Preprints
https://doi.org/10.5194/egusphere-2022-449
https://doi.org/10.5194/egusphere-2022-449
 
15 Jun 2022
15 Jun 2022

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

Elias Charbel Massoud1, Lauren Andrews2, Rolf Reichle2, Andrea Molod2, Jongmin Park3, Sophie Ruehr1, and Manuela Girotto1 Elias Charbel Massoud et al.
  • 1University of California Berkeley, Department of Environmental Science, Policy, and Management, Berkeley, CA, 94720 United States
  • 2NASA Goddard Space Flight Center, Global Modeling & Assimilation Office, Greenbelt, MD, 20771 United States
  • 3Goddard Earth Sciences Technology and Research (GESTAR II), University of Maryland, Baltimore, MD, 21250 United States

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 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 Ranom=0.18 for precipitation to Ranom=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 Ranom=0.13 for snow water equivalent to Ranom=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.

Elias Charbel Massoud et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-449', Anonymous Referee #1, 12 Jul 2022
  • RC2: 'Comment on egusphere-2022-449', Anonymous Referee #2, 07 Aug 2022
  • RC3: 'Comment on egusphere-2022-449', Anonymous Referee #3, 23 Sep 2022

Elias Charbel Massoud et al.

Elias Charbel Massoud et al.

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Short summary
In this study, we benchmark the forecast skill of the NASA’s Goddard Earth Observing System subseasonal-to-seasonal (GEOS-S2S version 2) hydrometeorological forecasts in the High Mountain Asia (HMA) region. 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.