Abstract
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 availability of water resources can change depending on local seasonality of the hydrologic cycle. Forecasting the atmospheric states and surface conditions, including hydrometeorologically 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) hydrometeorological forecasts at 1-3-month lead times in the HMA region, including a portion of the Indian subcontinent, during the retrospective forecast period, 1981-2016. To assess forecast skill, we evaluate 2ĝ€¯m air temperature, total precipitation, fractional snow cover, snow water equivalent, surface soil moisture, and terrestrial water storage forecasts against the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) and independent reanalysis data, satellite observations, and data fusion products. Anomaly correlation is highest when the forecasts are evaluated against MERRA-2 and particularly in variables with long memory in the climate system, likely due to the 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 range from an anomaly correlation of RanomCombining double low line0.18 for precipitation to RanomCombining double low line0.62 for soil moisture. Anomaly correlations are consistently lower when forecasts are evaluated against independent observations; results for the 1-month forecast skill range from RanomCombining double low line0.13 for snow water equivalent to RanomCombining double low line0.24 for fractional snow cover. We find that, generally, 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.
| Original language | English |
|---|---|
| Pages (from-to) | 147-171 |
| Number of pages | 25 |
| Journal | Earth System Dynamics |
| Volume | 14 |
| Issue number | 1 |
| DOIs | |
| State | Published - Feb 8 2023 |
Funding
This research has been supported by the NASA Headquarters (grant no. 80NSSC20K1301). The authors acknowledge the NASA HiMAT team for funding this work (grant no. 80NSSC20K1301), as well as for generous data sharing and broader discussions that helped shape the paper. GMAO's GEOS-S2S-V2 development was funded under the NASA Modeling, Analysis, and Prediction program GMAO “core” funding. Computational resources were provided by the NASA High-End Computing (HEC) Program through the NASA Center for Climate Simulation (NCCS) at the Goddard Space Flight Center and the NASA Advanced Supercomputing (NAS) division. The authors thank Agniv Sengupta for information on and preparation of the topography map. This paper has been authored by UT-Battelle, LLC, under contract no. DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this paper, or to allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan.
