Neglecting irrigation contributes to the simulated summertime warm-and-dry bias in the central United States

Abstract

A vast number of weather forecast and climate models have a common warm-and-dry bias, accompanied by the underestimation of evapotranspiration and overestimation of surface net radiation, over the central United States during boreal summer. Various theories have been proposed to explain these biases, but no studies have linked the biases with the missing representation of human perturbations, such as irrigation. Here we argue that neglecting the impact of irrigation contributes to the longstanding warm surface temperature and lack of precipitation biases over this region. By using convection-permitting multi-season simulations over the contiguous United States coupled with an operational-like irrigation scheme, we show that irrigation increases surface evapotranspiration and decreases surface temperature by increasing evaporative fraction. By increasing the frequency of mesoscale convective systems, irrigation reduces the summertime model precipitation deficit and improves the simulated precipitation diurnal cycle over the Great Plains. The increased precipitation also alleviates the warm bias in our simulation setup, likely by damping the positive feedback between soil moisture and temperature.

Impact of Lateral Flow on Surface Water and Energy Budgets Over the Southern Great Plains—A Modeling Study

“…For example, an underestimation in evaporative fraction (EF, defined as the ratio of LH to the sum of LH and SH) has been attributed as the dominant source of error in models with a large warming bias. Handling of anthropogenic impacts (or lack thereof), such as neglecting irrigation in the LSMs, has also been attributed to the warming bias (Pei et al, 2016;Qian et al, 2020;Z. Yang et al, 2019).…”

Convection‐permitting modeling with regional climate models: Latest developments and next steps

“…Moreover, CPRCMs are mainly atmospheric-only models. Large benefits could be achieved by adding Earth system components (Giorgi & Gao, 2018) to CPRCMs such as lake models (Wu et al, 2020), aerosol schemes (Chen, 2021), oceanic models providing improved sea surface temperature gradients (Van Pham et al, 2016), and more sophisticated surface schemes including irrigation (Qian et al, 2020) and groundwater interactions (Barlage et al, 2021).…”

Utilizing a Storm-Generating Hotspot to Study Convective Cloud Transitions: The CACTI Experiment

“…Many mountainous regions of the world exert a primary control on the initiation of deep convection that often grows upscale into mesoscale convective systems (MCSs), producing a majority of rainfall downstream of these regions (e.g., Laing and Fritsch 1997;Nesbitt et al 2006;. Poor prediction of deep convection initiation timing and location (e.g., Dai 2006), upscale growth from isolated to mesoscale systems (e.g., Hohenegger and Stevens 2013;Hagos et al 2014), propagation (e.g., Del Genio et al 2012Song et al 2013), and surface flux-precipitation interactions (e.g., Taylor et al 2012;Klein and Taylor 2020;Qian et al 2020) likely contribute to a warm, dry bias in climate models downstream of the SDC range (Carril et al 2012;Solman et al 2013) and other mountain ranges such as the Rockies (Anderson et al 2003;Klein et al 2006), which are key agricultural regions. Increasing model resolution has improved predictions, but even models without parameterized deep convection tend to display overly 6 strong updrafts (Varble et al 2014a, Marinescu et al 2016Fan et al 2017), excessive riming that results in high-biased radar reflectivity (e.g., Lang et al 2011;Varble et al 2011;Fridlind et al 2012;Stanford et al 2017), and low-biased stratiform rainfall (e.g., Hagos et al 2014;Varble et al 2014b, Han et al 2019.…”

“…The wide range of environmental conditions in central Argentina and the high frequency of orographic convective clouds that evolve into deeper congestus, initiate into deep convection Houze 2011, 2016;Mulholland et al 2018), and organize into mesoscale systems near the SDC range (Anabor et al 2008;Romatschke and Houze 2010;Rasmussen et al 2014Rasmussen et al , 2016 make it an ideal location to quantify interactions between convective clouds and their surrounding environment. Extreme storms in Argentina stand out as being some of the world's deepest (Zipser et al 2006), largest (Velasco and Fritsch 1987), and longest-lived with some of the highest lightning flash rates (Cecil et al 2015) and largest hail (Cecil and Blankenship 2012;Kumjian et al 2020) on Earth.…”

“…Extreme storms in Argentina stand out as being some of the world's deepest (Zipser et al 2006), largest (Velasco and Fritsch 1987), and longest-lived with some of the highest lightning flash rates (Cecil et al 2015) and largest hail (Cecil and Blankenship 2012;Kumjian et al 2020) on Earth. The convective lifecycle in this region is significantly influenced by orographic flows (Nicolini and Skabar 2011;Rasmussen and Houze 2011;Bueno Repinaldo et al 2015;Mulholland et al 2019Mulholland et al , 2020, the South American low level jet (Nicolini et al 2002;Salio et al 2002Salio et al , 2007Saulo et al 2004Saulo et al , 2007Borque et al 2010), and synoptic-scale troughs that induce the Northwestern Argentinean ("Chaco") Low (Seluchi et al 2003), free tropospheric subsidence (Ribeiro and Accepted for publication in Bulletin of the American Meteorological ociety. DOI S 10.1175/BAMS-D-20-0030.1.…”