2023
DOI: 10.1002/asl.1172
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Systematic daytime increases in atmospheric biases linked to dry soils in irrigated areas in Indian operational forecasts

Abstract: The representation of land–atmosphere coupling in forecast models can significantly impact weather prediction. A previous case study in Northern India incorporating both model and observational data identified atmospheric biases in a high‐resolution forecast linked to soil moisture that impacted the representation of the monsoon trough, an important driver of regional rainfall. The aim of the current work is to understand whether this behavior is present in operational forecasts run by the India National Centr… Show more

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Cited by 4 publications
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“…The Coupled Model Intercomparison Project (Phase 6) (CMIP6) climate models, for example, include a basic representation of land‐use and land‐cover change, but very few include irrigation. This can give rise to systematic warm‐and‐dry simulation biases (Barton et al, 2023). Studies have shown that the cooling effects of irrigation may be underestimated by global climate models due to their coarse spatial resolutions (Chen & Dirmeyer, 2020; Sorooshian et al, 2011).…”
Section: Introductionmentioning
confidence: 99%
“…The Coupled Model Intercomparison Project (Phase 6) (CMIP6) climate models, for example, include a basic representation of land‐use and land‐cover change, but very few include irrigation. This can give rise to systematic warm‐and‐dry simulation biases (Barton et al, 2023). Studies have shown that the cooling effects of irrigation may be underestimated by global climate models due to their coarse spatial resolutions (Chen & Dirmeyer, 2020; Sorooshian et al, 2011).…”
Section: Introductionmentioning
confidence: 99%