科研之友
微信
新浪微博
Facebook
分享链接
摘要
A multivariate multi-site statistical downscaling model (MMSDM) was developed for simultaneous downscaling of climate variables including daily maximum and minimum temperatures (T-max and T-min) for multiple observation sites. The MMSDM employs multivariate multiple linear regression (MMLR) to simulate deterministic series from large-scale reanalysis data and adds spatially correlated random series to the deterministic series of the MMLR to complement the underestimated variance and to reproduce a spatial correlation of T-max and T-min from multiple sites and an at-site temporal correlation between T-max and T-min. The MMSDM model is called MMLRc. The downscaled results of the MMLRc were compared to those of MMLR without random noise (MMLRn) and MMLR with uncorrelated random noise (MMLRi) over the southern Quebec area of Canada. The MMLRc almost exactly reproduced the cross-site correlation of T-max and T-min among multiple observation sites, and it accurately reproduced the at-site temporal correlation between T-max and T-min at each observation site. The MMLRi and MMLRc reproduced monthly standard deviations of daily T-max and T-min, the 90th percentile of T-max (T(max)90), the 10th percentile of T-min (T(min)10), and the frost and thaw cycle (Fr-Th) more accurately than the MMLRn model. However, both MMLRc and MMLRi yielded a larger standard error for the monthly mean of daily T-max and daily T-min, frost season length (FSL), and growing season length (GSL). For the Fr-Th and diurnal temperature range, the MMLRc performed better than the MMLRn and MMLRi. We conclude that the MMLRn may serve as an alternative to downscaling deterministic signals of a predictand, consistent with global climate model predictors, and it may serve to project the averaged central tendency of a predictand. The MMLRc, however, is recommended for reproduction of variance, extreme events, and the inter-annual variability of the predictands.
- 出版日期2012
