ALBERT

Description: In-situ observation, climate reanalyses, and satellite remote sensing are used to study the annual cycle of turbulent latent heat flux (LHF) in the Agulhas Current system. We assess if the datasets do represent the intense exchange of moisture that occurs above the Agulhas Current and the Retroflection region, especially the new reanalyses as the former, the National Centers for Environmental Prediction Reanalysis 2 (NCEP2) and the European Centre for Medium-Range Weather Forecast (ECMWF) reanalysis second-generation reanalysis (ERA-40) have lower sea and less distinct surface temperature (SST) in the Agulhas Current system due to their low spatial resolution thus do not adequately represent the Agulhas Current LHF. We use monthly fields of LHF, SST, surface wind speed, saturated specific humidity at the sea surface (Qss), and specific humidity at 10 m (Qa). The Climate Forecast System Reanalysis (CFSR), the European Centre for Medium-Range Weather Forecast fifth generation (ERA-5), and the Modern-Era Retrospective analysis for Research and Applications version-2 (MERRA-2) are similar to the air–sea turbulent fluxes (SEAFLUX) and do represent the signature of the Agulhas Current. ERA-Interim underestimates the LHF due to lower surface wind speeds than other datasets. The observation-based National Oceanography Center Southampton (NOCS) dataset is different from all other datasets. The highest LHF of 250 W/m2 is found in the Retroflection in winter. The lowest LHF (~100 W/m2) is off Port Elizabeth in summer. East of the Agulhas Current, Qss-Qa is the main driver of the amplitude of the annual cycle of LHF, while it is the wind speed in the Retroflection and both Qss-Qa and wind speed in between. The difference in LHF between product are due to differences in Qss-Qa wind speed and resolution of datasets.

Description: In-situ observation, climate reanalyses, and satellite remote sensing are used to study the annual cycle of turbulent latent heat flux (LHF) in the Agulhas Current system. We assess if the datasets do represent the intense exchange of moisture that occurs above the Agulhas Current and the Retroflection region, especially the new reanalyses as the former, the National Centers for Environmental Prediction Reanalysis 2 (NCEP2) and the European Centre for Medium-Range Weather Forecast (ECMWF) reanalysis second-generation reanalysis (ERA-40) have lower sea and less distinct surface temperature (SST) in the Agulhas Current system due to their low spatial resolution thus do not adequately represent the Agulhas Current LHF. We use monthly fields of LHF, SST, surface wind speed, saturated specific humidity at the sea surface (Qss), and specific humidity at 10 m (Qa). The Climate Forecast System Reanalysis (CFSR), the European Centre for Medium-Range Weather Forecast fifth generation (ERA-5), and the Modern-Era Retrospective analysis for Research and Applications version-2 (MERRA-2) are similar to the air–sea turbulent fluxes (SEAFLUX) and do represent the signature of the Agulhas Current. ERA-Interim underestimates the LHF due to lower surface wind speeds than other datasets. The observation-based National Oceanography Center Southampton (NOCS) dataset is different from all other datasets. The highest LHF of 250 W/m2 is found in the Retroflection in winter. The lowest LHF (~100 W/m2) is off Port Elizabeth in summer. East of the Agulhas Current, Qss-Qa is the main driver of the amplitude of the annual cycle of LHF, while it is the wind speed in the Retroflection and both Qss-Qa and wind speed in between. The difference in LHF between product are due to differences in Qss-Qa wind speed and resolution of datasets.

Description: The Agulhas Current (AC) creates a sharp temperature gradient with the surrounding ocean, leading to a large turbulent flux of moisture from ocean to atmosphere. We use two simulations of the Weather Research and Forecasting (WRF) model to show the seasonal impact of the warm core of the AC on southern Africa precipitation. In one simulation the sea surface temperature (SST) of the AC is similar to satellite observations, while the second uses satellite SST observations spatially smoothed to reduce the temperature of the core of the AC by ~1.5°C. We show that decreasing the SST of the AC reduces the precipitation of the wettest seasons (austral summer and autumn) inland. Over the ocean, reducing the SST reduces precipitation, low-level wind convergence, SST and SLP Laplacian above the AC in all seasons, consistent with the pressure adjustment mechanism. Moreover, winter precipitation above the Current may be also related to increased latent flux. In summer and autumn, the AC SST reduction is also associated with decreased precipitation further inland (more than 1.5 mm/day), caused by an atmospheric circulation that decreases the horizontal moisture flux from the AC to South Africa. The reduction is also associated with higher geopotential height extending from the surface east and over the AC to the mid-troposphere over southeastern Africa. The westward tilted geopotential height is consistent with the linear response to shallow diabatic heating in midlatitudes. An identical mechanism occurs in spring but is weaker. Winter rainfall response is confined above the AC.

Description: Twenty-first-century climate change projections are uncertain, especially on regional scales. An important source of uncertainty is that climate models exhibit biases, which limits their ability to predict climate. One of the largest biases is the too warm sea surface temperature (SST) in the eastern tropical Atlantic (TA), reflecting deficient atmospheric and oceanic circulation. Here, we show that CO2-forced TA-sector climate changes simulated by state-of-the-art climate models exhibit a strong mean-state dependence. In particular, models simulating largest SST warming in the eastern TA, consistent with the warming observed since the mid-20th century, typically exhibit a more realistic mean state than models simulating largest warming in the western TA. The former models exhibit a larger climate sensitivity, and predict stronger and in part qualitatively different climate changes over the TA sector, for example in precipitation. These findings may help to reducing uncertainty in TA-climate change projections.