Seasonal forecasting skill for the High Mountain Asia region in the Goddard Earth Observing System
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Published:2023-02-08
Issue:1
Volume:14
Page:147-171
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ISSN:2190-4987
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Container-title:Earth System Dynamics
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language:en
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Short-container-title:Earth Syst. Dynam.
Author:
Massoud Elias C.ORCID, Andrews Lauren, Reichle RolfORCID, Molod AndreaORCID, Park Jongmin, Ruehr Sophie, Girotto Manuela
Abstract
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 2 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
Ranom=0.18 for precipitation to Ranom=0.62 for soil moisture.
Anomaly correlations are consistently lower when forecasts are evaluated
against independent observations; results for the 1-month forecast skill
range from Ranom=0.13 for snow water equivalent to Ranom=0.24
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.
Publisher
Copernicus GmbH
Subject
General Earth and Planetary Sciences
Reference118 articles.
1. Arendt, A. A., Houser, P., Kapnick, S. B., Kargel, J. S., Kirschbaum, D.,
Kumar, S., Margulis, S. A., McDonald, K. C., Osmanoglu, B., Painter, T. H., and Raup, B. H.: NASA's High Mountain Asia Team (HiMAT):
collaborative research to study changes of the High Asia region, AGU Fall
Meeting Abstracts, Vol. 2017, C33D-1231, 2017AGUFM.C33D1231A,
2017. 2. Aquila, V., Baldwin, C., Mukherjee, N., Hackert, E., Li, F., Marshak, J.,
Molod, A., and Pawson, S.: Impacts of the Eruption of Mount Pinatubo on
Surface Temperatures and Precipitation Forecasts With the NASA GEOS
Subseasonal-to-Seasonal System, J. Geophys. Res.-Atmos.
126, e2021JD034830, https://doi.org/10.1029/2021JD034830, 2021. 3. Batbaatar, J., Gillespie, A. R., Koppes, M., Clark, D. H., Chadwick, O. A.,
Fink, D., Matmon, A., and Rupper, S.: Glacier development in continental
climate regions of central Asia, Untangling the Quaternary Period: A Legacy
of Stephen C. Porter, https://doi.org/10.1130/2020.2548(07), 2021. 4. Bekaert, D., Handwerger, A. L., Agram, P., and Kirschbaum, D. B.:
InSAR-based detection method for mapping and monitoring slow-moving
landslides in remote regions with steep and mountainous terrain: An
application to Nepal, Remote Sens. Environ., 249, 111983,
https://doi.org/10.1016/j.rse.2020.111983, 2020. 5. Bosilovich, M. G., Lucchesi, R., and Suarez, M.: MERRA-2: File
Specification, GMAO Office Note No. 9 (Version 1.1), 73 pp.,
http://gmao.gsfc.nasa.gov/pubs/office_notes (last access: August 2021),
2016.
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