Evaluating Skill of Seasonal Precipitation and Temperature Predictions of NCEP CFSv2 Forecasts over 17 Hydroclimatic Regions in China

Abstract

Seasonal predictions of precipitation and surface air temperature from the Climate Forecast System, version 2 (CFSv2), are evaluated against gridded daily observations from 1982 to 2007 over 17 hydroclimatic regions in China. The seasonal predictive skill is quantified with skill scores including correlation coefficient, RMSE, and mean bias for spatially averaged seasonal precipitation and temperature forecasts for each region. The evaluation focuses on identifying regions and seasons where significant skill exists, thus potentially contributing to skill in hydrological prediction. The authors find that the predictive skill of CFSv2 precipitation and temperature forecasts has a stronger dependence on seasons and regions than on lead times. Both temperature and precipitation forecasts show higher skill from late summer [July-September (JAS)] to late autumn [October-December (OND)] and from winter [December-February (DJF)] to spring [March-May (MAM)].The skill of CFSv2 precipitation forecasts is low during summer [June-August (JJA)] and winter (DJF) over all of China because of low potential predictability of the East Asian summer monsoon and the East Asian winter monsoon for China. As expected, temperature predictive skill is much higher than precipitation predictive skill in all regions. As observed precipitation shows significant correlation with the Oceanic Niño index over western, southwestern, and central China, the authors found that CFSv2 precipitation forecasts generally show similar correlation pattern, suggesting that CFSv2 precipitation forecasts can capture ENSO signals. This evaluation suggests that using CFSv2 forecasts for seasonal hydrological prediction over China is promising and challenging.

Microwave remote sensing of short‐term droughts during crop growing seasons

“…To analyze the interannual variability of short-term drought areas at river basin scale, the mainland China has been divided into 17 river basins ( Figure S3 in the supporting information) according to their hydroclimate characteristics [Lang et al, 2014]. For each basin, the drought (SPI-1 less than À0.8, or the soil moisture percentiles less than 20%) area fraction (the ratio of 0.25°drought grid elements to total grid elements within the basin) is calculated for each month , and the square correlation (R 2 ; coefficient of determination) is computed between SPI-1 drought area and reanalysis (or ESA CCI) products during the study period.…”

Seasonal drought predictability and forecast skill over China

“…Therefore, we are interested in how ENSO influences drought index (SPI3) in terms of predictability and forecast skill over China. The correlation analysis is carried out over 17 river basins ( Figure S1 in the supporting information) in China, where the basins have been divided according to their hydroclimate characteristics [Lang et al, 2014]. During December-January-February (DJF), higher Figure 1.…”

Relating anomaly correlation to lead time: Clustering analysis of CFSv2 forecasts of summer precipitation in China

“…[] investigated the forecasts at 1 month lead time considering forecasts at longer lead times would be less skillful; Lang et al . [] and Ma et al . [] paid attention to case study river basins, instead of GCM grid cells, and evaluated the forecast skill of spatial averaging precipitation.…”