Role of wind stress in driving SST biases in the Tropical Atlantic

Abstract

Coupled climate models used for long-term future climate projections and seasonal or decadal predictions share a systematic and persistent warm sea surface temperature (SST) bias in the tropical Atlantic. This study attempts to better understand the physical mechanisms responsible for the development of systematic biases in the tropical Atlantic using the so-called Transpose-CMIP protocol in a multi-model context. Six global climate models have been used to perform seasonal forecasts starting both in May and February over the period 2000-2009. In all models, the growth of SST biases is rapid. Significant biases are seen in the first month of forecast and, by 6 months, the root-mean-square SST bias is 80% of the climatological bias. These control experiments show that the equatorial warm SST bias is not driven by surface heat flux biases in all models, whereas in the southeastern Atlantic the solar heat flux could explain the setup of an initial warm bias in the first few days. A set of sensitivity experiments with prescribed wind stress confirm the leading role of wind stress biases in driving the equatorial SST bias, even if the amplitude of the SST bias is model dependent. A reduced SST bias leads to a reduced precipitation bias locally, but there is no robust remote effect on West African Monsoon rainfall. Over the southeastern part of the basin, local wind biases tend to have an impact on the local SST bias (except in the high resolution model). However, there is also a non-local effect of equatorial wind correction in two models. This can be explained by sub-surface advection of water from the equator, which is colder when the bias in equatorial wind stress is corrected. In terms of variability, it is also shown that improving the mean state in the equatorial Atlantic leads to a beneficial intensification of the Bjerknes feedback loop. In conclusion, we show a robust effect of wind stress biases on tropical mean climate and variability in multiple climate models.

The Southeastern Tropical Atlantic SST Bias Investigated with a Coupled Atmosphere–Ocean Single-Column Model at a PIRATA Mooring Site

“…The surface solar radiation bias, hence, cannot explain the warm SST bias, which is larger by almost two orders of magnitude. This conclusion is consistent with those of other studies using EC-Earth (Exarchou et al 2017;Voldoire et al 2019;Deppenmeier et al 2020).…”

“…A possible reason for the warm SST bias originating in the atmosphere is reduced forcing of the ocean due to underestimated winds. This theory has recently been supported by Xu et al (2014b) and Voldoire et al (2019). We test the influence of wind biases on the SST bias in the U,V ERA experiment.…”

“…The SST RMSE decreases only by 0.058C. Voldoire et al (2019) show EC-Earth to be the least sensitive CGCM to the wind stress replacement. This is due to the small wind stress bias in the model compared to ERA-Interim.…”

“…Harlaß et al (2015) and Milinski et al (2016) have recently shown the importance of atmospheric resolution for reducing these coastal biases, and Small et al (2014) stressed the importance of an additional high-resolution ocean component. Smaller, but sizeable, biases are found at the location of the buoy (Toniazzo and Woolnough 2014;Voldoire et al 2019). During the first five months of the year, the three-dimensional model accurately captures the SST in the southeastern tropical Atlantic.…”

“…Other origins of the warm bias have been suggested to arise in the atmosphere, for example, from excessive shortwave radiation (Huang et al 2007;Hu et al 2008) or insufficient wind forcing (Richter et al 2012;Voldoire et al 2014;Koseki et al 2018) or from an atmospheric moisture bias (Hourdin et al 2015). A recent multimodel study highlights the role of wind stress forcing in the bias development ), but also shows that it cannot explain the entire bias and sometimes even has limited effect (as is the case for EC-Earth, which we use here).…”