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Abstract

Statistics of regional sea level variability are analyzed in terms of probability density functions of a 100-member ensemble of monthly mean sea surface height (SSH) timeseries simulated during the low-resolution Max Planck Institute Grand Ensemble (MPI-GE) experiment for historical and climate change conditions. To analyze the impact of climate change on sea level statistics, fields of SSH variability, skewness and excess kurtosis representing the historical period 1986–2005 are compared with similar fields from projections for the period 2081–2100 obtained under moderate (RCP4.5) and strong (RCP8.5) climate forcing conditions. Overall, larger deviations from Gaussian SSH statistics are limited to the western and eastern tropical Pacific. Under future climate warming conditions, SSH variability of the western tropical Pacific tends to become more Gaussian in agreement with weaker zonal easterly wind stress pulses, suggesting a reduced El Niño Southern Oscillation activity in the western warm pool region. Otherwise SSH variability changes show a complex amplitude pattern with some regions becoming less variable, e.g., off the eastern coast of the north American continent, while other regions become more variable, notably the Southern Ocean. A west (decrease)-east (increase) gradient in variability changes across the subtropical Atlantic under RCP8.5 forcing is related to changes in the gyre circulation and a declining Atlantic Meridional Overturning Circulation in response to external forcing changes. We diagnosed regional changes of the 99th percentiles as well as global mean that increase by 16cm for RCP4.5 and by 24cm for RCP8.5, respectively, suggesting increased high-end sea level extremes for warmer climate conditions.