Seasonally Dependent Future Changes in the U.S. Midwest Hydroclimate and Extremes

Wenyu Zhou aPacific Northwest National Laboratory, Richland, Washington

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L. Ruby Leung aPacific Northwest National Laboratory, Richland, Washington

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Jian Lu aPacific Northwest National Laboratory, Richland, Washington

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Abstract

This study investigates the responses of the hydroclimate and extremes in the U.S. Midwest to global warming, based on ensemble projections of phase 6 of the Coupled Model Intercomparison Project and the multimodel initial-condition large-ensemble simulations. The precipitation response features a seasonally dependent change with increased precipitation in April–May but reduced precipitation in July–August. The late-spring wetting is attributed to the enhanced low-level moisture-transporting southerlies, which are induced by regional sea level pressure anomalies linked to the poleward shift of the North American westerly jet (NAWJ). The late-summer drying is attributed to the weakened storm track, which is also linked to the poleward NAWJ shift. The seasonally dependent future changes of the Midwest precipitation are analogous to its climatological seasonal progression, which increases over late spring as the NAWJ approaches the Midwest and decreases over late summer as the NAWJ migrates away. In response to the mean precipitation changes, extremely wet late springs (April–May precipitation above the 99th percentile of the historical period) and extremely dry late summers (below the 1st percentile) will occur much more frequently, implying increased late-spring floods and late-summer droughts. Future warming in the Midwest is amplified in late summer due to the reduced precipitation. With amplified background warming and increased occurrence, future late-summer droughts will be more devastating. Our results highlight that, under a time-invariant poleward jet shift, opposite precipitation changes arise before and after the peak rainy month, leading to substantial increases in the subseasonal extremes. The severity of such climate impacts is obscured in projections of the rainy-season mean.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Wenyu Zhou, wenyu.zhou@pnnl.gov

Abstract

This study investigates the responses of the hydroclimate and extremes in the U.S. Midwest to global warming, based on ensemble projections of phase 6 of the Coupled Model Intercomparison Project and the multimodel initial-condition large-ensemble simulations. The precipitation response features a seasonally dependent change with increased precipitation in April–May but reduced precipitation in July–August. The late-spring wetting is attributed to the enhanced low-level moisture-transporting southerlies, which are induced by regional sea level pressure anomalies linked to the poleward shift of the North American westerly jet (NAWJ). The late-summer drying is attributed to the weakened storm track, which is also linked to the poleward NAWJ shift. The seasonally dependent future changes of the Midwest precipitation are analogous to its climatological seasonal progression, which increases over late spring as the NAWJ approaches the Midwest and decreases over late summer as the NAWJ migrates away. In response to the mean precipitation changes, extremely wet late springs (April–May precipitation above the 99th percentile of the historical period) and extremely dry late summers (below the 1st percentile) will occur much more frequently, implying increased late-spring floods and late-summer droughts. Future warming in the Midwest is amplified in late summer due to the reduced precipitation. With amplified background warming and increased occurrence, future late-summer droughts will be more devastating. Our results highlight that, under a time-invariant poleward jet shift, opposite precipitation changes arise before and after the peak rainy month, leading to substantial increases in the subseasonal extremes. The severity of such climate impacts is obscured in projections of the rainy-season mean.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Wenyu Zhou, wenyu.zhou@pnnl.gov

1. Introduction

The U.S. Midwest, located in the north-central United States, represents one of the most agriculturally intense areas in the world. The region contributes to a significant portion of global corn and soybean production and is a major producer of fruits, dairy, and livestock. According to the 2017 U.S. Census of Agriculture, the Midwest had a market value of agricultural products sold over $100 billion (USDA 2017). Climatologically, the Midwest receives abundant precipitation during the crop-growing season. But there are extreme years when the Midwest receives too little or too much precipitation, leading to disastrous droughts and floods. The 1936 Dust Bowl drought and the 1993 Great Flood are two examples that brought catastrophic losses to the Midwest (Schubert et al. 2004; Cook et al. 2014; Kunkel et al. 1994). In recent years, the 2012 summer drought induced massive crop failure while the 2019 spring flood caused widespread delays in crop planting. It is of great importance to understand how hydroclimate and extremes in the Midwest may change in the future, as anthropogenically forced global warming can profoundly affect both the climatological mean (e.g., Held and Soden 2006; Seager et al. 2007; Xie et al. 2010; Simpson et al. 2016; Zhou et al. 2019) and extremes (e.g., Lu et al. 2014; O’Gorman 2015; Swain et al. 2018; Dong et al. 2018; Zhou et al. 2020) of the regional precipitation.

The response of the regional hydroclimate over the central United States to global warming has been considerably studied using scenario-based projections of both global climate and regional downscaling models (IPCC 2007, p. 4; IPCC 2013, p. 5; Kunkel et al. 2013; Patricola and Cook 2013; Mearns et al. 2013; Seager et al. 2014; Bukovsky et al. 2017; Byun and Hamlet 2018; Neri et al. 2020). There is an emerging consensus that the Midwest will receive more precipitation in spring but likely less in summer. The spring wetting has been linked to the intensification of the Great Plains low-level jet (GPLLJ) that transports more moisture inland (Cook et al. 2008; Song et al. 2018). Bukovsky et al. (2017) highlighted a systematic change in the regional circulation over the central United States, featuring the strengthening of the GPLLJ, the poleward expansion of the subtropical high, and the poleward shift of the westerly jet. While focusing on the southern Great Plains, they argued that these circulation changes support a poleward shift in the regional precipitation pattern. Zhou et al. (2021) looked into the coupling within these circulation changes and found that the GPLLJ intensification is driven by regional sea level pressure (SLP) anomalies associated with the poleward expansion of the subtropical high. A fuller understanding of these regional circulation changes and the pathways for them to influence the precipitation and its extremes, however, remains to be established.

Future changes in the monthly-to-seasonal precipitation extremes have great implications for floods and droughts. Accurately estimating the warming-induced changes in these low-probability extremes is challenging, due to model uncertainty and large natural variability (e.g., Villarini et al. 2020). Recognizing these challenges, large-ensemble projections with different models [e.g., the Coupled Model Intercomparison Project (CMIP)] and different initial conditions [e.g., multimodel initial-condition large-ensemble simulations (Multi-LENS)], have been conducted by climate modeling centers. These two datasets may be used in combination to isolate the effects of model uncertainty and natural variability and to better understand the response of climate extremes to global warming.

In this study, we investigate future changes in the Midwest mean hydroclimate, the underlying dynamics, and the associated responses of the monthly-to-seasonal extremes, based on the CMIP6 and Multi-LENS models. Section 2 describes the datasets and methods used in this study. Section 3 elucidates the seasonally dependent precipitation changes and illustrates the underlying mechanisms. Section 4 examines future changes in the occurrence likelihood of monthly to seasonal precipitation extremes. Section 5 highlights the coupled temperature responses. Section 6 provides a summary and discussion.

2. Datasets and methods

a. Observations/reanalysis datasets

The monthly precipitation over land and ocean is obtained from the GPCC (Schneider et al. 2014) and GPCP (Adler et al. 2018) datasets, respectively. Variables related to the atmospheric circulation are obtained from the ERA-Interim dataset (Dee et al. 2011).

b. CMIP6

The responses of the monthly climatology to global warming are estimated based on future projections of 20 climate models (Table 1) from CMIP6 (Eyring et al. 2016). The present and future climates are represented respectively by the 1980–99 period in the historical simulations and the 2080–99 period following the Shared Socioeconomic Pathway (SSP) based on representative concentration pathway 8.5 (SSP5–8.5). To maintain an equal weight for each model, we only use the first realization (r1i1p1), which is available for all models. Unless otherwise stated, no normalization has been applied to the projections of individual models.

Table 1.

Names and modeling centers of the 20 CMIP6 models.

Table 1.

c. Multi-LENS

LENS refers to large-ensemble simulations initialized from slightly different conditions (e.g., Maher et al. 2019; Deser et al. 2020) so that each run represents a different realization consistent with the external forcing. The effect of natural variability can be effectively isolated in the ensemble mean. Monthly outputs are obtained from seven models (Table 2) that provide large-ensemble runs from 1960 to 2100 under the historical and RCP8.5 radiative forcing. The different projections among these seven models represent the model uncertainty.

Table 2.

Seven models that provide initial-condition large-ensemble runs from 1960 to 2100 under historical and RCP8.5 radiative forcing.

Table 2.

d. Storm track intensity

The storm track intensity is estimated as the 24-h difference filtered variance (Wallace et al. 1988; Chang et al. 2012) of 6-hourly meridional wind, that is, [υ(t+24h)υ(t)]2¯, where the overbar indicates time averaging over a month. With a half power point at periods of 1.2 and 6 days (Chang and Fu 2002), this filter produces similar results to those from bandpass filters.

e. GPLLJ-induced low-level moisture convergence

The impact of the GPLLJ on the low-level moisture convergence is computed as
(V¯850q¯850),
where V¯850 is the monthly 850-hPa horizontal winds, q¯850 is the monthly 850-hPa specific humidity, and ∇ is the divergence operator.

f. Potential evapotranspiration

To evaluate land aridity, potential evapotranspiration (PET) is computed following the Penman–Monteith algorithm (Allen et al. 1998). Given the near-surface air temperature Ta, wind speed |u|, and net downward radiation flux Rn, PET is formulated as
PET=(RnG)Δ+ρaCpe*(1RH)CH|u|Δ+γ(1+rsCH|u|)/Lυ
where e* is the saturation vapor pressure at Ta, Δde*/dTa is the local slope of the saturation vapor pressure curve, ρa is the air density, Cp is the air specific heat, rs is the assumed bulk stomatal resistance of well-watered vegetation, CH is an assumed scalar transfer coefficient, Lυ is the heat of vaporization of water, γ ≡ (Cpps)/(εLυ), ps is the air pressure, ε ≈ 0.622 is the ratio of molar masses of water vapor and dry air, and G is the heat flux into the ground or soil. We parameterize G as 5% of Rn and use rs of 70 s m−1 and CH of 4.8 × 10−3, following Fu and Feng (2014).

3. Seasonally dependent precipitation changes and the mechanism

a. Climatology and future changes

The Midwest, denoted by the black box in Fig. 1, is located in the northern part of the central United States. The central U.S. warm-season hydroclimate is regulated by two major systems (Figs. 1a–c): the midlatitude storm track to the north that brings synoptic disturbances and the subtropical anticyclone to the south that transports moisture inland through low-level winds (i.e., the GPLLJ). Both systems are linked to the North American westerly jet (NAWJ), in that the storm track rides along the northern flank of the westerly jet while the low-level winds merge into the surface westerlies from the south. The regional precipitation in the central United States migrates seasonally with the NAWJ (Figs. 1a–c) and the Midwest precipitation peaks in June when the NAWJ migrates to the region with associated storm track and low-level southerly winds (Fig. 1d). The CMIP6 ensemble can capture the basic features of the regional circulation and precipitation but is less successful in fully simulating the precipitation peak over the U.S. Great Plains (Figs. 1e–g). Despite this weakness, the CMIP6 ensemble is able to capture the observed seasonal cycle of the Midwest precipitation (Fig. 1h).

Fig. 1.
Fig. 1.

(a)–(c) Spatial pattern of the climatological precipitation (shading), 500-hPa zonal wind (magenta contours at 10, 12, and 14 m s−1), 850-hPa storm track intensity (blue contours at a 30 m2 s−2 interval, starting from 90 m2 s−2), and 850-hPa horizontal winds (vectors) for (a) April–May, (b) June, and (c) July–August based on GPCP/GPCC and ERA-Interim. (d) The Midwest precipitation (green line) and the latitude of the NAWJ (purple line) as a function of month. The latitude of the Midwest is denoted by the yellow line. (e)–(h) As in (a)–(d), but for the climatology simulated by the CMIP6 ensemble. (h) Shading indicates the 25th and 75th percentiles of the model simulations. The dashed lines show the future climatology. (i)–(k) Future changes in the climatological precipitation (shading), 500-hPa zonal wind (contours at 0.5 m s−1 interval, dashed for negative) and 850-hPa horizontal winds (vectors) for (i) April–May, (j) June, and (k) July–August. (l) Future changes in the Midwest precipitation as a function of month. Projections of individual models are shown in dashes while the multimodel mean is shown in the green line. (m)–(o) Seasonal evolution of the climatological precipitation (shading), 500-hPa zonal wind (contours at 0.8 m s−1 interval) and 850-hPa horizontal winds (vectors), as indicated by changes from (m) April to June, (n) May to July, and (o) June to August. (p) Temporal changes in the Midwest precipitation, (Pnext_monthPprev_month)/2, as a function of month. The Midwest region is denoted by the black box.

Citation: Journal of Climate 35, 1; 10.1175/JCLI-D-21-0012.1

Future changes in the regional circulation and precipitation projected by the CMIP6 ensemble are shown in Figs. 1i–k. A major feature of the regional circulation changes is the poleward shift of the NAWJ, indicated by a meridional dipole pattern with enhanced westerlies to the north and reduced westerlies to the south. It is well known that the zonal-mean westerly jet will shift poleward under global warming (Yin 2005; Chen et al. 2008; Barnes and Polvani 2013); at regional scale, however, the westerly jet can shift either poleward or equatorward (Simpson et al. 2014). Over North America, the westerly jet is projected to shift poleward consistently throughout the warm season (Fig. 1h; jet latitudes in current and future climates indicated by the purple lines). The regional precipitation changes feature a meridional dipole pattern too, with wetting to the north but drying to the south. This dipole pattern migrates poleward gradually from April to August along with the climatological seasonal progression. Encompassed in this migrating meridional dipole pattern, precipitation changes in the Midwest feature wetting in April–May, little change in June, and drying in July–August (Figs. 1i–k). Among individual CMIP6 models, the late-spring wetting and late-summer drying are robustly projected by more than 90% of the models (Fig. 1l). With increased (decreased) precipitation before (after) the peak rainy month, the seasonal cycle of the Midwest precipitation will be advanced (Fig. 1h; dashed green line vs solid green line).

The correlated pattern changes between the NAWJ and the regional precipitation indicate potential physical links between them. Previous studies have also suggested correlations between the jet shift and the regional circulation changes in the U.S. southern Great Plains (Bukovsky et al. 2017) and in western Europe (Simpson et al. 2019). Here, we illustrate that the poleward NAWJ shift affects both the GPLLJ and the storm track, and consequently plays a central role in regulating future precipitation changes in the Midwest.

b. Late-spring wetting: Enhanced GPLLJ

A poleward jet shift with adjusted eddy momentum flux convergence can induce anomalous descent and positive SLP (e.g., Kang et al. 2011). Such a relation between the jet shift and the SLP anomaly is manifest in future zonal-mean changes in the Southern Hemisphere (Fig. 2a). In the Northern Hemisphere, the circulation changes are zonally inhomogeneous (Simpson et al. 2014). Nevertheless, the linkage between the poleward jet shift and the SLP anomaly is clear in that the positive SLP anomalies are only found in regions with the poleward shift of the regional westerly jet, such as over the west Pacific in February (Fig. 2b) and the west Atlantic in April (Fig. 2c). These regional positive SLP anomalies contribute to the zonal mean Hadley expansion in the Northern Hemisphere. Here, with the substantial poleward NAWJ shift in April–May (Fig. 3a), a notable positive SLP anomaly arises underneath it over the west Atlantic (Fig. 3d). The positive SLP anomaly excites anomalous southerlies to its west, leading to an intensified and northward-extended GPLLJ (Fig. 3d). Such a dynamical link from the poleward jet shift to the regional positive SLP anomaly and to the GPLLJ enhancement is also seen in autumn (Figs. 3c,f). With abundant moisture from the rainy-season southern Great Plains, the enhanced GPLLJ in late spring leads to increased low-level moisture flux convergence into the Midwest (blue contours in Fig. 3d) and contributes to the projected late-spring wetting.

Fig. 2.
Fig. 2.

(a) Future changes in the (top) zonal mean zonal wind (shading) and vertical velocity (contours at a 15 hPa day−1 interval) and (bottom) SLP averaged from February to April in the SH. (b) Future changes (shading) in the (top) 500-hPa zonal wind and (bottom) SLP for the NH in February. The climatological zonal wind (contour levels at 20 and 30 m s−1) and SLP (contours levels at 1018, 1021, and 1024 hPa) are shown in contours. (c) As in (b), but for the NH in April. Different from (b), the climatological zonal wind is contoured at the levels of 15 and 20 m s−1.

Citation: Journal of Climate 35, 1; 10.1175/JCLI-D-21-0012.1

Fig. 3.
Fig. 3.

(a)–(c) Future changes in the 500-hPa zonal winds (shading) for (a) April–May, (b) July–August, and (c) October–November. The climatological zonal wind is indicated by the gray contours (levels at 11, 14, and 17 m s−1). (d)–(f) Future changes in the SLP (shading) and 850-hPa horizontal winds (vectors) for (d) April–May, (e) July–August, and (f) October–November. The blue contours indicate changes in the moisture flux convergence (levels at 10−6, 2 × 10−6, and 3 × 10−6 kg kg−1 s−1). The climatological SLP is indicated by the brown solid contours (interval of 3 hPa; relative to 1015 hPa). (g)–(i) Future changes in the 850-hPa storm track (shading) for (g) April–May, (h) July–August, and (i) October–November. The climatological storm track is indicated by the black solid contours (levels at 40, 60, 80, and 100 m2 s−2).

Citation: Journal of Climate 35, 1; 10.1175/JCLI-D-21-0012.1

c. Late-summer drying: Suppressed storm track

In late summer, however, the poleward NAWJ shift acts at a more northward location than in spring and is weaker in magnitude than in autumn (Fig. 3b). The anomalous SLP is found mainly over the oceans close to the continent (Fig. 3e), suggesting an effect of enhanced land–sea contrast (Li et al. 2012; Shaw and Voigt 2015). As a result, the southerly winds are only enhanced over the Gulf of Mexico and there are no significant changes in the GPLLJ and moisture flux convergence over the Midwest (Fig. 3e).

In the absence of the GPLLJ changes, the late-summer drying is explained by the weakened storm track (Fig. 3h). In the climatology, the NAWJ is accompanied by a strong storm track. The storm track peaks slightly poleward of the westerly jet, as seen in both the reanalysis (Figs. 1a–c) and climate models (Figs. 1e–g). Under global warming, the storm track intensity in the central United States is substantially weakened equatorward of its climatology and slightly enhanced poleward of its climatology (Figs. 3g–i). Such a pattern change can be understood as a combined result of a poleward shift along with the NAWJ and a general weakening due to enhanced midlatitude static stability (Frierson 2006; Lehmann et al. 2014). As this pattern of storm track changes migrates poleward over the warm season, the storm track intensity in the Midwest is slightly enhanced in late spring (Fig. 3g) but substantially suppressed in late summer (Fig. 3h). The weakened storm track in late summer corresponds to reduced synoptic disturbances for triggering precipitation and can contribute to the projected late-summer drying. From the perspective of the atmospheric moisture budget, when synoptic storms trigger precipitation, moisture is converged into the region. The reduced storm track intensity thus implies decreased eddy moisture convergence which is balanced by the reduced precipitation.

d. Seasonally dependent precipitation changes under poleward jet shift analogous to climatological seasonal progression

The seasonally dependent precipitation changes are summarized by the Hovmöller diagrams of the longitudinal-mean changes over the central United States (Fig. 4). As the NAWJ shifts poleward consistently over the warm season (Fig. 4a), the Midwest precipitation increases in late spring but decreases in late summer (Fig. 4b). The late spring wetting is accomplished by the enhanced and northward-extended GPLLJ that converges more moisture into the Midwest (Fig. 4c) whereas the late summer drying is consistently explained by the weakened storm track (Fig. 4d).

Fig. 4.
Fig. 4.

(a) Future changes in the longitudinal-mean (80°–50°W) 500-hPa zonal wind (shading) as a function of month and latitude. The climatology is shown in contours (levels at 12, 14, and 16 m s−1), with black for current and red for future. (b) Future changes in the central U.S. longitudinal-mean (105°–85°W) precipitation as a function of month and latitude. The climatology is shown in contours (levels at 54, 72, 90, and 108 mm day−1), black for current and red for future. (c) Future changes in the central U.S. longitudinal-mean 850-hPa winds (vectors) and moisture-flux convergence of the mean circulation (shading). (d) Future changes in the central U.S. longitudinal-mean 850-hPa storm track intensity. The climatology is shown in contours (levels at 60, 80, 100, and 120 m−2 s−2). The white dots indicate that more than 75% of the models agree on the sign.

Citation: Journal of Climate 35, 1; 10.1175/JCLI-D-21-0012.1

The seasonally dependent precipitation changes under future poleward NAWJ shift (Figs. 4a,b and 1h,l) are analogous to the seasonal progression of the climatological precipitation under poleward NAWJ migration (Fig. 1p). In spring, the regional precipitation peaks south of the Midwest (Fig. 1a). As the NAWJ migrates poleward toward the Midwest, the GPLLJ enhances and extends farther northward, and the Midwest receives more precipitation (Figs. 1a,b,m). In June, the Midwest precipitation peaks (Fig. 1b) so there is little precipitation change in the Midwest associated with the seasonal jet migration from May to July (Fig. 1n). In late summer, as the NAWJ migrates farther northward, the storm track shifts away from the Midwest, and the Midwest precipitation declines from the June peak (Figs. 1b,c,o). In analogy to such seasonal evolution (Figs. 1m–o), the future poleward NAWJ shift brings more precipitation to the Midwest in late spring (Fig. 1i), has little effect on the June precipitation (Fig. 1j), and reduces the Midwest precipitation in late summer (Fig. 1k).

4. Substantial increases in extremely wet late spring and extremely dry late summer

In light of the substantial future changes in the mean precipitation, we look into how monthly to seasonal precipitation extremes in the Midwest will change under global warming. We first estimate the response of the precipitation extremes to global warming in the ensemble run of each LENS model. Based on the different projections of the seven LENS models, a linear relationship is established between the change in the climatological mean and the change in the precipitation extremes. We then estimate future changes in the precipitation extremes by applying this relationship to the climatological mean precipitation changes projected by the CMIP6 ensemble. The combined usage of the Multi-LENS and CMIP6 models allows us to develop a clear understanding of the forced response, the natural variability, and the intermodel uncertainty of the precipitation extremes.

Taking the CESM-LENS for an example, the extremely wet late springs and extremely dry late summers, identified based on the 1st and 99th historical percentiles, are projected to occur much more frequently as the climate warms (Figs. 5a,b). In particular, the CESM-LENS projects a 19-times increase in the likelihood of an extremely wet April–May (Fig. 5c) and a 6-times increase in the likelihood of an extremely dry July–August (Fig. 5d) over this century under the RCP8.5 scenario. The same analysis has been conducted for the other six LENS models. It is found that the increased likelihoods of the dry/wet extremes vary considerably among individual LENS models but are correlated with their projected climatological mean changes (Figs. 5e,f). In particular, the linear regression line indicates that a 10 mm month−1 climatological drying (wetting) would translate to a 10-times increase in the likelihood of extremely dry (wet) events. This relationship is then applied to the CMIP ensemble projections. As shown by the black dots in Figs. 5e and 5f, the CMIP6 ensemble predicts a 16 ± 5 mm month−1 (the range between the 25th and 75th percentiles of the CMIP6 projections) increase in the late-spring mean precipitation and a 12 ± 6 mm month−1 decrease in the late-summer mean precipitation. This translates to a 16 ± 5 times increase in the likelihood of extremely wet late springs (red star in Fig. 5e) and a 12 ± 6 times increase in the likelihood of extremely dry late summers (red star in Fig. 5f). The effect of the natural variability is measured by the range between the 25th and 75th percentiles of the LENS member projections (shading in Figs. 5c and 5d for the CESM-LENS and error bars in Figs. 5e and 5f for individual LENS models). Averaging over the seven LENS models, the natural variability accounts for a ±6-times change in the likelihood of the extremely wet late spring and a ±5-times change in the likelihood of the extremely dry late summer.

Fig. 5.
Fig. 5.

(a) Occurrences of the extremely wet (green bars; over the 99th historical percentile) and extremely dry (orange bars; below the 1st historical percentile) April–May in 40 members of the CESM-LENS (each row represents one member). (b) As in (a), but for occurrences of the extremely wet (green) and extremely dry (orange) July–August. (c) Changes in the likelihoods (counting over a 20-yr moving window) of extremely wet (green) and extremely dry (orange) April–May as the climate warms. Thick lines show the likelihoods calculated over all 40 members of the CESM-LENS and shading indicates the range from the 25th to the 75th percentiles of the ensemble projection. (d) As in (c), but for the likelihoods of the extremely wet and extremely dry July–August. (e) Correlation between the climatological April–May wetting (x axis) and the amplified likelihood (LFUTR/LHIST) of extremely wet April–May (y axis) among the seven LENS models. For each LENS model, the mean of its ensemble members is indicated by the dot while the intermodel spread is shown by the error bars. The climatological precipitation changes projected by the 20 CMIP6 models are shown in dots, with the ensemble mean indicated by the red triangle. The red star indicates the estimation. (f) As in (e), but for the climatological July–August drying and the amplified likelihood of the extremely dry July–August.

Citation: Journal of Climate 35, 1; 10.1175/JCLI-D-21-0012.1

5. Coupled temperature responses to future precipitation changes

The Midwest is known as a region with strong land–atmosphere coupling (Koster et al. 2004) so that precipitation changes may effectively modulate the response of the land surface temperature. Indeed, with late-spring wetting but late-summer drying, the surface warming in the Midwest is damped in late spring but amplified in late summer (Fig. 6a). The combination of reduced precipitation and enhanced warming in late summer suggests increased land aridity. Indeed, the ratio of potential evapotranspiration (PET; section 2f) to precipitation (P), an index that quantifies land aridity, increases substantially in the Midwest late summer under global warming (Fig. 6b).

Fig. 6.
Fig. 6.

(a) Future changes in the Midwest climatological precipitation (ΔP¯; green) and near-surface temperature (ΔTs¯; red) as a function of month. To highlight the monthly coupling between temperature and precipitation changes, projections of individual CMIP6 models have been normalized by their projected annual-mean global-mean surface warming and multiplied by the ensemble-mean warming of 4 K. (b) Future changes in the ratio between potential evapotranspiration and precipitation (ΔPET/P¯) as a function of month. The solid lines indicate the ensemble mean and the shading indicates the range from the 25th to 75th percentiles of the model ensemble.

Citation: Journal of Climate 35, 1; 10.1175/JCLI-D-21-0012.1

Drier (wetter) summers are often associated with warmer (cooler) land temperature and extreme droughts can be compounded by extreme heat (black dots in Fig. 7a). Such coupled response of drying and warming is consistently reproduced in the historical simulations of the CMIP6 models (gray dots in Fig. 7a). The comparison between the composites of extreme dry and wet late summers in CESM-LENS illustrates that the anomalous drying (Fig. 7b) and warming (Fig. 7c) are regionally coupled through radiative and soil moisture feedbacks. Specifically, declined precipitation leads to reduced soil moisture that weakens evaporative cooling (Fig. 7d) and enhanced surface solar radiation that warms the surface (Fig. 7e). In the future, such compound extremes will occur much more frequently and with much higher heat. Under SSP5–8.5 (orange dots in Fig. 7a), an extreme dry late summer similar to that in 1936 is expected to be 12 times more frequent (see the gray and orange dots left of the 1st percentile line) and will be associated with an excessive monthly mean temperature above 31°C! Note that the current climatology is ~22°C and the record high of 1936 is ~25°C (the enlarged red dot in Fig. 7a). The situation is less merciless but still grim under an intermediate emission scenario — SSP2–4.5 (green dots in Fig. 7a). The extreme drought will be 5 times more frequent and associated with a monthly mean temperature above 27°C.

Fig. 7.
Fig. 7.

(a) The Midwest July–August precipitation (x axis) and temperature (y axis) of each year in observations (1901–2015; black dots) and in the normalized CMIP6 ensembles of historical (1980–99; gray dots), SSP2–4.5 (2080–99; green dots), and SSP5–8.5 (2080–99; orange dots) periods. To highlight the interannual coupling, temperature and precipitation in individual CMIP models have been normalized so that the resultant climatological mean in individual models is identical and equal to the multimodel mean. The 1st percentile of the simulated historical precipitation (gray dots) is denoted by the gray dashed line. The Dust Bowl drought of 1936 is denoted. Also shown are differences in the (b) precipitation, (c) surface temperature, (d) soil moisture, and (e) net surface solar radiation between composites of the extremely dry and wet July–August in CESM-LENS. The white dots indicate that the differences are significant with p < 0.05 using the t test.

Citation: Journal of Climate 35, 1; 10.1175/JCLI-D-21-0012.1

6. Summary and discussion

This study investigates future changes in the Midwest hydroclimate and extremes based on the CMIP6 and Multi-LENS models. We show that future precipitation changes in the Midwest are seasonally dependent, featuring late-spring wetting but late-summer drying. Corresponding to such climatological changes, the frequency of extremely wet late springs (April–May precipitation above the 99th percentile of the historical period) and extremely dry late summers (July–August precipitation below the 1st percentile of the historical period) are both projected to increase by more than 10 times under the high emission scenario. A once-in-a-century event in the historical period can become once-in-a-decade by the end of this century. The seasonally dependent precipitation changes further modulate the temperature response. Specifically, the surface warming in the Midwest is damped in later spring with increased precipitation but amplified in late summer with reduced precipitation. The combination of the amplified warming and reduced precipitation in late summer leads to increased land aridity. The extremely dry late summers are compounded by extreme heat. In the future, they will become even more threatening with increased occurrence and higher extreme heat.

The seasonally dependent precipitation changes are regulated by changes in the GPLLJ and storm tracks, that are both linked to the poleward NAWJ shift. Specifically, the increased precipitation in late spring is accomplished by the enhanced and northward-extended GPLLJ, which converges more moisture into the Midwest. The GPLLJ changes are driven by regional SLP anomalies linked to the poleward NAWJ shift. The reduced precipitation in late summer is explained by the reduced storm track in the Midwest. The storm track weakens partly because the storm track shifts poleward along with the NAWJ.

With late-spring wetting but late-summer drying, the rainy-season (April–August) mean precipitation changes are small and deemed uncertain. The frequency of the extreme April–August precipitation only increases by 2 times, which is much smaller than the frequency increase in the extremely wet April–May and extremely dry July–August. This means that the rainy-season mean projection underestimates the severity of the climate impacts in the Midwest.

The seasonally dependent precipitation changes under future poleward jet shift are analogous to its climatological seasonal progression under poleward jet migration. One may interpret future precipitation changes as a result of the imprints of the poleward jet shift on the climatological precipitation pattern. This suggests that the model-projected precipitation changes may be sensitive to both the degree of the poleward NAWJ shift and the representation of the current climatology. While nearly all models project a poleward NAWJ shift in the warm season, the degree of the poleward shift varies substantially among models (Zhou et al. 2021). The zonal-mean poleward jet shift has been extensively studied, but future jet shifts in the Northern Hemisphere are longitudinally and seasonally dependent (Simpson et al. 2014). It is not well understood what processes govern such dependency. Future studies are needed to better understand the mechanisms underlying the regional poleward jet shift. The development of such understanding is critical for constraining the degree of the future poleward NAWJ shift. Given the potential sensitivity to the climatological seasonal progression, more effort is needed to improve the models’ representation of the observed climatology. We have shown that climate models have difficulty fully simulating the precipitation hotspot east of the Rockies. Previous studies also found that climate models have dry and warm biases over the central United States (Lin et al. 2017). These biases are not likely to affect the basic changes presented here, but they may affect the detailed changes at local scales.

Our study has emphasized the importance of large-scale circulation changes in controlling the Midwest precipitation change. This however does not rule out the possibility that other processes, such as the mesoscale convective system (Prein et al. 2017) and soil moisture feedbacks (Dirmeyer et al. 2012), may change with global warming and modulate the large-scale dynamically driven changes.

Acknowledgments

This study was supported by Office of Science, U.S. Department of Energy Biological and Environmental Research as part of the Regional and Global Model Analysis and Multisector Dynamics program areas. We acknowledge the WCRP Working Group on Coupled Modelling, which is responsible for the CMIP. We acknowledge the U.S. CLIVAR Working Group on Large Ensembles for providing the Multi- LENS archive. The Pacific Northwest National Laboratory (PNNL) is operated for DOE by Battelle Memorial Institute under Contract DE-AC05-76RLO1830. The authors declare that they have no competing interests.

Data availability statement

The CMIP outputs used in this study can be obtained from the CMIP5 and CMIP6 archives at https://esgf-node.llnl.gov/projects/esgf-llnl/. The ERA-Interim dataset is available at https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim. The GPCP and GPCC precipitation dataset are available at https://psl.noaa.gov/data/gridded/data.gpcp.html and https://psl.noaa.gov/data/gridded/data.gpcc.html, respectively.

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Save
  • Adler, R. F., and Coauthors, 2018: The Global Precipitation Climatology Project (GPCP) monthly analysis (new version 2.3) and a review of 2017 global precipitation. Atmosphere, 9, 138, https://doi.org/10.3390/atmos9040138.

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    • Search Google Scholar
    • Export Citation
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    • Crossref
    • Search Google Scholar
    • Export Citation
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    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chang, E. K. M., Y. Guo, and X. Xia, 2012: CMIP5 multimodel ensemble projection of storm track change under global warming. J. Geophys. Res., 117, D23118, https://doi.org/10.1029/2012JD018578.

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  • Chen, G., J. Lu, and D. M. W. Frierson, 2008: Phase speed spectra and the latitude of surface westerlies: Interannual variability and global warming trend. J. Climate, 21, 59425959, https://doi.org/10.1175/2008JCLI2306.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
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    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cook, K. H., E. K. Vizy, Z. S. Launer, and C. M. Patricola, 2008: Springtime intensification of the Great Plains low-level jet and Midwest precipitation in GCM simulations of the twenty-first century. J. Climate, 21, 63216340, https://doi.org/10.1175/2008JCLI2355.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
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    • Crossref
    • Search Google Scholar
    • Export Citation
  • Deser, C., and Coauthors, 2020: Insights from Earth system model initial-condition large ensembles and future prospects. Nat. Climate Change, 10, 277286, https://doi.org/10.1038/s41558-020-0731-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dirmeyer, P. A., and Coauthors, 2012: Evidence for enhanced land–atmosphere feedback in a warming climate. J. Hydrometeor., 13, 981995, https://doi.org/10.1175/JHM-D-11-0104.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dong, L., L. R. Leung, and F. Song, 2018: Future changes of subseasonal precipitation variability in North America during winter under global warming. Geophys. Res. Lett., 45, 12 46712 476, https://doi.org/10.1029/2018GL079900.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Eyring, V., S. Bony, G. A. Meehl, C. A. Senior, B. Stevens, R. J. Stouffer, and K. E. Taylor, 2016: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev., 9, 19371958, https://doi.org/10.5194/gmd-9-1937-2016.

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    • Search Google Scholar
    • Export Citation
  • Frierson, D. M. W., 2006: Robust increases in midlatitude static stability in simulations of global warming. Geophys. Res. Lett., 33, L24816, https://doi.org/10.1029/2006GL027504.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fu, Q., and S. Feng, 2014: Responses of terrestrial aridity to global warming. J. Geophys. Res. Atmos., 119, 78637875, https://doi.org/10.1002/2014JD021608.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Held, I. M., and B. J. Soden, 2006: Robust responses of the hydrological cycle to global warming. J. Climate, 19, 56865699, https://doi.org/10.1175/JCLI3990.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
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  • IPCC, 2013: Climate Change 2013: The Physical Science Basis. Cambridge University Press, 1535 pp., https://doi.org/10.1017/CBO9781107415324.

    • Crossref
    • Export Citation
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    • Crossref
    • Search Google Scholar
    • Export Citation
  • Koster, R. D., and Coauthors, 2004: Regions of strong coupling between soil moisture and precipitation. Science, 305, 11381140, https://doi.org/10.1126/science.1100217.

    • Crossref
    • Search Google Scholar
    • Export Citation
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  • Fig. 1.

    (a)–(c) Spatial pattern of the climatological precipitation (shading), 500-hPa zonal wind (magenta contours at 10, 12, and 14 m s−1), 850-hPa storm track intensity (blue contours at a 30 m2 s−2 interval, starting from 90 m2 s−2), and 850-hPa horizontal winds (vectors) for (a) April–May, (b) June, and (c) July–August based on GPCP/GPCC and ERA-Interim. (d) The Midwest precipitation (green line) and the latitude of the NAWJ (purple line) as a function of month. The latitude of the Midwest is denoted by the yellow line. (e)–(h) As in (a)–(d), but for the climatology simulated by the CMIP6 ensemble. (h) Shading indicates the 25th and 75th percentiles of the model simulations. The dashed lines show the future climatology. (i)–(k) Future changes in the climatological precipitation (shading), 500-hPa zonal wind (contours at 0.5 m s−1 interval, dashed for negative) and 850-hPa horizontal winds (vectors) for (i) April–May, (j) June, and (k) July–August. (l) Future changes in the Midwest precipitation as a function of month. Projections of individual models are shown in dashes while the multimodel mean is shown in the green line. (m)–(o) Seasonal evolution of the climatological precipitation (shading), 500-hPa zonal wind (contours at 0.8 m s−1 interval) and 850-hPa horizontal winds (vectors), as indicated by changes from (m) April to June, (n) May to July, and (o) June to August. (p) Temporal changes in the Midwest precipitation, (Pnext_monthPprev_month)/2, as a function of month. The Midwest region is denoted by the black box.

  • Fig. 2.

    (a) Future changes in the (top) zonal mean zonal wind (shading) and vertical velocity (contours at a 15 hPa day−1 interval) and (bottom) SLP averaged from February to April in the SH. (b) Future changes (shading) in the (top) 500-hPa zonal wind and (bottom) SLP for the NH in February. The climatological zonal wind (contour levels at 20 and 30 m s−1) and SLP (contours levels at 1018, 1021, and 1024 hPa) are shown in contours. (c) As in (b), but for the NH in April. Different from (b), the climatological zonal wind is contoured at the levels of 15 and 20 m s−1.

  • Fig. 3.

    (a)–(c) Future changes in the 500-hPa zonal winds (shading) for (a) April–May, (b) July–August, and (c) October–November. The climatological zonal wind is indicated by the gray contours (levels at 11, 14, and 17 m s−1). (d)–(f) Future changes in the SLP (shading) and 850-hPa horizontal winds (vectors) for (d) April–May, (e) July–August, and (f) October–November. The blue contours indicate changes in the moisture flux convergence (levels at 10−6, 2 × 10−6, and 3 × 10−6 kg kg−1 s−1). The climatological SLP is indicated by the brown solid contours (interval of 3 hPa; relative to 1015 hPa). (g)–(i) Future changes in the 850-hPa storm track (shading) for (g) April–May, (h) July–August, and (i) October–November. The climatological storm track is indicated by the black solid contours (levels at 40, 60, 80, and 100 m2 s−2).

  • Fig. 4.

    (a) Future changes in the longitudinal-mean (80°–50°W) 500-hPa zonal wind (shading) as a function of month and latitude. The climatology is shown in contours (levels at 12, 14, and 16 m s−1), with black for current and red for future. (b) Future changes in the central U.S. longitudinal-mean (105°–85°W) precipitation as a function of month and latitude. The climatology is shown in contours (levels at 54, 72, 90, and 108 mm day−1), black for current and red for future. (c) Future changes in the central U.S. longitudinal-mean 850-hPa winds (vectors) and moisture-flux convergence of the mean circulation (shading). (d) Future changes in the central U.S. longitudinal-mean 850-hPa storm track intensity. The climatology is shown in contours (levels at 60, 80, 100, and 120 m−2 s−2). The white dots indicate that more than 75% of the models agree on the sign.

  • Fig. 5.

    (a) Occurrences of the extremely wet (green bars; over the 99th historical percentile) and extremely dry (orange bars; below the 1st historical percentile) April–May in 40 members of the CESM-LENS (each row represents one member). (b) As in (a), but for occurrences of the extremely wet (green) and extremely dry (orange) July–August. (c) Changes in the likelihoods (counting over a 20-yr moving window) of extremely wet (green) and extremely dry (orange) April–May as the climate warms. Thick lines show the likelihoods calculated over all 40 members of the CESM-LENS and shading indicates the range from the 25th to the 75th percentiles of the ensemble projection. (d) As in (c), but for the likelihoods of the extremely wet and extremely dry July–August. (e) Correlation between the climatological April–May wetting (x axis) and the amplified likelihood (LFUTR/LHIST) of extremely wet April–May (y axis) among the seven LENS models. For each LENS model, the mean of its ensemble members is indicated by the dot while the intermodel spread is shown by the error bars. The climatological precipitation changes projected by the 20 CMIP6 models are shown in dots, with the ensemble mean indicated by the red triangle. The red star indicates the estimation. (f) As in (e), but for the climatological July–August drying and the amplified likelihood of the extremely dry July–August.

  • Fig. 6.

    (a) Future changes in the Midwest climatological precipitation (ΔP¯; green) and near-surface temperature (ΔTs¯; red) as a function of month. To highlight the monthly coupling between temperature and precipitation changes, projections of individual CMIP6 models have been normalized by their projected annual-mean global-mean surface warming and multiplied by the ensemble-mean warming of 4 K. (b) Future changes in the ratio between potential evapotranspiration and precipitation (ΔPET/P¯) as a function of month. The solid lines indicate the ensemble mean and the shading indicates the range from the 25th to 75th percentiles of the model ensemble.

  • Fig. 7.

    (a) The Midwest July–August precipitation (x axis) and temperature (y axis) of each year in observations (1901–2015; black dots) and in the normalized CMIP6 ensembles of historical (1980–99; gray dots), SSP2–4.5 (2080–99; green dots), and SSP5–8.5 (2080–99; orange dots) periods. To highlight the interannual coupling, temperature and precipitation in individual CMIP models have been normalized so that the resultant climatological mean in individual models is identical and equal to the multimodel mean. The 1st percentile of the simulated historical precipitation (gray dots) is denoted by the gray dashed line. The Dust Bowl drought of 1936 is denoted. Also shown are differences in the (b) precipitation, (c) surface temperature, (d) soil moisture, and (e) net surface solar radiation between composites of the extremely dry and wet July–August in CESM-LENS. The white dots indicate that the differences are significant with p < 0.05 using the t test.

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