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Dingwen Zeng
and
Xing Yuan

Abstract

Persistent drought events that cause serious damage to the economy and environment are usually intensified by the feedback between the land surface and atmosphere. Therefore, reasonably modeling land–atmosphere coupling is critical for skillful prediction of persistent droughts. However, most high-resolution regional climate modeling has focused on the amplification effect of land–atmosphere coupling on local anticyclonic circulation anomalies, while less attention has been paid to the nonlocal influence through altering large-scale atmospheric circulation. Here we investigate how the antecedent land–atmosphere coupling over the area south of Lake Baikal (ASLB) influences the drought events occurring over its downstream region [i.e., Northeast China (NEC)] by using the Weather Research and Forecasting (WRF) Model and a linear baroclinic model (LBM). When the ASLB region is artificially forced to be wet in the WRF simulations during March–May, the surface sensible heating is weakened and results in a cooling anomaly in low level atmosphere during May–July. Consequently, the anticyclonic circulation anomalies over ASLB and NEC are weakened, and the severity of NEC drought during May–July cannot be captured due to the upstream wetting in March–May. In the LBM experiments, idealized atmospheric heating anomaly that mimics the diabatic heating associated with surface wetness is imposed over ASLB, and the quasi-steady response pattern of 500-hPa geopotential height to the upstream wetting is highly consistent with that in the WRF simulation. In addition, the lower-level heating instead of the upper-level cooling makes a major contribution to the high pressure anomaly over NEC. This study implies the critical role of modeling upstream land–atmosphere coupling in capturing downstream persistent droughts.

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Dingwen Zeng
and
Xing Yuan

Abstract

The land surface, with a memory longer than the atmosphere in nature, has been recognized as an important source for Subseasonal to Seasonal (S2S) predictability through land–atmosphere coupling at multiple time scales. Understanding of the land–atmosphere coupling is important for improving subseasonal forecasting that is expected to fill the gap between medium-range weather forecasts and seasonal forecasts. Based on reanalysis and S2S reforecast datasets, land–atmosphere coupling is investigated over East Asia from daily to monthly time scales during summertime. Reanalysis results show that soil moisture–evapotranspiration (ET) coupling is closely related to the monsoonal rain belt shift. The coupling can be significant over humid regions (e.g., south China) during postmonsoon periods, where soil is usually drier, but insignificant over semiarid regions (e.g., north China) after the arrival of a monsoon, where soil is wetter. The dependence of soil moisture–ET coupling on soil wetness conditions decreases as the time scale increases, indicating more significant coupling at longer time scales. Similar sensitivities to time scales are found between ET and lifting condensation level (LCL), and between ET and precipitation, especially over land–atmosphere coupling hotspots. Monthly coupling strength analysis shows that ET–LCL coupling is a key process that determines the soil moisture–precipitation coupling, and the response of convective instability to ET is stronger at longer time scales. Subseasonal forecasting models also show more significant land–atmosphere coupling at monthly than daily time scales, where the ECMWF and NCEP models that best reproduce the coupling and its changes with monsoonal rain belt shifts have the best precipitation forecast skill among S2S models.

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Ruxuan Ma
and
Xing Yuan

Abstract

Flash droughts have been occurring frequently worldwide, which has a serious impact on food and water security. The rapid onset of flash droughts presents a challenge to the subseasonal forecast, but there is limited knowledge about their forecast skills due to the lack of appropriate identification and assessment procedures. Here, we investigate the forecast skill of flash droughts over China with lead times up to 3 weeks by using hindcast datasets from the Subseasonal-to-Seasonal Prediction (S2S) project. The flash droughts are identified by using weekly soil moisture percentiles from two S2S forecast models (ECMWF and NCEP). The comparison with reanalysis shows that ECMWF and NCEP forecast models underestimate flash drought occurrence by 5% and 19% for lead 1 week. The national mean hit rates for flash droughts are 0.22 and 0.16 for ECMWF and NCEP models for lead 1 week, and they can reach 0.29 and 0.18 over South China. The ensemble of the two models increases equitable threat score (ETS) from ECMWF and NCEP models by 8% and 40% for lead 1 week. In terms of probabilistic forecast, ECMWF has a higher Brier skill score than NCEP, especially over eastern China, which is consistent with higher temperature and precipitation forecast skill. The multimodel ensemble has the highest Brier skill score. This study suggests the importance of multimodel ensemble flash drought forecasting.

Significance Statement

Flash droughts have raised considerable concern, but whether they can be predicted at subseasonal time scales remains unclear. This study evaluates forecast skill of flash droughts over China based on ECMWF and NCEP hindcast data. Focusing on the historical flash drought events identified by the onset speed and duration, it is found that the ECMWF model outperformed the NCEP model with higher hit rates, lower false alarm ratios, and higher equitable threat scores, especially during the first week. However, less than 30% of the drought events can be captured in most regions by both models. An ensemble of the two models showed skill improvement against the ECMWF model for both deterministic and probabilistic forecasts.

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Dingwen Zeng
and
Xing Yuan

Abstract

Anomalies in atmospheric moisture transport are critical for drought formation, with East Asian monsoon anomalies identified as the primary driver of droughts in East Asia. However, the drought mechanism for the border region between monsoon and nonmonsoon land areas remains unclear due to complex interactions between latitudinal circulation patterns. Using a Lagrangian framework, we quantify moisture supply (MS) during spring and summer drought events within the border region, specifically Northeast China (NEC). Our results reveal that decreased MS from monsoon land areas is more crucial than nonmonsoon land areas and local areas for NEC droughts, with the local water recycling playing a more substantial role in summer than in spring. On average, summer droughts are 4 times more intense than spring droughts. Contributions to MS deficits from monsoon land areas, nonmonsoon land areas, and local areas during spring (summer) droughts are 43.2% (43.8%), 25.1% (19.2%), and 22% (33.8%), respectively. The weakened atmospheric circulation from source regions to NEC contributes to over 80% of MS deficits during droughts. Atmospheric wave trains triggered by North Atlantic sea surface temperature anomalies (SSTA) gradients, along with the weakening of the East Asian subtropical westerly jet in spring and a positive phase of the polar–Eurasian teleconnection in summer, contribute to spring and summer droughts, respectively. A sustained, record-breaking positive SSTA along the western European coast from the spring to summer excited a wave train, and resulted in the extreme 2017 spring–summer drought. These findings provide valuable insights into the drought mechanism within the interaction zone of circulation systems from different latitudes.

Significance Statement

The complex interactions between atmospheric circulation patterns at different latitudes have led to an insufficient understanding of the mechanisms causing extreme drought in the border region between the monsoon and nonmonsoon land areas. This study aims to investigate the drought mechanisms in the border region from the perspective of moisture tracking. Results indicate a moisture supply deficit from the monsoon region contributes the most (approximately half) to droughts, although the majority of air parcels reaching this region originate from the nonmonsoon region. The weakening of circulation contributes to more than 80% of the moisture supply deficit during droughts, far exceeding the impact of reduced evaporation from the moisture source regions. These findings are conducive to understanding the mechanisms of extreme drought.

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Tushar Apurv
,
Ximing Cai
, and
Xing Yuan

Abstract

Meteorological droughts in the continental United States (CONUS) are known to oscillate at the multidecadal time scale in response to the sea surface temperatures (SST) variability over the Pacific Ocean and the North Atlantic Ocean. While previous studies have focused on understanding the influence of SST oscillations on drought frequency over the CONUS, this information has not been integrated with global warming for future drought risk assessment at the decadal scale. In this study, we use the support vector machines (SVMs) to handle correlation between input variables for quantifying the influence of internal variability [Atlantic multidecadal oscillation (AMO) and Pacific decadal oscillation (PDO)] and global warming on the decadal changes in the severity of seasonal droughts over the CONUS during 1901–2015. The regional drivers of drought severity identified using SVMs are used for the assessment of decadal drought risk in the near future. We find internal variability as the dominant driver of decadal changes in drought severity in the southern and central Great Plains and global warming as the dominant driver for the southeastern and southwestern United States. In the southern Plains, the existing pattern of increasing drought severity is likely to persist in the near future if AMO and PDO remain in their positive and negative phases, respectively, while global warming is likely to contribute to increasing drought severity in the Southeast and Southwest. This study suggests an emerging role of global warming in drought risk over the southern states, where near-term climate change adaptation is necessary.

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Shanshan Wang
,
Jianping Huang
, and
Xing Yuan
Open access
Peng Ji
,
Xing Yuan
, and
Dan Li

Abstract

The Tibetan Plateau (TP), known as the world’s “Third Pole,” plays a vital role in regulating the regional and global climate and provides freshwater for about 1.5 billion people. Observations show an accelerated ground surface warming trend over the southeastern TP during the global warming slowdown period of 1998–2013, especially in the summer and winter seasons. The processes responsible for such acceleration are under debate as contributions from different radiative processes are still unknown. Here we estimate for the first time the contributions of each radiative component to the ground surface warming trend before and after 1998 by analyzing multisource datasets under an energy balance framework. Results show that declining cloud cover caused by the weakening of both the South Asian summer monsoon and local-scale atmospheric upward motion mainly led to the accelerated ground surface warming during the summers of 1998–2013, whereas the decreased surface albedo caused by the snow melting was the major warming factor in winter. Moreover, increased clear-sky longwave radiation induced by the warming middle and upper troposphere was the second largest factor, contributing to about 21%–48% of the ground surface warming trend in both the summer and winter seasons. Our results unravel the key processes driving the ground surface warming over the southeastern TP and have implications for the development of climate and Earth system models in simulating ground surface temperature change and other related complex cryosphere–hydrosphere–atmosphere interactions over high-altitude land areas.

Free access
Xing Yuan
and
Xin-Zhong Liang

Abstract

This study presents a comprehensive evaluation on a Conjunctive Surface–Subsurface Process Model (CSSP) in predicting soil temperature–moisture distributions, terrestrial hydrology variations, and land–atmosphere exchanges against various in situ measurements and synthetic observations at regional–local scales over the contiguous United States. The CSSP, rooted in the Common Land Model (CoLM) with a few updates from the Community Land Model version 3.5 (CLM3.5), incorporates significant advances in representing hydrology processes with realistic surface (soil and vegetation) characteristics. These include dynamic surface albedo based on satellite retrievals, subgrid soil moisture variability of topographic controls, surface–subsurface flow interactions, and bedrock constraint on water table depths. As compared with the AmeriFlux tower measurements, the CSSP and CLM3.5 reduce surface sensible and latent heat flux errors from CoLM by 10 W m−2 on average, and have much higher correlations with observations for daily latent heat variations. The CSSP outperforms the CLM3.5 over the crop, grass, and shrub sites in depicting the latent heat annual cycles. While retaining the improvement for soil moisture in deep layers, the CSSP shows further advantage over the CLM3.5 in representing seasonal and interannual variations in root zones. The CSSP reduces soil temperature errors from the CLM3.5 (CoLM) by 0.2 (0.7) K at 0.1 m and 0.3 (0.6) K at 1 m; more realistically captures seasonal–interannual extreme runoff and streamflow over most regions and snow depth anomalies in high latitude (45°–52°N); and alleviates climatological water table depth systematic bias (absolute error) by about 1.2 (0.4) m. Clearly, the CSSP performance is overall superior to both the CoLM and CLM3.5. The remaining CSSP deficiencies and future refinements are also discussed.

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Shanshan Wang
,
Xing Yuan
, and
Renguang Wu
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Xing Yuan
,
Linying Wang
, and
Eric F. Wood
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