Abstract
The overall performance of the simulated seasonal precipitation response to local terrestrial forcings, namely vegetation abundance and soil moisture, in the Sahel among the Coupled Model Intercomparison Project Phase Five (CMIP5) Earth System Models (ESMs) is systematically investigated and compared with its observational counterpart using a multivariate statistical method. The observed seasonal precipitation response is evaluated against a large ensemble of observational, reanalysis, and satellite data sets to provide quantification of uncertainties. The behaviour of models with and without a Dynamic Global Vegetation Model (DGVM) component is also explored, along with the mechanisms responsible for terrestrial feedback on rainfall. In general, the CMIP5 models can reasonably capture the seasonal evolution of Sahel precipitation and soil moisture, albeit with wet biases during the pre-monsoon period and dry biases during the peak monsoon period. The non-DGVM ESMs simulate comparable leaf area indices (LAIs) with observations, while DGVM-enabled ESMs simulate too much year-round LAI. The variance of precipitation that is attributed to oceanic forcings in CMIP5 is comparable with observations; however, the variance of precipitation that is attributed to terrestrial forcings is smaller in CMIP5 models than observed, especially for non-DGVM ESMs. CMIP5 models, especially those without DGVMs, undervalue precipitation's observed response strength to soil moisture anomalies. In both observations and CMIP5 models, none of the atmospheric variables show significant responses to direct vegetation forcing, except for the response in transpiration. Although vegetation has minimal direct effect on the atmospheric state, it can affect the atmosphere by modifying soil moisture and transpiration rate indirectly, which helps explain the more realistic simulation of rainfall in DGVM-enabled ESMs than non-DGVM ESMs. Coupling of an ESM to a DGVM is critical in generating reasonable land–atmosphere feedback and examining future ecological and climatic changes over the Sahel.
| Original language | English |
|---|---|
| Pages (from-to) | 99-122 |
| Number of pages | 24 |
| Journal | International Journal of Climatology |
| Volume | 43 |
| Issue number | 1 |
| DOIs | |
| State | Published - Jan 2023 |
Funding
The authors acknowledge funding from the Department of Energy (DOE) through grant DE-SC0012534. This research was also partially supported by the Reducing Uncertainties in Biogeochemical Interactions through Synthesis and Computation Science Focus Area (RUBISCO SFA DE-AC05-00OR2272). RUBISCO is sponsored by the Regional and Global Model Analysis activity of the Earth and Environmental Systems Modelling Program in the Earth and Environmental Systems Sciences Division of the Office of Biological and Environmental Research in the U.S. DOE Office of Science. Computational resources were provided by the National Energy Research Scientific Computing Center. We acknowledge the World Climate Research Programme's Working Group on Coupled Modelling, which is responsible for CMIP. We thank the climate modelling groups listed in Table 1 for producing and making available their model output. For CMIP, the U.S. DOE Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. The authors acknowledge funding from the Department of Energy (DOE) through grant DE‐SC0012534. This research was also partially supported by the Reducing Uncertainties in Biogeochemical Interactions through Synthesis and Computation Science Focus Area (RUBISCO SFA DE‐AC05‐00OR2272). RUBISCO is sponsored by the Regional and Global Model Analysis activity of the Earth and Environmental Systems Modelling Program in the Earth and Environmental Systems Sciences Division of the Office of Biological and Environmental Research in the U.S. DOE Office of Science. Computational resources were provided by the National Energy Research Scientific Computing Center. We acknowledge the World Climate Research Programme's Working Group on Coupled Modelling, which is responsible for CMIP. We thank the climate modelling groups listed in Table 1 for producing and making available their model output. For CMIP, the U.S. DOE Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals.
Keywords
- CMIP5
- DGVM
- Sahel
- land surface feedback
- precipitation