# Bias correction

A machine-learning postprocessing layer that learns to correct errors in existing forecasting models doubles the accuracy of a gold-standard model over the two-to-six-week subseasonal range and, for the first time, consistently beats climatological averages.

A machine-learning postprocessing layer that learns to correct errors in existing forecasting models doubles the accuracy of a gold-standard model over the two-to-six-week subseasonal range and, for the first time, consistently beats climatological averages.

Using MAWS observations, this study evaluated the bias in BMKG Ina-Flows sea surface temperature forecasts and compared three machine-learning bias-correction algorithms (SVR, LSTM, Bi-LSTM) at Karimun Jawa and Bira, finding a warm bias at both sites, with LSTM best at Karimun Jawa (RMSE 0.207, MAE 0.182, MBE -0.171, a 29.83% RMSE reduction) and Bi-LSTM best at Bira (RMSE 0.218, MAE 0.173, MBE 0.011, a 26.10% RMSE reduction), indicating that correction effectiveness is location-dependent and algorithm choice should rest on local validation.

Using MAWS observations, this study evaluated the bias in BMKG Ina-Flows sea surface temperature forecasts and compared three machine-learning bias-correction algorithms (SVR, LSTM, Bi-LSTM) at Karimun Jawa and Bira, finding a warm bias at both sites, with LSTM best at Karimun Jawa (RMSE 0.207, MAE 0.182, MBE -0.171, a 29.83% RMSE reduction) and Bi-LSTM best at Bira (RMSE 0.218, MAE 0.173, MBE 0.011, a 26.10% RMSE reduction), indicating that correction effectiveness is location-dependent and algorithm choice should rest on local validation.

Using soil moisture from 17 CMIP6 models bias-corrected against GLDAS and combined into a multi-model ensemble, the study characterizes 2015–2050 global soil moisture droughts with a non-parametric SSMI under SSP2-4.5 and SSP5-8.5 and assesses agricultural exposure with a Drought Exposure Index overlaying cropland, finding intensified drought characteristics toward mid-century, longer durations and wider spatial extent under SSP5-8.5, pronounced drying in South America, southern Europe, South Asia and parts of North America, and a global mean DEI rising from 0.71 under SSP2-4.5 to 0.78 under SSP5-8.5.

Using soil moisture from 17 CMIP6 models bias-corrected against GLDAS and combined into a multi-model ensemble, the study characterizes 2015–2050 global soil moisture droughts with a non-parametric SSMI under SSP2-4.5 and SSP5-8.5 and assesses agricultural exposure with a Drought Exposure Index overlaying cropland, finding intensified drought characteristics toward mid-century, longer durations and wider spatial extent under SSP5-8.5, pronounced drying in South America, southern Europe, South Asia and parts of North America, and a global mean DEI rising from 0.71 under SSP2-4.5 to 0.78 under SSP5-8.5.