Contribution of soil moisture feedback to hydroclimatic variability

Krakauer, Nir Y.; Cook, Benjamin I.; Puma, Michael J.

doi:10.5194/hess-14-505-2010

Cited by 31 publications

(26 citation statements)

References 54 publications

(60 reference statements)

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“…The importance of the nonlinear atmosphere response to periodic terms must be emphasized. Krakauer et al (2010) indicated that P variance has a nonlinear response to the combined interannual variability of sea surface temperature and SM. Stuecker et al (2013) used observational and climate model data to highlight the importance of the nonlinear atmospheric response to the annual cycle of sea surface temperature.…”

Section: Introductionmentioning

confidence: 99%

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

Shangguan

Deng

et al. 2020

Journal of Hydrometeorology

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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. 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.

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Section: Introductionmentioning

confidence: 99%

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

Shangguan

Deng

et al. 2020

Journal of Hydrometeorology

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“…Anthropogenic land cover and land use change (LCLUC) affects climate by modifying water, sensible heat, and radiation fluxes at the land surface (Chase et al, 2000;Gordon et al, 2005;Brovkin et al, 2006;Findell et al, 2007;Krakauer et al, 2010;Mahmood et al, 2014). One important mode of LCLUC has been the dramatic expansion in irrigated agriculture over the past century.…”

Section: Introductionmentioning

confidence: 99%

“…864 N. Y. Krakauer et al: Air-sea coupling and irrigation Many GCM studies of irrigation's climate impacts have been conducted with prescribed sea surface temperatures (SSTs) (Boucher et al, 2004;Puma and Cook, 2010;de Vrese et al, 2016), while several did include ocean-atmosphere interaction (Lobell et al, 2006;Cook et al, 2011Cook et al, , 2015. Various studies have highlighted the importance of interactive atmosphere-ocean coupling for accurately reproducing various phenomena in GCMs.…”

Section: Introductionmentioning

confidence: 99%

Ocean–atmosphere interactions modulate irrigation's climate impacts

et al. 2016

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Abstract. Numerous studies have focused on the local and regional climate effects of irrigated agriculture and other land cover and land use change (LCLUC) phenomena, but there are few studies on the role of oceanatmosphere interaction in modulating irrigation climate impacts. Here, we compare simulations with and without interactive sea surface temperatures of the equilibrium effect on climate of contemporary (year 2000) irrigation geographic extent and intensity. We find that ocean-atmosphere interaction does impact the magnitude of globalmean and spatially varying climate impacts, greatly increasing their global reach. Local climate effects in the irrigated regions remain broadly similar, while non-local effects, particularly over the oceans, tend to be larger. The interaction amplifies irrigation-driven standing wave patterns in the tropics and midlatitudes in our simulations, approximately doubling the global-mean amplitude of surface temperature changes due to irrigation. The fractions of global area experiencing significant annual-mean surface air temperature and precipitation change also approximately double with ocean-atmosphere interaction. Subject to confirmation with other models, these findings imply that LCLUC is an important contributor to climate change even in remote areas such as the Southern Ocean, and that attribution studies should include interactive oceans and need to consider LCLUC, including irrigation, as a truly global forcing that affects climate and the water cycle over ocean as well as land areas.

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“…Previous statistical and model assessments of East Asian or global land‐atmosphere coupling mainly focused on the timescales from days to months [e.g., Koster et al , 2004; Kim and Hong , 2007; Chow et al , 2008; Zhang et al , 2008b; Dirmeyer et al , 2009]. Several previous studies have used AGCM simulations to investigate the contribution of soil moisture feedbacks to interannual climate variability on the global scale [e.g., Koster et al , 2000; Reale and Dirmeyer , 2002; Krakauer et al , 2010]. However, as mentioned above, AGCM studies of land‐atmosphere coupling have large uncertainties on the local to regional scales.…”

Section: Introductionmentioning

confidence: 99%

Land-atmosphere coupling and summer climate variability over East Asia

Zhang¹,

Wu²,

Dong³

2011

J. Geophys. Res.

108

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[1] Two long-term simulations with the weather research and forecasting model are conducted to assess the contribution of land-atmosphere coupling to interannual variability of summer climate over East Asia. The control experiment (CTL) uses a fully coupled land surface model, while an additional experiment replaces soil moisture evolution at each time step with the climatology of CTL and thus removes the interannual variability of soil moisture. CTL is able to reproduce relatively well climatic means and interannual variability of summer climate over East Asia though some biases exist. It is found that land-atmosphere coupling plays a critical role in influencing summer climate variability, in particular over the climatic and ecological transition zones. Interactive soil moisture strongly amplifies daily mean temperature variability over the southern Siberia-northern Mongolia region, the region from northeast China to central China, and the eastern part of South Asia, accounting for half or more of the total variance. Soil moisture is found to exert substantially stronger impacts on daily maximum temperature variability than on daily mean temperature variability but generally has small effects on daily minimum temperature except for the eastern Tibetan Plateau and some other areas. Soil moisture makes a dominant contribution to precipitation variability over the climatic and ecological transition zones of the southern Siberia-northern Mongolia region and northern China and many areas of western China. While soil moisture-temperature coupling is largely determined by the ability of soil moisture to affect surface fluxes, soil moistureprecipitation coupling also depends on other physical processes, particularly moisture convection.

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Contribution of soil moisture feedback to hydroclimatic variability

Cited by 31 publications

References 54 publications

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

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

Ocean–atmosphere interactions modulate irrigation's climate impacts

Land-atmosphere coupling and summer climate variability over East Asia

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