David C. Bader

The Community Earth System Model: A Framework for Collaborative Research

The Community Earth System Model (CESM) is a flexible and extensible community tool used to investigate a diverse set of Earth system interactions across multiple time and space scales. This global coupled model significantly extends its predecessor, the Community Climate System Model, by incorporating new Earth system simulation capabilities. These comprise the ability to simulate biogeochemical cycles, including those of carbon and nitrogen, a variety of atmospheric chemistry options, the Greenland Ice Sheet, and an atmosphere that extends to the lower thermosphere. These and other new model capabilities are enabling investigations into a wide range of pressing scientific questions, providing new foresight into possible future climates and increasing our collective knowledge about the behavior and interactions of the Earth system. Simulations with numerous configurations of the CESM have been provided to phase 5 of the Coupled Model Intercomparison Project (CMIP5) and are being analyzed by the broad com...

A Unified Modeling Approach to Climate System Prediction

There is a new perspective of a continuum of prediction problems, with a blurring of the distinction between short-term predictions and long-term climate projections. At the heart of this new persp...

Secular trends and climate drift in coupled ocean-atmosphere general circulation models

Coupled ocean-atmosphere general circulation models (coupled GCMs) with interactive sea ice are the primary tool for investigating possible future global warming and numerous other issues in climate science. A long-standing problem with such models is that when different components of the physical climate system are linked together, the simulated climate can drift away from observations unless constrained by ad hoc adjustments to interface fluxes. However, eleven modern coupled GCMs--including three that do not employ flux adjustments--behave much better in this respect than the older generation of models. Surface temperature trends in control run simulations (with external climate forcing such as solar brightness and atmospheric carbon dioxide held constant) are small compared with observed trends, which include 20th century climate change due to both anthropogenic and natural factors. Sea ice changes in the models are dominated by interannual variations. Deep ocean temperature and salinity trends are small enough for model control runs to extend over 1000 simulated years or more, but trends in some regions, most notably the Arctic, are inconsistent among the models and may be problematic.

Evaluation of a CCSM3 Simulation with a Finite Volume Dynamical Core for the Atmosphere at 1° Latitude 1.25° Longitude Resolution

Abstract A simulation of the present-day climate by the Community Climate System Model version 3 (CCSM3) that uses a Finite Volume (FV) numerical method for solving the equations governing the atmospheric dynamics is presented. The simulation is compared to observations and to the well-documented simulation by the standard CCSM3, which uses the Eulerian spectral method for the atmospheric dynamics. The atmospheric component in the simulation herein uses a 1° latitude × 1.25° longitude grid, which is a slightly finer resolution than the T85-grid used in the spectral transform. As in the T85 simulation, the ocean and ice models use a nominal 1-degree grid. Although the physical parameterizations are the same and the resolution is comparable to the standard model, substantial testing and slight retuning were required to obtain an acceptable control simulation. There are significant improvements in the simulation of the surface wind stress and sea surface temperature. Improvements are also seen in the simulat...

Coupled Climate Model appraisal: A benchmark for future studies

Studies of future climate scenarios, such as those conducted in support of the Intergovernmental Panel on Climate Change (IPCC, http://www.ipcc.ch/), rely heavily on numerical experiments performed with coupled ocean-atmosphere general circulation models (OAGCMs). In order to assess the results of such climate change experiments, a benchmark for evaluating model performance is required.To provide this benchmark, Lawrence Livermore National Laboratorys Program for Climate Model Diagnosis and Intercomparison (PCMDI) conducted an extensive appraisal of multidecadal climate simulations by 11 coupled OAGCMs that were developed during the period of 1995–2002 [Bader etal., 2004]. While diverse representations of atmosphere, ocean, sea ice, land, and of their respective couplings were employed (see Table 1), all of these climate models were run with current values of solar and greenhouse gas radiative forcings. Thus, by comparing details of the OAGCM simulations with analogous facets of climate observations, the needed model-performance benchmark can be obtained. If, for instance, a model simulation closely replicates the salient features of the present climate, a necessary (though not sufficient) condition is met for placing some confidence in the models projections of the climate of the next several decades.

Preliminary Study of California Wintertime Model Wet Bias

Abstract The Weather Research and Forecasting (WRF) model version 3.0.1 is used in both short-range (days) and long-range (years) simulations to explore the California wintertime model wet bias. California is divided into four regions (the coast, central valley, mountains, and Southern California) for validation. Three sets of gridded surface observations are used to evaluate the impact of measurement uncertainty on the model wet bias. Short-range simulations are driven by the North American Regional Reanalysis (NARR) data and designed to test the sensitivity of model physics and grid resolution to the wet bias using eight winter storms chosen from four major types of large-scale conditions: the Pineapple Express, El Nino, La Nina, and synoptic cyclones. Control simulations are conducted with 12-km grid spacing (low resolution) but additional experiments are performed at 2-km (high) resolution to assess the robustness of microphysics and cumulus parameterizations to resolution changes. Additionally, long-...

A spectral transform dynamical core option within the Community Atmosphere Model (CAM4)

An ensemble of simulations covering the present day observational period using forced sea surface temperatures and prescribed sea-ice extent is configured with an 85 truncation resolution spectral transform dynamical core (T85) within the Community Atmosphere Model (CAM), version 4 and is evaluated relative to observed and model derived data sets and the one degree finite volume (FV) dynamical core. The spectral option provides a well-known base within the climate model community to assess climate behavior and statistics, and its relative computational efficiency for smaller computing platforms allows it to be extended to perform high-resolution climate length simulations. Overall, the quality of the T85 ensemble is similar to FV. Analyzing specific features of the T85 simulations show notable improvements to the representation of wintertime Arctic sea level pressure and summer precipitation over the Western Indian subcontinent. The mean and spatial patterns of the land surface temperature trends over the AMIP period are generally well simulated with the T85 ensemble relative to observations, however the model is not able to capture the extent nor magnitude of changes in temperature extremes over the boreal summer, where the changes are most dramatic. Biases in the wintertime Arctic surface temperature and annual mean surface stress fields persist with T85 as with the CAM3 version of T85, as compared to FV. An experiment to identify the source of differences between dycores has revealed that the longwave cloud forcing is sensitive to the choice of dycore, which has implications for tuning strategies of the physics parameter settings.

The use of the Climate-Science Computational end Station (CCES) development and grand challenge team for the next IPCC assessment : an operational plan.

The grand challenge of climate change science is to predict future climates based on scenarios of anthropogenic emissions and other changes resulting from options in energy and development policies. Addressing this challenge requires a Climate Science Computational End Station consisting of a sustained climate model research, development, and application program combined with world-class DOE leadership computing resources to enable advanced computational simulation of the Earth system. This project provides the primary computer allocations for the DOE SciDAC and Climate Change Prediction Program. It builds on the successful interagency collaboration of the National Science and the U.S. Department of Energy in developing and applying the Community Climate System Model (CCSM) for climate change science. It also includes collaboration with the National Aeronautics and Space Administration in carbon data assimilation and university partners with expertise in high-end computational climate research.

Celebrating Two Decades of the Program for Climate Model Diagnosis and Intercomparison

What: to celebrate the twentieth anniversary of PCmDi and to honor its founder, w. lawrence gates, more than 100 specialists and leaders in climate modeling met to discuss PCmDi’s history and the future of climate modeling When: 6 april 2009 Where: bethesda, maryland T wenty years ago, W. Lawrence (Larry) Gates approached the U.S. Department of Energy (DOE) Office of Energy Research (now the Office of Science) with a plan to coordinate the comparison and documentation of climate model differences. This effort would help improve our understanding of climate change through a systematic approach to model intercomparison. Early attempts at comparing results showed a surprisingly large range in control climate from such parameters as cloud cover, precipitation, and even atmospheric temperature. The DOE agreed to fund the effort at the Lawrence Livermore National Laboratory (LLNL), in part because of the existing computing environment and because of a preexisting atmospheric science group that contained a wide variety of expertise. The project was named the Program for Climate Model Diagnosis and Intercomparison (PCMDI), and it has changed the international landscape of climate modeling over the past 20 years. In spring 2009 the DOE hosted a 1-day symposium to celebrate the twentieth anniversary of PCMDI and to honor its founder, Larry Gates. Through their personal experiences, the morning presenters painted an image of climate science in the 1970s and 1980s, that generated early support from the international community for model intercomparison, thereby bringing PCMDI into existence. Four talks covered Gates’s early contributions to climate research at the University of California, Los Angeles (UCLA), the RAND Corporation, and Oregon State University through the founding of PCMDI to coordinate the Atmospheric Model Intercomparison Project (AMIP). The speakers were, in order of presentation, Warren Washington [National Center for Atmospheric Research (NCAR)], Kelly Redmond (Western Regional Climate Center), George Boer (Canadian Centre for Climate Modelling and Analysis), and Lennart Bengtsson [University of Reading, former director of the European Centre for Medium-Range Weather Forecasts (ECMWF)]. The afternoon session emphasized the scientific ideas that are the basis of PCMDI’s success, summarizing their AFFILIATIONS: Potter—University of California Davis, Davis, California; bader—oak ridge national laboratory, oak ridge, tennessee; riChes and josePh—office of biological and environmental research, U.s. Department of energy, washington, D.C.; baMzai—Climate and large-scale Dynamics Program, national science foundation, arlington, Virginia CORRESPONDING AUTHOR: gerald l. Potter, Department of geology, University of California, Davis, one shields avenue, Davis, Ca 95616 e-mail: jpotter@ucdavis.edu

[Comment on “On award to Crichton”] On the validity climate models

We object to contributor Kevin Corbetts assertions, in his article “On award to Crichton” (Eos, 87(43), 464, 2006), that “Too often now, models are taken as data and their results taken as fact, when the accuracy of the models in predicting even short-term effects is poor and the fundamental validity for most climate models is opaque…” Corbett cites (among other references) our Eos article “Coupled climate model appraisal: A benchmark for future studies” [Phillips et al, 2006], implying that our findings support his remarks. In fact, our evaluation of model simulations relative to observational data leads us to very different conclusions.