Impact of tropical SSTs on the monthly signal over the North Atlantic-European region

Abstract

Targeted numerical simulations were designed to test the potential impact of tropical sea surface temperatures (SSTs) on the geopotential heights at 200 hPa (GH200) signal over the North Atlantic-European region. Five experiments with SST anomalies prescribed in different areas, acting as lower boundary forcing, were created with an intermediately complex atmospheric general circulation model (ICTP AGCM). In the AGCM experiments, the SST forcing was prescribed globally, in the tropical zone of all oceans, only in the tropical Atlantic, tropical Indian Ocean and limited to the tropical Pacific. All of the simulations covered a 156-year-long period.The monthly GH200 signal was calculated based on the difference between the ensemble mean of each experiment and the climatological mean for the considered period. In addition, to inspect the impact of the El Niño-Southern Oscillation (ENSO), the signal was calculated for ENSO and non-ENSO years, respectively. Here, the ENSO years were classified according to the value of the late-winter Niño3.4 index.Additionally, each experiment’s monthly signal was averaged over the signal maximum over the North Atlantic-European region. The characteristics of the spatially averaged signal were compared to the signal averaged over a similar signal maximum observed over the Pacific North American region.Results have shown that the GH200 signal is the strongest in the late-winter months in all experiments. The AGCM experiment with SST boundary forcing prescribed only in the tropical Atlantic consistently had the smallest signal amplitude. The strongest signal linked to ENSO events was found in the experiment with the SST forcing prescribed only in the tropical Pacific. The signal averaged over the NAE maximum generally yields smaller values than the PNA maximum average. Also, the differences between the (non) ENSO signal and the signal for all years are less pronounced in the case of the NAE maximum average.