Improvements in a half degree atmosphere/land version of the CCSM

Gent, Peter R.; Yeager, Stephen; Neale, Richard; Levis, Samuel; Bailey, David A.

doi:10.1007/s00382-009-0614-8

Cited by 234 publications

(244 citation statements)

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“…The Gent et al (2009;hereafter G09) study is of particular relevance here and there is much discussion throughout this paper comparing the results from our simulations with the findings of G09. First, both studies used the same version of the National Center for Atmospheric Research (NCAR) Community Climate System Model (CCSM).…”

Section: Introductionmentioning

confidence: 84%

“…Several recent studies have focused on the importance of atmospheric model resolution in the simulation of climate (May and Roeckner 2001;Brankovic and Gregory 2001;Pope and Stratton 2002;Kobayashi and Sugi 2004;Hack et al 2006;Navarra et al 2008;Gent et al 2009). The reported results range from little or no change in the mean and variable climate (i.e., Hack et al 2006) to significant differences in the cycle of El Niño and the Southern Oscillation 1 Throughout this paper, model "resolution" refers to the spacing of model grid elements.…”

Section: Introductionmentioning

confidence: 99%

“…(ENSO; Navarra et al 2008) and in sea surface temperature (SST) biases in the upwelling regions (i.e., Gent et al 2009). …”

Section: Introductionmentioning

confidence: 99%

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Impact of ocean model resolution on CCSM climate simulations

et al. 2012

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The current literature provides compelling evidence suggesting that an eddy-resolving (as opposed to eddy-permitting or eddy-parameterized) ocean component model will significantly impact the simulation of the large-scale climate, although this has not been fully tested to date in multi-decadal global coupled climate simulations. The purpose of this paper is to document how increased ocean model resolution impacts the simulation of large-scale climate variability. The model used for this study is the NCAR Community Climate System Model version 3.5 (CCSM3.5) -the forerunner to CCSM4. Two experiments are reported here. The first experiment (i.e., control) is a 155-year present-day climate simulation using a 0.5º atmosphere component (zonal resolution 0.625º meridional resolution 0.5º) coupled to ocean and sea-ice components with zonal resolution of 1.2º and meridional resolution varying from 0.27º at the equator to 0.54º in the mid-latitudes. The second simulation uses the same atmospheric model coupled to 0.1º ocean and sea-ice component models. The simulations are compared in terms of how the representation of smaller scale features in the time mean ocean circulation and ocean eddies impact the mean and variable climate. In terms of the global mean surface temperature, the enhanced ocean resolution leads to a ubiquitous surface warming of about 0.2 o C. The warming is largest in the Arctic and regions of strong ocean fronts and ocean eddy activity (i.e., Southern Ocean, western boundary currents). The Arctic warming is associated with significant losses of sea-ice in the high-resolution simulation. The SST gradients in the North Atlantic, in particular, are better resolved in the high-resolution model leading to significantly sharper temperature gradients and associated large-scale shifts in the rainfall. In the extra-tropics, the interannual sea surface temperature anomaly (SSTA) variability is increased with the resolved eddies, but decreases in the deep tropics (i.e., the variance of El Niño and the Southern Oscillation is reduced). Changes in global SSTA teleconnections and local air-sea feedback are also documented and show large changes in ocean-atmosphere coupling.3

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Section: Introductionmentioning

confidence: 84%

Section: Introductionmentioning

confidence: 99%

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Impact of ocean model resolution on CCSM climate simulations

et al. 2012

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“…CLM4 is the latest version of the land component of the Community Earth System Model (CESM) [Collins et al, 2006;Gent et al, 2010;Lawrence et al, 2011], which has been designed and used for studies of interannual and interdecadal variability, paleoclimate regimes, and projections of future changes of the global earth system. Compared to previous versions, CLM4 represents significant improvements in its model parameterizations and structure, including runoff generation, soil hydrology thermodynamics, snow, and albedo parameters [Lawrence et al, 2011].…”

Section: Land Surface Modeling Approachesmentioning

confidence: 99%

Scalability of grid- and subbasin-based land surface modeling approaches for hydrologic simulations

Tesfa

Leung

Huang

et al. 2014

J. Geophys. Res. Atmos.

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This paper investigates the relative merits of grid-and subbasin-based land surface modeling approaches for hydrologic simulations, with a focus on their scalability (i.e., ability to perform consistently across spatial resolutions) in simulating runoff generation. Simulations are produced by the grid-and subbasin-based Community Land Model at 0.125°, 0.25°, 0.5°, and 1°spatial resolutions over the U.S. Pacific Northwest. Using the 0.125°simulation as the "reference" solution, statistical metrics are calculated by comparing simulations at 0.25°, 0.5°, and 1°resolutions with the 0.125°simulation for each approach. Statistical significance test results suggest significant scalability advantage for the subbasin-based approach compared to the grid-based approach. Basin level annual average relative errors of surface runoff at 0.25°, 0.5°, and 1°r esolutions compared to the 0.125°simulation are 3%, 4%, and 6% for the subbasin-based configuration and 4%, 7%, and 11% for the grid-based configuration, respectively. The scalability advantages are more pronounced during winter/spring and over mountainous regions. The source of runoff scalability is found to be related to the scalability of major meteorological and land surface parameters of runoff generation. More specifically, the subbasin-based approach is more consistent across spatial scales than the grid-based approach in snowfall/ rainfall partitioning because of scalability related to air temperature and surface elevation. Scalability of a topographic parameter used in runoff parameterization also contributes to improved scalability of the rain-driven saturated surface runoff component, particularly during winter. Hence, this study demonstrates the importance of spatial structure for multiscale modeling of hydrological processes.

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“…In order to assess the effect of the increased model resolution (0.25 ı ), these results were compared to a lower resolution (1 ı ) version of the same model. Similar resolution-dependent studies with earlier versions of CCSM were carried out by Hack et al [2006] with T42 1 and T85 1 CCSM3 and by Gent et al [2010] with 2 ı and 0.5 ı CCSM3.5 where the authors compared the biases in the mean climate of the simulations, focusing on certain areas of interest. Here we provide a different perspective for the intermodel comparison by assessing model performance at simulating climatologies at different spatial scales.…”

Section: Summary Of Model Resultsmentioning

confidence: 83%

Scale space methods for climate model analysis

2013

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[1] In this paper, we introduce methods for evaluating climate model performance across spatial scales. These techniques are based on the "scale space" framework widely used in the image processing and computer vision communities. We discuss why the diffusion equation on the sphere provides a particularly attractive means of smoothing two-dimensional maps of global climate data. We establish that no structure is introduced into a map as an artifact of the smoothing procedure. This allows for the comparison of models and observations at multiple scales. As a test case for these methods, we compare the ability of high-and low-resolution versions of the Community Climate System Model (CCSM) to simulate the seasonal climatologies of surface air temperature (TAS), sea level pressure (PSL), and total precipitation rate (PR). For TAS, we find that the high-resolution model is better able to capture the boreal summer (JJA) climatological pattern at fine scales, although there is no such improvement in winter (DJF). We find the performances of the high-and low-resolution models to be similarly capable of capturing the summertime sea level pressure climatology at all scales. However, the high-resolution model PSL climatology is degraded for DJF, especially at larger scales. For both JJA and DJF precipitation climatologies, we find larger precipitation errors in the high-resolution model at the finest scales; however, performance at larger scales is improved.

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Improvements in a half degree atmosphere/land version of the CCSM

Cited by 234 publications

References 25 publications

Impact of ocean model resolution on CCSM climate simulations

Impact of ocean model resolution on CCSM climate simulations

Scalability of grid- and subbasin-based land surface modeling approaches for hydrologic simulations

Scale space methods for climate model analysis

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