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

Diallo, Ismaïla; Xue, Yongkang; Li, Qian; Sales, Fernando De; Li, Wei

doi:10.1007/s00382-019-04630-6

Cited by 30 publications

(35 citation statements)

References 79 publications

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“…General circulation models (GCMs) are the most sophisticated tools available to investigate the climate system response to increases in radiative forcing and to identify the mechanisms driving that response (Taylor et al 2012;Flato et al 2013). However, despite potential progress over the last two decades in GCM simulations of past, present, and future climate, considerable regional biases, and other inadequacies still exist due to incomplete representation of key regional scale processes, poor parameterizations, imperfect initial conditions, and coarse resolutions (van Der Wiel et al 2016;Wehner et al 2014;Diallo et al 2019). These modeling deficiencies are in addition to the influences of internal variability of the Earth system that collectively lead to uncertainty in projections of climate change at global to regional scales.…”

Section: Introductionmentioning

confidence: 99%

Projected Changes in Temperature and Precipitation Over the United States, Central America, and the Caribbean in CMIP6 GCMs

et al. 2021

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The Coupled Model Intercomparison Project Phase 6 (CMIP6) dataset is used to examine projected changes in temperature and precipitation over the United States (U.S.), Central America and the Caribbean. The changes are computed using an ensemble of 31 models for three future time slices (2021–2040, 2041–2060, and 2080–2099) relative to the reference period (1995–2014) under three Shared Socioeconomic Pathways (SSPs; SSP1-2.6, SSP2-4.5, and SSP5-8.5). The CMIP6 ensemble reproduces the observed annual cycle and distribution of mean annual temperature and precipitation with biases between − 0.93 and 1.27 °C and − 37.90 to 58.45%, respectively, for most of the region. However, modeled precipitation is too large over the western and Midwestern U.S. during winter and spring and over the North American monsoon region in summer, while too small over southern Central America. Temperature is projected to increase over the entire domain under all three SSPs, by as much as 6 °C under SSP5-8.5, and with more pronounced increases in the northern latitudes over the regions that receive snow in the present climate. Annual precipitation projections for the end of the twenty-first century have more uncertainty, as expected, and exhibit a meridional dipole-like pattern, with precipitation increasing by 10–30% over much of the U.S. and decreasing by 10–40% over Central America and the Caribbean, especially over the monsoon region. Seasonally, precipitation over the eastern and central subregions is projected to increase during winter and spring and decrease during summer and autumn. Over the monsoon region and Central America, precipitation is projected to decrease in all seasons except autumn. The analysis was repeated on a subset of 9 models with the best performance in the reference period; however, no significant difference was found, suggesting that model bias is not strongly influencing the projections.

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Section: Introductionmentioning

confidence: 99%

Projected Changes in Temperature and Precipitation Over the United States, Central America, and the Caribbean in CMIP6 GCMs

et al. 2021

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“…

R_{adj}^{2}

for predictors in 2‐month lead is reduced by about 0.20 for all calendar month prediction, with significantly less reduction for spring prediction compared to the respective 1‐month prediction. The encouraging performance of spring LST prediction with middle‐layer SUBT and surface albedo as predictors also implies their important role as the initial condition for both weather and climate models (Diallo et al, 2019; Xia et al, 2013; Xue et al, 2016, 2018). The SM's contribution to extreme warm and cold years' soil memory has also been investigated.…”

Section: Resultsmentioning

confidence: 99%

“…Xue et al's (2018) conjecture is based on the significant lagged correlation between spring air temperature ( T air ) over the northern Rocky Mountain areas in North America and the TP in East Asia and their respective summer precipitation in their downstream regions. Although T air anomaly in spring directly interacts with atmospheric circulation and precipitation, Xue et al (2016, 2018) and Diallo et al (2019) found that specifying proper initial LST and subsurface temperature (SUBT) is the only way to reproduce the observed persistence in the climate models. Their observational analyses and model sensitivity experiments show that the T air /LST/SUBT effect is comparable to or in some areas/seasons probably more important than the well‐known sea surface temperature effects.…”

Section: Introductionmentioning

confidence: 99%

Investigation of the Variability of Near‐Surface Temperature Anomaly and Its Causes Over the Tibetan Plateau

Liu

Xue

et al. 2020

JGR Atmospheres

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Recent research has reported the great influence of springtime land surface temperature (LST) and subsurface temperature (SUBT) over the Tibetan Plateau (TP) on downstream region summer precipitation, indicating the potential application of LST/SUBT on subseasonal to seasonal (S2S) prediction. In this study, we employed both observational data and offline model simulation to explore the memory of surface and subsurface variables and assess the driving effects of snow/albedo and SUBT on the LST anomaly. Our composite analysis based on observation shows that the anomalous LST in the TP can sustain for seasons and is accompanied by persistent SUBT as well as snow and associated surface albedo anomalies. A multilayer frozen soil model reproduces the observed LST anomaly and shows surface albedo and middle-layer SUBT have 1-3 months memory, indicating the degree of persistence or dissipation of anomaly through time with more extended memory during spring. With simulated middle-layer SUBT as a predictor, the linear regression model produces R 2 adj of 0.44 and 0.26 for 1-and 2-month LST prediction, respectively. The predictability is higher during the spring. Our results also show February snowfall, May snowmelt, and aero in snow exert substantial impacts on springtime LST through snow albedo feedback. The long memory of SUBT allows it to preserve the surface thermal anomaly and release it gradually in the following months to seasons. Meanwhile, the sensitivity study indicates that the soil properties and soil column depth predominate SUBT memory, which suggests the key processes to improve LST/SUBT then downstream S2S prediction.

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“…The dynamical and empirical downscaling of climate observations and climate projections exemplifies the infusion of multiscale concepts into physical geography (Winkler et al, 1997). Physical geographers have been at the forefront of increasing the utility of climate projections from global climate models (GCMs) for assessing potential impacts and as input to decision making (Diallo et al, 2019). Downscaling transforms coarser scale outputs into finer scale products using geographic information and knowledge of physical processes at the finer scale.…”

Section: Processes At Multiple Geographic Scalesmentioning

confidence: 99%

Physical geography contributes

Harden

Luzzadder–Beach²,

MacDonald

et al. 2020

Progress in Physical Geography: Earth and Environment

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Physical geography is a process, conducted by people, of integration and synthesis of ideas and observations to advance scientific understanding of Earth’s surface and atmosphere and to apply this knowledge to the greater good of the planet and its people. Therefore, physical geography matters; that is, physical geography makes a difference to people and contributes to environmental decision making at various scales. Based upon presentations and discussion at the 2019 AAG Annual Meeting (see editorial above in this issue), we provide here a historical perspective and distillation of how and why, in our opinions, physical geography matters. Although we provide several specific examples, these represent only a small portion of the large body of excellent and relevant physical geography research.

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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

Cited by 30 publications

References 79 publications

Projected Changes in Temperature and Precipitation Over the United States, Central America, and the Caribbean in CMIP6 GCMs

Projected Changes in Temperature and Precipitation Over the United States, Central America, and the Caribbean in CMIP6 GCMs

Investigation of the Variability of Near‐Surface Temperature Anomaly and Its Causes Over the Tibetan Plateau

Physical geography contributes

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