In this study,we evaluate the forecast skill of the subseasonal-to-seasonal(S2S)prediction model of the Beijing Climate Center(BCC)for the boreal summer intraseasonal oscillation(BSISO).We also discuss the key factors...In this study,we evaluate the forecast skill of the subseasonal-to-seasonal(S2S)prediction model of the Beijing Climate Center(BCC)for the boreal summer intraseasonal oscillation(BSISO).We also discuss the key factors that inhibit the BSISO forecast skill in this model.Based on the bivariate anomaly correlation coefficient(ACC)of the BSISO index,defined by the first two EOF modes of outgoing longwave radiation and 850-hPa zonal wind anomalies over the Asian monsoon region,we found that the hindcast skill degraded as the lead time increased.The ACC dropped to below 0.5for lead times of 11 days and longer when the predicted BSISO showed weakened strength and insignificant northward propagation.To identify what causes the weakened forecast skill of BSISO at the forecast lead time of 11 days,we diagnosed the main mechanisms responsible for the BSISO northward propagation.The same analysis was also carried out using the observations and the outputs of the four-day forecast lead that successfully predicted the observed northward-propagating BSISO.We found that the lack of northward propagation at the 11-day forecast lead was due to insufficient increases in low-level cyclonic vorticity,moistening and warm temperature anomalies to the north of the convection,which were induced by the interaction between background mean flows and BSISO-related anomalous fields.The BCC S2S model can predict the background monsoon circulations,such as the low-level southerly and the northerly and easterly vertical shears,but has limited capability in forecasting the distributions of circulation and moisture anomalies.展开更多
Monthly mean temperatures at 562 stations in China are estimated using a statistical downscaling technique. The technique used is multiple linear regressions （MLRs） of principal components （PCs）. A stepwise screen...Monthly mean temperatures at 562 stations in China are estimated using a statistical downscaling technique. The technique used is multiple linear regressions （MLRs） of principal components （PCs）. A stepwise screening procedure is used for selecting the skilful PCs as predictors used in the regression equation. The predictors include temperature at 850 hPa （7）, the combination of sea-level pressure and temperature at 850 hPa （P＋T） and the combination of geo-potential height and temperature at 850 hPa （H＋T）. The downscaling procedure is tested with the three predictors over three predictor domains. The optimum statistical model is obtained for each station and month by finding the predictor and predictor domain corresponding to the highest correlation. Finally, the optimum statistical downscaling models are applied to the Hadley Centre Coupled Model, version 3 （HadCM3） outputs under the Special Report on Emission Scenarios （SRES） A2 and B2 scenarios to construct local future temperature change scenarios for each station and month, The results show that （1） statistical downscaling produces less warming than the HadCM3 output itself; （2） the downscaled annual cycles of temperature differ from the HadCM3 output, but are similar to the observation; （3） the downscaled temperature scenarios show more warming in the north than in the south; （4） the downscaled temperature scenarios vary with emission scenarios, and the A2 scenario produces more warming than the B2, especially in the north of China.展开更多
A running mean bias （RMB） correction ap- proach was applied to the forecasts of near-surface variables in a seasonal short-range ensemble forecasting experiment with 57 consecutive cases during summer 2010 in the no...A running mean bias （RMB） correction ap- proach was applied to the forecasts of near-surface variables in a seasonal short-range ensemble forecasting experiment with 57 consecutive cases during summer 2010 in the northern China region. To determine a proper training window length for calculating RMB, window lengths from 2 to 20 days were evaluated, and 16 days was taken as an optimal window length, since it receives most of the benefit from extending the window length. The raw and 16-day RMB corrected ensembles were then evaluated for their ensemble mean forecast skills. The results show that the raw ensemble has obvious bias in all near-surface variables. The RMB correction can remove the bias reasonably well, and generate an unbiased ensemble. The bias correction not only reduces the ensemble mean forecast error, but also results in a better spreaderror relationship. Moreover, two methods for computing calibrated probabilistic forecast （PF） were also evaluated through the 57 case dates： 1） using the relative frequency from the RMB-eorrected ensemble; 2） computing the forecasting probabilities based on a historical rank histogram. The first method outperforms the second one, as it can improve both the reliability and the resolution of the PFs, while the second method only has a small effect on the reliability, indicating the necessity and importance of removing the systematic errors from the ensemble.展开更多
基金supported by the National Basic Research Program of China (973 Program) (Grant No.2015CB453200)
文摘In this study,we evaluate the forecast skill of the subseasonal-to-seasonal(S2S)prediction model of the Beijing Climate Center(BCC)for the boreal summer intraseasonal oscillation(BSISO).We also discuss the key factors that inhibit the BSISO forecast skill in this model.Based on the bivariate anomaly correlation coefficient(ACC)of the BSISO index,defined by the first two EOF modes of outgoing longwave radiation and 850-hPa zonal wind anomalies over the Asian monsoon region,we found that the hindcast skill degraded as the lead time increased.The ACC dropped to below 0.5for lead times of 11 days and longer when the predicted BSISO showed weakened strength and insignificant northward propagation.To identify what causes the weakened forecast skill of BSISO at the forecast lead time of 11 days,we diagnosed the main mechanisms responsible for the BSISO northward propagation.The same analysis was also carried out using the observations and the outputs of the four-day forecast lead that successfully predicted the observed northward-propagating BSISO.We found that the lack of northward propagation at the 11-day forecast lead was due to insufficient increases in low-level cyclonic vorticity,moistening and warm temperature anomalies to the north of the convection,which were induced by the interaction between background mean flows and BSISO-related anomalous fields.The BCC S2S model can predict the background monsoon circulations,such as the low-level southerly and the northerly and easterly vertical shears,but has limited capability in forecasting the distributions of circulation and moisture anomalies.
基金supported by the National Natural Science Foundation of China under grant No.40705030the National Basic Research Program of China (Grant No.2006CB400504)
文摘Monthly mean temperatures at 562 stations in China are estimated using a statistical downscaling technique. The technique used is multiple linear regressions （MLRs） of principal components （PCs）. A stepwise screening procedure is used for selecting the skilful PCs as predictors used in the regression equation. The predictors include temperature at 850 hPa （7）, the combination of sea-level pressure and temperature at 850 hPa （P＋T） and the combination of geo-potential height and temperature at 850 hPa （H＋T）. The downscaling procedure is tested with the three predictors over three predictor domains. The optimum statistical model is obtained for each station and month by finding the predictor and predictor domain corresponding to the highest correlation. Finally, the optimum statistical downscaling models are applied to the Hadley Centre Coupled Model, version 3 （HadCM3） outputs under the Special Report on Emission Scenarios （SRES） A2 and B2 scenarios to construct local future temperature change scenarios for each station and month, The results show that （1） statistical downscaling produces less warming than the HadCM3 output itself; （2） the downscaled annual cycles of temperature differ from the HadCM3 output, but are similar to the observation; （3） the downscaled temperature scenarios show more warming in the north than in the south; （4） the downscaled temperature scenarios vary with emission scenarios, and the A2 scenario produces more warming than the B2, especially in the north of China.
基金supported by a project of the National Natural Science Foundation of China (Grant No. 41305099)
文摘A running mean bias （RMB） correction ap- proach was applied to the forecasts of near-surface variables in a seasonal short-range ensemble forecasting experiment with 57 consecutive cases during summer 2010 in the northern China region. To determine a proper training window length for calculating RMB, window lengths from 2 to 20 days were evaluated, and 16 days was taken as an optimal window length, since it receives most of the benefit from extending the window length. The raw and 16-day RMB corrected ensembles were then evaluated for their ensemble mean forecast skills. The results show that the raw ensemble has obvious bias in all near-surface variables. The RMB correction can remove the bias reasonably well, and generate an unbiased ensemble. The bias correction not only reduces the ensemble mean forecast error, but also results in a better spreaderror relationship. Moreover, two methods for computing calibrated probabilistic forecast （PF） were also evaluated through the 57 case dates： 1） using the relative frequency from the RMB-eorrected ensemble; 2） computing the forecasting probabilities based on a historical rank histogram. The first method outperforms the second one, as it can improve both the reliability and the resolution of the PFs, while the second method only has a small effect on the reliability, indicating the necessity and importance of removing the systematic errors from the ensemble.
