Biological and Environmental Research - Earth and Environmental System Sciences
Earth and Environmental System Modeling

A Causal Inference Model Based on Random Forest to Identify the Effect of Soil Moisture on Precipitation

TitleA Causal Inference Model Based on Random Forest to Identify the Effect of Soil Moisture on Precipitation
Publication TypeJournal Article
Year of Publication2020
JournalJournal of Hydrometeorology
Volume21
Number5
Pages1115-1131
Abstract / Summary

Soil moisture influences precipitation mainly through its impact on land-atmosphere interactions. Understanding and correctly modeling soil moisture–precipitation (SM–P) coupling is crucial for improving weather forecasting and sub-seasonal to seasonal climate predictions, especially when predicting the persistence and magnitude of drought. However, the sign and spatial structure of SM–P feedback are still being debated in the climate research community, mainly due to the difficulty in establishing causal relationships and the high degree of nonlinearity in land-atmosphere processes. To this end, we developed a causal inference model based on the Granger causality analysis and a nonlinear machine learning model. This model includes three steps: nonlinear anomaly decomposition, nonlinear Granger causality analysis, and evaluation of the quality of SM–P feedback, which eliminates the nonlinear response of interannual and seasonal variability and the memory effects of climatic factors and isolates the causal relationship of local SM–P feedback. We applied this model by using the National Climate Assessment–Land Data Assimilation System (NCA-LDAS) datasets over the United States. The results highlight the importance of nonlinear atmosphere responses in land-atmosphere interactions. In addition, the strong feedback over the southwestern United States and the Great Plains both highlight the impacts of topographic factors rather than only the sensitivity of evapotranspiration to soil moisture. Furthermore, the SM–P index defined by our framework is used to benchmark Earth system models (ESMs), which provides a new metric for efficiently identifying potential model biases in modeling local land-atmosphere interactions and may help the development of ESMs in improving simulations of water cycle variability and extremes.

URLhttp://dx.doi.org/10.1175/jhm-d-19-0209.1
DOI10.1175/jhm-d-19-0209.1
Journal: Journal of Hydrometeorology
Year of Publication: 2020
Volume: 21
Number: 5
Pages: 1115-1131
Publication Date: 05/2020

Soil moisture influences precipitation mainly through its impact on land-atmosphere interactions. Understanding and correctly modeling soil moisture–precipitation (SM–P) coupling is crucial for improving weather forecasting and sub-seasonal to seasonal climate predictions, especially when predicting the persistence and magnitude of drought. However, the sign and spatial structure of SM–P feedback are still being debated in the climate research community, mainly due to the difficulty in establishing causal relationships and the high degree of nonlinearity in land-atmosphere processes. To this end, we developed a causal inference model based on the Granger causality analysis and a nonlinear machine learning model. This model includes three steps: nonlinear anomaly decomposition, nonlinear Granger causality analysis, and evaluation of the quality of SM–P feedback, which eliminates the nonlinear response of interannual and seasonal variability and the memory effects of climatic factors and isolates the causal relationship of local SM–P feedback. We applied this model by using the National Climate Assessment–Land Data Assimilation System (NCA-LDAS) datasets over the United States. The results highlight the importance of nonlinear atmosphere responses in land-atmosphere interactions. In addition, the strong feedback over the southwestern United States and the Great Plains both highlight the impacts of topographic factors rather than only the sensitivity of evapotranspiration to soil moisture. Furthermore, the SM–P index defined by our framework is used to benchmark Earth system models (ESMs), which provides a new metric for efficiently identifying potential model biases in modeling local land-atmosphere interactions and may help the development of ESMs in improving simulations of water cycle variability and extremes.

DOI: 10.1175/jhm-d-19-0209.1
Citation:
Li, L, W Shangguan, Y Deng, J Mao, J Pan, N Wei, H Yuan, S Zhang, Y Zhang, and Y Dai.  2020.  "A Causal Inference Model Based on Random Forest to Identify the Effect of Soil Moisture on Precipitation."  Journal of Hydrometeorology 21(5): 1115-1131.  https://doi.org/10.1175/jhm-d-19-0209.1.