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An investigation of tropical Atlantic bias in a high-resolution coupled regional climate model

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Abstract

Coupled atmosphere–ocean general circulation models (AOGCMs) commonly fail to simulate the eastern equatorial Atlantic boreal summer cold tongue and produce a westerly equatorial trade wind bias. This tropical Atlantic bias problem is investigated with a high-resolution (27-km atmosphere represented by the Weather Research and Forecasting Model, 9-km ocean represented by the Regional Ocean Modeling System) coupled regional climate model. Uncoupled atmospheric simulations test climate sensitivity to cumulus, land-surface, planetary boundary layer, microphysics, and radiation parameterizations and reveal that the radiation scheme has a pronounced impact in the tropical Atlantic. The CAM radiation simulates a dry precipitation (up to −90%) and cold land-surface temperature (up to −8 K) bias over the Amazon related to an over-representation of low-level clouds and almost basin-wide westerly trade wind bias. The Rapid Radiative Transfer Model and Goddard radiation simulates doubled Amazon and Congo Basin precipitation rates and a weak eastern Atlantic trade wind bias. Season-long high-resolution coupled regional model experiments indicate that the initiation of the warm eastern equatorial Atlantic sea surface temperature (SST) bias is more sensitive to the local rather than basin-wide trade wind bias and to a wet Congo Basin instead of dry Amazon—which differs from AOGCM simulations. Comparisons between coupled and uncoupled simulations suggest a regional Bjerknes feedback confined to the eastern equatorial Atlantic amplifies the initial SST, wind, and deepened thermocline bias, while barrier layer feedbacks are relatively unimportant. The SST bias in some CRCM simulations resembles the typical AOGCM bias indicating that increasing resolution is unlikely a simple solution to this problem.

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Acknowledgments

This research was supported by the Office of Science (BER), U.S. Department of Energy, Grant Nos. DE-SC0006824, DE-FG02-07ER64443 and DE-SC0004966, and by National Science Foundation Grant# AGS-1067937 as well as by National Oceanic and Atmospheric Administration Grant # NA09OAR4310135. Simulations were run at the Texas A&M Supercomputing Facility. P.C. also acknowledges the supports from the Chinese National Basic Research Program (2007CB816005), the National Science Foundation of China (41028005, 40976004, 40921004, 40930844, and 40730843), and the Chinese Ministry of Education’s 111 project (B07036). The NCEP CFSR data are from NOAA’s National Operational Model Archive and Distribution System (NOMADS) which is maintained at NOAA’s National Climatic Data Center (NCDC), TRMM data are from NASA’s Goddard Earth Sciences (GES) Data and Information Services Center (DISC), and the NOAA OI SST are archived by NOAA’s NCDC National Environmental Satellite, Data, and Information Service (NESDIS). Finally, the authors thank Howard Seidel for his initial contribution to the development of TAMU-CRCM, Karthik Balaguru for providing code for the barrier layer calculation, and two anonymous reviewers, whose greatly appreciated comments helped improve this paper.

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Authors and Affiliations

  1. Department of Atmospheric Sciences, Texas A&M University, 3150 TAMU, College Station, TX, 77843-3150, USA

    Christina M. Patricola, R. Saravanan & Jen-Shan Hsieh

  2. Department of Oceanography, Texas A&M University, College Station, TX, USA

    Mingkui Li, Zhao Xu & Ping Chang

  3. Key Laboratory of Physical Oceanography of Ministry of Education, Ocean University of China, Qingdao, China

    Mingkui Li, Zhao Xu & Ping Chang

  4. State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Hangzhou, Zhejiang, China

    Ping Chang

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  1. Christina M. Patricola
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  2. Mingkui Li
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  3. Zhao Xu
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  4. Ping Chang
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  5. R. Saravanan
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  6. Jen-Shan Hsieh
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Correspondence to Christina M. Patricola.

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Patricola, C.M., Li, M., Xu, Z. et al. An investigation of tropical Atlantic bias in a high-resolution coupled regional climate model. Clim Dyn 39, 2443–2463 (2012). https://doi.org/10.1007/s00382-012-1320-5

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  • DOI: https://doi.org/10.1007/s00382-012-1320-5

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