Abstract
Sub-seasonal to seasonal (S2S) forecasts are useful for critical planning and management decisions in multiple sectors. Presently, in the United States, the primary source for real time seasonal climate forecast comes from the Climate Prediction Center within the National Center for climate Prediction (NCEP) which uses its model forecast component Climate Forecast System Version 2 (CFSv2) of North American Multi-Model Ensemble (NMME). In comparison to the cool season, the level of skill in warm season seasonal forecasts of precipitation produced by the NMME is much lower due to the poor climatological representation of warm season convective precipitation. This study shows that dynamical downscaling using a regional climate model at a convective permitting scale driven by boundary conditions from global reanalysis of CFS (CFSR) enhances regional forecast skill by better resolving the regional forcings and processes that generate the regional response from the large-scale circulation anomaly. Improvement in S2S forecast skill needs combination of skillful forecast of the large-scale circulation anomaly as well as its regional response in temperature and precipitation, the latter specially where it is modulated primarily by regional forcings like topography or land cover. To fully realize the potential in improving warm season seasonal forecasts using a dynamical modeling approach, we performed dynamically downscaled simulations with Weather Research and Forecasting model over the Upper and Lower Colorado basin at 12 km and 3 km grid spacing for 2000-2011. The additional convective-permitting nested domain of 3km resolution significantly reduces the bias in mean (~2mm/day) and extreme (~4 mm/day) summer precipitation when compared to coarser domain of 12 km resolution. The convective permitting modeling product also better represents eastward propagation of the moist convective summer systems, which are major source of summer rainfall in the southwest United Sates. Our findings provide important insights for S2S prediction using convective permitting model.
Highlights
Long-term decision making in applications like agriculture and water resources requires accurate seasonal forecast information on regional and local scales
RCM-generated 2 m air temperature was compared with North American Land Data Assimilation System (NLDAS) (Mitchell et al, 2004) with respect to mean and extreme temperature climatology
Where the performance is quite akin in terms of representing seasonal mean, value added can be interpreted in terms of a higher resolution product obtained from the Convection-permitting modeling (CPM) simulation
Summary
Long-term decision making in applications like agriculture and water resources requires accurate seasonal forecast information on regional and local scales. An increase in atmospheric blocking has been observed in recent decades (Croci-Maspoli et al, 2007), leading to increased and more persistent extreme events in the mid-latitudes (Francis and Vavrus, 2012; Screen et al, 2013) In this respect, warm season climate appears to become more extreme in conjunction with large-scale atmospheric circulation (or teleconnection) patterns that are the primary drivers of continental-scale variations in wet and dry conditions on seasonal timescales (Coumou et al, 2014; Chang et al, 2015). Predicting such events and assessing their seasonal impact in the near future remains a challenge
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