Abstract Atmospheric rivers (ARs) and Santa Ana winds (SAWs) are impactful weather events for California communities. Emergency planning efforts and resource management would benefit from extending lead times of skillful prediction for these and other types of extreme weather patterns. Here we describe a methodology for subseasonal prediction of impactful winter weather in California, including ARs, SAWs and heat extremes. The hybrid approach combines dynamical model and historical information to forecast probabilities of impactful weather outcomes at weeks 1–4 lead. This methodology uses dynamical model information considered most reliable, that is, planetary/synoptic‐scale atmospheric circulation, filters for dynamical model error/uncertainty at longer lead times and increases the sample of likely outcomes by utilizing the full historical record instead of a more limited suite of dynamical forecast model ensemble members. We demonstrate skill above climatology at subseasonal timescales, highlighting potential for use in water, health, land, and fire management decision support.
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Effect of Human-Induced Land Disturbance on Subseasonal Predictability of Near-Surface Variables Using an Atmospheric General Circulation Model
Irrigation can affect climate and weather patterns from regional to global scales through the alteration of surface water and energy balances. Here, we couple a land-surface model (LSM) that includes various human land-water management activities including irrigation with an atmospheric general circulation model (AGCM) to examine the impacts of irrigation-induced land disturbance on the subseasonal predictability of near-surface variables. Results indicate that the simulated global irrigation and groundwater withdrawals (circa 2000) are ~3600 and ~370 km3/year, respectively, which are in good agreement with previous estimates from country statistics and offline–LSMs. Subseasonal predictions for boreal summers during the 1986–1995 period suggest that the spread among ensemble simulations of air temperature can be substantially reduced by using realistic land initializations considering irrigation-induced changes in soil moisture. Additionally, it is found that the subseasonal forecast skill for near-surface temperature and sea level pressure significantly improves when human-induced land disturbance is accounted for in the AGCM. These results underscore the need to incorporate irrigation into weather forecast models, such as the global forecast system.
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- Award ID(s):
- 1752729
- PAR ID:
- 10148147
- Date Published:
- Journal Name:
- Atmosphere
- Volume:
- 10
- Issue:
- 11
- ISSN:
- 2073-4433
- Page Range / eLocation ID:
- 725
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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