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Coupled Model Biases in the Tropics

Tropical Atlantic Biases in Coupled Atmosphere-Ocean Models

Benguela low-level coastal jet

Generations of coupled atmosphere-ocean general circulation models have been plagued by persistent warm sea surface temperature (SST) biases in the southeastern tropical Atlantic.  The SST biases are most severe in the eastern boundary coastal upwelling region and are sensitive to surface wind stress and wind stress curl associated with the Benguela low-level coastal jet (BLLCJ), a southerly jet parallel to the Angola-Namibia coast. However, little has been documented about this atmospheric source of oceanic bias. Here we investigate the characteristics and dynamics of the BLLCJ using observations, reanalyses, and atmospheric model simulations.  Satellite wind products and high-resolution reanalyses and models represent the BLLCJ with two near-shore maxima, one near the Angola-Benguela front (ABF) at 17.5°S, and the other near 25-27.5°S, whereas coarse resolution reanalyses and models represent the BLLCJ poorly with a single, broad, more offshore maximum. Model experiments indicate that convex coastal geometry near the ABF supports the preferred location of the BLLCJ northern maximum by supporting conditions for a hydraulic expansion fan.  Intraseasonal variability of the BLLCJ is associated with large-scale variability in intensity and location of the South Atlantic subtropical high through modulation of the low-level zonal pressure gradient. 


  • Patricola CM, Chang P (2017) Structure and Dynamics of the Benguela Low-Level Coastal Jet. Climate Dynamics, 49, 2765-2788.
  • Patricola CM, Li M, Xu Z, Chang P, Saravanan R, Hsieh J-S (2012) An Investigation of Tropical Atlantic Bias in a High-Resolution Coupled Regional Climate Model. Climate Dynamics, 39, 2443–2463.

This research was supported by the U.S. National Science Foundation and the U.S. Department of Energy Office of Science (BER).  High-performance computing resources provided by the Texas Advanced Computing Center (TACC) at The University of Texas at Austin through the Extreme Science and Engineering Discovery Environment (XSEDE) and the Texas A&M Supercomputing Facility.