Land Atmosphere Coupling Research Articles

Soil Moisture-Latent Heat Flux Coupling in Climate Modeling: Insights and Implications from the Offline Land Model

Abstract Understanding the relationship between soil moisture (SM) and latent heat flux (LE) is crucial for forecasting seasonal extremes like heat waves and droughts and grasping long-term climate systems. Despite its importance, substantial disparities exist among various models’ interpretations of this relationship. This study defines four SM-LE indicators: wilting point, critical SM, saturated LE, and SM-LE slope, characterizing SM-LE regimes and corresponding breakpoints delineating the regimes using segmented regression and scrutinizes model disparities by contrasting conventional coupled simulations with matching offline land model (LM) simulations within the CMIP6 framework. It is revealed that those indicators have lower variability among ensemble members from the same model than among different models. Moreover, all indicators except for SM-LE slope demonstrate strong correlations between the coupled and uncoupled simulations using the same LMs. While the spatial distributions of breakpoint values correspond only moderately well with the indicators’ values, they improve after adjusting for model atmospheric biases of precipitation and downward radiation. The impact of the atmospheric fields on the four SM-LE indicators is well correlated between the coupled and offline experiments, except for the relationship between precipitation and the wilting point. Consequently, this indicates a significant constraint of the SM-LE relationship in coupled model simulations determined by the LM's behavior. Thus, one may determine much about the land-atmosphere coupling behavior parsimoniously in a model system without running the fully coupled model, justifying a hierarchical approach for model development and assessment.

Deducing land–atmosphere coupling regimes from SMAP soil moisture

Abstract. In recent years, there has been a growing recognition of the significance of land–atmosphere (L–A) interactions and feedback mechanisms in understanding and predicting Earth's water and energy cycles. Soil moisture plays a critical role in mediating the strength of L–A interactions and is important for understanding the complex and governing processes across this interface. This study aims to identify the significance of soil moisture in identifying L–A coupling strength within the convective triggering potential (CTP) and humidity index (HI) framework. To address this, a consistent and reliable dataset of atmospheric profiles is created by merging CTP and HI using triple collocation (TC) with three reanalysis datasets. The merged CTP and HI product demonstrates enhanced performance globally compared to the individual datasets when validated with radiosonde and satellite observations. This merged product of CTP and HI is then used to compare the L–A coupling strength based on Soil Moisture Active Passive Level 3 (SMAPL3) and SMAP Level 4 (SMAPL4) over 2 decades (2003–2022) where L–A coupling strength is defined as the persistence probability within the dry and wet coupling regimes. Results indicate that the persistency-based coupling strength is related to the ability of soil moisture to predict future atmospheric humidity and dry vs. wet coupling state. The coupling strength in SMAPL4 is consistently stronger than in SMAPL3 and is likely due to its reliance on a land surface model and reduced susceptibility to random noise. The difference in coupling strength based on the same CTP–HI underscores the importance of soil moisture data in estimating coupling strength within the CTP–HI framework. These findings lay the groundwork for understanding the role of L–A interactions and drought evolution due to soil moisture variations by providing insight into the quantification of coupling strength and its role in drought monitoring and forecast efforts.

Sensitivity of the Northern Hemisphere Warming Trend to Snowpack Variability

Abstract A marked decrease in land surface snow cover within the Northern Hemisphere under global warming will warm the atmosphere near the land surface. However, minimal information exists regarding the contribution of snowpack variation to interannual surface air temperature variability. The current study investigates the effects caused by snow water equivalent (SWE) change on surface air temperature (SAT). To this end, an SWE pacemaker experiment of land–atmosphere coupling (AMIP-type) is undertaken for the period 1901–2010, during which the Model for Interdisciplinary Research on Climate, version 6 (MIROC6), atmospheric general circulation model’s SWE is continuously nudged toward SWE derived from an land-only (LMIP-type) experiment. Compared to a reference MIROC6 simulation without SWE nudging, the spatial correlation of 1980–2010 interannual SWE trends in our experiment with those in GlobSnow observation data increased by 0.31 over the Northern Hemisphere. Similarly, the linear correlation of interannual SAT with reanalysis data is greater by 0.04, 0.04, 0.08, and 0.07 for autumn, winter, spring, and summer over the region from the reference experiment, respectively. It is shown that due to this SWE nudging, the modeled interannual SAT change in the Northern Hemisphere becomes more accurate. Through a surface energy budget analysis, changes in SAT are attributed to changes in surface albedo, soil evaporation, and soil temperature. Areas of greater contribution of SWE variability to SAT variability appear to shift from south to north areas as snow melts. These results highlight surface albedo, snow hydrological, and land heat storage effects through which the SWE interannual trend on the land surface significantly controls atmospheric air temperature variability near the land surface. Significance Statement This study investigates the contribution of land snowpack to surface air temperature in the Northern Hemisphere using a climate model updated with observation-based estimates of snow water equivalent. A marked decrease in snowpack under global warming is expected to warm the atmosphere near the land surface. Variations in surface albedo, soil evaporation, and soil temperature through the snow–soil–atmosphere processes during the early snow-melting season are crucial for accurately simulating surface air temperature in the Northern Hemisphere. Moreover, terrestrial snowpack is a significant factor in global warming rates.

Improved simulation of compound drought and heat extremes in eastern China through CWRF downscaling

Abstract Given their profound socio-economic impact and increasing occurrence, compound drought and heat extremes (CDHEs) have become a focal point of widespread concern. Studies have attempted to reproduce and predict these extremes using general circulation models (GCMs); however, the performance of these models in capturing compound events remains controversial. This study presents an improved simulation of CDHE trends over eastern China by using the regional Climate-Weather Research and Forecasting model (CWRF) to downscale the projections of two GCMs that participated in the Coupled Model Intercomparison Project Phase 6. The results show that CWRF downscaling significantly improved the underestimation of CDHE trends in GCM historical simulations, aligning better with observed trends. Moreover, the improvements of CWRF downscaling in simulating CDHEs are more pronounced than those for univariate events, i.e. extreme drought and extreme heat events. This enhancement largely results from CWRF’s better representation of land-atmosphere interaction processes, as indicated by the more realistic spatial distributions and intensities of the land-atmosphere coupling strength index. Under the SSP245 and SSP585 scenario, CWRF downscaling again predicts a more rapid increase in regional mean CDHE frequency compared to GCMs, with values nearing or exceeding 0.4 by the mid-21st century, suggesting a significant future threat to the study region. This study highlights the important role of land-atmosphere interactions in shaping CDHEs and the efficacy of regional climate models to reduce uncertainty in compound event simulations.

Future drought overestimations due to no constraints of CO2 physiological effect and land-atmosphere coupling on potential evapotranspiration

Abstract Various offline drought indices have been widely used to project dryness/wetness and drought changes. However, the results derived from these indices often differ from or even contradict observations and direct projections made by coupled climate models. Therefore, it is crucial to investigate this scientific debate thoroughly and identify the potential causes. This study adopts a water demand-side perspective, focusing on potential evapotranspiration (PET), to address such controversy. Employing the Standardized Precipitation-Evapotranspiration Index (SPEI), three PET models including the Food and Agriculture Organization of the United Nations’ report 56 (FAO-56) Penman-Monteith (PM) model, a corrected FAO-56 PM model incorporating CO2 physiological effect (PMCO2), and a land-atmosphere coupled PET model (PET-LAC) are further compared. Despite projected increases in PET across most land areas, the PM shows the most pronounced increases among these PET models. Compared to PMCO2 and PET-LAC, the PM model predicts the most significant drying, with the 3-month SPEI decreasing by 0.50±0.23 /yr. Additionally, it projects the most substantial drought intensification, with increases in areas, intensity, and duration of 28±6.9%, 0.70±0.20/yr, and 2.90±0.83 month/yr, respectively. Meanwhile, these projections correspond to the most extensive area percentages, with 78.5±10% for drying, 94.8±7.2% for drought intensity, and 93.6±7.9% for drought duration. These findings imply that the commonly used PM model overestimates future drought conditions. Differences and contradictions between the drought projections from PM-based offline indices and direct climate model outputs can be partly attributed to the omission of CO2 physiological effect and land-atmosphere coupling constraints in the PM model. This study highlights the importance of improving PET models by incorporating these constraints, thereby providing valuable insights for enhancing the accuracy of future drought assessments.

Impacts of early spring soil moisture over the Greater Mekong Subregion on the interannual variation of South China Sea summer monsoon onset

This study investigates the influence of early spring (March–April) soil moisture (SM) over the Greater Mekong Subregion (GMS) on the interannual variation of South China Sea summer monsoon (SCSSM) onset, using observational analyses and numerical experiments. It is found that when early spring SM over the GMS is wetter, westerly anomalies dominate the South China Sea, corresponding to an early onset of the SCSSM, and vice versa. The analyses of physical mechanism show that the positive anomalies of early spring SM decrease local surface air temperature by adjusting the surface latent heat flux and sensible heat flux. The persistence of anomalous ground cooling contributes to negative geopotential height anomalies in the middle troposphere, and further induces the eastward retreat of western North Pacific subtropical high in May. The anomalous cyclone over the South China Sea favors the early onset of SCSSM. The key physical processes linking the variability of early spring SM over the GMS and the following SCSSM onset are confirmed by the sensitivity experiments using a coupled atmosphere–land model (CAM6–CLM5). Specifically, the onset date of the SCSSM in the drier experiment is 11 days later than that in the wetter experiment. The present results have implications for the forecast of SCSSM onset.

A simple snow temperature index model exposes discrepancies between reanalysis snow water equivalent products

Abstract. Current global reanalyses show marked discrepancies in snow mass and snow cover extent for the Northern Hemisphere. Here, benchmark snow datasets are produced by driving a simple offline snow model, the Brown Temperature Index Model (B-TIM), with temperature and precipitation from three reanalyses. The B-TIM offline snow performs comparably to or better than online (coupled land–atmosphere) reanalysis snow when evaluated against in situ snow measurements. Sources of discrepancy in snow climatologies, which are difficult to isolate when comparing online reanalysis snow products amongst themselves, are partially elucidated by separately bias-adjusting temperature and precipitation in the B-TIM. Interannual variability in snow mass and snow spatial patterns is far more self-consistent amongst offline B-TIM snow products than amongst online reanalysis snow products, and the self-consistent products are more similar to in situ observations, as evaluated in a validation study. Specific artifacts related to temporal inhomogeneity in snow data assimilation are revealed in the analysis. The B-TIM, released here as an open-source, self-contained Python package, provides a simple benchmarking tool for future updates to more sophisticated online and offline snow datasets.

Improving subseasonal forecast skill in the Norwegian Climate Prediction Model using soil moisture data assimilation

This study shows the importance of soil moisture (SM) in subseasonal-to-seasonal (S2S) predictions at mid-latitudes. We do this through introducing the Norwegian Climate Prediction Model Land (NorCPM-Land), a land reanalysis framework tailored for integration with the Norwegian Climate Prediction Model (NorCPM). NorCPM-Land assimilates blended SM data from the European Space Agency’s Climate Change Initiative into a 30-member offline simulation of the Community Land Model with fluxes from the coupled model. The assimilation of SM data reduces error in SM by 10.5 % when validated against independent SM observations. It also improves latent heat flux estimates, illustrating that the adjustment of underlying SM significantly augments the capacity to model land surface dynamics. We evaluate the added value of land initialisation for subseasonal predictions, by comparing the performance of hindcasts (retrospective prediction) using the standard NorCPM with a version where the land initial condition is taken from NorCPM-Land reanalysis. The hindcast covers the period 2000 to 2019 with four start dates per year. Land initialisation enhances SM predictions, reducing error by up to 2.5-month lead time. Likewise, the error for precipitation and temperature shows improvement up to a lead time of 1.5-month. The largest improvements are observed in regions with significant land-atmospheric coupling, such as the Central United States, the Sahel, and Central India. This method further enhances the prediction of extreme temperature variations, both high and low, with the most notable improvements seen in regions at mid and high latitudes, including parts of Europe, the United States, and Asia. Overall, our study provides further evidence for the significant role of SM content in enhancing the accuracy of subseasonal predictions. This study introduces a technique for improved land initialisation, utilising the same model employed in climate predictions.

Automated calibration of Noah-MP land surface model for improved irrigation representation in the North China Plain

Irrigation significantly impacts the terrestrial water and energy cycle. Yet, due to insufficient agriculture management data, omitting important processes in models, and suboptimal model configuration, realistically representing irrigation processes in land surface models remains challenging. In this study, we employed the Noah-MP land surface model with a more realistic irrigation map and irrigation parameterization to replicate irrigation consumption over the North China Plain. For irrigation map, three different irrigation area maps are evaluated using prefecture-level census data. The best-performing, the Global Map of Irrigation Areas (GMIA), was chosen as the input of irrigation area to Noah-MP. Based on a widely-used calibration method, the Shuffled Complex Evolution method developed by the University of Arizona (SCE-UA), we constructed an automatic calibration framework for Noah-MP. The census data of irrigation water amount at the prefectural level was used to calibrate three key parameters in the flood irrigation module, namely, irrigation triggering point, flood irrigation loss, and flood application rate factor. Results show the calibrated irrigation parameters help mitigate the underestimation of irrigation water amount in the default simulation in good agreement with census data (R2 = 0.71, N = 624). The calibrated irrigation simulation increases latent heat flux (+20.8 %) and decreases sensible heat flux (−34.5 %), and cools the land surface (−0.89 K), roughly proportional to irrigation amount. Furthermore, the additional irrigation water in calibrated simulation is partitioned into evaporation (58.5 %), followed by total runoff (39.8 %) and soil moisture storage change (1.7 %), significantly impacting hydrology, land–atmosphere coupling, and regional climate.

How Does the Tibetan Plateau Land Thermal Initial Condition Influence the Subseasonal Prediction of 2020 Record‐Breaking Mei‐Yu Rainfall

AbstractAccurate subseasonal prediction of heavy rainfall is helpful for disaster mitigation but challenging. The land thermal condition of Tibetan Plateau (TP), usually with climate memory ranging from weeks to seasons, has been seen as a potential predictability source for subseasonal prediction. Aiming at 2020 record‐breaking Mei‐yu rainfall, this study attempts to investigate whether and how the influence of initial TP surface thermal condition near late June influences the July rainfall prediction over the Middle and Lower Yangtze River Region (MLYR), based on two contrasting prediction experiments using a global climate ensemble prediction system. The results show that the most distinguishable change in the downstream prediction in July is the anomalous low‐tropospheric cyclone and the associated increased rainfall over MLYR corresponding to the warmer initial condition of surface TP. Influenced by the invasion of the positive potential vorticity (PV) center that generated over TP and propagated eastward, this low‐level cyclone anomaly over MLYR is formed within the first week of prediction, and persists for the next 3 weeks maintained by the positive feedback between the low‐level cyclone and middle‐tropospheric latent heating over MLYR in the prediction. This study confirmed the significant effect of TP initial thermal condition on downstream prediction ahead of 3 weeks during the Mei‐yu season (peak summer) with strong land–atmosphere coupling over TP.

Flash Drought Intensification due to Enhanced Land–Atmospheric Coupling in India

Abstract Land–atmospheric feedback influences the occurrence and severity of flash droughts. However, the observed and projected changes in flash droughts and associated land–atmospheric coupling have not been examined over India. Moreover, the causes of the rapid depletion of soil moisture during flash droughts are not well known. We identify major flash droughts and associated soil moisture–vapor pressure deficit (SM–VPD) coupling in India using ERA5 and simulations from global climate models (CMIP6-GCMs). The summer monsoon season (June–September) witnesses more than 60% of the flash drought events and a relatively higher rate of flash drought development. The flash drought frequency has mainly decreased during India’s observed climate (1980–2019), which is projected to decline further in the future warming climate. On the other hand, the flash drought development rate has significantly increased during the observed period, which is projected to enhance further under the warming climate. SM–VPD coupling during the flash drought onset-development phase is considerably higher (threefold to fivefold) than during the normal condition (in the absence of flash drought). The high (low) SM–VPD coupling explains the faster (slower) flash drought development rate in the observed and future warming climate. The strength of SM–VPD coupling has increased in the recent period and is projected to increase further in the future warming climate. The increased SM–VPD coupling can intensify future flash droughts in India, especially during the summer monsoon season, with considerable implications for agriculture, water resources, and ecosystems. Significance Statement This study aims to understand better the role of land–atmospheric coupling in explaining flash drought characteristics (frequency and development rate) in India. Strong land–atmospheric (SM–VPD) feedback might influence the regional weather patterns during flash drought, which often negatively impacts humans and the ecosystem. We explain the causes and drivers of increasing (decreasing) flash drought development rate (frequency) in India. The long-term change in SM–VPD coupling drives the frequency of flash drought, whereas an anomalous instantaneous change in coupling controls the flash drought development rate. More intense flash droughts contributed by the increased land–atmospheric coupling are projected in the future. Predicting the SM–VPD coupling metric can facilitate more time for preparedness, resulting in minimizing the flash drought impacts.

The characterization, mechanism, predictability, and impacts of the unprecedented 2023 Southeast Asia heatwave

In April and May 2023, Southeast Asia (SEA) encountered an exceptional heatwave. The Continental SEA was hardest hit, where all the countries broke their highest temperature records with measurements exceeding 42 °C, and Thailand set the region’s new record of 49 °C. This study provides a comprehensive analysis of this event by investigating its spatiotemporal evolution, physical mechanisms, forecast performance, return period, and extensive impacts. The enhanced high-pressure influenced by tropical waves, moisture deficiency and strong land-atmosphere coupling are considered as the key drivers to this extreme heatwave event. The ECMWF exhibited limited forecast skills for the reduced soil moisture and failed to capture the land-atmosphere coupling, leading to a severe underestimation of the heatwave’s intensity. Although the return period of this heatwave event is 129 years based on the rarity of temperature records, the combination of near-surface drying and soil moisture deficiency that triggered strong positive land-atmosphere feedback and rapid warming was extremely uncommon, with an occurrence probability of just 0.08%. These analyses underscore the exceptional nature of this unparalleled heatwave event and its underlying physical mechanisms, revealing its broad impacts, including significant health repercussions, a marked increase in wildfires, and diminished agricultural yields.

A global–land snow scheme (GLASS) v1.0 for the GFDL Earth System Model: formulation and evaluation at instrumented sites

Abstract. Snowpacks modulate water storage over extended land regions and at the same time play a central role in the surface albedo feedback, impacting the climate system energy balance. Despite the complexity of snow processes and their importance for both land hydrology and global climate, several state-of-the-art land surface models and Earth System Models still employ relatively simple descriptions of the snowpack dynamics. In this study we present a newly-developed snow scheme tailored to the Geophysical Fluid Dynamics Laboratory (GFDL) land model version 4.1. This new snowpack model, named GLASS (Global LAnd–Snow Scheme), includes a refined and dynamical vertical-layering snow structure that allows us to track the temporal evolution of snow grain properties in each snow layer, while at the same time limiting the model computational expense, as is necessary for a model suited to global-scale climate simulations. In GLASS, the evolution of snow grain size and shape is explicitly resolved, with implications for predicted bulk snow properties, as they directly impact snow depth, snow thermal conductivity, and optical properties. Here we describe the physical processes in GLASS and their implementation, as well as the interactions with other surface processes and the land–atmosphere coupling in the GFDL Earth System Model. The performance of GLASS is tested over 10 experimental sites, where in situ observations allow for a comprehensive model evaluation. We find that when compared to the current GFDL snow model, GLASS improves predictions of seasonal snow water equivalent, primarily as a consequence of improved snow albedo. The simulated soil temperature under the snowpack also improves by about 1.5 K on average across the sites, while a negative bias of about 1 K in snow surface temperature is observed.

Pronounced spatial disparity of projected heatwave changes linked to heat domes and land-atmosphere coupling

Heatwaves are projected to substantially increase at a global scale, exacerbating worldwide heat-related risks in the future. However, understanding future heterogeneous heatwave changes and their origins remains challenging. By analyzing the output of various climate models from the Coupled Model Intercomparison Project Phase 6, we found pronounced spatial disparity of projected heatwave increases in the Northern Hemisphere, even outstretching seven-fold inter-regional differences in extreme heatwave occurrences, attributed primarily to future changes in heat-dome-like circulations and soil moisture–temperature coupling. Specifically, we found that by the end of the 21st century, the modulations of combined Pacific El Niño and positive Pacific Meridional Mode on magnified heat-dome-like circulations would be translated into summertime hotspots over western Asia and western North America. Amplified soil moisture–temperature couplings then further aggravate the heatwave intensity over these two hotspots. This study provides support for formulating impact-based mitigation strategies and efficiently addressing the potential future risks of heatwaves.

Spatially varying effect of soil moisture-atmosphere feedback on spring streamflow under future warming in China

Soil moisture–atmosphere feedback (SAF) is expected to accelerate warming in the future, but its influences on the terrestrial water cycle over non-traditional hotspots of land–atmosphere coupling (e.g., East Asia) remains elusive due to strong heterogeneity and uncertainties at model grid scales. Here we show robust evidence of the integral effects of SAF on streamflow under future warming across major river basins in China. Based on high-resolution hydrological modelings driven by CMIP6 simulations with/without land–atmosphere coupling, SAF is projected to significantly increase spring streamflow by 28–58% in mid-latitude China, but decrease it by 12–48% in other regions. Such contrary effects are attributed to the soil moisture-precipitation interaction, which causes spatially opposite changes in precipitation through thermodynamic (e.g., ascending/descending vertical motions) and thermal (e.g., weakened meridional water vapor transport) atmospheric adjustments. Our result highlights the importance of SAF in altering freshwater resources even in locations not typically considered to be SAF hotspots.

Sensitivity of aridity diagnoses to land-atmosphere coupling in South America

Sensitivity of aridity diagnoses to land-atmosphere coupling in South America

Contribution of land-atmosphere coupling in 2022 CONUS compound drought-heatwave events and implications for forecasting

Severe compound drought-heatwave events were observed over three regions of the Contiguous United States (CONUS), Northwest (NW), Great Plains (GP), and Northeast (NE) regions, during July and August 2022. In this study, we have found that the developments of these drought-heatwave events were shaped by different land-atmosphere coupling behaviors which are associated with water and energy limitation regimes in these regions. In the NW and GP regions, the surface soil moisture (SM) and evapotranspiration (ET) were coupled through water-limited processes. Heatwaves in these two regions were affected by the decrease of ET and the available SM due to the precipitation deficit. This type of land-atmosphere coupling was especially prominent in the GP. In the NE region, the heatwave governed ET through the increase of potential ET (PET) based on energy-limited coupling, which played a crucial role in the development of drought.The impacts of the different land-atmosphere coupling behaviors on the predictability of the 13-km Geophysical Fluid Dynamics Laboratory (GFDL) System for High-resolution prediction on Earth-to-Local Domains (SHiELD) were also investigated by checking its 10-day forecasts during the same period. The analysis was particularly focused on the GP and NE regions, where different land-atmosphere coupling behaviors were observed. The model's warm bias in the GP region was associated with the overestimated net radiation, and the bias was further amplified through the water-limited coupling. In the NE region, the PET-related variables, including surface air temperature, influenced the predictability of drought onset by limiting ET through the energy-limited coupling. Based on our findings, this study highlights the crucial role of land-atmosphere coupling behaviors and provides a scientific strategy for enhancing the model predictability of compound drought-heatwaves.

Flash drought and heatwave compound events increased in strength and length from 1980 to 2022 in China

Compound flash drought and heatwave (FDHW) events have garnered increasing amounts of attention due to their substantial impacts on agriculture, water resources, and public health. However, studies on their intensity and classification in China are limited. In this study, we classified FDHW events in China from 1980 to 2022 using a classification framework designed to address regional patterns and explore their characteristics further. The results showed that FDHW events in northern China mostly occurred in early to mid-summer, whereas in southern China, excluding the Southwest River Basin, they occurred predominantly in mid to late summer. Furthermore, the spatial coverage of FDHW events across China significantly expanded. From 1980 to 2022, FDHW events in China evolved toward higher intensities and longer durations. This trend was especially notable in the Jiang-Huai River Basin, the main grain-producing region and a densely populated area of China. From the perspective of land‒atmosphere coupling, the amplifying effect of flash droughts and high temperatures increased with their intensity. When high temperatures reached the extreme level, the amplification effect on flash droughts was evident: 35.76% from the water deficit perspective and 38.82% from the soil moisture perspective. During extreme flash droughts, the amplification effect on high temperatures intensified: 41.51% from the water deficit perspective and 45.06% from the soil moisture perspective. The Southwest River Basin became a hotspot for the interaction between flash droughts and high temperatures. This study has implications for developing science-based policies to tackle risks in the water, energy and food sectors in China.

Deep-learning-driven simulations of boundary layer clouds over the Southern Great Plains

Abstract. Based on long-term observations at the Southern Great Plains site by the Atmospheric Radiation Measurement (ARM) program for training and validation, a deep-learning model is developed to simulate the daytime evolution of boundary layer clouds (BLCs) from the perspective of land–atmosphere coupling. The model takes ARM measurements (including early-morning soundings and diurnally varying surface meteorological conditions and heat fluxes) as inputs and predicts hourly estimates (including cloud occurrence, the positions of cloud boundaries, and the vertical profile of the cloud fraction) as outputs. The deep-learning model offers good agreement with the observed cloud fields, especially in the accuracy with which cloud occurrence and base height are reproduced. When the inputs are substituted by reanalysis data from ERA5 and MERRA-2, the outputs of the deep-learning model provide a better agreement with observation than the cloud fields extracted from ERA5 and MERRA-2 themselves. Thus, the deep-learning model shows great potential to serve as a diagnostic tool for the performance of physics-based models in simulating stratiform and cumulus clouds. By quantifying biases in clouds and attributing them to the simulated atmospheric state variables versus the model-parameterized cloud processes, this observation-based deep-learning model may offer insights into the directions needed to improve the simulation of BLCs in physics-based models for weather forecasting and climate prediction.

Land-atmosphere coupling amplified the record-breaking heatwave at altitudes above 5000 meters on the Tibetan Plateau in July 2022

In July 2022, regions with elevations exceeding 5 000 m on the inner Tibetan Plateau (TP) witnessed a record-breaking heatwave. But how the atmospheric circulation and soil moisture play roles in the occurrence and maintenance of the heatwave in such high elevation climate sensitive region remains unknown. Here, by using the flow analogue method, we find that negative soil moisture anomalies explain more than half of the extreme high temperature during the heatwave, while atmospheric circulation explains less than half. The high soil moisture-temperature coupling metric and the increased correlation between latent heat flux and soil moisture during heatwave revealed strong land-atmosphere feedback in the Qiangtang Plateau which has amplified the heatwave. Analyses of numerical experiments confirm that the presence of interaction between soil moisture and the atmosphere has increased the intensity of hot extreme event under the same atmospheric circulation conditions. Under the warming background, land-atmosphere coupling leads to a faster increase in extreme high temperatures compared to the global mean warming rate, and it is twice as fast as the increase in extreme high temperatures without coupling. We highlight the increased probability of extreme high temperature over the TP in the future due to soil moisture feedback and the results are hoped to inform policymakers in making decisions for climate adaptation activities.