It is well known that climate and circulation model simulation output are often systematically biased. However, existing bias correction methods typically ignore spatial autocorrelation of the biases and correct only the overall mean and variance, resulting in mismatched spatial variability between bias‐corrected simulations and observations. In this study, we propose using regression kriging (RK) to correct for biased spatial patterns and apply this method to Regional Ocean Modeling System (ROMS) simulated ocean bottom temperature and salinity for the Mid‐Atlantic Bight, USA. RK combines modeling non‐stationary trends using (generalized) regression with ordinary kriging (OK) of the regression residuals. We compared the performance of RK to a simpler OK method and a quantile mapping (QM) method often used for bias correction of such model output. These methods were evaluated using the Structural Similarity (SSIM) index that can simultaneously evaluate model accuracy, precision, and spatial similarities. Our results show that while both RK and QM can correct for overall biases of the mean and variation, only RK can effectively reduce the spatial‐temporal autocorrelation of the biases. The RK method was able to bias correct while preserving the spatial‐temporal trends of the ROMS simulated bottom temperature and salinity surfaces. The RK approach can easily be applied to any similar climate and circulation model simulation output. This study has profound implications for studies that use the output from such a model for fine‐scale mapping, habitat suitability modeling, species distribution modeling, or predicting the effects of climate change.

中文翻译：

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使用回归克里金法从区域环流模型对海底温度和盐度模拟的偏差校正

众所周知，气候和环流模型模拟的输出通常会出现系统性偏差。但是，现有的偏差校正方法通常会忽略偏差的空间自相关，而仅校正总体均值和方差，从而导致偏差校正后的模拟和观测值之间的空间变异性不匹配。在这项研究中，我们建议使用回归克里金法（RK）纠正有偏见的空间格局，并将此方法应用于美国中大西洋海岸线的区域海洋模拟系统（ROMS）模拟的海底温度和盐度。RK将使用（广义）回归模型与非平稳趋势建模与回归残差的普通克里格法（OK）相结合。我们将RK的性能与更简单的OK方法和常用于此类模型输出的偏差校正的分位数映射（QM）方法进行了比较。使用结构相似性（SSIM）指数评估了这些方法，该指数可以同时评估模型的准确性，精度和空间相似性。我们的结果表明，尽管RK和QM都可以校正均值和方差的总体偏差，但只有RK可以有效地减少偏差的时空自相关。RK方法在保留ROMS模拟的底部温度和盐度表面的时空趋势的同时能够进行校正。RK方法可以轻松地应用于任何类似的气候和环流模型模拟输出。这项研究对于使用此类模型的输出进行精细比例制图，栖息地适应性建模，物种分布建模或预测气候变化影响的研究具有深远的意义。