Journal
BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY
Volume 103, Issue 12, Pages E2756-E2767Publisher
AMER METEOROLOGICAL SOC
DOI: 10.1175/BAMS-D-21-0270.1
Keywords
Atmosphere; Atmosphere-land interaction; Ensembles; Numerical weather prediction; forecasting; General circulation models; Model initialization
Categories
Funding
- World Climate Research Programme (WCRP)
- U.S. National Science Foundation [AGS-1849654]
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The surface temperature of the Tibetan Plateau is causally related to summer precipitation in multiple regions across the world, indicating that high-mountain land temperature could be a substantial source of subseasonal-to-seasonal precipitation predictability.
Subseasonal-to-seasonal (S2S) precipitation prediction in boreal spring and summer months, which contains a significant number of high-signal events, is scientifically challenging and prediction skill has remained poor for years. Tibetan Plateau (TP) spring observed surface -temperatures show a lag correlation with summer precipitation in several remote regions, but current global land-atmosphere coupled models are unable to represent this behavior due to significant errors in producing observed TP surface temperatures. To address these issues, the Global Energy and Water Exchanges (GEWEX) program launched the Impact of Initialized Land Temperature and Snowpack on Subseasonal-to-Seasonal Prediction (LS4P) initiative as a community effort to test the impact of land temperature in high-mountain regions on S2S prediction by climate models: more than 40 institutions worldwide are participating in this project. After using an innovative new land state initialization approach based on observed surface 2-m temperature over the TP in the LS4P experiment, results from a multimodel ensemble provide evidence for a causal relationship in the observed association between the Plateau spring land temperature and summer precipitation over several regions across the world through teleconnections. The influence is underscored by an out-of-phase oscillation between the TP and Rocky Mountain surface temperatures. This study reveals for the first time that high-mountain land temperature could be a substantial source of S2S precipitation predictability, and its effect is probably as large as ocean surface temperature over global hotspot regions identified here; the ensemble means in some hotspots produce more than 40% of the observed anomalies. This LS4P approach should stimulate more follow-on explorations.
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