Bias Correction of Ocean Bottom Temperature and Salinity Simulations From a Regional Circulation Model Using Regression Kriging

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