Tropical Atlantic Biases in Coupled Atmosphere-Ocean Models
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.
- Kurian J, Li P, Chang P, Patricola CM, Small J (2021) Impact of the Benguela Coastal Low-Level Jet on the Southeast Tropical Atlantic SST Bias in a Regional Ocean Model. Climate Dynamics, 56, 2773-2800.
- 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.
The Influence of Climate Model Sea Surface Temperature Biases on Simulated Tropical Cyclones
Sea surface temperature (SST) patterns both local to and remote from tropical cyclone (TC) development regions are important drivers of the variability of TC activity. Therefore, reliable simulations and predictions of TC activity depend on a realistic representation of tropical SST. Nevertheless, severe SST biases are common to the current generation of global climate models, especially in the tropical Pacific and Atlantic. These biases are strongly positive in the southeastern tropical basins, and negative, but weaker, in the northwestern tropical basins. To investigate the impact of the tropical SST biases on simulated TC activity, an atmospheric-only tropical channel model was used to conduct several sets of ensemble simulations. The simulations suggest an underrepresentation in Atlantic TC activity caused by the Atlantic cold bias alone, and an overrepresentation in Eastern North Pacific TC activity due to the Atlantic cold bias and Pacific warm bias jointly. The results indicate the importance of considering SST bias effects on simulated TC activity in climate model studies and highlight key regions where reducing SST biases could potentially improve TC representation in climate models.
- Hsu W-C, Patricola CM, Chang P (2019) The Impact of Climate Model Sea Surface Temperature Biases on Tropical Cyclone Simulations. Climate Dynamics, 53 (1-2), 173-192.
This research was supported by the U.S. National Science Foundation and the U.S. Department of Energy Office of Science (BER RGMA). 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.