Spring Land Surface and Subsurface Temperature Anomalies and Subsequent Downstream Late Spring‐Summer Droughts/Floods in North America and East Asia

Abstract

Sea surface temperature (SST) variability has been shown to have predictive value for land precipitation, although SSTs are unable to fully predict intraseasonal to interannual hydrologic extremes. The possible remote effects of large‐scale land surface temperature (LST) and subsurface temperature (SUBT) anomalies in geographical areas upstream and closer to the areas of drought/flood have largely been ignored. Here evidence from climate observations and model simulations addresses these effects. Evaluation of observational data using Maximum Covariance Analysis identifies significant correlations between springtime 2‐m air temperature (T2 m) cold (warm) anomalies in both the western U.S. and the Tibetan Plateau and downstream drought (flood) events in late spring/summer. To support these observational findings, climate models are used in sensitivity studies, in which initial LST/SUBT anomaly is imposed to produce observed T2 m anomaly, to demonstrate a causal relationship for two important cases: between spring warm T2 m/LST/SUBT anomalies in western U.S. and the extraordinary 2015 flood in Southern Great Plains and adjacent regions and between spring cold T2 m/LST/SUBT anomalies in the Tibetan Plateau and the severe 2003 drought south of the Yangtze River region. The LST/SUBT downstream effects in North America are associated with a large‐scale atmospheric stationary wave extending eastward from the LST/SUBT anomaly region. The effects of SST in these cases are also tested and compared with the LST/SUBT effects. These results suggest that consideration of LST/SUBT anomalies has the potential to add value to intraseasonal prediction of dry and wet conditions, especially extreme drought/flood events. The results suggest the importance of developing land data and models capable of preserving observed soil memory.

An Arctic‐Tibetan Connection on Subseasonal to Seasonal Time Scale

“…Our MCA analyses show that the spring LST is significantly coupled with the regional snow cover in preceding months. This provides observational evidence to the conjecture in Xue et al () that a temporally filtered response to snow anomalies is preserved in the LST memory, with the latter a more effective predictor to drought and flood events. The TP region snow cover can further originate back to the anomalous atmospheric circulation in middle and high latitudes in February, suggesting that the February anomalous circulation, through the resultant anomalies in snowfall in late winter and snow cover in early spring in TP region, can eventually lead to large‐scale anomalies in LST in spring time.…”

“…However, the subsequent studies showed that the effects of the snow cover on summer precipitation are highly variable (e.g., Bamzai & Shukla, 1999;Liu & Yanai, 2002;Wu & Qian, 2003;Xiao & Duan, 2016;Xue et al, 2012), suggesting the difficulty of using snow in TP directly as a predictor for droughts and flood events. Studies by Xue, Oaida, et al, (2016) and Xue et al (2018) therefore conjectured that a temporally filtered response to the snow anomalies may be preserved in the LST and found that, in both North America and East Asia, the springtime large-scale LST anomalies all significantly affect the early summer precipitation in its downstream region. Their observational analysis show that the connections between the LST and its downstream summer precipitation are as significant as the well-known SST-precipitation connections.…”

Dynamical downscaling the impact of spring Western US land surface temperature on the 2015 flood extremes at the Southern Great Plains: effect of domain choice, dynamic cores and land surface parameterization

“…For instance, during May 2015, Southern Great Plains and several adjacent cities (hereafter referred to as SGP) saw one of their wettest Mays on record, which was neither fully predicted nor anticipated. This flood event made headlines in many media outlets, while the property damage in Houston alone was estimated to more than $40 million (Wang et al 2015;Mekonnen et al 2016;Xue et al 2018). Despite considerable progress in understanding processes controlling the US seasonal/intraseasonal rainfall variability (see review by Koster et al 2017, and paper cited therein), accurately predicting these extreme events in models still remains a challenge .…”

Sensitivity of land precipitation to surface evapotranspiration: a nonlocal perspective based on water vapor transport

“…• For about 2/3 land areas, JJA precipitation is more sensitive to local ET than nonlocal ET in a point-to-point sense • For about 1/5 land areas, JJA precipitation is on average sensitive to ET of more than 1,000 km away • Remote sensitivities are much smaller than local sensitivities but could be large when combined and may be useful for some regions weather and climate through winds or atmospheric waves (Koster et al, 2016;Schumacher et al, 2019;Teng et al, 2019;Xue et al, 2018) or direct moisture transport (e.g., Alter et al, 2015;Herrera-Estrada et al, 2019;Lo & Famiglietti, 2013;Wei et al, 2013). Almost all these works are case studies, and a complete global picture is still elusive.…”