Multivariate Bias‐Correction of High‐Resolution Regional Climate Change Simulations for West Africa: Performance and Climate Change Implications

Cannon, Alex J.

Laux, Patrick

Hald, Cornelius
Adeyeri, Oluwafemi
Rahimi, Jaber

Srivastava, Amit K.

Mbaye, Mamadou Lamine
Kunstmann, Harald

DOI: https://doi.org/10.1029/2021JD034836
Persistent URL: http://resolver.sub.uni-goettingen.de/purl?gldocs-11858/9969
Laux, Patrick; 1 Karlsruhe Institute of Technology (KIT) Campus Alpin Institute of Meteorology and Climate Research ‐ Atmospheric Environmental Research (IMK‐IFU) Garmisch‐Partenkirchen Germany
Hald, Cornelius; 4 Meteorological Observatory Hohenpeißenberg German Weather Service Hohenpeißenberg Germany
Adeyeri, Oluwafemi; 5 School of Energy and Environment City University of Hong Kong Kowloon Hong‐Kong
Rahimi, Jaber; 1 Karlsruhe Institute of Technology (KIT) Campus Alpin Institute of Meteorology and Climate Research ‐ Atmospheric Environmental Research (IMK‐IFU) Garmisch‐Partenkirchen Germany
Srivastava, Amit K.; 6 Institute of Crop Science and Resource Conservation University of Bonn Bonn Germany
Mbaye, Mamadou Lamine; 7 Laboratoire d’Océanographie des Sciences de l’Environnement et du Climat (LOSEC) Université Assane SECK de Ziguinchor Ziguinchor Senegal
Kunstmann, Harald; 1 Karlsruhe Institute of Technology (KIT) Campus Alpin Institute of Meteorology and Climate Research ‐ Atmospheric Environmental Research (IMK‐IFU) Garmisch‐Partenkirchen Germany
Abstract
A multivariate bias correction based on N‐dimensional probability density function transform (MBCn) technique is applied to four different high‐resolution regional climate change simulations and key meteorological variables, namely precipitation, mean near‐surface air temperature, near‐surface maximum air temperature, near‐surface minimum air temperature, surface downwelling solar radiation, relative humidity, and wind speed. The impact of bias‐correction on the historical (1980–2005) period, the inter‐variable relationships, and the measures of spatio‐temporal consistency are investigated. The focus is on the discrepancies between the original and the bias‐corrected results over five agro‐ecological zones. We also evaluate relevant indices for agricultural applications such as climate extreme indices, under current and future (2020–2050) climate change conditions based on the RCP4.5. Results show that MBCn successfully corrects the seasonal biases in spatial patterns and intensities for all variables, their intervariable correlation, and the distributions of most of the analyzed variables. Relatively large bias reductions during the historical period give indication of possible benefits of MBCn when applied to future scenarios. Although the four regional climate models do not agree on the same positive/negative sign of the change of the seven climate variables for all grid points, the model ensemble mean shows a statistically significant change in rainfall, relative humidity in the Northern zone and wind speed in the Coastal zone of West Africa and increasing maximum summer temperature up to 2°C in the Sahara.
Key Points:
Multivariate bias‐correction (MBCn) of key meteorological variables accounting for their interdependency.
MBCn effectively removes the statistical biases as indicated by several measures and improves the representation of the probability density.
The corrected model ensemble mean preserves the climate change signal with a statistically significant change in precipitation.
Subjects
A multivariate bias correction (MBCn)onal climate simulationsWest Africa
High‐resolution regional climate change simulations
Climate extreme indices
The bias correction is found to influence the probability of extreme events
The model ensemble mean shows a statistically significant change