The Community Climate System Model version 4

Author Posting. © American Meteorological Society, 2011. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Climate 24 (2011): 4973–4991, doi:10.1175/2011JCLI4083.1.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 CCSM components, and documents fully coupled preindustrial control runs compared to the previous version, CCSM3. Using the standard atmosphere and land resolution of 1° results in the sea surface temperature biases in the major upwelling regions being comparable to the 1.4°-resolution CCSM3. Two changes to the deep convection scheme in the atmosphere component result in CCSM4 producing El Niño–Southern Oscillation variability with a much more realistic frequency distribution than in CCSM3, although the amplitude is too large compared to observations. These changes also improve 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 Gulf Stream path and the North Atlantic Ocean meridional overturning circulation. 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 CCSM3, and for several reasons the Arctic sea ice concentration is improved in CCSM4. An ensemble of twentieth-century simulations produces a good 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 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 shortwave and longwave cloud forcings.National Science Foundation, which sponsors NCAR and the CCSM Project. The project is also sponsored by the U.S. Department of Energy (DOE). Thanks are also due to the many other software engineers and scientists who worked on developing CCSM4, and to the Computational and Information Systems Laboratory at NCAR, which provided the computing resources through the Climate Simulation Laboratory. Hunke was supported within theClimate, Ocean and Sea Ice Modeling project at Los Alamos National Laboratory, which is funded by the Biological and Environmental Research division of the DOE Office of Science. The Los Alamos National Laboratory is operated by theDOENationalNuclear Security Administration under Contract DE-AC52-06NA25396. Raschwas supported by theDOEOffice of Science, Earth System Modeling Program, which is part of the DOE Climate Change Research Program. The Pacific Northwest National Laboratory is operated forDOEbyBattelle Memorial Institute under Contract DE-AC06-76RLO 1830. Worley was supported by the Climate Change Research Division of the Office of Biological and Environmental Research and by the Office ofAdvanced Scientific Computing Research, both in the DOE Office of Science, under Contract DE-AC05-00OR22725 with UT-Batelle, LLC

A framework for modeling uncertainty in regional climate change

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 (US) associated with four dimensions of uncertainty. The sources of uncertainty considered in this framework are the emissions projections, global climate system parameters, natural variability and model structural uncertainty. The modeling framework revolves around the Massachusetts Institute of Technology (MIT) Integrated Global System Model (IGSM), an integrated assessment model with an Earth System Model of Intermediate Complexity (EMIC) (with a two-dimensional zonal-mean atmosphere). Regional climate change over the US 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). Second, we use a pattern-scaling method that extends the IGSM zonal mean based on climate change patterns from various climate models. Results show that the range of annual mean 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 end-of-century precipitation changes, with natural variability dominating until 2050. For the set of scenarios used in this study, the choice of policy is the largest driver of uncertainty, defined as the range of warming and changes in precipitation, in future projections of climate change over the US

The Community Climate System Model version 3 (CCSM3)

Author Posting. © American Meteorological Society 2006. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Climate 19 (2006): 2122–2143, doi:10.1175/JCLI3761.1.The Community Climate System Model version 3 (CCSM3) has recently 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 dynamics, variability, and climate change. This paper will show results from the configuration used for climate-change simulations with a T85 grid for the atmosphere and land and a grid with approximately 1° resolution for the ocean and sea ice. The new system incorporates several significant improvements in the physical parameterizations. 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, tropical sea surface temperatures, and cloud radiative effects. CCSM3 can produce stable climate simulations of millennial 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 coastal regions west of continents, the spectrum of ENSO variability, the spatial distribution of precipitation in the tropical oceans, and continental precipitation and surface air temperatures. Work is under way to extend CCSM to a more accurate and comprehensive model of the earth's climate system.We would like to acknowledge the substantial contributions to and support for the CCSM project from the National Science Foundation (NSF), the Department of Energy (DOE), the National Oceanic and Atmospheric Administration, and the National Aeronautics and Space Administration

CESM1(WACCM) Stratospheric Aerosol Geoengineering Large Ensemble Project

This paper describes the Stratospheric Aerosol Geoengineering Large Ensemble (GLENS) project, which promotes the use of a unique model dataset, performed with the Community Earth System Model, with the Whole Atmosphere Community Climate Model as its atmospheric component [CESM1(WACCM)], to investigate global and regional impacts of geoengineering. The performed simulations were designed to achieve multiple simultaneous climate goals, by strategically placing sulfur injections at four different locations in the stratosphere, unlike many earlier studies that targeted globally averaged surface temperature by placing injections in regions at or around the equator. This advanced approach reduces some of the previously found adverse effects of stratospheric aerosol geoengineering, including uneven cooling between the poles and the equator and shifts in tropical precipitation. The 20-member ensemble increases the ability to distinguish between forced changes and changes due to climate variability in global and regional climate variables in the coupled atmosphere, land, sea ice, and ocean system. We invite the broader community to perform in-depth analyses of climate-related impacts and to identify processes that lead to changes in the climate system as the result of a strategic application of stratospheric aerosol geoengineering

Quantile-based bias correction and uncertainty quantification of extreme event attribution statements

Extreme event attribution characterizes how anthropogenic climate change may have influenced the probability and magnitude of selected individual extreme weather and climate events. Attribution statements often involve quantification of the fraction of attributable risk (FAR) or the risk ratio (RR) and associated confidence intervals. Many such analyses use climate model output to characterize extreme event behavior with and without anthropogenic influence. However, such climate models may have biases in their representation of extreme events. To account for discrepancies in the probabilities of extreme events between observational datasets and model datasets, we demonstrate an appropriate rescaling of the model output based on the quantiles of the datasets to estimate an adjusted risk ratio. Our methodology accounts for various components of uncertainty in estimation of the risk ratio. In particular, we present an approach to construct a one-sided confidence interval on the lower bound of the risk ratio when the estimated risk ratio is infinity. We demonstrate the methodology using the summer 2011 central US heatwave and output from the Community Earth System Model. In this example, we find that the lower bound of the risk ratio is relatively insensitive to the magnitude and probability of the actual event.Comment: 28 pages, 4 figures, 3 table

Global water cycle

The primary objective is to determine the scope and interactions of the global water cycle with all components of the Earth system and to understand how it stimulates and regulates changes on both global and regional scales. The following subject areas are covered: (1) water vapor variability; (2) multi-phase water analysis; (3) diabatic heating; (4) MSU (Microwave Sounding Unit) temperature analysis; (5) Optimal precipitation and streamflow analysis; (6) CCM (Community Climate Model) hydrological cycle; (7) CCM1 climate sensitivity to lower boundary forcing; and (8) mesoscale modeling of atmosphere/surface interaction

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

© The Authors, 2011. This article is distributed under the terms of the Creative Commons Attribution 3.0 License. The definitive version was published in Biogeosciences 8 (2011): 387-414, doi:10.5194/bg-8-387-2011.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.This work was done under the auspices of NASA NNG06G127G, NSF grants 0748369, 0932946, 0745961 and 0832782. The work of C. J. was supported by the Joint DECC/Defra Met Office Hadley Centre Climate Programme (GA01101)