Climate model biases in the eastern tropical oceans: causes, impacts and ways forward.

Richter, Ingo

doi:10.1002/wcc.338

Cited by 253Climate variability and modelsOceanographic and Atmospheric ProcessesMeteorological Phenomena and Simulations

Climate model biases in the eastern tropical oceans: causes, impacts and ways forward.

Richter, Ingo·2015·WIREs Climate Change

DOI 10.1002/wcc.338

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Abstract

The eastern boundaries of the tropical and subtropical oceans are regions of high biological productivity that support some of the world's largest fisheries. They also feature extensive stratocumulus cloud decks that play a pivotal role in the response of the climate system to greenhouse gas forcing. Global climate models experience great difficulties simulating eastern boundary regions, with one of the most notable shortcomings being warm sea‐surface temperature biases that often exceed 5 K. These model biases are due to several reasons. (1) Weaker than observed alongshore winds lead to an underrepresentation of upwelling and alongshore currents and the cooling associated with them. (2) Stratocumulus decks and their effects on shortwave radiation are underpredicted in the models. (3) The offshore transport of cool waters by mesoscale eddies is not adequately represented by global models due to insufficient resolution. (4) The sharp vertical temperature gradient separating the warm upper ocean layer from the deep ocean is too diffuse in the models. More work will be required to assess the relative importance of these error sources and to find ways of mitigating them. Coordinated multi‐model experiments are vital to achieve this goal, as are enhanced ocean and atmosphere observations of the eastern boundary regions. To what extent eastern ocean biases compromise the models' ability to produce accurate seasonal predictions, and climate change projections should be another focus…

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The warm SST bias in the Benguela coastal upwelling region is more severe than that at the equator, exceeding 2.5°C in the multi-model average of AOGCMs from CMIP5.
“The warm SST bias in the Benguela coastal upwelling region (Figure 9) is more severe than that at the equator, exceeding 2.5 • C in the multi-model average of atmosphereocean general circulation models (AOGCMs) from the Coupled Model Intercomparison Project Phase 5 (CMIP5; Toniazzo and Woolnough, 2014;Richter, 2015;Zuidema et al., 2016a).”
From The tropical atlantic observing system (2019) · cites this paper as Richter, 2015
The atmospheric component of the coupled models is the primary culprit for the Southeast Atlantic warm SST bias, with the ocean playing a vital but secondary role.
“This suggests the atmospheric component of the coupled models is the primary culprit (Lauer and Hamilton, 2013), with the ocean playing a vital but secondary role (Richter, 2015).”
From The tropical atlantic observing system (2019) · cites this paper as Richter, 2015

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Mutai, C. (2000) · doi:10.1175/1520-0442(2000)013
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Price, C. (1994) · doi:10.1175/1520-0442(1994)007

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“…upwelling region (Figure 9) is more severe than that at the equator, exceeding 2.5◦C in the multi-model average of atmosphereocean general circulation models (AOGCMs) from the Coupled Model Intercomparison Project Phase 5 (CMIP5; Toniazzo and Woolnough, 2014; Richter, 2015; Zuidema et al., 2016a).”
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Process‐Based Climate Model Development Harnessing Machine Learning: I. A Calibration Tool for Parameterization Improvement
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Evaluation of high-resolution atmospheric and oceanic simulations of the California Current System
Renault, L., McWilliams, J. C., Kessouri, F., Jousse, A., Frenzel, H., Chen, R., & Deutsch, C. A. (2020) · Progress In Oceanography · doi:10.1016/j.pocean.2021.102564 · Cited by 69 · see citing works
Role of wind stress in driving SST biases in the Tropical Atlantic
Voldoire, A., Exarchou, E., Sánchez-Gómez, E., Demissie, T. D., Deppenmeier, A., Frauen, C., Goubanova, K., Hazeleger, W., Keenlyside, N. S., Koseki, S., et al. (2019) · Climate Dynamics · doi:10.1007/s00382-019-04717-0 · Cited by 68 · see citing works
Broad Consistency Between Observed and Simulated Trends in Sea Surface Temperature Patterns
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El Niño–Southern Oscillation Evolution Modulated by Atlantic Forcing
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Evaluation of Dynamically Downscaled CMIP6‐CCAM Models Over Australia
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Seasonal predictions initialised by assimilating sea surface temperature observations with the EnKF
Wang, Y., Counillon, F., Keenlyside, N. S., Svendsen, L., Gleixner, S., Kimmritz, M., Dai, P., & Gao, Y. (2019) · Climate Dynamics · doi:10.1007/s00382-019-04897-9 · Cited by 64 · see citing works
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Exarchou, E., Ortega, P., Rodríguez‐Fonseca, B., Losada, T., Polo, I., & Prodhomme, C. (2021) · Nature Communications · doi:10.1038/s41467-021-21857-2 · Cited by 64 · see citing works
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Contrasting Impacts of Radiative Forcing in the Southern Ocean versus Southern Tropics on ITCZ Position and Energy Transport in One GFDL Climate Model
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A Process-Based Assessment of CMIP5 Rainfall in the Congo Basin: The September–November Rainy Season
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Scaife, A. A., Ferranti, L., Alves, O., Athanasiadis, P., Baehr, J., Déqué, M., Dippe, T., Dunstone, N., Fereday, D. R., Gudgel, R., et al. (2018) · International Journal of Climatology · doi:10.1002/joc.5855 · Cited by 58 · see citing works
High atmospheric horizontal resolution eliminates the wind‐driven coastal warm bias in the southeastern tropical Atlantic
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Richter, I., Doi, T., Behera, S. K., & Keenlyside, N. (2018) · Climate Dynamics · doi:10.1007/s00382-017-3809-4 · Cited by 53 · see citing works
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Weakening of the Senegalo–Mauritanian upwelling system under climate change
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The impact of climate model sea surface temperature biases on tropical cyclone simulations.
Hsu, W.-C., Patricola, C. M., & Chang, P. (2019) · Climate Dynamics · doi:10.1007/s00382-018-4577-5 · Cited by 51 · see citing works
Can reducing the incoming energy flux over the Southern Ocean in a CGCM improve its simulation of tropical climate?
Mechoso, C. R., Losada, T., Koseki, S., Mohíno, E., Keenlyside, N. S., Castaño‐Tierno, A., Myers, T. A., Rodríguez‐Fonseca, B., & Toniazzo, T. (2016) · Geophysical Research Letters · doi:10.1002/2016gl071150 · Cited by 51 · see citing works
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Patricola, C. M., & Chang, P. (2017) · Climate Dynamics · doi:10.1007/s00382-016-3479-7 · Cited by 51 · see citing works
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Cross‐Shore Flow and Implications for Carbon Export in the California Current Ecosystem: A Lagrangian Analysis
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Oxygen cycling in the northern Benguela Upwelling System: Modelling oxygen sources and sinks
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The Influence of Ocean Coupling on Simulated and Projected Tropical Cyclone Precipitation in the HighResMIP–PRIMAVERA Simulations
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Authors: Richter, I.
Affiliations: Japan Agency for Marine-Earth Science and Technology (JP)
Corresponding author: Richter, Ingo
ORCID: Richter, Ingo
Venue: WIREs Climate Change
Publisher: Wiley
Published: 2015-03-11
Volume: 6
Issue: 3
Pages: 345-358
ISSN: 1757-7780 (ISSN-L)
1757-7799
DOI: 10.1002/wcc.338
OpenAlex: W1508836477
Cited by: 253
References: 95
Citations by year: 2026: 8 · 2025: 27 · 2024: 23 · 2023: 20 · 2022: 29 · 2021: 25 · 2020: 39 · 2019: 26 · 2018: 27 · 2017: 14 · 2016: 12 · 2015: 3
FWCI: 15.29
Type: journal article
Language: en
Open access: closed
Licence: onlinelibrary.wiley.com/termsAndConditions#vor (vor)
Locations: Wiley Interdisciplinary Reviews Climate Change (publishedVersion)
Funders: U.S. Department of Energy
Topics: Physical Sciences › Environmental Science › Global and Planetary Change › Climate variability and models
Physical Sciences › Earth and Planetary Sciences › Oceanography › Oceanographic and Atmospheric Processes
Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › Meteorological Phenomena and Simulations
Keywords: climate model bias, eastern boundary upwelling, stratocumulus clouds, sea surface temperature bias, alongshore winds, mesoscale eddies, shortwave radiation, ocean-atmosphere interaction, model resolution, seasonal forecasting, climate change projections

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Climate model biases in the eastern tropical oceans: causes, impacts and ways forward.

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