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

Journal Article · Fri May 22 00:00:00 EDT 2020 · Journal of Hydrometeorology

DOI:https://doi.org/10.1175/jhm-d-19-0209.1· OSTI ID:1648900

Li, Lu ^[1]; Shangguan, Wei ^[1]; Deng, Yi ^[2]; Mao, Jiafu ^[3]; Pan, JinJing ^[1]; Wei, Nan ^[1]; Yuan, Hua ^[1]; Zhang, Shupeng ^[1]; Zhang, Yonggen ^[4]; Dai, Yongjiu ^[1]

---

1. Sun Yat-sen University, Guangdong (China)
2. Georgia Inst. of Technology, Atlanta, GA (United States)
3. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
4. Tianjin University (China)

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 subseasonal 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 National Climate Assessment–Land Data Assimilation System (NCA-LDAS) datasets over the United States. Here, 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.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: National Science Foundation Climate and Large-Scale Dynamics (CLD); Natural Science Foundation of China; USDOE Office of Science (SC), Biological and Environmental Research (BER)
• Grant/Contract Number: AC05-00OR22725
• OSTI ID: 1648900
• Journal Information: Journal of Hydrometeorology, Journal Name: Journal of Hydrometeorology Journal Issue: 5 Vol. 21; ISSN 1525-755X
• Publisher: American Meteorological Society
• Country of Publication: United States
• Language: English

Related Subjects

54 ENVIRONMENTAL SCIENCES
Hydrometeorology
Other artificial intelligence/machine learning
Precipitation
Probabilistic Quantitative Precipitation Forecasting (PQPF)
Soil moisture
Statistical techniques