TY - UNPB
T1 - CMIP6 model evaluation of multi-year droughts
AU - van Mourik, Jonna
AU - van der Wiel, Karin
AU - Hazeleger, Wilco
AU - Wanders, Niko
PY - 2025/12/24
Y1 - 2025/12/24
N2 - Multi-year droughts are extreme drought events leading to long-lasting impact. Due to their limited number in observational records, global climate models with large ensembles can contribute by increasing the sample size of multi-year droughts. However, the knowledge on their to simulate multi-year droughts is limited on a global scale. In this study, we evaluate six different CMIP6 models in simulating multi-year droughts by comparing them to ERA5. We test for frequency, time spent in multi-year droughts versus shorter droughts, seasonality, and drivers. The multi-model mean performs robustly across these metrics, with strong inter-model agreement in deviations from ERA5. These deviations can result from either model biases, ERA5 biases, or a limited sample size of multi-year droughts within ERA5. The differences between the multi-model mean and ERA5 are explained primarily by internal variability, which underscores the value of large ensembles for studying rare extremes such as multi-year droughts.
AB - Multi-year droughts are extreme drought events leading to long-lasting impact. Due to their limited number in observational records, global climate models with large ensembles can contribute by increasing the sample size of multi-year droughts. However, the knowledge on their to simulate multi-year droughts is limited on a global scale. In this study, we evaluate six different CMIP6 models in simulating multi-year droughts by comparing them to ERA5. We test for frequency, time spent in multi-year droughts versus shorter droughts, seasonality, and drivers. The multi-model mean performs robustly across these metrics, with strong inter-model agreement in deviations from ERA5. These deviations can result from either model biases, ERA5 biases, or a limited sample size of multi-year droughts within ERA5. The differences between the multi-model mean and ERA5 are explained primarily by internal variability, which underscores the value of large ensembles for studying rare extremes such as multi-year droughts.
UR - https://doi.org/10.22541/essoar.176659928.88003235/v1
U2 - 10.22541/essoar.176659928.88003235/v1
DO - 10.22541/essoar.176659928.88003235/v1
M3 - Preprint
BT - CMIP6 model evaluation of multi-year droughts
PB - ESS Open Archive
ER -
