Impact of Spatial Scales on the Intercomparison of Climate Scenarios

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

Luo, Wei; Steptoe, Michael; Chang, Zheng

2017-01-01

Scenario analysis has been widely applied in climate science to understand the impact of climate change on the future human environment, but intercomparison and similarity analysis of different climate scenarios based on multiple simulation runs remain challenging. Although spatial heterogeneity plays a key role in modeling climate and human systems, little research has been performed to understand the impact of spatial variations and scales on similarity analysis of climate scenarios. To address this issue, the authors developed a geovisual analytics framework that lets users perform similarity analysis of climate scenarios from the Global Change Assessment Model (GCAM) using a hierarchicalmore » clustering approach.« less

Impacts of correcting the inter-variable correlation of climate model outputs on hydrological modeling

NASA Astrophysics Data System (ADS)

Chen, Jie; Li, Chao; Brissette, François P.; Chen, Hua; Wang, Mingna; Essou, Gilles R. C.

2018-05-01

Bias correction is usually implemented prior to using climate model outputs for impact studies. However, bias correction methods that are commonly used treat climate variables independently and often ignore inter-variable dependencies. The effects of ignoring such dependencies on impact studies need to be investigated. This study aims to assess the impacts of correcting the inter-variable correlation of climate model outputs on hydrological modeling. To this end, a joint bias correction (JBC) method which corrects the joint distribution of two variables as a whole is compared with an independent bias correction (IBC) method; this is considered in terms of correcting simulations of precipitation and temperature from 26 climate models for hydrological modeling over 12 watersheds located in various climate regimes. The results show that the simulated precipitation and temperature are considerably biased not only in the individual distributions, but also in their correlations, which in turn result in biased hydrological simulations. In addition to reducing the biases of the individual characteristics of precipitation and temperature, the JBC method can also reduce the bias in precipitation-temperature (P-T) correlations. In terms of hydrological modeling, the JBC method performs significantly better than the IBC method for 11 out of the 12 watersheds over the calibration period. For the validation period, the advantages of the JBC method are greatly reduced as the performance becomes dependent on the watershed, GCM and hydrological metric considered. For arid/tropical and snowfall-rainfall-mixed watersheds, JBC performs better than IBC. For snowfall- or rainfall-dominated watersheds, however, the two methods behave similarly, with IBC performing somewhat better than JBC. Overall, the results emphasize the advantages of correcting the P-T correlation when using climate model-simulated precipitation and temperature to assess the impact of climate change on watershed hydrology. However, a thorough validation and a comparison with other methods are recommended before using the JBC method, since it may perform worse than the IBC method for some cases due to bias nonstationarity of climate model outputs.

An evaluation of 20th century climate for the Southeastern United States as simulated by Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models

USGS Publications Warehouse

David E. Rupp,

2016-05-05

The 20th century climate for the Southeastern United States and surrounding areas as simulated by global climate models used in the Coupled Model Intercomparison Project Phase 5 (CMIP5) was evaluated. A suite of statistics that characterize various aspects of the regional climate was calculated from both model simulations and observation-based datasets. CMIP5 global climate models were ranked by their ability to reproduce the observed climate. Differences in the performance of the models between regions of the United States (the Southeastern and Northwestern United States) warrant a regional-scale assessment of CMIP5 models.

Evaluating synoptic systems in the CMIP5 climate models over the Australian region

NASA Astrophysics Data System (ADS)

Gibson, Peter B.; Uotila, Petteri; Perkins-Kirkpatrick, Sarah E.; Alexander, Lisa V.; Pitman, Andrew J.

2016-10-01

Climate models are our principal tool for generating the projections used to inform climate change policy. Our confidence in projections depends, in part, on how realistically they simulate present day climate and associated variability over a range of time scales. Traditionally, climate models are less commonly assessed at time scales relevant to daily weather systems. Here we explore the utility of a self-organizing maps (SOMs) procedure for evaluating the frequency, persistence and transitions of daily synoptic systems in the Australian region simulated by state-of-the-art global climate models. In terms of skill in simulating the climatological frequency of synoptic systems, large spread was observed between models. A positive association between all metrics was found, implying that relative skill in simulating the persistence and transitions of systems is related to skill in simulating the climatological frequency. Considering all models and metrics collectively, model performance was found to be related to model horizontal resolution but unrelated to vertical resolution or representation of the stratosphere. In terms of the SOM procedure, the timespan over which evaluation was performed had some influence on model performance skill measures, as did the number of circulation types examined. These findings have implications for selecting models most useful for future projections over the Australian region, particularly for projections related to synoptic scale processes and phenomena. More broadly, this study has demonstrated the utility of the SOMs procedure in providing a process-based evaluation of climate models.

Effects of disturbance and climate change on ecosystem performance in the Yukon River Basin boreal forest

USGS Publications Warehouse

Wylie, Bruce K.; Rigge, Matthew B.; Brisco, Brian; Mrnaghan, Kevin; Rover, Jennifer R.; Long, Jordan

2014-01-01

A warming climate influences boreal forest productivity, dynamics, and disturbance regimes. We used ecosystem models and 250 m satellite Normalized Difference Vegetation Index (NDVI) data averaged over the growing season (GSN) to model current, and estimate future, ecosystem performance. We modeled Expected Ecosystem Performance (EEP), or anticipated productivity, in undisturbed stands over the 2000–2008 period from a variety of abiotic data sources, using a rule-based piecewise regression tree. The EEP model was applied to a future climate ensemble A1B projection to quantify expected changes to mature boreal forest performance. Ecosystem Performance Anomalies (EPA), were identified as the residuals of the EEP and GSN relationship and represent performance departures from expected performance conditions. These performance data were used to monitor successional events following fire. Results suggested that maximum EPA occurs 30–40 years following fire, and deciduous stands generally have higher EPA than coniferous stands. Mean undisturbed EEP is projected to increase 5.6% by 2040 and 8.7% by 2070, suggesting an increased deciduous component in boreal forests. Our results contribute to the understanding of boreal forest successional dynamics and its response to climate change. This information enables informed decisions to prepare for, and adapt to, climate change in the Yukon River Basin forest.

Evaluation of mean climate in a chemistry-climate model simulation

NASA Astrophysics Data System (ADS)

Hong, S.; Park, H.; Wie, J.; Park, R.; Lee, S.; Moon, B. K.

2017-12-01

Incorporation of the interactive chemistry is essential for understanding chemistry-climate interactions and feedback processes in climate models. Here we assess a newly developed chemistry-climate model (GRIMs-Chem), which is based on the Global/Regional Integrated Model system (GRIMs) including the aerosol direct effect as well as stratospheric linearized ozone chemistry (LINOZ). We conducted GRIMs-Chem with observed sea surface temperature during the period of 1979-2010, and compared the simulation results with observations and also with CMIP models. To measure the relative performance of our model, we define the quantitative performance metric using the Taylor diagram. This metric allow us to assess overall features in simulating multiple variables. Overall, our model better reproduce the zonal mean spatial pattern of temperature, horizontal wind, vertical motion, and relative humidity relative to other models. However, the model did not produce good simulations at upper troposphere (200 hPa). It is currently unclear which model processes are responsible for this. AcknowledgementsThis research was supported by the Korea Ministry of Environment (MOE) as "Climate Change Correspondence Program."

Beyond a Climate-Centric View of Plant Distribution: Edaphic Variables Add Value to Distribution Models

PubMed Central

Beauregard, Frieda; de Blois, Sylvie

2014-01-01

Both climatic and edaphic conditions determine plant distribution, however many species distribution models do not include edaphic variables especially over large geographical extent. Using an exceptional database of vegetation plots (n = 4839) covering an extent of ∼55000 km2, we tested whether the inclusion of fine scale edaphic variables would improve model predictions of plant distribution compared to models using only climate predictors. We also tested how well these edaphic variables could predict distribution on their own, to evaluate the assumption that at large extents, distribution is governed largely by climate. We also hypothesized that the relative contribution of edaphic and climatic data would vary among species depending on their growth forms and biogeographical attributes within the study area. We modelled 128 native plant species from diverse taxa using four statistical model types and three sets of abiotic predictors: climate, edaphic, and edaphic-climate. Model predictive accuracy and variable importance were compared among these models and for species' characteristics describing growth form, range boundaries within the study area, and prevalence. For many species both the climate-only and edaphic-only models performed well, however the edaphic-climate models generally performed best. The three sets of predictors differed in the spatial information provided about habitat suitability, with climate models able to distinguish range edges, but edaphic models able to better distinguish within-range variation. Model predictive accuracy was generally lower for species without a range boundary within the study area and for common species, but these effects were buffered by including both edaphic and climatic predictors. The relative importance of edaphic and climatic variables varied with growth forms, with trees being more related to climate whereas lower growth forms were more related to edaphic conditions. Our study identifies the potential for non-climate aspects of the environment to pose a constraint to range expansion under climate change. PMID:24658097

Beyond a climate-centric view of plant distribution: edaphic variables add value to distribution models.

PubMed

Beauregard, Frieda; de Blois, Sylvie

2014-01-01

Both climatic and edaphic conditions determine plant distribution, however many species distribution models do not include edaphic variables especially over large geographical extent. Using an exceptional database of vegetation plots (n = 4839) covering an extent of ∼55,000 km2, we tested whether the inclusion of fine scale edaphic variables would improve model predictions of plant distribution compared to models using only climate predictors. We also tested how well these edaphic variables could predict distribution on their own, to evaluate the assumption that at large extents, distribution is governed largely by climate. We also hypothesized that the relative contribution of edaphic and climatic data would vary among species depending on their growth forms and biogeographical attributes within the study area. We modelled 128 native plant species from diverse taxa using four statistical model types and three sets of abiotic predictors: climate, edaphic, and edaphic-climate. Model predictive accuracy and variable importance were compared among these models and for species' characteristics describing growth form, range boundaries within the study area, and prevalence. For many species both the climate-only and edaphic-only models performed well, however the edaphic-climate models generally performed best. The three sets of predictors differed in the spatial information provided about habitat suitability, with climate models able to distinguish range edges, but edaphic models able to better distinguish within-range variation. Model predictive accuracy was generally lower for species without a range boundary within the study area and for common species, but these effects were buffered by including both edaphic and climatic predictors. The relative importance of edaphic and climatic variables varied with growth forms, with trees being more related to climate whereas lower growth forms were more related to edaphic conditions. Our study identifies the potential for non-climate aspects of the environment to pose a constraint to range expansion under climate change.

Evaluation of global climate model on performances of precipitation simulation and prediction in the Huaihe River basin

NASA Astrophysics Data System (ADS)

Wu, Yenan; Zhong, Ping-an; Xu, Bin; Zhu, Feilin; Fu, Jisi

2017-06-01

Using climate models with high performance to predict the future climate changes can increase the reliability of results. In this paper, six kinds of global climate models that selected from the Coupled Model Intercomparison Project Phase 5 (CMIP5) under Representative Concentration Path (RCP) 4.5 scenarios were compared to the measured data during baseline period (1960-2000) and evaluate the simulation performance on precipitation. Since the results of single climate models are often biased and highly uncertain, we examine the back propagation (BP) neural network and arithmetic mean method in assembling the precipitation of multi models. The delta method was used to calibrate the result of single model and multimodel ensembles by arithmetic mean method (MME-AM) during the validation period (2001-2010) and the predicting period (2011-2100). We then use the single models and multimodel ensembles to predict the future precipitation process and spatial distribution. The result shows that BNU-ESM model has the highest simulation effect among all the single models. The multimodel assembled by BP neural network (MME-BP) has a good simulation performance on the annual average precipitation process and the deterministic coefficient during the validation period is 0.814. The simulation capability on spatial distribution of precipitation is: calibrated MME-AM > MME-BP > calibrated BNU-ESM. The future precipitation predicted by all models tends to increase as the time period increases. The order of average increase amplitude of each season is: winter > spring > summer > autumn. These findings can provide useful information for decision makers to make climate-related disaster mitigation plans.

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

PubMed

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

2005-11-01

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

Probabilistic modeling of the indoor climates of residential buildings using EnergyPlus

DOE PAGES

Buechler, Elizabeth D.; Pallin, Simon B.; Boudreaux, Philip R.; ...

2017-04-25

The indoor air temperature and relative humidity in residential buildings significantly affect material moisture durability, HVAC system performance, and occupant comfort. Therefore, indoor climate data is generally required to define boundary conditions in numerical models that evaluate envelope durability and equipment performance. However, indoor climate data obtained from field studies is influenced by weather, occupant behavior and internal loads, and is generally unrepresentative of the residential building stock. Likewise, whole-building simulation models typically neglect stochastic variables and yield deterministic results that are applicable to only a single home in a specific climate. The

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

NASA Astrophysics Data System (ADS)

Ragone, Francesco; Lucarini, Valerio; Lunkeit, Frank

2016-03-01

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

Interactive Correlation Analysis and Visualization of Climate Data

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

Ma, Kwan-Liu

The relationship between our ability to analyze and extract insights from visualization of climate model output and the capability of the available resources to make those visualizations has reached a crisis point. The large volume of data currently produced by climate models is overwhelming the current, decades-old visualization workflow. The traditional methods for visualizing climate output also have not kept pace with changes in the types of grids used, the number of variables involved, and the number of different simulations performed with a climate model or the feature-richness of high-resolution simulations. This project has developed new and faster methods formore » visualization in order to get the most knowledge out of the new generation of high-resolution climate models. While traditional climate images will continue to be useful, there is need for new approaches to visualization and analysis of climate data if we are to gain all the insights available in ultra-large data sets produced by high-resolution model output and ensemble integrations of climate models such as those produced for the Coupled Model Intercomparison Project. Towards that end, we have developed new visualization techniques for performing correlation analysis. We have also introduced highly scalable, parallel rendering methods for visualizing large-scale 3D data. This project was done jointly with climate scientists and visualization researchers at Argonne National Laboratory and NCAR.« less

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

PubMed

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

2010-06-01

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

Climate downscaling over South America for 1971-2000: application in SMAP rainfall-runoff model for Grande River Basin

NASA Astrophysics Data System (ADS)

da Silva, Felipe das Neves Roque; Alves, José Luis Drummond; Cataldi, Marcio

2018-03-01

This paper aims to validate inflow simulations concerning the present-day climate at Água Vermelha Hydroelectric Plant (AVHP—located on the Grande River Basin) based on the Soil Moisture Accounting Procedure (SMAP) hydrological model. In order to provide rainfall data to the SMAP model, the RegCM regional climate model was also used working with boundary conditions from the MIROC model. Initially, present-day climate simulation performed by RegCM model was analyzed. It was found that, in terms of rainfall, the model was able to simulate the main patterns observed over South America. A bias correction technique was also used and it was essential to reduce mistakes related to rainfall simulation. Comparison between rainfall simulations from RegCM and MIROC showed improvements when the dynamical downscaling was performed. Then, SMAP, a rainfall-runoff hydrological model, was used to simulate inflows at Água Vermelha Hydroelectric Plant. After calibration with observed rainfall, SMAP simulations were evaluated in two different periods from the one used in calibration. During calibration, SMAP captures the inflow variability observed at AVHP. During validation periods, the hydrological model obtained better results and statistics with observed rainfall. However, in spite of some discrepancies, the use of simulated rainfall without bias correction captured the interannual flow variability. However, the use of bias removal in the simulated rainfall performed by RegCM brought significant improvements to the simulation of natural inflows performed by SMAP. Not only the curve of simulated inflow became more similar to the observed inflow, but also the statistics improved their values. Improvements were also noticed in the inflow simulation when the rainfall was provided by the regional climate model compared to the global model. In general, results obtained so far prove that there was an added value in rainfall when regional climate model was compared to global climate model and that data from regional models must be bias-corrected so as to improve their results.

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.

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.

Estimating thermal performance curves from repeated field observations

USGS Publications Warehouse

Childress, Evan; Letcher, Benjamin H.

2017-01-01

Estimating thermal performance of organisms is critical for understanding population distributions and dynamics and predicting responses to climate change. Typically, performance curves are estimated using laboratory studies to isolate temperature effects, but other abiotic and biotic factors influence temperature-performance relationships in nature reducing these models' predictive ability. We present a model for estimating thermal performance curves from repeated field observations that includes environmental and individual variation. We fit the model in a Bayesian framework using MCMC sampling, which allowed for estimation of unobserved latent growth while propagating uncertainty. Fitting the model to simulated data varying in sampling design and parameter values demonstrated that the parameter estimates were accurate, precise, and unbiased. Fitting the model to individual growth data from wild trout revealed high out-of-sample predictive ability relative to laboratory-derived models, which produced more biased predictions for field performance. The field-based estimates of thermal maxima were lower than those based on laboratory studies. Under warming temperature scenarios, field-derived performance models predicted stronger declines in body size than laboratory-derived models, suggesting that laboratory-based models may underestimate climate change effects. The presented model estimates true, realized field performance, avoiding assumptions required for applying laboratory-based models to field performance, which should improve estimates of performance under climate change and advance thermal ecology.

Performance evaluation of a non-hydrostatic regional climate model over the Mediterranean/Black Sea area and climate projections for the XXI century

NASA Astrophysics Data System (ADS)

Mercogliano, Paola; Bucchignani, Edoardo; Montesarchio, Myriam; Zollo, Alessandra Lucia

2013-04-01

In the framework of the Work Package 4 (Developing integrated tools for environmental assessment) of PERSEUS Project, high resolution climate simulations have been performed, with the aim of furthering knowledge in the field of climate variability at regional scale, its causes and impacts. CMCC is a no profit centre whose aims are the promotion, research coordination and scientific activities in the field of climate changes. In this work, we show results of numerical simulation performed over a very wide area (13W-46E; 29-56N) at spatial resolution of 14 km, which includes the Mediterranean and Black Seas, using the regional climate model COSMO-CLM. It is a non-hydrostatic model for the simulation of atmospheric processes, developed by the DWD-Germany for weather forecast services; successively, the model has been updated by the CLM-Community, in order to develop climatic applications. It is the only documented numerical model system in Europe designed for spatial resolutions down to 1 km with a range of applicability encompassing operational numerical weather prediction, regional climate modelling the dispersion of trace gases and aerosol and idealised studies and applicable in all regions of the world for a wide range of available climate simulations from global climate and NWP models. Different reasons justify the development of a regional model: the first is the increasing number of works in literature asserting that regional models have also the features to provide more detailed description of the climate extremes, that are often more important then their mean values for natural and human systems. The second one is that high resolution modelling shows adequate features to provide information for impact assessment studies. At CMCC, regional climate modelling is a part of an integrated simulation system and it has been used in different European and African projects to provide qualitative and quantitative evaluation of the hydrogeological and public health risks. A simulation covering the period 1971-2000 and driven by ERA40 reanalysis has been performed, in order to assess the capability of the model to reproduce the present climate, with "perfect boundary conditions". A comparison, in terms of 2-metre temperature and precipitation, with EOBS dataset will be shown and discussed, in order to analyze the capabilities in simulating the main features of the observed climate over a wide area, at high spatial resolution. Then, a comparison between the results of COSMO-CLM driven by the global model CMCC-MED (whose atmospheric component is ECHAM5) and by ERA40 will be provided for a characterization of the errors induced by the global model. Finally, climate projections on the examined area for the XXI century, considering the RCP4.5 emission scenario for the future, will be provided. In this work a special emphasis will be issued to the analysis of the capability to reproduce not only the average climate trend but also extremes of the present and future climate, in terms of temperature, precipitation and wind.

The Future of Planetary Climate Modeling and Weather Prediction

NASA Technical Reports Server (NTRS)

Del Genio, A. D.; Domagal-Goldman, S. D.; Kiang, N. Y.; Kopparapu, R. K.; Schmidt, G. A.; Sohl, L. E.

2017-01-01

Modeling of planetary climate and weather has followed the development of tools for studying Earth, with lags of a few years. Early Earth climate studies were performed with 1-dimensionalradiative-convective models, which were soon fol-lowed by similar models for the climates of Mars and Venus and eventually by similar models for exoplan-ets. 3-dimensional general circulation models (GCMs) became common in Earth science soon after and within several years were applied to the meteorology of Mars, but it was several decades before a GCM was used to simulate extrasolar planets. Recent trends in Earth weather and and climate modeling serve as a useful guide to how modeling of Solar System and exoplanet weather and climate will evolve in the coming decade.

Uncertainty and the Social Cost of Methane Using Bayesian Constrained Climate Models

NASA Astrophysics Data System (ADS)

Errickson, F. C.; Anthoff, D.; Keller, K.

2016-12-01

Social cost estimates of greenhouse gases are important for the design of sound climate policies and are also plagued by uncertainty. One major source of uncertainty stems from the simplified representation of the climate system used in the integrated assessment models that provide these social cost estimates. We explore how uncertainty over the social cost of methane varies with the way physical processes and feedbacks in the methane cycle are modeled by (i) coupling three different methane models to a simple climate model, (ii) using MCMC to perform a Bayesian calibration of the three coupled climate models that simulates direct sampling from the joint posterior probability density function (pdf) of model parameters, and (iii) producing probabilistic climate projections that are then used to calculate the Social Cost of Methane (SCM) with the DICE and FUND integrated assessment models. We find that including a temperature feedback in the methane cycle acts as an additional constraint during the calibration process and results in a correlation between the tropospheric lifetime of methane and several climate model parameters. This correlation is not seen in the models lacking this feedback. Several of the estimated marginal pdfs of the model parameters also exhibit different distributional shapes and expected values depending on the methane model used. As a result, probabilistic projections of the climate system out to the year 2300 exhibit different levels of uncertainty and magnitudes of warming for each of the three models under an RCP8.5 scenario. We find these differences in climate projections result in differences in the distributions and expected values for our estimates of the SCM. We also examine uncertainty about the SCM by performing a Monte Carlo analysis using a distribution for the climate sensitivity while holding all other climate model parameters constant. Our SCM estimates using the Bayesian calibration are lower and exhibit less uncertainty about extremely high values in the right tail of the distribution compared to the Monte Carlo approach. This finding has important climate policy implications and suggests previous work that accounts for climate model uncertainty by only varying the climate sensitivity parameter may overestimate the SCM.

Evaluation of regional climate simulations over the Great Lakes region driven by three global data sets

Treesearch

Shiyuan Zhong; Xiuping Li; Xindi Bian; Warren E. Heilman; L. Ruby Leung; William I. Jr. Gustafson

2012-01-01

The performance of regional climate simulations is evaluated for the Great Lakes region. Three 10-year (1990-1999) current-climate simulations are performed using the MM5 regional climate model (RCM) with 36-km horizontal resolution. The simulations employed identical configuration and physical parameterizations, but different lateral boundary conditions and sea-...

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.

Validation of the Regional Climate Model ALARO with different dynamical downscaling approaches and different horizontal resolutions

NASA Astrophysics Data System (ADS)

Berckmans, Julie; Hamdi, Rafiq; De Troch, Rozemien; Giot, Olivier

2015-04-01

At the Royal Meteorological Institute of Belgium (RMI), climate simulations are performed with the regional climate model (RCM) ALARO, a version of the ALADIN model with improved physical parameterizations. In order to obtain high-resolution information of the regional climate, lateral bounary conditions (LBC) are prescribed from the global climate model (GCM) ARPEGE. Dynamical downscaling is commonly done in a continuous long-term simulation, with the initialisation of the model at the start and driven by the regularly updated LBCs of the GCM. Recently, more interest exists in the dynamical downscaling approach of frequent reinitializations of the climate simulations. For these experiments, the model is initialised daily and driven for 24 hours by the GCM. However, the surface is either initialised daily together with the atmosphere or free to evolve continuously. The surface scheme implemented in ALARO is SURFEX, which can be either run in coupled mode or in stand-alone mode. The regional climate is simulated on different domains, on a 20km horizontal resolution over Western-Europe and a 4km horizontal resolution over Belgium. Besides, SURFEX allows to perform a stand-alone or offline simulation on 1km horizontal resolution over Belgium. This research is in the framework of the project MASC: "Modelling and Assessing Surface Change Impacts on Belgian and Western European Climate", a 4-year project funded by the Belgian Federal Government. The overall aim of the project is to study the feedbacks between climate changes and land surface changes in order to improve regional climate model projections at the decennial scale over Belgium and Western Europe and thus to provide better climate projections and climate change evaluation tools to policy makers, stakeholders and the scientific community.

Visualization and Analysis of Climate Simulation Performance Data

NASA Astrophysics Data System (ADS)

Röber, Niklas; Adamidis, Panagiotis; Behrens, Jörg

2015-04-01

Visualization is the key process of transforming abstract (scientific) data into a graphical representation, to aid in the understanding of the information hidden within the data. Climate simulation data sets are typically quite large, time varying, and consist of many different variables sampled on an underlying grid. A large variety of climate models - and sub models - exist to simulate various aspects of the climate system. Generally, one is mainly interested in the physical variables produced by the simulation runs, but model developers are also interested in performance data measured along with these simulations. Climate simulation models are carefully developed complex software systems, designed to run in parallel on large HPC systems. An important goal thereby is to utilize the entire hardware as efficiently as possible, that is, to distribute the workload as even as possible among the individual components. This is a very challenging task, and detailed performance data, such as timings, cache misses etc. have to be used to locate and understand performance problems in order to optimize the model implementation. Furthermore, the correlation of performance data to the processes of the application and the sub-domains of the decomposed underlying grid is vital when addressing communication and load imbalance issues. High resolution climate simulations are carried out on tens to hundreds of thousands of cores, thus yielding a vast amount of profiling data, which cannot be analyzed without appropriate visualization techniques. This PICO presentation displays and discusses the ICON simulation model, which is jointly developed by the Max Planck Institute for Meteorology and the German Weather Service and in partnership with DKRZ. The visualization and analysis of the models performance data allows us to optimize and fine tune the model, as well as to understand its execution on the HPC system. We show and discuss our workflow, as well as present new ideas and solutions that greatly aided our understanding. The software employed is based on Avizo Green, ParaView and SimVis, as well as own developed software extensions.

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

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.

Data Sparsity Considerations in Climate Impact Analysis for the Water Sector (Invited)

NASA Astrophysics Data System (ADS)

Asante, K. O.; Khimsara, P.; Chan, A.

2013-12-01

Scientists and planners are helping governments and communities around the world to prepare for climate change by performing local impact studies and developing adaptation plans. Most studies begin by analyzing global climate models outputs to estimate the magnitude of projected change, assessing vulnerabilities and proposing adaptation measures. In these studies, climate projections from the Intergovernmental Panel on Climate Change (IPCC) Data Distribution Centre (DDC) are either used directly or downscaled using regional models. Since climate projections cover the entire global, climate change analysis can be performed for any location. However, selection of climate projections for use in historically data sparse regions presents special challenges. Key questions arise about the impact of historical data sparsity on quality of climate projections, spatial consistency of results and suitability for applications such as water resource planning. In this paper, a water-sector climate study conducted in a data-rich setting in California is compared to a similar study conducted a data-sparse setting in Mozambique. The challenges of selecting projections, performing analysis and interpreting the results for climate adaption planning are compared to illustrate the decision process and challenges encountered in these two very different settings.

Reliability of the North America CORDEX and NARCCAP simulations in the context of uncertainty in regional climate change projections

NASA Astrophysics Data System (ADS)

Karmalkar, A.

2017-12-01

Ensembles of dynamically downscaled climate change simulations are routinely used to capture uncertainty in projections at regional scales. I assess the reliability of two such ensembles for North America - NARCCAP and NA-CORDEX - by investigating the impact of model selection on representing uncertainty in regional projections, and the ability of the regional climate models (RCMs) to provide reliable information. These aspects - discussed for the six regions used in the US National Climate Assessment - provide an important perspective on the interpretation of downscaled results. I show that selecting general circulation models for downscaling based on their equilibrium climate sensitivities is a reasonable choice, but the six models chosen for NA-CORDEX do a poor job at representing uncertainty in winter temperature and precipitation projections in many parts of the eastern US, which lead to overconfident projections. The RCM performance is highly variable across models, regions, and seasons and the ability of the RCMs to provide improved seasonal mean performance relative to their parent GCMs seems limited in both RCM ensembles. Additionally, the ability of the RCMs to simulate historical climates is not strongly related to their ability to simulate climate change across the ensemble. This finding suggests limited use of models' historical performance to constrain their projections. Given these challenges in dynamical downscaling, the RCM results should not be used in isolation. Information on how well the RCM ensembles represent known uncertainties in regional climate change projections discussed here needs to be communicated clearly to inform maagement decisions.

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.

Susceptibility of the Batoka Gorge hydroelectric scheme to climate change

NASA Astrophysics Data System (ADS)

Harrison, Gareth P.; Whittington, H.(Bert) W.

2002-07-01

The continuing and increased use of renewable energy sources, including hydropower, is a key strategy to limit the extent of future climate change. Paradoxically, climate change itself may alter the availability of this natural resource, adversely affecting the financial viability of both existing and potential schemes. Here, a model is described that enables the assessment of the relationship between changes in climate and the viability, technical and financial, of hydro development. The planned Batoka Gorge scheme on the Zambezi River is used as a case study to validate the model and to predict the impact of climate change on river flows, electricity production and scheme financial performance. The model was found to perform well, given the inherent difficulties in the task, although there is concern regarding the ability of the hydrological model to reproduce the historic flow conditions of the upper Zambezi Basin. Simulations with climate change scenarios illustrate the sensitivity of the Batoka Gorge scheme to changes in climate. They suggest significant reductions in river flows, declining power production, reductions in electricity sales revenue and consequently an adverse impact on a range of investment measures.

Multilevel Safety Climate and Safety Performance in the Construction Industry: Development and Validation of a Top-Down Mechanism

PubMed Central

Gao, Ran; Chan, Albert P.C.; Utama, Wahyudi P.; Zahoor, Hafiz

2016-01-01

The character of construction projects exposes front-line workers to dangers and accidents. Safety climate has been confirmed to be a predictor of safety performance in the construction industry. This study aims to explore the underlying mechanisms of the relationship between multilevel safety climate and safety performance. An integrated model was developed to study how particular safety climate factors of one level affect those of other levels, and then affect safety performance from the top down. A questionnaire survey was administered on six construction sites in Vietnam. A total of 1030 valid questionnaires were collected from this survey. Approximately half of the data were used to conduct exploratory factor analysis (EFA) and the remaining data were submitted to structural equation modeling (SEM). Top management commitment (TMC) and supervisors’ expectation (SE) were identified as factors to represent organizational safety climate (OSC) and supervisor safety climate (SSC), respectively, and coworkers’ caring and communication (CCC) and coworkers’ role models (CRM) were identified as factors to denote coworker safety climate (CSC). SEM results show that OSC factor is positively related to SSC factor and CSC factors significantly. SSC factor could partially mediate the relationship between OSC factor and CSC factors, as well as the relationship between OSC factor and safety performance. CSC factors partially mediate the relationship between OSC factor and safety performance, and the relationship between SSC factor and safety performance. The findings imply that a positive safety culture should be established both at the organizational level and the group level. Efforts from all top management, supervisors, and coworkers should be provided to improve safety performance in the construction industry. PMID:27834823

Multilevel Safety Climate and Safety Performance in the Construction Industry: Development and Validation of a Top-Down Mechanism.

PubMed

Gao, Ran; Chan, Albert P C; Utama, Wahyudi P; Zahoor, Hafiz

2016-11-08

The character of construction projects exposes front-line workers to dangers and accidents. Safety climate has been confirmed to be a predictor of safety performance in the construction industry. This study aims to explore the underlying mechanisms of the relationship between multilevel safety climate and safety performance. An integrated model was developed to study how particular safety climate factors of one level affect those of other levels, and then affect safety performance from the top down. A questionnaire survey was administered on six construction sites in Vietnam. A total of 1030 valid questionnaires were collected from this survey. Approximately half of the data were used to conduct exploratory factor analysis (EFA) and the remaining data were submitted to structural equation modeling (SEM). Top management commitment (TMC) and supervisors' expectation (SE) were identified as factors to represent organizational safety climate (OSC) and supervisor safety climate (SSC), respectively, and coworkers' caring and communication (CCC) and coworkers' role models (CRM) were identified as factors to denote coworker safety climate (CSC). SEM results show that OSC factor is positively related to SSC factor and CSC factors significantly. SSC factor could partially mediate the relationship between OSC factor and CSC factors, as well as the relationship between OSC factor and safety performance. CSC factors partially mediate the relationship between OSC factor and safety performance, and the relationship between SSC factor and safety performance. The findings imply that a positive safety culture should be established both at the organizational level and the group level. Efforts from all top management, supervisors, and coworkers should be provided to improve safety performance in the construction industry.

Upgrades to the REA method for producing probabilistic climate change projections

NASA Astrophysics Data System (ADS)

Xu, Ying; Gao, Xuejie; Giorgi, Filippo

2010-05-01

We present an augmented version of the Reliability Ensemble Averaging (REA) method designed to generate probabilistic climate change information from ensembles of climate model simulations. Compared to the original version, the augmented one includes consideration of multiple variables and statistics in the calculation of the performance-based weights. In addition, the model convergence criterion previously employed is removed. The method is applied to the calculation of changes in mean and variability for temperature and precipitation over different sub-regions of East Asia based on the recently completed CMIP3 multi-model ensemble. Comparison of the new and old REA methods, along with the simple averaging procedure, and the use of different combinations of performance metrics shows that at fine sub-regional scales the choice of weighting is relevant. This is mostly because the models show a substantial spread in performance for the simulation of precipitation statistics, a result that supports the use of model weighting as a useful option to account for wide ranges of quality of models. The REA method, and in particular the upgraded one, provides a simple and flexible framework for assessing the uncertainty related to the aggregation of results from ensembles of models in order to produce climate change information at the regional scale. KEY WORDS: REA method, Climate change, CMIP3

Examining the Performance of Statistical Downscaling Methods: Toward Matching Applications to Data Products

NASA Astrophysics Data System (ADS)

Dixon, K. W.; Lanzante, J. R.; Adams-Smith, D.

2017-12-01

Several challenges exist when seeking to use future climate model projections in a climate impacts study. A not uncommon approach is to utilize climate projection data sets derived from more than one future emissions scenario and from multiple global climate models (GCMs). The range of future climate responses represented in the set is sometimes taken to be indicative of levels of uncertainty in the projections. Yet, GCM outputs are deemed to be unsuitable for direct use in many climate impacts applications. GCM grids typically are viewed as being too coarse. Additionally, regional or local-scale biases in a GCM's simulation of the contemporary climate that may not be problematic from a global climate modeling perspective may be unacceptably large for a climate impacts application. Statistical downscaling (SD) of climate projections - a type of post-processing that uses observations to inform the refinement of GCM projections - is often used in an attempt to account for GCM biases and to provide additional spatial detail. "What downscaled climate projection is the best one to use" is a frequently asked question, but one that is not always easy to answer, as it can be dependent on stakeholder needs and expectations. Here we present results from a perfect model experimental design illustrating how SD method performance can vary not only by SD method, but how performance can also vary by location, season, climate variable of interest, amount of projected climate change, SD configuration choices, and whether one is interested in central tendencies or the tails of the distribution. Awareness of these factors can be helpful when seeking to determine the suitability of downscaled climate projections for specific climate impacts applications. It also points to the potential value of considering more than one SD data product in a study, so as to acknowledge uncertainties associated with the strengths and weaknesses of different downscaling methods.

Impacts of weighting climate models for hydro-meteorological climate change studies

NASA Astrophysics Data System (ADS)

Chen, Jie; Brissette, François P.; Lucas-Picher, Philippe; Caya, Daniel

2017-06-01

Weighting climate models is controversial in climate change impact studies using an ensemble of climate simulations from different climate models. In climate science, there is a general consensus that all climate models should be considered as having equal performance or in other words that all projections are equiprobable. On the other hand, in the impacts and adaptation community, many believe that climate models should be weighted based on their ability to better represent various metrics over a reference period. The debate appears to be partly philosophical in nature as few studies have investigated the impact of using weights in projecting future climate changes. The present study focuses on the impact of assigning weights to climate models for hydrological climate change studies. Five methods are used to determine weights on an ensemble of 28 global climate models (GCMs) adapted from the Coupled Model Intercomparison Project Phase 5 (CMIP5) database. Using a hydrological model, streamflows are computed over a reference (1961-1990) and future (2061-2090) periods, with and without post-processing climate model outputs. The impacts of using different weighting schemes for GCM simulations are then analyzed in terms of ensemble mean and uncertainty. The results show that weighting GCMs has a limited impact on both projected future climate in term of precipitation and temperature changes and hydrology in terms of nine different streamflow criteria. These results apply to both raw and post-processed GCM model outputs, thus supporting the view that climate models should be considered equiprobable.

Pan evaporation modeling using six different heuristic computing methods in different climates of China

NASA Astrophysics Data System (ADS)

Wang, Lunche; Kisi, Ozgur; Zounemat-Kermani, Mohammad; Li, Hui

2017-01-01

Pan evaporation (Ep) plays important roles in agricultural water resources management. One of the basic challenges is modeling Ep using limited climatic parameters because there are a number of factors affecting the evaporation rate. This study investigated the abilities of six different soft computing methods, multi-layer perceptron (MLP), generalized regression neural network (GRNN), fuzzy genetic (FG), least square support vector machine (LSSVM), multivariate adaptive regression spline (MARS), adaptive neuro-fuzzy inference systems with grid partition (ANFIS-GP), and two regression methods, multiple linear regression (MLR) and Stephens and Stewart model (SS) in predicting monthly Ep. Long-term climatic data at various sites crossing a wide range of climates during 1961-2000 are used for model development and validation. The results showed that the models have different accuracies in different climates and the MLP model performed superior to the other models in predicting monthly Ep at most stations using local input combinations (for example, the MAE (mean absolute errors), RMSE (root mean square errors), and determination coefficient (R2) are 0.314 mm/day, 0.405 mm/day and 0.988, respectively for HEB station), while GRNN model performed better in Tibetan Plateau (MAE, RMSE and R2 are 0.459 mm/day, 0.592 mm/day and 0.932, respectively). The accuracies of above models ranked as: MLP, GRNN, LSSVM, FG, ANFIS-GP, MARS and MLR. The overall results indicated that the soft computing techniques generally performed better than the regression methods, but MLR and SS models can be more preferred at some climatic zones instead of complex nonlinear models, for example, the BJ (Beijing), CQ (Chongqing) and HK (Haikou) stations. Therefore, it can be concluded that Ep could be successfully predicted using above models in hydrological modeling studies.

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.

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.

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

PubMed

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

2016-04-01

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

Results from the VALUE perfect predictor experiment: process-based evaluation

NASA Astrophysics Data System (ADS)

Maraun, Douglas; Soares, Pedro; Hertig, Elke; Brands, Swen; Huth, Radan; Cardoso, Rita; Kotlarski, Sven; Casado, Maria; Pongracz, Rita; Bartholy, Judit

2016-04-01

Until recently, the evaluation of downscaled climate model simulations has typically been limited to surface climatologies, including long term means, spatial variability and extremes. But these aspects are often, at least partly, tuned in regional climate models to match observed climate. The tuning issue is of course particularly relevant for bias corrected regional climate models. In general, a good performance of a model for these aspects in present climate does therefore not imply a good performance in simulating climate change. It is now widely accepted that, to increase our condidence in climate change simulations, it is necessary to evaluate how climate models simulate relevant underlying processes. In other words, it is important to assess whether downscaling does the right for the right reason. Therefore, VALUE has carried out a broad process-based evaluation study based on its perfect predictor experiment simulations: the downscaling methods are driven by ERA-Interim data over the period 1979-2008, reference observations are given by a network of 85 meteorological stations covering all European climates. More than 30 methods participated in the evaluation. In order to compare statistical and dynamical methods, only variables provided by both types of approaches could be considered. This limited the analysis to conditioning local surface variables on variables from driving processes that are simulated by ERA-Interim. We considered the following types of processes: at the continental scale, we evaluated the performance of downscaling methods for positive and negative North Atlantic Oscillation, Atlantic ridge and blocking situations. At synoptic scales, we considered Lamb weather types for selected European regions such as Scandinavia, the United Kingdom, the Iberian Pensinsula or the Alps. At regional scales we considered phenomena such as the Mistral, the Bora or the Iberian coastal jet. Such process-based evaluation helps to attribute biases in surface variables to underlying processes and ultimately to improve climate models.

Assessment of regional climate change and development of climate adaptation decision aids in the Southwestern US

NASA Astrophysics Data System (ADS)

Darmenova, K.; Higgins, G.; Kiley, H.; Apling, D.

2010-12-01

Current General Circulation Models (GCMs) provide a valuable estimate of both natural and anthropogenic climate changes and variability on global scales. At the same time, future climate projections calculated with GCMs are not of sufficient spatial resolution to address regional needs. Many climate impact models require information at scales of 50 km or less, so dynamical downscaling is often used to estimate the smaller-scale information based on larger scale GCM output. To address current deficiencies in local planning and decision making with respect to regional climate change, our research is focused on performing a dynamical downscaling with the Weather Research and Forecasting (WRF) model and developing decision aids that translate the regional climate data into actionable information for users. Our methodology involves development of climatological indices of extreme weather and heating/cooling degree days based on WRF ensemble runs initialized with the NCEP-NCAR reanalysis and the European Center/Hamburg Model (ECHAM5). Results indicate that the downscale simulations provide the necessary detailed output required by state and local governments and the private sector to develop climate adaptation plans. In addition we evaluated the WRF performance in long-term climate simulations over the Southwestern US and validated against observational datasets.

Risky future for Mediterranean forests unless they undergo extreme carbon fertilization.

PubMed

Gea-Izquierdo, Guillermo; Nicault, Antoine; Battipaglia, Giovanna; Dorado-Liñán, Isabel; Gutiérrez, Emilia; Ribas, Montserrat; Guiot, Joel

2017-07-01

Forest performance is challenged by climate change but higher atmospheric [CO 2 ] (c a ) could help trees mitigate the negative effect of enhanced water stress. Forest projections using data assimilation with mechanistic models are a valuable tool to assess forest performance. Firstly, we used dendrochronological data from 12 Mediterranean tree species (six conifers and six broadleaves) to calibrate a process-based vegetation model at 77 sites. Secondly, we conducted simulations of gross primary production (GPP) and radial growth using an ensemble of climate projections for the period 2010-2100 for the high-emission RCP8.5 and low-emission RCP2.6 scenarios. GPP and growth projections were simulated using climatic data from the two RCPs combined with (i) expected c a ; (ii) constant c a  = 390 ppm, to test a purely climate-driven performance excluding compensation from carbon fertilization. The model accurately mimicked the growth trends since the 1950s when, despite increasing c a , enhanced evaporative demands precluded a global net positive effect on growth. Modeled annual growth and GPP showed similar long-term trends. Under RCP2.6 (i.e., temperatures below +2 °C with respect to preindustrial values), the forests showed resistance to future climate (as expressed by non-negative trends in growth and GPP) except for some coniferous sites. Using exponentially growing c a and climate as from RCP8.5, carbon fertilization overrode the negative effect of the highly constraining climatic conditions under that scenario. This effect was particularly evident above 500 ppm (which is already over +2 °C), which seems unrealistic and likely reflects model miss-performance at high c a above the calibration range. Thus, forest projections under RCP8.5 preventing carbon fertilization displayed very negative forest performance at the regional scale. This suggests that most of western Mediterranean forests would successfully acclimate to the coldest climate change scenario but be vulnerable to a climate warmer than +2 °C unless the trees developed an exaggerated fertilization response to [CO 2 ]. © 2017 John Wiley & Sons Ltd.

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.

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

Buechler, Elizabeth D.; Pallin, Simon B.; Boudreaux, Philip R.

The indoor air temperature and relative humidity in residential buildings significantly affect material moisture durability, HVAC system performance, and occupant comfort. Therefore, indoor climate data is generally required to define boundary conditions in numerical models that evaluate envelope durability and equipment performance. However, indoor climate data obtained from field studies is influenced by weather, occupant behavior and internal loads, and is generally unrepresentative of the residential building stock. Likewise, whole-building simulation models typically neglect stochastic variables and yield deterministic results that are applicable to only a single home in a specific climate. The

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.

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

USGS Publications Warehouse

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

2018-01-01

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

Assessment of temperature and precipitation over Mediterranean Area and Black Sea with non hydrostatic high resolution regional climate model

NASA Astrophysics Data System (ADS)

Mercogliano, P.; Montesarchio, M.; Zollo, A.; Bucchignani, E.

2012-12-01

In the framework of the Italian GEMINA Project (program of expansion and development of the Euro-Mediterranean Center for Climate Change (CMCC), high resolution climate simulations have been performed, with the aim of furthering knowledge in the field of climate variability at regional scale, its causes and impacts. CMCC is a no profit centre whose aims are the promotion, research coordination and scientific activities in the field of climate changes. In this work, we show results of numerical simulation performed over a very wide area (13W-46E; 29-56N) at spatial resolution of 14 km, which includes all the Mediterranean Sea, using the regional climate model COSMO-CLM. It is a non-hydrostatic model for the simulation of atmospheric processes, developed by the DWD-Germany for weather forecast services; successively, the model has been updated by the CLM-Community, in order to develop climatic applications. It is the only documented numerical model system in Europe designed for spatial resolutions down to 1 km with a range of applicability encompassing operational numerical weather prediction, regional climate modelling the dispersion of trace gases and aerosol and idealised studies and applicable in all regions of the world for a wide range of available climate simulations from global climate and NWP models. Different reasons justify the development of a regional model: the first is the increasing number of works in literature asserting that regional models have also the features to provide more detailed description of the climate extremes, that are often more important then their mean values for natural and human systems. The second one is that high resolution modelling shows adequate features to provide information for impact assessment studies. At CMCC, regional climate modelling is a part of an integrated simulation system and it has been used in different European and African projects to provide qualitative and quantitative evaluation of the hydrogeological and public health risks. A simulation covering the period 1971-2000 and driven by ERA40 reanalysis has been performed, in order to assess the capability of the model to reproduce the present climate, with "perfect boundary conditions". A comparison, in terms of 2-metre temperature and precipitation, with EOBS dataset will be shown and discussed, in order to analyze the capabilities in simulating the main features of the observed climate over a wide area, at high spatial resolution. Then, a comparison between the results of COSMO-CLM driven by the global model CMCC-MED (whose atmospheric component is ECHAM5) and by ERA40 will be provided for a characterization of the errors induced by the global model. Finally, climate projections on the examined area for the XXI century, considering the RCP4.5 emission scenario for the future, will be provided. In this work a special emphasis will be issued to the analysis of the capability to reproduce not only the average climate patterns but also extremes of the present and future climate, in terms of temperature, precipitation and wind.

Atmospheric, climatic and environmental research

NASA Technical Reports Server (NTRS)

Broecker, Wallace S.; Gornitz, Vivien M.

1992-01-01

Work performed on the three tasks during the report period is summarized. The climate and atmospheric modeling studies included work on climate model development and applications, paleoclimate studies, climate change applications, and SAGE II. Climate applications of Earth and planetary observations included studies on cloud climatology and planetary studies. Studies on the chemistry of the Earth and the environment are briefly described. Publications based on the above research are listed; two of these papers are included in the appendices.

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.

Climate Projections from the NARCliM Project: Bayesian Model Averaging of Maximum Temperature Projections

NASA Astrophysics Data System (ADS)

Olson, R.; Evans, J. P.; Fan, Y.

2015-12-01

NARCliM (NSW/ACT Regional Climate Modelling Project) is a regional climate project for Australia and the surrounding region. It dynamically downscales 4 General Circulation Models (GCMs) using three Regional Climate Models (RCMs) to provide climate projections for the CORDEX-AustralAsia region at 50 km resolution, and for south-east Australia at 10 km resolution. The project differs from previous work in the level of sophistication of model selection. Specifically, the selection process for GCMs included (i) conducting literature review to evaluate model performance, (ii) analysing model independence, and (iii) selecting models that span future temperature and precipitation change space. RCMs for downscaling the GCMs were chosen based on their performance for several precipitation events over South-East Australia, and on model independence.Bayesian Model Averaging (BMA) provides a statistically consistent framework for weighing the models based on their likelihood given the available observations. These weights are used to provide probability distribution functions (pdfs) for model projections. We develop a BMA framework for constructing probabilistic climate projections for spatially-averaged variables from the NARCliM project. The first step in the procedure is smoothing model output in order to exclude the influence of internal climate variability. Our statistical model for model-observations residuals is a homoskedastic iid process. Comparing RCMs with Australian Water Availability Project (AWAP) observations is used to determine model weights through Monte Carlo integration. Posterior pdfs of statistical parameters of model-data residuals are obtained using Markov Chain Monte Carlo. The uncertainty in the properties of the model-data residuals is fully accounted for when constructing the projections. We present the preliminary results of the BMA analysis for yearly maximum temperature for New South Wales state planning regions for the period 2060-2079.

Flexible Environments for Grand-Challenge Simulation in Climate Science

NASA Astrophysics Data System (ADS)

Pierrehumbert, R.; Tobis, M.; Lin, J.; Dieterich, C.; Caballero, R.

2004-12-01

Current climate models are monolithic codes, generally in Fortran, aimed at high-performance simulation of the modern climate. Though they adequately serve their designated purpose, they present major barriers to application in other problems. Tailoring them to paleoclimate of planetary simulations, for instance, takes months of work. Theoretical studies, where one may want to remove selected processes or break feedback loops, are similarly hindered. Further, current climate models are of little value in education, since the implementation of textbook concepts and equations in the code is obscured by technical detail. The Climate Systems Center at the University of Chicago seeks to overcome these limitations by bringing modern object-oriented design into the business of climate modeling. Our ultimate goal is to produce an end-to-end modeling environment capable of configuring anything from a simple single-column radiative-convective model to a full 3-D coupled climate model using a uniform, flexible interface. Technically, the modeling environment is implemented as a Python-based software component toolkit: key number-crunching procedures are implemented as discrete, compiled-language components 'glued' together and co-ordinated by Python, combining the high performance of compiled languages and the flexibility and extensibility of Python. We are incrementally working towards this final objective following a series of distinct, complementary lines. We will present an overview of these activities, including PyOM, a Python-based finite-difference ocean model allowing run-time selection of different Arakawa grids and physical parameterizations; CliMT, an atmospheric modeling toolkit providing a library of 'legacy' radiative, convective and dynamical modules which can be knitted into dynamical models, and PyCCSM, a version of NCAR's Community Climate System Model in which the coupler and run-control architecture are re-implemented in Python, augmenting its flexibility and adaptability.

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

Global Climate Models Intercomparison of Anthropogenic Aerosols Effects on Regional Climate over North Pacific

NASA Astrophysics Data System (ADS)

Hu, J.; Zhang, R.; Wang, Y.; Ming, Y.; Lin, Y.; Pan, B.

2015-12-01

Aerosols can alter atmospheric radiation and cloud physics, which further exert impacts on weather and global climate. With the development and industrialization of the developing Asian countries, anthropogenic aerosols have received considerable attentions and remain to be the largest uncertainty in the climate projection. Here we assess the performance of two stat-of-art global climate models (National Center for Atmospheric Research-Community Atmosphere Model 5 (CAM5) and Geophysical Fluid Dynamics Laboratory Atmosphere Model 3 (AM3)) in simulating the impacts of anthropogenic aerosols on North Pacific storm track region. By contrasting two aerosol scenarios, i.e. present day (PD) and pre-industrial (PI), both models show aerosol optical depth (AOD) enhanced by about 22%, with CAM5 AOD 40% lower in magnitude due to the long range transport of anthropogenic aerosols. Aerosol effects on the ice water path (IWP), stratiform precipitation, convergence and convection strengths in the two models are distinctive in patterns and magnitudes. AM3 shows qualitatively good agreement with long-term satellite observations, while CAM5 overestimates convection and liquid water path resulting in an underestimation of large-scale precipitation and IWP. Due to coarse resolution and parameterization in convection schemes, both models' performance on convection needs to be improved. Aerosols performance on large-scale circulation and radiative budget are also examined in this study.

Translating Extreme Precipitation Data from Climate Change Projections into Resilient Engineering Applications

NASA Astrophysics Data System (ADS)

Cook, L. M.; Samaras, C.; Anderson, C.

2016-12-01

Engineers generally use historical precipitation trends to inform assumptions and parameters for long-lived infrastructure designs. However, resilient design calls for the adjustment of current engineering practice to incorporate a range of future climate conditions that are likely to be different than the past. Despite the availability of future projections from downscaled climate models, there remains a considerable mismatch between climate model outputs and the inputs needed in the engineering community to incorporate climate resiliency. These factors include differences in temporal and spatial scales, model uncertainties, and a lack of criteria for selection of an ensemble of models. This research addresses the limitations to working with climate data by providing a framework for the use of publicly available downscaled climate projections to inform engineering resiliency. The framework consists of five steps: 1) selecting the data source based on the engineering application, 2) extracting the data at a specific location, 3) validating for performance against observed data, 4) post-processing for bias or scale, and 5) selecting the ensemble and calculating statistics. The framework is illustrated with an example application to extreme precipitation-frequency statistics, the 25-year daily precipitation depth, using four publically available climate data sources: NARCCAP, USGS, Reclamation, and MACA. The attached figure presents the results for step 5 from the framework, analyzing how the 24H25Y depth changes when the model ensemble is culled based on model performance against observed data, for both post-processing techniques: bias-correction and change factor. Culling the model ensemble increases both the mean and median values for all data sources, and reduces range for NARCCAP and MACA ensembles due to elimination of poorer performing models, and in some cases, those that predict a decrease in future 24H25Y precipitation volumes. This result is especially relevant to engineers who wish to reduce the range of the ensemble and remove contradicting models; however, this result is not generalizable for all cases. Finally, this research highlights the need for the formation of an intermediate entity that is able to translate climate projections into relevant engineering information.

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.

Transferability of optimally-selected climate models in the quantification of climate change impacts on hydrology

NASA Astrophysics Data System (ADS)

Chen, Jie; Brissette, François P.; Lucas-Picher, Philippe

2016-11-01

Given the ever increasing number of climate change simulations being carried out, it has become impractical to use all of them to cover the uncertainty of climate change impacts. Various methods have been proposed to optimally select subsets of a large ensemble of climate simulations for impact studies. However, the behaviour of optimally-selected subsets of climate simulations for climate change impacts is unknown, since the transfer process from climate projections to the impact study world is usually highly non-linear. Consequently, this study investigates the transferability of optimally-selected subsets of climate simulations in the case of hydrological impacts. Two different methods were used for the optimal selection of subsets of climate scenarios, and both were found to be capable of adequately representing the spread of selected climate model variables contained in the original large ensemble. However, in both cases, the optimal subsets had limited transferability to hydrological impacts. To capture a similar variability in the impact model world, many more simulations have to be used than those that are needed to simply cover variability from the climate model variables' perspective. Overall, both optimal subset selection methods were better than random selection when small subsets were selected from a large ensemble for impact studies. However, as the number of selected simulations increased, random selection often performed better than the two optimal methods. To ensure adequate uncertainty coverage, the results of this study imply that selecting as many climate change simulations as possible is the best avenue. Where this was not possible, the two optimal methods were found to perform adequately.

Douglas-fir plantations in Europe: a retrospective test of assisted migration to address climate change.

PubMed

Isaac-Renton, Miriam G; Roberts, David R; Hamann, Andreas; Spiecker, Heinrich

2014-08-01

We evaluate genetic test plantations of North American Douglas-fir provenances in Europe to quantify how tree populations respond when subjected to climate regime shifts, and we examined whether bioclimate envelope models developed for North America to guide assisted migration under climate change can retrospectively predict the success of these provenance transfers to Europe. The meta-analysis is based on long-term growth data of 2800 provenances transferred to 120 European test sites. The model was generally well suited to predict the best performing provenances along north-south gradients in Western Europe, but failed to predict superior performance of coastal North American populations under continental climate conditions in Eastern Europe. However, model projections appear appropriate when considering additional information regarding adaptation of Douglas-fir provenances to withstand frost and drought, even though the model partially fails in a validation against growth traits alone. We conclude by applying the partially validated model to climate change scenarios for Europe, demonstrating that climate trends observed over the last three decades warrant changes to current use of Douglas-fir provenances in plantation forestry throughout Western and Central Europe. © 2014 John Wiley & Sons Ltd.

Climate change impact assessment on hydrology of a small watershed using semi-distributed model

NASA Astrophysics Data System (ADS)

Pandey, Brij Kishor; Gosain, A. K.; Paul, George; Khare, Deepak

2017-07-01

This study is an attempt to quantify the impact of climate change on the hydrology of Armur watershed in Godavari river basin, India. A GIS-based semi-distributed hydrological model, soil and water assessment tool (SWAT) has been employed to estimate the water balance components on the basis of unique combinations of slope, soil and land cover classes for the base line (1961-1990) and future climate scenarios (2071-2100). Sensitivity analysis of the model has been performed to identify the most critical parameters of the watershed. Average monthly calibration (1987-1994) and validation (1995-2000) have been performed using the observed discharge data. Coefficient of determination (R2), Nash-Sutcliffe efficiency (ENS) and root mean square error (RMSE) were used to evaluate the model performance. Calibrated SWAT setup has been used to evaluate the changes in water balance components of future projection over the study area. HadRM3, a regional climatic data, have been used as input of the hydrological model for climate change impact studies. In results, it was found that changes in average annual temperature (+3.25 °C), average annual rainfall (+28 %), evapotranspiration (28 %) and water yield (49 %) increased for GHG scenarios with respect to the base line scenario.

Uncertainty of a hydrological climate change impact assessment - Is it really all about climate uncertainty?

NASA Astrophysics Data System (ADS)

Honti, Mark; Reichert, Peter; Scheidegger, Andreas; Stamm, Christian

2013-04-01

Climate change impact assessments have become more and more popular in hydrology since the middle 1980's with another boost after the publication of the IPCC AR4 report. During hundreds of impact studies a quasi-standard methodology emerged, which is mainly shaped by the growing public demand for predicting how water resources management or flood protection should change in the close future. The ``standard'' workflow considers future climate under a specific IPCC emission scenario simulated by global circulation models (GCMs), possibly downscaled by a regional climate model (RCM) and/or a stochastic weather generator. The output from the climate models is typically corrected for bias before feeding it into a calibrated hydrological model, which is run on the past and future meteorological data to analyse the impacts of climate change on the hydrological indicators of interest. The impact predictions are as uncertain as any forecast that tries to describe the behaviour of an extremely complex system decades into the future. Future climate predictions are uncertain due to the scenario uncertainty and the GCM model uncertainty that is obvious on finer resolution than continental scale. Like in any hierarchical model system, uncertainty propagates through the descendant components. Downscaling increases uncertainty with the deficiencies of RCMs and/or weather generators. Bias correction adds a strong deterministic shift to the input data. Finally the predictive uncertainty of the hydrological model ends the cascade that leads to the total uncertainty of the hydrological impact assessment. There is an emerging consensus between many studies on the relative importance of the different uncertainty sources. The prevailing perception is that GCM uncertainty dominates hydrological impact studies. There are only few studies, which found that the predictive uncertainty of hydrological models can be in the same range or even larger than climatic uncertainty. We carried out a climate change impact assessment and estimated the relative importance of the uncertainty sources. The study was performed on 2 small catchments in the Swiss Plateau with a lumped conceptual rainfall runoff model. In the climatic part we applied the standard ensemble approach to quantify uncertainty but in hydrology we used formal Bayesian uncertainty assessment method with 2 different likelihood functions. One was a time-series error model that was able to deal with the complicated statistical properties of hydrological model residuals. The second was a likelihood function for the flow quantiles directly. Due to the better data coverage and smaller hydrological complexity in one of our test catchments we had better performance from the hydrological model and thus could observe that the relative importance of different uncertainty sources varied between sites, boundary conditions and flow indicators. The uncertainty of future climate was important, but not dominant. The deficiencies of the hydrological model were on the same scale, especially for the sites and flow components where model performance for the past observations was further from optimal (Nash-Sutcliffe index = 0.5 - 0.7). The overall uncertainty of predictions was well beyond the expected change signal even for the best performing site and flow indicator.

WRF-Cordex simulations for Europe: mean and extreme precipitation for present and future climates

NASA Astrophysics Data System (ADS)

Cardoso, Rita M.; Soares, Pedro M. M.; Miranda, Pedro M. A.

2013-04-01

The Weather Research and Forecast (WRF-ARW) model, version 3.3.1, was used to perform the European domain Cordex simulations, at 50km resolution. A first simulation, forced by ERA-Interim (1989-2009), was carried out to evaluate the models performance to represent the mean and extreme precipitation in present European climate. This evaluation is based in the comparison of WRF results against the ECAD regular gridded dataset of daily precipitation. Results are comparable to recent studies with other models for the European region, at this resolution. For the same domain a control and a future scenario (RCP8.5) simulation was performed to assess the climate change impact on the mean and extreme precipitation. These regional simulations were forced by EC-EARTH model results, and, encompass the periods from 1960-2006 and 2006-2100, respectively.

SEEPLUS: A SIMPLE ONLINE CLIMATE MODEL

NASA Astrophysics Data System (ADS)

Tsutsui, Junichi

A web application for a simple climate model - SEEPLUS (a Simple climate model to Examine Emission Pathways Leading to Updated Scenarios) - has been developed. SEEPLUS consists of carbon-cycle and climate-change modules, through which it provides the information infrastructure required to perform climate-change experiments, even on a millennial-timescale. The main objective of this application is to share the latest scientific knowledge acquired from climate modeling studies among the different stakeholders involved in climate-change issues. Both the carbon-cycle and climate-change modules employ impulse response functions (IRFs) for their key processes, thereby enabling the model to integrate the outcome from an ensemble of complex climate models. The current IRF parameters and forcing manipulation are basically consistent with, or within an uncertainty range of, the understanding of certain key aspects such as the equivalent climate sensitivity and ocean CO2 uptake data documented in representative literature. The carbon-cycle module enables inverse calculation to determine the emission pathway required in order to attain a given concentration pathway, thereby providing a flexible way to compare the module with more advanced modeling studies. The module also enables analytical evaluation of its equilibrium states, thereby facilitating the long-term planning of global warming mitigation.

Response of the tropical Pacific to abrupt climate change 8,200 years ago

NASA Astrophysics Data System (ADS)

Atwood, A. R.; Battisti, D.; Bitz, C. M.; Sachs, J. P.

2017-12-01

The relatively stable climate of the Holocene epoch was punctuated by a period of large and abrupt climate change ca. 8,200 yr BP, when an outburst of glacial meltwater into the Labrador Sea drove large and abrupt climate changes across the globe. However, little is known about the response of the tropical Pacific to this event. We present the first evidence for large perturbations to the eastern tropical Pacific climate, based on sedimentary biomarker and hydrogen isotopic records from a freshwater lake in the Galápagos Islands. We inform these reconstructions with freshwater forcing simulations performed with the Community Climate System Model version 4. Together, the biomarker records and model simulations provide evidence for a mechanistic link between (1) a southward shift of the Intertropical Convergence Zone in the eastern equatorial Pacific and (2) decreased frequency and/or intensity of Eastern Pacific El Niño events during the 8,200 BP event. While climate theory and modeling studies support a southward shift of the ITCZ in response to a weakened AMOC, the dynamical drivers for the observed change in ENSO variability are less well developed. To explore these linkages, we perform simulations with an intermediate complexity model of the tropical Pacific. These results provide valuable insight into the controls of tropical Pacific climate variability and the mechanisms behind the global response to abrupt climate change.

Implementation of Web Processing Services (WPS) over IPSL Earth System Grid Federation (ESGF) node

NASA Astrophysics Data System (ADS)

Kadygrov, Nikolay; Denvil, Sebastien; Carenton, Nicolas; Levavasseur, Guillaume; Hempelmann, Nils; Ehbrecht, Carsten

2016-04-01

The Earth System Grid Federation (ESGF) is aimed to provide access to climate data for the international climate community. ESGF is a system of distributed and federated nodes that dynamically interact with each other. ESGF user may search and download climatic data, geographically distributed over the world, from one common web interface and through standardized API. With the continuous development of the climate models and the beginning of the sixth phase of the Coupled Model Intercomparison Project (CMIP6), the amount of data available from ESGF will continuously increase during the next 5 years. IPSL holds a replication of the different global and regional climate models output, observations and reanalysis data (CMIP5, CORDEX, obs4MIPs, etc) that are available on the IPSL ESGF node. In order to let scientists perform analysis of the models without downloading vast amount of data the Web Processing Services (WPS) were installed at IPSL compute node. The work is part of the CONVERGENCE project founded by French National Research Agency (ANR). PyWPS implementation of the Web processing Service standard from Open Geospatial Consortium (OGC) in the framework of birdhouse software is used. The processes could be run by user remotely through web-based WPS client or by using command-line tool. All the calculations are performed on the server side close to the data. If the models/observations are not available at IPSL it will be downloaded and cached by WPS process from ESGF network using synda tool. The outputs of the WPS processes are available for download as plots, tar-archives or as NetCDF files. We present the architecture of WPS at IPSL along with the processes for evaluation of the model performance, on-site diagnostics and post-analysis processing of the models output, e.g.: - regriding/interpolation/aggregation - ocgis (OpenClimateGIS) based polygon subsetting of the data - average seasonal cycle, multimodel mean, multimodel mean bias - calculation of the climate indices with icclim library (CERFACS) - atmospheric modes of variability In order to evaluate performance of any new model, once it became available in ESGF, we implement WPS with several model diagnostics and performance metrics calculated using ESMValTool (Eyring et al., GMDD 2015). As a further step we are developing new WPS processes and core-functions to be implemented at ISPL ESGF compute node following the scientific community needs.

76 FR 33923 - Endangered and Threatened Wildlife and Plants; 12-Month Finding on a Petition To List Abronia...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-06-09

... Sipes 2006, p. 76). The leaf blades are succulent (fleshy) and oval or diamond-shaped with smooth edges... of climate model runs performed at modeling centers worldwide using 22 global climate models (Ray et...

Assessing the Added Value of Dynamical Downscaling in the Context of Hydrologic Implication

NASA Astrophysics Data System (ADS)

Lu, M.; IM, E. S.; Lee, M. H.

2017-12-01

There is a scientific consensus that high-resolution climate simulations downscaled by Regional Climate Models (RCMs) can provide valuable refined information over the target region. However, a significant body of hydrologic impact assessment has been performing using the climate information provided by Global Climate Models (GCMs) in spite of a fundamental spatial scale gap. It is probably based on the assumption that the substantial biases and spatial scale gap from GCMs raw data can be simply removed by applying the statistical bias correction and spatial disaggregation. Indeed, many previous studies argue that the benefit of dynamical downscaling using RCMs is minimal when linking climate data with the hydrological model, from the comparison of the impact between bias-corrected GCMs and bias-corrected RCMs on hydrologic simulations. It may be true for long-term averaged climatological pattern, but it is not necessarily the case when looking into variability across various temporal spectrum. In this study, we investigate the added value of dynamical downscaling focusing on the performance in capturing climate variability. For doing this, we evaluate the performance of the distributed hydrological model over the Korean river basin using the raw output from GCM and RCM, and bias-corrected output from GCM and RCM. The impacts of climate input data on streamflow simulation are comprehensively analyzed. [Acknowledgements]This research is supported by the Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (Grant 17AWMP-B083066-04).

Identification of the prediction model for dengue incidence in Can Tho city, a Mekong Delta area in Vietnam.

PubMed

Phung, Dung; Huang, Cunrui; Rutherford, Shannon; Chu, Cordia; Wang, Xiaoming; Nguyen, Minh; Nguyen, Nga Huy; Manh, Cuong Do

2015-01-01

The Mekong Delta is highly vulnerable to climate change and a dengue endemic area in Vietnam. This study aims to examine the association between climate factors and dengue incidence and to identify the best climate prediction model for dengue incidence in Can Tho city, the Mekong Delta area in Vietnam. We used three different regression models comprising: standard multiple regression model (SMR), seasonal autoregressive integrated moving average model (SARIMA), and Poisson distributed lag model (PDLM) to examine the association between climate factors and dengue incidence over the period 2003-2010. We validated the models by forecasting dengue cases for the period of January-December, 2011 using the mean absolute percentage error (MAPE). Receiver operating characteristics curves were used to analyze the sensitivity of the forecast of a dengue outbreak. The results indicate that temperature and relative humidity are significantly associated with changes in dengue incidence consistently across the model methods used, but not cumulative rainfall. The Poisson distributed lag model (PDLM) performs the best prediction of dengue incidence for a 6, 9, and 12-month period and diagnosis of an outbreak however the SARIMA model performs a better prediction of dengue incidence for a 3-month period. The simple or standard multiple regression performed highly imprecise prediction of dengue incidence. We recommend a follow-up study to validate the model on a larger scale in the Mekong Delta region and to analyze the possibility of incorporating a climate-based dengue early warning method into the national dengue surveillance system. Copyright © 2014 Elsevier B.V. All rights reserved.

Measures of GCM Performance as Functions of Model Parameters Affecting Clouds and Radiation

NASA Astrophysics Data System (ADS)

Jackson, C.; Mu, Q.; Sen, M.; Stoffa, P.

2002-05-01

This abstract is one of three related presentations at this meeting dealing with several issues surrounding optimal parameter and uncertainty estimation of model predictions of climate. Uncertainty in model predictions of climate depends in part on the uncertainty produced by model approximations or parameterizations of unresolved physics. Evaluating these uncertainties is computationally expensive because one needs to evaluate how arbitrary choices for any given combination of model parameters affects model performance. Because the computational effort grows exponentially with the number of parameters being investigated, it is important to choose parameters carefully. Evaluating whether a parameter is worth investigating depends on two considerations: 1) does reasonable choices of parameter values produce a large range in model response relative to observational uncertainty? and 2) does the model response depend non-linearly on various combinations of model parameters? We have decided to narrow our attention to selecting parameters that affect clouds and radiation, as it is likely that these parameters will dominate uncertainties in model predictions of future climate. We present preliminary results of ~20 to 30 AMIPII style climate model integrations using NCAR's CCM3.10 that show model performance as functions of individual parameters controlling 1) critical relative humidity for cloud formation (RHMIN), and 2) boundary layer critical Richardson number (RICR). We also explore various definitions of model performance that include some or all observational data sources (surface air temperature and pressure, meridional and zonal winds, clouds, long and short-wave cloud forcings, etc...) and evaluate in a few select cases whether the model's response depends non-linearly on the parameter values we have selected.

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

NASA Astrophysics Data System (ADS)

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

2014-12-01

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

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

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

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

2011-12-05

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

The interplay of climate and land use change affects the distribution of EU bumblebees.

PubMed

Marshall, Leon; Biesmeijer, Jacobus C; Rasmont, Pierre; Vereecken, Nicolas J; Dvorak, Libor; Fitzpatrick, Una; Francis, Frédéric; Neumayer, Johann; Ødegaard, Frode; Paukkunen, Juho P T; Pawlikowski, Tadeusz; Reemer, Menno; Roberts, Stuart P M; Straka, Jakub; Vray, Sarah; Dendoncker, Nicolas

2018-01-01

Bumblebees in Europe have been in steady decline since the 1900s. This decline is expected to continue with climate change as the main driver. However, at the local scale, land use and land cover (LULC) change strongly affects the occurrence of bumblebees. At present, LULC change is rarely included in models of future distributions of species. This study's objective is to compare the roles of dynamic LULC change and climate change on the projected distribution patterns of 48 European bumblebee species for three change scenarios until 2100 at the scales of Europe, and Belgium, Netherlands and Luxembourg (BENELUX). We compared three types of models: (1) only climate covariates, (2) climate and static LULC covariates and (3) climate and dynamic LULC covariates. The climate and LULC change scenarios used in the models include, extreme growth applied strategy (GRAS), business as might be usual and sustainable European development goals. We analysed model performance, range gain/loss and the shift in range limits for all bumblebees. Overall, model performance improved with the introduction of LULC covariates. Dynamic models projected less range loss and gain than climate-only projections, and greater range loss and gain than static models. Overall, there is considerable variation in species responses and effects were most pronounced at the BENELUX scale. The majority of species were predicted to lose considerable range, particularly under the extreme growth scenario (GRAS; overall mean: 64% ± 34). Model simulations project a number of local extinctions and considerable range loss at the BENELUX scale (overall mean: 56% ± 39). Therefore, we recommend species-specific modelling to understand how LULC and climate interact in future modelling. The efficacy of dynamic LULC change should improve with higher thematic and spatial resolution. Nevertheless, current broad scale representations of change in major land use classes impact modelled future distribution patterns. © 2017 The Authors. Global Change Biology Published by John Wiley & Sons Ltd.

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.

Investigating the Capacity of Hydrological Models to Project Impacts of Climate Change in the Context of Water Allocation

NASA Astrophysics Data System (ADS)

Velez, Carlos; Maroy, Edith; Rocabado, Ivan; Pereira, Fernando

2017-04-01

To analyse the impacts of climate changes, hydrological models are used to project the hydrology responds under future conditions that normally differ from those for which they were calibrated. The challenge is to assess the validity of the projected effects when there is not data to validate it. A framework for testing the ability of models to project climate change was proposed by Refsgaard et al., (2014). The authors recommend the use of the differential-split sample test (DSST) in order to build confidence in the model projections. The method follow three steps: 1. A small number of sub-periods are selected according to one climate characteristics, 2. The calibration - validation test is applied on these periods, 3. The validation performances are compered to evaluate whether they vary significantly when climatic characteristics differ between calibration and validation. DSST rely on the existing records of climate and hydrological variables; and performances are estimated based on indicators of error between observed and simulated variables. Other authors suggest that, since climate models are not able to reproduce single events but rather statistical properties describing the climate, this should be reflected when testing hydrological models. Thus, performance criteria such as RMSE should be replaced by for instance flow duration curves or other distribution functions. Using this type of performance criteria, Van Steenbergen and Willems, (2012) proposed a method to test the validity of hydrological models in a climate changing context. The method is based on the evaluation of peak flow increases due to different levels of rainfall increases. In contrast to DSST, this method use the projected climate variability and it is especially useful to compare different modelling tools. In the framework of a water allocation project for the region of Flanders (Belgium) we calibrated three hydrological models: NAM, PDM and VHM; for 67 gauged sub-catchments with approx. 40 years of records. This paper investigates the capacity of the three hydrological models to project the impacts of climate change scenarios. It is proposed a general testing framework which combine the use of the existing information through an adapted form of DSST with the approach proposed by Van Steenbergen and Willems, (2012) adapted to assess statistical properties of flows useful in the context of water allocation. To assess the model we use robustness criteria based on a Log Nash-Sutcliffe, BIAS on cummulative volumes and relative changes based on Q50/Q90 estimated from the duration curve. The three conceptual rainfall-runoff models yielded different results per sub-catchments. A relation was found between robustness criteria and changes in mean rainfall and changes in mean potential evapotranspiration. Biases are greatly affected by changes in precipitation, especially when the climate scenarios involve changes in precipitation volume beyond the range used for calibration. Using the combine approach we were able to classify the modelling tools per sub-catchments and create an ensemble of best models to project the impacts of climate variability for the catchments of 10 main rivers in Flanders. Thus, managers could understand better the usability of the modelling tools and the credibility of its outputs for water allocation applications. References Refsgaard, J.C., Madsen, H., Andréassian, V., Arnbjerg-Nielsen, K., Davidson, T.A., Drews, M., Hamilton, D.P., Jeppesen, E., Kjellström, E., Olesen, J.E., Sonnenborg, T.O., Trolle, D., Willems, P., Christensen, J.H., 2014. A framework for testing the ability of models to project climate change and its impacts. Clim. Change. Van Steenbergen, N., Willems, P., 2012. Method for testing the accuracy of rainfall - runoff models in predicting peak flow changes due to rainfall changes , in a climate changing context. J. Hydrol. 415, 425-434.

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.

Designing ecological climate change impact assessments to reflect key climatic drivers

USGS Publications Warehouse

Sofaer, Helen R.; Barsugli, Joseph J.; Jarnevich, Catherine S.; Abatzoglou, John T.; Talbert, Marian; Miller, Brian W.; Morisette, Jeffrey T.

2017-01-01

Identifying the climatic drivers of an ecological system is a key step in assessing its vulnerability to climate change. The climatic dimensions to which a species or system is most sensitive – such as means or extremes – can guide methodological decisions for projections of ecological impacts and vulnerabilities. However, scientific workflows for combining climate projections with ecological models have received little explicit attention. We review Global Climate Model (GCM) performance along different dimensions of change and compare frameworks for integrating GCM output into ecological models. In systems sensitive to climatological means, it is straightforward to base ecological impact assessments on mean projected changes from several GCMs. Ecological systems sensitive to climatic extremes may benefit from what we term the ‘model space’ approach: a comparison of ecological projections based on simulated climate from historical and future time periods. This approach leverages the experimental framework used in climate modeling, in which historical climate simulations serve as controls for future projections. Moreover, it can capture projected changes in the intensity and frequency of climatic extremes, rather than assuming that future means will determine future extremes. Given the recent emphasis on the ecological impacts of climatic extremes, the strategies we describe will be applicable across species and systems. We also highlight practical considerations for the selection of climate models and data products, emphasizing that the spatial resolution of the climate change signal is generally coarser than the grid cell size of downscaled climate model output. Our review illustrates how an understanding of how climate model outputs are derived and downscaled can improve the selection and application of climatic data used in ecological modeling.

Designing ecological climate change impact assessments to reflect key climatic drivers.

PubMed

Sofaer, Helen R; Barsugli, Joseph J; Jarnevich, Catherine S; Abatzoglou, John T; Talbert, Marian K; Miller, Brian W; Morisette, Jeffrey T

2017-07-01

Identifying the climatic drivers of an ecological system is a key step in assessing its vulnerability to climate change. The climatic dimensions to which a species or system is most sensitive - such as means or extremes - can guide methodological decisions for projections of ecological impacts and vulnerabilities. However, scientific workflows for combining climate projections with ecological models have received little explicit attention. We review Global Climate Model (GCM) performance along different dimensions of change and compare frameworks for integrating GCM output into ecological models. In systems sensitive to climatological means, it is straightforward to base ecological impact assessments on mean projected changes from several GCMs. Ecological systems sensitive to climatic extremes may benefit from what we term the 'model space' approach: a comparison of ecological projections based on simulated climate from historical and future time periods. This approach leverages the experimental framework used in climate modeling, in which historical climate simulations serve as controls for future projections. Moreover, it can capture projected changes in the intensity and frequency of climatic extremes, rather than assuming that future means will determine future extremes. Given the recent emphasis on the ecological impacts of climatic extremes, the strategies we describe will be applicable across species and systems. We also highlight practical considerations for the selection of climate models and data products, emphasizing that the spatial resolution of the climate change signal is generally coarser than the grid cell size of downscaled climate model output. Our review illustrates how an understanding of how climate model outputs are derived and downscaled can improve the selection and application of climatic data used in ecological modeling. © 2017 John Wiley & Sons Ltd.

Teams as innovative systems: multilevel motivational antecedents of innovation in R&D teams.

PubMed

Chen, Gilad; Farh, Jiing-Lih; Campbell-Bush, Elizabeth M; Wu, Zhiming; Wu, Xin

2013-11-01

Integrating theories of proactive motivation, team innovation climate, and motivation in teams, we developed and tested a multilevel model of motivators of innovative performance in teams. Analyses of multisource data from 428 members of 95 research and development (R&D) teams across 33 Chinese firms indicated that team-level support for innovation climate captured motivational mechanisms that mediated between transformational leadership and team innovative performance, whereas members' motivational states (role-breadth self-efficacy and intrinsic motivation) mediated between proactive personality and individual innovative performance. Furthermore, individual motivational states and team support for innovation climate uniquely promoted individual innovative performance, and, in turn, individual innovative performance linked team support for innovation climate to team innovative performance. (c) 2013 APA, all rights reserved.

Assessing the capability of high resolution climatic model experiments to simulate Mediterranean cyclonic tracks

NASA Astrophysics Data System (ADS)

Hatzaki, M.; Flocas, H. A.; Giannakopoulos, C.; Kostopoulou, E.; Kouroutzoglou, I.; Keay, K.; Simmonds, I.

2010-09-01

In this study, a comparison of a reanalysis driven simulation to a GCM driven simulation of a regional climate model is performed in order to assess the model's ability to capture the climatic characteristics of cyclonic tracks in the Mediterranean in the present climate. The ultimate scope of the study will be to perform a future climate projection related to cyclonic tracks in order to better understand and assess climate change in the Mediterranean. The climatology of the cyclonic tracks includes inter-monthly variations, classification of tracks according to their origin domain, dynamic and kinematic characteristics, as well as trend analysis. For this purpose, the ENEA model is employed based on PROTHEUS system composed of the RegCM atmospheric regional model and the MITgcm ocean model, coupled through the OASIS3 flux coupler. These model data became available through the EU Project CIRCE which aims to perform, for the first time, climate change projections with a realistic representation of the Mediterranean Sea. Two experiments are employed; a) the ERA402 with lateral Boundary conditions from ERA40 for the 43-year period 1958-2000, and b) the EH5OM_20C3M where the lateral boundary conditions for the atmosphere (1951-2000) are taken from the ECHAM5-MPIOM 20c3m global simulation (run3) included in the IPCC-AR4. The identification and tracking of cyclones is performed with the aid of the Melbourne University algorithm (MS algorithm), according to the Lagrangian perspective. MS algorithm characterizes a cyclone only if a vorticity maximum could be connected with a local pressure minimum. This approach is considered to be crucial, since open lows are also incorporated into the storm life-cycle, preventing possible inappropriate time series breaks, if a temporary weakening to an open-low state occurs. The model experiments verify that considerable inter-monthly variations of track density occur in the Mediterranean region, consistent with previous studies. The classification of the tracks according to their origin domain show that the vast majority originate within the examined area itself. The study of the kinematic and dynamic parameters of tracks according to their origin demonstrate that deeper cyclones follow the SW track. ACKNOWLEDGMENTS: M. Hatzaki would like to thank the Greek State Scholarships Foundation for financial support through the program of postdoctoral research. The support of EU-FP6 project CIRCE Integrated Project-Climate Change and Impact Research: the Mediterranean Environment (http://www.circeproject.eu) for climate model data provision is also greatly acknowledged.

HPC Aspects of Variable-Resolution Global Climate Modeling using a Multi-scale Convection Parameterization

EPA Science Inventory

High performance computing (HPC) requirements for the new generation variable grid resolution (VGR) global climate models differ from that of traditional global models. A VGR global model with 15 km grids over the CONUS stretching to 60 km grids elsewhere will have about ~2.5 tim...

A Global Climate Model for Instruction.

ERIC Educational Resources Information Center

Burt, James E.

This paper describes a simple global climate model useful in a freshman or sophomore level course in climatology. There are three parts to the paper. The first part describes the model, which is a global model of surface air temperature averaged over latitude and longitude. Samples of the types of calculations performed in the model are provided.…

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

Modeling climate change impacts on water trading.

PubMed

Luo, Bin; Maqsood, Imran; Gong, Yazhen

2010-04-01

This paper presents a new method of evaluating the impacts of climate change on the long-term performance of water trading programs, through designing an indicator to measure the mean of periodic water volume that can be released by trading through a water-use system. The indicator is computed with a stochastic optimization model which can reflect the random uncertainty of water availability. The developed method was demonstrated in the Swift Current Creek watershed of Prairie Canada under two future scenarios simulated by a Canadian Regional Climate Model, in which total water availabilities under future scenarios were estimated using a monthly water balance model. Frequency analysis was performed to obtain the best probability distributions for both observed and simulated water quantity data. Results from the case study indicate that the performance of a trading system is highly scenario-dependent in future climate, with trading effectiveness highly optimistic or undesirable under different future scenarios. Trading effectiveness also largely depends on trading costs, with high costs resulting in failure of the trading program. (c) 2010 Elsevier B.V. All rights reserved.

Projection of climatic suitability for Aedes albopictus Skuse (Culicidae) in Europe under climate change conditions

NASA Astrophysics Data System (ADS)

Fischer, Dominik; Thomas, Stephanie Margarete; Niemitz, Franziska; Reineking, Björn; Beierkuhnlein, Carl

2011-07-01

During the last decades the disease vector Aedes albopictus ( Ae. albopictus) has rapidly spread around the globe. The spread of this species raises serious public health concerns. Here, we model the present distribution and the future climatic suitability of Europe for this vector in the face of climate change. In order to achieve the most realistic current prediction and future projection, we compare the performance of four different modelling approaches, differentiated by the selection of climate variables (based on expert knowledge vs. statistical criteria) and by the geographical range of presence records (native range vs. global range). First, models of the native and global range were built with MaxEnt and were either based on (1) statistically selected climatic input variables or (2) input variables selected with expert knowledge from the literature. Native models show high model performance (AUC: 0.91-0.94) for the native range, but do not predict the European distribution well (AUC: 0.70-0.72). Models based on the global distribution of the species, however, were able to identify all regions where Ae. albopictus is currently established, including Europe (AUC: 0.89-0.91). In a second step, the modelled bioclimatic envelope of the global range was projected to future climatic conditions in Europe using two emission scenarios implemented in the regional climate model COSMO-CLM for three time periods 2011-2040, 2041-2070, and 2071-2100. For both global-driven models, the results indicate that climatically suitable areas for the establishment of Ae. albopictus will increase in western and central Europe already in 2011-2040 and with a temporal delay in eastern Europe. On the other hand, a decline in climatically suitable areas in southern Europe is pronounced in the Expert knowledge based model. Our projections appear unaffected by non-analogue climate, as this is not detected by Multivariate Environmental Similarity Surface analysis. The generated risk maps can aid in identifying suitable habitats for Ae. albopictus and hence support monitoring and control activities to avoid disease vector establishment.

Efficient design based on perturbed parameter ensembles to identify plausible and diverse variants of a model for climate change projections

NASA Astrophysics Data System (ADS)

Karmalkar, A.; Sexton, D.; Murphy, J.

2017-12-01

We present exploratory work towards developing an efficient strategy to select variants of a state-of-the-art but expensive climate model suitable for climate projection studies. The strategy combines information from a set of idealized perturbed parameter ensemble (PPE) and CMIP5 multi-model ensemble (MME) experiments, and uses two criteria as basis to select model variants for a PPE suitable for future projections: a) acceptable model performance at two different timescales, and b) maintaining diversity in model response to climate change. We demonstrate that there is a strong relationship between model errors at weather and climate timescales for a variety of key variables. This relationship is used to filter out parts of parameter space that do not give credible simulations of historical climate, while minimizing the impact on ranges in forcings and feedbacks that drive model responses to climate change. We use statistical emulation to explore the parameter space thoroughly, and demonstrate that about 90% can be filtered out without affecting diversity in global-scale climate change responses. This leads to identification of plausible parts of parameter space from which model variants can be selected for projection studies.

Modelling Bambara Groundnut Yield in Southern Africa: Towards a Climate-Resilient Future

NASA Technical Reports Server (NTRS)

Karunaratne, A. S.; Walker, S.; Ruane, A. C.

2015-01-01

Current agriculture depends on a few major species grown as monocultures that are supported by global research underpinning current productivity. However, many hundreds of alternative crops have the potential to meet real world challenges by sustaining humanity, diversifying agricultural systems for food and nutritional security, and especially responding to climate change through their resilience to certain climate conditions. Bambara groundnut (Vigna subterranea (L.) Verdc.), an underutilised African legume, is an exemplar crop for climate resilience. Predicted yield performances of Bambara groundnut by AquaCrop (a crop-water productivity model) were evaluated for baseline (1980-2009) and mid-century climates (2040-2069) under 20 downscaled Global Climate Models (CMIP5-RCP8.5), as well as for climate sensitivities (AgMIPC3MP) across 3 locations in Southern Africa (Botswana, South Africa, Namibia). Different land - races of Bambara groundnut originating from various semi-arid African locations showed diverse yield performances with diverse sensitivities to climate. S19 originating from hot-dry conditions in Namibia has greater future yield potential compared to the Swaziland landrace Uniswa Red-UN across study sites. South Africa has the lowest yield under the current climate, indicating positive future yield trends. Namibia reported the highest baseline yield at optimum current temperatures, indicating less yield potential in future climates. Bambara groundnut shows positive yield potential at temperatures of up to 31degC, with further warming pushing yields down. Thus, many regions in Southern Africa can utilize Bambara groundnut successfully in the coming decades. This modelling exercise supports decisions on genotypic suitability for present and future climates at specific locations.

Performance of the general circulation models in simulating temperature and precipitation over Iran

NASA Astrophysics Data System (ADS)

Abbasian, Mohammadsadegh; Moghim, Sanaz; Abrishamchi, Ahmad

2018-03-01

General Circulation Models (GCMs) are advanced tools for impact assessment and climate change studies. Previous studies show that the performance of the GCMs in simulating climate variables varies significantly over different regions. This study intends to evaluate the performance of the Coupled Model Intercomparison Project phase 5 (CMIP5) GCMs in simulating temperature and precipitation over Iran. Simulations from 37 GCMs and observations from the Climatic Research Unit (CRU) were obtained for the period of 1901-2005. Six measures of performance including mean bias, root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), linear correlation coefficient (r), Kolmogorov-Smirnov statistic (KS), Sen's slope estimator, and the Taylor diagram are used for the evaluation. GCMs are ranked based on each statistic at seasonal and annual time scales. Results show that most GCMs perform reasonably well in simulating the annual and seasonal temperature over Iran. The majority of the GCMs have a poor skill to simulate precipitation, particularly at seasonal scale. Based on the results, the best GCMs to represent temperature and precipitation simulations over Iran are the CMCC-CMS (Euro-Mediterranean Center on Climate Change) and the MRI-CGCM3 (Meteorological Research Institute), respectively. The results are valuable for climate and hydrometeorological studies and can help water resources planners and managers to choose the proper GCM based on their criteria.

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

PubMed

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

2015-06-01

Long-term carbon (C) cycle feedbacks to climate depend on the future dynamics of soil organic carbon (SOC). Current models show low predictive accuracy at simulating contemporary SOC pools, which can be improved through parameter estimation. However, major uncertainty remains in global soil responses to climate change, particularly uncertainty in how the activity of soil microbial communities will respond. To date, the role of microbes in SOC dynamics has been implicitly described by decay rate constants in most conventional global carbon cycle models. Explicitly including microbial biomass dynamics into C cycle model formulations has shown potential to improve model predictive performance when assessed against global SOC databases. This study aimed to data-constrained parameters of two soil microbial models, evaluate the improvements in performance of those calibrated models in predicting contemporary carbon stocks, and compare the SOC responses to climate change and their uncertainties between microbial and conventional models. Microbial models with calibrated parameters explained 51% of variability in the observed total SOC, whereas a calibrated conventional model explained 41%. The microbial models, when forced with climate and soil carbon input predictions from the 5th Coupled Model Intercomparison Project (CMIP5), produced stronger soil C responses to 95 years of climate change than any of the 11 CMIP5 models. The calibrated microbial models predicted between 8% (2-pool model) and 11% (4-pool model) soil C losses compared with CMIP5 model projections which ranged from a 7% loss to a 22.6% gain. Lastly, we observed unrealistic oscillatory SOC dynamics in the 2-pool microbial model. The 4-pool model also produced oscillations, but they were less prominent and could be avoided, depending on the parameter values. © 2014 John Wiley & Sons Ltd.

Morbidity Rate Prediction of Dengue Hemorrhagic Fever (DHF) Using the Support Vector Machine and the Aedes aegypti Infection Rate in Similar Climates and Geographical Areas

PubMed Central

Kesorn, Kraisak; Ongruk, Phatsavee; Chompoosri, Jakkrawarn; Phumee, Atchara; Thavara, Usavadee; Tawatsin, Apiwat; Siriyasatien, Padet

2015-01-01

Background In the past few decades, several researchers have proposed highly accurate prediction models that have typically relied on climate parameters. However, climate factors can be unreliable and can lower the effectiveness of prediction when they are applied in locations where climate factors do not differ significantly. The purpose of this study was to improve a dengue surveillance system in areas with similar climate by exploiting the infection rate in the Aedes aegypti mosquito and using the support vector machine (SVM) technique for forecasting the dengue morbidity rate. Methods and Findings Areas with high incidence of dengue outbreaks in central Thailand were studied. The proposed framework consisted of the following three major parts: 1) data integration, 2) model construction, and 3) model evaluation. We discovered that the Ae. aegypti female and larvae mosquito infection rates were significantly positively associated with the morbidity rate. Thus, the increasing infection rate of female mosquitoes and larvae led to a higher number of dengue cases, and the prediction performance increased when those predictors were integrated into a predictive model. In this research, we applied the SVM with the radial basis function (RBF) kernel to forecast the high morbidity rate and take precautions to prevent the development of pervasive dengue epidemics. The experimental results showed that the introduced parameters significantly increased the prediction accuracy to 88.37% when used on the test set data, and these parameters led to the highest performance compared to state-of-the-art forecasting models. Conclusions The infection rates of the Ae. aegypti female mosquitoes and larvae improved the morbidity rate forecasting efficiency better than the climate parameters used in classical frameworks. We demonstrated that the SVM-R-based model has high generalization performance and obtained the highest prediction performance compared to classical models as measured by the accuracy, sensitivity, specificity, and mean absolute error (MAE). PMID:25961289

Analysis of high-resolution simulations for the Black Forest region from a point of view of tourism climatology - a comparison between two regional climate models (REMO and CLM)

NASA Astrophysics Data System (ADS)

Endler, Christina; Matzarakis, Andreas

2011-03-01

An analysis of climate simulations from a point of view of tourism climatology based on two regional climate models, namely REMO and CLM, was performed for a regional domain in the southwest of Germany, the Black Forest region, for two time frames, 1971-2000 that represents the twentieth century climate and 2021-2050 that represents the future climate. In that context, the Intergovernmental Panel on Climate Change (IPCC) scenarios A1B and B1 are used. The analysis focuses on human-biometeorological and applied climatologic issues, especially for tourism purposes - that means parameters belonging to thermal (physiologically equivalent temperature, PET), physical (precipitation, snow, wind), and aesthetic (fog, cloud cover) facets of climate in tourism. In general, both models reveal similar trends, but differ in their extent. The trend of thermal comfort is contradicting: it tends to decrease in REMO, while it shows a slight increase in CLM. Moreover, REMO reveals a wider range of future climate trends than CLM, especially for sunshine, dry days, and heat stress. Both models are driven by the same global coupled atmosphere-ocean model ECHAM5/MPI-OM. Because both models are not able to resolve meso- and micro-scale processes such as cloud microphysics, differences between model results and discrepancies in the development of even those parameters (e.g., cloud formation and cover) are due to different model parameterization and formulation. Climatic changes expected by 2050 are small compared to 2100, but may have major impacts on tourism as for example, snow cover and its duration are highly vulnerable to a warmer climate directly affecting tourism in winter. Beyond indirect impacts are of high relevance as they influence tourism as well. Thus, changes in climate, natural environment, demography, tourists' demands, among other things affect economy in general. The analysis of the CLM results and its comparison with the REMO results complete the analysis performed within the project Climate Trends and Sustainable Development of Tourism in Coastal and Low Mountain Range Regions (CAST) funded by the German Federal Ministry of Education and Research (BMBF).

The Regional Climate Model Evaluation System: A Systematic Evaluation Of CORDEX Simulations Using Obs4MIPs

NASA Astrophysics Data System (ADS)

Goodman, A.; Lee, H.; Waliser, D. E.; Guttowski, W.

2017-12-01

Observation-based evaluations of global climate models (GCMs) have been a key element for identifying systematic model biases that can be targeted for model improvements and for establishing uncertainty associated with projections of global climate change. However, GCMs are limited in their ability to represent physical phenomena which occur on smaller, regional scales, including many types of extreme weather events. In order to help facilitate projections in changes of such phenomena, simulations from regional climate models (RCMs) for 14 different domains around the world are being provided by the Coordinated Regional Climate Downscaling Experiment (CORDEX; www.cordex.org). However, although CORDEX specifies standard simulation and archiving protocols, these simulations are conducted independently by individual research and modeling groups representing each of these domains often with different output requirements and data archiving and exchange capabilities. Thus, with respect to similar efforts using GCMs (e.g., the Coupled Model Intercomparison Project, CMIP), it is more difficult to achieve a standardized, systematic evaluation of the RCMs for each domain and across all the CORDEX domains. Using the Regional Climate Model Evaluation System (RCMES; rcmes.jpl.nasa.gov) developed at JPL, we are developing easy to use templates for performing systematic evaluations of CORDEX simulations. Results from the application of a number of evaluation metrics (e.g., biases, centered RMS, and pattern correlations) will be shown for a variety of physical quantities and CORDEX domains. These evaluations are performed using products from obs4MIPs, an activity initiated by DOE and NASA, and now shepherded by the World Climate Research Program's Data Advisory Council.

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.

How will climate novelty influence ecological forecasts? Using the Quaternary to assess future reliability.

PubMed

Fitzpatrick, Matthew C; Blois, Jessica L; Williams, John W; Nieto-Lugilde, Diego; Maguire, Kaitlin C; Lorenz, David J

2018-03-23

Future climates are projected to be highly novel relative to recent climates. Climate novelty challenges models that correlate ecological patterns to climate variables and then use these relationships to forecast ecological responses to future climate change. Here, we quantify the magnitude and ecological significance of future climate novelty by comparing it to novel climates over the past 21,000 years in North America. We then use relationships between model performance and climate novelty derived from the fossil pollen record from eastern North America to estimate the expected decrease in predictive skill of ecological forecasting models as future climate novelty increases. We show that, in the high emissions scenario (RCP 8.5) and by late 21st century, future climate novelty is similar to or higher than peak levels of climate novelty over the last 21,000 years. The accuracy of ecological forecasting models is projected to decline steadily over the coming decades in response to increasing climate novelty, although models that incorporate co-occurrences among species may retain somewhat higher predictive skill. In addition to quantifying future climate novelty in the context of late Quaternary climate change, this work underscores the challenges of making reliable forecasts to an increasingly novel future, while highlighting the need to assess potential avenues for improvement, such as increased reliance on geological analogs for future novel climates and improving existing models by pooling data through time and incorporating assemblage-level information. © 2018 John Wiley & Sons Ltd.

Maritime Continent seasonal climate biases in AMIP experiments of the CMIP5 multimodel ensemble

NASA Astrophysics Data System (ADS)

Toh, Ying Ying; Turner, Andrew G.; Johnson, Stephanie J.; Holloway, Christopher E.

2018-02-01

The fidelity of 28 Coupled Model Intercomparison Project phase 5 (CMIP5) models in simulating mean climate over the Maritime Continent in the Atmospheric Model Intercomparison Project (AMIP) experiment is evaluated in this study. The performance of AMIP models varies greatly in reproducing seasonal mean climate and the seasonal cycle. The multi-model mean has better skill at reproducing the observed mean climate than the individual models. The spatial pattern of 850 hPa wind is better simulated than the precipitation in all four seasons. We found that model horizontal resolution is not a good indicator of model performance. Instead, a model's local Maritime Continent biases are somewhat related to its biases in the local Hadley circulation and global monsoon. The comparison with coupled models in CMIP5 shows that AMIP models generally performed better than coupled models in the simulation of the global monsoon and local Hadley circulation but less well at simulating the Maritime Continent annual cycle of precipitation. To characterize model systematic biases in the AMIP runs, we performed cluster analysis on Maritime Continent annual cycle precipitation. Our analysis resulted in two distinct clusters. Cluster I models are able to capture both the winter monsoon and summer monsoon shift, but they overestimate the precipitation; especially during the JJA and SON seasons. Cluster II models simulate weaker seasonal migration than observed, and the maximum rainfall position stays closer to the equator throughout the year. The tropics-wide properties of these clusters suggest a connection between the skill of simulating global properties of the monsoon circulation and the skill of simulating the regional scale of Maritime Continent precipitation.

Multi-year downscaling application of two-way coupled WRF v3.4 and CMAQ v5.0.2 over east Asia for regional climate and air quality modeling: model evaluation and aerosol direct effects

NASA Astrophysics Data System (ADS)

Hong, Chaopeng; Zhang, Qiang; Zhang, Yang; Tang, Youhua; Tong, Daniel; He, Kebin

2017-06-01

In this study, a regional coupled climate-chemistry modeling system using the dynamical downscaling technique was established by linking the global Community Earth System Model (CESM) and the regional two-way coupled Weather Research and Forecasting - Community Multi-scale Air Quality (WRF-CMAQ) model for the purpose of comprehensive assessments of regional climate change and air quality and their interactions within one modeling framework. The modeling system was applied over east Asia for a multi-year climatological application during 2006-2010, driven with CESM downscaling data under Representative Concentration Pathways 4.5 (RCP4.5), along with a short-term air quality application in representative months in 2013 that was driven with a reanalysis dataset. A comprehensive model evaluation was conducted against observations from surface networks and satellite observations to assess the model's performance. This study presents the first application and evaluation of the two-way coupled WRF-CMAQ model for climatological simulations using the dynamical downscaling technique. The model was able to satisfactorily predict major meteorological variables. The improved statistical performance for the 2 m temperature (T2) in this study (with a mean bias of -0.6 °C) compared with the Coupled Model Intercomparison Project Phase 5 (CMIP5) multi-models might be related to the use of the regional model WRF and the bias-correction technique applied for CESM downscaling. The model showed good ability to predict PM2. 5 in winter (with a normalized mean bias (NMB) of 6.4 % in 2013) and O3 in summer (with an NMB of 18.2 % in 2013) in terms of statistical performance and spatial distributions. Compared with global models that tend to underpredict PM2. 5 concentrations in China, WRF-CMAQ was able to capture the high PM2. 5 concentrations in urban areas. In general, the two-way coupled WRF-CMAQ model performed well for both climatological and air quality applications. The coupled modeling system with direct aerosol feedbacks predicted aerosol optical depth relatively well and significantly reduced the overprediction in downward shortwave radiation at the surface (SWDOWN) over polluted regions in China. The performance of cloud variables was not as good as other meteorological variables, and underpredictions of cloud fraction resulted in overpredictions of SWDOWN and underpredictions of shortwave and longwave cloud forcing. The importance of climate-chemistry interactions was demonstrated via the impacts of aerosol direct effects on climate and air quality. The aerosol effects on climate and air quality in east Asia (e.g., SWDOWN and T2 decreased by 21.8 W m-2 and 0.45 °C, respectively, and most pollutant concentrations increased by 4.8-9.5 % in January over China's major cities) were more significant than in other regions because of higher aerosol loadings that resulted from severe regional pollution, which indicates the need for applying online-coupled models over east Asia for regional climate and air quality modeling and to study the important climate-chemistry interactions. This work established a baseline for WRF-CMAQ simulations for a future period under the RCP4.5 climate scenario, which will be presented in a future paper.

Climate Classification is an Important Factor in ­Assessing Hospital Performance Metrics

NASA Astrophysics Data System (ADS)

Boland, M. R.; Parhi, P.; Gentine, P.; Tatonetti, N. P.

2017-12-01

Context/Purpose: Climate is a known modulator of disease, but its impact on hospital performance metrics remains unstudied. Methods: We assess the relationship between Köppen-Geiger climate classification and hospital performance metrics, specifically 30-day mortality, as reported in Hospital Compare, and collected for the period July 2013 through June 2014 (7/1/2013 - 06/30/2014). A hospital-level multivariate linear regression analysis was performed while controlling for known socioeconomic factors to explore the relationship between all-cause mortality and climate. Hospital performance scores were obtained from 4,524 hospitals belonging to 15 distinct Köppen-Geiger climates and 2,373 unique counties. Results: Model results revealed that hospital performance metrics for mortality showed significant climate dependence (p<0.001) after adjusting for socioeconomic factors. Interpretation: Currently, hospitals are reimbursed by Governmental agencies using 30-day mortality rates along with 30-day readmission rates. These metrics allow Government agencies to rank hospitals according to their `performance' along these metrics. Various socioeconomic factors are taken into consideration when determining individual hospitals performance. However, no climate-based adjustment is made within the existing framework. Our results indicate that climate-based variability in 30-day mortality rates does exist even after socioeconomic confounder adjustment. Use of standardized high-level climate classification systems (such as Koppen-Geiger) would be useful to incorporate in future metrics. Conclusion: Climate is a significant factor in evaluating hospital 30-day mortality rates. These results demonstrate that climate classification is an important factor when comparing hospital performance across the United States.

The seasonal response of the Held-Suarez climate model to prescribed ocean temperature anomalies. I - Results of decadal integrations

NASA Technical Reports Server (NTRS)

Phillips, T. J.; Semtner, A. J., Jr.

1984-01-01

Anomalies in ocean surface temperature have been identified as possible causes of variations in the climate of particular seasons or as a source of interannual climatic variability, and attempts have been made to forecast seasonal climate by using ocean temperatures as predictor variables. However, the seasonal atmospheric response to ocean temperature anomalies has not yet been systematically investigated with nonlinear models. The present investigation is concerned with ten-year integrations involving a model of intermediate complexity, the Held-Suarez climate model. The calculations have been performed to investigate the changes in seasonal climate which result from a fixed anomaly imposed on a seasonally varying, global ocean temperature field. Part I of the paper provides a report on the results of these decadal integrations. Attention is given to model properties, the experimental design, and the anomaly experiments.

Misleading prioritizations from modelling range shifts under climate change

USGS Publications Warehouse

Sofaer, Helen R.; Jarnevich, Catherine S.; Flather, Curtis H.

2018-01-01

AimConservation planning requires the prioritization of a subset of taxa and geographical locations to focus monitoring and management efforts. Integration of the threats and opportunities posed by climate change often relies on predictions from species distribution models, particularly for assessments of vulnerability or invasion risk for multiple taxa. We evaluated whether species distribution models could reliably rank changes in species range size under climate and land use change.LocationConterminous U.S.A.Time period1977–2014.Major taxa studiedPasserine birds.MethodsWe estimated ensembles of species distribution models based on historical North American Breeding Bird Survey occurrences for 190 songbirds, and generated predictions to recent years given c. 35 years of observed land use and climate change. We evaluated model predictions using standard metrics of discrimination performance and a more detailed assessment of the ability of models to rank species vulnerability to climate change based on predicted range loss, range gain, and overall change in range size.ResultsSpecies distribution models yielded unreliable and misleading assessments of relative vulnerability to climate and land use change. Models could not accurately predict range expansion or contraction, and therefore failed to anticipate patterns of range change among species. These failures occurred despite excellent overall discrimination ability and transferability to the validation time period, which reflected strong performance at the majority of locations that were either always or never occupied by each species.Main conclusionsModels failed for the questions and at the locations of greatest interest to conservation and management. This highlights potential pitfalls of multi-taxa impact assessments under global change; in our case, models provided misleading rankings of the most impacted species, and spatial information about range changes was not credible. As modelling methods and frameworks continue to be refined, performance assessments and validation efforts should focus on the measures of risk and vulnerability useful for decision-making.

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.

Crop Yield Simulations Using Multiple Regional Climate Models in the Southwestern United States

NASA Astrophysics Data System (ADS)

Stack, D.; Kafatos, M.; Kim, S.; Kim, J.; Walko, R. L.

2013-12-01

Agricultural productivity (described by crop yield) is strongly dependent on climate conditions determined by meteorological parameters (e.g., temperature, rainfall, and solar radiation). California is the largest producer of agricultural products in the United States, but crops in associated arid and semi-arid regions live near their physiological limits (e.g., in hot summer conditions with little precipitation). Thus, accurate climate data are essential in assessing the impact of climate variability on agricultural productivity in the Southwestern United States and other arid regions. To address this issue, we produced simulated climate datasets and used them as input for the crop production model. For climate data, we employed two different regional climate models (WRF and OLAM) using a fine-resolution (8km) grid. Performances of the two different models are evaluated in a fine-resolution regional climate hindcast experiment for 10 years from 2001 to 2010 by comparing them to the North American Regional Reanalysis (NARR) dataset. Based on this comparison, multi-model ensembles with variable weighting are used to alleviate model bias and improve the accuracy of crop model productivity over large geographic regions (county and state). Finally, by using a specific crop-yield simulation model (APSIM) in conjunction with meteorological forcings from the multi-regional climate model ensemble, we demonstrate the degree to which maize yields are sensitive to the regional climate in the Southwestern United States.

Hydrological modeling as an evaluation tool of EURO-CORDEX climate projections and bias correction methods

NASA Astrophysics Data System (ADS)

Hakala, Kirsti; Addor, Nans; Seibert, Jan

2017-04-01

Streamflow stemming from Switzerland's mountainous landscape will be influenced by climate change, which will pose significant challenges to the water management and policy sector. In climate change impact research, the determination of future streamflow is impeded by different sources of uncertainty, which propagate through the model chain. In this research, we explicitly considered the following sources of uncertainty: (1) climate models, (2) downscaling of the climate projections to the catchment scale, (3) bias correction method and (4) parameterization of the hydrological model. We utilize climate projections at the 0.11 degree 12.5 km resolution from the EURO-CORDEX project, which are the most recent climate projections for the European domain. EURO-CORDEX is comprised of regional climate model (RCM) simulations, which have been downscaled from global climate models (GCMs) from the CMIP5 archive, using both dynamical and statistical techniques. Uncertainties are explored by applying a modeling chain involving 14 GCM-RCMs to ten Swiss catchments. We utilize the rainfall-runoff model HBV Light, which has been widely used in operational hydrological forecasting. The Lindström measure, a combination of model efficiency and volume error, was used as an objective function to calibrate HBV Light. Ten best sets of parameters are then achieved by calibrating using the genetic algorithm and Powell optimization (GAP) method. The GAP optimization method is based on the evolution of parameter sets, which works by selecting and recombining high performing parameter sets with each other. Once HBV is calibrated, we then perform a quantitative comparison of the influence of biases inherited from climate model simulations to the biases stemming from the hydrological model. The evaluation is conducted over two time periods: i) 1980-2009 to characterize the simulation realism under the current climate and ii) 2070-2099 to identify the magnitude of the projected change of streamflow under the climate scenarios RCP4.5 and RCP8.5. We utilize two techniques for correcting biases in the climate model output: quantile mapping and a new method, frequency bias correction. The FBC method matches the frequencies between observed and GCM-RCM data. In this way, it can be used to correct for all time scales, which is a known limitation of quantile mapping. A novel approach for the evaluation of the climate simulations and bias correction methods was then applied. Streamflow can be thought of as the "great integrator" of uncertainties. The ability, or the lack thereof, to correctly simulate streamflow is a way to assess the realism of the bias-corrected climate simulations. Long-term monthly mean as well as high and low flow metrics are used to evaluate the realism of the simulations under current climate and to gauge the impacts of climate change on streamflow. Preliminary results show that under present climate, calibration of the hydrological model comprises of a much smaller band of uncertainty in the modeling chain as compared to the bias correction of the GCM-RCMs. Therefore, for future time periods, we expect the bias correction of climate model data to have a greater influence on projected changes in streamflow than the calibration of the hydrological model.

An approach to secure weather and climate models against hardware faults

NASA Astrophysics Data System (ADS)

Düben, Peter D.; Dawson, Andrew

2017-03-01

Enabling Earth System models to run efficiently on future supercomputers is a serious challenge for model development. Many publications study efficient parallelization to allow better scaling of performance on an increasing number of computing cores. However, one of the most alarming threats for weather and climate predictions on future high performance computing architectures is widely ignored: the presence of hardware faults that will frequently hit large applications as we approach exascale supercomputing. Changes in the structure of weather and climate models that would allow them to be resilient against hardware faults are hardly discussed in the model development community. In this paper, we present an approach to secure the dynamical core of weather and climate models against hardware faults using a backup system that stores coarse resolution copies of prognostic variables. Frequent checks of the model fields on the backup grid allow the detection of severe hardware faults, and prognostic variables that are changed by hardware faults on the model grid can be restored from the backup grid to continue model simulations with no significant delay. To justify the approach, we perform model simulations with a C-grid shallow water model in the presence of frequent hardware faults. As long as the backup system is used, simulations do not crash and a high level of model quality can be maintained. The overhead due to the backup system is reasonable and additional storage requirements are small. Runtime is increased by only 13 % for the shallow water model.

weather@home 2: validation of an improved global-regional climate modelling system

NASA Astrophysics Data System (ADS)

Guillod, Benoit P.; Jones, Richard G.; Bowery, Andy; Haustein, Karsten; Massey, Neil R.; Mitchell, Daniel M.; Otto, Friederike E. L.; Sparrow, Sarah N.; Uhe, Peter; Wallom, David C. H.; Wilson, Simon; Allen, Myles R.

2017-05-01

Extreme weather events can have large impacts on society and, in many regions, are expected to change in frequency and intensity with climate change. Owing to the relatively short observational record, climate models are useful tools as they allow for generation of a larger sample of extreme events, to attribute recent events to anthropogenic climate change, and to project changes in such events into the future. The modelling system known as weather@home, consisting of a global climate model (GCM) with a nested regional climate model (RCM) and driven by sea surface temperatures, allows one to generate a very large ensemble with the help of volunteer distributed computing. This is a key tool to understanding many aspects of extreme events. Here, a new version of the weather@home system (weather@home 2) with a higher-resolution RCM over Europe is documented and a broad validation of the climate is performed. The new model includes a more recent land-surface scheme in both GCM and RCM, where subgrid-scale land-surface heterogeneity is newly represented using tiles, and an increase in RCM resolution from 50 to 25 km. The GCM performs similarly to the previous version, with some improvements in the representation of mean climate. The European RCM temperature biases are overall reduced, in particular the warm bias over eastern Europe, but large biases remain. Precipitation is improved over the Alps in summer, with mixed changes in other regions and seasons. The model is shown to represent the main classes of regional extreme events reasonably well and shows a good sensitivity to its drivers. In particular, given the improvements in this version of the weather@home system, it is likely that more reliable statements can be made with regards to impact statements, especially at more localized scales.

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.

Combining surface reanalysis and remote sensing data for monitoring evapotranspiration

USGS Publications Warehouse

Marshall, M.; Tu, K.; Funk, C.; Michaelsen, J.; Williams, Pat; Williams, C.; Ardö, J.; Marie, B.; Cappelaere, B.; Grandcourt, A.; Nickless, A.; Noubellon, Y.; Scholes, R.; Kutsch, W.

2012-01-01

Climate change is expected to have the greatest impact on the world's poor. In the Sahel, a climatically sensitive region where rain-fed agriculture is the primary livelihood, expected decreases in water supply will increase food insecurity. Studies on climate change and the intensification of the water cycle in sub-Saharan Africa are few. This is due in part to poor calibration of modeled actual evapotranspiration (AET), a key input in continental-scale hydrologic models. In this study, a model driven by dynamic canopy AET was combined with the Global Land Data Assimilation System realization of the NOAH Land Surface Model (GNOAH) wet canopy and soil AET for monitoring purposes in sub-Saharan Africa. The performance of the hybrid model was compared against AET from the GNOAH model and dynamic model using eight eddy flux towers representing major biomes of sub-Saharan Africa. The greatest improvements in model performance are at humid sites with dense vegetation, while performance at semi-arid sites is poor, but better than individual models. The reduction in errors using the hybrid model can be attributed to the integration of a dynamic vegetation component with land surface model estimates, improved model parameterization, and reduction of multiplicative effects of uncertain data.

An approach to secure weather and climate models against hardware faults

NASA Astrophysics Data System (ADS)

Düben, Peter; Dawson, Andrew

2017-04-01

Enabling Earth System models to run efficiently on future supercomputers is a serious challenge for model development. Many publications study efficient parallelisation to allow better scaling of performance on an increasing number of computing cores. However, one of the most alarming threats for weather and climate predictions on future high performance computing architectures is widely ignored: the presence of hardware faults that will frequently hit large applications as we approach exascale supercomputing. Changes in the structure of weather and climate models that would allow them to be resilient against hardware faults are hardly discussed in the model development community. We present an approach to secure the dynamical core of weather and climate models against hardware faults using a backup system that stores coarse resolution copies of prognostic variables. Frequent checks of the model fields on the backup grid allow the detection of severe hardware faults, and prognostic variables that are changed by hardware faults on the model grid can be restored from the backup grid to continue model simulations with no significant delay. To justify the approach, we perform simulations with a C-grid shallow water model in the presence of frequent hardware faults. As long as the backup system is used, simulations do not crash and a high level of model quality can be maintained. The overhead due to the backup system is reasonable and additional storage requirements are small. Runtime is increased by only 13% for the shallow water model.

Modelling the Climate - Greenland Ice Sheet Interaction in the Coupled Ice-sheet/Climate Model EC-EARTH - PISM

NASA Astrophysics Data System (ADS)

Yang, S.; Madsen, M. S.; Rodehacke, C. B.; Svendsen, S. H.; Adalgeirsdottir, G.

2014-12-01

Recent observations show that the Greenland ice sheet (GrIS) has been losing mass with an increasing speed during the past decades. Predicting the GrIS changes and their climate consequences relies on the understanding of the interaction of the GrIS with the climate system on both global and local scales, and requires climate model systems with an explicit and physically consistent ice sheet module. A fully coupled global climate model with a dynamical ice sheet model for the GrIS has recently been developed. The model system, EC-EARTH - PISM, consists of the EC-EARTH, an atmosphere, ocean and sea ice model system, and the Parallel Ice Sheet Model (PISM). The coupling of PISM includes a modified surface physical parameterization in EC-EARTH adapted to the land ice surface over glaciated regions in Greenland. The PISM ice sheet model is forced with the surface mass balance (SMB) directly computed inside the EC-EARTH atmospheric module and accounting for the precipitation, the surface evaporation, and the melting of snow and ice over land ice. PISM returns the simulated basal melt, ice discharge and ice cover (extent and thickness) as boundary conditions to EC-EARTH. This coupled system is mass and energy conserving without being constrained by any anomaly correction or flux adjustment, and hence is suitable for investigation of ice sheet - climate feedbacks. Three multi-century experiments for warm climate scenarios under (1) the RCP85 climate forcing, (2) an abrupt 4xCO2 and (3) an idealized 1% per year CO2 increase are performed using the coupled model system. The experiments are compared with their counterparts of the standard CMIP5 simulations (without the interactive ice sheet) to evaluate the performance of the coupled system and to quantify the GrIS feedbacks. In particular, the evolution of the Greenland ice sheet under the warm climate and its impacts on the climate system are investigated. Freshwater fluxes from the Greenland ice sheet melt to the Arctic and North Atlantic basin and their influence on the ocean stratification and ocean circulation are analysed. The changes in the surface climate and the atmospheric circulation associated with the impact of the Greenland ice sheet changes are quantified. The interaction between the Greenland ice sheet and Arctic sea ice is also examined.

Vegetation zones in changing climate

NASA Astrophysics Data System (ADS)

Belda, Michal; Holtanova, Eva; Halenka, Tomas; Kalvova, Jaroslava

2017-04-01

Climate patterns analysis can be performed for individual climate variables separately or the data can be aggregated using e.g. some kind of climate classification. These classifications usually correspond to vegetation distribution in the sense that each climate type is dominated by one vegetation zone or eco-region. Thus, the Köppen-Trewartha classification provides integrated assessment of temperature and precipitation together with their annual cycle as well. This way climate classifications also can be used as a convenient tool for the assessment and validation of climate models and for the analysis of simulated future climate changes. The Köppen-Trewartha classification is applied on full CMIP5 family of more than 40 GCM simulations and CRU dataset for comparison. This evaluation provides insight on the GCM performance and errors for simulations of the 20th century climate. Common regions are identified, such as Australia or Amazonia, where many state-of-the-art models perform inadequately. Moreover, the analysis of the CMIP5 ensemble for future under RCP 4.5 and RCP 8.5 is performed to assess the climate change for future. There are significant changes for some types in most models e.g. increase of savanna and decrease of tundra for the future climate. For some types significant shifts in latitude can be seen when studying their geographical location in selected continental areas, e.g. toward higher latitudes for boreal climate. Quite significant uncertainty can be seen for some types. For Europe, EuroCORDEX results for both 0.11 and 0.44 degree resolution are validated using Köppen-Trewartha types in comparison to E-OBS based classification. ERA-Interim driven simulations are compared to both present conditions of CMIP5 models as well as their downscaling by EuroCORDEX RCMs. Finally, the climate change signal assessment is provided using the individual climate types. In addition to the changes assessed similarly as for GCMs analysis in terms of the area of individual types, in the continental scale some shifts of boundaries between the selected types can be studied as well providing the information on climate change signal. The shift of the boundary between the boreal zone and continental temperate zone to the north is clearly seen in most simulations as well as eastern move of the boundary of the maritime and continental type of temperate zone. However, there can be quite clear problem with model biases in climate types association. When analysing climate types in Europe and their shifts under climate change using Köppen-Trewartha classification (KTC), for the temperate climate type there are subtypes defined following the continentality patterns, and we can see their generally meridionally located divide across Europe shifted to the east. There is a question whether this is realistic or rather due to the simplistic condition in KTC following the winter minimum temperature, while other continentality indices consider rather the amplitude of temperature during the year. This leads us to connect our analysis of climate change effects using climate classification to the more detailed analysis of continentality patterns development in Europe to provide better insight on the climate regimes and to verify the continentality conditions, their definitions and climate change effects on them. The comparison of several selected continentality indices is shown.

Simulating forage crop production in a northern climate with the Integrated Farm System Model

USDA-ARS?s Scientific Manuscript database

Whole-farm simulation models are useful tools for evaluating the effect of management practices and climate variability on the agro-environmental and economic performance of farms. A few process-based farm-scale models have been developed, but none have been evaluated in a northern region with a sho...

Doing the right thing without being told: joint effects of initiative climate and general self-efficacy on employee proactive customer service performance.

PubMed

Raub, Steffen; Liao, Hui

2012-05-01

We developed and tested a cross-level model of the antecedents and outcomes of proactive customer service performance. Results from a field study of 900 frontline service employees and their supervisors in 74 establishments of a multinational hotel chain located in Europe, the Middle East, Africa, and Asia demonstrated measurement equivalence and suggested that, after controlling for service climate, initiative climate at the establishment level and general self-efficacy at the individual level predicted employee proactive customer service performance and interacted in a synergistic way. Results also showed that at the establishment level, controlling for service climate and collective general service performance, initiative climate was positively and indirectly associated with customer service satisfaction through the mediation of aggregated proactive customer service performance. We discuss important theoretical and practical implications of these findings. (PsycINFO Database Record (c) 2012 APA, all rights reserved).

Performance assessment and parameterization of the SWAP-WOFOST model for peat soil under agricultural use in northern Europe.

NASA Astrophysics Data System (ADS)

Bertram, Sascha; Bechtold, Michel; Hendriks, Rob; Piayda, Arndt; Regina, Kristiina; Myllys, Merja; Tiemeyer, Bärbel

2017-04-01

Peat soils form a major share of soil suitable for agriculture in northern Europe. Successful agricultural production depends on hydrological and pedological conditions, local climate and agricultural management. Climate change impact assessment on food production and development of mitigation and adaptation strategies require reliable yield forecasts under given emission scenarios. Coupled soil hydrology - crop growth models, driven by regionalized future climate scenarios are a valuable tool and widely used for this purpose. Parameterization on local peat soil conditions and crop breed or grassland specie performance, however, remains a major challenge. The subject of this study is to evaluate the performance and sensitivity of the SWAP-WOFOST coupled soil hydrology and plant growth model with respect to the application on peat soils under different regional conditions across northern Europe. Further, the parameterization of region-specific crop and grass species is discussed. First results of the model application and parameterization at deep peat sites in southern Finland are presented. The model performed very well in reproducing two years of observed, daily ground water level data on four hydrologically contrasting sites. Naturally dry and wet sites could be modelled with the same performance as sites with active water table management by regulated drains in order to improve peat conservation. A simultaneous multi-site calibration scheme was used to estimate plant growth parameters of the local oat breed. Cross-site validation of the modelled yields against two years of observations proved the robustness of the chosen parameter set and gave no indication of possible overparameterization. This study proves the suitability of the coupled SWAP-WOFOST model for the prediction of crop yields and water table dynamics of peat soils in agricultural use under given climate conditions.

The Effect of Mitigation Policy on Regional Climate Impacts on the U.S. Electric Sector

NASA Astrophysics Data System (ADS)

Cohen, S. M.; Sun, Y.; Strzepek, K.; McFarland, J.; Boehlert, B.; Fant, C.

2017-12-01

Climate change can influence the U.S. electricity sector in many ways, the nature of which can be shaped by energy and environmental policy choices. Changing temperatures affect electricity demand largely through heating and cooling needs, and temperatures also affect generation and transmission system performance. Altered precipitation patterns affect the regional and seasonal distribution of surface water runoff, which changes hydropower operation and thermal cooling water availability. The extent to which these stimuli influence U.S. power sector operation and planning will depend to some extent on whether or not proactive policies are enacted to mitigate these impacts. Mitigation policies such as CO2 emissions limits or technology restrictions can change the makeup of the electricity system while reducing the extent of climate change itself. We use the National Renewable Energy Laboratory's Regional Energy Deployment System (ReEDS), a U.S. electric sector capacity expansion model, to explore electric sector evolution through 2050 under alternative climate and policy assumptions. The model endogenously represents climate impacts on load, power system performance, cooling water availability, and hydropower, allowing internally consistent system responses to climate change along with projected technology, market, and policy conditions. We compare climate impacts across 5 global circulation models for a 8.5 W/m2 representative concentration pathway (RCP) without a climate mitigation policy and a 4.5 W/m2 RCP with climate mitigation. Climate drivers affect the capacity and generation mix at the national and regional levels, with relative growth of wind, solar, and natural gas-based technologies depending on local electricity system characteristics. These differences affect regional economic impacts, measured here as changes to electricity price and system costs. Mitigation policy reduces the economic and system impacts of climate change largely by moderating temperature-induced load but also by lessening water- and temperature-based performance constraints. Policy impacts are nuanced and region-specific, and this analysis underscores the importance of climate mitigation policy to regional electricity system planning decisions.

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

NASA Astrophysics Data System (ADS)

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

2014-05-01

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

Continuously on-­going regional climate hindcast simulations for impact applications

NASA Astrophysics Data System (ADS)

Anders, Ivonne; Piringer, Martin; Kaufmann, Hildegard; Knauder, Werner; Resch, Gernot; Andre, Konrad

2017-04-01

Observational data for e.g. temperature, precipitation, radiation, or wind are often used as meteorological forcing for different impact models, like e.g. crop models, urban models, economic models and energy system models. To assess a climate signal, the time period covered by the observation is often too short, they have gaps in between, and are inhomogeneous over time, due to changes in the measurements itself or in the near surrounding. Thus output from global and regional climate models can close the gap and provide homogeneous and physically consistent time series of meteorological parameters. CORDEX evaluation runs performed for the IPCC-AR5 provide a good base for the regional scale. However, with respect to climate services, continuously on-going hindcast simulations are required for regularly updated applications. The Climate Research group at the national Austrian weather service, ZAMG, is focusing on high mountain regions and, especially on the Alps. The hindcast-simulation performed with the regional climate model COSMO-CLM is forced by ERAinterim and optimized for the Alpine Region. The simulation available for the period of 1979-2015 in a spatial resolution of about 10km is prolonged ongoing and fullfils the customer's needs with respect of output variables, levels, intervals and statistical measures. One of the main tasks is to capture strong precipitation events which often occur during summer when low pressure systems develop over the Golf of Genoa, moving to the Northeast. This leads to floods and landslide events in Austria, Czech Republic and Germany. Such events are not sufficiently represented in the CORDEX-evaluation runs. ZAMG use high quality gridded precipitation and temperature data for the Alpine Region (1-6km) to evaluate the model performance. Data is provided e.g. to hydrological modellers (high water, low water), but also to assess icing capability of infrastructure or the calculation the separation distances between livestock farming and residential area.

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.

A public health approach to the impact of climate change on health in southern Africa - identifying priority modifiable risks.

PubMed

Myers, J; Young, T; Galloway, M; Manyike, P; Tucker, T

2011-11-01

Anthropogenic climate change and anticipated adverse impacts on human health as outlined by the Intergovernmental Panel on Climate Change (IPCC) are taken as given. A conceptual model for thinking about the spectrum of climate-related health risks ranging from distal and infrastructural to proximal and behavioural and their relation to the burden of disease pattern typical of sub-Saharan Africa is provided. The model provides a tool for identifying modifiable risk factors with a view to future research, specifically into the performance of interventions to reduce the impact of climate change.

Climate modeling. [for use in understanding earth's radiation budget

NASA Technical Reports Server (NTRS)

1978-01-01

The requirements for radiation measurements suitable for the understanding, improvement, and verification of models used in performing climate research are considered. Both zonal energy balance models and three dimensional general circulation models are considered, and certain problems are identified as common to all models. Areas of emphasis include regional energy balance observations, spectral band observations, cloud-radiation interaction, and the radiative properties of the earth's surface.

Influence of climate change on flood magnitude and seasonality in the Arga River catchment in Spain

NASA Astrophysics Data System (ADS)

Garijo, Carlos; Mediero, Luis

2018-04-01

Climate change projections suggest that extremes, such as floods, will modify their behaviour in the future. Detailed catchment-scale studies are needed to implement the European Union Floods Directive and give recommendations for flood management and design of hydraulic infrastructure. In this study, a methodology to quantify changes in future flood magnitude and seasonality due to climate change at a catchment scale is proposed. Projections of 24 global climate models are used, with 10 being downscaled by the Spanish Meteorological Agency (Agencia Estatal de Meteorología, AEMET) and 14 from the EURO-CORDEX project, under two representative concentration pathways (RCPs) 4.5 and 8.5, from the Fifth Assessment Report provided by the Intergovernmental Panel on Climate Change. Downscaled climate models provided by the AEMET were corrected in terms of bias. The HBV rainfall-runoff model was selected to simulate the catchment hydrological behaviour. Simulations were analysed through both annual maximum and peaks-over-threshold (POT) series. The results show a decrease in the magnitude of extreme floods for the climate model projections downscaled by the AEMET. However, results for the climate model projections downscaled by EURO-CORDEX show differing trends, depending on the RCP. A small decrease in the flood magnitude was noticed for the RCP 4.5, while an increase was found for the RCP 8.5. Regarding the monthly seasonality analysis performed by using the POT series, a delay in the flood timing from late-autumn to late-winter is identified supporting the findings of recent studies performed with observed data in recent decades.

Near-global climate simulation at 1 km resolution: establishing a performance baseline on 4888 GPUs with COSMO 5.0

NASA Astrophysics Data System (ADS)

Fuhrer, Oliver; Chadha, Tarun; Hoefler, Torsten; Kwasniewski, Grzegorz; Lapillonne, Xavier; Leutwyler, David; Lüthi, Daniel; Osuna, Carlos; Schär, Christoph; Schulthess, Thomas C.; Vogt, Hannes

2018-05-01

The best hope for reducing long-standing global climate model biases is by increasing resolution to the kilometer scale. Here we present results from an ultrahigh-resolution non-hydrostatic climate model for a near-global setup running on the full Piz Daint supercomputer on 4888 GPUs (graphics processing units). The dynamical core of the model has been completely rewritten using a domain-specific language (DSL) for performance portability across different hardware architectures. Physical parameterizations and diagnostics have been ported using compiler directives. To our knowledge this represents the first complete atmospheric model being run entirely on accelerators on this scale. At a grid spacing of 930 m (1.9 km), we achieve a simulation throughput of 0.043 (0.23) simulated years per day and an energy consumption of 596 MWh per simulated year. Furthermore, we propose a new memory usage efficiency (MUE) metric that considers how efficiently the memory bandwidth - the dominant bottleneck of climate codes - is being used.

Evaluation of CMIP5 twentieth century rainfall simulation over the equatorial East Africa

NASA Astrophysics Data System (ADS)

Ongoma, Victor; Chen, Haishan; Gao, Chujie

2018-02-01

This study assesses the performance of 22 Coupled Model Intercomparison Project Phase 5 (CMIP5) historical simulations of rainfall over East Africa (EA) against reanalyzed datasets during 1951-2005. The datasets were sourced from Global Precipitation Climatology Centre (GPCC) and Climate Research Unit (CRU). The metrics used to rank CMIP5 Global Circulation Models (GCMs) based on their performance in reproducing the observed rainfall include correlation coefficient, standard deviation, bias, percentage bias, root mean square error, and trend. Performances of individual models vary widely. The overall performance of the models over EA is generally low. The models reproduce the observed bimodal rainfall over EA. However, majority of them overestimate and underestimate the October-December (OND) and March-May (MAM) rainfall, respectively. The monthly (inter-annual) correlation between model and reanalyzed is high (low). More than a third of the models show a positive bias of the annual rainfall. High standard deviation in rainfall is recorded in the Lake Victoria Basin, central Kenya, and eastern Tanzania. A number of models reproduce the spatial standard deviation of rainfall during MAM season as compared to OND. The top eight models that produce rainfall over EA relatively well are as follows: CanESM2, CESM1-CAM5, CMCC-CESM, CNRM-CM5, CSIRO-Mk3-6-0, EC-EARTH, INMCM4, and MICROC5. Although these results form a fairly good basis for selection of GCMs for carrying out climate projections and downscaling over EA, it is evident that there is still need for critical improvement in rainfall-related processes in the models assessed. Therefore, climate users are advised to use the projections of rainfall from CMIP5 models over EA cautiously when making decisions on adaptation to or mitigation of climate change.

CEOS SEO and GISS Meeting

NASA Technical Reports Server (NTRS)

Killough, Brian; Stover, Shelley

2008-01-01

The Committee on Earth Observation Satellites (CEOS) provides a brief to the Goddard Institute for Space Studies (GISS) regarding the CEOS Systems Engineering Office (SEO) and current work on climate requirements and analysis. A "system framework" is provided for the Global Earth Observation System of Systems (GEOSS). SEO climate-related tasks are outlined including the assessment of essential climate variable (ECV) parameters, use of the "systems framework" to determine relevant informational products and science models and the performance of assessments and gap analyses of measurements and missions for each ECV. Climate requirements, including instruments and missions, measurements, knowledge and models, and decision makers, are also outlined. These requirements would establish traceability from instruments to products and services allowing for benefit evaluation of instruments and measurements. Additionally, traceable climate requirements would provide a better understanding of global climate models.

Application of Inverse Modeling to Estimate Groundwater Recharge under Future Climate Scenario

NASA Astrophysics Data System (ADS)

Akbariyeh, S.; Wang, T.; Bartelt-Hunt, S.; Li, Y.

2016-12-01

Climate variability and change will impose profound influences on groundwater systems. Accurate estimation of groundwater recharge is extremely important for predicting the flow and contaminant transport in the subsurface, which, however, remains as one of the most challenging tasks in the field of hydrology. Using an inverse modeling technique and HYDRUS 1D software, we predicted the spatial distribution of groundwater recharge across the Upper Platte basin in Nebraska, USA, based on 5-year projected future climate and soil moisture data (2057-2060). The climate data was obtained from Weather Research and Forecasting (WRF) model under RCP 8.5 scenario, which was downscaled from global CCSM4 model to a resolution of 24 by 24 km2. Precipitation, potential evapotranspiration, and soil moisture data were extracted from 76 grids located within the Upper Platte basin to perform the inverse modeling. Hargreaves equation was used to calculate the potential evapotranspiration according to latitude, maximum and minimum temperature, and leaf area index (LAI) data at each node. Van-Genuchten parameters were optimized using the inverse algorithm to minimize the error between input and modeled soil moisture data. The groundwater recharge was calculated as the amount of water that passed the lower boundary of the best fitted model. The year of 2057 was used as a spin-up period to minimize the impact of initial conditions. The model was calibrated for years 2058 to 2059 and validation was performed for 2060. This work demonstrates an efficient approach to estimating groundwater recharge based on climate modeling results, which will aid groundwater resources management under future climate scenarios.

Improving operational land surface model canopy evapotranspiration in Africa using a direct remote sensing approach

NASA Astrophysics Data System (ADS)

Marshall, M.; Tu, K.; Funk, C.; Michaelsen, J.; Williams, P.; Williams, C.; Ardö, J.; Boucher, M.; Cappelaere, B.; de Grandcourt, A.; Nickless, A.; Nouvellon, Y.; Scholes, R.; Kutsch, W.

2013-03-01

Climate change is expected to have the greatest impact on the world's economically poor. In the Sahel, a climatically sensitive region where rain-fed agriculture is the primary livelihood, expected decreases in water supply will increase food insecurity. Studies on climate change and the intensification of the water cycle in sub-Saharan Africa are few. This is due in part to poor calibration of modeled evapotranspiration (ET), a key input in continental-scale hydrologic models. In this study, a remote sensing model of transpiration (the primary component of ET), driven by a time series of vegetation indices, was used to substitute transpiration from the Global Land Data Assimilation System realization of the National Centers for Environmental Prediction, Oregon State University, Air Force, and Hydrology Research Laboratory at National Weather Service Land Surface Model (GNOAH) to improve total ET model estimates for monitoring purposes in sub-Saharan Africa. The performance of the hybrid model was compared against GNOAH ET and the remote sensing method using eight eddy flux towers representing major biomes of sub-Saharan Africa. The greatest improvements in model performance were at humid sites with dense vegetation, while performance at semi-arid sites was poor, but better than the models before hybridization. The reduction in errors using the hybrid model can be attributed to the integration of a simple canopy scheme that depends primarily on low bias surface climate reanalysis data and is driven primarily by a time series of vegetation indices.

Centennial-scale Holocene climate variations amplified by Antarctic Ice Sheet discharge

NASA Astrophysics Data System (ADS)

Bakker, Pepijn; Clark, Peter U.; Golledge, Nicholas R.; Schmittner, Andreas; Weber, Michael E.

2017-01-01

Proxy-based indicators of past climate change show that current global climate models systematically underestimate Holocene-epoch climate variability on centennial to multi-millennial timescales, with the mismatch increasing for longer periods. Proposed explanations for the discrepancy include ocean-atmosphere coupling that is too weak in models, insufficient energy cascades from smaller to larger spatial and temporal scales, or that global climate models do not consider slow climate feedbacks related to the carbon cycle or interactions between ice sheets and climate. Such interactions, however, are known to have strongly affected centennial- to orbital-scale climate variability during past glaciations, and are likely to be important in future climate change. Here we show that fluctuations in Antarctic Ice Sheet discharge caused by relatively small changes in subsurface ocean temperature can amplify multi-centennial climate variability regionally and globally, suggesting that a dynamic Antarctic Ice Sheet may have driven climate fluctuations during the Holocene. We analysed high-temporal-resolution records of iceberg-rafted debris derived from the Antarctic Ice Sheet, and performed both high-spatial-resolution ice-sheet modelling of the Antarctic Ice Sheet and multi-millennial global climate model simulations. Ice-sheet responses to decadal-scale ocean forcing appear to be less important, possibly indicating that the future response of the Antarctic Ice Sheet will be governed more by long-term anthropogenic warming combined with multi-centennial natural variability than by annual or decadal climate oscillations.

Assessing Climate Change Risks Using a Multi-Model Approach

NASA Astrophysics Data System (ADS)

Knorr, W.; Scholze, M.; Prentice, C.

2007-12-01

We quantify the risks of climate-induced changes in key ecosystem processes during the 21st century by forcing a dynamic global vegetation model with multiple scenarios from the IPCC AR4 data archive using 16 climate models and mapping the proportions of model runs showing exceedance of natural variability in wildfire frequency and freshwater supply or shifts in vegetation cover. Our analysis does not assign probabilities to scenarios. Instead, we consider the distribution of outcomes within three sets of model runs grouped according to the amount of global warming they simulate: < 2 degree C (including committed climate change simulations), 2-3 degree C, and >3 degree C. Here, we are contrasting two different methods for calculating the risks: first we use an equal weighting approach giving every model within one of the three sets the same weight, and second, we weight the models according to their ability to model ENSO. The differences are underpinning the need for the development of more robust performance metrics for global climate models.

Performance of the Hydrological Portion of a Simple Water Quality Model in Different Climatic Regions

NASA Astrophysics Data System (ADS)

Moore, K.; Pierson, D.; Pettersson, K.; Naden, P.; Allott, N.; Jennings, E.; Tamm, T.; Järvet, A.; Nickus, U.; Thies, H.; Arvola, L.; Järvinen, M.; Schneiderman, E.; Zion, M.; Lounsbury, D.

2004-05-01

We are applying an existing watershed model in the EU CLIME (Climate and Lake Impacts in Europe) project to evaluate the effects of weather on seasonal and annual delivery of N, P, and DOC to lakes. Model calibration is based on long-term records of weather and water quality data collected from sites in different climatic regions spread across Europe and in New York State. The overall aim of the CLIME project is to develop methods and models to support lake and catchment management under current climate conditions and make predictions under future climate scenarios. Scientists from 10 partner countries are collaborating on developing a consistent approach to defining model parameters for the Generalized Watershed Loading Functions (GWLF) model, one of a larger suite of models used in the project. An example of the approach for the hydrological portion of the GWLF model will be presented, with consideration of the balance between model simplicity, ease of use, data requirements, and realistic predictions.

Influence of the management strategy model on estimating water system performance under climate change

NASA Astrophysics Data System (ADS)

Francois, Baptiste; Hingray, Benoit; Creutin, Jean-Dominique; Hendrickx, Frederic

2015-04-01

The performance of water systems used worldwide for the management of water resources is expected to be influenced by future changes in regional climates and water uses. Anticipating possible performance changes of a given system requires a modeling chain simulating its management. Operational management is usually not trivial especially when several conflicting objectives have to be accounted for. Management models are therefore often a crude representation of the real system and they only approximate its performance. Estimated performance changes are expected to depend on the management model used, but this is often not assessed. This communication analyzes the influence of the management strategy representation on the performance of an Alpine reservoir (Serre-Ponçon, South-East of France) for which irrigation supply, hydropower generation and recreational activities are the main objectives. We consider three ways to construct the strategy named as clear-, short- and far-sighted management. They are based on different forecastability degrees of seasonal inflows into the reservoir. The strategies are optimized using a Dynamic Programming algorithm (deterministic for clear-sighted and implicit stochastic for short- and far-sighted). System performance is estimated for an ensemble of future hydro-meteorological projections obtained in the RIWER2030 research project (http://www.lthe.fr/RIWER2030/) from a suite of climate experiments from the EU - ENSEMBLES research project. Our results show that changes in system performance is much more influenced by changes in hydro-meteorological variables than by the choice of strategy modeling. They also show that a simple strategy representation (i.e. clear-sighted management) leads to similar estimates of performance modifications than those obtained with a representation supposedly closer to real world (i.e. the far-sighted management). The Short-Sighted management approach lead to significantly different results, especially when inter-annual inflow variability is high. Key words: Climate change, water resource, impact, management strategy modelling

Monitoring the performance of the next Climate Forecast System version 3, throughout its development stage at EMC/NCEP

NASA Astrophysics Data System (ADS)

Peña, M.; Saha, S.; Wu, X.; Wang, J.; Tripp, P.; Moorthi, S.; Bhattacharjee, P.

2016-12-01

The next version of the operational Climate Forecast System (version 3, CFSv3) will be a fully coupled six-components system with diverse applications to earth system modeling, including weather and climate predictions. This system will couple the earth's atmosphere, land, ocean, sea-ice, waves and aerosols for both data assimilation and modeling. It will also use the NOAA Environmental Modeling System (NEMS) software super structure to couple these components. The CFSv3 is part of the next Unified Global Coupled System (UGCS), which will unify the global prediction systems that are now operational at NCEP. The UGCS is being developed through the efforts of dedicated research and engineering teams and through coordination across many CPO/MAPP and NGGPS groups. During this development phase, the UGCS is being tested for seasonal purposes and undergoes frequent revisions. Each new revision is evaluated to quickly discover, isolate and solve problems that negatively impact its performance. In the UGCS-seasonal model, components (e.g., ocean, sea-ice, atmosphere, etc.) are coupled through a NEMS-based "mediator". In this numerical infrastructure, model diagnostics and forecast validation are carried out, both component by component, and as a whole. The next stage, model optimization, will require enhanced performance diagnostics tools to help prioritize areas of numerical improvements. After the technical development of the UGCS-seasonal is completed, it will become the first realization of the CFSv3. All future development of this system will be carried out by the climate team at NCEP, in scientific collaboration with the groups that developed the individual components, as well as the climate community. A unique challenge to evaluate this unified weather-climate system is the large number of variables, which evolve over a wide range of temporal and spatial scales. A small set of performance measures and scorecard displays are been created, and collaboration and software contributions from research and operational centers are being incorporated. A status of the CFSv3/UGCS-seasonal development and examples of its performance and measuring tools will be presented.

Attributing regional effects of the 2014 Jordanian extreme drought to external climate drivers

NASA Astrophysics Data System (ADS)

Bergaoui, Karim; Mitchell, Dann; Zaaboul, Rashyd; Otto, Friederike; McDonnell, Rachael; Dadson, Simon; Allen, Myles

2015-04-01

Throughout 2014, the regions of Jordan, Israel, Lebanon and Syria have experienced a persistent draught with clear impacts on the local populations. In this study we perform an extreme event attribution analysis of how such a draught has changed under climate change, with a specific focus on the flow rate of the Upper Jordan river and the water level of Lake Tiberious (AKA the Sea of Galilee). Both of which hold major societal, political and religious importance. To perform the analysis we make use of distributed computing power to run thousands of modelled years of 2014 with slightly different initial conditions. We use an atmosphere only model (HadAM3p) with a nested 50 km regional model covering Africa and the Middle East. The 50 km model atmospheric variables will be used directly to force offline our 1 km LIS surface model. Two separate experiments and simulations are performed, 1. for all known climate forcings that are present in 2014, and 2. for a naturalised 2014 scenario where we assume humans never impacted the climate. We perform sensitivity analyses on the observed precipitation over the regions of interest, and determine that the TRMM data is in good agreement with station data obtained from the Jordanian Ministry of Water. Using a combination of the TRMM and model data we are able to make clear statements on the attribution of a 2014-like extreme draught event to human causal factors.

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

PubMed

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

2011-07-01

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

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.

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

Rainer, Leo I.; Hoeschele, Marc A.; Apte, Michael G.

This report addresses the results of detailed monitoring completed under Program Element 6 of Lawrence Berkeley National Laboratory's High Performance Commercial Building Systems (HPCBS) PIER program. The purpose of the Energy Simulations and Projected State-Wide Energy Savings project is to develop reasonable energy performance and cost models for high performance relocatable classrooms (RCs) across California climates. A key objective of the energy monitoring was to validate DOE2 simulations for comparison to initial DOE2 performance projections. The validated DOE2 model was then used to develop statewide savings projections by modeling base case and high performance RC operation in the 16 Californiamore » climate zones. The primary objective of this phase of work was to utilize detailed field monitoring data to modify DOE2 inputs and generate performance projections based on a validated simulation model. Additional objectives include the following: (1) Obtain comparative performance data on base case and high performance HVAC systems to determine how they are operated, how they perform, and how the occupants respond to the advanced systems. This was accomplished by installing both HVAC systems side-by-side (i.e., one per module of a standard two module, 24 ft by 40 ft RC) on the study RCs and switching HVAC operating modes on a weekly basis. (2) Develop projected statewide energy and demand impacts based on the validated DOE2 model. (3) Develop cost effectiveness projections for the high performance HVAC system in the 16 California climate zones.« less

Climate, soil or both? Which variables are better predictors of the distributions of Australian shrub species?

PubMed Central

Esperón-Rodríguez, Manuel; Baumgartner, John B.; Beaumont, Linda J.

2017-01-01

Background Shrubs play a key role in biogeochemical cycles, prevent soil and water erosion, provide forage for livestock, and are a source of food, wood and non-wood products. However, despite their ecological and societal importance, the influence of different environmental variables on shrub distributions remains unclear. We evaluated the influence of climate and soil characteristics, and whether including soil variables improved the performance of a species distribution model (SDM), Maxent. Methods This study assessed variation in predictions of environmental suitability for 29 Australian shrub species (representing dominant members of six shrubland classes) due to the use of alternative sets of predictor variables. Models were calibrated with (1) climate variables only, (2) climate and soil variables, and (3) soil variables only. Results The predictive power of SDMs differed substantially across species, but generally models calibrated with both climate and soil data performed better than those calibrated only with climate variables. Models calibrated solely with soil variables were the least accurate. We found regional differences in potential shrub species richness across Australia due to the use of different sets of variables. Conclusions Our study provides evidence that predicted patterns of species richness may be sensitive to the choice of predictor set when multiple, plausible alternatives exist, and demonstrates the importance of considering soil properties when modeling availability of habitat for plants. PMID:28652933

Regional Climate Simulations with COSMO-CLM for West Africa using different soil-vegetation-atmosphere-transfer module's (SVAT's)

NASA Astrophysics Data System (ADS)

Breil, Marcus; Panitz, Hans-Jürgen

2013-04-01

Climate predictions on decadal timescales constitute a new field of research, closing the gap between short-term and seasonal weather predictions and long-term climate projections. Therefore, the Federal Ministry of Education and Research in Germany (BMBF) has recently funded the research program MiKlip (Mittelfristige Klimaprognosen), which aims to create a model system that can provide reliable decadal climate forecasts. Recent studies have suggested that one region with high potential decadal predictability is West Africa. Therefore, the DEPARTURE project (DEcadal Prediction of African Rainfall and ATlantic HURricanE Activity) was established within the MiKlip program to assess the feasibility and the potential added value of regional decadal climate predictions for West Africa. To quantify the potential decadal climate predictability, a multi-model approach with the three different regional climate models REMO, WRF and COSMO-CLM (CCLM) will be realized. The presented research will contribute to DEPARTURE by performing hindcast ensemble simulations with CCLM, based on SST-driven global MPI-ESM-LR simulations. Thereby, one focus is on the dynamic soil-vegetation-climate interaction on decadal timescales. Recent studies indicate that there are significant feedbacks between the land-surface and the atmosphere, which might influence the decadal climate variability substantially. To investigate this connection, three different SVAT's (TERRA_ML, Community Land Model (CLM), and VEG3D) will be coupled with the CCLM. Thus, sensitive model parameters shall be identified, whereby the understanding of important processes might be improved. As a first step, the influence of the model domain on the CCLM results was examined. For this purpose, recent CCLM results from simulations for the official CORDEX domain were compared with CCLM results achieved by using an extended DEPARTURE model domain to about 60°W. This sensitivity analysis was performed with a horizontal resolution of 0.44°. Thereby, the analysis showed that the domain size doesn't affect the quality of the simulation results significantly. The impact of different SVAT's on the model performance is supposed to be higher. To investigate this assumption, TERRA_ML, the standard SVAT implemented in CCLM, is replaced by VEG3D using the OASIS3-MCT coupling software. Compared to TERRA_ML, VEG3D includes an explicit vegetation layer, inducing higher correlations with observations as it has been shown in previous studies. The results of both model configurations are analysed and presented for the DEPARTURE model domain.

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.

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.

Regional climate projection of the Maritime Continent using the MIT Regional Climate Model

NASA Astrophysics Data System (ADS)

IM, E. S.; Eltahir, E. A. B.

2014-12-01

Given that warming of the climate system is unequivocal (IPCC AR5), accurate assessment of future climate is essential to understand the impact of climate change due to global warming. Modelling the climate change of the Maritime Continent is particularly challenge, showing a high degree of uncertainty. Compared to other regions, model agreement of future projections in response to anthropogenic emission forcings is much less. Furthermore, the spatial and temporal behaviors of climate projections seem to vary significantly due to a complex geographical condition and a wide range of scale interactions. For the fine-scale climate information (27 km) suitable for representing the complexity of climate change over the Maritime Continent, dynamical downscaling is performed using the MIT regional climate model (MRCM) during two thirty-year period for reference (1970-1999) and future (2070-2099) climate. Initial and boundary conditions are provided by Community Earth System Model (CESM) simulations under the emission scenarios projected by MIT Integrated Global System Model (IGSM). Changes in mean climate as well as the frequency and intensity of extreme climate events are investigated at various temporal and spatial scales. Our analysis is primarily centered on the different behavior of changes in convective and large-scale precipitation over land vs. ocean during dry vs. wet season. In addition, we attempt to find the added value to downscaled results over the Maritime Continent through the comparison between MRCM and CESM projection. Acknowledgements.This research was supported by the National Research Foundation Singapore through the Singapore MIT Alliance for Research and Technology's Center for Environmental Sensing and Modeling interdisciplinary research program.

Impact of global warming on tropical cyclone genesis in coupled and forced simulations: role of SST spatial anomalies

NASA Astrophysics Data System (ADS)

Royer, Jean-François; Chauvin, Fabrice; Daloz, Anne-Sophie

2010-05-01

The response of tropical cyclones (TC) activity to global warming has not yet reached a clear consensus in the Fourth Assessment Report (AR4) published by the Intergovernmental Panel on Climate Change (IPCC, 2007) or in the recent scientific literature. Observed series are neither long nor reliable enough for a statistically significant detection and attribution of past TC trends, and coupled climate models give widely divergent results for the future evolution of TC activity in the different ocean basins. The potential importance of the spatial structure of the future SST warming has been pointed out by Chauvin et al. (2006) in simulations performed at CNRM with the ARPEGE-Climat GCM. The current presentation describes a new set of simulations that have been performed with the ARPEGE-Climat model to try to understand the possible role of SST patterns in the TC cyclogenesis response in 15 CMIP3 coupled simulations analysed by Royer et al (2009). The new simulations have been performed with the atmospheric component of the ARPEGE-Climat GCM forced in 10 year simulations by the SST patterns from each of 15 CMIP3 simulations with different climate model at the end of the 21st century according to scenario A2. The TC analysis is based on the computation of a Convective Yearly Genesis Parameter (CYGP) and the Genesis Potential Index (GPI). The computed genesis indices for each of the ARPEGE-Climat forced simulations is compared with the indices computed directly from the initial coupled simulation. The influence of SST patterns can then be more easily assessed since all the ARPEGE-Climat simulations are performed with the same atmospheric model, whereas the original simulations used models with different parameterization and resolutions. The analysis shows that CYGP or GPI anomalies obtained with ARPEGE are as variable between each other as those obtained originally by the different IPCC models. The variety of SST patterns used to force ARPEGE explains a large part of the dispersion, though for a given SST pattern, ARPEGE does not necessarily reproduce the anomaly produced originally by the IPCC model which produced the SST anomaly. Many factors can contribute to this discrepancy, but the most prominent seems to be the absence of coupling between the forced atmospheric ARPEGE simulation and the underlying ocean. When the atmospheric model is forced by prescribed SST anomalies some retroactions between cyclogenesis and ocean are missing. There are however areas over the globe were models agree about the CYGP or GPI anomalies induced by global warming, such as the Indian Ocean that shows a better coherency in the coupled and forced responses. This could be an indication that interaction between ocean and atmosphere is not as strong there as in the other basins. Details of the results for all the other ocean basins will be presented. References: Chauvin F. and J.-F. Royer and M. Déqué , 2006: Response of hurricane-type vortices to global warming as simulated by ARPEGE-Climat at high resolution. Climate Dynamics 27(4), 377-399. IPCC [Intergovernmental Panel for Climate Change], Climate change 2007: The physical science basis, in: S. Solomon et al. (eds.), Cambridge University Press. Royer JF, F Chauvin, 2009: Response of tropical cyclogenesis to global warming in an IPCC AR-4 scenario assessed by a modified yearly genesis parameter. "Hurricanes and Climate Change", J. B. Elsner and T. H. Jagger (Eds.), Springer, ISBN: 978-0-387-09409-0, pp 213-234.

A new climate modeling framework for convection-resolving simulation at continental scale

NASA Astrophysics Data System (ADS)

Charpilloz, Christophe; di Girolamo, Salvatore; Arteaga, Andrea; Fuhrer, Oliver; Hoefler, Torsten; Schulthess, Thomas; Schär, Christoph

2017-04-01

Major uncertainties remain in our understanding of the processes that govern the water cycle in a changing climate and their representation in weather and climate models. Of particular concern are heavy precipitation events of convective origin (thunderstorms and rain showers). The aim of the crCLIM project [1] is to propose a new climate modeling framework that alleviates the I/O-bottleneck in large-scale, convection-resolving climate simulations and thus to enable new analysis techniques for climate scientists. Due to the large computational costs, convection-resolving simulations are currently restricted to small computational domains or very short time scales, unless the largest available supercomputers system such as hybrid CPU-GPU architectures are used [3]. Hence, the COSMO model has been adapted to run on these architectures for research and production purposes [2]. However, the amount of generated data also increases and storing this data becomes infeasible making the analysis of simulations results impractical. To circumvent this problem and enable high-resolution models in climate we propose a data-virtualization layer (DVL) that re-runs simulations on demand and transparently manages the data for the analysis, that means we trade off computational effort (time) for storage (space). This approach also requires a bit-reproducible version of the COSMO model that produces identical results on different architectures (CPUs and GPUs) [4] that will be coupled with a performance model in order enable optimal re-runs depending on requirements of the re-run and available resources. In this contribution, we discuss the strategy to develop the DVL, a first performance model, the challenge of bit-reproducibility and the first results of the crCLIM project. [1] http://www.c2sm.ethz.ch/research/crCLIM.html [2] O. Fuhrer, C. Osuna, X. Lapillonne, T. Gysi, M. Bianco, and T. Schulthess. "Towards gpu-accelerated operational weather forecasting." In The GPU Technology Conference, GTC. 2013. [3] D. Leutwyler, O. Fuhrer, X. Lapillonne, D. Lüthi, and C. Schär. "Towards European-scale convection-resolving climate simulations with GPUs: a study with COSMO 4.19." Geoscientific Model Development 9, no. 9 (2016): 3393. [4] A. Arteaga, O. Fuhrer, and T. Hoefler. "Designing bit-reproducible portable high-performance applications." In Parallel and Distributed Processing Symposium, 2014 IEEE 28th International, pp. 1235-1244. IEEE, 2014.

Evaluation of a new satellite-based precipitation dataset for climate studies in the Xiang River basin, Southern China

NASA Astrophysics Data System (ADS)

Zhu, Q.; Xu, Y. P.; Hsu, K. L.

2017-12-01

A new satellite-based precipitation dataset, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) with long-term time series dating back to 1983 can be one valuable dataset for climate studies. This study investigates the feasibility of using PERSIANN-CDR as a reference dataset for climate studies. Sixteen CMIP5 models are evaluated over the Xiang River basin, southern China, by comparing their performance on precipitation projection and streamflow simulation, particularly on extreme precipitation and streamflow events. The results show PERSIANN-CDR is a valuable dataset for climate studies, even on extreme precipitation events. The precipitation estimates and their extreme events from CMIP5 models are improved significantly compared with rain gauge observations after bias-correction by the PERSIANN-CDR precipitation estimates. Given streamflows simulated with raw and bias-corrected precipitation estimates from 16 CMIP5 models, 10 out of 16 are improved after bias-correction. The impact of bias-correction on extreme events for streamflow simulations are unstable, with eight out of 16 models can be clearly claimed they are improved after the bias-correction. Concerning the performance of raw CMIP5 models on precipitation, IPSL-CM5A-MR excels the other CMIP5 models, while MRI-CGCM3 outperforms on extreme events with its better performance on six extreme precipitation metrics. Case studies also show that raw CCSM4, CESM1-CAM5, and MRI-CGCM3 outperform other models on streamflow simulation, while MIROC5-ESM-CHEM, MIROC5-ESM and IPSL-CM5A-MR behaves better than the other models after bias-correction.

Evaluation of climatic changes in South-Asia

NASA Astrophysics Data System (ADS)

Kjellstrom, Erik; Rana, Arun; Grigory, Nikulin; Renate, Wilcke; Hansson, Ulf; Kolax, Michael

2016-04-01

Literature has sufficient evidences of climate change impact all over the world and its impact on various sectors. In light of new advancements made in climate modeling, availability of several climate downscaling approaches, the more robust bias correction methods with varying complexities and strengths, in the present study we performed a systematic evaluation of climate change impact over South-Asia region. We have used different Regional Climate Models (RCMs) (from CORDEX domain), (Global Climate Models GCMs) and gridded observations for the study area to evaluate the models in historical/control period (1980-2010) and changes in future period (2010-2099). Firstly, GCMs and RCMs are evaluated against the Gridded observational datasets in the area using precipitation and temperature as indicative variables. Observational dataset are also evaluated against the reliable set of observational dataset, as pointed in literature. Bias, Correlation, and changes (among other statistical measures) are calculated for the entire region and both the variables. Eventually, the region was sub-divided into various smaller domains based on homogenous precipitation zones to evaluate the average changes over time period. Spatial and temporal changes for the region are then finally calculated to evaluate the future changes in the region. Future changes are calculated for 2 Representative Concentration Pathways (RCPs), the middle emission (RCP4.5) and high emission (RCP8.5) and for both climatic variables, precipitation and temperature. Lastly, Evaluation of Extremes is performed based on precipitation and temperature based indices for whole region in future dataset. Results have indicated that the whole study region is under extreme stress in future climate scenarios for both climatic variables i.e. precipitation and temperature. Precipitation variability is dependent on the location in the area leading to droughts and floods in various regions in future. Temperature is hinting towards a constant increase throughout the region regardless of location.

Objective calibration of regional climate models

NASA Astrophysics Data System (ADS)

Bellprat, O.; Kotlarski, S.; Lüthi, D.; SchäR, C.

2012-12-01

Climate models are subject to high parametric uncertainty induced by poorly confined model parameters of parameterized physical processes. Uncertain model parameters are typically calibrated in order to increase the agreement of the model with available observations. The common practice is to adjust uncertain model parameters manually, often referred to as expert tuning, which lacks objectivity and transparency in the use of observations. These shortcomings often haze model inter-comparisons and hinder the implementation of new model parameterizations. Methods which would allow to systematically calibrate model parameters are unfortunately often not applicable to state-of-the-art climate models, due to computational constraints facing the high dimensionality and non-linearity of the problem. Here we present an approach to objectively calibrate a regional climate model, using reanalysis driven simulations and building upon a quadratic metamodel presented by Neelin et al. (2010) that serves as a computationally cheap surrogate of the model. Five model parameters originating from different parameterizations are selected for the optimization according to their influence on the model performance. The metamodel accurately estimates spatial averages of 2 m temperature, precipitation and total cloud cover, with an uncertainty of similar magnitude as the internal variability of the regional climate model. The non-linearities of the parameter perturbations are well captured, such that only a limited number of 20-50 simulations are needed to estimate optimal parameter settings. Parameter interactions are small, which allows to further reduce the number of simulations. In comparison to an ensemble of the same model which has undergone expert tuning, the calibration yields similar optimal model configurations, but leading to an additional reduction of the model error. The performance range captured is much wider than sampled with the expert-tuned ensemble and the presented methodology is effective and objective. It is argued that objective calibration is an attractive tool and could become standard procedure after introducing new model implementations, or after a spatial transfer of a regional climate model. Objective calibration of parameterizations with regional models could also serve as a strategy toward improving parameterization packages of global climate models.

Uncertainties in the Modelled CO2 Threshold for Antarctic Glaciation

NASA Technical Reports Server (NTRS)

Gasson, E.; Lunt, D. J.; DeConto, R.; Goldner, A.; Heinemann, M.; Huber, M.; LeGrande, A. N.; Pollard, D.; Sagoo, N.; Siddall, M.;

Organizational climate, occupational stress, and employee mental health: mediating effects of organizational efficiency.

PubMed

Arnetz, Bengt B; Lucas, Todd; Arnetz, Judith E

2011-01-01

To determine whether the relationship between organizational climate and employee mental health is consistent (ie, invariant) or differs across four large hospitals, and whether organizational efficiency mediates this relationship. Participants (total N = 5316) completed validated measures of organizational climate variables (social climate, participatory management, goal clarity, and performance feedback), organizational efficiency, occupational stress, and mental health. Path analysis best supported a model in which organizational efficiency partially mediated relationships between organizational climate, occupational stress, and mental health. Focusing on improving both the psychosocial work environment and organizational efficiency might contribute to decreased employee stress, improved mental well-being, and organizational performance.

Performance analysis of the lineal model for estimating the maximum power of a HCPV module in different climate conditions

NASA Astrophysics Data System (ADS)

Fernández, Eduardo F.; Almonacid, Florencia; Sarmah, Nabin; Mallick, Tapas; Sanchez, Iñigo; Cuadra, Juan M.; Soria-Moya, Alberto; Pérez-Higueras, Pedro

2014-09-01

A model based on easily obtained atmospheric parameters and on a simple lineal mathematical expression has been developed at the Centre of Advanced Studies in Energy and Environment in southern Spain. The model predicts the maximum power of a HCPV module as a function of direct normal irradiance, air temperature and air mass. Presently, the proposed model has only been validated in southern Spain and its performance in locations with different atmospheric conditions still remains unknown. In order to address this issue, several HCPV modules have been measured in two different locations with different climate conditions than the south of Spain: the Environment and Sustainability Institute in southern UK and the National Renewable Energy Center in northern Spain. Results show that the model has an adequate match between actual and estimated data with a RMSE lower than 3.9% at locations with different climate conditions.

Biological Interactions and Simulated Climate Change Modulates the Ecophysiological Performance of Colobanthus quitensis in the Antarctic Ecosystem

PubMed Central

Torres-Díaz, Cristian; Gallardo-Cerda, Jorge; Lavin, Paris; Oses, Rómulo; Carrasco-Urra, Fernando; Atala, Cristian; Acuña-Rodríguez, Ian S.; Convey, Peter; Molina-Montenegro, Marco A.

2016-01-01

Most climate and environmental change models predict significant increases in temperature and precipitation by the end of the 21st Century, for which the current functional output of certain symbioses may also be altered. In this context we address the following questions: 1) How the expected changes in abiotic factors (temperature, and water) differentially affect the ecophysiological performance of the plant Colobanthus quitensis? and 2) Will this environmental change indirectly affect C. quitensis photochemical performance and biomass accumulation by modifying its association with fungal endophytes? Plants of C. quitensis from King George Island in the South Shetland archipelago (62°09′ S), and Lagotellerie Island in the Antarctic Peninsula (65°53′ S) were put under simulated abiotic conditions in growth chambers following predictive models of global climate change (GCC). The indirect effect of GCC on the interaction between C. quitensis and fungal endophytes was assessed in a field experiment carried out in the Antarctica, in which we eliminated endophytes under contemporary conditions and applied experimental watering to simulate increased precipitation input. We measured four proxies of plant performance. First, we found that warming (+W) significantly increased plant performance, however its effect tended to be less than watering (+W) and combined warming and watering (+T°+W). Second, the presence of fungal endophytes improved plant performance, and its effect was significantly decreased under experimental watering. Our results indicate that both biotic and abiotic factors affect ecophysiological performance, and the directions of these influences will change with climate change. Our findings provide valuable information that will help to predict future population spread and evolution through using ecological niche models under different climatic scenarios. PMID:27776181

Biological Interactions and Simulated Climate Change Modulates the Ecophysiological Performance of Colobanthus quitensis in the Antarctic Ecosystem.

PubMed

Torres-Díaz, Cristian; Gallardo-Cerda, Jorge; Lavin, Paris; Oses, Rómulo; Carrasco-Urra, Fernando; Atala, Cristian; Acuña-Rodríguez, Ian S; Convey, Peter; Molina-Montenegro, Marco A

2016-01-01

Most climate and environmental change models predict significant increases in temperature and precipitation by the end of the 21st Century, for which the current functional output of certain symbioses may also be altered. In this context we address the following questions: 1) How the expected changes in abiotic factors (temperature, and water) differentially affect the ecophysiological performance of the plant Colobanthus quitensis? and 2) Will this environmental change indirectly affect C. quitensis photochemical performance and biomass accumulation by modifying its association with fungal endophytes? Plants of C. quitensis from King George Island in the South Shetland archipelago (62°09' S), and Lagotellerie Island in the Antarctic Peninsula (65°53' S) were put under simulated abiotic conditions in growth chambers following predictive models of global climate change (GCC). The indirect effect of GCC on the interaction between C. quitensis and fungal endophytes was assessed in a field experiment carried out in the Antarctica, in which we eliminated endophytes under contemporary conditions and applied experimental watering to simulate increased precipitation input. We measured four proxies of plant performance. First, we found that warming (+W) significantly increased plant performance, however its effect tended to be less than watering (+W) and combined warming and watering (+T°+W). Second, the presence of fungal endophytes improved plant performance, and its effect was significantly decreased under experimental watering. Our results indicate that both biotic and abiotic factors affect ecophysiological performance, and the directions of these influences will change with climate change. Our findings provide valuable information that will help to predict future population spread and evolution through using ecological niche models under different climatic scenarios.

Data in support of energy performance of double-glazed windows.

PubMed

Shakouri, Mahmoud; Banihashemi, Saeed

2016-06-01

This paper provides the data used in a research project to propose a new simplified windows rating system based on saved annual energy ("Developing an empirical predictive energy-rating model for windows by using Artificial Neural Network" (Shakouri Hassanabadi and Banihashemi Namini, 2012) [1], "Climatic, parametric and non-parametric analysis of energy performance of double-glazed windows in different climates" (Banihashemi et al., 2015) [2]). A full factorial simulation study was conducted to evaluate the performance of 26 different types of windows in a four-story residential building. In order to generalize the results, the selected windows were tested in four climates of cold, tropical, temperate, and hot and arid; and four different main orientations of North, West, South and East. The accompanied datasets include the annual saved cooling and heating energy in different climates and orientations by using the selected windows. Moreover, a complete dataset is provided that includes the specifications of 26 windows, climate data, month, and orientation of the window. This dataset can be used to make predictive models for energy efficiency assessment of double glazed windows.

Climate Model Diagnostic Analyzer

NASA Technical Reports Server (NTRS)

Lee, Seungwon; Pan, Lei; Zhai, Chengxing; Tang, Benyang; Kubar, Terry; Zhang, Zia; Wang, Wei

2015-01-01

The comprehensive and innovative evaluation of climate models with newly available global observations is critically needed for the improvement of climate model current-state representation and future-state predictability. A climate model diagnostic evaluation process requires physics-based multi-variable analyses that typically involve large-volume and heterogeneous datasets, making them both computation- and data-intensive. With an exploratory nature of climate data analyses and an explosive growth of datasets and service tools, scientists are struggling to keep track of their datasets, tools, and execution/study history, let alone sharing them with others. In response, we have developed a cloud-enabled, provenance-supported, web-service system called Climate Model Diagnostic Analyzer (CMDA). CMDA enables the physics-based, multivariable model performance evaluations and diagnoses through the comprehensive and synergistic use of multiple observational data, reanalysis data, and model outputs. At the same time, CMDA provides a crowd-sourcing space where scientists can organize their work efficiently and share their work with others. CMDA is empowered by many current state-of-the-art software packages in web service, provenance, and semantic search.

Seasonal to interannual Arctic sea ice predictability in current global climate models

NASA Astrophysics Data System (ADS)

Tietsche, S.; Day, J. J.; Guemas, V.; Hurlin, W. J.; Keeley, S. P. E.; Matei, D.; Msadek, R.; Collins, M.; Hawkins, E.

2014-02-01

We establish the first intermodel comparison of seasonal to interannual predictability of present-day Arctic climate by performing coordinated sets of idealized ensemble predictions with four state-of-the-art global climate models. For Arctic sea ice extent and volume, there is potential predictive skill for lead times of up to 3 years, and potential prediction errors have similar growth rates and magnitudes across the models. Spatial patterns of potential prediction errors differ substantially between the models, but some features are robust. Sea ice concentration errors are largest in the marginal ice zone, and in winter they are almost zero away from the ice edge. Sea ice thickness errors are amplified along the coasts of the Arctic Ocean, an effect that is dominated by sea ice advection. These results give an upper bound on the ability of current global climate models to predict important aspects of Arctic climate.

Are we using the right fuel to drive hydrological models? A climate impact study in the Upper Blue Nile

NASA Astrophysics Data System (ADS)

Liersch, Stefan; Tecklenburg, Julia; Rust, Henning; Dobler, Andreas; Fischer, Madlen; Kruschke, Tim; Koch, Hagen; Fokko Hattermann, Fred

2018-04-01

Climate simulations are the fuel to drive hydrological models that are used to assess the impacts of climate change and variability on hydrological parameters, such as river discharges, soil moisture, and evapotranspiration. Unlike with cars, where we know which fuel the engine requires, we never know in advance what unexpected side effects might be caused by the fuel we feed our models with. Sometimes we increase the fuel's octane number (bias correction) to achieve better performance and find out that the model behaves differently but not always as was expected or desired. This study investigates the impacts of projected climate change on the hydrology of the Upper Blue Nile catchment using two model ensembles consisting of five global CMIP5 Earth system models and 10 regional climate models (CORDEX Africa). WATCH forcing data were used to calibrate an eco-hydrological model and to bias-correct both model ensembles using slightly differing approaches. On the one hand it was found that the bias correction methods considerably improved the performance of average rainfall characteristics in the reference period (1970-1999) in most of the cases. This also holds true for non-extreme discharge conditions between Q20 and Q80. On the other hand, bias-corrected simulations tend to overemphasize magnitudes of projected change signals and extremes. A general weakness of both uncorrected and bias-corrected simulations is the rather poor representation of high and low flows and their extremes, which were often deteriorated by bias correction. This inaccuracy is a crucial deficiency for regional impact studies dealing with water management issues and it is therefore important to analyse model performance and characteristics and the effect of bias correction, and eventually to exclude some climate models from the ensemble. However, the multi-model means of all ensembles project increasing average annual discharges in the Upper Blue Nile catchment and a shift in seasonal patterns, with decreasing discharges in June and July and increasing discharges from August to November.

Using statistical model to simulate the impact of climate change on maize yield with climate and crop uncertainties

NASA Astrophysics Data System (ADS)

Zhang, Yi; Zhao, Yanxia; Wang, Chunyi; Chen, Sining

2017-11-01

Assessment of the impact of climate change on crop productions with considering uncertainties is essential for properly identifying and decision-making agricultural practices that are sustainable. In this study, we employed 24 climate projections consisting of the combinations of eight GCMs and three emission scenarios representing the climate projections uncertainty, and two crop statistical models with 100 sets of parameters in each model representing parameter uncertainty within the crop models. The goal of this study was to evaluate the impact of climate change on maize ( Zea mays L.) yield at three locations (Benxi, Changling, and Hailun) across Northeast China (NEC) in periods 2010-2039 and 2040-2069, taking 1976-2005 as the baseline period. The multi-models ensembles method is an effective way to deal with the uncertainties. The results of ensemble simulations showed that maize yield reductions were less than 5 % in both future periods relative to the baseline. To further understand the contributions of individual sources of uncertainty, such as climate projections and crop model parameters, in ensemble yield simulations, variance decomposition was performed. The results indicated that the uncertainty from climate projections was much larger than that contributed by crop model parameters. Increased ensemble yield variance revealed the increasing uncertainty in the yield simulation in the future periods.

Evaluating climate models: Should we use weather or climate observations?

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

Oglesby, Robert J; Erickson III, David J

2009-12-01

Calling the numerical models that we use for simulations of climate change 'climate models' is a bit of a misnomer. These 'general circulation models' (GCMs, AKA global climate models) and their cousins the 'regional climate models' (RCMs) are actually physically-based weather simulators. That is, these models simulate, either globally or locally, daily weather patterns in response to some change in forcing or boundary condition. These simulated weather patterns are then aggregated into climate statistics, very much as we aggregate observations into 'real climate statistics'. Traditionally, the output of GCMs has been evaluated using climate statistics, as opposed to their abilitymore » to simulate realistic daily weather observations. At the coarse global scale this may be a reasonable approach, however, as RCM's downscale to increasingly higher resolutions, the conjunction between weather and climate becomes more problematic. We present results from a series of present-day climate simulations using the WRF ARW for domains that cover North America, much of Latin America, and South Asia. The basic domains are at a 12 km resolution, but several inner domains at 4 km have also been simulated. These include regions of complex topography in Mexico, Colombia, Peru, and Sri Lanka, as well as a region of low topography and fairly homogeneous land surface type (the U.S. Great Plains). Model evaluations are performed using standard climate analyses (e.g., reanalyses; NCDC data) but also using time series of daily station observations. Preliminary results suggest little difference in the assessment of long-term mean quantities, but the variability on seasonal and interannual timescales is better described. Furthermore, the value-added by using daily weather observations as an evaluation tool increases with the model resolution.« less

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.

Mid-Holocene and last glacial maximum climate simulations with the IPSL model: part II: model-data comparisons

NASA Astrophysics Data System (ADS)

Kageyama, Masa; Braconnot, Pascale; Bopp, Laurent; Mariotti, Véronique; Roy, Tilla; Woillez, Marie-Noëlle; Caubel, Arnaud; Foujols, Marie-Alice; Guilyardi, Eric; Khodri, Myriam; Lloyd, James; Lombard, Fabien; Marti, Olivier

2013-05-01

The climates of the mid-Holocene (MH, 6,000 years ago) and the Last Glacial Maximum (LGM, 21,000 years ago) have been extensively documented and as such, have become targets for the evaluation of climate models for climate contexts very different from the present. In Part 1 of the present work, we have studied the MH and LGM simulations performed with the last two versions of the IPSL model: IPSL_CM4, run for the PMIP2/CMIP3 (Coupled Model Intercomparion Project) projects and IPSL_CM5A, run for the most recent PMIP3/CMIP5 projets. We have shown that not only are these models different in their simulations of the PI climate, but also in their simulations of the climatic anomalies for the MH and LGM. In the Part 2 of this paper, we first examine whether palaeo-data can help discriminate between the model performances. This is indeed the case for the African monsoon for the MH or for North America south of the Laurentide ice sheet, the South Atlantic or the southern Indian ocean for the LGM. For the LGM, off-line vegetation modelling appears to offer good opportunities to distinguish climate model results because glacial vegetation proves to be very sensitive to even small differences in LGM climate. For other cases such as the LGM North Atlantic or the LGM equatorial Pacific, the large uncertainty on the SST reconstructions, prevents model discrimination. We have examined the use of other proxy-data for model evaluation, which has become possible with the inclusion of the biogeochemistry morel PISCES in the IPSL_CM5A model. We show a broad agreement of the LGM-PI export production changes with reconstructions. These changes are related to the mixed layer depth in most regions and to sea-ice variations in the high latitudes. We have also modelled foraminifer abundances with the FORAMCLIM model and shown that the changes in foraminifer abundance in the equatorial Pacific are mainly forced by changes in SSTs, hence confirming the SST-foraminifer abundance relationship. Yet, this is not the case in all regions in the North Atlantic, where food availability can have a strong impact of foraminifer abundances. Further work will be needed to exhaustively examine the role of factors other than climate in piloting changes in palaeo-indicators.

On The Impact of Climate Change to Agricultural Productivity in East Java

NASA Astrophysics Data System (ADS)

Kuswanto, Heri; Salamah, Mutiah; Mumpuni Retnaningsih, Sri; Dwi Prastyo, Dedy

2018-03-01

Many researches showed that climate change has significant impact on agricultural sector, which threats the food security especially in developing countries. It has been observed also that the climate change increases the intensity of extreme events. This research investigated the impact climate to the agricultural productivity in East Java, as one of the main rice producers in Indonesia. Standard regression as well as panel regression models have been performed in order to find the best model which is able to describe the climate change impact. The analysis found that the fixed effect model of panel regression outperforms the others showing that climate change had negatively impacted the rice productivity in East Java. The effect in Malang and Pasuruan were almost the same, while the impact in Sumenep was the least one compared to other districts.

Climate and dengue transmission: evidence and implications.

PubMed

Morin, Cory W; Comrie, Andrew C; Ernst, Kacey

2013-01-01

Climate influences dengue ecology by affecting vector dynamics, agent development, and mosquito/human interactions. Although these relationships are known, the impact climate change will have on transmission is unclear. Climate-driven statistical and process-based models are being used to refine our knowledge of these relationships and predict the effects of projected climate change on dengue fever occurrence, but results have been inconsistent. We sought to identify major climatic influences on dengue virus ecology and to evaluate the ability of climate-based dengue models to describe associations between climate and dengue, simulate outbreaks, and project the impacts of climate change. We reviewed the evidence for direct and indirect relationships between climate and dengue generated from laboratory studies, field studies, and statistical analyses of associations between vectors, dengue fever incidence, and climate conditions. We assessed the potential contribution of climate-driven, process-based dengue models and provide suggestions to improve their performance. Relationships between climate variables and factors that influence dengue transmission are complex. A climate variable may increase dengue transmission potential through one aspect of the system while simultaneously decreasing transmission potential through another. This complexity may at least partly explain inconsistencies in statistical associations between dengue and climate. Process-based models can account for the complex dynamics but often omit important aspects of dengue ecology, notably virus development and host-species interactions. Synthesizing and applying current knowledge of climatic effects on all aspects of dengue virus ecology will help direct future research and enable better projections of climate change effects on dengue incidence.

Impact of the climate change on the performance of the steam and gas turbines in Russia

NASA Astrophysics Data System (ADS)

Fedotova (Kasilova, E. V.; Klimenko, V. V.; Klimenko, A. V.; Tereshin, A. G.

2017-11-01

The power generating industry is known to be vulnerable to the climate change due to the deteriorating efficiency of the power equipment. Effects for Russia are not completely understood yet. But they are already detected and will be more pronounced during the entire current century, as the Russian territory is one of the areas around the world where the climate change is developing most rapidly. An original climate model was applied to simulate the change of the air temperature across Russia for the twenty-first century. The results of the climate simulations were used to conduct impact analysis for the steam and gas turbine performance taking into account seasonal and spatial heterogeneity of the climate change across the Russian territory. Sensitivity of the turbines to the climatic conditions was simulated using both results of fundamental heat transfer research and empirical performance curves for the units being in operation nowadays. The integral effect of the climate change on the power generating industry was estimated. Some possible challenges and opportunities resulted from the climate change were identified.

Developmental Climate: A Cross-level Analysis of Voluntary Turnover and Job Performance

PubMed Central

Spell, Hannah B.; Eby, Lillian T.; Vandenberg, Robert J.

2014-01-01

This research investigates the influence of shared perceptions of developmental climate on individual-level perceptions of organizational commitment, engagement, and perceived competence, and whether these attitudes mediate the relationship between developmental climate and both individual voluntary turnover and supervisor-rated job performance. Survey data were collected from 361 intact employee-supervisory mentoring dyads and matched with employee turnover data collected one year later to test the proposed framework using multilevel modeling techniques. As expected, shared perceptions of developmental climate were significantly and positively related to all three individual work attitudes. In addition, both organizational commitment and perceived competence were significant mediators of the positive relationship between shared perceptions of developmental climate and voluntary turnover, as well as shared perceptions of developmental climate and supervisor-rated job performance. By contrast, no significant mediating effects were found for engagement. Theoretical implications, limitations, and future research are discussed. PMID:24748681

Analysis and Experimental Investigation of Optimum Design of Thermoelectric Cooling/Heating System for Car Seat Climate Control (CSCC)

NASA Astrophysics Data System (ADS)

Elarusi, Abdulmunaem; Attar, Alaa; Lee, HoSung

2018-02-01

The optimum design of a thermoelectric system for application in car seat climate control has been modeled and its performance evaluated experimentally. The optimum design of the thermoelectric device combining two heat exchangers was obtained by using a newly developed optimization method based on the dimensional technique. Based on the analytical optimum design results, commercial thermoelectric cooler and heat sinks were selected to design and construct the climate control heat pump. This work focuses on testing the system performance in both cooling and heating modes to ensure accurate analytical modeling. Although the analytical performance was calculated using the simple ideal thermoelectric equations with effective thermoelectric material properties, it showed very good agreement with experiment for most operating conditions.

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.

Reservoir Performance Under Future Climate For Basins With Different Hydrologic Sensitivities

NASA Astrophysics Data System (ADS)

Mateus, M. C.; Tullos, D. D.

2013-12-01

In addition to long-standing uncertainties related to variable inflows and market price of power, reservoir operators face a number of new uncertainties related to hydrologic nonstationarity, changing environmental regulations, and rapidly growing water and energy demands. This study investigates the impact, sensitivity, and uncertainty of changing hydrology on hydrosystem performance across different hydrogeologic settings. We evaluate the performance of reservoirs in the Santiam River basin, including a case study in the North Santiam Basin, with high permeability and extensive groundwater storage, and the South Santiam Basin, with low permeability, little groundwater storage and rapid runoff response. The modeling objective is to address the following study questions: (1) for the two hydrologic regimes, how does the flood management, water supply, and environmental performance of current reservoir operations change under future 2.5, 50 and 97.5 percentile streamflow projections; and (2) how much change in inflow is required to initiate a failure to meet downstream minimum or maximum flows in the future. We couple global climate model results with a rainfall-runoff model and a formal Bayesian uncertainty analysis to simulate future inflow hydrographs as inputs to a reservoir operations model. To evaluate reservoir performance under a changing climate, we calculate reservoir refill reliability, changes in flood frequency, and reservoir time and volumetric reliability of meeting minimum spring and summer flow target. Reservoir performance under future hydrology appears to vary with hydrogeology. We find higher sensitivity to floods for the North Santiam Basin and higher sensitivity to minimum flow targets for the South Santiam Basin. Higher uncertainty is related with basins with a more complex hydrologeology. Results from model simulations contribute to understanding of the reliability and vulnerability of reservoirs to a changing climate.

Simulations of the Montréal urban heat island

NASA Astrophysics Data System (ADS)

Roberge, François; Sushama, Laxmi; Fanta, Gemechu

2017-04-01

The current population of Montreal is around 3.8 million and this number is projected to go up in the coming years to decades, which will lead to vast expansion of urban areas. It is well known that urban morphology impacts weather and climate, and therefore should be taken into consideration in urban planning. This is particularly important in the context of a changing climate, as the intensity and frequency of temperature extremes such as hot spells are projected to increase in future climate, and Urban Heat Island (UHI) can potentially raise already stressful temperatures during such events, which can have significant effects on human health and energy consumption. High-resolution regional climate model simulations can be utilized to understand better urban-weather/climate interactions in current and future climates, particularly the spatio-temporal characteristics of the Urban Heat Island and its impact on other weather/climate characteristics such as urban flows, precipitation etc. This paper will focus on two high-resolution (250 m) simulations performed with (1) the Canadian Land Surface Scheme (CLASS) and (2) CLASS and TEB (Town Energy Balance) model; TEB is a single layer urban canopy model and is used to model the urban fractions. The two simulations are performed over a domain covering Montreal for the 1960-2015 period, driven by atmospheric forcing data coming from a high-resolution Canadian Regional Climate Model (CRCM5) simulation, driven by ERA-Interim. The two simulations are compared to assess the impact of urban regions on selected surface fields and the simulation with both CLASS and TEB is then used to study the spatio-temporal characteristics of the UHI over the study domain. Some preliminary results from a coupled simulation, i.e. CRCM5+CLASS+TEB, for selected years, including extreme warm years, will also be presented.

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.

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.

WRF model forecasts and their use for hydroclimate monitoring over southern South America

NASA Astrophysics Data System (ADS)

Muller, Omar; Lovino, Miguel; Berbery, E. Hugo

2017-04-01

Weather forecasting and monitoring systems based on regional models are becoming increasingly relevant for decision support in agriculture and water management. This work evaluates the predictive and monitoring capabilities of a system based on WRF model simulations at 15 km grid spacing over a domain that encompasses La Plata Basin (LPB) in southern South America, where agriculture and water resources are essential. The model's skill up to a lead-time of 7 days is evaluated with daily precipitation and 2m temperature in-situ observations. Results show high prediction performance with 7 days lead-time throughout the domain and particularly over LPB, where about 70% of rain and no-rain days are correctly predicted. The scores tend to be better over humid climates than over arid-to-semiarid climates. Compared to the arid-semiarid climate, the humid climate has a higher probability of detection and less false alarms. The ranges of the skill scores are similar to those found over the United States, suggesting that proper choice of parameterizations lead to no loss of performance of the model. Daily mean, minimum and maximum forecast temperatures are highly correlated with observations up to 7 day lead time. The best performance is for daily mean temperature, followed by minimum temperature and a slightly weaker performance for maximum temperature over arid regions. The usefulness of WRF products for hydroclimate monitoring was tested for an unprecedented drought in southern Brazil and for a slightly above normal precipitation season in northeastern Argentina. In both cases the model products reproduce the observed precipitation conditions with consistent impacts on soil moisture, evapotranspiration and runoff. This evaluation validates the model's usefulness to fore-cast weather up to one week and to monitor climate conditions in real time. The scores suggest that the forecast lead-time can be extended into week two, while bias correction methods can reduce part of the systematic errors.

Can air temperature be used to project influences of climate change on stream temperature?

Treesearch

Ivan Arismendi; Mohammad Safeeq; Jason B Dunham; Sherri L Johnson

2014-01-01

Worldwide, lack of data on stream temperature has motivated the use of regression-based statistical models to predict stream temperatures based on more widely available data on air temperatures. Such models have been widely applied to project responses of stream temperatures under climate change, but the performance of these models has not been fully evaluated. To...

Intercomparison of model response and internal variability across climate model ensembles

NASA Astrophysics Data System (ADS)

Kumar, Devashish; Ganguly, Auroop R.

2017-10-01

Characterization of climate uncertainty at regional scales over near-term planning horizons (0-30 years) is crucial for climate adaptation. Climate internal variability (CIV) dominates climate uncertainty over decadal prediction horizons at stakeholders' scales (regional to local). In the literature, CIV has been characterized indirectly using projections of climate change from multi-model ensembles (MME) instead of directly using projections from multiple initial condition ensembles (MICE), primarily because adequate number of initial condition (IC) runs were not available for any climate model. Nevertheless, the recent availability of significant number of IC runs from one climate model allows for the first time to characterize CIV directly from climate model projections and perform a sensitivity analysis to study the dominance of CIV compared to model response variability (MRV). Here, we measure relative agreement (a dimensionless number with values ranging between 0 and 1, inclusive; a high value indicates less variability and vice versa) among MME and MICE and find that CIV is lower than MRV for all projection time horizons and spatial resolutions for precipitation and temperature. However, CIV exhibits greater dominance over MRV for seasonal and annual mean precipitation at higher latitudes where signals of climate change are expected to emerge sooner. Furthermore, precipitation exhibits large uncertainties and a rapid decline in relative agreement from global to continental, regional, or local scales for MICE compared to MME. The fractional contribution of uncertainty due to CIV is invariant for precipitation and decreases for temperature as lead time progresses towards the end of the century.

A Regional Climate Model Evaluation System based on contemporary Satellite and other Observations for Assessing Regional Climate Model Fidelity

NASA Astrophysics Data System (ADS)

Waliser, D. E.; Kim, J.; Mattman, C.; Goodale, C.; Hart, A.; Zimdars, P.; Lean, P.

2011-12-01

Evaluation of climate models against observations is an essential part of assessing the impact of climate variations and change on regionally important sectors and improving climate models. Regional climate models (RCMs) are of a particular concern. RCMs provide fine-scale climate needed by the assessment community via downscaling global climate model projections such as those contributing to the Coupled Model Intercomparison Project (CMIP) that form one aspect of the quantitative basis of the IPCC Assessment Reports. The lack of reliable fine-resolution observational data and formal tools and metrics has represented a challenge in evaluating RCMs. Recent satellite observations are particularly useful as they provide a wealth of information and constraints on many different processes within the climate system. Due to their large volume and the difficulties associated with accessing and using contemporary observations, however, these datasets have been generally underutilized in model evaluation studies. Recognizing this problem, NASA JPL and UCLA have developed the Regional Climate Model Evaluation System (RCMES) to help make satellite observations, in conjunction with in-situ and reanalysis datasets, more readily accessible to the regional modeling community. The system includes a central database (Regional Climate Model Evaluation Database: RCMED) to store multiple datasets in a common format and codes for calculating and plotting statistical metrics to assess model performance (Regional Climate Model Evaluation Tool: RCMET). This allows the time taken to compare model data with satellite observations to be reduced from weeks to days. RCMES is a component of the recent ExArch project, an international effort for facilitating the archive and access of massive amounts data for users using cloud-based infrastructure, in this case as applied to the study of climate and climate change. This presentation will describe RCMES and demonstrate its utility using examples from RCMs applied to the southwest US as well as to Africa based on output from the CORDEX activity. Application of RCMES to the evaluation of multi-RCM hindcast for CORDEX-Africa will be presented in a companion paper in A41.

High resolution global climate modelling; the UPSCALE project, a large simulation campaign

NASA Astrophysics Data System (ADS)

Mizielinski, M. S.; Roberts, M. J.; Vidale, P. L.; Schiemann, R.; Demory, M.-E.; Strachan, J.; Edwards, T.; Stephens, A.; Lawrence, B. N.; Pritchard, M.; Chiu, P.; Iwi, A.; Churchill, J.; del Cano Novales, C.; Kettleborough, J.; Roseblade, W.; Selwood, P.; Foster, M.; Glover, M.; Malcolm, A.

2014-01-01

The UPSCALE (UK on PRACE: weather-resolving Simulations of Climate for globAL Environmental risk) project constructed and ran an ensemble of HadGEM3 (Hadley centre Global Environment Model 3) atmosphere-only global climate simulations over the period 1985-2011, at resolutions of N512 (25 km), N216 (60 km) and N96 (130 km) as used in current global weather forecasting, seasonal prediction and climate modelling respectively. Alongside these present climate simulations a parallel ensemble looking at extremes of future climate was run, using a time-slice methodology to consider conditions at the end of this century. These simulations were primarily performed using a 144 million core hour, single year grant of computing time from PRACE (the Partnership for Advanced Computing in Europe) in 2012, with additional resources supplied by the Natural Environmental Research Council (NERC) and the Met Office. Almost 400 terabytes of simulation data were generated on the HERMIT supercomputer at the high performance computing center Stuttgart (HLRS), and transferred to the JASMIN super-data cluster provided by the Science and Technology Facilities Council Centre for Data Archival (STFC CEDA) for analysis and storage. In this paper we describe the implementation of the project, present the technical challenges in terms of optimisation, data output, transfer and storage that such a project involves and include details of the model configuration and the composition of the UPSCALE dataset. This dataset is available for scientific analysis to allow assessment of the value of model resolution in both present and potential future climate conditions.

High-resolution global climate modelling: the UPSCALE project, a large-simulation campaign

NASA Astrophysics Data System (ADS)

Mizielinski, M. S.; Roberts, M. J.; Vidale, P. L.; Schiemann, R.; Demory, M.-E.; Strachan, J.; Edwards, T.; Stephens, A.; Lawrence, B. N.; Pritchard, M.; Chiu, P.; Iwi, A.; Churchill, J.; del Cano Novales, C.; Kettleborough, J.; Roseblade, W.; Selwood, P.; Foster, M.; Glover, M.; Malcolm, A.

2014-08-01

The UPSCALE (UK on PRACE: weather-resolving Simulations of Climate for globAL Environmental risk) project constructed and ran an ensemble of HadGEM3 (Hadley Centre Global Environment Model 3) atmosphere-only global climate simulations over the period 1985-2011, at resolutions of N512 (25 km), N216 (60 km) and N96 (130 km) as used in current global weather forecasting, seasonal prediction and climate modelling respectively. Alongside these present climate simulations a parallel ensemble looking at extremes of future climate was run, using a time-slice methodology to consider conditions at the end of this century. These simulations were primarily performed using a 144 million core hour, single year grant of computing time from PRACE (the Partnership for Advanced Computing in Europe) in 2012, with additional resources supplied by the Natural Environment Research Council (NERC) and the Met Office. Almost 400 terabytes of simulation data were generated on the HERMIT supercomputer at the High Performance Computing Center Stuttgart (HLRS), and transferred to the JASMIN super-data cluster provided by the Science and Technology Facilities Council Centre for Data Archival (STFC CEDA) for analysis and storage. In this paper we describe the implementation of the project, present the technical challenges in terms of optimisation, data output, transfer and storage that such a project involves and include details of the model configuration and the composition of the UPSCALE data set. This data set is available for scientific analysis to allow assessment of the value of model resolution in both present and potential future climate conditions.

AerChemMIP: Quantifying the effects of chemistry and aerosols in CMIP6

DOE PAGES

Collins, William J.; Lamarque, Jean -François; Schulz, Michael; ...

2017-02-09

The Aerosol Chemistry Model Intercomparison Project (AerChemMIP) is endorsed by the Coupled-Model Intercomparison Project 6 (CMIP6) and is designed to quantify the climate and air quality impacts of aerosols and chemically reactive gases. These are specifically near-term climate forcers (NTCFs: methane, tropospheric ozone and aerosols, and their precursors), nitrous oxide and ozone-depleting halocarbons. The aim of AerChemMIP is to answer four scientific questions. 1. How have anthropogenic emissions contributed to global radiative forcing and affected regional climate over the historical period? 2. How might future policies (on climate, air quality and land use) affect the abundances of NTCFs and theirmore » climate impacts? 3.How do uncertainties in historical NTCF emissions affect radiative forcing estimates? 4. How important are climate feedbacks to natural NTCF emissions, atmospheric composition, and radiative effects? These questions will be addressed through targeted simulations with CMIP6 climate models that include an interactive representation of tropospheric aerosols and atmospheric chemistry. These simulations build on the CMIP6 Diagnostic, Evaluation and Characterization of Klima (DECK) experiments, the CMIP6 historical simulations, and future projections performed elsewhere in CMIP6, allowing the contributions from aerosols and/or chemistry to be quantified. As a result, specific diagnostics are requested as part of the CMIP6 data request to highlight the chemical composition of the atmosphere, to evaluate the performance of the models, and to understand differences in behaviour between them.« less

AerChemMIP: Quantifying the effects of chemistry and aerosols in CMIP6

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

Collins, William J.; Lamarque, Jean -François; Schulz, Michael

The Aerosol Chemistry Model Intercomparison Project (AerChemMIP) is endorsed by the Coupled-Model Intercomparison Project 6 (CMIP6) and is designed to quantify the climate and air quality impacts of aerosols and chemically reactive gases. These are specifically near-term climate forcers (NTCFs: methane, tropospheric ozone and aerosols, and their precursors), nitrous oxide and ozone-depleting halocarbons. The aim of AerChemMIP is to answer four scientific questions. 1. How have anthropogenic emissions contributed to global radiative forcing and affected regional climate over the historical period? 2. How might future policies (on climate, air quality and land use) affect the abundances of NTCFs and theirmore » climate impacts? 3.How do uncertainties in historical NTCF emissions affect radiative forcing estimates? 4. How important are climate feedbacks to natural NTCF emissions, atmospheric composition, and radiative effects? These questions will be addressed through targeted simulations with CMIP6 climate models that include an interactive representation of tropospheric aerosols and atmospheric chemistry. These simulations build on the CMIP6 Diagnostic, Evaluation and Characterization of Klima (DECK) experiments, the CMIP6 historical simulations, and future projections performed elsewhere in CMIP6, allowing the contributions from aerosols and/or chemistry to be quantified. As a result, specific diagnostics are requested as part of the CMIP6 data request to highlight the chemical composition of the atmosphere, to evaluate the performance of the models, and to understand differences in behaviour between them.« less

The WASCAL high-resolution climate projection ensemble for West Africa

NASA Astrophysics Data System (ADS)

Kunstmann, Harald; Heinzeller, Dominikus; Dieng, Diarra; Smiatek, Gerhard; Bliefernicht, Jan; Hamann, Ilse; Salack, Seyni

2017-04-01

With climate change being one of the most severe challenges to rural Africa in the 21st century, West Africa is facing an urgent need to develop effective adaptation and mitigation measures to protect its constantly growing population. We perform ensemble-based regional climate simulations at a high resolution of 12km for West Africa to allow a scientifically sound derivation of climate change adaptation measures. Based on the RCP4.5 scenario, our ensemble consist of three simulation experiments with the Weather Research & Forecasting Tool (WRF) and one additional experiment with the Consortium for Small-scale Modelling Model COSMO in Climate Mode (COSMO-CLM). We discuss the model performance over the validation period 1980-2010, including a novel, station-based precipitation database for West Africa obtained within the WASCAL (West African Science Service Centre for Climate Change and Adapted Land Use) program. Particular attention is paid to the representation of the dynamics of the West African Summer Monsoon and to the added value of our high-resolution models over existing data sets. We further present results on the climate change signal obtained for the two future periods 2020-2050 and 2070-2100 and compare them to current state-of-the-art projections from the CORDEX-Africa project. While the temperature change signal is similar to that obtained within CORDEX-Africa, our simulations predict a wetter future for the Coast of Guinea and the southern Soudano area and a slight drying in the northernmost part of the Sahel.

Land use and climate change impacts on runoff and soil erosion at the hillslope scale in the Brazilian Cerrado.

PubMed

Anache, Jamil A A; Flanagan, Dennis C; Srivastava, Anurag; Wendland, Edson C

2018-05-01

Land use and climate change can influence runoff and soil erosion, threatening soil and water conservation in the Cerrado biome in Brazil. The adoption of a process-based model was necessary due to the lack of long-term observed data. Our goals were to calibrate the WEPP (Water Erosion Prediction Project) model for different land uses under subtropical conditions in the Cerrado biome; predict runoff and soil erosion for these different land uses; and simulate runoff and soil erosion considering climate change. We performed the model calibration using a 5-year dataset (2012-2016) of observed runoff and soil loss in four different land uses (wooded Cerrado, tilled fallow without plant cover, pasture, and sugarcane) in experimental plots. Selected soil and management parameters were optimized for each land use during the WEPP model calibration with the existing field data. The simulations were conducted using the calibrated WEPP model components with a 100-year climate dataset created with CLIGEN (weather generator) based on regional climate statistics. We obtained downscaled General Circulation Model (GCM) projections, and runoff and soil loss were predicted with WEPP using future climate scenarios for 2030, 2060, and 2090 considering different Representative Concentration Pathways (RCPs). The WEPP model had an acceptable performance for the subtropical conditions. Land use can influence runoff and soil loss rates in a significant way. Potential climate changes, which indicate the increase of rainfall intensities and depths, may increase the variability and rates of runoff and soil erosion. However, projected climate changes did not significantly affect the runoff and soil erosion for the four analyzed land uses at our location. Finally, the runoff behavior was distinct for each land use, but for soil loss we found similarities between pasture and wooded Cerrado, suggesting that the soil may attain a sustainable level when the land management follows conservation principles. Copyright © 2017 Elsevier B.V. All rights reserved.

Using historical and projected future climate model simulations as drivers of agricultural and biological models (Invited)

NASA Astrophysics Data System (ADS)

Stefanova, L. B.

2013-12-01

Climate model evaluation is frequently performed as a first step in analyzing climate change simulations. Atmospheric scientists are accustomed to evaluating climate models through the assessment of model climatology and biases, the models' representation of large-scale modes of variability (such as ENSO, PDO, AMO, etc) and the relationship between these modes and local variability (e.g. the connection between ENSO and the wintertime precipitation in the Southeast US). While these provide valuable information about the fidelity of historical and projected climate model simulations from an atmospheric scientist's point of view, the application of climate model data to fields such as agriculture, ecology and biology may require additional analyses focused on the particular application's requirements and sensitivities. Typically, historical climate simulations are used to determine a mapping between the model and observed climate, either through a simple (additive for temperature or multiplicative for precipitation) or a more sophisticated (such as quantile matching) bias correction on a monthly or seasonal time scale. Plants, animals and humans however are not directly affected by monthly or seasonal means. To assess the impact of projected climate change on living organisms and related industries (e.g. agriculture, forestry, conservation, utilities, etc.), derivative measures such as the heating degree-days (HDD), cooling degree-days (CDD), growing degree-days (GDD), accumulated chill hours (ACH), wet season onset (WSO) and duration (WSD), among others, are frequently useful. We will present a comparison of the projected changes in such derivative measures calculated by applying: (a) the traditional temperature/precipitation bias correction described above versus (b) a bias correction based on the mapping between the historical model and observed derivative measures themselves. In addition, we will present and discuss examples of various application-based climate model evaluations, such as: (a) agricultural crop yield estimates and (b) species population viability estimates modeled using observed climate data vs. historical climate simulations.

Climatic Variables and Malaria Morbidity in Mutale Local Municipality, South Africa: A 19-Year Data Analysis

PubMed Central

Botai, Joel O.; Rautenbach, Hannes; Ncongwane, Katlego P.; Botai, Christina M.

2017-01-01

The north-eastern parts of South Africa, comprising the Limpopo Province, have recorded a sudden rise in the rate of malaria morbidity and mortality in the 2017 malaria season. The epidemiological profiles of malaria, as well as other vector-borne diseases, are strongly associated with climate and environmental conditions. A retrospective understanding of the relationship between climate and the occurrence of malaria may provide insight into the dynamics of the disease’s transmission and its persistence in the north-eastern region. In this paper, the association between climatic variables and the occurrence of malaria was studied in the Mutale local municipality in South Africa over a period of 19-year. Time series analysis was conducted on monthly climatic variables and monthly malaria cases in the Mutale municipality for the period of 1998–2017. Spearman correlation analysis was performed and the Seasonal Autoregressive Integrated Moving Average (SARIMA) model was developed. Microsoft Excel was used for data cleaning, and statistical software R was used to analyse the data and develop the model. Results show that both climatic variables’ and malaria cases’ time series exhibited seasonal patterns, showing a number of peaks and fluctuations. Spearman correlation analysis indicated that monthly total rainfall, mean minimum temperature, mean maximum temperature, mean average temperature, and mean relative humidity were significantly and positively correlated with monthly malaria cases in the study area. Regression analysis showed that monthly total rainfall and monthly mean minimum temperature (R2 = 0.65), at a two-month lagged effect, are the most significant climatic predictors of malaria transmission in Mutale local municipality. A SARIMA (2,1,2) (1,1,1) model fitted with only malaria cases has a prediction performance of about 51%, and the SARIMAX (2,1,2) (1,1,1) model with climatic variables as exogenous factors has a prediction performance of about 72% in malaria cases. The model gives a close comparison between the predicted and observed number of malaria cases, hence indicating that the model provides an acceptable fit to predict the number of malaria cases in the municipality. To sum up, the association between the climatic variables and malaria cases provides clues to better understand the dynamics of malaria transmission. The lagged effect detected in this study can help in adequate planning for malaria intervention. PMID:29117114

Climatic Variables and Malaria Morbidity in Mutale Local Municipality, South Africa: A 19-Year Data Analysis.

PubMed

Adeola, Abiodun M; Botai, Joel O; Rautenbach, Hannes; Adisa, Omolola M; Ncongwane, Katlego P; Botai, Christina M; Adebayo-Ojo, Temitope C

2017-11-08

The north-eastern parts of South Africa, comprising the Limpopo Province, have recorded a sudden rise in the rate of malaria morbidity and mortality in the 2017 malaria season. The epidemiological profiles of malaria, as well as other vector-borne diseases, are strongly associated with climate and environmental conditions. A retrospective understanding of the relationship between climate and the occurrence of malaria may provide insight into the dynamics of the disease's transmission and its persistence in the north-eastern region. In this paper, the association between climatic variables and the occurrence of malaria was studied in the Mutale local municipality in South Africa over a period of 19-year. Time series analysis was conducted on monthly climatic variables and monthly malaria cases in the Mutale municipality for the period of 1998-2017. Spearman correlation analysis was performed and the Seasonal Autoregressive Integrated Moving Average (SARIMA) model was developed. Microsoft Excel was used for data cleaning, and statistical software R was used to analyse the data and develop the model. Results show that both climatic variables' and malaria cases' time series exhibited seasonal patterns, showing a number of peaks and fluctuations. Spearman correlation analysis indicated that monthly total rainfall, mean minimum temperature, mean maximum temperature, mean average temperature, and mean relative humidity were significantly and positively correlated with monthly malaria cases in the study area. Regression analysis showed that monthly total rainfall and monthly mean minimum temperature ( R ² = 0.65), at a two-month lagged effect, are the most significant climatic predictors of malaria transmission in Mutale local municipality. A SARIMA (2,1,2) (1,1,1) model fitted with only malaria cases has a prediction performance of about 51%, and the SARIMAX (2,1,2) (1,1,1) model with climatic variables as exogenous factors has a prediction performance of about 72% in malaria cases. The model gives a close comparison between the predicted and observed number of malaria cases, hence indicating that the model provides an acceptable fit to predict the number of malaria cases in the municipality. To sum up, the association between the climatic variables and malaria cases provides clues to better understand the dynamics of malaria transmission. The lagged effect detected in this study can help in adequate planning for malaria intervention.

Species-specific ecological niche modelling predicts different range contractions for Lutzomyia intermedia and a related vector of Leishmania braziliensis following climate change in South America.

PubMed

McIntyre, Shannon; Rangel, Elizabeth F; Ready, Paul D; Carvalho, Bruno M

2017-03-24

Before 1996 the phlebotomine sand fly Lutzomyia neivai was usually treated as a synonym of the morphologically similar Lutzomyia intermedia, which has long been considered a vector of Leishmania braziliensis, the causative agent of much cutaneous leishmaniasis in South America. This report investigates the likely range changes of both sand fly species in response to a stabilisation climate change scenario (RCP4.5) and a high greenhouse gas emissions one (RCP8.5). Ecological niche modelling was used to identify areas of South America with climates currently suitable for each species, and then the future distributions of these climates were predicted based on climate change scenarios. Compared with the previous ecological niche model of L. intermedia (sensu lato) produced using the GARP algorithm in 2003, the current investigation modelled the two species separately, making use of verified presence records and additional records after 2001. Also, the new ensemble approach employed ecological niche modelling algorithms (including Maximum Entropy, Random Forests and Support Vector Machines) that have been widely adopted since 2003 and perform better than GARP, as well as using a more recent climate change model (HadGEM2) considered to have better performance at higher resolution than the earlier one (HadCM2). Lutzomyia intermedia was shown to be the more tropical of the two species, with its climatic niche defined by higher annual mean temperatures and lower temperature seasonality, in contrast to the more subtropical L. neivai. These different latitudinal ranges explain the two species' predicted responses to climate change by 2050, with L. intermedia mostly contracting its range (except perhaps in northeast Brazil) and L. neivai mostly shifting its range southwards in Brazil and Argentina. This contradicts the findings of the 2003 report, which predicted more range expansion. The different findings can be explained by the improved data sets and modelling methods. Our findings indicate that climate change will not always lead to range expansion of disease vectors such as sand flies. Ecological niche models should be species specific, carefully selected and combined in an ensemble approach.

Climate data induced uncertainty in model-based estimations of terrestrial primary productivity

NASA Astrophysics Data System (ADS)

Wu, Zhendong; Ahlström, Anders; Smith, Benjamin; Ardö, Jonas; Eklundh, Lars; Fensholt, Rasmus; Lehsten, Veiko

2017-06-01

Model-based estimations of historical fluxes and pools of the terrestrial biosphere differ substantially. These differences arise not only from differences between models but also from differences in the environmental and climatic data used as input to the models. Here we investigate the role of uncertainties in historical climate data by performing simulations of terrestrial gross primary productivity (GPP) using a process-based dynamic vegetation model (LPJ-GUESS) forced by six different climate datasets. We find that the climate induced uncertainty, defined as the range among historical simulations in GPP when forcing the model with the different climate datasets, can be as high as 11 Pg C yr-1 globally (9% of mean GPP). We also assessed a hypothetical maximum climate data induced uncertainty by combining climate variables from different datasets, which resulted in significantly larger uncertainties of 41 Pg C yr-1 globally or 32% of mean GPP. The uncertainty is partitioned into components associated to the three main climatic drivers, temperature, precipitation, and shortwave radiation. Additionally, we illustrate how the uncertainty due to a given climate driver depends both on the magnitude of the forcing data uncertainty (climate data range) and the apparent sensitivity of the modeled GPP to the driver (apparent model sensitivity). We find that LPJ-GUESS overestimates GPP compared to empirically based GPP data product in all land cover classes except for tropical forests. Tropical forests emerge as a disproportionate source of uncertainty in GPP estimation both in the simulations and empirical data products. The tropical forest uncertainty is most strongly associated with shortwave radiation and precipitation forcing, of which climate data range contributes higher to overall uncertainty than apparent model sensitivity to forcing. Globally, precipitation dominates the climate induced uncertainty over nearly half of the vegetated land area, which is mainly due to climate data range and less so due to the apparent model sensitivity. Overall, climate data ranges are found to contribute more to the climate induced uncertainty than apparent model sensitivity to forcing. Our study highlights the need to better constrain tropical climate, and demonstrates that uncertainty caused by climatic forcing data must be considered when comparing and evaluating carbon cycle model results and empirical datasets.

Influence of Lake Malawi on regional climate from a double-nested regional climate model experiment

NASA Astrophysics Data System (ADS)

Diallo, Ismaïla; Giorgi, Filippo; Stordal, Frode

2017-07-01

We evaluate the performance of the regional climate model (RCM) RegCM4 coupled to a one dimensional lake model for Lake Malawi (also known as Lake Nyasa in Tanzania and Lago Niassa in Mozambique) in simulating the main characteristics of rainfall and near surface air temperature patterns over the region. We further investigate the impact of the lake on the simulated regional climate. Two RCM simulations, one with and one without Lake Malawi, are performed for the period 1992-2008 at a grid spacing of 10 km by nesting the model within a corresponding 25 km resolution run ("mother domain") encompassing all Southern Africa. The performance of the model in simulating the mean seasonal patterns of near surface air temperature and precipitation is good compared with previous applications of this model. The temperature biases are generally less than 2.5 °C, while the seasonal cycle of precipitation over the region matches observations well. Moreover, the one-dimensional lake model reproduces fairly well the geographical pattern of observed (from satellite measurements) lake surface temperature as well as its mean month-to-month evolution. The Malawi Lake-effects on the moisture and atmospheric circulation of the surrounding region result in an increase of water vapor mixing ratio due to increased evaporation in the presence of the lake, which combines with enhanced rising motions and low-level moisture convergence to yield a significant precipitation increase over the lake and neighboring areas during the whole austral summer rainy season.

Enhanced science-stakeholder communication to improve ecosystem model performances for climate change impact assessments.

PubMed

Jönsson, Anna Maria; Anderbrant, Olle; Holmér, Jennie; Johansson, Jacob; Schurgers, Guy; Svensson, Glenn P; Smith, Henrik G

2015-04-01

In recent years, climate impact assessments of relevance to the agricultural and forestry sectors have received considerable attention. Current ecosystem models commonly capture the effect of a warmer climate on biomass production, but they rarely sufficiently capture potential losses caused by pests, pathogens and extreme weather events. In addition, alternative management regimes may not be integrated in the models. A way to improve the quality of climate impact assessments is to increase the science-stakeholder collaboration, and in a two-way dialog link empirical experience and impact modelling with policy and strategies for sustainable management. In this paper we give a brief overview of different ecosystem modelling methods, discuss how to include ecological and management aspects, and highlight the importance of science-stakeholder communication. By this, we hope to stimulate a discussion among the science-stakeholder communities on how to quantify the potential for climate change adaptation by improving the realism in the models.

A bottom-up, vulnerability-based framework for identifying the adaptive capacity of water resources systems in a changing climate

NASA Astrophysics Data System (ADS)

Culley, Sam; Noble, Stephanie; Timbs, Michael; Yates, Adam; Giuliani, Matteo; Castelletti, Andrea; Maier, Holger; Westra, Seth

2015-04-01

Water resource system infrastructure and operating policies are commonly designed on the assumption that the statistics of future rainfall, temperature and other hydrometeorological variables are equal to those of the historical record. There is now substantial evidence demonstrating that this assumption is no longer valid, and that climate change will significantly impact water resources systems worldwide. Under different climatic inputs, the performance of these systems may degrade to a point where they become unable to meet the primary objectives for which they were built. In such a changing context, using existing infrastructure more efficiently - rather than planning additional infrastructure - becomes key to restore the system's performance at acceptable levels and minimize financial investments and associated risk. The traditional top-down approach for assessing climate change impacts relies on the use of a cascade of models from the global to the local scale. However, it is often difficult to utilize this top-down approach in a decision-making procedure, as there is disparity amongst various climate projections, arising from incomplete scientific understanding of the complicated processes and feedbacks within the climate system, and model limitations in reproducing those relationships. In contrast with this top-down approach, this study contributes a framework to identify the adaptive capacity of water resource systems under changing climatic conditions adopting a bottom-up, vulnerability-based approach. The performance of the current system management is first assessed for a comprehensive range of climatic conditions, which are independent of climate model forecasts. The adaptive capacity of the system is then estimated by re-evaluating the performance of a set of adaptive operating policies, which are optimized for each climatic condition under which the system is simulated. The proposed framework reverses the perspective by identifying water system vulnerability drivers and by enhancing the adaptive capacity of the system to respond to unforeseen events, in order to design robust and resilient adaptation measures. The approach is demonstrated on the multipurpose operation of the Lake Como system, located in Northern Italy, accounting for flood protection and irrigation supply. Numerical results show that our framework successfully identified the failure boundary based on current system management policies, which is demonstrated as being particularly sensitive to decreases in both precipitation and temperature. To estimate the likelihood of the climate being in states causing system failures and to provide a time frame for reaching such states, we consider 22 climate model projections; these projections suggest that the current management policies will lead to a high chance of failure over the next 40 years. The adaptive capacity of the re-optimized operating policies exhibits the potential for partially mitigating adverse climate change impacts and for extending the life of the system.

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.

Identification of reliable gridded reference data for statistical downscaling methods in Alberta

NASA Astrophysics Data System (ADS)

Eum, H. I.; Gupta, A.

2017-12-01

Climate models provide essential information to assess impacts of climate change at regional and global scales. However, statistical downscaling methods have been applied to prepare climate model data for various applications such as hydrologic and ecologic modelling at a watershed scale. As the reliability and (spatial and temporal) resolution of statistically downscaled climate data mainly depend on a reference data, identifying the most reliable reference data is crucial for statistical downscaling. A growing number of gridded climate products are available for key climate variables which are main input data to regional modelling systems. However, inconsistencies in these climate products, for example, different combinations of climate variables, varying data domains and data lengths and data accuracy varying with physiographic characteristics of the landscape, have caused significant challenges in selecting the most suitable reference climate data for various environmental studies and modelling. Employing various observation-based daily gridded climate products available in public domain, i.e. thin plate spline regression products (ANUSPLIN and TPS), inverse distance method (Alberta Townships), and numerical climate model (North American Regional Reanalysis) and an optimum interpolation technique (Canadian Precipitation Analysis), this study evaluates the accuracy of the climate products at each grid point by comparing with the Adjusted and Homogenized Canadian Climate Data (AHCCD) observations for precipitation, minimum and maximum temperature over the province of Alberta. Based on the performance of climate products at AHCCD stations, we ranked the reliability of these publically available climate products corresponding to the elevations of stations discretized into several classes. According to the rank of climate products for each elevation class, we identified the most reliable climate products based on the elevation of target points. A web-based system was developed to allow users to easily select the most reliable reference climate data at each target point based on the elevation of grid cell. By constructing the best combination of reference data for the study domain, the accurate and reliable statistically downscaled climate projections could be significantly improved.

Modelling Spatial Dependence Structures Between Climate Variables by Combining Mixture Models with Copula Models

NASA Astrophysics Data System (ADS)

Khan, F.; Pilz, J.; Spöck, G.

2017-12-01

Spatio-temporal dependence structures play a pivotal role in understanding the meteorological characteristics of a basin or sub-basin. This further affects the hydrological conditions and consequently will provide misleading results if these structures are not taken into account properly. In this study we modeled the spatial dependence structure between climate variables including maximum, minimum temperature and precipitation in the Monsoon dominated region of Pakistan. For temperature, six, and for precipitation four meteorological stations have been considered. For modelling the dependence structure between temperature and precipitation at multiple sites, we utilized C-Vine, D-Vine and Student t-copula models. For temperature, multivariate mixture normal distributions and for precipitation gamma distributions have been used as marginals under the copula models. A comparison was made between C-Vine, D-Vine and Student t-copula by observational and simulated spatial dependence structure to choose an appropriate model for the climate data. The results show that all copula models performed well, however, there are subtle differences in their performances. The copula models captured the patterns of spatial dependence structures between climate variables at multiple meteorological sites, however, the t-copula showed poor performance in reproducing the dependence structure with respect to magnitude. It was observed that important statistics of observed data have been closely approximated except of maximum values for temperature and minimum values for minimum temperature. Probability density functions of simulated data closely follow the probability density functions of observational data for all variables. C and D-Vines are better tools when it comes to modelling the dependence between variables, however, Student t-copulas compete closely for precipitation. Keywords: Copula model, C-Vine, D-Vine, Spatial dependence structure, Monsoon dominated region of Pakistan, Mixture models, EM algorithm.

Predicting Dengue Fever Outbreaks in French Guiana Using Climate Indicators.

PubMed

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

2016-04-01

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

IN11B-1621: Quantifying How Climate Affects Vegetation in the Amazon Rainforest

NASA Technical Reports Server (NTRS)

Das, Kamalika; Kodali, Anuradha; Szubert, Marcin; Ganguly, Sangram; Bongard, Joshua

2016-01-01

Amazon droughts in 2005 and 2010 have raised serious concern about the future of the rainforest. Amazon forests are crucial because of their role as the largest carbon sink in the world which would effect the global warming phenomena with decreased photosynthesis activity. Especially, after a decline in plant growth in 1.68 million km2 forest area during the once-in-a-century severe drought in 2010, it is of primary importance to understand the relationship between different climatic variables and vegetation. In an earlier study, we have shown that non-linear models are better at capturing the relation dynamics of vegetation and climate variables such as temperature and precipitation, compared to linear models. In this research, we learn precise models between vegetation and climatic variables (temperature, precipitation) for normal conditions in the Amazon region using genetic programming based symbolic regression. This is done by removing high elevation and drought affected areas and also considering the slope of the region as one of the important factors while building the model. The model learned reveals new and interesting ways historical and current climate variables affect the vegetation at any location. MAIAC data has been used as a vegetation surrogate in our study. For temperature and precipitation, we have used TRMM and MODIS Land Surface Temperature data sets while learning the non-linear regression model. However, to generalize the model to make it independent of the data source, we perform transfer learning where we regress a regularized least squares to learn the parameters of the non-linear model using other data sources such as the precipitation and temperature from the Climatic Research Center (CRU). This new model is very similar in structure and performance compared to the original learned model and verifies the same claims about the nature of dependency between these climate variables and the vegetation in the Amazon region. As a result of this study, we are able to learn, for the very first time how exactly different climate factors influence vegetation at any location in the Amazon rainforests, independent of the specific sources from which the data has been obtained.

Quantifying How Climate Affects Vegetation in the Amazon Rainforest

NASA Astrophysics Data System (ADS)

Das, K.; Kodali, A.; Szubert, M.; Ganguly, S.; Bongard, J.

2016-12-01

Amazon droughts in 2005 and 2010 have raised serious concern about the future of the rainforest. Amazon forests are crucial because of their role as the largest carbon sink in the world which would effect the global warming phenomena with decreased photosynthesis activity. Especially, after a decline in plant growth in 1.68 million km2 forest area during the once-in-a-century severe drought in 2010, it is of primary importance to understand the relationship between different climatic variables and vegetation. In an earlier study, we have shown that non-linear models are better at capturing the relation dynamics of vegetation and climate variables such as temperature and precipitation, compared to linear models. In this research, we learn precise models between vegetation and climatic variables (temperature, precipitation) for normal conditions in the Amazon region using genetic programming based symbolic regression. This is done by removing high elevation and drought affected areas and also considering the slope of the region as one of the important factors while building the model. The model learned reveals new and interesting ways historical and current climate variables affect the vegetation at any location. MAIAC data has been used as a vegetation surrogate in our study. For temperature and precipitation, we have used TRMM and MODIS Land Surface Temperature data sets while learning the non-linear regression model. However, to generalize the model to make it independent of the data source, we perform transfer learning where we regress a regularized least squares to learn the parameters of the non-linear model using other data sources such as the precipitation and temperature from the Climatic Research Center (CRU). This new model is very similar in structure and performance compared to the original learned model and verifies the same claims about the nature of dependency between these climate variables and the vegetation in the Amazon region. As a result of this study, we are able to learn, for the very first time how exactly different climate factors influence vegetation at any location in the Amazon rainforests, independent of the specific sources from which the data has been obtained.

Validation of non-stationary precipitation series for site-specific impact assessment: comparison of two statistical downscaling techniques

NASA Astrophysics Data System (ADS)

Mullan, Donal; Chen, Jie; Zhang, Xunchang John

2016-02-01

Statistical downscaling (SD) methods have become a popular, low-cost and accessible means of bridging the gap between the coarse spatial resolution at which climate models output climate scenarios and the finer spatial scale at which impact modellers require these scenarios, with various different SD techniques used for a wide range of applications across the world. This paper compares the Generator for Point Climate Change (GPCC) model and the Statistical DownScaling Model (SDSM)—two contrasting SD methods—in terms of their ability to generate precipitation series under non-stationary conditions across ten contrasting global climates. The mean, maximum and a selection of distribution statistics as well as the cumulative frequencies of dry and wet spells for four different temporal resolutions were compared between the models and the observed series for a validation period. Results indicate that both methods can generate daily precipitation series that generally closely mirror observed series for a wide range of non-stationary climates. However, GPCC tends to overestimate higher precipitation amounts, whilst SDSM tends to underestimate these. This infers that GPCC is more likely to overestimate the effects of precipitation on a given impact sector, whilst SDSM is likely to underestimate the effects. GPCC performs better than SDSM in reproducing wet and dry day frequency, which is a key advantage for many impact sectors. Overall, the mixed performance of the two methods illustrates the importance of users performing a thorough validation in order to determine the influence of simulated precipitation on their chosen impact sector.

A common fallacy in climate model evaluation

NASA Astrophysics Data System (ADS)

Annan, J. D.; Hargreaves, J. C.; Tachiiri, K.

2012-04-01

We discuss the assessment of model ensembles such as that arising from the CMIP3 coordinated multi-model experiments. An important aspect of this is not merely the closeness of the models to observations in absolute terms but also the reliability of the ensemble spread as an indication of uncertainty. In this context, it has been widely argued that the multi-model ensemble of opportunity is insufficiently broad to adequately represent uncertainties regarding future climate change. For example, the IPCC AR4 summarises the consensus with the sentence: "Those studies also suggest that the current AOGCMs may not cover the full range of uncertainty for climate sensitivity." Similar claims have been made in the literature for other properties of the climate system, including the transient climate response and efficiency of ocean heat uptake. Comparison of model outputs with observations of the climate system forms an essential component of model assessment and is crucial for building our confidence in model predictions. However, methods for undertaking this comparison are not always clearly justified and understood. Here we show that the popular approach which forms the basis for the above claims, of comparing the ensemble spread to a so-called "observationally-constrained pdf", can be highly misleading. Such a comparison will almost certainly result in disagreement, but in reality tells us little about the performance of the ensemble. We present an alternative approach based on an assessment of the predictive performance of the ensemble, and show how it may lead to very different, and rather more encouraging, conclusions. We additionally outline some necessary conditions for an ensemble (or more generally, a probabilistic prediction) to be challenged by an observation.

High Resolution Simulations of Future Climate in West Africa Using a Variable-Resolution Atmospheric Model

NASA Astrophysics Data System (ADS)

Adegoke, J. O.; Engelbrecht, F.; Vezhapparambu, S.

2013-12-01

In previous work demonstrated the application of a var¬iable-resolution global atmospheric model, the conformal-cubic atmospheric model (CCAM), across a wide range of spatial and time scales to investigate the ability of the model to provide realistic simulations of present-day climate and plausible projections of future climate change over sub-Saharan Africa. By applying the model in stretched-grid mode the versatility of the model dynamics, numerical formulation and physical parameterizations to function across a range of length scales over the region of interest, was also explored. We primarily used CCAM to illustrate the capability of the model to function as a flexible downscaling tool at the climate-change time scale. Here we report on additional long term climate projection studies performed by downscaling at much higher resolutions (8 Km) over an area that stretches from just south of Sahara desert to the southern coast of the Niger Delta and into the Gulf of Guinea. To perform these simulations, CCAM was provided with synoptic-scale forcing of atmospheric circulation from 2.5 deg resolution NCEP reanalysis at 6-hourly interval and SSTs from NCEP reanalysis data uses as lower boundary forcing. CCAM 60 Km resolution downscaled to 8 Km (Schmidt factor 24.75) then 8 Km resolution simulation downscaled to 1 Km (Schmidt factor 200) over an area approximately 50 Km x 50 Km in the southern Lake Chad Basin (LCB). Our intent in conducting these high resolution model runs was to obtain a deeper understanding of linkages between the projected future climate and the hydrological processes that control the surface water regime in this part of sub-Saharan Africa.

An assessment of precipitation and surface air temperature over China by regional climate models

NASA Astrophysics Data System (ADS)

Wang, Xueyuan; Tang, Jianping; Niu, Xiaorui; Wang, Shuyu

2016-12-01

An analysis of a 20-year summer time simulation of present-day climate (1989-2008) over China using four regional climate models coupled with different land surface models is carried out. The climatic means, interannual variability, linear trends, and extremes are examined, with focus on precipitation and near surface air temperature. The models are able to reproduce the basic features of the observed summer mean precipitation and temperature over China and the regional detail due to topographic forcing. Overall, the model performance is better for temperature than that of precipitation. The models reasonably grasp the major anomalies and standard deviations over China and the five subregions studied. The models generally reproduce the spatial pattern of high interannual variability over wet regions, and low variability over the dry regions. The models also capture well the variable temperature gradient increase to the north by latitude. Both the observed and simulated linear trend of precipitation shows a drying tendency over the Yangtze River Basin and wetting over South China. The models capture well the relatively small temperature trends in large areas of China. The models reasonably simulate the characteristics of extreme precipitation indices of heavy rain days and heavy precipitation fraction. Most of the models also performed well in capturing both the sign and magnitude of the daily maximum and minimum temperatures over China.

Physically-based distributed mass balance modeling of a tropical glacier: An application to backward modeling of past climate

NASA Astrophysics Data System (ADS)

Moelg, T.; Cullen, N. J.; Hardy, D. R.; Winkler, M.; Kaser, G.

2009-04-01

The use of spatially distributed (2-D) mass balance models has increased in recent years, but mostly focuses on extratropical glacier surfaces. Here we present the first application of a process-based 2-D model to an African glacier: Kersten Glacier on Kilimanjaro. Multi-year data from an automatic weather station (AWS) at 5873 m a.s.l. (500 hPa) serve to force the model. Validation variables comprise surface temperature, surface height change, snow depth, and incoming radiation - all of which indicate a good model performance. Analyses of the interannual variability in the most significant total mass budget terms (surface accumulation, melt, and sublimation), as well as in the related energy fluxes, exhibit a strong link to atmospheric moisture of a particular year. This is because net shortwave radiation (a result of both cloudiness and surface albedo) is the most variable energy flux on monthly to annual time scales. Internal accumulation (refreezing of melt water), however, shows a time lag and is strongest after a very wet year. Due to the limited validation data at lower elevations, we also perform a detailed sensitivity study by varying 17 model parameters - which yields a total mass loss estimate of 522 +/- 105 kg/m2/year under present climate conditions. Moreover, the verified model allows us to perform backward modeling of the last maximum extent of Kersten Glacier in the 1880s, which is indicated by a well preserved terminal moraine. This step reveals decreases in precipitation (30-45%), water vapor pressure (0.1-0.3 hPa) and cloud cover (2-4 percentage units) as the most likely local climate change between late 19th century and present. Thus, the study also demonstrates how 2-D modeling can help reconstruct past climate for a remote place prior to the availability of measurements. In our case these findings have great relevance for the debate of surface versus mid-tropospheric climate change in the tropics.

Sensitivity of simulated maize crop yields to regional climate in the Southwestern United States

NASA Astrophysics Data System (ADS)

Kim, S.; Myoung, B.; Stack, D.; Kim, J.; Hatzopoulos, N.; Kafatos, M.

2013-12-01

The sensitivity of maize yield to the regional climate in the Southwestern United States (SW US) has been investigated by using a crop-yield simulation model (APSIM) in conjunction with meteorological forcings (daily minimum and maximum temperature, precipitation, and radiation) from the North American Regional Reanalysis (NARR) dataset. The primary focus of this study is to look at the effects of interannual variations of atmospheric components on the crop productivity in the SW US over the 21-year period (1991 to 2011). First of all, characteristics and performance of APSIM was examined by comparing simulated maize yields with observed yields from United States Department of Agriculture (USDA) and the leaf-area index (LAI) from MODIS satellite data. Comparisons of the simulated maize yield with the available observations show that the crop model can reasonably reproduce observed maize yields. Sensitivity tests were performed to assess the relative contribution of each climate driver to regional crop yield. Sensitivity experiments show that potential crop production responds nonlinearly to climate drivers and the yield sensitivity varied among geographical locations depending on their mean climates. Lastly, a detailed analysis of both the spatial and temporal variations of each climate driver in the regions where maize is actually grown in three states (CA, AZ, and NV) in the SW US was performed.

How does the sensitivity of climate affect stratospheric solar radiation management?

NASA Astrophysics Data System (ADS)

Ricke, K.; Rowlands, D. J.; Ingram, W.; Keith, D.; Morgan, M. G.

2011-12-01

If implementation of proposals to engineer the climate through solar radiation management (SRM) ever occurs, it is likely to be contingent upon climate sensitivity. Despite this, no modeling studies have examined how the effectiveness of SRM forcings differs between the typical Atmosphere-Ocean General Circulation Models (AOGCMs) with climate sensitivities close to the Coupled Model Intercomparison Project (CMIP) mean and ones with high climate sensitivities. Here, we use a perturbed physics ensemble modeling experiment to examine variations in the response of climate to SRM under different climate sensitivities. When SRM is used as a substitute for mitigation its ability to maintain the current climate state gets worse with increased climate sensitivity and with increased concentrations of greenhouse gases. However, our results also demonstrate that the potential of SRM to slow climate change, even at the regional level, grows with climate sensitivity. On average, SRM reduces regional rates of temperature change by more than 90 percent and rates of precipitation change by more than 50 percent in these higher sensitivity model configurations. To investigate how SRM might behave in models with high climate sensitivity that are also consistent with recent observed climate change we perform a "perturbed physics" ensemble (PPE) modelling experiment with the climateprediction.net (cpdn) version of the HadCM3L AOGCM. Like other perturbed physics climate modelling experiments, we simulate past and future climate scenarios using a wide range of model parameter combinations that both reproduce past climate within a specified level of accuracy and simulate future climates with a wide range of climate sensitivities. We chose 43 members ("model versions") from a subset of the 1,550 from the British Broadcasting Corporation (BBC) climateprediction.net project that have data that allow restarts. We use our results to explore how much assessments of SRM that use best-estimate models, and so near-median climate sensitivity, may be ignoring important contingencies associated with implementing SRM in reality. A primary motivation for studying SRM via the injection of aerosols in the stratosphere is to evaluate its potential effectiveness as "insurance" in the case of higher-than-expected climate response to global warming. We find that this is precisely when SRM appears to be least effective in returning regional climates to their baseline states and reducing regional rates of precipitation change. On the other hand, given the very high regional temperature anomalies associated with rising greenhouse gas concentrations in high sensitivity models, it is also where SRM is most effective in reducing rates of change relative to a no SRM alternative.

Development and Performance of Alternative Electricity Sector Pathways Subject to Multiple Climate and Water Projections

NASA Astrophysics Data System (ADS)

Newmark, R. L.; Vorosmarty, C. J.; Miara, A.; Cohen, S.; Macknick, J.; Sun, Y.; Corsi, F.; Fekete, B. M.; Tidwell, V. C.

2017-12-01

Climate change impacts on air temperatures and water availability have the potential to alter future electricity sector investment decisions as well as the reliability and performance of the power sector. Different electricity sector configurations are more or less vulnerable to climate-induced changes. For example, once-through cooled thermal facilities are the most cost-effective and efficient technologies under cooler and wetter conditions, but can be substantially affected by and vulnerable to warmer and drier conditions. Non-thermal renewable technologies, such as PV and wind, are essentially "drought-proof" but have other integration and reliability challenges. Prior efforts have explored the impacts of climate change on electric sector development for a limited set of climate and electricity scenarios. Here, we provide a comprehensive suite of scenarios that evaluate how different electricity sector pathways could be affected by a range of climate and water resource conditions. We use four representative concentration pathway (RCP) scenarios under five global circulation models (GCM) as climate drivers to a Water Balance Model (WBM), to provide twenty separate future climate-water conditions. These climate-water conditions influence electricity sector development from present day to 2050 as determined using the Regional Energy Deployment Systems (ReEDS) model. Four unique electricity sector pathways will be considered, including business-as-usual, carbon cap, high renewable energy technology costs, and coal reliance scenarios. The combination of climate-water and electricity sector pathway scenarios leads to 80 potential future cases resulting in different national and regional electricity infrastructure configurations. The vulnerability of these configurations in relation to climate change (including in-stream thermal pollution impacts and environmental regulations) is evaluated using the Thermoelectric Power and Thermal Pollution (TP2M) model, providing quantitative estimates of the power sector's ability to meet loads, given changes in air temperature, water temperature, and water availability.

Significance of model credibility in estimating climate projection distributions for regional hydroclimatological risk assessments

USGS Publications Warehouse

Brekke, L.D.; Dettinger, M.D.; Maurer, E.P.; Anderson, M.

2008-01-01

Ensembles of historical climate simulations and climate projections from the World Climate Research Programme's (WCRP's) Coupled Model Intercomparison Project phase 3 (CMIP3) multi-model dataset were investigated to determine how model credibility affects apparent relative scenario likelihoods in regional risk assessments. Methods were developed and applied in a Northern California case study. An ensemble of 59 twentieth century climate simulations from 17 WCRP CMIP3 models was analyzed to evaluate relative model credibility associated with a 75-member projection ensemble from the same 17 models. Credibility was assessed based on how models realistically reproduced selected statistics of historical climate relevant to California climatology. Metrics of this credibility were used to derive relative model weights leading to weight-threshold culling of models contributing to the projection ensemble. Density functions were then estimated for two projected quantities (temperature and precipitation), with and without considering credibility-based ensemble reductions. An analysis for Northern California showed that, while some models seem more capable at recreating limited aspects twentieth century climate, the overall tendency is for comparable model performance when several credibility measures are combined. Use of these metrics to decide which models to include in density function development led to local adjustments to function shapes, but led to limited affect on breadth and central tendency, which were found to be more influenced by 'completeness' of the original ensemble in terms of models and emissions pathways. ?? 2007 Springer Science+Business Media B.V.

School Climate of Educational Institutions: Design and Validation of a Diagnostic Scale

ERIC Educational Resources Information Center

Becerra, Sandra

2016-01-01

School climate is recognized as a relevant factor for the improvement of educative processes, favoring the administrative processes and optimum school performance. The present article is the result of a quantitative research model which had the objective of psychometrically designing and validating a scale to diagnose the organizational climate of…

Curve number method response to historical climate variability and trends

USDA-ARS?s Scientific Manuscript database

With the dependence on the curve number (CN) model by the engineering community, the question arises as to whether changes in climate may affect the performance of the CN algorithm which impacts estimates of runoff. A study was conducted to determine the effects of “climate period” (period of unifor...

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

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

Investigation of Climate Change Impact on Water Resources for an Alpine Basin in Northern Italy: Implications for Evapotranspiration Modeling Complexity

PubMed Central

Ravazzani, Giovanni; Ghilardi, Matteo; Mendlik, Thomas; Gobiet, Andreas; Corbari, Chiara; Mancini, Marco

2014-01-01

Assessing the future effects of climate change on water availability requires an understanding of how precipitation and evapotranspiration rates will respond to changes in atmospheric forcing. Use of simplified hydrological models is required beacause of lack of meteorological forcings with the high space and time resolutions required to model hydrological processes in mountains river basins, and the necessity of reducing the computational costs. The main objective of this study was to quantify the differences between a simplified hydrological model, which uses only precipitation and temperature to compute the hydrological balance when simulating the impact of climate change, and an enhanced version of the model, which solves the energy balance to compute the actual evapotranspiration. For the meteorological forcing of future scenario, at-site bias-corrected time series based on two regional climate models were used. A quantile-based error-correction approach was used to downscale the regional climate model simulations to a point scale and to reduce its error characteristics. The study shows that a simple temperature-based approach for computing the evapotranspiration is sufficiently accurate for performing hydrological impact investigations of climate change for the Alpine river basin which was studied. PMID:25285917

Investigation of climate change impact on water resources for an Alpine basin in northern Italy: implications for evapotranspiration modeling complexity.

PubMed

Ravazzani, Giovanni; Ghilardi, Matteo; Mendlik, Thomas; Gobiet, Andreas; Corbari, Chiara; Mancini, Marco

2014-01-01

Assessing the future effects of climate change on water availability requires an understanding of how precipitation and evapotranspiration rates will respond to changes in atmospheric forcing. Use of simplified hydrological models is required because of lack of meteorological forcings with the high space and time resolutions required to model hydrological processes in mountains river basins, and the necessity of reducing the computational costs. The main objective of this study was to quantify the differences between a simplified hydrological model, which uses only precipitation and temperature to compute the hydrological balance when simulating the impact of climate change, and an enhanced version of the model, which solves the energy balance to compute the actual evapotranspiration. For the meteorological forcing of future scenario, at-site bias-corrected time series based on two regional climate models were used. A quantile-based error-correction approach was used to downscale the regional climate model simulations to a point scale and to reduce its error characteristics. The study shows that a simple temperature-based approach for computing the evapotranspiration is sufficiently accurate for performing hydrological impact investigations of climate change for the Alpine river basin which was studied.

Combined climate and carbon-cycle effects of large-scale deforestation

PubMed Central

Bala, G.; Caldeira, K.; Wickett, M.; Phillips, T. J.; Lobell, D. B.; Delire, C.; Mirin, A.

2007-01-01

The prevention of deforestation and promotion of afforestation have often been cited as strategies to slow global warming. Deforestation releases CO2 to the atmosphere, which exerts a warming influence on Earth's climate. However, biophysical effects of deforestation, which include changes in land surface albedo, evapotranspiration, and cloud cover also affect climate. Here we present results from several large-scale deforestation experiments performed with a three-dimensional coupled global carbon-cycle and climate model. These simulations were performed by using a fully three-dimensional model representing physical and biogeochemical interactions among land, atmosphere, and ocean. We find that global-scale deforestation has a net cooling influence on Earth's climate, because the warming carbon-cycle effects of deforestation are overwhelmed by the net cooling associated with changes in albedo and evapotranspiration. Latitude-specific deforestation experiments indicate that afforestation projects in the tropics would be clearly beneficial in mitigating global-scale warming, but would be counterproductive if implemented at high latitudes and would offer only marginal benefits in temperate regions. Although these results question the efficacy of mid- and high-latitude afforestation projects for climate mitigation, forests remain environmentally valuable resources for many reasons unrelated to climate. PMID:17420463

Combined climate and carbon-cycle effects of large-scale deforestation.

PubMed

Bala, G; Caldeira, K; Wickett, M; Phillips, T J; Lobell, D B; Delire, C; Mirin, A

2007-04-17

The prevention of deforestation and promotion of afforestation have often been cited as strategies to slow global warming. Deforestation releases CO(2) to the atmosphere, which exerts a warming influence on Earth's climate. However, biophysical effects of deforestation, which include changes in land surface albedo, evapotranspiration, and cloud cover also affect climate. Here we present results from several large-scale deforestation experiments performed with a three-dimensional coupled global carbon-cycle and climate model. These simulations were performed by using a fully three-dimensional model representing physical and biogeochemical interactions among land, atmosphere, and ocean. We find that global-scale deforestation has a net cooling influence on Earth's climate, because the warming carbon-cycle effects of deforestation are overwhelmed by the net cooling associated with changes in albedo and evapotranspiration. Latitude-specific deforestation experiments indicate that afforestation projects in the tropics would be clearly beneficial in mitigating global-scale warming, but would be counterproductive if implemented at high latitudes and would offer only marginal benefits in temperate regions. Although these results question the efficacy of mid- and high-latitude afforestation projects for climate mitigation, forests remain environmentally valuable resources for many reasons unrelated to climate.

Combined Climate and Carbon-Cycle Effects of Large-Scale Deforestation

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

Bala, G; Caldeira, K; Wickett, M

2006-10-17

The prevention of deforestation and promotion of afforestation have often been cited as strategies to slow global warming. Deforestation releases CO{sub 2} to the atmosphere, which exerts a warming influence on Earth's climate. However, biophysical effects of deforestation, which include changes in land surface albedo, evapotranspiration, and cloud cover also affect climate. Here we present results from several large-scale deforestation experiments performed with a three-dimensional coupled global carbon-cycle and climate model. These are the first such simulations performed using a fully three-dimensional model representing physical and biogeochemical interactions among land, atmosphere, and ocean. We find that global-scale deforestation has amore » net cooling influence on Earth's climate, since the warming carbon-cycle effects of deforestation are overwhelmed by the net cooling associated with changes in albedo and evapotranspiration. Latitude-specific deforestation experiments indicate that afforestation projects in the tropics would be clearly beneficial in mitigating global-scale warming, but would be counterproductive if implemented at high latitudes and would offer only marginal benefits in temperate regions. While these results question the efficacy of mid- and high-latitude afforestation projects for climate mitigation, forests remain environmentally valuable resources for many reasons unrelated to climate.« less

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.

Quantifying climate feedbacks in polar regions.

PubMed

Goosse, Hugues; Kay, Jennifer E; Armour, Kyle C; Bodas-Salcedo, Alejandro; Chepfer, Helene; Docquier, David; Jonko, Alexandra; Kushner, Paul J; Lecomte, Olivier; Massonnet, François; Park, Hyo-Seok; Pithan, Felix; Svensson, Gunilla; Vancoppenolle, Martin

2018-05-15

The concept of feedback is key in assessing whether a perturbation to a system is amplified or damped by mechanisms internal to the system. In polar regions, climate dynamics are controlled by both radiative and non-radiative interactions between the atmosphere, ocean, sea ice, ice sheets and land surfaces. Precisely quantifying polar feedbacks is required for a process-oriented evaluation of climate models, a clear understanding of the processes responsible for polar climate changes, and a reduction in uncertainty associated with model projections. This quantification can be performed using a simple and consistent approach that is valid for a wide range of feedbacks, offering the opportunity for more systematic feedback analyses and a better understanding of polar climate changes.

An analysis of relationships among climate forcing and time-integrated NDVI of grasslands over the U.S. northern and central Great Plains

USGS Publications Warehouse

Yang, Limin; Wylie, Bruce K.; Tieszen, Larry L.; Reed, Bradley C.

1998-01-01

Time-integrated normalized difference vegetation index (TI NDVI) derived from the multitemporal satellite imagery (1989–1993) was used as a surrogate for primary production to investigate climate impacts on grassland performance for central and northern Great Plains grasslands. Results suggest that spatial and temporal variability in growing season precipitation, potential evapotranspiration, and growing degree days are the most important controls on grassland performance and productivity. When TI NDVI and climate data of all grassland land cover classes were examined as a whole, a statistical model showed significant positive correlation between the TI NDVI and accumulated spring and summer precipitation, and a negative correlation between TI NDVI and spring potential evapotranspiration. The coefficient of determination (R2) of the general model was 0.45. When the TI NDVI-climate relationship was examined by individual land cover type, the relationship was generally better defined in terms of the variance accounted for by class-specific models . The photosynthetic pathway is an important determinant of grassland performance with northern mixed prairie (mixture of C3 and C4 grassland) TI NDVI affected by both thermal and moisture conditions during the growing season while southern plains grasslands (primarily C4grassland) were predominantly influenced by spring and summer precipitation. Grassland land cover classes associated with sandy soils also demonstrated a strong relationship between TI NDVI and growing season rainfall. Significant impact of interannual climate variability on the TI NDVI–climate relationship was also observed. The study suggests an integrated approach involving numerical models, satellite remote sensing, and field observations to monitor grassland ecosystem dynamics on a regional scale.

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.

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

NASA Astrophysics Data System (ADS)

Baker, Noel; Taylor, Patrick

2014-05-01

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

Modelling the impact of climate change and atmospheric N deposition on French forests biodiversity.

PubMed

Rizzetto, Simon; Belyazid, Salim; Gégout, Jean-Claude; Nicolas, Manuel; Alard, Didier; Corcket, Emmanuel; Gaudio, Noémie; Sverdrup, Harald; Probst, Anne

2016-06-01

A dynamic coupled biogeochemical-ecological model was used to simulate the effects of nitrogen deposition and climate change on plant communities at three forest sites in France. The three sites had different forest covers (sessile oak, Norway spruce and silver fir), three nitrogen loads ranging from relatively low to high, different climatic regions and different soil types. Both the availability of vegetation time series and the environmental niches of the understory species allowed to evaluate the model for predicting the composition of the three plant communities. The calibration of the environmental niches was successful, with a model performance consistently reasonably high throughout the three sites. The model simulations of two climatic and two deposition scenarios showed that climate change may entirely compromise the eventual recovery from eutrophication of the simulated plant communities in response to the reductions in nitrogen deposition. The interplay between climate and deposition was strongly governed by site characteristics and histories in the long term, while forest management remained the main driver of change in the short term. Copyright © 2016 Elsevier Ltd. All rights reserved.

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

Natural climate variability and teleconnections to precipitation over the Pacific-North American region in CMIP3 and CMIP5 models

NASA Astrophysics Data System (ADS)

Polade, Suraj D.; Gershunov, Alexander; Cayan, Daniel R.; Dettinger, Michael D.; Pierce, David W.

2013-05-01

climate variability will continue to be an important aspect of future regional climate even in the midst of long-term secular changes. Consequently, the ability of climate models to simulate major natural modes of variability and their teleconnections provides important context for the interpretation and use of climate change projections. Comparisons reported here indicate that the CMIP5 generation of global climate models shows significant improvements in simulations of key Pacific climate mode and their teleconnections to North America compared to earlier CMIP3 simulations. The performance of 14 models with simulations in both the CMIP3 and CMIP5 archives are assessed using singular value decomposition analysis of simulated and observed winter Pacific sea surface temperatures (SSTs) and concurrent precipitation over the contiguous United States and northwestern Mexico. Most of the models reproduce basic features of the key natural mode and their teleconnections, albeit with notable regional deviations from observations in both SST and precipitation. Increasing horizontal resolution in the CMIP5 simulations is an important, but not a necessary, factor in the improvement from CMIP3 to CMIP5.

Natural climate variability and teleconnections to precipitation over the Pacific-North American region in CMIP3 and CMIP5 models

USGS Publications Warehouse

Polade, Suraj D.; Gershunov, Alexander; Cayan, Daniel R.; Dettinger, Michael D.; Pierce, David W.

2013-01-01

Natural climate variability will continue to be an important aspect of future regional climate even in the midst of long-term secular changes. Consequently, the ability of climate models to simulate major natural modes of variability and their teleconnections provides important context for the interpretation and use of climate change projections. Comparisons reported here indicate that the CMIP5 generation of global climate models shows significant improvements in simulations of key Pacific climate mode and their teleconnections to North America compared to earlier CMIP3 simulations. The performance of 14 models with simulations in both the CMIP3 and CMIP5 archives are assessed using singular value decomposition analysis of simulated and observed winter Pacific sea surface temperatures (SSTs) and concurrent precipitation over the contiguous United States and northwestern Mexico. Most of the models reproduce basic features of the key natural mode and their teleconnections, albeit with notable regional deviations from observations in both SST and precipitation. Increasing horizontal resolution in the CMIP5 simulations is an important, but not a necessary, factor in the improvement from CMIP3 to CMIP5.

Record dry summer in 2015 challenges precipitation projections in Central Europe.

PubMed

Orth, René; Zscheischler, Jakob; Seneviratne, Sonia I

2016-06-21

Central Europe was characterized by a humid-temperate climate in the 20(th) century. Climate change projections suggest that climate in this area will shift towards warmer temperatures by the end of the 21(st) century, while projected precipitation changes are highly uncertain. Here we show that the 2015 summer rainfall was the lowest on record since 1901 in Central Europe, and that climate models that perform best in the three driest years of the historical time period 1901-2015 project stronger drying trends in the 21(st) century than models that perform best in the remaining years. Analyses of precipitation and derived soil moisture reveal that the 2015 event was drier than both the recent 2003 or 2010 extreme summers in Central Europe. Additionally there are large anomalies in satellite-derived vegetation greenness. In terms of precipitation and temperature anomalies, the 2015 summer in Central Europe is found to lie between historical climate in the region and that characteristic of the Mediterranean area. Even though the models best capturing past droughts are not necessarily generally more reliable in the future, the 2015 drought event illustrates that potential future drying trends have severe implications and could be stronger than commonly assumed from the entire IPCC AR5 model ensemble.

A framework for evaluating statistical downscaling performance under changing climatic conditions (Invited)

NASA Astrophysics Data System (ADS)

Dixon, K. W.; Balaji, V.; Lanzante, J.; Radhakrishnan, A.; Hayhoe, K.; Stoner, A. K.; Gaitan, C. F.

2013-12-01

Statistical downscaling (SD) methods may be viewed as generating a value-added product - a refinement of global climate model (GCM) output designed to add finer scale detail and to address GCM shortcomings via a process that gleans information from a combination of observations and GCM-simulated climate change responses. Making use of observational data sets and GCM simulations representing the same historical period, cross-validation techniques allow one to assess how well an SD method meets this goal. However, lacking observations of future, the extent to which a particular SD method's skill might degrade when applied to future climate projections cannot be assessed in the same manner. Here we illustrate and describe extensions to a 'perfect model' experimental design that seeks to quantify aspects of SD method performance both for a historical period (1979-2008) and for late 21st century climate projections. Examples highlighting cases in which downscaling performance deteriorates in future climate projections will be discussed. Also, results will be presented showing how synthetic datasets having known statistical properties may be used to further isolate factors responsible for degradations in SD method skill under changing climatic conditions. We will describe a set of input files used to conduct these analyses that are being made available to researchers who wish to utilize this experimental framework to evaluate SD methods they have developed. The gridded data sets cover a region centered on the contiguous 48 United States with a grid spacing of approximately 25km, have daily time resolution (e.g., maximum and minimum near-surface temperature and precipitation), and represent a total of 120 years of model simulations. This effort is consistent with the 2013 National Climate Predictions and Projections Platform Quantitative Evaluation of Downscaling Workshop goal of supporting a community approach to promote the informed use of downscaled climate projections.

Ecological Niche Modelling Predicts Southward Expansion of Lutzomyia (Nyssomyia) flaviscutellata (Diptera: Psychodidae: Phlebotominae), Vector of Leishmania (Leishmania) amazonensis in South America, under Climate Change.

PubMed

Carvalho, Bruno M; Rangel, Elizabeth F; Ready, Paul D; Vale, Mariana M

2015-01-01

Vector borne diseases are susceptible to climate change because distributions and densities of many vectors are climate driven. The Amazon region is endemic for cutaneous leishmaniasis and is predicted to be severely impacted by climate change. Recent records suggest that the distributions of Lutzomyia (Nyssomyia) flaviscutellata and the parasite it transmits, Leishmania (Leishmania) amazonensis, are expanding southward, possibly due to climate change, and sometimes associated with new human infection cases. We define the vector's climatic niche and explore future projections under climate change scenarios. Vector occurrence records were compiled from the literature, museum collections and Brazilian Health Departments. Six bioclimatic variables were used as predictors in six ecological niche model algorithms (BIOCLIM, DOMAIN, MaxEnt, GARP, logistic regression and Random Forest). Projections for 2050 used 17 general circulation models in two greenhouse gas representative concentration pathways: "stabilization" and "high increase". Ensemble models and consensus maps were produced by overlapping binary predictions. Final model outputs showed good performance and significance. The use of species absence data substantially improved model performance. Currently, L. flaviscutellata is widely distributed in the Amazon region, with records in the Atlantic Forest and savannah regions of Central Brazil. Future projections indicate expansion of the climatically suitable area for the vector in both scenarios, towards higher latitudes and elevations. L. flaviscutellata is likely to find increasingly suitable conditions for its expansion into areas where human population size and density are much larger than they are in its current locations. If environmental conditions change as predicted, the range of the vector is likely to expand to southeastern and central-southern Brazil, eastern Paraguay and further into the Amazonian areas of Bolivia, Peru, Ecuador, Colombia and Venezuela. These areas will only become endemic for L. amazonensis, however, if they have competent reservoir hosts and transmission dynamics matching those in the Amazon region.

Ecological Niche Modelling Predicts Southward Expansion of Lutzomyia (Nyssomyia) flaviscutellata (Diptera: Psychodidae: Phlebotominae), Vector of Leishmania (Leishmania) amazonensis in South America, under Climate Change

PubMed Central

Carvalho, Bruno M.; Ready, Paul D.

2015-01-01

Vector borne diseases are susceptible to climate change because distributions and densities of many vectors are climate driven. The Amazon region is endemic for cutaneous leishmaniasis and is predicted to be severely impacted by climate change. Recent records suggest that the distributions of Lutzomyia (Nyssomyia) flaviscutellata and the parasite it transmits, Leishmania (Leishmania) amazonensis, are expanding southward, possibly due to climate change, and sometimes associated with new human infection cases. We define the vector’s climatic niche and explore future projections under climate change scenarios. Vector occurrence records were compiled from the literature, museum collections and Brazilian Health Departments. Six bioclimatic variables were used as predictors in six ecological niche model algorithms (BIOCLIM, DOMAIN, MaxEnt, GARP, logistic regression and Random Forest). Projections for 2050 used 17 general circulation models in two greenhouse gas representative concentration pathways: “stabilization” and “high increase”. Ensemble models and consensus maps were produced by overlapping binary predictions. Final model outputs showed good performance and significance. The use of species absence data substantially improved model performance. Currently, L. flaviscutellata is widely distributed in the Amazon region, with records in the Atlantic Forest and savannah regions of Central Brazil. Future projections indicate expansion of the climatically suitable area for the vector in both scenarios, towards higher latitudes and elevations. L. flaviscutellata is likely to find increasingly suitable conditions for its expansion into areas where human population size and density are much larger than they are in its current locations. If environmental conditions change as predicted, the range of the vector is likely to expand to southeastern and central-southern Brazil, eastern Paraguay and further into the Amazonian areas of Bolivia, Peru, Ecuador, Colombia and Venezuela. These areas will only become endemic for L. amazonensis, however, if they have competent reservoir hosts and transmission dynamics matching those in the Amazon region. PMID:26619186

Running climate model on a commercial cloud computing environment: A case study using Community Earth System Model (CESM) on Amazon AWS

NASA Astrophysics Data System (ADS)

Chen, Xiuhong; Huang, Xianglei; Jiao, Chaoyi; Flanner, Mark G.; Raeker, Todd; Palen, Brock

2017-01-01

The suites of numerical models used for simulating climate of our planet are usually run on dedicated high-performance computing (HPC) resources. This study investigates an alternative to the usual approach, i.e. carrying out climate model simulations on commercially available cloud computing environment. We test the performance and reliability of running the CESM (Community Earth System Model), a flagship climate model in the United States developed by the National Center for Atmospheric Research (NCAR), on Amazon Web Service (AWS) EC2, the cloud computing environment by Amazon.com, Inc. StarCluster is used to create virtual computing cluster on the AWS EC2 for the CESM simulations. The wall-clock time for one year of CESM simulation on the AWS EC2 virtual cluster is comparable to the time spent for the same simulation on a local dedicated high-performance computing cluster with InfiniBand connections. The CESM simulation can be efficiently scaled with the number of CPU cores on the AWS EC2 virtual cluster environment up to 64 cores. For the standard configuration of the CESM at a spatial resolution of 1.9° latitude by 2.5° longitude, increasing the number of cores from 16 to 64 reduces the wall-clock running time by more than 50% and the scaling is nearly linear. Beyond 64 cores, the communication latency starts to outweigh the benefit of distributed computing and the parallel speedup becomes nearly unchanged.

Mechanistic species distribution modelling as a link between physiology and conservation.

PubMed

Evans, Tyler G; Diamond, Sarah E; Kelly, Morgan W

2015-01-01

Climate change conservation planning relies heavily on correlative species distribution models that estimate future areas of occupancy based on environmental conditions encountered in present-day ranges. The approach benefits from rapid assessment of vulnerability over a large number of organisms, but can have poor predictive power when transposed to novel environments and reveals little in the way of causal mechanisms that define changes in species distribution or abundance. Having conservation planning rely largely on this single approach also increases the risk of policy failure. Mechanistic models that are parameterized with physiological information are expected to be more robust when extrapolating distributions to future environmental conditions and can identify physiological processes that set range boundaries. Implementation of mechanistic species distribution models requires knowledge of how environmental change influences physiological performance, and because this information is currently restricted to a comparatively small number of well-studied organisms, use of mechanistic modelling in the context of climate change conservation is limited. In this review, we propose that the need to develop mechanistic models that incorporate physiological data presents an opportunity for physiologists to contribute more directly to climate change conservation and advance the field of conservation physiology. We begin by describing the prevalence of species distribution modelling in climate change conservation, highlighting the benefits and drawbacks of both mechanistic and correlative approaches. Next, we emphasize the need to expand mechanistic models and discuss potential metrics of physiological performance suitable for integration into mechanistic models. We conclude by summarizing other factors, such as the need to consider demography, limiting broader application of mechanistic models in climate change conservation. Ideally, modellers, physiologists and conservation practitioners would work collaboratively to build models, interpret results and consider conservation management options, and articulating this need here may help to stimulate collaboration.

Framework for Detection and Localization of Extreme Climate Event with Pixel Recursive Super Resolution

NASA Astrophysics Data System (ADS)

Kim, S. K.; Lee, J.; Zhang, C.; Ames, S.; Williams, D. N.

2017-12-01

Deep learning techniques have been successfully applied to solve many problems in climate and geoscience using massive-scaled observed and modeled data. For extreme climate event detections, several models based on deep neural networks have been recently proposed and attend superior performance that overshadows all previous handcrafted expert based method. The issue arising, though, is that accurate localization of events requires high quality of climate data. In this work, we propose framework capable of detecting and localizing extreme climate events in very coarse climate data. Our framework is based on two models using deep neural networks, (1) Convolutional Neural Networks (CNNs) to detect and localize extreme climate events, and (2) Pixel recursive recursive super resolution model to reconstruct high resolution climate data from low resolution climate data. Based on our preliminary work, we have presented two CNNs in our framework for different purposes, detection and localization. Our results using CNNs for extreme climate events detection shows that simple neural nets can capture the pattern of extreme climate events with high accuracy from very coarse reanalysis data. However, localization accuracy is relatively low due to the coarse resolution. To resolve this issue, the pixel recursive super resolution model reconstructs the resolution of input of localization CNNs. We present a best networks using pixel recursive super resolution model that synthesizes details of tropical cyclone in ground truth data while enhancing their resolution. Therefore, this approach not only dramat- ically reduces the human effort, but also suggests possibility to reduce computing cost required for downscaling process to increase resolution of data.

Assessing the sensitivity of avian species abundance to land cover and climate

USGS Publications Warehouse

LeBrun, Jaymi J.; Thogmartin, Wayne E.; Thompson, Frank R.; Dijak, William D.; Millspaugh, Joshua J.

2016-01-01

Climate projections for the Midwestern United States predict southerly climates to shift northward. These shifts in climate could alter distributions of species across North America through changes in climate (i.e., temperature and precipitation), or through climate-induced changes on land cover. Our objective was to determine the relative impacts of land cover and climate on the abundance of five bird species in the Central United States that have habitat requirements ranging from grassland and shrubland to forest. We substituted space for time to examine potential impacts of a changing climate by assessing climate and land cover relationships over a broad latitudinal gradient. We found positive and negative relationships of climate and land cover factors with avian abundances. Habitat variables drove patterns of abundance in migratory and resident species, although climate was also influential in predicting abundance for some species occupying more open habitat (i.e., prairie warbler, blue-winged warbler, and northern bobwhite). Abundance of northern bobwhite increased with winter temperature and was the species exhibiting the most significant effect of climate. Models for birds primarily occupying early successional habitats performed better with a combination of habitat and climate variables whereas models of species found in contiguous forest performed best with land cover alone. These varied species-specific responses present unique challenges to land managers trying to balance species conservation over a variety of land covers. Management activities focused on increasing forest cover may play a role in mitigating effects of future climate by providing habitat refugia to species vulnerable to projected changes. Conservation efforts would be best served focusing on areas with high species abundances and an array of habitats. Future work managing forests for resilience and resistance to climate change could benefit species already susceptible to climate impacts.

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.

Atmospheric response and feedback to radiative forcing from biomass burning in tropical South America

Treesearch

Yongqiang Liu

2005-01-01

Simulations are performed to understand the importance of smoke from biomass burning in tropical South America to regional radiation and climate. The National Center for Atmospheric Research (NCAR) regional climate model coupled with the NCAR column radiative model is used to estimate smoke direct radiative forcing and consequent atmospheric perturbations during a...

Theory and Programs for Dynamic Modeling of Tree Rings from Climate

Treesearch

Paul C. van Deusen; Jennifer Koretz

1988-01-01

Computer programs written in GAUSS(TM) for IBM compatible personal computers are described that perform dynamic tree ring modeling with climate data; the underlying theory is also described. The programs and a separate users manual are available from the authors, although users must have the GAUSS software package on their personal computer. An example application of...

Forward modeling of tree-ring data: a case study with a global network

NASA Astrophysics Data System (ADS)

Breitenmoser, P. D.; Frank, D.; Brönnimann, S.

2012-04-01

Information derived from tree-rings is one of the most powerful tools presently available for studying past climatic variability as well as identifying fundamental relationships between tree-growth and climate. Climate reconstructions are typically performed by extending linear relationships, established during the overlapping period of instrumental and climate proxy archives into the past. Such analyses, however, are limited by methodological assumptions, including stationarity and linearity of the climate-proxy relationship. We investigate climate and tree-ring data using the Vaganov-Shashkin-Lite (VS-Lite) forward model of tree-ring width formation to examine the relations among actual tree growth and climate (as inferred from the simulated chronologies) to reconstruct past climate variability. The VS-lite model has been shown to produce skill comparable to that achieved using classical dendrochronological statistical modeling techniques when applied on simulations of a network of North American tree-ring chronologies. Although the detailed mechanistic processes such as photosynthesis, storage, or cell processes are not modeled directly, the net effect of the dominating nonlinear climatic controls on tree-growth are implemented into the model by the principle of limiting factors and threshold growth response functions. The VS-lite model requires as inputs only latitude, monthly mean temperature and monthly accumulated precipitation. Hence, this simple, process-based model enables ring-width simulation at any location where monthly climate records exist. In this study, we analyse the growth response of simulated tree-rings to monthly climate conditions obtained from the 20th century reanalysis project back to 1871. These simulated tree-ring chronologies are compared to the climate-driven variability in worldwide observed tree-ring chronologies from the International Tree Ring Database. Results point toward the suitability of the relationship among actual tree growth and climate (as inferred from the simulated chronologies) for use in global palaeoclimate reconstructions.

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.

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

NASA Technical Reports Server (NTRS)

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

2015-01-01

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

Evaluating the sensitivity of agricultural model performance to different climate inputs

PubMed Central

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

2017-01-01

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

A 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.

Climate data induced uncertainty in model based estimations of terrestrial primary productivity

NASA Astrophysics Data System (ADS)

Wu, Z.; Ahlström, A.; Smith, B.; Ardö, J.; Eklundh, L.; Fensholt, R.; Lehsten, V.

2016-12-01

Models used to project global vegetation and carbon cycle differ in their estimates of historical fluxes and pools. These differences arise not only from differences between models but also from differences in the environmental and climatic data that forces the models. Here we investigate the role of uncertainties in historical climate data, encapsulated by a set of six historical climate datasets. We focus on terrestrial gross primary productivity (GPP) and analyze the results from a dynamic process-based vegetation model (LPJ-GUESS) forced by six different climate datasets and two empirical datasets of GPP (derived from flux towers and remote sensing). We find that the climate induced uncertainty, defined as the difference among historical simulations in GPP when forcing the model with the different climate datasets, can be as high as 33 Pg C yr-1 globally (19% of mean GPP). The uncertainty is partitioned into the three main climatic drivers, temperature, precipitation, and shortwave radiation. Additionally, we illustrate how the uncertainty due to a given climate driver depends both on the magnitude of the forcing data uncertainty (the data range) and the sensitivity of the modeled GPP to the driver (the ecosystem sensitivity). The analysis is performed globally and stratified into five land cover classes. We find that the dynamic vegetation model overestimates GPP, compared to empirically based GPP data over most areas, except for the tropical region. Both the simulations and empirical estimates agree that the tropical region is a disproportionate source of uncertainty in GPP estimation. This is mainly caused by uncertainties in shortwave radiation forcing, of which climate data range contributes slightly higher uncertainty than ecosystem sensitivity to shortwave radiation. We also find that precipitation dominated the climate induced uncertainty over nearly half of terrestrial vegetated surfaces, which is mainly due to large ecosystem sensitivity to precipitation. Overall, climate data ranges are found to contribute more to the climate induced uncertainty than ecosystem sensitivity. Our study highlights the need to better constrain tropical climate and demonstrate that uncertainty caused by climatic forcing data must be considered when comparing and evaluating model results and empirical datasets.

Efficient and Flexible Climate Analysis with Python in a Cloud-Based Distributed Computing Framework

NASA Astrophysics Data System (ADS)

Gannon, C.

2017-12-01

As climate models become progressively more advanced, and spatial resolution further improved through various downscaling projects, climate projections at a local level are increasingly insightful and valuable. However, the raw size of climate datasets presents numerous hurdles for analysts wishing to develop customized climate risk metrics or perform site-specific statistical analysis. Four Twenty Seven, a climate risk consultancy, has implemented a Python-based distributed framework to analyze large climate datasets in the cloud. With the freedom afforded by efficiently processing these datasets, we are able to customize and continually develop new climate risk metrics using the most up-to-date data. Here we outline our process for using Python packages such as XArray and Dask to evaluate netCDF files in a distributed framework, StarCluster to operate in a cluster-computing environment, cloud computing services to access publicly hosted datasets, and how this setup is particularly valuable for generating climate change indicators and performing localized statistical analysis.

The Community Climate System Model.

NASA Astrophysics Data System (ADS)

Blackmon, Maurice; Boville, Byron; Bryan, Frank; Dickinson, Robert; Gent, Peter; Kiehl, Jeffrey; Moritz, Richard; Randall, David; Shukla, Jagadish; Solomon, Susan; Bonan, Gordon; Doney, Scott; Fung, Inez; Hack, James; Hunke, Elizabeth; Hurrell, James; Kutzbach, John; Meehl, Jerry; Otto-Bliesner, Bette; Saravanan, R.; Schneider, Edwin K.; Sloan, Lisa; Spall, Michael; Taylor, Karl; Tribbia, Joseph; Washington, Warren

2001-11-01

The Community Climate System Model (CCSM) has been created to represent the principal components of the climate system and their interactions. Development and applications of the model are carried out by the U.S. climate research community, thus taking advantage of both wide intellectual participation and computing capabilities beyond those available to most individual U.S. institutions. This article outlines the history of the CCSM, its current capabilities, and plans for its future development and applications, with the goal of providing a summary useful to present and future users. The initial version of the CCSM included atmosphere and ocean general circulation models, a land surface model that was grafted onto the atmosphere model, a sea-ice model, and a flux coupler that facilitates information exchanges among the component models with their differing grids. This version of the model produced a successful 300-yr simulation of the current climate without artificial flux adjustments. The model was then used to perform a coupled simulation in which the atmospheric CO2 concentration increased by 1% per year. In this version of the coupled model, the ocean salinity and deep-ocean temperature slowly drifted away from observed values. A subsequent correction to the roughness length used for sea ice significantly reduced these errors. An updated version of the CCSM was used to perform three simulations of the twentieth century's climate, and several pro-jections of the climate of the twenty-first century. The CCSM's simulation of the tropical ocean circulation has been significantly improved by reducing the background vertical diffusivity and incorporating an anisotropic horizontal viscosity tensor. The meridional resolution of the ocean model was also refined near the equator. These changes have resulted in a greatly improved simulation of both the Pacific equatorial undercurrent and the surface countercurrents. The interannual variability of the sea surface temperature in the central and eastern tropical Pacific is also more realistic in simulations with the updated model. Scientific challenges to be addressed with future versions of the CCSM include realistic simulation of the whole atmosphere, including the middle and upper atmosphere, as well as the troposphere; simulation of changes in the chemical composition of the atmosphere through the incorporation of an integrated chemistry model; inclusion of global, prognostic biogeochemical components for land, ocean, and atmosphere; simulations of past climates, including times of extensive continental glaciation as well as times with little or no ice; studies of natural climate variability on seasonal-to-centennial timescales; and investigations of anthropogenic climate change. In order to make such studies possible, work is under way to improve all components of the model. Plans call for a new version of the CCSM to be released in 2002. Planned studies with the CCSM will require much more computer power than is currently available.

Mesoscale Convective Systems During SCSMEX: Simulations with a Regional Climate Model and a Cloud-Resolving Model

NASA Technical Reports Server (NTRS)

Tao, W. K.; Wang, Y.; Qian, J.; Shie, C. -L.; Lau, W. K. -M.; Kakar, R.; Starr, David O' C. (Technical Monitor)

2002-01-01

The South China Sea Monsoon Experiment (SCSMEX) was conducted in May-June 1998. One of its major objectives is to better understand the key physical processes for the onset and evolution of the summer monsoon over Southeast Asia and southern China (Lau et al. 2000). Multiple observation platforms (e.g., soundings, Doppler radar, ships, wind seafarers, radiometers, etc.) during SCSMEX provided a first attempt at investigating the detailed characteristics of convection and circulation changes, associated with monsoons over the South China Sea region. SCSMEX also provided precipitation derived from atmospheric budgets (Johnson and Ciesielski 2002) and comparison to those obtained from the Tropical Rainfall Measuring Mission (TRMM). In this paper, a regional climate model and a cloud-resolving model are used to perform multi-day integrations to understand the precipitation processes associated with the summer monsoon over Southeast Asia and southern China. The regional climate model is used to understand the soil - precipitation interaction and feedback associated with a flood event that occurred in and around China's Atlantic River during SCSMEX. Sensitivity tests on various land surface models, cumulus parameterization schemes (CASE), sea surface temperature (SST) variations and midlatitude influences are also performed to understand the processes associated with the onset of the monsoon over the S. China Sea during SCSMEX. Cloud-resolving models (CRMs) use more sophisticated and physically realistic parameterizations of cloud microphysical processes with very fine spatial and temporal resolution. One of the major characteristics of CRMs is an explicit interaction between clouds, radiation and the land/ocean surface. It is for this reason that GEWEX (Global Energy and Water Cycle Experiment) has formed the GCSS (GEWEX Cloud System Study) expressly for the purpose of improving the representation of the moist processes in large-scale models using CRMs. The Goddard Cumulus Ensemble (GCE) model is a CRM and is used to simulate convective systems associated with the onset of the South China Sea monsoon in 1998. The BRUCE model includes the same land surface model, cloud physics, and radiation scheme used in the regional climate model. A comparison between the results from the GCE model and regional climate model is performed.

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.

A National Energy-Water System Assessment Framework (NEWS): Synopsis of Stage 1 Research Strategy and Results

NASA Astrophysics Data System (ADS)

Vorosmarty, C. J.; Miara, A.; Macknick, J.; Newmark, R. L.; Cohen, S.; Sun, Y.; Tidwell, V. C.; Corsi, F.; Melillo, J. M.; Fekete, B. M.; Proussevitch, A. A.; Glidden, S.; Suh, S.

2017-12-01

The focus of this talk is on climate adaptation and the reliability of power supply infrastructure when viewed through the lens of strategic water issues. Power supply is critically dependent upon water resources, particularly to cool thermoelectric plants, making the sector particularly sensitive to any shifts in the geography or seasonality of water supply. We report on results from an NSF-Funded Water Sustainability and Climate effort aimed at uncovering key energy and economic system vulnerabilities. We have developed the National Energy-Water System assessment framework (NEWS) to systematically evaluate: a) the performance of the nation's electricity sector under multiple climate scenarios; b) the feasibility of alternative pathways to improve climate adaptation; and, c) the impacts of energy technology and investment tradeoffs on the economic productivity, water availability and aquatic ecosystem condition. Our project combines core engineering and geophysical models (ReEDS [Regional Energy Deployment System], TP2M [Thermoelectric Power and Thermal Pollution], and WBM [Water Balance]) through unique digital "handshake" protocols that operate across different institutions and modeling platforms. Combined system outputs are fed into a regional-to-national scale economic input/output model to evaluate economic consequences of climate constraints, technology choices, and environmental regulation. The impact assessments in NEWS are carried out through a series of climate/energy policy scenario studies to 2050. We find that despite significant climate-water impacts on individual plants, the current US power supply infrastructure shows potential for adaptation to future climates by capitalizing on the size of regional power systems, grid configuration and improvements in thermal efficiencies. However, the magnitude and implications of climate-water impacts vary depending on the configuration of the future power sector. To evaluate future power supply performance, we model alternative electricity sector pathways in combination with varying climate-water conditions. Further, water-linked disruptions in electricity supply yield substantial impacts on regional economies yet system-level shocks can be attenuated through different technology mixes and infrastructure.

Reservoirs performances under climate variability: a case study

NASA Astrophysics Data System (ADS)

Longobardi, A.; Mautone, M.; de Luca, C.

2014-09-01

A case study, the Piano della Rocca dam (southern Italy) is discussed here in order to quantify the system performances under climate variability conditions. Different climate scenarios have been stochastically generated according to the tendencies in precipitation and air temperature observed during recent decades for the studied area. Climate variables have then been filtered through an ARMA model to generate, at the monthly scale, time series of reservoir inflow volumes. Controlled release has been computed considering the reservoir is operated following the standard linear operating policy (SLOP) and reservoir performances have been assessed through the calculation of reliability, resilience and vulnerability indices (Hashimoto et al. 1982), comparing current and future scenarios of climate variability. The proposed approach can be suggested as a valuable tool to mitigate the effects of moderate to severe and persistent droughts periods, through the allocation of new water resources or the planning of appropriate operational rules.

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.

Model uncertainties do not affect observed patterns of species richness in the Amazon.

PubMed

Sales, Lilian Patrícia; Neves, Olívia Viana; De Marco, Paulo; Loyola, Rafael

2017-01-01

Climate change is arguably a major threat to biodiversity conservation and there are several methods to assess its impacts on species potential distribution. Yet the extent to which different approaches on species distribution modeling affect species richness patterns at biogeographical scale is however unaddressed in literature. In this paper, we verified if the expected responses to climate change in biogeographical scale-patterns of species richness and species vulnerability to climate change-are affected by the inputs used to model and project species distribution. We modeled the distribution of 288 vertebrate species (amphibians, birds and mammals), all endemic to the Amazon basin, using different combinations of the following inputs known to affect the outcome of species distribution models (SDMs): 1) biological data type, 2) modeling methods, 3) greenhouse gas emission scenarios and 4) climate forecasts. We calculated uncertainty with a hierarchical ANOVA in which those different inputs were considered factors. The greatest source of variation was the modeling method. Model performance interacted with data type and modeling method. Absolute values of variation on suitable climate area were not equal among predictions, but some biological patterns were still consistent. All models predicted losses on the area that is climatically suitable for species, especially for amphibians and primates. All models also indicated a current East-western gradient on endemic species richness, from the Andes foot downstream the Amazon river. Again, all models predicted future movements of species upwards the Andes mountains and overall species richness losses. From a methodological perspective, our work highlights that SDMs are a useful tool for assessing impacts of climate change on biodiversity. Uncertainty exists but biological patterns are still evident at large spatial scales. As modeling methods are the greatest source of variation, choosing the appropriate statistics according to the study objective is also essential for estimating the impacts of climate change on species distribution. Yet from a conservation perspective, we show that Amazon endemic fauna is potentially vulnerable to climate change, due to expected reductions on suitable climate area. Climate-driven faunal movements are predicted towards the Andes mountains, which might work as climate refugia for migrating species.

Model uncertainties do not affect observed patterns of species richness in the Amazon

PubMed Central

Sales, Lilian Patrícia; Neves, Olívia Viana; De Marco, Paulo

2017-01-01

Background Climate change is arguably a major threat to biodiversity conservation and there are several methods to assess its impacts on species potential distribution. Yet the extent to which different approaches on species distribution modeling affect species richness patterns at biogeographical scale is however unaddressed in literature. In this paper, we verified if the expected responses to climate change in biogeographical scale—patterns of species richness and species vulnerability to climate change—are affected by the inputs used to model and project species distribution. Methods We modeled the distribution of 288 vertebrate species (amphibians, birds and mammals), all endemic to the Amazon basin, using different combinations of the following inputs known to affect the outcome of species distribution models (SDMs): 1) biological data type, 2) modeling methods, 3) greenhouse gas emission scenarios and 4) climate forecasts. We calculated uncertainty with a hierarchical ANOVA in which those different inputs were considered factors. Results The greatest source of variation was the modeling method. Model performance interacted with data type and modeling method. Absolute values of variation on suitable climate area were not equal among predictions, but some biological patterns were still consistent. All models predicted losses on the area that is climatically suitable for species, especially for amphibians and primates. All models also indicated a current East-western gradient on endemic species richness, from the Andes foot downstream the Amazon river. Again, all models predicted future movements of species upwards the Andes mountains and overall species richness losses. Conclusions From a methodological perspective, our work highlights that SDMs are a useful tool for assessing impacts of climate change on biodiversity. Uncertainty exists but biological patterns are still evident at large spatial scales. As modeling methods are the greatest source of variation, choosing the appropriate statistics according to the study objective is also essential for estimating the impacts of climate change on species distribution. Yet from a conservation perspective, we show that Amazon endemic fauna is potentially vulnerable to climate change, due to expected reductions on suitable climate area. Climate-driven faunal movements are predicted towards the Andes mountains, which might work as climate refugia for migrating species. PMID:29023503

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.

The value of model averaging and dynamical climate model predictions for improving statistical seasonal streamflow forecasts over Australia

NASA Astrophysics Data System (ADS)

Pokhrel, Prafulla; Wang, Q. J.; Robertson, David E.

2013-10-01

Seasonal streamflow forecasts are valuable for planning and allocation of water resources. In Australia, the Bureau of Meteorology employs a statistical method to forecast seasonal streamflows. The method uses predictors that are related to catchment wetness at the start of a forecast period and to climate during the forecast period. For the latter, a predictor is selected among a number of lagged climate indices as candidates to give the "best" model in terms of model performance in cross validation. This study investigates two strategies for further improvement in seasonal streamflow forecasts. The first is to combine, through Bayesian model averaging, multiple candidate models with different lagged climate indices as predictors, to take advantage of different predictive strengths of the multiple models. The second strategy is to introduce additional candidate models, using rainfall and sea surface temperature predictions from a global climate model as predictors. This is to take advantage of the direct simulations of various dynamic processes. The results show that combining forecasts from multiple statistical models generally yields more skillful forecasts than using only the best model and appears to moderate the worst forecast errors. The use of rainfall predictions from the dynamical climate model marginally improves the streamflow forecasts when viewed over all the study catchments and seasons, but the use of sea surface temperature predictions provide little additional benefit.

Hygrothermal Anaylsis of Wood-Frame Wall Assemblies in a Mixed-Humid Climate

Treesearch

Samuel V. Glass

2013-01-01

This study uses a one-dimensional hygrothermal model to investigate the moisture performance of 10 residential wood-frame wall assemblies in a representative mixed-humid climate location of Baltimore, Maryland (climate zone 4A). All the assemblies include oriented strandboard (OSB) sheathing and vinyl siding. The walls differ in stud cavity thickness, level of cavity...

Identifying a key physical factor sensitive to the performance of Madden-Julian oscillation simulation in climate models

NASA Astrophysics Data System (ADS)

Kim, Go-Un; Seo, Kyong-Hwan

2018-01-01

A key physical factor in regulating the performance of Madden-Julian oscillation (MJO) simulation is examined by using 26 climate model simulations from the World Meteorological Organization's Working Group for Numerical Experimentation/Global Energy and Water Cycle Experiment Atmospheric System Study (WGNE and MJO-Task Force/GASS) global model comparison project. For this, intraseasonal moisture budget equation is analyzed and a simple, efficient physical quantity is developed. The result shows that MJO skill is most sensitive to vertically integrated intraseasonal zonal wind convergence (ZC). In particular, a specific threshold value of the strength of the ZC can be used as distinguishing between good and poor models. An additional finding is that good models exhibit the correct simultaneous convection and large-scale circulation phase relationship. In poor models, however, the peak circulation response appears 3 days after peak rainfall, suggesting unfavorable coupling between convection and circulation. For an improving simulation of the MJO in climate models, we propose that this delay of circulation in response to convection needs to be corrected in the cumulus parameterization scheme.

The Early Eocene equable climate problem: can perturbations of climate model parameters identify possible solutions?

PubMed

Sagoo, Navjit; Valdes, Paul; Flecker, Rachel; Gregoire, Lauren J

2013-10-28

Geological data for the Early Eocene (56-47.8 Ma) indicate extensive global warming, with very warm temperatures at both poles. However, despite numerous attempts to simulate this warmth, there are remarkable data-model differences in the prediction of these polar surface temperatures, resulting in the so-called 'equable climate problem'. In this paper, for the first time an ensemble with a perturbed climate-sensitive model parameters approach has been applied to modelling the Early Eocene climate. We performed more than 100 simulations with perturbed physics parameters, and identified two simulations that have an optimal fit with the proxy data. We have simulated the warmth of the Early Eocene at 560 ppmv CO2, which is a much lower CO2 level than many other models. We investigate the changes in atmospheric circulation, cloud properties and ocean circulation that are common to these simulations and how they differ from the remaining simulations in order to understand what mechanisms contribute to the polar warming. The parameter set from one of the optimal Early Eocene simulations also produces a favourable fit for the last glacial maximum boundary climate and outperforms the control parameter set for the present day. Although this does not 'prove' that this model is correct, it is very encouraging that there is a parameter set that creates a climate model able to simulate well very different palaeoclimates and the present-day climate. Interestingly, to achieve the great warmth of the Early Eocene this version of the model does not have a strong future climate change Charney climate sensitivity. It produces a Charney climate sensitivity of 2.7(°)C, whereas the mean value of the 18 models in the IPCC Fourth Assessment Report (AR4) is 3.26(°)C±0.69(°)C. Thus, this value is within the range and below the mean of the models included in the AR4.

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.

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).

Assessment of the performance of CORDEX-SA experiments in simulating seasonal mean temperature over the Himalayan region for the present climate: Part I

NASA Astrophysics Data System (ADS)

Nengker, T.; Choudhary, A.; Dimri, A. P.

2018-04-01

The ability of an ensemble of five regional climate models (hereafter RCMs) under Coordinated Regional Climate Downscaling Experiments-South Asia (hereafter, CORDEX-SA) in simulating the key features of present day near surface mean air temperature (Tmean) climatology (1970-2005) over the Himalayan region is studied. The purpose of this paper is to understand the consistency in the performance of models across the ensemble, space and seasons. For this a number of statistical measures like trend, correlation, variance, probability distribution function etc. are applied to evaluate the performance of models against observation and simultaneously the underlying uncertainties between them for four different seasons. The most evident finding from the study is the presence of a large cold bias (-6 to -8 °C) which is systematically seen across all the models and across space and time over the Himalayan region. However, these RCMs with its fine resolution perform extremely well in capturing the spatial distribution of the temperature features as indicated by a consistently high spatial correlation (greater than 0.9) with the observation in all seasons. In spite of underestimation in simulated temperature and general intensification of cold bias with increasing elevation the models show a greater rate of warming than the observation throughout entire altitudinal stretch of study region. During winter, the simulated rate of warming gets even higher at high altitudes. Moreover, a seasonal response of model performance and its spatial variability to elevation is found.

Many-objective robust decision making for water allocation under climate change.

PubMed

Yan, Dan; Ludwig, Fulco; Huang, He Qing; Werners, Saskia E

2017-12-31

Water allocation is facing profound challenges due to climate change uncertainties. To identify adaptive water allocation strategies that are robust to climate change uncertainties, a model framework combining many-objective robust decision making and biophysical modeling is developed for large rivers. The framework was applied to the Pearl River basin (PRB), China where sufficient flow to the delta is required to reduce saltwater intrusion in the dry season. Before identifying and assessing robust water allocation plans for the future, the performance of ten state-of-the-art MOEAs (multi-objective evolutionary algorithms) is evaluated for the water allocation problem in the PRB. The Borg multi-objective evolutionary algorithm (Borg MOEA), which is a self-adaptive optimization algorithm, has the best performance during the historical periods. Therefore it is selected to generate new water allocation plans for the future (2079-2099). This study shows that robust decision making using carefully selected MOEAs can help limit saltwater intrusion in the Pearl River Delta. However, the framework could perform poorly due to larger than expected climate change impacts on water availability. Results also show that subjective design choices from the researchers and/or water managers could potentially affect the ability of the model framework, and cause the most robust water allocation plans to fail under future climate change. Developing robust allocation plans in a river basin suffering from increasing water shortage requires the researchers and water managers to well characterize future climate change of the study regions and vulnerabilities of their tools. Copyright © 2017 Elsevier B.V. All rights reserved.

Earth System Grid II (ESG): Turning Climate Model Datasets Into Community Resources

NASA Astrophysics Data System (ADS)

Williams, D.; Middleton, D.; Foster, I.; Nevedova, V.; Kesselman, C.; Chervenak, A.; Bharathi, S.; Drach, B.; Cinquni, L.; Brown, D.; Strand, G.; Fox, P.; Garcia, J.; Bernholdte, D.; Chanchio, K.; Pouchard, L.; Chen, M.; Shoshani, A.; Sim, A.

2003-12-01

High-resolution, long-duration simulations performed with advanced DOE SciDAC/NCAR climate models will produce tens of petabytes of output. To be useful, this output must be made available to global change impacts researchers nationwide, both at national laboratories and at universities, other research laboratories, and other institutions. To this end, we propose to create a new Earth System Grid, ESG-II - a virtual collaborative environment that links distributed centers, users, models, and data. ESG-II will provide scientists with virtual proximity to the distributed data and resources that they require to perform their research. The creation of this environment will significantly increase the scientific productivity of U.S. climate researchers by turning climate datasets into community resources. In creating ESG-II, we will integrate and extend a range of Grid and collaboratory technologies, including the DODS remote access protocols for environmental data, Globus Toolkit technologies for authentication, resource discovery, and resource access, and Data Grid technologies developed in other projects. We will develop new technologies for (1) creating and operating "filtering servers" capable of performing sophisticated analyses, and (2) delivering results to users. In so doing, we will simultaneously contribute to climate science and advance the state of the art in collaboratory technology. We expect our results to be useful to numerous other DOE projects. The three-year R&D program will be undertaken by a talented and experienced team of computer scientists at five laboratories (ANL, LBNL, LLNL, NCAR, ORNL) and one university (ISI), working in close collaboration with climate scientists at several sites.

A modelling study of the event-based retention performance of green roof under the hot-humid tropical climate in Kuching.

PubMed

Chai, C T; Putuhena, F J; Selaman, O S

2017-12-01

The influences of climate on the retention capability of green roof have been widely discussed in existing literature. However, knowledge on how the retention capability of green roof is affected by the tropical climate is limited. This paper highlights the retention performance of the green roof situated in Kuching under hot-humid tropical climatic conditions. Using the green roof water balance modelling approach, this study simulated the hourly runoff generated from a virtual green roof from November 2012 to October 2013 based on past meteorological data. The result showed that the overall retention performance was satisfactory with a mean retention rate of 72.5% from 380 analysed rainfall events but reduced to 12.0% only for the events that potentially trigger the occurrence of flash flood. By performing the Spearman rank's correlation analysis, it was found that the rainfall depth and mean rainfall intensity, individually, had a strong negative correlation with event retention rate, suggesting that the retention rate increases with decreased rainfall depth. The expected direct relationship between retention rate and antecedent dry weather period was found to be event size dependent.

Effect of climate variables on cocoa black pod incidence in Sabah using ARIMAX model

NASA Astrophysics Data System (ADS)

Ling Sheng Chang, Albert; Ramba, Haya; Mohd. Jaaffar, Ahmad Kamil; Kim Phin, Chong; Chong Mun, Ho

2016-06-01

Cocoa black pod disease is one of the major diseases affecting the cocoa production in Malaysia and also around the world. Studies have shown that the climate variables have influenced the cocoa black pod disease incidence and it is important to quantify the black pod disease variation due to the effect of climate variables. Application of time series analysis especially auto-regressive moving average (ARIMA) model has been widely used in economics study and can be used to quantify the effect of climate variables on black pod incidence to forecast the right time to control the incidence. However, ARIMA model does not capture some turning points in cocoa black pod incidence. In order to improve forecasting performance, other explanatory variables such as climate variables should be included into ARIMA model as ARIMAX model. Therefore, this paper is to study the effect of climate variables on the cocoa black pod disease incidence using ARIMAX model. The findings of the study showed ARIMAX model using MA(1) and relative humidity at lag 7 days, RHt - 7 gave better R square value compared to ARIMA model using MA(1) which could be used to forecast the black pod incidence to assist the farmers determine timely application of fungicide spraying and culture practices to control the black pod incidence.

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

Forecasting distributions of an aquatic invasive species (Nitellopsis obtusa) under future climate scenarios

PubMed Central

Varela, Sara; Larkin, Daniel J.; Phelps, Nicholas B. D.

2017-01-01

Starry stonewort (Nitellopsis obtusa) is an alga that has emerged as an aquatic invasive species of concern in the United States. Where established, starry stonewort can interfere with recreational uses of water bodies and potentially have ecological impacts. Incipient invasion of starry stonewort in Minnesota provides an opportunity to predict future expansion in order to target early detection and strategic management. We used ecological niche models to identify suitable areas for starry stonewort in Minnesota based on global occurrence records and present-day and future climate conditions. We assessed sensitivity of forecasts to different parameters, using four emission scenarios (i.e., RCP 2.6, RCP 4.5, RCP 6, and RCP 8.5) from five future climate models (i.e., CCSM, GISS, IPSL, MIROC, and MRI). From our niche model analyses, we found that (i) occurrences from the entire range, instead of occurrences restricted to the invaded range, provide more informed models; (ii) default settings in Maxent did not provide the best model; (iii) the model calibration area and its background samples impact model performance; (iv) model projections to future climate conditions should be restricted to analogous environments; and (v) forecasts in future climate conditions should include different future climate models and model calibration areas to better capture uncertainty in forecasts. Under present climate, the most suitable areas for starry stonewort are predicted to be found in central and southeastern Minnesota. In the future, suitable areas for starry stonewort are predicted to shift in geographic range under some future climate models and to shrink under others, with most permutations indicating a net decrease of the species’ suitable range. Our suitability maps can serve to design short-term plans for surveillance and education, while future climate models suggest a plausible reduction of starry stonewort spread in the long-term if the trends in climate warming remain. PMID:28704433

Forecasting distributions of an aquatic invasive species (Nitellopsis obtusa) under future climate scenarios.

PubMed

Romero-Alvarez, Daniel; Escobar, Luis E; Varela, Sara; Larkin, Daniel J; Phelps, Nicholas B D

2017-01-01

Starry stonewort (Nitellopsis obtusa) is an alga that has emerged as an aquatic invasive species of concern in the United States. Where established, starry stonewort can interfere with recreational uses of water bodies and potentially have ecological impacts. Incipient invasion of starry stonewort in Minnesota provides an opportunity to predict future expansion in order to target early detection and strategic management. We used ecological niche models to identify suitable areas for starry stonewort in Minnesota based on global occurrence records and present-day and future climate conditions. We assessed sensitivity of forecasts to different parameters, using four emission scenarios (i.e., RCP 2.6, RCP 4.5, RCP 6, and RCP 8.5) from five future climate models (i.e., CCSM, GISS, IPSL, MIROC, and MRI). From our niche model analyses, we found that (i) occurrences from the entire range, instead of occurrences restricted to the invaded range, provide more informed models; (ii) default settings in Maxent did not provide the best model; (iii) the model calibration area and its background samples impact model performance; (iv) model projections to future climate conditions should be restricted to analogous environments; and (v) forecasts in future climate conditions should include different future climate models and model calibration areas to better capture uncertainty in forecasts. Under present climate, the most suitable areas for starry stonewort are predicted to be found in central and southeastern Minnesota. In the future, suitable areas for starry stonewort are predicted to shift in geographic range under some future climate models and to shrink under others, with most permutations indicating a net decrease of the species' suitable range. Our suitability maps can serve to design short-term plans for surveillance and education, while future climate models suggest a plausible reduction of starry stonewort spread in the long-term if the trends in climate warming remain.

Uncertainties in assessing climate change impacts on the hydrology of Mediterranean basins

NASA Astrophysics Data System (ADS)

Ludwig, Ralf

2013-04-01

There is substantial evidence in historical and recent observations that the Mediterranean and neighboring regions are especially vulnerable to the impacts of climate change. Numerous climate projections, stemming from ensembles of global and regional climate models, agree on severe changes in the climate forcing which are likely to exacerbate subsequent ecological, economic and social impacts. Many of these causal connections are closely linked to the general expectation that water availability will decline in the already water-stressed basins of Africa, the Mediterranean region and the Near East, even though considerable regional variances must be expected. Consequently, climate change impacts on water resources are raising concerns regarding their possible management and security implications. Decreasing access to water resources and other related factors could be a cause or a 'multiplier' of tensions within and between countries. Whether security threats arise from climate impacts or options for cooperation evolve does not depend only on the severity of the impacts themselves, but on social, economic, and institutional vulnerabilities or resilience as well as factors that influence local, national and international relations. However, an assessment of vulnerability and risks hinges on natural, socio-economic, and political conditions and responses, all of which are uncertain. Multidisciplinary research is needed to tackle the multi-facet complexity of climate change impacts on water resources in the Mediterranean and neighboring countries. This is particularly true in a region of overall data scarcity and poor data management and exchange structures. The current potential to develop appropriate regional adaptation measures towards climate change impacts suffers heavily from large uncertainties. These spread along a long chain of components, starting from the definition of emission scenarios to global and regional climate modeling to impact models and a subsequent variety of management options and adaptation strategies. Therefore, the 4-year FP7-project CLIMB (Climate induced changes on the hydrology of Mediterranean basins, GA: 244151) includes a major focus on the assessment and quantification of uncertainties. First, CLIMB employs a rigorous climate change model analysis, auditing the Global and Regional Climate Model data available through the ENSEMBLES and PRUDENCE initiatives. The audits lead to select the best regional performers as compared to observed values during the climatic reference period (1971- 2000). Specific bias correction and downscaling procedures are applied to provide the driving inputs and meet the demands of the subsequent impact models, transferring a future climate signal (2041-2070) into hydrological quantities at the catchment or landscape scale. However, very limited quantitative knowledge is as yet available about the role of hydrological model complexity for climate change impact assessment, where predictive power becomes more and more important and raises the demand for process-based and spatially explicit model types. Thus, CLIMB uses hydrological model ensembles to analyze the performance of existing models and works to identify the appropriate level of model complexity, and thus to determine the data specifications required to provide robust results in a climate change context. The presentation focuses on the CLIMB multi-level strategy to uncertainty assessment and highlights latest findings in some of the seven CLIMB case studies. In particular, the presentation will demonstrate the current constraints of hydro-meteorological data availability and processing and searches for solutions that can eventually be provided by integrating hydro-meteorology and ICT research communities.

Future Bloom and Blossom Frost Risk for Malus domestica Considering Climate Model and Impact Model Uncertainties

PubMed Central

Hoffmann, Holger; Rath, Thomas

2013-01-01

The future bloom and risk of blossom frosts for Malus domestica were projected using regional climate realizations and phenological ( = impact) models. As climate impact projections are susceptible to uncertainties of climate and impact models and model concatenation, the significant horizon of the climate impact signal was analyzed by applying 7 impact models, including two new developments, on 13 climate realizations of the IPCC emission scenario A1B. Advancement of phenophases and a decrease in blossom frost risk for Lower Saxony (Germany) for early and late ripeners was determined by six out of seven phenological models. Single model/single grid point time series of bloom showed significant trends by 2021–2050 compared to 1971–2000, whereas the joint signal of all climate and impact models did not stabilize until 2043. Regarding blossom frost risk, joint projection variability exceeded the projected signal. Thus, blossom frost risk cannot be stated to be lower by the end of the 21st century despite a negative trend. As a consequence it is however unlikely to increase. Uncertainty of temperature, blooming date and blossom frost risk projection reached a minimum at 2078–2087. The projected phenophases advanced by 5.5 d K−1, showing partial compensation of delayed fulfillment of the winter chill requirement and faster completion of the following forcing phase in spring. Finally, phenological model performance was improved by considering the length of day. PMID:24116022

Future bloom and blossom frost risk for Malus domestica considering climate model and impact model uncertainties.

PubMed

Hoffmann, Holger; Rath, Thomas

2013-01-01

The future bloom and risk of blossom frosts for Malus domestica were projected using regional climate realizations and phenological ( = impact) models. As climate impact projections are susceptible to uncertainties of climate and impact models and model concatenation, the significant horizon of the climate impact signal was analyzed by applying 7 impact models, including two new developments, on 13 climate realizations of the IPCC emission scenario A1B. Advancement of phenophases and a decrease in blossom frost risk for Lower Saxony (Germany) for early and late ripeners was determined by six out of seven phenological models. Single model/single grid point time series of bloom showed significant trends by 2021-2050 compared to 1971-2000, whereas the joint signal of all climate and impact models did not stabilize until 2043. Regarding blossom frost risk, joint projection variability exceeded the projected signal. Thus, blossom frost risk cannot be stated to be lower by the end of the 21st century despite a negative trend. As a consequence it is however unlikely to increase. Uncertainty of temperature, blooming date and blossom frost risk projection reached a minimum at 2078-2087. The projected phenophases advanced by 5.5 d K(-1), showing partial compensation of delayed fulfillment of the winter chill requirement and faster completion of the following forcing phase in spring. Finally, phenological model performance was improved by considering the length of day.

A review on regional convection permitting climate modeling

NASA Astrophysics Data System (ADS)

van Lipzig, Nicole; Prein, Andreas; Brisson, Erwan; Van Weverberg, Kwinten; Demuzere, Matthias; Saeed, Sajjad; Stengel, Martin

2016-04-01

With the increase of computational resources, it has recently become possible to perform climate model integrations where at least part the of convection is resolved. Since convection-permitting models (CPMs) are performing better than models where convection is parameterized, especially for high-impact weather like extreme precipitation, there is currently strong scientific progress in this research domain (Prein et al., 2015). Another advantage of CPMs, that have a horizontal grid spacing <4 km, is that they better resolve complex orography and land use. The regional climate model COSMO-CLM is frequently applied for CPM simulations, due to its non-hydrostatic dynamics and open international network of scientists. This presentation consists of an overview of the recent progress in CPM, with a focus on COSMO-CLM. It consists of three parts, namely the discussion of i) critical components of CPM, ii) the added value of CPM in the present-day climate and iii) the difference in climate sensitivity in CPM compared to coarser scale models. In terms of added value, the CPMs especially improve the representation of precipitation's, diurnal cycle, intensity and spatial distribution. However, an in depth-evaluation of cloud properties with CCLM over Belgium indicates a strong underestimation of the cloud fraction, causing an overestimation of high temperature extremes (Brisson et al., 2016). In terms of climate sensitivity, the CPMs indicate a stronger increase in flash floods, changes in hail storm characteristics, and reductions in the snowpack over mountains compared to coarser scale models. In conclusion, CPMs are a very promising tool for future climate research. However, additional efforts are necessary to overcome remaining deficiencies, like improving the cloud characteristics. This will be a challenging task due to compensating deficiencies that currently exist in `state-of-the-art' models, yielding a good representation of average climate conditions. In the light of using CPMs to study climate change it is necessary that these deficiencies are addressed in future research. Coordinated modeling programs are crucially needed to advance parameterizations of unresolved physics and to assess the full potential of CPMs. Brisson, E., K. Van Weverberg, M. Demuzere, A. Devis, S. Saeed, M. Stengel, N.P.M. van Lipzig, 2016. How well can a convection-permitting climate model reproduce 1 decadal statistics of precipitation, temperature and cloud characteristics? Clim. Dyn. (minor revisions). Prein, Andreas F., Wolfgang Langhans, Giorgia Fosser, Andrew Ferrone, Nikolina Ban, Klaus Goergen, Michael Keller, Merja Tölle, Oliver Gutjahr, Frauke Feser, Erwan Brisson, Stefan Kollet, Juerg Schmidli, Nicole P. M. van Lipzig, Ruby Leung. (2015) A review on regional convection-permitting climate modeling: Demonstrations, prospects, and challenges. Reviews of Geophysics 53:10.1002/rog.v53.2, 323-361

A diagram for evaluating multiple aspects of model performance in simulating vector fields

NASA Astrophysics Data System (ADS)

Xu, Zhongfeng; Hou, Zhaolu; Han, Ying; Guo, Weidong

2016-12-01

Vector quantities, e.g., vector winds, play an extremely important role in climate systems. The energy and water exchanges between different regions are strongly dominated by wind, which in turn shapes the regional climate. Thus, how well climate models can simulate vector fields directly affects model performance in reproducing the nature of a regional climate. This paper devises a new diagram, termed the vector field evaluation (VFE) diagram, which is a generalized Taylor diagram and able to provide a concise evaluation of model performance in simulating vector fields. The diagram can measure how well two vector fields match each other in terms of three statistical variables, i.e., the vector similarity coefficient, root mean square length (RMSL), and root mean square vector difference (RMSVD). Similar to the Taylor diagram, the VFE diagram is especially useful for evaluating climate models. The pattern similarity of two vector fields is measured by a vector similarity coefficient (VSC) that is defined by the arithmetic mean of the inner product of normalized vector pairs. Examples are provided, showing that VSC can identify how close one vector field resembles another. Note that VSC can only describe the pattern similarity, and it does not reflect the systematic difference in the mean vector length between two vector fields. To measure the vector length, RMSL is included in the diagram. The third variable, RMSVD, is used to identify the magnitude of the overall difference between two vector fields. Examples show that the VFE diagram can clearly illustrate the extent to which the overall RMSVD is attributed to the systematic difference in RMSL and how much is due to the poor pattern similarity.

Evaluating CMIP5 Simulations of Historical Continental Climate with Koeppen Bioclimatic Metrics

NASA Astrophysics Data System (ADS)

Phillips, T. J.; Bonfils, C.

2013-12-01

The classic Koeppen bioclimatic classification scheme associates generic vegetation types (e.g. grassland, tundra, broadleaf or evergreen forests, etc.) with regional climate zones defined by their annual cycles of continental temperature (T) and precipitation (P), considered together. The locations or areas of Koeppen vegetation types derived from observational data thus can provide concise metrical standards for simultaneously evaluating climate simulations of T and P in naturally defined regions. The CMIP5 models' collective ability to correctly represent two variables that are critically important for living organisms at regional scales is therefore central to this evaluation. For this study, 14 Koeppen vegetation types are derived from annual-cycle climatologies of T and P in some 3 dozen CMIP5 simulations of the 1980-1999 period. Metrics for evaluating the ability of the CMIP5 models to simulate the correct locations and areas of each vegetation type, as well as measures of overall model performance, also are developed. It is found that the CMIP5 models are generally most deficient in simulating: 1) climates of drier Koeppen zones (e.g. desert, savanna, grassland, steppe vegetation types) located in the southwestern U.S. and Mexico, eastern Europe, southern Africa, and central Australia; 2) climates of regions such as central Asia and western South America where topography plays a key role. Details of regional T or P biases in selected simulations that exemplify general model performance problems also will be presented. Acknowledgments: This work was funded by the U.S. Department of Energy Office of Science and was performed at the Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. Map of Koeppen vegetation types derived from observed T and P.

Assessing Disparities of Dengue Virus Transmission Risk across the US-Mexican Border Using a Climate Driven Vector-Epidemiological Model

NASA Technical Reports Server (NTRS)

Morin, Cory; Monaghan, Andrew; Quattrochi, Dale; Crosson, William; Hayden, Mary; Ernst, Kacey

2015-01-01

Dengue fever is a mosquito-borne viral disease reemerging throughout much of the tropical Americas. Dengue virus transmission is explicitly influenced by climate and the environment through its primary vector, Aedes aegypti. Temperature regulates Ae. aegypti development, survival, and replication rates as well as the incubation period of the virus within the mosquito. Precipitation provides water for many of the preferred breeding habitats of the mosquito, including buckets, old tires, and other places water can collect. Although transmission regularly occurs along the border region in Mexico, dengue virus transmission in bordering Arizona has not occurred. Using NASA's TRMM (Tropical Rainfall Measuring Mission) satellite for precipitation input and Daymet for temperature and supplemental precipitation input, we modeled dengue transmission along a US-Mexico transect using a dynamic dengue transmission model that includes interacting vector ecology and epidemiological components. Model runs were performed for 5 cities in Sonora, Mexico and southern Arizona. Employing a Monte Carlo approach, we performed ensembles of several thousands of model simulations in order to resolve the model uncertainty arising from using different combinations of parameter values that are not well known. For cities with reported dengue case data, the top model simulations that best reproduced dengue case numbers were retained and their parameter values were extracted for comparison. These parameter values were used to run simulations in areas where dengue virus transmission does not occur or where dengue fever case data was unavailable. Additional model runs were performed to reveal how changes in climate or parameter values could alter transmission risk along the transect. The relative influence of climate variability and model parameters on dengue virus transmission is assessed to help public health workers prepare location specific infection prevention strategies.

Climatic and biotic drivers of tropical evergreen forest photosynthesis: integrating field, eddy flux, remote sensing and modelling

NASA Astrophysics Data System (ADS)

Wu, J.; Serbin, S.; Xu, X.; Guan, K.; Albert, L.; Hayek, M.; Restrepo-Coupe, N.; Lopes, A. P.; Wiedemann, K. T.; Christoffersen, B. O.; Meng, R.; De Araujo, A. C.; Oliveira Junior, R. C.; Camargo, P. B. D.; Silva, R. D.; Nelson, B. W.; Huete, A. R.; Rogers, A.; Saleska, S. R.

2016-12-01

Tropical evergreen forest photosynthetic metabolism is an important driver of large-scale carbon, water, and energy cycles, generating various climate feedbacks. However, considerable uncertainties remain regarding how best to represent evergreen forest photosynthesis in current terrestrial biosphere models (TBMs), especially its sensitivity to climatic vs. biotic variation. Here, we develop a new approach to partition climatic and biotic controls on tropical forest photosynthesis from hourly to inter-annual timescales. Our results show that climatic factors dominate photosynthesis dynamics at shorter-time scale (i.e. hourly), while biotic factors dominate longer-timescale (i.e. monthly and longer) photosynthetic dynamics. Focusing on seasonal timescales, we combine camera and ecosystem carbon flux observations of forests across a rainfall gradient in Amazonia to show that high dry season leaf turnover shifts canopy composition towards younger more efficient leaves. This seasonal variation in leaf quality (per-area leaf photosynthetic capacity) thus can explain the high photosynthetic seasonality observed in the tropics. Finally, we evaluated the performance of models with different phenological schemes (i.e. leaf quantity versus leaf quality; with and without leaf phenological variation alone the vertical canopy profile). We found that models which represented the phenology of leaf quality and its within-canopy variation performed best in simulating photosynthetic seasonality in tropical evergreen forests. This work highlights the importance of incorporating improved understanding of climatic and biotic controls in next generation TBMs to project future carbon and water cycles in the tropics.

The Role of Climate and Socialization in Developing Interfunctional Coordination.

ERIC Educational Resources Information Center

Wooldridge, Barbara Ross; Minsky, Barbara D.

2002-01-01

Develops a model illustrating that two elements of organizational culture--climate and socialization processes--foster acceptance of organizational values and facilitate the development of interfunctional coordination, which in turn influences firm performance. (Contains 42 references.) (JOW)

Economic impacts of climate change on agriculture: the AgMIP approach

NASA Astrophysics Data System (ADS)

Delincé, Jacques; Ciaian, Pavel; Witzke, Heinz-Peter

2015-01-01

The current paper investigates the long-term global impacts on crop productivity under different climate scenarios using the AgMIP approach (Agricultural Model Intercomparison and Improvement Project). The paper provides horizontal model intercomparison from 11 economic models as well as a more detailed analysis of the simulated effects from the Common Agricultural Policy Regionalized Impact (CAPRI) model to systematically compare its performance with other AgMIP models and specifically for the Chinese agriculture. CAPRI is a comparative static partial equilibrium model extensively used for medium and long-term economic and environmental policy impact applications. The results indicate that, at the global level, the climate change will cause an agricultural productivity decrease (between -2% and -15% by 2050), a food price increase (between 1.3% and 56%) and an expansion of cultivated area (between 1% and 4%) by 2050. The results for China indicate that the climate change effects tend to be smaller than the global impacts. The CAPRI-simulated effects are, in general, close to the median across all AgMIP models. Model intercomparison analyses reveal consistency in terms of direction of change to climate change but relatively strong heterogeneity in the magnitude of the effects between models.

A modelling study of long term green roof retention performance.

PubMed

Stovin, Virginia; Poë, Simon; Berretta, Christian

2013-12-15

This paper outlines the development of a conceptual hydrological flux model for the long term continuous simulation of runoff and drought risk for green roof systems. A green roof's retention capacity depends upon its physical configuration, but it is also strongly influenced by local climatic controls, including the rainfall characteristics and the restoration of retention capacity associated with evapotranspiration during dry weather periods. The model includes a function that links evapotranspiration rates to substrate moisture content, and is validated against observed runoff data. The model's application to typical extensive green roof configurations is demonstrated with reference to four UK locations characterised by contrasting climatic regimes, using 30-year rainfall time-series inputs at hourly simulation time steps. It is shown that retention performance is dependent upon local climatic conditions. Volumetric retention ranges from 0.19 (cool, wet climate) to 0.59 (warm, dry climate). Per event retention is also considered, and it is demonstrated that retention performance decreases significantly when high return period events are considered in isolation. For example, in Sheffield the median per-event retention is 1.00 (many small events), but the median retention for events exceeding a 1 in 1 yr return period threshold is only 0.10. The simulation tool also provides useful information about the likelihood of drought periods, for which irrigation may be required. A sensitivity study suggests that green roofs with reduced moisture-holding capacity and/or low evapotranspiration rates will tend to offer reduced levels of retention, whilst high moisture-holding capacity and low evapotranspiration rates offer the strongest drought resistance. Copyright © 2013 Elsevier Ltd. All rights reserved.

Impact of individual resilience and safety climate on safety performance and psychological stress of construction workers: A case study of the Ontario construction industry.

PubMed

Chen, Yuting; McCabe, Brenda; Hyatt, Douglas

2017-06-01

The construction industry has hit a plateau in terms of safety performance. Safety climate is regarded as a leading indicator of safety performance; however, relatively little safety climate research has been done in the Canadian construction industry. Safety climate may be geographically sensitive, thus it is necessary to examine how the construct of safety climate is defined and used to improve safety performance in different regions. On the other hand, more and more attention has been paid to job related stress in the construction industry. Previous research proposed that individual resilience may be associated with a better safety performance and may help employees manage stress. Unfortunately, few empirical research studies have examined this hypothesis. This paper aims to examine the role of safety climate and individual resilience in safety performance and job stress in the Canadian construction industry. The research was based on 837 surveys collected in Ontario between June 2015 and June 2016. Structural equation modeling (SEM) techniques were used to explore the impact of individual resilience and safety climate on physical safety outcomes and on psychological stress among construction workers. The results show that safety climate not only affected construction workers' safety performance but also indirectly affected their psychological stress. In addition, it was found that individual resilience had a direct negative impact on psychological stress but had no impact on physical safety outcomes. These findings highlight the roles of both organizational and individual factors in individual safety performance and in psychological well-being. Construction organizations need to not only monitor employees' safety performance, but also to assess their employees' psychological well-being. Promoting a positive safety climate together with developing training programs focusing on improving employees' psychological health - especially post-trauma psychological health - can improve the safety performance of an organization. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

A Regional Climate Model Evaluation System based on Satellite and other Observations

NASA Astrophysics Data System (ADS)

Lean, P.; Kim, J.; Waliser, D. E.; Hall, A. D.; Mattmann, C. A.; Granger, S. L.; Case, K.; Goodale, C.; Hart, A.; Zimdars, P.; Guan, B.; Molotch, N. P.; Kaki, S.

2010-12-01

Regional climate models are a fundamental tool needed for downscaling global climate simulations and projections, such as those contributing to the Coupled Model Intercomparison Projects (CMIPs) that form the basis of the IPCC Assessment Reports. The regional modeling process provides the means to accommodate higher resolution and a greater complexity of Earth System processes. Evaluation of both the global and regional climate models against observations is essential to identify model weaknesses and to direct future model development efforts focused on reducing the uncertainty associated with climate projections. However, the lack of reliable observational data and the lack of formal tools are among the serious limitations to addressing these objectives. Recent satellite observations are particularly useful as they provide a wealth of information on many different aspects of the climate system, but due to their large volume and the difficulties associated with accessing and using the data, these datasets have been generally underutilized in model evaluation studies. Recognizing this problem, NASA JPL / UCLA is developing a model evaluation system to help make satellite observations, in conjunction with in-situ, assimilated, and reanalysis datasets, more readily accessible to the modeling community. The system includes a central database to store multiple datasets in a common format and codes for calculating predefined statistical metrics to assess model performance. This allows the time taken to compare model simulations with satellite observations to be reduced from weeks to days. Early results from the use this new model evaluation system for evaluating regional climate simulations over California/western US regions will be presented.

Evolving Storage and Cyber Infrastructure at the NASA Center for Climate Simulation

NASA Technical Reports Server (NTRS)

Salmon, Ellen; Duffy, Daniel; Spear, Carrie; Sinno, Scott; Vaughan, Garrison; Bowen, Michael

2018-01-01

This talk will describe recent developments at the NASA Center for Climate Simulation, which is funded by NASAs Science Mission Directorate, and supports the specialized data storage and computational needs of weather, ocean, and climate researchers, as well as astrophysicists, heliophysicists, and planetary scientists. To meet requirements for higher-resolution, higher-fidelity simulations, the NCCS augments its High Performance Computing (HPC) and storage retrieval environment. As the petabytes of model and observational data grow, the NCCS is broadening data services offerings and deploying and expanding virtualization resources for high performance analytics.

Quantifying climate feedbacks in polar regions

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

Goosse, Hugues; Kay, Jennifer E.; Armour, Kyle C.

The concept of feedback is key in assessing whether a perturbation to a system is amplified or damped by mechanisms internal to the system. In polar regions, climate dynamics are controlled by both radiative and non-radiative interactions between the atmosphere, ocean, sea ice, ice sheets and land surfaces. Precisely quantifying polar feedbacks is required for a process-oriented evaluation of climate models, a clear understanding of the processes responsible for polar climate changes, and a reduction in uncertainty associated with model projections. This quantification can be performed using a simple and consistent approach that is valid for a wide range ofmore » feedbacks, thus offering the opportunity for more systematic feedback analyses and a better understanding of polar climate changes.« less

Quantifying climate feedbacks in polar regions

DOE PAGES

Goosse, Hugues; Kay, Jennifer E.; Armour, Kyle C.; ...

2018-05-15

The concept of feedback is key in assessing whether a perturbation to a system is amplified or damped by mechanisms internal to the system. In polar regions, climate dynamics are controlled by both radiative and non-radiative interactions between the atmosphere, ocean, sea ice, ice sheets and land surfaces. Precisely quantifying polar feedbacks is required for a process-oriented evaluation of climate models, a clear understanding of the processes responsible for polar climate changes, and a reduction in uncertainty associated with model projections. This quantification can be performed using a simple and consistent approach that is valid for a wide range ofmore » feedbacks, thus offering the opportunity for more systematic feedback analyses and a better understanding of polar climate changes.« less

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

PubMed

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

2013-09-01

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

Climates of U.S. cities in the 21st century

NASA Astrophysics Data System (ADS)

Krayenhoff, E. S.; Georgescu, M.; Moustaoui, M.

2017-12-01

Urban climates are projected to warm over the 21st century due to global climate change and urban development. To assess this projected warming, Weather Research and Forecasting (WRF) model simulations are performed at 20 km resolution over the contiguous U.S. for three 10-year periods: contemporary (2000-2009), mid-century (2050-2059), and end-of-century (2090-2099). Urban land use projections are derived from the EPA's ICLUS data set, and future climate projections are based on two global climate models and two greenhouse gas emissions scenarios. The potential for design implementations such as `green' roofs and high albedo roofs to offset the projected warming is considered. Effects of urban expansion, urban densification and infrastructure adaptation on urban climate are compared over the century. Assessment considers impacts at both seasonal and diurnal scales, isolates fair weather impacts, and considers multiple climate variables: air temperature, precipitation, humidity, wind speed, and surface energy budget partitioning.

Assessing the Robustness of Green Infrastructure under Stochastic Design Storms and Climate Change Scenarios

NASA Astrophysics Data System (ADS)

Chui, T. F. M.; Yang, Y.

2017-12-01

Green infrastructures (GI) have been widely used to mitigate flood risk, improve surface water quality, and to restore predevelopment hydrologic regimes. Commonly-used GI include, bioretention system, porous pavement and green roof, etc. They are normally sized to fulfil different design criteria (e.g. providing certain storage depths, limiting peak surface flow rates) that are formulated for current climate conditions. While GI commonly have long lifespan, the sensitivity of their performance to climate change is however unclear. This study first proposes a method to formulate suitable design criteria to meet different management interests (e.g. different levels of first flush reduction and peak flow reduction). Then typical designs of GI are proposed. In addition, a high resolution stochastic design storm generator using copulas and random cascade model is developed, which is calibrated using recorded rainfall time series. Then, few climate change scenarios are generated by varying the duration and depth of design storms, and changing the parameters of the calibrated storm generator. Finally, the performance of GI with typical designs under the random synthesized design storms are then assessed using numerical modeling. The robustness of the designs is obtained by the comparing their performance in the future scenarios to the current one. This study overall examines the robustness of the current GI design criteria under uncertain future climate conditions, demonstrating whether current GI design criteria should be modified to account for climate change.

A new framework for the analysis of continental-scale convection-resolving climate simulations

NASA Astrophysics Data System (ADS)

Leutwyler, D.; Charpilloz, C.; Arteaga, A.; Ban, N.; Di Girolamo, S.; Fuhrer, O.; Hoefler, T.; Schulthess, T. C.; Christoph, S.

2017-12-01

High-resolution climate simulations at horizontal resolution of O(1-4 km) allow explicit treatment of deep convection (thunderstorms and rain showers). Explicitly treating convection by the governing equations reduces uncertainties associated with parametrization schemes and allows a model formulation closer to physical first principles [1,2]. But kilometer-scale climate simulations with long integration periods and large computational domains are expensive and data storage becomes unbearably voluminous. Hence new approaches to perform analysis are required. In the crCLIM project we propose a new climate modeling framework that allows scientists to conduct analysis at high spatial and temporal resolution. We tackle the computational cost by using the largest available supercomputers such as hybrid CPU-GPU architectures. For this the COSMO model has been adapted to run on such architectures [2]. We then alleviate the I/O-bottleneck by employing a simulation data-virtualizer (SDaVi) that allows to trade-off storage (space) for computational effort (time). This is achieved by caching the simulation outputs and efficiently launching re-simulations in case of cache misses. All this is done transparently from the analysis applications [3]. For the re-runs this approach requires a bit-reproducible version of COSMO. That is to say a model that produces identical results on different architectures to ensure coherent recomputation of the requested data [4]. In this contribution we present a version of SDaVi, a first performance model, and a strategy to obtain bit-reproducibility across hardware architectures.[1] N. Ban, J. Schmidli, C. Schär. Evaluation of the convection-resolving regional climate modeling approach in decade-long simulations. J. Geophys. Res. Atmos., 7889-7907, 2014.[2] D. Leutwyler, O. Fuhrer, X. Lapillonne, D. Lüthi, C. Schär. Towards European-scale convection-resolving climate simulations with GPUs: a study with COSMO 4.19. Geosci. Model Dev, 3393-3412, 2016.[3] S. Di Girolamo, P. Schmid, T. Schulthess, T. Hoefler. Virtualized Big Data: Reproducing Simulation Output on Demand. Submit. to the 23rd ACM Symposium on PPoPP 18, Vienna, Austria.[4] A. Arteaga, O. Fuhrer, T. Hoefler. Designing Bit-Reproducible Portable High-Performance Applications. IEEE 28th IPDPS, 2014.

Multi-site precipitation downscaling using a stochastic weather generator

NASA Astrophysics Data System (ADS)

Chen, Jie; Chen, Hua; Guo, Shenglian

2018-03-01

Statistical downscaling is an efficient way to solve the spatiotemporal mismatch between climate model outputs and the data requirements of hydrological models. However, the most commonly-used downscaling method only produces climate change scenarios for a specific site or watershed average, which is unable to drive distributed hydrological models to study the spatial variability of climate change impacts. By coupling a single-site downscaling method and a multi-site weather generator, this study proposes a multi-site downscaling approach for hydrological climate change impact studies. Multi-site downscaling is done in two stages. The first stage involves spatially downscaling climate model-simulated monthly precipitation from grid scale to a specific site using a quantile mapping method, and the second stage involves the temporal disaggregating of monthly precipitation to daily values by adjusting the parameters of a multi-site weather generator. The inter-station correlation is specifically considered using a distribution-free approach along with an iterative algorithm. The performance of the downscaling approach is illustrated using a 10-station watershed as an example. The precipitation time series derived from the National Centers for Environment Prediction (NCEP) reanalysis dataset is used as the climate model simulation. The precipitation time series of each station is divided into 30 odd years for calibration and 29 even years for validation. Several metrics, including the frequencies of wet and dry spells and statistics of the daily, monthly and annual precipitation are used as criteria to evaluate the multi-site downscaling approach. The results show that the frequencies of wet and dry spells are well reproduced for all stations. In addition, the multi-site downscaling approach performs well with respect to reproducing precipitation statistics, especially at monthly and annual timescales. The remaining biases mainly result from the non-stationarity of NCEP precipitation. Overall, the proposed approach is efficient for generating multi-site climate change scenarios that can be used to investigate the spatial variability of climate change impacts on hydrology.

An ARM data-oriented diagnostics package to evaluate the climate model simulation

NASA Astrophysics Data System (ADS)

Zhang, C.; Xie, S.

2016-12-01

A set of diagnostics that utilize long-term high frequency measurements from the DOE Atmospheric Radiation Measurement (ARM) program is developed for evaluating the regional simulation of clouds, radiation and precipitation in climate models. The diagnostics results are computed and visualized automatically in a python-based package that aims to serve as an easy entry point for evaluating climate simulations using the ARM data, as well as the CMIP5 multi-model simulations. Basic performance metrics are computed to measure the accuracy of mean state and variability of simulated regional climate. The evaluated physical quantities include vertical profiles of clouds, temperature, relative humidity, cloud liquid water path, total column water vapor, precipitation, sensible and latent heat fluxes, radiative fluxes, aerosol and cloud microphysical properties. Process-oriented diagnostics focusing on individual cloud and precipitation-related phenomena are developed for the evaluation and development of specific model physical parameterizations. Application of the ARM diagnostics package will be presented in the AGU session. This work is performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344, IM release number is: LLNL-ABS-698645.

Evaluating the variability in surface water reservoir planning characteristics during climate change impacts assessment

NASA Astrophysics Data System (ADS)

Soundharajan, Bankaru-Swamy; Adeloye, Adebayo J.; Remesan, Renji

2016-07-01

This study employed a Monte-Carlo simulation approach to characterise the uncertainties in climate change induced variations in storage requirements and performance (reliability (time- and volume-based), resilience, vulnerability and sustainability) of surface water reservoirs. Using a calibrated rainfall-runoff (R-R) model, the baseline runoff scenario was first simulated. The R-R inputs (rainfall and temperature) were then perturbed using plausible delta-changes to produce simulated climate change runoff scenarios. Stochastic models of the runoff were developed and used to generate ensembles of both the current and climate-change-perturbed future runoff scenarios. The resulting runoff ensembles were used to force simulation models of the behaviour of the reservoir to produce 'populations' of required reservoir storage capacity to meet demands, and the performance. Comparing these parameters between the current and the perturbed provided the population of climate change effects which was then analysed to determine the variability in the impacts. The methodology was applied to the Pong reservoir on the Beas River in northern India. The reservoir serves irrigation and hydropower needs and the hydrology of the catchment is highly influenced by Himalayan seasonal snow and glaciers, and Monsoon rainfall, both of which are predicted to change due to climate change. The results show that required reservoir capacity is highly variable with a coefficient of variation (CV) as high as 0.3 as the future climate becomes drier. Of the performance indices, the vulnerability recorded the highest variability (CV up to 0.5) while the volume-based reliability was the least variable. Such variabilities or uncertainties will, no doubt, complicate the development of climate change adaptation measures; however, knowledge of their sheer magnitudes as obtained in this study will help in the formulation of appropriate policy and technical interventions for sustaining and possibly enhancing water security for irrigation and other uses served by Pong reservoir.

Incorporating Student Activities into Climate Change Education

NASA Astrophysics Data System (ADS)

Steele, H.; Kelly, K.; Klein, D.; Cadavid, A. C.

2013-12-01

Under a NASA grant, Mathematical and Geospatial Pathways to Climate Change Education, students at California State University, Northridge integrated Geographic Information Systems (GIS), remote sensing, satellite data technologies, and climate modelling into the study of global climate change under a Pathway for studying the Mathematics of Climate Change (PMCC). The PMCC, which is an interdisciplinary option within the BS in Applied Mathematical Sciences, consists of courses offered by the departments of Mathematics, Physics, and Geography and is designed to prepare students for careers and Ph.D. programs in technical fields relevant to global climate change. Under this option students are exposed to the science, mathematics, and applications of climate change science through a variety of methods including hands-on experience with computer modeling and image processing software. In the Geography component of the program, ESRI's ArcGIS and ERDAS Imagine mapping, spatial analysis and image processing software were used to explore NASA satellite data to examine the earth's atmosphere, hydrosphere and biosphere in areas that are affected by climate change or affect climate. These technology tools were incorporated into climate change and remote sensing courses to enhance students' knowledge and understanding of climate change through hands-on application of image processing techniques to NASA data. Several sets of exercises were developed with specific learning objectives in mind. These were (1) to increase student understanding of climate change and climate change processes; (2) to develop student skills in understanding, downloading and processing satellite data; (3) to teach remote sensing technology and GIS through applications to climate change; (4) to expose students to climate data and methods they can apply to solve real world problems and incorporate in future research projects. In the Math and Physics components of the course, students learned about atmospheric circulation with applications of the Lorenz model, explored the land-sea breeze problem with the Dynamics and Thermodynamics Circulation Model (DTDM), and developed simple radiative transfer models. Class projects explored the effects of varying the content of CO2 and CH4 in the atmosphere, as well as the properties of paleoclimates in atmospheric simulations using EdGCM. Initial assessment of student knowledge, attitudes, and behaviors associated with these activities, particularly about climate change, was measured. Pre- and post-course surveys provided student perspectives about the courses and their learning about remote sensing and climate change concepts. Student performance on the tutorials and course projects evaluated students' ability to learn and apply their knowledge about climate change and skills with remote sensing to assigned problems or proposed projects of their choice. Survey and performance data illustrated that the exercises were successful in meeting their intended learning objectives as well as opportunities for further refinement and expansion.

Development of a Cloud Resolving Model for Heterogeneous Supercomputers

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

Development of a High-Resolution Climate Model for Future Climate Change Projection on the Earth Simulator

NASA Astrophysics Data System (ADS)

Kanzawa, H.; Emori, S.; Nishimura, T.; Suzuki, T.; Inoue, T.; Hasumi, H.; Saito, F.; Abe-Ouchi, A.; Kimoto, M.; Sumi, A.

2002-12-01

The fastest supercomputer of the world, the Earth Simulator (total peak performance 40TFLOPS) has recently been available for climate researches in Yokohama, Japan. We are planning to conduct a series of future climate change projection experiments on the Earth Simulator with a high-resolution coupled ocean-atmosphere climate model. The main scientific aims for the experiments are to investigate 1) the change in global ocean circulation with an eddy-permitting ocean model, 2) the regional details of the climate change including Asian monsoon rainfall pattern, tropical cyclones and so on, and 3) the change in natural climate variability with a high-resolution model of the coupled ocean-atmosphere system. To meet these aims, an atmospheric GCM, CCSR/NIES AGCM, with T106(~1.1o) horizontal resolution and 56 vertical layers is to be coupled with an oceanic GCM, COCO, with ~ 0.28ox 0.19o horizontal resolution and 48 vertical layers. This coupled ocean-atmosphere climate model, named MIROC, also includes a land-surface model, a dynamic-thermodynamic seaice model, and a river routing model. The poles of the oceanic model grid system are rotated from the geographic poles so that they are placed in Greenland and Antarctic land masses to avoild the singularity of the grid system. Each of the atmospheric and the oceanic parts of the model is parallelized with the Message Passing Interface (MPI) technique. The coupling of the two is to be done with a Multi Program Multi Data (MPMD) fashion. A 100-model-year integration will be possible in one actual month with 720 vector processors (which is only 14% of the full resources of the Earth Simulator).

Climate Change in Nicaragua: a dynamical downscaling of precipitation and temperature.

NASA Astrophysics Data System (ADS)

Porras, Ignasi; Domingo-Dalmau, Anna; Sole, Josep Maria; Arasa, Raul; Picanyol, Miquel; Ángeles Gonzalez-Serrano, M.°; Masdeu, Marta

2016-04-01

Climate Change affects weather patterns and modifies meteorological extreme events like tropical cyclones, heavy rainfalls, dry events, extreme temperatures, etc. The aim of this study is to show the Climate Change projections over Nicaragua for the period 2010-2040 focused on precipitation and temperature. In order to obtain the climate change signal, the results obtained by modelling a past period (1980-2009) were compared with the ones obtained by modelling a future period (2010-2040). The modelling method was based on a dynamical downscaling, coupling global and regional models. The MPI-ESM-MR global climate model was selected due to the better performance over Nicaragua. Moreover, a detailed sensitivity analysis for different parameterizations and schemes of the Weather Research and Forecast (WRF-ARW) model was made to minimize the model uncertainty. To evaluate and validate the methodology, a comparison between model outputs and satellite measurements data was realized. The results show an expected increment of the temperature and an increment of the number of days per year with temperatures higher than 35°C. Monthly precipitation patterns will change although annual total precipitation will be similar. In addition, number of dry days are expected to increase.

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.

Assessments of Maize Yield Potential in the Korean Peninsula Using Multiple Crop Models

NASA Astrophysics Data System (ADS)

Kim, S. H.; Myoung, B.; Lim, C. H.; Lee, S. G.; Lee, W. K.; Kafatos, M.

2015-12-01

The Korean Peninsular has unique agricultural environments due to the differences in the political and socio-economical systems between the Republic of Korea (SK, hereafter) and the Democratic Peoples' Republic of Korea (NK, hereafter). NK has been suffering from the lack of food supplies caused by natural disasters, land degradation and failed political system. The neighboring developed country SK has a better agricultural system but very low food self-sufficiency rate (around 1% of maize). 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 have utilized multiple process-based crop models capable of regional-scale assessments to evaluate maize Yp over the Korean Peninsula - the GIS version of EPIC model (GEPIC) and APSIM model that can be expanded to regional scales (APSIM regions). First we evaluated model performance and skill for 20 years from 1991 to 2010 using reanalysis data (Local Data Assimilation and Prediction System (LDAPS); 1.5km resolution) and observed data. Each model's performances were compared over different regions within the Korean Peninsula of different regional climate characteristics. To quantify the major influence of individual climate variables, we also conducted a sensitivity test using 20 years of climatology. Lastly, a multi-model ensemble analysis was performed to reduce crop model uncertainties. The results will provide valuable information for estimating the climate change or variability impacts on Yp over the Korean Peninsula.

Comparison of climate envelope models developed using expert-selected variables versus statistical selection

USGS Publications Warehouse

Brandt, Laura A.; Benscoter, Allison; Harvey, Rebecca G.; Speroterra, Carolina; Bucklin, David N.; Romañach, Stephanie; Watling, James I.; Mazzotti, Frank J.

2017-01-01

Climate envelope models are widely used to describe potential future distribution of species under different climate change scenarios. It is broadly recognized that there are both strengths and limitations to using climate envelope models and that outcomes are sensitive to initial assumptions, inputs, and modeling methods Selection of predictor variables, a central step in modeling, is one of the areas where different techniques can yield varying results. Selection of climate variables to use as predictors is often done using statistical approaches that develop correlations between occurrences and climate data. These approaches have received criticism in that they rely on the statistical properties of the data rather than directly incorporating biological information about species responses to temperature and precipitation. We evaluated and compared models and prediction maps for 15 threatened or endangered species in Florida based on two variable selection techniques: expert opinion and a statistical method. We compared model performance between these two approaches for contemporary predictions, and the spatial correlation, spatial overlap and area predicted for contemporary and future climate predictions. In general, experts identified more variables as being important than the statistical method and there was low overlap in the variable sets (<40%) between the two methods Despite these differences in variable sets (expert versus statistical), models had high performance metrics (>0.9 for area under the curve (AUC) and >0.7 for true skill statistic (TSS). Spatial overlap, which compares the spatial configuration between maps constructed using the different variable selection techniques, was only moderate overall (about 60%), with a great deal of variability across species. Difference in spatial overlap was even greater under future climate projections, indicating additional divergence of model outputs from different variable selection techniques. Our work is in agreement with other studies which have found that for broad-scale species distribution modeling, using statistical methods of variable selection is a useful first step, especially when there is a need to model a large number of species or expert knowledge of the species is limited. Expert input can then be used to refine models that seem unrealistic or for species that experts believe are particularly sensitive to change. It also emphasizes the importance of using multiple models to reduce uncertainty and improve map outputs for conservation planning. Where outputs overlap or show the same direction of change there is greater certainty in the predictions. Areas of disagreement can be used for learning by asking why the models do not agree, and may highlight areas where additional on-the-ground data collection could improve the models.

Improvements in the Scalability of the NASA Goddard Multiscale Modeling Framework for Hurricane Climate Studies

NASA Technical Reports Server (NTRS)

Shen, Bo-Wen; Tao, Wei-Kuo; Chern, Jiun-Dar

2007-01-01

Improving our understanding of hurricane inter-annual variability and the impact of climate change (e.g., doubling CO2 and/or global warming) on hurricanes brings both scientific and computational challenges to researchers. As hurricane dynamics involves multiscale interactions among synoptic-scale flows, mesoscale vortices, and small-scale cloud motions, an ideal numerical model suitable for hurricane studies should demonstrate its capabilities in simulating these interactions. The newly-developed multiscale modeling framework (MMF, Tao et al., 2007) and the substantial computing power by the NASA Columbia supercomputer show promise in pursuing the related studies, as the MMF inherits the advantages of two NASA state-of-the-art modeling components: the GEOS4/fvGCM and 2D GCEs. This article focuses on the computational issues and proposes a revised methodology to improve the MMF's performance and scalability. It is shown that this prototype implementation enables 12-fold performance improvements with 364 CPUs, thereby making it more feasible to study hurricane climate.

A network-base analysis of CMIP5 "historical" experiments

NASA Astrophysics Data System (ADS)

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

2012-12-01

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

Evaluation of regional climate simulations for air quality modelling purposes

NASA Astrophysics Data System (ADS)

Menut, Laurent; Tripathi, Om P.; Colette, Augustin; Vautard, Robert; Flaounas, Emmanouil; Bessagnet, Bertrand

2013-05-01

In order to evaluate the future potential benefits of emission regulation on regional air quality, while taking into account the effects of climate change, off-line air quality projection simulations are driven using weather forcing taken from regional climate models. These regional models are themselves driven by simulations carried out using global climate models (GCM) and economical scenarios. Uncertainties and biases in climate models introduce an additional "climate modeling" source of uncertainty that is to be added to all other types of uncertainties in air quality modeling for policy evaluation. In this article we evaluate the changes in air quality-related weather variables induced by replacing reanalyses-forced by GCM-forced regional climate simulations. As an example we use GCM simulations carried out in the framework of the ERA-interim programme and of the CMIP5 project using the Institut Pierre-Simon Laplace climate model (IPSLcm), driving regional simulations performed in the framework of the EURO-CORDEX programme. In summer, we found compensating deficiencies acting on photochemistry: an overestimation by GCM-driven weather due to a positive bias in short-wave radiation, a negative bias in wind speed, too many stagnant episodes, and a negative temperature bias. In winter, air quality is mostly driven by dispersion, and we could not identify significant differences in either wind or planetary boundary layer height statistics between GCM-driven and reanalyses-driven regional simulations. However, precipitation appears largely overestimated in GCM-driven simulations, which could significantly affect the simulation of aerosol concentrations. The identification of these biases will help interpreting results of future air quality simulations using these data. Despite these, we conclude that the identified differences should not lead to major difficulties in using GCM-driven regional climate simulations for air quality projections.

Evaluation of energy savings potential of variable refrigerant flow (VRF) from variable air volume (VAV) in the U.S. climate locations

DOE PAGES

Kim, Dongsu; Cox, Sam J.; Cho, Heejin; ...

2017-05-22

Variable refrigerant flow (VRF) systems are known for their high energy performance and thus can improve energy efficiency both in residential and commercial buildings. The energy savings potential of this system has been demonstrated in several studies by comparing the system performance with conventional HVAC systems such as rooftop variable air volume systems (RTU-VAV) and central chiller and boiler systems. This paper evaluates the performance of VRF and RTU-VAV systems in a simulation environment using widely-accepted whole building energy modeling software, EnergyPlus. A medium office prototype building model, developed by the U.S. Department of Energy (DOE), is used to assessmore » the performance of VRF and RTU-VAV systems. Each system is placed in 16 different locations, representing all U.S. climate zones, to evaluate the performance variations. Both models are compliant with the minimum energy code requirements prescribed in ASHRAE standard 90.1-2010 — energy standard for buildings except low-rise residential buildings. Finally, a comparison study between the simulation results of VRF and RTU-VAV models is made to demonstrate energy savings potential of VRF systems. The simulation results show that the VRF systems would save around 15–42% and 18–33% for HVAC site and source energy uses compared to the RTU-VAV systems. In addition, calculated results for annual HVAC cost savings point out that hot and mild climates show higher percentage cost savings for the VRF systems than cold climates mainly due to the differences in electricity and gas use for heating sources.« less

Evaluation of energy savings potential of variable refrigerant flow (VRF) from variable air volume (VAV) in the U.S. climate locations

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

Kim, Dongsu; Cox, Sam J.; Cho, Heejin

Variable refrigerant flow (VRF) systems are known for their high energy performance and thus can improve energy efficiency both in residential and commercial buildings. The energy savings potential of this system has been demonstrated in several studies by comparing the system performance with conventional HVAC systems such as rooftop variable air volume systems (RTU-VAV) and central chiller and boiler systems. This paper evaluates the performance of VRF and RTU-VAV systems in a simulation environment using widely-accepted whole building energy modeling software, EnergyPlus. A medium office prototype building model, developed by the U.S. Department of Energy (DOE), is used to assessmore » the performance of VRF and RTU-VAV systems. Each system is placed in 16 different locations, representing all U.S. climate zones, to evaluate the performance variations. Both models are compliant with the minimum energy code requirements prescribed in ASHRAE standard 90.1-2010 — energy standard for buildings except low-rise residential buildings. Finally, a comparison study between the simulation results of VRF and RTU-VAV models is made to demonstrate energy savings potential of VRF systems. The simulation results show that the VRF systems would save around 15–42% and 18–33% for HVAC site and source energy uses compared to the RTU-VAV systems. In addition, calculated results for annual HVAC cost savings point out that hot and mild climates show higher percentage cost savings for the VRF systems than cold climates mainly due to the differences in electricity and gas use for heating sources.« less

Risk assessment of climate systems for national security.

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

Backus, George A.; Boslough, Mark Bruce Elrick; Brown, Theresa Jean

2012-10-01

Climate change, through drought, flooding, storms, heat waves, and melting Arctic ice, affects the production and flow of resource within and among geographical regions. The interactions among governments, populations, and sectors of the economy require integrated assessment based on risk, through uncertainty quantification (UQ). This project evaluated the capabilities with Sandia National Laboratories to perform such integrated analyses, as they relate to (inter)national security. The combining of the UQ results from climate models with hydrological and economic/infrastructure impact modeling appears to offer the best capability for national security risk assessments.

Uncertainty of climate change impact on groundwater reserves - Application to a chalk aquifer

NASA Astrophysics Data System (ADS)

Goderniaux, Pascal; Brouyère, Serge; Wildemeersch, Samuel; Therrien, René; Dassargues, Alain

2015-09-01

Recent studies have evaluated the impact of climate change on groundwater resources for different geographical and climatic contexts. However, most studies have either not estimated the uncertainty around projected impacts or have limited the analysis to the uncertainty related to climate models. In this study, the uncertainties around impact projections from several sources (climate models, natural variability of the weather, hydrological model calibration) are calculated and compared for the Geer catchment (465 km2) in Belgium. We use a surface-subsurface integrated model implemented using the finite element code HydroGeoSphere, coupled with climate change scenarios (2010-2085) and the UCODE_2005 inverse model, to assess the uncertainty related to the calibration of the hydrological model. This integrated model provides a more realistic representation of the water exchanges between surface and subsurface domains and constrains more the calibration with the use of both surface and subsurface observed data. Sensitivity and uncertainty analyses were performed on predictions. The linear uncertainty analysis is approximate for this nonlinear system, but it provides some measure of uncertainty for computationally demanding models. Results show that, for the Geer catchment, the most important uncertainty is related to calibration of the hydrological model. The total uncertainty associated with the prediction of groundwater levels remains large. By the end of the century, however, the uncertainty becomes smaller than the predicted decline in groundwater levels.

Tempest: Tools for Addressing the Needs of Next-Generation Climate Models

NASA Astrophysics Data System (ADS)

Ullrich, P. A.; Guerra, J. E.; Pinheiro, M. C.; Fong, J.

2015-12-01

Tempest is a comprehensive simulation-to-science infrastructure that tackles the needs of next-generation, high-resolution, data intensive climate modeling activities. This project incorporates three key components: TempestDynamics, a global modeling framework for experimental numerical methods and high-performance computing; TempestRemap, a toolset for arbitrary-order conservative and consistent remapping between unstructured grids; and TempestExtremes, a suite of detection and characterization tools for identifying weather extremes in large climate datasets. In this presentation, the latest advances with the implementation of this framework will be discussed, and a number of projects now utilizing these tools will be featured.

Parameter identification of the SWAT model on the BANI catchment (West Africa) under limited data condition

NASA Astrophysics Data System (ADS)

Chaibou Begou, Jamilatou; Jomaa, Seifeddine; Benabdallah, Sihem; Rode, Michael

2015-04-01

Due to the climate change, drier conditions have prevailed in West Africa, since the seventies, and the consequences are important on water resources. In order to identify and implement management strategies of adaptation to climate change in the sector of water, it is crucial to improve our physical understanding of water resources evolution in the region. To this end, hydrologic modelling is an appropriate tool for flow predictions under changing climate and land use conditions. In this study, the applicability and performance of the recent version of Soil and Water Assessment Tool (SWAT2012) model were tested on the Bani catchment in West Africa under limited data condition. Model parameters identification was also tested using one site and multisite calibration approaches. The Bani is located in the upper part of the Niger River and drains an area of about 101, 000 km2 at the outlet of Douna. The climate is tropical, humid to semi-arid from the South to the North with an average annual rainfall of 1050 mm (period 1981-2000). Global datasets were used for the model setup such as: USGS hydrosheds DEM, USGS LCI GlobCov2009 and the FAO Digital Soil Map of the World. Daily measured rainfall from nine rain gauges and maximum and minimum temperature from five weather stations covering the period 1981-1997 were used for model setup. Sensitivity analysis, calibration and validation are performed within SWATCUP using GLUE procedure at Douna station first (one site calibration), then at three additional internal stations, Bougouni, Pankourou and Kouoro1 (multi-site calibration). Model parameters were calibrated at daily time step for the period 1983-1992, then validated for the period 1993-1997. A period of two years (1981-1982) was used for model warming up. Results of one-site calibration showed that the model performance is evaluated by 0.76 and 0.79 for Nash-Sutcliffe (NS) and correlation coefficient (R2), respectively. While for the validation period the performance improved considerably with NS and R2 equal to 0.84 and 0.87, respectively. The degree of total uncertainties is quantified by a minimum P-factor of 0.61 and a maximum R-factor of 0.59. These statistics suggest that the model performance can be judged as very good, especially considering limited data condition and high climate, land use and soil variability in the studied basin. The most sensitive parameters (CN2, OVN and SLSUBBSN) are related to surface runoff reflecting the dominance of this process on the streamflow generation. In the next step, multisite calibration approach will be performed on the BANI basin to assess how much additional observations improve the model parameter identification.

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.

The Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP): Overview and Description of Models, Simulations and Climate Diagnostics

NASA Technical Reports Server (NTRS)

Lamarque, J.-F.; Shindell, D. T.; Naik, V.; Plummer, D.; Josse, B.; Righi, M.; Rumbold, S. T.; Schulz, M.; Skeie, R. B.; Strode, S.;

Evaluation of surface water budget and assessment the global water cycle for the IPCC AR4 A1B scenario simulations

NASA Astrophysics Data System (ADS)

Baek, H.; Park, E.; Kwon, W.

2009-12-01

Water balance calculations are becoming increasingly important for earth-system studies, because humans require water for their survival. Especially, the relationship between climate change and freshwater resources is of primary concern to human society and also has implications for all living species. The goal of this study is to assess the closure and annual variations of the water cycles based on the multi-model ensemble approach. In this study, the projection results of the previous works focusing on global and six sub-regions are updated using sixteen atmosphere-ocean general circulation model (AOGCM) simulations based on the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) A1B scenario. Before projecting future climate, model performances are evaluated on the simulation of the present-day climate. From the result, we construct and use mainly multi-model ensembles (MMEs), which is referred to as MME9, defined from nine selected AOGCMs of higher performance. Analyzed variables include annual and seasonal precipitation, evaporation, and runoff. The overall projection results from MME9 show that most regions will experience warmer and wetter climate at the end of 21st century. The evaporation shows a very similar trend to precipitation, but not in the runoff projection. The internal and inter-model variabilities are larger in the runoff than both precipitation and evaporation. Moreover, the runoff is notably reduced in Europe at the end of 21st century.

VALUE - A Framework to Validate Downscaling Approaches for Climate Change Studies

NASA Astrophysics Data System (ADS)

Maraun, Douglas; Widmann, Martin; Gutiérrez, José M.; Kotlarski, Sven; Chandler, Richard E.; Hertig, Elke; Wibig, Joanna; Huth, Radan; Wilke, Renate A. I.

2015-04-01

VALUE is an open European network to validate and compare downscaling methods for climate change research. VALUE aims to foster collaboration and knowledge exchange between climatologists, impact modellers, statisticians, and stakeholders to establish an interdisciplinary downscaling community. A key deliverable of VALUE is the development of a systematic validation framework to enable the assessment and comparison of both dynamical and statistical downscaling methods. Here, we present the key ingredients of this framework. VALUE's main approach to validation is user-focused: starting from a specific user problem, a validation tree guides the selection of relevant validation indices and performance measures. Several experiments have been designed to isolate specific points in the downscaling procedure where problems may occur: what is the isolated downscaling skill? How do statistical and dynamical methods compare? How do methods perform at different spatial scales? Do methods fail in representing regional climate change? How is the overall representation of regional climate, including errors inherited from global climate models? The framework will be the basis for a comprehensive community-open downscaling intercomparison study, but is intended also to provide general guidance for other validation studies.

VALUE: A framework to validate downscaling approaches for climate change studies

NASA Astrophysics Data System (ADS)

Maraun, Douglas; Widmann, Martin; Gutiérrez, José M.; Kotlarski, Sven; Chandler, Richard E.; Hertig, Elke; Wibig, Joanna; Huth, Radan; Wilcke, Renate A. I.

2015-01-01

VALUE is an open European network to validate and compare downscaling methods for climate change research. VALUE aims to foster collaboration and knowledge exchange between climatologists, impact modellers, statisticians, and stakeholders to establish an interdisciplinary downscaling community. A key deliverable of VALUE is the development of a systematic validation framework to enable the assessment and comparison of both dynamical and statistical downscaling methods. In this paper, we present the key ingredients of this framework. VALUE's main approach to validation is user- focused: starting from a specific user problem, a validation tree guides the selection of relevant validation indices and performance measures. Several experiments have been designed to isolate specific points in the downscaling procedure where problems may occur: what is the isolated downscaling skill? How do statistical and dynamical methods compare? How do methods perform at different spatial scales? Do methods fail in representing regional climate change? How is the overall representation of regional climate, including errors inherited from global climate models? The framework will be the basis for a comprehensive community-open downscaling intercomparison study, but is intended also to provide general guidance for other validation studies.

Statistical surrogate models for prediction of high-consequence climate change.

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

Constantine, Paul; Field, Richard V., Jr.; Boslough, Mark Bruce Elrick

2011-09-01

In safety engineering, performance metrics are defined using probabilistic risk assessments focused on the low-probability, high-consequence tail of the distribution of possible events, as opposed to best estimates based on central tendencies. We frame the climate change problem and its associated risks in a similar manner. To properly explore the tails of the distribution requires extensive sampling, which is not possible with existing coupled atmospheric models due to the high computational cost of each simulation. We therefore propose the use of specialized statistical surrogate models (SSMs) for the purpose of exploring the probability law of various climate variables of interest.more » A SSM is different than a deterministic surrogate model in that it represents each climate variable of interest as a space/time random field. The SSM can be calibrated to available spatial and temporal data from existing climate databases, e.g., the Program for Climate Model Diagnosis and Intercomparison (PCMDI), or to a collection of outputs from a General Circulation Model (GCM), e.g., the Community Earth System Model (CESM) and its predecessors. Because of its reduced size and complexity, the realization of a large number of independent model outputs from a SSM becomes computationally straightforward, so that quantifying the risk associated with low-probability, high-consequence climate events becomes feasible. A Bayesian framework is developed to provide quantitative measures of confidence, via Bayesian credible intervals, in the use of the proposed approach to assess these risks.« less

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.

Consequences of Team Job Demands: Role Ambiguity Climate, Affective Engagement, and Extra-Role Performance

PubMed Central

Mañas, Miguel A.; Díaz-Fúnez, Pedro; Pecino, Vicente; López-Liria, Remedios; Padilla, David; Aguilar-Parra, José M.

2018-01-01

In the absence of clearly established procedures in the workplace, employees will experience a negative affective state. This situation influences their well-being and their intention to behave in ways that benefit the organization beyond their job demands. This impact is more relevant on teamwork where members share the perception of ambiguity through emotional contagion (role ambiguity climate). In the framework of the job demands-resources model, the present study analyzes how high levels of role ambiguity climate can have such an effect to reduce employee affective engagement. Over time it has been associated with negative results for the organization due to a lack of extra-role performance. The sample included 706 employees from a multinational company, who were divided into 11 work teams. In line with the formulated hypotheses, the results confirm the negative influence of the role ambiguity climate on extra-role performance, and the mediated effect of affective engagement in the relationship between the role ambiguity climate and extra-role performance. These findings indicate that the role ambiguity climate is related to the adequate or inadequate functioning of employees within a work context. PMID:29375424

Consequences of Team Job Demands: Role Ambiguity Climate, Affective Engagement, and Extra-Role Performance.

PubMed

Mañas, Miguel A; Díaz-Fúnez, Pedro; Pecino, Vicente; López-Liria, Remedios; Padilla, David; Aguilar-Parra, José M

2017-01-01

In the absence of clearly established procedures in the workplace, employees will experience a negative affective state. This situation influences their well-being and their intention to behave in ways that benefit the organization beyond their job demands. This impact is more relevant on teamwork where members share the perception of ambiguity through emotional contagion (role ambiguity climate). In the framework of the job demands-resources model, the present study analyzes how high levels of role ambiguity climate can have such an effect to reduce employee affective engagement. Over time it has been associated with negative results for the organization due to a lack of extra-role performance. The sample included 706 employees from a multinational company, who were divided into 11 work teams. In line with the formulated hypotheses, the results confirm the negative influence of the role ambiguity climate on extra-role performance, and the mediated effect of affective engagement in the relationship between the role ambiguity climate and extra-role performance. These findings indicate that the role ambiguity climate is related to the adequate or inadequate functioning of employees within a work context.

The variability of runoff and soil erosion in the Brazilian Cerrado biome due to the potential land use and climate changes

NASA Astrophysics Data System (ADS)

Alexandre Ayach Anache, Jamil; Wendland, Edson; Malacarne Pinheiro Rosalem, Lívia; Srivastava, Anurag; Flanagan, Dennis

2017-04-01

Changes in land use and climate can influence runoff and soil loss, threatening soil and water conservation in the Cerrado biome in Brazil. Due to the lack of long term observed data for runoff and soil erosion in Brazil, the adoption of a process-based model was necessary, representing the variability of both variables in a continuous simulation approach. Thus, we aimed to calibrate WEPP (Water Erosion Prediction Project) model for different land uses (undisturbed Cerrado, fallow, pasture, and sugarcane) under subtropical conditions inside the Cerrado biome; predict runoff and soil erosion for these different land uses; and simulate runoff and soil erosion considering climate change scenarios. We performed the model calibration using a 4-year dataset of observed runoff and soil loss in four different land uses (undisturbed Cerrado, fallow, pasture, and sugarcane). The WEPP model components (climate, topography, soil, and management) were calibrated according to field data. However, soil and management were optimized according to each land use using a parameter estimation tool. The observations were conducted between 2012 and 2015 in experimental plots (5 m width, 20 m length, 9% slope gradient, 3 replicates per treatment). The simulations were done using the calibrated WEPP model components, but changing the 4-year observed climate file by a 100-year dataset created with CLIGEN (weather generator) based on regional climate statistics. Afterwards, using MarkSim DSSAT Weather File Generator, runoff and soil loss were simulated using future climate scenarios for 2030, 2060, and 2090. To analyze the data, we used non-parametric statistics as data do not follow normal distribution. The results show that WEPP model had an acceptable performance for the considered conditions. In addition, both land use and climate can influence on runoff and soil loss rates. Potential climate changes which consider the increase of rainfall intensities and depths in the studied region may increase the variability and rates for runoff and soil erosion. However, the climate did not change the differences and similarities between the rates of the four analyzed land uses. The runoff behavior is distinct for all land uses, but for soil loss we found similarities between pasture and undisturbed Cerrado, suggesting that soil sustainability could be reached when the management follows conservation principles.

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

Bond-Lamberty, Benjamin; Bunn, Andrew G.; Thomson, Allison M.

High-latitude northern ecosystems are experiencing rapid climate changes, and represent a large potential climate feedback because of their high soil carbon densities and shifting disturbance regimes. A significant carbon flow from these ecosystems is soil respiration (RS, the flow of carbon dioxide, generated by plant roots and soil fauna, from the soil surface to atmosphere), and any change in the high-latitude carbon cycle might thus be reflected in RS observed in the field. This study used two variants of a machine-learning algorithm and least squares regression to examine how remotely-sensed canopy greenness (NDVI), climate, and other variables are coupled tomore » annual RS based on 105 observations from 64 circumpolar sites in a global database. The addition of NDVI roughly doubled model performance, with the best-performing models explaining ~62% of observed RS variability« less

Integrated numerical modeling of a landslide early warning system in a context of adaptation to future climatic pressures

NASA Astrophysics Data System (ADS)

Khabarov, Nikolay; Huggel, Christian; Obersteiner, Michael; Ramírez, Juan Manuel

2010-05-01

Mountain regions are typically characterized by rugged terrain which is susceptible to different types of landslides during high-intensity precipitation. Landslides account for billions of dollars of damage and many casualties, and are expected to increase in frequency in the future due to a projected increase of precipitation intensity. Early warning systems (EWS) are thought to be a primary tool for related disaster risk reduction and climate change adaptation to extreme climatic events and hydro-meteorological hazards, including landslides. An EWS for hazards such as landslides consist of different components, including environmental monitoring instruments (e.g. rainfall or flow sensors), physical or empirical process models to support decision-making (warnings, evacuation), data and voice communication, organization and logistics-related procedures, and population response. Considering this broad range, EWS are highly complex systems, and it is therefore difficult to understand the effect of the different components and changing conditions on the overall performance, ultimately being expressed as human lives saved or structural damage reduced. In this contribution we present a further development of our approach to assess a landslide EWS in an integral way, both at the system and component level. We utilize a numerical model using 6 hour rainfall data as basic input. A threshold function based on a rainfall-intensity/duration relation was applied as a decision criterion for evacuation. Damage to infrastructure and human lives was defined as a linear function of landslide magnitude, with the magnitude modelled using a power function of landslide frequency. Correct evacuation was assessed with a ‘true' reference rainfall dataset versus a dataset of artificially reduced quality imitating the observation system component. Performance of the EWS using these rainfall datasets was expressed in monetary terms (i.e. damage related to false and correct evacuation). We applied this model to a landslide EWS in Colombia that is currently being implemented within a disaster prevention project. We evaluated the EWS against rainfall data with artificially introduced error and computed with multiple model runs the probabilistic damage functions depending on rainfall error. Then we modified the original precipitation pattern to reflect possible climatic changes e.g. change in annual precipitation as well as change in precipitation intensity with annual values remaining constant. We let the EWS model adapt for changed conditions to function optimally. Our results show that for the same errors in rainfall measurements the system's performance degrades with expected changing climatic conditions. The obtained results suggest that EWS cannot internally adapt to climate change and require exogenous adaptive measures to avoid increase in overall damage. The model represents a first attempt to integrally simulate and evaluate EWS under future possible climatic pressures. Future work will concentrate on refining model components and spatially explicit climate scenarios.

Handling Uncertainty in Palaeo-Climate Models and Data

NASA Astrophysics Data System (ADS)

Voss, J.; Haywood, A. M.; Dolan, A. M.; Domingo, D.

2017-12-01

The study of palaeoclimates can provide data on the behaviour of the Earth system with boundary conditions different from the ones we observe in the present. One of the main challenges in this approach is that data on past climates comes with large uncertainties, since quantities of interest cannot be observed directly, but must be derived from proxies instead. We consider proxy-derived data from the Pliocene (around 3 millions years ago; the last interval in Earth history when CO2 was at modern or near future levels) and contrast this data to the output of complex climate models. In order to perform a meaningful data-model comparison, uncertainties must be taken into account. In this context, we discuss two examples of complex data-model comparison problems. Both examples have in common that they involve fitting a statistical model to describe how the output of the climate simulations depends on various model parameters, including atmospheric CO2 concentration and orbital parameters (obliquity, excentricity, and precession). This introduces additional uncertainties, but allows to explore a much larger range of model parameters than would be feasible by only relying on simulation runs. The first example shows how Gaussian process emulators can be used to perform data-model comparison when simulation runs only differ in the choice of orbital parameters, but temperature data is given in the (somewhat inconvenient) form of "warm peak averages". The second example shows how a simpler approach, based on linear regression, can be used to analyse a more complex problem where we use a larger and more varied ensemble of climate simulations with the aim to estimate Earth System Sensitivity.

Changes of Mediterranean cyclones in the future climate employing high resolution climate simulations

NASA Astrophysics Data System (ADS)

Hatzaki, M.; Flocas, H. A.; Kouroutzoglou, J.; Keay, K.; Simmonds, I.; Giannakopoulos, C. A.; Brikolas, V.

2011-12-01

A number of studies suggest that cyclone activity over both hemispheres has changed over the second half of the 20th century. The assessment of the future changes of the cyclonic activity as imposed by global warming conditions is very important since these cyclones can be associated with extreme precipitation conditions, severe storms and floods. This is more important for the Mediterranean that has been found to be more vulnerable to climate change. The main objective of the current study is to better understand and assess future changes in the main characteristics of Mediterranean cyclones, including temporal and spatial variations of frequency of cyclonic tracks, and dynamic and kinematic parameters, such as intensity, size, propagation velocity, as well as trend analysis. For this purpose, the MPI-HH regional coupled climate model of the Max Planck Institute for Meteorology is employed consisting of the REgional atmosphere MOdel (REMO), the Max-Planck-Institute for Meteorology ocean model (MPI-OM) and the Hydrological Discharge Model (HD Model). A 25 km resolution domain is established on a rotated latitude-longitude coordinate system, while the physical parameterizations are taken from the global climate model ECHAM-4. These model data became available through the EU Project CIRCE which aims to perform, for the first time, climate change projections with a realistic representation of the Mediterranean Sea. The model results for the present climate are evaluated against ERA-40 Reanalysis (available through ECMWF), for the period 1962-2001. The identification and tracking of cyclones is performed with the aid of the Melbourne University algorithm (MS algorithm), according to the Lagrangian perspective. MS algorithm characterizes a cyclone only if a vorticity maximum could be connected with a local pressure minimum. According to the results, a decrease of the storm number and a tendency towards deeper cyclones is expected in the future, in general agreement with the results of previous studies. However, new findings reveal with respect to the dynamic/kinematic characteristics of the cyclonic tracks. The model experiments verify that considerable inter-monthly variations of track density occur in the Mediterranean region. The study of the kinematic and dynamic parameters of the cyclonic tracks according to their origin domain show that the vast majority originate within the examined area itself. ACKNOWLEDGMENTS: M. Hatzaki would like to thank the Greek State Scholarships Foundation for financial support through the program of postdoctoral research. The support of EU-FP6 project CIRCE Integrated Project-Climate Change and Impact Research: the Mediterranean Environment (http://www.circeproject.eu) for climate model data provision is also greatly acknowledged.

A climate model projection weighting scheme accounting for performance and interdependence

NASA Astrophysics Data System (ADS)

Knutti, Reto; Sedláček, Jan; Sanderson, Benjamin M.; Lorenz, Ruth; Fischer, Erich M.; Eyring, Veronika

2017-02-01

Uncertainties of climate projections are routinely assessed by considering simulations from different models. Observations are used to evaluate models, yet there is a debate about whether and how to explicitly weight model projections by agreement with observations. Here we present a straightforward weighting scheme that accounts both for the large differences in model performance and for model interdependencies, and we test reliability in a perfect model setup. We provide weighted multimodel projections of Arctic sea ice and temperature as a case study to demonstrate that, for some questions at least, it is meaningless to treat all models equally. The constrained ensemble shows reduced spread and a more rapid sea ice decline than the unweighted ensemble. We argue that the growing number of models with different characteristics and considerable interdependence finally justifies abandoning strict model democracy, and we provide guidance on when and how this can be achieved robustly.

Role of genetics in adapting forests under climate change: lessons learned from common garden experiments in central Europe

NASA Astrophysics Data System (ADS)

Chakraborty, Debojyoti; Schueler, Silvio

2017-04-01

Adaptive management aiming at reducing vulnerability and enhancing the resilience of forested ecosystems is a key to preserving the potential of forests to provide multiple ecosystem services under climate change. Planting alternative or non native tree species adapted to future conditions and also utilizing the genetic variation within tree species has also been suggested as an important adaptive management strategy under climate change. Therefore, knowledge on suitable provenances/populations is a key issue. Provenance trial experiments, where several populations of a species are planted in a particular climate or throughout an appropriate climatic gradient offers a great opportunity to understand adaptive genetic variation within a tree species. These trials were primarily established, for identifying populations with desired growth and fitness characteristics. Due to the increasing interest in climate change, such trials were revisited to understand the relation between growth performance and climate and to recommend suitable populations for future conditions. Here we present the lessons learned from provenance trials of Norway spruce and Douglas -fir in central Europe. With data from provenance trials planted across a wide range of environmental conditions in central Europe we developed multivariate models, Universal Response Functions (URFs). The URFs predict growth performance as a function of climate of planting locations (i.e. environmental factors) and provenance/ population origin (i.e. genetic factors). The flexibility of the URFs as a decision making tool is remarkable. The model can be used as to identify suitable planting material for a give site, and vice versa and also as a species distribution model (SDM) with integrated genetic variation. Under current and climate change scenarios, the URFs were applied to predict populations with higher growth performance in central Europe and also as species distribution models for Douglas-fir (Pseudotsuga menziesii [Mirbel] Franco) and Norway spruce (Picea abies (L.) Karst). For both Douglas-fir and Norway spruce wide variation in growth performance were detected. Populations of Douglas-fir identified by the URFs to be optimum for central Europe current climate and climate change scenarios originate from western Cascades and coastal areas of British Columbia, Washington and Oregon. The current seed stands of Douglas-fir in North America, providing planting materials for Central Europe under the legal framework of the Organization for Economic Cooperation and Development (OECD) were found to be suitable for under future conditions. In case of Norway spruce provenances originating from warm and drier regions of south east Europe were found to be suitable for central Europe under future conditions. Even though calibrated with data from Central Europe, when applied as SDMs, the URFs predicted the observed occurrence of Douglas-fir in its native range in North America with reasonable accuracy compared to contemporary SDMs developed in North America. For both Douglas-fir and Norway spruce significant variation in habitat suitability was found depending on the planted population or seed source indicating the role of intraspecific variation in buffering effects of climate change.

Characterization of the Sahelian-Sudan rainfall based on observations and regional climate models

NASA Astrophysics Data System (ADS)

Salih, Abubakr A. M.; Elagib, Nadir Ahmed; Tjernström, Michael; Zhang, Qiong

2018-04-01

The African Sahel region is known to be highly vulnerable to climate variability and change. We analyze rainfall in the Sahelian Sudan in terms of distribution of rain-days and amounts, and examine whether regional climate models can capture these rainfall features. Three regional models namely, Regional Model (REMO), Rossby Center Atmospheric Model (RCA) and Regional Climate Model (RegCM4), are evaluated against gridded observations (Climate Research Unit, Tropical Rainfall Measuring Mission, and ERA-interim reanalysis) and rain-gauge data from six arid and semi-arid weather stations across Sahelian Sudan over the period 1989 to 2008. Most of the observed rain-days are characterized by weak (0.1-1.0 mm/day) to moderate (> 1.0-10.0 mm/day) rainfall, with average frequencies of 18.5% and 48.0% of the total annual rain-days, respectively. Although very strong rainfall events (> 30.0 mm/day) occur rarely, they account for a large fraction of the total annual rainfall (28-42% across the stations). The performance of the models varies both spatially and temporally. RegCM4 most closely reproduces the observed annual rainfall cycle, especially for the more arid locations, but all of the three models fail to capture the strong rainfall events and hence underestimate its contribution to the total annual number of rain-days and rainfall amount. However, excessive moderate rainfall compensates this underestimation in the models in an annual average sense. The present study uncovers some of the models' limitations in skillfully reproducing the observed climate over dry regions, will aid model users in recognizing the uncertainties in the model output and will help climate and hydrological modeling communities in improving models.

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

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

NASA Astrophysics Data System (ADS)

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

2014-12-01

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

A multiscale climate emulator for long-term morphodynamics (MUSCLE-morpho)

NASA Astrophysics Data System (ADS)

Antolínez, José Antonio A.; Méndez, Fernando J.; Camus, Paula; Vitousek, Sean; González, E. Mauricio; Ruggiero, Peter; Barnard, Patrick

2016-01-01

Interest in understanding long-term coastal morphodynamics has recently increased as climate change impacts become perceptible and accelerated. Multiscale, behavior-oriented and process-based models, or hybrids of the two, are typically applied with deterministic approaches which require considerable computational effort. In order to reduce the computational cost of modeling large spatial and temporal scales, input reduction and morphological acceleration techniques have been developed. Here we introduce a general framework for reducing dimensionality of wave-driver inputs to morphodynamic models. The proposed framework seeks to account for dependencies with global atmospheric circulation fields and deals simultaneously with seasonality, interannual variability, long-term trends, and autocorrelation of wave height, wave period, and wave direction. The model is also able to reproduce future wave climate time series accounting for possible changes in the global climate system. An application of long-term shoreline evolution is presented by comparing the performance of the real and the simulated wave climate using a one-line model. This article was corrected on 2 FEB 2016. See the end of the full text for details.

Controlled comparison of species- and community-level models across novel climates and communities

PubMed Central

Maguire, Kaitlin C.; Blois, Jessica L.; Fitzpatrick, Matthew C.; Williams, John W.; Ferrier, Simon; Lorenz, David J.

2016-01-01

Species distribution models (SDMs) assume species exist in isolation and do not influence one another's distributions, thus potentially limiting their ability to predict biodiversity patterns. Community-level models (CLMs) capitalize on species co-occurrences to fit shared environmental responses of species and communities, and therefore may result in more robust and transferable models. Here, we conduct a controlled comparison of five paired SDMs and CLMs across changing climates, using palaeoclimatic simulations and fossil-pollen records of eastern North America for the past 21 000 years. Both SDMs and CLMs performed poorly when projected to time periods that are temporally distant and climatically dissimilar from those in which they were fit; however, CLMs generally outperformed SDMs in these instances, especially when models were fit with sparse calibration datasets. Additionally, CLMs did not over-fit training data, unlike SDMs. The expected emergence of novel climates presents a major forecasting challenge for all models, but CLMs may better rise to this challenge by borrowing information from co-occurring taxa. PMID:26962143

Assessment of bias correction under transient climate change

NASA Astrophysics Data System (ADS)

Van Schaeybroeck, Bert; Vannitsem, Stéphane

2015-04-01

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

Cluster analysis of Southeastern U.S. climate stations

NASA Astrophysics Data System (ADS)

Stooksbury, D. E.; Michaels, P. J.

1991-09-01

A two-step cluster analysis of 449 Southeastern climate stations is used to objectively determine general climate clusters (groups of climate stations) for eight southeastern states. The purpose is objectively to define regions of climatic homogeneity that should perform more robustly in subsequent climatic impact models. This type of analysis has been successfully used in many related climate research problems including the determination of corn/climate districts in Iowa (Ortiz-Valdez, 1985) and the classification of synoptic climate types (Davis, 1988). These general climate clusters may be more appropriate for climate research than the standard climate divisions (CD) groupings of climate stations, which are modifications of the agro-economic United States Department of Agriculture crop reporting districts. Unlike the CD's, these objectively determined climate clusters are not restricted by state borders and thus have reduced multicollinearity which makes them more appropriate for the study of the impact of climate and climatic change.

Relevance of Regional Hydro-Climatic Projection Data for Hydrodynamics and Water Quality Modelling of the Baltic Sea

NASA Astrophysics Data System (ADS)

Goldenberg, R.; Vigouroux, G.; Chen, Y.; Bring, A.; Kalantari, Z.; Prieto, C.; Destouni, G.

2017-12-01

The Baltic Sea, located in Northern Europe, is one of the world's largest body of brackish water, enclosed and surrounded by nine different countries. The magnitude of climate change may be particularly large in northern regions, and identifying its impacts on vulnerable inland waters and their runoff and nutrient loading to the Baltic Sea is an important and complex task. Exploration of such hydro-climatic impacts is needed to understand potential future changes in physical, ecological and water quality conditions in the regional coastal and marine waters. In this study, we investigate hydro-climatic changes and impacts on the Baltic Sea by synthesizing multi-model climate projection data from the CORDEX regional downscaling initiative (EURO- and Arctic- CORDEX domains, http://www.cordex.org/). We identify key hydro-climatic variable outputs of these models and assess model performance with regard to their projected temporal and spatial change behavior and impacts on different scales and coastal-marine parts, up to the whole Baltic Sea. Model spreading, robustness and impact implications for the Baltic Sea system are investigated for and through further use in simulations of coastal-marine hydrodynamics and water quality based on these key output variables and their change projections. Climate model robustness in this context is assessed by inter-model spreading analysis and observation data comparisons, while projected change implications are assessed by forcing of linked hydrodynamic and water quality modeling of the Baltic Sea based on relevant hydro-climatic outputs for inland water runoff and waterborne nutrient loading to the Baltic sea, as well as for conditions in the sea itself. This focused synthesis and analysis of hydro-climatically relevant output data of regional climate models facilitates assessment of reliability and uncertainty in projections of driver-impact changes of key importance for Baltic Sea physical, water quality and ecological conditions and their future evolution.

CMIP5 Scientific Gaps and Recommendations for CMIP6

DOE PAGES

Stouffer, R. J.; Eyring, V.; Meehl, G. A.; ...

2017-01-23

The Coupled Model Intercomparison Project (CMIP) is an ongoing coordinated international activity of numerical experimentation of unprecedented scope and impact on climate science. Its most recent phase, the fifth phase (CMIP5), has created nearly 2 PB of output from dozens of experiments performed by dozens of comprehensive climate models available to the climate science research community. In so doing, it has greatly advanced climate science. While CMIP5 has given answers to important science questions, with the help of a community survey we identify and motivate three broad topics here that guided the scientific framework of the next phase of CMIP,more » that is, CMIP6: (1) How does the Earth system respond to changes in forcing? (2) What are the origins and consequences of systematic model biases? (3) How can we assess future climate changes given internal climate variability, predictability, and uncertainties in scenarios? CMIP has demonstrated the power of idealized experiments to better understand how the climate system works. We expect that these idealized approaches will continue to contribute to CMIP6. The quantification of radiative forcings and responses was poor, and thus it requires new methods and experiments to address this gap. There are a number of systematic model biases that appear in all phases of CMIP that remain a major climate modeling challenge. In conclusion, these biases need increased attention to better understand their origins and consequences through targeted experiments. Improving understanding of the mechanisms’ underlying internal climate variability for more skillful decadal climate predictions and long-term projections remains another challenge for CMIP6.« less

Water resources sensitivity to the isolated effects of land use, water demand and climate change under 2 degree global warming

NASA Astrophysics Data System (ADS)

Bisselink, Berny; Bernhard, Jeroen; de Roo, Ad

2017-04-01

One of the key impacts of global change are the future water resources. These water resources are influenced by changes in land use (LU), water demand (WD) and climate change. Recent developments in scenario modelling opened new opportunities for an integrated assessment. However, for identifying water resource management strategies it is helpful to focus on the isolated effects of possible changes in LU, WD and climate that may occur in the near future. In this work, we quantify the isolated contribution of LU, WD and climate to the integrated total water resources assuming a linear model behavior. An ensemble of five EURO-CORDEX RCP8.5 climate projections for the 31-year periods centered on the year of exceeding the global-mean temperature of 2 degree is used to drive the fully distributed hydrological model LISFLOOD for multiple river catchments in Europe. The JRC's Land Use Modelling Platform LUISA was used to obtain a detailed pan-European reference land use scenario until 2050. Water demand is estimated based on socio-economic (GDP, population estimates etc.), land use and climate projections as well. For each climate projection, four model runs have been performed including an integrated (LU, WD and climate) simulation and other three simulations to isolate the effect of LU, WD and climate. Changes relative to the baseline in terms of water resources indicators of the ensemble means of the 2 degree warming period and their associated uncertainties will reveal the integrated and isolated effect of LU, WD and climate change on water resources.

CMIP5 Scientific Gaps and Recommendations for CMIP6

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

Stouffer, R. J.; Eyring, V.; Meehl, G. A.

The Coupled Model Intercomparison Project (CMIP) is an ongoing coordinated international activity of numerical experimentation of unprecedented scope and impact on climate science. Its most recent phase, the fifth phase (CMIP5), has created nearly 2 PB of output from dozens of experiments performed by dozens of comprehensive climate models available to the climate science research community. In so doing, it has greatly advanced climate science. While CMIP5 has given answers to important science questions, with the help of a community survey we identify and motivate three broad topics here that guided the scientific framework of the next phase of CMIP,more » that is, CMIP6: (1) How does the Earth system respond to changes in forcing? (2) What are the origins and consequences of systematic model biases? (3) How can we assess future climate changes given internal climate variability, predictability, and uncertainties in scenarios? CMIP has demonstrated the power of idealized experiments to better understand how the climate system works. We expect that these idealized approaches will continue to contribute to CMIP6. The quantification of radiative forcings and responses was poor, and thus it requires new methods and experiments to address this gap. There are a number of systematic model biases that appear in all phases of CMIP that remain a major climate modeling challenge. In conclusion, these biases need increased attention to better understand their origins and consequences through targeted experiments. Improving understanding of the mechanisms’ underlying internal climate variability for more skillful decadal climate predictions and long-term projections remains another challenge for CMIP6.« less

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

PubMed

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

2017-12-01

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

Low robustness of increasing reservoir capacity for adaptation to climate change: A case study for an agricultural river basin

NASA Astrophysics Data System (ADS)

Kim, Daeha; Eum, Hyung-Il

2017-04-01

With growing concerns of the uncertain climate change, investments in water infrastructures are considered as adaptation policies for water managers and stakeholders despite their negative impacts on the environment. Particularly in regions with limited water availability or conflicting demands, building reservoirs and/or augmenting their storage capacity were already adopted for alleviating influences of the climate change. This study provides a probabilistic assessment of climate change impacts on water scarcity in a river system regulated by an agricultural reservoir in South Korea, which already increased its storage capacity for water supply. For the assessment, we developed the climate response functions (CRFs) defined as relationships between bi-decadal system performance indicators (reservoir reliability and vulnerability) and corresponding climatic conditions, using hydrological models with 10,000-year long stochastic generation of daily precipitation and temperatures. The climate change impacts were assessed by plotting 52 downscaled climate projections of general circulation models (GCMs) on the CRFs. Results indicated that augmented reservoir capacity makes the reservoir system more sensitive to changes in long-term averages of precipitation and temperatures despite improved system performances. Increasing reservoir capacity is unlikely to be "no regret" adaptation policy for the river system. On the other hand, converting the planting strategy from transplanting to direct sowing (i.e., a demand control) could be a more robust to bi-decadal climatic changes based on CRFs and thus could be good to be a no-regret policy.

Implementation of BMP Strategies for Adaptation to Climate Change and Land Use Change in a Pasture-Dominated Watershed

PubMed Central

Chiang, Li-Chi; Chaubey, Indrajeet; Hong, Nien-Ming; Lin, Yu-Pin; Huang, Tao

2012-01-01

Implementing a suite of best management practices (BMPs) can reduce non-point source (NPS) pollutants from various land use activities. Watershed models are generally used to evaluate the effectiveness of BMP performance in improving water quality as the basis for watershed management recommendations. This study evaluates 171 management practice combinations that incorporate nutrient management, vegetated filter strips (VFS) and grazing management for their performances in improving water quality in a pasture-dominated watershed with dynamic land use changes during 1992–2007 by using the Soil and Water Assessment Tool (SWAT). These selected BMPs were further examined with future climate conditions (2010–2069) downscaled from three general circulation models (GCMs) for understanding how climate change may impact BMP performance. Simulation results indicate that total nitrogen (TN) and total phosphorus (TP) losses increase with increasing litter application rates. Alum-treated litter applications resulted in greater TN losses, and fewer TP losses than the losses from untreated poultry litter applications. For the same litter application rates, sediment and TP losses are greater for summer applications than fall and spring applications, while TN losses are greater for fall applications. Overgrazing management resulted in the greatest sediment and phosphorus losses, and VFS is the most influential management practice in reducing pollutant losses. Simulations also indicate that climate change impacts TSS losses the most, resulting in a larger magnitude of TSS losses. However, the performance of selected BMPs in reducing TN and TP losses was more stable in future climate change conditions than in the BMP performance in the historical climate condition. We recommend that selection of BMPs to reduce TSS losses should be a priority concern when multiple uses of BMPs that benefit nutrient reductions are considered in a watershed. Therefore, the BMP combination of spring litter application, optimum grazing management and filter strip with a VFS ratio of 42 could be a promising alternative for use in mitigating future climate change. PMID:23202767

Collaborative Project: Understanding Climate Model Biases in Tropical Atlantic and Their Impact on Simulations of Extreme Climate Events

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

Chang, Ping

Recent studies have revealed that among all the tropical oceans, the tropical Atlantic has experienced the most pronounced warming trend over the 20th century. Many extreme climate events affecting the U.S., such as hurricanes, severe precipitation and drought events, are influenced by conditions in the Gulf of Mexico and the Atlantic Ocean. It is therefore imperative to have accurate simulations of the climatic mean and variability in the Atlantic region to be able to make credible projections of future climate change affecting the U.S. and other countries adjoining the Atlantic Ocean. Unfortunately, almost all global climate models exhibit large biasesmore » in their simulations of tropical Atlantic climate. The atmospheric convection simulation errors in the Amazon region and the associated errors in the trade wind simulations are hypothesized to be a leading cause of the tropical Atlantic biases in climate models. As global climate models have resolutions that are too coarse to resolve some of the atmospheric and oceanic processes responsible for the model biases, we propose to use a high-resolution coupled regional climate model (CRCM) framework to address the tropical bias issue. We propose to combine the expertise in tropical coupled atmosphere-ocean modeling at Texas A&M University (TAMU) and the coupled land-atmosphere modeling expertise at Pacific Northwest National Laboratory (PNNL) to develop a comprehensive CRCM for the Atlantic sector within a general and flexible modeling framework. The atmospheric component of the CRCM will be the NCAR WRF model and the oceanic component will be the Rutgers/UCLA ROMS. For the land component, we will use CLM modified at PNNL to include more detailed representations of vegetation and soil hydrology processes. The combined TAMU-PNNL CRCM model will be used to simulate the Atlantic climate, and the associated land-atmosphere-ocean interactions at a horizontal resolution of 9 km or finer. A particular focus of the model development effort will be to optimize the performance of WRF and ROMS over several thousand of cores by focusing on both the parallel communication libraries and the I/O interfaces, in order to achieve the sustained throughput needed to perform simulations on such fine resolution grids. The CRCM model will be developed within the framework of the Coupler (CPL7) software that is part of the NCAR Community Earth System Model (CESM). Through efforts at PNNL and within the community, WRF and CLM have already been coupled via CPL7. Using the flux coupler approach for the whole CRCM model will allow us to flexibly couple WRF, ROMS, and CLM with each model running on its own grid at different resolutions. In addition, this framework will allow us to easily port parameterizations between CESM and the CRCM, and potentially allow partial coupling between the CESM and the CRCM. TAMU and PNNL will contribute cooperatively to this research endeavor. The TAMU team led by Chang and Saravanan has considerable experience in studying atmosphere-ocean interactions within tropical Atlantic sector and will focus on modeling issues that relate to coupling WRF and ROMS. The PNNL team led by Leung has extensive expertise in atmosphere-land interaction and will be responsible for improving the land surface parameterization. Both teams will jointly work on integrating WRF-ROMS and WRF-CLM to couple WRF, ROMS, and CLM through CPL7. Montuoro of the TAMU Supercomputing Center will be responsible for improving the MPI and Parallel IO interfaces of the CRCM. Both teams will contribute to the design and execution of the proposed numerical experiments and jointly perform analysis of the numerical experiments.« less

NASA Downscaling Project: Final Report

NASA Technical Reports Server (NTRS)

Ferraro, Robert; Waliser, Duane; Peters-Lidard, Christa

2017-01-01

A team of researchers from NASA Ames Research Center, Goddard Space Flight Center, the Jet Propulsion Laboratory, and Marshall Space Flight Center, along with university partners at UCLA, conducted an investigation to explore whether downscaling coarse resolution global climate model (GCM) predictions might provide valid insights into the regional impacts sought by decision makers. Since the computational cost of running global models at high spatial resolution for any useful climate scale period is prohibitive, the hope for downscaling is that a coarse resolution GCM provides sufficiently accurate synoptic scale information for a regional climate model (RCM) to accurately develop fine scale features that represent the regional impacts of a changing climate. As a proxy for a prognostic climate forecast model, and so that ground truth in the form of satellite and in-situ observations could be used for evaluation, the MERRA and MERRA - 2 reanalyses were used to drive the NU - WRF regional climate model and a GEOS - 5 replay. This was performed at various resolutions that were at factors of 2 to 10 higher than the reanalysis forcing. A number of experiments were conducted that varied resolution, model parameterizations, and intermediate scale nudging, for simulations over the continental US during the period from 2000 - 2010. The results of these experiments were compared to observational datasets to evaluate the output.

NASA Downscaling Project

NASA Technical Reports Server (NTRS)

Ferraro, Robert; Waliser, Duane; Peters-Lidard, Christa

2017-01-01

A team of researchers from NASA Ames Research Center, Goddard Space Flight Center, the Jet Propulsion Laboratory, and Marshall Space Flight Center, along with university partners at UCLA, conducted an investigation to explore whether downscaling coarse resolution global climate model (GCM) predictions might provide valid insights into the regional impacts sought by decision makers. Since the computational cost of running global models at high spatial resolution for any useful climate scale period is prohibitive, the hope for downscaling is that a coarse resolution GCM provides sufficiently accurate synoptic scale information for a regional climate model (RCM) to accurately develop fine scale features that represent the regional impacts of a changing climate. As a proxy for a prognostic climate forecast model, and so that ground truth in the form of satellite and in-situ observations could be used for evaluation, the MERRA and MERRA-2 reanalyses were used to drive the NU-WRF regional climate model and a GEOS-5 replay. This was performed at various resolutions that were at factors of 2 to 10 higher than the reanalysis forcing. A number of experiments were conducted that varied resolution, model parameterizations, and intermediate scale nudging, for simulations over the continental US during the period from 2000-2010. The results of these experiments were compared to observational datasets to evaluate the output.

Climate conditions and drought assessment with the Palmer Drought Severity Index in Iran: evaluation of CORDEX South Asia climate projections (2070-2099)

NASA Astrophysics Data System (ADS)

Senatore, Alfonso; Hejabi, Somayeh; Mendicino, Giuseppe; Bazrafshan, Javad; Irannejad, Parviz

2018-03-01

Climate change projections were evaluated over both the whole Iran and six zones having different precipitation regimes considering the CORDEX South Asia dataset, for assessing space-time distribution of drought occurrences in the future period 2070-2099 under RCP4.5 scenario. Initially, the performances of eight available CORDEX South Asia Regional Climate Models (RCMs) were assessed for the baseline period 1970-2005 through the GPCC v.7 precipitation dataset and the CFSR temperature dataset, which were previously selected as the most reliable within a set of five global datasets compared to 41 available synoptic stations. Though the CCLM RCM driven by the MPI-ESM-LR General Circulation Model is in general the most suitable for temperature and, together with the REMO 2009 RCM also driven by MPI-ESM-LR, for precipitation, their performances do not overwhelm other models for every season and zone in which Iranian territory was divided according to a principal component analysis approach. Hence, a weighting approach was tested and adopted to take into account useful information from every RCM in each of the six zones. The models resulting more reliable compared to current climate show a strong precipitation decrease. Weighted average predicts an overall yearly precipitation decrease of about 20%. Temperature projections provide a mean annual increase of 2.4 °C. Future drought scenarios were depicted by means of the self-calibrating version of the Palmer drought severity index (SC-PDSI) model. Weighted average predicts a sharp drying that can be configured as a real shift in mean climate conditions, drastically affecting water resources of the country.

Attribution of declining Western U.S. Snowpack to human effects

USGS Publications Warehouse

Pierce, D.W.; Barnett, T.P.; Hidalgo, H.G.; Das, T.; Bonfils, Celine; Santer, B.D.; Bala, G.; Dettinger, M.D.; Cayan, D.R.; Mirin, A.; Wood, A.W.; Nozawa, T.

2008-01-01

Observations show snowpack has declined across much of the western United States over the period 1950-99. This reduction has important social and economic implications, as water retained in the snowpack from winter storms forms an important part of the hydrological cycle and water supply in the region. A formal model-based detection and attribution (D-A) study of these reductions is performed. The detection variable is the ratio of 1 April snow water equivalent (SWE) to water-year-to-date precipitation (P), chosen to reduce the effect of P variability on the results. Estimates of natural internal climate variability are obtained from 1600 years of two control simulations performed with fully coupled ocean-atmosphere climate models. Estimates of the SWE/P response to anthropogenic greenhouse gases, ozone, and some aerosols are taken from multiple-member ensembles of perturbation experiments run with two models. The D-A shows the observations and anthropogenically forced models have greater SWE/P reductions than can be explained by natural internal climate variability alone. Model-estimated effects of changes in solar and volcanic forcing likewise do not explain the SWE/P reductions. The mean model estimate is that about half of the SWE/P reductions observed in the west from 1950 to 1999 are the result of climate changes forced by anthropogenic greenhouse gases, ozone, and aerosols. ?? 2008 American Meteorological Society.

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

PubMed

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

2015-01-01

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

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

PubMed Central

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

2015-01-01

Global climate is changing due to increasing anthropogenic emissions of greenhouse gases. Forest managers need growth and yield models that can be used to predict future forest dynamics during the transition period of present-day forests under a changing climatic regime. In this study, we developed a forest growth and yield model that can be used to predict individual-tree growth under current and projected future climatic conditions. The model was constructed by integrating historical tree growth records with predictions from an ecological process-based model using neural networks. The new model predicts basal area (BA) and volume growth for individual trees in pure or mixed species forests. For model development, tree-growth data under current climatic conditions were obtained using over 3000 permanent sample plots from the Province of Nova Scotia, Canada. Data to reflect tree growth under a changing climatic regime were projected with JABOWA-3 (an ecological process-based model). Model validation with designated data produced model efficiencies of 0.82 and 0.89 in predicting individual-tree BA and volume growth. Model efficiency is a relative index of model performance, where 1 indicates an ideal fit, while values lower than zero means the predictions are no better than the average of the observations. Overall mean prediction error (BIAS) of basal area and volume growth predictions was nominal (i.e., for BA: -0.0177 cm2 5-year-1 and volume: 0.0008 m3 5-year-1). Model variability described by root mean squared error (RMSE) in basal area prediction was 40.53 cm2 5-year-1 and 0.0393 m3 5-year-1 in volume prediction. The new modelling approach has potential to reduce uncertainties in growth and yield predictions under different climate change scenarios. This novel approach provides an avenue for forest managers to generate required information for the management of forests in transitional periods of climate change. Artificial intelligence technology has substantial potential in forest modelling. PMID:26173081

Evaluating global reanalysis datasets for provision of boundary conditions in regional climate modelling

NASA Astrophysics Data System (ADS)

Moalafhi, Ditiro B.; Evans, Jason P.; Sharma, Ashish

2016-11-01

Regional climate modelling studies often begin by downscaling a reanalysis dataset in order to simulate the observed climate, allowing the investigation of regional climate processes and quantification of the errors associated with the regional model. To date choice of reanalysis to perform such downscaling has been made based either on convenience or on performance of the reanalyses within the regional domain for relevant variables such as near-surface air temperature and precipitation. However, the only information passed from the reanalysis to the regional model are the atmospheric temperature, moisture and winds at the location of the boundaries of the regional domain. Here we present a methodology to evaluate reanalyses derived lateral boundary conditions for an example domain over southern Africa using satellite data. This study focusses on atmospheric temperature and moisture which are easily available. Five commonly used global reanalyses (NCEP1, NCEP2, ERA-I, 20CRv2, and MERRA) are evaluated against the Atmospheric Infrared Sounder satellite temperature and relative humidity over boundaries of two domains centred on southern Africa for the years 2003-2012 inclusive. The study reveals that MERRA is the most suitable for climate mean with NCEP1 the next most suitable. For climate variability, ERA-I is the best followed by MERRA. Overall, MERRA is preferred for generating lateral boundary conditions for this domain, followed by ERA-I. While a "better" LBC specification is not the sole precursor to an improved downscaling outcome, any reduction in uncertainty associated with the specification of LBCs is a step in the right direction.

Can air temperature be used to project influences of climate change on stream temperature?

USGS Publications Warehouse

Arismendi, Ivan; Safeeq, Mohammad; Dunham, Jason B.; Johnson, Sherri L.

2014-01-01

Worldwide, lack of data on stream temperature has motivated the use of regression-based statistical models to predict stream temperatures based on more widely available data on air temperatures. Such models have been widely applied to project responses of stream temperatures under climate change, but the performance of these models has not been fully evaluated. To address this knowledge gap, we examined the performance of two widely used linear and nonlinear regression models that predict stream temperatures based on air temperatures. We evaluated model performance and temporal stability of model parameters in a suite of regulated and unregulated streams with 11–44 years of stream temperature data. Although such models may have validity when predicting stream temperatures within the span of time that corresponds to the data used to develop them, model predictions did not transfer well to other time periods. Validation of model predictions of most recent stream temperatures, based on air temperature–stream temperature relationships from previous time periods often showed poor performance when compared with observed stream temperatures. Overall, model predictions were less robust in regulated streams and they frequently failed in detecting the coldest and warmest temperatures within all sites. In many cases, the magnitude of errors in these predictions falls within a range that equals or exceeds the magnitude of future projections of climate-related changes in stream temperatures reported for the region we studied (between 0.5 and 3.0 °C by 2080). The limited ability of regression-based statistical models to accurately project stream temperatures over time likely stems from the fact that underlying processes at play, namely the heat budgets of air and water, are distinctive in each medium and vary among localities and through time.

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

NASA Astrophysics Data System (ADS)

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

2016-12-01

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

Integrating modelling and remote sensing to identify ecosystem performance anomalies in the boreal forest, Yukon River Basin, Alaska

USGS Publications Warehouse

Wylie, B.K.; Zhang, L.; Bliss, Norman B.; Ji, Lei; Tieszen, Larry L.; Jolly, W. M.

2008-01-01

High-latitude ecosystems are exposed to more pronounced warming effects than other parts of the globe. We develop a technique to monitor ecological changes in a way that distinguishes climate influences from disturbances. In this study, we account for climatic influences on Alaskan boreal forest performance with a data-driven model. We defined ecosystem performance anomalies (EPA) using the residuals of the model and made annual maps of EPA. Most areas (88%) did not have anomalous ecosystem performance for at least 6 of 8 years between 1996 and 2004. Areas with underperforming EPA (10%) often indicate areas associated with recent fires and areas of possible insect infestation or drying soil related to permafrost degradation. Overperforming areas (2%) occurred in older fire recovery areas where increased deciduous vegetation components are expected. The EPA measure was validated with composite burn index data and Landsat vegetation indices near and within burned areas.

Enabling the use of climate model data in the Dutch climate effect community

NASA Astrophysics Data System (ADS)

Som de Cerff, Wim; Plieger, Maarten

2010-05-01

Within the climate effect community the usage of climate model data is emerging. Where mostly climate time series and weather generators were used, there is a shift to incorporate climate model data into climate effect models. The use of climate model data within the climate effect models is difficult, due to missing metadata, resolution and projection issues, data formats and availability of the parameters of interest. Often the climate effect modelers are not aware of available climate model data or are not aware of how they can use it. Together with seven other partners (CERFACS, CNR-IPSL, SMHI, INHGA, CMCC, WUR, MF-CNRM), KNMI is involved in the FP7 IS ENES (http://www.enes.org) project work package 10/JRA5 ‘Bridging Climate Research Data and the Needs of the Impact Community. The aims of this work package are to enhance the use of Climate Research Data and to enhance the interaction with climate effect/impact communities. Phase one is to define use cases together with the Dutch climate effect community, which describe the intended use of climate model data in climate effect models. We defined four use cases: 1) FEWS hydrological Framework (Deltares) 2) METAPHOR, a plants and species dispersion model (Wageningen University) 3) Natuurplanner, an Ecological model suite (Wageningen University) 4) Land use models (Free University/JRC). Also the other partners in JRA5 have defined use cases, which are representative for the climate effect and impact communities in their country. Goal is to find commonalities between all defined use cases. The common functionality will be implemented as e-tools and incorporated in the IS-ENES data portal. Common issues relate to e.g., need for high resolution: downscaling from GCM to local scale (also involves interpolation); parameter selection; finding extremes; averaging methods. At the conference we will describe the FEWS case in more detail: Delft FEWS is an open shell system (in development since 1995) for performing hydrological predictions and the handling of time series data. The most important capabilities of FEWS are importing of meteorological and hydrological data and organizing the workflows of the different models which can be used within FEWS, like the Netherlands Hydrological Instrumentarium (NHI). Besides predictions, the system is currently being used for hydrological climate effects studies. Currently regionally downscaled data are used, but using model data will be the next step. This coupling of climate model data to FEWS will open a wider rage of climate impact and effect research, but it is a difficult task to accomplish. Issues to be dealt with are: regridding, downscaling, format conversion, extraction of required data and addition of descriptive metadata, including quality and uncertainty parameters. Finding an appropriate solution involves several iterations: first, the use case was defined, then we just provided a single data file containing some data of interest provided via FTP, next this data was offered through OGC services. Currently we are working on providing larger datasets and improving on the parameters and metadata. We will present the results (e-tools/data) and experiences gained on implementing the described use cases. Note that we are currently using experimental data, as the official climate model runs are not available yet.

Testing For The Linearity of Responses To Multiple Anthropogenic Climate Forcings

NASA Astrophysics Data System (ADS)

Forest, C. E.; Stone, P. H.; Sokolov, A. P.

To test whether climate forcings are additive, we compare climate model simulations in which anthropogenic forcings are applied individually and in combination. Tests are performed with different values for climate system properties (climate sensitivity and rate of heat uptake by the deep ocean) as well as for different strengths of the net aerosol forcing, thereby testing for the dependence of linearity on these properties. The MIT 2D Land-Ocean Climate Model used in this study consists of a zonally aver- aged statistical-dynamical atmospheric model coupled to a mixed-layer Q-flux ocean model, with heat anomalies diffused into the deep ocean. Following our previous stud- ies, the anthropogenic forcings are the changes in concentrations of greenhouse gases (1860-1995), sulfate aerosol (1860-1995), and stratospheric and tropospheric ozone (1979-1995). The sulfate aerosol forcing is applied as a surface albedo change. For an aerosol forcing of -1.0 W/m2 and an effective ocean diffusitivity of 2.5 cm2/s, the nonlinearity of the response of global-mean surface temperatures to the combined forcing shows a strong dependence on climate sensitivity. The fractional change in decadal averages ([(TG + TS + TO) - TGSO]/TGSO) for the 1986-1995 period compared to pre-industrial times are 0.43, 0.90, and 1.08 with climate sensitiv- ities of 3.0, 4.5, and 6.2 C, respectively. The values of TGSO for these three cases o are 0.52, 0.62, and 0.76 C. The dependence of linearity on climate system properties, o the role of climate system feedbacks, and the implications for the detection of climate system's response to individual forcings will be presented. Details of the model and forcings can be found at http://web.mit.edu/globalchange/www/.

Testing for the linearity of responses to multiple anthropogenic climate forcings

NASA Astrophysics Data System (ADS)

Forest, C. E.; Stone, P. H.; Sokolov, A. P.

2001-12-01

To test whether climate forcings are additive, we compare climate model simulations in which anthropogenic forcings are applied individually and in combination. Tests are performed with different values for climate system properties (climate sensitivity and rate of heat uptake by the deep ocean) as well as for different strengths of the net aerosol forcing, thereby testing for the dependence of linearity on these properties. The MIT 2D Land-Ocean Climate Model used in this study consists of a zonally averaged statistical-dynamical atmospheric model coupled to a mixed-layer Q-flux ocean model, with heat anomalies diffused into the deep ocean. Following our previous studies, the anthropogenic forcings are the changes in concentrations of greenhouse gases (1860-1995), sulfate aerosol (1860-1995), and stratospheric and tropospheric ozone (1979-1995). The sulfate aerosol forcing is applied as a surface albedo change. For an aerosol forcing of -1.0 W/m2 and an effective ocean diffusitivity of 2.5 cm2/s, the nonlinearity of the response of global-mean surface temperatures to the combined forcing shows a strong dependence on climate sensitivity. The fractional change in decadal averages ([(Δ TG + Δ TS + Δ TO) - Δ TGSO ]/ Δ TGSO) for the 1986-1995 period compared to pre-industrial times are 0.43, 0.90, and 1.08 with climate sensitivities of 3.0, 4.5, and 6.2 oC, respectively. The values of Δ TGSO for these three cases are 0.52, 0.62, and 0.76 oC. The dependence of linearity on climate system properties, the role of climate system feedbacks, and the implications for the detection of climate system's response to individual forcings will be presented. Details of the model and forcings can be found at http://web.mit.edu/globalchange/www/.

Large-scale, high-performance and cloud-enabled multi-model analytics experiments in the context of the Earth System Grid Federation

NASA Astrophysics Data System (ADS)

Fiore, S.; Płóciennik, M.; Doutriaux, C.; Blanquer, I.; Barbera, R.; Williams, D. N.; Anantharaj, V. G.; Evans, B. J. K.; Salomoni, D.; Aloisio, G.

2017-12-01

The increased models resolution in the development of comprehensive Earth System Models is rapidly leading to very large climate simulations output that pose significant scientific data management challenges in terms of data sharing, processing, analysis, visualization, preservation, curation, and archiving.Large scale global experiments for Climate Model Intercomparison Projects (CMIP) have led to the development of the Earth System Grid Federation (ESGF), a federated data infrastructure which has been serving the CMIP5 experiment, providing access to 2PB of data for the IPCC Assessment Reports. In such a context, running a multi-model data analysis experiment is very challenging, as it requires the availability of a large amount of data related to multiple climate models simulations and scientific data management tools for large-scale data analytics. To address these challenges, a case study on climate models intercomparison data analysis has been defined and implemented in the context of the EU H2020 INDIGO-DataCloud project. The case study has been tested and validated on CMIP5 datasets, in the context of a large scale, international testbed involving several ESGF sites (LLNL, ORNL and CMCC), one orchestrator site (PSNC) and one more hosting INDIGO PaaS services (UPV). Additional ESGF sites, such as NCI (Australia) and a couple more in Europe, are also joining the testbed. The added value of the proposed solution is summarized in the following: it implements a server-side paradigm which limits data movement; it relies on a High-Performance Data Analytics (HPDA) stack to address performance; it exploits the INDIGO PaaS layer to support flexible, dynamic and automated deployment of software components; it provides user-friendly web access based on the INDIGO Future Gateway; and finally it integrates, complements and extends the support currently available through ESGF. Overall it provides a new "tool" for climate scientists to run multi-model experiments. At the time this contribution is being written, the proposed testbed represents the first implementation of a distributed large-scale, multi-model experiment in the ESGF/CMIP context, joining together server-side approaches for scientific data analysis, HPDA frameworks, end-to-end workflow management, and cloud computing.

A Simple Approach to Account for Climate Model Interdependence in Multi-Model Ensembles

NASA Astrophysics Data System (ADS)

Herger, N.; Abramowitz, G.; Angelil, O. M.; Knutti, R.; Sanderson, B.

2016-12-01

Multi-model ensembles are an indispensable tool for future climate projection and its uncertainty quantification. Ensembles containing multiple climate models generally have increased skill, consistency and reliability. Due to the lack of agreed-on alternatives, most scientists use the equally-weighted multi-model mean as they subscribe to model democracy ("one model, one vote").Different research groups are known to share sections of code, parameterizations in their model, literature, or even whole model components. Therefore, individual model runs do not represent truly independent estimates. Ignoring this dependence structure might lead to a false model consensus, wrong estimation of uncertainty and effective number of independent models.Here, we present a way to partially address this problem by selecting a subset of CMIP5 model runs so that its climatological mean minimizes the RMSE compared to a given observation product. Due to the cancelling out of errors, regional biases in the ensemble mean are reduced significantly.Using a model-as-truth experiment we demonstrate that those regional biases persist into the future and we are not fitting noise, thus providing improved observationally-constrained projections of the 21st century. The optimally selected ensemble shows significantly higher global mean surface temperature projections than the original ensemble, where all the model runs are considered. Moreover, the spread is decreased well beyond that expected from the decreased ensemble size.Several previous studies have recommended an ensemble selection approach based on performance ranking of the model runs. Here, we show that this approach can perform even worse than randomly selecting ensemble members and can thus be harmful. We suggest that accounting for interdependence in the ensemble selection process is a necessary step for robust projections for use in impact assessments, adaptation and mitigation of climate change.

Development of virtual research environment for regional climatic and ecological studies and continuous education support

NASA Astrophysics Data System (ADS)

Gordov, Evgeny; Lykosov, Vasily; Krupchatnikov, Vladimir; Bogomolov, Vasily; Gordova, Yulia; Martynova, Yulia; Okladnikov, Igor; Titov, Alexander; Shulgina, Tamara

2014-05-01

Volumes of environmental data archives are growing immensely due to recent models, high performance computers and sensors development. It makes impossible their comprehensive analysis in conventional manner on workplace using in house computing facilities, data storage and processing software at hands. One of possible answers to this challenge is creation of virtual research environment (VRE), which should provide a researcher with an integrated access to huge data resources, tools and services across disciplines and user communities and enable researchers to process structured and qualitative data in virtual workspaces. VRE should integrate data, network and computing resources providing interdisciplinary climatic research community with opportunity to get profound understanding of ongoing and possible future climatic changes and their consequences. Presented are first steps and plans for development of VRE prototype element aimed at regional climatic and ecological monitoring and modeling as well as at continuous education and training support. Recently developed experimental software and hardware platform aimed at integrated analysis of heterogeneous georeferenced data "Climate" (http://climate.scert.ru/, Gordov et al., 2013; Shulgina et al., 2013; Okladnikov et al., 2013) is used as a VRE element prototype and approach test bench. VRE under development will integrate on the base of geoportal distributed thematic data storage, processing and analysis systems and set of models of complex climatic and environmental processes run on supercomputers. VRE specific tools are aimed at high resolution rendering on-going climatic processes occurring in Northern Eurasia and reliable and found prognoses of their dynamics for selected sets of future mankind activity scenaria. Currently the VRE element is accessible via developed geoportal at the same link (http://climate.scert.ru/) and integrates the WRF and «Planet Simulator» models, basic reanalysis and instrumental measurements data and support profound statistical analysis of storaged and modeled on demand data. In particular, one can run the integrated models, preprocess modeling results data, using dedicated modules for numerical processing perform analysys and visualize obtained results. New functionality recently has been added to the statistical analysis tools set aimed at detailed studies of climatic extremes occurring in Northern Asia. The VRE element is also supporting thematic educational courses for students and post-graduate students of the Tomsk State University. In particular, it allow students to perform on-line thematic laboratory work cycles on the basics of analysis of current and potential future regional climate change using Siberia territory as an example (Gordova et al, 2013). We plan to expand the integrated models set and add comprehensive surface and Arctic Ocean description. Developed VRE element "Climate" provides specialists involved into multidisciplinary research projects with reliable and practical instruments for integrated research of climate and ecosystems changes on global and regional scales. With its help even a user without programming skills can process and visualize multidimensional observational and model data through unified web-interface using a common graphical web-browser. This work is partially supported by SB RAS project VIII.80.2.1, RFBR grant 13-05-12034, grant 14-05-00502, and integrated project SB RAS 131. References 1. Gordov E.P., Lykosov V.N., Krupchatnikov V.N., Okladnikov I.G., Titov A.G., Shulgina T.M. Computationaland information technologies for monitoring and modeling of climate changes and their consequences. Novosibirsk: Nauka, Siberian branch, 2013. - 195 p. (in Russian) 2. T.M. Shulgina, E.P. Gordov, I.G. Okladnikov, A.G., Titov, E.Yu. Genina, N.P. Gorbatenko, I.V. Kuzhevskaya,A.S. Akhmetshina. Software complex for a regional climate change analysis. // Vestnik NGU. Series: Information technologies. 2013. Vol. 11. Issue 1. P. 124-131. (in Russian) 3. I.G. Okladnikov, A.G. Titov, T.M. Shulgina, E.P. Gordov, V.Yu. Bogomolov, Yu.V. Martynova, S.P. Suschenko,A.V. Skvortsov. Software for analysis and visualization of climate change monitoring and forecasting data //Numerical methods and programming, 2013. Vol. 14. P. 123-131.(in Russian) 4. Yu.E. Gordova, E.Yu. Genina, V.P. Gorbatenko, E.P. Gordov, I.V. Kuzhevskaya, Yu.V. Martynova , I.G. Okladnikov, A.G. Titov, T.M. Shulgina, N.K. Barashkova Support of the educational process in modern climatology within the web-gis platform «Climate». Open and Distant Education. 2013, No 1(49)., P. 14-19.(in Russian)

Promoting Climate Literacy and Conceptual Understanding among In-service Secondary Science Teachers requires an Epistemological Perspective

NASA Astrophysics Data System (ADS)

Bhattacharya, D.; Forbes, C.; Roehrig, G.; Chandler, M. A.

2017-12-01

Promoting climate literacy among in-service science teachers necessitates an understanding of fundamental concepts about the Earth's climate System (USGCRP, 2009). Very few teachers report having any formal instruction in climate science (Plutzer et al., 2016), therefore, rather simple conceptions of climate systems and their variability exist, which has implications for students' science learning (Francies et al., 1993; Libarkin, 2005; Rebich, 2005). This study uses the inferences from a NASA Innovations in Climate Education (NICE) teacher professional development program (CYCLES) to establish the necessity for developing an epistemological perspective among teachers. In CYCLES, 19 middle and high school (male=8, female=11) teachers were assessed for their understanding of global climate change (GCC). A qualitative analysis of their concept maps and an alignment of their conceptions with the Essential Principles of Climate Literacy (NOAA, 2009) demonstrated that participants emphasized on EPCL 1, 3, 6, 7 focusing on the Earth system, atmospheric, social and ecological impacts of GCC. However, EPCL 4 (variability in climate) and 5 (data-based observations and modeling) were least represented and emphasized upon. Thus, participants' descriptions about global climatic patterns were often factual rather than incorporating causation (why the temperatures are increasing) and/or correlation (describing what other factors might influence global temperatures). Therefore, engaging with epistemic dimensions of climate science to understand the processes, tools, and norms through which climate scientists study the Earth's climate system (Huxter et al., 2013) is critical for developing an in-depth conceptual understanding of climate. CLiMES (Climate Modeling and Epistemology of Science), a NSF initiative proposes to use EzGCM (EzGlobal Climate Model) to engage students and teachers in designing and running simulations, performing data processing activities, and analyzing computational models to develop their own evidence-based claims about the Earth's climate system. We describe how epistemological investigations can be conducted using EzGCM to bring the scientific process and authentic climate science practice to middle and high school classrooms.

The sensitivity of a general circulation model to Saharan dust heating

NASA Technical Reports Server (NTRS)

Randall, D. A.; Carlson, T.; Mintz, Y.

1984-01-01

During the Northern summer, sporadic outbreaks of wind borne Saharan dust are carried out over the Atlantic by the tropical easterlies. Optical depths due to the dust can reach 3 near the African coast, and the dust cloud can be detected as far west as the Caribbean Sea (Carlson, 1979). In order to obtain insight into the possible effects of Saharan dust on the weather and climate of North Africa and the tropical Atlantic Ocean, simulation experiments have been performed with the Climate Model of the Goddard Laboratory for Atmospheric Sciences. The most recent version of the model is described by Randall (1982). The model produces realistic simulations of many aspects of the observed climate and its seasonal variation.

Grapevine phenology and climate change in Georgia.

PubMed

Cola, G; Failla, O; Maghradze, D; Megrelidze, L; Mariani, L

2017-04-01

While the climate of Western Europe has been deeply affected by the abrupt climate change that took place in the late '1980s of the twentieth century, a similar signal is detected only few years later, in 1994, in Georgia. Grapevine phenology is deeply influenced by climate and this paper aimed to analyze how phenological timing changed before and after the climatic change of 1994. Availability of thermal resources in the two climatic phases for the five altitudinal belts in the 0-1250-m range was analyzed. A phenological dataset gathered in two experimental sites during the period 2012-2014, and a suitable thermal dataset was used to calibrate a phenological model based on the normal approach and able to describe BBCH phenological stages 61 (beginning of flowering), 71 (fruit set), and 81 (veraison). Calibration was performed for four relevant Georgian varieties (Mtsvane Kakhuri, Rkatsiteli, Ojaleshi, and Saperavi). The model validation was performed on an independent 3-year dataset gathered in Gorizia (Italy). Furthermore, in the case of variety Rkatsiteli, the model was applied to the 1974-2013 thermal time series in order to obtain phenological maps of the Georgian territory. Results show that after the climate change of 1994, Rkatsiteli showed an advance, more relevant at higher altitudes where the whole increase of thermal resource was effectively translated in phenological advance. For instance the average advance of veraison was 5.9 days for 250-500 m asl belt and 18.1 days for 750-1000 m asl). On the other hand, at lower altitudes, phenological advance was depleted by superoptimal temperatures. As a final result, some suggestions for the adaptation of viticultural practices to the current climatic phase are provided.

Climate Ocean Modeling on a Beowulf Class System

NASA Technical Reports Server (NTRS)

Cheng, B. N.; Chao, Y.; Wang, P.; Bondarenko, M.

2000-01-01

With the growing power and shrinking cost of personal computers. the availability of fast ethernet interconnections, and public domain software packages, it is now possible to combine them to build desktop parallel computers (named Beowulf or PC clusters) at a fraction of what it would cost to buy systems of comparable power front supercomputer companies. This led as to build and assemble our own sys tem. specifically for climate ocean modeling. In this article, we present our experience with such a system, discuss its network performance, and provide some performance comparison data with both HP SPP2000 and Cray T3E for an ocean Model used in present-day oceanographic research.

Using worldwide edaphic data to model plant species niches: An assessment at a continental extent

PubMed Central

Galvão, Franklin; Villalobos, Fabricio; De Marco Júnior, Paulo

2017-01-01

Ecological niche modeling (ENM) is a broadly used tool in different fields of plant ecology. Despite the importance of edaphic conditions in determining the niche of terrestrial plant species, edaphic data have rarely been included in ENMs of plant species perhaps because such data are not available for many regions. Recently, edaphic data has been made available at a global scale allowing its potential inclusion and evaluation on ENM performance for plant species. Here, we take advantage of such data and address the following main questions: What is the influence of distinct predictor variables (e.g. climatic vs edaphic) on different ENM algorithms? and what is the relationship between the performance of different predictors and geographic characteristics of species? We used 125 plant species distributed over the Neotropical region to explore the effect on ENMs of using edaphic data available from the SoilGrids database and its combination with climatic data from the CHELSA database. In addition, we related these different predictor variables to geographic characteristics of the target species and different ENM algorithms. The use of different predictors (climatic, edaphic, and both) significantly affected model performance and spatial complexity of the predictions. We showed that the use of global edaphic plus climatic variables generates ENMs with similar or better accuracy compared to those constructed only with climate variables. Moreover, the performance of models considering these different predictors, separately or jointly, was related to geographic properties of species records, such as number and distribution range. The large geographic extent, the variability of environments and the different species’ geographical characteristics considered here allowed us to demonstrate that global edaphic data adds useful information for plant ENMs. This is particularly valuable for studies of species that are distributed in regions where more detailed information on soil properties is poor or does not even exist. PMID:29049298

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

Impact of Variable-Resolution Meshes on Regional Climate Simulations

NASA Astrophysics Data System (ADS)

Fowler, L. D.; Skamarock, W. C.; Bruyere, C. L.

2014-12-01

The Model for Prediction Across Scales (MPAS) is currently being used for seasonal-scale simulations on globally-uniform and regionally-refined meshes. Our ongoing research aims at analyzing simulations of tropical convective activity and tropical cyclone development during one hurricane season over the North Atlantic Ocean, contrasting statistics obtained with a variable-resolution mesh against those obtained with a quasi-uniform mesh. Analyses focus on the spatial distribution, frequency, and intensity of convective and grid-scale precipitations, and their relative contributions to the total precipitation as a function of the horizontal scale. Multi-month simulations initialized on May 1st 2005 using ERA-Interim re-analyses indicate that MPAS performs satisfactorily as a regional climate model for different combinations of horizontal resolutions and transitions between the coarse and refined meshes. Results highlight seamless transitions for convection, cloud microphysics, radiation, and land-surface processes between the quasi-uniform and locally- refined meshes, despite the fact that the physics parameterizations were not developed for variable resolution meshes. Our goal of analyzing the performance of MPAS is twofold. First, we want to establish that MPAS can be successfully used as a regional climate model, bypassing the need for nesting and nudging techniques at the edges of the computational domain as done in traditional regional climate modeling. Second, we want to assess the performance of our convective and cloud microphysics parameterizations as the horizontal resolution varies between the lower-resolution quasi-uniform and higher-resolution locally-refined areas of the global domain.

Impact of Variable-Resolution Meshes on Regional Climate Simulations

NASA Astrophysics Data System (ADS)

Fowler, L. D.; Skamarock, W. C.; Bruyere, C. L.

2013-12-01

The Model for Prediction Across Scales (MPAS) is currently being used for seasonal-scale simulations on globally-uniform and regionally-refined meshes. Our ongoing research aims at analyzing simulations of tropical convective activity and tropical cyclone development during one hurricane season over the North Atlantic Ocean, contrasting statistics obtained with a variable-resolution mesh against those obtained with a quasi-uniform mesh. Analyses focus on the spatial distribution, frequency, and intensity of convective and grid-scale precipitations, and their relative contributions to the total precipitation as a function of the horizontal scale. Multi-month simulations initialized on May 1st 2005 using NCEP/NCAR re-analyses indicate that MPAS performs satisfactorily as a regional climate model for different combinations of horizontal resolutions and transitions between the coarse and refined meshes. Results highlight seamless transitions for convection, cloud microphysics, radiation, and land-surface processes between the quasi-uniform and locally-refined meshes, despite the fact that the physics parameterizations were not developed for variable resolution meshes. Our goal of analyzing the performance of MPAS is twofold. First, we want to establish that MPAS can be successfully used as a regional climate model, bypassing the need for nesting and nudging techniques at the edges of the computational domain as done in traditional regional climate modeling. Second, we want to assess the performance of our convective and cloud microphysics parameterizations as the horizontal resolution varies between the lower-resolution quasi-uniform and higher-resolution locally-refined areas of the global domain.

Exploring Climate Niches of Ponderosa Pine (Pinus ponderosa Douglas ex Lawson) Haplotypes in the Western United States: Implications for Evolutionary History and Conservation

PubMed Central

Shinneman, Douglas J.; Potter, Kevin M.; Hipkins, Valerie D.

2016-01-01

Ponderosa pine (Pinus ponderosa Douglas ex Lawson) occupies montane environments throughout western North America, where it is both an ecologically and economically important tree species. A recent study using mitochondrial DNA analysis demonstrated substantial genetic variation among ponderosa pine populations in the western U.S., identifying 10 haplotypes with unique evolutionary lineages that generally correspond spatially with distributions of the Pacific (P. p. var. ponderosa) and Rocky Mountain (P. p. var. scopulorum) varieties. To elucidate the role of climate in shaping the phylogeographic history of ponderosa pine, we used nonparametric multiplicative regression to develop predictive climate niche models for two varieties and 10 haplotypes and to hindcast potential distribution of the varieties during the last glacial maximum (LGM), ~22,000 yr BP. Our climate niche models performed well for the varieties, but haplotype models were constrained in some cases by small datasets and unmeasured microclimate influences. The models suggest strong relationships between genetic lineages and climate. Particularly evident was the role of seasonal precipitation balance in most models, with winter- and summer-dominated precipitation regimes strongly associated with P. p. vars. ponderosa and scopulorum, respectively. Indeed, where present-day climate niches overlap between the varieties, introgression of two haplotypes also occurs along a steep clinal divide in western Montana. Reconstructed climate niches for the LGM suggest potentially suitable climate existed for the Pacific variety in the California Floristic province, the Great Basin, and Arizona highlands, while suitable climate for the Rocky Mountain variety may have existed across the southwestern interior highlands. These findings underscore potentially unique phylogeographic origins of modern ponderosa pine evolutionary lineages, including potential adaptations to Pleistocene climates associated with discrete temporary glacial refugia. Our predictive climate niche models may inform strategies for further genetic research (e.g., sampling design) and conservation that promotes haplotype compatibility with projected changes in future climate. PMID:26985674

Exploring Climate Niches of Ponderosa Pine (Pinus ponderosa Douglas ex Lawson) Haplotypes in the Western United States: Implications for Evolutionary History and Conservation.

PubMed

Shinneman, Douglas J; Means, Robert E; Potter, Kevin M; Hipkins, Valerie D

2016-01-01

Ponderosa pine (Pinus ponderosa Douglas ex Lawson) occupies montane environments throughout western North America, where it is both an ecologically and economically important tree species. A recent study using mitochondrial DNA analysis demonstrated substantial genetic variation among ponderosa pine populations in the western U.S., identifying 10 haplotypes with unique evolutionary lineages that generally correspond spatially with distributions of the Pacific (P. p. var. ponderosa) and Rocky Mountain (P. p. var. scopulorum) varieties. To elucidate the role of climate in shaping the phylogeographic history of ponderosa pine, we used nonparametric multiplicative regression to develop predictive climate niche models for two varieties and 10 haplotypes and to hindcast potential distribution of the varieties during the last glacial maximum (LGM), ~22,000 yr BP. Our climate niche models performed well for the varieties, but haplotype models were constrained in some cases by small datasets and unmeasured microclimate influences. The models suggest strong relationships between genetic lineages and climate. Particularly evident was the role of seasonal precipitation balance in most models, with winter- and summer-dominated precipitation regimes strongly associated with P. p. vars. ponderosa and scopulorum, respectively. Indeed, where present-day climate niches overlap between the varieties, introgression of two haplotypes also occurs along a steep clinal divide in western Montana. Reconstructed climate niches for the LGM suggest potentially suitable climate existed for the Pacific variety in the California Floristic province, the Great Basin, and Arizona highlands, while suitable climate for the Rocky Mountain variety may have existed across the southwestern interior highlands. These findings underscore potentially unique phylogeographic origins of modern ponderosa pine evolutionary lineages, including potential adaptations to Pleistocene climates associated with discrete temporary glacial refugia. Our predictive climate niche models may inform strategies for further genetic research (e.g., sampling design) and conservation that promotes haplotype compatibility with projected changes in future climate.

Exploring climate niches of ponderosa pine (Pinus ponderosa Douglas ex Lawson) haplotypes in the western United States: Implications for evolutionary history and conservation

USGS Publications Warehouse

Shinneman, Douglas; Means, Robert E.; Potter, Kevin M.; Hipkins, Valerie D.

2016-01-01

Ponderosa pine (Pinus ponderosa Douglas ex Lawson) occupies montane environments throughout western North America, where it is both an ecologically and economically important tree species. A recent study using mitochondrial DNA analysis demonstrated substantial genetic variation among ponderosa pine populations in the western U.S., identifying 10 haplotypes with unique evolutionary lineages that generally correspond spatially with distributions of the Pacific (P. p. var. ponderosa) and Rocky Mountain (P. p. var. scopulorum) varieties. To elucidate the role of climate in shaping the phylogeographic history of ponderosa pine, we used nonparametric multiplicative regression to develop predictive climate niche models for two varieties and 10 haplotypes and to hindcast potential distribution of the varieties during the last glacial maximum (LGM), ~22,000 yr BP. Our climate niche models performed well for the varieties, but haplotype models were constrained in some cases by small datasets and unmeasured microclimate influences. The models suggest strong relationships between genetic lineages and climate. Particularly evident was the role of seasonal precipitation balance in most models, with winter- and summer-dominated precipitation regimes strongly associated with P. p. vars. ponderosa and scopulorum, respectively. Indeed, where present-day climate niches overlap between the varieties, introgression of two haplotypes also occurs along a steep clinal divide in western Montana. Reconstructed climate niches for the LGM suggest potentially suitable climate existed for the Pacific variety in the California Floristic province, the Great Basin, and Arizona highlands, while suitable climate for the Rocky Mountain variety may have existed across the southwestern interior highlands. These findings underscore potentially unique phylogeographic origins of modern ponderosa pine evolutionary lineages, including potential adaptations to Pleistocene climates associated with discrete temporary glacial refugia. Our predictive climate niche models may inform strategies for further genetic research (e.g., sampling design) and conservation that promotes haplotype compatibility with projected changes in future climate.

Benchmarking sensitivity of biophysical processes to leaf area changes in land surface models

NASA Astrophysics Data System (ADS)

Forzieri, Giovanni; Duveiller, Gregory; Georgievski, Goran; Li, Wei; Robestson, Eddy; Kautz, Markus; Lawrence, Peter; Ciais, Philippe; Pongratz, Julia; Sitch, Stephen; Wiltshire, Andy; Arneth, Almut; Cescatti, Alessandro

2017-04-01

Land surface models (LSM) are widely applied as supporting tools for policy-relevant assessment of climate change and its impact on terrestrial ecosystems, yet knowledge of their performance skills in representing the sensitivity of biophysical processes to changes in vegetation density is still limited. This is particularly relevant in light of the substantial impacts on regional climate associated with the changes in leaf area index (LAI) following the observed global greening. Benchmarking LSMs on the sensitivity of the simulated processes to vegetation density is essential to reduce their uncertainty and improve the representation of these effects. Here we present a novel benchmark system to assess model capacity in reproducing land surface-atmosphere energy exchanges modulated by vegetation density. Through a collaborative effort of different modeling groups, a consistent set of land surface energy fluxes and LAI dynamics has been generated from multiple LSMs, including JSBACH, JULES, ORCHIDEE, CLM4.5 and LPJ-GUESS. Relationships of interannual variations of modeled surface fluxes to LAI changes have been analyzed at global scale across different climatological gradients and compared with satellite-based products. A set of scoring metrics has been used to assess the overall model performances and a detailed analysis in the climate space has been provided to diagnose possible model errors associated to background conditions. Results have enabled us to identify model-specific strengths and deficiencies. An overall best performing model does not emerge from the analyses. However, the comparison with other models that work better under certain metrics and conditions indicates that improvements are expected to be potentially achievable. A general amplification of the biophysical processes mediated by vegetation is found across the different land surface schemes. Grasslands are characterized by an underestimated year-to-year variability of LAI in cold climates, ultimately affecting the amount of absorbed radiation. In addition patterns of simulated turbulent fluxes appear opposite to observations. Such systematic errors shed light on the current partial understanding of some of the mechanisms controlling the surface energy balance. In contrast forests appear reasonably well represented with respect to the interactions between LAI and turbulent fluxes across most climatological gradients, while for net radiation this is only true for warm climates. These proven strengths increase the confidence on how certain processes are simulated in LSMs. The model capacity to mimic the vegetation-biophysics interplay has been tested over the real scenario of greening that occurred in the last 30 years. We found that the modeled trends in surface heat fluxes associated with the long-term changes in leaf area could vary largely from those observed, with different discrepancies across models and climate zones. Our findings help to identify knowledge gaps and improve model representation of the sensitivity of biophysical processes to changes in leaf area density. In particular, comparing models and observations over a wide range of climate and vegetation conditions, as analyzed here, allowed capturing non-linearity of system responses that may emerge more frequently in future climate scenarios.

An Overview of the Future Development of Climate and Earth System Models for Scientific and Policy Use (Invited)

NASA Astrophysics Data System (ADS)

Washington, W. M.

2010-12-01

The development of climate and earth system models has been regarded primarily as the making of scientific tools to study the complex nature of the Earth’s climate. These models have a long history starting with very simple physical models based on fundamental physics in the 1960s and over time they have become much more complex with atmospheric, ocean, sea ice, land/vegetation, biogeochemical, glacial and ecological components. The policy use aspects of these models did not start in the 1960s and 1970s as decision making tools but were used to answer fundamental scientific questions such as what happens when the atmospheric carbon dioxide concentration increases or is doubled. They gave insights into the various interactions and were extensively compared with observations. It was realized that models of the earlier time periods could only give first order answers to many of the fundamental policy questions. As societal concerns about climate change rose, the policy questions of anthropogenic climate change became better defined; they were mostly concerned with the climate impacts of increasing greenhouse gases, aerosols, and land cover change. In the late 1980s, the United Nations set up the Intergovernmental Panel on Climate Change to perform assessments of the published literature. Thus, the development of climate and Earth system models became intimately linked to the need to not only improve our scientific understanding but also answering fundamental policy questions. In order to meet this challenge, the models became more complex and realistic so that they could address these policy oriented science questions such as rising sea level. The presentation will discuss the past and future development of global climate and earth system models for science and policy purposes. Also to be discussed is their interactions with economic integrated assessment models, regional and specialized models such as river transport or ecological components. As an example of one development pathway, the NSF/Department of Energy supported Community Climate System and Earth System Models will be featured in the presentation. Computational challenges will also part of the discussion.

Commensurate comparisons of models with energy budget observations reveal consistent climate sensitivities

NASA Astrophysics Data System (ADS)

Armour, K.

2017-12-01

Global energy budget observations have been widely used to constrain the effective, or instantaneous climate sensitivity (ICS), producing median estimates around 2°C (Otto et al. 2013; Lewis & Curry 2015). A key question is whether the comprehensive climate models used to project future warming are consistent with these energy budget estimates of ICS. Yet, performing such comparisons has proven challenging. Within models, values of ICS robustly vary over time, as surface temperature patterns evolve with transient warming, and are generally smaller than the values of equilibrium climate sensitivity (ECS). Naively comparing values of ECS in CMIP5 models (median of about 3.4°C) to observation-based values of ICS has led to the suggestion that models are overly sensitive. This apparent discrepancy can partially be resolved by (i) comparing observation-based values of ICS to model values of ICS relevant for historical warming (Armour 2017; Proistosescu & Huybers 2017); (ii) taking into account the "efficacies" of non-CO2 radiative forcing agents (Marvel et al. 2015); and (iii) accounting for the sparseness of historical temperature observations and differences in sea-surface temperature and near-surface air temperature over the oceans (Richardson et al. 2016). Another potential source of discrepancy is a mismatch between observed and simulated surface temperature patterns over recent decades, due to either natural variability or model deficiencies in simulating historical warming patterns. The nature of the mismatch is such that simulated patterns can lead to more positive radiative feedbacks (higher ICS) relative to those engendered by observed patterns. The magnitude of this effect has not yet been addressed. Here we outline an approach to perform fully commensurate comparisons of climate models with global energy budget observations that take all of the above effects into account. We find that when apples-to-apples comparisons are made, values of ICS in models are consistently in good agreement with values of ICS inferred from global energy budget constraints. This suggests that the current generation of coupled climate models are not overly sensitive. However, since global energy budget observations do not constrain ECS, it is less certain whether model ECS values are realistic.

Work climate and the mediating role of workplace bullying related to job performance, job satisfaction, and work ability: A study among hospital nurses.

PubMed

Olsen, Espen; Bjaalid, Gunhild; Mikkelsen, Aslaug

2017-11-01

To increase understanding of workplace bullying and its relation to work climate and different outcomes among nurses. Examine a proposed bullying model including both job resource and job demands, as well as nurse outcomes reflected in job performance, job satisfaction, and work ability. Workplace bullying has been identified as some of the most damaging mechanisms in workplace settings. It is important to increase understanding of workplace bullying in relation to work climate and different outcomes among nurses. This study adopted a cross-sectional web based survey design. A sample of 2946 Registered Nurses from four public Norwegian hospitals were collected during October 2014. We analysed data using descriptive statistics, correlations, Cronbach's alpa, confirmatory factor analyses, and structural equation modelling. The majority of work climate characteristics confirmed to influence workplace bullying, and additionally had direct influence on nurse outcomes; job performance, job satisfaction, and work ability. Bullying had a mediational role between most of the work climate dimensions and nurse outcomes. This study increases our understanding of organizational antecedent of bullying among nurses. Workplace bullying among nurses functions as a mediator between the majority of work climate dimensions and outcomes related to job satisfaction and work ability. Strategies to reduce bullying should look at the study finding and specifically job resources and job demands that influence bullying and nurse outcomes. © 2017 John Wiley & Sons Ltd.

Simulating the onset of spring vegetation growth across the Northern Hemisphere.

PubMed

Liu, Qiang; Fu, Yongshuo H; Liu, Yongwen; Janssens, Ivan A; Piao, Shilong

2018-03-01

Changes in the spring onset of vegetation growth in response to climate change can profoundly impact climate-biosphere interactions. Thus, robust simulation of spring onset is essential to accurately predict ecosystem responses and feedback to ongoing climate change. To date, the ability of vegetation phenology models to reproduce spatiotemporal patterns of spring onset at larger scales has not been thoroughly investigated. In this study, we took advantage of phenology observations via remote sensing to calibrate and evaluated six models, including both one-phase (considering only forcing temperatures) and two-phase (involving forcing, chilling, and photoperiod) models across the Northern Hemisphere between 1982 and 2012. Overall, we found that the model that integrated the photoperiod effect performed best at capturing spatiotemporal patterns of spring phenology in boreal and temperate forests. By contrast, all of the models performed poorly in simulating the onset of growth in grasslands. These results suggest that the photoperiod plays a role in controlling the onset of growth in most Northern Hemisphere forests, whereas other environmental factors (e.g., precipitation) should be considered when simulating the onset of growth in grasslands. We also found that the one-phase model performed as well as the two-phase models in boreal forests, which implies that the chilling requirement is probably fulfilled across most of the boreal zone. Conversely, two-phase models performed better in temperate forests than the one-phase model, suggesting that photoperiod and chilling play important roles in these temperate forests. Our results highlight the significance of including chilling and photoperiod effects in models of the spring onset of forest growth at large scales, and indicate that the consideration of additional drivers may be required for grasslands. © 2017 John Wiley & Sons Ltd.

Coupled Global-Regional Climate Model Simulations of Future Changes in Hydrology over Central America

NASA Astrophysics Data System (ADS)

Oglesby, R. J.; Erickson, D. J.; Hernandez, J. L.; Irwin, D.

2005-12-01

Central America covers a relatively small area, but is topographically very complex, has long coast-lines, large inland bodies of water, and very diverse land cover which is both natural and human-induced. As a result, Central America is plagued by hydrologic extremes, especially major flooding and drought events, in a region where many people still barely manage to eke out a living through subsistence. Therefore, considerable concern exists about whether these extreme events will change, either in magnitude or in number, as climate changes in the future. To address this concern, we have used global climate model simulations of future climate change to drive a regional climate model centered on Central America. We use the IPCC `business as usual' scenario 21st century run made with the NCAR CCSM3 global model to drive the regional model MM5 at 12 km resolution. We chose the `business as usual' scenario to focus on the largest possible changes that are likely to occur. Because we are most interested in near-term changes, our simulations are for the years 2010, 2015, and 2025. A long `present-day run (for 2005) allows us to distinguish between climate variability and any signal due to climate change. Furthermore, a multi-year run with MM5 forced by NCEP reanalyses allows an assessment of how well the coupled global-regional model performs over Central America. Our analyses suggest that the coupled model does a credible job simulating the current climate and hydrologic regime, though lack of sufficient observations strongly complicates this comparison. The suite of model runs for the future years is currently nearing completion, and key results will be presented at the meeting.

Reassessing regime shifts in the North Pacific: incremental climate change and commercial fishing are necessary for explaining decadal-scale biological variability.

PubMed

Litzow, Michael A; Mueter, Franz J; Hobday, Alistair J

2014-01-01

In areas of the North Pacific that are largely free of overfishing, climate regime shifts - abrupt changes in modes of low-frequency climate variability - are seen as the dominant drivers of decadal-scale ecological variability. We assessed the ability of leading modes of climate variability [Pacific Decadal Oscillation (PDO), North Pacific Gyre Oscillation (NPGO), Arctic Oscillation (AO), Pacific-North American Pattern (PNA), North Pacific Index (NPI), El Niño-Southern Oscillation (ENSO)] to explain decadal-scale (1965-2008) patterns of climatic and biological variability across two North Pacific ecosystems (Gulf of Alaska and Bering Sea). Our response variables were the first principle component (PC1) of four regional climate parameters [sea surface temperature (SST), sea level pressure (SLP), freshwater input, ice cover], and PCs 1-2 of 36 biological time series [production or abundance for populations of salmon (Oncorhynchus spp.), groundfish, herring (Clupea pallasii), shrimp, and jellyfish]. We found that the climate modes alone could not explain ecological variability in the study region. Both linear models (for climate PC1) and generalized additive models (for biology PC1-2) invoking only the climate modes produced residuals with significant temporal trends, indicating that the models failed to capture coherent patterns of ecological variability. However, when the residual climate trend and a time series of commercial fishery catches were used as additional candidate variables, resulting models of biology PC1-2 satisfied assumptions of independent residuals and out-performed models constructed from the climate modes alone in terms of predictive power. As measured by effect size and Akaike weights, the residual climate trend was the most important variable for explaining biology PC1 variability, and commercial catch the most important variable for biology PC2. Patterns of climate sensitivity and exploitation history for taxa strongly associated with biology PC1-2 suggest plausible mechanistic explanations for these modeling results. Our findings suggest that, even in the absence of overfishing and in areas strongly influenced by internal climate variability, climate regime shift effects can only be understood in the context of other ecosystem perturbations. © 2013 John Wiley & Sons Ltd.

PRMS-IV, the precipitation-runoff modeling system, version 4

USGS Publications Warehouse

Markstrom, Steven L.; Regan, R. Steve; Hay, Lauren E.; Viger, Roland J.; Webb, Richard M.; Payn, Robert A.; LaFontaine, Jacob H.

2015-01-01

Computer models that simulate the hydrologic cycle at a watershed scale facilitate assessment of variability in climate, biota, geology, and human activities on water availability and flow. This report describes an updated version of the Precipitation-Runoff Modeling System. The Precipitation-Runoff Modeling System is a deterministic, distributed-parameter, physical-process-based modeling system developed to evaluate the response of various combinations of climate and land use on streamflow and general watershed hydrology. Several new model components were developed, and all existing components were updated, to enhance performance and supportability. This report describes the history, application, concepts, organization, and mathematical formulation of the Precipitation-Runoff Modeling System and its model components. This updated version provides improvements in (1) system flexibility for integrated science, (2) verification of conservation of water during simulation, (3) methods for spatial distribution of climate boundary conditions, and (4) methods for simulation of soil-water flow and storage.

Built Expansion and Global Climate Change Drive Projected Urban Heat: Relative Magnitudes, Interactions, and Mitigation

NASA Astrophysics Data System (ADS)

Krayenhoff, E. S.; Georgescu, M.; Moustaoui, M.

2016-12-01

Surface climates are projected to warm due to global climate change over the course of the 21st century, and demographic projections suggest urban areas in the United States will continue to expand and develop, with associated local climate outcomes. Interactions between these two drivers of urban heat have not been robustly quantified to date. Here, simulations with the Weather Research and Forecasting model (coupled to a Single-Layer Urban Canopy Model) are performed at 20 km resolution over the continental U.S. for two 10-year periods: contemporary (2000-2009) and end-of-century (2090-2099). Present and end of century urban land use are derived from the Environmental Protection Agency's Integrated Climate and Land-Use Scenarios. Modelled effects on urban climates are evaluated regionally. Sensitivity to climate projection (Community Climate System Model 4.0, RCP 4.5 vs. RCP 8.5) and associated urban development scenarios are assessed. Effects on near-surface urban air temperature of RCP8.5 climate change are greater than those attributable to the corresponding urban development in many regions. Interaction effects vary by region, and while of lesser magnitude, are not negligible. Moreover, urban development and its interactions with RCP8.5 climate change modify the distribution of convective precipitation over the eastern US. Interaction effects result from the different meteorological effects of urban areas under current and future climate. Finally, the potential for design implementations such as green roofs and high albedo roofs to offset the projected warming is considered. Impacts of these implementations on precipitation are also assessed.

Future Climate Change in the Baltic Sea Area

NASA Astrophysics Data System (ADS)

Bøssing Christensen, Ole; Kjellström, Erik; Zorita, Eduardo; Sonnenborg, Torben; Meier, Markus; Grinsted, Aslak

2015-04-01

Regional climate models have been used extensively since the first assessment of climate change in the Baltic Sea region published in 2008, not the least for studies of Europe (and including the Baltic Sea catchment area). Therefore, conclusions regarding climate model results have a better foundation than was the case for the first BACC report of 2008. This presentation will report model results regarding future climate. What is the state of understanding about future human-driven climate change? We will cover regional models, statistical downscaling, hydrological modelling, ocean modelling and sea-level change as it is projected for the Baltic Sea region. Collections of regional model simulations from the ENSEMBLES project for example, financed through the European 5th Framework Programme and the World Climate Research Programme Coordinated Regional Climate Downscaling Experiment, have made it possible to obtain an increasingly robust estimation of model uncertainty. While the first Baltic Sea assessment mainly used four simulations from the European 5th Framework Programme PRUDENCE project, an ensemble of 13 transient regional simulations with twice the horizontal resolution reaching the end of the 21st century has been available from the ENSEMBLES project; therefore it has been possible to obtain more quantitative assessments of model uncertainty. The literature about future climate change in the Baltic Sea region is largely built upon the ENSEMBLES project. Also within statistical downscaling, a considerable number of papers have been published, encompassing now the application of non-linear statistical models, projected changes in extremes and correction of climate model biases. The uncertainty of hydrological change has received increasing attention since the previous Baltic Sea assessment. Several studies on the propagation of uncertainties originating in GCMs, RCMs, and emission scenarios are presented. The number of studies on uncertainties related to downscaling and impact models is relatively small, but more are emerging. A large number of coupled climate-environmental scenario simulations for the Baltic Sea have been performed within the BONUS+ projects (ECOSUPPORT, INFLOW, AMBER and Baltic-C (2009-2011)), using various combinations of output from GCMs, RCMs, hydrological models and scenarios for load and emission of nutrients as forcing for Baltic Sea models. Such a large ensemble of scenario simulations for the Baltic Sea has never before been produced and enables for the first time an estimation of uncertainties.

Modelling shifts in agroclimate and crop cultivar response under climate change.

PubMed

Rötter, Reimund P; Höhn, Jukka; Trnka, Mirek; Fronzek, Stefan; Carter, Timothy R; Kahiluoto, Helena

2013-10-01

(i) to identify at national scale areas where crop yield formation is currently most prone to climate-induced stresses, (ii) to evaluate how the severity of these stresses is likely to develop in time and space, and (iii) to appraise and quantify the performance of two strategies for adapting crop cultivation to a wide range of (uncertain) climate change projections. To this end we made use of extensive climate, crop, and soil data, and of two modelling tools: N-AgriCLIM and the WOFOST crop simulation model. N-AgriCLIM was developed for the automatic generation of indicators describing basic agroclimatic conditions and was applied over the whole of Finland. WOFOST was used to simulate detailed crop responses at four representative locations. N-AgriCLIM calculations have been performed nationally for 3829 grid boxes at a 10 × 10 km resolution and for 32 climate scenarios. Ranges of projected shifts in indicator values for heat, drought and other crop-relevant stresses across the scenarios vary widely - so do the spatial patterns of change. Overall, under reference climate the most risk-prone areas for spring cereals are found in south-west Finland, shifting to south-east Finland towards the end of this century. Conditions for grass are likely to improve. WOFOST simulation results suggest that CO2 fertilization and adjusted sowing combined can lead to small yield increases of current barley cultivars under most climate scenarios on favourable soils, but not under extreme climate scenarios and poor soils. This information can be valuable for appraising alternative adaptation strategies. It facilitates the identification of regions in which climatic changes might be rapid or otherwise notable for crop production, requiring a more detailed evaluation of adaptation measures. The results also suggest that utilizing the diversity of cultivar responses seems beneficial given the high uncertainty in climate change projections.

Air-sea exchange over Black Sea estimated from high resolution regional climate simulations

NASA Astrophysics Data System (ADS)

Velea, Liliana; Bojariu, Roxana; Cica, Roxana

2013-04-01

Black Sea is an important influencing factor for the climate of bordering countries, showing cyclogenetic activity (Trigo et al, 1999) and influencing Mediterranean cyclones passing over. As for other seas, standard observations of the atmosphere are limited in time and space and available observation-based estimations of air-sea exchange terms present quite large ranges of uncertainty. The reanalysis datasets (e.g. ERA produced by ECMWF) provide promising validation estimates of climatic characteristics against the ones in available climatic data (Schrum et al, 2001), while cannot reproduce some local features due to relatively coarse horizontal resolution. Detailed and realistic information on smaller-scale processes are foreseen to be provided by regional climate models, due to continuous improvements of physical parameterizations and numerical solutions and thus affording simulations at high spatial resolution. The aim of the study is to assess the potential of three regional climate models in reproducing known climatological characteristics of air-sea exchange over Black Sea, as well as to explore the added value of the model compared to the input (reanalysis) data. We employ results of long-term (1961-2000) simulations performed within ENSEMBLE project (http://ensemblesrt3.dmi.dk/) using models ETHZ-CLM, CNRM-ALADIN, METO-HadCM, for which the integration domain covers the whole area of interest. The analysis is performed for the entire basin for several variables entering the heat and water budget terms and available as direct output from the models, at seasonal and annual scale. A comparison with independent data (ERA-INTERIM) and findings from other studies (e.g. Schrum et al, 2001) is also presented. References: Schrum, C., Staneva, J., Stanev, E. and Ozsoy, E., 2001: Air-sea exchange in the Black Sea estimated from atmospheric analysis for the period 1979-1993, J. Marine Systems, 31, 3-19 Trigo, I. F., T. D. Davies, and G. R. Bigg (1999): Objective climatology of cyclones in the Mediterranean region. J. Climate, 12, 1685- 169

FACE-IT. A Science Gateway for Food Security Research

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

Montella, Raffaele; Kelly, David; Xiong, Wei

Progress in sustainability science is hindered by challenges in creating and managing complex data acquisition, processing, simulation, post-processing, and intercomparison pipelines. To address these challenges, we developed the Framework to Advance Climate, Economic, and Impact Investigations with Information Technology (FACE-IT) for crop and climate impact assessments. This integrated data processing and simulation framework enables data ingest from geospatial archives; data regridding, aggregation, and other processing prior to simulation; large-scale climate impact simulations with agricultural and other models, leveraging high-performance and cloud computing; and post-processing to produce aggregated yields and ensemble variables needed for statistics, for model intercomparison, and to connectmore » biophysical models to global and regional economic models. FACE-IT leverages the capabilities of the Globus Galaxies platform to enable the capture of workflows and outputs in well-defined, reusable, and comparable forms. We describe FACE-IT and applications within the Agricultural Model Intercomparison and Improvement Project and the Center for Robust Decision-making on Climate and Energy Policy.« less

High mean water vapour pressure promotes the transmission of bacillary dysentery.

PubMed

Li, Guo-Zheng; Shao, Feng-Feng; Zhang, Hao; Zou, Chun-Pu; Li, Hui-Hui; Jin, Jue

2015-01-01

Bacillary dysentery is an infectious disease caused by Shigella dysenteriae, which has a seasonal distribution. External environmental factors, including climate, play a significant role in its transmission. This paper identifies climate-related risk factors and their role in bacillary dysentery transmission. Harbin, in northeast China, with a temperate climate, and Quzhou, in southern China, with a subtropical climate, are chosen as the study locations. The least absolute shrinkage and selectionator operator is applied to select relevant climate factors involved in the transmission of bacillary dysentery. Based on the selected relevant climate factors and incidence rates, an AutoRegressive Integrated Moving Average (ARIMA) model is established successfully as a time series prediction model. The numerical results demonstrate that the mean water vapour pressure over the previous month results in a high relative risk for bacillary dysentery transmission in both cities, and the ARIMA model can successfully perform such a prediction. These results provide better explanations for the relationship between climate factors and bacillary dysentery transmission than those put forth in other studies that use only correlation coefficients or fitting models. The findings in this paper demonstrate that the mean water vapour pressure over the previous month is an important predictor for the transmission of bacillary dysentery.

Potential climate effect of mineral aerosols over West Africa. Part I: model validation and contemporary climate evaluation

NASA Astrophysics Data System (ADS)

Ji, Zhenming; Wang, Guiling; Pal, Jeremy S.; Yu, Miao

2016-02-01

Mineral dusts present in the atmosphere can play an important role in climate over West Africa and surrounding regions. However, current understanding regarding how dust aerosols influence climate of West Africa is very limited. In this study, a regional climate model is used to investigate the potential climatic impacts of dust aerosols. Two sets of simulations driven by reanalysis and Earth System Model boundary conditions are performed with and without the representation of dust processes. The model, regardless of the boundary forcing, captures the spatial and temporal variability of the aerosol optical depth and surface concentration. The shortwave radiative forcing of dust is negative at the surface and positive in the atmosphere, with greater changes in the spring and summer. The presence of mineral dusts causes surface cooling and lower troposphere heating, resulting in a stabilization effect and reduction in precipitation in the northern portion of the monsoon close to the dust emissions region. This results in an enhancement of precipitation to the south. While dusts cause the lower troposphere to stabilize, upper tropospheric cooling makes the region more prone to intense deep convection as is evident by a simulated increase in extreme precipitation. In a companion paper, the impacts of dust emissions on future West African climate are investigated.

A Quantitative Climate-Match Score for Risk-Assessment Screening of Reptile and Amphibian Introductions

NASA Astrophysics Data System (ADS)

van Wilgen, Nicola J.; Roura-Pascual, Núria; Richardson, David M.

2009-09-01

Assessing climatic suitability provides a good preliminary estimate of the invasive potential of a species to inform risk assessment. We examined two approaches for bioclimatic modeling for 67 reptile and amphibian species introduced to California and Florida. First, we modeled the worldwide distribution of the biomes found in the introduced range to highlight similar areas worldwide from which invaders might arise. Second, we modeled potentially suitable environments for species based on climatic factors in their native ranges, using three sources of distribution data. Performance of the three datasets and both approaches were compared for each species. Climate match was positively correlated with species establishment success (maximum predicted suitability in the introduced range was more strongly correlated with establishment success than mean suitability). Data assembled from the Global Amphibian Assessment through NatureServe provided the most accurate models for amphibians, while ecoregion data compiled by the World Wide Fund for Nature yielded models which described reptile climatic suitability better than available point-locality data. We present three methods of assigning a climate-match score for use in risk assessment using both the mean and maximum climatic suitabilities. Managers may choose to use different methods depending on the stringency of the assessment and the available data, facilitating higher resolution and accuracy for herpetofaunal risk assessment. Climate-matching has inherent limitations and other factors pertaining to ecological interactions and life-history traits must also be considered for thorough risk assessment.

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.

Assessing the link between Atlantic Niño 1 and drought over West Africa using CORDEX regional climate models

NASA Astrophysics Data System (ADS)

Adeniyi, Mojisola Oluwayemisi; Dilau, Kabiru Alabi

2018-02-01

The skill of Coordinated Regional Climate Downscaling Experiment (CORDEX) models (ARPEGE, CCLM, HIRHAM, RACMO, REMO, PRECIS, RegCM3, RCA, WRF and CRCM) in simulating the climate (precipitation, temperature and drought) of West Africa is determined using a process-based metric. This is done by comparing the CORDEX models' simulated and observed correlation coefficients between Atlantic Niño Index 1 (ATLN1) and the climate over West Africa. Strong positive correlation is observed between ATLN1 and the climate parameters at the Guinea Coast (GC). The Atlantic Ocean has Niño behaviours through the ATLN indices which influence the climate of the tropics. Drought has distinct dipole structure of correlation with ATLN1 (negative at the Sahel); precipitation does not have distinct dipole structure of correlation, while temperature has almost a monopole correlation structure with ATLN1 over West Africa. The magnitude of the correlation increases with closeness to the equatorial eastern Atlantic. Correlations between ATLN1 and temperature are mostly stronger than those between ATLN1 and precipitation over the region. Most models have good performance over the GC, but ARPEGE has the highest skill at GC. The PRECIS is the most skilful over Savannah and RCA over Sahel. These models can be used to downscale the projected climate at the region of their highest skill.

A Three-Legged Stool or Race? Governance Models for NOAA RISAs, DOI CSCs, and USDA Climate Hubs

NASA Astrophysics Data System (ADS)

Foster, J. G.

2014-12-01

NOAAs Regional Integrated Sciences and Assessments (RISA) Teams, DOIs Climate Science Centers (CSCs), and USDAs Regional Climate Hubs (RCHs) have common missions of integrating climate and related knowledge across scientific disciplines and regions to create "actionable" information that decision-makes can use to manage climate risks and impacts at state and local scales. However, the sponsoring agency programs, university investigators, and local federal officials govern each differently. The three models of program and center governance are 1) exclusively university (RISAs), 2) a hybrid of Federal government and (host) university (CSCs,), and 3) exclusively Federal (Hubs). Each model has it's advantages and disadvantages in terms of legal definition and authority, scientific mission requirements and strategies, flexibility and legitimacy to conduct research and to collaborate regionally with constituencies, leadership and governance approach and "friction points,", staff capacity and ability to engage stakeholders, necessity to deliver products and services, bureaucratic oversight, performance evaluation, and political support at Congressional, state, and local levels. Using available sources of information and data, this paper will compare and contrast the strengths and weakness of these three regional applied climate science center governance models.

A Three-Legged Stool or Race? Governance Models for NOAA RISAs, DOI CSCs, and USDA Climate Hub

NASA Astrophysics Data System (ADS)

Foster, J. G.

2014-12-01

NOAAs Regional Integrated Sciences and Assessments (RISA) Teams, DOIs Climate Science Centers (CSCs), and USDAs Regional Climate Hubs (RCHs) have common missions of integrating climate and related knowledge across scientific disciplines and regions to create "actionable" information that decision-makes can use to manage climate risks and impacts at state and local scales. However, the sponsoring agency programs, university investigators, and local federal officials govern each differently. The three models of program and center governance are 1) exclusively university (RISAs), 2) a hybrid of Federal government and (host) university (CSCs,), and 3) exclusively Federal (Hubs). Each model has it's advantages and disadvantages in terms of legal definition and authority, scientific mission requirements and strategies, flexibility and legitimacy to conduct research and to collaborate regionally with constituencies, leadership and governance approach and "friction points,", staff capacity and ability to engage stakeholders, necessity to deliver products and services, bureaucratic oversight, performance evaluation, and political support at Congressional, state, and local levels. Using available sources of information and data, this paper will compare and contrast the strengths and weakness of these three regional applied climate science center governance models.

Wintertime urban heat island modified by global climate change over Japan

NASA Astrophysics Data System (ADS)

Hara, M.

2015-12-01

Urban thermal environment change, especially, surface air temperature (SAT) rise in metropolitan areas, is one of the major recent issues in urban areas. The urban thermal environmental change affects not only human health such as heat stroke, but also increasing infectious disease due to spreading out virus vectors habitat and increase of industry and house energy consumption. The SAT rise is mostly caused by global climate change and urban heat island (hereafter UHI) by urbanization. The population in Tokyo metropolitan area is over 30 millions and the Tokyo metropolitan area is one of the biggest megacities in the world. The temperature rise due to urbanization seems comparable to the global climate change in the major megacities. It is important to project how the urbanization and the global climate change affect to the future change of urban thermal environment to plan the adaptation and mitigation policy. To predict future SAT change in urban scale, we should estimate future UHI modified by the global climate change. This study investigates change in UHI intensity (UHII) of major metropolitan areas in Japan by effects of the global climate change. We performed a series of climate simulations. Present climate simulations with and without urban process are conducted for ten seasons using a high-resolution numerical climate model, the Weather Research and Forecasting (WRF) model. Future climate projections with and without urban process are also conducted. The future projections are performed using the pseudo global warming method, assuming 2050s' initial and boundary conditions estimated by a GCM under the RCP scenario. Simulation results indicated that UHII would be enhanced more than 30% in Tokyo during the night due to the global climate change. The enhancement of urban heat island is mostly caused by change of lower atmospheric stability.

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

NASA Astrophysics Data System (ADS)

Yoshida, K.; Naoe, H.

2016-12-01

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

A Process-based, Climate-Sensitive Model to Derive Methane Emissions from Natural Wetlands: Application to 5 Wetland Sites, Sensitivity to Model Parameters and Climate

NASA Technical Reports Server (NTRS)

Walter, Bernadette P.; Heimann, Martin

1999-01-01

Methane emissions from natural wetlands constitutes the largest methane source at present and depends highly on the climate. In order to investigate the response of methane emissions from natural wetlands to climate variations, a 1-dimensional process-based climate-sensitive model to derive methane emissions from natural wetlands is developed. In the model the processes leading to methane emission are simulated within a 1-dimensional soil column and the three different transport mechanisms diffusion, plant-mediated transport and ebullition are modeled explicitly. The model forcing consists of daily values of soil temperature, water table and Net Primary Productivity, and at permafrost sites the thaw depth is included. The methane model is tested using observational data obtained at 5 wetland sites located in North America, Europe and Central America, representing a large variety of environmental conditions. It can be shown that in most cases seasonal variations in methane emissions can be explained by the combined effect of changes in soil temperature and the position of the water table. Our results also show that a process-based approach is needed, because there is no simple relationship between these controlling factors and methane emissions that applies to a variety of wetland sites. The sensitivity of the model to the choice of key model parameters is tested and further sensitivity tests are performed to demonstrate how methane emissions from wetlands respond to climate variations.

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.

Seasonal precipitation forecasting for the Melbourne region using a Self-Organizing Maps approach

NASA Astrophysics Data System (ADS)

Pidoto, Ross; Wallner, Markus; Haberlandt, Uwe

2017-04-01

The Melbourne region experiences highly variable inter-annual rainfall. For close to a decade during the 2000s, below average rainfall seriously affected the environment, water supplies and agriculture. A seasonal rainfall forecasting model for the Melbourne region based on the novel approach of a Self-Organizing Map has been developed and tested for its prediction performance. Predictor variables at varying lead times were first assessed for inclusion within the model by calculating their importance via Random Forests. Predictor variables tested include the climate indices SOI, DMI and N3.4, in addition to gridded global sea surface temperature data. Five forecasting models were developed: an annual model and four seasonal models, each individually optimized for performance through Pearson's correlation r and the Nash-Sutcliffe Efficiency. The annual model showed a prediction performance of r = 0.54 and NSE = 0.14. The best seasonal model was for spring, with r = 0.61 and NSE = 0.31. Autumn was the worst performing seasonal model. The sea surface temperature data contributed fewer predictor variables compared to climate indices. Most predictor variables were supplied at a minimum lead, however some predictors were found at lead times of up to a year.

Global Potential for Hydro-generated Electricity and Climate Change Impact

NASA Astrophysics Data System (ADS)

Zhou, Y.; Hejazi, M. I.; Leon, C.; Calvin, K. V.; Thomson, A. M.; Li, H. Y.

2014-12-01

Hydropower is a dominant renewable energy source at the global level, accounting for more than 15% of the world's total power supply. It is also very vulnerable to climate change. Improved understanding of climate change impact on hydropower can help develop adaptation measures to increase the resilience of energy system. In this study, we developed a comprehensive estimate of global hydropower potential using runoff and stream flow data derived from a global hydrologic model with a river routing sub-model, along with turbine technology performance, cost assumptions, and environmental consideration (Figure 1). We find that hydropower has the potential to supply a significant portion of the world energy needs, although this potential varies substantially by regions. Resources in a number of countries exceed by multiple folds the total current demand for electricity, e.g., Russia and Indonesia. A sensitivity analysis indicates that hydropower potential can be highly sensitive to a number of parameters including designed flow for capacity, cost and financing, turbine efficiency, and stream flow. The climate change impact on hydropower potential was evaluated by using runoff outputs from 4 climate models (HadCM3, PCM, CGCM2, and CSIRO2). It was found that the climate change on hydropower shows large variation not only by regions, but also climate models, and this demonstrates the importance of incorporating climate change into infrastructure-planning at the regional level though the existing uncertainties.

Safety climate and culture: Integrating psychological and systems perspectives.

PubMed

Casey, Tristan; Griffin, Mark A; Flatau Harrison, Huw; Neal, Andrew

2017-07-01

Safety climate research has reached a mature stage of development, with a number of meta-analyses demonstrating the link between safety climate and safety outcomes. More recently, there has been interest from systems theorists in integrating the concept of safety culture and to a lesser extent, safety climate into systems-based models of organizational safety. Such models represent a theoretical and practical development of the safety climate concept by positioning climate as part of a dynamic work system in which perceptions of safety act to constrain and shape employee behavior. We propose safety climate and safety culture constitute part of the enabling capitals through which organizations build safety capability. We discuss how organizations can deploy different configurations of enabling capital to exert control over work systems and maintain safe and productive performance. We outline 4 key strategies through which organizations to reconcile the system control problems of promotion versus prevention, and stability versus flexibility. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6

DOE PAGES

O'Neill, Brian C.; Tebaldi, Claudia; van Vuuren, Detlef P.; ...

2016-09-28

Projections of future climate change play a fundamental role in improving understanding of the climate system as well as characterizing societal risks and response options. The Scenario Model Intercomparison Project (ScenarioMIP) is the primary activity within Phase 6 of the Coupled Model Intercomparison Project (CMIP6) that will provide multi-model climate projections based on alternative scenarios of future emissions and land use changes produced with integrated assessment models. Here, we describe ScenarioMIP's objectives, experimental design, and its relation to other activities within CMIP6. The ScenarioMIP design is one component of a larger scenario process that aims to facilitate a wide rangemore » of integrated studies across the climate science, integrated assessment modeling, and impacts, adaptation, and vulnerability communities, and will form an important part of the evidence base in the forthcoming Intergovernmental Panel on Climate Change (IPCC) assessments. Furthermore, it will provide the basis for investigating a number of targeted science and policy questions that are especially relevant to scenario-based analysis, including the role of specific forcings such as land use and aerosols, the effect of a peak and decline in forcing, the consequences of scenarios that limit warming to below 2°C, the relative contributions to uncertainty from scenarios, climate models, and internal variability, and long-term climate system outcomes beyond the 21st century. In order to serve this wide range of scientific communities and address these questions, a design has been identified consisting of eight alternative 21st century scenarios plus one large initial condition ensemble and a set of long-term extensions, divided into two tiers defined by relative priority. Some of these scenarios will also provide a basis for variants planned to be run in other CMIP6-Endorsed MIPs to investigate questions related to specific forcings. Harmonized, spatially explicit emissions and land use scenarios generated with integrated assessment models will be provided to participating climate modeling groups by late 2016, with the climate model simulations run within the 2017–2018 time frame, and output from the climate model projections made available and analyses performed over the 2018–2020 period.« less

The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6

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

O'Neill, Brian C.; Tebaldi, Claudia; van Vuuren, Detlef P.

2016-01-01

Projections of future climate change play a fundamental role in improving understanding of the climate system as well as characterizing societal risks and response options. The Scenario Model Intercomparison Project (ScenarioMIP) is the primary activity within Phase 6 of the Coupled Model Intercomparison Project (CMIP6) that will provide multi-model climate projections based on alternative scenarios of future emissions and land use changes produced with integrated assessment models. In this paper, we describe ScenarioMIP's objectives, experimental design, and its relation to other activities within CMIP6. The ScenarioMIP design is one component of a larger scenario process that aims to facilitate amore » wide range of integrated studies across the climate science, integrated assessment modeling, and impacts, adaptation, and vulnerability communities, and will form an important part of the evidence base in the forthcoming Intergovernmental Panel on Climate Change (IPCC) assessments. At the same time, it will provide the basis for investigating a number of targeted science and policy questions that are especially relevant to scenario-based analysis, including the role of specific forcings such as land use and aerosols, the effect of a peak and decline in forcing, the consequences of scenarios that limit warming to below 2 °C, the relative contributions to uncertainty from scenarios, climate models, and internal variability, and long-term climate system outcomes beyond the 21st century. To serve this wide range of scientific communities and address these questions, a design has been identified consisting of eight alternative 21st century scenarios plus one large initial condition ensemble and a set of long-term extensions, divided into two tiers defined by relative priority. Some of these scenarios will also provide a basis for variants planned to be run in other CMIP6-Endorsed MIPs to investigate questions related to specific forcings. Harmonized, spatially explicit emissions and land use scenarios generated with integrated assessment models will be provided to participating climate modeling groups by late 2016, with the climate model simulations run within the 2017–2018 time frame, and output from the climate model projections made available and analyses performed over the 2018–2020 period.« less

The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6

NASA Astrophysics Data System (ADS)

O'Neill, Brian C.; Tebaldi, Claudia; van Vuuren, Detlef P.; Eyring, Veronika; Friedlingstein, Pierre; Hurtt, George; Knutti, Reto; Kriegler, Elmar; Lamarque, Jean-Francois; Lowe, Jason; Meehl, Gerald A.; Moss, Richard; Riahi, Keywan; Sanderson, Benjamin M.

2016-09-01

Projections of future climate change play a fundamental role in improving understanding of the climate system as well as characterizing societal risks and response options. The Scenario Model Intercomparison Project (ScenarioMIP) is the primary activity within Phase 6 of the Coupled Model Intercomparison Project (CMIP6) that will provide multi-model climate projections based on alternative scenarios of future emissions and land use changes produced with integrated assessment models. In this paper, we describe ScenarioMIP's objectives, experimental design, and its relation to other activities within CMIP6. The ScenarioMIP design is one component of a larger scenario process that aims to facilitate a wide range of integrated studies across the climate science, integrated assessment modeling, and impacts, adaptation, and vulnerability communities, and will form an important part of the evidence base in the forthcoming Intergovernmental Panel on Climate Change (IPCC) assessments. At the same time, it will provide the basis for investigating a number of targeted science and policy questions that are especially relevant to scenario-based analysis, including the role of specific forcings such as land use and aerosols, the effect of a peak and decline in forcing, the consequences of scenarios that limit warming to below 2 °C, the relative contributions to uncertainty from scenarios, climate models, and internal variability, and long-term climate system outcomes beyond the 21st century. To serve this wide range of scientific communities and address these questions, a design has been identified consisting of eight alternative 21st century scenarios plus one large initial condition ensemble and a set of long-term extensions, divided into two tiers defined by relative priority. Some of these scenarios will also provide a basis for variants planned to be run in other CMIP6-Endorsed MIPs to investigate questions related to specific forcings. Harmonized, spatially explicit emissions and land use scenarios generated with integrated assessment models will be provided to participating climate modeling groups by late 2016, with the climate model simulations run within the 2017-2018 time frame, and output from the climate model projections made available and analyses performed over the 2018-2020 period.

Show Me the Evidence: How a Unit Challenge Can Support Middle School Teachers and Students in Investigating Climate Change Using Real-World Data and Science Practices

NASA Astrophysics Data System (ADS)

Gochis, E. E.; Tubman, S.; Grazul, K.; Bluth, G.; Huntoon, J. E.

2017-12-01

Michigan Science Teaching and Assessment Reform (Mi-STAR) is developing an NGSS-aligned integrated science middle school curriculum and associated teacher professional learning program that addresses all performance expectations for the 6-8 grade-band. The Mi-STAR instructional model is a unit- and lesson-level model that scaffolds students in using science practices to investigate scientific phenomena and apply engineering principles to address a real-world challenge. Mi-STAR has developed an 8th grade unit on climate change based on the Mi-STAR instructional model and NGSS performance expectations. The unit was developed in collaboration with Michigan teachers, climate scientists, and curriculum developers. The unit puts students in the role of advisers to local officials who need an evidence-based explanation of climate change and recommendations about community-based actions to address it. Students discover puzzling signs of global climate change, ask questions about these signs, and engage in a series of investigations using simulations and real data to develop scientific models for the mechanisms of climate change. Students use their models as the basis for evidence-based arguments about the causes and impacts of climate change and employ engineering practices to propose local actions in their community to address climate change. Dedicated professional learning supports teachers before and during implementation of the unit. Before implementing the unit, all teachers complete an online self-paced "unit primer" during which they assume the role of their students as they are introduced to the unit challenge. During this experience, teachers experience science as a practice by using real data and simulations to develop a model of the causes of climate change, just as their students will later do. During unit implementation, teachers are part of a professional learning community led by a teacher facilitator in their local area or school. This professional learning community serves as a resource both for implementing student-directed pedagogy and for the development of content knowledge. Eight teachers pilot tested the unit with more than 500 students in spring 2017, and teachers who participated in the first professional learning cohort are currently implementing the unit around Michigan.

Regional Climate Simulations with COSMO-CLM for West Africa using three different soil-vegetation-atmosphere-transfer (SVAT) module

NASA Astrophysics Data System (ADS)

Breil, Marcus; Panitz, Hans-Jürgen

2014-05-01

Climate predictions on decadal timescales constitute a new field of research, closing the gap between short-term and seasonal weather predictions and long-term climate projections. Therefore, the Federal Ministry of Education and Research in Germany (BMBF) has recently funded the research program MiKlip (Mittelfristige Klimaprognosen), which aims to create a model system that can provide reliable decadal climate forecasts. Recent studies have suggested that one region with high potential decadal predictability is West Africa. Therefore, the project DEPARTURE (DEcadal Prediction of African Rainfall and ATlantic HURricanE Activity) was established within the MiKlip program to assess the feasibility and the potential added value of regional decadal climate predictions for West Africa. To quantify the potential decadal climate predictability, a multi-model approach with the three different regional climate models REMO, WRF and COSMO-CLM (CCLM) will be realized. The presented research will contribute to DEPARTURE by performing hindcast ensemble simulations with CCLM, driven by global decadal MPI-ESM-LR simulations. Thereby, one focus is on the dynamic soil-vegetation-climate interaction on decadal timescales. Recent studies indicate that there are significant feedbacks between the land-surface and the atmosphere, which might influence the decadal climate variability substantially. To investigate this connection, two different SVATs (Community Land Model (CLM), and VEG3D) will be coupled with the CCLM, replacing TERRA_ML, the standard SVAT implemented in CCLM. Thus, sensitive model parameters shall be identified, whereby the understanding of important processes might be improved. As a first step, TERRA_ML is substituted by VEG3D, a SVAT developed at the IMK-TRO, Karlsruhe, Germany. Compared to TERRA_ML, VEG3D includes an explicit vegetation layer by using a big leaf approach, inducing higher correlations with observations as it has been shown in previous studies. The coupling of VEG3D with CCLM is performed by using the OASIS3-MCT coupling software, developed by CERFACS, Toulouse, France. Results of CCLM simulations using both SVATs are analysed and compared for the DEPARTURE model domain. Thereby ERA-Interim driven CCLM simulations with VEG3D showed better agreement with observational data than simulations with TERRA_ML, especially for dense vegetaded areas. This will be demonstrated exemplarily. Additionally, results for MPI-ESM-LR driven decadal hindcast simulations (1966 - 1975) are analysed and presented.

Sensitivity of the regional climate in the Middle East and North Africa to volcanic perturbations

NASA Astrophysics Data System (ADS)

Dogar, Muhammad Mubashar; Stenchikov, Georgiy; Osipov, Sergey; Wyman, Bruce; Zhao, Ming

2017-08-01

The Middle East and North Africa (MENA) regional climate appears to be extremely sensitive to volcanic eruptions. Winter cooling after the 1991 Pinatubo eruption far exceeded the mean hemispheric temperature anomaly, even causing snowfall in Israel. To better understand MENA climate variability, the climate responses to the El Chichón and Pinatubo volcanic eruptions are analyzed using observations, NOAA/National Centers for Environmental Prediction Climate Forecast System Reanalysis, and output from the Geophysical Fluid Dynamics Laboratory's High-Resolution Atmospheric Model. A multiple regression analysis both for the observations and the model output is performed on seasonal summer and winter composites to separate out the contributions from climate trends, El Niño-Southern Oscillation (ENSO), North Atlantic Oscillation (NAO), Indian summer monsoon, and volcanic aerosols. Strong regional temperature and precipitation responses over the MENA region are found in both winter and summer. The model and the observations both show that a positive NAO amplifies the MENA volcanic winter cooling. In boreal summer, the patterns of changing temperature and precipitation suggest a weakening and southward shift of the Intertropical Convergence Zone, caused by volcanic surface cooling and weakening of the Indian and West African monsoons. The model captures the main features of the climate response; however, it underestimates the total cooling, especially in winter, and exhibits a different spatial pattern of the NAO climate response in MENA compared to the observations. The conducted analysis sheds light on the internal mechanisms of MENA climate variability and helps to selectively diagnose the model deficiencies.

Validation of an organizational communication climate assessment toolkit.

PubMed

Wynia, Matthew K; Johnson, Megan; McCoy, Thomas P; Griffin, Leah Passmore; Osborn, Chandra Y

2010-01-01

Effective communication is critical to providing quality health care and can be affected by a number of modifiable organizational factors. The authors performed a prospective multisite validation study of an organizational communication climate assessment tool in 13 geographically and ethnically diverse health care organizations. Communication climate was measured across 9 discrete domains. Patient and staff surveys with matched items in each domain were developed using a national consensus process, which then underwent psychometric field testing and assessment of domain coherence. The authors found meaningful within-site and between-site performance score variability in all domains. In multivariable models, most communication domains were significant predictors of patient-reported quality of care and trust. The authors conclude that these assessment tools provide a valid empirical assessment of organizational communication climate in 9 domains. Assessment results may be useful to track organizational performance, to benchmark, and to inform tailored quality improvement interventions.

Performance Investigation of a Solar Heat Driven Adsorption Chiller under Two Different Climatic Conditions

NASA Astrophysics Data System (ADS)

Choudhury, Biplab; Chatterjee, Pradip Kumar; Habib, Khairul; Saha, Bidyut Baran

2018-06-01

The demand for cooling, especially in the developing economies, is rising at a fast rate. Fast-depleting sources of fossil fuel and environmental concerns necessitate looking for alternative cooling solutions. Solar heat driven adsorption based cooling cycles are environmentally friendly due to their use of natural refrigerants and the thermal compression process. In this paper, a performance simulation study of a basic two-bed solar adsorption chiller has been performed through a transient model for two different climatic locations in India. Effect of operating temperatures and cycle time on the chiller performance has been studied. It is observed that the solar hot water temperature obtained in the composite climate of Delhi (28.65°N, 77.25°E) can run the basic adsorption cooling cycle efficiently throughout the year. Whereas, the monsoon months of July and August in the warm and humid climate of Durgapur (23.48°N, 87.32°E) are unable to supply the required driving heat.

High Resolution Climate Modeling of the Water Cycle over the Contiguous United States Including Potential Climate Change Scenarios

NASA Astrophysics Data System (ADS)

Rasmussen, R.; Ikeda, K.; Liu, C.; Gochis, D.; Chen, F.; Barlage, M. J.; Dai, A.; Dudhia, J.; Clark, M. P.; Gutmann, E. D.; Li, Y.

2015-12-01

The NCAR Water System program strives to improve the full representation of the water cycle in both regional and global models. Our previous high-resolution simulations using the WRF model over the Rocky Mountains revealed that proper spatial and temporal depiction of snowfall adequate for water resource and climate change purposes can be achieved with the appropriate choice of model grid spacing (< 6 km horizontal) and parameterizations. The climate sensitivity experiment consistent with expected climate change showed an altered hydrological cycle with increased fraction of rain versus snow, increased snowfall at high altitudes, earlier melting of snowpack, and decreased total runoff. In order to investigate regional differences between the Rockies and other major mountain barriers and to study climate change impacts over other regions of the contiguous U.S. (CONUS), we have expanded our prior CO Headwaters modeling study to encompass most of North America at a horizontal grid spacing of 4 km. A domain expansion provides the opportunity to assess changes in orographic precipitation across different mountain ranges in the western USA, as well as the very dominant role of convection in the eastern half of the USA. The high resolution WRF-downscaled climate change data will also become a valuable community resource for many university groups who are interested in studying regional climate changes and impacts but unable to perform such long-duration and high-resolution WRF-based downscaling simulations of their own. The scientific goals and details of the model dataset will be presented including some preliminary results.

A prognostic pollen emissions model for climate models (PECM1.0)

NASA Astrophysics Data System (ADS)

Wozniak, Matthew C.; Steiner, Allison L.

2017-11-01

We develop a prognostic model called Pollen Emissions for Climate Models (PECM) for use within regional and global climate models to simulate pollen counts over the seasonal cycle based on geography, vegetation type, and meteorological parameters. Using modern surface pollen count data, empirical relationships between prior-year annual average temperature and pollen season start dates and end dates are developed for deciduous broadleaf trees (Acer, Alnus, Betula, Fraxinus, Morus, Platanus, Populus, Quercus, Ulmus), evergreen needleleaf trees (Cupressaceae, Pinaceae), grasses (Poaceae; C3, C4), and ragweed (Ambrosia). This regression model explains as much as 57 % of the variance in pollen phenological dates, and it is used to create a climate-flexible phenology that can be used to study the response of wind-driven pollen emissions to climate change. The emissions model is evaluated in the Regional Climate Model version 4 (RegCM4) over the continental United States by prescribing an emission potential from PECM and transporting pollen as aerosol tracers. We evaluate two different pollen emissions scenarios in the model using (1) a taxa-specific land cover database, phenology, and emission potential, and (2) a plant functional type (PFT) land cover, phenology, and emission potential. The simulated surface pollen concentrations for both simulations are evaluated against observed surface pollen counts in five climatic subregions. Given prescribed pollen emissions, the RegCM4 simulates observed concentrations within an order of magnitude, although the performance of the simulations in any subregion is strongly related to the land cover representation and the number of observation sites used to create the empirical phenological relationship. The taxa-based model provides a better representation of the phenology of tree-based pollen counts than the PFT-based model; however, we note that the PFT-based version provides a useful and climate-flexible emissions model for the general representation of the pollen phenology over the United States.

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.

A Reusable Framework for Regional Climate Model Evaluation

NASA Astrophysics Data System (ADS)

Hart, A. F.; Goodale, C. E.; Mattmann, C. A.; Lean, P.; Kim, J.; Zimdars, P.; Waliser, D. E.; Crichton, D. J.

2011-12-01

Climate observations are currently obtained through a diverse network of sensors and platforms that include space-based observatories, airborne and seaborne platforms, and distributed, networked, ground-based instruments. These global observational measurements are critical inputs to the efforts of the climate modeling community and can provide a corpus of data for use in analysis and validation of climate models. The Regional Climate Model Evaluation System (RCMES) is an effort currently being undertaken to address the challenges of integrating this vast array of observational climate data into a coherent resource suitable for performing model analysis at the regional level. Developed through a collaboration between the NASA Jet Propulsion Laboratory (JPL) and the UCLA Joint Institute for Regional Earth System Science and Engineering (JIFRESSE), the RCMES uses existing open source technologies (MySQL, Apache Hadoop, and Apache OODT), to construct a scalable, parametric, geospatial data store that incorporates decades of observational data from a variety of NASA Earth science missions, as well as other sources into a consistently annotated, highly available scientific resource. By eliminating arbitrary partitions in the data (individual file boundaries, differing file formats, etc), and instead treating each individual observational measurement as a unique, geospatially referenced data point, the RCMES is capable of transforming large, heterogeneous collections of disparate observational data into a unified resource suitable for comparison to climate model output. This facility is further enhanced by the availability of a model evaluation toolkit which consists of a set of Python libraries, a RESTful web service layer, and a browser-based graphical user interface that allows for orchestration of model-to-data comparisons by composing them visually through web forms. This combination of tools and interfaces dramatically simplifies the process of interacting with and utilizing large volumes of observational data for model evaluation research. We feel that the RCMES is particularly appealing in that it represents a principled, reusable architectural approach rather than a one-off technological implementation. In fact, early RCMES prototypes have already utilized a variety of implementation technologies in an effort to address different performance and scalability concerns. This has been greatly facilitated by the fact that, at the architectural level, the RCMES is fundamentally domain agnostic. Strictly separating the data model from the implementation has enabled us to create a reusable architecture that we believe can be modified and configured to suit the demands of researchers in other domains.

Robust Decision Making to Support Water Quality Climate Adaptation: a Case Study in the Chesapeake Bay Watershed

NASA Astrophysics Data System (ADS)

Fischbach, J. R.; Lempert, R. J.; Molina-Perez, E.

2017-12-01

The U.S. Environmental Protection Agency (USEPA), together with state and local partners, develops watershed implementation plans designed to meet water quality standards. Climate uncertainty, along with uncertainty about future land use changes or the performance of water quality best management practices (BMPs), may make it difficult for these implementation plans to meet water quality goals. In this effort, we explored how decision making under deep uncertainty (DMDU) methods such as Robust Decision Making (RDM) could help USEPA and its partners develop implementation plans that are more robust to future uncertainty. The study focuses on one part of the Chesapeake Bay watershed, the Patuxent River, which is 2,479 sq km in area, highly urbanized, and has a rapidly growing population. We simulated the contribution of stormwater contaminants from the Patuxent to the overall Total Maximum Daily Load (TMDL) for the Chesapeake Bay under multiple scenarios reflecting climate and other uncertainties. Contaminants considered included nitrogen, phosphorus, and sediment loads. The assessment included a large set of scenario simulations using the USEPA Chesapeake Bay Program's Phase V watershed model. Uncertainties represented in the analysis included 18 downscaled climate projections (based on 6 general circulation models and 3 emissions pathways), 12 land use scenarios with different population projections and development patterns, and alternative assumptions about BMP performance standards and efficiencies associated with different suites of stormwater BMPs. Finally, we developed cost estimates for each of the performance standards and compared cost to TMDL performance as a key tradeoff for future water quality management decisions. In this talk, we describe how this research can help inform climate-related decision support at USEPA's Chesapeake Bay Program, and more generally how RDM and other DMDU methods can support improved water quality management under climate uncertainty.

The Rate of Seasonal Changes in Temperature Alters Acclimation of Performance under Climate Change.

PubMed

Nilsson-Örtman, Viktor; Johansson, Frank

2017-12-01

How the ability to acclimate will impact individual performance and ecological interactions under climate change remains poorly understood. Theory predicts that the benefit an organism can gain from acclimating depends on the rate at which temperatures change relative to the time it takes to induce beneficial acclimation. Here, we present a conceptual model showing how slower seasonal changes under climate change can alter species' relative performance when they differ in acclimation rate and magnitude. To test predictions from theory, we performed a microcosm experiment where we reared a mid- and a high-latitude damselfly species alone or together under the rapid seasonality currently experienced at 62°N and the slower seasonality predicted for this latitude under climate change and measured larval growth and survival. To separate acclimation effects from fixed thermal responses, we simulated growth trajectories based on species' growth rates at constant temperatures and quantified how much and how fast species needed to acclimate to match the observed growth trajectories. Consistent with our predictions, the results showed that the midlatitude species had a greater capacity for acclimation than the high-latitude species. Furthermore, since acclimation occurred at a slower rate than seasonal temperature changes, the midlatitude species had a small growth advantage over the high-latitude species under the current seasonality but a greater growth advantage under the slower seasonality predicted for this latitude under climate change. In addition, the two species did not differ in survival under the current seasonality, but the midlatitude species had higher survival under the predicted climate change scenario, possibly because rates of cannibalism were lower when smaller heterospecifics were present. These findings highlight the need to incorporate acclimation rates in ecological models.

A parametric approach for simultaneous bias correction and high-resolution downscaling of climate model rainfall

NASA Astrophysics Data System (ADS)

Mamalakis, Antonios; Langousis, Andreas; Deidda, Roberto; Marrocu, Marino

2017-03-01

Distribution mapping has been identified as the most efficient approach to bias-correct climate model rainfall, while reproducing its statistics at spatial and temporal resolutions suitable to run hydrologic models. Yet its implementation based on empirical distributions derived from control samples (referred to as nonparametric distribution mapping) makes the method's performance sensitive to sample length variations, the presence of outliers, the spatial resolution of climate model results, and may lead to biases, especially in extreme rainfall estimation. To address these shortcomings, we propose a methodology for simultaneous bias correction and high-resolution downscaling of climate model rainfall products that uses: (a) a two-component theoretical distribution model (i.e., a generalized Pareto (GP) model for rainfall intensities above a specified threshold u*, and an exponential model for lower rainrates), and (b) proper interpolation of the corresponding distribution parameters on a user-defined high-resolution grid, using kriging for uncertain data. We assess the performance of the suggested parametric approach relative to the nonparametric one, using daily raingauge measurements from a dense network in the island of Sardinia (Italy), and rainfall data from four GCM/RCM model chains of the ENSEMBLES project. The obtained results shed light on the competitive advantages of the parametric approach, which is proved more accurate and considerably less sensitive to the characteristics of the calibration period, independent of the GCM/RCM combination used. This is especially the case for extreme rainfall estimation, where the GP assumption allows for more accurate and robust estimates, also beyond the range of the available data.

Applicability of AgMERRA Forcing Dataset to Fill Gaps in Historical in-situ Meteorological Data

NASA Astrophysics Data System (ADS)

Bannayan, M.; Lashkari, A.; Zare, H.; Asadi, S.; Salehnia, N.

2015-12-01

Integrated assessment studies of food production systems use crop models to simulate the effects of climate and socio-economic changes on food security. Climate forcing data is one of those key inputs of crop models. This study evaluated the performance of AgMERRA climate forcing dataset to fill gaps in historical in-situ meteorological data for different climatic regions of Iran. AgMERRA dataset intercompared with in- situ observational dataset for daily maximum and minimum temperature and precipitation during 1980-2010 periods via Root Mean Square error (RMSE), Mean Absolute Error (MAE) and Mean Bias Error (MBE) for 17 stations in four climatic regions included humid and moderate, cold, dry and arid, hot and humid. Moreover, probability distribution function and cumulative distribution function compared between model and observed data. The results of measures of agreement between AgMERRA data and observed data demonstrated that there are small errors in model data for all stations. Except for stations which are located in cold regions, model data in the other stations illustrated under-prediction for daily maximum temperature and precipitation. However, it was not significant. In addition, probability distribution function and cumulative distribution function showed the same trend for all stations between model and observed data. Therefore, the reliability of AgMERRA dataset is high to fill gaps in historical observations in different climatic regions of Iran as well as it could be applied as a basis for future climate scenarios.

Improved Upper Ocean/Sea Ice Modeling in the GISS GCM for Investigating Climate Change

NASA Technical Reports Server (NTRS)

1997-01-01

This project built on our previous results in which we highlighted the importance of sea ice in overall climate sensitivity by determining that for both warming and cooling climates, when sea ice was not allowed to change, climate sensitivity was reduced by 35-40%. We also modified the Goddard Institute for Space Studies (GISS) 8 deg x lO deg atmospheric General Circulation Model (GCM) to include an upper-ocean/sea-ice model involving the Semtner three-layer ice/snow thermodynamic model, the Price et al. (1986) ocean mixed layer model and a general upper ocean vertical advection/diffusion scheme for maintaining and fluxing properties across the pycnocline. This effort, in addition to improving the sea ice representation in the AGCM, revealed a number of sensitive components of the sea ice/ocean system. For example, the ability to flux heat through the ice/snow properly is critical in order to resolve the surface temperature properly, since small errors in this lead to unrestrained climate drift. The present project, summarized in this report, had as its objectives: (1) introducing a series of sea ice and ocean improvements aimed at overcoming remaining weaknesses in the GCM sea ice/ocean representation, and (2) performing a series of sensitivity experiments designed to evaluate the climate sensitivity of the revised model to both Antarctic and Arctic sea ice, determine the sensitivity of the climate response to initial ice distribution, and investigate the transient response to doubling CO2.

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

PubMed

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

2014-09-01

Climate change may facilitate alien species invasion into new areas, particularly for species from warm native ranges introduced into areas currently marginal for temperature. Although conclusions from modelling approaches and experimental studies are generally similar, combining the two approaches has rarely occurred. The aim of this study was to validate species distribution models by conducting field trials in sites of differing suitability as predicted by the models, thus increasing confidence in their ability to assess invasion risk. Three recently naturalized alien plants in New Zealand were used as study species (Archontophoenix cunninghamiana, Psidium guajava and Schefflera actinophylla): they originate from warm native ranges, are woody bird-dispersed species and of concern as potential weeds. Seedlings were grown in six sites across the country, differing both in climate and suitability (as predicted by the species distribution models). Seedling growth and survival were recorded over two summers and one or two winter seasons, and temperature and precipitation were monitored hourly at each site. Additionally, alien seedling performances were compared to those of closely related native species (Rhopalostylis sapida, Lophomyrtus bullata and Schefflera digitata). Furthermore, half of the seedlings were sprayed with pesticide, to investigate whether enemy release may influence performance. The results showed large differences in growth and survival of the alien species among the six sites. In the more suitable sites, performance was frequently higher compared to the native species. Leaf damage from invertebrate herbivory was low for both alien and native seedlings, with little evidence that the alien species should have an advantage over the native species because of enemy release. Correlations between performance in the field and predicted suitability of species distribution models were generally high. The projected increase in minimum temperature and reduced frosts with climate change may provide more suitable habitats and enable the spread of these species. © 2014 John Wiley & Sons Ltd.

Analyzing Multidecadal Trends in Cloudiness Over the Subtropical Andes Mountains of South America Using a Regional Climate Model.

NASA Astrophysics Data System (ADS)

Zaitchik, B. F.; Russell, A.; Gnanadesikan, A.

2016-12-01

Satellite-based products indicate that many parts of South America have been experiencing increases in outgoing longwave radiation (OLR) and corresponding decreases in cloudiness over the last few decades, with the strongest trends occurring in the subtropical Andes Mountains - an area that is highly vulnerable to climate change due to its reliance on glacial melt for dry-season runoff. Changes in cloudiness may be contributing to increases in atmospheric temperature, thereby raising the freezing level height (FLH) - a critical geophysical parameter. Yet these trends are only partially captured in reanalysis products, while AMIP climate models generally show no significant trend in OLR over this timeframe, making it difficult to determine the underlying drivers. Therefore, controlled numerical experiments with a regional climate model are performed in order to investigate drivers of the observed OLR and cloudiness trends. The Weather Research and Forecasting model (WRF) is used here because it offers several advantages over global models, including higher resolution - a critical asset in areas of complex topography - as well as flexible physics, parameterization, and data assimilation capabilities. It is likely that changes in the mean states and meridional gradients of SSTs in the Pacific and Atlantic oceans are driving regional trends in clouds. A series of lower boundary manipulations are performed with WRF to determine to what extent changes in SSTs influence regional OLR.

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.

Assessing the Role of Climate Variability on Liver Fluke Risk in the UK Through Mechanistic Hydro-Epidemiological Modelling

NASA Astrophysics Data System (ADS)

Beltrame, L.; Dunne, T.; Rose, H.; Walker, J.; Morgan, E.; Vickerman, P.; Wagener, T.

2016-12-01

Liver fluke is a flatworm parasite infecting grazing animals worldwide. In the UK, it causes considerable production losses to cattle and sheep industries and costs farmers millions of pounds each year due to reduced growth rates and lower milk yields. Large part of the parasite life-cycle takes place outside of the host, with its survival and development strongly controlled by climatic and hydrologic conditions. Evidence of climate-driven changes in the distribution and seasonality of fluke disease already exists, as the infection is increasingly expanding to new areas and becoming a year-round problem. Therefore, it is crucial to assess current and potential future impacts of climate variability on the disease to guide interventions at the farm scale and mitigate risk. Climate-based fluke risk models have been available since the 1950s, however, they are based on empirical relationships derived between historical climate and incidence data, and thus are unlikely to be robust for simulating risk under changing conditions. Moreover, they are not dynamic, but estimate risk over large regions in the UK based on monthly average climate conditions, so they do not allow investigating the effects of climate variability for supporting farmers' decisions. In this study, we introduce a mechanistic model for fluke, which represents habitat suitability for disease development at 25m resolution with a daily time step, explicitly linking the parasite life-cycle to key hydro-climate conditions. The model is used on a case study in the UK and sensitivity analysis is performed to better understand the role of climate variability on the space-time dynamics of the disease, while explicitly accounting for uncertainties. Comparisons are presented with experts' knowledge and a widely used empirical model.

Simulating Climate Change in Ireland

NASA Astrophysics Data System (ADS)

Nolan, P.; Lynch, P.

2012-04-01

At the Meteorology & Climate Centre at University College Dublin, we are using the CLM-Community's COSMO-CLM Regional Climate Model (RCM) and the WRF RCM (developed at NCAR) to simulate the climate of Ireland at high spatial resolution. To address the issue of model uncertainty, a Multi-Model Ensemble (MME) approach is used. The ensemble method uses different RCMs, driven by several Global Climate Models (GCMs), to simulate climate change. Through the MME approach, the uncertainty in the RCM projections is quantified, enabling us to estimate the probability density function of predicted changes, and providing a measure of confidence in the predictions. The RCMs were validated by performing a 20-year simulation of the Irish climate (1981-2000), driven by ECMWF ERA-40 global re-analysis data, and comparing the output to observations. Results confirm that the output of the RCMs exhibit reasonable and realistic features as documented in the historical data record. Projections for the future Irish climate were generated by downscaling the Max Planck Institute's ECHAM5 GCM, the UK Met Office HadGEM2-ES GCM and the CGCM3.1 GCM from the Canadian Centre for Climate Modelling. Simulations were run for a reference period 1961-2000 and future period 2021-2060. The future climate was simulated using the A1B, A2, B1, RCP 4.5 & RCP 8.5 greenhouse gas emission scenarios. Results for the downscaled simulations show a substantial overall increase in precipitation and wind speed for the future winter months and a decrease during the summer months. The predicted annual change in temperature is approximately 1.1°C over Ireland. To date, all RCM projections are in general agreement, thus increasing our confidence in the robustness of the results.

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

PubMed Central

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

2005-01-01

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

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

PubMed Central

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

2016-01-01

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

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.

On temporal stochastic modeling of precipitation, nesting models across scales

NASA Astrophysics Data System (ADS)

Paschalis, Athanasios; Molnar, Peter; Fatichi, Simone; Burlando, Paolo

2014-01-01

We analyze the performance of composite stochastic models of temporal precipitation which can satisfactorily reproduce precipitation properties across a wide range of temporal scales. The rationale is that a combination of stochastic precipitation models which are most appropriate for specific limited temporal scales leads to better overall performance across a wider range of scales than single models alone. We investigate different model combinations. For the coarse (daily) scale these are models based on Alternating renewal processes, Markov chains, and Poisson cluster models, which are then combined with a microcanonical Multiplicative Random Cascade model to disaggregate precipitation to finer (minute) scales. The composite models were tested on data at four sites in different climates. The results show that model combinations improve the performance in key statistics such as probability distributions of precipitation depth, autocorrelation structure, intermittency, reproduction of extremes, compared to single models. At the same time they remain reasonably parsimonious. No model combination was found to outperform the others at all sites and for all statistics, however we provide insight on the capabilities of specific model combinations. The results for the four different climates are similar, which suggests a degree of generality and wider applicability of the approach.

Representation of deforestation impacts on climate, water, and nutrient cycles in the ACME earth system model

NASA Astrophysics Data System (ADS)

Cai, X.; Riley, W. J.; Zhu, Q.

2017-12-01

Deforestation causes a series of changes to the climate, water, and nutrient cycles. Employing a state-of-the-art earth system model—ACME (Accelerated Climate Modeling for Energy), we comprehensively investigate the impacts of deforestation on these processes. We first assess the performance of the ACME Land Model (ALM) in simulating runoff, evapotranspiration, albedo, and plant productivity at 42 FLUXNET sites. The single column mode of ACME is then used to examine climate effects (temperature cooling/warming) and responses of runoff, evapotranspiration, and nutrient fluxes to deforestation. This approach separates local effects of deforestation from global circulation effects. To better understand the deforestation effects in a global context, we use the coupled (atmosphere, land, and slab ocean) mode of ACME to demonstrate the impacts of deforestation on global climate, water, and nutrient fluxes. Preliminary results showed that the land component of ACME has advantages in simulating these processes and that local deforestation has potentially large impacts on runoff and atmospheric processes.

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

USGS Publications Warehouse

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

2014-01-01

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

Projected Changes to Streamflow Characteristics in Quebec Basins as Simulated by the Canadian Regional Climate Model (CRCM4)

NASA Astrophysics Data System (ADS)

Huziy, O.; Sushama, L.; Khaliq, M.; Lehner, B.; Laprise, R.; Roy, R.

2011-12-01

According to the Intergovernmental Panel on Climate Change (IPCC, 2007), an intensification of the global hydrological cycle and increase in precipitation for some regions around the world, including the northern mid- to high-latitudes, is expected in future climate. This will have an impact on mean and extreme flow characteristics, which need to be assessed for better development of adaptation strategies. Analysis of the mean and extreme streamflow characteristics for Quebec (North-eastern Canada) basins in current climate and their projected changes in future climate are assessed using a 10 member ensemble of current (1970 - 1999) and future (2041 - 2070) Canadian RCM (CRCM4) simulations. Validation of streamflow characteristics, performed by comparing modeled values with those observed, available from the Centre d'expertise hydrique du Quebec (CEHQ) shows that the model captures reasonably well the high flows. Results suggest increase in mean and 10 year return levels of 1 day high flows, which appear significant for most of the northern basins.

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.

Optimization of black-box models with uncertain climatic inputs—Application to sunflower ideotype design

PubMed Central

Picheny, Victor; Trépos, Ronan; Casadebaig, Pierre

2017-01-01

Accounting for the interannual climatic variations is a well-known issue for simulation-based studies of environmental systems. It often requires intensive sampling (e.g., averaging the simulation outputs over many climatic series), which hinders many sequential processes, in particular optimization algorithms. We propose here an approach based on a subset selection in a large basis of climatic series, using an ad-hoc similarity function and clustering. A non-parametric reconstruction technique is introduced to estimate accurately the distribution of the output of interest using only the subset sampling. The proposed strategy is non-intrusive and generic (i.e. transposable to most models with climatic data inputs), and can be combined to most “off-the-shelf” optimization solvers. We apply our approach to sunflower ideotype design using the crop model SUNFLO. The underlying optimization problem is formulated as a multi-objective one to account for risk-aversion. Our approach achieves good performances even for limited computational budgets, outperforming significantly standard strategies. PMID:28542198

The Ocean Colour Climate Change Initiative: III. A Round-Robin Comparison on In-Water Bio-Optical Algorithms

NASA Technical Reports Server (NTRS)

Brewin, Robert J.W.; Sathyendranath, Shubha; Muller, Dagmar; Brockmann, Carsten; Deschamps, Pierre-Yves; Devred, Emmanuel; Doerffer, Roland; Fomferra, Norman; Franz, Bryan; Grant, Mike;

The thermal environment of the human being on the global scale.

PubMed

Jendritzky, Gerd; Tinz, Birger

2009-11-11

The close relationship between human health, performance, well-being and the thermal environment is obvious. Nevertheless, most studies of climate and climate change impacts show amazing shortcomings in the assessment of the environment. Populations living in different climates have different susceptibilities, due to socio-economic reasons, and different customary behavioural adaptations. The global distribution of risks of hazardous thermal exposure has not been analysed before. To produce maps of the baseline and future bioclimate that allows a direct comparison of the differences in the vulnerability of populations to thermal stress across the world. The required climatological data fields are obtained from climate simulations with the global General Circulation Model ECHAM4 in T106-resolution. For the thermo-physiologically relevant assessment of these climate data a complete heat budget model of the human being, the 'Perceived Temperature' procedure has been applied which already comprises adaptation by clothing to a certain degree. Short-term physiological acclimatisation is considered via Health Related Assessment of the Thermal Environment. The global maps 1971-1980 (control run, assumed as baseline climate) show a pattern of thermal stress intensities as frequencies of heat. The heat load for people living in warm-humid climates is the highest. Climate change will lead to clear differences in health-related thermal stress between baseline climate and the future bioclimate 2041-2050 based on the 'business-as-usual' greenhouse gas scenario IS92a. The majority of the world's population will be faced with more frequent and more intense heat strain in spite of an assumed level of acclimatisation. Further adaptation measures are crucial in order to reduce the vulnerability of the populations. This bioclimatology analysis provides a tool for various questions in climate and climate change impact research. Considerations of regional or local scale require climate simulations with higher resolution. As adaptation is the key term in understanding the role of climate/climate change for human health, performance and well-being, further research in this field is crucial.

Configuration of the thermal landscape determines thermoregulatory performance of ectotherms

PubMed Central

Sears, Michael W.; Angilletta, Michael J.; Schuler, Matthew S.; Borchert, Jason; Dilliplane, Katherine F.; Stegman, Monica; Rusch, Travis W.; Mitchell, William A.

2016-01-01

Although most organisms thermoregulate behaviorally, biologists still cannot easily predict whether mobile animals will thermoregulate in natural environments. Current models fail because they ignore how the spatial distribution of thermal resources constrains thermoregulatory performance over space and time. To overcome this limitation, we modeled the spatially explicit movements of animals constrained by access to thermal resources. Our models predict that ectotherms thermoregulate more accurately when thermal resources are dispersed throughout space than when these resources are clumped. This prediction was supported by thermoregulatory behaviors of lizards in outdoor arenas with known distributions of environmental temperatures. Further, simulations showed how the spatial structure of the landscape qualitatively affects responses of animals to climate. Biologists will need spatially explicit models to predict impacts of climate change on local scales. PMID:27601639

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.

Shallow Horizontal GCHP Effectiveness in Arid Climate Soils

NASA Astrophysics Data System (ADS)

North, Timothy James

Ground coupled heat pumps (GCHPs) have been used successfully in many environments to improve the heating and cooling efficiency of both small and large scale buildings. In arid climate regions, such as the Phoenix, Arizona metropolitan area, where the air condi-tioning load is dominated by cooling in the summer, GCHPs are difficult to install and operate. This is because the nature of soils in arid climate regions, in that they are both dry and hot, renders them particularly ineffective at dissipating heat. The first part of this thesis addresses applying the SVHeat finite element modeling soft-ware to create a model of a GCHP system. Using real-world data from a prototype solar-water heating system coupled with a ground-source heat exchanger installed in Menlo Park, California, a relatively accurate model was created to represent a novel GCHP panel system installed in a shallow vertical trench. A sensitivity analysis was performed to evaluate the accuracy of the calibrated model. The second part of the thesis involved adapting the calibrated model to represent an ap-proximation of soil conditions in arid climate regions, using a range of thermal properties for dry soils. The effectiveness of the GCHP in the arid climate region model was then evaluated by comparing the thermal flux from the panel into the subsurface profile to that of the prototype GCHP. It was shown that soils in arid climate regions are particularly inefficient at heat dissipation, but that it is highly dependent on the thermal conductivity inputted into the model. This demonstrates the importance of proper site characterization in arid climate regions. Finally, several soil improvement methods were researched to evaluate their potential for use in improving the effectiveness of shallow horizontal GCHP systems in arid climate regions.

Combined impacts due to deforestation and the forest fires in the Amazonia and the global climate change on the future climate of South America: a modelling study

NASA Astrophysics Data System (ADS)

de Oliveira, G. S.; Cardoso, M. F.; Sanches, M. B.; Alexandre, F. F.

2014-12-01

Since the late 1980s a large number of numerical experiments with atmospheric general circulation models has been used to assess the impacts of deforestation on global and regional climate, and one of the main motivations is the Amazon rainforest. In the same way, in the last decade several studies have shown that a higher concentration of CO2 in the atmosphere could lead changes in climate in the Amazon region. In this study we performed new analyses to quantify how deforestation, fire and the increase in atmospheric CO2 concentration may combine to affect the climate in Amazonia during this century. For the projection of land use was considered a spatially explicit land use scenario from Aguiar et al. (2013). The scenario was built using LuccME generic modelling framework and the potential of change considering the proximity to previously deforested areas and also spatial drivers (roads and protected areas). In order to quantify the response of Brazilian Earth System Model, with INLAND-IBIS surface model, to climate change, deforestation and forest fire we performed a suite of simulations in two main categories: 1) the model was running under historical and RCP8.5 greenhouse gas concentration, and 2) the model was forced by the same configuration in 1 but also considering the effects of deforestation and forest fire in Amazon. In summary, the most important changes occur in the East/Northeast and South of the Amazonia and are more evident when are considered all effects (climate change, deforestation and fire). The results show warmer near-surface air temperature in all cases compared to the control case. This relative warming of the deforested land surface is consistent with the reduction in evapotranspiration, the lower leaf area and the lower surface roughness length. There is a reduction in annual precipitation in both cases mainly over eastern/northeastern Amazonia. The reduction in precipitation occurs mainly during the dry season (June-November) in both cases, and there is an increase in dry season length that is more evident when are considered all effects. In summary, we conclude that the synergistic combination of deforestation and climate change resulting from global warming may lead to important impacts that add considerably to the vulnerability of tropical forest ecosystems in the region.

Is temperature the main cause of dengue rise in non-endemic countries? The case of Argentina

PubMed Central

2012-01-01

Background Dengue cases have increased during the last decades, particularly in non-endemic areas, and Argentina was no exception in the southern transmission fringe. Although temperature rise has been blamed for this, human population growth, increased travel and inefficient vector control may also be implicated. The relative contribution of geographic, demographic and climatic of variables on the occurrence of dengue cases was evaluated. Methods According to dengue history in the country, the study was divided in two decades, a first decade corresponding to the reemergence of the disease and the second including several epidemics. Annual dengue risk was modeled by a temperature-based mechanistic model as annual days of possible transmission. The spatial distribution of dengue occurrence was modeled as a function of the output of the mechanistic model, climatic, geographic and demographic variables for both decades. Results According to the temperature-based model dengue risk increased between the two decades, and epidemics of the last decade coincided with high annual risk. Dengue spatial occurrence was best modeled by a combination of climatic, demographic and geographic variables and province as a grouping factor. It was positively associated with days of possible transmission, human population number, population fall and distance to water bodies. When considered separately, the classification performance of demographic variables was higher than that of climatic and geographic variables. Conclusions Temperature, though useful to estimate annual transmission risk, does not fully describe the distribution of dengue occurrence at the country scale. Indeed, when taken separately, climatic variables performed worse than geographic or demographic variables. A combination of the three types was best for this task. PMID:22768874

Evaluating the fidelity of CMIP5 models in producing large-scale meteorological patterns over the Northwestern United States

NASA Astrophysics Data System (ADS)

Lintner, B. R.; Loikith, P. C.; Pike, M.; Aragon, C.

2017-12-01

Climate change information is increasingly required at impact-relevant scales. However, most state-of-the-art climate models are not of sufficiently high spatial resolution to resolve features explicitly at such scales. This challenge is particularly acute in regions of complex topography, such as the Pacific Northwest of the United States. To address this scale mismatch problem, we consider large-scale meteorological patterns (LSMPs), which can be resolved by climate models and associated with the occurrence of local scale climate and climate extremes. In prior work, using self-organizing maps (SOMs), we computed LSMPs over the northwestern United States (NWUS) from daily reanalysis circulation fields and further related these to the occurrence of observed extreme temperatures and precipitation: SOMs were used to group LSMPs into 12 nodes or clusters spanning the continuum of synoptic variability over the regions. Here this observational foundation is utilized as an evaluation target for a suite of global climate models from the Fifth Phase of the Coupled Model Intercomparison Project (CMIP5). Evaluation is performed in two primary ways. First, daily model circulation fields are assigned to one of the 12 reanalysis nodes based on minimization of the mean square error. From this, a bulk model skill score is computed measuring the similarity between the model and reanalysis nodes. Next, SOMs are applied directly to the model output and compared to the nodes obtained from reanalysis. Results reveal that many of the models have LSMPs analogous to the reanalysis, suggesting that the models reasonably capture observed daily synoptic states.

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

Multi-objective optimization of GENIE Earth system models.

PubMed

Price, Andrew R; Myerscough, Richard J; Voutchkov, Ivan I; Marsh, Robert; Cox, Simon J

2009-07-13

The tuning of parameters in climate models is essential to provide reliable long-term forecasts of Earth system behaviour. We apply a multi-objective optimization algorithm to the problem of parameter estimation in climate models. This optimization process involves the iterative evaluation of response surface models (RSMs), followed by the execution of multiple Earth system simulations. These computations require an infrastructure that provides high-performance computing for building and searching the RSMs and high-throughput computing for the concurrent evaluation of a large number of models. Grid computing technology is therefore essential to make this algorithm practical for members of the GENIE project.

The Carbon-Land Model Intercomparison Project (C-LAMP): A Model-Data Comparison System for Evaluation of Coupled Biosphere-Atmosphere Models

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

Hoffman, Forrest M; Randerson, Jim; Thornton, Peter E

2009-01-01

The need to capture important climate feebacks in general circulation models (GCMs) has resulted in new efforts to include atmospheric chemistry and land and ocean biogeochemistry into the next generation of production climate models, now often referred to as Earth System Models (ESMs). While many terrestrial and ocean carbon models have been coupled to GCMs, recent work has shown that such models can yield a wide range of results, suggesting that a more rigorous set of offline and partially coupled experiments, along with detailed analyses of processes and comparisons with measurements, are warranted. The Carbon-Land Model Intercomparison Project (C-LAMP) providesmore » a simulation protocol and model performance metrics based upon comparisons against best-available satellite- and ground-based measurements (Hoffman et al., 2007). C-LAMP provides feedback to the modeling community regarding model improvements and to the measurement community by suggesting new observational campaigns. C-LAMP Experiment 1 consists of a set of uncoupled simulations of terrestrial carbon models specifically designed to examine the ability of the models to reproduce surface carbon and energy fluxes at multiple sites and to exhibit the influence of climate variability, prescribed atmospheric carbon dioxide (CO{sub 2}), nitrogen (N) deposition, and land cover change on projections of terrestrial carbon fluxes during the 20th century. Experiment 2 consists of partially coupled simulations of the terrestrial carbon model with an active atmosphere model exchanging energy and moisture fluxes. In all experiments, atmospheric CO{sub 2} follows the prescribed historical trajectory from C{sup 4}MIP. In Experiment 2, the atmosphere model is forced with prescribed sea surface temperatures (SSTs) and corresponding sea ice concentrations from the Hadley Centre; prescribed CO{sub 2} is radiatively active; and land, fossil fuel, and ocean CO{sub 2} fluxes are advected by the model. Both sets of experiments have been performed using two different terrestrial biogeochemistry modules coupled to the Community Land Model version 3 (CLM3) in the Community Climate System Model version 3 (CCSM3): The CASA model of Fung, et al., and the carbon-nitrogen (CN) model of Thornton. Comparisons against Ameriflus site measurements, MODIS satellite observations, NOAA flask records, TRANSCOM inversions, and Free Air CO{sub 2} Enrichment (FACE) site measurements, and other datasets have been performed and are described in Randerson et al. (2009). The C-LAMP diagnostics package was used to validate improvements to CASA and CN for use in the next generation model, CLM4. It is hoped that this effort will serve as a prototype for an international carbon-cycle model benchmarking activity for models being used for the Inter-governmental Panel on Climate Change (IPCC) Fifth Assessment Report. More information about C-LAMP, the experimental protocol, performance metrics, output standards, and model-data comparisons from the CLM3-CASA and CLM3-CN models are available at http://www.climatemodeling.org/c-lamp.« less

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.

WASCAL - West African Science Service Center on Climate Change and Adapted Land Use Regional Climate Simulations and Land-Atmosphere Simulations for West Africa at DKRZ and elsewhere

NASA Astrophysics Data System (ADS)

Hamann, Ilse; Arnault, Joel; Bliefernicht, Jan; Klein, Cornelia; Heinzeller, Dominikus; Kunstmann, Harald

2014-05-01

Changing climate and hydro-meteorological boundary conditions are among the most severe challenges to Africa in the 21st century. In particular West Africa faces an urgent need to develop effective adaptation and mitigation strategies to cope with negative impacts on humans and environment due to climate change, increased hydro-meteorological variability and land use changes. To help meet these challenges, the German Federal Ministry of Education and Research (BMBF) started an initiative with institutions in Germany and West African countries to establish together a West African Science Service Center on Climate Change and Adapted Land Use (WASCAL). This activity is accompanied by an establishment of trans-boundary observation networks, an interdisciplinary core research program and graduate research programs on climate change and related issues for strengthening the analytical capabilities of the Science Service Center. A key research activity of the WASCAL Competence Center is the provision of regional climate simulations in a fine spatio-temporal resolution for the core research sites of WASCAL for the present and the near future. The climate information is needed for subsequent local climate impact studies in agriculture, water resources and further socio-economic sectors. The simulation experiments are performed using regional climate models such as COSMO-CLM, RegCM and WRF and statistical techniques for a further refinement of the projections. The core research sites of WASCAL are located in the Sudanian Savannah belt in Northern Ghana, Southern Burkina Faso and Northern Benin. The climate in this region is semi-arid with six rainy months. Due to the strong population growth in West Africa, many areas of the Sudanian Savannah have been already converted to farmland since the majority of the people are living directly or indirectly from the income produced in agriculture. The simulation experiments of the Competence Center and the Core Research Program are accompanied by the WASCAL Graduate Research Program on the West African Climate System. The GRP-WACS provides ten scholarships per year for West African PhD students with a duration of three years. Present and future WASCAL PhD students will constitute one important user group of the Linux cluster that will be installed at the Competence Center in Ouagadougou, Burkina Faso. Regional Land-Atmosphere Simulations A key research activity of the WASCAL Core Research Program is the analysis of interactions between the land surface and the atmosphere to investigate how land surface changes affect hydro-meteorological surface fluxes such as evapotranspiration. Since current land surface models of global and regional climate models neglect dominant lateral hydrological processes such as surface runoff, a novel land surface model is used, the NCAR Distributed Hydrological Modeling System (NDHMS). This model can be coupled to WRF (WRF-Hydro) to perform two-way coupled atmospheric-hydrological simulations for the watershed of interest. Hardware and network prerequisites include a HPC cluster, network switches, internal storage media, Internet connectivity of sufficient bandwidth. Competences needed are HPC, storage, and visualization systems optimized for climate research, parallelization and optimization of climate models and workflows, efficient management of highest data volumes.

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).

A water resources model to explore the implications of energy alternatives in the southwestern US

NASA Astrophysics Data System (ADS)

Yates, D.; Averyt, Kristen; Flores-Lopez, Francisco; Meldrum, J.; Sattler, S.; Sieber, J.; Young, C.

2013-12-01

This letter documents the development and validation of a climate-driven, southwestern-US-wide water resources planning model that is being used to explore the implications of extended drought and climate warming on the allocation of water among competing uses. These model uses include a separate accounting for irrigated agriculture; municipal indoor use based on local population and per-capita consumption; climate-driven municipal outdoor turf and amenity watering; and thermoelectric cooling. The model simulates the natural and managed flows of rivers throughout the southwest, including the South Platte, the Arkansas, the Colorado, the Green, the Salt, the Sacramento, the San Joaquin, the Owens, and more than 50 others. Calibration was performed on parameters of land cover, snow accumulation and melt, and water capacity and hydraulic conductivity of soil horizons. Goodness of fit statistics and other measures of performance are shown for a select number of locations and are used to summarize the model’s ability to represent monthly streamflow, reservoir storages, surface and ground water deliveries, etc, under 1980-2010 levels of sectoral water use.

Koppen bioclimatic evaluation of CMIP historical climate simulations

DOE PAGES

Phillips, Thomas J.; Bonfils, Celine J. W.

2015-06-05

Köppen bioclimatic classification relates generic vegetation types to characteristics of the interactive annual-cycles of continental temperature (T) and precipitation (P). In addition to predicting possible bioclimatic consequences of past or prospective climate change, a Köppen scheme can be used to pinpoint biases in model simulations of historical T and P. In this study a Köppen evaluation of Coupled Model Intercomparison Project (CMIP) simulations of historical climate is conducted for the period 1980–1999. Evaluation of an example CMIP5 model illustrates how errors in simulating Köppen vegetation types (relative to those derived from observational reference data) can be deconstructed and related tomore » model-specific temperature and precipitation biases. Measures of CMIP model skill in simulating the reference Köppen vegetation types are also developed, allowing the bioclimatic performance of a CMIP5 simulation of T and P to be compared quantitatively with its CMIP3 antecedent. Although certain bioclimatic discrepancies persist across model generations, the CMIP5 models collectively display an improved rendering of historical T and P relative to their CMIP3 counterparts. Additionally, the Köppen-based performance metrics are found to be quite insensitive to alternative choices of observational reference data or to differences in model horizontal resolution.« less

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

PubMed

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

2010-01-01

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

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

NASA Astrophysics Data System (ADS)

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

2016-04-01

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

Multi-criteria objective based climate change impact assessment for multi-purpose multi-reservoir systems

NASA Astrophysics Data System (ADS)

Müller, Ruben; Schütze, Niels

2014-05-01

Water resources systems with reservoirs are expected to be sensitive to climate change. Assessment studies that analyze the impact of climate change on the performance of reservoirs can be divided in two groups: (1) Studies that simulate the operation under projected inflows with the current set of operational rules. Due to non adapted operational rules the future performance of these reservoirs can be underestimated and the impact overestimated. (2) Studies that optimize the operational rules for best adaption of the system to the projected conditions before the assessment of the impact. The latter allows for estimating more realistically future performance and adaption strategies based on new operation rules are available if required. Multi-purpose reservoirs serve various, often conflicting functions. If all functions cannot be served simultaneously at a maximum level, an effective compromise between multiple objectives of the reservoir operation has to be provided. Yet under climate change the historically preferenced compromise may no longer be the most suitable compromise in the future. Therefore a multi-objective based climate change impact assessment approach for multi-purpose multi-reservoir systems is proposed in the study. Projected inflows are provided in a first step using a physically based rainfall-runoff model. In a second step, a time series model is applied to generate long-term inflow time series. Finally, the long-term inflow series are used as driving variables for a simulation-based multi-objective optimization of the reservoir system in order to derive optimal operation rules. As a result, the adapted Pareto-optimal set of diverse best compromise solutions can be presented to the decision maker in order to assist him in assessing climate change adaption measures with respect to the future performance of the multi-purpose reservoir system. The approach is tested on a multi-purpose multi-reservoir system in a mountainous catchment in Germany. A climate change assessment is performed for climate change scenarios based on the SRES emission scenarios A1B, B1 and A2 for a set of statistically downscaled meteorological data. The future performance of the multi-purpose multi-reservoir system is quantified and possible intensifications of trade-offs between management goals or reservoir utilizations are shown.

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.

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.

Temporal variability of total cloud cover at a Mediterranean megacity in the 20th century: Evidence from visual observations and climate models

NASA Astrophysics Data System (ADS)

Founda, Dimitra; Giannakopoulos, Christos; Pierros, Fragiskos

2013-04-01

Cloud cover is one of the major factors that determine the radiation budget and the climate system of the Earth. Moreover, the response of clouds has always been an important source of uncertainty in global climate models. Visual surface observations of clouds have been conducted at the National Observatory of Athens (NOA) since the mid 19th century. The historical archive of cloud reports at NOA since 1860 has been digitized and updated, spanning now a period of one and a half century. Mean monthly values of total cloud cover were derived by averaging subdaily observations of cloud cover (3 observations/day). Changes in observational practice (e.g. from 1/10 to 1/8 units) were considered, however, subjective measures of cloud cover from trained observers introduces some kind of uncertainty in the time series. Data before 1884 were considered unreliable, so the analysis was restricted to the series from 1884 to 2012. The time series of total cloud cover at NOA is validated and correlated with historical time series of other (physically related) variables such as the total sunshine duration as well as DTR (Diurnal Temperature Range) which are independently measured. Trend analysis was performed on the mean annual and seasonal series of total cloud cover from 1884-2012. The mean annual values show a marked temporal variability with sub periods of decreasing and increasing tendencies, however, the overall linear trend is positive and statistically significant (p <0.001) amounting to +2% per decade and implying a total increase of almost 25% for the whole analysed period. These results are in agreement qualitatively with the trends reported in other studies worldwide, especially concerning the period before the mid 20th century. On a seasonal basis, spring and summer series present outstanding positive long term trends, while in winter and autumn total cloud cover reveals also positive but less pronounced long term trends Additionally, an evaluation of cloud cover and/or sunshine duration/diurnal temperature range as depicted by regional climate models over Athens will be performed. Regional climate models are valuable tools for projections of future climate change but their performance is typically assessed only in terms of temperature and precipitation. The representation of non-standard parameters such as cloud cover and/or sunshine duration/diurnal temperature range has so far seen little or no evaluation in the models and can therefore be prone to large uncertainties. Regional climate models developed in the framework of recent EU projects, such as the ENSEMBLES (www.ensembles-eu.org) and the CIRCE (www.circeproject.eu) projects, will be used and an initial validation of these parameters against the historical archive of NOA will be performed.

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

NASA Astrophysics Data System (ADS)

Dairaku, K.

2017-12-01

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

Examination of multi-model ensemble seasonal prediction methods using a simple climate system

NASA Astrophysics Data System (ADS)

Kang, In-Sik; Yoo, Jin Ho

2006-02-01

A simple climate model was designed as a proxy for the real climate system, and a number of prediction models were generated by slightly perturbing the physical parameters of the simple model. A set of long (240 years) historical hindcast predictions were performed with various prediction models, which are used to examine various issues of multi-model ensemble seasonal prediction, such as the best ways of blending multi-models and the selection of models. Based on these results, we suggest a feasible way of maximizing the benefit of using multi models in seasonal prediction. In particular, three types of multi-model ensemble prediction systems, i.e., the simple composite, superensemble, and the composite after statistically correcting individual predictions (corrected composite), are examined and compared to each other. The superensemble has more of an overfitting problem than the others, especially for the case of small training samples and/or weak external forcing, and the corrected composite produces the best prediction skill among the multi-model systems.

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.

Evaluations of high-resolution dynamically downscaled ensembles over the contiguous United States Climate Dynamics

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

Zobel, Zachary; Wang, Jiali; Wuebbles, Donald J.

This study uses Weather Research and Forecast (WRF) model to evaluate the performance of six dynamical downscaled decadal historical simulations with 12-km resolution for a large domain (7200 x 6180 km) that covers most of North America. The initial and boundary conditions are from three global climate models (GCMs) and one reanalysis data. The GCMs employed in this study are the Geophysical Fluid Dynamics Laboratory Earth System Model with Generalized Ocean Layer Dynamics component, Community Climate System Model, version 4, and the Hadley Centre Global Environment Model, version 2-Earth System. The reanalysis data is from the National Centers for Environmentalmore » Prediction-US. Department of Energy Reanalysis II. We analyze the effects of bias correcting, the lateral boundary conditions and the effects of spectral nudging. We evaluate the model performance for seven surface variables and four upper atmospheric variables based on their climatology and extremes for seven subregions across the United States. The results indicate that the simulation’s performance depends on both location and the features/variable being tested. We find that the use of bias correction and/or nudging is beneficial in many situations, but employing these when running the RCM is not always an improvement when compared to the reference data. The use of an ensemble mean and median leads to a better performance in measuring the climatology, while it is significantly biased for the extremes, showing much larger differences than individual GCM driven model simulations from the reference data. This study provides a comprehensive evaluation of these historical model runs in order to make informed decisions when making future projections.« less

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.

Leaf-trait plasticity and species vulnerability to climate change in a Mongolian steppe.

PubMed

Liancourt, Pierre; Boldgiv, Bazartseren; Song, Daniel S; Spence, Laura A; Helliker, Brent R; Petraitis, Peter S; Casper, Brenda B

2015-09-01

Climate change is expected to modify plant assemblages in ways that will have major consequences for ecosystem functions. How climate change will affect community composition will depend on how individual species respond, which is likely related to interspecific differences in functional traits. The extraordinary plasticity of some plant traits is typically neglected in assessing how climate change will affect different species. In the Mongolian steppe, we examined whether leaf functional traits under ambient conditions and whether plasticity in these traits under altered climate could explain climate-induced biomass responses in 12 co-occurring plant species. We experimentally created three probable climate change scenarios and used a model selection procedure to determine the set of baseline traits or plasticity values that best explained biomass response. Under all climate change scenarios, plasticity for at least one leaf trait correlated with change in species performance, while functional leaf-trait values in ambient conditions did not. We demonstrate that trait plasticity could play a critical role in vulnerability of species to a rapidly changing environment. Plasticity should be considered when examining how climate change will affect plant performance, species' niche spaces, and ecological processes that depend on plant community composition. © 2015 John Wiley & Sons Ltd.

Selecting a climate model subset to optimise key ensemble properties

NASA Astrophysics Data System (ADS)

Herger, Nadja; Abramowitz, Gab; Knutti, Reto; Angélil, Oliver; Lehmann, Karsten; Sanderson, Benjamin M.

2018-02-01

End users studying impacts and risks caused by human-induced climate change are often presented with large multi-model ensembles of climate projections whose composition and size are arbitrarily determined. An efficient and versatile method that finds a subset which maintains certain key properties from the full ensemble is needed, but very little work has been done in this area. Therefore, users typically make their own somewhat subjective subset choices and commonly use the equally weighted model mean as a best estimate. However, different climate model simulations cannot necessarily be regarded as independent estimates due to the presence of duplicated code and shared development history. Here, we present an efficient and flexible tool that makes better use of the ensemble as a whole by finding a subset with improved mean performance compared to the multi-model mean while at the same time maintaining the spread and addressing the problem of model interdependence. Out-of-sample skill and reliability are demonstrated using model-as-truth experiments. This approach is illustrated with one set of optimisation criteria but we also highlight the flexibility of cost functions, depending on the focus of different users. The technique is useful for a range of applications that, for example, minimise present-day bias to obtain an accurate ensemble mean, reduce dependence in ensemble spread, maximise future spread, ensure good performance of individual models in an ensemble, reduce the ensemble size while maintaining important ensemble characteristics, or optimise several of these at the same time. As in any calibration exercise, the final ensemble is sensitive to the metric, observational product, and pre-processing steps used.

Energy technologies evaluated against climate targets using a cost and carbon trade-off curve.

PubMed

Trancik, Jessika E; Cross-Call, Daniel

2013-06-18

Over the next few decades, severe cuts in emissions from energy will be required to meet global climate-change mitigation goals. These emission reductions imply a major shift toward low-carbon energy technologies, and the economic cost and technical feasibility of mitigation are therefore highly dependent upon the future performance of energy technologies. However, existing models do not readily translate into quantitative targets against which we can judge the dynamic performance of technologies. Here, we present a simple, new model for evaluating energy-supply technologies and their improvement trajectories against climate-change mitigation goals. We define a target for technology performance in terms of the carbon intensity of energy, consistent with emission reduction goals, and show how the target depends upon energy demand levels. Because the cost of energy determines the level of adoption, we then compare supply technologies to one another and to this target based on their position on a cost and carbon trade-off curve and how the position changes over time. Applying the model to U.S. electricity, we show that the target for carbon intensity will approach zero by midcentury for commonly cited emission reduction goals, even under a high demand-side efficiency scenario. For Chinese electricity, the carbon intensity target is relaxed and less certain because of lesser emission reductions and greater variability in energy demand projections. Examining a century-long database on changes in the cost-carbon space, we find that the magnitude of changes in cost and carbon intensity that are required to meet future performance targets is not unprecedented, providing some evidence that these targets are within engineering reach. The cost and carbon trade-off curve can be used to evaluate the dynamic performance of existing and new technologies against climate-change mitigation goals.

Multi-RCM ensemble downscaling of global seasonal forecasts (MRED)

NASA Astrophysics Data System (ADS)

Arritt, R. W.

2008-12-01

The Multi-RCM Ensemble Downscaling (MRED) project was recently initiated to address the question, Can regional climate models provide additional useful information from global seasonal forecasts? MRED will use a suite of regional climate models to downscale seasonal forecasts produced by the new National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS) seasonal forecast system and the NASA GEOS5 system. The initial focus will be on wintertime forecasts in order to evaluate topographic forcing, snowmelt, and the potential usefulness of higher resolution, especially for near-surface fields influenced by high resolution orography. Each regional model will cover the conterminous US (CONUS) at approximately 32 km resolution, and will perform an ensemble of 15 runs for each year 1982-2003 for the forecast period 1 December - 30 April. MRED will compare individual regional and global forecasts as well as ensemble mean precipitation and temperature forecasts, which are currently being used to drive macroscale land surface models (LSMs), as well as wind, humidity, radiation, turbulent heat fluxes, which are important for more advanced coupled macro-scale hydrologic models. Metrics of ensemble spread will also be evaluated. Extensive analysis will be performed to link improvements in downscaled forecast skill to regional forcings and physical mechanisms. Our overarching goal is to determine what additional skill can be provided by a community ensemble of high resolution regional models, which we believe will eventually define a strategy for more skillful and useful regional seasonal climate forecasts.

Eco-hydrological Modeling in the Framework of Climate Change

NASA Astrophysics Data System (ADS)

Fatichi, Simone; Ivanov, Valeriy Y.; Caporali, Enrica

2010-05-01

A blueprint methodology for studying climate change impacts, as inferred from climate models, on eco-hydrological dynamics at the plot and small catchment scale is presented. Input hydro-meteorological variables for hydrological and eco-hydrological models for present and future climates are reproduced using a stochastic downscaling technique and a weather generator, "AWE-GEN". The generated time series of meteorological variables for the present climate and an ensemble of possible future climates serve as input to a newly developed physically-based eco-hydrological model "Tethys-Chloris". An application of the proposed methodology is realized reproducing the current (1961-2000) and multiple future (2081-2100) climates for the location of Tucson (Arizona). A general reduction of precipitation and a significant increase of air temperature are inferred. The eco-hydrological model is successively applied to detect changes in water recharge and vegetation dynamics for a desert shrub ecosystem, typical of the semi-arid climate of south Arizona. Results for the future climate account for uncertainties in the downscaling and are produced in terms of probability density functions. A comparison of control and future scenarios is discussed in terms of changes in the hydrological balance components, energy fluxes, and indices of vegetation productivity. An appreciable effect of climate change can be observed in metrics of vegetation performance. The negative impact on vegetation due to amplification of water stress in a warmer and dryer climate is offset by a positive effect of carbon dioxide augment. This implies a positive shift in plant capabilities to exploit water. Consequently, the plant water use efficiency and rain use efficiency are expected to increase. Interesting differences in the long-term vegetation productivity are also observed for the ensemble of future climates. The reduction of precipitation and the substantial maintenance of vegetation cover ultimately leads to the depletion of soil moisture and recharge to deeper layers. Such an outcome can affect the long-tem water availability in semi-arid systems and expose plants to more severe and frequent periods of stress.

Impacts of climate change on TN load and its control in a River Basin with complex pollution sources.

PubMed

Yang, Xiaoying; Warren, Rachel; He, Yi; Ye, Jinyin; Li, Qiaoling; Wang, Guoqing

2018-02-15

It is increasingly recognized that climate change could affect the quality of water through complex natural and anthropogenic mechanisms. Previous studies on climate change and water quality have mostly focused on assessing its impact on pollutant loads from agricultural runoff. A sub-daily SWAT model was developed to simulate the discharge, transport, and transformation of nitrogen from all known anthropogenic sources including industries, municipal sewage treatment plants, concentrated and scattered feedlot operations, rural households, and crop production in the Upper Huai River Basin. This is a highly polluted basin with total nitrogen (TN) concentrations frequently exceeding Class V of the Chinese Surface Water Quality Standard (GB3838-2002). Climate change projections produced by 16 Global Circulation Models (GCMs) under the RCP 4.5 and RCP 8.5 scenarios in the mid (2040-2060) and late (2070-2090) century were used to drive the SWAT model to evaluate the impacts of climate change on both the TN loads and the effectiveness of three water pollution control measures (reducing fertilizer use, constructing vegetative filter strips, and improving septic tank performance) in the basin. SWAT simulation results have indicated that climate change is likely to cause an increase in both monthly average and extreme TN loads in February, May, and November. The projected impact of climate change on TN loads in August is more varied between GCMs. In addition, climate change is projected to have a negative impact on the effectiveness of septic tanks in reducing TN loads, while its impacts on the other two measures are more uncertain. Despite the uncertainty, reducing fertilizer use remains the most effective measure for reducing TN loads under different climate change scenarios. Meanwhile, improving septic tank performance is relatively more effective in reducing annual TN loads, while constructing vegetative filter strips is more effective in reducing annual maximum monthly TN loads. Copyright © 2017 Elsevier B.V. All rights reserved.

Changing Permafrost in the Arctic and its Global Effects in the 21st Century (PAGE21): A very large international and integrated project to measure the impact of permafrost degradation on the climate system

NASA Astrophysics Data System (ADS)

Lantuit, Hugues; Boike, Julia; Dahms, Melanie; Hubberten, Hans-Wolfgang

2013-04-01

The northern permafrost region contains approximately 50% of the estimated global below-ground organic carbon pool and more than twice as much as is contained in the current atmos-pheric carbon pool. The sheer size of this carbon pool, together with the large amplitude of predicted arctic climate change im-plies that there is a high potential for global-scale feedbacks from arctic climate change if these carbon reservoirs are desta-bilized. Nonetheless, significant gaps exist in our current state of knowledge that prevent us from producing accurate assess-ments of the vulnerability of the arctic permafrost to climate change, or of the implications of future climate change for global greenhouse gas (GHG) emissions. Specifically: • Our understanding of the physical and biogeochemical processes at play in permafrost areas is still insuffi-cient in some key aspects • Size estimates for the high latitude continental carbon and nitrogen stocks vary widely between regions and research groups. • The representation of permafrost-related processes in global climate models still tends to be rudimentary, and is one reason for the frequently poor perform-ances of climate models at high latitudes. The key objectives of PAGE21 are: • to improve our understanding of the processes affect-ing the size of the arctic permafrost carbon and nitro-gen pools through detailed field studies and monitor-ing, in order to quantify their size and their vulnerability to climate change, • to produce, assemble and assess high-quality datasets in order to develop and evaluate representations of permafrost and related processes in global models, • to improve these models accordingly, • to use these models to reduce the uncertainties in feed-backs from arctic permafrost to global change, thereby providing the means to assess the feasibility of stabili-zation scenarios, and • to ensure widespread dissemination of our results in order to provide direct input into the ongoing debate on climate-change mitigation. The concept of PAGE21 is to directly address these questions through a close interaction between monitoring activities, proc-ess studies and modeling on the pertinent temporal and spatial scales. Field sites have been selected to cover a wide range of environmental conditions for the validation of large scale mod-els, the development of permafrost monitoring capabilities, the study of permafrost processes, and for overlap with existing monitoring programs. PAGE21 will contribute to upgrading the project sites with the objective of providing a measurement baseline, both for process studies and for modeling programs. PAGE21 is determined to break down the traditional barriers in permafrost sciences between observational and model-supported site studies and large-scale climate modeling. Our concept for the interaction between site-scale studies and large-scale modeling is to establish and maintain a direct link be-tween these two areas for developing and evaluating, on all spatial scales, the land-surface modules of leading European global climate models taking part in the Coupled Model Inter-comparison Project Phase 5 (CMIP5), designed to inform the IPCC process. The timing of this project is such that the main scientific results from PAGE21, and in particular the model-based assessments will build entirely on new outputs and results from the CMIP5 Climate Model Intercomparison Project designed to inform the IPCC Fifth Assessment Report. However, PAGE21 is designed to leave a legacy that will en-dure beyond the lifetime of the projections that it produces. This legacy will comprise • an improved understanding of the key processes and parameters that determine the vulnerability of arctic permafrost to climate change, • the production of a suite of major European coupled climate models including detailed and validated repre-sentations of permafrost-related processes, that will reduce uncertainties in future climate projections pro-duced well beyond the lifetime of PAGE21, and • the training of a new generation of permafrost scien-tists who will bridge the long-standing gap between permafrost field science and global climate modeling, for the long-term benefit of science and society.

Development of ALARO-Climate regional climate model for a very high resolution

NASA Astrophysics Data System (ADS)

Skalak, Petr; Farda, Ales; Brozkova, Radmila; Masek, Jan

2013-04-01

ALARO-Climate is a new regional climate model (RCM) derived from the ALADIN LAM model family. It is based on the numerical weather prediction model ALARO and developed at the Czech Hydrometeorological Institute. The model is expected to able to work in the so called "grey zone" physics (horizontal resolution of 4 - 7 km) and at the same time retain its ability to be operated in resolutions in between 20 and 50 km, which are typical for contemporary generation of regional climate models. Here we present the main features of the RCM ALARO-Climate and results of the first model simulations on longer time-scales (1961-1990). The model was driven by the ERA-40/Interim re-analyses and run on the large pan-European integration domain ("ENSEMBLES / Euro-Cordex domain") with spatial resolution of 25 km. The simulated model climate was compared with the gridded observation of air temperature (mean, maximum, minimum) and precipitation from the E-OBS version 7 dataset. The validation of the first ERA-40 simulation has revealed significant cold biases in all seasons (between -4 and -2 °C) and overestimation of precipitation on 20% to 60% in the selected Central Europe target area (0° - 30° eastern longitude ; 40° - 60° northern latitude). The consequent adaptations in the model and their effect on the simulated properties of climate variables are illustrated. Acknowledgements: This study was performed within the frame of projects ALARO (project P209/11/2405 sponsored by the Czech Science Foundation) and CzechGlobe Centre (CZ.1.05/1.1.00/02.0073). The partial support was also provided under the projects P209-11-0956 of the Czech Science Foundation and CZ.1.07/2.4.00/31.0056 (Operational Programme of Education for Competitiveness of Ministry of Education, Youth and Sports of the Czech Republic).

Simulation of Drought-induced Tree Mortality Using a New Individual and Hydraulic Trait-based Model (S-TEDy)

NASA Astrophysics Data System (ADS)

Sinha, T.; Gangodagamage, C.; Ale, S.; Frazier, A. G.; Giambelluca, T. W.; Kumagai, T.; Nakai, T.; Sato, H.

2017-12-01

Drought-related tree mortality at a regional scale causes drastic shifts in carbon and water cycling in Southeast Asian tropical rainforests, where severe droughts are projected to occur more frequently, especially under El Niño conditions. To provide a useful tool for projecting the tropical rainforest dynamics under climate change conditions, we developed the Spatially Explicit Individual-Based (SEIB) Dynamic Global Vegetation Model (DGVM) applicable to simulating mechanistic tree mortality induced by the climatic impacts via individual-tree-scale ecophysiology such as hydraulic failure and carbon starvation. In this study, we present the new model, SEIB-originated Terrestrial Ecosystem Dynamics (S-TEDy) model, and the computation results were compared with observations collected at a field site in a Bornean tropical rainforest. Furthermore, after validating the model's performance, numerical experiments addressing a future of the tropical rainforest were conducted using some global climate model (GCM) simulation outputs.

Application of a stochastic weather generator to assess climate change impacts in a semi-arid climate: The Upper Indus Basin

NASA Astrophysics Data System (ADS)

Forsythe, N.; Fowler, H. J.; Blenkinsop, S.; Burton, A.; Kilsby, C. G.; Archer, D. R.; Harpham, C.; Hashmi, M. Z.

2014-09-01

Assessing local climate change impacts requires downscaling from Global Climate Model simulations. Here, a stochastic rainfall model (RainSim) combined with a rainfall conditioned weather generator (CRU WG) have been successfully applied in a semi-arid mountain climate, for part of the Upper Indus Basin (UIB), for point stations at a daily time-step to explore climate change impacts. Validation of the simulated time-series against observations (1961-1990) demonstrated the models' skill in reproducing climatological means of core variables with monthly RMSE of <2.0 mm for precipitation and ⩽0.4 °C for mean temperature and daily temperature range. This level of performance is impressive given complexity of climate processes operating in this mountainous context at the boundary between monsoonal and mid-latitude (westerly) weather systems. Of equal importance the model captures well the observed interannual variability as quantified by the first and last decile of 30-year climatic periods. Differences between a control (1961-1990) and future (2071-2100) regional climate model (RCM) time-slice experiment were then used to provide change factors which could be applied within the rainfall and weather models to produce perturbed ‘future' weather time-series. These project year-round increases in precipitation (maximum seasonal mean change:+27%, annual mean change: +18%) with increased intensity in the wettest months (February, March, April) and year-round increases in mean temperature (annual mean +4.8 °C). Climatic constraints on the productivity of natural resource-dependent systems were also assessed using relevant indices from the European Climate Assessment (ECA) and indicate potential future risk to water resources and local agriculture. However, the uniformity of projected temperature increases is in stark contrast to recent seasonally asymmetrical trends in observations, so an alternative scenario of extrapolated trends was also explored. We conclude that interannual variability in climate will continue to have the dominant impact on water resources management whichever trajectory is followed. This demonstrates the need for sophisticated downscaling methods which can evaluate changes in variability and sequencing of events to explore climate change impacts in this region.

Growing Land-Sea Temperature Contrast and the Intensification of Arctic Cyclones

NASA Astrophysics Data System (ADS)

Day, Jonathan J.; Hodges, Kevin I.

2018-04-01

Cyclones play an important role in the coupled dynamics of the Arctic climate system on a range of time scales. Modeling studies suggest that storminess will increase in Arctic summer due to enhanced land-sea thermal contrast along the Arctic coastline, in a region known as the Arctic Frontal Zone (AFZ). However, the climate models used in these studies are poor at reproducing the present-day Arctic summer cyclone climatology and so their projections of Arctic cyclones and related quantities, such as sea ice, may not be reliable. In this study we perform composite analysis of Arctic cyclone statistics using AFZ variability as an analog for climate change. High AFZ years are characterized both by increased cyclone frequency and dynamical intensity, compared to low years. Importantly, the size of the response in this analog suggests that General Circulation Models may underestimate the response of Arctic cyclones to climate change, given a similar change in baroclinicity.

Generation of future potential scenarios in an Alpine Catchment by applying bias-correction techniques, delta-change approaches and stochastic Weather Generators at different spatial scale. Analysis of their influence on basic and drought statistics.

NASA Astrophysics Data System (ADS)

Collados-Lara, Antonio-Juan; Pulido-Velazquez, David; Pardo-Iguzquiza, Eulogio

2017-04-01

Assessing impacts of potential future climate change scenarios in precipitation and temperature is essential to design adaptive strategies in water resources systems. The objective of this work is to analyze the possibilities of different statistical downscaling methods to generate future potential scenarios in an Alpine Catchment from historical data and the available climate models simulations performed in the frame of the CORDEX EU project. The initial information employed to define these downscaling approaches are the historical climatic data (taken from the Spain02 project for the period 1971-2000 with a spatial resolution of 12.5 Km) and the future series provided by climatic models in the horizon period 2071-2100 . We have used information coming from nine climate model simulations (obtained from five different Regional climate models (RCM) nested to four different Global Climate Models (GCM)) from the European CORDEX project. In our application we have focused on the Representative Concentration Pathways (RCP) 8.5 emissions scenario, which is the most unfavorable scenario considered in the fifth Assessment Report (AR5) by the Intergovernmental Panel on Climate Change (IPCC). For each RCM we have generated future climate series for the period 2071-2100 by applying two different approaches, bias correction and delta change, and five different transformation techniques (first moment correction, first and second moment correction, regression functions, quantile mapping using distribution derived transformation and quantile mapping using empirical quantiles) for both of them. Ensembles of the obtained series were proposed to obtain more representative potential future climate scenarios to be employed to study potential impacts. In this work we propose a non-equifeaseble combination of the future series giving more weight to those coming from models (delta change approaches) or combination of models and techniques that provides better approximation to the basic and drought statistic of the historical data. A multi-objective analysis using basic statistics (mean, standard deviation and asymmetry coefficient) and droughts statistics (duration, magnitude and intensity) has been performed to identify which models are better in terms of goodness of fit to reproduce the historical series. The drought statistics have been obtained from the Standard Precipitation index (SPI) series using the Theory of Runs. This analysis allows discriminate the best RCM and the best combination of model and correction technique in the bias-correction method. We have also analyzed the possibilities of using different Stochastic Weather Generators to approximate the basic and droughts statistics of the historical series. These analyses have been performed in our case study in a lumped and in a distributed way in order to assess its sensibility to the spatial scale. The statistic of the future temperature series obtained with different ensemble options are quite homogeneous, but the precipitation shows a higher sensibility to the adopted method and spatial scale. The global increment in the mean temperature values are 31.79 %, 31.79 %, 31.03 % and 31.74 % for the distributed bias-correction, distributed delta-change, lumped bias-correction and lumped delta-change ensembles respectively and in the precipitation they are -25.48 %, -28.49 %, -26.42 % and -27.35% respectively. Acknowledgments: This research work has been partially supported by the GESINHIMPADAPT project (CGL2013-48424-C2-2-R) with Spanish MINECO funds. We would also like to thank Spain02 and CORDEX projects for the data provided for this study and the R package qmap.

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.

Redesigning mental healthcare delivery: is there an effect on organizational climate?

PubMed

Joosten, T C M; Bongers, I M B; Janssen, R T J M

2014-02-01

Many studies have investigated the effect of redesign on operational performance; fewer studies have evaluated the effects on employees' perceptions of their working environment (organizational climate). Some authors state that redesign will lead to poorer organizational climate, while others state the opposite. The goal of this study was to empirically investigate this relation. Organizational climate was measured in a field experiment, before and after a redesign intervention. At one of the sites, a redesign project was conducted. At the other site, no redesign efforts took place. Two Dutch child- and adolescent-mental healthcare providers. Professionals that worked at one of the units at the start and/or the end of the intervention period. The main intervention was a redesign project aimed at improving timely delivery of services (modeled after the breakthrough series). Scores on the four models of the organizational climate measure, a validated questionnaire that measures organizational climate. Our analysis showed that climate at the intervention site changed on factors related to productivity and goal achievement (rational goal model). The intervention group scored worse than the comparison group on the part of the questionnaire that focuses on sociotechnical elements of organizational climate. However, observed differences were so small, that their practical relevance seems rather limited. Redesign efforts in healthcare, so it seems, do not influence organizational climate as much as expected.

Optimization for energy efficiency of underground building envelope thermal performance in different climate zones of China

NASA Astrophysics Data System (ADS)

Shi, Luyang; Liu, Jing; Zhang, Huibo

2017-11-01

The object of this article is to investigate the influence of thermal performance of envelopes in shallow-buried buildings on energy consumption for different climate zones of China. For the purpose of this study, an effective building energy simulation tool (DeST) developed by Tsinghua University was chosen to model the heat transfer in underground buildings. Based on the simulative results, energy consumption for heating and cooling for the whole year was obtained. The results showed that the relationship between energy consumption and U-value of envelopes for underground buildings is different compared with above-ground buildings: improving thermal performance of exterior walls cannot reduce energy consumption, on the contrary, may result in more energy cost. Besides, it is can be derived that optimized U-values of underground building envelopes vary with climate zones of China in this study. For severe cold climate zone, the optimized U-value of underground building envelopes is 0.8W/(m2·K); for cold climate zone, the optimized U-value is 1.5W/(m2·K); for warm climate zone, the U-value is 2.0W/(m2·K).

Cold truths: how winter drives responses of terrestrial organisms to climate change.

PubMed

Williams, Caroline M; Henry, Hugh A L; Sinclair, Brent J

2015-02-01

Winter is a key driver of individual performance, community composition, and ecological interactions in terrestrial habitats. Although climate change research tends to focus on performance in the growing season, climate change is also modifying winter conditions rapidly. Changes to winter temperatures, the variability of winter conditions, and winter snow cover can interact to induce cold injury, alter energy and water balance, advance or retard phenology, and modify community interactions. Species vary in their susceptibility to these winter drivers, hampering efforts to predict biological responses to climate change. Existing frameworks for predicting the impacts of climate change do not incorporate the complexity of organismal responses to winter. Here, we synthesise organismal responses to winter climate change, and use this synthesis to build a framework to predict exposure and sensitivity to negative impacts. This framework can be used to estimate the vulnerability of species to winter climate change. We describe the importance of relationships between winter conditions and performance during the growing season in determining fitness, and demonstrate how summer and winter processes are linked. Incorporating winter into current models will require concerted effort from theoreticians and empiricists, and the expansion of current growing-season studies to incorporate winter. © 2014 The Authors. Biological Reviews © 2014 Cambridge Philosophical Society.

Two-way against one-way nesting for climate downscaling in Europe and the Mediterranean region using LMDZ4

NASA Astrophysics Data System (ADS)

Li, Shan; Li, Laurent; Le Treut, Hervé

2016-04-01

In the 21st century, the estimated surface temperature warming projected by General Circulation Models (GCMs) is between 0.3 and 4.8 °C, depending on the scenario considered. GCMs exhibit a good representation of climate on a global scale, but they are not able to reproduce regional climate processes with the same level of accuracy. Society and policymakers need model projections to define climate change adaptation and mitigation policies on a global, regional and local scale. Climate downscaling is mostly conducted with a regional model nested into the outputs of a global model. This one-way nesting approach is generally used in the climate community without feedbacks from Regional Climate Models (RCMs) to GCMs. This lack of interaction between the two models may affect regional modes of variability, in particular those with a boundary conflict. The objective of this study is to evaluate a two-way nesting configuration that makes an interactive coupling between the RCM and the GCM, an approach against the traditional configuration of one-way nesting system. An additional aim of this work is to examine if the two-way nesting system can improve the RCM performance. The atmospheric component of the IPSL integrated climate model (LMDZ) is configured at both regional (LMDZ-regional) and global (LMDZ-global) scales. The two models have the same configuration for the dynamical framework and the physical forcings. The climatology values of sea surface temperature (SST) are prescribed for the two models. The stretched-grid of LMDZ-global is applied to a region defined by Europe, the Mediterranean, North Africa and Western North Atlantic. To ensure a good statistical significance of results, all simulations last at least 80 years. The nesting process of models is performed by a relaxation procedure of a time scale of 90 minutes. In the case of two-way nesting, the exchange between the two models is every two hours. The relaxation procedure induces a boundary conflict, particularly in the eastern boundary for temperature and geopotential height. A correlation analysis on the synoptic scale evaluates the relationship between the GCM and the RCM. The beginning of the simulations shows a great consistency of the two models. When dominant dynamics apply, RCM inherits most of the GCM signal with a consistent spatial structure. On the contrary, when the atmospheric circulation is weak, there are not that many effects transferred from the GCM to the RCM. When the RCM has its own dynamics, the boundary conflict is more pronounced. Winter season is chosen for the two-way nesting test due to the predominant role of the atmospheric dynamics in winter. The new approach of a two-way nesting system reduces boundary bias, having a influence in some cases in climate model projections. The effect of two-way nesting is enhanced when using a finer grid.

Sensitivity of Statistical Downscaling Techniques to Reanalysis Choice and Implications for Regional Climate Change Scenarios

NASA Astrophysics Data System (ADS)

Manzanas, R., Sr.; Brands, S.; San Martin, D., Sr.; Gutiérrez, J. M., Sr.

2014-12-01

This work shows that local-scale climate projections obtained by means of statistical downscaling are sensitive to the choice of reanalysis used for calibration. To this aim, a Generalized Linear Model (GLM) approach is applied to downscale daily precipitation in the Philippines. First, the GLMs are trained and tested -under a cross-validation scheme- separately for two distinct reanalyses (ERA-Interim and JRA-25) for the period 1981-2000. When the observed and downscaled time-series are compared, the attained performance is found to be sensitive to the reanalysis considered if climate change signal bearing variables (temperature and/or specific humidity) are included in the predictor field. Moreover, performance differences are shown to be in correspondence with the disagreement found between the raw predictors from the two reanalyses. Second, the regression coefficients calibrated either with ERA-Interim or JRA-25 are subsequently applied to the output of a Global Climate Model (MPI-ECHAM5) in order to assess the sensitivity of local-scale climate change projections (up to 2100) to reanalysis choice. In this case, the differences detected in present climate conditions are considerably amplified, leading to "delta-change" estimates differing by up to a 35% (on average for the entire country) depending on the reanalysis used for calibration. Therefore, reanalysis choice is shown to importantly contribute to the uncertainty of local-scale climate change projections, and, consequently, should be treated with equal care as other, well-known, sources of uncertainty -e.g., the choice of the GCM and/or downscaling method.- Implications of the results for the entire tropics, as well as for the Model Output Statistics downscaling approach are also briefly discussed.

Climate Prediction for Brazil's Nordeste: Performance of Empirical and Numerical Modeling Methods.

NASA Astrophysics Data System (ADS)

Moura, Antonio Divino; Hastenrath, Stefan

2004-07-01

Comparisons of performance of climate forecast methods require consistency in the predictand and a long common reference period. For Brazil's Nordeste, empirical methods developed at the University of Wisconsin use preseason (October January) rainfall and January indices of the fields of meridional wind component and sea surface temperature (SST) in the tropical Atlantic and the equatorial Pacific as input to stepwise multiple regression and neural networking. These are used to predict the March June rainfall at a network of 27 stations. An experiment at the International Research Institute for Climate Prediction, Columbia University, with a numerical model (ECHAM4.5) used global SST information through February to predict the March June rainfall at three grid points in the Nordeste. The predictands for the empirical and numerical model forecasts are correlated at +0.96, and the period common to the independent portion of record of the empirical prediction and the numerical modeling is 1968 99. Over this period, predicted versus observed rainfall are evaluated in terms of correlation, root-mean-square error, absolute error, and bias. Performance is high for both approaches. Numerical modeling produces a correlation of +0.68, moderate errors, and strong negative bias. For the empirical methods, errors and bias are small, and correlations of +0.73 and +0.82 are reached between predicted and observed rainfall.

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

NASA Astrophysics Data System (ADS)

Van Uytven, Els; Willems, Patrick

2017-04-01

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

Developing Models for Predictive Climate Science

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

Drake, John B; Jones, Philip W

2007-01-01

The Community Climate System Model results from a multi-agency collaboration designed to construct cutting-edge climate science simulation models for a broad research community. Predictive climate simulations are currently being prepared for the petascale computers of the near future. Modeling capabilities are continuously being improved in order to provide better answers to critical questions about Earth's climate. Climate change and its implications are front page news in today's world. Could global warming be responsible for the July 2006 heat waves in Europe and the United States? Should more resources be devoted to preparing for an increase in the frequency of strongmore » tropical storms and hurricanes like Katrina? Will coastal cities be flooded due to a rise in sea level? The National Climatic Data Center (NCDC), which archives all weather data for the nation, reports that global surface temperatures have increased over the last century, and that the rate of increase is three times greater since 1976. Will temperatures continue to climb at this rate, will they decline again, or will the rate of increase become even steeper? To address such a flurry of questions, scientists must adopt a systematic approach and develop a predictive framework. With responsibility for advising on energy and technology strategies, the DOE is dedicated to advancing climate research in order to elucidate the causes of climate change, including the role of carbon loading from fossil fuel use. Thus, climate science--which by nature involves advanced computing technology and methods--has been the focus of a number of DOE's SciDAC research projects. Dr. John Drake (ORNL) and Dr. Philip Jones (LANL) served as principal investigators on the SciDAC project, 'Collaborative Design and Development of the Community Climate System Model for Terascale Computers.' The Community Climate System Model (CCSM) is a fully-coupled global system that provides state-of-the-art computer simulations of the Earth's past, present, and future climate states. The collaborative SciDAC team--including over a dozen researchers at institutions around the country--developed, validated, documented, and optimized the performance of CCSM using the latest software engineering approaches, computational technology, and scientific knowledge. Many of the factors that must be accounted for in a comprehensive model of the climate system are illustrated in figure 1.« less

Building an ensemble of climate scenarios for decision-making in hydrology: benefits, pitfalls and uncertainties

NASA Astrophysics Data System (ADS)

Braun, Marco; Chaumont, Diane

2013-04-01

Using climate model output to explore climate change impacts on hydrology requires several considerations, choices and methods in the post treatment of the datasets. In the effort of producing a comprehensive data base of climate change scenarios for over 300 watersheds in the Canadian province of Québec, a selection of state of the art procedures were applied to an ensemble comprising 87 climate simulations. The climate data ensemble is based on global climate simulations from the Coupled Model Intercomparison Project - Phase 3 (CMIP3) and regional climate simulations from the North American Regional Climate Change Assessment Program (NARCCAP) and operational simulations produced at Ouranos. Information on the response of hydrological systems to changing climate conditions can be derived by linking climate simulations with hydrological models. However, the direct use of raw climate model output variables as drivers for hydrological models is limited by issues such as spatial resolution and the calibration of hydro models with observations. Methods for downscaling and bias correcting the data are required to achieve seamless integration of climate simulations with hydro models. The effects on the results of four different approaches to data post processing were explored and compared. We present the lessons learned from building the largest data base yet for multiple stakeholders in the hydro power and water management sector in Québec putting an emphasis on the benefits and pitfalls in choosing simulations, extracting the data, performing bias corrections and documenting the results. A discussion of the sources and significance of uncertainties in the data will also be included. The climatological data base was subsequently used by the state owned hydro power company Hydro-Québec and the Centre d'expertise hydrique du Québec (CEHQ), the provincial water authority, to simulate future stream flows and analyse the impacts on hydrological indicators. While this submission focuses on the production of climatic scenarios for application in hydrology, the submission « The (cQ)2 project: assessing watershed scale hydrological changes for the province of Québec at the 2050 horizon, a collaborative framework » by Catherine Guay describes how Hydro-Québec and CEHQ put the data into use.

Academic climate, well-being and academic performance in a university degree course.

PubMed

Rania, Nadia; Siri, Anna; Bagnasco, Annamaria; Aleo, Giuseppe; Sasso, Loredana

2014-09-01

The psychological climate within organisations affects not only the behaviour and the attitude of group members, but also the performance of the group itself. According to the ecological model, this research examines how learning in different classroom contexts of the same nursing degree programme can affect academic performance, well-being, self-esteem and perceived climate. Four scales were used to assess students' perceptions by collecting primary data while academic performance was measured by obtaining students' academic records. A questionnaire completed by 391 first-year nursing students was administered. Differences were observed in the perceptions of climate and academic performance in different classroom contexts with trends, which did not always overlap; however, strong correlations were observed among self-esteem, well-being and climate, and schoolmate relationships. Universities should not merely train competent professionals but also build learning communities that support the well-being of relationships and the development of well-being contexts. The findings support the need for an educational intervention for improving the quality of life and well-being of the community and individual students. This type of intervention requires a 'compliant' organisational environment that puts studetns, teachers and professionals in the condition to practice their professional skills. © 2013 John Wiley & Sons Ltd.

An Improved Radiative Transfer Model for Climate Calculations

NASA Technical Reports Server (NTRS)

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

1998-01-01

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

Three-dimensional hydrogeological modeling to assess the elevated-water-table technique for controlling acid generation from an abandoned tailings site in Quebec, Canada

NASA Astrophysics Data System (ADS)

Ethier, Marie-Pier; Bussière, Bruno; Broda, Stefan; Aubertin, Michel

2018-01-01

The Manitou Mine sulphidic-tailings storage facility No. 2, near Val D'Or, Canada, was reclaimed in 2009 by elevating the water table and applying a monolayer cover made of tailings from nearby Goldex Mine. Previous studies showed that production of acid mine drainage can be controlled by lowering the oxygen flux through Manitou tailings with a water table maintained at the interface between the cover and reactive tailings. Simulations of different scenarios were performed using numerical hydrogeological modeling to evaluate the capacity of the reclamation works to maintain the phreatic surface at this interface. A large-scale numerical model was constructed and calibrated using 3 years of field measurements. This model reproduced the field measurements, including the existence of a western zone on the site where the phreatic level targeted is not always met during the summer. A sensitivity analysis was performed to assess the response of the model to varying saturated hydraulic conductivities, porosities, and grain-size distributions. Higher variations of the hydraulic heads, with respect to the calibrated scenario results, were observed when simulating a looser or coarser cover material. Long-term responses were simulated using: the normal climatic data, data for a normal climate with a 2-month dry spell, and a simplified climate-change case. Environmental quality targets were reached less frequently during summer for the dry spell simulation as well as for the simplified climate-change scenario. This study illustrates how numerical simulations can be used as a key tool to assess the eventual performance of various mine-site reclamation scenarios.

Three-dimensional hydrogeological modeling to assess the elevated-water-table technique for controlling acid generation from an abandoned tailings site in Quebec, Canada

NASA Astrophysics Data System (ADS)

Ethier, Marie-Pier; Bussière, Bruno; Broda, Stefan; Aubertin, Michel

2018-06-01

The Manitou Mine sulphidic-tailings storage facility No. 2, near Val D'Or, Canada, was reclaimed in 2009 by elevating the water table and applying a monolayer cover made of tailings from nearby Goldex Mine. Previous studies showed that production of acid mine drainage can be controlled by lowering the oxygen flux through Manitou tailings with a water table maintained at the interface between the cover and reactive tailings. Simulations of different scenarios were performed using numerical hydrogeological modeling to evaluate the capacity of the reclamation works to maintain the phreatic surface at this interface. A large-scale numerical model was constructed and calibrated using 3 years of field measurements. This model reproduced the field measurements, including the existence of a western zone on the site where the phreatic level targeted is not always met during the summer. A sensitivity analysis was performed to assess the response of the model to varying saturated hydraulic conductivities, porosities, and grain-size distributions. Higher variations of the hydraulic heads, with respect to the calibrated scenario results, were observed when simulating a looser or coarser cover material. Long-term responses were simulated using: the normal climatic data, data for a normal climate with a 2-month dry spell, and a simplified climate-change case. Environmental quality targets were reached less frequently during summer for the dry spell simulation as well as for the simplified climate-change scenario. This study illustrates how numerical simulations can be used as a key tool to assess the eventual performance of various mine-site reclamation scenarios.

Nasal airflow simulations suggest convergent adaptation in Neanderthals and modern humans.

PubMed

de Azevedo, S; González, M F; Cintas, C; Ramallo, V; Quinto-Sánchez, M; Márquez, F; Hünemeier, T; Paschetta, C; Ruderman, A; Navarro, P; Pazos, B A; Silva de Cerqueira, C C; Velan, O; Ramírez-Rozzi, F; Calvo, N; Castro, H G; Paz, R R; González-José, R

2017-11-21

Both modern humans (MHs) and Neanderthals successfully settled across western Eurasian cold-climate landscapes. Among the many adaptations considered as essential to survival in such landscapes, changes in the nasal morphology and/or function aimed to humidify and warm the air before it reaches the lungs are of key importance. Unfortunately, the lack of soft-tissue evidence in the fossil record turns difficult any comparative study of respiratory performance. Here, we reconstruct the internal nasal cavity of a Neanderthal plus two representatives of climatically divergent MH populations (southwestern Europeans and northeastern Asians). The reconstruction includes mucosa distribution enabling a realistic simulation of the breathing cycle in different climatic conditions via computational fluid dynamics. Striking across-specimens differences in fluid residence times affecting humidification and warming performance at the anterior tract were found under cold/dry climate simulations. Specifically, the Asian model achieves a rapid air conditioning, followed by the Neanderthals, whereas the European model attains a proper conditioning only around the medium-posterior tract. In addition, quantitative-genetic evolutionary analyses of nasal morphology provided signals of stabilizing selection for MH populations, with the removal of Arctic populations turning covariation patterns compatible with evolution by genetic drift. Both results indicate that, departing from important craniofacial differences existing among Neanderthals and MHs, an advantageous species-specific respiratory performance in cold climates may have occurred in both species. Fluid dynamics and evolutionary biology independently provided evidence of nasal evolution, suggesting that adaptive explanations regarding complex functional phenotypes require interdisciplinary approaches aimed to quantify both performance and evolutionary signals on covariation patterns.

Towards Cloud-Resolving European-Scale Climate Simulations using a fully GPU-enabled Prototype of the COSMO Regional Model

NASA Astrophysics Data System (ADS)

Leutwyler, David; Fuhrer, Oliver; Cumming, Benjamin; Lapillonne, Xavier; Gysi, Tobias; Lüthi, Daniel; Osuna, Carlos; Schär, Christoph

2014-05-01

The representation of moist convection is a major shortcoming of current global and regional climate models. State-of-the-art global models usually operate at grid spacings of 10-300 km, and therefore cannot fully resolve the relevant upscale and downscale energy cascades. Therefore parametrization of the relevant sub-grid scale processes is required. Several studies have shown that this approach entails major uncertainties for precipitation processes, which raises concerns about the model's ability to represent precipitation statistics and associated feedback processes, as well as their sensitivities to large-scale conditions. Further refining the model resolution to the kilometer scale allows representing these processes much closer to first principles and thus should yield an improved representation of the water cycle including the drivers of extreme events. Although cloud-resolving simulations are very useful tools for climate simulations and numerical weather prediction, their high horizontal resolution and consequently the small time steps needed, challenge current supercomputers to model large domains and long time scales. The recent innovations in the domain of hybrid supercomputers have led to mixed node designs with a conventional CPU and an accelerator such as a graphics processing unit (GPU). GPUs relax the necessity for cache coherency and complex memory hierarchies, but have a larger system memory-bandwidth. This is highly beneficial for low compute intensity codes such as atmospheric stencil-based models. However, to efficiently exploit these hybrid architectures, climate models need to be ported and/or redesigned. Within the framework of the Swiss High Performance High Productivity Computing initiative (HP2C) a project to port the COSMO model to hybrid architectures has recently come to and end. The product of these efforts is a version of COSMO with an improved performance on traditional x86-based clusters as well as hybrid architectures with GPUs. We present our redesign and porting approach as well as our experience and lessons learned. Furthermore, we discuss relevant performance benchmarks obtained on the new hybrid Cray XC30 system "Piz Daint" installed at the Swiss National Supercomputing Centre (CSCS), both in terms of time-to-solution as well as energy consumption. We will demonstrate a first set of short cloud-resolving climate simulations at the European-scale using the GPU-enabled COSMO prototype and elaborate our future plans on how to exploit this new model capability.

Comparison of hybrid spectral-decomposition artificial neural network models for understanding climatic forcing of groundwater levels

NASA Astrophysics Data System (ADS)

Abrokwah, K.; O'Reilly, A. M.

2017-12-01

Groundwater is an important resource that is extracted every day because of its invaluable use for domestic, industrial and agricultural purposes. The need for sustaining groundwater resources is clearly indicated by declining water levels and has led to modeling and forecasting accurate groundwater levels. In this study, spectral decomposition of climatic forcing time series was used to develop hybrid wavelet analysis (WA) and moving window average (MWA) artificial neural network (ANN) models. These techniques are explored by modeling historical groundwater levels in order to provide understanding of potential causes of the observed groundwater-level fluctuations. Selection of the appropriate decomposition level for WA and window size for MWA helps in understanding the important time scales of climatic forcing, such as rainfall, that influence water levels. Discrete wavelet transform (DWT) is used to decompose the input time-series data into various levels of approximate and details wavelet coefficients, whilst MWA acts as a low-pass signal-filtering technique for removing high-frequency signals from the input data. The variables used to develop and validate the models were daily average rainfall measurements from five National Atmospheric and Oceanic Administration (NOAA) weather stations and daily water-level measurements from two wells recorded from 1978 to 2008 in central Florida, USA. Using different decomposition levels and different window sizes, several WA-ANN and MWA-ANN models for simulating the water levels were created and their relative performances compared against each other. The WA-ANN models performed better than the corresponding MWA-ANN models; also higher decomposition levels of the input signal by the DWT gave the best results. The results obtained show the applicability and feasibility of hybrid WA-ANN and MWA-ANN models for simulating daily water levels using only climatic forcing time series as model inputs.

Evaluation of Historical and Projected Agricultural Climate Risk Over the Continental US

NASA Astrophysics Data System (ADS)

Zhu, X.; Troy, T. J.; Devineni, N.

2016-12-01

Food demands are rising due to an increasing population with changing food preferences, which places pressure on agricultural systems. In addition, in the past decade climate extremes have highlighted the vulnerability of our agricultural production to climate variability. Quantitative analyses in the climate-agriculture research field have been performed in many studies. However, climate risk still remains difficult to evaluate at large scales yet shows great potential of help us better understand historical climate change impacts and evaluate the future risk given climate projections. In this study, we developed a framework to evaluate climate risk quantitatively by applying statistical methods such as Bayesian regression, distribution fitting, and Monte Carlo simulation. We applied the framework over different climate regions in the continental US both historically and for modeled climate projections. The relative importance of any major growing season climate index, such as maximum dry period or heavy precipitation, was evaluated to determine what climate indices play a role in affecting crop yields. The statistical modeling framework was applied using county yields, with irrigated and rainfed yields separated to evaluate the different risk. This framework provides estimates of the climate risk facing agricultural production in the near-term that account for the full uncertainty of climate occurrences, range of crop response, and spatial correlation in climate. In particular, the method provides robust estimates of importance of irrigation in mitigating agricultural climate risk. The results of this study can contribute to decision making about crop choice and water use in an uncertain climate.

Surface Mass Balance of the Greenland Ice Sheet Derived from Paleoclimate Reanalysis

NASA Astrophysics Data System (ADS)

Badgeley, J.; Steig, E. J.; Hakim, G. J.; Anderson, J.; Tardif, R.

2017-12-01

Modeling past ice-sheet behavior requires independent knowledge of past surface mass balance. Though models provide useful insight into ice-sheet response to climate forcing, if past climate is unknown, then ascertaining the rate and extent of past ice-sheet change is limited to geological and geophysical constraints. We use a novel data-assimilation framework developed under the Last Millennium Reanalysis Project (Hakim et al., 2016) to reconstruct past climate over ice sheets with the intent of creating an independent surface mass balance record for paleo ice-sheet modeling. Paleoclimate data assimilation combines the physics of climate models and the time series evidence of proxy records in an offline, ensemble-based approach. This framework allows for the assimilation of numerous proxy records and archive types while maintaining spatial consistency with known climate dynamics and physics captured by the models. In our reconstruction, we use the Community Climate System Model version 4, CMIP5 last millennium simulation (Taylor et al., 2012; Landrum et al., 2013) and a nearly complete database of ice core oxygen isotope records to reconstruct Holocene surface temperature and precipitation over the Greenland Ice Sheet on a decadal timescale. By applying a seasonality to this reconstruction (from the TraCE-21ka simulation; Liu et al., 2009), our reanalysis can be used in seasonally-based surface mass balance models. Here we discuss the methods behind our reanalysis and the performance of our reconstruction through prediction of unassimilated proxy records and comparison to paleoclimate reconstructions and reanalysis products.

Operative air temperature data for different measures applied on a building envelope in warm climate.

PubMed

Baglivo, Cristina; Congedo, Paolo Maria

2018-04-01

Several technical combinations have been evaluated in order to design high energy performance buildings for the warm climate. The analysis has been developed in several steps, avoiding the use of HVAC systems. The methodological approach of this study is based on a sequential search technique and it is shown on the paper entitled "Envelope Design Optimization by Thermal Modeling of a Building in a Warm Climate" [1]. The Operative Air Temperature trends (TOP), for each combination, have been plotted through a dynamic simulation performed using the software TRNSYS 17 (a transient system simulation program, University of Wisconsin, Solar Energy Laboratory, USA, 2010). Starting from the simplest building configuration consisting of 9 rooms (equal-sized modules of 5 × 5 m 2 ), the different building components are sequentially evaluated until the envelope design is optimized. The aim of this study is to perform a step-by-step simulation, simplifying as much as possible the model without making additional variables that can modify their performances. Walls, slab-on-ground floor, roof, shading and windows are among the simulated building components. The results are shown for each combination and evaluated for Brindisi, a city in southern Italy having 1083 degrees day, belonging to the national climatic zone C. The data show the trends of the TOP for each measure applied in the case study for a total of 17 combinations divided into eight steps.

How does a servant leader fuel the service fire? A multilevel model of servant leadership, individual self identity, group competition climate, and customer service performance.

PubMed

Chen, Zhijun; Zhu, Jing; Zhou, Mingjian

2015-03-01

Building on a social identity framework, our cross-level process model explains how a manager's servant leadership affects frontline employees' service performance, measured as service quality, customer-focused citizenship behavior, and customer-oriented prosocial behavior. Among a sample of 238 hairstylists in 30 salons and 470 of their customers, we found that hair stylists' self-identity embedded in the group, namely, self-efficacy and group identification, partially mediated the positive effect of salon managers' servant leadership on stylists' service performance as rated by the customers, after taking into account the positive influence of transformational leadership. Moreover, group competition climate strengthened the positive relationship between self-efficacy and service performance. PsycINFO Database Record (c) 2015 APA, all rights reserved.

Modeling impacts of climate change on the potential distribution of the carcinogenic liver fluke, Opisthorchis viverrini, in Thailand.

PubMed

Suwannatrai, A; Pratumchart, K; Suwannatrai, K; Thinkhamrop, K; Chaiyos, J; Kim, C S; Suwanweerakamtorn, R; Boonmars, T; Wongsaroj, T; Sripa, B

2017-01-01

Global climate change is now regarded as imposing a significant threat of enhancing transmission of parasitic diseases. Maximum entropy species distribution modeling (MaxEnt) was used to explore how projected climate change could affect the potential distribution of the carcinogenic liver fluke, Opisthorchis viverrini, in Thailand. A range of climate variables was used: the Hadley Global Environment Model 2-Earth System (HadGEM2-ES) climate change model and also the IPCC scenarios A2a for 2050 and 2070. Occurrence data from surveys conducted in 2009 and 2014 were obtained from the Department of Disease Control, Ministry of Public Health, Thailand. The MaxEnt model performed better than random for O. viverrini with training AUC values greater than 0.8 under current and future climatic conditions. The current distribution of O. viverrini is significantly affected by precipitation and minimum temperature. According to current conditions, parts of Thailand climatically suitable for O. viverrini are mostly in the northeast and north, but the parasite is largely absent from southern Thailand. Under future climate change scenarios, the distribution of O. viverrini in 2050 should be significantly affected by precipitation, maximum temperature, and mean temperature of the wettest quarter, whereas in 2070, significant factors are likely to be precipitation during the coldest quarter, maximum, and minimum temperatures. Maps of predicted future distribution revealed a drastic decrease in presence of O. viverrini in the northeast region. The information gained from this study should be a useful reference for implementing long-term prevention and control strategies for O. viverrini in Thailand.

The Effects of Temperature and Precipitation on the Yield of Zea Mays L. I the Southeastern United States

NASA Astrophysics Data System (ADS)

Stooksbury, David Emory

Three families of straightforward maize (Zea mays L.) yield/climate models using monthly temperature and precipitation terms are produced. One family of models uses USDA's Crop Reporting Districts (CRD) as its scale of aggregation. The other two families of models use three different district aggregates based on climate or yield patterns. The climate and yield districts are determined by using a two-stage cluster analysis. The CRD-based family of models perform as well as the climate and yield based models. All models explain between 80% and 90% of the variance in maize yield. The most important climate term affecting maize yield in the South is the daily maximum temperature at pollination time. The higher the maximum temperature, the lower the yield. Above normal minimum temperature during pollination increases yield in the Middle South. Weather that favors early planting and rapid vegetative growth increases yield. Ideal maize yield weather includes a dry period during planting followed by a warm period during vegetative growth. Moisture variables are important only during the planting and harvest periods when above normal precipitation delays field work and thereby reduces yield. The model results indicate that the dire predictions about the fate of Southern agriculture in a trace gas warmed world may not be true. This is due to the overwhelming influence of the daily maximum temperature on yield. An optimum aggregate for climate impact studies was not found. I postulate that this is due to the dynamic nature of the American maize production system. For most climate impact studies on a dynamic agricultural system, there does not need to be a concern about the model aggregation.

Evaluation of skill at simulating heatwave and heat-humidity indices in Global and Regional Climate Models

NASA Astrophysics Data System (ADS)

Goldie, J. K.; Alexander, L. V.; Lewis, S. C.; Sherwood, S. C.

2017-12-01

A wide body of literature now establishes the harm of extreme heat on human health, and work is now emerging on the projection of future health impacts. However, heat-health relationships vary across different populations (Gasparrini et al. 2015), so accurate simulation of regional climate is an important component of joint health impact projection. Here, we evaluate the ability of nine Global Climate Models (GCMs) from CMIP5 and the NARCliM Regional Climate Model to reproduce a selection of 15 health-relevant heatwave and heat-humidity indices over the historical period (1990-2005) using the Perkins skill score (Perkins et al. 2007) in five Australian cities. We explore the reasons for poor model skill, comparing these modelled distributions to both weather station observations and gridded reanalysis data. Finally, we show changes in the modelled distributions from the highest-performing models under RCP4.5 and RCP8.5 greenhouse gas scenarios and discuss the implications of simulated heat stress for future climate change adaptation. ReferencesGasparrini, Antonio, Yuming Guo, Masahiro Hashizume, Eric Lavigne, Antonella Zanobetti, Joel Schwartz, Aurelio Tobias, et al. "Mortality Risk Attributable to High and Low Ambient Temperature: A Multicountry Observational Study." The Lancet 386, no. 9991 (July 31, 2015): 369-75. doi:10.1016/S0140-6736(14)62114-0. Perkins, S. E., A. J. Pitman, N. J. Holbrook, and J. McAneney. "Evaluation of the AR4 Climate Models' Simulated Daily Maximum Temperature, Minimum Temperature, and Precipitation over Australia Using Probability Density Functions." Journal of Climate 20, no. 17 (September 1, 2007): 4356-76. doi:10.1175/JCLI4253.1.

CFD Study of the Performance of an Operational Wind Farm and its Impact on the Local Climate: CFD sensitivity to forestry modelling

NASA Astrophysics Data System (ADS)

Wylie, Scott; Watson, Simon

2013-04-01

Any past, current or projected future wind farm developments are highly dependent on localised climatic conditions. For example the mean wind speed, one of the main factors in assessing the economic feasibility of a wind farm, can vary significantly over length scales no greater than the size of a typical wind farm. Any additional heterogeneity at a potential site, such as forestry, can affect the wind resource further not accounting for the additional difficulty of installation. If a wind farm is sited in an environmentally sensitive area then the ability to predict the wind farm performance and possible impacts on the important localised climatic conditions are of increased importance. Siting of wind farms in environmentally sensitive areas is not uncommon, such as areas of peat-land as in this example. Areas of peat-land are important sinks for carbon in the atmosphere but their ability to sequester carbon is highly dependent on the local climatic conditions. An operational wind farm's impact on such an area was investigated using CFD. Validation of the model outputs were carried out using field measurements from three automatic weather stations (AWS) located throughout the site. The study focuses on validation of both wind speed and turbulence measurement, whilst also assessing the models ability to predict wind farm performance. The use of CFD to model the variation in wind speed over heterogeneous terrain, including wind turbines effects, is increasing in popularity. Encouraging results have increased confidence in the ability of CFD performance in complex terrain with features such as steep slopes and forests, which are not well modelled by the widely used linear models such as WAsP and MS-Micro. Using concurrent measurements from three stationary AWS across the wind farm will allow detailed validation of the model predicted flow characteristics, whilst aggregated power output information will allow an assessment of how accurate the model setup can predict wind farm performance. Given the dependence of the local climatic conditions influence on the peat-land's ability to sequester carbon, accurate predictions of the local wind and turbulence features will allow us to quantify any possible wind farm influences. This work was carried out using the commercially available Reynolds Averaged Navier-Stokes (RANS) CFD package ANSYS CFX. Utilising the Windmodeller add-on in CFX, a series of simulations were carried out to assess wind flow interactions through and around the wind farm, incorporating features such as terrain, forestry and rotor wake interactions. Particular attention was paid to forestry effects, as the AWS are located close to the vicinity of forestry. Different Leaf Area Densities (LAD) were tested to assess how sensitive the models output was to this change.

(Un)certainty in climate change impacts on global energy consumption

NASA Astrophysics Data System (ADS)

van Ruijven, B. J.; De Cian, E.; Sue Wing, I.

2017-12-01

Climate change is expected to have an influence on the energy sector, especially on energy demand. For many locations, this change in energy demand is a balance between increase of demand for space cooling and a decrease of space heating demand. We perform a large-scale uncertainty analysis to characterize climate change risk on energy consumption as driven by climate and socioeconomic uncertainty. We combine a dynamic econometric model1 with multiple realizations of temperature projections from all 21 CMIP5 models (from the NASA Earth Exchange Global Daily Downscaled Projections2) under moderate (RCP4.5) and vigorous (RCP8.5) warming. Global spatial population projections for five SSPs are combined with GDP projections to construct scenarios for future energy demand driven by socioeconomic change. Between the climate models, we find a median global increase in climate-related energy demand of around 24% by 2050 under RCP8.5 with an interquartile range of 18-38%. Most climate models agree on increases in energy demand of more than 25% or 50% in tropical regions, the Southern USA and Southern China (see Figure). With respect to socioeconomic scenarios, we find wide variations between the SSPs for the number of people in low-income countries who are exposed to increases in energy demand. Figure attached: Number of models that agree on total climate-related energy consumption to increase or decrease by more than 0, 10, 25 or 50% by 2050 under RCP8.5 and SSP5 as result of the CMIP5 ensemble of temperature projections. References1. De Cian, E. & Sue Wing, I. Global Energy Demand in a Warming Climate. (FEEM, 2016). 2. Thrasher, B., Maurer, E. P., McKellar, C. & Duffy, P. B. Technical Note: Bias correcting climate model simulated daily temperature extremes with quantile mapping. Hydrol Earth Syst Sci 16, 3309-3314 (2012).

Implications of Climate Change for Bird Conservation in the Southwestern U.S. under Three Alternative Futures

PubMed Central

Friggens, Megan M.; Finch, Deborah M.

2015-01-01

Future expected changes in climate and human activity threaten many riparian habitats, particularly in the southwestern U.S. Using Maximum Entropy (MaxEnt3.3.3) modeling, we characterized habitat relationships and generated spatial predictions of habitat suitability for the Lucy’s warbler (Oreothlypis luciae), the Southwestern willow flycatcher (Empidonax traillii extimus) and the Western yellow-billed cuckoo (Coccyzus americanus). Our goal was to provide site- and species-specific information that can be used by managers to identify areas for habitat conservation and/or restoration along the Rio Grande in New Mexico. We created models of suitable habitat for each species based on collection and survey samples and climate, biophysical, and vegetation data. We projected habitat suitability under future climates by applying these models to conditions generated from three climate models for 2030, 2060 and 2090. By comparing current and future distributions, we identified how habitats are likely to change as a result of changing climate and the consequences of those changes for these bird species. We also examined whether land ownership of high value sites shifts under changing climate conditions. Habitat suitability models performed well. Biophysical characteristics were more important that climate conditions for predicting habitat suitability with distance to water being the single most important predictor. Climate, though less important, was still influential and led to declines of suitable habitat of more than 60% by 2090. For all species, suitable habitat tended to shrink over time within the study area leaving a few core areas of high importance. Overall, climate changes will increase habitat fragmentation and reduce breeding habitat patch size. The best strategy for conserving bird species within the Rio Grande will include measures to maintain and restore critical habitat refugia. This study provides an example of a presence-only habitat model that can be used to inform the management of species at intermediate scales. PMID:26700871