Abstract
In this study, an improved method named spatial disaggregation and detrended bias correction (SDDBC) based on spatial disaggregation and bias correction (SDBC) combined with trend correction was proposed. Using data from meteorological stations over China from 1991 to 2020 and the seasonal hindcast data from the Beijing Climate Center Climate System Model (BCC_CSM1.1 (m)), the performances of the model, SDBC, and SDDBC in spring temperature forecasts were evaluated. The results showed that the observed spring temperature exhibits a significant increasing trend in most of China, but the warming trend simulated by the model was obviously smaller. SDBC performed poorly in temperature trend correction. With SDDBC, the model’s deviation in temperature trend was corrected, and consequently, the temporal correlation between the model’s simulation and the observation as well as the forecasting skill on the phase of temperature were improved, thus improving the MSSS and the ACC. From the perspective of probabilistic prediction, the relative operating characteristic skill score (ROCSS) and the Brier skill score (BSS) of the SDDBC for three categorical forecasts were higher than those of the model and SDBC. The SDDBC’s BSS increased as the effect of the increasing resolution component was greater than that of the decreasing reliability component. Therefore, it is necessary to correct the predicted temperature trend in post-processing for the output of numerical prediction models.
Highlights
Excluding parts of South China and Southwest China, the simulated air temperature warming rates were lower than the observed rates in most regions
Larger differences were found in North China, Central China, Northwest China, and East China, ranging between 0.22 and 0.25 ◦ C/decade
spatial disaggregation and detrended bias correction (SDDBC) retains the advantages of spatial disaggregation and bias correction (SDBC), and effectively improves probabilistic and deterministic forecast skills by correcting the temperature trend bias
Summary
Recent studies have used the archived BCC_CSM reforecasts for different applications, such as evaluating the forecast skill of Asian–Western Pacific summer monsoon [4], Asian summer monsoon [5], Madden–Julian oscillation [6], summer precipitation [7], synoptic eddy and low-frequency flow [8], Indian Ocean basin mode and dipole mode [9], stratospheric sudden warming [10], primary East Asian summer circulation patterns [11], and winter temperature [12]. The model has shown a considerable ability to predict important climate phenomena, tropical large-scale atmospheric circulation anomalies and primary climate variability modes. The prediction skill of weak anomaly signals and atmospheric circulation in middle and high latitudes still needs to be improved
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