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Winter precipitation predictability in Central Southwest Asia and its representation in seasonal forecast systems

Journal Article · · npj Climate and Atmospheric Science
 [1];  [2];  [3];  [4]
  1. Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States); Univ. of Tennessee, Knoxville, TN (United States); Air Force Institute of Technology, Wright-Patterson AFB, OH (United States)
  2. Abdus Salam International Centre for Theoretical Physics, Trieste (Italy)
  3. National Oceanic and Atmospheric Administration (NOAA), Princeton, NJ (United States). Geophysical Fluid Dynamics Laboratory
  4. Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
In Central Southwest Asia (CSWA; 22°N to 40°N and 30°E to 70°E), winter (November to February) precipitation contributes up to 70% of the annual mean, but substantial interannual variations exist. Dynamical models exhibit subpar predictability in this region, but the limits of their skills are not well established. Here, we identify the tropical and extratropical forcings that explain ~75% of area-averaged seasonal variability in CSWA winter precipitation. Tropical forcing comes from the indirect El Niño-Southern Oscillation (ENSO) pathway, the leading mode of tropical Indian Ocean precipitation variability. This mode is coupled with ENSO-related Pacific Ocean sea surface temperature variability. A direct ENSO influence on CSWA does not extend beyond its Indian Ocean connection. Extratropical forcing comes from a large-scale mode of internal atmospheric variability. The spatial structure, variability of tropical forcing, and teleconnection with CSWA winter precipitation are skillfully depicted in two seasonal forecasting systems: the fifth-generation seasonal forecasting system (SEAS5) and Seamless System for Prediction and Earth System Research (SPEAR). Extratropical forcing’s spatial structure is also produced skillfully in the two modelling systems; however, the representation of its interannual variability and teleconnection requires improvement. While SEAS5 displays skill in representing extratropical forcing influence on CSWA winter precipitation and marginal skill in reproducing interannual variability, SPEAR has negligible ability in both areas. Consequently, these models have limited predictive skills over CSWA in winter. While improvements in representing extratropical forcing may be inherently limited as it arises from internal atmospheric variability, further research is needed to establish its predictability limits fully.
Research Organization:
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities (SUF)
Grant/Contract Number:
AC05-00OR22725
OSTI ID:
2333836
Journal Information:
npj Climate and Atmospheric Science, Journal Name: npj Climate and Atmospheric Science Journal Issue: 1 Vol. 7; ISSN 2397-3722
Publisher:
Springer NatureCopyright Statement
Country of Publication:
United States
Language:
English

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