This study compare the characteristics of global mesoscale convective systems (MCSs) simulated in a global storm-resolving model (GSRM) and a high-resolution (~25-km) General Circulation Model (GCM), both developed at the Geophysical Fluid Dynamics Laboratory. By comparing with two satellite datasets, we examine the spatial distribution, seasonal/diurnal cycles, and event based features such as duration, size, intensity, and propagation of MCSs across six global hotspots. MCS-related precipitation features and their contribution to the total precipitation are also analyzed. Our results show that both models effectively capture the observed spatial patterns and seasonal cycles of MCSs, although notable differences exist in absolute values, particularly in the GCM. Both models simulate event-based statistics, but show large geographical variations with an overall tendency to produce longer-lasting and larger MCSs. The GSRM performs better in simulating MCSs diurnal cycle and MCS intensity. While both models replicate spatial patterns of MCS-related precipitation, they struggle with accurately capturing intensity, and their contributions to total precipitation vary. This comparison highlights strengths and limitations of these two types of models, calling for further process level investigation of model deficiencies and a detailed evaluation of observations due to dataset discrepancies.
Winter atmospheric rivers (ARs) are crucial for water supply and extreme weather events over the western (WUS) and eastern US (EUS), yet their long-term trends and interplay remain unclear. Here we fill this gap by analyzing multiple observational AR products over the past four decades. Contrasting yet interrelated trends emerge in AR frequency, intensity, and associated mean precipitation. A decline in AR activity over WUS contributes to a drying trend, while notable increases over EUS foster a wetter climate. These trends are driven by large-scale atmospheric and oceanic variability in the Pacific, which strengthens anticyclonic circulation patterns near both coasts. These anticyclonic patterns, however, have opposing effects–impeding ARs from steering to WUS while facilitating their development over EUS. Our findings present a unified explanation for the observed AR trends and have co-beneficial implications for mitigating concerns related to AR-induced extreme events across both densely populated coastal regions.
Feng, Zhe, Andreas F Prein, Julia Kukulies, Thomas Fiolleau, William K Jones, Ben Maybee, Zachary L Moon, Kelly M Núñez Ocasio, and Wenhao Dong, et al., April 2025: Mesoscale Convective Systems Tracking Method Intercomparison (MCSMIP): Application to DYAMOND global km-scale simulations. JGR Atmospheres, 130(8), DOI:10.1029/2024JD042204. Abstract
Global kilometer-scale models represent the future of Earth system modeling, enabling explicit simulation of organized convective storms and their associated extreme weather. Here, we comprehensively evaluate tropical mesoscale convective system (MCS) characteristics in the DYAMOND (DYnamics of the atmospheric general circulation modeled on non-hydrostatic domains) simulations for both summer and winter phases. Using 10 different feature trackers applied to simulations and satellite observations, we assess MCS frequency, precipitation, and other key characteristics. Substantial differences (a factor of 2–3) arise among trackers in observed MCS frequency and their precipitation contribution, but model-observation differences in MCS statistics are more consistent across trackers. DYAMOND models are generally skillful in simulating tropical mean MCS frequency, with multi-model mean biases ranging from −2%–8% over land and −8%–8% over ocean (summer vs. winter). However, most DYAMOND models underestimate MCS precipitation amount (23%) and their contribution to total precipitation (17%). Biases in precipitation contributions are generally smaller over land (13%) than over ocean (21%), with moderate inter-model variability. While models better simulate MCS diurnal cycles and cloud shield characteristics, they overestimate MCS precipitation intensity and underestimate stratiform rain contributions (up to a factor of 2), particularly over land, albeit observational uncertainties exist. Additionally, models exhibit a wide range of precipitable water in the tropics compared to reanalysis and satellite observations, with many models showing exaggerated sensitivity of MCS precipitation intensity to precipitable water. The MCS metrics developed here provide process-oriented diagnostics to guide future model development.
Cooper, Vincent T., Kyle Armour, Gregory J Hakim, Jessica E Tierney, Matthew B Osman, Cristian Proistosescu, Yue Dong, Natalie J Burls, Timothy Andrews, Daniel E Amrhein, Jiang Zhu, Wenhao Dong, Yi Ming, and Philip Chmielowiec, April 2024: Last Glacial Maximum pattern effects reduce climate sensitivity estimates. Science Advances, 10(16), DOI:10.1126/sciadv.adk9461. Abstract
Here, we show that the Last Glacial Maximum (LGM) provides a stronger constraint on equilibrium climate sensitivity (ECS), the global warming from increasing greenhouse gases, after accounting for temperature patterns. Feedbacks governing ECS depend on spatial patterns of surface temperature (“pattern effects”); hence, using the LGM to constrain future warming requires quantifying how temperature patterns produce different feedbacks during LGM cooling versus modern-day warming. Combining data assimilation reconstructions with atmospheric models, we show that the climate is more sensitive to LGM forcing because ice sheets amplify extratropical cooling where feedbacks are destabilizing. Accounting for LGM pattern effects yields a median modern-day ECS of 2.4°C, 66% range 1.7° to 3.5°C (1.4° to 5.0°C, 5 to 95%), from LGM evidence alone. Combining the LGM with other lines of evidence, the best estimate becomes 2.9°C, 66% range 2.4° to 3.5°C (2.1° to 4.1°C, 5 to 95%), substantially narrowing uncertainty compared to recent assessments.
Atmospheric rivers (ARs) play important roles in various extreme weather events across the US. While AR features in western US have been extensively studied, there remains limited understanding of their variability in the eastern US (EUS). Using both observations and a state-of-the-art climate model, we find a significant increase (~10% dec−1) in winter AR frequency in the EUS during the past four decades. This trend is closely linked to recent changes in the Pacific/North America (PNA) teleconnection pattern, accompanied by a poleward shift of the mid-latitude jet stream. We further reveal a strong correlation (R = 0.8; P < 0.001) between interannual variations in AR occurrence and the PNA index. This linkage has been verified in various model simulations. A statistical model, built on this linkage, has proven effective in predicting the AR frequency using the PNA index at both monthly and seasonal scales. These promising results have important implications for addressing concerns related to AR-associated extreme precipitation and flooding in this region.
The Taklamakan and Gobi Desert (TGD) region has experienced a pronounced increase in summer precipitation, including high-impact extreme events, over recent decades. Despite identifying large-scale circulation changes as a key driver of the wetting trend, understanding the relative contributions of internal variability and external forcings remains limited. Here, we approach this problem by using a hierarchy of numerical simulations, complemented by diverse statistical analysis tools. Our results offer strong evidence that the atmospheric internal variations primarily drive this observed trend. Specifically, recent changes in the North Atlantic Oscillation have redirected the storm track, leading to increased extratropical storms entering TGD and subsequently more precipitation. A clustering analysis further demonstrates that these linkages predominantly operate at the synoptic scale, with larger contributions from large precipitation events. Our analysis highlights the crucial role of internal variability, in addition to anthropogenic forcing, when seeking a comprehensive understanding of future precipitation trends in TGD.
Mesoscale convective systems (MCSs) are pivotal in global energy/water cycles and typically produce extreme weather events. Despite their importance, our understanding of their future change remains limited, largely due to inadequate representation in current climate models. Here, using a global storm-resolving model that accurately simulates MCSs, we conclude contrasting responses to increased SST in their occurrence, that is, notable decreases over land but increases over ocean. This land-ocean contrast is attributed to the changes in convective available potential energy (CAPE) and convective inhibition (CIN). Over land, notable rises in CIN alongside moderate increases in CAPE effectively suppress (favor) weak to moderate (intense) MCSs, resulting in an overall reduction in MCS occurrences. In contrast, substantial increases in CAPE with minimal changes in CIN over ocean contribute to a significant rise in MCS occurrences. The divergent response in MCS occurrence has profound impacts on both mean and extreme precipitation.
Precipitation changes in full response to CO2 increase are widely studied but confidence in future projections remains low. Mechanistic understanding of the direct radiative effect of CO2 on precipitation changes, independent from CO2-induced SST changes, is therefore necessary. Utilizing global atmospheric models, we identify robust summer precipitation decreases across North America in response to direct CO2 forcing. We find that spatial distribution of CO2 forcing at land surface is likely shaped by climatological distribution of water vapor and clouds. This, coupled with local feedback processes, changes in convection, and moisture supply resulting from CO2-induced circulation changes, could determine North American hydroclimate changes. In central North America, increasing CO2 may decrease summertime precipitation by warming the surface and inducing dry advection into the region to reduce moisture supply. Meanwhile, for the southwest and the east, CO2-induced shift of subtropical highs generates wet advection, which might mitigate the drying effect from warming.
You, Zhenyu, Yi Deng, Yi Ming, and Wenhao Dong, February 2024: A multiscale assessment of the springtime U.S. mesoscale convective systems in the NOAA GFDL AM4. Climate Dynamics, DOI:10.1007/s00382-024-07114-4. Abstract
This study presents a multiscale assessment of the springtime U.S. Mesoscale Convective Systems (MCSs) in the NOAA Geophysical Fluid Dynamics Laboratory (GFDL)’s Atmosphere Model version 4 (AM4). In AM4, MCSs exhibit lower intensity but longer duration, producing more precipitation compared to observation. The overall MCS activity demonstrates a “location bias” with its peak shifting from the Southern Great Plains to the Midwest in AM4, causing an eastward shift in associated precipitation. However, the dry bias of MCS precipitation over the Great Plains due to this shift is compensated by additional precipitation from amplified extratropical cyclone activities. Further analysis reveals that AM4 effectively reproduces the spatiotemporal distribution and relative frequency contribution of large-scale forcing patterns driving MCS genesis. The MCS location bias emerges under all forms of large-scale forcing patterns and is further attributed to local dynamic and thermodynamic factors including weaker surface lows, eastward-shifted fronts, and suppressed low-level jets (LLJs). Here we argue that the MCS location bias results from AM4 biases in both synoptic-mesoscale anomalies (i.e., fronts and LLJs) and seasonal mean circulations. The lack of two-way air-sea interaction in AM4 creates a hemispheric-scale sea level pressure bias, which is ultimately responsible for a seasonal mean northerly bias in lower-tropospheric winds and the subsequent weakening of LLJs. The existence of such biases in prescribed sea surface temperature (SST) experiments implies the need for extra caution when utilizing extended-range forecasts for MCSs over the continental U.S.
Accurate representation of mesoscale scale convective systems (MCSs) in climate models is of vital importance to understanding global energy, water cycles, and extreme weather. In this study, we evaluate the simulated MCS features over the United States from the newly developed GFDL global high-resolution (∼50 km) AM4 model by comparing them with the observations during spring to early summer (April–June) and late summer (July–August). The results show that the spatial distribution and seasonality of occurrence and genesis frequency of MCSs are reasonably simulated over the central United States in both seasons. The model reliably reproduces the observed features of MCS duration, translation speed, and size over the central United States, as well as the favorable large-scale circulation pattern associated with MCS development over the central United States during spring and early summer. However, the model misrepresents the amplitude and the phase of the diurnal cycle of MCSs during both seasons. In addition, the spatial distribution of occurrence and genesis frequency of MCSs over the eastern United States is substantially overestimated, with larger biases in early spring and summer. Furthermore, while large-scale circulation patterns are reasonably simulated in spring and early summer, they are misrepresented in the model during summer. Finally, we examine MCS-related precipitation, finding that the model overestimates MCS-related precipitation during spring and early summer, but this bias is insufficient to explain the significant dry bias observed in total precipitation over the central United States. Nonetheless, the dry biases in MCS-associated precipitation during late summer likely contribute to the overall precipitation deficit in the model.
The diurnal cycle of precipitation and precipitation variances at different time scales are analyzed in this study based on multiple high-resolution 3-h precipitation datasets. The results are used to evaluate nine CMIP6 models and a series of GFDL-AM4.0 model simulations, with the goal of examining the impact of SST diurnal cycle, varying horizontal resolutions, and different microphysics schemes on these two precipitation features. It is found that although diurnal amplitudes are reasonably simulated, models generally generate too early diurnal peaks over land, with a diurnal phase peaking around noon instead of the observed late afternoon (or early evening) peak. As for precipitation variances, irregular subdaily fluctuations dominate the total variance, followed by variance of daily mean precipitation and variance associated with the mean diurnal cycle. While the spatial and zonal distributions of precipitation variances are generally captured by the models, significant biases are present in tropical regions, where large mean precipitation biases are observed. The comparisons based on AM4.0 model simulations demonstrate that the inclusion of ocean coupling, adoption of a new microphysics scheme, and increasing of horizontal resolution have limited impacts on these two simulated features, emphasizing the need for future investigation into these model deficiencies at the process level. Conducting routine examinations of these metrics would be a crucial first step toward better simulation of precipitation intermittence in future model development. Last, distinct differences in these two features are found among observational datasets, highlighting the urgent need for a detailed evaluation of precipitation observations, especially at subdaily time scales, as model evaluation heavily relies on high-quality observations.
Dong, Wenhao, and Yanluan Lin, December 2023: Observed Scaling of Precipitation Extremes With Surface Temperature and Convective Available Potential Energy In Clouds and Their Climatic Impacts (eds S.C. Sullivan and C. Hoose), DOI:10.1002/9781119700357.ch14.
Neelin, J D., John P Krasting, Aparna Radhakrishnan, Jessica Liptak, Thomas Jackson, Yi Ming, Wenhao Dong, Andrew Gettelman, Danielle R Coleman, Eric Maloney, Allison A Wing, and Yi-Hung Kuo, et al., August 2023: Process-oriented diagnostics: Principles, practice, community development and common standards. Bulletin of the American Meteorological Society, 104(8), DOI:10.1175/BAMS-D-21-0268.1E1452-E1468. Abstract
Process-oriented diagnostics (PODs) aim to provide feedback for model developers through model analysis based on physical hypotheses. However, the step from a diagnostic based on relationships among variables, even when hypothesis driven, to specific guidance for revising model formulation or parameterizations can be substantial. The POD may provide more information than a purely performance-based metric, but a gap between POD principles and providing actionable information for specific model revisions can remain. Furthermore, in coordinating diagnostics development, there is a trade-off between freedom for the developer, aiming to capture innovation, and near-term utility to the modeling center. Best practices that allow for the former, while conforming to specifications that aid the latter, are important for community diagnostics development that leads to tangible model improvements. Promising directions to close the gap between principles and practice include the interaction of PODs with perturbed physics experiments and with more quantitative process models as well as the inclusion of personnel from modeling centers in diagnostics development groups for immediate feedback during climate model revisions. Examples are provided, along with best-practice recommendations, based on practical experience from the NOAA Model Diagnostics Task Force (MDTF). Common standards for metrics and diagnostics that have arisen from a collaboration between the MDTF and the Department of Energy’s Coordinated Model Evaluation Capability are advocated as a means of uniting community diagnostics efforts.
Xie, Yongkun, Jianping Huang, Guoxiong Wu, Yimin Liu, and Wenhao Dong, et al., October 2023: Oceanic repeaters boost the global climatic impact of the Tibetan Plateau. Science Bulletin, 68(19), DOI:10.1016/j.scib.2023.07.0192225-2235. Abstract
The topography of the Tibetan Plateau (TP) has shaped the paleoclimatic evolution of the Asian monsoon. However, the influence of the TP on the global climate, beyond the domain of the Asian monsoon, remains unclear. Here we show that the Pacific and Atlantic Oceans act as efficient repeaters that boost the global climatic impact of the TP. The simulations demonstrate that oceanic repeaters enable TP heating to induce a wide-ranging climate response across the globe. A 1 °C TP warming can result in a 0.73 °C temperature increase over North America. Oceanic repeaters exert their influence by enhancing the air-sea interaction-mediated horizontal heat and moisture transport, as well as relevant atmospheric circulation pathways including westerlies, stationary waves, and zonal-vertical cells. Air-sea interactions were further tied to local feedbacks, mainly the decreased air-sea latent heat flux from the weakening air-sea humidity difference and the increased shortwave radiation from sinking motion-induced cloud reduction over the North Pacific and Atlantic Oceans. Our findings highlight the crucial influence of TP heating variation on the current climate under a quasi-fixed topography, in contrast to topography change previously studied in paleoclimate evolution. Therefore, TP heating should be considered in research on global climate change.
Zhao, Dingchi, Yanluan Lin, Wenhao Dong, Yi Qin, Wenchao Chu, Kun Yang, Husi Letu, and Lei Huang, October 2023: Alleviated WRF summer wet bias over the Tibetan Plateau using a new cloud macrophysics scheme. Journal of Advances in Modeling Earth Systems, 15(10), DOI:10.1029/2023MS003616. Abstract
Reliable precipitation simulation over the Tibetan Plateau (TP) remains a challenge, manifested by a prominent systematic wet bias in the warm season. Previous studies have generally neglected the potential linkage between surface radiation energy budget and precipitation bias. Prevalent scattered cumulus and thunderstorms over the TP in summer strongly influence surface radiation. A cloud fraction scheme considering subgrid temperature and humidity fluctuations is implemented in the WRF model and tested for a month-long simulation. It is found that the scheme better reproduces the surface solar radiation compared to a default cloud fraction scheme in the WRF model. Using abundant surface observations, we find that overestimation of the downward surface shortwave radiation (DSSR) would lead to wet bias. DSSR overestimation contributes to higher surface temperature and larger evaporation and enhanced atmospheric instability, which favor more simulated convective precipitation. The study suggests that a better simulation of clouds and surface radiation would benefit precipitation simulation over the plateau.
Dong, Wenhao, and Yi Ming, September 2022: Seasonality and variability of snowfall to total precipitation ratio over high mountain Asia simulated by the GFDL high-resolution AM4. Journal of Climate, 35(17), DOI:10.1175/JCLI-D-22-0026.15573-5589. Abstract
The ratio of snowfall to total precipitation (S/P ratio) is an important metric that is widely used to detect and monitor hydrologic responses to climate change over mountainous areas. Changes in the S/P ratio over time have proved to be reliable indicators of climatic warming. In this study, the seasonality and interannual variability of monthly S/P ratios over High Mountain Asia (HMA) have been examined during the period 1950–2014 based on a three-member ensemble of simulations using the latest GFDL AM4 model. The results show a significant decreasing trend in S/P ratios during the analysis period, which has mainly resulted from reductions in snowfall, with increases in total precipitation playing a secondary role. Significant regime shifts in S/P ratios are detected around the mid-1990s, with rainfall becoming the dominant form of precipitation over HMA after the changepoints. Attribution analysis demonstrates that increases in rainfall during recent decades were primarily caused by a transformation of snowfall to rainfall as temperature warmed. A logistic equation is used to explore the relationship between the S/P ratio and surface temperature, allowing calculation of a threshold temperature at which the S/P ratio equals 50% (i.e., precipitation is equally likely to take the form of rainfall or snowfall). These temperature thresholds are higher over higher elevations. This study provides an extensive evaluation of simulated S/P ratios over the HMA that helps clarify the seasonality and interannual variability of this metric over the past several decades. The results have important socioeconomic and environmental implications, particularly with respect to water management in Asia under climate change.
An event-based assessment of the sea surface temperature (SST) threshold at the genesis of tropical mesoscale convective systems (MCSs) is performed in this study. We show that this threshold (SSTG) has undergone a significant warming trend at a rate of ∼0.2°C per decade. The SSTG shows a remarkable correspondence with the tropical mean SST and upper-tropospheric temperature on interannual and longer timescales. Using a high-resolution global climate model that permits realistic simulations of tropical MCSs, we find that the observed features of SSTG are well simulated. Both observation and model simulations demonstrate that the upward tendency in SSTG primarily results from the environmental SST warming over MCS genesis regions rather than the changes in MCS genesis location. A continuous increase in SSTG is projected in a warming simulation, but the relationship between SSTG and upper-tropospheric temperature remains unchanged, suggesting that the tropical tropospheric temperature generally follows a moist-adiabatic adjustment.
The characteristics of tropical mesoscale convective systems (MCSs) simulated with a finer-resolution (~50 km) version of the Geophysical Fluid Dynamics Laboratory (GFDL) AM4 model are evaluated by comparing with a comprehensive long-term observational dataset. It is shown that the model can capture the various aspects of MCSs reasonably well. The simulated spatial distribution of MCSs is broadly in agreement with the observations. This is also true for seasonality and interannual variability over different land and oceanic regions. The simulated MCSs are generally longer-lived, weaker, and larger than observed. Despite these biases, an event-scale analysis suggests that their duration, intensity, and size are strongly correlated. Specifically, longer-lived and stronger events tend to be bigger, which is consistent with the observations. The same model is used to investigate the response of tropical MCSs to global warming using time-slice simulations forced by prescribed sea surface temperatures and sea ice. There is an overall decrease in occurrence frequency, and the reduction over land is more prominent than over ocean.
Monsoon low-pressure systems (MLPSs) are among the most important synoptic-scale disturbances of the South Asian summer monsoon. Potential changes in their characteristics in a warmer climate would have broad societal impacts. Yet, the findings from a few existing studies are inconclusive. We use the Geophysical Fluid Dynamics Laboratory (GFDL) coupled climate model CM4.0 to examine the projected changes in the simulated MLPS activity under a future emission scenario. It is shown that CM4.0 can skillfully simulate the number, genesis location, intensity and lifetime of MLPSs. Global warming gives rise to a significant decrease in MLPS activity. An analysis of several large-scale environmental variables, both dynamic and thermodynamic, suggests that the decrease in MLPS activity can be attributed mainly to a reduction in low-level relative vorticity over the core genesis region. The decreased vorticity is consistent with weaker large-scale ascent, which leads to less vorticity production through the stretching term in the vorticity equation. Assuming a fixed radius of influence, the projected reduction in MLPSs would significantly lower the associated precipitation over the north central India, despite an overall increase in mean precipitation.
https://doi.org/10.1175/JCLI-D-20-0168.1
Qiu, T, Wenyu Huang, Jonathon S Wright, Yanluan Lin, Ping Lu, Xinsheng He, Zifan Yang, and Wenhao Dong, et al., December 2019: Moisture Sources for Wintertime Intense Precipitation Events Over the Three Snowy Subregions of the Tibetan Plateau. Journal of Geophysical Research: Atmospheres, 124(23), DOI:10.1029/2019JD031110. Abstract
Wintertime intense precipitation events often lead to severe snow disasters. In this study, a Lagrangian approach is employed to examine the evaporative moisture sources for wintertime intense precipitation events over the three snowy subregions of the Tibetan Plateau (TP) during 1979–2016, including the western TP (WTP), south central TP (SCTP), and southeastern TP (SETP). More than 80.0% of the moisture for intense precipitation over each subregion originates from terrestrial areas. Although prevailing westerly winds dominate above the TP and its surrounding areas during winter, half of the precipitation over the three subregions is supplied by evaporation from the south (i.e., the Indian Peninsula). Specifically, evaporation from the Indian Peninsula contributes 68.0%, 65.0%, and 45.0% of the moisture for intense precipitation over the WTP, SCTP, and SETP, respectively. The two primary oceanic moisture source regions for intense precipitation are the Arabian Sea and the Bay of Bengal, playing complementary roles in supplying moisture. The relative contributions of the Arabian Sea to intense precipitation over the WTP, SCTP, and SETP are 9.2%, 6.9%, and 1.1%, while those of the Bay of Bengal are 1.1%, 12.1%, and 8.6%. Southerly winds downstream of a cyclonic anomaly over the Indian Peninsula are crucial for the low‐level moisture transport from the south to the Himalayan foothills. Under the combined effects of orographic lifting and favorable large‐scale circulation patterns, moisture ascends further into the three subregions. Changes in the position and intensity of the cyclonic anomaly are particularly crucial to facilitating moisture contributions from the key source regions.
Dong, Wenhao, Yanluan Lin, Jonathon S Wright, Yuanyu Xie, and Yi Ming, et al., August 2018: Regional disparities in warm season rainfall changes over arid eastern-central Asia. Scientific Reports, 8, 13051, DOI:10.1038/s41598-018-31246-3. Abstract
Multiple studies have reported a shift in the trend of warm season rainfall over arid eastern–central Asia (AECA) around the turn of the new century, from increasing over the second half of the twentieth century to decreasing during the early years of the twenty-first. Here, a closer look based on multiple precipitation datasets reveals important regional disparities in these changes. Warm-season rainfall increased over both basin areas and mountain ranges during 1961–1998 due to enhanced moisture flux convergence associated with changes in the large-scale circulation and increases in atmospheric moisture content. Despite a significant decrease in warm-season precipitation over the high mountain ranges after the year 1998, warm season rainfall has remained large over low-lying basin areas. This discrepancy, which is also reflected in changes in river flow, soil moisture, and vegetation, primarily results from disparate responses to enhanced warming in the mountain and basin areas of AECA. In addition to changes in the prevailing circulation and moisture transport patterns, the decrease in precipitation over the mountains has occurred mainly because increases in local water vapor saturation capacity (which scales with temperature) have outpaced the available moisture supply, reducing relative humidity and suppressing precipitation. By contrast, rainfall over basin areas has been maintained by accelerated moisture recycling driven by rapid glacier retreat, snow melt, and irrigation expansion. This trend is unsustainable and is likely to reverse as these cryospheric buffers disappear, with potentially catastrophic implications for local agriculture and ecology.
Wang, Yan, Yuanyu Xie, Wenhao Dong, and Yi Ming, et al., October 2017: Adverse Effects of Increasing Drought on Air Quality via Natural Processes. Atmospheric Chemistry and Physics, 17(20), DOI:10.5194/acp-17-12827-2017. Abstract
Drought is a recurring extreme of the climate system with well-documented impacts on agriculture and water resources. The strong perturbation of drought to the land biosphere and atmospheric water cycle will affect atmospheric composition, the nature and extent of which are not well understood. Here we present observational evidence that surface ozone and PM2.5 in the US are significantly correlated with drought severity, with 3.5 ppbv (8 %) and 1.6 μg m−3 (17 %) increases respectively under severe drought. These enhancements show little sensitivity to the decreasing trend of US anthropogenic emissions, indicating natural processes as the primary cause. Elevated ozone and PM2.5 are attributed to the combined effects of drought on natural emissions (wildfires, biogenic VOCs and dust), deposition, and chemistry. Most climate-chemistry models are not able to reproduce the observed responses of ozone and PM2.5 to drought severity, suggesting a lack of mechanistic understanding of drought effects on atmospheric composition. The model deficiencies are partly attributed to the lack of drought-induced changes in land-atmosphere exchanges of reactive gases and particles and misrepresentation of cloud changes under drought conditions. By applying the observed relationships between drought and air pollutants to climate model projected drought occurrences, we estimate an increase of 1–6 % for ground-level O3 and 1–16 % for PM2.5 in the US by 2100 compared to the 2000s due to increasing drought alone. Drought thus poses another aspect of climate change penalty on air quality not recognized before. Improvements in the models are imperative to facilitate better prediction of air quality challenges due to changing hydroclimate and atmospheric composition feedback to climate.
Dong, Wenhao, Yanluan Lin, Jonathon S Wright, and Yi Ming, et al., March 2016: Summer rainfall over the southwestern Tibetan Plateau controlled by deep convection over the Indian subcontinent. Nature Communications, 7, 10925, DOI:10.1038/ncomms10925. Abstract
Despite the importance of precipitation and moisture transport over the Tibetan Plateau for
glacier mass balance, river runoff and local ecology, changes in these quantities remain highly
uncertain and poorly understood. Here we use observational data and model simulations to
explore the close relationship between summer rainfall variability over the southwestern
Tibetan Plateau (SWTP) and that over central-eastern India (CEI), which exists despite the
separation of these two regions by the Himalayas. We show that this relationship is maintained
primarily by ‘up-and-over’ moisture transport, in which hydrometeors and moisture are
lifted by convective storms over CEI and the Himalayan foothills and then swept over the
SWTP by the mid-tropospheric circulation, rather than by upslope flow over the Himalayas.
Sensitivity simulations confirm the importance of up-and-over transport at event scales, and
an objective storm classification indicates that this pathway accounts for approximately half
of total summer rainfall over the SWTP.
