Land Surface Heat Research Articles

Synchronization of the Recent Decline of East African Long Rains and Northwestern Eurasian Warming

AbstractThe East African March–April–May (MAM, “long rains”) precipitation decline in recent decades remains a puzzle marked by various proposed large‐scale drivers. Here, the interannual variability of the long rains and their recent drying trend are examined using global model simulations and observations. Comparison of a control simulation and re‐initialized simulations in which land‐surface feedback is suppressed shows that much of the long rains deficit experienced between 1980 and 2014 is synchronized with the warming of the Northwestern Eurasian landmass. In agreement with the modeling results, multiple observational data sets reveal a strong negative correlation between MAM mean East African rainfall amount and the surface temperature over Northwestern Eurasia. Idealized simulations further indicate that warming in Northwestern Eurasia weakens the regional Hadley Cell and diverts the monsoonal transport of moisture away from Eastern Africa toward Europe and southern Africa, highlighting the role of remote land surface warming on the observed precipitation decline.

Forest fire size amplifies postfire land surface warming

Climate warming has caused a widespread increase in extreme fire weather, making forest fires longer-lived and larger1–3. The average forest fire size in Canada, the USA and Australia has doubled or even tripled in recent decades4,5. In return, forest fires feed back to climate by modulating land–atmospheric carbon, nitrogen, aerosol, energy and water fluxes6–8. However, the surface climate impacts of increasingly large fires and their implications for land management remain to be established. Here we use satellite observations to show that in temperate and boreal forests in the Northern Hemisphere, fire size persistently amplified decade-long postfire land surface warming in summer per unit burnt area. Both warming and its amplification with fire size were found to diminish with an increasing abundance of broadleaf trees, consistent with their lower fire vulnerability compared with coniferous species9,10. Fire-size-enhanced warming may affect the success and composition of postfire stand regeneration11,12 as well as permafrost degradation13, presenting previously overlooked, additional feedback effects to future climate and fire dynamics. Given the projected increase in fire size in northern forests14,15, climate-smart forestry should aim to mitigate the climate risks of large fires, possibly by increasing the share of broadleaf trees, where appropriate, and avoiding active pyrophytes.

Heating pattern and effects of built-up land in response to influencing factors: A statistical estimation of 172 Chinese cities

The increase of the land surface temperature (LST) caused by an urban expansion poses a great threat to the humanity and society. However, the heating characteristics of built-up land and the environmental impacts on these mechanisms remain poorly understood over large areas. Therefore, the change trend (k) and structural distribution of LST during the process of increasing the built-up area in 172 cities in China was quantified. Using correlation analysis and statistical methods to analyze the correlation and possible driving characteristics of influencing factors (climate, topography, land use type, and soil) on k. The results indicated that k varied from −5.955 to 8.240 and showed a spatial characteristic of small in the northwest and large in the southeast. The research emphasized the heating difference of built-up land varied significantly in cities with different natural environmental conditions, with the maximum difference reaching 6 °C. The heating mechanism of built-up land under the interference of influencing factors was explained from the perspective of land surface heat flux, and the mitigation strategies for urban temperature were proposed. Besides, the threshold for the impact of environmental factors on the heating of built-up land was found, which provides a reference for environment-adapted urban design and planning standards tailored by considering the specific characteristics of each city.

Assessing the Correlation Between Spectral Indices and Land Surface Heat Fluxes by Remote Sensing Technology: A Case Study in Thai Binh Province, Red River Delta, Vietnam

Assessing the Correlation Between Spectral Indices and Land Surface Heat Fluxes by Remote Sensing Technology: A Case Study in Thai Binh Province, Red River Delta, Vietnam

Integrating Spatiotemporal Analysis of Land Transformation and Urban Growth in Peshawar Valley and Its Implications on Temperature in Response to Climate Change

Examining the interconnected dynamics of urbanization and climate change is crucial due to their implications for environmental, social, and public health systems. This study provides a comprehensive analysis of these dynamics in the Peshawar Valley, a rapidly urbanizing region in Khyber Pakhtunkhwa, Pakistan, over a 30-year period (1990–2020). A novel methodological framework integrating remote sensing, GIS techniques, and Google Earth Engine (GEE) was developed to analyze land use/land cover (LULC) changes, particularly the expansion of the built-up environment, along with the land surface temperature (LST) and heat index (HI). This framework intricately links these elements, providing a unique perspective on the environmental transformations occurring in the Peshawar Valley. Unlike previous studies that focused on individual aspects, this research offers a holistic understanding of the complex interplay between urbanization, land use changes, temperature dynamics, and heat index variations. Over three decades, urbanization expanded significantly, with built-up areas increasing from 6.35% to 14.13%. The population surged from 5.3 million to 12.6 million, coupled with significant increases in registered vehicles (from 0.171 million to 1.364 million) and operational industries (from 327 to 1155). These transitions influenced air quality and temperature dynamics, as evidenced by a highest mean LST of 30.30 °C and a maximum HI of 55.48 °C, marking a notable increase from 50.54 °C. These changes show strong positive correlations with built-up areas, population size, registered vehicles, and industrial activity. The findings highlight the urgent need for adaptive strategies, public health interventions, and sustainable practices to mitigate the environmental impacts of urbanization and climate change in the Peshawar Valley. Sustainable urban development strategies and climate change mitigation measures are crucial for ensuring a livable and resilient future for the region. This long-term analysis provides a robust foundation for future projections and policy recommendations.

Impact of decreasing land–sea horizontal pressure gradient on the lightning activity over western India

AbstractWith future global warming projections, how lightning activity changes in the warmer world is still a debated and challenging question. During the Indian pre‐monsoon season (March–May), land surface heating and moisture availability due to prevailing winds from the neighbouring oceans provide favourable conditions for thunderstorm formation. Based on 24 years of lightning data from 2000 to 2023 detected by Lightning Imaging Sensor/Optical Transient Detector (LIS/OTD) and Indian Lightning Location Network (ILLN), the trend of lightning flashes over western India (15°–22°N, 72.5°–81°E) has been investigated. Our results demonstrate a steady decline in lightning activity during the pre‐monsoon season over western India, which contradicts the previous studies suggesting an increasing lightning trend over the Indian Subcontinent and other parts of the world. Our analysis has shown a falling trend of lightning activity at a rate of −0.066 flashes·km−2 year−1 from 2000 to 2013 (LIS/OTD) and −0.14 M flashes·year−1 from 2014 to 2023 (ILLN). Our observation and previous research strongly suggested that the pressure difference between the land and the neighbouring oceans during pre‐monsoon and monsoon has been weakening for a long time over the Indian region, and we have found a consistent reduction in wind speed over the study region. Here, we propose that the enhanced Indian Ocean warming potentially weakens the land–sea thermal contrast and, thereby, reduces the horizontal pressure gradient. Further, the decreasing trend in the land–sea horizontal pressure gradient resulted in a declining rate of wind speed over western India, affecting moisture transport over land. Thus, the study emphasizes the impact of the decreased land–sea horizontal pressure gradient on declining lighting activity in western India.

How Does Plant CO2 Physiological Forcing Amplify Amazon Warming in CMIP6 Earth System Models?

AbstractThe physiological response to increasing CO2 concentrations will lead to land surface warming through a redistribution of the energy balance. As the Amazon is one of the most plant‐rich regions, the increase in surface temperature, caused by plant CO2 physiological forcing, is particularly large compared to other regions. In this study, we analyze the outputs of the 11 models in the Coupled Model Intercomparison Project Phase 6 to find out how CO2 physiological forcing amplifies Amazonian warming under elevated CO2 levels. With the CO2 concentration increase from 285 to 823 ppm, the Amazon temperature increased by 0.48 ± 0.42 K as a result of plant physiological forcing. Moreover, we assess the contributions of each climate feedback to the surface warming due to physiological forcing by quantifying climate feedbacks based on radiative kernels. Lapse rate feedback and cloud feedback, analyzed as the primary contributors, accounted for 53% and 37% of Amazon warming, respectively. The warming contributions of these two feedbacks also exhibit a significant spread, which contributes to the predictive uncertainty. The surface warming due to reduced evapotranspiration is larger than the upper tropospheric warming in the Amazon, resulting in surface warming by lapse rate feedback. In addition, cloud cover in the Amazon region decreases due to the reduced evapotranspiration. Decreased cloud cover amplifies surface warming through the shortwave cloud feedback. Furthermore, differences in circulation and local convection caused by physiological effect contribute to the inter‐model spread of the cloud feedback.

The Temperature of the Deep Ocean Is a Robust Proxy for Global Mean Surface Temperature During the Cenozoic

AbstractReconstructing global mean surface temperature (GMST) is one of the key contributions that paleoclimate science can make in addressing societally relevant questions and is required to determine equilibrium climate sensitivity (ECS). GMST has been derived from the temperature of the deep ocean (Td), with previous work suggesting a simple Td‐GMST scaling factor of 1 prior to the Pliocene. However, this factor lacks a robust mechanistic basis, and indeed, is intuitively difficult to envisage given that polar amplification is a ubiquitous feature of past warm climate states and deep water overwhelmingly forms at high latitudes. Here, we interrogate whether and crucially, why, this relationship exists using a suite of curated data compilations and two sets of paleoclimate model simulations. We show that models and data are in full agreement that a 1:1 relationship is a good approximation. Taken together, the two sets of climate models suggest that (a) a lower sensitivity of SST in the season of deep water formation than high latitude mean annual SST in response to climate forcing, and moreover (b) a greater degree of land versus ocean surface warming are the two processes that act to counterbalance a possible polar amplification‐derived bias on Td‐derived GMST. Using this knowledge, we provide a new Cenozoic record of GMST. Our estimates are substantially warmer than similar previous efforts for much of the Paleogene and are thus consistent with a substantially higher‐than‐modern ECS during deep‐time high CO2 climate states.

Summer drought weakens land surface cooling of tundra vegetation

Siberia experienced a prolonged heatwave in the spring of 2020, resulting in extreme summer drought and major wildfires in the North-Eastern Siberian lowland tundra. In the Arctic tundra, plants play a key role in regulating the summer land surface energy budget by contributing to land surface cooling through evapotranspiration. Yet we know little about how drought conditions impact land surface cooling by tundra plant communities, potentially contributing to high air temperatures through a positive plant-mediated feedback. Here we used high-resolution land surface temperature and vegetation maps based on drone imagery to determine the impact of an extreme summer drought on land surface cooling in the lowland tundra of North-Eastern Siberia. We found that land surface cooling differed strongly among plant communities between the drought year 2020 and the reference year 2021. Further, we observed a decrease in the normalized land surface cooling (measured as water deficit index) in the drought year 2020 across all plant communities. This indicates a shift towards an energy budget dominated by sensible heat fluxes, contributing to land surface warming. Overall, our findings suggest significant variation in land surface cooling among common Arctic plant communities in the North-Eastern Siberian lowland tundra and a pronounced effect of drought on all community types. Based on our results, we suggest discriminating between functional tundra plant communities when predicting the drought impacts on energy flux related processes such as land surface cooling, permafrost thaw and wildfires.

Spatio-temporal urban land surface temperature variations and heat stress vulnerability index in Thiruvananthapuram city of Kerala, India

ABSTRACT Land use/Land cover (LU/LC) modifications leads to rise in surface temperature is a global concern. This study investigates the effects of LU/LC changes in Land Surface Temperature (LST) variations and effects of Urban Heat Island (UHI) in Thiruvananthapuram city. Furthermore, the study proposed a novel heat stress vulnerability index (HSVI) using Analytical Hierarchy Process (AHP) method. Results shows that transformation of all land uses is to built-up areas, which shows an increase of +118.46% where as others such as vegetation (-24.27%), open area (-64.315) and waterbody (-43.88%) shows drastic decrease over 34 years. The relationship between Normalized Difference Vegetation Index and LST shows a significant change with R2 values between 0.223 in 1988, 0.382 in 2000 and 0.253 in 2022. Moreover, this also contributed to the increase of the mean LST of 26.96 ºC in 1988 to 28.52 ºC in 2022. HSVI of the study indicates that 30.83% of the study area is categorized as very low heat stress vulnerable area followed by 37.22% area in low heat stress vulnerability, 17.88% in moderate heat stress vulnerability, 8.87% in high heat stress vulnerability and 5.19% of the area falling in very high heat stress Vulnerability class.

Intensifying urban imprint on land surface warming: Insights from local to global scale

Increasing urbanization exacerbates surface energy balance perturbations and the health risks of climate warming; however, it has not been determined whether urban-induced warming and attributions vary from local, regional, to global scale. Here, the local surface urban heat island (SUHI) is evidenced to manifest with an annual daily mean intensity of 0.99°C-1.10°C during 2003-2018 using satellite observations over 536 cities worldwide. Spatiotemporal patterns and mechanisms of SUHI tightly link with climate-vegetation conditions, with regional warming effect reaching up to 0.015°C-0.138°C (annual average) due to surface energy alterations. Globally, the SUHI footprint of 1,860 cities approximates to 1% of the terrestrial lands, about 1.8-2.9 times far beyond the urban impervious areas, suggesting the enlargements of the imprint of urban warming from local to global scales. With continuous development of urbanization, the implications for SUHI-added warming and scaling effects are considerably important on accelerating global warming.

Evaluating land-surface warming and cooling environments across urban–rural local climate zone gradients in subtropical megacities

The influence of vegetated land cover on the land surface has been extensively explored in urban environmental studies, both regionally and globally. However, addressing the challenge of surface urban heat islands (SUHI) in megacities remains complex at the landscape level because of factors such as intricate urban structures, urban–rural functions, and insufficient land surface classification for impervious surfaces. In this study, we propose a novel approach that uses Earth observation data from 2000 to 2020 to examine the warming and cooling impacts of vegetation on SUHI in the densely populated Greater Bay Area (GBA) of China. Our methodology integrates urban–rural local climate zone (LCZ) gradients with vegetation greenness modeling. Our findings highlight that dense tree coverage exhibited the most substantial cooling effect across all four seasons. Low vegetation types, such as grassland and cropland, demonstrated moderate cooling during daytime throughout the year, exhibiting slight warming during winter nights. These cooling and warming effects were consistently varied along urban–rural gradients. The SUHI tended to decrease in strength with an increase in gradient, while showing an increasing trend across downtown areas from 2000 to 2020. Extreme daytime warming was observed from 2010 to 2015, whereas extreme nighttime warming occurred between 2015 and 2020. Urban–rural and interannual changes in cooling or warming effects were more strongly associated with evapotranspiration than with albedo. Our analysis provides valuable insights for guiding local greenspace planning and combating the adverse impacts of SUHI.

Simulation Evaluation of Transpiration Ratio (T/ET) in the Yellow River Basin

It is of great significance to improve the simulation accuracy of vegetation dynamics in land surface models for the study of dynamic changes of vegetation on land surface water and heat cycling. Parameter optimization is an effective means to improve the simulation accuracy of land surface model. Aiming at the application of Noah-MP land surface model in the Yellow River Basin, this paper improves the simulation performance of regional evAPOtranspiration and transpiration ratio by parameter optimization.

Risk Burden of Heat-Related Morbidity Due to Urban Heat Island Effect in Tamalanrea District, Makassar

One of the problems in the urban environment is the rise in land surface temperature and heat buildup (Urban Heat Island) around built-up areas. People living in UHI areas will experience an increased risk of health burdens. Therefore, early identification for disaster mitigation is needed to achieve a sustainable city. This study aims to provide an overview of the risk of the spread of heat-related diseases based on the relative risk value in Tamalanrea District. This study considers the relationship between Land Surface Temperature (LST) values in 2019, 2020, and 2021 with the incidence of heat-related diseases obtained from the Integrated Health Center Recording and Reporting System (IHCRRS) in Tamalanrea District, Makassar City in 2019, 2020 and 2021. Data analysis used Pearson correlation test and Standardized Morbidity Ratio (SMR) epidemiological approach. The results showed a significant relationship between LST values and the incidence of hypertension, heart disease, and asthma in 2019 and the incidence of headache and hypertension in 2021. Bira and Kapasa sub-districts were at the highest risk for the spread of heat-related diseases than other sub-districts with a risk score > 4.00 (very high). These findings can be used to help guide public health interventions and preventive urban planning efforts.

Global impacts of vegetation clumping on regulating land surface heat fluxes

The clumping index (CI) quantifies the non-random distribution of vegetation across space, which regulates the canopy radiative transfer processes and land surface carbon, water, and energy cycles. However, its impact on global surface energy budget, particularly sensible heat fluxes and surface temperature, is not well understood. Additionally, while there have been studies showing significant seasonal variations in CI, the impacts of these variations on surface energy fluxes remain unclear. In this study, we incorporated satellite-derived spatially and temporally explicit CI data into the Community Land Model version 5 (CLM5) to evaluate the effects of CI on global land energy fluxes. Our results showed that including CI increased the global mean sensible heat flux dissipated from ground by 3.9 W m−2 (∼18%), while decreasing the global mean vegetation sensible heat flux by 4.9 W m−2 (∼65%), resulting in a total sensible heat decrease of 1.0 W m−2 (∼3%). In contrast, CI increased the global mean latent heat flux by 0.8 W m−2 (∼2%), primarily due to increased evapotranspiration (up to 11 W m−2) in tropical regions. We also found considerable impacts of seasonal variations in CI, particularly on sensible heat fluxes from ground and vegetation in evergreen needleleaf forests and deciduous needleleaf forests. Using constant CI rather than considering seasonal variations resulted in significant overestimation and underestimation of the sensible heat fluxes from vegetation and ground, respectively, in boreal summer. These changes in surface energy fluxes caused by CI and its seasonal variations led to up to 1.7 and 0.5 K differences in simulated mean ground temperature. These findings highlight the importance of including CI and considering its seasonal variations in modeling land surface energy fluxes.

Potential causes of megapluvials in the Yangtze–Huaihe River valley over the last millennium

The potential causes of megapluvials over Yangtze–Huaihe River valley (YHRV) during the last millennium were investigated using the Community Earth System Model-Last Millennium Ensemble (CESM-LME) simulation data and multiple proxy data (i.e., reconstruction and paleoclimate assimilation data). Herein, we first depicted the spatial hydroclimatic pattern over the Northern Hemisphere when megapluvials occurred in the YHRV during the last millennium. The proxy and simulation data both showed that megapluvials in YHRV were accompanied by extensive decadal drought in western Asia and northwestern India, also located in mid to low latitudes. Then, by diagnosing the dynamic processes, we found that the baroclinic structure over western Asia and northwestern India caused anomalous geopotential heights, and hence triggered the negative phase of the Circumglobal Teleconnection (CGT). The eastwards wave train at mid-latitudes disturbed by the CGT mode, triggered lower-level northerlies and upper-level southerlies over the northern part of the YHRV and, as a consequence, led to anomalous strong convection near the YHRV. Together with abundant water vapour from the Pacific Northwest, there was a synergistic effect on megapluvial events over the YHRV. Finally, we analysed the linkage of surface temperature in western Asia and northwestern India and megapluvials in the YHRV. Land surface heating over western Asia and northwestern India may largely stimulate the formation of a negative CGT mode through local feedback of air–land interactions and, as a result, affect precipitation anomalies downstream, such as in the YHRV. It is therefore necessary to consider anomalous high surface temperatures in western Asia and northwestern India as important predictors for interdecadal severe floods in the YHRV.

The spatiotemporal variation of land surface heat fluxes in Tibetan Plateau during 2001–2022

The surface energy budget is important for understanding of energy and water cycle processes in the Tibetan Plateau (TP). In this study, the daily sensible (SH) and latent (LE) heat fluxes at the horizontal resolution of 1° are first estimated using the maximum entropy production (MEP) model (hereinafter SHMEP and LEMEP) in the entire TP during 2001–2022. The MEP model is built on physical and statistical principles to simulate surface heat fluxes. The surface net radiation, soil moisture (SM), and land surface temperature (LST) are the main driving variables for MEP model. To select the relatively accurate MEP input data, the merged surface net radiation (Rn–merged) under all-sky conditions are generated from CERES, ISCCP-FH, and ERA5 using the Bayesian Model Averaging scheme. Besides, the TP SM and LST from various data sources are evaluated using the in-situ observations at site scale. Based on the daily Rn–merged, ERA5 SM, CERES LST, and the MEP model, the daily SH and LE are estimated in the entire TP. The results show the daily SHMEP and LEMEP perform well at the validation sites, with the regional mean correlation coefficient (R) above 0.7, root-mean-square error (RMSE) of <19 W m−2, absolute value of bias and mean absolute error (MAE) below 11 W m−2. The monthly SHMEP has the regional mean R of 0.96 and RMSE of 6.30 W m−2 at all the measurement stations. For LEMEP, the regional mean R and RMSE values are 0.93 and 10.01 W m−2, respectively. The MEP simulation results are superior to the SH and LE in ERA5, ERA-Interim, MERRA-2, and JRA-55 reanalysis datasets and previous studies, especially for LE. Based on this new dataset, the spatial and temporal varying characteristics of SH and LE in the TP are analyzed. The annual mean SHMEP value are large in the western TP, the Qaidam Basin in the northern TP, and the Himalaya ranges, and small in the southeastern TP. The annual mean LEMEP has the maximum value in southeastern TP, and minimum value in western TP and the Qaidam Basin. The annual mean SHMEP and LEMEP over the entire TP are 34.79 W m−2 and 20.16 W m−2, with the significant declining trends of −0.17 W m−2 year−1 and − 0.052 W m−2 year−1 during the study period, respectively. The spatial distributions of the MEP surface heat fluxes and their trends are mainly influenced by the model inputs of Rn and SM in the TP.

Anthropogenic disturbance exacerbates resilience loss in the Amazon rainforests.

Uncovering the mechanisms that lead to Amazon forest resilience variations is crucial to predict the impact of future climatic and anthropogenic disturbances. Here, we apply a previously used empirical resilience metrics, lag-1 month temporal autocorrelation (TAC), to vegetation optical depth data in C-band (a good proxy of the whole canopy water content) in order to explore how forest resilience variations are impacted by human disturbances and environmental drivers in the Brazilian Amazon. We found that human disturbances significantly increase the risk of critical transitions, and that the median TAC value is ~2.4 times higher in human-disturbed forests than that in intact forests, suggesting a much lower resilience in disturbed forests. Additionally, human-disturbed forests are less resilient to land surface heat stress and atmospheric water stress than intact forests. Among human-disturbed forests, forests with a more closed and thicker canopy structure, which is linked to a higher forest cover and a lower disturbance fraction, are comparably more resilient. These results further emphasize the urgent need to limit deforestation and degradation through policy intervention to maintain the resilience of the Amazon rainforests.

The Detection and Attribution of Northern Hemisphere Land Surface Warming (1850–2018) in Terms of Human and Natural Factors: Challenges of Inadequate Data

A statistical analysis was applied to Northern Hemisphere land surface temperatures (1850–2018) to try to identify the main drivers of the observed warming since the mid-19th century. Two different temperature estimates were considered—a rural and urban blend (that matches almost exactly with most current estimates) and a rural-only estimate. The rural and urban blend indicates a long-term warming of 0.89 °C/century since 1850, while the rural-only indicates 0.55 °C/century. This contradicts a common assumption that current thermometer-based global temperature indices are relatively unaffected by urban warming biases. Three main climatic drivers were considered, following the approaches adopted by the Intergovernmental Panel on Climate Change (IPCC)’s recent 6th Assessment Report (AR6): two natural forcings (solar and volcanic) and the composite “all anthropogenic forcings combined” time series recommended by IPCC AR6. The volcanic time series was that recommended by IPCC AR6. Two alternative solar forcing datasets were contrasted. One was the Total Solar Irradiance (TSI) time series that was recommended by IPCC AR6. The other TSI time series was apparently overlooked by IPCC AR6. It was found that altering the temperature estimate and/or the choice of solar forcing dataset resulted in very different conclusions as to the primary drivers of the observed warming. Our analysis focused on the Northern Hemispheric land component of global surface temperatures since this is the most data-rich component. It reveals that important challenges remain for the broader detection and attribution problem of global warming: (1) urbanization bias remains a substantial problem for the global land temperature data; (2) it is still unclear which (if any) of the many TSI time series in the literature are accurate estimates of past TSI; (3) the scientific community is not yet in a position to confidently establish whether the warming since 1850 is mostly human-caused, mostly natural, or some combination. Suggestions for how these scientific challenges might be resolved are offered.

Impact of ENSO on Wintertime Land Surface Variables in Northern Hemisphere Extratropics: Role of Atmospheric Moisture Processes

Abstract El Niño–Southern Oscillation (ENSO) is the strongest interannual signature in the tropical air–sea system and can affect global atmospheric variables, but ENSO’s climatic impact on land surface variables such as skin temperature, snow cover, and soil moisture that can serve as seasonal climate predictors remains relatively little known. Here we examine ENSO’s impact on the three land surface variables in Northern Hemisphere extratropics by regression analysis, using a combination of ERA5 reanalysis, GLDAS, satellite-based observations, and CESM2 simulations. Our results show that during El Niño winters anomalous land surface warming occurs in Northern Hemisphere midlatitudes, accompanied by snow cover reduction, especially in northern North America and eastern Europe, while anomalous cooling occurs in southern North America and the Tibetan Plateau, accompanied by increased snow cover. Meanwhile, increased soil moisture is observed in southern North America, central Asia, and southeast China. Further analysis indicates that atmospheric moisture processes dominate the formation of the land surface anomalies, for which the changes in water vapor and precipitation induced by the ENSO-related large-scale atmospheric teleconnection are critical. The anomalous land surface warming in midlatitudes mainly results from the increased downward longwave radiation due to the increase of water vapor, whereas the physical pathway causing water vapor anomalies is different over North America and Eurasia. Controlled by ENSO-induced changes in atmospheric circulation and moisture transport, anomalous precipitation explains most of the snow cover and soil moisture anomalies, which play a certain role in shaping the skin temperature anomalies in some regions through snow–albedo feedback and evaporation, respectively.