Science.gov

Sample records for climate models final

  1. Exploitation of parallelism in climate models. Final report

    SciTech Connect

    Baer, Ferdinand; Tribbia, Joseph J.; Williamson, David L.

    2001-02-05

    This final report includes details on the research accomplished by the grant entitled 'Exploitation of Parallelism in Climate Models' to the University of Maryland. The purpose of the grant was to shed light on (a) how to reconfigure the atmospheric prediction equations such that the time iteration process could be compressed by use of MPP architecture; (b) how to develop local subgrid scale models which can provide time and space dependent parameterization for a state-of-the-art climate model to minimize the scale resolution necessary for a climate model, and to utilize MPP capability to simultaneously integrate those subgrid models and their statistics; and (c) how to capitalize on the MPP architecture to study the inherent ensemble nature of the climate problem. In the process of addressing these issues, we created parallel algorithms with spectral accuracy; we developed a process for concurrent climate simulations; we established suitable model reconstructions to speed up computation; we identified and tested optimum realization statistics; we undertook a number of parameterization studies to better understand model physics; and we studied the impact of subgrid scale motions and their parameterization in atmospheric models.

  2. Final Technical Report: "Representing Endogenous Technological Change in Climate Policy Models: General Equilibrium Approaches"

    SciTech Connect

    Ian Sue Wing

    2006-04-18

    The research supported by this award pursued three lines of inquiry: (1) The construction of dynamic general equilibrium models to simulate the accumulation and substitution of knowledge, which has resulted in the preparation and submission of several papers: (a) A submitted pedagogic paper which clarifies the structure and operation of computable general equilibrium (CGE) models (C.2), and a review article in press which develops a taxonomy for understanding the representation of technical change in economic and engineering models for climate policy analysis (B.3). (b) A paper which models knowledge directly as a homogeneous factor, and demonstrates that inter-sectoral reallocation of knowledge is the key margin of adjustment which enables induced technical change to lower the costs of climate policy (C.1). (c) An empirical paper which estimates the contribution of embodied knowledge to aggregate energy intensity in the U.S. (C.3), followed by a companion article which embeds these results within a CGE model to understand the degree to which autonomous energy efficiency improvement (AEEI) is attributable to technical change as opposed to sub-sectoral shifts in industrial composition (C.4) (d) Finally, ongoing theoretical work to characterize the precursors and implications of the response of innovation to emission limits (E.2). (2) Data development and simulation modeling to understand how the characteristics of discrete energy supply technologies determine their succession in response to emission limits when they are embedded within a general equilibrium framework. This work has produced two peer-reviewed articles which are currently in press (B.1 and B.2). (3) Empirical investigation of trade as an avenue for the transmission of technological change to developing countries, and its implications for leakage, which has resulted in an econometric study which is being revised for submission to a journal (E.1). As work commenced on this topic, the U.S. withdrawal

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

    SciTech Connect

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

    2006-08-04

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

  4. Learning and enhanced climate representation in integrated assessment models. Final report, September 1994--May 1997

    SciTech Connect

    Kolstad, C.D.

    1997-12-31

    The objective of the project is to enhance capabilities for integrated-assessment modeling in two major areas: learning/R and D/information acquisition and the nexus between climate dynamics and climate impacts. In the first of these areas, the author`s objective is to improve the way in which economic models deal with learning (endogenous and/or exogenous) within an economy. This would obviously include the R and D process, whereby knowledge about climate change (and many other things) is acquired over time and influences regulatory actions. The work in climate dynamics is focused in part on incorporating the regional climate-change results from equilibrium and transient general circulation model (GCM) simulations in the simplified integrated-assessment model. While the work is generic and therefore applicable to any integrated-assessment model, it is done in the context of a standard Ramsey growth model. Thus, the work involves theoretical conceptualization, empirical implementation in an integrated-assessment model, and analysis using that model.

  5. 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.

  6. Climate system modeling on massively parallel systems: LDRD Project 95-ERP-47 final report

    SciTech Connect

    Mirin, A.A.; Dannevik, W.P.; Chan, B.; Duffy, P.B.; Eltgroth, P.G.; Wehner, M.F.

    1996-12-01

    Global warming, acid rain, ozone depletion, and biodiversity loss are some of the major climate-related issues presently being addressed by climate and environmental scientists. Because unexpected changes in the climate could have significant effect on our economy, it is vitally important to improve the scientific basis for understanding and predicting the earth`s climate. The impracticality of modeling the earth experimentally in the laboratory together with the fact that the model equations are highly nonlinear has created a unique and vital role for computer-based climate experiments. However, today`s computer models, when run at desired spatial and temporal resolution and physical complexity, severely overtax the capabilities of our most powerful computers. Parallel processing offers significant potential for attaining increased performance and making tractable simulations that cannot be performed today. The principal goals of this project have been to develop and demonstrate the capability to perform large-scale climate simulations on high-performance computing systems (using methodology that scales to the systems of tomorrow), and to carry out leading-edge scientific calculations using parallelized models. The demonstration platform for these studies has been the 256-processor Cray-T3D located at Lawrence Livermore National Laboratory. Our plan was to undertake an ambitious program in optimization, proof-of-principle and scientific study. These goals have been met. We are now regularly using massively parallel processors for scientific study of the ocean and atmosphere, and preliminary parallel coupled ocean/atmosphere calculations are being carried out as well. Furthermore, our work suggests that it should be possible to develop an advanced comprehensive climate system model with performance scalable to the teraflops range. 9 refs., 3 figs.

  7. Final Progress Report [Testing Climate Model Simulations of Tropical Cirrus Lifecycles: A Lagrangian

    SciTech Connect

    Soden, Brian J

    2009-06-30

    This project integrates ARM data sets with satellite observations and model simulations to improve the representation of tropical cloud systems in climate models. We focus on describing and understanding relevant features of the lifecycle of tropical cirrus cloud systems using an innovative method which combines the Eulerian-based ARM measurements with Lagrangian information from geostationary satellites.

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

    SciTech Connect

    Saravanan, Ramalingam

    2011-10-30

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

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

    SciTech Connect

    Russell, Lynn M

    2013-06-18

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

  10. Second generation integrated climate-change modeling: The NEWDICE model [Final report] [RICE-99

    SciTech Connect

    Nordhaus, William D.

    2001-04-01

    Under this grant, the Principal Investigator developed a second-generation integrated assessment model called the RICE-99 model. This fully revised model of the economics of global warning builds upon earlier work by the author and collaborators. The primary product was published in a volume from MIT Press in 2000 entitled 'Warming the World: Economic Models of Global Warming,' jointly with Joseph Bayer. The book and the underlying computer models are available on the Internet.

  11. Colorado river/Yuma desalting plant forecasting model. Global climate change response program. Final report

    SciTech Connect

    Hirai, L.S.

    1993-05-01

    There is a financial and economic incentive to examine and study advance climatological weather forecasting relating to the operation of the Colorado River, particularly relating to the Yuma Desalting Plant (YDP). Operation and maintenance costs of YDP are highly variable depending on the accuracy and reliability of the long-term forecast. The report details progress of the Bureau of Reclamation's study, begun in 1988, to determine the possibility of improving accuracy and reliability of short- and long-range weather and climate forecasts. Modifications to the initial study have been made following consultation with 12 weather and climate experts. The study has been broken into three phases: (1) establishing a network of experts to facilitate data exchange; (2) deriving and/or integrating data and existing models for future operation of the Colorado River and YDP; and (3) testing, adjusting, and implementing a forecasting model.

  12. Scientific development of a massively parallel ocean climate model. Final report

    SciTech Connect

    Semtner, A.J.; Chervin, R.M.

    1996-09-01

    Over the last three years, very significant advances have been made in refining the grid resolution of ocean models and in improving the physical and numerical treatments of ocean hydrodynamics. Some of these advances have occurred as a result of the successful transition of ocean models onto massively parallel computers, which has been led by Los Alamos investigators. Major progress has been made in simulating global ocean circulation and in understanding various ocean climatic aspects such as the effect of wind driving on heat and freshwater transports. These steps have demonstrated the capability to conduct realistic decadal to century ocean integrations at high resolution on massively parallel computers.

  13. An experimental climate modeling laboratory. DOE CHAMMP Program, year 2 final report

    SciTech Connect

    1996-02-01

    The major focus of this two year duration CHAMMP science team project is the development and in-model testing of new numerical methods and dynamical algorithms which are particularly well suited to massively parallel computers. The project includes efforts relevant to both global ocean circulation models and atmospheric GCMs. During the course of the authors investigations they focused on two basic areas. The first of these was the implementation and testing of a global non-linear dynamics code using the Local Spectral (LS) formalism. The LS method is of considerable interest for atmospheric GCMs since it has a computational complexity of N{sup 2} as opposed to the N{sup 3} log N complexity of Spectral Transform (ST) implementations while maintaining many of the same properties of the ST models which have become the dominant method employed for global climate model studies. A second element of the investigation has been the evaluation of alternate dynamical systems for use in global ocean circulation models. Some of the investigations have focused on the use of split-explicit hydrostatic models while others have made use of non-hydrostatic dynamics with vertically implicit integrations of equation systems using artificial compressibility.

  14. Modeling the effects of vegetation on methane oxidation and emissions through soil landfill final covers across different climates.

    PubMed

    Abichou, Tarek; Kormi, Tarek; Yuan, Lei; Johnson, Terry; Francisco, Escobar

    2015-02-01

    Plant roots are reported to enhance the aeration of soil by creating secondary macropores which improve the diffusion of oxygen into soil as well as the supply of methane to bacteria. Therefore, methane oxidation can be improved considerably by the soil structuring processes of vegetation, along with the increase of organic biomass in the soil associated with plant roots. This study consisted of using a numerical model that combines flow of water and heat with gas transport and oxidation in soils, to simulate methane emission and oxidation through simulated vegetated and non-vegetated landfill covers under different climatic conditions. Different simulations were performed using different methane loading flux (5-200 g m(-2) d(-1)) as the bottom boundary. The lowest modeled surface emissions were always obtained with vegetated soil covers for all simulated climates. The largest differences in simulated surface emissions between the vegetated and non-vegetated scenarios occur during the growing season. Higher average yearly percent oxidation was obtained in simulations with vegetated soil covers as compared to non-vegetated scenario. The modeled effects of vegetation on methane surface emissions and percent oxidation were attributed to two separate mechanisms: (1) increase in methane oxidation associated with the change of the physical properties of the upper vegetative layer and (2) increase in organic matter associated with vegetated soil layers. Finally, correlations between percent oxidation and methane loading into simulated vegetated and non-vegetated covers were proposed to allow decision makers to compare vegetated versus non-vegetated soil landfill covers. These results were obtained using a modeling study with several simplifying assumptions that do not capture the complexities of vegetated soils under field conditions. PMID:25475118

  15. Analysis and synthesis of models for effects of climate change on agricultural systems. Final report

    SciTech Connect

    Geng, S.; Plant, R.; Loomis, R.

    1992-07-27

    Our objectives are to develop a new integrative physiological-morphological model of the wheat crop that will behave realistically in high-CO{sub 2} environments, and to update the ALFALFA model to match the wheat model`s photosynthetic structures and microclimates.

  16. Land Processes in a High Resolution Community Climate Model with Sub-Grid Scale Parameterizations Final Report

    SciTech Connect

    R. E. Dickinson; Andrea N. Hahmann

    2002-07-17

    The characteristics of land important for climate are very heterogeneous, as are the key atmospheric inputs to land, i.e. precipitation and radiation. To adequately represent this heterogeneity, state-of-the-art climate models should represent atmospheric inputs to land, land properties, and the dynamical changes of land at the highest resolution accessible by climate models. The research funded under this project focused on the development of an alternative approach to this problem in which a sub-mesh is imposed on each atmospheric model grid square. This allows representation of the land climate dynamics at a higher resolution than that achievable in the global atmospheric models. The high spatial detail of the fine-mesh treatment provides not only a more accurate representation of land processes to the atmospheric model, but also the opportunity for direct downscaling of the surface climate. The proposed project continued the development and refinement of a high-resolution land surface model that is compatible for inclusion into the National Center for Atmospheric Research (NCAR) Community Climate Model (CCM), a state-of-the-art atmospheric general circulation model (GCM) that is used for climate simulation and prediction.

  17. Transforming the representation of the boundary layer and low clouds for high-resolution regional climate modeling: Final report

    SciTech Connect

    Huang, Hsin-Yuan; Hall, Alex

    2013-07-24

    Stratocumulus and shallow cumulus clouds in subtropical oceanic regions (e.g., Southeast Pacific) cover thousands of square kilometers and play a key role in regulating global climate (e.g., Klein and Hartmann, 1993). Numerical modeling is an essential tool to study these clouds in regional and global systems, but the current generation of climate and weather models has difficulties in representing them in a realistic way (e.g., Siebesma et al., 2004; Stevens et al., 2007; Teixeira et al., 2011). While numerical models resolve the large-scale flow, subgrid-scale parameterizations are needed to estimate small-scale properties (e.g. boundary layer turbulence and convection, clouds, radiation), which have significant influence on the resolved scale due to the complex nonlinear nature of the atmosphere. To represent the contribution of these fine-scale processes to the resolved scale, climate models use various parameterizations, which are the main pieces in the model that contribute to the low clouds dynamics and therefore are the major sources of errors or approximations in their representation. In this project, we aim to 1) improve our understanding of the physical processes in thermal circulation and cloud formation, 2) examine the performance and sensitivity of various parameterizations in the regional weather model (Weather Research and Forecasting model; WRF), and 3) develop, implement, and evaluate the advanced boundary layer parameterization in the regional model to better represent stratocumulus, shallow cumulus, and their transition. Thus, this project includes three major corresponding studies. We find that the mean diurnal cycle is sensitive to model domain in ways that reveal the existence of different contributions originating from the Southeast Pacific land-masses. The experiments suggest that diurnal variations in circulations and thermal structures over this region are influenced by convection over the Peruvian sector of the Andes cordillera, while

  18. Final Technical Report for Collaborative Research: Regional climate-change projections through next-generation empirical and dynamical models, DE-FG02-07ER64429

    SciTech Connect

    Smyth, Padhraic

    2013-07-22

    This is the final report for a DOE-funded research project describing the outcome of research on non-homogeneous hidden Markov models (NHMMs) and coupled ocean-atmosphere (O-A) intermediate-complexity models (ICMs) to identify the potentially predictable modes of climate variability, and to investigate their impacts on the regional-scale. The main results consist of extensive development of the hidden Markov models for rainfall simulation and downscaling specifically within the non-stationary climate change context together with the development of parallelized software; application of NHMMs to downscaling of rainfall projections over India; identification and analysis of decadal climate signals in data and models; and, studies of climate variability in terms of the dynamics of atmospheric flow regimes.

  19. Final Report for Formulation of Moist Dynamics and Physics for Future Climate Models

    SciTech Connect

    Celal S. Konor

    2008-04-30

    In this project, one of our goals is to develop atmospheric models, in which innovative ideas on improving the quality of moisture predictions can be tested. Our other goal is to develop an explicit time integration scheme based on the multi-point differencing (MED) that does the same job as an implicit trapezoidal scheme but uses information only from limited number of grid points. Below we discuss the work performed at UCLA toward these goals during the funding period indicated above.

  20. Assessing the climatic effect of carbon dioxide and other trace gases using an interactive two-dimensional climate-chemistry model. Final report, December 1992--August 1996

    SciTech Connect

    Ko, M.K.W.

    1996-12-31

    In the recent IPCC report, the role of tropospheric aerosols, stratospheric aerosols, and natural solar variability have also been identified as having sizable effects on climate, both by direct perturbation of the radiative balance and indirectly by changing ozone. Although the effect of changing CO{sub 2} is by far the dominant factor on a century time scale, the effects from the other identified factors are important on a decade time scale. It is important to understand the mechanisms that relate these changes to climatic responses. Developing appropriate numerical models with the capability to simulate these mechanisms will enable one to correctly interpret the observed climate changes that have occurred to data, as well as predict future changes in climate. It is presently impractical to run comprehensive 3-D general circulation model simulations of the interactions between atmospheric chemistry and the rest of the climate system on time scales of decades to centuries. Thus, 2-D models and other lower resolution models play an essential role in understanding the complex interactions of the integrated climate system.

  1. Carbon dioxide, climate and the deep ocean circulation: Carbon chemistry model. Final report

    SciTech Connect

    Menawat, A.S.

    1992-09-21

    The objective of this study was to investigate the role of oceanic carbon chemistry in modulating the atmospheric levels of CO{sub 2}. It is well known that the oceans are the primary sink of the excess carbon pumped into the atmosphere since the beginning of the industrial period. The suspended particulate and the dissolved organic matters in the deep ocean play important roles as carriers of carbon and other elements critical to the fate of CO{sub 2}. In addition, the suspended particulate matter provides sites for oxidation-reduction reactions and microbial activities. The problem is of an intricate system with complex chemical, physical and biological processes. This report describes a methodology to describe the interconversions of different forms of the organic and inorganic nutrients, that may be incorporated in the ocean circulation models. Our approach includes the driving force behind the transfers in addition to balancing the elements. Such thermodynamic considerations of describing the imbalance in the chemical potentials is a new and unique feature of our approach.

  2. Final Technical Report for "Collaborative Research. Regional climate-change projections through next-generation empirical and dynamical models"

    SciTech Connect

    Kravtsov, S.; Robertson, Andrew W.; Ghil, Michael; Smyth, Padhraic J.

    2011-04-08

    This project was a continuation of previous work under DOE CCPP funding in which we developed a twin approach of non-homogeneous hidden Markov models (NHMMs) and coupled ocean-atmosphere (O-A) intermediate-complexity models (ICMs) to identify the potentially predictable modes of climate variability, and to investigate their impacts on the regional-scale. We have developed a family of latent-variable NHMMs to simulate historical records of daily rainfall, and used them to downscale seasonal predictions. We have also developed empirical mode reduction (EMR) models for gaining insight into the underlying dynamics in observational data and general circulation model (GCM) simulations. Using coupled O-A ICMs, we have identified a new mechanism of interdecadal climate variability, involving the midlatitude oceans mesoscale eddy field and nonlinear, persistent atmospheric response to the oceanic anomalies. A related decadal mode is also identified, associated with the oceans thermohaline circulation. The goal of the continuation was to build on these ICM results and NHMM/EMR model developments and software to strengthen two key pillars of support for the development and application of climate models for climate change projections on time scales of decades to centuries, namely: (a) dynamical and theoretical understanding of decadal-to-interdecadal oscillations and their predictability; and (b) an interface from climate models to applications, in order to inform societal adaptation strategies to climate change at the regional scale, including model calibration, correction, downscaling and, most importantly, assessment and interpretation of spread and uncertainties in multi-model ensembles. Our main results from the grant consist of extensive further development of the hidden Markov models for rainfall simulation and downscaling specifically within the non-stationary climate change context together with the development of parallelized software; application of NHMMs to

  3. Final Technical Report for "Collaborative Research: Regional climate-change projections through next-generation empirical and dynamical models"

    SciTech Connect

    Robertson, A.W.; Ghil, M.; Kravtsov, K.; Smyth, P.J.

    2011-04-08

    This project was a continuation of previous work under DOE CCPP funding in which we developed a twin approach of non-homogeneous hidden Markov models (NHMMs) and coupled ocean-atmosphere (O-A) intermediate-complexity models (ICMs) to identify the potentially predictable modes of climate variability, and to investigate their impacts on the regional-scale. We have developed a family of latent-variable NHMMs to simulate historical records of daily rainfall, and used them to downscale seasonal predictions. We have also developed empirical mode reduction (EMR) models for gaining insight into the underlying dynamics in observational data and general circulation model (GCM) simulations. Using coupled O-A ICMs, we have identified a new mechanism of interdecadal climate variability, involving the midlatitude oceans mesoscale eddy field and nonlinear, persistent atmospheric response to the oceanic anomalies. A related decadal mode is also identified, associated with the oceans thermohaline circulation. The goal of the continuation was to build on these ICM results and NHMM/EMR model developments and software to strengthen two key pillars of support for the development and application of climate models for climate change projections on time scales of decades to centuries, namely: (a) dynamical and theoretical understanding of decadal-to-interdecadal oscillations and their predictability; and (b) an interface from climate models to applications, in order to inform societal adaptation strategies to climate change at the regional scale, including model calibration, correction, downscaling and, most importantly, assessment and interpretation of spread and uncertainties in multi-model ensembles. Our main results from the grant consist of extensive further development of the hidden Markov models for rainfall simulation and downscaling specifically within the non-stationary climate change context together with the development of parallelized software; application of NHMMs to

  4. Final Report on Evaluating the Representation and Impact of Convective Processes in the NCAR Community Climate System Model

    SciTech Connect

    X. Wu, G. J. Zhang

    2008-04-23

    Convection and clouds affect atmospheric temperature, moisture and wind fields through the heat of condensation and evaporation and through redistributions of heat, moisture and momentum. Individual clouds have a spatial scale of less than 10 km, much smaller than the grid size of several hundred kilometers used in climate models. Therefore the effects of clouds must be approximated in terms of variables that the model can resolve. Deriving such formulations for convection and clouds has been a major challenge for the climate modeling community due to the lack of observations of cloud and microphysical properties. The objective of our DOE CCPP project is to evaluate and improve the representation of convection schemes developed by PIs in the NCAR (National Center for Atmospheric Research) Community Climate System Model (CCSM) and study its impact on global climate simulations. • The project resulted in nine peer-reviewed publications and numerous scientific presentations that directly address the CCPP’s scientific objective of improving climate models. • We developed a package of improved convection parameterization that includes improved closure, trigger condition for convection, and comprehensive treatment of convective momentum transport. • We implemented the new convection parameterization package into several versions of the NCAR models (both coupled and uncoupled). This has led to 1) Improved simulation of seasonal migration of ITCZ; 2) Improved shortwave cloud radiative forcing response to El Niño in CAM3; 3) Improved MJO simulation in both uncoupled and coupled model; and 4) Improved simulation of ENSO in coupled model. • Using the dynamic core of CCM3, we isolated the dynamic effects of convective momentum transport. • We implemented mosaic treatment of subgrid-scale cloud-radiation interaction in CCM3.

  5. Climate evolution with a coupled two dimensional atmosphere/ocean model. Final report, [March 1, 1992--August 31, 1993

    SciTech Connect

    Hoffert, M.I.; Kheshgi, H.

    1994-02-01

    To test the impact of a density-dependent eddy diffusivity law on global warming predictions, transient calculations with a standard upwelling-diffusion ocean/climate model were conducted. This report discusses that research. In addition, this report also presents the findings from research conducted on internal wave-breaking turbulence in an upwelling-diffusion ocean.

  6. A reduced-form approach to characterizing sulfate aerosol effects on climate in integrated assessment models. Final report

    SciTech Connect

    Wigley, T.M.L.

    1996-04-01

    The objective of this study was to devise a methodology for estimating the spatial patterns of future climate change accounting for the effects of both greenhouse gases and sulfate aerosols under a wide range of emissions scenarios, using the results of General Circulation Models.

  7. Theoretical models of the impact of climate change on natural populations, communities and ecosystems. Final report, 1989--1992

    SciTech Connect

    Wiegert, R.

    1992-12-31

    Land use change is a relatively understudied aspect of global change. In many cases, the impact of land use on plant and animal species may be far greater than the impact of climate change per se. As an integral part of our long-term studies of the response of animal populations to global change, we have focused on land use change as a dominant driving force. Climate change, no doubt, will also play a role in determining the future abundance and distribution of many species, but, for many species, the signal from climate change per se may be difficult to detect if we do not first understand the impact of land use change. This formed the dominant theme of the research by the PI (Pulliam). Both land use change and year to year climate change can directly affect other populations and two examples of this formed the focus of the remaining research, models of invertebrates in Carolina Bays and a model of a commercial estuarine population of blue crabs.

  8. Climate system modeling program

    SciTech Connect

    1995-12-31

    The Climate System Modeling Project is a component activity of NSF's Climate Modeling, Analysis and Prediction Program, supported by the Atmospheric Sciences Program, Geosciences Directorate. Its objective is to accelerate progress toward reliable prediction of global and regional climate changes in the decades ahead. CSMP operates through workshops, support for post-docs and graduate students and other collaborative activities designed to promote interdisciplinary and strategic work in support of the overall objective (above) and specifically in three areas, (1) Causes of interdecadal variability in the climate system, (2) Interactions of regional climate forcing with global processes, and (3) Scientific needs of climate assessment.

  9. Natural and anthropogenic climate change. Final report

    SciTech Connect

    Wang, W.C.; Ronberg, B.; Gutowski, W.; Molnar, G.; Li, K.R.

    1986-08-01

    The report describes a one-year research project which was the initial phase of a research program intended: (1) to refine and validate a 2-D climate model for studying the CO/sub 2/ and trace gases climatic effects; and (2) to participate in the United States of America (USA) Department of Energy/The People's Republic of China (PRC) Academia Sinica research project on CO/sub 2/-induced climate changes. The overall objective is to find ways to model regional climate change in a global warming environment potentially induced by CO/sub 2/ increase. The first task has two subtasks: (a) to incorporate a boundary layer parameterization into the 2-D radiative-dynamical model of Wang et al. (1984) and study its impact on climate sensitivity; and (b) to validate the 2-D radiative-dynamical models through comparisons with data and with other more comprehensive climate models so that our confidence in the model simulation of trace gases climatic effects can be increased. The second task is intended to: (a) analyze the climate data to improve our understanding of local/regional climate changes (in particular the desertification problem); and (b) coordinate the various research programs within the USA/PRC CO/sub 2/ project, which is critical in successfully achieving the research project scientific goals.

  10. FY08 LDRD Final Report Regional Climate

    SciTech Connect

    Bader, D C; Chin, H; Caldwell, P M

    2009-05-19

    An integrated, multi-model capability for regional climate change simulation is needed to perform original analyses to understand and prepare for the impacts of climate change on the time and space scales that are critical to California's future environmental quality and economic prosperity. Our intent was to develop a very high resolution regional simulation capability to address consequences of climate change in California to complement the global modeling capability that is supported by DOE at LLNL and other institutions to inform national and international energy policies. The California state government, through the California Energy Commission (CEC), institutionalized the State's climate change assessment process through its biennial climate change reports. The bases for these reports, however, are global climate change simulations for future scenarios designed to inform international policy negotiations, and are primarily focused on the global to continental scale impacts of increasing emissions of greenhouse gases. These simulations do not meet the needs of California public and private officials who will make major decisions in the next decade that require an understanding of climate change in California for the next thirty to fifty years and its effects on energy use, water utilization, air quality, agriculture and natural ecosystems. With the additional development of regional dynamical climate modeling capability, LLNL will be able to design and execute global simulations specifically for scenarios important to the state, then use those results to drive regional simulations of the impacts of the simulated climate change for regions as small as individual cities or watersheds. Through this project, we systematically studied the strengths and weaknesses of downscaling global model results with a regional mesoscale model to guide others, particularly university researchers, who are using the technique based on models with less complete parameterizations or

  11. Final scientific report for DOE award title: Improving the Representation of Ice Sedimentation Rates in Global Climate Models

    SciTech Connect

    Mitchell, David L.

    2013-09-05

    It is well known that cirrus clouds play a major role in regulating the earth’s climate, but the details of how this works are just beginning to be understood. This project targeted the main property of cirrus clouds that influence climate processes; the ice fall speed. That is, this project improves the representation of the mass-weighted ice particle fall velocity, Vm, in climate models, used to predict future climate on global and regional scales. Prior to 2007, the dominant sizes of ice particles in cirrus clouds were poorly understood, making it virtually impossible to predict how cirrus clouds interact with sunlight and thermal radiation. Due to several studies investigating the performance of optical probes used to measure the ice particle size distribution (PSD), as well as the remote sensing results from our last ARM project, it is now well established that the anomalously high concentrations of small ice crystals often reported prior to 2007 were measurement artifacts. Advances in the design and data processing of optical probes have greatly reduced these ice artifacts that resulted from the shattering of ice particles on the probe tips and/or inlet tube, and PSD measurements from one of these improved probes (the 2-dimensional Stereo or 2D-S probe) are utilized in this project to parameterize Vm for climate models. Our original plan in the proposal was to parameterize the ice PSD (in terms of temperature and ice water content) and ice particle mass and projected area (in terms of mass- and area-dimensional power laws or m-D/A-D expressions) since these are the microphysical properties that determine Vm, and then proceed to calculate Vm from these parameterized properties. But the 2D-S probe directly measures ice particle projected area and indirectly estimates ice particle mass for each size bin. It soon became apparent that the original plan would introduce more uncertainty in the Vm calculations

  12. Research on the climatic effects of nuclear winter: Final report

    SciTech Connect

    Dickinson, R.E.

    1986-12-03

    The National Center for Atmospheric Research (NCAR) has undertaken a series of research efforts to develop and implement improvements to the Community Climate Model (CCM) needed to make the model more applicable to studies of the climatic effects of nuclear war. The development of the model improvements has reached a stage where implementation may proceed, and several of the developed routines are being incorporated into the next approved version of the CCM (CCM1). Formal documentation is being completed describing the specific model improvements that have been successfully implemented. This final report includes the series of annual proposals and progress reports that have guided the project.

  13. A dynamically-coupled groundwater, land surface and regional climate model to predict seasonal watershed flow and groundwater response, FINAL LDRD REPORT.

    SciTech Connect

    Maxwell, R; Kollet, S; Chow, F; Granvold, P; Duan, Q

    2007-02-23

    This final report is organized in four sections. Section 1 is the project summary (below), Section 2 is a submitted manuscript that describes the offline, or spinup simulations in detail, Section 3 is also a submitted manuscript that describes the online, or fully-coupled simulations in detail and Section 3, which is report that describes work done via a subcontract with UC Berkeley. The goal of this project was to develop and apply a coupled regional climate, land-surface, groundwater flow model as a means to further understand important mass and energy couplings between regional climate, the land surface, and groundwater. The project involved coupling three distinct submodels that are traditionally used independently with abstracted and potentially oversimplified (inter-model) boundary conditions. This coupled model lead to (1) an improved understanding of the sensitivity and importance of coupled physical processes from the subsurface to the atmosphere; (2) a new tool for predicting hydrologic conditions (rainfall, temperature, snowfall, snowmelt, runoff, infiltration and groundwater flow) at the watershed scale over a range of timeframes; (3) a simulation of hydrologic response of a characteristic watershed that will provide insight into the certainty of hydrologic forecasting, dominance and sensitivity of groundwater dynamics on land-surface fluxes; and (4) a more realistic model representation of weather predictions, precipitation and temperature, at the regional scale. Regional climate models are typically used for the simulation of weather, precipitation and temperature behavior over 10-1000 km domains for weather or climate prediction purposes, and are typically driven by boundary conditions derived from global climate models (GCMs), observations or both. The land or ocean surface typically represents a bottom boundary condition of these models, where important mass (water) and energy fluxes are approximated. The viability and influence of these

  14. Regional Climate Modeling: Progress, Challenges, and Prospects

    SciTech Connect

    Wang, Yuqing; Leung, Lai R.; McGregor, John L.; Lee, Dong-Kyou; Wang, Wei-Chyung; Ding, Yihui; Kimura, Fujio

    2004-12-01

    Regional climate modeling with regional climate models (RCMs) has matured over the past decade and allows for meaningful utilization in a broad spectrum of applications. In this paper, latest progresses in regional climate modeling studies are reviewed, including RCM development, applications of RCMs to dynamical downscaling for climate change assessment, seasonal climate predictions and climate process studies, and the study of regional climate predictability. Challenges and potential directions of future research in this important area are discussed, with the focus on those to which less attention has been given previously, such as the importance of ensemble simulations, further development and improvement of regional climate modeling approach, modeling extreme climate events and sub-daily variation of clouds and precipitation, model evaluation and diagnostics, applications of RCMs to climate process studies and seasonal predictions, and development of regional earth system models. It is believed that with both the demonstrated credibility of RCMs’ capability in reproducing not only monthly to seasonal mean climate and interannual variability but also the extreme climate events when driven by good quality reanalysis and the continuous improvements in the skill of global general circulation models (GCMs) in simulating large-scale atmospheric circulation, regional climate modeling will remain an important dynamical downscaling tool for providing the needed information for assessing climate change impacts and seasonal climate predictions, and a powerful tool for improving our understanding of regional climate processes. An internationally coordinated effort can be developed with different focuses by different groups to advance regional climate modeling studies. It is also recognized that since the final quality of the results from nested RCMs depends in part on the realism of the large-scale forcing provided by GCMs, the reduction of errors and improvement in

  15. Refining climate models

    ScienceCinema

    Warren, Jeff; Iversen, Colleen; Brooks, Jonathan; Ricciuto, Daniel

    2014-06-26

    Using dogwood trees, Oak Ridge National Laboratory researchers are gaining a better understanding of the role photosynthesis and respiration play in the atmospheric carbon dioxide cycle. Their findings will aid computer modelers in improving the accuracy of climate simulations.

  16. Refining climate models

    SciTech Connect

    Warren, Jeff; Iversen, Colleen; Brooks, Jonathan; Ricciuto, Daniel

    2012-10-31

    Using dogwood trees, Oak Ridge National Laboratory researchers are gaining a better understanding of the role photosynthesis and respiration play in the atmospheric carbon dioxide cycle. Their findings will aid computer modelers in improving the accuracy of climate simulations.

  17. Modeling Climate Dynamically

    ERIC Educational Resources Information Center

    Walsh, Jim; McGehee, Richard

    2013-01-01

    A dynamical systems approach to energy balance models of climate is presented, focusing on low order, or conceptual, models. Included are global average and latitude-dependent, surface temperature models. The development and analysis of the differential equations and corresponding bifurcation diagrams provides a host of appropriate material for…

  18. Modeling glacial climates

    NASA Technical Reports Server (NTRS)

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

    1984-01-01

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

  19. Climate Change and Climate Modeling

    NASA Astrophysics Data System (ADS)

    Schmidt, Gavin

    2011-06-01

    In long-established fields like fluid mechanics or quantum theory, the contents of introductory textbooks are mostly predictable: The basics are covered in more or less the same order, and while cutting-edge research occasionally gets a look-in (depending on the inclinations of the authors), the contents are far more frequently reworkings of previous textbooks than a synthesis of recent primary literature. In a field like climate science, however, where there is a much shorter history of textbook writing, much of the subject matter is extracted directly from papers published in the past 10 years. This makes the resulting textbooks far more varied and interesting.

  20. Aerosol Climate Interactions in Climate System Models

    NASA Astrophysics Data System (ADS)

    Kiehl, J. T.

    2002-12-01

    Aerosols are widely recognized as an important process in Earth's climate system. Observations over the past decade have improved our understanding of the physical and chemical properties of aerosols. Recently, field observations have highlighted the pervasiveness of absorbing aerosols in the atmosphere. These aerosols are of particular interest, since they alter the vertical distribution of shortwave radiative heating between the surface and atmosphere. Given this increased knowledge of aerosols from various field programs, interest is focusing on how to integrate this understanding into global climate models. These types of models provide the best tool available to comprehensively study the potential effects of aerosols on Earth's climate system. Results from climate system model simulations that include aerosol effects will be presented to illustrate key aerosol climate interactions. These simulations employ idealized and realistic distributions of absorbing aerosols. The idealized aerosol simulations provide insight into the role of aerosol shortwave absorption on the global hydrologic cycle. The realistic aerosol distributions provide insight into the local response of aerosol forcing in the Indian subcontinent region. Emphasis from these simulations will be on the hydrologic cycle, since water availability is of emerging global environmental concern. This presentation will also consider what more is needed to significantly improve our ability to model aerosol processes in climate system models. Uncertainty in aerosol climate interactions remains a major source of uncertainty in our ability to project future climate change. Focus will be on interactions between aerosols and various physical, chemical and biogeochemical aspects of the Earth system.

  1. Energy balance climate models

    NASA Technical Reports Server (NTRS)

    North, G. R.; Cahalan, R. F.; Coakley, J. A., Jr.

    1981-01-01

    An introductory survey of the global energy balance climate models is presented with an emphasis on analytical results. A sequence of increasingly complicated models involving ice cap and radiative feedback processes are solved, and the solutions and parameter sensitivities are studied. The model parameterizations are examined critically in light of many current uncertainties. A simple seasonal model is used to study the effects of changes in orbital elements on the temperature field. A linear stability theorem and a complete nonlinear stability analysis for the models are developed. Analytical solutions are also obtained for the linearized models driven by stochastic forcing elements. In this context the relation between natural fluctuation statistics and climate sensitivity is stressed.

  2. Modeling the response of plants and ecosystems to CO{sub 2} and climate change. Final technical report, September 1, 1992--August 31, 1996

    SciTech Connect

    Reynolds, J.F.

    1998-04-10

    Objectives can be divided into those for plant modeling and those for ecosystem modeling and experimental work in support of both. The author worked in a variety of ecosystem types, including pine, arctic, desert, and grasslands. Plant modeling objectives are: (1) to construct generic models of leaf, canopy, and whole-plant response to elevated CO{sub 2} and climate change; (2) to validate predictions of whole-plant response against various field studies of elevated CO{sub 2} and climate change; (3) to use these models to test specific hypotheses and to make predictions about primary, secondary and tertiary effects of elevated CO{sub 2} and climate change on individual plants for conditions and time frames beyond those used to calibrate the model; and (4) to provide information to higher-level models, such as community models and ecosystem models. Ecosystem level modeling objectives are: (1) to incorporate models of plant responses to elevated CO{sub 2} into a generic ecosystem model in order to predict the direct and indirect effects of elevated CO{sub 2} and climate change on ecosystems; (2) to validate model predictions of total system-level response (including decomposition) against various ecosystem field studies of elevated CO{sub 2} and climate change; (3) to use the ecosystem model to test specific hypotheses and to make predictions about primary, secondary and tertiary effects of elevated CO{sub 2} and climate change on ecosystems for conditions and time frames beyond those used to calibrate the model; and (4) to use the ecosystem model to study effects of change in CO{sub 2} and climate at regional and global scales. Occasionally the author conducted some experimental work that was deemed important to the development of the models. This work was mainly physiological work that could be performed in the Duke University Phytotron, using existing facilities.

  3. Final Report for DOE Grant DE-FG02-07ER64470 [“Incorporation of the HYbrid Coordinate Ocean Model (HYCOM) into the Community Climate System Model (CCSM): Evaluation and Climate Applications”

    SciTech Connect

    Chassignet, Eric P

    2013-03-18

    The primary goal of the project entitled “Incorporation of the HYbrid Coordinate Ocean Model (HYCOM) into the Community Climate System Model (CCSM): Evaluation and Climate Applications” was to systematically investigate the performance of the HYbrid Coordinate Ocean Model (HYCOM) as an alternative oceanic component of the NCAR’s Community Climate System Model (CCSM). We have configured two versions of the fully coupled CCSM3/HYCOM: one with a medium resolution (T42) Community Atmospheric Model (CAM) and the other with higher resolution (T85). We have performed a comprehensive analysis of the 400-year fully coupled CCSM3/HYCOM simulations and compared the results with those from CCSM3/POP and with climatological observations, and also we have performed tuning of critical model parameters, including Smagorinsky viscosity, isopycnal diffusivity, and background vertical diffusivity. The analysis shows that most oceanic features are well represented in the CCSM3/HYCOM. The coupled CCSM3/HYCOM (T42) has been integrated for 400 years, and the results have been archived and transferred to the High Performance Computer in the Florida State Univesity. In the last year, we have made comprehensive diagnostics of the long-term simulations by the comparison with the original CCSM3/POP simulation and with the observations. To gain some understanding of the model biases, the mean climate and modes of climate variability of the two models are compared with observations. The examination includes the Northern and Southern Annular Modes (NAM and SAM), the Pacific-North-American (PNA) pattern, the Atlantic Multidecadal Oscillation (AMO), and the main Southern Ocean SST mode. We also compared the performance of ENSO simulation in the coupled models. This report summarizes the main findings from the comparison of long-term CCSM3/HYCOM and CCSM3/POP simulations.

  4. Final Report: Fine-Mesh Treatment of the Land Component of a Global Climate Model, September 1, 1994 - August 31, 1998

    SciTech Connect

    Dickinson, Robert E.

    1998-08-31

    The characteristics of land important for climate are very heterogeneous, as are the key atmospheric inputs to land, i.e. precipitation and radiation. To adequately represent this heterogeneity, state-of-the-art climate models should represent atmospheric inputs to land, land properties, and the dynamical changes of land at the highest resolution accessible by climate models. The research funded under this project focused on the development of an alternative approach to this problem in which a sub-mesh is imposed on each atmospheric model grid square. This allows representation of the land climate dynamics at a higher resolution than that achievable in the global atmospheric models. The high spatial detail of the fine-mesh treatment provides not only a more accurate representation of land processes to the atmospheric model, but also the opportunity for direct downscaling of the surface climate. The principal objectives were: (1) To complete the development of fine-mesh data structures in the VBATS model and its link to CCM2; (2) To improve BATS model parameterizations; (3) To complete and refine fine-mesh atmospheric parameterizations; and (4) To conduct sensitivity studies. The primary shift in goals has been to include and emphasize linkages to CCM3 which has been publicly released as of May 1996.

  5. Climate Model Output Rewriter

    SciTech Connect

    Taylor, K. E.; Doutriaux, C.

    2004-06-21

    CMOR comprises a set of FORTRAN 90 dunctions that can be used to produce CF-compliant netCDF files. The structure of the files created by CMOR and the metadata they contain fulfill the requirements of many of the climate community’s standard model experiments (which are referred to here as "MIPS", which stands for "model intercomparison project", including, for example, AMIP, CMIP, CFMIP, PMIP, APE, and IPCC scenario runs), CMOR was not designed to serve as an all-purpose wfiter of CF-compliant netCDF files, but simply to reduce the effort required to prepare and manage MIP data. Although MIPs encourage systematic analysis of results across models, this is only easy to do if the model output is written in a common format with files structured similarly and with sufficient metadata uniformly stored according to a common standard. Individual modeling groups store their data in different ways. but if a group can read its own data with FORTRAN, then it should easily be able to transform the data, using CMOR, into the common format required by the MIPs, The adoption of CMOR as a standard code for exchanging climate data will facilitate participation in MIPs because after learning how to satisfy the output requirements of one MIP, it will be easy to prepare output for the other MIPs.

  6. Climate Model Output Rewriter

    Energy Science and Technology Software Center (ESTSC)

    2004-06-21

    CMOR comprises a set of FORTRAN 90 dunctions that can be used to produce CF-compliant netCDF files. The structure of the files created by CMOR and the metadata they contain fulfill the requirements of many of the climate community’s standard model experiments (which are referred to here as "MIPS", which stands for "model intercomparison project", including, for example, AMIP, CMIP, CFMIP, PMIP, APE, and IPCC scenario runs), CMOR was not designed to serve as anmore » all-purpose wfiter of CF-compliant netCDF files, but simply to reduce the effort required to prepare and manage MIP data. Although MIPs encourage systematic analysis of results across models, this is only easy to do if the model output is written in a common format with files structured similarly and with sufficient metadata uniformly stored according to a common standard. Individual modeling groups store their data in different ways. but if a group can read its own data with FORTRAN, then it should easily be able to transform the data, using CMOR, into the common format required by the MIPs, The adoption of CMOR as a standard code for exchanging climate data will facilitate participation in MIPs because after learning how to satisfy the output requirements of one MIP, it will be easy to prepare output for the other MIPs.« less

  7. Natural and anthropogenic climate change. Final report, 1 March 1986--31 August 1992

    SciTech Connect

    Portman, D.A.; Gutowski, W.J. Jr.; Wang, W.C.; Iacono, M.J.; Yang, S.

    1992-08-31

    This final report provides a broad overview of program accomplishments. Brief descriptions are provided for accomplishments with respect to intercomparisions and improvements in general circulation models, analysis of climatic data and climate model statistics, and accomplishments in the China Meteorology coordination.

  8. Watershed Modeling to Assess the Sensitivity of Streamflow, Nutrient, and Sediment Loads to Potential Climate Change and Urban Development in 20 U.S. Watersheds (Final Report)

    EPA Science Inventory

    Watershed modeling was conducted in 20 large, U.S. watersheds to assess the sensitivity of streamflow, nutrient (nitrogen and phosphorus), and sediment loading to a range of plausible mid-21st Century climate change and urban development scenarios in different regions of the nati...

  9. Integrating Remote Sensing, Field Observations, and Models to Understand Disturbance and Climate Effects on the Carbon Balance of the West Coast U.S., Final Report

    SciTech Connect

    Beverly E. Law

    2011-10-05

    As an element of NACP research, the proposed investigation is a two pronged approach that derives and evaluates a regional carbon (C) budget for Oregon, Washington, and California. Objectives are (1) Use multiple data sources, including AmeriFlux data, inventories, and multispectral remote sensing data to investigate trends in carbon storage and exchanges of CO2 and water with variation in climate and disturbance history; (2) Develop and apply regional modeling that relies on these multiple data sources to reduce uncertainty in spatial estimates of carbon storage and NEP, and relative contributions of terrestrial ecosystems and anthropogenic emissions to atmospheric CO2 in the region; (3) Model terrestrial carbon processes across the region, using the Biome-BGC terrestrial ecosystem model, and an atmospheric inverse modeling approach to estimate variation in rate and timing of terrestrial uptake and feedbacks to the atmosphere in response to climate and disturbance.

  10. Climate and atmospheric modeling studies

    NASA Technical Reports Server (NTRS)

    1992-01-01

    The climate and atmosphere modeling research programs have concentrated on the development of appropriate atmospheric and upper ocean models, and preliminary applications of these models. Principal models are a one-dimensional radiative-convective model, a three-dimensional global model, and an upper ocean model. Principal applications were the study of the impact of CO2, aerosols, and the solar 'constant' on climate.

  11. Several Computational Opportunities and Challenges Associated with Climate Change Modeling

    SciTech Connect

    Wang, Dali; Post, Wilfred M; Wilson, Bruce E

    2010-01-01

    One of the key factors in the improved understanding of climate science is the development and improvement of high fidelity climate models. These models are critical for projections of future climate scenarios, as well as for highlighting the areas where further measurement and experimentation are needed for knowledge improvement. In this paper, we focus on several computing issues associated with climate change modeling. First, we review a fully coupled global simulation and a nested regional climate model to demonstrate key design components, and then we explain the underlying restrictions associated with the temporal and spatial scale for climate change modeling. We then discuss the role of high-end computers in climate change sciences. Finally, we explain the importance of fostering regional, integrated climate impact analysis. Although we discuss the computational challenges associated with climate change modeling, and we hope those considerations can also be beneficial to many other modeling research programs involving multiscale system dynamics.

  12. Building an advanced climate model: Program plan for the CHAMMP (Computer Hardware, Advanced Mathematics, and Model Physics) Climate Modeling Program

    SciTech Connect

    Not Available

    1990-12-01

    The issue of global warming and related climatic changes from increasing concentrations of greenhouse gases in the atmosphere has received prominent attention during the past few years. The Computer Hardware, Advanced Mathematics, and Model Physics (CHAMMP) Climate Modeling Program is designed to contribute directly to this rapid improvement. The goal of the CHAMMP Climate Modeling Program is to develop, verify, and apply a new generation of climate models within a coordinated framework that incorporates the best available scientific and numerical approaches to represent physical, biogeochemical, and ecological processes, that fully utilizes the hardware and software capabilities of new computer architectures, that probes the limits of climate predictability, and finally that can be used to address the challenging problem of understanding the greenhouse climate issue through the ability of the models to simulate time-dependent climatic changes over extended times and with regional resolution.

  13. ICRCCM phase II: Verification and calibration of radiation codes in climate models. Final report, 1 May 1990--30 April 1993

    SciTech Connect

    Ellingson, R.G.; Wiscombe, W.J.; Murcray, D.; Smith, W.; Strauch, R.

    1993-12-31

    Following the finding by the InterComparison of Radiation Codes used in Climate Models (ICRCCM) of large differences among fluxes predicted by sophisticated radiation models that could not be sorted out because of the lack of a set of accurate atmospheric spectral radiation data measured simultaneously with the important radiative properties of the atmosphere, the team of scientists proposed to remedy the situation by carrying out a comprehensive program of measurement and analysis called SPECTRE (Spectral Radiance Experiment). SPECTRE was to establish an absolute standard against which to compare models, and aimed to remove the hidden variables (unknown humidities, aerosols, etc.) which radiation modelers had invoked to excuse disagreements with observation. The data collected during SPECTRE were to form the test bed for the second phase of ICRCCM, namely verification and calibration of radiation codes used in climate models. This should lead to more accurate radiation models for use in parameterizing climate models, which in turn play a key role in the prediction of trace-gas greenhouse effects. This report summarizes the activities during the project`s Third year to meet stated objectives. The report is divided into three sections entitled: (1) SPECTRE Activities, (2) ICRCCM Activities, and (3) Summary Information. The section on SPECTRE activities summarizes the field portion of the project during 1991, and the data reduction/analysis performed by the various participants. The section on ICRCCM activities summarizes their initial attempts to select data for distribution to ICRCCM participants and at comparison of observations with calculations as will be done by the ICRCCM participants. The Summary Information section lists data concerning publications, presentations, graduate students supported, and post-doctoral appointments during the project.

  14. Do regional climate models represent regional climate?

    NASA Astrophysics Data System (ADS)

    Maraun, Douglas; Widmann, Martin

    2014-05-01

    When using climate change scenarios - either from global climate models or further downscaled - to assess localised real world impacts, one has to ensure that the local simulation indeed correctly represents the real world local climate. Representativeness has so far mainly been discussed as a scale issue: simulated meteorological variables in general represent grid box averages, whereas real weather is often expressed by means of point values. As a result, in particular simulated extreme values are not directly comparable with observed local extreme values. Here we argue that the issue of representativeness is more general. To illustrate this point, assume the following situations: first, the (GCM or RCM) simulated large scale weather, e.g., the mid-latitude storm track, might be systematically distorted compared to observed weather. If such a distortion at the synoptic scale is strong, the simulated local climate might be completely different from the observed. Second, the orography even of high resolution RCMs is only a coarse model of true orography. In particular in mountain ranges the simulated mesoscale flow might therefore considerably deviate from the observed flow, leading to systematically displaced local weather. In both cases, the simulated local climate does not represent observed local climate. Thus, representativeness also encompasses representing a particular location. We propose to measure this aspect of representativeness for RCMs driven with perfect boundary conditions as the correlation between observations and simulations at the inter-annual scale. In doing so, random variability generated by the RCMs is largely averaged out. As an example, we assess how well KNMIs RACMO2 RCM at 25km horizontal resolution represents winter precipitation in the gridded E-OBS data set over the European domain. At a chosen grid box, RCM precipitation might not be representative of observed precipitation, in particular in the rain shadow of major moutain ranges

  15. Thermohaline circulations and global climate change. Final report

    SciTech Connect

    Hanson, H.P.

    1996-10-01

    This report discusses results from the project entitled Thermohaline Circulations and Global Climate Change. Results are discussed in three sections related to the development of the Miami Isopycnic Coordinate Ocean Model (MICOM), surface forcing of the ocean by the atmosphere, and experiments with the MICOM related to the problem of the ocean`s response to global climate change. It will require the use of a global, coupled ocean-atmospheric climate model to quantify the feedbacks between ocean and atmosphere associated with climate changes. The results presented here do provide guidance for such studies in the future.

  16. Modeling Earth's Climate

    ERIC Educational Resources Information Center

    Pallant, Amy; Lee, Hee-Sun; Pryputniewicz, Sara

    2012-01-01

    Systems thinking suggests that one can best understand a complex system by studying the interrelationships of its component parts rather than looking at the individual parts in isolation. With ongoing concern about the effects of climate change, using innovative materials to help students understand how Earth's systems connect with each other is…

  17. Stereolithography models. Final report

    SciTech Connect

    Smith, R.E.

    1995-03-01

    This report describes the first stereolithographic models made, which proved in a new release of ProEngineer software (Parametric Technologies, or PTC) and 3D Systems (Valencia, California) software for the SLA 250 machine. They are a model of benzene and the {alpha}-carbon backbone of the variable region of an antibody.

  18. The Finer Details: Climate Modeling

    NASA Technical Reports Server (NTRS)

    2000-01-01

    If you want to know whether you will need sunscreen or an umbrella for tomorrow's picnic, you can simply read the local weather report. However, if you are calculating the impact of gas combustion on global temperatures, or anticipating next year's rainfall levels to set water conservation policy, you must conduct a more comprehensive investigation. Such complex matters require long-range modeling techniques that predict broad trends in climate development rather than day-to-day details. Climate models are built from equations that calculate the progression of weather-related conditions over time. Based on the laws of physics, climate model equations have been developed to predict a number of environmental factors, for example: 1. Amount of solar radiation that hits the Earth. 2. Varying proportions of gases that make up the air. 3. Temperature at the Earth's surface. 4. Circulation of ocean and wind currents. 5. Development of cloud cover. Numerical modeling of the climate can improve our understanding of both the past and, the future. A model can confirm the accuracy of environmental measurements taken. in, the past and can even fill in gaps in those records. In addition, by quantifying the relationship between different aspects of climate, scientists can estimate how a future change in one aspect may alter the rest of the world. For example, could an increase in the temperature of the Pacific Ocean somehow set off a drought on the other side of the world? A computer simulation could lead to an answer for this and other questions. Quantifying the chaotic, nonlinear activities that shape our climate is no easy matter. You cannot run these simulations on your desktop computer and expect results by the time you have finished checking your morning e-mail. Efficient and accurate climate modeling requires powerful computers that can process billions of mathematical calculations in a single second. The NCCS exists to provide this degree of vast computing capability.

  19. Aggregate Models of Climate Change

    NASA Astrophysics Data System (ADS)

    Hooss, G.; Voss, R.; Hasselmann, K.; Maier-Reimer, E.; Joos, F.

    Integrated assessment of climate change generally requires the evaluation of many transient scenario simulations of century-timescale changes in atmospheric compo- sition and climate, desirably with the accuracy of state-of-the-art three-dimensional (3D) coupled atmosphere-ocean general circulation models (GCMs). Such multi- scenario GCM computations are possible through appropriate representation of the models in aggregate forms. For this purpose, we developed Nonlinear Impulse- response projections of 3D models of the global (oceanic and terrestrial) Carbon cycle and the atmosphere-ocean Climate System (NICCS). For higher CO2 forcing, appli- cability is extended beyond the linear response domain through explicit treatment of dominant nonlinear effects. The climate change module was furthermore augmented with spatial patterns of change in some of the most impact-relevant fields. Applied to three long-term CO2 emission scenarios, the model demonstrates (a) the minor rela- tive role of the terrestrial carbon sink through CO2 fertilization, and (b) the necessity to reduce fossil carbon emissions to a very small fraction of today's rates within the next few decades if a major climate change is to be avoided.

  20. Model confirmation in climate economics.

    PubMed

    Millner, Antony; McDermott, Thomas K J

    2016-08-01

    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

  1. Energy-balance climate models

    NASA Technical Reports Server (NTRS)

    North, G. R.; Cahalan, R. F.; Coakley, J. A., Jr.

    1980-01-01

    An introductory survey of the global energy balance climate models is presented with an emphasis on analytical results. A sequence of increasingly complicated models involving ice cap and radiative feedback processes are solved and the solutions and parameter sensitivities are studied. The model parameterizations are examined critically in light of many current uncertainties. A simple seasonal model is used to study the effects of changes in orbital elements on the temperature field. A linear stability theorem and a complete nonlinear stability analysis for the models are developed. Analytical solutions are also obtained for the linearized models driven by stochastic forcing elements. In this context the relation between natural fluctuation statistics and climate sensitivity is stressed.

  2. Modeling of hydrologic conditions and solute movement in processed oil shale waste embankments under simulated climatic conditions. Final report, November 1995

    SciTech Connect

    1995-12-31

    A study is described on the hydrological and geotechnical behavior of an oil shale solid waste. The objective was to obtain information which can be used to assess the environmental impacts of oil shale solid waste disposal in the Green River Basin. The spent shale used in this study was combusted by the Lurgi-Ruhrgas process by Rio Blanco Oil Shale Company, Inc. Laboratory bench-scale testing included index properties, such as grain size distribution and Atterberg limits, and tests for engineering properties including hydraulic conductivity and shear strength. Large-scale tests were conducted on model spent shale waste embankments to evaluate hydrological response, including infiltration, runoff, and seepage. Large-scale tests were conducted at a field site in western Colorado and in the Environmental Simulation Laboratory (ESL)at the University of Wyoming. The ESL tests allowed the investigators to control rainfall and temperature, providing information on the hydrological response of spent shale under simulated severe climatic conditions. All experimental methods, materials, facilities, and instrumentation are described in detail, and results are given and discussed. 34 refs.

  3. Integrated Climate and Carbon-cycle Model

    Energy Science and Technology Software Center (ESTSC)

    2006-03-06

    The INCCA model is a numerical climate and carbon cycle modeling tool for use in studying climate change and carbon cycle science. The model includes atmosphere, ocean, land surface, and sea ice components.

  4. Contribution to the development of DOE ARM Climate Modeling Best Estimate Data (CMBE) products: Satellite data over the ARM permanent and AMF sites: Final Report

    SciTech Connect

    Xie, B; Dong, X; Xie, S

    2012-05-18

    To support the LLNL ARM infrastructure team Climate Modeling Best Estimate (CMBE) data development, the University of North Dakota (UND)'s group will provide the LLNL team the NASA CERES and ISCCP satellite retrieved cloud and radiative properties for the periods when they are available over the ARM permanent research sites. The current available datasets, to date, are as follows: the CERES/TERRA during 200003-200812; the CERES/AQUA during 200207-200712; and the ISCCP during 199601-200806. The detailed parameters list below: (1) CERES Shortwave radiative fluxes (net and downwelling); (2) CERES Longwave radiative fluxes (upwelling) - (items 1 & 2 include both all-sky and clear-sky fluxes); (3) CERES Layered clouds (total, high, middle, and low); (4) CERES Cloud thickness; (5) CERES Effective cloud height; (6) CERES cloud microphysical/optical properties; (7) ISCCP optical depth cloud top pressure matrix; (8) ISCCP derived cloud types (r.g., cirrus, stratus, etc.); and (9) ISCCP infrared derived cloud top pressures. (10) The UND group shall apply necessary quality checks to the original CERES and ISCCP data to remove suspicious data points. The temporal resolution for CERES data should be all available satellite overpasses over the ARM sites; for ISCCP data, it should be 3-hourly. The spatial resolution is the closest satellite field of view observations to the ARM surface sites. All the provided satellite data should be in a format that is consistent with the current ARM CMBE dataset so that the satellite data can be easily merged into the CMBE dataset.

  5. An Analog Earth Climate Model

    NASA Astrophysics Data System (ADS)

    Varekamp, J. C.

    2010-12-01

    The earth climate is broadly governed by the radiative power of the sun as well as the heat retention and convective cooling of the atmosphere. I have constructed an analog earth model for an undergraduate climate class that simulates mean climate using these three parameters. The ‘earth’ is a hollow, black, bronze sphere (4 cm diameter) mounted on a thin insulated rod, and illuminated by two opposite optic fibers, with light focused on the sphere by a set of lenses. The sphere is encased in a large double-walled aluminum cylinder (34 cm diameter by 26 cm high) with separate water cooling jackets at the top, bottom, and sides. The cylinder can be filled with a gas of choice at a variety of pressures or can be run in vacuum. The exterior is cladded with insulation, and the temperature of the sphere, atmosphere and walls is monitored with thermocouples. The temperature and waterflow of the three cooling jackets can be monitored to establish the energy output of the whole system; the energy input is the energy yield of the two optic fibers. A small IR transmissive lens at the top provides the opportunity to hook up the fiber of a hyper spectrometer to monitor the emission spectrum of the black ‘earth’ sphere. A pressure gauge and gas inlet-outlet system for flushing of the cell completes it. The heat yield of the cooling water at the top is the sum of the radiative and convective components, whereas the bottom jacket only carries off the radiative heat of the sphere. Undergraduate E&ES students at Wesleyan University have run experiments with dry air, pure CO2, N2 and Ar at 1 atmosphere, and a low vacuum run was accomplished to calibrate the energy input. For each experiment, the lights are flipped on, the temperature acquisition routine is activated, and the sphere starts to warm up until an equilibrium temperature has been reached. The lights are then flipped off and the cooling sequence towards ambient is registered. The energy input is constant for a given

  6. The Software Architecture of Global Climate Models

    NASA Astrophysics Data System (ADS)

    Alexander, K. A.; Easterbrook, S. M.

    2011-12-01

    It has become common to compare and contrast the output of multiple global climate models (GCMs), such as in the Climate Model Intercomparison Project Phase 5 (CMIP5). However, intercomparisons of the software architecture of GCMs are almost nonexistent. In this qualitative study of seven GCMs from Canada, the United States, and Europe, we attempt to fill this gap in research. We describe the various representations of the climate system as computer programs, and account for architectural differences between models. Most GCMs now practice component-based software engineering, where Earth system components (such as the atmosphere or land surface) are present as highly encapsulated sub-models. This architecture facilitates a mix-and-match approach to climate modelling that allows for convenient sharing of model components between institutions, but it also leads to difficulty when choosing where to draw the lines between systems that are not encapsulated in the real world, such as sea ice. We also examine different styles of couplers in GCMs, which manage interaction and data flow between components. Finally, we pay particular attention to the varying levels of complexity in GCMs, both between and within models. Many GCMs have some components that are significantly more complex than others, a phenomenon which can be explained by the respective institution's research goals as well as the origin of the model components. In conclusion, although some features of software architecture have been adopted by every GCM we examined, other features show a wide range of different design choices and strategies. These architectural differences may provide new insights into variability and spread between models.

  7. Use of ARM data to test an improved parameterization of upper tropospheric clouds for use in climate models. Final report, December 1, 1991--May 30, 1995

    SciTech Connect

    Randall, D.A.; Xu, KuanMan

    1995-03-01

    We have developed new cloud parameterizations, and tested them in an SCM, a CEM, and a full three-dimensional GCM. We have demonstrated the ability to drive the SCM and the CEM with observations similar to those that we are about to receive from ARM. We have used the three-dimensional GCM to investigate some aspects of the role of clouds in climate. The climate simulation, and especially the Earth`s radiation budget, has been substantially improved through use of the new parameterizations.

  8. Environmental, genetic, and ecophysiological variation of western and Utah juniper and their hybrids: A model system for vegetation response to climate change. Final report

    SciTech Connect

    Nowak, R.S.; Tausch, R.J.

    1998-11-01

    This report focuses on the following two research projects relating to the biological effects of climate change: Hybridization and genetic diversity populations of Utah (Juniperus osteosperma) and western (Juniperus occidentalis) juniper: Evidence from nuclear ribosomal and chloroplast DNA; and Ecophysiological patterns of pinyon and juniper.

  9. Likelihood-Based Climate Model Evaluation

    NASA Technical Reports Server (NTRS)

    Braverman, Amy; Cressie, Noel; Teixeira, Joao

    2012-01-01

    Climate models are deterministic, mathematical descriptions of the physics of climate. Confidence in predictions of future climate is increased if the physics are verifiably correct. A necessary, (but not sufficient) condition is that past and present climate be simulated well. Quantify the likelihood that a (summary statistic computed from a) set of observations arises from a physical system with the characteristics captured by a model generated time series. Given a prior on models, we can go further: posterior distribution of model given observations.

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

    SciTech Connect

    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 ability 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.

  11. Uncertainty Quantification in Climate Modeling

    NASA Astrophysics Data System (ADS)

    Sargsyan, K.; Safta, C.; Berry, R.; Debusschere, B.; Najm, H.

    2011-12-01

    We address challenges that sensitivity analysis and uncertainty quantification methods face when dealing with complex computational models. In particular, climate models are computationally expensive and typically depend on a large number of input parameters. We consider the Community Land Model (CLM), which consists of a nested computational grid hierarchy designed to represent the spatial heterogeneity of the land surface. Each computational cell can be composed of multiple land types, and each land type can incorporate one or more sub-models describing the spatial and depth variability. Even for simulations at a regional scale, the computational cost of a single run is quite high and the number of parameters that control the model behavior is very large. Therefore, the parameter sensitivity analysis and uncertainty propagation face significant difficulties for climate models. This work employs several algorithmic avenues to address some of the challenges encountered by classical uncertainty quantification methodologies when dealing with expensive computational models, specifically focusing on the CLM as a primary application. First of all, since the available climate model predictions are extremely sparse due to the high computational cost of model runs, we adopt a Bayesian framework that effectively incorporates this lack-of-knowledge as a source of uncertainty, and produces robust predictions with quantified uncertainty even if the model runs are extremely sparse. In particular, we infer Polynomial Chaos spectral expansions that effectively encode the uncertain input-output relationship and allow efficient propagation of all sources of input uncertainties to outputs of interest. Secondly, the predictability analysis of climate models strongly suffers from the curse of dimensionality, i.e. the large number of input parameters. While single-parameter perturbation studies can be efficiently performed in a parallel fashion, the multivariate uncertainty analysis

  12. A National Strategy for Advancing Climate Modeling

    SciTech Connect

    Dunlea, Edward; Elfring, Chris

    2012-12-04

    Climate models are the foundation for understanding and projecting climate and climate-related changes and are thus critical tools for supporting climate-related decision making. This study developed a holistic strategy for improving the nation's capability to accurately simulate climate and related Earth system changes on decadal to centennial timescales. The committee's report is a high level analysis, providing a strategic framework to guide progress in the nation's climate modeling enterprise over the next 10-20 years. This study was supported by DOE, NSF, NASA, NOAA, and the intelligence community.

  13. On the software quality of climate models

    NASA Astrophysics Data System (ADS)

    Pipitone, J.; Easterbrook, S.

    2009-12-01

    A climate model is an executable theory of the climate; the model encapsulates climatological theories in software so that they can be simulated and their implications investigated directly. Thus, in order to trust a climate model one must trust that the software it is built from is robust. Our study explores the nature of software quality in the context of climate modelling: How do we characterise and assess the quality of climate modelling software? We use two major research strategies: (1) analysis of defect densities of leading global climate models and (2) semi-structured interviews with researchers from several climate modelling centres. Defect density analysis is an established software engineering technique for studying software quality. We collected our defect data from bug tracking systems, version control repository comments, and from static analysis of the source code. As a result of our analysis, we characterise common defect types found in climate model software and we identify the software quality factors that are relevant for climate scientists. We also provide a roadmap to achieve proper benchmarks for climate model software quality, and we discuss the implications of our findings for the assessment of climate model software trustworthiness.

  14. Questions of bias in climate models

    SciTech Connect

    Smith, Steven J.; Wigley, Tom M.; Meinshausen, Malte; Rogelj, Joeri

    2014-08-27

    The recent work by Shindell usefully contributes to the debate over estimating climate sensitivity by highlighting an important aspect of the climate system: that climate forcings that occur over land result in a more rapid temperature response than forcings that are distributed more uniformly over the globe. While, as noted in this work, simple climate models may be biased by assuming the same temperature response for all forcing agents, the implication that the MAGICC model is biased in this way is not correct.

  15. Paleoclimate validation of a numerical climate model

    SciTech Connect

    Schelling, F.J.; Church, H.W.; Zak, B.D.; Thompson, S.L.

    1994-04-01

    An analysis planned to validate regional climate model results for a past climate state at Yucca Mountain, Nevada, against paleoclimate evidence for the period is described. This analysis, which will use the GENESIS model of global climate nested with the RegCM2 regional climate model, is part of a larger study for DOE`s Yucca Mountain Site Characterization Project that is evaluating the impacts of long term future climate change on performance of the potential high level nuclear waste repository at Yucca Mountain. The planned analysis and anticipated results are presented.

  16. Climate change models and forest research

    SciTech Connect

    Cooter, E.J.; Eder, B.K.; LeDuc, S.K.; Truppi, L. )

    1993-09-01

    Sophisticated climate models have projected that global warming of 1.5[degrees]-4.5[degrees]C will take place over a period of 50-100 years (Bretherton et al. 1990). They also predict changes in the global water cycle in response to this warming. Some regions could become wetter while others become drier. Seasonal patterns of precipitation would likely change. Although historical and paleoclimatic records provide examples of a warmer earth in some geographic locations, other regions could experience conditions unlike those of any period in the earth's history (Cooperative Holocene Mapping Project 1988). General circulation models (GCM) offer one means of obtaining a portrait of what the climatological future holds based on our current understanding of the global environment. GCMs are powerful tools, but our knowledge of the processes and interactions that they attempt to model is incomplete. As many as 19 GCMs have been identified (Randall et al. 1992). No particular model can hope to be completely accurate; and, in fact, no two are in complete agreement concerning the present climate of our world. Nevertheless, policy recommendations must be made with the information available. Although the perfect climate forecast is still far in the future, we know enough to be able to make responsible use of the data produced by GCMs. Responsible use requires knowing which predictions have been evaluated against historical records and the degree to which processes critical to forest assessments are explicitly modeled (or missing) from a particular GCM. These insights can guide the scientist in selecting appropriate GCMs, using their predictions in particular applications, and interpreting the final results. 17 refs., 1 fig., 3 tabs.

  17. Use of regional climate models in climate based ecosystem studies

    SciTech Connect

    Hostetler, S.

    1995-09-01

    Regional climate models (RCMs) use horizontal grid spacings on the order of tens of kilometers and thus are able to simulate the climate of a limited area at resolutions much higher than can be attained by general circulation models (horizontal scales of several degrees of hundreds of kilometers). The fine mesh of RCMs allows regional scale features that exert forcings on climate (e.g., lakes, mountains, coastlines) to be resolved. As a result, RCM simulations begin to reflect the heterogeneity of climate that supports the spatially diverse distribution of ecosystems in western North American. Examples of model simulations and comparisons with reconstructions of vegetation during the last glacial maximum (21 K CAL) will be presented.

  18. Climate Change Vulnerability Assessments: Four Case Studies of Water Utility Practices (2011 Final)

    EPA Science Inventory

    EPA is releasing the final report titled, Climate Change Vulnerability Assessments: Four Case Studies of Water Utility Practices. This report was prepared by the National Center for Environmental Assessment's Global Climate Research Staff in the Office of Research and Developmen...

  19. Modeling the Arctic climate system using the regional climate model HIRHAM

    NASA Astrophysics Data System (ADS)

    Rinke, A.; Dethloff, K.; Dorn, W.; Matthes, H.; Mielke, M.; Klaus, D.

    2012-12-01

    The regional climate model HIRHAM is used as a tool for coupled modeling of the Arctic climate system. Various approaches are pursued which will finally be combined into a regional Earth system model. Compared to data from the 35th North Pole drifting station of 2007-2008, the HIRHAM model has been evaluated over the central Arctic concerning atmospheric boundary layer and cloud cover. Modifications of the stability functions impact the regional circulation but cannot satisfactorily improve the boundary layer structure. A prognostic statistical cloud scheme performs better than a relative humidity-based scheme. With the coupled atmosphere-ocean-ice model HIRHAM-NAOSIM, ensemble simulations were conducted for the period 1948-2008. It is demonstrated that a realistic simulation of the atmospheric circulation and its internal variability is required to reproduce the observed sea ice extent in summer. Alongside, the internal variability of the atmospheric HIRHAM model is quantified, also based on ensemble simulations for 1979-2008. Coupled atmosphere-land HIRHAM simulations for future Arctic climate scenarios are discussed with respect to the influence of vegetation changes as well as its implications for frozen ground conditions.

  20. A study of longwave radiation codes for climate studies: Validation with ARM observations and tests in general circulation models. Final report, September 15, 1990--October 31, 1994

    SciTech Connect

    Ellingson, R.G.; Baer, F.

    1998-09-01

    DOE has launched a major initiative -- the Atmospheric Radiation Measurements (ARM) Program -- directed at improving the parameterization of the physics governing cloud and radiative processes in general circulation models (GCMs). One specific goal of ARM is to improve the treatment of radiative transfer in GCMs under clear-sky, general overcast and broken cloud conditions. In 1990, the authors proposed to contribute to this goal by attacking major problems connected with one of the dominant radiation components of the problem -- longwave radiation. In particular, their long-term research goals are to: develop an optimum longwave radiation model for use in GCMs that has been calibrated with state-of-the-art observations, assess the impact of the longwave radiative forcing in a GCM, determine the sensitivity of a GCM to the radiative model used in it, and determine how the longwave radiative forcing contributes relatively when compared to shortwave radiative forcing, sensible heating, thermal advection and expansion.

  1. Interactive Puzzles for the mean climate dyanmics and climate change with the Monash Simple Climate Model

    NASA Astrophysics Data System (ADS)

    Dommenget, D.

    2014-12-01

    The Monash university interactive simple climate model is a web-based interface that allows students and the general public to explore the physical simulation of the climate system with a real global climate model. It is based on the Globally Resolved Energy Balance (GREB) model that simulates most of the main physical processes in the climate system in a very simplistic way and therefore allows very fast and simple climate model simulations. Despite its simplicity the model simulates the mean climate and its response to external forcings, such as doubling of the CO2 concentrations very realistically.The Monash simple climate model web-interface allows you to do some entertaining and educational puzzles about the interaction of climate dynamics. By turning switches OFF and ON you control physical processes in the climate system, but you do not know what these processes. By testing a number of experiments you learn about the interactions in the climate system and thereby figure out which switch controls what process in the climate system. The presentation will illustrate how this web-base tool works and what are the possibilities in teaching students with this tool are.

  2. Thermohaline circulations and global climate change. Final report

    SciTech Connect

    Hanson, H.P.

    1994-09-01

    This research is ultimately concerned with investigating the hypothesis that changes in surface thermal and hydrological forcing of the North Atlantic, changes that might be expected to accompany CO2-induced global warming, could result in ocean-atmosphere interactions` exerting a positive feedback on the climate system. This report concerns research conducted with funding from the Carbon Dioxide Research Program (now the Global Climate Change Program) of the US Department of Energy via grant no. DE-FG02-90ER61019 during the period 15 July 1990 - 14 July 1994. This was a three-year award, extended to a fourth year (15 July 1993 - 14 July 1994) via a no-cost extension. It is important to emphasize that this award has been renewed for an additional two years (15 July 1993 - 14 July 1995) via grant no. DE-FG03-93ER61646 (with the same title). Because the project was originally envisioned to be a five-year effort, many of the important results and conclusions will be available for the Final Report of that second award. This report therefore concerns mainly preliminary conclusions and a discussion of progress toward understanding the central hypothesis of the research.

  3. Developing a Common Information Model for climate models and data

    NASA Astrophysics Data System (ADS)

    Valcke, S.; Balaji, V.; Bentley, P.; Guilyardi, E.; Lawrence, B.; Pascoe, C.; Steenman-Clark, L.; Toussaint, F.; Treshansky, A.

    2009-04-01

    The Metafor project, funded under the EU Framework Programme 7, proposes a Common Information Model (CIM) to describe in a standard way climate data and the models and modelling environments that produced this data. To establish the CIM, Metafor first considered the metadata models developed by other groups engaged in similar efforts in Europe and worlwide, such as the US Earth System Curator, explored fragmentation and gaps as well as duplication of information present in these metadata models, and reviewed current problems in identifying, accessing or using climate data present in existing repositories. Based on this analysis and on different use cases, the first version of the CIM is composed of 5 packages. The "data" package is used to describe the data objects that can be collected and stored in any number of ways; the "activity" package details the simulations and experiments and related requirements that were performed with numerical (possibly coupled) models described with the "software" packages. Both data and models can be associated with numerical grids represented by the "grid" package and finally the "shared" package gathers concepts shared among the other packages. The CIM is defined and implemented in the Unified Modelling Language (UML) and application schema have been generated in XML schema. Aiming at a wide adoption of the CIM, Metafor will optimize the way climate data infrastructures are used to store knowledge, thereby adding value to primary research data and information, and providing an essential asset for the numerous stakeholders actively engaged in climate change issues (policy, research, impacts, mitigation, private sector).

  4. Selecting global climate models for regional climate change studies

    PubMed Central

    Pierce, David W.; Barnett, Tim P.; Santer, Benjamin D.; Gleckler, Peter J.

    2009-01-01

    Regional or local climate change modeling studies currently require starting with a global climate model, then downscaling to the region of interest. How should global models be chosen for such studies, and what effect do such choices have? This question is addressed in the context of a regional climate detection and attribution (D&A) study of January-February-March (JFM) temperature over the western U.S. Models are often selected for a regional D&A analysis based on the quality of the simulated regional climate. Accordingly, 42 performance metrics based on seasonal temperature and precipitation, the El Nino/Southern Oscillation (ENSO), and the Pacific Decadal Oscillation are constructed and applied to 21 global models. However, no strong relationship is found between the score of the models on the metrics and results of the D&A analysis. Instead, the importance of having ensembles of runs with enough realizations to reduce the effects of natural internal climate variability is emphasized. Also, the superiority of the multimodel ensemble average (MM) to any 1 individual model, already found in global studies examining the mean climate, is true in this regional study that includes measures of variability as well. Evidence is shown that this superiority is largely caused by the cancellation of offsetting errors in the individual global models. Results with both the MM and models picked randomly confirm the original D&A results of anthropogenically forced JFM temperature changes in the western U.S. Future projections of temperature do not depend on model performance until the 2080s, after which the better performing models show warmer temperatures. PMID:19439652

  5. Final Report. Evaluating the Climate Sensitivity of Dissipative Subgrid-Scale Mixing Processes and Variable Resolution in NCAR's Community Earth System Model

    SciTech Connect

    Jablonowski, Christiane

    2015-12-14

    The goals of this project were to (1) assess and quantify the sensitivity and scale-dependency of unresolved subgrid-scale mixing processes in NCAR’s Community Earth System Model (CESM), and (2) to improve the accuracy and skill of forthcoming CESM configurations on modern cubed-sphere and variable-resolution computational grids. The research thereby contributed to the description and quantification of uncertainties in CESM’s dynamical cores and their physics-dynamics interactions.

  6. A personal perspective on modelling the climate system

    PubMed Central

    Palmer, T. N.

    2016-01-01

    Given their increasing relevance for society, I suggest that the climate science community itself does not treat the development of error-free ab initio models of the climate system with sufficient urgency. With increasing levels of difficulty, I discuss a number of proposals for speeding up such development. Firstly, I believe that climate science should make better use of the pool of post-PhD talent in mathematics and physics, for developing next-generation climate models. Secondly, I believe there is more scope for the development of modelling systems which link weather and climate prediction more seamlessly. Finally, here in Europe, I call for a new European Programme on Extreme Computing and Climate to advance our ability to simulate climate extremes, and understand the drivers of such extremes. A key goal for such a programme is the development of a 1 km global climate system model to run on the first exascale supercomputers in the early 2020s. PMID:27274686

  7. Dynamic Integrated Climate Economy model (DICE)

    EPA Science Inventory

    The DICE model is an Integrated Assessment model of climate change impacts and costs, which “integrate[s] in an end-to-end fashion the economics, carbon cycle, climate science, and impacts in a highly aggregated model that allow[s] a weighing of the costs and benefits of taking s...

  8. Climate Sensitivity of the Community Climate System Model, Version 4

    SciTech Connect

    Bitz, Cecilia M.; Shell, K. M.; Gent, P. R.; Bailey, D. A.; Danabasoglu, G.; Armour, K. C.; Holland, M. M.; Kiehl, J. T.

    2012-05-01

    Equilibrium climate sensitivity of the Community Climate System Model Version 4 (CCSM4) is 3.20°C for 1° horizontal resolution in each component. This is about a half degree Celsius higher than in the previous version (CCSM3). The transient climate sensitivity of CCSM4 at 1° resolution is 1.72°C, which is about 0.2°C higher than in CCSM3. These higher climate sensitivities in CCSM4 cannot be explained by the change to a preindustrial baseline climate. We use the radiative kernel technique to show that from CCSM3 to CCSM4, the global mean lapse-rate feedback declines in magnitude, and the shortwave cloud feedback increases. These two warming effects are partially canceled by cooling due to slight decreases in the global mean water-vapor feedback and longwave cloud feedback from CCSM3 to CCSM4. A new formulation of the mixed-layer, slab ocean model in CCSM4 attempts to reproduce the SST and sea ice climatology from an integration with a full-depth ocean, and it is integrated with a dynamic sea ice model. These new features allow an isolation of the influence of ocean dynamical changes on the climate response when comparing integrations with the slab ocean and full-depth ocean. The transient climate response of the full-depth ocean version is 0.54 of the equilibrium climate sensitivity when estimated with the new slab ocean model version for both CCSM3 and CCSM4. We argue the ratio is the same in both versions because they have about the same zonal mean pattern of change in ocean surface heat flux, which broadly resembles the zonal mean pattern of net feedback strength.

  9. Climate Sensitivity of the Community Climate System Model, Version 4

    DOE PAGESBeta

    Bitz, Cecilia M.; Shell, K. M.; Gent, P. R.; Bailey, D. A.; Danabasoglu, G.; Armour, K. C.; Holland, M. M.; Kiehl, J. T.

    2012-05-01

    Equilibrium climate sensitivity of the Community Climate System Model Version 4 (CCSM4) is 3.20°C for 1° horizontal resolution in each component. This is about a half degree Celsius higher than in the previous version (CCSM3). The transient climate sensitivity of CCSM4 at 1° resolution is 1.72°C, which is about 0.2°C higher than in CCSM3. These higher climate sensitivities in CCSM4 cannot be explained by the change to a preindustrial baseline climate. We use the radiative kernel technique to show that from CCSM3 to CCSM4, the global mean lapse-rate feedback declines in magnitude, and the shortwave cloud feedback increases. These twomore » warming effects are partially canceled by cooling due to slight decreases in the global mean water-vapor feedback and longwave cloud feedback from CCSM3 to CCSM4. A new formulation of the mixed-layer, slab ocean model in CCSM4 attempts to reproduce the SST and sea ice climatology from an integration with a full-depth ocean, and it is integrated with a dynamic sea ice model. These new features allow an isolation of the influence of ocean dynamical changes on the climate response when comparing integrations with the slab ocean and full-depth ocean. The transient climate response of the full-depth ocean version is 0.54 of the equilibrium climate sensitivity when estimated with the new slab ocean model version for both CCSM3 and CCSM4. We argue the ratio is the same in both versions because they have about the same zonal mean pattern of change in ocean surface heat flux, which broadly resembles the zonal mean pattern of net feedback strength.« less

  10. Fully nonlinear data-assimilation in climate models

    NASA Astrophysics Data System (ADS)

    van Leeuwen, P. J.; Browne, P.

    2014-12-01

    While climate modelling requires huge computational resources, initialising climate models using data assimilation is even more demanding. As the climate system is highly nonlinear both through nonlinear dynamics and strong feed backs the data-assimilation methodology has to be nonlinear too. Furthermore, it has been realised that one best forecast is not that useful and proper uncertainty quantification is essential for advances in the field. Both requirements point to fully nonlinear ensemble techniques, such as particle filters. Recently particle filters have been generated that allow for efficient ensemble members for climate model initialisation, by generating proper samples from the posterior probability density function in huge dimensional spaces. Another issue is to connect the complex climate model to the data-assimilation code. We have developed a very efficient framework to do this using only MPI communication between separate model and data-assimilation executables, called EMPIRE. This framework allows for very fast connection of any complex model to state-of-the-art ensemble data-assimilation methods. We will show an example of use of this new methodology to the HadCM3 climate model, which has over 2 million model variables, using the EMPIRE framework. We will discuss timings and efficiency, as well as some of the physical results. Finally, we will discuss the coupling with the very high resolution UM vn8.2 with close to 300 million model variables.

  11. Climate Modeling using High-Performance Computing

    SciTech Connect

    Mirin, A A

    2007-02-05

    The Center for Applied Scientific Computing (CASC) and the LLNL Climate and Carbon Science Group of Energy and Environment (E and E) are working together to improve predictions of future climate by applying the best available computational methods and computer resources to this problem. Over the last decade, researchers at the Lawrence Livermore National Laboratory (LLNL) have developed a number of climate models that provide state-of-the-art simulations on a wide variety of massively parallel computers. We are now developing and applying a second generation of high-performance climate models. Through the addition of relevant physical processes, we are developing an earth systems modeling capability as well.

  12. Conceptual Understanding of Climate Change with a Simple Climate Model

    NASA Astrophysics Data System (ADS)

    Dommenget, Dietmar; Floeter, Janine

    2010-05-01

    The future climate change projections are essentially based on coupled general circulation model (CGCM) simulations, which give a distinct global warming pattern with arctic winter amplification, an equilibrium land-sea warming contrast and an inter-hemispheric warming gradient. While these simulations are the most important tool of the Intergovernmental Panel on Climate Change (IPCC) predictions, the conceptual understanding of these predicted structures of climate change and the causes of their uncertainties is very difficult to reach if only based on these highly complex CGCM simulations. In the study presented here we will introduce a very simple, globally resolved energy balance (GREB) model, which is capable of simulating the main characteristics of global warming. The model shall give a bridge between the strongly simplified energy balance models and the fully coupled 4-dimensional complex CGCMs. It provides a fast tool for the conceptual understanding and development of hypotheses for climate change studies and teaching. It is based on the surface energy balance by very simple representations of solar and thermal radiation, the atmospheric hydrological cycle, sensible turbulent heat flux, the transport by the mean atmospheric circulation and heat exchange with the deeper ocean. It can be run on any PC computer and compute 200yrs climate scenarios within minutes. The simple model's climate sensitivity and the spatial structure of the warming pattern are within the uncertainties of the IPCC models simulations. It is capable of simulating the arctic winter amplification, the equilibrium land-sea warming contrast and the inter-hemispheric warming gradient with good agreement to the IPCC models in amplitude and structure.

  13. Climate Change Projections Using Regional Regression Models

    NASA Astrophysics Data System (ADS)

    Griffis, V. W.; Gyawali, R.; Watkins, D. W.

    2012-12-01

    A typical approach to project climate change impacts on water resources systems is to downscale general circulation model (GCM) or regional climate model (RCM) outputs as forcing data for a watershed model. With downscaled climate model outputs becoming readily available, multi-model ensemble approaches incorporating mutliple GCMs, multiple emissions scenarios and multiple initializations are increasingly being used. While these multi-model climate ensembles represent a range of plausible futures, different hydrologic models and methods may complicate impact assessment. In particular, associated loss, flow routing, snowmelt and evapotranspiration computation methods can markedly increase hydrological modeling uncertainty. Other challenges include properly calibrating and verifying the watershed model and maintaining a consistent energy budget between climate and hydrologic models. An alternative approach, particularly appealing for ungauged basins or locations where record lengths are short, is to directly predict selected streamflow quantiles from regional regression equations that include physical basin characteristics as well as meteorological variables output by climate models (Fennessey 2011). Two sets of regional regression models are developed for the Great Lakes states using ordinary least squares and weighted least squares regression. The regional regression modeling approach is compared with physically based hydrologic modeling approaches for selected Great Lakes watersheds using downscaled outputs from the Coupled Model Intercomparison Project (CMIP3) as inputs to the Large Basin Runoff Model (LBRM) and the U.S. Army Corps Hydrologic Modeling System (HEC-HMS).

  14. Crop response to climate: ecophysical models

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Ecophysiological models were the dominant tools used to estimate the potential impact of climate change in agroecosystems in the Third and Fourth Assessment Reports of the IPCC and are widely used elsewhere in climate change research. These models, also known as “crop models” or “simulation models”,...

  15. Climate Model Diagnostic Analyzer Web Service System

    NASA Astrophysics Data System (ADS)

    Lee, S.; Pan, L.; Zhai, C.; Tang, B.; Kubar, T. L.; Li, J.; Zhang, J.; Wang, W.

    2015-12-01

    Both the National Research Council Decadal Survey and the latest Intergovernmental Panel on Climate Change Assessment Report stressed the need for the comprehensive and innovative evaluation of climate models with the synergistic use of global satellite observations in order to improve our weather and climate simulation and prediction capabilities. The abundance of satellite observations for fundamental climate parameters and the availability of coordinated model outputs from CMIP5 for the same parameters offer a great opportunity to understand and diagnose model biases in climate models. In addition, the Obs4MIPs efforts have created several key global observational datasets that are readily usable for model evaluations. However, a model diagnostic evaluation process requires physics-based multi-variable comparisons that typically involve large-volume and heterogeneous datasets, making them both computationally- and data-intensive. In response, we have developed a novel methodology to diagnose model biases in contemporary climate models and implementing the methodology as a web-service based, cloud-enabled, provenance-supported climate-model evaluation system. The evaluation system is named Climate Model Diagnostic Analyzer (CMDA), which is the product of the research and technology development investments of several current and past NASA ROSES programs. The current technologies and infrastructure of CMDA are designed and selected to address several technical challenges that the Earth science modeling and model analysis community faces in evaluating and diagnosing climate models. In particular, we have three key technology components: (1) diagnostic analysis methodology; (2) web-service based, cloud-enabled technology; (3) provenance-supported technology. The diagnostic analysis methodology includes random forest feature importance ranking, conditional probability distribution function, conditional sampling, and time-lagged correlation map. We have implemented the

  16. Global climate change model natural climate variation: Paleoclimate data base, probabilities and astronomic predictors

    SciTech Connect

    Kukla, G.; Gavin, J.

    1994-05-01

    This report was prepared at the Lamont-Doherty Geological Observatory of Columbia University at Palisades, New York, under subcontract to Pacific Northwest Laboratory it is a part of a larger project of global climate studies which supports site characterization work required for the selection of a potential high-level nuclear waste repository and forms part of the Performance Assessment Scientific Support (PASS) Program at PNL. The work under the PASS Program is currently focusing on the proposed site at Yucca Mountain, Nevada, and is under the overall direction of the Yucca Mountain Project Office US Department of Energy, Las Vegas, Nevada. The final results of the PNL project will provide input to global atmospheric models designed to test specific climate scenarios which will be used in the site specific modeling work of others. The primary purpose of the data bases compiled and of the astronomic predictive models is to aid in the estimation of the probabilities of future climate states. The results will be used by two other teams working on the global climate study under contract to PNL. They are located at and the University of Maine in Orono, Maine, and the Applied Research Corporation in College Station, Texas. This report presents the results of the third year`s work on the global climate change models and the data bases describing past climates.

  17. Climate system studies: final report to the U.S. Department of Energy

    SciTech Connect

    Bradley, Raymond S.; Diaz, Henry F.

    2000-03-01

    In this final report, we summarize research on climate variability and forcing mechanisms responsible for these changes. We report on research related to high elevation climate change, changes in the hydrological cycle and the seasonality of precipitation and on changes in climatic extremes. A comprehensive bibliography of research articles and books arising from this grant is included as an appendix.

  18. Weather Forecaster Understanding of Climate Models

    NASA Astrophysics Data System (ADS)

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

    2013-12-01

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

  19. An Appraisal of Coupled Climate Model Simulations

    SciTech Connect

    Sperber, K; Gleckler, P; Covey, C; Taylor, K; Bader, D; Phillips, T; Fiorino, M; Achutarao, K

    2004-02-24

    In 2002, the Program for Climate Model Diagnosis and Intercomparison (PCMDI) proposed the concept for a state-of-the-science appraisal of climate models to be performed approximately every two years. Motivation for this idea arose from the perceived needs of the international modeling groups and the broader climate research community to document progress more frequently than provided by the Intergovernmental Panel on Climate Change (IPCC) Assessment Reports. A committee of external reviewers, which included senior researchers from four leading international modeling centers, supported the concept by stating in its review: ''The panel enthusiastically endorses the suggestion that PCMDI develop an independent appraisal of coupled model performance every 2-3 years. This would provide a useful 'mid-course' evaluation of modeling progress in the context of larger IPCC and national assessment activities, and should include both coupled and single-component model evaluations.''

  20. Historical and idealized climate model experiments: an EMIC intercomparison

    NASA Astrophysics Data System (ADS)

    Eby, M.; Weaver, A. J.; Alexander, K.; Zickfeld, K.; Abe-Ouchi, A.; Cimatoribus, A. A.; Crespin, E.; Drijfhout, S. S.; Edwards, N. R.; Eliseev, A. V.; Feulner, G.; Fichefet, T.; Forest, C. E.; Goosse, H.; Holden, P. B.; Joos, F.; Kawamiya, M.; Kicklighter, D.; Kienert, H.; Matsumoto, K.; Mokhov, I. I.; Monier, E.; Olsen, S. M.; Pedersen, J. O. P.; Perrette, M.; Philippon-Berthier, G.; Ridgwell, A.; Schlosser, A.; Schneider von Deimling, T.; Shaffer, G.; Smith, R. S.; Spahni, R.; Sokolov, A. P.; Steinacher, M.; Tachiiri, K.; Tokos, K.; Yoshimori, M.; Zeng, N.; Zhao, F.

    2012-08-01

    Both historical and idealized climate model experiments are performed with a variety of Earth System Models of Intermediate Complexity (EMICs) as part of a community contribution to the Intergovernmental Panel on Climate Change Fifth Assessment Report. Historical simulations start at 850 CE and continue through to 2005. The standard simulations include changes in forcing from solar luminosity, Earth's orbital configuration, CO2, additional greenhouse gases, land-use, and sulphate and volcanic aerosols. In spite of very different modelled pre-industrial global surface air temperatures, overall 20th century trends in surface air temperature and carbon uptake are reasonably well simulated when compared to observed trends. Land carbon fluxes show much more variation between models than ocean carbon fluxes, and recent land fluxes seem to be underestimated. It is possible that recent modelled climate trends or climate-carbon feedbacks are overestimated resulting in too much land carbon loss or that carbon uptake due to CO2 and/or nitrogen fertilization is underestimated. Several one thousand year long, idealized, 2x and 4x CO2 experiments are used to quantify standard model characteristics, including transient and equilibrium climate sensitivities, and climate-carbon feedbacks. The values from EMICs generally fall within the range given by General Circulation Models. Seven additional historical simulations, each including a single specified forcing, are used to assess the contributions of different climate forcings to the overall climate and carbon cycle response. The response of surface air temperature is the linear sum of the individual forcings, while the carbon cycle response shows considerable synergy between land-use change and CO2 forcings for some models. Finally, the preindustrial portions of the last millennium simulations are used to assess historical model carbon-climate feedbacks. Given the specified forcing, there is a tendency for the EMICs to

  1. Modeling and assessing international climate financing

    NASA Astrophysics Data System (ADS)

    Wu, Jing; Tang, Lichun; Mohamed, Rayman; Zhu, Qianting; Wang, Zheng

    2016-06-01

    Climate financing is a key issue in current negotiations on climate protection. This study establishes a climate financing model based on a mechanism in which donor countries set up funds for climate financing and recipient countries use the funds exclusively for carbon emission reduction. The burden-sharing principles are based on GDP, historical emissions, and consumptionbased emissions. Using this model, we develop and analyze a series of scenario simulations, including a financing program negotiated at the Cancun Climate Change Conference (2010) and several subsequent programs. Results show that sustained climate financing can help to combat global climate change. However, the Cancun Agreements are projected to result in a reduction of only 0.01°C in global warming by 2100 compared to the scenario without climate financing. Longer-term climate financing programs should be established to achieve more significant benefits. Our model and simulations also show that climate financing has economic benefits for developing countries. Developed countries will suffer a slight GDP loss in the early stages of climate financing, but the longterm economic growth and the eventual benefits of climate mitigation will compensate for this slight loss. Different burden-sharing principles have very similar effects on global temperature change and economic growth of recipient countries, but they do result in differences in GDP changes for Japan and the FSU. The GDP-based principle results in a larger share of financial burden for Japan, while the historical emissions-based principle results in a larger share of financial burden for the FSU. A larger burden share leads to a greater GDP loss.

  2. Evaluating models of climate and forest vegetation

    NASA Technical Reports Server (NTRS)

    Clark, James S.

    1992-01-01

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

  3. Weighting climate model projections using observational constraints

    PubMed Central

    Gillett, Nathan P.

    2015-01-01

    Projected climate change integrates the net response to multiple climate feedbacks. Whereas existing long-term climate change projections are typically based on unweighted individual climate model simulations, as observed climate change intensifies it is increasingly becoming possible to constrain the net response to feedbacks and hence projected warming directly from observed climate change. One approach scales simulated future warming based on a fit to observations over the historical period, but this approach is only accurate for near-term projections and for scenarios of continuously increasing radiative forcing. For this reason, the recent Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR5) included such observationally constrained projections in its assessment of warming to 2035, but used raw model projections of longer term warming to 2100. Here a simple approach to weighting model projections based on an observational constraint is proposed which does not assume a linear relationship between past and future changes. This approach is used to weight model projections of warming in 2081–2100 relative to 1986–2005 under the Representative Concentration Pathway 4.5 forcing scenario, based on an observationally constrained estimate of the Transient Climate Response derived from a detection and attribution analysis. The resulting observationally constrained 5–95% warming range of 0.8–2.5 K is somewhat lower than the unweighted range of 1.1–2.6 K reported in the IPCC AR5. PMID:26438283

  4. The Monash Simple Climate Model: An interactive climate model for teaching

    NASA Astrophysics Data System (ADS)

    Dommenget, Dietmar

    2015-04-01

    The Monash university interactive simple climate model is a web-based interface that allows students and the general public to explore the physical simulation of the climate system with a real global climate model. It is based on the Globally Resolved Energy Balance (GREB) model that simulates most of the main physical processes in the climate system in a very simplistic way and therefore allows very fast and simple climate model simulations. Despite its simplicity the model simulates the mean climate and its response to external forcings, such as doubling of the CO2 concentrations very realistically. The Monash simple climate model web-interface allows you to explore thousands of experiments, scenarios and tutorials in an interactive way. You can do some entertaining and educational puzzles about the interaction of climate dynamics. By turning switches OFF and ON you control physical processes in the climate system ansd see how the interaction of the processes builds up the climate. By testing a number of experiments you learn about the interactions in the climate system and thereby figure out which switch controls what process in the climate system. The presentation will illustrate how this web-base tool works and what are the possibilities in teaching students with this tool are.

  5. Exploitation of Parallelism in Climate Models

    SciTech Connect

    Baer, F.; Tribbia, J.J.; Williamson, D.L.

    1999-03-01

    The US Department of Energy (DOE), through its CHAMMP initiative, hopes to develop the capability to make meaningful regional climate forecasts on time scales exceeding a decade, such capability to be based on numerical prediction type models. We propose research to contribute to each of the specific items enumerated in the CHAMMP announcement (Notice 91-3); i.e., to consider theoretical limits to prediction of climate and climate change on appropriate time scales, to develop new mathematical techniques to utilize massively parallel processors (MPP), to actually utilize MPPs as a research tool, and to develop improved representations of some processes essential to climate prediction. In particular, our goals are to: (1) Reconfigure the prediction equations such that the time iteration process can be compressed by use of MMP architecture, and to develop appropriate algorithms. (2) Develop local subgrid scale models which can provide time and space dependent parameterization for a state- of-the-art climate model to minimize the scale resolution necessary for a climate model, and to utilize MPP capability to simultaneously integrate those subgrid models and their statistics. (3) Capitalize on the MPP architecture to study the inherent ensemble nature of the climate problem. By careful choice of initial states, many realizations of the climate system can be determined concurrently and more realistic assessments of the climate prediction can be made in a realistic time frame. To explore these initiatives, we will exploit all available computing technology, and in particular MPP machines. We anticipate that significant improvements in modeling of climate on the decadal and longer time scales for regional space scales will result from our efforts.

  6. OVERVIEW OF CLIMATE INFORMATION NEEDS FOR ECOLOGICAL EFFECTS MODELS

    EPA Science Inventory

    Atmospheric scientists engaged in climate change research require a basic understanding of how ecological effects models incorporate climate. This report provides an overview of existing ecological models that might be used to model climate change effects on vegetation. ome agric...

  7. DOE SBIR Phase II Final Technical Report - Assessing Climate Change Effects on Wind Energy

    SciTech Connect

    Whiteman, Cameron; Capps, Scott

    2014-11-05

    Specialized Vertum Partners software tools were prototyped, tested and commercialized to allow wind energy stakeholders to assess the uncertainties of climate change on wind power production and distribution. This project resulted in three commercially proven products and a marketing tool. The first was a Weather Research and Forecasting Model (WRF) based resource evaluation system. The second was a web-based service providing global 10m wind data from multiple sources to wind industry subscription customers. The third product addressed the needs of our utility clients looking at climate change effects on electricity distribution. For this we collaborated on the Santa Ana Wildfire Threat Index (SAWTi), which was released publicly last quarter. Finally to promote these products and educate potential users we released “Gust or Bust”, a graphic-novel styled marketing publication.

  8. Modeling the Climatic Consequences of Geoengineering

    NASA Astrophysics Data System (ADS)

    Somerville, R. C.

    2005-12-01

    The last half-century has seen the development of physically comprehensive computer models of the climate system. These models are the primary tool for making predictions of climate change due to human activities, such as emitting greenhouse gases into the atmosphere. Because scientific understanding of the climate system is incomplete, however, any climate model will necessarily have imperfections. The inevitable uncertainties associated with these models have sometimes been cited as reasons for not taking action to reduce such emissions. Climate models could certainly be employed to predict the results of various attempts at geoengineering, but many questions would arise. For example, in considering proposals to increase the planetary reflectivity by brightening parts of the land surface or by orbiting mirrors, can models be used to bound the results and to warm of unintended consequences? How could confidence limits be placed on such model results? How can climate changes due to proposed geoengineering be distinguished from natural variability? There are historical parallels on smaller scales, in which models have been employed to predict the results of attempts to alter the weather, such as the use of cloud seeding for precipitation enhancement, hail suppression and hurricane modification. However, there are also many lessons to be learned from the recent record of using models to simulate the effects of the great unintended geoengineering experiment involving greenhouse gases, now in progress. In this major research effort, the same types of questions have been studied at length. The best modern models have demonstrated an impressive ability to predict some aspects of climate change. A large body of evidence has already accumulated through comparing model predictions to many observed aspects of recent climate change, ranging from increases in ocean heat content to changes in atmospheric water vapor to reductions in glacier extent. The preponderance of expert

  9. 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.…

  10. Climate Modeling and Prediction at NSIPP

    NASA Technical Reports Server (NTRS)

    Suarez, Max; Einaudi, Franco (Technical Monitor)

    2001-01-01

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

  11. Using climate model ensemble forecasts for seasonal hydrologic prediction

    NASA Astrophysics Data System (ADS)

    Wood, Andrew Whitaker

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

  12. Whither low-order climate models?

    NASA Astrophysics Data System (ADS)

    Viebahn, Jan; Dijkstra, Henk A.

    2015-04-01

    A zoo of low-order (small degrees of freedom) deterministic and stochastic climate models has appeared in the literature with each focussing on specific aspects of (paleo)climate variability. The advantage of these models is that their behavior can be analyzed in detail and hence cause and effect (mechanisms) can be disentangled efficiently. Indeed, much insight has been obtained by `thinking deep about simple models'. However, the disadvantage is that each model usually contains idealizations and severe approximations such that the mechanisms underlying a certain phenomenon in these models may not represent the mechanisms which are at work in more detailed models and in observations. The danger is thus that low-order model results will be ignored by many of the climate science community. In this presentation, focus will be on several issues related to the use of low-order model results. Did the results of these models contribute to a better understanding of observed climate variability or did they only aggravate the confusion about cause and effect? In the spirit of `essentially all models are wrong but some are useful' (George Box), which type of models (e.g. stochastic versus deterministic, ad hoc versus truncated, etc.) has been more useful than others (has been the best fit for purpose)? Does this provide future guidelines on the development and usage of these models? Example models and their results will serve to address these issues.

  13. How well do climate models simulate precipitation?

    NASA Astrophysics Data System (ADS)

    Schaller, Nathalie; Mahlstein, Irina; Knutti, Reto; Cermak, Jan

    2010-05-01

    This study compares three different methods to evaluate the ability of Atmosphere Ocean General Circulation Models (AOGCMs) to simulate precipitation. Currently, AOGCMs are the most powerful tool to investigate the future climate but how to evaluate them is a relatively new research field. Thus, no standardized metric for defining a climate model's skill has been defined so far. The common way to proceed is to evaluate the model simulations against observations using statistical measures. However, precipitation is highly variable on both the spatial and temporal scales. We therefore suspect that metrics representing regional features of the modelled precipitation response to climate change are more suitable to identify the good models than statistical measures defined on a global scale. Here, we compare three different ways of ranking the climate models: a) biases in a broad range of climate variables, b) only biases in global precipitation and c) regional features of modelled precipitation in areas where future changes are expected to be pronounced. Surprisingly, the multimodel mean performs only average for the feature-based ranking, while it outperforms all single models in the two bias-based rankings. In the feature-based ranking, the models performing best can be different for each region or zonal band considered and identifying them each time newly depending on the purpose may allow for more reliable projections. Further, this study reveals that many models have similar biases and that the observation datasets are often located at one end of the model range. Our results suggest that weighting the models according to their ability to simulate the present climate might lead to more reliable projections than the "one model, one vote" approach that has been favored so far.

  14. Modeling Renewable Water Resources under Climate Change

    NASA Astrophysics Data System (ADS)

    Liu, X.; Tang, Q.

    2014-12-01

    The impacts of climate change on renewable water resources are usually assessed using hydrological models driven by downscaled climate outputs from global climate models. Most hydrological models do not have explicit parameterization of vegetation and thus are unable to assess the effects of elevated atmospheric CO2 on stomatal conductance and water loss of leaf. The response of vegetation to elevated atmospheric CO2 would reduce evaporation and affect runoff and renewable water resources. To date, the impacts of elevated CO2 on vegetation transpiration were not well addressed in assessment of water resources under climate change. In this study, the distributed biosphere-hydrological (DBH) model, which incorporates a simple biosphere model into a distributed hydrological scheme, was used to assess the impacts of elevated CO2 on vegetation transpiration and consequent runoff. The DBH model was driven by five General Circulation Models (GCMs) under four Representative Concentration Pathways (RCPs). For each climate scenario, two model experiments were conducted. The atmospheric CO2 concentration in one experiment was assumed to remain at the level of 2000 and increased as described by the RCPs in the other experiment. The results showed that the elevated CO2 would result in decrease in evapotranspiration, increase in runoff, and have considerable impacts on water resources. However, CO2 induced runoff change is generally small in dry areas likely because vegetation is usually sparse in the arid area.

  15. Testing Stochastic Models for Climate Field Reconstructions using Instrumental Data

    NASA Astrophysics Data System (ADS)

    Werner, J.; Toreti, A.; Luterbacher, J.

    2012-12-01

    Over the last decades, several different methods have been used to reconstruct past climatic change. These methods consist of an - often statistical - model and a related inference step. While recently a lot of the discussion has been focused on the latter (Smerdon et al. 2011, Christiansen et al. 2011), we here turn to the modelling part. A series of recent pseudoproxy experiments (PPE) focused on climate field reconstructions (Tingley+Huybers 2010a,b; Werner et al. 2012) has used Bayesian inference together with a localized stochastic description of the spatio-temporal evolution of climate field variables: Rather than using large patterns over the full spatial domain to describe the climate field variables, local temporal evolution and spatial coherence were modelled directly. While the stochastic model, a multivariate AR(1) process, was based on few simple assumptions it could nevertheless reconstruct most of the climate variability in the used dataset. Here we show how such a simple localized model could be derived from available observational data or at least be validated using the Kramers-Moyal-Expansion (KME). While KME often can require large amounts of data, we show that at least some results are stable in the context of PPEs with respect to data availability. Finally we apply this method to real world climate data from the CRU and the Global Historical Climate Network (GHCN) to arrive at a suitable model for European gridded mean summer temperature reconstructions. Smerdon J.E. et al. JClim 24, 1284-1309 (2011) Tingley M.P. and Huybers P. JClim 10, 2759-2781, 2782-2800 (2010a,b) Christiansen, B. and Ljundqvist, F.C. JClim 24, 6013-6034 (2011) Werner J.P. et al. JClim accepted (2012)

  16. Integrating a 1D Thermal Lake Model into a Global and Regional Climate Model: Model Evaluation and Regional Climate Simulation

    NASA Astrophysics Data System (ADS)

    Subin, Z. M.; Riley, W. J.

    2009-12-01

    Compared to solid ground, lakes tend to have decreased albedo, increased ground heat conductance, and increased effective ground heat capacity. These features alter local surface fluxes compared to nearby vegetation, which in turn alter the climate of the nearby atmosphere and surrounding land areas. Interest in feedbacks between lake behavior and climate change provides motivation for including lakes in global climate models, as does the desire to do effective regional downscaling of climate model predictions over regions with large lake area fraction, like the Great Lakes region. Finally, the initiation, warming, and expansion of Arctic thermokarst lakes could provide an important geophysical and biogeochemical feedback to climate warming. The Community Land Model (CLM) 3.5 currently uses a 1D Hostetler lake scheme. We have updated this model to improve the characterization of surface fluxes, eddy diffusivity, and convective mixing. We also link the lake model with the full snow physics found over other land surface types (including 5 snow layers, aerosol deposition, partial transparency of snow layers, and snow aging), add phase change & ice physics to the lake model, and include soil layers beneath lakes. These soil layers will be an important component of future thermokarst lake modeling, as thermokarst lakes tend to form regions of unfrozen soil (talik) beneath them that become active sites for anaerobic decomposition of pre-modern peat. We have also integrated the updated lake model into a modified version of the Weather Research and Forecasting (WRF) Model 3.0. We will present comparisons between predicted and observed thermal conditions, snow and ice depths, and surface energy fluxes at several lake sites, using local meteorological forcing or integrated regional atmospheric coupling. The thermal predictions are generally reasonable and show a marked improvement from runs performed with the baseline CLM 3.5 version of the lake model. Over Sparkling Lake

  17. The Community Climate System Model: CCSM3

    SciTech Connect

    Collins, W D; Blackmon, M; Bitz, C; Bonan, G; Bretherton, C S; Carton, J A; Chang, P; Doney, S; Hack, J J; Kiehl, J T; Henderson, T; Large, W G; McKenna, D; Santer, B D; Smith, R D

    2004-12-27

    A new version of the Community Climate System Model (CCSM) has been developed and released to the climate community. CCSM3 is a coupled climate model with components representing the atmosphere, ocean, sea ice, and land surface connected by a flux coupler. CCSM3 is designed to produce realistic simulations over a wide range of spatial resolutions, enabling inexpensive simulations lasting several millennia or detailed studies of continental-scale climate change. This paper will show results from the configuration used for climate-change simulations with a T85 grid for atmosphere and land and a 1-degree grid for ocean and sea-ice. The new system incorporates several significant improvements in the scientific formulation. The enhancements in the model physics are designed to reduce or eliminate several systematic biases in the mean climate produced by previous editions of CCSM. These include new treatments of cloud processes, aerosol radiative forcing, land-atmosphere fluxes, ocean mixed-layer processes, and sea-ice dynamics. There are significant improvements in the sea-ice thickness, polar radiation budgets, equatorial sea-surface temperatures, ocean currents, cloud radiative effects, and ENSO teleconnections. CCSM3 can produce stable climate simulations of millenial duration without ad hoc adjustments to the fluxes exchanged among the component models. Nonetheless, there are still systematic biases in the ocean-atmosphere fluxes in western coastal regions, the spectrum of ENSO variability, the spatial distribution of precipitation in the Pacific and Indian Oceans, and the continental precipitation and surface air temperatures. We conclude with the prospects for extending CCSM to a more comprehensive model of the Earth's climate system.

  18. ARM Climate Modeling Best Estimate Data

    SciTech Connect

    Xie, S.; Jensen, M.; McCoy, R. B.; Klein, S. A.; Cederwall, R. T.; Wiscombe, W. J.; Clothiaux, E. E.; Gaustad, K. L.; Golaz, J.-C.; Hall, S.; Johnson, K. L.; Lin, Y.; Long, C. N.; Mather, J. H.; McCord, R. A.; McFarlane, S. A.; Palanisamy, G.; Shi, Y.; Turner, D. D.

    2010-01-01

    The U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Program (www.arm.gov) was created in 1989 to address scientific uncertainties related to global climate change, with a focus on the crucial role of clouds and their influence on the transfer of radiation in the atmosphere. A central activity is the acquisition of detailed observations of clouds and radiation, as well as related atmospheric variables for climate model evaluation and improvement. Since 1992, ARM has established six permanent ARM Climate Research Facility (ACRF) sites and deployed an ARM Mobile Facility (AMF) in diverse climate regimes around the world (Fig. 1) to perform long-term continuous field measurements. The time record of ACRF data now exceeds a decade at most ACRF fixed sites and ranges from several months to one year for AMF deployments. Billions of measurements are currently stored in millions of data files in the ACRF Data Archive. The long-term continuous ACRF data provide invaluable information to improve our understanding of the interaction between clouds and radiation, and an observational basis for model validation and improvement and climate studies. Given the huge number of data files and current diversity of archived ACRF data structures, however, it can be difficult for an outside user such as a climate modeler to quickly find the ACRF data product(s) that best meets their research needs. The required geophysical quantities may exist in multiple data streams, and over the history of ACRF operations, the measurements could be obtained by a variety of instruments, reviewed with different levels of data quality assurance, or derived using different algorithms. In addition, most ACRF data are stored in daily-based files with a temporal resolution that ranges from a few seconds to a few minutes, which is much finer than that sought by some users. Therefore, it is not as convenient for data users to perform quick comparisons over large spans of data, and this

  19. Climate Forcings and Climate Sensitivities Diagnosed from Coupled Climate Model Integrations

    SciTech Connect

    Forster, P M A F; Taylor, K E

    2006-07-25

    A simple technique is proposed for calculating global mean climate forcing from transient integrations of coupled Atmosphere Ocean General Circulation Models (AOGCMs). This 'climate forcing' differs from the conventionally defined radiative forcing as it includes semi-direct effects that account for certain short timescale responses in the troposphere. Firstly, we calculate a climate feedback term from reported values of 2 x CO{sub 2} radiative forcing and surface temperature time series from 70-year simulations by twenty AOGCMs. In these simulations carbon dioxide is increased by 1%/year. The derived climate feedback agrees well with values that we diagnose from equilibrium climate change experiments of slab-ocean versions of the same models. These climate feedback terms are associated with the fast, quasi-linear response of lapse rate, clouds, water vapor and albedo to global surface temperature changes. The importance of the feedbacks is gauged by their impact on the radiative fluxes at the top of the atmosphere. We find partial compensation between longwave and shortwave feedback terms that lessens the inter-model differences in the equilibrium climate sensitivity. There is also some indication that the AOGCMs overestimate the strength of the positive longwave feedback. These feedback terms are then used to infer the shortwave and longwave time series of climate forcing in 20th and 21st Century simulations in the AOGCMs. We validate the technique using conventionally calculated forcing time series from four AOGCMs. In these AOGCMs the shortwave and longwave climate forcings we diagnose agree with the conventional forcing time series within {approx}10%. The shortwave forcing time series exhibit order of magnitude variations between the AOGCMs, differences likely related to how both natural forcings and/or anthropogenic aerosol effects are included. There are also factor of two differences in the longwave climate forcing time series, which may indicate problems

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

    SciTech Connect

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

    2011-12-05

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

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

    NASA Astrophysics Data System (ADS)

    Daron, Joseph

    2010-05-01

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

  2. Modelling climate change and malaria transmission.

    PubMed

    Parham, Paul E; Michael, Edwin

    2010-01-01

    The impact of climate change on human health has received increasing attention in recent years, with potential impacts due to vector-borne diseases only now beginning to be understood. As the most severe vector-borne disease, with one million deaths globally in 2006, malaria is thought most likely to be affected by changes in climate variables due to the sensitivity of its transmission dynamics to environmental conditions. While considerable research has been carried out using statistical models to better assess the relationship between changes in environmental variables and malaria incidence, less progress has been made on developing process-based climate-driven mathematical models with greater explanatory power. Here, we develop a simple model of malaria transmission linked to climate which permits useful insights into the sensitivity of disease transmission to changes in rainfall and temperature variables. Both the impact of changes in the mean values of these key external variables and importantly temporal variation in these values are explored. We show that the development and analysis of such dynamic climate-driven transmission models will be crucial to understanding the rate at which P. falciparum and P. vivax may either infect, expand into or go extinct in populations as local environmental conditions change. Malaria becomes endemic in a population when the basic reproduction number R0 is greater than unity and we identify an optimum climate-driven transmission window for the disease, thus providing a useful indicator for determing how transmission risk may change as climate changes. Overall, our results indicate that considerable work is required to better understand ways in which global malaria incidence and distribution may alter with climate change. In particular, we show that the roles of seasonality, stochasticity and variability in environmental variables, as well as ultimately anthropogenic effects, require further study. The work presented here

  3. CLIMATE CHANGE EFFECTS ON STREAM AND RIVER BIOLOGICAL INDICATORS: A PRELIMINARY ANALYSIS (FINAL REPORT)

    EPA Science Inventory

    This final report is a preliminary assessment that describes how biological indicators are likely to respond to climate change, how well current sampling schemes may detect climate-driven changes, and how likely it is that these sampling schemes will continue to detect impairmen...

  4. Final Report for "Analyzing and visualizing next generation climate data"

    SciTech Connect

    Pletzer, Alexander

    2012-11-13

    The project "Analyzing and visualizing next generation climate data" adds block-structured (mosaic) grid support, parallel processing, and 2D/3D curvilinear interpolation to the open-source UV-CDAT climate data analysis tool. Block structured grid support complies to the Gridspec extension submitted to the Climate and Forecast metadata conventions. It contains two parts: aggregation of data spread over multiple mosaic tiles (M-SPEC) and aggregation of temporal data stored in different files (F-SPEC). Together, M-SPEC and F-SPEC allow users to interact with data stored in multiple files as if the data were in a single file. For computational expensive tasks, a flexible, multi-dimensional, multi-type distributed array class allows users to process data in parallel using remote memory access. Both nodal and cell based interpolation is supported; users can choose between different interpolation libraries including ESMF and LibCF depending on the their particular needs.

  5. Physics modeling support contract: Final report

    SciTech Connect

    Not Available

    1987-09-30

    This document is the final report for the Physics Modeling Support contract between TRW, Inc. and the Lawrence Livermore National Laboratory for fiscal year 1987. It consists of following projects: TIBER physics modeling and systems code development; advanced blanket modeling task; time dependent modeling; and free electron maser for TIBER II.

  6. Climate model uncertainty versus conceptual geological uncertainty in hydrological modeling

    NASA Astrophysics Data System (ADS)

    Sonnenborg, T. O.; Seifert, D.; Refsgaard, J. C.

    2015-09-01

    Projections of climate change impact are associated with a cascade of uncertainties including in CO2 emission scenarios, climate models, downscaling and impact models. The relative importance of the individual uncertainty sources is expected to depend on several factors including the quantity that is projected. In the present study the impacts of climate model uncertainty and geological model uncertainty on hydraulic head, stream flow, travel time and capture zones are evaluated. Six versions of a physically based and distributed hydrological model, each containing a unique interpretation of the geological structure of the model area, are forced by 11 climate model projections. Each projection of future climate is a result of a GCM-RCM model combination (from the ENSEMBLES project) forced by the same CO2 scenario (A1B). The changes from the reference period (1991-2010) to the future period (2081-2100) in projected hydrological variables are evaluated and the effects of geological model and climate model uncertainties are quantified. The results show that uncertainty propagation is context-dependent. While the geological conceptualization is the dominating uncertainty source for projection of travel time and capture zones, the uncertainty due to the climate models is more important for groundwater hydraulic heads and stream flow.

  7. Climate model uncertainty vs. conceptual geological uncertainty in hydrological modeling

    NASA Astrophysics Data System (ADS)

    Sonnenborg, T. O.; Seifert, D.; Refsgaard, J. C.

    2015-04-01

    Projections of climate change impact are associated with a cascade of uncertainties including CO2 emission scenario, climate model, downscaling and impact model. The relative importance of the individual uncertainty sources is expected to depend on several factors including the quantity that is projected. In the present study the impacts of climate model uncertainty and geological model uncertainty on hydraulic head, stream flow, travel time and capture zones are evaluated. Six versions of a physically based and distributed hydrological model, each containing a unique interpretation of the geological structure of the model area, are forced by 11 climate model projections. Each projection of future climate is a result of a GCM-RCM model combination (from the ENSEMBLES project) forced by the same CO2 scenario (A1B). The changes from the reference period (1991-2010) to the future period (2081-2100) in projected hydrological variables are evaluated and the effects of geological model and climate model uncertainties are quantified. The results show that uncertainty propagation is context dependent. While the geological conceptualization is the dominating uncertainty source for projection of travel time and capture zones, the uncertainty on the climate models is more important for groundwater hydraulic heads and stream flow.

  8. 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

  9. Challenges in Modeling Regional Climate Change (Invited)

    NASA Astrophysics Data System (ADS)

    Leung, L.

    2013-12-01

    Precipitation, soil moisture, and runoff are vital to ecosystems and human activities. Predicting changes in the space-time characteristics of these water cycle processes has been a longstanding challenge in climate modeling. Different modeling approaches have been developed to allow high resolution to be achieved using available computing resources. Although high resolution is necessary to better resolve regional forcing and processes, improvements in simulating water cycle response are difficult to demonstrate and climate models have so far shown irreducible sensitivity to model resolution, dynamical framework, and physics parameterizations that confounds reliable predictions of regional climate change. Additionally, regional climate responds to both regional and global forcing but predicting changes in regional and global forcing such as related to land use/land cover and aerosol requires improved understanding and modeling of the dynamics of human-earth system interactions. Furthermore, regional response and regional forcing may be related through complex interactions that are dependent on the regional climate regimes, making decisions on regional mitigation and adaptation more challenging. Examples of the aforementioned challenges from on-going research and possible future directions will be discussed.

  10. Ionospheric climate and weather modeling

    SciTech Connect

    Schunk, R.W.; Sojka, J.J.

    1988-03-01

    Simulations of the ionospheric model of Schunk et al. (1986) have been used for climatology and weather modeling. Steady state empirical models were used in the climatology model to provide plasma convection and particle precipitation patterns in the northern high-latitude region. The climatology model also depicts the ionospheric electron density and ion and electron temperatures for solar maximum, winter solstice, and strong geomagnetic activity conditions. The weather model describes the variations of ionospheric features during the solar cycle, seasonal changes, and geomagnetic activity. Prospects for future modeling are considered. 23 references.

  11. Modelling rainfall erosion resulting from climate change

    NASA Astrophysics Data System (ADS)

    Kinnell, Peter

    2016-04-01

    It is well known that soil erosion leads to agricultural productivity decline and contributes to water quality decline. The current widely used models for determining soil erosion for management purposes in agriculture focus on long term (~20 years) average annual soil loss and are not well suited to determining variations that occur over short timespans and as a result of climate change. Soil loss resulting from rainfall erosion is directly dependent on the product of runoff and sediment concentration both of which are likely to be influenced by climate change. This presentation demonstrates the capacity of models like the USLE, USLE-M and WEPP to predict variations in runoff and erosion associated with rainfall events eroding bare fallow plots in the USA with a view to modelling rainfall erosion in areas subject to climate change.

  12. 21 Layer troposphere-stratosphere climate model

    NASA Technical Reports Server (NTRS)

    Rind, D.; Suozzo, R.; Lacis, A.; Russell, G.; Hansen, J.

    1984-01-01

    The global climate model is extended through the stratosphere by increasing the vertical resolution and raising the rigid model top to the 0.01 mb (75 km) level. The inclusion of a realistic stratosphere is necessary for the investigation of the climate effects of stratospheric perturbations, such as changes of ozone, aerosols or solar ultraviolet irradiance, as well as for studying the effect on the stratosphere of tropospheric climate changes. The observed temperature and wind patterns throughout the troposphere and stratosphere are simulated. In addition to the excess planetary wave amplitude in the upper stratosphere, other model deficiences include the Northern Hemisphere lower stratospheric temperatures being 5 to 10 C too cold in winter at high latitudes and the temperature at 50 to 60 km altitude near the equator are too cold. Methods of correcting these deficiencies are discussed.

  13. Assessing effects of variation in global climate data sets on spatial predictions from climate envelope models

    USGS Publications Warehouse

    Romanach, Stephanie; Watling, James I.; Fletcher, Robert J., Jr.; Speroterra, Carolina; Bucklin, David N.; Brandt, Laura A.; Pearlstine, Leonard G.; Escribano, Yesenia; Mazzotti, Frank J.

    2014-01-01

    Climate change poses new challenges for natural resource managers. Predictive modeling of species–environment relationships using climate envelope models can enhance our understanding of climate change effects on biodiversity, assist in assessment of invasion risk by exotic organisms, and inform life-history understanding of individual species. While increasing interest has focused on the role of uncertainty in future conditions on model predictions, models also may be sensitive to the initial conditions on which they are trained. Although climate envelope models are usually trained using data on contemporary climate, we lack systematic comparisons of model performance and predictions across alternative climate data sets available for model training. Here, we seek to fill that gap by comparing variability in predictions between two contemporary climate data sets to variability in spatial predictions among three alternative projections of future climate. Overall, correlations between monthly temperature and precipitation variables were very high for both contemporary and future data. Model performance varied across algorithms, but not between two alternative contemporary climate data sets. Spatial predictions varied more among alternative general-circulation models describing future climate conditions than between contemporary climate data sets. However, we did find that climate envelope models with low Cohen's kappa scores made more discrepant spatial predictions between climate data sets for the contemporary period than did models with high Cohen's kappa scores. We suggest conservation planners evaluate multiple performance metrics and be aware of the importance of differences in initial conditions for spatial predictions from climate envelope models.

  14. Final technical report on atmospheric ozone as a climate gas

    SciTech Connect

    Wang, Wei-Chyung

    1998-11-12

    This report summarizes the major research accomplishments of the project ''Atmospheric Ozone as a Climate Gas'' for the period July 1, 1994--March 31, 1998. The report is divided into three sctions: research summary, publications and participation of graduate students. The objectives of the research program were: (1) to improve understanding of the physical, chemical and dynamical processes that control mid-latitute O{sub 3} in the lower stratosphere and free troposphere; and (2) to develop improved predictions of future O{sub 3} changes in these regions and their influence on (and response to) future climate changes. The research term includes a subcontractor, Professor Ivar Isaksen of the University of Oslo.

  15. High dimensional decision dilemmas in climate models

    NASA Astrophysics Data System (ADS)

    Bracco, A.; Neelin, J. D.; Luo, H.; McWilliams, J. C.; Meyerson, J. E.

    2013-05-01

    An important source of uncertainty in climate models is linked to the calibration of model parameters. Interest in systematic and automated parameter optimization procedures stems from the desire to improve the model climatology and to quantify the average sensitivity associated with potential changes in the climate system. Neelin et al. (2010) used a quadratic metamodel to objectively calibrate an atmospheric circulation model (AGCM) around four adjustable parameters. The metamodel accurately estimates global spatial averages of common fields of climatic interest, from precipitation, to low and high level winds, from temperature at various levels to sea level pressure and geopotential height, while providing a computationally cheap strategy to explore the influence of parameter settings. Here, guided by the metamodel, the ambiguities or dilemmas related to the decision making process in relation to model sensitivity and optimization are examined. Simulations of current climate are subject to considerable regional-scale biases. Those biases may vary substantially depending on the climate variable considered, and/or on the performance metric adopted. Common dilemmas are associated with model revisions yielding improvement in one field or regional pattern or season, but degradation in another, or improvement in the model climatology but degradation in the interannual variability representation. Challenges are posed to the modeler by the high dimensionality of the model output fields and by the large number of adjustable parameters. The use of the metamodel in the optimization strategy helps visualize trade-offs at a regional level, e.g. how mismatches between sensitivity and error spatial fields yield regional errors under minimization of global objective functions.

  16. High dimensional decision dilemmas in climate models

    NASA Astrophysics Data System (ADS)

    Bracco, A.; Neelin, J. D.; Luo, H.; McWilliams, J. C.; Meyerson, J. E.

    2013-10-01

    An important source of uncertainty in climate models is linked to the calibration of model parameters. Interest in systematic and automated parameter optimization procedures stems from the desire to improve the model climatology and to quantify the average sensitivity associated with potential changes in the climate system. Building upon on the smoothness of the response of an atmospheric circulation model (AGCM) to changes of four adjustable parameters, Neelin et al. (2010) used a quadratic metamodel to objectively calibrate the AGCM. The metamodel accurately estimates global spatial averages of common fields of climatic interest, from precipitation, to low and high level winds, from temperature at various levels to sea level pressure and geopotential height, while providing a computationally cheap strategy to explore the influence of parameter settings. Here, guided by the metamodel, the ambiguities or dilemmas related to the decision making process in relation to model sensitivity and optimization are examined. Simulations of current climate are subject to considerable regional-scale biases. Those biases may vary substantially depending on the climate variable considered, and/or on the performance metric adopted. Common dilemmas are associated with model revisions yielding improvement in one field or regional pattern or season, but degradation in another, or improvement in the model climatology but degradation in the interannual variability representation. Challenges are posed to the modeler by the high dimensionality of the model output fields and by the large number of adjustable parameters. The use of the metamodel in the optimization strategy helps visualize trade-offs at a regional level, e.g., how mismatches between sensitivity and error spatial fields yield regional errors under minimization of global objective functions.

  17. Optimal Empirical Prognostic Models of Climate Dynamics

    NASA Astrophysics Data System (ADS)

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

    2014-12-01

    In this report the empirical methodology for prediction of climate dynamics is suggested. We construct the dynamical models of data patterns connected with climate indices, from observed spatially distributed time series. The models are based on artificial neural network (ANN) parameterization and have a form of discrete stochastic evolution operator mapping some sequence of systems state on the next one [1]. Different approaches to reconstruction of empirical basis (phase variables) for system's phase space representation, which is appropriate for forecasting the climate index of interest, are discussed in the report; for this purpose both linear and non-linear data expansions are considered. The most important point of the methodology is finding the optimal structural parameters of the model such as dimension of variable vector, i.e. number of principal components used for modeling, the time lag used for prediction, and number of neurons in ANN determining the quality of approximation. Actually, we need to solve the model selection problem, i.e. we want to obtain a model of optimal complexity in relation to analyzed time series. We use MDL approach [2] for this purpose: the model providing best data compression is chosen. The method is applied to space-distributed time-series of sea surface temperature and sea level pressure taken from IRI datasets [3]: the ability of proposed models to predict different climate indices (incl. Multivariate ENSO index, Pacific Decadal Oscillation index, North-Atlantic Oscillation index) is investigated. References:1. Molkov Ya. I., E. M. Loskutov, D. N. Mukhin, and A. M. Feigin, Random dynamical models from time series. Phys. Rev. E, 85, 036216, 2012.2. Molkov, Ya.I., D.N. Mukhin, E.M. Loskutov, A.M. Feigin, and G.A. Fidelin, Using the minimum description length principle for global reconstruction of dynamic systems from noisy time series. Phys. Rev. E, 80, 046207, 2009.3. IRI/LDEO Climate Data Library (http://iridl.ldeo.columbia.edu/)

  18. Mapping model agreement on future climate projections

    NASA Astrophysics Data System (ADS)

    Tebaldi, Claudia; Arblaster, Julie M.; Knutti, Reto

    2011-12-01

    Climate change projections are often based on simulations from multiple global climate models and are presented as maps with some form of stippling or measure of robustness to indicate where different models agree on the projected anthropogenically forced changes. The criteria used to determine model agreement, however, often ignore the presence of natural internal variability. We demonstrate that this leads to misleading presentations of the degree of model consensus on the sign and magnitude of the change if the ratio of the signal from the externally forced change to internal variability is low. We present a simple alternative method of depicting multimodel projections which clearly separates lack of climate change signal from lack of model agreement by assessing the degree of consensus on the significance of the change as well as the sign of the change. Our results demonstrate that the common interpretation of lack of model agreement in precipitation projections is largely an artifact of the large noise from climate variability masking the signal, an issue exacerbated by performing analyses at the grid point scale. We argue that separating more clearly the case of lack of agreement from the case of lack of signal will add valuable information for stake-holders' decision making, since adaptation measures required in the two cases are potentially very different.

  19. Basins and Wepp Climate Assessment Tools (Cat): Case Study Guide to Potential Applications (Final Report)

    EPA Science Inventory

    Cover of the BASINS and WEPP <span class=Climate Assessment Tool: Case Study Final report"> This final report supports application of two recently developed...

  20. The Community Climate System Model, Version 2.

    NASA Astrophysics Data System (ADS)

    Kiehl, Jeffrey T.; Gent, Peter R.

    2004-10-01

    The Community Climate System Model, version 2 (CCSM2) is briefly described. A 1000-yr control simulation of the present day climate has been completed without flux adjustments. Minor modifications were made at year 350, which included all five components using the same physical constants. There are very small trends in the upper-ocean, sea ice, atmosphere, and land fields after year 150 of the control simulation. The deep ocean has small but significant trends; however, these are not large enough that the control simulation could not be continued much further. The equilibrium climate sensitivity of CCSM2 is 2.2 K, which is slightly larger than the Climate System Model, version 1 (CSM1) value of 2.0 K.Several aspects of the control simulation's mean climate and interannual variability are described, and good and bad properties of the control simulation are documented. In particular, several aspects of the simulation, especially in the Arctic region, are much improved over those obtained in CSM1. Other aspects, such as the tropical Pacific region simulation, have not been improved much compared to those in CSM1. Priorities for further model development are discussed in the conclusions section.


  1. 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

  2. Modeling Coastline Response to Changing Storm Climate

    NASA Astrophysics Data System (ADS)

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

    2009-12-01

    Gradients in wave-driven alongshore sediment transport cause long-term change in the shape of sandy coastlines. Recent modeling work (Ashton, et. al. 2001; Ashton and Murray 2006) suggests coastlines can attain shapes that are in quasi-equilibrium with a regional wave climate—the distribution of wave influences as a function of deep-water wave-approach angles. Changes in storm frequency and/or magnitude will alter the wave climates affecting coastlines. Such a shift in wave forcing will tend to alter large-scale shapes of sedimentary coastlines (Slott et al., 2007). Even moderate changes in wave climate may cause coastlines to change shape rapidly, compared to a steady-wave-climate scenario. Such large-scale shape changes involve greatly accentuated rates of local erosion, and highly variable erosion/accretion rates. A recent analysis of wave records from the Southeastern US (Komar and Allen, 2007) indicates that wave climates have already been changing; for the past three decades, the heights of waves attributable to tropical storms have been increasing, changing the angular distribution of wave influences. These observations motivate ongoing, more refined modeling of how coastlines in this region should already be changing shape. Simulating patterns of shoreline change on actual coastlines involves examining the role of varying dynamical approximations in sub models of different environments (including wave propagation over the continental shelf) and uncertainties in model forcing (including the relationship between offshore buoy records and the wave climates affecting the coastline, when storm tracks often extend onshore of the buoy). Results suggest that modifications to the wave climate as recently seen along the Southeastern US give rise to rapid changes in shoreline shape and associated changes in patterns of erosion and accretion. Comparisons with results from related work, in which we examine historical and recent patterns of shoreline change for the

  3. Utilizing Cloud Computing to Improve Climate Modeling and Studies

    NASA Astrophysics Data System (ADS)

    Li, Z.; Yang, C.; Liu, K.; Sun, M.; XIA, J.; Huang, Q.

    2013-12-01

    Climate studies have become increasingly important due to the global climate change, one of the biggest challenges for the human in the 21st century. Climate data, not only observations data collected from various sensors but also simulated data generated from diverse climate models, are essential for scientists to explore the potential climate change patterns and analyze the complex climate dynamics. Climate modeling and simulation, a critical methodology for simulating the past and predicting the future climate conditions, can produce huge amount of data that contains potentially valuable information for climate studies. However, using modeling method in climate studies poses at least two challenges for scientists. First, running climate models is a computing intensive process, which requires large amounts of computation resources. Second, running climate models is also a data intensive process generating Big geospatial Data (model output), which demands large storage for managing the data and large computing power to process and analyze these data. This presentation introduces a novel framework to tackle the two challenges by 1) running climate models in a cloud environment in an automated fashion, and 2) managing and parallel processing Big model output Data by leveraging cloud computing technologies. A prototype system is developed based on the framework using ModelE as the climate model. Experiment results show that this framework can improve climate modeling in the research cycle by accelerating big data generation (model simulation), big data management (storage and processing) and on demand big data analytics.

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

    NASA Astrophysics Data System (ADS)

    Soliman, E.; Jeuland, M.

    2009-04-01

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

  5. 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.

  6. World Climate Research Programme (WCRP) Open Science Conference Final Report

    SciTech Connect

    Amy Honchar

    2012-11-07

    Travel support was provided for a range of invited speakers, students, early-career, and developing-country, and key scientists who required financial assistance to participate, and would otherwise be unable to attend, to contribute to, and benefit from, this important event. This support also allowed participants to present their research findings, provide input to WCRP planning and plans, and encourage collaboration with other research scientists. In particular, the participation and engagement of regional scientists in the OSC helped to ensure communication and advocacy in identifying the climate research needs of the region and their inclusion in the WCRP long-range research priorities.

  7. Historical and idealized climate model experiments: an intercomparison of Earth system models of intermediate complexity

    NASA Astrophysics Data System (ADS)

    Eby, M.; Weaver, A. J.; Alexander, K.; Zickfeld, K.; Abe-Ouchi, A.; Cimatoribus, A. A.; Crespin, E.; Drijfhout, S. S.; Edwards, N. R.; Eliseev, A. V.; Feulner, G.; Fichefet, T.; Forest, C. E.; Goosse, H.; Holden, P. B.; Joos, F.; Kawamiya, M.; Kicklighter, D.; Kienert, H.; Matsumoto, K.; Mokhov, I. I.; Monier, E.; Olsen, S. M.; Pedersen, J. O. P.; Perrette, M.; Philippon-Berthier, G.; Ridgwell, A.; Schlosser, A.; Schneider von Deimling, T.; Shaffer, G.; Smith, R. S.; Spahni, R.; Sokolov, A. P.; Steinacher, M.; Tachiiri, K.; Tokos, K.; Yoshimori, M.; Zeng, N.; Zhao, F.

    2013-05-01

    Both historical and idealized climate model experiments are performed with a variety of Earth system models of intermediate complexity (EMICs) as part of a community contribution to the Intergovernmental Panel on Climate Change Fifth Assessment Report. Historical simulations start at 850 CE and continue through to 2005. The standard simulations include changes in forcing from solar luminosity, Earth's orbital configuration, CO2, additional greenhouse gases, land use, and sulphate and volcanic aerosols. In spite of very different modelled pre-industrial global surface air temperatures, overall 20th century trends in surface air temperature and carbon uptake are reasonably well simulated when compared to observed trends. Land carbon fluxes show much more variation between models than ocean carbon fluxes, and recent land fluxes appear to be slightly underestimated. It is possible that recent modelled climate trends or climate-carbon feedbacks are overestimated resulting in too much land carbon loss or that carbon uptake due to CO2 and/or nitrogen fertilization is underestimated. Several one thousand year long, idealized, 2 × and 4 × CO2 experiments are used to quantify standard model characteristics, including transient and equilibrium climate sensitivities, and climate-carbon feedbacks. The values from EMICs generally fall within the range given by general circulation models. Seven additional historical simulations, each including a single specified forcing, are used to assess the contributions of different climate forcings to the overall climate and carbon cycle response. The response of surface air temperature is the linear sum of the individual forcings, while the carbon cycle response shows a non-linear interaction between land-use change and CO2 forcings for some models. Finally, the preindustrial portions of the last millennium simulations are used to assess historical model carbon-climate feedbacks. Given the specified forcing, there is a tendency for the

  8. Model Tech Prep Demonstration Project. Final Report.

    ERIC Educational Resources Information Center

    Southern Maryland Educational Consortium, La Plata.

    The Southern Maryland Educational Consortium's Tech Prep Model Demonstration project is described in this final report. The consortium members are Calvert, Charles, and St. Mary's county school districts and Charles County Community College in southern Maryland. The project is based on a 4 + 2 model in which ninth-grade students develop career…

  9. Transitional Employment Experimental Model (TEEM). Final Report.

    ERIC Educational Resources Information Center

    California State Personnel Board, Sacramento.

    The final report of the Transitional Employemnt Experimental Model (TEEM) Project, a research and development project providing a potential model for a large scale manpower absorption program in times of economic need, is presented. One major purpose of the project was to demonstrate the viability of providing suitable job placement for the…

  10. Modeling Evapotranspiration in Subtropical Climate

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Evapotranspiration loss is estimated at about 80% of annual precipitation in south Florida. Accurate prediction of evapotranspiration is important during and beyond the implementation of the Comprehensive Everglades Restoration Project(CERP). In the USDA’s Everglades Agro-Hydrology Model (EAHM) the...

  11. FOAM: Expanding the horizons of climate modeling

    SciTech Connect

    Tobis, M.; Foster, I.T.; Schafer, C.M.

    1997-10-01

    We report here on a project that expands the applicability of dynamic climate modeling to very long time scales. The Fast Ocean Atmosphere Model (FOAM) is a coupled ocean atmosphere model that incorporates physics of interest in understanding decade to century time scale variability. It addresses the high computational cost of this endeavor with a combination of improved ocean model formulation, low atmosphere resolution, and efficient coupling. It also uses message passing parallel processing techniques, allowing for the use of cost effective distributed memory platforms. The resulting model runs over 6000 times faster than real time with good fidelity, and has yielded significant results.

  12. The impact of ARM on climate modeling

    DOE PAGESBeta

    Randall, David A.; Del Genio, Anthony D.; Donner, Lee J.; Collins, William D.; Klein, Stephen A.

    2016-07-15

    Climate models are among humanity’s most ambitious and elaborate creations. They are designed to simulate the interactions of the atmosphere, ocean, land surface, and cryosphere on time scales far beyond the limits of deterministic predictability and including the effects of time-dependent external forcings. The processes involved include radiative transfer, fluid dynamics, microphysics, and some aspects of geochemistry, biology, and ecology. The models explicitly simulate processes on spatial scales ranging from the circumference of Earth down to 100 km or smaller and implicitly include the effects of processes on even smaller scales down to a micron or so. In addition, themore » atmospheric component of a climate model can be called an atmospheric global circulation model (AGCM).« less

  13. Climate Model Diagnostic Analyzer Web Service System

    NASA Astrophysics Data System (ADS)

    Lee, S.; Pan, L.; Zhai, C.; Tang, B.; Jiang, J. H.

    2013-12-01

    The latest Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report stressed the need for the comprehensive and innovative evaluation of climate models with newly available global observations. The traditional approach to climate model evaluation, which compares a single parameter at a time, identifies symptomatic model biases and errors but fails to diagnose the model problems. The model diagnosis process requires physics-based multi-variable comparisons that typically involve large-volume and heterogeneous datasets, making them both computationally- and data-intensive. To address these challenges, we are developing a parallel, distributed web-service system that enables the physics-based multi-variable model performance evaluations and diagnoses through the comprehensive and synergistic use of multiple observational data, reanalysis data, and model outputs. We have developed a methodology to transform an existing science application code into a web service using a Python wrapper interface and Python web service frameworks (i.e., Flask, Gunicorn, and Tornado). The web-service system, called Climate Model Diagnostic Analyzer (CMDA), currently supports (1) all the datasets from Obs4MIPs and a few ocean datasets from NOAA and Argo, which can serve as observation-based reference data for model evaluation and (2) many of CMIP5 model outputs covering a broad range of atmosphere, ocean, and land variables from the CMIP5 specific historical runs and AMIP runs. Analysis capabilities currently supported by CMDA are (1) the calculation of annual and seasonal means of physical variables, (2) the calculation of time evolution of the means in any specified geographical region, (3) the calculation of correlation between two variables, and (4) the calculation of difference between two variables. A web user interface is chosen for CMDA because it not only lowers the learning curve and removes the adoption barrier of the tool but also enables instantaneous use

  14. Modeling the Martian Atmosphere with the LMD Global Climate Model

    NASA Astrophysics Data System (ADS)

    Forget, F.; Millour, E.; Gonzalez-Galindo, F.; Lebonnois, S.; Madeleine, J.-B.; Meslin, P.-Y.; Montabone, L.; Spiga, A.; Hourdin, F.; Lefevre, F.; Montmessin, F.; Lewis, S. R.; Read, P.; Lopez-Valverde, M. A.; Gilli, G.

    2008-11-01

    The Global Climate Model developed at LMD (Paris) in collaboration with IAA (Spain), AOPP and the OU (UK) has been improved. It is used for many applications (water, dust, CO2, radon cycles, photochemistry, thermosphere, ionosphere, etc.).

  15. A Practical Philosophy of Complex Climate Modelling

    NASA Technical Reports Server (NTRS)

    Schmidt, Gavin A.; Sherwood, Steven

    2014-01-01

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

  16. The Swedish Regional Climate Modelling Programme, SWECLIM: a review.

    PubMed

    Rummukainen, Markku; Bergström, Sten; Persson, Gunn; Rodhe, Johan; Tjernström, Michael

    2004-06-01

    The Swedish Regional Climate Modelling Programme, SWECLIM, was a 6.5-year national research network for regional climate modeling, regional climate change projections and hydrological impact assessment and information to a wide range of stakeholders. Most of the program activities focussed on the regional climate system of Northern Europe. This led to the establishment of an advanced, coupled atmosphere-ocean-hydrology regional climate model system, a suite of regional climate change projections and progress on relevant data and process studies. These were, in turn, used for information and educational purposes, as a starting point for impact analyses on different societal sectors and provided contributions also to international climate research. PMID:15264594

  17. Climate Modeling Computing Needs Assessment

    NASA Astrophysics Data System (ADS)

    Petraska, K. E.; McCabe, J. D.

    2011-12-01

    This paper discusses early findings of an assessment of computing needs for NASA science, engineering and flight communities. The purpose of this assessment is to document a comprehensive set of computing needs that will allow us to better evaluate whether our computing assets are adequately structured to meet evolving demand. The early results are interesting, already pointing out improvements we can make today to get more out of the computing capacity we have, as well as potential game changing innovations for the future in how we apply information technology to science computing. Our objective is to learn how to leverage our resources in the best way possible to do more science for less money. Our approach in this assessment is threefold: Development of use case studies for science workflows; Creating a taxonomy and structure for describing science computing requirements; and characterizing agency computing, analysis, and visualization resources. As projects evolve, science data sets increase in a number of ways: in size, scope, timelines, complexity, and fidelity. Generating, processing, moving, and analyzing these data sets places distinct and discernable requirements on underlying computing, analysis, storage, and visualization systems. The initial focus group for this assessment is the Earth Science modeling community within NASA's Science Mission Directorate (SMD). As the assessment evolves, this focus will expand to other science communities across the agency. We will discuss our use cases, our framework for requirements and our characterizations, as well as our interview process, what we learned and how we plan to improve our materials after using them in the first round of interviews in the Earth Science Modeling community. We will describe our plans for how to expand this assessment, first into the Earth Science data analysis and remote sensing communities, and then throughout the full community of science, engineering and flight at NASA.

  18. OpenClimateGIS - A Web Service Providing Climate Model Data in Commonly Used Geospatial Formats

    NASA Astrophysics Data System (ADS)

    Erickson, T. A.; Koziol, B. W.; Rood, R. B.

    2011-12-01

    The goal of the OpenClimateGIS project is to make climate model datasets readily available in commonly used, modern geospatial formats used by GIS software, browser-based mapping tools, and virtual globes.The climate modeling community typically stores climate data in multidimensional gridded formats capable of efficiently storing large volumes of data (such as netCDF, grib) while the geospatial community typically uses flexible vector and raster formats that are capable of storing small volumes of data (relative to the multidimensional gridded formats). OpenClimateGIS seeks to address this difference in data formats by clipping climate data to user-specified vector geometries (i.e. areas of interest) and translating the gridded data on-the-fly into multiple vector formats. The OpenClimateGIS system does not store climate data archives locally, but rather works in conjunction with external climate archives that expose climate data via the OPeNDAP protocol. OpenClimateGIS provides a RESTful API web service for accessing climate data resources via HTTP, allowing a wide range of applications to access the climate data.The OpenClimateGIS system has been developed using open source development practices and the source code is publicly available. The project integrates libraries from several other open source projects (including Django, PostGIS, numpy, Shapely, and netcdf4-python).OpenClimateGIS development is supported by a grant from NOAA's Climate Program Office.

  19. A scalable climate health justice assessment model

    PubMed Central

    McDonald, Yolanda J.; Grineski, Sara E.; Collins, Timothy W.; Kim, Young-An

    2014-01-01

    This paper introduces a scalable “climate health justice” model for assessing and projecting incidence, treatment costs, and sociospatial disparities for diseases with well-documented climate change linkages. The model is designed to employ low-cost secondary data, and it is rooted in a perspective that merges normative environmental justice concerns with theoretical grounding in health inequalities. Since the model employs International Classification of Diseases, Ninth Revision Clinical Modification (ICD-9-CM) disease codes, it is transferable to other contexts, appropriate for use across spatial scales, and suitable for comparative analyses. We demonstrate the utility of the model through analysis of 2008–2010 hospitalization discharge data at state and county levels in Texas (USA). We identified several disease categories (i.e., cardiovascular, gastrointestinal, heat-related, and respiratory) associated with climate change, and then selected corresponding ICD-9 codes with the highest hospitalization counts for further analyses. Selected diseases include ischemic heart disease, diarrhea, heat exhaustion/cramps/stroke/syncope, and asthma. Cardiovascular disease ranked first among the general categories of diseases for age-adjusted hospital admission rate (5286.37 per 100,000). In terms of specific selected diseases (per 100,000 population), asthma ranked first (517.51), followed by ischemic heart disease (195.20), diarrhea (75.35), and heat exhaustion/cramps/stroke/syncope (7.81). Charges associated with the selected diseases over the 3-year period amounted to US$5.6 billion. Blacks were disproportionately burdened by the selected diseases in comparison to non-Hispanic whites, while Hispanics were not. Spatial distributions of the selected disease rates revealed geographic zones of disproportionate risk. Based upon a downscaled regional climate-change projection model, we estimate a >5% increase in the incidence and treatment costs of asthma attributable to

  20. LINKING MICROBES TO CLIMATE: INCORPORATING MICROBIAL ACTIVITY INTO CLIMATE MODELS COLLOQUIUM

    SciTech Connect

    DeLong, Edward; Harwood, Caroline; Reid, Ann

    2011-01-01

    This report explains the connection between microbes and climate, discusses in general terms what modeling is and how it applied to climate, and discusses the need for knowledge in microbial physiology, evolution, and ecology to contribute to the determination of fluxes and rates in climate models. It recommends with a multi-pronged approach to address the gaps.

  1. Forward in time methods for global climate research. Final report

    SciTech Connect

    Margolin, L.G.; Smolarkiewicz, P.K.

    1996-05-01

    Purpose is to demonstrate feasibility and utility of nonoscillatory forward-in-time (NFT) methods formodeling the global dynamics of the atmosphere and oceans. This includes development of new algorithms, construction of numerical models, and testing these models. One aspect of the research is to compare two variants of NFT methods, one based on Eulerian approximations and the other based on semi-Lagrangian approximations.

  2. Climate Modeling with a Linux Cluster

    NASA Astrophysics Data System (ADS)

    Renold, M.; Beyerle, U.; Raible, C. C.; Knutti, R.; Stocker, T. F.; Craig, T.

    2004-08-01

    Until recently, computationally intensive calculations in many scientific disciplines have been limited to institutions which have access to supercomputing centers. Today, the computing power of PC processors permits the assembly of inexpensive PC clusters that nearly approach the power of supercomputers. Moreover, the combination of inexpensive network cards and Open Source software provides an easy linking of standard computer equipment to enlarge such clusters. Universities and other institutions have taken this opportunity and built their own mini-supercomputers on site. Computing power is a particular issue for the climate modeling and impacts community. The purpose of this article is to make available a Linux cluster version of the Community Climate System Model developed by the National Center for Atmospheric Research (NCAR; http://www.cgd.ucar.edu/csm).

  3. Infrared radiation parameterizations in numerical climate models

    NASA Technical Reports Server (NTRS)

    Chou, Ming-Dah; Kratz, David P.; Ridgway, William

    1991-01-01

    This study presents various approaches to parameterizing the broadband transmission functions for utilization in numerical climate models. One-parameter scaling is applied to approximate a nonhomogeneous path with an equivalent homogeneous path, and the diffuse transmittances are either interpolated from precomputed tables or fit by analytical functions. Two-parameter scaling is applied to parameterizing the carbon dioxide and ozone transmission functions in both the lower and middle atmosphere. Parameterizations are given for the nitrous oxide and methane diffuse transmission functions.

  4. Present-day climate of Antarctica : A study with a regional atmospheric climate model

    NASA Astrophysics Data System (ADS)

    van de Berg, W. J.

    2008-02-01

    The present-day climate of Antarctica is studied with a regional climate model. In this research, foci are the surface mass balance, i.e. the accumulation of snow on the ice cap, and the heat budget of the atmosphere above Antarctica. Insight in the surface mass balance of the Antarctic ice cap is gained by explanation, evaluation and subsequently calibration of model-simulated surface mass balance patterns using more than 1900 in situ observations. Furthermore, a method is developed to quantify the uncertainty in the local and spatially integrated surface mass balance estimate. In our study, a good correlation between the observations and model results is found, giving reliability to the results presented. The final surface mass balance estimate primarily deviates in the coastal zones of Antarctica from earlier estimates. Our results strongly suggest much higher accumulation rates than previously assumed, leading to a 15% higher overall accumulation. Unfortunately, the coastal zone is poorly covered by observations, which makes a final assessment difficult. Analysis of the heat budget of the Antarctic atmosphere clarifies the dynamics of the Antarctic boundary layer and shows the coupling to the global climate. Our results show how the boundary layer develops from the interior of Antarctica to the coast; from shallow and extremely stable to deeper, mixed but still stable. Furthermore, the effect of surface undulations on the local near surface temperature is explained. Domes and ridges have a weakening effect on the surface inversion of the temperature through enhanced divergence of the near-surface wind field. Oppositely, valleys strengthen the surface inversion. This coupling of topography and temperature causes the spatial variability of surface temperatures on scales of typical a few hundred kilometers.

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

    NASA Astrophysics Data System (ADS)

    Anisimov, O.

    2003-04-01

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

  6. Explosive cyclones in CMIP5 climate models

    NASA Astrophysics Data System (ADS)

    Seiler, C.; Zwiers, F. W.

    2014-12-01

    Explosive cyclones are rapidly intensifying low pressure systems with severe wind speeds and precipitation, affecting livelihoods and infrastructure primarily in coastal and marine environments. A better understanding of the potential impacts of climate change on these so called meteorological bombs is therefore of great societal relevance. This study evaluates how well CMIP5 climate models reproduce explosive cyclones in the extratropics of the northern hemisphere, and how these bombs respond to global warming. For this purpose an objective-feature tracking algorithm was used to identify and track extratropical cyclones from 25 CMIP5 models and 3 reanalysis products for the periods 1980 to 2005 and 2070 to 2099. Cyclones were identified as the maxima of T42 vorticity of 6h wind speed at 850 hPa. Explosive and non-explosive cyclones were separated based on the corresponding deepening rates of mean sea level pressure. Most models accurately reproduced the spatial distribution of bombs when compared to results from reanalysis data (R2 = 0.84, p-value = 0.00), with high frequencies along the Kuroshio Current and the Gulf Stream, as well as the exit regions of the polar jet streaks. Most models however significantly underestimated bomb frequencies by a third on average, and by 74% in the most extreme case. This negative frequency bias coincided with significant underestimations of either meridional sea surface temperature (SST) gradients, or wind speeds of the polar jet streaks. Bomb frequency biases were significantly correlated with the number vertical model levels (R2= 0.36, p-value = 0.001), suggesting that the vertical atmospheric model resolution is crucial for simulating bomb frequencies accurately. The impacts of climate change on the location, frequency, and intensity of explosive cyclones were then explored for the Representative Concentration Pathway 8.5. Projections were related to model bias, resolution, projected changes of SST gradients, and wind speeds

  7. An Earth radiation budget climate model

    NASA Technical Reports Server (NTRS)

    Bartman, Fred L.

    1988-01-01

    A 2-D Earth Radiation Budget Climate Model has been constructed from an OLWR (Outgoing Longwave Radiation) model and an Earth albedo model. Each of these models uses the same cloud cover climatology modified by a factor GLCLC which adjusts the global annual average cloud cover. The two models are linked by a set of equations which relate the cloud albedos to the cloud top temperatures of the OLWR model. These equations are derived from simultaneous narrow band satellite measurements of cloud top temperature and albedo. Initial results include global annual average values of albedo and latitude/longitude radiation for 45 percent and 57 percent global annual average cloud cover and two different forms of the cloud albedo-cloud top temperature equations.

  8. Testing Connections between Campanian Ignimbrite Volcanism, Climate, and the Final Decline of the Neanderthals

    NASA Astrophysics Data System (ADS)

    Black, B. A.; Manga, M.; Neely, R. R., III

    2014-12-01

    The eruption of the Campanian Ignimbrite 40,000 years ago coincided approximately with the final decline of the Neanderthals and a technological and cultural transition from the Middle to Upper Paleolithic. Two end-member hypotheses have been advanced to explain Neanderthal decline: competition with anatomically modern humans and failure to adapt in the face of environmental stresses. The eruption of the Campanian Ignimbrite has been cited as a potentially major cause of such environmental stress. In this work, we draw on published datasets including ice core records, maps and simulations of ash dispersal, and petrologic measurements to constrain the characteristics of the Campanian Ignimbrite eruption. To investigate the climatic effects of the eruption, we use a three-dimensional sectional aerosol model to simulate the global aerosol cloud after 25 Tg and 100 Tg sulfur release scenarios. We couple these aerosol properties to a comprehensive earth system model under last glacial conditions. We find that summer temperatures were colder for several years after the eruption, with some simulations predicting temperature decreases of up to 10 degrees in Eastern Europe and Asia. While this cold interval may have impacted hominid communities in Siberia, the overall distribution of the cooling we observe in our model is inconsistent with catastrophic collapse of Neanderthal populations in Europe. Nonetheless, the volcanic cooling could have influenced daily life for a generation of Neanderthals and anatomically modern humans.

  9. New Gravity Wave Treatments for GISS Climate Models

    NASA Technical Reports Server (NTRS)

    Geller, Marvin A.; Zhou, Tiehan; Ruedy, Reto; Aleinov, Igor; Nazarenko, Larissa; Tausnev, Nikolai L.; Sun, Shan; Kelley, Maxwell; Cheng, Ye

    2011-01-01

    Previous versions of GISS climate models have either used formulations of Rayleigh drag to represent unresolved gravity wave interactions with the model-resolved flow or have included a rather complicated treatment of unresolved gravity waves that, while being climate interactive, involved the specification of a relatively large number of parameters that were not well constrained by observations and also was computationally very expensive. Here, the authors introduce a relatively simple and computationally efficient specification of unresolved orographic and nonorographic gravity waves and their interaction with the resolved flow. Comparisons of the GISS model winds and temperatures with no gravity wave parameterization; with only orographic gravity wave parameterization; and with both orographic and nonorographic gravity wave parameterizations are shown to illustrate how the zonal mean winds and temperatures converge toward observations. The authors also show that the specifications of orographic and nonorographic gravity waves must be different in the Northern and Southern Hemispheres. Then results are presented where the nonorographic gravity wave sources are specified to represent sources from convection in the intertropical convergence zone and spontaneous emission from jet imbalances. Finally, a strategy to include these effects in a climate-dependent manner is suggested.

  10. New Gravity Wave Treatments for GISS Climate Models

    NASA Technical Reports Server (NTRS)

    Geller, Marvin A.; Zhou, Tiehan; Ruedy, Reto; Aleinov, Igor; Nazarenko, Larissa; Tausnev, Nikolai L.; Sun, Shan; Kelley, Maxwell; Cheng, Ye

    2010-01-01

    Previous versions of GISS climate models have either used formulations of Rayleigh drag to represent unresolved gravity wave interactions with the model resolved flow or have included a rather complicated treatment of unresolved gravity waves that, while being climate interactive, involved the specification of a relatively large number of parameters that were not well constrained by observations and also was computationally very expensive. Here, we introduce a relatively simple and computationally efficient specification of unresolved orographic and non-orographic gravity waves and their interaction with the resolved flow. We show comparisons of the GISS model winds and temperatures with no gravity wave parametrization; with only orographic gravity wave parameterization; and with both orographic and non-orographic gravity wave parameterizations to illustrate how the zonal mean winds and temperatures converge toward observations. We also show that the specifications of orographic and nonorographic gravity waves must be different in the Northern and Southern Hemispheres. We then show results where the non-orographic gravity wave sources are specified to represent sources from convection in the Intertropical Convergence Zone and spontaneous emission from jet imbalances. Finally, we suggest a strategy to include these effects in a climate dependent manner.

  11. Implications of Climate Change for State Bioassessment Programs and Approaches to Account for Effects (Final Report)

    EPA Science Inventory

    This final report uses biological data collected by four states in wadeable rivers and streams to examine the components of state and tribal bioassessment and biomonitoring programs that may be vulnerable to climate change. The study investigates the potential to identify biologi...

  12. INCCA: Integrated Climate and Carbon Final Report of the LLNL LDRD Strategic Initiative

    SciTech Connect

    Thompson, S L

    2004-02-13

    The INCCA (Integrated Climate and Carbon) strategic initiative developed and applied the ability to simulate the fate and climate impact of fossil fuel-derived carbon dioxide (CO{sub 2}) on a global scale. Coupled climate and carbon cycle modeling like that of INCCA is required to understand and predict the future environmental impacts of fossil fuel burning. At present, atmospheric CO{sub 2} concentrations are prescribed, not simulated, in large climate models. Credible simulations of the entire climate system, however, need to predict time-evolving climate forcing using anthropogenic emissions as the fundamental input. Predicting atmospheric CO{sub 2} concentrations represents a substantial scientific advance because there are large natural sources and sinks of carbon that are likely to change as a result of climate change. Both terrestrial (e.g., vegetation on land) and oceanic components of the carbon cycle are known to be sensitive to climate change. Estimates of the amount of man-made CO{sub 2} that will accumulate in the atmosphere depend on understanding the carbon cycle. For this reason, models that use CO{sub 2} emissions, not prescribed atmospheric concentrations, as fundamental inputs are required to directly address greenhouse-related questions of interest to policymakers.

  13. The seasonal-cycle climate model

    NASA Technical Reports Server (NTRS)

    Marx, L.; Randall, D. A.

    1981-01-01

    The seasonal cycle run which will become the control run for the comparison with runs utilizing codes and parameterizations developed by outside investigators is discussed. The climate model currently exists in two parallel versions: one running on the Amdahl and the other running on the CYBER 203. These two versions are as nearly identical as machine capability and the requirement for high speed performance will allow. Developmental changes are made on the Amdahl/CMS version for ease of testing and rapidity of turnaround. The changes are subsequently incorporated into the CYBER 203 version using vectorization techniques where speed improvement can be realized. The 400 day seasonal cycle run serves as a control run for both medium and long range climate forecasts alsensitivity studies.

  14. Coupling Climate Models and Forward-Looking Economic Models

    NASA Astrophysics Data System (ADS)

    Judd, K.; Brock, W. A.

    2010-12-01

    Authors: Dr. Kenneth L. Judd, Hoover Institution, and Prof. William A. Brock, University of Wisconsin Current climate models range from General Circulation Models (GCM’s) with millions of degrees of freedom to models with few degrees of freedom. Simple Energy Balance Climate Models (EBCM’s) help us understand the dynamics of GCM’s. The same is true in economics with Computable General Equilibrium Models (CGE’s) where some models are infinite-dimensional multidimensional differential equations but some are simple models. Nordhaus (2007, 2010) couples a simple EBCM with a simple economic model. One- and two- dimensional ECBM’s do better at approximating damages across the globe and positive and negative feedbacks from anthroprogenic forcing (North etal. (1981), Wu and North (2007)). A proper coupling of climate and economic systems is crucial for arriving at effective policies. Brock and Xepapadeas (2010) have used Fourier/Legendre based expansions to study the shape of socially optimal carbon taxes over time at the planetary level in the face of damages caused by polar ice cap melt (as discussed by Oppenheimer, 2005) but in only a “one dimensional” EBCM. Economists have used orthogonal polynomial expansions to solve dynamic, forward-looking economic models (Judd, 1992, 1998). This presentation will couple EBCM climate models with basic forward-looking economic models, and examine the effectiveness and scaling properties of alternative solution methods. We will use a two dimensional EBCM model on the sphere (Wu and North, 2007) and a multicountry, multisector regional model of the economic system. Our aim will be to gain insights into intertemporal shape of the optimal carbon tax schedule, and its impact on global food production, as modeled by Golub and Hertel (2009). We will initially have limited computing resources and will need to focus on highly aggregated models. However, this will be more complex than existing models with forward

  15. Megacity project: Liwa, climate and water balance modeling

    NASA Astrophysics Data System (ADS)

    Chamorro, Alejandro; Bardossy, Andras

    2010-05-01

    Megacity project: Liwa, climate and water balance modeling Peru uses to face different natural phenomena such as El Nino and La Nina phenomena and, like many cities around the word, the climate change effects. Its capital Lima, located in a region where annual precipitation is about 9 mm, has a high hydrological cycle vulnerability which is demonstrated in periods of drought and extreme drought. Accurate and reliable methodology is requiring studying the impact of all these problems in the water supply of Lima. A statistical downscaling scheme (Bardossy, 2002) will be used to generate time series of different local climate scenarios in order to be applied in hydrological models. The conceptual model HBV (Bergström, 1995) is used to simulate water discharges at certain points of the catchments under study, water balance groundwater and for the estimation of storage volume in different reservoirs. As already mentioned, El Nino and La Nina currents influence the hydrological cycle. Previous studies have shown that these phenomena have serious impacts in Peru. In order to quantify these impacts in the area of interest we have analyzed the magnitude of the precipitation in several stations in years in which El Nino occurred, and in years where El Nino did not occurred. The next step is to increase the temporal resolution by incorporating new data. Due to the high vulnerability of the water supply system in Lima, potential new water sources are required. In particular, the catchment of Mantaro (including existing lakes) on the other side of Los Andes Mountains provides potential new alternatives for adding water to the current system. Alternatives for water transportation include using existing long tunnels which connect Mantaro with Rimac, where the majority of the lakes are located. Finally, the global climate models simulations for the coming years, considering different scenarios, will be used as an indicator and to estimate water availability for human use (city

  16. Current climate and climate change over India as simulated by the Canadian Regional Climate Model

    NASA Astrophysics Data System (ADS)

    Alexandru, Adelina; Sushama, Laxmi

    2015-08-01

    The performance of the fifth generation of the Canadian Regional Climate Model (CRCM5) in reproducing the main climatic characteristics over India during the southwest (SW)-, post- and pre-monsoon seasons are presented in this article. To assess the performance of CRCM5, European Centre for Medium- Range Weather Forecasts (ECMWF) Re- Analysis (ERA- 40) and Interim re-analysis (ERA-Interim) driven CRCM5 simulation is compared against independent observations and reanalysis data for the 1971-2000 period. Projected changes for two future periods, 2041-2070 and 2071-2100, with respect to the 1971-2000 current period are assessed based on two transient climate change simulations of CRCM5 spanning the 1950-2100 period. These two simulations are driven by the Canadian Earth System Model version 2 (CanESM2) and the Max Planck Institute for Meteorology's Earth System Low Resolution Model (MPI-ESM-LR), respectively. The boundary forcing errors associated with errors in the driving global climate models are also studied by comparing the 1971-2000 period of the CanESM2 and MPI-ESM-LR driven simulations with that of the CRCM5 simulation driven by ERA-40/ERA-Interim. Results show that CRCM5 driven by ERA-40/ERA-Interim is in general able to capture well the temporal and spatial patterns of 2 m-temperature, precipitation, wind, sea level pressure, total runoff and soil moisture over India in comparison with available reanalysis and observations. However, some noticeable differences between the model and observational data were found during the SW-monsoon season within the domain of integration. CRCM5 driven by ERA-40/ERA-Interim is 1-2 °C colder than CRU observations and generates more precipitation over the Western Ghats and central regions of India, and not enough in the northern and north-eastern parts of India and along the Konkan west coast in comparison with the observed precipitation. The monsoon onset seems to be relatively well captured over the southwestern coast of

  17. Modelling the hydrological cycle in assessments of climate change

    NASA Technical Reports Server (NTRS)

    Rind, D.; Rosenzweig, C.; Goldberg, R.

    1992-01-01

    The predictions of climate change studies depend crucially on the hydrological cycles embedded in the different models used. It is shown here that uncertainties in hydrological processes and inconsistencies in both climate and impact models limit confidence in current assessments of climate change. A future course of action to remedy this problem is suggested.

  18. Downscaling GISS ModelE Boreal Summer Climate over Africa

    NASA Technical Reports Server (NTRS)

    Druyan, Leonard M.; Fulakeza, Matthew

    2015-01-01

    The study examines the perceived added value of downscaling atmosphere-ocean global climate model simulations over Africa and adjacent oceans by a nested regional climate model. NASA/Goddard Institute for Space Studies (GISS) coupled ModelE simulations for June- September 1998-2002 are used to form lateral boundary conditions for synchronous simulations by the GISS RM3 regional climate model. The ModelE computational grid spacing is 2deg latitude by 2.5deg longitude and the RM3 grid spacing is 0.44deg. ModelE precipitation climatology for June-September 1998-2002 is shown to be a good proxy for 30-year means so results based on the 5-year sample are presumed to be generally representative. Comparison with observational evidence shows several discrepancies in ModelE configuration of the boreal summer inter-tropical convergence zone (ITCZ). One glaring shortcoming is that ModelE simulations do not advance the West African rain band northward during the summer to represent monsoon precipitation onset over the Sahel. Results for 1998-2002 show that onset simulation is an important added value produced by downscaling with RM3. ModelE Eastern South Atlantic Ocean computed sea-surface temperatures (SST) are some 4 K warmer than reanalysis, contributing to large positive biases in overlying surface air temperatures (Tsfc). ModelE Tsfc are also too warm over most of Africa. RM3 downscaling somewhat mitigates the magnitude of Tsfc biases over the African continent, it eliminates the ModelE double ITCZ over the Atlantic and it produces more realistic orographic precipitation maxima. Parallel ModelE and RM3 simulations with observed SST forcing (in place of the predicted ocean) lower Tsfc errors but have mixed impacts on circulation and precipitation biases. Downscaling improvements of the meridional movement of the rain band over West Africa and the configuration of orographic precipitation maxima are realized irrespective of the SST biases.

  19. A Solar-luminosity Model and Climate

    NASA Technical Reports Server (NTRS)

    Perry, Charles A.

    1990-01-01

    Although the mechanisms of climatic change are not completely understood, the potential causes include changes in the Sun's luminosity. Solar activity in the form of sunspots, flares, proton events, and radiation fluctuations has displayed periodic tendencies. Two types of proxy climatic data that can be related to periodic solar activity are varved geologic formations and freshwater diatom deposits. A model for solar luminosity was developed by using the geometric progression of harmonic cycles that is evident in solar and geophysical data. The model assumes that variation in global energy input is a result of many periods of individual solar-luminosity variations. The 0.1-percent variation of the solar constant measured during the last sunspot cycle provided the basis for determining the amplitude of each luminosity cycle. Model output is a summation of the amplitudes of each cycle of a geometric progression of harmonic sine waves that are referenced to the 11-year average solar cycle. When the last eight cycles in Emiliani's oxygen-18 variations from deep-sea cores were standardized to the average length of glaciations during the Pleistocene (88,000 years), correlation coefficients with the model output ranged from 0.48 to 0.76. In order to calibrate the model to real time, model output was graphically compared to indirect records of glacial advances and retreats during the last 24,000 years and with sea-level rises during the Holocene. Carbon-14 production during the last millenium and elevations of the Great Salt Lake for the last 140 years demonstrate significant correlations with modeled luminosity. Major solar flares during the last 90 years match well with the time-calibrated model.

  20. Modeling the uncertain impacts of climate change

    SciTech Connect

    Liebetrau, A.M.

    1992-08-01

    Human and earth systems are extremely complex processes. The modeling of these systems to assess the effects of climate change is an activity fraught with uncertainty. System models typically involve the linking of a series of computer codes, each of which is a detailed model of some physical or social process in its own right. In such system models, the output from one process model is the input to another. Traditional methods for dealing with uncertainty are inadequate because of the sheer complexity of the modeling effort: Monte Carlo methods and the exhaustive evaluation of ``what if?`` scenarios estimate sensitivities fail because of the heavy computational burden. More efficient methods are required for learning about system models that are constructed from a collection of computer codes. A two-tiered modeling approach is being developed to estimate the distribution of outcomes from a series of nested models. The basic strategy is to develop a simplified executive, or simplified system code (SSC), that is analogous to the more complex underlying code. An essential feature of the SSC is that it uses information abstracted from the detailed underlying process codes in a manner that preserves their essential features and interactions among them. Of course, to be useful, the SSC must be much faster to run than its complex counterpart. The success of the SSC modeling strategy depends on the methods used to extract essential features of the complex underlying codes.

  1. Modeling the uncertain impacts of climate change

    SciTech Connect

    Liebetrau, A.M.

    1992-08-01

    Human and earth systems are extremely complex processes. The modeling of these systems to assess the effects of climate change is an activity fraught with uncertainty. System models typically involve the linking of a series of computer codes, each of which is a detailed model of some physical or social process in its own right. In such system models, the output from one process model is the input to another. Traditional methods for dealing with uncertainty are inadequate because of the sheer complexity of the modeling effort: Monte Carlo methods and the exhaustive evaluation of what if '' scenarios estimate sensitivities fail because of the heavy computational burden. More efficient methods are required for learning about system models that are constructed from a collection of computer codes. A two-tiered modeling approach is being developed to estimate the distribution of outcomes from a series of nested models. The basic strategy is to develop a simplified executive, or simplified system code (SSC), that is analogous to the more complex underlying code. An essential feature of the SSC is that it uses information abstracted from the detailed underlying process codes in a manner that preserves their essential features and interactions among them. Of course, to be useful, the SSC must be much faster to run than its complex counterpart. The success of the SSC modeling strategy depends on the methods used to extract essential features of the complex underlying codes.

  2. Towards Systematic Benchmarking of Climate Model Performance

    NASA Astrophysics Data System (ADS)

    Gleckler, P. J.

    2014-12-01

    The process by which climate models are evaluated has evolved substantially over the past decade, with the Coupled Model Intercomparison Project (CMIP) serving as a centralizing activity for coordinating model experimentation and enabling research. Scientists with a broad spectrum of expertise have contributed to the CMIP model evaluation process, resulting in many hundreds of publications that have served as a key resource for the IPCC process. For several reasons, efforts are now underway to further systematize some aspects of the model evaluation process. First, some model evaluation can now be considered routine and should not require "re-inventing the wheel" or a journal publication simply to update results with newer models. Second, the benefit of CMIP research to model development has not been optimal because the publication of results generally takes several years and is usually not reproducible for benchmarking newer model versions. And third, there are now hundreds of model versions and many thousands of simulations, but there is no community-based mechanism for routinely monitoring model performance changes. An important change in the design of CMIP6 can help address these limitations. CMIP6 will include a small set standardized experiments as an ongoing exercise (CMIP "DECK": ongoing Diagnostic, Evaluation and Characterization of Klima), so that modeling groups can submit them at any time and not be overly constrained by deadlines. In this presentation, efforts to establish routine benchmarking of existing and future CMIP simulations will be described. To date, some benchmarking tools have been made available to all CMIP modeling groups to enable them to readily compare with CMIP5 simulations during the model development process. A natural extension of this effort is to make results from all CMIP simulations widely available, including the results from newer models as soon as the simulations become available for research. Making the results from routine

  3. From climate model ensembles to statistics: Introducing the "wux" package

    NASA Astrophysics Data System (ADS)

    Mendlik, Thomas; Heinrich, Georg; Gobiet, Andreas; Leuprecht, Armin

    2015-04-01

    We present the R package "wux", a toolbox to analyze climate change uncertainties projected by numerical climate model simulations. The focus of this package is to automatically process big amounts of climate simulations from multi-model ensembles in a user-friendly way. For that, climate model output in binary NetCDF format is read in and stored in a data frame, after first being aggregated to a desired temporal resolution and then being averaged over spatial domains of interest. The data processing can be performed for any number of meteorological parameters at one go, which allows multivariate statistical analysis of the climate model ensemble. The data to be processed is not restricted to any specific type of climate simulation: Global circulation models (GCMs), as the CMIP5 or CMIP3 simulations, can be read in the same way as Regional Climate Models (RCMs), as e.g. the CORDEX or ENSEMBLES simulations.

  4. Emulating AOGCM results using simple climate models

    NASA Astrophysics Data System (ADS)

    Olivié, Dirk; Stuber, Nicola

    2010-12-01

    Three simple climate models (SCMs) are calibrated using simulations from atmosphere ocean general circulation models (AOGCMs). In addition to using two conventional SCMs, results from a third simpler model developed specifically for this study are obtained. An easy to implement and comprehensive iterative procedure is applied that optimises the SCM emulation of global-mean surface temperature and total ocean heat content, and, if available in the SCM, of surface temperature over land, over the ocean and in both hemispheres, and of the global-mean ocean temperature profile. The method gives best-fit estimates as well as uncertainty intervals for the different SCM parameters. For the calibration, AOGCM simulations with two different types of forcing scenarios are used: pulse forcing simulations performed with 2 AOGCMs and gradually changing forcing simulations from 15 AOGCMs obtained within the framework of the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. The method is found to work well. For all possible combinations of SCMs and AOGCMs the emulation of AOGCM results could be improved. The obtained SCM parameters depend both on the AOGCM data and the type of forcing scenario. SCMs with a poor representation of the atmosphere thermal inertia are better able to emulate AOGCM results from gradually changing forcing than from pulse forcing simulations. Correct simultaneous emulation of both atmospheric temperatures and the ocean temperature profile by the SCMs strongly depends on the representation of the temperature gradient between the atmosphere and the mixed layer. Introducing climate sensitivities that are dependent on the forcing mechanism in the SCMs allows the emulation of AOGCM responses to carbon dioxide and solar insolation forcings equally well. Also, some SCM parameters are found to be very insensitive to the fitting, and the reduction of their uncertainty through the fitting procedure is only marginal, while other parameters

  5. A Tool for Sharing Empirical Models of Climate Impacts

    NASA Astrophysics Data System (ADS)

    Rising, J.; Kopp, R. E.; Hsiang, S. M.

    2013-12-01

    Scientists, policy advisors, and the public struggle to synthesize the quickly evolving empirical work on climate change impacts. The Integrated Assessment Models (IAMs) used to estimate the impacts of climate change and the effects of adaptation and mitigation policies can also benefit greatly from recent empirical results (Kopp, Hsiang & Oppenheimer, Impacts World 2013 discussion paper). This paper details a new online tool for exploring, analyzing, combining, and communicating a wide range of impact results, and supporting their integration into IAMs. The tool uses a new database of statistical results, which researchers can expand both in depth (by providing additional results that describing existing relationships) and breadth (by adding new relationships). Scientists can use the tool to quickly perform meta-analyses of related results, using Bayesian techniques to produce pooled and partially-pooled posterior distributions. Policy advisors can apply the statistical results to particular contexts, and combine different kinds of results in a cost-benefit framework. For example, models of the impact of temperature changes on agricultural yields can be first aggregated to build a best-estimate of the effect under given assumptions, then compared across countries using different temperature scenarios, and finally combined to estimate a social cost of carbon. The general public can better understand the many estimates of climate impacts and their range of uncertainty by exploring these results dynamically, with maps, bar charts, and dose-response-style plots. Front page of the climate impacts tool website. Sample "collections" of models, within which all results are estimates of the same fundamental relationship, are shown on the right. Simple pooled result for Gelman's "8 schools" example. Pooled results are calculated analytically, while partial-pooling (Bayesian hierarchical estimation) uses posterior simulations.

  6. Borehole climatology: a discussion based on contributions from climate modeling

    NASA Astrophysics Data System (ADS)

    González-Rouco, J. F.; Beltrami, H.; Zorita, E.; Stevens, M. B.

    2008-01-01

    Progress in understanding climate variability through the last millennium leans on simulation and reconstruction efforts. Exercises blending both approaches present a great potential for answering questions relevant both for the simulation and reconstruction of past climate, and depend on the specific peculiarities of proxies and methods involved in climate reconstructions, as well as on the realism and limitations of model simulations. This paper explores research specifically related to paleoclimate modeling and borehole climatology as a branch of climate reconstruction that has contributed significantly to our knowledge of the low frequency climate evolution during the last five centuries. The text flows around three main issues that group most of the interaction between model and geothermal efforts: the use of models as a validation tool for borehole climate reconstructions; comparison of geothermal information and model simulations as a means of either model validation or inference about past climate; and implications of the degree of realism on simulating subsurface climate on estimations of future climate change. The use of multi-centennial simulations as a surrogate reality for past climate suggests that within the simplified reality of climate models, methods and assumptions in borehole reconstructions deliver a consistent picture of past climate evolution at long time scales. Comparison of model simulations and borehole profiles indicate that borehole temperatures are responding to past external forcing and that more realism in the development of the soil model components in climate models is desirable. Such an improved degree of realism is important for the simulation of subsurface climate and air-ground interaction; results indicate it could also be crucial for simulating the adequate energy balance within climate change scenario experiments.

  7. Borehole climatology: a discussion based on contributions from climate modeling

    NASA Astrophysics Data System (ADS)

    González-Rouco, J. F.; Beltrami, H.; Zorita, E.; Stevens, M. B.

    2009-03-01

    Progress in understanding climate variability through the last millennium leans on simulation and reconstruction efforts. Exercises blending both approaches present a great potential for answering questions relevant both for the simulation and reconstruction of past climate, and depend on the specific peculiarities of proxies and methods involved in climate reconstructions, as well as on the realism and limitations of model simulations. This paper explores research specifically related to paleoclimate modeling and borehole climatology as a branch of climate reconstruction that has contributed significantly to our knowledge of the low frequency climate evolution during the last five centuries. The text flows around three main issues that group most of the interaction between model and geothermal efforts: the use of models as a validation tool for borehole climate reconstructions; comparison of geothermal information and model simulations as a means of either model validation or inference about past climate; and implications of the degree of realism on simulating subsurface climate on estimations of future climate change. The use of multi-centennial simulations as a surrogate reality for past climate suggests that within the simplified reality of climate models, methods and assumptions in borehole reconstructions deliver a consistent picture of past climate evolution at long time scales. Comparison of model simulations and borehole profiles indicate that borehole temperatures are responding to past external forcing and that more realism in the development of the soil model components in climate models is desirable. Such an improved degree of realism is important for the simulation of subsurface climate and air-ground interaction; results indicate it could also be crucial for simulating the adequate energy balance within climate change scenario experiments.

  8. Mixing parameterizations in ocean climate modeling

    NASA Astrophysics Data System (ADS)

    Moshonkin, S. N.; Gusev, A. V.; Zalesny, V. B.; Byshev, V. I.

    2016-03-01

    Results of numerical experiments with an eddy-permitting ocean circulation model on the simulation of the climatic variability of the North Atlantic and the Arctic Ocean are analyzed. We compare the ocean simulation quality with using different subgrid mixing parameterizations. The circulation model is found to be sensitive to a mixing parametrization. The computation of viscosity and diffusivity coefficients by an original splitting algorithm of the evolution equations for turbulence characteristics is found to be as efficient as traditional Monin-Obukhov parameterizations. At the same time, however, the variability of ocean climate characteristics is simulated more adequately. The simulation of salinity fields in the entire study region improves most significantly. Turbulent processes have a large effect on the circulation in the long-term through changes in the density fields. The velocity fields in the Gulf Stream and in the entire North Atlantic Subpolar Cyclonic Gyre are reproduced more realistically. The surface level height in the Arctic Basin is simulated more faithfully, marking the Beaufort Gyre better. The use of the Prandtl number as a function of the Richardson number improves the quality of ocean modeling.

  9. Using climate model output to assess the impacts of climate change on water resources

    SciTech Connect

    Cushman, R.M.

    1990-01-01

    The use of general circulation models (GCMs) to provide climate data for regional assessments of the impacts of changing climate on water resources stretches the limits of what the models were designed for. Problems that must be addressed include disagreement on a regional scale among GCMs and between the modeled and observed climate; coarse spatial resolution of the models; and simplistic representation of surface hydrology. It is important that continued progress be made in developing the methodology for using GCM output in climate-impact assessments. 18 refs.

  10. Sensitivity of climate models: Comparison of simulated and observed patterns for past climates

    SciTech Connect

    Prell, W.L.; Webb, T. III.

    1992-08-01

    Predicting the potential climatic effects of increased concentrations of atmospheric carbon dioxide requires the continuing development of climate models. Confidence in the predictions will be much enhanced once the models are thoroughly tested in terms of their ability to simulate climates that differ significantly from today's climate. As one index of the magnitude of past climate change, the global mean temperature increase during the past 18,000 years is similar to that predicted for carbon dioxide--doubling. Simulating the climatic changes of the past 18,000 years, as well as the warmer-than-present climate of 6000 years ago and the climate of the last interglacial, around 126,000 years ago, provides an excellent opportunity to test the models that are being used in global climate change research. During the past several years, we have used paleoclimatic data to test the accuracy of the National Center for Atmospheric Research, Community Climate Model, Version 0, after changing its boundary conditions to those appropriate for past climates. We have assembled regional and near-global paleoclimatic data sets of pollen, lake level, and marine plankton data and calibrated many of the data in terms of climatic variables. We have also developed methods that permit direct quantitative comparisons between the data and model results. Our research has shown that comparing the model results with the data is an evolutionary process, because the models, the data, and the methods for comparison are continually being improved. During 1992, we have completed new modeling experiments, further analyzed previous model experiments, compiled new paleodata, made new comparisons between data and model results, and participated in workshops on paleoclimatic modeling.

  11. Soil Moisture Memory in Climate Models

    NASA Technical Reports Server (NTRS)

    Koster, Randal D.; Suarez, Max J.; Zukor, Dorothy J. (Technical Monitor)

    2000-01-01

    Water balance considerations at the soil surface lead to an equation that relates the autocorrelation of soil moisture in climate models to (1) seasonality in the statistics of the atmospheric forcing, (2) the variation of evaporation with soil moisture, (3) the variation of runoff with soil moisture, and (4) persistence in the atmospheric forcing, as perhaps induced by land atmosphere feedback. Geographical variations in the relative strengths of these factors, which can be established through analysis of model diagnostics and which can be validated to a certain extent against observations, lead to geographical variations in simulated soil moisture memory and thus, in effect, to geographical variations in seasonal precipitation predictability associated with soil moisture. The use of the equation to characterize controls on soil moisture memory is demonstrated with data from the modeling system of the NASA Seasonal-to-Interannual Prediction Project.

  12. Evolution of Climate Science Modelling Language within international standards frameworks

    NASA Astrophysics Data System (ADS)

    Lowe, Dominic; Woolf, Andrew

    2010-05-01

    The Climate Science Modelling Language (CSML) was originally developed as part of the NERC Data Grid (NDG) project in the UK. It was one of the first Geography Markup Language (GML) application schemas describing complex feature types for the metocean domain. CSML feature types can be used to describe typical climate products such as model runs or atmospheric profiles. CSML has been successfully used within NDG to provide harmonised access to a number of different data sources. For example, meteorological observations held in heterogeneous databases by the British Atmospheric Data Centre (BADC) and Centre for Ecology and Hydrology (CEH) were served uniformly as CSML features via Web Feature Service. CSML has now been substantially revised to harmonise it with the latest developments in OGC and ISO conceptual modelling for geographic information. In particular, CSML is now aligned with the near-final ISO 19156 Observations & Measurements (O&M) standard. CSML combines the O&M concept of 'sampling features' together with an observation result based on the coverage model (ISO 19123). This general pattern is specialised for particular data types of interest, classified on the basis of sampling geometry and topology. In parallel work, the OGC Met Ocean Domain Working Group has established a conceptual modelling activity. This is a cross-organisational effort aimed at reaching consensus on a common core data model that could be re-used in a number of met-related application areas: operational meteorology, aviation meteorology, climate studies, and the research community. It is significant to note that this group has also identified sampling geometry and topology as a key classification axis for data types. Using the Model Driven Architecture (MDA) approach as adopted by INSPIRE we demonstrate how the CSML application schema is derived from a formal UML conceptual model based on the ISO TC211 framework. By employing MDA tools which map consistently between UML and GML we

  13. Integrated approaches to climate-crop modelling: needs and challenges.

    PubMed

    Betts, Richard A

    2005-11-29

    This paper discusses the need for a more integrated approach to modelling changes in climate and crops, and some of the challenges posed by this. While changes in atmospheric composition are expected to exert an increasing radiative forcing of climate change leading to further warming of global mean temperatures and shifts in precipitation patterns, these are not the only climatic processes which may influence crop production. Changes in the physical characteristics of the land cover may also affect climate; these may arise directly from land use activities and may also result from the large-scale responses of crops to seasonal, interannual and decadal changes in the atmospheric state. Climate models used to drive crop models may, therefore, need to consider changes in the land surface, either as imposed boundary conditions or as feedbacks from an interactive climate-vegetation model. Crops may also respond directly to changes in atmospheric composition, such as the concentrations of carbon dioxide (CO2), ozone (03) and compounds of sulphur and nitrogen, so crop models should consider these processes as well as climate change. Changes in these, and the responses of the crops, may be intimately linked with meteorological processes so crop and climate models should consider synergies between climate and atmospheric chemistry. Some crop responses may occur at scales too small to significantly influence meteorology, so may not need to be included as feedbacks within climate models. However, the volume of data required to drive the appropriate crop models may be very large, especially if short-time-scale variability is important. Implementation of crop models within climate models would minimize the need to transfer large quantities of data between separate modelling systems. It should also be noted that crop responses to climate change may interact with other impacts of climate change, such as hydrological changes. For example, the availability of water for irrigation

  14. Mixing parametrizations for ocean climate modelling

    NASA Astrophysics Data System (ADS)

    Gusev, Anatoly; Moshonkin, Sergey; Diansky, Nikolay; Zalesny, Vladimir

    2016-04-01

    The algorithm is presented of splitting the total evolutionary equations for the turbulence kinetic energy (TKE) and turbulence dissipation frequency (TDF), which is used to parameterize the viscosity and diffusion coefficients in ocean circulation models. The turbulence model equations are split into the stages of transport-diffusion and generation-dissipation. For the generation-dissipation stage, the following schemes are implemented: the explicit-implicit numerical scheme, analytical solution and the asymptotic behavior of the analytical solutions. The experiments were performed with different mixing parameterizations for the modelling of Arctic and the Atlantic climate decadal variability with the eddy-permitting circulation model INMOM (Institute of Numerical Mathematics Ocean Model) using vertical grid refinement in the zone of fully developed turbulence. The proposed model with the split equations for turbulence characteristics is similar to the contemporary differential turbulence models, concerning the physical formulations. At the same time, its algorithm has high enough computational efficiency. Parameterizations with using the split turbulence model make it possible to obtain more adequate structure of temperature and salinity at decadal timescales, compared to the simpler Pacanowski-Philander (PP) turbulence parameterization. Parameterizations with using analytical solution or numerical scheme at the generation-dissipation step of the turbulence model leads to better representation of ocean climate than the faster parameterization using the asymptotic behavior of the analytical solution. At the same time, the computational efficiency left almost unchanged relative to the simple PP parameterization. Usage of PP parametrization in the circulation model leads to realistic simulation of density and circulation with violation of T,S-relationships. This error is majorly avoided with using the proposed parameterizations containing the split turbulence model

  15. Regional climate simulations over Vietnam using the WRF model

    NASA Astrophysics Data System (ADS)

    Raghavan, S. V.; Vu, M. T.; Liong, S. Y.

    2015-07-01

    We present an analysis of the present-day (1961-1990) regional climate simulations over Vietnam. The regional climate model Weather Research and Forecasting (WRF) was driven by the global reanalysis ERA40. The performance of the regional climate model in simulating the observed climate is evaluated with a main focus on precipitation and temperature. The regional climate model was able to reproduce the observed spatial patterns of the climate, although with some biases. The model also performed better in reproducing the extreme precipitation and the interannual variability. Overall, the WRF model was able to simulate the main regional signatures of climate variables, seasonal cycles, and frequency distributions. This study is an evaluation of the present-day climate simulations of a regional climate model at a resolution of 25 km. Given that dynamical downscaling has become common for studying climate change and its impacts, the study highlights that much more improvements in modeling might be necessary to yield realistic simulations of climate at high resolutions before they can be used for impact studies at a local scale. The need for a dense network of observations is also realized as observations at high resolutions are needed when it comes to evaluations and validations of models at sub-regional and local scales.

  16. Climate Impact of Transportation A Model Comparison

    SciTech Connect

    Girod, Bastien; Van Vuuren, Detlef; Grahn, Maria; Kitous, Alban; Kim, Son H.; Kyle, G. Page

    2013-06-01

    Transportation contributes to a significant and rising share of global energy use and GHG emissions. Therefore modeling future travel demand, its fuel use, and resulting CO2 emission is highly relevant for climate change mitigation. In this study we compare the baseline projections for global service demand (passenger-kilometers, ton-kilometers), fuel use, and CO2 emissions of five different global transport models using harmonized input assumptions on income and population. For four models we also evaluate the impact of a carbon tax. All models project a steep increase in service demand over the century. Technology is important for limiting energy consumption and CO2 emissions, but quite radical changes in the technology mix are required to stabilize or reverse the trend. While all models project liquid fossil fuels dominating up to 2050, they differ regarding the use of alternative fuels (natural gas, hydrogen, biofuels, and electricity), because of different fuel price projections. The carbon tax of US$200/tCO2 in 2050 stabilizes or reverses global emission growth in all models. Besides common findings many differences in the model assumptions and projections indicate room for improvement in modeling and empirical description of the transport system.

  17. Nested-model approach to investigate climate change

    USGS Publications Warehouse

    Hay, Lauren E.; Leavesley, George H.

    1994-01-01

    Determination of the spatial and temporal distribution of precipitation in mountainous regions is critical for assessing the effects of climate variability on water resources in these regions. Potential effects of climate change on water resources in the Gunnison River basin of southwestern Colorado currently are being studied using a nested-model approach to disaggregate large-scale general circulation model output to account for smaller-scale processes. This paper presents a disaggregation technique in which scenarios of possible climate change will be developed by nesting a global general circulation model, a mesoscale climate model, a local orographic precipitation model, and a watershed model.

  18. A 'Common Information Model' for the climate modelling process

    NASA Astrophysics Data System (ADS)

    Treshansky, Allyn; Devine, Gerard

    2010-05-01

    The Common Information Model (CIM), developed by the EU-funded METAFOR project (http://metaforclimate.eu), is a formal model of the climate modeling process. It provides a rich structured description of not only climate data but also the "provenance" of that data: the software models and tools used to generate that data, the simulations those models implement, the experiments those simulations conform to, etc.. This formal metadata model is expected to add value to those datasets by firstly codifying what is currently found only in the heads of climate experts (the aforementioned provenance of climate datasets), and secondly by allowing tools to be developed that make searching for and analysing climate datasets a much more intuitive process than it has been in the past. This paper will describe the structure of the CIM, concentrating on how it works with and what it adds to other metadata standards. As alluded to above, current metadata standards concentrate on the contents of a climate dataset. Scientific detail and relevance of the model components that generated that data as well as the context for why it was run are missing. The CIM addresses this gap. However, it does not aim to replace existing standards. Rather, wherever possible it re-uses them. It also attempts to standardise our understanding of climate modeling at a very high level, at a conceptual level. This results in a UML description of climate modeling, the CONCIM. METAFOR extracts from this high-level UML the bits of the CIM that we want to use in our applications; These bits get converted into a set of XSD application schemas, the APPCIM. Other user groups may derive a different APPCIM (in a different format) that suits them from the same CONCIM. Thus there is a common understanding of the concepts used in climate modeling even if the implementation differs. In certain key places the CIM describes a general structure over which a specific Controlled Vocabulary (CV) can be applied. For example

  19. Climate Model Dependency and Understanding the Antarctic Ice Sheet during the Warm Late Pliocene

    NASA Astrophysics Data System (ADS)

    Dolan, Aisling; de Boer, Bas; Bernales, Jorge; Hunter, Stephen; Haywood, Alan

    2016-04-01

    In the context of future climate change, understanding the nature and behaviour of ice sheets during warm intervals of Earth history is fundamentally important. A warm period in the Late Pliocene (3.264 to 3.025 million years before present) can serve as a potential analogue for projected future climates. Although Pliocene ice locations and extents are still poorly constrained, a significant contribution to sea-level rise should be expected from both the Greenland ice sheet and the West and East Antarctic ice sheets based on palaeo sea-level reconstructions and geological evidence. Following a five year international project PLISMIP (Pliocene Ice Sheet Modeling Intercomparison Project) we present the final set of results which quantify uncertainty in climate model-based predictions of the Antarctic ice sheet. In this study we use an ensemble of climate model forcings within a multi-ice sheet model framework to assess the climate (model) dependency of large scale features of the Antarctic ice sheet. Seven coupled atmosphere-ocean climate models are used to derive surface temperature, precipitation and oceanic forcing that drive three ice sheet models (over the grounded and floating domain). Similar to results presented over Greenland, we show that the reconstruction of the Antarctic ice sheet is sensitive to which climate model is used to provide the forcing field. Key areas of uncertainty include West Antarctica, the large subglacial basins of East Antarctica and the overall thickness of the continental interior of East Antarctica. We relate the results back to geological proxy data, such as those relating to exposure rates which provide information on potential ice sheet thickness. Finally we discuss as to whether the choice of modelling framework (i.e. climate model and ice sheet model used) or the choice of boundary conditions causes the greatest uncertainty in ice sheet reconstructions of the warm Pliocene.

  20. The climate of the Iberian Peninsula during the last five centuries from a regional climate model perspective.

    NASA Astrophysics Data System (ADS)

    Gomez-Navarro, J. J.; Montavez, J. P.; Jerez, S.; Garcia-Valero, J. A.; Jimenez-Guerrero, P.; Zorita, E.; Gonzalez-Rouco, J. F.

    2009-09-01

    During the last years the use of paleoclimate simulations with models of different complexity has become an usual tool in paleoclimate studies. Progress in understanding climate variability leans on simulation and reconstruction efforts. Exercises blending both approaches present a great potential for answering questions relevant for both the simulation and reconstruction of past climate, and depend on the specific peculiarities of proxies and methods involved in climate reconstructions, as well as on the realism and limitations of model simulations. Most of paleoclimate integrations available in the literature covering the last millennium have been performed with relative rough resolution which does not allow to analyze regional climate features that can be of interest in the context of proxies evidence. In this work we present a new high resolution (30 km) regional climate simulation over the Iberian Peninsula of the last five. The regional simulations were performed with a climate version of the MM5 model coupled to the Noah LSM. The driving conditions used follow the Erik1 experiment, performed with the ECHO-G global circulation model. The results indicate that the seasonal modes of variation for near surface air temperature and precipitation obtained within the regional paleoclimate experiment are consistent with the obtained using the observational databases and equivalent to regional climate integrations driven by reanalysis data. On the other hand, the main modes of variation show strong signals in historical periods such as the Maunder and Dalton Minimum. Finally, some preliminary comparisons between the global and the regional model against reconstructions are also reported in this contribution.

  1. Using Weather Data and Climate Model Output in Economic Analyses of Climate Change

    SciTech Connect

    Auffhammer, Maximilian; Hsiang, Solomon M.; Schlenker, Wolfram; Sobel, Adam H.

    2013-06-28

    Economists are increasingly using weather data and climate model output in analyses of the economic impacts of climate change. This article introduces a set of weather data sets and climate models that are frequently used, discusses the most common mistakes economists make in using these products, and identifies ways to avoid these pitfalls. We first provide an introduction to weather data, including a summary of the types of datasets available, and then discuss five common pitfalls that empirical researchers should be aware of when using historical weather data as explanatory variables in econometric applications. We then provide a brief overview of climate models and discuss two common and significant errors often made by economists when climate model output is used to simulate the future impacts of climate change on an economic outcome of interest.

  2. Aerosol Properties and Processes: A Path from Field and Laboratory Measurements to Global Climate Models

    SciTech Connect

    Ghan, Steven J.; Schwartz, Stephen E.

    2007-07-01

    Aerosols exert a substantial influence on climate and climate change through a variety of complex mechanisms. Consequently there is a need to represent aerosol effects in global climate models, and models have begun to include representations of these effects. However, the treatment of aerosols in current global climate models is presently highly simplified, omitting many important processes and feedbacks. Consequently there is need for substantial improvement. Here we describe the U. S. Department of Energy strategy for improving the treatment of aerosol properties and processes in global climate models. The strategy begins with a foundation of field and laboratory measurements that provide the basis for modules of selected aerosol properties and processes. These modules are then integrated in regional aerosol models, which are evaluated by comparing with field measurements. Issues of scale are then addressed so that the modules can be applied to global aerosol models, which are evaluated by comparing with global satellite measurements. Finally, the validated set of modules are applied to global climate models for multi-century simulations. This strategy can be applied to successive generations of global climate models.

  3. Coupled Climate Model Appraisal a Benchmark for Future Studies

    SciTech Connect

    Phillips, T J; AchutaRao, K; Bader, D; Covey, C; Doutriaux, C M; Fiorino, M; Gleckler, P J; Sperber, K R; Taylor, K E

    2005-08-22

    The Program for Climate Model Diagnosis and Intercomparison (PCMDI) has produced an extensive appraisal of simulations of present-day climate by eleven representative coupled ocean-atmosphere general circulation models (OAGCMs) which were developed during the period 1995-2002. Because projections of potential future global climate change are derived chiefly from OAGCMs, there is a continuing need to test the credibility of these predictions by evaluating model performance in simulating the historically observed climate. For example, such an evaluation is an integral part of the periodic assessments of climate change that are reported by the Intergovernmental Panel on Climate Change. The PCMDI appraisal thus provides a useful benchmark for future studies of this type. The appraisal mainly analyzed multi-decadal simulations of present-day climate by models that employed diverse representations of climate processes for atmosphere, ocean, sea ice, and land, as well as different techniques for coupling these components (see Table). The selected models were a subset of those entered in phase 2 of the Coupled Model Intercomparison Project (CMIP2, Covey et al. 2003). For these ''CMIP2+ models'', more atmospheric or oceanic variables were provided than the minimum requirements for participation in CMIP2. However, the appraisal only considered those climate variables that were supplied from most of the CMIP2+ models. The appraisal focused on three facets of the simulations of current global climate: (1) secular trends in simulation time series which would be indicative of a problematical ''coupled climate drift''; (2) comparisons of temporally averaged fields of simulated atmospheric and oceanic climate variables with available observational climatologies; and (3) correspondences between simulated and observed modes of climatic variability. Highlights of these climatic aspects manifested by different CMIP2+ simulations are briefly discussed here.

  4. Multi-Wheat-Model Ensemble Responses to Interannual Climate Variability

    NASA Technical Reports Server (NTRS)

    Ruane, Alex C.; Hudson, Nicholas I.; Asseng, Senthold; Camarrano, Davide; Ewert, Frank; Martre, Pierre; Boote, Kenneth J.; Thorburn, Peter J.; Aggarwal, Pramod K.; Angulo, Carlos

    2016-01-01

    We compare 27 wheat models' yield responses to interannual climate variability, analyzed at locations in Argentina, Australia, India, and The Netherlands as part of the Agricultural Model Intercomparison and Improvement Project (AgMIP) Wheat Pilot. Each model simulated 1981e2010 grain yield, and we evaluate results against the interannual variability of growing season temperature, precipitation, and solar radiation. The amount of information used for calibration has only a minor effect on most models' climate response, and even small multi-model ensembles prove beneficial. Wheat model clusters reveal common characteristics of yield response to climate; however models rarely share the same cluster at all four sites indicating substantial independence. Only a weak relationship (R2 0.24) was found between the models' sensitivities to interannual temperature variability and their response to long-termwarming, suggesting that additional processes differentiate climate change impacts from observed climate variability analogs and motivating continuing analysis and model development efforts.

  5. Modeling the Earth: Climate on an Icosphere

    NASA Astrophysics Data System (ADS)

    Fouts, Stephanie; Cook, L. Jonathan

    The totally asymmetric simple exclusion process with Langmuir kinetics is a one-dimensional transport model used to study the motion of particles through a lattice. Its applications include systems in the fields of biology, climatology, mathematics, civil engineering, and physics. In our research, we examine the temporal dynamics through the power spectra, as well as the time-averaged particle distribution on the lattice via Monte Carlo simulations. We have applied our particle transport model to an icosahedron in an attempt to model Earth's changing climate. In our research, we examine the temporal dynamics of the particle distribution on the lattice, as they correspond to seasonal heat fluctuations in the polar and equatorial regions of the globe. Using Monte Carlos simulations, we alter the input parameters of the system to explore the resultant actions of the Earth-system model. Our findings include seasonal oscillations consistent with those seen in reality. We also built a mathematical framework for our model which, when solved numerically, matches the oscillations seen in our physical system.

  6. A transient stochastic weather generator incorporating climate model uncertainty

    NASA Astrophysics Data System (ADS)

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

    2015-11-01

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

  7. Hybrid Surface Mesh Adaptation for Climate Modeling

    SciTech Connect

    Khamayseh, Ahmed K; de Almeida, Valmor F; Hansen, Glen

    2008-01-01

    Solution-driven mesh adaptation is becoming quite popular for spatial error control in the numerical simulation of complex computational physics applications, such as climate modeling. Typically, spatial adaptation is achieved by element subdivision (h adaptation) with a primary goal of resolving the local length scales of interest. A second, less-popular method of spatial adaptivity is called "mesh motion" (r adaptation); the smooth repositioning of mesh node points aimed at resizing existing elements to capture the local length scales. This paper proposes an adaptation method based on a combination of both element subdivision and node point repositioning (rh adaptation). By combining these two methods using the notion of a mobility function, the proposed approach seeks to increase the flexibility and extensibility of mesh motion algorithms while providing a somewhat smoother transition between refined regions than is produced by element subdivision alone. Further, in an attempt to support the requirements of a very general class of climate simulation applications, the proposed method is designed to accommodate unstructured, polygonal mesh topologies in addition to the most popular mesh types.

  8. Hybrid Surface Mesh Adaptation for Climate Modeling

    SciTech Connect

    Ahmed Khamayseh; Valmor de Almeida; Glen Hansen

    2008-10-01

    Solution-driven mesh adaptation is becoming quite popular for spatial error control in the numerical simulation of complex computational physics applications, such as climate modeling. Typically, spatial adaptation is achieved by element subdivision (h adaptation) with a primary goal of resolving the local length scales of interest. A second, less-popular method of spatial adaptivity is called “mesh motion” (r adaptation); the smooth repositioning of mesh node points aimed at resizing existing elements to capture the local length scales. This paper proposes an adaptation method based on a combination of both element subdivision and node point repositioning (rh adaptation). By combining these two methods using the notion of a mobility function, the proposed approach seeks to increase the flexibility and extensibility of mesh motion algorithms while providing a somewhat smoother transition between refined regions than is produced by element subdivision alone. Further, in an attempt to support the requirements of a very general class of climate simulation applications, the proposed method is designed to accommodate unstructured, polygonal mesh topologies in addition to the most popular mesh types.

  9. Assessing Statistical Model Assumptions under Climate Change

    NASA Astrophysics Data System (ADS)

    Varotsos, Konstantinos V.; Giannakopoulos, Christos; Tombrou, Maria

    2016-04-01

    The majority of the studies assesses climate change impacts on air-quality using chemical transport models coupled to climate ones in an off-line mode, for various horizontal resolutions and different present and future time slices. A complementary approach is based on present-day empirical relations between air-pollutants and various meteorological variables which are then extrapolated to the future. However, the extrapolation relies on various assumptions such as that these relationships will retain their main characteristics in the future. In this study we focus on the ozone-temperature relationship. It is well known that among a number of meteorological variables, temperature is found to exhibit the highest correlation with ozone concentrations. This has led, in the past years, to the development and application of statistical models with which the potential impact of increasing future temperatures on various ozone statistical targets was examined. To examine whether the ozone-temperature relationship retains its main characteristics under warmer temperatures we analyze the relationship during the heatwaves events of 2003 and 2006 in Europe. More specifically, we use available gridded daily maximum temperatures (E-OBS) and hourly ozone observations from different non-urban stations (EMEP) within the areas that were impacted from the two heatwave events. In addition, we compare the temperature distributions of the two events with temperatures from two different future time periods 2021-2050 and 2071-2100 from a number of regional climate models developed under the framework of the Cordex initiative (http://www.cordex.org) with a horizontal resolution of 12 x 12km, based on different IPCC RCPs emissions scenarios. A statistical analysis is performed on the ozone-temperature relationship for each station and for the two aforementioned years which are then compared against the ozone-temperature relationships obtained from the rest of the available dataseries. The

  10. Climate Model Diagnostic Analyzer Web Service System

    NASA Astrophysics Data System (ADS)

    Lee, S.; Pan, L.; Zhai, C.; Tang, B.; Jiang, J. H.

    2014-12-01

    We have developed a cloud-enabled web-service system that empowers physics-based, multi-variable model performance evaluations and diagnoses through the comprehensive and synergistic use of multiple observational data, reanalysis data, and model outputs. We have developed a methodology to transform an existing science application code into a web service using a Python wrapper interface and Python web service frameworks. The web-service system, called Climate Model Diagnostic Analyzer (CMDA), currently supports (1) all the observational datasets from Obs4MIPs and a few ocean datasets from NOAA and Argo, which can serve as observation-based reference data for model evaluation, (2) many of CMIP5 model outputs covering a broad range of atmosphere, ocean, and land variables from the CMIP5 specific historical runs and AMIP runs, and (3) ECMWF reanalysis outputs for several environmental variables in order to supplement observational datasets. Analysis capabilities currently supported by CMDA are (1) the calculation of annual and seasonal means of physical variables, (2) the calculation of time evolution of the means in any specified geographical region, (3) the calculation of correlation between two variables, (4) the calculation of difference between two variables, and (5) the conditional sampling of one physical variable with respect to another variable. A web user interface is chosen for CMDA because it not only lowers the learning curve and removes the adoption barrier of the tool but also enables instantaneous use, avoiding the hassle of local software installation and environment incompatibility. CMDA will be used as an educational tool for the summer school organized by JPL's Center for Climate Science in 2014. In order to support 30+ simultaneous users during the school, we have deployed CMDA to the Amazon cloud environment. The cloud-enabled CMDA will provide each student with a virtual machine while the user interaction with the system will remain the same

  11. Sensitivity of climate models: Comparison of simulated and observed patterns for past climates

    SciTech Connect

    Prell, W.L.; Webb, T. III; Oglesby, R.J.

    1991-10-01

    Predicting the potential climatic effects of increased concentrations of atmospheric carbon dioxide requires the continuing development of climate models. As one index of the magnitude of past climates change, the global mean temperature increase during the past 18,000 years is similar to that predicted for carbon dioxide doubling. Simulating the climate changes of the past 18,000 years, as well as the warmer-than-present climate of 6000 years ago and the climate of the last interglacial, around 126,000 years ago, provides an excellent opportunity to test the models that are being used in global climate change research. During the past several years, we have used paleoclimatic data to test the accuracy of the NCAR CCMO (National Center for Atmospheric Research, Community Climate Model, Version 0), after changing its boundary conditions to those appropriate for past climates. We have assembled near-global paleoclimatic data sets of pollen, lake level, and marine plankton data and calibrated many of the data in terms of climatic variables. We have also developed methods that permit direct quantitative comparisons between the data and model results. Our comparisons have shown both some of the strengths and weaknesses of the model. The research so far has shown the feasibility of our methods for comparing paleoclimatic data and model results. Our research has also shown that comparing the model results with the data is an evolutionary process, because the models, the data, and the methods for comparison are continually being improved. During 1991, we have continued our studies and this Progress Report documents the results to date. During this year, we have completed new modeling experiments, compiled new data sets, made new comparisons between data and model results, and participated in workshops on paleoclimatic modeling. 37 refs.

  12. Comparing the effects of climate and impact model uncertainty on climate impacts estimates for grain maize

    NASA Astrophysics Data System (ADS)

    Holzkämper, Annelie; Honti, Mark; Fuhrer, Jürg

    2015-04-01

    Crop models are commonly applied to estimate impacts of projected climate change and to anticipate suitable adaptation measures. Thereby, uncertainties from global climate models, regional climate models, and impacts models cascade down to impact estimates. It is essential to quantify and understand uncertainties in impact assessments in order to provide informed guidance for decision making in adaptation planning. A question that has hardly been investigated in this context is how sensitive climate impact estimates are to the choice of the impact model approach. In a case study for Switzerland we compare results of three different crop modelling approaches to assess the relevance of impact model choice in relation to other uncertainty sources. The three approaches include an expert-based, a statistical and a process-based model. With each approach impact model parameter uncertainty and climate model uncertainty (originating from climate model chain and downscaling approach) are accounted for. ANOVA-based uncertainty partitioning is performed to quantify the relative importance of different uncertainty sources. Results suggest that uncertainty in estimated yield changes originating from the choice of the crop modelling approach can be greater than uncertainty from climate model chains. The uncertainty originating from crop model parameterization is small in comparison. While estimates of yield changes are highly uncertain, the directions of estimated changes in climatic limitations are largely consistent. This leads us to the conclusion that by focusing on estimated changes in climate limitations, more meaningful information can be provided to support decision making in adaptation planning - especially in cases where yield changes are highly uncertain.

  13. Climate Science for a Sustainable Energy Future Test Bed and Data Infrastructure Final Report

    SciTech Connect

    Williams, Dean N.; Foster, I.; Van Dam, Kerstin Kleese; Shipman, G.

    2014-05-04

    The collaborative Climate Science for a Sustainable Energy Future (CSSEF) project started in July 2011 with the goal of accelerating the development of climate model components (i.e., atmosphere, ocean and sea ice, and land surface) and enhancing their predictive capabilities while incorporating uncertainty quantification (UQ). This effort required accessing and converting observational data sets into specialized model testing and verification data sets and building a model development test bed, where model components and sub-models can be rapidly evaluated. CSSEF’s prototype test bed demonstrated, how an integrated testbed could eliminate tedious activities associated with model development and evaluation, by providing the capability to constantly compare model output—where scientists store, acquire, reformat, regrid, and analyze data sets one-by-one—to observational measurements in a controlled test bed.

  14. Potato model uncertainty across common datasets and varying climate

    Technology Transfer Automated Retrieval System (TEKTRAN)

    A potato crop multi-model assessment was conducted to quantify variation among models and evaluate responses to climate change. Nine modeling groups simulated agronomic and climatic responses at low- (Chinoli, Bolivia and Gisozi, Burundi) and high- (Jyndevad, Denmark and Washington, United States) ...

  15. An Earth longwave radiation climate model

    NASA Technical Reports Server (NTRS)

    Yang, S. K.

    1984-01-01

    An Earth outgoing longwave radiation (OLWR) climate model was constructed for radiation budget study. Required information is provided by on empirical 100mb water vapor mixing ratio equation of the mixing ratio interpolation scheme. Cloud top temperature is adjusted so that the calculation would agree with NOAA scanning radiometer measurements. Both clear sky and cloudy sky cases are calculated and discussed for global average, zonal average and world-wide distributed cases. The results agree well with the satellite observations. The clear sky case shows that the OLWR field is highly modulated by water vapor, especially in the tropics. The strongest longitudinal variation occurs in the tropics. This variation can be mostly explained by the strong water vapor gradient. Although in the zonal average case the tropics have a minimum in OLWR, the minimum is essentially contributed by a few very low flux regions, such as the Amazon, Indonesian and the Congo.

  16. Linking Output from regional Climat Models with Cryosphere Models

    NASA Astrophysics Data System (ADS)

    Winter, S.

    2003-04-01

    This study has the objective of linking the results of a low-resolution regional climate model (RCM) with high-resolution cryosphere models in order to determine the manner in which Alpine snow, ice and permafrost is likely to respond to enhanced atmospheric warming resulting from an increase in anthropogenic greenhouse gases. There are several constraints that need to be overcome prior to applying solutions to this problem. Firstly, as a result of the long response time of glaciers and alpine permafrost to climate change, long-term simulations of at least 30 years are required. Secondly, the smallest possible spatial resolution of current RCM still remains quite coarse (~ 50 km) because of the complex mathematical equations to be resolved in the RCM, the limited computer performance and the above mentioned long simulation period. On the other hand, cryosphere models used in the present study require gridded input climate variables with a typical mesh width of 50 m. The proposed solution consists in combining climate change data based on RCM scenarios with meteorological data of high elevation Alpine stations measured during a reference period. A RCM control run matching this reference period is required in order to quantify the expected change for each climate parameter. This approach allows breaking down the initial downscaling problem into two separate steps. First, the quantified change derived from RCM-control and scenario simulations is used to predict change for meteorological stations. Second, data sets of predicted change and meteorological measures of these stations are summed and then regionalized for the study area based on advanced algorithms and GIS techniques. Selecting a case study area close to one or more meteorological stations should minimize the associated regionalization error. A pilot study for a small area at Piz Corvatsch in the Eastern Swiss Alps has been designed. The A2 scenario of the IPCC (Intergovernmental Panel on Climate Change

  17. Evaluation of the regional climate model ALADIN to simulate the climate over North America in the CORDEX framework

    NASA Astrophysics Data System (ADS)

    Lucas-Picher, Philippe; Somot, Samuel; Déqué, Michel; Decharme, Bertrand; Alias, Antoinette

    2013-09-01

    In this study, an ensemble of four multi-year climate simulations is performed with the regional climate model ALADIN to evaluate its ability to simulate the climate over North America in the CORDEX framework. The simulations differ in their driving fields (ERA-40 or ERA-Interim) and the nudging technique (with or without large-scale nudging). The validation of the simulated 2-m temperature and precipitation with observationally-based gridded data sets shows that ALADIN performs similarly to other regional climate models that are commonly used over North America. Large-scale nudging improves the temporal correlation of the atmospheric circulation between ALADIN and its driving field, and also reduces the warm and dry summer biases in central North America. The differences between the simulations driven with different reanalyses are small and are likely related to the regional climate model’s induced internal variability. In general, the impact of different driving fields on ALADIN is smaller than that of large-scale nudging. The analysis of the multi-year simulations over the prairie and the east taiga indicates that the ALADIN 2-m temperature and precipitation interannual variability is similar or larger than that observed. Finally, a comparison of the simulations with observations for the summer 1993 shows that ALADIN underestimates the flood in central North America mainly due to its systematic dry bias in this region. Overall, the results indicate that ALADIN can produce a valuable contribution to CORDEX over North America.

  18. 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

  19. Evaluation of the new EMAC-SWIFT chemistry climate model

    NASA Astrophysics Data System (ADS)

    Scheffler, Janice; Langematz, Ulrike; Wohltmann, Ingo; Rex, Markus

    2016-04-01

    It is well known that the representation of atmospheric ozone chemistry in weather and climate models is essential for a realistic simulation of the atmospheric state. Including atmospheric ozone chemistry into climate simulations is usually done by prescribing a climatological ozone field, by including a fast linear ozone scheme into the model or by using a climate model with complex interactive chemistry. While prescribed climatological ozone fields are often not aligned with the modelled dynamics, a linear ozone scheme may not be applicable for a wide range of climatological conditions. Although interactive chemistry provides a realistic representation of atmospheric chemistry such model simulations are computationally very expensive and hence not suitable for ensemble simulations or simulations with multiple climate change scenarios. A new approach to represent atmospheric chemistry in climate models which can cope with non-linearities in ozone chemistry and is applicable to a wide range of climatic states is the Semi-empirical Weighted Iterative Fit Technique (SWIFT) that is driven by reanalysis data and has been validated against observational satellite data and runs of a full Chemistry and Transport Model. SWIFT has recently been implemented into the ECHAM/MESSy (EMAC) chemistry climate model that uses a modular approach to climate modelling where individual model components can be switched on and off. Here, we show first results of EMAC-SWIFT simulations and validate these against EMAC simulations using the complex interactive chemistry scheme MECCA, and against observations.

  20. Integrated assessment models of global climate change

    SciTech Connect

    Parson, E.A.; Fisher-Vanden, K.

    1997-12-31

    The authors review recent work in the integrated assessment modeling of global climate change. This field has grown rapidly since 1990. Integrated assessment models seek to combine knowledge from multiple disciplines in formal integrated representations; inform policy-making, structure knowledge, and prioritize key uncertainties; and advance knowledge of broad system linkages and feedbacks, particularly between socio-economic and bio-physical processes. They may combine simplified representations of the socio-economic determinants of greenhouse gas emissions, the atmosphere and oceans, impacts on human activities and ecosystems, and potential policies and responses. The authors summarize current projects, grouping them according to whether they emphasize the dynamics of emissions control and optimal policy-making, uncertainty, or spatial detail. They review the few significant insights that have been claimed from work to date and identify important challenges for integrated assessment modeling in its relationships to disciplinary knowledge and to broader assessment seeking to inform policy- and decision-making. 192 refs., 2 figs.

  1. On quantifying the climate of the nonautonomous Lorenz-63 model.

    PubMed

    Daron, J D; Stainforth, D A

    2015-04-01

    The Lorenz-63 model has been frequently used to inform our understanding of the Earth's climate and provide insight for numerical weather and climate prediction. Most studies have focused on the autonomous (time invariant) model behaviour in which the model's parameters are constants. Here, we investigate the properties of the model under time-varying parameters, providing a closer parallel to the challenges of climate prediction, in which climate forcing varies with time. Initial condition (IC) ensembles are used to construct frequency distributions of model variables, and we interpret these distributions as the time-dependent climate of the model. Results are presented that demonstrate the impact of ICs on the transient behaviour of the model climate. The location in state space from which an IC ensemble is initiated is shown to significantly impact the time it takes for ensembles to converge. The implication for climate prediction is that the climate may-in parallel with weather forecasting-have states from which its future behaviour is more, or less, predictable in distribution. Evidence of resonant behaviour and path dependence is found in model distributions under time varying parameters, demonstrating that prediction in nonautonomous nonlinear systems can be sensitive to the details of time-dependent forcing/parameter variations. Single model realisations are shown to be unable to reliably represent the model's climate; a result which has implications for how real-world climatic timeseries from observation are interpreted. The results have significant implications for the design and interpretation of Global Climate Model experiments. PMID:25933651

  2. Geospatial Issues in Energy-Climate Modeling: Implications for Modelers, Economists, Climate Scientists and Policy Makers

    NASA Astrophysics Data System (ADS)

    Newmark, R. L.; Arent, D.; Sullivan, P.; Short, W.

    2010-12-01

    Accurate characterizations of renewable energy technologies, particularly wind, solar, geothermal, and biomass, require an increasingly sophisticated understanding of location-specific attributes, including generation or production costs and the cost of transmission or transportation to a point of use, and climate induced changes to the resource base. Capturing these site-specific characteristics in national and global models presents both unique opportunities and challenges. National and global decisions, ideally, should be informed by geospatially rich data and analysis. Here we describe issues related to and initial advances in representing renewable energy technologies in global models, and the resulting implications for climate stabilization analysis and global assessments, including IPCC’s Assessment Round 5 and IEA’s World Energy Outlook.

  3. Climate Modeling with a Million CPUs

    NASA Astrophysics Data System (ADS)

    Tobis, M.; Jackson, C. S.

    2010-12-01

    Michael Tobis, Ph.D. Research Scientist Associate University of Texas Institute for Geophysics Charles S. Jackson Research Scientist University of Texas Institute for Geophysics Meteorological, oceanographic, and climatological applications have been at the forefront of scientific computing since its inception. The trend toward ever larger and more capable computing installations is unabated. However, much of the increase in capacity is accompanied by an increase in parallelism and a concomitant increase in complexity. An increase of at least four additional orders of magnitude in the computational power of scientific platforms is anticipated. It is unclear how individual climate simulations can continue to make effective use of the largest platforms. Conversion of existing community codes to higher resolution, or to more complex phenomenology, or both, presents daunting design and validation challenges. Our alternative approach is to use the expected resources to run very large ensembles of simulations of modest size, rather than to await the emergence of very large simulations. We are already doing this in exploring the parameter space of existing models using the Multiple Very Fast Simulated Annealing algorithm, which was developed for seismic imaging. Our experiments have the dual intentions of tuning the model and identifying ranges of parameter uncertainty. Our approach is less strongly constrained by the dimensionality of the parameter space than are competing methods. Nevertheless, scaling up remains costly. Much could be achieved by increasing the dimensionality of the search and adding complexity to the search algorithms. Such ensemble approaches scale naturally to very large platforms. Extensions of the approach are anticipated. For example, structurally different models can be tuned to comparable effectiveness. This can provide an objective test for which there is no realistic precedent with smaller computations. We find ourselves inventing new code to

  4. Usage of web-GIS platform Climate to prepare specialists in climate changes modeling and analysis

    NASA Astrophysics Data System (ADS)

    Gordova, Yulia; Martynova, Yulia; Shulgina, Tamara

    2014-05-01

    A web-GIS based platform "Climate" developed in our institute (http://climate.scert.ru/) has a set of tools and data bases to perform climate changes analysis on the selected territory. The platform is functioning and open for registration and all these tools are available. Besides that the platform has a potential to be used in education. It contains several educational courses followed by tests and trainings which are performed within the platform "Climate" using its web-gis tools. The main purpose of a new "Climatic and environmental modeling" module course is to enable students and graduates meteorological departments to improve their knowledge and skills in modern climatology. Although the emphasis is on climate science, the course is directly related to the part of the ecological science, which refers to the environment. This is due to the fact that the current global climate models have become models of the Earth system and include models of environment as well. The module includes a main course of lectures devoted to basic aspects of modern climatology , including analysis of the current climate change and its possible consequences , a special course on geophysical hydrodynamics, several on-line computing labs dedicated to specific monitoring and modeling of climate and climate change , as well as information kit , which not only includes the usual list of recommended reading, but also contains the files of many publications , the distribution of which is not limited by copyright law. Laboratory exercises are designed to consolidate students' knowledge of discipline, to instill in them the skills to work independently with large amounts of geophysical data using modern processing and analysis tools of web-GIS platform "Climate". The results obtained on laboratory work are presented as reports with the statement of the problem, the results of calculations and logically justified conclusion. Now the following labs are used to train and prepare young

  5. A framework for modeling uncertainty in regional climate change (Invited)

    NASA Astrophysics Data System (ADS)

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

    2013-12-01

    In this study, we present a new modeling framework and a large ensemble of climate projections to investigate the uncertainty in regional climate change over the United States associated with four dimensions of uncertainty. The sources of uncertainty considered in this framework are the emissions projections (using different climate policies), the climate system response (represented by different values of climate sensitivity and net aerosol forcing), natural variability (by perturbing initial conditions) and structural uncertainty (using different climate models). The modeling framework revolves around the Massachusetts Institute of Technology (MIT) Integrated Global System Model (IGSM), an integrated assessment model with an intermediate complexity earth system model (with a two-dimensional zonal-mean atmosphere). Regional climate change over the United States is obtained through a two-pronged approach. First, we use the IGSM-CAM framework which links the IGSM to the National Center for Atmospheric Research (NCAR) Community Atmosphere Model (CAM). Secondly, we use a pattern-scaling method that extends the IGSM zonal mean based on climate change patterns from various climate models. Results show that uncertainty in temperature changes are mainly driven by policy choices and the range of climate sensitivity considered. Meanwhile, the four sources of uncertainty contribute more equally to precipitation changes, with natural variability having a large impact in the first part of the 21st century. Overall, the choice of policy is the largest driver of uncertainty in future projections of climate change over the United States. In light of these results, we recommend that when investigating climate change impacts over specific regions, studies consider all four sources of uncertainty analyzed in this paper.

  6. Projections of Extreme Precipitation Events in India from regional and global climate model

    NASA Astrophysics Data System (ADS)

    Modi, P. A.; Shah, R.; Mishra, V.

    2014-12-01

    Extreme precipitation events pose tremendous challenges for humans and infrastructure. Precipitation extremes are projected to increase under the future climate. We examined changes in extreme precipitation events under the projected future climate in India from regional and global climate models. We obtained CMIP5 projections for 32 general circulation models (GCMs), while data for regional climate models (RCMs) were obtained from the CORDEX South Asia program. The data were analyzed for the historic (1971-1999) and projected future climate (2006-2060) for annual maximum precipitation, frequency of extreme precipitation events, mean intensity of top five precipitation events, and ratio of heavy to non-heavy precipitation. Out of the 32 GCMs, we selected the four best GCMs (BEST-GCMs) that performed better for extreme precipitation events in India. Moreover, we selected the host GCMs (HOST-GCMs) that were used as a boundary condition for the CORDEX-RCMs. We finally compared projections of extreme precipitation events from the BEST-GCMs, HOST-GCMs, and CORDEX-RCMs under the future climate. We find that the CORDEX-RCMs show a large inter-model variation leading to a high uncertainty in projections. Overall, most of the models indicate increases in extreme precipitation events under the projected future climate predominantly in the Southern peninsula.

  7. Deficiencies in the simulation of the geographic distribution of climate types by global climate models

    NASA Astrophysics Data System (ADS)

    Zhang, Xianliang; Yan, Xiaodong

    2016-05-01

    The performances of General Circulation Models (GCMs) when checked with conventional methods (i.e. correlation, bias, root-mean-square error) can only be evaluated for each variable individually. The geographic distribution of climate type in GCM simulations, which reflects the spatial attributes of models and is related closely to the terrestrial biosphere, has not yet been evaluated. Thus, whether the geographic distribution of climate types was well simulated by GCMs was evaluated in this study for nine GCMs. The results showed that large areas of climate zones classified by the GCMs were allocated incorrectly when compared to the basic climate zones established by observed data. The percentages of wrong areas covered approximately 30-50 % of the total land area for most models. In addition, the temporal shift in the distribution of climate zones according to the GCMs was found to be inaccurate. Not only were the locations of shifts poorly simulated, but also the areas of shift in climate zones. Overall, the geographic distribution of climate types was not simulated well by the GCMs, nor was the temporal shift in the distribution of climate zones. Thus, a new method on how to evaluate the simulated distribution of climate types for GCMs was provided in this study.

  8. Socioeconomic Drought in a Changing Climate: Modeling and Management

    NASA Astrophysics Data System (ADS)

    AghaKouchak, Amir; Mehran, Ali; Mazdiyasni, Omid

    2016-04-01

    Drought is typically defined based on meteorological, hydrological and land surface conditions. However, in many parts of the world, anthropogenic changes and water management practices have significantly altered local water availability. Socioeconomic drought refers to conditions whereby the available water supply cannot satisfy the human and environmental water needs. Surface water reservoirs provide resilience against local climate variability (e.g., droughts), and play a major role in regional water management. This presentation focuses on a framework for describing socioeconomic drought based on both water supply and demand information. We present a multivariate approach as a measure of socioeconomic drought, termed Multivariate Standardized Reliability and Resilience Index (MSRRI; Mehran et al., 2015). This model links the information on inflow and surface reservoir storage to water demand. MSRRI integrates a "top-down" and a "bottom-up" approach for describing socioeconomic drought. The "top-down" component describes processes that cannot be simply controlled or altered by local decision-makers and managers (e.g., precipitation, climate variability, climate change), whereas the "bottom-up" component focuses on the local resilience, and societal capacity to respond to droughts. The two components (termed, Inflow-Demand Reliability (IDR) indicator and Water Storage Resilience (WSR) indicator) are integrated using a nonparametric multivariate approach. We use this framework to assess the socioeconomic drought during the Australian Millennium Drought (1998-2010) and the 2011-2014 California Droughts. MSRRI provides additional information on socioeconomic drought onset, development and termination based on local resilience and human demand that cannot be obtained from the commonly used drought indicators. We show that MSRRI can be used for water management scenario analysis (e.g., local water availability based on different human water demands scenarios). Finally

  9. Climate Forced Alpine Tundra Ecosystem Dynamics: A Model Approach

    NASA Astrophysics Data System (ADS)

    Jarosch, A. H.; Clarke, G. K.; Danby, R. K.; Hik, D. S.

    2007-12-01

    Insights concerning the future evolution of alpine ecosystems depend on understanding and simulating their response to climate change. Comprehensive studies of these regions require novel spatio-temporal computational models of climate-forced landscape/ecosystem interactions. As part of the International Polar Year (IPY) we are examining alpine tundra landscapes and ecosystems in the Kluane region of southwest Yukon, Canada. Based on the combination of long-term geophysical and ecological field studies and driven by different climate change scenarios, such a model is being used to explore the range of possible future scenarios for the region. As the first step in building such a complex model, we present a simplified, grid-based model to demonstrate potential changes in plant community distribution driven by key climate variables such as temperature and precipitation. A linear orographic precipitation model is used to downscale climate data which, in combination with a digital elevation model, forms the geophysical input for the model. Simplified ecological rules describing the potential state transition of different plant communities and land cover types are incorporated in the model in a cellular automation fashion. The response of the ecosystem to several different climate scenarios will be presented, including a set of North American Regional Reanalysis climate data. This simplified model is used to demonstrate the potential of such interdisciplinary simulations to gain deeper understanding of ecosystem evolution with climate change.

  10. The Community Climate System Model Version 4

    SciTech Connect

    Gent, Peter R.; Danabasoglu, Gokhan; Donner, Leo J.; Holland, Marika M.; Hunke, Elizabeth C.; Jayne, Steve R.; Lawrence, David M.; Neale, Richard; Rasch, Philip J.; Vertenstein, Mariana; Worley, Patrick; Yang, Zong-Liang; Zhang, Minghua

    2011-10-01

    The fourth version of the Community Climate System Model (CCSM4) was recently completed and released to the climate community. This paper describes developments to all the CCSM components, and documents fully coupled pre-industrial control runs compared to the previous version, CCSM3. Using the standard atmosphere and land resolution of 1{sup o} results in the sea surface temperature biases in the major upwelling regions being comparable to the 1.4{sup o} resolution CCSM3. Two changes to the deep convection scheme in the atmosphere component result in the CCSM4 producing El Nino/Southern Oscillation variability with a much more realistic frequency distribution than the CCSM3, although the amplitude is too large compared to observations. They also improve the representation of the Madden-Julian Oscillation, and the frequency distribution of tropical precipitation. A new overflow parameterization in the ocean component leads to an improved simulation of the deep ocean density structure, especially in the North Atlantic. Changes to the CCSM4 land component lead to a much improved annual cycle of water storage, especially in the tropics. The CCSM4 sea ice component uses much more realistic albedos than the CCSM3, and the Arctic sea ice concentration is improved in the CCSM4. An ensemble of 20th century simulations runs produce an excellent match to the observed September Arctic sea ice extent from 1979 to 2005. The CCSM4 ensemble mean increase in globally-averaged surface temperature between 1850 and 2005 is larger than the observed increase by about 0.4 C. This is consistent with the fact that the CCSM4 does not include a representation of the indirect effects of aerosols, although other factors may come into play. The CCSM4 still has significant biases, such as the mean precipitation distribution in the tropical Pacific Ocean, too much low cloud in the Arctic, and the latitudinal distributions of short-wave and long-wave cloud forcings.

  11. Final Report for the portion performed in the University of Illinois on the project entitled "Optimizing the Cloud-Aerosol-Radiation Ensemble Modeling System to Improve Future Climate Change Projections at Regional to Local Scales"

    SciTech Connect

    Liang, Xin-Zhong

    2011-01-31

    This is the final report for the closure of the research tasks on the project that have performed during the entire reporting period in the University of Illinois. It contains a summary of the achievements and details of key results as well as the future plan for this project to be continued in the University of Maryland.

  12. Design of a regional climate modelling projection ensemble experiment - NARCliM

    NASA Astrophysics Data System (ADS)

    Evans, J. P.; Ji, F.; Lee, C.; Smith, P.; Argüeso, D.; Fita, L.

    2014-04-01

    Including the impacts of climate change in decision making and planning processes is a challenge facing many regional governments including the New South Wales (NSW) and Australian Capital Territory (ACT) governments in Australia. NARCliM (NSW/ACT Regional Climate Modelling project) is a regional climate modelling project that aims to provide a comprehensive and consistent set of climate projections that can be used by all relevant government departments when considering climate change. To maximise end user engagement and ensure outputs are relevant to the planning process, a series of stakeholder workshops were run to define key aspects of the model experiment including spatial resolution, time slices, and output variables. As with all such experiments, practical considerations limit the number of ensemble members that can be simulated such that choices must be made concerning which global climate models (GCMs) to downscale from, and which regional climate models (RCMs) to downscale with. Here a methodology for making these choices is proposed that aims to sample the uncertainty in both GCM and RCM ensembles, as well as spanning the range of future climate projections present in the GCM ensemble. The RCM selection process uses performance evaluation metrics to eliminate poor performing models from consideration, followed by explicit consideration of model independence in order to retain as much information as possible in a small model subset. In addition to these two steps the GCM selection process also considers the future change in temperature and precipitation projected by each GCM. The final GCM selection is based on a subjective consideration of the GCM independence and future change. The created ensemble provides a more robust view of future regional climate changes. Future research is required to determine objective criteria that could replace the subjective aspects of the selection process.

  13. Modeling climate change impacts on groundwater resources using transient stochastic climatic scenarios

    NASA Astrophysics Data System (ADS)

    Goderniaux, Pascal; BrouyèRe, Serge; Blenkinsop, Stephen; Burton, Aidan; Fowler, Hayley J.; Orban, Philippe; Dassargues, Alain

    2011-12-01

    Several studies have highlighted the potential negative impact of climate change on groundwater reserves, but additional work is required to help water managers plan for future changes. In particular, existing studies provide projections for a stationary climate representative of the end of the century, although information is demanded for the near future. Such time-slice experiments fail to account for the transient nature of climatic changes over the century. Moreover, uncertainty linked to natural climate variability is not explicitly considered in previous studies. In this study we substantially improve upon the state-of-the-art by using a sophisticated transient weather generator in combination with an integrated surface-subsurface hydrological model (Geer basin, Belgium) developed with the finite element modeling software "HydroGeoSphere." This version of the weather generator enables the stochastic generation of large numbers of equiprobable climatic time series, representing transient climate change, and used to assess impacts in a probabilistic way. For the Geer basin, 30 equiprobable climate change scenarios from 2010 to 2085 have been generated for each of six different regional climate models (RCMs). Results show that although the 95% confidence intervals calculated around projected groundwater levels remain large, the climate change signal becomes stronger than that of natural climate variability by 2085. Additionally, the weather generator's ability to simulate transient climate change enabled the assessment of the likely time scale and associated uncertainty of a specific impact, providing managers with additional information when planning further investment. This methodology constitutes a real improvement in the field of groundwater projections under climate change conditions.

  14. Climate and Land Use Change Effects on Ecological Resources in Three Watersheds: A Synthesis Report (Final Report)

    EPA Science Inventory

    The purpose of this final report is to provide a summary of climate change impacts to selected watersheds and recommendations for how to improve the process of conducting watershed assessments in the future.

  15. Understanding climate: A strategy for climate modeling and predictability research, 1985-1995

    NASA Technical Reports Server (NTRS)

    Thiele, O. (Editor); Schiffer, R. A. (Editor)

    1985-01-01

    The emphasis of the NASA strategy for climate modeling and predictability research is on the utilization of space technology to understand the processes which control the Earth's climate system and it's sensitivity to natural and man-induced changes and to assess the possibilities for climate prediction on time scales of from about two weeks to several decades. Because the climate is a complex multi-phenomena system, which interacts on a wide range of space and time scales, the diversity of scientific problems addressed requires a hierarchy of models along with the application of modern empirical and statistical techniques which exploit the extensive current and potential future global data sets afforded by space observations. Observing system simulation experiments, exploiting these models and data, will also provide the foundation for the future climate space observing system, e.g., Earth observing system (EOS), 1985; Tropical Rainfall Measuring Mission (TRMM) North, et al. NASA, 1984.

  16. THE REGRESSION MODEL OF IRAN LIBRARIES ORGANIZATIONAL CLIMATE

    PubMed Central

    Jahani, Mohammad Ali; Yaminfirooz, Mousa; Siamian, Hasan

    2015-01-01

    Background: The purpose of this study was to drawing a regression model of organizational climate of central libraries of Iran’s universities. Methods: This study is an applied research. The statistical population of this study consisted of 96 employees of the central libraries of Iran’s public universities selected among the 117 universities affiliated to the Ministry of Health by Stratified Sampling method (510 people). Climate Qual localized questionnaire was used as research tools. For predicting the organizational climate pattern of the libraries is used from the multivariate linear regression and track diagram. Results: of the 9 variables affecting organizational climate, 5 variables of innovation, teamwork, customer service, psychological safety and deep diversity play a major role in prediction of the organizational climate of Iran’s libraries. The results also indicate that each of these variables with different coefficient have the power to predict organizational climate but the climate score of psychological safety (0.94) plays a very crucial role in predicting the organizational climate. Track diagram showed that five variables of teamwork, customer service, psychological safety, deep diversity and innovation directly effects on the organizational climate variable that contribution of the team work from this influence is more than any other variables. Conclusions: Of the indicator of the organizational climate of climateQual, the contribution of the team work from this influence is more than any other variables that reinforcement of teamwork in academic libraries can be more effective in improving the organizational climate of this type libraries. PMID:26622203

  17. The cascade of uncertainty in modeling the impacts of climate change on Europe's forests

    NASA Astrophysics Data System (ADS)

    Reyer, Christopher; Lasch-Born, Petra; Suckow, Felicitas; Gutsch, Martin

    2015-04-01

    decreasing trends are mostly found in already warm and dry regions despite large differences in model structure, model parameters and climate change scenarios that induce considerable uncertainty into future projections. We also show that there are data assimilation techniques available to assess some types of uncertainties but also that many climate change impact assessment in forest ecosystems (including those presented here as well as observational and experimental studies) have focused to a large extent on testing the response of plants to changes in mean climate rather than climatic extremes. The latter may however ultimately shape the responses to a driving variable in reality. Finally, we highlight how these uncertainties culminate in increasingly complex management of natural resources in coupled social-ecological systems.

  18. Pleistocene Climate, Phylogeny, and Climate Envelope Models: An Integrative Approach to Better Understand Species' Response to Climate Change

    PubMed Central

    Lawing, A. Michelle; Polly, P. David

    2011-01-01

    Mean annual temperature reported by the Intergovernmental Panel on Climate Change increases at least 1.1°C to 6.4°C over the next 90 years. In context, a change in climate of 6°C is approximately the difference between the mean annual temperature of the Last Glacial Maximum (LGM) and our current warm interglacial. Species have been responding to changing climate throughout Earth's history and their previous biological responses can inform our expectations for future climate change. Here we synthesize geological evidence in the form of stable oxygen isotopes, general circulation paleoclimate models, species' evolutionary relatedness, and species' geographic distributions. We use the stable oxygen isotope record to develop a series of temporally high-resolution paleoclimate reconstructions spanning the Middle Pleistocene to Recent, which we use to map ancestral climatic envelope reconstructions for North American rattlesnakes. A simple linear interpolation between current climate and a general circulation paleoclimate model of the LGM using stable oxygen isotope ratios provides good estimates of paleoclimate at other time periods. We use geologically informed rates of change derived from these reconstructions to predict magnitudes and rates of change in species' suitable habitat over the next century. Our approach to modeling the past suitable habitat of species is general and can be adopted by others. We use multiple lines of evidence of past climate (isotopes and climate models), phylogenetic topology (to correct the models for long-term changes in the suitable habitat of a species), and the fossil record, however sparse, to cross check the models. Our models indicate the annual rate of displacement in a clade of rattlesnakes over the next century will be 2 to 3 orders of magnitude greater (430-2,420 m/yr) than it has been on average for the past 320 ky (2.3 m/yr). PMID:22164305

  19. 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. PMID:24043872

  20. Storm Water Management Model Climate Adjustment Tool (SWMM-CAT)

    EPA Science Inventory

    The US EPA’s newest tool, the Stormwater Management Model (SWMM) – Climate Adjustment Tool (CAT) is meant to help municipal stormwater utilities better address potential climate change impacts affecting their operations. SWMM, first released in 1971, models hydrology and hydrauli...

  1. CONSTABLE: A Global Climate Model for Classroom Use.

    ERIC Educational Resources Information Center

    Cerveny, Randall S.; And Others

    1985-01-01

    Described is the global climate model CONSTABLE (Climatic One-Dimensional Numerical Simulation of the Annual Balance of Latitudinal Energy), which can be used in undergraduate and graduate level climatology courses. Classroom exercises that can be used with the model are also included. (RM)

  2. Arctic Climate Change Analysed By Two 30-year Scenario Regional Climate Model Runs

    NASA Astrophysics Data System (ADS)

    Kiilsholm, S.; Christensen, J. H.

    High-resolution climate change simulations for an area covering the entire Arctic have been conducted with the regional climate model (RCM) HIRHAM. The emission sce- narios used were the IPCC SRES1 marker scenarios A2 and B2. Three 30-year time slice experiments were conducted with HIRHAM for periods representing present-day (1961-1990) and the future (2071-2100) in the two scenarios. Changes of the climate between these two periods will be presented with special emphasize on the climate of Greenland.

  3. Forest fire risk assessment in Sweden using climate model data: bias correction and future changes

    NASA Astrophysics Data System (ADS)

    Yang, W.; Gardelin, M.; Olsson, J.; Bosshard, T.

    2015-01-01

    As the risk for a forest fire is largely influenced by weather, evaluating its tendency under a changing climate becomes important for management and decision making. Currently, biases in climate models make it difficult to realistically estimate the future climate and consequent impact on fire risk. A distribution-based scaling (DBS) approach was developed as a post-processing tool that intends to correct systematic biases in climate modelling outputs. In this study, we used two projections, one driven by historical reanalysis (ERA40) and one from a global climate model (ECHAM5) for future projection, both having been dynamically downscaled by a regional climate model (RCA3). The effects of the post-processing tool on relative humidity and wind speed were studied in addition to the primary variables precipitation and temperature. Finally, the Canadian Fire Weather Index system was used to evaluate the influence of changing meteorological conditions on the moisture content in fuel layers and the fire-spread risk. The forest fire risk results using DBS are proven to better reflect risk using observations than that using raw climate outputs. For future periods, southern Sweden is likely to have a higher fire risk than today, whereas northern Sweden will have a lower risk of forest fire.

  4. Regional-Scale Climate Change: Observations and Model Simulations

    SciTech Connect

    Bradley, Raymond S; Diaz, Henry F

    2010-12-14

    This collaborative proposal addressed key issues in understanding the Earth's climate system, as highlighted by the U.S. Climate Science Program. The research focused on documenting past climatic changes and on assessing future climatic changes based on suites of global and regional climate models. Geographically, our emphasis was on the mountainous regions of the world, with a particular focus on the Neotropics of Central America and the Hawaiian Islands. Mountain regions are zones where large variations in ecosystems occur due to the strong climate zonation forced by the topography. These areas are particularly susceptible to changes in critical ecological thresholds, and we conducted studies of changes in phonological indicators based on various climatic thresholds.

  5. A Model Based Mars Climate Database for the Mission Design

    NASA Technical Reports Server (NTRS)

    2005-01-01

    A viewgraph presentation on a model based climate database is shown. The topics include: 1) Why a model based climate database?; 2) Mars Climate Database v3.1 Who uses it ? (approx. 60 users!); 3) The new Mars Climate database MCD v4.0; 4) MCD v4.0: what's new ? 5) Simulation of Water ice clouds; 6) Simulation of Water ice cycle; 7) A new tool for surface pressure prediction; 8) Acces to the database MCD 4.0; 9) How to access the database; and 10) New web access

  6. Evaluation of climate models in terms of relationship between cloud fraction and cloud albedo

    NASA Astrophysics Data System (ADS)

    Liu, Y.; Song, H.; Lin, W.; Wu, W.; Jensen, M. P.; Toto, T.

    2011-12-01

    Cloud fraction and cloud albedo have been investigated extensively but often separately in studying cloud-climate interaction; their quantitative relationship has been much less studied and understood, in both observations and climate models. In this study, we first examine this crucial relationship by empirical analysis of observational data, both surface-based and satellite measurements. We then decipher the key variables/processes that determine the relationship using designed simulations of the single-column model of the NCAR CAM (SCAM). Finally we explore how well or poor this relationship is simulated in the AR4 climate simulations. The preliminary results indicate that all the AR4 models as a group produce a cloud fraction-albedo relationship that is literally opposite to that observed. This stark contrast between model and observational results calls for new strategies and approaches in future development of cloud parameterizations and application of observations as model constraints.

  7. Modelling interactions of carbon dioxide, forests, and climate

    SciTech Connect

    Luxmoore, R.J.; Baldocchi, D.D.

    1994-09-01

    Atmospheric carbon dioxide is rising and forests and climate is changing! This combination of fact and premise may be evaluated at a range of temporal and spatial scales with the aid of computer simulators describing the interrelationships between forest vegetation, litter and soil characteristics, and appropriate meteorological variables. Some insights on the effects of climate on the transfers of carbon and the converse effect of carbon transfer on climate are discussed as a basis for assessing the significance of feedbacks between vegetation and climate under conditions of rising atmospheric carbon dioxide. Three main classes of forest models are reviewed. These are physiologically-based models, forest succession simulators based on the JABOWA model, and ecosystem-carbon budget models that use compartment transfer rates with empirically estimated coefficients. Some regression modeling approaches are also outlined. Energy budget models applied to forests and grasslands are also reviewed. This review presents examples of forest models; a comprehensive discussion of all available models is not undertaken.

  8. 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

    2016-02-01

    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.

  9. The Mechanisms of Natural Variability and its Interaction with Anthropogenic Climate Change Final Report

    SciTech Connect

    Vallis, Geoffrey K.

    2015-02-01

    The project had two main components. The first concerns estimating the climate sensitivity in the presence of forcing uncertainty and natural variability. Climate sensitivity is the increase in the average surface temperature for a given increase in greenhouse gases, for example a doubling of carbon dioxide. We have provided new, probabilistic estimates of climate sensitivity using a simple climate model an the observed warming in the 20th century, in conjunction with ideas in data assimilation and parameter estimation developed in the engineering community. The estimates combine the uncertainty in the anthropogenic aerosols with the uncertainty arising because of natural variability. The second component concerns how the atmospheric circulation itself might change with anthropogenic global warming. We have shown that GCMs robustly predict an increase in the length scale of eddies, and we have also explored the dynamical mechanisms whereby there might be a shift in the latitude of the jet stream associated with anthropogenic warming. Such shifts in the jet might cause large changes in regional climate, potentially larger than the globally-averaged signal itself. We have also shown that the tropopause robustly increases in height with global warming, and that the Hadley Cell expands, and that the expansion of the Hadley Cell is correlated with the polewards movement of the mid-latitude jet.

  10. Modeling High-Impact Weather and Climate: Lessons From a Tropical Cyclone Perspective

    SciTech Connect

    Done, James; Holland, Greg; Bruyere, Cindy; Leung, Lai-Yung R.; Suzuki-Parker, Asuka

    2012-06-01

    Although the societal impact of a weather event increases with the rarity of the event, our current ability to assess extreme events and their impacts is limited by not only rarity but also by current model fidelity and a lack of understanding of the underlying physical processes. This challenge is driving fresh approaches to assess high-impact weather and climate. Recent lessons learned in modeling high-impact weather and climate are presented using the case of tropical cyclones as an illustrative example. Through examples using the Nested Regional Climate Model to dynamically downscale large-scale climate data the need to treat bias in the driving data is illustrated. Domain size, location, and resolution are also shown to be critical and should be guided by the need to: include relevant regional climate physical processes; resolve key impact parameters; and to accurately simulate the response to changes in external forcing. The notion of sufficient model resolution is introduced together with the added value in combining dynamical and statistical assessments to fill out the parent distribution of high-impact parameters. Finally, through the example of a tropical cyclone damage index, direct impact assessments are presented as powerful tools that distill complex datasets into concise statements on likely impact, and as highly effective communication devices. Capsule: "Combining dynamical modeling of high-impact weather using traditional regional climate models with statistical techniques allows for comprehensive sampling of the full distribution, uncertainty estimation, direct assessment of impacts, and increased confidence in future changes."

  11. Modeling deoxynivalenol contamination of wheat in northwestern Europe for climate change assessments.

    PubMed

    van der Fels-Klerx, H J; Goedhart, P W; Elen, O; Börjesson, T; Hietaniemi, V; Booij, C J H

    2012-06-01

    Climate change will affect mycotoxin contamination of feed and food. Mathematical models for predicting mycotoxin concentrations in cereal grains are useful for estimating the impact of climate change on these toxins. The objective of the current study was to construct a descriptive model to estimate climate change impacts on deoxynivalenol (DON) contamination of mature wheat grown in northwestern Europe. Observational data from 717 wheat fields in Norway, Sweden, Finland, and The Netherlands were analyzed, including the DON concentrations in mature wheat, agronomical practices, and local weather. Multiple regression analyses were conducted, and the best set of explanatory variables, mainly including weather factors, was selected. The final model included the following variables: flowering date, length of time between flowering and harvest, wheat resistance to Fusarium infection, and several climatic variables related to relative humidity, temperature, and rainfall during critical stages of wheat cultivation. The model accounted for 50 % of the variance, which was sufficient to make this model useful for estimating the trends of climate change on DON contamination of wheat in northwestern Europe. Application of the model in possible climate change scenarios is illustrated. PMID:22691478

  12. Modelling Changes to Crop Yield Under Climate Change Scenarios

    NASA Astrophysics Data System (ADS)

    Gerber, J. S.; Deryng, D.; Ray, D. K.; Mueller, N. D.; Foley, J. A.; Ramankutty, N.

    2010-12-01

    This paper presents two sets of quantitative predictions for global soy and maize yields under changes to temperature and precipitation. The climatic changes considered are based on IPCC scenarios A1B and B1 as calculated with a variety of GCMs. One set of crop yield predictions is calculated with the process-based PEGASUS model, the other is based on an empirical climate-analog approach. The core of PEGASUS is a simple global surface energy, water, and carbon balance model. In addition, PEGASUS simulates planting dates and optimum cultivars at different locations of the world, allocates carbon to a grain pool, and uses an empirical relationship to estimate the influence of fertilizer application. In the empirical climate analog approach, recently published global data sets are used to empirically determine maximum attainable (potential) crop yields for a given set of climatic and soil conditions. Farmers are then quantified by their abilities to reach potential yields and as new climatically-limited potential yields obtain under climate change scenarios, farmers’ yields are assumed to evolve proportionally. Preliminary results indicate that global average yields in the future are sensitive to the climate model used to generate the future climate. However, all models indicate a decrease in yields under climate scenarios A1B and B1.

  13. Regional forecasting with global atmospheric models; Final report

    SciTech Connect

    Crowley, T.J.; Smith, N.R.

    1994-05-01

    The purpose of the project was to conduct model simulations for past and future climate change with respect to the proposed Yucca Mtn. repository. The authors report on three main topics, one of which is boundary conditions for paleo-hindcast studies. These conditions are necessary for the conduction of three to four model simulations. The boundary conditions have been prepared for future runs. The second topic is (a) comparing the atmospheric general circulation model (GCM) with observations and other GCMs; and (b) development of a better precipitation data base for the Yucca Mtn. region for comparisons with models. These tasks have been completed. The third topic is preliminary assessments of future climate change. Energy balance model (EBM) simulations suggest that the greenhouse effect will likely dominate climate change at Yucca Mtn. for the next 10,000 years. The EBM study should improve rational choice of GCM CO{sub 2} scenarios for future climate change.

  14. Landscape-scale modelling of soil carbon dynamics under land use and climate change

    NASA Astrophysics Data System (ADS)

    Lacoste, Marine; Viaud, Valérie; Michot, Didier; Christian, Walter

    2013-04-01

    Soil organic carbon (SOC) sequestration is highly linked to soil use and farming practices, but also to soil redistributions, soil properties, and climate. In a global change context, landscape, farming practice and climate changes are expected; and they will most probably impact SOC dynamics. To assess their respective impacts, we modelled the SOC contents and stocks evolution at the scale of an agricultural landscape, by taking into account the soil redistribution by tillage and water processes. The simulations were conducted from 2010 to 2100 under different scenarios of landscape and climate. These scenarios combined different land uses associated to specific farming practices (mixed dairy with rotations of crops and grasslands, intensive cropping with only crops rotations or permanent grasslands), landscape managements (hedges planting or removal), and climates (business-as-usual climate and climate change, with temperature and precipitations increase). We used a spatially SOC dynamic model (adapted from RothC), coupled to a soil redistribution model (LandSoil). SOC dynamics were spatially modelled with a lateral resolution of 2-m and for soil organic layers up to 105 cm. Initial SOC stocks were described with a 2-m resolution map based on field data and produced with digital soil mapping methods. The major factor of change in SOC stocks was land use change, the second factor of importance was climate change, and finally landscape management: for the total SOC stocks (0-to-105 cm soil layer) the change of land use, climate and landscape management induced a respective mean absolute variation of 10 to 20 tC ha-1, 9 tC ha-1 and 0.4 tC ha-1. When considering the 0-to-105 cm soil layer, the different modelled landscapes showed the same sensitivity to climate change, with induced a mean decrease of 10 tC ha-1. However, the impact of climate change was found different according to the different modelled landscape when considering the 0-to-7.5 and 0-to-30 cm soil

  15. Climate model studies of synchronously rotating planets.

    PubMed

    Joshi, Manoj

    2003-01-01

    M stars constitute 75% of main sequence stars though, until recently, their star systems have not been considered suitable places for habitable planets to exist. In this study the climate of a synchronously rotating planet around an M dwarf star is evaluated using a three-dimensional global atmospheric circulation model. The presence of clouds and evaporative cooling at the surface of the planet result in a cooler surface temperature at the subsolar point. Water ice forms at the polar regions and on the dark side, where the minimum temperature lies between -30 degrees C and 0 degrees C. As expected, rainfall is extremely high on the starlit side and extremely low on the dark side. The presence of a dry continent causes higher temperatures on the dayside, and allows accumulation of snow on the nightside. The absence of any oceans leads to higher day-night temperature differences, consistent with previous work. The present study reinforces recent conclusions that synchronously rotating planets within the circumstellar habitable zones of M dwarf stars should be habitable, and therefore M dwarf systems should not be excluded in future searches for exoplanets. PMID:14577888

  16. Initiative to improve process representation in chemistry-climate models

    SciTech Connect

    Doherty, Sarah J.; Rasch, Philip J.; Ravishankara, A.R.

    2009-06-16

    The Atmospheric Chemistry and Climate Initiative (AC&C) will address the current large uncertainties in our understanding of chemistry-climate interactions for short-lived atmospheric chemical constituents (e.g. aerosols, ozone, and methane). Understanding what controls the distribution of these species, how they affect climate, and how their distributions might change with a changing climate are important for air quality and climate forecasts. AC&C will address this issue in its first phase through a series of modeling exercises designed to test models’ ability to reproduce observed changes in these species distributions, to produce a set of coordinated forecasts for their future distribution, and to understand how processes are represented in different models. Observational databases will be used to test the models and to better understand processes represented in the models. This article describes the plans for this first phase of activities and seeks participation from the research community.

  17. Estimating the Health Impact of Climate Change with Calibrated Climate Model Output

    PubMed Central

    Zhou, Jingwen; Chang, Howard H.; Fuentes, Montserrat

    2013-01-01

    Studies on the health impacts of climate change routinely use climate model output as future exposure projection. Uncertainty quantification, usually in the form of sensitivity analysis, has focused predominantly on the variability arise from different emission scenarios or multi-model ensembles. This paper describes a Bayesian spatial quantile regression approach to calibrate climate model output for examining to the risks of future temperature on adverse health outcomes. Specifically, we first estimate the spatial quantile process for climate model output using nonlinear monotonic regression during a historical period. The quantile process is then calibrated using the quantile functions estimated from the observed monitoring data. Our model also down-scales the gridded climate model output to the point-level for projecting future exposure over a specific geographical region. The quantile regression approach is motivated by the need to better characterize the tails of future temperature distribution where the greatest health impacts are likely to occur. We applied the methodology to calibrate temperature projections from a regional climate model for the period 2041 to 2050. Accounting for calibration uncertainty, we calculated the number of of excess deaths attributed to future temperature for three cities in the US state of Alabama. PMID:24039385

  18. Effects of dynamical heat fluxes on model climate sensitivity

    NASA Technical Reports Server (NTRS)

    Wang, W.-C.; Molnar, G.; Mitchell, T. P.; Stone, P. H.

    1984-01-01

    A coupled high and low latitude radiative-dynamical model of the annual mean northern hemisphere has been constructed in order to study the interactions of the vertical and meridional heat fluxes and their feedback effect on model climate sensitivity. The model's climate sensitivity to solar constant changes and CO2 increases is investigated, and the effect of feedback in the dynamical fluxes on model climate sensitivity is examined. Nonlinear interactions between heat fluxes and other feedbacks such as radiation-temperature, ice albedo, and humidity are also discussed.

  19. The Community Climate System Model Project from an Interagency Perspective

    SciTech Connect

    Bader, D C; Bamzai, A; Fein, J; Patrinos, A; Leinen, M

    2005-06-16

    In 2007, the Intergovernmental Panel on Climate Change (IPCC) will publish its Fourth Assessment Report of the Scientific Basis of Climate Change (AR4). A significant portion of the AR4 will be the analysis of coupled general circulation model (GCM) simulations of the climate of the past century as well as scenarios of future climates under prescribed emission scenarios. Modeling groups worldwide have contributed to AR4, including three from the U.S., the Community Climate System Model (CCSM) project, the National Aeronautics and Space Administration (NASA) Goddard Institute for Space Sciences, and the National Oceanic and Atmospheric Administration (NOAA) Geophysical Fluid Dynamics Laboratory (GFDL). This collection of model results is providing a wealth of new information that will be used to examine the state of climate science, the potential impacts from climate changes, and the policy consequences that they imply. Our focus here is on the CCSM project. Although it is centered at the National Center for Atmospheric Research (NCAR), the CCSM version 3 (CCSM3) was designed, developed, and applied in a uniquely distributed fashion with participation by many institutions. This model has produced some of the most scientifically complete and highest resolution simulations of climate change to date, thanks to the teamwork of many scientists and software engineers. Their contributions will become obvious as a steady stream of peer-reviewed publications appears in the scientific literature. Less obvious, however, is the largely hidden, unprecedented level of interagency cooperation and multi-institutional coordination that provided the direction and resources necessary to make the CCSM project successful. Contrary to the widely-held opinion that the US climate research effort in general, and the climate modeling effort in particular, is fragmented and disorganized (NRC 1998, 2001), the success of the CCSM project demonstrates that a uniquely US approach to model

  20. Grassland/atmosphere response to changing climate: Coupling regional and local scales. Final report

    SciTech Connect

    Coughenour, M.B.; Kittel, T.G.F.; Pielke, R.A.; Eastman, J.

    1993-10-01

    The objectives of the study were: to evaluate the response of grassland ecosystems to atmospheric change at regional and site scales, and to develop multiscaled modeling systems to relate ecological and atmospheric models with different spatial and temporal resolutions. A menu-driven shell was developed to facilitate use of models at different temporal scales and to facilitate exchange information between models at different temporal scales. A detailed ecosystem model predicted that C{sub 3} temperate grasslands wig respond more strongly to elevated CO{sub 2} than temperate C{sub 4} grasslands in the short-term while a large positive N-PP response was predicted for a C{sub 4} Kenyan grassland. Long-term climate change scenarios produced either decreases or increases in Colorado plant productivity (NPP) depending on rainfall, but uniform increases in N-PP were predicted in Kenya. Elevated CO{sub 2} is likely to have little effect on ecosystem carbon storage in Colorado while it will increase carbon storage in Kenya. A synoptic climate classification processor (SCP) was developed to evaluate results of GCM climate sensitivity experiments. Roughly 80% agreement was achieved with manual classifications. Comparison of lx and 2xCO{sub 2} GCM Simulations revealed relatively small differences.

  1. Physical basis for climate change models

    SciTech Connect

    Goody, R.; Gerstell, M.

    1993-10-18

    The objectives for this research were two-fold: To identify means of using measurements of the outgoing radiation stream from earth to identify mechanisms of climate change; and to develop a flexible radiation code based upon the correlated-k method to enable rapid and accurate calculations of the outgoing radiation. The intended products are three papers and a radiation code. The three papers are to be on Entropy fluxes and the dissipation of the climate system, Radiation fingerprints of climate change, and A rapid correlated-k code.

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

    NASA Astrophysics Data System (ADS)

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

    2007-12-01

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

  3. A new approach in climate modelling strategies to provide climate information based on user needs

    NASA Astrophysics Data System (ADS)

    Dell'Aquila, Alessandro; Somot, Samuel; Dubois, Clotilde; Nabat, Pierre; Coppola, Erika

    2014-05-01

    In the framework of CLIMRUN EU FP7 project a new approach to plan the climate modelling activities has been proposed and applied. In particular a bottom-up approach mainly driven by the specific needs of end users has been adopted. In this perspective, the new climate information for Mediterranean region provided from new modelling activity have been tailored on the users needs raised in several Stakeholders Workshops organized at the early stages of the project. At the beginning of the project, several different options of possible developments for new modelling tools have been proposed by climate researchers involved in the project. Taking carefully into account the ranking of priorities suggested by end-users, the climate researchers could set a more focused research line fitting the expectations of stakeholders. New modelling tools to improve the representation and projection of surface wind speed, surface solar radiation, trend of extreme events, temperature of lakes and islands of Mediterranean have been successfully developed. Here we report some of the major outcomes from the new tools and more in general some recommendations about the future role of climate researchers in developing climate services.The results here reported could be useful also in the othe ongoing experiences about climate services such as projects SPECS, EUPORIAS...

  4. A toy climate model for Mars

    NASA Astrophysics Data System (ADS)

    Savijärvi, Hannu

    2014-11-01

    A "toy climate model" TCM was constructed for Mars. It returns the midday surface and near-surface air temperatures, given the orbital parameters, season (Ls), latitude, thermal inertia, albedo, surface pressure and dust visible optical depth (τ). The TCM is based on the surface energy balance with radiation terms calibrated against line-by-line calculations and surface heat flux terms against 1D model simulations. The TCM air temperatures match various lander observations reasonably well, e.g. the 3.4 martian years of recovered T1.6m from Viking Lander 1. The results demonstrate strong sensitivity of Ts and T1.6m to the dust load. All the VL1 years suggest major dust storms around Ls 270-300°, while τ appears only moderate in the simultaneous VL2 observations. The TCM was further extended to increased surface pressures, using moist 1D simulations. The greenhouse warming remained modest and the diurnal range was small in a thick CO2 atmosphere. As the CO2 condensation temperature Tc increases rapidly with pressure, the range of afternoon temperatures at various latitudes remains quite narrow in a thick atmosphere. The TCM can also deal with orbital parameter variations. A high-eccentricity, high-obliquity case was demonstrated for the present 7 mb (Tc 150 K) and a 1 bar CO2 atmosphere (Tc 195 K). High obliquity of 45° led to quite wide winter polar ice caps, which extended down to the subtropics. In the 1 bar case even the equatorial Ts was close to Tc at aphelion; a major dust storm at that time led to a tropical CO2 ice cover.

  5. Modeling Climate Impacts on Agriculture in South East South America

    NASA Astrophysics Data System (ADS)

    Ines, A. M.; Baethgen, W.; Greene, A. M.; Goddard, L. M.

    2013-12-01

    In the past two decades, a rapid expansion of croplands in South East South America is observed. This drastic change in landuse is seen to be due to two major factors - climate and economics. Converting marginal lands into agricultural lands is possible due to the increase in annual precipitation in the region and the increasing prices of soybeans and higher demands for grain crops have played a key role to this expansion. But the question is, how sustainable is the current trend in the future? A modeling study is conducted to evaluate the impacts of climate on agriculture in the Southern Cone of South America. We examine the impacts of climate variability and current climate change to crop yields using crop simulation models. Using the results of our current climate analysis as a baseline, we evaluate the impacts of future climate change in the next 10-30 years. Climate projections include scenarios considering only global warming, ozone and both impacting the near-term climate of the future in the region and considering decadal variability. We aim to evaluate the vulnerability of the current system to climate change. This paper will present the results of our modeling study.

  6. Comparison of climate model simulated and observed borehole temperature profiles

    NASA Astrophysics Data System (ADS)

    Gonzalez-Rouco, J. F.; Stevens, M. B.; Beltrami, H.; Goosse, H.; Rath, V.; Zorita, E.; Smerdon, J.

    2009-04-01

    Advances in understanding climate variability through the last millennium lean on simulation and reconstruction efforts. Progress in the integration of both approaches can potentially provide new means of assessing confidence on model projections of future climate change, of constraining the range of climate sensitivity and/or attributing past changes found in proxy evidence to external forcing. This work addresses specifically possible strategies for comparison of paleoclimate model simulations and the information recorded in borehole temperature profiles (BTPs). First efforts have allowed to design means of comparison of model simulated and observed BTPs in the context of the climate of the last millennium. This can be done by diffusing the simulated temperatures into the ground in order to produce synthetic BTPs that can be in turn assigned to collocated, real BTPs. Results suggest that there is sensitivity of borehole temperatures at large and regional scales to changes in external forcing over the last centuries. The comparison between borehole climate reconstructions and model simulations may also be subjected to non negligible uncertainties produced by the influence of past glacial and Holocene changes. While the thermal climate influence of the last deglaciation can be found well below 1000 m depth, such type of changes can potentially exert an influence on our understanding of subsurface climate in the top ca. 500 m. This issue is illustrated in control and externally forced climate simulations of the last millennium with the ECHO-G and LOVECLIM models, respectively.

  7. The Urgent Need for Improved Climate Models and Predictions

    NASA Astrophysics Data System (ADS)

    Goddard, Lisa; Baethgen, Walter; Kirtman, Ben; Meehl, Gerald

    2009-09-01

    An investment over the next 10 years of the order of US$2 billion for developing improved climate models was recommended in a report (http://wcrp.wmo.int/documents/WCRP_WorldModellingSummit_Jan2009.pdf) from the May 2008 World Modelling Summit for Climate Prediction, held in Reading, United Kingdom, and presented by the World Climate Research Programme. The report indicated that “climate models will, as in the past, play an important, and perhaps central, role in guiding the trillion dollar decisions that the peoples, governments and industries of the world will be making to cope with the consequences of changing climate.” If trillions of dollars are going to be invested in making decisions related to climate impacts, an investment of $2 billion, which is less than 0.1% of that amount, to provide better climate information seems prudent. One example of investment in adaptation is the World Bank's Climate Investment Fund, which has drawn contributions of more than $6 billion for work on clean technologies and adaptation efforts in nine pilot countries and two pilot regions. This is just the beginning of expenditures on adaptation efforts by the World Bank and other mechanisms, focusing on only a small fraction of the nations of the world and primarily aimed at anticipated anthropogenic climate change. Moreover, decisions are being made now, all around the world—by individuals, companies, and governments—that affect people and their livelihoods today, not just 50 or more years in the future. Climate risk management, whether related to projects of the scope of the World Bank's or to the planning and decisions of municipalities, will be best guided by meaningful climate information derived from observations of the past and model predictions of the future.

  8. Modelling urban climate under global climate change in Central European cities

    NASA Astrophysics Data System (ADS)

    Zuvela-Aloise, Maja; Bokwa, Anita; Dobrovolny, Petr; Gal, Tamas; Geletic, Jan; Gulyas, Agnes; Hajto, Monika; Hollosi, Brigitta; Kielar, Rafal; Lehnert, Michal; Skarbit, Nora; Stastny, Pavel; Svec, Marek; Unger, Janos; Vysoudil, Miroslav; Walawender, Jakub P.

    2015-04-01

    The global and regional climate warming is expected to increase the heat load in urban areas. In order to develop adaptation and mitigation strategies in particular cities, it is necessary to evaluate possible range of heat load increase, in terms of both its magnitude and spatial extent. The present study shows preliminary results of an international project aimed to evaluate the expected heat load increase in four Central European cities (Krakow, Poland; Bratislava, Slovakia; Brno, Czech Republic and Szeged, Hungary) using the non-hydrostatic MUKLIMO 3 model developed by DWD (Deutscher Wetterdienst) for micro-scale urban climate and planning applications. The investigation is focused on the spatial gradients of temperature during potential summer day conditions and possible change in heat load signal under future climate conditions. In order to identify thermally sensitive areas within the city, idealized simulations of temperature, wind and relative humidity in the urban area are performed based on the orography and land use data with 100 m resolution. The model setup uses standardize classification of land use properties based on local climate zones (LCZ) classification system (Stewart and Oke, 2012) which allows inter-comparison of the modelling results. The Landsat satellite images are used to identify typical land use classes in all the cities. The climatological changes in urban heat load are evaluated in terms of expected increase in the mean annual number of summer days (Tmax ≥ 25°C). The 30-year climatological indices are calculated based on the cuboid method. Timeseries of mean daily temperature, wind and relative humidity from a local meteorological station are used to evaluate the climatic indices for the recent climatic period, while the future climate signal is based on the data from regional climate projections of the EURO-CORDEX project. The project "Urban climate in Central European cities and global climate change" is funded within the

  9. The use of multi-model ensembles from global climate models for impact assessment of climate change

    NASA Astrophysics Data System (ADS)

    Semenov, M. A.

    2009-04-01

    The IPCC 4th Assessment Report was based on large datasets of projections of future climate produced by eighteen modelling groups worldwide who performed a set of coordinated climate experiments in which numerous global climate models (GCMs) have been run for a common set of experiments and various emission scenarios. These datasets are freely available form the IPCC Data Distribution Centre (www.ipcc-data.org) and can be used by the research community to assess the impact of changing climate on various systems of interest including impacts on agricultural crops and natural ecosystems, biodiversity and plant diseases. Multi-model ensembles (MME) emphasize the uncertainty in climate predictions resulting from structural differences in the global climate model design as well as uncertainty to variations of initial conditions or model parameters. This paper describes a methodology based on a stochastic weather generator for linking MME of predictions from GCMs with process-based impact models to assess impacts of climate change on biological or ecological systems. The latest version of the LARS-WG weather generator is described which allows seamlessly generating daily site-specific climate scenarios worldwide by utilising local daily weather and MME from GCMs. Examples of impacts on wheat in Europe, based on MME, are discussed, including changes in severity of drought and heat stress around flowering.

  10. Statistical Properties of Downscaled CMIP3 Global Climate Model Simulations

    NASA Astrophysics Data System (ADS)

    Duffy, P.; Tyan, S.; Thrasher, B.; Maurer, E. P.; Tebaldi, C.

    2009-12-01

    Spatial downscaling of global climate model projections adds physically meaningful spatial detail, and brings the results down to a scale that is more relevant to human and ecological systems. Statistical/empirical downscaling methods are computationally inexpensive, and thus can be applied to large ensembles of global climate model projections. Here we examine some of the statistical properties of a large ensemble of empirically downscale global climate projections. The projections are the CMIP3 global climate model projections that were performed by modeling groups around the world and archived by the Program for Climate Model Diagnosis and Intercomparison at Lawrence Livermore National Laboratory. Downscaled versions of 112 of these simulations were created on 2007 and are archived at http://gdo-dcp.ucllnl.org/downscaled_cmip3_projections/dcpInterface.html. The downscaling methodology employed, “Bias Correction/Spatial Downscaling” (BCSD), includes a correction of GCM biases relative to observations during a historical reference period, as well as empirical downscaling to grid scale of ~12 km. We analyzed these downscaled projections and some of the original global model results to assess effects of the bias correction and downscaling on the statistical properties of the ensemble. We also assessed uncertainty in the climate response to increased greenhouse gases from initial conditions relative to the uncertainty introduced by choice of global climate model.

  11. Modeling U.S. water resources under climate change

    NASA Astrophysics Data System (ADS)

    Blanc, Elodie; Strzepek, Kenneth; Schlosser, Adam; Jacoby, Henry; Gueneau, Arthur; Fant, Charles; Rausch, Sebastian; Reilly, John

    2014-04-01

    Water is at the center of a complex and dynamic system involving climatic, biological, hydrological, physical, and human interactions. We demonstrate a new modeling system that integrates climatic and hydrological determinants of water supply with economic and biological drivers of sectoral and regional water requirement while taking into account constraints of engineered water storage and transport systems. This modeling system is an extension of the Massachusetts Institute of Technology (MIT) Integrated Global System Model framework and is unique in its consistent treatment of factors affecting water resources and water requirements. Irrigation demand, for example, is driven by the same climatic conditions that drive evapotranspiration in natural systems and runoff, and future scenarios of water demand for power plant cooling are consistent with energy scenarios driving climate change. To illustrate the modeling system we select "wet" and "dry" patterns of precipitation for the United States from general circulation models used in the Climate Model Intercomparison Project (CMIP3). Results suggest that population and economic growth alone would increase water stress in the United States through mid-century. Climate change generally increases water stress with the largest increases in the Southwest. By identifying areas of potential stress in the absence of specific adaptation responses, the modeling system can help direct attention to water planning that might then limit use or add storage in potentially stressed regions, while illustrating how avoiding climate change through mitigation could change likely outcomes.

  12. Agricultural climate impacts assessment for economic modeling and decision support

    NASA Astrophysics Data System (ADS)

    Thomson, A. M.; Izaurralde, R. C.; Beach, R.; Zhang, X.; Zhao, K.; Monier, E.

    2013-12-01

    A range of approaches can be used in the application of climate change projections to agricultural impacts assessment. Climate projections can be used directly to drive crop models, which in turn can be used to provide inputs for agricultural economic or integrated assessment models. These model applications, and the transfer of information between models, must be guided by the state of the science. But the methodology must also account for the specific needs of stakeholders and the intended use of model results beyond pure scientific inquiry, including meeting the requirements of agencies responsible for designing and assessing policies, programs, and regulations. Here we present methodology and results of two climate impacts studies that applied climate model projections from CMIP3 and from the EPA Climate Impacts and Risk Analysis (CIRA) project in a crop model (EPIC - Environmental Policy Indicator Climate) in order to generate estimates of changes in crop productivity for use in an agricultural economic model for the United States (FASOM - Forest and Agricultural Sector Optimization Model). The FASOM model is a forward-looking dynamic model of the US forest and agricultural sector used to assess market responses to changing productivity of alternative land uses. The first study, focused on climate change impacts on the UDSA crop insurance program, was designed to use available daily climate projections from the CMIP3 archive. The decision to focus on daily data for this application limited the climate model and time period selection significantly; however for the intended purpose of assessing impacts on crop insurance payments, consideration of extreme event frequency was critical for assessing periodic crop failures. In a second, coordinated impacts study designed to assess the relative difference in climate impacts under a no-mitigation policy and different future climate mitigation scenarios, the stakeholder specifically requested an assessment of a

  13. Albedo, clouds and climate sensitivity in the CMIP3 models

    NASA Astrophysics Data System (ADS)

    Bender, F.; Rodhe, H.; Ekman, A. M.; Charlson, R.

    2010-12-01

    The albedo is a key parameter in the radiative budget of the Earth and a primary determinant of the planetary temperature and is therefore also central to questions regarding climate stability, climate change and climate sensitivity. Global climate models are essential for studying the albedo, and the parameters determining it (specifically clouds), on large spatial and temporal scales. Although models (here represented by the CMIP3 models) are able to capture the large-scale characteristics of the albedo, a bias is found between modelled and observed global albedo estimates, and on a regional scale particularly problematic regions can be identified. Many cloud parameters are poorly constrained by observations, and vary widely among models. This freedom of variability can be used in tuning models to the better constrained radiative budget, which may influence the model climate sensitivity. The effect can be kept small, compared to the range of climate sensitivities estimated by different models. Despite their different parameterizations of clouds, aerosols etc., models do have fundamental features in common, which can further the understanding of the real climate system. For instance, sensitivity to volcanic forcing is related to climate sensitivity in an ensemble of CMIP3 models. If this relation is valid for the real climate as well, observations of the volcanic sensitivity can help restrict estimates of climate sensitivity. The range of climate sensitivity estimates in models can largely be attributed to variations in cloud response to external forcing. In models with high (low) climate sensitivity, changes in cloud cover and cloud reflectivity generally enhance (counteract) a positive radiative forcing due to increased CO2 concentrations, feeding back on (damping) the warming, with a more (less) negative albedo response to the forcing. Cloud albedo is important in this regard, yet not well known. Regional cloud albedo, particularly for low-level marine

  14. Institutional Climate and Student Departure: A Multinomial Multilevel Modeling Approach

    ERIC Educational Resources Information Center

    Yi, Pyong-sik

    2008-01-01

    This study applied a multinomial HOLM technique to examine the extent to which the institutional climate for diversity influences the different types of college student withdrawal, such as stop out, drop out, and transfer. Based on a reformulation of Tinto's model along with the conceptualization of institutional climate for diversity by Hurtado…

  15. Climate and Agriculture: Model Inter-Comparison for Evaluating the Uncertainties in Climate Change Impact Assessment

    NASA Astrophysics Data System (ADS)

    Geethalakshmi, V.; Lakshmanan, A.; Bhuvaneswari, K.; Rajalakshmi, D.; Sekhar, N. U.; Anbhazhagan, R.; Gurusamy, L.

    2011-12-01

    Presence of large uncertainties in climate models (CM) and in future emission scenarios makes it difficult to predict the long-term climate changes at regional scales. Climate models do a reasonable job of capturing the large-scale aspects of current climate but still contain systematic model errors adding uncertainty to the future projections. Using CM outputs in impact models also cascade the uncertainty in climate change research. A study was undertaken with the objective of evaluating the uncertainty of climate change predictions by comparing the outputs from Regional Climate Models (RCM) and their resultant impact on rice productivity in Bhavani basin of Tamil Nadu, India. Current and future climate data were developed using RCMs viz., RegCM3 and PRECIS considering SRES A1B scenario for 130 years (1971-2100). The RCM outputs were used in DSSAT and EPIC models for assessing the impact of climate change. Results were compared to assess the magnitude of uncertainty in predicting the future climate and the resultant impacts. Comparison of predicted current climate with observed data indicated that RegCM3 under estimates maximum temperature by 1.8 °C while, PRECIS over estimates by 1.1°C over 40 years (1971 - 2010). The minimum temperature was under estimated by both the models, but with varying magnitude (3.8 °C for RegCM3 and 1 °C for PRECIS). RegCM3 over predicted rainfall (14 %), in contrast, PRECIS underpredicted (30.9 %) the same. Future climate projections indicated gradual increase in maximum and minimum temperatures with progress of time. Increase of maximum and minimum temperatures in PRECIS was 3.7oC and 4.2oC respectively and in RegCM3, it was 3.1oC and 3.7oC by 2100. No clear trend could be observed for rainfall other than increase in the quantum compared to current rainfall. Rice yield simulated over Bhavani basin for current and future climate by DSSAT, without CO2 fertilization effect, indicated reduction of 356 and 217 Kg ha-1decade-1 for

  16. Climate sensitivity from fluctuation dissipation - Some simple model tests

    NASA Technical Reports Server (NTRS)

    Bell, T. L.

    1980-01-01

    Leith has suggested that climatic response to change in external forcing parameters of the climate system may be estimated via the fluctuation-dissipation theorem (FDT). The method, which uses the natural fluctuations of the atmosphere to probe its dynamics, is tested here using a twenty-variable truncation model of the barotropic vorticity equation. Dissipative terms are added to the equations, so that the model is pushed away from the region where it is expected to satisfy the FDT. It is found that, even though the FDT is no longer satisfied in every detail, the FDT continues to provide an excellent estimate of the climatic sensitivity of the model.

  17. Assessing Climate Impacts on Air Pollution from Models and Measurements

    NASA Astrophysics Data System (ADS)

    Holloway, T.; Plachinski, S. D.; Morton, J. L.; Spak, S.

    2011-12-01

    It is well known that large-scale patterns in temperature, humidity, solar radiation and atmospheric circulation affect formation and transport of atmospheric constituents. These relationships have supported a growing body of work projecting changes in ozone (O3), and to a lesser extent aerosols, as a function of changing climate. Typically, global and regional chemical transport models are used to quantify climate impacts on air pollution, but the ability of these models to assess weather-dependent chemical processes has not been thoroughly evaluated. Quantifying model sensitivity to climate poses the additional challenge of isolating the local to synoptic scale effects of meteorological conditions on chemistry and transport from concurrent trends in emissions, hemispheric background concentrations, and land cover change. Understanding how well models capture historic climate-chemistry relationships is essential in projecting future climate impacts, in that it allows for better evaluation of model skill and improved understanding of climate-chemistry relationships. We compare the sensitivity of chemistry-climate relationships, as simulated by the EPA Community Multiscale Air Quality (CMAQ) model, with observed historical response characteristics from EPA Air Quality System (AQS) monitoring data. We present results for O3, sulfate and nitrate aerosols, and ambient mercury concentrations. Despite the fact that CMAQ over-predicts daily maximum 8-hour ground-level O3 concentrations relative to AQS data, the model does an excellent job at simulating the response of O3 to daily maximum temperature. In both model and observations, we find that higher temperatures produce higher O3 across most of the U.S., as expected in summertime conditions. However, distinct regions appear in both datasets where temperature and O3 are anti-correlated - for example, over the Upper Midwestern U.S. states of Iowa, Missouri, Illinois, and Indiana in July 2002. Characterizing uncertainties

  18. Systematic Characterization of Cyclogenesis in High Resolution Climate Model Simulations

    NASA Astrophysics Data System (ADS)

    Liu, Y.; Rao, P.; Kashinath, K.; Prabhat, M.; O'Brien, T. A.

    2015-12-01

    In this study we develop a systematic methodology to analyze cyclogenesis in high resolution climate model simulations. The motivation for this study is to understand how cyclones develop in simulations with the objective of improving the theoretical foundations of cyclogenesis. We use the toolkit for extreme climate analysis (TECA) [Prabhat et al., ICCS 2012] to detect and track cyclones (TCs) in recent high resolution simulations (25km) of current day and climate change scenarios [Wehner et al, J Climate 2015], as well as reanalyses. We systematically adjust the tracking criteria to identify developing and non-developing TCs. The detection and tracking criteria are based on (i) the local relative vorticity maximum being above a certain value, (ii) the colocation of vorticity maximum, surface pressure minimum and warm core temperature maximum, (iii) surface pressure gradient around the storm center to be above a certain value, and (iv) temperature gradient around the warm core center to be above a certain value. To identify non-developing TCs, we systematically characterize the sensitivity of cyclone detection to these criteria using a principal component analysis on the criteria. First, we composite vorticity, pressure and temperature fields around the start of each cyclone's trajectory. Second, we find the covariance of pairs of thresholded variables, for example, vorticity and pressure gradient. Finally, we construct a cross-correlation matrix with these covariances and find the eigenvectors. The eigenvector corresponding to the largest eigenvalue describes the direction of maximum sensitivity.We simultaneously lower thresholds along the direction of maximum sensitivity, which results in an increase in the number of TC-like systems and trajectory lengths compared to the baseline case. We contrast the behavior of developing and non-developing TCs by constructing multivariate joint PDFs of various environmental conditions along their trajectories. We also compute

  19. Combining Global Climate Model Outputs and Insights from Downscaling for Australian Climate Projections

    NASA Astrophysics Data System (ADS)

    Grose, M. R.; Timbal, B.; Katzfey, J. J.; Moise, A. F.; Eksrtrom, M.; Whetton, P.

    2013-12-01

    Dynamical and statistical downscaling of global climate model (GCM) outputs has the potential to provide valuable insights when making regional climate projections. It may reveal regional detail in the projected climate change signal through higher resolution and accounting for local influences such as topography and coastlines. However, climate change adaptation research and planning desires a coherent view of possible future climate that accounts for the various sources of uncertainty and at a relevant spatial scale. This means there is value in combining the most useful insights from all available downscaling with a more comprehensive set of designed global climate model (GCM) projections (e.g. the CMIP5 archive), and this is done for the next set of national climate projections products in Australia. There are several practical considerations in this process that affect the process, primarily because downscaling is done using various disparate methods for a limited set of models and scenarios. There is no objective framework to combine different sets of ad hoc downscaling simulations with a set of GCMs, so some degree of expert judgment is used. We emphasize cases where there is the most apparent ';added value' and report these insights in complement, and in some cases in preference to, GCM projections. Confidence in such insights first requires understanding of what input data is used from the host model, what biases are reduced and what new biases are potentially introduced. We then seek an understanding of how the climate change signal differs from that of the host model, and an attribution of the cause of this difference. Several case studies within Australia are discussed.

  20. Emulation of MIROC5 with a simple climate model

    NASA Astrophysics Data System (ADS)

    Ishizaki, Yasuhiro; Emori, Seita; Shiogama, Hideo; Takahashi, Kiyoshi; Yokohata, Tokuta; Yoshimori, Masakazu

    2014-05-01

    We developed a simple climate model based on MAGICC6, and investigated the ability of the simple climate model to emulate global mean surface air temperature (SAT) changes of an atmosphere-ocean general circulation model (MIROC5) in the twenty-first century in representative concentration pathways (RCPs). Some previous research indicated that climate sensitivity, ocean vertical diffusion and forcing of anthropogenic aerosols (direct and indirect effects of sulfate aerosol, black carbon and organic carbon) are important factors to emulate global mean SAT changes of atmosphere-ocean general circulation models CMIP3. We therefore estimate these important parameters in the simple climate model using a Metropolis-Hastings Markov chain Monte Carlo (MCMC) approach. The estimated values of the important parameters by the MCMC are physically valid, and our simple climate model can successfully emulate global mean SAT changes of MIROC5 in RCPs with the estimated parameters by the MCMC approach. In addition, we estimated the relative contributions f each important parameter in sensitivity experiments, in which we change the value of an important parameter from the estimated one by the MCMC to the default value of MAGICC6. As a result, we found that the estimation of climate sensitivity is the most important factor for the emulation of the AOGCM, and the stimation of ocean vertical diffusion is also important factor. Although the estimations of the anthropogenic aerosols forcing are very important for the emulation of the AOGCM in the twenty century, the influence of them on the emulation of the AOGCM in the twenty first century is very small. This is because emissions of anthropogenic aerosols are projected to decrease in the twenty first century, and relative contributions of the forcing of anthropogenic aerosols also decrease. Carbon cycle models are not incorporated into our simple climate model yet. A sophisticated carbon cycle model is required to be incorporated into

  1. Model experiments on climate change in the Tokyo metropolitan area using regional climate scenarios

    NASA Astrophysics Data System (ADS)

    Tsunematsu, N.; Dairaku, K.

    2011-12-01

    There is a possibility that the future atmospheric warming leads to more frequent heavy rainfall in the metropolitan area, thereby increasing the risk of floods. As part of REsearch Program on Climate Change Adaptation (RECCA) funded by Ministry of Education, Culture, Sports, Science and Technology, Japan, we started numerical model experiments for investigating the vulnerability and adaptation to climate change in water hazard assessments in the metropolitan area by the use of regional climate scenarios. The model experiments adopt dynamical downscaling techniques. Future climate projections obtained from regional climate model simulations at 20 km horizontal grid spacing are downscaled into finer grids (less than 5 km resolutions) of Regional Atmospheric Modeling System Version 6.0 modified by National Research Institute for Earth Science and Disaster Prevention (NIED-RAMS). Prior to performing the dynamical downscaling experiments, the NIED-RAMS model biases are evaluated by comparing long-term surface meteorological observations with results of the model simulations that are carried out by using the Japanese Re-Analysis (JRA) data and Japan Meteorological Agency Meso-Scale Model outputs as the initial and boundary conditions.

  2. Advances in the ARM Climate Research Facility for Model Evaluation

    NASA Astrophysics Data System (ADS)

    Mather, J. H., II; Voyles, J.

    2014-12-01

    The DOE Atmospheric Radiation Measurement (ARM) Climate Research Facility has been operating for over 20 years providing observations of clouds, aerosols, and radiation to obtain a detailed description of the atmospheric state, support the study of atmospheric processes, and support the improvement of the parameterization of these processes in climate models. ARM facilities include long-term, multi-year deployments as well shorter mobile facility deployments in diverse climate regimes. With the goal of exploiting this increasingly diverse data set, we will explore relationships of cloud distributions with environmental parameters across climate regimes for the purpose of providing constraints on climate model simulations. We will also explore a new strategy ARM is developing to make use of high-resolution process models as another means to impact climate models. The Southern Great Plains and North Slope of Alaska sites are undergoing a reconfiguration to support the routine operation of high-resolution process-models. Enhancements are being targeted to both enable the initialization and evaluation of process models with the goal of using the combination of high-resolution observations and simulations to study key atmospheric processes. This type of observation/model integration is not new at ARM sites, but the creation of two new "supersites" will enable the application of this observation/model synergy on a routine basis, further exploiting the long-term nature of the ARM observations.

  3. Combined effects of climate models, hydrological model structures and land use scenarios on hydrological impacts of climate change

    NASA Astrophysics Data System (ADS)

    Karlsson, Ida B.; Sonnenborg, Torben O.; Refsgaard, Jens Christian; Trolle, Dennis; Børgesen, Christen Duus; Olesen, Jørgen E.; Jeppesen, Erik; Jensen, Karsten H.

    2016-04-01

    Impact studies of the hydrological response of future climate change are important for the water authorities when risk assessment, management and adaptation to a changing climate are carried out. The objective of this study was to model the combined effect of land use and climate changes on hydrology for a 486 km2 catchment in Denmark and to evaluate the sensitivity of the results to the choice of hydrological model. Three hydrological models, NAM, SWAT and MIKE SHE, were constructed and calibrated using similar methods. Each model was forced with results from four climate models and four land use scenarios. The results revealed that even though the hydrological models all showed similar performance during calibration, the mean discharge response to climate change varied up to 30%, and the variations were even higher for extreme events (1th and 99th percentile). Land use changes appeared to cause little change in mean hydrological responses and little variation between hydrological models. Differences in hydrological model responses to land use were, however, significant for extremes due to dissimilarities in hydrological model structure and process equations. The climate model choice remained the dominant factor for mean discharge, low and high flows as well as hydraulic head at the end of the century.

  4. ARM Climate Modeling Best Estimate Data, A New Data Product for Climate Studies

    SciTech Connect

    Xie, Shaocheng; McCoy, Renata B.; Klein, Stephen A.; Cederwall, Richard T.; Wiscombe, Warren J.; Clothiaux, Eugene E.; Gaustad, Krista L.; Golaz, Jean-Christophe; Shamblin, Stefanie H; Jensen, Michael P.; Johnson, Karen L.; Lin, Yanluan; Long, Charles N.; Mather, James H.; McCord, Raymond A; McFarlane, Sally A.; Palanisamy, Giri; Shi, Yan; Turner, David D.

    2010-01-01

    The U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Program (www.arm.gov) was created in 1989 to address scientific uncertainties related to global climate change, with a focus on the crucial role of clouds and their influence on the transfer of radiation in the atmosphere. A central activity is the acquisition of detailed observations of clouds and radiation, as well as related atmospheric variables for climate model evaluation and improvement. Since 1992, ARM has established six permanent ARM Climate Research Facility (ACRF) sites and deployed an ARM Mobile Facility (AMF) in diverse climate regimes around the world (Fig. 1) to perform long-term continuous field measurements. The time record of ACRF data now exceeds a decade at most ACRF fixed sites and ranges from several months to one year for AMF deployments. Billions of measurements are currently stored in millions of data files in the ACRF Data Archive. The long-term continuous ACRF data provide invaluable information to improve our understanding of the interaction between clouds and radiation, and an observational basis for model validation and improvement and climate studies. Given the huge number of data files and current diversity of archived ACRF data structures, however, it can be difficult for an outside user such as a climate modeler to quickly find the ACRF data product(s) that best meets their research needs. The required geophysical quantities may exist in multiple data streams, and over the history of ACRF operations, the measurements could be obtained by a variety of instruments, reviewed with different levels of data quality assurance, or derived using different algorithms. In addition, most ACRF data are stored in daily-based files with a temporal resolution that ranges from a few seconds to a few minutes, which is much finer than that sought by some users. Therefore, it is not as convenient for data users to perform quick comparisons over large spans of data, and this

  5. Very High Resolution Climate Modelling in Northern Russia

    NASA Astrophysics Data System (ADS)

    Stendel, M.; Christensen, J. H.

    2009-04-01

    Simulations with global climate models (GCMs) clearly indicate that major climate changes for the Arctic can be expected during the 21st century. Already now, there are substantial changes in sea-ice extent and thickness and a considerable increase in air temperature in several regions. Contemporary GCMs are unable to give a realistic representation of the climate and climate change in regions with steep orography, due to their coarse resolution. But even relatively high resolution regional climate models (RCMs) fail in this respect. We have therefore conducted a transient simulation with the newest version of the HIRHAM RCM, covering the period 1958-2001 over a region in northeast European Russia, including the Ural Mountains, with the unprecedented horizontal resolution of 4 km. For this simulation, a double downscaling procedure was applied. Average and extreme values will be discussed, and a comparison of subsurface temperatures to a set of observations from the region will be presented.

  6. Towards Better Integration of Climate Models and Models for the Terrestrial Cryosphere (Permafrost and Glaciers)

    NASA Astrophysics Data System (ADS)

    Etzelmuller, B.; Westermann, S.; Gisnas, K.; Aas, K. S.; Schuler, T.; Dunse, T.; Ostby, T.; Berntsen, T.; Kristjansson, J. E.; Stordal, F.

    2014-12-01

    , which is not possible without subgrid representation of snow depths. Finally, we evaluate the possibility to improve simulations of surface energy fluxes also in atmosphere and climate models, through better representation of sub-grid scale variability of variables relevant for atmosphere-cryosphere interactions.

  7. Coupled Climate Model Simulations of a Late Cretaceous (Maastrichtian) Greenhouse Climate: Comparison with Proxy Data

    NASA Astrophysics Data System (ADS)

    Upchurch, G. R.; Kiehl, J. T.; Shields, C. A.; Scotese, C.

    2009-12-01

    Earth’s future climate is expected to warm considerably due to increased atmospheric carbon dioxide. Paleoclimate records indicate that pre-Quaternary time periods provide the best possible view of Earth under warm greenhouse conditions. Thus, past warm greenhouse climates provide an important tool to evaluate fully coupled climate models that are currently used to study future climate change. In this study, we use the Community Climate System Model (CCSM3) to investigate the climate of the latest Cretaceous (Maastrichtian). CCSM3 is a fully coupled three-dimensional global model that includes atmospheric, oceanic, sea-ice and terrestrial processes. The CCSM3 simulations employ slight modifications of the paleogeographic and global vegetation reconstructions used in earlier simulations of the late Maastrichtian with the GENESIS Earth System Model (Upchurch, Otto-Bliesner, and Scotese, 1999). CCSM3 simulations include two levels of atmospheric carbon dioxide (2XPAL and 6XPAL), best estimates of atmospheric methane, changes to low level liquid cloud properties based on the hypothesis of Kump and Pollard (2008), and different paleoelevations for the interior of Siberia. A coupled simulation of multi-century length is carried out to study steady state conditions for the surface ocean. For terrestrial regions, model mean annual temperatures and seasonality are compared with data from angiosperm leaf physiognomy, plant life form distribution, and other climatic indicators to determine how well the model represents high latitude warmth on a zonal and regional basis. Model precipitation is compared with a database of climatically restricted sediments and angiosperm leaf physiognomy for specific sites. For oceanic regions, the CCSM3 simulations are compared to marine proxies of surface and benthic temperatures, especially the δ18O of exceptionally preserved carbonate. Our simulations reproduce many features of Maastrichtian climate, such as the latitudinal gradient of

  8. Assessing climate model software quality: a defect density analysis of three models

    NASA Astrophysics Data System (ADS)

    Pipitone, J.; Easterbrook, S.

    2012-08-01

    A climate model is an executable theory of the climate; the model encapsulates climatological theories in software so that they can be simulated and their implications investigated. Thus, in order to trust a climate model, one must trust that the software it is built from is built correctly. Our study explores the nature of software quality in the context of climate modelling. We performed an analysis of defect reports and defect fixes in several versions of leading global climate models by collecting defect data from bug tracking systems and version control repository comments. We found that the climate models all have very low defect densities compared to well-known, similarly sized open-source projects. We discuss the implications of our findings for the assessment of climate model software trustworthiness.

  9. Assessing climate model software quality: a defect density analysis of three models

    NASA Astrophysics Data System (ADS)

    Pipitone, J.; Easterbrook, S.

    2012-02-01

    A climate model is an executable theory of the climate; the model encapsulates climatological theories in software so that they can be simulated and their implications investigated. Thus, in order to trust a climate model one must trust that the software it is built from is built correctly. Our study explores the nature of software quality in the context of climate modelling. We performed an analysis of defect reports and defect fixes in several versions of leading global climate models by collecting defect data from bug tracking systems and version control repository comments. We found that the climate models all have very low defect densities compared to well-known, similarly sized open-source projects. We discuss the implications of our findings for the assessment of climate model software trustworthiness.

  10. Modeling lakes and reservoirs in the climate system

    USGS Publications Warehouse

    MacKay, M.D.; Neale, P.J.; Arp, C.D.; De Senerpont Domis, L. N.; Fang, X.; Gal, G.; Jo, K.D.; Kirillin, G.; Lenters, J.D.; Litchman, E.; MacIntyre, S.; Marsh, P.; Melack, J.; Mooij, W.M.; Peeters, F.; Quesada, A.; Schladow, S.G.; Schmid, M.; Spence, C.; Stokes, S.L.

    2009-01-01

    Modeling studies examining the effect of lakes on regional and global climate, as well as studies on the influence of climate variability and change on aquatic ecosystems, are surveyed. Fully coupled atmosphere-land surface-lake climate models that could be used for both of these types of study simultaneously do not presently exist, though there are many applications that would benefit from such models. It is argued here that current understanding of physical and biogeochemical processes in freshwater systems is sufficient to begin to construct such models, and a path forward is proposed. The largest impediment to fully representing lakes in the climate system lies in the handling of lakes that are too small to be explicitly resolved by the climate model, and that make up the majority of the lake-covered area at the resolutions currently used by global and regional climate models. Ongoing development within the hydrological sciences community and continual improvements in model resolution should help ameliorate this issue.

  11. Clouds and more: ARM climate modeling best estimate data: A new data product for climate studies

    DOE PAGESBeta

    Xie, Shaocheng; McCoy, Renata B.; Klein, Stephen A.; Cederwall, Richard T.; Wiscombe, Warren J.; Clothiaux, Eugene E.; Gaustad, Krista L.; Golaz, Jean -Christophe; Hall, Stephanie D.; Jensen, Michael P.; et al

    2010-01-01

    The U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Program (www.arm.gov) was created in 1989 to address scientific uncertainties related to global climate change, with a focus on the crucial role of clouds and their influence on the transfer of radiation atmosphere. Here, a central activity is the acquisition of detailed observations of clouds and radiation, as well as related atmospheric variables for climate model evaluation and improvement.

  12. Regional and Global Climate Response to Anthropogenic SO2 Emissions from China in Three Climate Models

    NASA Technical Reports Server (NTRS)

    Kasoar, M.; Voulgarakis, Apostolos; Lamarque, Jean-Francois; Shindell, Drew T.; Bellouin, Nicholas; Collins, William J.; Faluvegi, Greg; Tsigaridis, Kostas

    2016-01-01

    We use the HadGEM3-GA4, CESM1, and GISS ModelE2 climate models to investigate the global and regional aerosol burden, radiative flux, and surface temperature responses to removing anthropogenic sulfur dioxide (SO2) emissions from China. We find that the models differ by up to a factor of 6 in the simulated change in aerosol optical depth (AOD) and shortwave radiative flux over China that results from reduced sulfate aerosol, leading to a large range of magnitudes in the regional and global temperature responses. Two of the three models simulate a near-ubiquitous hemispheric warming due to the regional SO2 removal, with similarities in the local and remote pattern of response, but overall with a substantially different magnitude. The third model simulates almost no significant temperature response. We attribute the discrepancies in the response to a combination of substantial differences in the chemical conversion of SO2 to sulfate, translation of sulfate mass into AOD, cloud radiative interactions, and differences in the radiative forcing efficiency of sulfate aerosol in the models. The model with the strongest response (HadGEM3-GA4) compares best with observations of AOD regionally, however the other two models compare similarly (albeit poorly) and still disagree substantially in their simulated climate response, indicating that total AOD observations are far from sufficient to determine which model response is more plausible. Our results highlight that there remains a large uncertainty in the representation of both aerosol chemistry as well as direct and indirect aerosol radiative effects in current climate models, and reinforces that caution must be applied when interpreting the results of modelling studies of aerosol influences on climate. Model studies that implicate aerosols in climate responses should ideally explore a range of radiative forcing strengths representative of this uncertainty, in addition to thoroughly evaluating the models used against

  13. Climate change hotspots in the CMIP5 global climate model ensemble.

    PubMed

    Diffenbaugh, Noah S; Giorgi, Filippo

    2012-01-10

    We use a statistical metric of multi-dimensional climate change to quantify the emergence of global climate change hotspots in the CMIP5 climate model ensemble. Our hotspot metric extends previous work through the inclusion of extreme seasonal temperature and precipitation, which exert critical influence on climate change impacts. The results identify areas of the Amazon, the Sahel and tropical West Africa, Indonesia, and the Tibetan Plateau as persistent regional climate change hotspots throughout the 21(st) century of the RCP8.5 and RCP4.5 forcing pathways. In addition, areas of southern Africa, the Mediterranean, the Arctic, and Central America/western North America also emerge as prominent regional climate change hotspots in response to intermediate and high levels of forcing. Comparisons of different periods of the two forcing pathways suggest that the pattern of aggregate change is fairly robust to the level of global warming below approximately 2°C of global warming (relative to the late-20(th)-century baseline), but not at the higher levels of global warming that occur in the late-21(st)-century period of the RCP8.5 pathway, with areas of southern Africa, the Mediterranean, and the Arctic exhibiting particular intensification of relative aggregate climate change in response to high levels of forcing. Although specific impacts will clearly be shaped by the interaction of climate change with human and biological vulnerabilities, our identification of climate change hotspots can help to inform mitigation and adaptation decisions by quantifying the rate, magnitude and causes of the aggregate climate response in different parts of the world. PMID:24014154

  14. Application of paleoenvironmental data for testing climate models and understanding past and future climate variations

    NASA Astrophysics Data System (ADS)

    Izumi, Kenji

    Paleo data-model comparison is the process of comparing output from model simulations of past periods with paleoenvironmental data. It enables us to understand both the paleoclimate mechanism and responses of the earth environment to the climate and to evaluate how models work. This dissertation has two parts that each involve the development and application of approaches for data-model comparisons. In part 1, which is focused on the understanding of both past and future climatic changes/variations, I compare paleoclimate and historical simulations with future climate projections exploiting the fact that climate-model configurations are exactly the same in the paleo and future simulations in the Coupled Model Intercomparison Project Phase 5. In practice, I investigated large-scale temperature responses (land-ocean contrast, high-latitude amplification, and change in temperature seasonality) in paleo and future simulations, found broadly consistent relationships across the climate states, and validated the responses using modern observations and paleoclimate reconstructions. Furthermore, I examined the possibility that a small set of common mechanisms controls the large-scale temperature responses using a simple energy-balance model to decompose the temperature changes shown in warm and cold climate simulations and found that the clear-sky longwave downward radiation is a key control of the robust responses. In part 2, I applied the equilibrium terrestrial biosphere models, BIOME4 and BIOME5 (developed from BIOME4 herein), for reconstructing paleoclimate. I applied inverse modeling through the iterative forward-modeling (IMIFM) approach that uses the North American vegetation data to infer the mid-Holocene (MH, 6000 years ago) and the Last Glacial Maximum (LGM, 21,000 years ago) climates that control vegetation distributions. The IMIFM approach has the potential to provide more accurate quantitative climate estimates from pollen records than statistical approaches

  15. Emulation of a couple atmosphere-ocean general circulation model with a simple climate model

    NASA Astrophysics Data System (ADS)

    Ishizaki, Y.; Emori, S.; Oki, T.; Shiogama, H.; Yokohata, T.; Yoshimori, M.

    2013-12-01

    Simple climate models have been used to investigate uncertainty of future projections under a very wide range of emission scenarios because the use of Atmosphere-ocean general circulation models (AOGCMs) requires very huge computer resources to project future climate changes under many different socio-economic scenarios. We developed a simple climate model, and investigated the ability of the simple climate model to emulate global mean surface air temperature (SAT) changes of an AOGCM (MIROC5) in a representative concentration pathway (RCP8.5). Some previous research indicated that climate sensitivity, ocean vertical diffusion and anthropogenic aerosol forcing (direct and indirect effects of sulfate aerosol, black carbon and organic carbon) are essentially important factors to emulate of global mean SAT changes of AOGCMs. We, therefore, estimate these important factors in the simple climate model using a Metropolis-Hastings Markov chain Monte Carlo (MCMC) approach, and compared the results of the emulation of the simple climate model with those of AIM/impact[policy] simple climate model. Although root mean square error (RMSE) in decadal means of global mean SAT changes during the period of 2001-2100 in the AIM/impact[policy] simple climate model are large (0.6), the RMSE in our new simple climate model are dramatically improved (0.02). Thus, the estimation of these important factors by a MCMC is very useful for emulation of AOGCMs by the use of simple climate models.

  16. Urban Climate Resilience - Connecting climate models with decision support cyberinfrastructure using open standards

    NASA Astrophysics Data System (ADS)

    Bermudez, L. E.; Percivall, G.; Idol, T. A.

    2015-12-01

    Experts in climate modeling, remote sensing of the Earth, and cyber infrastructure must work together in order to make climate predictions available to decision makers. Such experts and decision makers worked together in the Open Geospatial Consortium's (OGC) Testbed 11 to address a scenario of population displacement by coastal inundation due to the predicted sea level rise. In a Policy Fact Sheet "Harnessing Climate Data to Boost Ecosystem & Water Resilience", issued by White House Office of Science and Technology (OSTP) in December 2014, OGC committed to increase access to climate change information using open standards. In July 2015, the OGC Testbed 11 Urban Climate Resilience activity delivered on that commitment with open standards based support for climate-change preparedness. Using open standards such as the OGC Web Coverage Service and Web Processing Service and the NetCDF and GMLJP2 encoding standards, Testbed 11 deployed an interoperable high-resolution flood model to bring climate model outputs together with global change assessment models and other remote sensing data for decision support. Methods to confirm model predictions and to allow "what-if-scenarios" included in-situ sensor webs and crowdsourcing. A scenario was in two locations: San Francisco Bay Area and Mozambique. The scenarios demonstrated interoperation and capabilities of open geospatial specifications in supporting data services and processing services. The resultant High Resolution Flood Information System addressed access and control of simulation models and high-resolution data in an open, worldwide, collaborative Web environment. The scenarios examined the feasibility and capability of existing OGC geospatial Web service specifications in supporting the on-demand, dynamic serving of flood information from models with forecasting capacity. Results of this testbed included identification of standards and best practices that help researchers and cities deal with climate-related issues

  17. Parameterization of clouds and radiation in climate models

    SciTech Connect

    Roeckner, E.

    1995-09-01

    Clouds are a very important, yet poorly modeled element in the climate system. There are many potential cloud feedbacks, including those related to cloud cover, height, water content, phase change, and droplet concentration and size distribution. As a prerequisite to studying the cloud feedback issue, this research reports on the simulation and validation of cloud radiative forcing under present climate conditions using the ECHAM general circulation model and ERBE top-of-atmosphere radiative fluxes.

  18. Verification of regional climate models over the territory of Ukraine

    NASA Astrophysics Data System (ADS)

    Krakovska, S.; Palamarchuk, L.; Shedemenko, I.; Djukel, G.; Gnatjuk, N.

    2009-04-01

    Verification of regional climate models (RCMs) over the territory of Ukraine was the first stage of the National project for assessment of possible climate change and its impact on the economic and social life in Ukraine in XXI century. Since Ukraine has pretty different climates in different parts, the territory of Ukraine was divided on 11 regions with more or less uniform climate conditions: 7 almost equal in space regions in plain terrain, 2 - in coastal zones near the Black and Azov seas and 2 - in the Carpathian and the Crimean mountains. Verification of RCMs for climate characteristics was carried out for each defined region separately. Data of meteorological network in Ukraine (187 stations) and the Climate Research Unit (CRU 10-min global data-set) for multy-year monthly, season and annual means of temperature and precipitation for the period 1961-90 were used for verification of models' results. Two RCMs were used in the analysis of the past climate of Ukraine: REMO (MPI-M, Hamburg) and RegCM3 (ICTP, Trieste). Both models were constructed with initial and boundary conditions from ERA-40 data-set with horizontal spacing of ~25 km and vertically 27 (REMO) and 18 (RegCM3) Z-σ levels. In a whole, both models demonstrated better ability for temperature than precipitation characteristics. Very high correlation of 0.9 was found between models, network and CRU for temperatures and 0.7-0.8 for precipitation. Generally, models were warmer especially for summer months up to 2 oC. More precipitation in the models was found for winter season and less - for summer and in the mountainous subregions comparably with observations. In perspective we intend to run RCMs initialized with GCMs for the same period and for XXI century and account for the obtained systematic models' errors in the analysis of possible climate change over the territory of Ukraine.

  19. 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).

  20. CLIMATE CHANGE MODELS AND FOREST IMPACTS RESEARCH

    EPA Science Inventory

    The recent Earth Summit in Rio has once again focused world attention on the importance of climate, human and biological interactions. nder programs such as the Forest Service Global Change Research Program (FSGCRP), scientists are exploring and assessing a variety of forest reso...

  1. Desert dust and anthropogenic aerosol interactions in the Community Climate System Model coupled-carbon-climate model

    SciTech Connect

    Mahowald, Natalie; Rothenberg, D.; Lindsay, Keith; Doney, Scott C.; Moore, Jefferson Keith; Randerson, James T.; Thornton, Peter E; Jones, C. D.

    2011-02-01

    Coupled-carbon-climate simulations are an essential tool for predicting the impact of human activity onto the climate and biogeochemistry. Here we incorporate prognostic desert dust and anthropogenic aerosols into the CCSM3.1 coupled carbon-climate model and explore the resulting interactions with climate and biogeochemical dynamics through a series of transient anthropogenic simulations (20th and 21st centuries) and sensitivity studies. The inclusion of prognostic aerosols into this model has a small net global cooling effect on climate but does not significantly impact the globally averaged carbon cycle; we argue that this is likely to be because the CCSM3.1 model has a small climate feedback onto the carbon cycle. We propose a mechanism for including desert dust and anthropogenic aerosols into a simple carbon-climate feedback analysis to explain the results of our and previous studies. Inclusion of aerosols has statistically significant impacts on regional climate and biogeochemistry, in particular through the effects on the ocean nitrogen cycle and primary productivity of altered iron inputs from desert dust deposition.

  2. Inconsistencies and Fallacies: IPCC 20th Century Simulations, Multi-Model Ensembles and Climate Sensitivity

    NASA Astrophysics Data System (ADS)

    Rogers, N. L.

    2009-12-01

    The IPCC used an experiment that had approximately 20 different climate models fit the temperature history of the 20th century. A remarkably good and convincing fit was obtained by combining selected models into a multi-model ensemble. This may be seen in figure 9.5 of the AR4 Scientific Basis report. The fallacy is that each modeling group used different forcings, effectively simulating a different imaginary planet. Since the IPCC models differ by more than 2-1 in climate sensitivity it would be quite amazing if they could all agree on temperature in the late 20th century when CO2 was rapidly increasing. Allowing each model to be excited by different forcings effectively makes the model be a rather complicated curve fitting program. If one accepts that the models are being used to do curve fitting then the supposedly better results obtained by averaging multiple models is easily explainable as the reduction of error that results from averaging approximations to a function with uncorrelated errors. Finally the late 20th century temperature rise is too small for a 3 degree climate sensitivity for doubling of CO2 and the explanations for the warming shortfall that rely on aerosol cooling or ocean warming are easily refuted.There may be alternative explanations for the shortfall or it may be that climate sensitivity is much lower than projected by the IPCC.

  3. Modeling of GE Appliances: Final Presentation

    SciTech Connect

    Fuller, Jason C.; Vyakaranam, Bharat; Leistritz, Sean M.; Parker, Graham B.

    2013-01-31

    This report is the final in a series of three reports funded by U.S. Department of Energy Office of Electricity Delivery and Energy Reliability (DOE-OE) in collaboration with GE Appliances’ through a Cooperative Research and Development Agreement (CRADA) to describe the potential of GE Appliances’ DR-enabled appliances to provide benefits to the utility grid.

  4. A climate robust integrated modelling framework for regional impact assessment of climate change

    NASA Astrophysics Data System (ADS)

    Janssen, Gijs; Bakker, Alexander; van Ek, Remco; Groot, Annemarie; Kroes, Joop; Kuiper, Marijn; Schipper, Peter; van Walsum, Paul; Wamelink, Wieger; Mol, Janet

    2013-04-01

    Decision making towards climate proofing the water management of regional catchments can benefit greatly from the availability of a climate robust integrated modelling framework, capable of a consistent assessment of climate change impacts on the various interests present in the catchments. In the Netherlands, much effort has been devoted to developing state-of-the-art regional dynamic groundwater models with a very high spatial resolution (25x25 m2). Still, these models are not completely satisfactory to decision makers because the modelling concepts do not take into account feedbacks between meteorology, vegetation/crop growth, and hydrology. This introduces uncertainties in forecasting the effects of climate change on groundwater, surface water, agricultural yields, and development of groundwater dependent terrestrial ecosystems. These uncertainties add to the uncertainties about the predictions on climate change itself. In order to create an integrated, climate robust modelling framework, we coupled existing model codes on hydrology, agriculture and nature that are currently in use at the different research institutes in the Netherlands. The modelling framework consists of the model codes MODFLOW (groundwater flow), MetaSWAP (vadose zone), WOFOST (crop growth), SMART2-SUMO2 (soil-vegetation) and NTM3 (nature valuation). MODFLOW, MetaSWAP and WOFOST are coupled online (i.e. exchange information on time step basis). Thus, changes in meteorology and CO2-concentrations affect crop growth and feedbacks between crop growth, vadose zone water movement and groundwater recharge are accounted for. The model chain WOFOST-MetaSWAP-MODFLOW generates hydrological input for the ecological prediction model combination SMART2-SUMO2-NTM3. The modelling framework was used to support the regional water management decision making process in the 267 km2 Baakse Beek-Veengoot catchment in the east of the Netherlands. Computations were performed for regionalized 30-year climate change

  5. Climate condition in the Central Europe during the Weichselian Ice Sheet according to the Educational Global Climate Modeling Project

    NASA Astrophysics Data System (ADS)

    Szuman, Izabela; Czernecki, Bartosz

    2010-05-01

    condition in Poland. In this area occurred a huge ice-lobe, distinct in the geomorphology, during the Weichselian Ice Sheet. Authors try to define the role of such a big ice-barrier on the climate changes at the foreland, between the western and eastern side. It is necessary to consider the ice cap thickness in the lobe estimated from separately prepared in GIS software (GRASS) 3D ice-sheet surface elevation model, together with the climatic data from the GCM for regional situation. The results of modeling are also related to available abiotic parameters for Poland. Finally, it is suggested that the ice-lobe was high enough barrier to cause the differences in temperature distribution due to limitation of delivery the warm Atlantic air masses to the eastern region. It has also significant impact on local wind field, especially in transition areas.

  6. Assessing climate change impact by integrated hydrological modelling

    NASA Astrophysics Data System (ADS)

    Lajer Hojberg, Anker; Jørgen Henriksen, Hans; Olsen, Martin; der Keur Peter, van; Seaby, Lauren Paige; Troldborg, Lars; Sonnenborg, Torben; Refsgaard, Jens Christian

    2013-04-01

    Future climate may have a profound effect on the freshwater cycle, which must be taken into consideration by water management for future planning. Developments in the future climate are nevertheless uncertain, thus adding to the challenge of managing an uncertain system. To support the water managers at various levels in Denmark, the national water resources model (DK-model) (Højberg et al., 2012; Stisen et al., 2012) was used to propagate future climate to hydrological response under considerations of the main sources of uncertainty. The DK-model is a physically based and fully distributed model constructed on the basis of the MIKE SHE/MIKE11 model system describing groundwater and surface water systems and the interaction between the domains. The model has been constructed for the entire 43.000 km2 land area of Denmark only excluding minor islands. Future climate from General Circulation Models (GCM) was downscaled by Regional Climate Models (RCM) by a distribution-based scaling method (Seaby et al., 2012). The same dataset was used to train all combinations of GCM-RCMs and they were found to represent the mean and variance at the seasonal basis equally well. Changes in hydrological response were computed by comparing the short term development from the period 1990 - 2010 to 2021 - 2050, which is the time span relevant for water management. To account for uncertainty in future climate predictions, hydrological response from the DK-model using nine combinations of GCMs and RCMs was analysed for two catchments representing the various hydrogeological conditions in Denmark. Three GCM-RCM combinations displaying high, mean and low future impacts were selected as representative climate models for which climate impact studies were carried out for the entire country. Parameter uncertainty was addressed by sensitivity analysis and was generally found to be of less importance compared to the uncertainty spanned by the GCM-RCM combinations. Analysis of the simulations

  7. Observationally-Based Data/Model Metrics from the Southern Ocean Climate Model Atlas

    NASA Astrophysics Data System (ADS)

    Abell, J.; Russell, J. L.; Goodman, P. J.

    2015-12-01

    The Southern Ocean Climate Model Atlas makes available observationally-based standardized data/model metrics of the latest simulations of climate and projections of climate change from available climate models. Global climate model simulations differ greatly in the Southern Ocean, so the development of consistent, observationally-based metrics, by which to assess the fidelity of model simulations is essential. We will present metrics showing and quantifying the results of the modern day climate simulations over the Southern Ocean from models submitted as part of the CMIP5/IPCC-AR5 process. Our analysis will focus on the simulations of the temperature, salinity and carbon at various depths and along significant hydrographic sections. The models exhibit different skill levels with various metrics between models and also within individual models.

  8. Tropical deforestation: Modeling local- to regional-scale climate change

    SciTech Connect

    Henderson-Sellers, A.; Durbidge, T.B.; Pitman, A.J. ); Dickinson, R.E. ); Kennedy, P.J. ); McGuffie, K. )

    1993-04-20

    The authors report results from a model study using the National Center for Atmospheric Research Community Climate Model (Version 1) general circulation model to assess the impact of regional scale deforestation on climate change. In the model a large parcel in the Amazon basin is changed from tropical rain forest to scrub grassland. Impacts can include adding CO[sub 2] to the atmosphere by biomass burning, increasing surface albedo, changing precipitation and evaporation rates, impacting soil moisture, and general weather patterns. They compare their model results with earlier work which has looked at this same problem.

  9. Planetary boundary layer energetics simulated from a regional climate model over Europe for present climate and climate change conditions

    NASA Astrophysics Data System (ADS)

    Sánchez, E.; Yagüe, C.; Gaertner, M. A.

    2007-01-01

    This paper presents a description of the planetary boundary layer (PBL) for current (1960-1990) and future (2070-2100) climate periods as obtained from a regional climate model (RCM) centered on the Mediterranean basin. Vertically integrated turbulent kinetic energy (TKEZ) and boundary layer height (z i ) are used to describe PBL energetics. Present climate shows a TKEZ annual cycle with a clear summer maximum for southern regions, while northern regions of Europe exhibit a smoother or even a lack of cycle. Future climate conditions exhibit a similar behaviour, with an increase in the summer maximum peaks. A detailed analysis of summer surface climate change energetics over land shows an increased Bowen ratio and decreases in the evaporative fraction. The enhanced sensible heat flux responsible for these results causes an energy surplus inside the PBL, resulting in increased convective activity and corresponding TKEZ. These results are consistent with temperature increases obtained by several other model simulations, and also indicate that changes in the turbulent transport from the PBL to the free troposphere can affect atmospheric circulations.

  10. Climate-methane cycle feedback in global climate model model simulations forced by RCP scenarios

    NASA Astrophysics Data System (ADS)

    Eliseev, Alexey V.; Denisov, Sergey N.; Arzhanov, Maxim M.; Mokhov, Igor I.

    2013-04-01

    Methane cycle module of the global climate model of intermediate complexity developed at the A.M. Obukhov Institute of Atmospheric Physics, Russian Academy of Sciences (IAP RAS CM) is extended by coupling with a detailed module for thermal and hydrological processes in soil (Deep Soil Simulator, (Arzhanov et al., 2008)). This is an important improvement with respect with the earlier IAP RAS CM version (Eliseev et al., 2008) which has employed prescribed soil hydrology to simulate CH4 emissions from soil. Geographical distribution of water inundated soil in the model was also improved by replacing the older Olson's ecosystem data base by the data based on the SCIAMACHY retrievals (Bergamaschi et al., 2007). New version of the IAP RAS CM module for methane emissions from soil is validated by using the simulation protocol adopted in the WETCHIMP (Wetland and Wetland CH4 Inter-comparison of Models Project). In addition, atmospheric part of the IAP RAS CM methane cycle is extended by temperature dependence of the methane life-time in the atmosphere in order to mimic the respective dependence of the atmospheric methane chemistry (Denisov et al., 2012). The IAP RAS CM simulations are performed for the 18th-21st centuries according with the CMIP5 protocol taking into account natural and anthropogenic forcings. The new IAP RAS CM version realistically reproduces pre-industrial and present-day characteristics of the global methane cycle including CH4 concentration qCH4 in the atmosphere and CH4 emissions from soil. The latter amounts 150 - 160 TgCH4-yr for the late 20th century and increases to 170 - 230 TgCH4-yr in the late 21st century. Atmospheric methane concentration equals 3900 ppbv under the most aggressive anthropogenic scenario RCP 8.5 and 1850 - 1980 ppbv under more moderate scenarios RCP 6.0 and RCP 4.5. Under the least aggressive scenario RCP 2.6 qCH4 reaches maximum 1730 ppbv in 2020s and declines afterwards. Climate change impact on the methane emissions from

  11. Berry composition and climate: responses and empirical models

    NASA Astrophysics Data System (ADS)

    Barnuud, Nyamdorj N.; Zerihun, Ayalsew; Gibberd, Mark; Bates, Bryson

    2014-08-01

    Climate is a strong modulator of berry composition. Accordingly, the projected change in climate is expected to impact on the composition of berries and of the resultant wines. However, the direction and extent of climate change impact on fruit composition of winegrape cultivars are not fully known. This study utilised a climate gradient along a 700 km transect, covering all wine regions of Western Australia, to explore and empirically describe influences of climate on anthocyanins, pH and titratable acidity (TA) levels in two or three cultivars of Vitis vinifera (Cabernet Sauvignon, Chardonnay and Shiraz). The results showed that, at a common maturity of 22° Brix total soluble solids, berries from the warmer regions had low levels of anthocyanins and TA as well as high pH compared to berries from the cooler regions. Most of these regional variations in berry composition reflected the prevailing climatic conditions of the regions. Thus, depending on cultivar, 82-87 % of TA, 83 % of anthocyanins and about half of the pH variations across the gradient were explained by climate-variable-based empirical models. Some of the variables that were relevant in describing the variations in berry attributes included: diurnal ranges and ripening period temperature (TA), vapour pressure deficit in October and growing degree days (pH), and ripening period temperatures (anthocyanins). Further, the rates of change in these berry attributes in response to climate variables were cultivar dependent. Based on the observed patterns along the climate gradient, it is concluded that: (1) in a warming climate, all other things being equal, berry anthocyanins and TA levels will decline whereas pH levels will rise; and (2) despite variations in non-climatic factors (e.g. soil type and management) along the sampling transect, variations in TA and anthocyanins were satisfactorily described using climate-variable-based empirical models, indicating the overriding impact of climate on berry

  12. Evaluation of the Australian Community Climate and Earth-System Simulator Chemistry-Climate Model

    NASA Astrophysics Data System (ADS)

    Stone, K. A.; Morgenstern, O.; Karoly, D. J.; Klekociuk, A. R.; French, W. J. R.; Abraham, N. L.; Schofield, R.

    2015-07-01

    Chemistry climate models are important tools for addressing interactions of composition and climate in the Earth System. In particular, they are used for assessing the combined roles of greenhouse gases and ozone in Southern Hemisphere climate and weather. Here we present an evaluation of the Australian Community Climate and Earth System Simulator-Chemistry Climate Model, focusing on the Southern Hemisphere and the Australian region. This model is used for the Australian contribution to the international Chemistry-Climate Model Initiative, which is soliciting hindcast, future projection and sensitivity simulations. The model simulates global total column ozone (TCO) distributions accurately, with a slight delay in the onset and recovery of springtime Antarctic ozone depletion, and consistently higher ozone values. However, October averaged Antarctic TCO from 1960 to 2010 show a similar amount of depletion compared to observations. A significant innovation is the evaluation of simulated vertical profiles of ozone and temperature with ozonesonde data from Australia, New Zealand and Antarctica from 38 to 90° S. Excess ozone concentrations (up to 26.4 % at Davis during winter) and stratospheric cold biases (up to 10.1 K at the South Pole) outside the period of perturbed springtime ozone depletion are seen during all seasons compared to ozonesondes. A disparity in the vertical location of ozone depletion is seen: centered around 100 hPa in ozonesonde data compared to above 50 hPa in the model. Analysis of vertical chlorine monoxide profiles indicates that colder Antarctic stratospheric temperatures (possibly due to reduced mid-latitude heat flux) are artificially enhancing polar stratospheric cloud formation at high altitudes. The models inability to explicitly simulated supercooled ternary solution may also explain the lack of depletion at lower altitudes. The simulated Southern Annular Mode (SAM) index compares well with ERA-Interim data. Accompanying these

  13. Representation of Clear and Cloudy Boundary Layers in Climate Models. Chapter 14

    NASA Technical Reports Server (NTRS)

    Randall, D. A.; Shao, Q.; Branson, M.

    1997-01-01

    The atmospheric general circulation models which are being used as components of climate models rely on their boundary layer parameterizations to produce realistic simulations of the surface turbulent fluxes of sensible heat. moisture. and momentum: of the boundary-layer depth over which these fluxes converge: of boundary layer cloudiness: and of the interactions of the boundary layer with the deep convective clouds that grow upwards from it. Two current atmospheric general circulation models are used as examples to show how these requirements are being addressed: these are version 3 of the Community Climate Model. which has been developed at the U.S. National Center for Atmospheric Research. and the Colorado State University atmospheric general circulation model. The formulations and results of both models are discussed. Finally, areas for future research are suggested.

  14. Psyplot: Visualizing rectangular and triangular Climate Model Data with Python

    NASA Astrophysics Data System (ADS)

    Sommer, Philipp

    2016-04-01

    The development and use of climate models often requires the visualization of geo-referenced data. Creating visualizations should be fast, attractive, flexible, easily applicable and easily reproducible. There is a wide range of software tools available for visualizing raster data, but they often are inaccessible to many users (e.g. because they are difficult to use in a script or have low flexibility). In order to facilitate easy visualization of geo-referenced data, we developed a new framework called "psyplot," which can aid earth system scientists with their daily work. It is purely written in the programming language Python and primarily built upon the python packages matplotlib, cartopy and xray. The package can visualize data stored on the hard disk (e.g. NetCDF, GeoTIFF, any other file format supported by the xray package), or directly from the memory or Climate Data Operators (CDOs). Furthermore, data can be visualized on a rectangular grid (following or not following the CF Conventions) and on a triangular grid (following the CF or UGRID Conventions). Psyplot visualizes 2D scalar and vector fields, enabling the user to easily manage and format multiple plots at the same time, and to export the plots into all common picture formats and movies covered by the matplotlib package. The package can currently be used in an interactive python session or in python scripts, and will soon be developed for use with a graphical user interface (GUI). Finally, the psyplot framework enables flexible configuration, allows easy integration into other scripts that uses matplotlib, and provides a flexible foundation for further development.

  15. Coastal Ecosystems and Climate Change: Is Modeling and Monitoring Enough?

    NASA Astrophysics Data System (ADS)

    Cronin, T. M.; Walker, H. A.

    2005-05-01

    Many coastal ecosystems are severely degraded due to a variety of human factors, requiring large and expensive monitoring and modeling efforts for restoration and management. Climate variability, including abrupt climate change, is seldom factored into coastal ecosystem management despite growing evidence for climate forcing of precipitation, river discharge, water quality, salinity, turbidity, faunal and phytoplankton dynamics, dissolved oxygen, and other ecosystem processes. We will review evidence from long-term monitoring records, multi-proxy paleoclimatic and paleoecological records, and climatic modeling that suggests that the effects of climate can override local and regional human activities and may potentially diminish the success of restoration efforts. Because ecosystem restoration often involves long-term objectives requiring decades to achieve, our focus will be on examples from sub-tropical and temperate estuaries in North America that show ecosystem response over decadal timescales to variability related to El Niño-Southern Oscillation, the Pacific Decadal Oscillation and the North Atlantic Oscillation. Climatic variability evident from paleo-records of the past few centuries exceeds that recorded in most 20th century monitoring records. This raises issues about the efficacy of local and regional ecosystem and hydrodynamic models designed to simulate ecosystem response to anthropogenic changes in sediment and nutrient input, fresh-water discharge, and land-use because such models, though tested with rigorous validation procedures, use calibration data sets limited to a few years. Thus, they might not be appropriate for simulating response to climatic extremes on the scale and duration of past events outside their calibration range. Understanding the complexities of ecosystem response to climatic forcing, especially in the context of local and regional ecosystem disturbance, raises formidable challenges, but attempts to integrate climate

  16. Using a Global Climate Model in an On-line Climate Change Course

    NASA Astrophysics Data System (ADS)

    Randle, D. E.; Chandler, M. A.; Sohl, L. E.

    2012-12-01

    Seminars on Science: Climate Change is an on-line, graduate-level teacher professional development course offered by the American Museum of Natural History. It is an intensive 6-week course covering a broad range of global climate topics, from the fundamentals of the climate system, to the causes of climate change, the role of paleoclimate investigations, and a discussion of potential consequences and risks. The instructional method blends essays, videos, textbooks, and linked websites, with required participation in electronic discussion forums that are moderated by an experienced educator and a course scientist. Most weeks include additional assignments. Three of these assignments employ computer models, including two weeks spent working with a full-fledged 3D global climate model (GCM). The global climate modeling environment is supplied through a partnership with Columbia University's Educational Global Climate Modeling Project (EdGCM). The objective is to have participants gain hands-on experience with one of the most important, yet misunderstood, aspects of climate change research. Participants in the course are supplied with a USB drive that includes installers for the software and sample data. The EdGCM software includes a version of NASA's global climate model fitted with a graphical user interface and pre-loaded with several climate change simulations. Step-by-step assignments and video tutorials help walk people through these challenging exercises and the course incorporates a special assignment discussion forum to help with technical problems and questions about the NASA GCM. There are several takeaways from our first year and a half of offering this course, which has become one of the most popular out of the twelve courses offered by the Museum. Participants report a high level of satisfaction in using EdGCM. Some report frustration at the initial steps, but overwhelmingly claim that the assignments are worth the effort. Many of the difficulties that

  17. Response of plants and ecosystems to CO{sub 2} and climate change. Final technical report

    SciTech Connect

    Reynolds, J.F.

    1993-12-31

    In recognition of the important role of vegetation in the bio-geosphere carbon cycle, the Carbon Dioxide Research Program of the US Department of Energy established the research program: Direct Effects of increasing Carbon Dioxide on Vegetation. The ultimate goal is to develop a general ecosystem model to investigate, via hypothesis testing, the potential responses of different terrestrial ecosystems to changes in the global environment over the next century. The approach involves the parallel development of models at several hierarchical levels, from the leaf to the ecosystem. At the plant level, mechanism and the direct effects of CO{sub 2} in the development of a general plant growth model, GEPSI - GEneral Plant SImulator has been stressed. At the ecosystem level, we have stressed the translation Of CO{sub 2} effects and other aspects of climate change throughout the ecosystem, including feedbacks and constraints to system response, in the development of a mechanistic, general ecosystem model SERECO - Simulation of Ecosystem Response to Elevated CO{sub 2} and Climate Change has been stressed.

  18. Modeling Climate Change Effects on Stream Temperatures in Regulated Rivers

    NASA Astrophysics Data System (ADS)

    Null, S. E.; Akhbari, M.; Ligare, S. T.; Rheinheimer, D. E.; Peek, R.; Yarnell, S. M.; Viers, J. H.

    2013-12-01

    We provide a method for examining mesoscale stream temperature objectives downstream of dams with anticipated climate change using an integrated multi-model approach. Changing hydroclimatic conditions will likely impact stream temperatures within reservoirs and below dams, and affect downstream ecology. We model hydrology and water temperature using a series of linked models that includes a hydrology model to predict natural unimpaired flows in upstream reaches, a reservoir temperature simulation model , an operations model to simulate reservoir releases, and a stream temperature simulation model to simulate downstream conditions . All models are 1-dimensional and operate on either a weekly or daily timestep. First, we model reservoir thermal dynamics and release operations of hypothetical reservoirs of different sizes, elevations, and latitudes with climate-forced inflow hydrologies to examine the potential to manage stream temperatures for coldwater habitat. Results are presented as stream temperature change from the historical time period and indicate that reservoir releases are cooler than upstream conditions, although the absolute temperatures of reaches below dams warm with climate change. We also apply our method to a case study in California's Yuba River watershed to evaluate water regulation and hydropower operation effects on stream temperatures with climate change. Catchments of the upper Yuba River are highly-engineered, with multiple, interconnected infrastructure to provide hydropower, water supply, flood control, environmental flows, and recreation. Results illustrate climate-driven versus operations-driven changes to stream temperatures. This work highlights the need for methods to consider reservoir regulation effects on stream temperatures with climate change, particularly for hydropower relicensing (which currently ignores climate change) such that impacts to other beneficial uses like coldwater habitat and instream ecosystems can be

  19. Investigations of the Climate System Response to Climate Engineering in a Hierarchy of Models

    NASA Astrophysics Data System (ADS)

    McCusker, Kelly E.

    Global warming due to anthropogenic emissions of greenhouse gases is causing negative impacts on diverse ecological and human systems around the globe, and these impacts are projected to worsen as climate continues to warm. In the absence of meaningful greenhouse gas emissions reductions, new strategies have been proposed to engineer the climate, with the aim of preventing further warming and avoiding associated climate impacts. We investigate one such strategy here, falling under the umbrella of `solar radiation management', in which sulfate aerosols are injected into the stratosphere. We use a global climate model with a coupled mixed-layer depth ocean and with a fully-coupled ocean general circulation model to simulate the stabilization of climate by balancing increasing carbon dioxide with increasing stratospheric sulfate concentrations. We evaluate whether or not severe climate impacts, such as melting Arctic sea ice, tropical crop failure, or destabilization of the West Antarctic ice sheet, could be avoided. We find that while tropical climate emergencies might be avoided by use of stratospheric aerosol injections, avoiding polar emergencies cannot be guaranteed due to large residual climate changes in those regions, which are in part due to residual atmospheric circulation anomalies. We also find that the inclusion of a fully-coupled ocean is important for determining the regional climate response because of its dynamical feedbacks. The efficacy of stratospheric sulfate aerosol injections, and solar radiation management more generally, depends on its ability to be maintained indefinitely, without interruption from a variety of possible sources, such as technological failure, a breakdown in global cooperation, lack of funding, or negative unintended consequences. We next consider the scenario in which stratospheric sulfate injections are abruptly terminated after a multi- decadal period of implementation while greenhouse gas emissions have continued unabated

  20. Climate change decision-making: Model & parameter uncertainties explored

    SciTech Connect

    Dowlatabadi, H.; Kandlikar, M.; Linville, C.

    1995-12-31

    A critical aspect of climate change decision-making is uncertainties in current understanding of the socioeconomic, climatic and biogeochemical processes involved. Decision-making processes are much better informed if these uncertainties are characterized and their implications understood. Quantitative analysis of these uncertainties serve to inform decision makers about the likely outcome of policy initiatives, and help set priorities for research so that outcome ambiguities faced by the decision-makers are reduced. A family of integrated assessment models of climate change have been developed at Carnegie Mellon. These models are distinguished from other integrated assessment efforts in that they were designed from the outset to characterize and propagate parameter, model, value, and decision-rule uncertainties. The most recent of these models is ICAM 2.1. This model includes representation of the processes of demographics, economic activity, emissions, atmospheric chemistry, climate and sea level change and impacts from these changes and policies for emissions mitigation, and adaptation to change. The model has over 800 objects of which about one half are used to represent uncertainty. In this paper we show, that when considering parameter uncertainties, the relative contribution of climatic uncertainties are most important, followed by uncertainties in damage calculations, economic uncertainties and direct aerosol forcing uncertainties. When considering model structure uncertainties we find that the choice of policy is often dominated by model structure choice, rather than parameter uncertainties.

  1. Advances in ocean modeling for climate change research

    NASA Astrophysics Data System (ADS)

    Holland, William R.; Capotondi, Antonietta; Holland, Marika M.

    1995-07-01

    An adequate understanding of climate variability and the eventual prediction of climate change are among the most urgent and far-reaching efforts of the scientific community. The climate system is in an ever-changing state with vast impact on mankind in all his activities. Both short and long-term aspects of climate variability are of concern, and the unravelling of "natural" variability from "man-induced" climate change is required to prepare for and ameliorate, if possible, the potentially devastating aspects of such change. In terms of scientific effort, the climate community can be thought of as the union of the disciplinary sciences of meteorology, oceanography, sea ice and glaciology, and land surface processes. Since models are based upon mathematical and numerical constructs, mathematics and computer sciences are also directly involved. In addition, some of the problems of man-induced climate change (release of greenhouse gases, the ozone-hole problem, etc.) are basically chemical in nature, and the expertise of the atmospheric and oceanic chemist is also required. In addition, some part of the response to climate perturbations will arise in the biological world, due to upsetting the balance in the great food web that binds communities together on both the land and the sea. Thus, the problems to be solved are extraordinarily complex and require the efforts of many kinds of scientist.

  2. Regional Climate Model Projections for the State of Washington

    SciTech Connect

    Salathe, E.; Leung, Lai-Yung R.; Qian, Yun; Zhang, Yongxin

    2010-05-05

    Global climate models do not have sufficient spatial resolution to represent the atmospheric and land surface processes that determine the unique regional heterogeneity of the climate of the State of Washington. If future large-scale weather patterns interact differently with the local terrain and coastlines than current weather patterns, local changes in temperature and precipitation could be quite different from the coarse-scale changes projected by global models. Regional climate models explicitly simulate the interactions between the large-scale weather patterns simulated by a global model and the local terrain. We have performed two 100-year climate simulations using the Weather and Research Forecasting (WRF) model developed at the National Center for Atmospheric Research (NCAR). One simulation is forced by the NCAR Community Climate System Model version 3 (CCSM3) and the second is forced by a simulation of the Max Plank Institute, Hamburg, global model (ECHAM5). The mesoscale simulations produce regional changes in snow cover, cloudiness, and circulation patterns associated with interactions between the large-scale climate change and the regional topography and land-water contrasts. These changes substantially alter the temperature and precipitation trends over the region relative to the global model result or statistical downscaling. To illustrate this effect, we analyze the changes from the current climate (1970-1999) to the mid 21st century (2030-2059). Changes in seasonal-mean temperature, precipitation, and snowpack are presented. Several climatological indices of extreme daily weather are also presented: precipitation intensity, fraction of precipitation occurring in extreme daily events, heat wave frequency, growing season length, and frequency of warm nights. Despite somewhat different changes in seasonal precipitation and temperature from the two regional simulations, consistent results for changes in snowpack and extreme precipitation are found in

  3. Modeling High-Impact Weather and Climate: Lessons From a Tropical Cyclone Perspective

    SciTech Connect

    Done, James; Holland, Greg; Bruyere, Cindy; Leung, Lai-Yung R.; Suzuki-Parker, Asuka

    2013-10-19

    Although the societal impact of a weather event increases with the rarity of the event, our current ability to assess extreme events and their impacts is limited by not only rarity but also by current model fidelity and a lack of understanding of the underlying physical processes. This challenge is driving fresh approaches to assess high-impact weather and climate. Recent lessons learned in modeling high-impact weather and climate are presented using the case of tropical cyclones as an illustrative example. Through examples using the Nested Regional Climate Model to dynamically downscale large-scale climate data the need to treat bias in the driving data is illustrated. Domain size, location, and resolution are also shown to be critical and should be guided by the need to: include relevant regional climate physical processes; resolve key impact parameters; and to accurately simulate the response to changes in external forcing. The notion of sufficient model resolution is introduced together with the added value in combining dynamical and statistical assessments to fill out the parent distribution of high-impact parameters. Finally, through the example of a tropical cyclone damage index, direct impact assessments are resented as powerful tools that distill complex datasets into concise statements on likely impact, and as highly effective communication devices.

  4. The origins of computer weather prediction and climate modeling

    SciTech Connect

    Lynch, Peter

    2008-03-20

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

  5. The origins of computer weather prediction and climate modeling

    NASA Astrophysics Data System (ADS)

    Lynch, Peter

    2008-03-01

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

  6. Final Project Report Load Modeling Transmission Research

    SciTech Connect

    Lesieutre, Bernard; Bravo, Richard; Yinger, Robert; Chassin, Dave; Huang, Henry; Lu, Ning; Hiskens, Ian; Venkataramanan, Giri

    2012-03-31

    The research presented in this report primarily focuses on improving power system load models to better represent their impact on system behavior. The previous standard load model fails to capture the delayed voltage recovery events that are observed in the Southwest and elsewhere. These events are attributed to stalled air conditioner units after a fault. To gain a better understanding of their role in these events and to guide modeling efforts, typical air conditioner units were testing in laboratories. Using data obtained from these extensive tests, new load models were developed to match air conditioner behavior. An air conditioner model is incorporated in the new WECC composite load model. These models are used in dynamic studies of the West and can impact power transfer limits for California. Unit-level and systemlevel solutions are proposed as potential solutions to the delayed voltage recovery problem.

  7. Do bioclimate variables improve performance of climate envelope models?

    USGS Publications Warehouse

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

    2012-01-01

    Climate envelope models are widely used to forecast potential effects of climate change on species distributions. A key issue in climate envelope modeling is the selection of predictor variables that most directly influence species. To determine whether model performance and spatial predictions were related to the selection of predictor variables, we compared models using bioclimate variables with models constructed from monthly climate data for twelve terrestrial vertebrate species in the southeastern USA using two different algorithms (random forests or generalized linear models), and two model selection techniques (using uncorrelated predictors or a subset of user-defined biologically relevant predictor variables). There were no differences in performance between models created with bioclimate or monthly variables, but one metric of model performance was significantly greater using the random forest algorithm compared with generalized linear models. Spatial predictions between maps using bioclimate and monthly variables were very consistent using the random forest algorithm with uncorrelated predictors, whereas we observed greater variability in predictions using generalized linear models.

  8. Estimation of the fractional coverage of rainfall in climate models

    NASA Technical Reports Server (NTRS)

    Eltahir, E. A. B.; Bras, R. L.

    1993-01-01

    The fraction of the grid cell area covered by rainfall, mu, is an essential parameter in descriptions of land surface hydrology in climate models. A simple procedure is presented for estimating this fraction, based on extensive observations of storm areas and rainfall volumes. Storm area and rainfall volume are often linearly related; this relation can be used to compute the storm area from the volume of rainfall simulated by a climate model. A formula is developed for computing mu, which describes the dependence of the fractional coverage of rainfall on the season of the year, the geographical region, rainfall volume, and the spatial and temporal resolution of the model. The new formula is applied in computing mu over the Amazon region. Significant temporal variability in the fractional coverage of rainfall is demonstrated. The implications of this variability for the modeling of land surface hydrology in climate models are discussed.

  9. The UC-LLNL Regional Climate System Model

    SciTech Connect

    Miller, N.L.; Kim, Jinwon

    1996-09-01

    The UC-LLNL Regional Climate System Model has been under development since 1991. The unique system simulates climate from the global scale down to the watershed catchment scale, and consists of data pre- and post- processors, and four model components. The four model components are (1) a mesoscale atmospheric simulation model, (2) a soil-plant-snow model, (3) a watershed hydrology-riverflow model, and (4) a suite of crop response models. The first three model components have been coupled, and the system includes two-way feedbacks between the soil-plant-snow model and the mesoscale atmospheric simulation model. This three-component version of RCSM has been tested, validated, and successfully used for operational quantitative precipitation forecasts and seasonal water resource studies over the southwestern US. We are currently implementation and validating the fourth component, the Decision Support system for Agrotechnology Transfer (DSSAT). A description of the UC-LLNL RCSM and some recent results are presented.

  10. 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

    2014-05-01

    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 results of the RCM ALARO-Climate model simulations in 25 and 6.25 km resolutions on the longer time-scale (1961-1990). The model was driven by the ERA-40 re-analyses and run on the integration domain of ~ 2500 x 2500 km size covering the central Europe. The simulated model climate was compared with the gridded observation of air temperature (mean, maximum, minimum) and precipitation from the E-OBS version dataset 8. Other simulated parameters (e.g., cloudiness, radiation or components of water cycle) were compared to the ERA-40 re-analyses. The validation of the first ERA-40 simulation in both, 25 km and 6.25 km resolutions, revealed significant cold biases in all seasons and overestimation of precipitation in the selected Central Europe target area (0° - 30° eastern longitude ; 40° - 60° northern latitude). The differences between these simulations were small and thus revealed a robustness of the model's physical parameterization on the resolution change. The series of 25 km resolution simulations with several model adaptations was carried out to study their effect on the simulated properties of climate variables and thus possibly identify a source of major errors in the simulated climate. The current investigation suggests the main reason for biases is related to the model physic. 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

  11. Dynamically combining climate models to "supermodel" the tropical Pacific

    NASA Astrophysics Data System (ADS)

    Shen, Mao-Lin; Keenlyside, Noel; Selten, Frank; Wiegerinck, Wim; Duane, Gregory S.

    2016-01-01

    We construct an interactive ensemble of two different climate models to improve simulation of key aspects of tropical Pacific climate. Our so-called supermodel is based on two atmospheric general circulation models (AGCMs) coupled to a single ocean GCM, which is driven by a weighted average of the air-sea fluxes. Optimal weights are determined using a machine learning algorithm to minimize sea surface temperature errors over the tropical Pacific. This coupling strategy synchronizes atmospheric variability in the two AGCMs over the equatorial Pacific, where it improves the representation of ocean-atmosphere interaction and the climate state. In particular, the common double Intertropical Convergence Zone error is suppressed, and the positive Bjerknes feedback improves substantially to match observations well, and the negative heat flux feedback is also much improved. This study supports the concept of supermodeling as a promising multimodel ensemble strategy to improve weather and climate predictions.

  12. The potential effects of climate change on the native vascular flora of North America. A preliminary climate envelopes analysis: Final report

    SciTech Connect

    Morse, L.E.; Kutner, L.S.; Maddox, G.D.; Honey, L.L.; Thurman, C.M.; Kartesz, J.T.; Chaplin, S.J.

    1993-11-01

    To assess the potential effects of global warming on the North American flora, the reported geographical distributions of the 15,148 native North American vascular plant species were matched with climate data for 194 geographical areas to estimate the current ``climate envelope`` for each species. Three methods of analysis were used to construct these envelopes, all based on the limits of mean annual temperatures currently experienced by each species within its present range. Published models of future climates predict a possible increase in mean annual temperatures of 3{degree}C (5.4{degree}F) within the next century. Assuming that species might be eliminated from areas outside their present climate envelope, then about 7% to 11% of North America`s native plant species would be entirely out of their envelopes in a +3{degree}C climate. Rare species would be disproportionately affected -- between 10% and 18% of these species would be entirely out of their climate envelopes. However, some rare species may be able to persist at their present sites due to the availability of suitable microhabitats or genetic variation in climate tolerances. Of the more common species, only about 1% to 2% would be vulnerable in a +3{degree}C climate. The local effects of climate change on plant species would vary considerably if species withdraw from the southern or low-elevation portions of their ranges. Species may expand their ranges northwards as new areas become climatically suitable for them, producing significant changes in local floras. Species vary in their ability to make such migrations, depending upon limitations imposed by dispersal ability and/or specialized habitat requirements. An estimate of dispersibility suggests that species with narrow climate envelopes tend to lack characteristics promoting mobility.

  13. Spatial-temporal assessment of climate model drifts

    NASA Astrophysics Data System (ADS)

    Zanchettin, Davide; Woldeyes Arisido, Maeregu; Gaetan, Carlo; Rubino, Angelo

    2016-04-01

    Decadal climate forecasts with full-field initialized coupled climate models are affected by a growing error signal that develops due to the adjustment of the simulations from the assimilated state consistent with observations to the state consistent with the biased model's climatology. Sea-surface temperature (SST) drifts and biases are a major concern due to the central role of SST properties for the dynamical coupling between the atmosphere and the ocean, and for the associated variability. Therefore, strong SST drifts complicate the initialization and assessment of decadal climate prediction experiments, and can be detrimental for their overall quality. We propose a dynamic linear model based on a state-space approach and developed within a Bayesian hierarchical framework for probabilistic assessment of spatial and temporal characteristics of SST drifts in ensemble climate simulations. The state-space approach uses unobservable state variables to directly model the processes generating the observed variability. The statistical model is based on a sequential definition of the process having a conditional dependency only on the previous time step, which therefore corresponds to the Kalman filter formulas. In our formulation, the statistical model distinguishes between seasonal and longer-term drift components, and between large-scale and local drifts. We apply the Bayesian method to make inferences on the variance components of the Gaussian errors in both the observation and system equations of the state-space model. To this purpose, we draw samples from their posterior distributions using a Monte Carlo Markov Chain simulation technique with a Gibbs sampler. In this contribution we illustrate a first application of the model using the MiKlip prototype system for decadal climate predictions. We focus on the tropical Atlantic Ocean - a region where climate models are typically affected by a severe warm SST bias - to demonstrate how our approach allows for a more

  14. Multi-model drought estimation using regional climate model output

    NASA Astrophysics Data System (ADS)

    McCabe, M. F.; Sung, B.; Evans, J. P.; Sheffield, J.

    2012-12-01

    Drought is a recurring climatic phenomenon in Australia and many other regions of the world. Apart from the considerable social and health repercussions that widespread drought has at a community level, there are major implications to the landscape, economy and water resources sectors. One of the key outputs in drought characterisation is determining the degree, extent and severity of the actual drought. However, there exist a range of techniques to quantify drought (each with its own definition) that adds to the level of uncertainty in accurate estimation. To examine the range and variability in multi-model drought prediction, a study of drought characteristics is undertaken, focusing on one of Australia's most significant agricultural regions: the Murray Darling Basin (MDB). Common drought indices including the Reconnaissance Drought Index (RDI), Standard Runoff Index (SRI), Soil Moisture Percentiles (SMP) and Palmer Drought Severity Index (PDSI) were derived using output from a high resolution regional climate simulation of the MDB for the period from 1985 to 2008. Spatial and temporal analyses were conducted by comparing these indices across regional scales. A severity-area-duration analysis and drought clustering approach were also used to characterize the extent and severity of these events across south-eastern Australia. Overall it was found that the four drought indices responded similarly to precipitation anomalies and successfully captured the major droughts over the nearly 25 years of simulation. The recent Australian drought from 2002-2008 was the most severe as shown by various analyses. Indeed, the Murray Darling Basin experienced contiguous moderate to extreme drought conditions for long periods, covering almost 100% of both the Darling and Murray Basins. Analysis of results also showed that the duration of droughts varied greatly between indices, as drought assessments using soil moisture parameters tended to recover in response to precipitation at

  15. Model biases in rice phenology under warmer climates

    NASA Astrophysics Data System (ADS)

    Zhang, Tianyi; Li, Tao; Yang, Xiaoguang; Simelton, Elisabeth

    2016-06-01

    Climate-induced crop yields model projections are constrained by the accuracy of the phenology simulation in crop models. Here, we use phenology observations from 775 trials with 19 rice cultivars in 5 Asian countries to compare the performance of four rice phenology models (growing-degree-day (GDD), exponential, beta and bilinear models) when applied to warmer climates. For a given cultivar, the difference in growing season temperature (GST) varied between 2.2 and 8.2 °C in different trials, which allowed us to calibrate the models for lower GST and validate under higher GST, with three calibration experiments. The results show that in warmer climates the bilinear and beta phenology models resulted in gradually increasing bias for phenology predication and double yield bias per percent increase in phenology simulation bias, while the GDD and exponential models maintained a comparatively constant bias. The phenology biases were primarily attributed to varying phenological patterns to temperature in models, rather than on the size of the calibration dataset. Additionally, results suggest that model simulations based on multiple cultivars provide better predictability than using one cultivar. Therefore, to accurately capture climate change impacts on rice phenology, we recommend simulations based on multiple cultivars using the GDD and exponential phenology models.

  16. Model biases in rice phenology under warmer climates

    PubMed Central

    Zhang, Tianyi; Li, Tao; Yang, Xiaoguang; Simelton, Elisabeth

    2016-01-01

    Climate-induced crop yields model projections are constrained by the accuracy of the phenology simulation in crop models. Here, we use phenology observations from 775 trials with 19 rice cultivars in 5 Asian countries to compare the performance of four rice phenology models (growing-degree-day (GDD), exponential, beta and bilinear models) when applied to warmer climates. For a given cultivar, the difference in growing season temperature (GST) varied between 2.2 and 8.2 °C in different trials, which allowed us to calibrate the models for lower GST and validate under higher GST, with three calibration experiments. The results show that in warmer climates the bilinear and beta phenology models resulted in gradually increasing bias for phenology predication and double yield bias per percent increase in phenology simulation bias, while the GDD and exponential models maintained a comparatively constant bias. The phenology biases were primarily attributed to varying phenological patterns to temperature in models, rather than on the size of the calibration dataset. Additionally, results suggest that model simulations based on multiple cultivars provide better predictability than using one cultivar. Therefore, to accurately capture climate change impacts on rice phenology, we recommend simulations based on multiple cultivars using the GDD and exponential phenology models. PMID:27273847

  17. Model biases in rice phenology under warmer climates.

    PubMed

    Zhang, Tianyi; Li, Tao; Yang, Xiaoguang; Simelton, Elisabeth

    2016-01-01

    Climate-induced crop yields model projections are constrained by the accuracy of the phenology simulation in crop models. Here, we use phenology observations from 775 trials with 19 rice cultivars in 5 Asian countries to compare the performance of four rice phenology models (growing-degree-day (GDD), exponential, beta and bilinear models) when applied to warmer climates. For a given cultivar, the difference in growing season temperature (GST) varied between 2.2 and 8.2 °C in different trials, which allowed us to calibrate the models for lower GST and validate under higher GST, with three calibration experiments. The results show that in warmer climates the bilinear and beta phenology models resulted in gradually increasing bias for phenology predication and double yield bias per percent increase in phenology simulation bias, while the GDD and exponential models maintained a comparatively constant bias. The phenology biases were primarily attributed to varying phenological patterns to temperature in models, rather than on the size of the calibration dataset. Additionally, results suggest that model simulations based on multiple cultivars provide better predictability than using one cultivar. Therefore, to accurately capture climate change impacts on rice phenology, we recommend simulations based on multiple cultivars using the GDD and exponential phenology models. PMID:27273847

  18. Towards Ultra-High Resolution Models of Climate and Weather

    SciTech Connect

    Wehner, Michael; Oliker, Leonid; Shalf, John

    2007-01-01

    We present a speculative extrapolation of the performance aspects of an atmospheric general circulation model to ultra-high resolution and describe alternative technological paths to realize integration of such a model in the relatively near future. Due to a superlinear scaling of the computational burden dictated by stability criterion, the solution of the equations of motion dominate the calculation at ultra-high resolutions. From this extrapolation, it is estimated that a credible kilometer scale atmospheric model would require at least a sustained ten petaflop computer to provide scientifically useful climate simulations. Our design study portends an alternate strategy for practical power-efficient implementations of petaflop scale systems. Embedded processor technology could be exploited to tailor a custom machine designed to ultra-high climate model specifications at relatively affordable cost and power considerations. The major conceptual changes required by a kilometer scale climate model are certain to be difficult to implement. Although the hardware, software, and algorithms are all equally critical in conducting ultra-high climate resolution studies, it is likely that the necessary petaflop computing technology will be available in advance of a credible kilometer scale climate model.

  19. Climate Model Intercomparison at the Dynamics Level (Invited)

    NASA Astrophysics Data System (ADS)

    Tsonis, A.; Steinhaeuser, K.

    2013-12-01

    Until now, climate model intercomparison has focused primarily on annual and global averages of various quantities or on specific components, not on how well the general dynamics in the models compare to each other. In order to address how well models agree when it comes to dynamics they generate, we have adopted a new approach based on climate networks. We have considered 28 pre-industrial control runs as well as 70 20th-century forced runs from 23 climate models and have constructed networks for the 500 hPa, surface air temperature (SAT), sea level pressure (SLP), and precipitation fields for each run. Then we employed a widely used algorithm to derive the community structure in these networks. Communities separate 'nodes' in the network sharing similar dynamics. It has been shown that these communities, or sub-systems, in the climate system are associated with major climate modes and physics of the atmosphere. Once the community structure for all runs is derived, we use a pattern matching statistic to obtain a measure of how well any two models agree with each other. We find that, with possibly the exception of the 500 hPa field, the consistency for the SAT, SLP, and precipitation fields is questionable. More importantly, none of the models comes close to the community structure of the actual observations (reality). This is a significant finding especially for the temperature and precipitation fields, as these are the fields widely used to produce future projections in time and in space.

  20. A climate model intercomparison at the dynamics level

    NASA Astrophysics Data System (ADS)

    Tsonis, A.; Steinhaeuser, K.

    2012-12-01

    Up to now climate model intercomparison has focused on annual and global averages of various quantities or on specific components, not on how well the general dynamics in the models compare to each other. In order to address how well the models agree when it comes to dynamics they generate, we have adopted a new approach based on climate networks. We have considered 57 20th-century control runs from 17 climate models and have constructed networks for the 500 hPa, surface air temperature (SAT), and sea level pressure (SLP) fields for each run. Then we used two different algorithms to derive the community structure in these networks. Communities separate "nodes' in the network sharing similar dynamics. It has been shown that these communities, or sub-systems, in the climate system are associated with major climate modes and physics of the atmosphere 1-3. Once the community structure for all runs is derived we use a pattern matching statistic to obtain a measure of how well any two models agree with each other. We find that with possibly the exception of the 500 hPa filed, the consistency for the SLP and especially SAT fields is questionable. More importantly none of the models comes close to the community structure of the actual observations (reality). This is a significant finding especially for the temperature field, as this is the field predicted to produce temperature projections in time and in space.

  1. A climate model intercomparison at the dynamics level

    NASA Astrophysics Data System (ADS)

    Tsonis, Anastasios; Steinhaeuser, Karsten

    2013-04-01

    Until now, climate model intercomparison has focused primarily on annual and global averages of various quantities or on specific components, not on how well the general dynamics in the models compare to each other. In order to address how well models agree when it comes to dynamics they generate, we have adopted a new approach based on climate networks. We have considered 28 pre-industrial control runs as well as 70 20th-century forced runs from 23 climate models and have constructed networks for the 500 hPa, surface air temperature (SAT), sea level pressure (SLP), and precipitation fields for each run. Then we employed a widely used algorithm to derive the community structure in these networks. Communities separate "nodes" in the network sharing similar dynamics. It has been shown that these communities, or sub-systems, in the climate system are associated with major climate modes and physics of the atmosphere. Once the community structure for all runs is derived, we use a pattern matching statistic to obtain a measure of how well any two models agree with each other. We find that, with possibly the exception of the 500 hPa field, the consistency for the SAT, SLP, and precipitation fields is questionable. More importantly, none of the models comes close to the community structure of the actual observations (reality). This is a significant finding especially for the temperature and precipitation fields, as these are the fields widely used to produce future projections in time and in space.

  2. Statistical modeling of electrical components: Final report

    SciTech Connect

    Jolly, R.L.

    1988-07-01

    A method of forecasting production yields based on SPICE (University of California at Berkeley) circuit simulation and Monte Carlo techniques was evaluated. This method involved calculating functionally accurate component models using statistical techniques and using these component models in a SPICE electrical circuit simulation program. The results of the simulation program allow production yields to be calculated using standard statistical techniques.

  3. Modeling Transient Response of Forests to Climate Change

    SciTech Connect

    Dale, Virginia H; Tharp, M Lynn; Lannom, Karen O.; Hodges, Donald G

    2010-01-01

    Our hypothesis is that a high diversity of dominant life forms in Tennessee forests conveys resilience to disturbance such as climate change. Because of uncertainty in climate change and their effects, three climate change scenarios for 2030 and 2080 from three General Circulation Models (GCMs) were used to simulate a range of potential climate conditions for the state. These climate changes derive from the Intergovernmental Panel on Climate Change (IPCC) A1B storyline that assumes rapid global economic growth. The precipitation and temperature projections from the three GCMs for 2030 and 2080 were related to changes in five ecological provinces using the monthly record of temperature and precipitation from 1980 to 1997 for each 1 km cell across the state as aggregated into the provinces. Temperatures are projected to increase in all ecological provinces in all months for all three GCMs for both 2030 and 2080. Precipitation differences from the long-term average are more complex but less striking. The forest ecosystem model LINKAGES was used to simulate conditions for five ecological provinces from 1989 to 2300. Average output projects changes in tree diversity and species composition in all ecological provinces in Tennessee with the greatest changes in the Southern Mixed Forest province. Projected declines in total tree biomass are followed by biomass recovery as species replacement occurs in stands. The Southern Mixed Forest province results in less diversity in dominant trees as well as lower overall biomass than projections for the other four provinces. The biomass and composition changes projected in this study differ from forest dynamics expected without climate change. These results suggest that biomass recovery following climate change is linked to dominant tree diversity in the southeastern forest of the US. The generality of this observation warrants further investigation, for it relates to ways that forest management may influence climate change effects.

  4. Chesapeake Bay sediment flux model. Final report

    SciTech Connect

    Di Toro, D.M.; Fitzpatrick, J.J.

    1993-06-01

    Formulation and application of a predictive diagenetic sediment model are described in this report. The model considers two benthic sediment layers: a thin aerobic layer in contact with the water column and a thicker anaerobic layer. Processes represented include diagenesis, diffusion, particle mixing, and burial. Deposition of organic matter, water column concentrations, and temperature are treated as independent variables that influence sediment-water fluxes. Sediment oxygen demand and sediment-water fluxes of sulfide, ammonium, nitrate, phosphate, and silica are predicted. The model was calibrated using sediment-water flux observations collected in Chesapeake Bay 1985-1988. When independent variables were specified based on observations, the model correctly represented the time series of sediment-water fluxes observed at eight stations in the Bay and tributaries.... Chesapeake Bay, Models, Sediments, Dissolved oxygen, Nitrogen Eutrophication, Phosphorus.

  5. SEISMIC MODELING ENGINES PHASE 1 FINAL REPORT

    SciTech Connect

    BRUCE P. MARION

    2006-02-09

    Seismic modeling is a core component of petroleum exploration and production today. Potential applications include modeling the influence of dip on anisotropic migration; source/receiver placement in deviated-well three-dimensional surveys for vertical seismic profiling (VSP); and the generation of realistic data sets for testing contractor-supplied migration algorithms or for interpreting AVO (amplitude variation with offset) responses. This project was designed to extend the use of a finite-difference modeling package, developed at Lawrence Berkeley Laboratories, to the advanced applications needed by industry. The approach included a realistic, easy-to-use 2-D modeling package for the desktop of the practicing geophysicist. The feasibility of providing a wide-ranging set of seismic modeling engines was fully demonstrated in Phase I. The technical focus was on adding variable gridding in both the horizontal and vertical directions, incorporating attenuation, improving absorbing boundary conditions and adding the optional coefficient finite difference methods.

  6. Modeling climate change impacts on overwintering bald eagles

    PubMed Central

    Harvey, Chris J; Moriarty, Pamela E; Salathé Jr, Eric P

    2012-01-01

    Bald eagles (Haliaeetus leucocephalus) are recovering from severe population declines, and are exerting pressure on food resources in some areas. Thousands of bald eagles overwinter near Puget Sound, primarily to feed on chum salmon (Oncorhynchus keta) carcasses. We used modeling techniques to examine how anticipated climate changes will affect energetic demands of overwintering bald eagles. We applied a regional downscaling method to two global climate change models to obtain hourly temperature, precipitation, wind, and longwave radiation estimates at the mouths of three Puget Sound tributaries (the Skagit, Hamma Hamma, and Nisqually rivers) in two decades, the 1970s and the 2050s. Climate data were used to drive bald eagle bioenergetics models from December to February for each river, year, and decade. Bald eagle bioenergetics were insensitive to climate change: despite warmer winters in the 2050s, particularly near the Nisqually River, bald eagle food requirements declined only slightly (<1%). However, the warming climate caused salmon carcasses to decompose more rapidly, resulting in 11% to 14% less annual carcass biomass available to eagles in the 2050s. That estimate is likely conservative, as it does not account for decreased availability of carcasses due to anticipated increases in winter stream flow. Future climate-driven declines in winter food availability, coupled with a growing bald eagle population, may force eagles to seek alternate prey in the Puget Sound area or in more remote ecosystems. PMID:22822430

  7. Modeling climate change impacts on overwintering bald eagles.

    PubMed

    Harvey, Chris J; Moriarty, Pamela E; Salathé, Eric P

    2012-03-01

    Bald eagles (Haliaeetus leucocephalus) are recovering from severe population declines, and are exerting pressure on food resources in some areas. Thousands of bald eagles overwinter near Puget Sound, primarily to feed on chum salmon (Oncorhynchus keta) carcasses. We used modeling techniques to examine how anticipated climate changes will affect energetic demands of overwintering bald eagles. We applied a regional downscaling method to two global climate change models to obtain hourly temperature, precipitation, wind, and longwave radiation estimates at the mouths of three Puget Sound tributaries (the Skagit, Hamma Hamma, and Nisqually rivers) in two decades, the 1970s and the 2050s. Climate data were used to drive bald eagle bioenergetics models from December to February for each river, year, and decade. Bald eagle bioenergetics were insensitive to climate change: despite warmer winters in the 2050s, particularly near the Nisqually River, bald eagle food requirements declined only slightly (<1%). However, the warming climate caused salmon carcasses to decompose more rapidly, resulting in 11% to 14% less annual carcass biomass available to eagles in the 2050s. That estimate is likely conservative, as it does not account for decreased availability of carcasses due to anticipated increases in winter stream flow. Future climate-driven declines in winter food availability, coupled with a growing bald eagle population, may force eagles to seek alternate prey in the Puget Sound area or in more remote ecosystems. PMID:22822430

  8. Aerosols and clouds in chemical transport models and climate models.

    SciTech Connect

    Lohmann,U.; Schwartz, S. E.

    2008-03-02

    Clouds exert major influences on both shortwave and longwave radiation as well as on the hydrological cycle. Accurate representation of clouds in climate models is a major unsolved problem because of high sensitivity of radiation and hydrology to cloud properties and processes, incomplete understanding of these processes, and the wide range of length scales over which these processes occur. Small changes in the amount, altitude, physical thickness, and/or microphysical properties of clouds due to human influences can exert changes in Earth's radiation budget that are comparable to the radiative forcing by anthropogenic greenhouse gases, thus either partly offsetting or enhancing the warming due to these gases. Because clouds form on aerosol particles, changes in the amount and/or composition of aerosols affect clouds in a variety of ways. The forcing of the radiation balance due to aerosol-cloud interactions (indirect aerosol effect) has large uncertainties because a variety of important processes are not well understood precluding their accurate representation in models.

  9. 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.

    2016-06-01

    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

  10. Community Climate System Model (CCSM) Experiments and Output Data

    DOE Data Explorer

    The National Center for Atmospheric Research (NCAR) created the first version of the Community Climate Model (CCM) in 1983 as a global atmosphere model. It was improved in 1994 when NCAR, with support from the National Science Foundation (NSF), developed and incorporated a Climate System Model (CSM) that included atmosphere, land surface, ocean, and sea ice. As the capabilities of the model grew, so did interest in its applications and changes in how it would be managed. A workshop in 1996 set the future management structure, marked the beginning of the second phase of the model, a phase that included full participation of the scientific community, and also saw additional financial support, including support from the Department of Energy. In recognition of these changes, the model was renamed to the Community Climate System Model (CCSM). It began to function as a model with the interactions of land, sea, and air fully coupled, providing computer simulations of Earth's past climate, its present climate, and its possible future climate. The CCSM website at http://www2.cesm.ucar.edu/ describes some of the research that has been done since then: A 300-year run has been performed using the CSM, and results from this experiment have appeared in a special issue of theJournal of Climate, 11, June, 1998. A 125-year experiment has been carried out in which carbon dioxide was described to increase at 1% per year from its present concentration to approximately three times its present concentration. More recently, the Climate of the 20th Century experiment was run, with carbon dioxide and other greenhouse gases and sulfate aerosols prescribed to evolve according to our best knowledge from 1870 to the present. Three scenarios for the 21st century were developed: a "business as usual" experiment, in which greenhouse gases are assumed to increase with no economic constraints; an experiment using the Intergovernmental Panel on Climate Change (IPCC) Scenario A1; and a "policy

  11. The Validation of Climate Models: The Development of Essential Practice

    NASA Astrophysics Data System (ADS)

    Rood, R. B.

    2011-12-01

    It is possible from both a scientific and philosophical perspective to state that climate models cannot be validated. However, with the realization that the scientific investigation of climate change is as much a subject of politics as of science, maintaining this formal notion of "validation" has significant consequences. For example, it relegates the bulk of work of many climate scientists to an exercise of model evaluation that can be construed as ill-posed. Even within the science community this motivates criticism of climate modeling as an exercise of weak scientific practice. Stepping outside of the science community, statements that validation is impossible are used in political arguments to discredit the scientific investigation of climate, to maintain doubt about projections of climate change, and hence, to prohibit the development of public policy to regulate the emissions of greenhouse gases. With the acceptance of the impossibility of validation, scientists often state that the credibility of models can be established through an evaluation process. A robust evaluation process leads to the quantitative description of the modeling system against a standard set of measures. If this process is standardized as institutional practice, then this provides a measure of model performance from one modeling release to the next. It is argued, here, that such a robust and standardized evaluation of climate models can be structured and quantified as "validation." Arguments about the nuanced meaning of validation and evaluation are a subject about which the climate modeling community needs to develop a standard. It does injustice to a body of science-based knowledge to maintain that validation is "impossible." Rather than following such a premise, which immediately devalues the knowledge base, it is more useful to develop a systematic, standardized approach to robust, appropriate validation. This stands to represent the complexity of the Earth's climate and its

  12. Sensitivity of climate model to hydrology

    NASA Astrophysics Data System (ADS)

    Ye, Duzheng

    The importance of land—surface processes in affecting climate change has been analyzed and discussed by Namias [1962,1963]. The physics of the land-surface processes affect the climate because the ground hydrology, together with the vegetation and soil, determine the surface moisture availability which, in turn, controls the partition between the sensible and latent heat fluxes [Rowntree, 1984] and also the transfer of momentum. Further the vegetation cover and the soil moisture content can determine the ground surface albedo in snowless conditions. Therefore the heat balance and water balance in the planetary boundary layer are highly influenced by the hydrological processes. Through the planetary boundary layer, the influence of the ground hydrological processes can be felt through the whole troposphere [Yeh, et al., 1984; Rowntree and Bolton, 1983]. Here a crucial factor is the soil moisture content. In the region of dry anomalies, the following sequence of events tends to occur: a decrease of evaporation, a ground surface warming with an increase of sensible heat flux, a wanning of lower layers of the atmosphere with a decrease of relative humidity (due to decrease of evaporation and warming of lower atmophere), a decrease of precipitation with a cooling of the atmosphere higher up (due to decrease of latent heat), and then a change of upper air circulation [Rowntree and Bolton, 1983]. It is the decrease of precipitation which will cause the initial dry anomaly to persist. In the region of moist anomalies, the opposite sequence of events tends to occur.

  13. Intercomparison of hydrologic processes in global climate models

    NASA Technical Reports Server (NTRS)

    Lau, W. K.-M.; Sud, Y. C.; Kim, J.-H.

    1995-01-01

    In this report, we address the intercomparison of precipitation (P), evaporation (E), and surface hydrologic forcing (P-E) for 23 Atmospheric Model Intercomparison Project (AMIP) general circulation models (GCM's) including relevant observations, over a variety of spatial and temporal scales. The intercomparison includes global and hemispheric means, latitudinal profiles, selected area means for the tropics and extratropics, ocean and land, respectively. In addition, we have computed anomaly pattern correlations among models and observations for different seasons, harmonic analysis for annual and semiannual cycles, and rain-rate frequency distribution. We also compare the joint influence of temperature and precipitation on local climate using the Koeppen climate classification scheme.

  14. Watershed Model Parameterization for Assessing Impacts due to Climate Change

    NASA Astrophysics Data System (ADS)

    Yactayo, G. A.; Bhatt, G.

    2014-12-01

    The Chesapeake Bay (CB) Total Maximum Daily Load (TMDL) program drives water quality policy and management in parts of six states — Delaware, Maryland, New York, Pennsylvania, Virginia, and West Virginia — along with the District of Columbia to achieve water quality standards in the Bay through reductions in nutrient and sediment pollution. The HSPF Watershed Model (WSM) is used as an accounting tool in the development of the TMDL to track progress and guide implementations of best management practices. Published research has shown that precipitation has increased in the US during 20th century by about ten percent, and half of the increase is due to changes in frequency and intensity in the upper tenth percentile of the distribution. Projections from global climate models suggest that these trends are anticipated to continue over the next century. Our analysis of climate data over the last three decades show similar trends in observed precipitation in the CB Watershed. The impact of climate change on the CB TMDL will be examined in a 2017 assessment of progress in the State and Federal partnership of the Chesapeake Bay Program. This is consistent with the CB Executive Order of May 12, 2009 mandates assessment of the impacts of climate change on the CB TMDL. The WSM has a simulation period of more than 3 decades from 1985 to 2011. Over the simulation period precipitation intensity, temperatures, and CO2 levels are increasing. A study conducted by Najjar et al. (2010) that included regional climate projections suggests that pollutant loads in the CB region will increase over the next century. Butcher et al. (2014) demonstrated that a watershed model parameter needs to be adjusted to compensate for the effect of elevated CO2 concentrations on plant transpiration in climate projection applications. This raises the question of whether parameters within a watershed model calibrated using historical climate data are sufficient for assessing hydrologic and water

  15. Implementation of the Stochastic Multicloud Model in the NCEP Climate Forecast System version 2 (CFSv2)

    NASA Astrophysics Data System (ADS)

    Goswami, B. B.; Krishna, R. P. M.; Khouider, B.; Mukhopadhyay, P.; Majda, A.

    2015-12-01

    We present here the implementation of the stochastic multicloud model (SMCM) (khouider et al 2010) in the NCEP Climate forecast system version 2 (CFSv2). The final goal of this effort is to improve the Indian Summer Monsoon weather and climate through better-organized tropical convection in CFSv2. The fidelity of CFSv2 in simulating the mean state of the global climate, particularly the Indian summer monsoon, relative to the CMIP5 models (Sabeer et al 2013) is the reason behind choosing CFSv2 as the GCM to implement SMCM. We expect to see an improved climate simulation in SMCM-CFSv2 because of the theoretically sound and tested design of the multicloud approach (Khouider and Majda 2006, and the relevant subsequent work thereafter). In order to implement SMCM in CFSv2, first we identify different climatic regions based on the mean state of the global climate (using the CFSR 20year monthly climatology). Then we initialize the climatological values (computed from the CFSR 20year monthly climatology) of the variables required in the multicloud parameterization scheme, for the different climatic zones. We input moisture, temperature and PBL height from the CFSv2 to the multicloud parameterization module and then compute the corresponding variables that were initialized from the mean state. Then we compute the deviation of those variables from the background state. Based on middle troposphere dryness, we compute the heating rates for the deep, congestus and stratiform convection from these deviations from the background (deterministic approach). The stochastic extension involves the evolution of the cloud area fractions, associated to each one of the three cloud types, which are represented by a stochastic lattice subgrid model whose random transitions depend on CAPE and large-scale tropospheric dryness. The stochastic model feedback, to the GCM dynamics, occurs through the modulation of the heating rates by the cloud area fractions.

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

    NASA Technical Reports Server (NTRS)

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

    2010-01-01

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

  17. Climate effects of anthropogenic sulfate: Simulations from a coupled chemistry/climate model

    SciTech Connect

    Chuang, C.C.; Penner, J.E.; Taylor, K.E.; Walton, J.J.

    1993-09-01

    In this paper, we use a more comprehensive approach by coupling a climate model with a 3-D global chemistry model to investigate the forcing by anthropogenic aerosol sulfate. The chemistry model treats the global-scale transport, transformation, and removal of SO{sub 2}, DMS and H{sub 2}SO{sub 4} species in the atmosphere. The mass concentration of anthropogenic sulfate from fossil fuel combustion and biomass burning is calculated in the chemistry model and provided to the climate model where it affects the shortwave radiation. We also investigate the effect, with cloud nucleation parameterized in terms of local aerosol number, sulfate mass concentration and updraft velocity. Our simulations indicate that anthropogenic sulfate may result in important increases in reflected solar radiation, which would mask locally the radiative forcing from increased greenhouse gases. Uncertainties in these results will be discussed.

  18. World agriculture and climate change: Current modeling issues

    SciTech Connect

    Darwin, R.

    1996-12-31

    Recent studies suggest that although global increases in temperature and changes in precipitation patterns during the next century will affect world agriculture, farmer adaptations are likely to prevent climate change from jeopardizing world food production. The costs and benefits of global climate change, however, are not equally distributed around the world. Agricultural production may increase in high latitude and alpine areas, but decrease in tropical and some other areas. Also, land use changes that accompany climate-induced shifts in cropland and permanent pasture are likely to raise additional social and environmental issues. Despite these advances, some important aspects of climate change have not been adequately simulated in global models. These include the effects that climate-induced changes in water resources are likely to have on agricultural production, the well-documented beneficial effects of higher concentrations of atmospheric carbon dioxide on plant growth and water use, and the cooling effects of tropospheric emissions of sulfur dioxide. In addition, past research generally relied on equilibrium climates based on a doubling of atmospheric carbon dioxide. Now, however, results from transient climate change experiments are available.

  19. Modeling Climate Change Impacts on Landscape Evolution, Fire, and Hydrology

    NASA Astrophysics Data System (ADS)

    Sheppard, B. S.; O Connor, C.; Falk, D. A.; Garfin, G. M.

    2015-12-01

    Landscape disturbances such as wildfire interact with climate variability to influence hydrologic regimes. We coupled landscape, fire, and hydrologic models and forced them using projected climate to demonstrate climate change impacts anticipated at Fort Huachuca in southeastern Arizona, USA. The US Department of Defense (DoD) recognizes climate change as a trend that has implications for military installations, national security and global instability. The goal of this DoD Strategic Environmental Research and Development Program (SERDP) project (RC-2232) is to provide decision making tools for military installations in the southwestern US to help them adapt to the operational realities associated with climate change. For this study we coupled the spatially explicit fire and vegetation dynamics model FireBGCv2 with the Automated Geospatial Watershed Assessment tool (AGWA) to evaluate landscape vegetation change, fire disturbance, and surface runoff in response to projected climate forcing. A projected climate stream for the years 2005-2055 was developed from the Multivariate Adaptive Constructed Analogs (MACA) 4 km statistical downscaling of the CanESM2 GCM using Representative Concentration Pathway (RCP) 8.5. AGWA, an ArcGIS add-in tool, was used to automate the parameterization and execution of the Soil Water Assessment Tool (SWAT) and the KINematic runoff and EROSion2 (KINEROS2) models based on GIS layers. Landscape raster data generated by FireBGCv2 project an increase in fire and drought associated tree mortality and a decrease in vegetative basal area over the years of simulation. Preliminary results from SWAT modeling efforts show an increase to surface runoff during years following a fire, and for future winter rainy seasons. Initial results from KINEROS2 model runs show that peak runoff rates are expected to increase 10-100 fold as a result of intense rainfall falling on burned areas.

  20. 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

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

    NASA Astrophysics Data System (ADS)

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

    2015-05-01

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

  2. Climate Modeling in the Calculus and Differential Equations Classroom

    ERIC Educational Resources Information Center

    Kose, Emek; Kunze, Jennifer

    2013-01-01

    Students in college-level mathematics classes can build the differential equations of an energy balance model of the Earth's climate themselves, from a basic understanding of the background science. Here we use variable albedo and qualitative analysis to find stable and unstable equilibria of such a model, providing a problem or perhaps a…

  3. Modeling Two Types of Adaptation to Climate Change

    EPA Science Inventory

    Mitigation and adaptation are the two key responses available to policymakers to reduce the risks of climate change. We model these two policies together in a new DICE-based integrated assessment model that characterizes adaptation as either short-lived flow spending or long-live...

  4. Constructing Scientific Arguments Using Evidence from Dynamic Computational Climate Models

    ERIC Educational Resources Information Center

    Pallant, Amy; Lee, Hee-Sun

    2015-01-01

    Modeling and argumentation are two important scientific practices students need to develop throughout school years. In this paper, we investigated how middle and high school students (N = 512) construct a scientific argument based on evidence from computational models with which they simulated climate change. We designed scientific argumentation…

  5. Reconstructing the climate states of the Late Pleistocene with the MIROC climate model

    NASA Astrophysics Data System (ADS)

    Chan, Wing-Le; Abe-Ouchi, Ayako; O'ishi, Ryouta; Takahashi, Kunio

    2014-05-01

    The Late Pleistocene was a period which lasted from the Eemian interglacial period to the start of the warm Holocene and was characterized mostly by widespread glacial ice. It was also a period which saw modern humans spread throughout the world and other species of the same genus, like the Neanderthals, become extinct. Various hypotheses have been put forward to explain the extinction of Neanderthals, about 30,000 years ago. Among these is one which involves changes in past climate and the inability of Neanderthals to adapt to such changes. The last traces of Neanderthals coincide with the end of Marine Isotope Stage 3 (MIS3) which was marked by large fluctuations in temperature and so-called Heinrich events, as suggested by geochemical records from ice cores. It is thought that melting sea ice or icebergs originating from the Laurentide ice sheet led to a large discharge of freshwater into the North Atlantic Ocean during the Heinrich events and severely weakened the Atlantic meridional overturning circulation, with important environmental ramifications across parts of Europe such as sharp decreases in temperature and reduction in forest cover. In order to assess the effects of past climate change on past hominin migration and on the extinction of certain species, it is first important to have a good understanding of the past climate itself. In this study, we have used three variants of MIROC (The Model for Interdisciplinary Research on Climate), a global climate model, for a time slice experiment within the Late Pleistocene: two mid-resolution models (an atmosphere model and a coupled atmosphere-ocean model) and a high-resolution atmosphere model. To obtain a fuller picture, we also look at a cool stadial state as obtained from a 'freshwater hosing' coupled-model experiment, designed to mimic the effects of freshwater discharge in the North Atlantic. We next use the sea surface temperature response from this experiment to drive the atmosphere models. We discuss

  6. Assessment of the future climate change in the Czech Republic based on ALADIN-CLIMATE/CZ and AR4 models

    NASA Astrophysics Data System (ADS)

    Kalvova, J.; Holtanova, E.; Crhova, L.; Miksovsky, J.; Pisoft, P.; Motl, M.

    2010-09-01

    The regional climate model ALADIN-CLIMATE/CZ (25 km resolution) is going to be used for the new climate change scenarios for the Czech Republic. However, for effective use of such scenario for impact studies, it is necessary to provide an estimate of related uncertainty. The driving global model is an important source of uncertainty in RCM simulations. We present a comparison of changes in basic climate characteristics simulated by ALADIN-CLIMATE/CZ and a set of eight AR4 models for the periods of 2010-39, 2040-69, 2070-99. The global climate models were chosen based on its ability to simulate observed climate characteristics in the reference period (1961-1990).

  7. Modeling Climate Change in the Absence of Climate Change Data. Editorial Comment

    NASA Technical Reports Server (NTRS)

    Skiles, J. W.

    1995-01-01

    Practitioners of climate change prediction base many of their future climate scenarios on General Circulation Models (GCM's), each model with differing assumptions and parameter requirements. For representing the atmosphere, GCM's typically contain equations for calculating motion of particles, thermodynamics and radiation, and continuity of water vapor. Hydrology and heat balance are usually included for continents, and sea ice and heat balance are included for oceans. The current issue of this journal contains a paper by Van Blarcum et al. (1995) that predicts runoff from nine high-latitude rivers under a doubled CO2 atmosphere. The paper is important since river flow is an indicator variable for climate change. The authors show that precipitation will increase under the imposed perturbations and that owing to higher temperatures earlier in the year that cause the snow pack to melt sooner, runoff will also increase. They base their simulations on output from a GCM coupled with an interesting water routing scheme they have devised. Climate change models have been linked to other models to predict deforestation.

  8. Can we trust climate models to realistically represent severe European windstorms?

    NASA Astrophysics Data System (ADS)

    Trzeciak, Tomasz M.; Knippertz, Peter; Pirret, Jennifer S. R.; Williams, Keith D.

    2015-08-01

    Cyclonic windstorms are one of the most important natural hazards for Europe, but robust climate projections of the position and the strength of the North Atlantic storm track are not yet possible, bearing significant risks to European societies and the (re)insurance industry. Previous studies addressing the problem of climate model uncertainty through statistical comparisons of simulations of the current climate with (re-)analysis data show large disagreement between different climate models, different ensemble members of the same model and observed climatologies of intense cyclones. One weakness of such evaluations lies in the difficulty to separate influences of the climate model's basic state from the influence of fast processes on the development of the most intense storms, which could create compensating effects and therefore suggest higher reliability than there really is. This work aims to shed new light into this problem through a cost-effective "seamless" approach of hindcasting 20 historical severe storms with the two global climate models, ECHAM6 and GA4 configuration of the Met Office Unified Model, run in a numerical weather prediction mode using different lead times, and horizontal and vertical resolutions. These runs are then compared to re-analysis data. The main conclusions from this work are: (a) objectively identified cyclone tracks are represented satisfactorily by most hindcasts; (b) sensitivity to vertical resolution is low; (c) cyclone depth is systematically under-predicted for a coarse resolution of T63 by both climate models; (d) no systematic bias is found for the higher resolution of T127 out to about three days, demonstrating that climate models are in fact able to represent the complex dynamics of explosively deepening cyclones well, if given the correct initial conditions; (e) an analysis using a recently developed diagnostic tool based on the surface pressure tendency equation points to too weak diabatic processes, mainly latent

  9. Can we trust climate models to realistically represent severe European windstorms?

    NASA Astrophysics Data System (ADS)

    Trzeciak, Tomasz M.; Knippertz, Peter; Pirret, Jennifer S. R.; Williams, Keith D.

    2016-06-01

    Cyclonic windstorms are one of the most important natural hazards for Europe, but robust climate projections of the position and the strength of the North Atlantic storm track are not yet possible, bearing significant risks to European societies and the (re)insurance industry. Previous studies addressing the problem of climate model uncertainty through statistical comparisons of simulations of the current climate with (re-)analysis data show large disagreement between different climate models, different ensemble members of the same model and observed climatologies of intense cyclones. One weakness of such evaluations lies in the difficulty to separate influences of the climate model's basic state from the influence of fast processes on the development of the most intense storms, which could create compensating effects and therefore suggest higher reliability than there really is. This work aims to shed new light into this problem through a cost-effective "seamless" approach of hindcasting 20 historical severe storms with the two global climate models, ECHAM6 and GA4 configuration of the Met Office Unified Model, run in a numerical weather prediction mode using different lead times, and horizontal and vertical resolutions. These runs are then compared to re-analysis data. The main conclusions from this work are: (a) objectively identified cyclone tracks are represented satisfactorily by most hindcasts; (b) sensitivity to vertical resolution is low; (c) cyclone depth is systematically under-predicted for a coarse resolution of T63 by both climate models; (d) no systematic bias is found for the higher resolution of T127 out to about three days, demonstrating that climate models are in fact able to represent the complex dynamics of explosively deepening cyclones well, if given the correct initial conditions; (e) an analysis using a recently developed diagnostic tool based on the surface pressure tendency equation points to too weak diabatic processes, mainly latent

  10. Winter and summer simulations with the GLAS climate model

    NASA Technical Reports Server (NTRS)

    Shukla, J.; Straus, D.; Randall, D.; Sud, Y.; Marx, L.

    1981-01-01

    The GLAS climate model is a general circulation model based on the primitive equations in sigma coordinates on a global domain in the presence of orography. The model incorporates parameterizations of the effects of radiation, convection, large scale latent heat release, turbulent and boundary layer fluxes, and ground hydrology. Winter and summer simulations were carried out with this model, and the resulting data are compared to observations.

  11. Climate Models from the Joint Global Change Research Institute

    DOE Data Explorer

    Staff at the Joint Institute develop and use models to simulate the economic and physical impacts of global change policy options. The GCAM, for example, gives analysts insight into how regional and national economies might respond to climate change mitigation policies including carbon taxes, carbon trading, and accelerated deployment of energy technology. Three available models are Phoenix, GCAM, and EPIC. Phoenix is a global, dynamic recursive, computable general equilibrium model that is solved in five-year time steps from 2005 through 2100 and divides the world into twenty-four regions. Each region includes twenty-six industrial sectors. Particular attention is paid to energy production in Phoenix. There are nine electricity-generating technologies (coal, natural gas, oil, biomass, nuclear, hydro, wind, solar, and geothermal) and four additional energy commodities: crude oil, refined oil products, coal, and natural gas. Phoenix is designed to answer economic questions related to international climate and energy policy and international trade. Phoenix replaces the Second Generation Model (SGM) that was formerly used for general equilibrium analysis at JGCRI. GCAM is the Global Change Assessment Model, a partial equilibrium model of the world with 14 regions. GCAM operates in 5 year time steps from 1990 to 2095 and is designed to examine long-term changes in the coupled energy, agriculture/land-use, and climate system. GCAM includes a 151-region agriculture land-use module and a reduced form carbon cycle and climate module in addition to its incorporation of demographics, resources, energy production and consumption. The model has been used extensively in a number of assessment and modeling activities such as the Energy Modeling Forum (EMF), the U.S. Climate Change Technology Program, and the U.S. Climate Change Science Program and IPCC assessment reports. GCAM is now freely available as a community model. The Environmental Policy Integrated Climate (EPIC) Model

  12. Modeled impact of anthropogenic land cover change on climate

    USGS Publications Warehouse

    Findell, K.L.; Shevliakova, E.; Milly, P.C.D.; Stouffer, R.J.

    2007-01-01

    Equilibrium experiments with the Geophysical Fluid Dynamics Laboratory's climate model are used to investigate the impact of anthropogenic land cover change on climate. Regions of altered land cover include large portions of Europe, India, eastern China, and the eastern United States. Smaller areas of change are present in various tropical regions. This study focuses on the impacts of biophysical changes associated with the land cover change (albedo, root and stomatal properties, roughness length), which is almost exclusively a conversion from forest to grassland in the model; the effects of irrigation or other water management practices and the effects of atmospheric carbon dioxide changes associated with land cover conversion are not included in these experiments. The model suggests that observed land cover changes have little or no impact on globally averaged climatic variables (e.g., 2-m air temperature is 0.008 K warmer in a simulation with 1990 land cover compared to a simulation with potential natural vegetation cover). Differences in the annual mean climatic fields analyzed did not exhibit global field significance. Within some of the regions of land cover change, however, there are relatively large changes of many surface climatic variables. These changes are highly significant locally in the annual mean and in most months of the year in eastern Europe and northern India. They can be explained mainly as direct and indirect consequences of model-prescribed increases in surface albedo, decreases in rooting depth, and changes of stomatal control that accompany deforestation. ?? 2007 American Meteorological Society.

  13. Hospitable archean climates simulated by a general circulation model.

    PubMed

    Wolf, E T; Toon, O B

    2013-07-01

    Evidence from ancient sediments indicates that liquid water and primitive life were present during the Archean despite the faint young Sun. To date, studies of Archean climate typically utilize simplified one-dimensional models that ignore clouds and ice. Here, we use an atmospheric general circulation model coupled to a mixed-layer ocean model to simulate the climate circa 2.8 billion years ago when the Sun was 20% dimmer than it is today. Surface properties are assumed to be equal to those of the present day, while ocean heat transport varies as a function of sea ice extent. Present climate is duplicated with 0.06 bar of CO2 or alternatively with 0.02 bar of CO2 and 0.001 bar of CH4. Hot Archean climates, as implied by some isotopic reconstructions of ancient marine cherts, are unattainable even in our warmest simulation having 0.2 bar of CO2 and 0.001 bar of CH4. However, cooler climates with significant polar ice, but still dominated by open ocean, can be maintained with modest greenhouse gas amounts, posing no contradiction with CO2 constraints deduced from paleosols or with practical limitations on CH4 due to the formation of optically thick organic hazes. Our results indicate that a weak version of the faint young Sun paradox, requiring only that some portion of the planet's surface maintain liquid water, may be resolved with moderate greenhouse gas inventories. Thus, hospitable late Archean climates are easily obtained in our climate model. PMID:23808659

  14. High Resolution Modelling of Crop Response to Climate Change

    NASA Astrophysics Data System (ADS)

    Mirmasoudi, S. S.; Byrne, J. M.; MacDonald, R. J.; Lewis, D.

    2014-12-01

    Crop production is one of the most vulnerable sectors to climatic variability and change. Increasing atmospheric CO2 concentration and other greenhouse gases are causing increases in global temperature. In western North America, water supply is largely derived from mountain snowmelt. Climate change will have a significant impact on mountain snowpack and subsequently, the snow-derived water supply. This will strain water supplies and increase water demand in areas with substantial irrigation agriculture. Increasing temperatures may create heat stress for some crops regardless of soil water supply, and increasing surface O3 and other pollutants may damage crops and ecosystems. CO2 fertilization may or may not be an advantage in future. This work is part of a larger study that will address a series of questions based on a range of future climate scenarios for several watersheds in western North America. The key questions are: (1) how will snowmelt and rainfall runoff vary in future; (2) how will seasonal and inter-annual soil water supply vary, and how might that impacts food supplies; (3) how might heat stress impact (some) crops even with adequate soil water; (4) will CO2 fertilization alter crop yields; and (5) will pollution loads, particularly O3, cause meaningful changes to crop yields? The Generate Earth Systems Science (GENESYS) Spatial Hydrometeorological Model is an innovative, efficient, high-resolution model designed to assess climate driven changes in mountain snowpack derived water supplies. We will link GENESYS to the CROPWAT crop model system to assess climate driven changes in water requirement and associated crop productivity for a range of future climate scenarios. Literature bases studies will be utilised to develop approximate crop response functions for heat stress, CO2 fertilization and for O3 damages. The overall objective is to create modeling systems that allows meaningful assessment of agricultural productivity at a watershed scale under a

  15. Evaluation Model for Career Programs. Final Report.

    ERIC Educational Resources Information Center

    Byerly, Richard L.; And Others

    A study was conducted to provide and test an evaluative model that could be utilized in providing curricular evaluation of the various career programs. Two career fields, dental assistant and auto mechanic, were chosen for study. A questionnaire based upon the actual job performance was completed by six groups connected with the auto mechanics and…

  16. Final Report for CAEL Operational Models Project.

    ERIC Educational Resources Information Center

    Cooperative Assessment of Experiential Learning, Columbia, MD.

    Twelve institutions with experiential learning programs in higher education were selected to develop practical models that could be useful to similar institutions. Attention was to be focused on either or both of two areas of concern for experiential learning programs: the establishment of criterion standards for assessment and the financial…

  17. Climate science: Unexpected fix for ocean models

    NASA Astrophysics Data System (ADS)

    Kelly, Kathryn A.; Thompson, Luanne

    2016-07-01

    Computational models persistently underestimate strong currents that redistribute ocean heat. This problem is solved in models in which ocean eddies are damped by coupling of the atmosphere with the sea. See Letter p.533

  18. The effects of climate model similarity on probabilistic climate projections and the implications for local, risk-based adaptation planning

    NASA Astrophysics Data System (ADS)

    Steinschneider, Scott; McCrary, Rachel; Mearns, Linda O.; Brown, Casey

    2015-06-01

    Approaches for probability density function (pdf) development of future climate often assume that different climate models provide independent information, despite model similarities that stem from a common genealogy (models with shared code or developed at the same institution). Here we use an ensemble of projections from the Coupled Model Intercomparison Project Phase 5 to develop probabilistic climate information, with and without an accounting of intermodel correlations, for seven regions across the United States. We then use the pdfs to estimate midcentury climate-related risks to a water utility in one of the regions. We show that the variance of climate changes is underestimated across all regions if model correlations are ignored, and in some cases, the mean change shifts as well. When coupled with impact models of the hydrology and infrastructure of a water utility, the underestimated likelihood of large climate changes significantly alters the quantification of risk for water shortages by midcentury.

  19. Peformance Tuning and Evaluation of a Parallel Community Climate Model

    SciTech Connect

    Drake, J.B.; Worley, P.H.; Hammond, S.

    1999-11-13

    The Parallel Community Climate Model (PCCM) is a message-passing parallelization of version 2.1 of the Community Climate Model (CCM) developed by researchers at Argonne and Oak Ridge National Laboratories and at the National Center for Atmospheric Research in the early to mid 1990s. In preparation for use in the Department of Energy's Parallel Climate Model (PCM), PCCM has recently been updated with new physics routines from version 3.2 of the CCM, improvements to the parallel implementation, and ports to the SGIKray Research T3E and Origin 2000. We describe our experience in porting and tuning PCCM on these new platforms, evaluating the performance of different parallel algorithm options and comparing performance between the T3E and Origin 2000.

  20. Congo Basin rainfall climatology: can we believe the climate models?

    PubMed Central

    Washington, Richard; James, Rachel; Pearce, Helen; Pokam, Wilfried M.; Moufouma-Okia, Wilfran

    2013-01-01

    The Congo Basin is one of three key convective regions on the planet which, during the transition seasons, dominates global tropical rainfall. There is little agreement as to the distribution and quantity of rainfall across the basin with datasets differing by an order of magnitude in some seasons. The location of maximum rainfall is in the far eastern sector of the basin in some datasets but the far western edge of the basin in others during March to May. There is no consistent pattern to this rainfall distribution in satellite or model datasets. Resolving these differences is difficult without ground-based data. Moisture flux nevertheless emerges as a useful variable with which to study these differences. Climate models with weak (strong) or even divergent moisture flux over the basin are dry (wet). The paper suggests an approach, via a targeted field campaign, for generating useful climate information with which to confront rainfall products and climate models. PMID:23878328

  1. Oscillations in a simple climate-vegetation model

    NASA Astrophysics Data System (ADS)

    Rombouts, J.; Ghil, M.

    2015-05-01

    We formulate and analyze a simple dynamical systems model for climate-vegetation interaction. The planet we consider consists of a large ocean and a land surface on which vegetation can grow. The temperature affects vegetation growth on land and the amount of sea ice on the ocean. Conversely, vegetation and sea ice change the albedo of the planet, which in turn changes its energy balance and hence the temperature evolution. Our highly idealized, conceptual model is governed by two nonlinear, coupled ordinary differential equations, one for global temperature, the other for vegetation cover. The model exhibits either bistability between a vegetated and a desert state or oscillatory behavior. The oscillations arise through a Hopf bifurcation off the vegetated state, when the death rate of vegetation is low enough. These oscillations are anharmonic and exhibit a sawtooth shape that is characteristic of relaxation oscillations, as well as suggestive of the sharp deglaciations of the Quaternary. Our model's behavior can be compared, on the one hand, with the bistability of even simpler, Daisyworld-style climate-vegetation models. On the other hand, it can be integrated into the hierarchy of models trying to simulate and explain oscillatory behavior in the climate system. Rigorous mathematical results are obtained that link the nature of the feedbacks with the nature and the stability of the solutions. The relevance of model results to climate variability on various timescales is discussed.

  2. A Model for Collaborative Learning in Undergraduate Climate Change Courses

    NASA Astrophysics Data System (ADS)

    Teranes, J. L.

    2008-12-01

    Like several colleges and universities across the nation, the University of California, San Diego, has introduced climate change topics into many existing and new undergraduate courses. I have administered a program in this area at UCSD and have also developed and taught a new lower-division UCSD course entitled "Climate Change and Society", a general education course for non-majors. This class covers the basics of climate change, such as the science that explains it, the causes of climate change, climate change impacts, and mitigation strategies. The teaching methods for this course stress interdisciplinary approaches. I find that inquiry-based and collaborative modes of learning are particularly effective when applied to science-based climate, environmental and sustainability topics. Undergraduate education is often dominated by a competitive and individualistic approach to learning. In this approach, individual success is frequently perceived as contingent on others being less successful. Such a model is at odds with commonly stated goals of teaching climate change and sustainability, which are to equip students to contribute to the debate on global environmental change and societal adaptation strategies; and to help students become better informed citizens and decision makers. I present classroom-tested strategies for developing collaborative forms of learning in climate change and environmental courses, including team projects, group presentations and group assessment exercises. I show how critical thinking skills and long-term retention of information can benefit in the collaborative mode of learning. I find that a collaborative learning model is especially appropriate to general education courses in which the enrolled student body represents a wide diversity of majors, class level and expertise. I also connect collaborative coursework in interdisciplinary environmental topics directly to applications in the field, where so much "real-world" achievement in

  3. Improving plot- and regional-scale crop models for simulating impacts of climate variability and extremes

    NASA Astrophysics Data System (ADS)

    Tao, F.; Rötter, R.

    2013-12-01

    Many studies on global climate report that climate variability is increasing with more frequent and intense extreme events1. There are quite large uncertainties from both the plot- and regional-scale models in simulating impacts of climate variability and extremes on crop development, growth and productivity2,3. One key to reducing the uncertainties is better exploitation of experimental data to eliminate crop model deficiencies and develop better algorithms that more adequately capture the impacts of extreme events, such as high temperature and drought, on crop performance4,5. In the present study, in a first step, the inter-annual variability in wheat yield and climate from 1971 to 2012 in Finland was investigated. Using statistical approaches the impacts of climate variability and extremes on wheat growth and productivity were quantified. In a second step, a plot-scale model, WOFOST6, and a regional-scale crop model, MCWLA7, were calibrated and validated, and applied to simulate wheat growth and yield variability from 1971-2012. Next, the estimated impacts of high temperature stress, cold damage, and drought stress on crop growth and productivity based on the statistical approaches, and on crop simulation models WOFOST and MCWLA were compared. Then, the impact mechanisms of climate extremes on crop growth and productivity in the WOFOST model and MCWLA model were identified, and subsequently, the various algorithm and impact functions were fitted against the long-term crop trial data. Finally, the impact mechanisms, algorithms and functions in WOFOST model and MCWLA model were improved to better simulate the impacts of climate variability and extremes, particularly high temperature stress, cold damage and drought stress for location-specific and large area climate impact assessments. Our studies provide a good example of how to improve, in parallel, the plot- and regional-scale models for simulating impacts of climate variability and extremes, as needed for

  4. Climate extremes in multi-model simulations of stratospheric aerosol and marine cloud brightening climate engineering

    NASA Astrophysics Data System (ADS)

    Aswathy, V. N.; Boucher, O.; Quaas, M.; Niemeier, U.; Muri, H.; Quaas, J.

    2014-12-01

    Simulations from a multi-model ensemble for the RCP4.5 climate change scenario for the 21st century, and for two solar radiation management schemes (stratospheric sulfate injection, G3, and marine cloud brightening, G3SSCE) have been analyzed in terms of changes in the mean and extremes for surface air temperature and precipitation. The climate engineered (SRM 2060s - RCP4.5 2010s) and termination (2080s - 2060s) periods are investigated. During the climate engineering period, both schemes, as intended, offset temperature increases by about 60% globally, but are more effective in the low latitudes and exhibit some residual warming in the Arctic (especially in the case of marine cloud brightening that is only applied in the low latitudes). In both climate engineering scenarios, extreme temperatures changes are similar to the mean temperature changes over much of the globe. The exception is in Northern Hemisphere high latitudes, where high temperatures (90th percentile of the distribution) of climate engineering relative to RCP4.5 rise less than the mean and cold temperatures (10th percentile) much more than the mean. When defining temperature extremes by fixed thresholds, namely number of frost days and summer days, it is found that both climate engineering experiments are not completely alleviating the changes relative to RCP 4.5. The reduction in 2060s dry spell occurrence over land region in G3-SSCE is is more pronounced than over oceans. Experiment G3 exhibits same pattern as G3-SSCE albeit, stronger in magnitude. A strong termination effect is found for the two climate engineering schemes, with large temperature increases especially in the Arctic. Mean temperatures rise faster than the extremes, especially over oceans, with the exception of the Tropics. Conversely precipitation extremes rise much more than the mean, even more so over the ocean, and especially in the Tropics.

  5. Modelling of maize production in Croatia: present and future climate.

    PubMed

    Vučetić, V

    2011-04-01

    Maize is one of the most important agricultural crops in Croatia, and was selected for research of the effect of climate warming on yields. The Decision Support System for the Agrotechnology Transfer model (DSSAT) is one of the most utilized crop-weather models in the world, and was used in this paper for the investigation of maize growth and production in the present and future climate. The impact of present climate on maize yield was studied using DSSAT 4.0 with meteorological data from the Zagreb-Maksimir station covering the period 1949-2004. Pedological, physiological and genetic data from a 1999 field maize experiment at the same location were added. The location is representative of the continental climate in central Croatia. The linear trends of model outputs and the non-parametric Mann-Kendall test indicate that the beginning of silking has advanced significantly by 1·4 days/decade since the mid-1990s, and maturity by 4·5 days/decade. It also shows a decrease in biomass by 122 kg/ha and in maize yield by 216 kg/ha in 10 years.Estimates of the sensitivity of maize growth and yield in future climates were made by changing the initial weather and CO(2) conditions of the DSSAT 4.0 model according to the different climatic scenarios for Croatia at the end of the 21st century. Changed climate suggests increases in global solar radiation, minimal temperature and maximal temperature (×1·07, 2 and 4°C, respectively), but a decrease in the amount of precipitation (×0·92), compared with weather data from the period 1949-2004. The reduction of maize yield was caused by the increase in minimal and maximal temperature and the decrease in precipitation amount, related to the present climate, is 6, 12 and 3%, respectively. A doubling of CO(2) concentration stimulates leaf assimilation, but maize yield is only 1% higher, while global solar radiation growth by 7% increases evapotranspiration by 3%. Simultaneous application of all these climate changes suggested that

  6. Modelling of maize production in Croatia: present and future climate

    PubMed Central

    VUČETIĆ, V.

    2011-01-01

    SUMMARY Maize is one of the most important agricultural crops in Croatia, and was selected for research of the effect of climate warming on yields. The Decision Support System for the Agrotechnology Transfer model (DSSAT) is one of the most utilized crop–weather models in the world, and was used in this paper for the investigation of maize growth and production in the present and future climate. The impact of present climate on maize yield was studied using DSSAT 4.0 with meteorological data from the Zagreb–Maksimir station covering the period 1949–2004. Pedological, physiological and genetic data from a 1999 field maize experiment at the same location were added. The location is representative of the continental climate in central Croatia. The linear trends of model outputs and the non-parametric Mann–Kendall test indicate that the beginning of silking has advanced significantly by 1·4 days/decade since the mid-1990s, and maturity by 4·5 days/decade. It also shows a decrease in biomass by 122 kg/ha and in maize yield by 216 kg/ha in 10 years. Estimates of the sensitivity of maize growth and yield in future climates were made by changing the initial weather and CO2 conditions of the DSSAT 4.0 model according to the different climatic scenarios for Croatia at the end of the 21st century. Changed climate suggests increases in global solar radiation, minimal temperature and maximal temperature (×1·07, 2 and 4°C, respectively), but a decrease in the amount of precipitation (×0·92), compared with weather data from the period 1949–2004. The reduction of maize yield was caused by the increase in minimal and maximal temperature and the decrease in precipitation amount, related to the present climate, is 6, 12 and 3%, respectively. A doubling of CO2 concentration stimulates leaf assimilation, but maize yield is only 1% higher, while global solar radiation growth by 7% increases evapotranspiration by 3%. Simultaneous application of all these climate changes

  7. Final Progress Report: Collaborative Research: Decadal-to-Centennial Climate & Climate Change Studies with Enhanced Variable and Uniform Resolution GCMs Using Advanced Numerical Techniques

    SciTech Connect

    Fox-Rabinovitz, M; Cote, J

    2009-06-05

    The joint U.S-Canadian project has been devoted to: (a) decadal climate studies using developed state-of-the-art GCMs (General Circulation Models) with enhanced variable and uniform resolution; (b) development and implementation of advanced numerical techniques; (c) research in parallel computing and associated numerical methods; (d) atmospheric chemistry experiments related to climate issues; (e) validation of regional climate modeling strategies for nested- and stretched-grid models. The variable-resolution stretched-grid (SG) GCMs produce accurate and cost-efficient regional climate simulations with mesoscale resolution. The advantage of the stretched grid approach is that it allows us to preserve the high quality of both global and regional circulations while providing consistent interactions between global and regional scales and phenomena. The major accomplishment for the project has been the successful international SGMIP-1 and SGMIP-2 (Stretched-Grid Model Intercomparison Project, phase-1 and phase-2) based on this research developments and activities. The SGMIP provides unique high-resolution regional and global multi-model ensembles beneficial for regional climate modeling and broader modeling community. The U.S SGMIP simulations have been produced using SciDAC ORNL supercomputers. Collaborations with other international participants M. Deque (Meteo-France) and J. McGregor (CSIRO, Australia) and their centers and groups have been beneficial for the strong joint effort, especially for the SGMIP activities. The WMO/WCRP/WGNE endorsed the SGMIP activities in 2004-2008. This project reflects a trend in the modeling and broader communities to move towards regional and sub-regional assessments and applications important for the U.S. and Canadian public, business and policy decision makers, as well as for international collaborations on regional, and especially climate related issues.

  8. A Simple Climate Model Program for High School Education

    NASA Astrophysics Data System (ADS)

    Dommenget, D.

    2012-04-01

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

  9. Science of Global Climate Modeling: Confirmation from Discoveries on Mars

    NASA Astrophysics Data System (ADS)

    Hartmann, William K.

    2012-10-01

    As early as 1993, analysis of obliquity changes on Mars revealed irregular cycles of high excursion, over 45°1. Further obliquity analyses indicated that insolation and climatic conditions vary with time, with the four most recent episodes of obliquity >45° occurring about 5.5, 8, 9, and 15 My.2 Various researchers applied global climate models, using Martian parameters and obliquity changes. The models (independent of Martian geomorphological observations) indicate exceptional climate conditions during the high-obliquity episodes at >45°3,4, with localized massive ice deposition effects east of Hellas and on the west slopes of Tharsis.5 At last year’s DPS my co-authors and I detailed evidence of unusual active glaciation in Greg crater, near the center of the predicted area of ice accumulation during high obliquity.6 We found that the timescale of glacial surface layer activity matches the general 5-15 My timescale of the last episodes of high obliquity and ice deposition. Radar results confirm ice deposits in debris aprons concentrated in the same area.7 Less direct evidence has also been found for glacial ice deposits in the west Tharsis region.8 Here I emphasize that if the models can be adjusted to Mars and then successfully indicate unusual, specific features that we see there, it is an argument for the robustness of climate modeling in general. In recent years we have see various public figures casting doubt on the validity of terrestrial global modeling. The successful match of Martian climate modeling with direct Martian geological and chronometric observations provides an interesting and teachable refutation of the attacks on climate science. References: 1. Science 259:1294-1297; 2. LPSC XXXV, Abs. 1600; 3. Nature 412:411-413; 4. Science 295:110-113; 5. Science 311:368-371; 6. EPSC-DPS Abs. 1394; 7. Science 322:1235-1238; 8. Nature 434:346-351.

  10. Catalogue of abrupt shifts in Intergovernmental Panel on Climate Change climate models.

    PubMed

    Drijfhout, Sybren; Bathiany, Sebastian; Beaulieu, Claudie; Brovkin, Victor; Claussen, Martin; Huntingford, Chris; Scheffer, Marten; Sgubin, Giovanni; Swingedouw, Didier

    2015-10-27

    Abrupt transitions of regional climate in response to the gradual rise in atmospheric greenhouse gas concentrations are notoriously difficult to foresee. However, such events could be particularly challenging in view of the capacity required for society and ecosystems to adapt to them. We present, to our knowledge, the first systematic screening of the massive climate model ensemble informing the recent Intergovernmental Panel on Climate Change report, and reveal evidence of 37 forced regional abrupt changes in the ocean, sea ice, snow cover, permafrost, and terrestrial biosphere that arise after a certain global temperature increase. Eighteen out of 37 events occur for global warming levels of less than 2°, a threshold sometimes presented as a safe limit. Although most models predict one or more such events, any specific occurrence typically appears in only a few models. We find no compelling evidence for a general relation between the overall number of abrupt shifts and the level of global warming. However, we do note that abrupt changes in ocean circulation occur more often for moderate warming (less than 2°), whereas over land they occur more often for warming larger than 2°. Using a basic proportion test, however, we find that the number of abrupt shifts identified in Representative Concentration Pathway (RCP) 8.5 scenarios is significantly larger than in other scenarios of lower radiative forcing. This suggests the potential for a gradual trend of destabilization of the climate with respect to such shifts, due to increasing global mean temperature change. PMID:26460042

  11. Catalogue of abrupt shifts in Intergovernmental Panel on Climate Change climate models

    PubMed Central

    Drijfhout, Sybren; Bathiany, Sebastian; Beaulieu, Claudie; Brovkin, Victor; Claussen, Martin; Huntingford, Chris; Scheffer, Marten; Sgubin, Giovanni; Swingedouw, Didier

    2015-01-01

    Abrupt transitions of regional climate in response to the gradual rise in atmospheric greenhouse gas concentrations are notoriously difficult to foresee. However, such events could be particularly challenging in view of the capacity required for society and ecosystems to adapt to them. We present, to our knowledge, the first systematic screening of the massive climate model ensemble informing the recent Intergovernmental Panel on Climate Change report, and reveal evidence of 37 forced regional abrupt changes in the ocean, sea ice, snow cover, permafrost, and terrestrial biosphere that arise after a certain global temperature increase. Eighteen out of 37 events occur for global warming levels of less than 2°, a threshold sometimes presented as a safe limit. Although most models predict one or more such events, any specific occurrence typically appears in only a few models. We find no compelling evidence for a general relation between the overall number of abrupt shifts and the level of global warming. However, we do note that abrupt changes in ocean circulation occur more often for moderate warming (less than 2°), whereas over land they occur more often for warming larger than 2°. Using a basic proportion test, however, we find that the number of abrupt shifts identified in Representative Concentration Pathway (RCP) 8.5 scenarios is significantly larger than in other scenarios of lower radiative forcing. This suggests the potential for a gradual trend of destabilization of the climate with respect to such shifts, due to increasing global mean temperature change. PMID:26460042

  12. Intercomparison of the capabilities of simplified climate models to project the effects of aviation CO2 on climate

    NASA Astrophysics Data System (ADS)

    Khodayari, Arezoo; Wuebbles, Donald J.; Olsen, Seth C.; Fuglestvedt, Jan S.; Berntsen, Terje; Lund, Marianne T.; Waitz, Ian; Wolfe, Philip; Forster, Piers M.; Meinshausen, Malte; Lee, David S.; Lim, Ling L.

    2013-08-01

    This study evaluates the capabilities of the carbon cycle and energy balance treatments relative to the effect of aviation CO2 emissions on climate in several existing simplified climate models (SCMs) that are either being used or could be used for evaluating the effects of aviation on climate. Since these models are used in policy-related analyses, it is important that the capabilities of such models represent the state of understanding of the science. We compare the Aviation Environmental Portfolio Management Tool (APMT) Impacts climate model, two models used at the Center for International Climate and Environmental Research-Oslo (CICERO-1 and CICERO-2), the Integrated Science Assessment Model (ISAM) model as described in Jain et al. (1994), the simple Linear Climate response model (LinClim) and the Model for the Assessment of Greenhouse-gas Induced Climate Change version 6 (MAGICC6). In this paper we select scenarios to illustrate the behavior of the carbon cycle and energy balance models in these SCMs. This study is not intended to determine the absolute and likely range of the expected climate response in these models but to highlight specific features in model representations of the carbon cycle and energy balance models that need to be carefully considered in studies of aviation effects on climate. These results suggest that carbon cycle models that use linear impulse-response-functions (IRF) in combination with separate equations describing air-sea and air-biosphere exchange of CO2 can account for the dominant nonlinearities in the climate system that would otherwise not have been captured with an IRF alone, and hence, produce a close representation of more complex carbon cycle models. Moreover, results suggest that an energy balance model with a 2-box ocean sub-model and IRF tuned to reproduce the response of coupled Earth system models produces a close representation of the globally-averaged temperature response of more complex energy balance models.

  13. Global Climate Models of Titan, Uranus, and Neptune

    NASA Astrophysics Data System (ADS)

    Friedson, A. J.; Orton, G.; West, R.

    2007-12-01

    We present the formulation of and some results from global climate models for Titan, Uranus, and Neptune. The model for Titan is a fully three-dimensional, modified version of NCAR's terrestrial global climate model, CAM-3. It includes forcing by Saturn's gravitational tides, a treatment of the planetary boundary layer and surface interactions, scattering and absorption of short-wave radiation, and absorption and emission of long-wave radiation. The physical properties and distribution of aerosols are constrained by Cassini observations. The climate models for Uranus and Neptune are two-dimensional, radiative-diffusive models which calculate sensible heat fluxes and latent-heat fluxes due to ortho-para hydrogen conversion in terms of a mixing-length formulation. The vertical pressure range in these models extends from 100 bars up to 0.1 mbar. Our main goal for the 2-d models is to establish the relative roles of sensible and ortho-para latent-heat fluxes in transporting heat laterally and vertically. We are also currently developing three-dimensional models of Uranus and Neptune based on modification of CAM-3. We will discuss how the parameterization of heat fluxes in the 2-d models can be adapted to model small-scale convection in the presence of ortho-para conversion in the 3-d models. This research is supported by the NASA Outer Planet Research Program.

  14. Constructing Scientific Arguments Using Evidence from Dynamic Computational Climate Models

    NASA Astrophysics Data System (ADS)

    Pallant, Amy; Lee, Hee-Sun

    2015-04-01

    Modeling and argumentation are two important scientific practices students need to develop throughout school years. In this paper, we investigated how middle and high school students ( N = 512) construct a scientific argument based on evidence from computational models with which they simulated climate change. We designed scientific argumentation tasks with three increasingly complex dynamic climate models. Each scientific argumentation task consisted of four parts: multiple-choice claim, openended explanation, five-point Likert scale uncertainty rating, and open-ended uncertainty rationale. We coded 1,294 scientific arguments in terms of a claim's consistency with current scientific consensus, whether explanations were model based or knowledge based and categorized the sources of uncertainty (personal vs. scientific). We used chi-square and ANOVA tests to identify significant patterns. Results indicate that (1) a majority of students incorporated models as evidence to support their claims, (2) most students used model output results shown on graphs to confirm their claim rather than to explain simulated molecular processes, (3) students' dependence on model results and their uncertainty rating diminished as the dynamic climate models became more and more complex, (4) some students' misconceptions interfered with observing and interpreting model results or simulated processes, and (5) students' uncertainty sources reflected more frequently on their assessment of personal knowledge or abilities related to the tasks than on their critical examination of scientific evidence resulting from models. These findings have implications for teaching and research related to the integration of scientific argumentation and modeling practices to address complex Earth systems.

  15. 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

    2016-01-01

    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.

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

    NASA Technical Reports Server (NTRS)

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

    1988-01-01

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

  17. FOREST-SAGE, a new deforestation model for climate models and an example deforestation climate impact experiment in the Congo.

    NASA Astrophysics Data System (ADS)

    Tompkins, A. M.; Bell, J.-P.; Caporaso, L.

    2012-04-01

    The impact of deforestation on climate is often studied using highly idealized "instant deforestation" experiments due to the lack of generalized deforestation scenario generators coupled to climate model land-surface schemes. A new deforestation scenario generator has been therefore developed to fulfill this role known as the deFORESTation ScenArio GEnerator, or FOREST-SAGE. The model produces distributed maps of deforestation rates that account for local factors such as proximity to transport networks, distance weighted population density, forest fragmentation and presence of protected areas and logging concessions. The integrated deforestation risk is scaled to give the deforestation rate as specified by macro-region scenarios such as "business as usual" or "increased protection legislation" which are a function of future time. FOREST-SAGE is based on the framework of the widely used Community Land Model (CLM), which is the land model for the Community Earth System Model (CESM), the Community Atmosphere Model (CAM) and the 4th generation ICTP regional climate model REGCM4. Example potential future deforestation scenarios for central Africa are shown, along with the resulting climate impact as modelled by REGCM coupled to CLM.

  18. Using Web 2.0 Techniques To Bring Global Climate Modeling To More Users

    NASA Astrophysics Data System (ADS)

    Chandler, M. A.; Sohl, L. E.; Tortorici, S.

    2012-12-01

    The Educational Global Climate Model has been used for many years in undergraduate courses and professional development settings to teach the fundamentals of global climate modeling and climate change simulation to students and teachers. While course participants have reported a high level of satisfaction in these courses and overwhelmingly claim that EdGCM projects are worth the effort, there is often a high level of frustration during the initial learning stages. Many of the problems stem from issues related to installation of the software suite and to the length of time it can take to run initial experiments. Two or more days of continuous run time may be required before enough data has been gathered to begin analyses. Asking users to download existing simulation data has not been a solution because the GCM data sets are several gigabytes in size, requiring substantial bandwidth and stable dedicated internet connections. As a means of getting around these problems we have been developing a Web 2.0 utility called EzGCM (Easy G-G-M) which emphasizes that participants learn the steps involved in climate modeling research: constructing a hypothesis, designing an experiment, running a computer model and assessing when an experiment has finished (reached equilibrium), using scientific visualization to support analysis, and finally communicating the results through social networking methods. We use classic climate experiments that can be "rediscovered" through exercises with EzGCM and are attempting to make this Web 2.0 tool an entry point into climate modeling for teachers with little time to cover the subject, users with limited computer skills, and for those who want an introduction to the process before tackling more complex projects with EdGCM.

  19. Evaluating the effect of climate change on areal reduction factors using regional climate model projections

    NASA Astrophysics Data System (ADS)

    Li, Jingwan; Sharma, Ashish; Johnson, Fiona; Evans, Jason

    2015-09-01

    Areal reduction factors (ARFs) are commonly used to transform point design rainfall to represent the average design rainfall for a catchment area. While there has been considerable attention paid in the research and engineering communities to the likely changes in rainfall intensity in future climates, the issue of changes to design areal rainfall has been largely ignored. This paper investigates the impact of climate change on ARFs. A new methodology for estimating changes in ARFs is presented. This method is used to assess changes in ARFs in the greater Sydney region using a high-resolution regional climate model (RCM). ARFs under present (1990-2009) and future (2040-2059) climate conditions were derived and compared for annual exceedance probabilities (AEPs) from 50% to 5% for durations ranging from 1 h to 120 h. The analysis shows two main trends in the future changes in ARFs. For the shortest duration events (1-h) the ARFs are found to increase which implies that these events will tend to have a larger spatial structure in the future than the current climate. In contrast, storms with durations between 6 and 72 h are likely to have decreased ARFs in the future, suggesting a more restricted spatial coverage of storms under a warming climate. The extent of the decrease varies with event frequency and catchment size. The largest decreases are found for large catchments and rare events. Although the results here are based on a single RCM and need to be confirmed in future work with multiple models, the framework that is proposed will be useful for future studies considering changes in the areal extent of rainfall extremes.

  20. Connecting climate model projections of global temperature change with the real world

    NASA Astrophysics Data System (ADS)

    Hawkins, Ed; Sutton, Rowan

    2016-04-01

    Current state-of-the-art global climate models produce different values for Earth's mean temperature. When comparing simulations with each other and with observations it is standard practice to compare temperature anomalies with respect to a reference period. It is not always appreciated that the choice of reference period can affect conclusions, both about the skill of simulations of past climate, and about the magnitude of expected future changes in climate. We discuss some of the key issues that arise when using anomalies relative to a reference period to generate climate projections and highlight that there is no perfect choice of reference period. When evaluating models against observations, a long reference period should generally be used, but how long depends on the quality of the observations available. The IPCC AR5 choice to use a 1986-2005 reference period for future global temperature projections was reasonable, but a case-by-case approach is needed for different purposes and when assessing projections of different climate variables. Finally, we recommend that any studies that involve the use of a reference period should explicitly examine the robustness of the conclusions to alternative choices.

  1. Visualizing Life Zone Boundary Sensitivities Across Climate Models and Temporal Spans

    SciTech Connect

    Sisneros, Roberto R; Huang, Jian; Ostrouchov, George; Hoffman, Forrest M

    2011-01-01

    Life zones are a convenient and quantifiable method for delineating areas with similar plant and animal communities based on bioclimatic conditions. Such ecoregionalization techniques have proved useful for defining habitats and for studying how these habitats may shift due to environmental change. The ecological impacts of climate change are of particular interest. Here we show that visualizations of the geographic projection of life zones may be applied to the investigation of potential ecological impacts of climate change using the results of global climate model simulations. Using a multi-factor classification scheme, we show how life zones change over time based on quantitative model results into the next century. Using two straightforward metrics, we identify regions of high sensitivity to climate changes from two global climate simulations under two different greenhouse gas emissions scenarios. Finally, we identify how preferred human habitats may shift under these scenarios. We apply visualization methods developed for the purpose of displaying multivariate relationships within data, especially for situations that involve a large number of concurrent relationships. Our method is based on the concept of multivariate classification, and is implemented directly in VisIt, a production quality visualization package.

  2. The regional aerosol-climate model REMO-HAM

    NASA Astrophysics Data System (ADS)

    Pietikäinen, J.-P.; O'Donnell, D.; Teichmann, C.; Karstens, U.; Pfeifer, S.; Kazil, J.; Podzun, R.; Fiedler, S.; Kokkola, H.; Birmili, W.; O'Dowd, C.; Baltensperger, U.; Weingartner, E.; Gehrig, R.; Spindler, G.; Kulmala, M.; Feichter, J.; Jacob, D.; Laaksonen, A.

    2012-03-01

    REMO-HAM is a new regional aerosol-climate model. It is based on the REMO regional climate model and includes all of the major aerosol processes. The structure for aerosol is similar to the global aerosol-climate model ECHAM5-HAM, for example the aerosol module HAM-M7 has been coupled with a two-moment stratiform cloud scheme. In this work, we have evaluated the model and compared the results against ECHAM5-HAM and measurements. Four different measurement sites was chosen for the comparison of total number concentrations, size distributions and gas phase sulfur dioxide concentrations: Hyytiälä in Finland, Melpitz in Germany, Mace Head in Ireland and Jungfraujoch in Switzerland. REMO-HAM is run with two different resolutions: 50×50 km2 and 10×10 km2. Based on our simulations, REMO-HAM can represent the measured values reasonably well. The total number concentrations are slightly underestimated, which is probably due to the missing boundary layer nucleation and online secondary organic aerosol model. The differences in the total number concentrations between REMO-HAM and ECHAM5-HAM can be mainly explained by the difference in the nucleation mode. From the meteorological point of view, REMO-HAM represents the precipitation fields and 2 m temperature profile very well compared to measurement. Overall, we have shown that REMO-HAM is a functional aerosol-climate model, which will be used in further studies.

  3. Modeling productivity and transpiration of Pinus radiata: climatic effects.

    PubMed

    Sheriff, D. W.; Mattay, J. P.; McMurtrie, R. E.

    1996-01-01

    Climatic effects on annual net carbon gain, stem biomass and annual transpiration were simulated for Pinus radiata D. Don at Canberra and Mt. Gambier. Simulations were conducted with an existing process-based forest growth model (BIOMASS, Model 1) and with a modified version of the BIOMASS model (Model 2) in which response functions for carbon assimilation and leaf conductance were replaced with those derived from field gas exchange data collected at Mt. Gambier. Simulated carbon gain was compared with a published report stating that mean annual stem volume increment (MAI) at Mt. Gambier was 1.8 times greater than at Canberra and that the difference could be the result solely of differences in climate. Regional differences in climate resulted in a 20% greater simulated annual transpiration at Canberra than at Mt. Gambier but only small differences in simulated productivity, indicating that climatic differences did not account for the reported differences in productivity. With Model 1, simulated annual net carbon gain and annual increase in stem biomass were greater at Canberra than at Mt. Gambier, whereas Model 2 indicated a similar annual net carbon gain and annual stem biomass increase in both regions. PMID:14871762

  4. Climate Change and Groundwater: A Coupling of Models

    NASA Astrophysics Data System (ADS)

    Chesebrough, E.; Gorokhovich, Y.

    2012-12-01

    Groundwater is the largest source of readily available freshwater on our planet. Aquifers are vulnerable to climate change and require new groundwater management plans to account for changing precipitation patterns and sea level rise, among other factors. Atmospheric General Circulation Models (GCMs) use algorithms applied to historic and modern data to simulate current climatic conditions and predict future changes on a global scale. However, these GCMs are limited in their application at a regional level, thus making hydrogeological predictions difficult. Models designed specifically for hydrogeology are most commonly designed for regional assessment, and they can incorporate GCM outputs. Some of the challenges in coupling GCM outputs and hydrogeological models are the differences in spatial and temporal scales. In addition, the different scenarios of climate response to Greenhouse Gas forcings create a range of outputs from GCMs, affecting the predicting capacity of hydrogeological models. The use of dynamic and statistical downscaling of GCMs make it possible to overcome these challenges by taking the climate simulation output from GCMs and incorporating it as the input for hydrogeological models. This coupling of GCMs to groundwater models makes new groundwater management plans possible, which will ensure the sustainability of these resources in the future. The studies referenced within this paper highlight the advantages and disadvantages of various combinations of coupling and downscaling methodologies.

  5. Linking the Weather Generator with Regional Climate Model

    NASA Astrophysics Data System (ADS)

    Dubrovsky, Martin; Farda, Ales; Skalak, Petr; Huth, Radan

    2013-04-01

    One of the downscaling approaches, which transform the raw outputs from the climate models (GCMs or RCMs) into data with more realistic structure, is based on linking the stochastic weather generator with the climate model output. The present contribution, in which the parametric daily surface weather generator (WG) M&Rfi is linked to the RCM output, follows two aims: (1) Validation of the new simulations of the present climate (1961-1990) made by the ALADIN-Climate Regional Climate Model at 25 km resolution. The WG parameters are derived from the RCM-simulated surface weather series and compared to those derived from weather series observed in 125 Czech meteorological stations. The set of WG parameters will include statistics of the surface temperature and precipitation series (including probability of wet day occurrence). (2) Presenting a methodology for linking the WG with RCM output. This methodology, which is based on merging information from observations and RCM, may be interpreted as a downscaling procedure, whose product is a gridded WG capable of producing realistic synthetic multivariate weather series for weather-ungauged locations. In this procedure, WG is calibrated with RCM-simulated multi-variate weather series in the first step, and the grid specific WG parameters are then de-biased by spatially interpolated correction factors based on comparison of WG parameters calibrated with gridded RCM weather series and spatially scarcer observations. The quality of the weather series produced by the resultant gridded WG will be assessed in terms of selected climatic characteristics (focusing on characteristics related to variability and extremes of surface temperature and precipitation). Acknowledgements: The present experiment is made within the frame of projects ALARO-Climate (project P209/11/2405 sponsored by the Czech Science Foundation), WG4VALUE (project LD12029 sponsored by the Ministry of Education, Youth and Sports of CR) and VALUE (COST ES 1102

  6. Atmospheric Climate Model Experiments Performed at Multiple Horizontal Resolutions

    SciTech Connect

    Phillips, T; Bala, G; Gleckler, P; Lobell, D; Mirin, A; Maxwell, R; Rotman, D

    2007-12-21

    This report documents salient features of version 3.3 of the Community Atmosphere Model (CAM3.3) and of three climate simulations in which the resolution of its latitude-longitude grid was systematically increased. For all these simulations of global atmospheric climate during the period 1980-1999, observed monthly ocean surface temperatures and sea ice extents were prescribed according to standard Atmospheric Model Intercomparison Project (AMIP) values. These CAM3.3 resolution experiments served as control runs for subsequent simulations of the climatic effects of agricultural irrigation, the focus of a Laboratory Directed Research and Development (LDRD) project. The CAM3.3 model was able to replicate basic features of the historical climate, although biases in a number of atmospheric variables were evident. Increasing horizontal resolution also generally failed to ameliorate the large-scale errors in most of the climate variables that could be compared with observations. A notable exception was the simulation of precipitation, which incrementally improved with increasing resolution, especially in regions where orography plays a central role in determining the local hydroclimate.

  7. A New Method of Comparing Forcing Agents in Climate Models

    SciTech Connect

    Kravitz, Benjamin S.; MacMartin, Douglas; Rasch, Philip J.; Jarvis, Andrew

    2015-10-14

    We describe a new method of comparing different climate forcing agents (e.g., CO2, CH4, and solar irradiance) that avoids many of the ambiguities introduced by temperature-related climate feedbacks. This is achieved by introducing an explicit feedback loop external to the climate model that adjusts one forcing agent to balance another while keeping global mean surface temperature constant. Compared to current approaches, this method has two main advantages: (i) the need to define radiative forcing is bypassed and (ii) by maintaining roughly constant global mean temperature, the effects of state dependence on internal feedback strengths are minimized. We demonstrate this approach for several different forcing agents and derive the relationships between these forcing agents in two climate models; comparisons between forcing agents are highly linear in concordance with predicted functional forms. Transitivity of the relationships between the forcing agents appears to hold within a wide range of forcing. The relationships between the forcing agents obtained from this method are consistent across both models but differ from relationships that would be obtained from calculations of radiative forcing, highlighting the importance of controlling for surface temperature feedback effects when separating radiative forcing and climate response.

  8. Modelling hydrological responses of Nerbioi River Basin to Climate Change

    NASA Astrophysics Data System (ADS)

    Mendizabal, Maddalen; Moncho, Roberto; Chust, Guillem; Torp, Peter

    2010-05-01

    Future climate change will affect aquatic systems on various pathways. Regarding the hydrological cycle, which is a very important pathway, changes in hydrometeorological variables (air temperature, precipitation, evapotranspiration) in first order impact discharges. The fourth report assessment of the Intergovernmental Panel for Climate Change indicates there is evidence that the recent warming of the climate system would result in more frequent extreme precipitation events, increased winter flood likelihoods, increased and widespread melting of snow and ice, longer and more widespread droughts, and rising sea level. Available research and climate model outputs indicate a range of hydrological impacts with likely to very likely probabilities (67 to 99%). For example, it is likely that up to 20% of the world population will live in areas where river flood potential could increase by the 2080s. In Spain, within the Atlantic basin, the hydrological variability will increase in the future due to the intensification of the positive phase of the North Atlantic Oscillation (NAO) index. This might cause flood frequency decreases, but its magnitude does not decrease. The generation of flood, its duration and magnitude are closely linked to changes in winter precipitation. The climatic conditions and relief of the Iberian Peninsula favour the generation of floods. In Spain, floods had historically strong socio-economic impacts, with more than 1525 victims in the past five decades. This upward trend of hydrological variability is expected to remain in the coming decades (medium uncertainty) when the intensification of the positive phase of the NAO index (MMA, 2006) is considered. In order to adapt or minimize climate change impacts in water resources, it is necessary to use climate projections as well as hydrological modelling tools. The main objective of this paper is to evaluate and assess the hydrological response to climate changes in flow conditions in Nerbioi river

  9. The Co-evolution of Climate Models and the Intergovernmental Panel on Climate Change

    NASA Astrophysics Data System (ADS)

    Somerville, R. C.

    2010-12-01

    As recently as the 1950s, global climate models, or GCMs, did not exist, and the notion that man-made carbon dioxide might lead to significant climate change was not regarded as a serious possibility by most experts. Today, of course, the prospect or threat of exactly this type of climate change dominates the science and ranks among the most pressing issues confronting all mankind. Indeed, the prevailing scientific view throughout the first half of the twentieth century was that adding carbon dioxide to the atmosphere would have only a negligible effect on climate. The science of climate change caused by atmospheric carbon dioxide changes has thus undergone a genuine revolution. An extraordinarily rapid development of global climate models has also characterized this period, especially in the three decades since about 1980. In these three decades, the number of GCMs has greatly increased, and their physical and computational aspects have both markedly improved. Modeling progress has been enabled by many scientific advances, of course, but especially by a massive increase in available computer power, with supercomputer speeds increasing by roughly a factor of a million in the three decades from about 1980 to 2010. This technological advance has permitted a rapid increase in the physical comprehensiveness of GCMs as well as in spatial computational resolution. In short, GCMs have dramatically evolved over time, in exactly the same recent period as popular interest and scientific concern about anthropogenic climate change have markedly increased. In parallel, a unique international organization, the Intergovernmental Panel on Climate Change, or IPCC, has also recently come into being and also evolved rapidly. Today, the IPCC has become widely respected and globally influential. The IPCC was founded in 1988, and its history is thus even shorter than that of GCMs. Yet, its stature today is such that a series of IPCC reports assessing climate change science has already

  10. An integrated modeling study of ocean circulation, the ocean carbon cycle, marine ecosystems, and climate change

    NASA Astrophysics Data System (ADS)

    Cao, Long

    The unifying theme of this study is to conduct an extensive exploration of various interactions between ocean circulation, the carbon cycle, marine ecosystems, and climate change using an earth system model of intermediate complexity, ISAM-2.5D (Integrated Science Assessment Model). First, through the simulation of radiocarbon (in terms of Delta14C) it is demonstrated that the inclusion of isopycnal diffusion and a parameterization of eddy-induced circulation in the ISAM-2.5D model yields the most realistic representation of ocean mixing and circulation. Secondly, I demonstrate the value of the simulation of multiple tracers, combined with a variety of observational data, in constraining the ISAM-2.5D model that has been constrained by the simulation of Delta14C. Through the simulation of ocean biogeochemical cycles and CFC-11 and the use of the updated observational data of bomb radiocarbon, I improve the Delta14C-constrained ISAM-2.5D model's performance in simulating ocean circulation and air-sea gas exchange, as well as its credibility in predicting oceanic carbon uptake. Third, I use the ISAM-2.5D model to assess the efficiency of direct carbon injection into the deep ocean with the influence of climate change. It is shown that the consideration of climate change enhances the retention time of injected carbon into the Atlantic Ocean as a result of weakened North Atlantic overturning circulation in a warming climate. However, the climatic effect is insignificant on the efficiency of carbon injection into the Pacific and Indian Oceans. Finally, I quantify that increased atmospheric CO2 concentrations would be mainly responsible for future ocean acidification, including lowering in ocean pH and sea water saturation state with respect to carbonate minerals. The consideration of climate change produces a second-order modification to projected ocean acidification. Therefore, in addition to its radiative effects on climate change, increased atmospheric CO2

  11. Agent Model Development for Assessing Climate-Induced Geopolitical Instability.

    SciTech Connect

    Boslough, Mark B.; Backus, George A.

    2005-12-01

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

  12. Regional climate modeling of heat stress, frost, and water stress events in the agricultural region of Southwest Western Australia under the current climate and future climate scenarios.

    NASA Astrophysics Data System (ADS)

    Kala, Jatin; Lyons, Tom J.; Abbs, Deborah J.; Foster, Ian J.

    2010-05-01

    Heat stress, frost, and water stress events have significant impacts on grain quality and production within the agricultural region (wheat-belt) of Southwest Western Australia (SWWA) (Cramb, 2000) and understanding how the frequency and intensity of these events will change in the future is crucial for management purposes. Hence, the Regional Atmospheric Modeling System (Pielke et al, 1992) (RAMS Version 6.0) is used to simulate the past 10 years of the climate of SWWA at a 20 km grid resolution by down-scaling the 6-hourly 1.0 by 1.0 degree National Center for Environmental Prediction Final Analyses from December 1999 to Present. Daily minimum and maximum temperatures, as well as daily rainfall are validated against observations. Simulations of future climate are carried out by down-scaling the Commonwealth Scientific and Industrial Research Organization (CSIRO) Mark 3.5 General Circulation Model (Gordon et al, 2002) for 10 years (2046-2055) under the SRES A2 scenario using the Cubic Conformal Atmospheric Model (CCAM) (McGregor and Dix, 2008). The 6-hourly CCAM output is then downscaled to a 20 km resolution using RAMS. Changes in extreme events are discussed within the context of the continued viability of agriculture in SWWA. Cramb, J. (2000) Climate in relation to agriculture in south-western Australia. In: The Wheat Book (Eds W. K. Anderson and J. R. Garlinge). Bulletin 4443. Department of Agriculture, Western Australia. Gordon, H. B., Rotstayn, L. D., McGregor, J. L., Dix, M. R., Kowalczyk, E. A., O'Farrell, S. P., Waterman, L. J., Hirst, A. C., Wilson, S. G., Collier, M. A., Watterson, I. G., and Elliott, T. I. (2002). The CSIRO Mk3 Climate System Model [Electronic publication]. Aspendale: CSIRO Atmospheric Research. (CSIRO Atmospheric Research technical paper; no. 60). 130 p McGregor, J. L., and Dix, M. R., (2008) An updated description of the conformal-cubic atmospheric model. High Resolution Simulation of the Atmosphere and Ocean, Hamilton, K. and Ohfuchi

  13. Chemistry-Climate Models of the Stratosphere

    NASA Technical Reports Server (NTRS)

    Austin, J.; Shindell, D.; Bruehl, C.; Dameris, M.; Manzini, E.; Nagashima, T.; Newman, P.; Pawson, S.; Pitari, G.; Rozanov, E.; Bhartia, P. K. (Technical Monitor)

    2001-01-01

    Over the last decade, improved computer power has allowed three-dimensional models of the stratosphere to be developed that can be used to simulate polar ozone levels over long periods. This paper compares the meteorology between these models, and discusses the future of polar ozone levels over the next 50 years.

  14. Improving hydrology models for a changing climate

    NASA Astrophysics Data System (ADS)

    Palus, Shannon

    2014-12-01

    Changes over time in the relationship between rainfall and catchment runoff pose a significant challenge for hydrological models, which are often calibrated under the assumption that the future relationship will be consistent with that of the past. In a recent paper, Westra et al. outlined a method for diagnosing, interpreting, and improving the capacity of models to develop predictions under such conditions.

  15. Drought risk assessment of food security in a regional climate model perspective

    NASA Astrophysics Data System (ADS)

    Calmanti, Sandro; Yohannes Gelassie, Tinebeb; Syroka, Joanna; Nikulin, Grigory; Jones, Colin

    2013-04-01

    We test the potential added value of high-resolution dynamical downscalings of climate simulations for impact studies in the sector of food-security. We use the risk assessment platform - Africa RiskView (ARV) - developed by the office of Climate and Disaster Risk Solutions of WFP as the underlying impact model. As a test, retrospective case, we consider the ERA-Interim reanalysis and three downscalings performed by Swedish Meteorological and Hydrological Institute (SMHI) at three different horizontal resolutions (44Km, 22Km and 11 km). Aim is to compare the outcome of the impact model under different climate forcings with a unique historical record of humanitarian interventions maintained by WFP. The analysis shows a consistent picture in which coarser donwscalings imply a downgrading of the predicting skills of the impact model with respect to the driving glabal scale data. Some skill is recovered at intermediate horizontal resolution and finally the 11-Km resolution downscaling shows a predicting skill that is in line with that of the rainfall observations is routinely adopted at WFP for monitoring and early warning. Building on the calibration described above, we perform a climate change stress test using model simulation included in the CORDEX database.

  16. Global changes in seasonal means and extremes of precipitation from daily climate model data

    NASA Astrophysics Data System (ADS)

    Russo, Simone; Sterl, Andreas

    2012-01-01

    We investigate simulated changes of seasonal precipitation maxima and means in a future, warmer climate. We use data from the ESSENCE project, in which a 17-member ensemble of climate change simulations in response to the SRES A1b scenario has been carried out using the ECHAM5/MPI-OM climate model. The large size of the data set gives the opportunity to detect the changes of climate extremes and means with high statistical confidence. Daily precipitation data are used to calculate the seasonal precipitation maximum and the seasonal mean. Modeled precipitation data appear consistent with observation-based data from the Global Precipitation Climatology Project. The data are split into six time periods of 25 years to get independent time series. The seasonal peaks are modeled by using the generalized extreme value distribution, while empirical distributions are used to study changes of the seasonal precipitation mean. Finally, we use an empirical method to detect changes of occurrence of very wet and dry periods. Results from these model simulations indicate that over most of the world precipitation maxima will increase in the future. Seasonal means behave differently. In many regions they are decreasing or not increasing. The occurrence of very wet periods is strongly increasing during boreal winter in the extratropics and decreasing in the tropics. In summary, wet regions become wetter and dry regions become drier.

  17. 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

  18. 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

    . 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.

  19. An observational and modeling study of the regional impacts of climate variability

    NASA Astrophysics Data System (ADS)

    Horton, Radley M.

    during El Nino events. Based on the results from Chapter One, the analysis is expanded in several ways in Chapter Two. To gain a more complete and statistically meaningful understanding of ENSO, a 25 year time period is used instead of a single event. To gain a fuller understanding of climate variability, additional patterns are analyzed. Finally analysis is conducted at the regional scales that are of interest to farmers and agricultural planners. Key findings are that GISS ModelE can reproduce: (1) the spatial pattern associated with two additional related modes, the Arctic Oscillation (AO) and the North Atlantic Oscillation (NAO); (2) rainfall patterns in Indonesia; and (3) dynamical features such as sea level pressure (SLP) gradients and wind in the study regions. When run in coupled mode, the same model reproduces similar modes spatially but with reduced variance and weak teleconnections. Since Chapter Two identified Western Indonesia as the region where GCMs hold the most promise for agricultural applications, in Chapter Three a finer spatial and temporal scale analysis of ENSO's effects is presented. Agricultural decision-making is also linked to ENSO's climate effects. Early rainy season precipitation and circulation, and same-season planting and harvesting dates, are shown to be sensitive to ENSO. The locus of ENSO convergence and rainfall anomalies is shown to be near the axis of rainy season establishment, defined as the 6--8 mm/day isohyet, an approximate threshold for irrigated rice cultivation. As the axis tracks south and east between October and January, so do ENSO anomalies. Circulation anomalies associated with ENSO are shown to be similar to those associated with rainfall anomalies, suggesting that long lead-time ENSO forecasts may allow more adaptation than 'wait and see' methods, with little loss of forecast skill. Additional findings include: (1) rice and corn yields are lower (higher) during dry (wet) trimesters and El Nino (La Nina) years; and (2

  20. The Eemian climate simulated by two models of different complexities

    NASA Astrophysics Data System (ADS)

    Nikolova, Irina; Yin, Qiuzhen; Berger, Andre; Singh, Umesh; Karami, Pasha

    2013-04-01

    The Eemian period, also known as MIS-5, experienced warmer than today climate, reduction in ice sheets and important sea-level rise. These interesting features have made the Eemian appropriate to evaluate climate models when forced with astronomical and greenhouse gas forcings different from today. In this work, we present the simulated Eemian climate by two climate models of different complexities, LOVECLIM (LLN Earth system model of intermediate complexity) and CCSM3 (NCAR atmosphere-ocean general circulation model). Feedbacks from sea ice, vegetation, monsoon and ENSO phenomena are discussed to explain the regional similarities/dissimilarities in both models with respect to the pre-industrial (PI) climate. Significant warming (cooling) over almost all the continents during boreal summer (winter) leads to a largely increased (reduced) seasonal contrast in the northern (southern) hemisphere, mainly due to the much higher (lower) insolation received by the whole Earth in boreal summer (winter). The arctic is warmer than at PI through the whole year, resulting from its much higher summer insolation and its remnant effect in the following fall-winter through the interactions between atmosphere, ocean and sea ice. Regional discrepancies exist in the sea-ice formation zones between the two models. Excessive sea-ice formation in CCSM3 results in intense regional cooling. In both models intensified African monsoon and vegetation feedback are responsible for the cooling during summer in North Africa and on the Arabian Peninsula. Over India precipitation maximum is found further west, while in Africa the precipitation maximum migrates further north. Trees and grassland expand north in Sahel/Sahara, trees being more abundant in the results from LOVECLIM than from CCSM3. A mix of forest and grassland occupies continents and expand deep in the high northern latitudes in line with proxy records. Desert areas reduce significantly in Northern Hemisphere, but increase in North

  1. Present and future near-surface wind climate of Greenland from high resolution regional climate modelling

    NASA Astrophysics Data System (ADS)

    Gorter, W.; van Angelen, J. H.; Lenaerts, J. T. M.; van den Broeke, M. R.

    2014-03-01

    The present and twenty-first century near-surface wind climate of Greenland is presented using output from the regional atmospheric climate model RACMO2. The modelled wind variability and wind distribution compare favourably to observations from three automatic weather stations in the ablation zone of southwest Greenland. The Weibull shape parameter is used to classify the wind climate. High values (κ > 4) are found in northern Greenland, indicative of uniform winds and a dominant katabatic forcing, while lower values (κ < 3) are found over the ocean and southern Greenland, where the synoptic forcing dominates. Very high values of the shape parameter are found over concave topography where confluence strengthens the katabatic circulation, while very low values are found in a narrow band along the coast due to barrier winds. To simulate the future (2081-2098) wind climate RACMO2 was forced with the HadGEM2-ES general circulation model using a scenario of mid-range radiative forcing of +4.5 W m-2 by 2100. For the future simulated climate, the near-surface potential temperature deficit reduces in all seasons in regions where the surface temperature is below the freezing point, indicating a reduction in strength of the near-surface temperature inversion layer. This leads to a wind speed reduction over the central ice sheet where katabatic forcing dominates, and a wind speed increase over steep coastal topography due to counteracting effects of thermal and katabatic forcing. Thermally forced winds over the seasonally sea ice covered region of the Greenland Sea are reduced by up to 2.5 m s-1.

  2. Space-Time Modeling of Climate Change Effects on the Water Balance of Southwestern Alberta

    NASA Astrophysics Data System (ADS)

    Mutulu, P.; Blais, J.

    2005-12-01

    Changes and variabilities in space and time characteristics of hydro-climatic processes play a major role in the water balance of a watershed. Formulating suitable spatio-temporal models for interactive behavior of such processes is crucial in understanding and investigating potential climate change effects on hydrology of a watershed. Historical climatic and hydrologic data are herein used to calibrate a conceptual stochastic monthly water balance for southwestern Alberta watersheds in Canada for the present day climate. The inputs to the model are monthly precipitation, evapotranspiration and temperature time series and the outputs are monthly soil moisture storages and streamflow. To account for vegetation influence on the precipitation-runoff transformation, a moisture index parameter based on the Jansen-Haise formulation and HDF-EOS data is incorporated into the hydrologic model. An adaptive technique is used to describe time evolution and uncertainties in the moisture index parameter. To simulate the effects of global warming due to the doubling of CO2, outputs from the Canadian Global Coupled Model 1 (CGCM1) are used to provide inputs that are downscaled to drive the hydrologic model. Spatial estimation of unknown data are performed using a modified Kriging approach combined with Canonical Correlation analysis. Preliminary results indicate that the formulated model is statistically adequate for estimating monthly streamflow at the 95% level. It is also shown that a temperature based approach can be used to estimate the evapotranspiration time series as an immediate process when data for the latter are either unavailable or insufficient. This result is attractive in the sense that only temperature and precipitation are needed to drive the hydrologic model. Finally, the sensitivity of moisture index and streamflow to potential climate changes is briefly discussed.

  3. Uncertainties and ensembles in global climate models: open issues with model dependence, performance, and robustness (Invited)

    NASA Astrophysics Data System (ADS)

    Knutti, R.; Tebaldi, C.

    2013-12-01

    Climate projections have often been summarized as multi model means, assuming that the average of models is better than a single model. Yet averaging models is problematic, because the models are not independent and share biases, and the models may not span the full uncertainty range. A model average can significantly underestimate the climate change signal if the changes are spatially heterogeneous. A seemingly obvious step is to select individual models based on how well they simulate the past and present climate. But metrics of model performance and model weighting is a thorny issue. The lack of verification of the actual climate projections means that we do not know, or cannot agree on which metrics are most relevant to identify a good model. An overview of recent coupled model intercomparisons is given along with a set of major challenges in interpreting them. It is shown that uncertainty in climate projections is difficult to quantify, and has not decreased significantly in the past few years, partly as a result of irreducible climate variability. Progress in model evaluation as well as statistical methods to interpret and combine model projections is urgently needed, in particular as more models of different quality and complexity, including perturbed physics ensembles and ensembles with structurally different models become available.

  4. Predictive modelling of boiler fouling. Final report.

    SciTech Connect

    Chatwani, A

    1990-12-31

    A spectral element method embodying Large Eddy Simulation based on Re- Normalization Group theory for simulating Sub Grid Scale viscosity was chosen for this work. This method is embodied in a computer code called NEKTON. NEKTON solves the unsteady, 2D or 3D,incompressible Navier Stokes equations by a spectral element method. The code was later extended to include the variable density and multiple reactive species effects at low Mach numbers, and to compute transport of large particles governed by inertia. Transport of small particles is computed by treating them as trace species. Code computations were performed for a number of test conditions typical of flow past a deep tube bank in a boiler. Results indicate qualitatively correct behavior. Predictions of deposition rates and deposit shape evolution also show correct qualitative behavior. These simulations are the first attempts to compute flow field results at realistic flow Reynolds numbers of the order of 10{sup 4}. Code validation was not done; comparison with experiment also could not be made as many phenomenological model parameters, e.g., sticking or erosion probabilities and their dependence on experimental conditions were not known. The predictions however demonstrate the capability to predict fouling from first principles. Further work is needed: use of large or massively parallel machine; code validation; parametric studies, etc.

  5. The climate responses of tropical and boreal ecosystems with an improved land surface model (JULES)

    NASA Astrophysics Data System (ADS)

    Harper, Anna; Friedlingstein, Pierre; Cox, Peter; Wiltshire, Andy; Jones, Chris

    2016-04-01

    The Joint UK Land Environment Simulator (JULES) is the land surface of the next generation UK Earth System Model (UKESM1). Recently, JULES was updated with new plant functional types and physiology based on a global plant trait database. These developments improved the simulation of terrestrial gross and net primary productivity on local and global scales, and enabled a more realistic representation of the global distribution of vegetation. In this study, we explore the present-day distribution of ecosystems and their vulnerability to climate change in JULES with these improvements, focusing on tropical and boreal ecosystems. Changes to these ecosystems will have implications for biogeophysical and biogeochemical feedbacks to climate change and need to be understood. First, we examine the simulated and observed rainforest-savannah boundary, which is strongly related to annual precipitation and the maximum climatological water deficit. Second, we assess the length of growing season and biomass stored in boreal ecosystems, where 20th century warming has likely extended the growing season. In each case, we first evaluate the ability of JULES to capture observed climate-vegetation relationships and trends. Finally, we run JULES to 2100 using climate data from 3 models and 2 RCP scenarios, and examine potential 21st century changes to these ecosystems. For example, do the tropical forests shrink in response to changes in tropical rainfall seasonality? And, how does the composition of boreal ecosystems change in response to climate warming? Given the potential for climate feedbacks and the inherent value in these ecosystems, it is essential to assess their responses to a range of climate change scenarios.

  6. High Resolution Regional Climate Modeling for Lebanon, Eastern Mediterranean Coast

    NASA Astrophysics Data System (ADS)

    Katurji, Marwan; Soltanzadeh, Iman; Kuhnlein, Meike; Zawar-Reza, Peyman

    2013-04-01

    The Eastern Mediterranean coast consists of Lebanon, Palestine, Syria, Israel and a small part of southern Turkey. The region lies between latitudes 30 degrees S and 40 degrees N, which makes its climate affected by westerly propagating wintertime cyclones spinning off mid-latitude troughs (December, January and February), while during summer (June, July and August) the area is strongly affected by the sub-tropical anti-cyclonic belt as a result of the descending air of the Hadley cell circulation system. The area is considered to be in a transitional zone between tropical to mid-latitude climate regimes, and having a coastal topography up to 3000 m in elevation (like in the Western Ranges of Lebanon), which emphasizes the complexity of climate variability in this area under future predictions of climate change. This research incorporates both regional climate numerical simulations, Tropical Rainfall Measuring Mission (TRMM) satellite derived and surface rain gauge rainfall data to evaluate the Regional Climate Model (RegCM) version 4 ability to represent both the mean and variance of observed precipitation in the Eastern Mediterranean Region, with emphasis on the Lebanese coastal terrain and mountain ranges. The adopted methodology involves dynamically down scaling climate data from reanalysis synoptic files through a double nesting procedure. The retrospective analysis of 13 years with both 50 and 10 km spatial resolution allows for the assessment of the model results on both a climate scale and specific high intensity precipitating events. The spatial averaged mean bias error in precipitation rate for the rainy season predicted by RegCM 50 and 10 km resolution grids was 0.13 and 0.004 mm hr-1 respectively. When correlating RegCM and TRMM precipitation rate for the domain covering Lebanon's coastal mountains, the root mean square error (RMSE) for the mean quantities over the 13-year period was only 0.03, while the RMSE for the standard deviation was higher by one

  7. Climate model biases and statistical downscaling for application in hydrologic model

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Climate change impact studies use global climate model (GCM) simulations to define future temperature and precipitation. The best available bias-corrected GCM output was obtained from Coupled Model Intercomparison Project phase 5 (CMIP5). CMIP5 data (temperature and precipitation) are available in d...

  8. Modeling impacts of climate change on freshwater availability in Africa

    NASA Astrophysics Data System (ADS)

    Faramarzi, Monireh; Abbaspour, Karim C.; Ashraf Vaghefi, Saeid; Farzaneh, Mohammad Reza; Zehnder, Alexander J. B.; Srinivasan, Raghavan; Yang, Hong

    2013-02-01

    SummaryThis study analyzes the impact of climate change on freshwater availability in Africa at the subbasin level for the period of 2020-2040. Future climate projections from five global circulation models (GCMs) under the four IPCC emission scenarios were fed into an existing SWAT hydrological model to project the impact on different components of water resources across the African continent. The GCMs have been downscaled based on observed data of Climate Research Unit to represent local climate conditions at 0.5° grid spatial resolution. The results show that for Africa as a whole, the mean total quantity of water resources is likely to increase. For individual subbasins and countries, variations are substantial. Although uncertainties are high in the simulated results, we found that in many regions/countries, most of the climate scenarios projected the same direction of changes in water resources, suggesting a relatively high confidence in the projections. The assessment of the number of dry days and the frequency of their occurrences suggests an increase in the drought events and their duration in the future. Overall, the dry regions have higher uncertainties than the wet regions in the projected impacts on water resources. This poses additional challenge to the agriculture in dry regions where water shortage is already severe while irrigation is expected to become more important to stabilize and increase food production.

  9. An Interactive Multi-Model for Consensus on Climate Change

    SciTech Connect

    Kocarev, Ljupco

    2014-07-02

    This project purports to develop a new scheme for forming consensus among alternative climate models, that give widely divergent projections as to the details of climate change, that is more intelligent than simply averaging the model outputs, or averaging with ex post facto weighting factors. The method under development effectively allows models to assimilate data from one another in run time with weights that are chosen in an adaptive training phase using 20th century data, so that the models synchronize with one another as well as with reality. An alternate approach that is being explored in parallel is the automated combination of equations from different models in an expert-system-like framework.

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

    SciTech Connect

    Lamarque, J.-F.; Shindell, Drew; Josse, B.; Young, P. J.; Cionni, I.; Eyring, Veronika; Bergmann, D.; Cameron-Smith, Philip; Collins, W. J.; Doherty, R.; Dalsoren, S.; Faluvegi, G.; Folberth, G.; Ghan, Steven J.; Horowitz, L.; Lee, Y. H.; MacKenzie, I. A.; Nagashima, T.; Naik, Vaishali; Plummer, David; Righi, M.; Rumbold, S.; Schulz, M.; Skeie, R. B.; Stevenson, D. S.; Strode, S.; Sudo, K.; Szopa, S.; Voulgarakis, A.; Zeng, G.

    2013-02-07

    The Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP) consists of a series of timeslice experiments targeting the long-term changes in atmospheric composition between 1850 and 2100, with the goal of documenting radiative forcing and the associated composition changes. Here we introduce the various simulations performed under ACCMIP and the associated model output. The ACCMIP models have a wide range of horizontal and vertical resolutions, vertical extent, chemistry schemes and interaction with radiation and clouds. While anthropogenic and biomass burning emissions were specified for all time slices in the ACCMIP protocol, it is found that the natural emissions lead to a significant range in emissions, mostly for ozone precursors. The analysis of selected present-day climate diagnostics (precipitation, temperature, specific humidity and zonal wind) reveals biases consistent with state-of-the-art climate models. The model-to-model comparison of changes in temperature, specific humidity and zonal wind between 1850 and 2000 and between 2000 and 2100 indicates mostly consistent results, but with outliers different enough to possibly affect their representation of climate impact on chemistry.

  11. Representing Icebergs In A Fully Coupled Climate Model

    NASA Astrophysics Data System (ADS)

    Bügelmayer, Marianne; Roche, Didier; Renssen, Hans

    2014-05-01

    Changes in the global climate during past and current times strongly impact the Polar Regions, which in turn affect the global climate due to several mechanisms, such as albedo, topography, ablation and ice discharge. Icebergs are an important part of the climate system as they interact with the ocean, atmosphere and cryosphere. Several approaches have been taken to incorporate iceberg calving into numerical models under different climate forcings. The studies done so far have in common that the icebergs were moved by reconstructed or modelled forcing fields and that the initial size distribution of the icebergs was prescribed according to present day observations. Hence, uncertainties in the forcing fields and in the parameterization of the iceberg size may alter the results. To investigate the impact of the background forcing (atmosphere, ocean) and the pre-defined size distribution on the icebergs and consequently on the Northern hemisphere climate and the Greenland ice sheet, we have coupled an earth system model of intermediate complexity (iLOVECLIM, Roche et al., 2013) to an ice sheet/ice shelf model (GRISLI, Ritz et al., 2001) and an iceberg module (Jongma et al., 2009; Bügelmayer et al., 2014). Using this set-up, we performed 15 sensitivity experiments that differ in the applied forcing (atmosphere, ocean), the applied boundary conditions (pre-industrial, 4xCO2, 1/4 x CO2) and the initial size distribution of the icebergs. In the presented study only the Greenland ice sheet is considered. We find that, under pre-industrial conditions, the atmospheric forcing pushes the icebergs further away from their calving sites and further into the North Atlantic, whereas the ocean currents transport the bergs along the Greenland coast and southward along the Canadian coast. Although the purely atmospheric-forced bergs cause warmer oceanic conditions than the oceanic-driven bergs, the overall effect on climate and the resulting ice sheet due to variations in the

  12. Integration of climatic indices in an objective probabilistic model for establishing and mapping viticultural climatic zones in a region

    NASA Astrophysics Data System (ADS)

    Moral, Francisco J.; Rebollo, Francisco J.; Paniagua, Luis L.; García, Abelardo; Honorio, Fulgencio

    2016-05-01

    Different climatic indices have been proposed to determine the wine suitability in a region. Some of them are related to the air temperature, but the hydric component of climate should also be considered which, in turn, is influenced by the precipitation during the different stages of the grapevine growing and ripening periods. In this study, we propose using the information obtained from ten climatic indices [heliothermal index (HI), cool night index (CI), dryness index (DI), growing season temperature (GST), the Winkler index (WI), September mean thermal amplitude (MTA), annual precipitation (AP), precipitation during flowering (PDF), precipitation before flowering (PBF), and summer precipitation (SP)] as inputs in an objective and probabilistic model, the Rasch model, with the aim of integrating the individual effects of them, obtaining the climate data that summarize all main climatic indices, which could influence on wine suitability from a climate viewpoint, and utilizing the Rasch measures to generate homogeneous climatic zones. The use of the Rasch model to estimate viticultural climatic suitability constitutes a new application of great practical importance, enabling to rationally determine locations in a region where high viticultural potential exists and establishing a ranking of the climatic indices which exerts an important influence on wine suitability in a region. Furthermore, from the measures of viticultural climatic suitability at some locations, estimates can be computed using a geostatistical algorithm, and these estimates can be utilized to map viticultural climatic zones in a region. To illustrate the process, an application to Extremadura, southwestern Spain, is shown.

  13. Modelling mid-Pliocene climate with COSMOS

    NASA Astrophysics Data System (ADS)

    Stepanek, C.; Lohmann, G.

    2012-10-01

    In this manuscript we describe the experimental procedure employed at the Alfred Wegener Institute in Germany in the preparation of the simulations for the Pliocene Model Intercomparison Project (PlioMIP). We present a description of the utilized Community Earth System Models (COSMOS, version: COSMOS-landveg r2413, 2009) and document the procedures that we applied to transfer the Pliocene Research, Interpretation and Synoptic Mapping (PRISM) Project mid-Pliocene reconstruction into model forcing fields. The model setup and spin-up procedure are described for both the paleo- and preindustrial (PI) time slices of PlioMIP experiments 1 and 2, and general results that depict the performance of our model setup for mid-Pliocene conditions are presented. The mid-Pliocene, as simulated with our COSMOS setup and PRISM boundary conditions, is both warmer and wetter in the global mean than the PI. The globally averaged annual mean surface air temperature in the mid-Pliocene standalone atmosphere (fully coupled atmosphere-ocean) simulation is 17.35 °C (17.82 °C), which implies a warming of 2.23 °C (3.40 °C) relative to the respective PI control simulation.

  14. Modelling mid-Pliocene climate with COSMOS

    NASA Astrophysics Data System (ADS)

    Stepanek, C.; Lohmann, G.

    2012-04-01

    In this manuscript we describe the experimental procedure employed at the Alfred Wegener Institute in Germany in the preparation of the simulations for the Pliocene Model Intercomparison Project (PlioMIP). We present a description of the utilized community earth system models (COSMOS) and document the procedures which we applied to transfer the Pliocene Research, Interpretation and Synoptic Mapping Project (PRISM) mid-Pliocene reconstruction into model forcing fields. The model setup and spin-up procedure are described for both the paleo and preindustrial (PI) time-slices of PlioMIP experiments 1 and 2, and general results that depict the performance of our model setup for mid-Pliocene conditions are presented. The mid-Pliocene as simulated with our COSMOS-setup and PRISM boundary conditions is both warmer and wetter than the PI. The globally averaged annual mean surface air temperature in the mid-Pliocene standalone atmosphere (fully coupled atmosphere-ocean) simulation is 17.35 °C (17.82 °C), which implies a warming of 2.23 °C (3.40 °C) relative to the respective PI control simulation.

  15. A climate model intercomparison at the dynamics level

    NASA Astrophysics Data System (ADS)

    Steinhaeuser, Karsten; Tsonis, Anastasios A.

    2014-03-01

    Until now, climate model intercomparison has focused primarily on annual and global averages of various quantities or on specific components, not on how well the general dynamics in the models compare to each other. In order to address how well models agree when it comes to the dynamics they generate, we have adopted a new approach based on climate networks. We have considered 28 pre-industrial control runs as well as 70 20th-century forced runs from 23 climate models and have constructed networks for the 500 hPa, surface air temperature (SAT), sea level pressure (SLP), and precipitation fields for each run. We then employed a widely used algorithm to derive the community structure in these networks. Communities separate "nodes" in the network sharing similar dynamics. It has been shown that these communities, or sub-systems, in the climate system are associated with major climate modes and physics of the atmosphere (Tsonis AA, Swanson KL, Wang G, J Clim 21: 2990-3001 in 2008; Tsonis AA, Wang G, Swanson KL, Rodrigues F, da Fontura Costa L, Clim Dyn, 37: 933-940 in 2011; Steinhaeuser K, Ganguly AR, Chawla NV, Clim Dyn 39: 889-895 in 2012). Once the community structure for all runs is derived, we use a pattern matching statistic to obtain a measure of how well any two models agree with each other. We find that, with the possible exception of the 500 hPa field, consistency for the SAT, SLP, and precipitation fields is questionable. More importantly, none of the models comes close to the community structure of the actual observations (reality). This is a significant finding especially for the temperature and precipitation fields, as these are the fields widely used to produce future projections in time and in space.

  16. Big Data and Data Models for Climate System Energetics

    NASA Astrophysics Data System (ADS)

    Fillmore, D. W.; Habermann, T.; Goedecke, W. B.

    2015-12-01

    Multi-decade satellite missions, such as the NASA CERES mission designed to place observational constraints on the distribution of reflected solar radiation and emitted thermal radiation, present a significant challenge both in the analysis of heterogeneous Big Data and in data continuity. The NASA CERES EBAF dataset is a part of a broader effort to increase the usability of satellite observational data for the climate modeling community. Issues of accessibility, consistency, and reproducibility are paramount. Here we describe the transformation of CERES measurements from source to high level data products intended for direct use by the climate community. At each stage we examine data storage and processing patterns, metadata and potential challenges in reproducibility. The spatial distribution of net energy uptake and transport in the climate system, and its evolution over interannual and decadal time scales, is fundamental to the development of Earth system models. The workflow begins with the CERES footprint radiance seen by a polar orbiter, to the conversion of radiance to radiometric fluxes based on scene identification from MODIS and VIIRS imagery, followed by diurnal interpolation through the use of geostationary satellite imagery and eventually to the creation of high level gridded data products, the ultimate being the Energy Balanced and Filled flux product for direct comparison to climate models. Based on this CERES case study we try to anticipate future questions the may arise in the context of these massive satellite data collections, and what new data models may facilitate future data analysis.

  17. The Arctic Climate Modeling Program: Professional Development for Rural Teachers

    ERIC Educational Resources Information Center

    Bertram, Kathryn Berry

    2010-01-01

    The Arctic Climate Modeling Program (ACMP) offered yearlong science, technology, engineering, and math (STEM) professional development to teachers in rural Alaska. Teacher training focused on introducing youth to workforce technologies used in Arctic research. Due to challenges in making professional development accessible to rural teachers, ACMP…

  18. GIS and crop simulation modelling applications in climate change research

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The challenges that climate change presents humanity require an unprecedented ability to predict the responses of crops to environment and management. Geographic information systems (GIS) and crop simulation models are two powerful and highly complementary tools that are increasingly used for such p...

  19. DEVELOPMENT AND MODELING OF REACTIVE BUILDING SYSTEMS: CLIMATE AND ILLUMINATION

    EPA Science Inventory

    Desirability barriers regarding the human comfort level still remain in the public acceptance of passive solar energy homes. The goal of this project is to model sensing climate control and illumination building systems as they apply to a zero-energy Midwest home. In develop...

  20. Cyclones and extreme windstorm events over Europe under climate change: Global and regional climate model diagnostics

    NASA Astrophysics Data System (ADS)

    Leckebusch, G. C.; Ulbrich, U.

    2003-04-01

    More than any changes of the climate system mean state conditions, the development of extreme events may influence social, economic and legal aspects of our society. This linkage results from the impact of extreme climate events (natural hazards) on environmental systems which again are directly linked to human activities. Prominent examples from the recent past are the record breaking rainfall amounts of August 2002 in central Europe which produced widespread floodings or the wind storm Lothar of December 1999. Within the MICE (Modelling the Impact of Climate Extremes) project framework an assessment of the impact of changes in extremes will be done. The investigation is carried out for several different impact categories as agriculture, energy use and property damage. Focus is laid on the diagnostics of GCM and RCM simulations under different climate change scenarios. In this study we concentrate on extreme windstorms and their relationship to cyclone activity in the global HADCM3 as well as in the regional HADRM3 model under two climate change scenarios (SRESA2a, B2a). In order to identify cyclones we used an objective algorithm from Murry and Simmonds which was widely tested under several different conditions. A slight increase in the occurrence of systems is identified above northern parts of central Europe for both scenarios. For more severe systems (core pressure < 990 hPa) we find an increase for western Europe. Strong wind events can be defined via different percentile values of the windspeed (e.g. above the 95 percentile). By this means the relationship between strong wind events and cyclones is also investigated. For several regions (e.g. Germany, France, Spain) a shift to more deep cyclones connected with an increasing number of strong wind events is found.

  1. Modeling Shasta Dam operations to regulate temperatures for Chinook salmon under extreme climate and climate change

    NASA Astrophysics Data System (ADS)

    Dai, A.; Saito, L.; Sapin, J. R.; Rajagopalan, B.; Hanna, R. B.; Kauneckis, D. L.

    2014-12-01

    Chinook salmon populations have declined significantly after the construction of Shasta Dam on the Sacramento River in 1945 prevented them from spawning in the cold waters upstream. In 1994, the winter-run Chinook were listed under the Endangered Species Act and 3 years later the US Bureau of Reclamation began operating a temperature control device (TCD) on the dam that allows for selective withdrawal for downstream temperature control to promote salmon spawning while also maximizing power generation. However, dam operators are responsible to other interests that depend on the reservoir for water such as agriculture, municipalities, industry, and recreation. An increase in temperatures due to climate change may place additional strain on the ability of dam operations to maintain spawning habitat for salmon downstream of the dam. We examined the capability of Shasta Dam to regulate downstream temperatures under extreme climates and climate change by using stochastically generated streamflow, stream temperature, and weather inputs with a two-dimensional CE-QUAL-W2 model under several operational options. Operation performance was evaluated using degree days and cold pool volume (volume of water below a temperature threshold). Model results indicated that a generalized operations release schedule, in which release elevations varied over the year to match downstream temperature targets, performed best overall in meeting temperature targets while preserving cold pool volume. Releasing all water out the bottom throughout the year tended to meet temperature targets at the expense of depleting the cold pool, and releasing all water out uppermost gates preserved the cold pool, but released water that was too warm during the critical spawning period. With higher air temperatures due to climate change, both degree day and cold pool volume metrics were worse than baseline conditions, which suggests that Chinook salmon may be more negatively affected under climate change.

  2. 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

  3. Radiative heating in global climate models

    SciTech Connect

    Baer, F.; Arsky, N.; Rocque, K.

    1996-04-01

    LWR algorithms from various GCMs vary significantly from one another for the same clear sky input data. This variability becomes pronounced when clouds are included. We demonstrate this effect by intercomparing the various models` output using observed data including clouds from ARM/CART data taken in Oklahoma.

  4. Continental-scale river flow in climate models

    NASA Technical Reports Server (NTRS)

    Miller, James R.; Russell, Gary L.; Caliri, Guilherme

    1994-01-01

    The hydrologic cycle is a major part of the global climate system. There is an atmospheric flux of water from the ocean surface to the continents. The cycle is closed by return flow in rivers. In this paper a river routing model is developed to use with grid box climate models for the whole earth. The routing model needs an algorithm for the river mass flow and a river direction file, which has been compiled for 4 deg x 5 deg and 2 deg x 2.5 deg resolutions. River basins are defined by the direction files. The river flow leaving each grid box depends on river and lake mass, downstream distance, and an effective flow speed that depends on topography. As input the routing model uses monthly land source runoff from a 5-yr simulation of the NASA/GISS atmospheric climate model (Hansen et al.). The land source runoff from the 4 deg x 5 deg resolution model is quartered onto a 2 deg x 2.5 deg grid, and the effect of grid resolution is examined. Monthly flow at the mouth of the world's major rivers is compared with observations, and a global error function for river flow is used to evaluate the routing model and its sensitivity to physical parameters. Three basinwide parameters are introduced: the river length weighted by source runoff, the turnover rate, and the basinwide speed. Although the values of these parameters depend on the resolution at which the rivers are defined, the values should converge as the grid resolution becomes finer. When the routing scheme described here is coupled with a climate model's source runoff, it provides the basis for closing the hydrologic cycle in coupled atmosphere-ocean models by realistically allowing water to return to the ocean at the correct location and with the proper magnitude and timing.

  5. Climatic impact of Amazon deforestation - a mechanistic model study

    SciTech Connect

    Ning Zeng; Dickinson, R.E.; Xubin Zeng

    1996-04-01

    Recent general circulation model (GCM) experiments suggest a drastic change in the regional climate, especially the hydrological cycle, after hypothesized Amazon basinwide deforestation. To facilitate the theoretical understanding os such a change, we develop an intermediate-level model for tropical climatology, including atmosphere-land-ocean interaction. The model consists of linearized steady-state primitive equations with simplified thermodynamics. A simple hydrological cycle is also included. Special attention has been paid to land-surface processes. It generally better simulates tropical climatology and the ENSO anomaly than do many of the previous simple models. The climatic impact of Amazon deforestation is studied in the context of this model. Model results show a much weakened Atlantic Walker-Hadley circulation as a result of the existence of a strong positive feedback loop in the atmospheric circulation system and the hydrological cycle. The regional climate is highly sensitive to albedo change and sensitive to evapotranspiration change. The pure dynamical effect of surface roughness length on convergence is small, but the surface flow anomaly displays intriguing features. Analysis of the thermodynamic equation reveals that the balance between convective heating, adiabatic cooling, and radiation largely determines the deforestation response. Studies of the consequences of hypothetical continuous deforestation suggest that the replacement of forest by desert may be able to sustain a dry climate. Scaling analysis motivated by our modeling efforts also helps to interpret the common results of many GCM simulations. When a simple mixed-layer ocean model is coupled with the atmospheric model, the results suggest a 1{degrees}C decrease in SST gradient across the equatorial Atlantic Ocean in response to Amazon deforestation. The magnitude depends on the coupling strength. 66 refs., 16 figs., 4 tabs.

  6. 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-