Land Surface Air Temperature Research Articles

Is home where the heat is? comparing residence-based with mobility-based measures of heat exposure in San Diego, California.

Heat can vary spatially within an urban area. Individual-level heat exposure may thus depend on an individual's day-to-day travel patterns (also called mobility patterns or activity space), yet heat exposure is commonly measured based on place of residence. In this study, we compared measures assessing exposure to two heat indicators using place of residence with those defined considering participants' day-to-day mobility patterns. Participants (n = 599; aged 35-80 years old [mean =59 years]) from San Diego County, California wore a GPS device to measure their day-to-day travel over 14-day intervals between 2014-10-17 and 2017-10-06. We measured exposure to two heat indicators (land-surface temperature [LST] and air temperature) using an approach considering their mobility patterns and an approach considering only their place of residence. We compared participant mean and maximum exposure values from each method for each indicator. The overall mobility-based mean LST exposure (34.7 °C) was almost equivalent to the corresponding residence-based mean (34.8 °C; mean difference in means = -0.09 °C). Similarly, the mean difference between the overall mobility-based mean air temperature exposure (19.2 °C) and the corresponding residence-based mean (19.2 °C) was negligible (-0.02 °C). Meaningful differences emerged, however, when comparing maximums, particularly for LST. The mean mobility-based maximum LST was 40.3 °C compared with a mean residence-based maximum of 35.8 °C, a difference of 4.51 °C. The difference in maximums was considerably smaller for air temperature (mean = 0.40 °C; SD = 1.41 °C) but nevertheless greater than the corresponding difference in means. As the climate warms, assessment of heat exposure both at and away from home is important for understanding its health impacts. We compared two approaches to estimate exposure to two heat measures (land surface temperature and air temperature). The first approach only considered exposure at home, and the second considered day-to-day travel. Considering the average exposure estimated by each approach, the results were almost identical. Considering the maximum exposure experienced (specific definition in text), the differences between the two approaches were more considerable, especially for land surface temperature.

Growth Simulation of Lyophyllum decastes and Coprinus comatus and Their Influencing Factors in a Forested Catchment

Wild edible mushrooms are an important food source globally and have a crucial role in forest ecosystems. However, there is limited research on the growth characteristics and the contribution of agronomic traits to biomass, and the environmental factors affecting mushroom growth are limited. This study was conducted in the Qilian Mountains, China, and focused on investigating the growth patterns and agronomic traits of Lyophyllum decastes and Coprinus comatus. The results revealed that the growth of these mushrooms followed a logical growth curve. By calculating the model parameters, we obtained the maximum daily growth of height (PH), pileus diameter (PD), and cluster perimeter (CP) of L. decastes on the 5th, 7th, and 7th days, respectively, with values of 0.55 cm d−1, 0.54 cm d−1, and 4.54 cm d−1, respectively. However, the maximum daily growth of PH, pileus length (PL), and PD of the C. comatus appeared on the 3rd day, 2nd day, and 2nd day of the observation, respectively. This study identified near-surface relative humidity, air relative humidity, and rainfall as the primary factors influencing mushroom growth, as indicated by Pearson’s correlation analysis, redundancy analysis (RDA), and multiple linear and stepwise regression. Additionally, land surface temperature and air temperature were also identified as important factors affecting mushroom growth. By utilizing random forest and stepwise regression analysis, this study identified PH and stipe diameter (SD) as the most crucial agronomic traits affecting mushroom biomass. Overall, this study offers insights for industrial mushroom cultivation and basic fungal research.

A Dynamically Consistent ENsemble of Temperature at the Earth surface since 1850 from the DCENT dataset

Accurate historical records of Earth’s surface temperatures are central to climate research and policy development. Widely-used estimates based on instrumental measurements from land and sea are, however, not fully consistent at either global or regional scales. To address these challenges, we develop the Dynamically Consistent ENsemble of Temperature (DCENT), a 200-member ensemble of monthly surface temperature anomalies relative to the 1982–2014 climatology. Each DCENT member starts from 1850 and has a 5° × 5° resolution. DCENT leverages several updated or recently-developed approaches of data homogenization and bias adjustments: an optimized pairwise homogenization algorithm for identifying breakpoints in land surface air temperature records, a physics-informed inter-comparison method to adjust systematic offsets in sea-surface temperatures recorded by ships, and a coupled energy balance model to homogenize continental and marine records. Each approach was published individually, and this paper describes a combined approach and its application in developing a gridded analysis. A notable difference of DCENT relative to existing temperature estimates is a cooler baseline for 1850–1900 that implies greater historical warming.

Assessing the Impacts of Falling Ice Radiative Effects on the Seasonal Variation of Land Surface Properties

AbstractThe impacts of falling ice radiative effects (FIREs) on land‐atmosphere feedback processes were examined, with a focus on the fidelity of land surface properties and their variability as inferred by global climate models (GCMs). We conducted a pair of sensitivity experiments using the National Center for Atmospheric Research (NCAR) Community Earth System Model Version 1 (CESM1) in fully coupled modes with FIREs turned on and off. This allowed us to investigate the seasonal response of land surface properties to changes in radiation fluxes and land surface temperature (LST) associated with FIREs across global land areas. Our findings indicate that during boreal winter, excluding FIREs results in less surface downward longwave and net flux (∼5–15 Wm−2), leading to a colder land surface (∼2–4 K) and air temperatures (∼1–4 K) at mid‐ and high latitudes. Consequently, the surface frozen soil layer and snow cover persist through spring, delaying snowmelt and thawing until summer. This delay reduces liquid soil moisture, thereby suppressing vegetation productivity in subsequent seasons. Conversely, tropical regions, exhibit contrasting responses, with a warmer land surface (∼0.5 K) and warmer air temperatures (∼0.1–0.5 K) due to increased surface downward shortwave and net flux (∼2–10 Wm−2). This enhancement in radiation fosters increased vegetation productivity throughout the seasonal cycle. These findings illustrate a local response of land surface properties to changes in the surface energy balance and LST, highlighting the significant role that FIREs play in land surface modeling within GCMs.

Performance of CMIP5 and CMIP6 models in reproducing the Interdecadal Pacific Oscillation and its global impacts

AbstractThis study assessed the capability of the historical simulations of phase 5 and 6 of the Coupled Model Intercomparison Project (CMIP5/6) in reproducing the temporal and spatial characteristics of the Interdecadal Pacific Oscillation (IPO) and its impact on global surface air temperature (SAT), surface equivalent potential temperature (Thetae_sfc) and precipitation. The IPO index time series simulated by CMIP5/6 models deviated from observations and struggled to capture the phase evolution characteristics of the IPO. Nevertheless, CMIP5/6 models successfully captured the horseshoe‐shaped sea surface temperature anomaly in the Pacific. Additionally, the CMIP5/6 models were able to simulate the IPO's 10–30‐year period. Notably, the simulated IPO index exhibited a statistically significant upward trend, which was absent in observations. Additionally, the IPO‐related global land SAT, Thetae_sfc and precipitation simulated by CMIP5/6 models performed differently in boreal winter and boreal summer. Furthermore, the IPO‐related global land SAT performed better in CMIP5/6 models during boreal winter than that in boreal summer. In CMIP6 models, it improved during both boreal winter and summer compared to CMIP5 models. In terms of the IPO‐related global land Thetae_sfc, CMIP5/6 models also performed better during boreal winter than in boreal summer. However, CMIP5 models outperformed CMIP6 models during the boreal summer. In terms of the IPO‐related global land precipitation, CMIP5/6 models performed better during boreal summer compared to boreal winter. Moreover, the IPO‐related global land precipitation in CMIP6 models improved significantly in boreal winter, but almost the same in boreal summer, compared to CMIP5 models. Further studies showed that the enhancements in simulating IPO's spatial pattern did not correspond to improvements in the model's ability to simulate IPO's global teleconnections.

Improving global temperature datasets to better account for non‐uniform warming

AbstractTo estimate changes in global mean surface temperature (GMST), one must infer past temperatures for regions of the planet that lacked observations. While current global instrumental temperature datasets (GITDs) estimate different rates of warming for different regions of the planet, this non‐uniform warming is often modelled as residuals relative to underlying trends of spatially uniform warming. To better account for spatial non‐uniform trends in warming, a new GITD was created that used maximum likelihood estimation (MLE) to combine the land surface air temperature (LSAT) anomalies of non‐infilled HadCRUT5 with the sea surface temperature (SST) anomalies of HadSST4. This GITD better accounts for non‐uniform trends in warming in two ways. Firstly, the underlying warming trends in the model are allowed to vary spatially and by the time of year. Secondly, climatological differences between open‐sea and sea ice regions are used to better account for changes in sea ice concentrations (SICs). These improvements increase the estimate of GMST change from the late 19th century (1850–1900) to 2023 by 0.006°C and 0.079°C, respectively. Although, for the latter improvement, tests suggest that there may be an overcorrection by a factor of two and estimates of SICs for the late 19th century are a significant source of unquantified uncertainty. In addition, this new GITD has other improvements compared to the HadCRUT5 Analysis dataset, including correcting for a small underestimation of LSAT warming between 1961 and 1990, taking advantage of temporal correlations of observations, taking advantage of correlations between land and open‐sea observations, and better treatment of the El Niño Southern Oscillation (ENSO). Overall, the median estimate of GMST change from the late 19th century to 2023 is 1.548°C, with a 95% confidence interval of [1.449°C, 1.635°C].

Assessing the Relationship Between Land Surface Temperature and Air Temperature for Thermal Comfort Analysis in Can Tho City: Implications for Sustainable Spatial Planning

This study aims to assess the relationship between land surface temperature (LST) and air temperature (Tair) and thermal comfort analysis in Can Tho City. LST is derived from the Landsat thermal band, while Tair is measured using 15 monitoring stations. A Temperature Humidity Index (THI) is employed as a preferred metric for analyzing the thermal comfort level, calculated based on a combination of Tair and relative humidity (RH). Besides quantitative data collection, the study also survey residents in high heat risk areas to acquire insights into their current experiences and perceptions heat. The results reveal that individuals in high SUHI areas experience discomfort across varying temperature ranges, influenced by particular working conditions and personal characteristics. Moreover, the findings highlight a significant correlation between LST and Tair, reaching a coefficient of 0.73, indicating the applicability of land surface temperature in large-scale temperature calculations. These results underscore the need for further research on the extent of heat-related health effects and the development of strategies for heat stress mitigation. Understanding of thermal comfort and the impacts of SUHI is crucial for addressing climate and societal challenges and enhance the well-being of urban populations.

Detection and attribution for land surface air temperature warming in China based on a perfect model selection analysis of optimal fingerprint strategy

Detection and attribution for land surface air temperature warming in China based on a perfect model selection analysis of optimal fingerprint strategy

An Improved Ensemble of Land Surface Air Temperatures Since 1880 Using Revised Pair-Wise Homogenization Algorithms Accounting for Autocorrelation

Abstract Land surface air temperatures (LSAT) inferred from weather station data differ among major research groups. The estimate by NOAA’s monthly Global Historical Climatology Network (GHCNm) averages 0.02°C cooler between 1880 and 1940 than Berkeley Earth’s and 0.14°C cooler than the Climate Research Unit estimates. Such systematic offsets can arise from differences in how poorly documented changes in measurement characteristics are detected and adjusted. Building upon an existing pairwise homogenization algorithm used in generating the fourth version of NOAA’s GHCNm(V4), PHA0, we propose two revisions to account for autocorrelation in climate variables. One version, PHA1, makes minimal modification to PHA0 by extending the threshold used in breakpoint detection to be a function of LSAT autocorrelation. The other version, PHA2, uses penalized likelihood to detect breakpoints through optimizing a model-selection problem globally. To facilitate efficient optimization for series with more than 1000 time steps, a multiparent genetic algorithm is proposed for PHA2. Tests on synthetic data generated by adding breakpoints to CMIP6 simulations and realizations from a Gaussian process indicate that PHA1 and PHA2 both similarly outperform PHA0 in recovering accurate climatic trends. Applied to unhomogenized GHCNmV4, both revised algorithms detect breakpoints that correspond with available station metadata. Uncertainties are estimated by perturbing algorithmic parameters, and an ensemble is constructed by pooling 50 PHA1- and 50 PHA2-based members. The continental-mean warming in this new ensemble is consistent with that of Berkeley Earth, despite using different homogenization approaches. Relative to unhomogenized data, our homogenization increases the 1880–2022 trend by 0.16 [0.12, 0.19]°C century−1 (95% confidence interval), leading to continental-mean warming of 1.65 [1.62, 1.69]°C over 2010–22 relative to 1880–1900. Significance Statement Accurately correcting for systematic errors in observational records of land surface air temperature (LSAT) is critical for quantifying historical warming. Existing LSAT estimates are subject to systematic offsets associated with processes including changes in instrumentation and station movement. This study improves a pairwise homogenization algorithm by accounting for the fact that climate signals are correlated over time. The revised algorithms outperform the original in identifying discontinuities and recovering accurate warming trends. Applied to monthly station temperatures, the revised algorithms adjust trends in continental mean LSAT since the 1880s to be 0.16°C century−1 greater relative to raw data. Our estimate is most consistent with that from Berkeley Earth and indicates lesser and greater warming than estimates from NOAA and the Met Office, respectively.

Implications of Russia-Ukraine war on land surface temperature and air quality: long-term and short-term analysis.

The world is currently witnessing the military operations of Russia invading Ukraine, leading to missile bombing and shelling on various parts. Although the economic ill effects are more conspicuous and much talked about, the environmental impacts are grimmer and more devastating but ironically are less in the news. Hence, in this work, we focused on the environmental impact of the Russia-Ukraine war by quantifying the long-term(2001 to 2023) and short-term temperature changes using land surface temperature (LST) and air temperature (AT) as proxies and monitoring changes in air quality, mainly methane (CH4), carbon monoxide (CO) and carbon dioxide (CO2), between 2021 and 2022. We used NASA MODIS FIRMS fire points from 24th February 2022 to 08th September 2023 to prepare the heat map for identifying the regions heavily devastated by bombing. Thus, parts of Kiev, Lviv, Luhansk, Odesa, Donetsk, Kherson, etc., in Ukraine were chosen for assessing the LST, AT variations during the peak season of war along with analysis of long-term and short-term changes. We used MODIS Terra LST and Emissivity, ERA 5 AT, CH4, CO2 from AIRS and CO from Sentinel 5P. The results of the LST showed an average increase of around 2.32°C (2022-2023), 3.44°C (2021 and 2022) in parts of Ukraine and an increase of about 2°C from COVID time, whilst a decrease of about 1°C during COVID. This increase in LST will cause enhanced warming, thus changing the regional climate in a shorter time frame. A consistent upward trend in CH4, CO and CO2 is seen from 2019 to 2023. These heat waves and pollution will grip Ukraine and cause menace due to the cumulative effect of heat waves, changing climate and the aftermaths of war. This would be catastrophic as it might lead to a widespread impact on human health, agricultural yield and infrastructure, to name a few.

An analysis of land surface temperature and land-use from sentinel satellite data

Land surface temperature is a study of the heat on the earth’s surface; it can be recognized by contacting the surface from Thermal Infrared wavelength which can be the roof, the top of buildings, water, or ice. Therefore, land surface temperature and air temperature are not the same. This study aims to analyze land surface temperature and land-use from Sentinel-2 Satellite data. The procedure is divided into 3 main steps: 1) Using data from Sentinel-3 Satellite to analyze temperature by using The Supplemental Nutrition Assistance Program (SNAP) program, 2) Land-use classification by using data from Sentinel-2 Satellite in Supervised Classification in the form of Maximum Likelihood Classifier. This study has classified land-use into 4 types: water, forest, urban, and agriculture, 3) Accuracy assessment. The result found that the land surface temperature of urban areas has the highest average land surface temperature, followed by forest, agriculture, and water.

Quantifying exposure biases in early instrumental land surface air temperature observations

AbstractExposure biases are a pervasive non‐climatic change in land air temperature records which have been introduced as a result of changes in the way thermometers were sheltered from solar radiation and the elements over time. Exposure biases have not been widely accounted for in observational records, due to difficulties detecting/correcting the bias using traditional homogenisation techniques; therefore, exposure biases still contribute significant uncertainty to the early period in global temperature compilations. Here, an empirical approach to address the bias arising from the introduction of Stevenson‐type screens from the late‐19th century is presented. The approach consists of: (1) an empirical analysis of 54 parallel measurement series to identify the characteristics of the exposure bias in four exposure classes; (2) the development of bias‐estimation models based on an analysis of which variables influence the bias; and (3) the application of the models to an extended version of CRUTEM5 (CRUTEM5_ext), based on exposure metadata, to quantify and reduce the bias. Step one identified differences between the temperatures recorded in Stevenson screens and early exposures, which vary seasonally, diurnally, and with location and exposure class. The largest biases (in mean temperatures) were found in freestanding exposures (up to −0.78°C annually) and in summer, while the smallest biases were generally found in wall‐mounted exposures (near‐0°C annually) and in winter. Significant relationships between the bias and temperature, downward top of atmosphere and/or received shortwave downward solar radiation were found in each exposure class and led to the development of three regression‐based bias‐estimation models. Application of these models to 1,960 mid‐latitude stations in CRUTEM5_ext, resulted in small (≤0.016°C) positive adjustments to the Northern Hemisphere mid‐latitude mean before 1880, and larger (≤−0.1°C) negative adjustments to the Northern and Southern Hemisphere mid‐latitude means between 1882–1934 and 1856–1900, respectively. Larger adjustments were estimated regionally: up to −0.57°C annually and −0.79°C seasonally in individual grid cells.

Multi-decadal pacemaker simulations with an intermediate-complexity climate model

Abstract. In this paper, we first describe the main features of a new version of the International Centre for Theoretical Physics global atmospheric model (SPEEDY) with improved simulation of surface fluxes and the formulation of a three-layer thermodynamic ocean model (TOM3) suitable to explore the coupled extratropical response to tropical ocean variability. Then, we present results on the atmospheric model climatology, highlighting the impact of the modifications introduced in the model code, and show how important features of interdecadal and interannual variability are simulated in a “pacemaker” coupled ensemble of 70-year runs, where portions of the tropical Indo-Pacific are constrained to follow the observed variability. Despite the very basic representation of variations in greenhouse forcing and heat transport to the deep ocean (below the 300 m domain of the TOM3 model), the coupled ensemble reproduces the variations in surface temperature over land and sea with very good accuracy, confirming the role of the Indo-Pacific as a “pacemaker” for the natural fluctuations of global-mean surface temperatures found in earlier studies. Atmospheric zonal-mean temperature trends over 50 years are also realistically simulated in the extratropical lower troposphere and up to 100 hPa in the tropics. On the interannual scale, sea-surface temperature (SST) variability in sub-tropical and tropical regions not affected by SST relaxation is underestimated (mostly because of the absence of dynamically induced variability), while extratropical SST variability during the cold seasons is comparable to that observed. Atmospheric teleconnection patterns and their connections with SST are reproduced with high fidelity, although with local differences in the amplitude of regional features (such as a larger-than-observed response of extratropical SST to North Atlantic Oscillation variability). The SPEEDY-TOM3 model also reproduces the observed connection between averages of surface heat fluxes over the oceans and land surface air temperature in the wintertime northern extratropics. Overall, as in earlier versions of SPEEDY, the fidelity of the simulations (both in terms of climatological means and variability) is higher near the surface and in the lower troposphere, while the negative impacts of the coarse vertical resolution and simplified parameterizations are mostly felt in the stratosphere. However, the improved simulation of surface heat fluxes and their impact on extratropical SST variability in this model version (obtained at a very modest computational cost) make the SPEEDY-TOM3 model a suitable tool to investigate the coupled response of the extratropical circulation to interannual and inter-decadal changes of tropical SST in ensemble experiments.

Disentangling the response of vegetation dynamics to natural and anthropogenic drivers over the Qinghai-Tibet Plateau using dimensionality reduction and structural equation model

The Qinghai-Tibet Plateau (QTP), also referred to as the “roof of the world”, is a distinct alpine ecosystem located at an average elevation of more than 4000 m. Climate variation and anthropogenic activity have affected vegetation development on the QTP during the past few decades, but it is still debatable how significant a role any of these causes has played. According to land surface air temperature data around the world, during the past 50 years, temperatures on the QTP have risen twice as rapidly as the average global level. Previous studies have employed the residual method to examine the impacts of anthropogenic activities on vegetation development, with a lack of actual data validation and less attention to explaining the complex interactions and pathways between vegetation growth, natural factors (climate change and topography), and anthropogenic drivers. Furthermore, although the relationships between vegetation dynamics and terrain have been addressed, the interactions in which meteorological factors influence vegetation growth in accordance with topographical variations remain inadequately elucidated. The raster datasets employed in this study include MODIS NDVI, meteorology, topography, as well as anthropogenic activity from 2000 to 2018. A suite of methods including partial correlation coefficient (PCC), principal component analysis (PCA), and partial least squares structural equation model (PLS-SEM) were applied to explore the interrelationships between various influencing factors and regional vegetation development over the past two decades. The results suggested that: (1) an elevation-specific temperature threshold existed, indicating that temperature could promote or inhibit vegetation growth depending on whether it is below or above this threshold, respectively, which was controlled by a trendline equation; (2) the PLS-SEM and PCA results showed that vegetation development was dominantly promoted by temperature and was most sensitive to precipitation; (3) topography, especially elevation, affected vegetation growth indirectly by influencing climatic change, while anthropogenic drivers had positively contributed to vegetation dynamics on the QTP under the influence of implementing ecological management such as the nature reserves establishment.

Two-decadal climate impacts on growth of major forest types of Eastern Himalaya

Forests affect regional climates, livelihoods and global cycles of water, carbon and nitrogen. Anthropogenic activities and climatic change affect forest health and national growth. Therefore, developing effective forest management plans requires understanding of the drivers of forest growth. The primary objective of this study was to understand the long-term effect of abiotic factors on the growth of forests in the region. This study used Moderate Resolution Imaging Spectroradiometer (MODIS) data for vegetation indices like NDVI and EVI and NASA's Land Assimilation datasets (wind speed, evapotranspiration, soil moisture and temperature) to understand their role on forest growth through statistical techniques such as Pearson's correlation and Multiple Linear Regression. The study examined the relationship between standard monthly vegetation indices and abiotic variables (i.e., moisture and temperature at different soil profiles up to 2 m depth, land surface temperature, evapotranspiration, relative humidity, wind velocity, and air temperature at 2 m height) in selected forests of the Eastern Himalayas for two decades (2001–2020, n = 240). Rainfall, temperature, and other associated factors significantly affected forest growth in the region. It was observed that rainfall alone affected forest growth in the region. However, its impact was maximum after two months of the rain events, reflecting a significant lag effect. Soil moisture at different depths affected vegetation growth in all forest types. Reduced soil moisture had a more significant effect on old-growth forests than younger forests. Multiple Linear Regression models developed with the abiotic factors explained higher variability in forest growth. In conclusion, this study reveals that rainfall, temperature, and their associated variables significantly affected forest growth in the study area. The study has significant implications for forest management in the region for formulating better strategies to mitigate climate change effects on forests in the region in future.

Global land degradation hotspots based on multiple methods and indicators

Land degradation is a major impediment to achieving sustainable development. However, there is currently no harmonized global map of land degradation status and hotspots. This paper aims to obtain the status and hotspots map of global land degradation by multiple methods and indicators to give essential references for land degradation neutrality. The results show that there are significant differences in the distribution and degree of land degradation between the different methods and indicators. Validation through observation points reveals that most of the methods and indicators can reflect land degradation in arid and semi-arid areas, while there are suitable methods or indicators in tropical and high-latitude areas. The degree of degradation has a large difference after overlay analysis, which shows that there are shortcomings of different methods and indicators for monitoring the degree of land degradation. However, the overlay of land degradation extent displays a high consistency, reflecting the current state of global land degradation to a certain extent. These areas with high overlay value can be recognized as hotspots of land degradation. It is also found there are consistent water-energy change characteristics in the hotspot area, such as increased land surface temperature and air temperature and decreased soil moisture and precipitation. These results conclude that studies on the degree of land degradation need to be considered in an integrated manner about the regional background. The combination of multiple methods and indicators is recommended for land degradation extent studies in large areas. Comparison of different methods and indicators is important guidance for global land degradation research. Accelerating ecological monitoring and restoration of land degradation hotspots is the first step towards land degradation neutrality.

The Relationship between Land Surface Temperature and Air Temperature in the Douro Demarcated Region, Portugal

Climatic studies of agricultural regions normally use gauge-based air temperature datasets, which are produced with interpolation methods. The informative quality of these datasets varies depending on the density of the weather stations in a particular region. A way to overcome this limitation is to use the land surface temperature calculated from satellite imagery. To show this, the MODIS land surface temperature was compared with the PTHRES gridded dataset for air temperature in the Douro Demarcated Region (Portugal) between the years 2002 and 2020. The MODIS land surface temperature was able to detect a more pronounced maritime–continental gradient, a higher lapse rate, and thermal inversions in valley areas in winter. This information could prove to be crucial for farmers looking to adapt their practices and crops to extreme events, such as heat waves or heavy frost. However, the use of land surface temperature in climate studies should consider the differences in air temperature, which, on some occasions and locations, can be up to ten degrees in the summer.

Understanding the differences in the effect of urbanization on land surface temperature and air temperature in China: insights from heatwave and non-heatwave conditions

Accelerated urbanization and frequent heatwave events pose significant threats to human health. Analyses of the differences in air and land surface temperature (LST) under extreme climates can aid in understanding human-nature ecosystem coupling and the required adaptations to climate change. In this study, we quantified differences in urban and rural temperatures in China under heatwave (CHW) and non-heatwave periods (NHW) conditions and the influence of meteorological factors on these differences. Based on impervious surface data, 2421 urban and rural stations were dynamically classified from 2008 to 2017. Heatwaves were identified using relative thresholds, and differences were explored using meteorological data and MODIS LST data. For LST, urban–rural temperature difference (U-RTempdiff) was highest during the day, whereas air temperature peaks occurred at night, under both NHW and CHW conditions. During CHWs, the daytime U-RTempdiff was greater for LST than for air temperature, reaching 4.24 ± 3.38 °C. At night, U-RTempdiff was slightly lower (1.04 ± 1.41 °C). The proportion of air U-RTempdiff contributed by rural air temperature was significantly higher during CHW nights than during NHW nights, whereas the proportion of land surface and air U-RTempdiff remained relatively stable during daytime. Spatially, the daytime temperature difference in the north decreased with latitude, whereas the difference in the south was lower. Under CHWs, urbanization had a stronger effect on LST than on air temperature, with a slightly smaller difference (0.01 °C yr−1) during the day and a slightly larger difference (0.03 °C yr−1) at night. The contribution of urbanization to LST was higher than that to air temperature, particularly during the day (16.34%). The effects of wind speed and precipitation on the average air urban–rural temperature difference was greater than those of LST under CHW, accounting for 16.13%, with the effects of wind speed being more significant. These results show that a comprehensive perspective is needed to understand the risks associated with a temperature rise risk under extreme climate conditions and to formulate effective mitigation measures that will they improve human thermal comfort under climate change.

Decoupling the response of vegetation dynamics to asymmetric warming over the Qinghai-Tibet plateau from 2001 to 2020

Global land surface air temperature data show that in the past 50 years, the rate of nighttime warming has been much faster than that of daytime, with the minimum daily temperature (Tmin) increasing about 40% faster than the maximum daily temperature (Tmax), resulting in a decreased diurnal temperature difference. The Qinghai-Tibet Plateau (QTP) is known as the "roof of the world", where temperatures have risen twice as fast as the global average warming rate in the last few decades. The factors affecting vegetation growth on the QTP are complex and still not fully understood to some extent. Previous studies paid less attention to the explanations of the complicated interactions and pathways between elements that influence vegetation growth, such as climate (especially asymmetric warming) and topography. In this study, we characterized the spatial and temporal trends of vegetation coverage and investigated the response of vegetation dynamics to asymmetric warming and topography in the QTP during 2001–2020 using trend analysis, partial correlation analysis, and partial least squares structural equation model (PLS-SEM) analysis. We found that from 2001 to 2020, the entire QTP demonstrated a greening trend in the growing season (April to October) at a rate of 0.0006/a (p < 0.05). The spatial distribution pattern of partial correlation between NDVI and Tmax differed from that of NDVI and Tmin. PLS-SEM results indicated that asymmetric warming (both Tmax and Tmin) had a consistent effect on vegetation development by directly promoting greening in the QTP, with NDVI values being more sensitive to Tmin, while topographic factors, especially elevation, mainly played an indirect role in influencing vegetation growth by affecting climate change. This study offers new insights into how vegetation responds to asymmetric warming and references for local ecological preservation.

Comparison of approaches to spatiotemporally interpolate land surface air temperature for the Qinghai–Tibet Plateau

Comparison of approaches to spatiotemporally interpolate land surface air temperature for the Qinghai–Tibet Plateau