Seasonal forecasting skill for the High Mountain Asia region in the Goddard Earth Observing System


Seasonal variability of the global hydrologic cycle directly impacts human activities, including hazard assessment and mitigation, agricultural decisions, and water resources management. This is particularly true across the High Mountain Asia (HMA) region, where water resource needs change depending on the seasonality and intensity of the hydrologic cycle. Forecasting the atmospheric states and surface conditions, including hydrometeorological relevant variables, at subseasonal-to-seasonal (S2S) lead times of weeks-to-months is an area of active research and development. NASA’s Goddard Earth Observing System (GEOS) S2S prediction system has been developed with this research goal in mind. Here, we benchmark the forecast skill of GEOS-S2S (version 2) seasonal hydrometeorological forecasts in the HMA region, including a portion of the Indian Subcontinent, at 1-, 2-, and 3-month lead times during the retrospective forecast period, 1981–2016. To assess forecast skill, we evaluate 2-m air temperature, total precipitation, fractional snow cover, snow water equivalent, surface soil moisture, and terrestrial water storage forecasts against MERRA-2 and independent reanalysis, satellite observations, and data fusion products. Anomaly correlation is highest when the forecasts are evaluated against MERRA-2 and especially in variables with long memory in the climate system, possibly due to similar initial conditions and model architecture used in GEOS-S2S and MERRA-2. When compared to MERRA-2, results for the 1-month forecast skill ranges from anomaly correlation of R$_textrmanom$=0.18 for precipitation to R$_textrmanom$=0.62 for soil moisture. Anomaly correlations are persistently lower when forecasts are evaluated against independent observations; results for the 1-month forecast skill ranges from R$_textrmanom$=0.13 for snow water equivalent to R$_textrmanom$=0.24 for fractional snow cover. Hydrometeorological forecast skill is dependent on the forecast lead time, the memory of the variable within the physical system, and the validation dataset used. Overall, these results benchmark the GEOS-S2S system’s ability to forecast HMA hydrometeorology on the seasonal timescale.