Global seasonal climate predictability in a two tiered forecast system: part I: boreal summer and fall seasons
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This paper shows demonstrable improvement in the global seasonal climate predictability of boreal summer (at zero lead) and fall (at one season lead) seasonal mean precipitation and surface temperature from a two-tiered seasonal hindcast forced with forecasted SST relative to two other contemporary operational coupled ocean–atmosphere climate models. The results from an extensive set of seasonal hindcasts are analyzed to come to this conclusion. This improvement is attributed to: (1) The multi-model bias corrected SST used to force the atmospheric model. (2) The global atmospheric model which is run at a relatively high resolution of 50 km grid resolution compared to the two other coupled ocean–atmosphere models. (3) The physics of the atmospheric model, especially that related to the convective parameterization scheme. The results of the seasonal hindcast are analyzed for both deterministic and probabilistic skill. The probabilistic skill analysis shows that significant forecast skill can be harvested from these seasonal hindcasts relative to the deterministic skill analysis. The paper concludes that the coupled ocean–atmosphere seasonal hindcasts have reached a reasonable fidelity to exploit their SST anomaly forecasts to force such relatively higher resolution two tier prediction experiments to glean further boreal summer and fall seasonal prediction skill.
This paper is dedicated to the memory of Dr. Masao Kanamitsu, without whose pioneering development of the FISH50 AGCM this work would not have been possible. This work was supported by Grants from NOAA (NA12OAR4310078, NA10OAR4310215, NA11OAR4310110), USGS (06HQGR0125), and USDA (027865). All computations for this paper were done on the computational resources provided by the Extreme Science and Engineering Discovery Environment (XSEDE) under TG-ATM120017 and TG-ATM120010.
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