On the long-term memory characteristic in land surface air temperatures:How well do CMIP6 models perform?

利用去趋势涨落分析(DFA)方法计算序列的长程记忆性(LTM),以CRUTEM5数据集的结果作为观测参照,评估了60个参与第六次国际耦合模式比较计划(CMIP6)的气候模式对地表气温LTM的再现能力.结果表明:大部分模式可以再现全球平均地表气温序列的LTM特征,其中AWI-ESM-1-1-LR和E3SM-1-0的模拟效果最好;60个模式均能模拟LTM随纬度带的变化;综合来说,全球水平上CNRM-CM6-1和HadGEM3-GC31-LL对地表气温LTM的模拟性能最好;多模式平均相比单一模式模拟性能更好;多模式平均与观测结果的偏差以及模式之间的模拟差异显著体现在赤道和沿海区域,这种偏差可能源于模式对海气耦合过程的模拟差异.

Changes and spatial patterns of the differences between ground and air temperature over the Qinghai-Xizang Plateau

The difference between ground soil and air temperature （Ts-Ta） was studied by using the data of ground and air temperature of 99 stations over the Qinghai-Xizang （Tibet） Plateau from 1960 to 2000,and its spatial distribution and time changing tendency have been diagnosed by principal component analysis and power spectral analysis methods. The results show that the values of （Ts-Ta） are the maximum in June and the minimum in December. The first three loading eigenvectors, which reflect the main spatially anomalous structure of （Ts-Ta） over the Qinghai-Xizang Plateau, contain the contrary changing pattern between the northwestern and the southeastern regions, the pattern response of the sea level elevation and the geography, and the pattern response of the distribution of the permafrost. There are four patterns of time evolution including the patterns of monotonous increasing or decreasing trends, the basic stability pattern and the parabola pattern with the minimum value. （Ts-Ta） has a periodic variation about 2 years. According to the spatial distribution of the third loading eigenvectors of （Ts-Ta） over the Qinghai-Xizang Plateau in cold season, the permafrost response region and the seasonal frozen ground response region are identified.

On the relationship between convection intensity of South China Sea summer monsoon and air-sea temperature difference in the tropical oceans

The annual, interannual and inter-decadal variability of convection intensity of South China Sea (SCS) summer monsoon and air-sea temperature difference in the tropical ocean is analyzed, and their relationship is discussed using two data sets of 48-a SODA (simple ocean data assimilation) and NCEP/NCAR. Analyses show that in wintertime Indian Ocean (WIO), springtime central tropical Pacific (SCTP) and summertime South China Sea-West Pacific (SSCSWP), air-sea temperature difference is significantly associated with the convection intensity of South China Sea summer monsoon. Correlation of the inter-decadal time scale (above 10 a) is higher and more stable. There is inter-decadal variability of correlation in scales less than 10 a and it is related with the air-sea temperature difference itself for corresponding waters. The inter-decadal variability of the convection intensity during the South China Sea summer monsoon is closely related to the inter-decadal variability of the general circulation of the atmosphere. Since the late period of the 1970s, in the lower troposphere, the cross-equatorial flow from the Southern Hemisphere has intensified. At the upper troposphere layer, the South Asian high and cross-equatorial flow from the Northern Hemisphere has intensified at the same time. Then the monsoon cell has also strengthened and resulted in the reinforcing of the convection of South China Sea summer monsoon.

COMPARATIVE ANALYSIS OF VARIOUS DATA OF AIR-SEA TEMPERATURE DIFFERENCE AND ITS VARIATION ACROSS SOUTH CHINA SEA IN THE PAST 35 YEARS

Using the International Comprehensive Ocean-Atmosphere Data Set(ICOADS) and ERA-Interim data, spatial distributions of air-sea temperature difference(ASTD) in the South China Sea(SCS) for the past 35 years are compared,and variations of spatial and temporal distributions of ASTD in this region are addressed using empirical orthogonal function decomposition and wavelet analysis methods. The results indicate that both ICOADS and ERA-Interim data can reflect actual distribution characteristics of ASTD in the SCS, but values of ASTD from the ERA-Interim data are smaller than those of the ICOADS data in the same region. In addition, the ASTD characteristics from the ERA-Interim data are not obvious inshore. A seesaw-type, north-south distribution of ASTD is dominant in the SCS; i.e., a positive peak in the south is associated with a negative peak in the north in November, and a negative peak in the south is accompanied by a positive peak in the north during April and May. Interannual ASTD variations in summer or autumn are decreasing. There is a seesaw-type distribution of ASTD between Beibu Bay and most of the SCS in summer, and the center of large values is in the Nansha Islands area in autumn. The ASTD in the SCS has a strong quasi-3a oscillation period in all seasons, and a quasi-11 a period in winter and spring. The ASTD is positively correlated with the Nio3.4 index in summer and autumn but negatively correlated in spring and winter.

Estimating Monthly Surface Air Temperature Using MODIS LST Data and an Artificial Neural Network in the Loess Plateau, China

Air temperature(Ta)datasets with high spatial and temporal resolutions are needed in a wide range of applications,such as hydrology,ecology,agriculture,and climate change studies.Nonetheless,the density of weather station networks is insufficient,especially in sparsely populated regions,greatly limiting the accuracy of estimates of spatially distributed Ta.Due to their continuous spatial coverage,remotely sensed land surface temperature(LST)data provide the possibility of exploring spatial estimates of Ta.However,because of the complex interaction of land and climate,retrieval of Ta from the LST is still far from straightforward.The estimation accuracy varies greatly depending on the model,particularly for maximum Ta.This study estimated monthly average daily minimum temperature(Tmin),average daily maximum temperature(Tmax)and average daily mean temperature(Tmean)over the Loess Plateau in China based on Moderate Resolution Imaging Spectroradiometer(MODIS)LST data(MYD11A2)and some auxiliary data using an artificial neural network(ANN)model.The data from 2003 to 2010 were used to train the ANN models,while 2011 to 2012 weather station temperatures were used to test the trained model.The results showed that the nighttime LST and mean LST provide good estimates of Tmin and Tmean,with root mean square errors(RMSEs)of 1.04℃ and 1.01℃,respectively.Moreover,the best RMSE of Tmax estimation was 1.27℃.Compared with the other two published Ta gridded datasets,the produced 1 km×1 km dataset accurately captured both the temporal and spatial patterns of Ta.The RMSE of Tmin estimation was more sensitive to elevation,while that of Tmax was more sensitive to month.Except for land cover type as the input variable,which reduced the RMSE by approximately 0.01℃,the other vegetation-related variables did not improve the performance of the model.The results of this study indicated that ANN,a type of machine learning method,is effective for long-term and large-scale Ta estimation.

Soil temperature estimation at different depths,using remotely-sensed data

Soil temperatures at different depths down the soil profile are important agro-meteorological indicators which are necessary for ecological modeling and precision agricultural activities. In this paper, using time series of soil temperature(ST) measured at different depths(0, 5, 10, 20, and 40 cm) at agro-meteorological stations in northern China as reference data, ST was estimated from land surface temperature(LST) and normalized difference vegetation index(NDVI) derived from AQUA/TERRA MODIS data, and solar declination(Ds) in univariate and multivariate linear regression models. Results showed that when daytime LST is used as predictor, the coefficient of determination(R^2) values decrease from the 0 cm layer to the 40 cm layer. Additionally, with the use of nighttime LST as predictor, the R^2 values were relatively higher at 5, 10 and 15 cm depths than those at 0, 20 and 40 cm depths. It is further observed that the multiple linear regression models for soil temperature estimation outperform the univariate linear regression models based on the root mean squared errors(RMSEs) and R^2. These results have demonstrated the potential of MODIS data in tandem with the Ds parameter for soil temperature estimation at the upper layers of the soil profile where plant roots grow in. To the best of our knowledge, this is the first attempt at the synergistic use of LST, NDVI and Ds for soil temperature estimation at different depths of the upper layers of the soil profile, representing a significant contribution to soil remote sensing.

Impact of land cover change on land surface temperature: A case study of Spiti Valley

Land surface temperature(LST) is the skin temperature of the earth surface. LST depends on the amount of sunlight received by any geographical area. Apart from sun light, LST is also affected by the land cover, which leads to change in land surface temperature. Impact of land cover change(LCC) on LST has been assessed using Landsat TM5, Landsat 8 TIRS/OLI and Digital Elevation Model(ASTER) for Spiti Valley, Himachal Pradesh, India. In the present study, Spiti valley was divided into three altitudinal zones to check the pattern of changing land cover along different altitudes and LST was calculated for all the four land cover categories extracted from remote sensing data for the years of 1990 and 2015. Matrix table was used as a technique to evaluate the land cover change between two different years. Matrix table shows that as a whole, about 2,151,647 ha(30%) area of Spiti valley experienced change in land cover in the last 25 years. The result also shows vegetation and water bodies increased by 107,560.2 ha(605.87%) and 45 ha(0.98%), respectively. Snow cover and barren land decreased by 19,016.5 ha(23.92%) and 88,589(14.14%), during the study period. A significant increase has been noticed in vegetation amongst all land cover types. Minimum, maximum and mean LST for three altitudinal zones have been calculated. The mean LST recorded was 11℃ in 1990 but it rose by 2℃ and reached to 13℃ in 2015. Changes in LST were obtained for each land cover categories. The mean temperature of different land cover types was calculated by averaging value of all pixels of a given land cover types. The mean LST of vegetation, barren land, snow cover and water body increased by 6℃, 9℃, 1℃, and 7℃, respectively. Further, relationships between LST, Normalized Difference Snow Index(NDSI), and Normalised Difference Vegetation Index(NDVI) were established using Linear Regression.

Air Temperature Estimation with MODIS Data over the Northern Tibetan Plateau

Time series of MODIS land surface temperature(Ts) and normalized difference vegetation index(NDVI) products,combined with digital elevation model(DEM) and meteorological data from 2001 to 2012,were used to map the spatial distribution of monthly mean air temperature over the Northern Tibetan Plateau(NTP). A time series analysis and a regression analysis of monthly mean land surface temperature(Ts) and air temperature(Ta) were conducted using ordinary linear regression(OLR) and geographical weighted regression(GWR). The analyses showed that GWR,which considers MODIS Ts,NDVI and elevation as independent variables,yielded much better results [RAdj2> 0.79; root-mean-square error(RMSE) =0.51℃–1.12℃] associated with estimating Tacompared to those from OLR(RAdj2= 0.40-0.78; RMSE = 1.60℃–4.38℃).In addition,some characteristics of the spatial distribution of monthly Taand the difference between the surface and air temperature(Td) are as follows. According to the analysis of the 0℃ and 10℃ isothermals,Tavalues over the NTP at elevations of 4000–5000 m were greater than 10℃ in the summer(from May to October),and Tavalues at an elevation of3200 m dropped below 0℃ in the winter(from November to April). Taexhibited an increasing trend from northwest to southeast. Except in the southeastern area of the NTP,T d values in other areas were all larger than 0℃ in the winter.

Estimation of Land Surface Temperature from Landsat-8 OLI Thermal Infrared Satellite Data. A Comparative Analysis of Two Cities in Ghana

This study employs Landsat-8 Operational Land Imager (OLI) thermal infrared satellite data to compare land surface temperature of two cities in Ghana: Accra and Kumasi. These cities have human populations above 2 million and the corresponding anthropogenic impact on their environments significantly. Images were acquired with minimum cloud cover (<10%) from both dry and rainy seasons between December to August. Image preprocessing and rectification using ArcGIS 10.8 software were used. The shapefiles of Accra and Kumasi were used to extract from the full scenes to subset the study area. Thermal band data numbers were converted to Top of Atmospheric Spectral Radiance using radiance rescaling factors. To determine the density of green on a patch of land, normalized difference vegetation index (NDVI) was calculated by using red and near-infrared bands i.e. Band 4 and Band 5. Land surface emissivity (LSE) was also calculated to determine the efficiency of transmitting thermal energy across the surface into the atmosphere. Results of the study show variation of temperatures between different locations in two urban areas. The study found Accra to have experienced higher and lower dry season and wet season temperatures, respectively. The temperature ranges corresponding to the dry and wet seasons were found to be 21.0985oC to 46.1314oC, and, 18.3437oC to 30.9693oC respectively. Results of Kumasi also show a higher range of temperatures from 32.6986oC to 19.1077oC during the dry season. In the wet season, temperatures ranged from 26.4142oC to -0.898728oC. Among the reasons for the cities of Accra and Kumasi recorded higher than corresponding rural areas’ values can be attributed to the urban heat islands’ phenomenon.

New methods for calculating bare soil land surface temperature over mountainous terrain

Land surface temperature(LST) causes the phase change of water, links to the partitioning of surface water and energy budget, and becomes an important parameter to hydrology, meteorology, ecohydrology, and other researches in the high mountain cold regions. Unlike air temperature, which has common altitudinal lapse rates in the mountainous regions, the influence of terrain leads to complicated estimation for soil LST. This study presents two methods that use air temperature and solar position,to estimate bare LST with high temporal resolution over horizontal sites and mountainous terrain with a random slope azimuth. The data from three horizontal meteorological stations and fourteen LST observation fields with different aspects and slopes were used to test the proposed LST methods. The calculated and measured LST were compared in a range of statistical analysis, and the analysis showed that the average RMSE(root mean square error),MAD(mean absolute deviation), and R^2(correlation coefficient) for three horizontal sites were 5.09℃,3.66℃, 0.92, and 5.03℃, 3.52℃, 0.85 for the fourteen complex terrain sites. The proposed methods showed acceptable accuracy, provide a simple way to estimate LST, and will be helpful for simulating the water and energy cycles in alpine mountainous terrain.

Land Characterization Analysis of Surface Temperature of Semi-Arid Mountainous City Abha, Saudi Arabia Using Remote Sensing and GIS

This knowledge of land surface temperature and its spatial variations within a city environment is of prime importance to the study of urban climate and human-environment interactions. Few studies have examined the influence of land use and terrain on the surface temperature effects of semi-arid mountainous urban areas. This study investigates the urban environment characterization and its effects on surface temperature using remote sensing. The methodologies adapted for this study are geometric and radiometric corrections of satellite data, extraction of land use/land cover and digital elevation model, estimation of vegetation density using Normalized Difference Vegetation Index (NDVI), and estimation of surface temperature and emissivity using temperature emissivity separation (TES) algorithm. Finally geospatial model and statistical techniques are used for assessing the overall impact of urban environmental characterization on urban climate of semi-arid region of Abha, Kingdom of Saudi Arabia. Herein, results reveal that the spatial distribution of surface temperature was affected by land use/land cover (LULC) and topography. The high dense built-up and commercial/industrial areas display higher surface temperature in comparison with surrounding lands. There is gradual decrease of LULC classes’ surface temperature with the increase in altitude. The cooling effect towards the surrounding urban built-up area is found increasing at the hill located vegetated area, the downward slope and valley terrain inside the recreational park. Therefore the spatial variation in surface temperature also reflected the effects of topography on LULC classes. Suitable mountainous land use utilization would help to expand the cooling effect. In the future, the outcomes of this study could be used to build environmentally sustainable urban planning suitable to semi-arid regions and to create practices that consider the local weather environment in urban planning.

Determination of the Correlation between the Air Temperature Measured <i>in Situ</i>and Remotely Sensed Data from MODIS and SEVIRI in Congo-Brazzaville

This study compared data from the MODerate-resolution Imaging Spectroradiometer (MODIS) onboard NASA’s Terra satellite and the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on EUMETSAT’s Meterosat Second Generation (MSG) satellite with in situ data obtained from ground observation stations in Congo-Brazzaville. Remote sensing instruments can be used to estimate air temperature, which has an important role in monitoring the effects of climate change. Congo-Brazzaville is located in equatorial forest, which is difficult to access, and has a limited number of ground meteorological stations measuring air temperature. This study used MODIS and MSG data for the period 2009-2014 to assess the performance of land surface temperature data from satellites against in situ data from ground-based stations in Congo-Brazzaville using a linear regression model. This work has allowed us to determine which satellite is best adapted for use in Central Africa.

Thermal sensation and percentage of dissatisfied in thermal environments with positive and negative vertical air temperature differences

Considering the percentage of dissatisfied due to local thermal sensation(PD LTSV),a vertical air temperature difference(ΔT_(d))threshold of about 3°C was recommended in standards.However,some novel air distribution methods might create large positive(which means the head warmer than the feet,vice versa)or negativeΔT_(d),with no suitable proved criteria to be used.In this study,sixteen subjects were seated in a climatic box placed in a climate chamber to evaluate thermal sensation and percentage of dissatisfied with negative and positiveΔT_(d) in different whole-body thermal conditions.Air temperatures were controlled independently at the upper and lower body parts,with 13 different air temperature sets combined with 22°C,25°C,28°C,and 31°C(i.e.-9°C≤ΔT_(d)≤9°C).Results showed that subjects were more thermally sensitive at the upper body and with positiveΔT_(d) than at the lower body or with negativeΔT_(d).The 80%acceptableΔT_(d) range is about-8 to 7°C in overall neutral(TSV=0),-7°C to 6°C in slightly cool(TSV=-0.5)conditions,which is wider than-3 to 3°C in slightly warm conditions(TSV=+0.5).By considering the factors of both TSV andΔT_(d),a new overall percentage of dissatisfied index(OPD P)was proposed.Case studies show that the new OPD P index is more precise and suitable for the evaluations of different air distributions to predict overall percentage of dissatisfied in thermal environments with vertical air temperature difference.

Difference in the Interdecadal Variability of Spring and Summer Sensible Heat Fluxes over Northwest China

The present study investigates the difference in interdecadal variability of the spring and summer sensible heat fluxes over Northwest China by using station observations from 1960 to 2000. It was found that the spring sensible heat flux over Northwest China was greater during the period from the late 1970s to the 1990s than during the period from the 1960s to the mid-1970s. The summer sensible heat flux was smaller in the late 1980s through the 1990s than it was in the 1970s through the early 1980s. Both the spring and summer land-air temperature differences over Northwest China displayed an obvious interdecadal increase in the late 1970s. Both the spring and summer surface wind speeds experienced an obvious interdecadal weakening in the late 1970s. The change in the surface wind speed played a more important role in the interdecadal variations in sensible heat flux during the summer, whereas the change in the land-air temperature difference was more important for the interdecadal variations in sensible heat flux in the spring. This difference was related to seasonal changes in the mean land-air temperature difference and the surface wind speed. Further analysis indicated that the increase in the spring land surface temperature in Northwest China was related to an increase in surface net radiation.

Urbanization-related warming in local temperature records:a review

城市化影响局地气温观测记录,是气候变化领域关注的问题之一。曾有研究认为,近几十年中国气温观测中城市化增温达0.1°C/10年甚至更高。然而,近年基于均一化观测而得到的结果要小得多。其中的不确定性主要源于数据质量和评估方法。典型例子如‘观测减再分析(OMR)’方法。由于再分析资料中的多年代际变率相对于实际气温观测存在系统性偏差,该方法通常会高估城市化效应。近年发展的高分辨率气候模拟及动态陆面演变信息将有助于理解和量化城市化效应。

HRCLDAS与ERA5-Land气温日统计数据在山东地区的对比评估

基于山东省2021年3月—2022年2月1519个气象观测站2 m气温观测数据,对中国气象局高分辨率陆面数据同化系统(High Resolution China Meteorological Administration Land Data Assimilation System,HRCLDAS)和欧洲中期天气预报中心第五代陆面再分析数据集(ERA5-Land)逐小时2 m气温分析的日统计数据(平均气温、最高气温、最低气温)进行对比评估。结果显示:(1)HRCLDAS/ERA5-Land日统计平均气温、最高气温、最低气温的均方根误差分别为0.1/1.2℃、0.6/1.9℃、0.4/1.7℃,表明HRCLDAS具有更高的精度,且在不同地理区域、不同海拔高度的表现均优于ERA5-Land,大部地区的偏差(-0.5~0.5℃)远低于ERA5-Land(-2.0~2.0℃)。(2)两套数据对高温及寒潮过程的监测能力对比评估表明,HRCLDAS能够捕捉到大部分的高温以及寒潮过程,其与观测的高温日数及寒潮日数空间分布较为相似,但对影响范围存在一定的低估;ERA5-Land则只能监测到部分高温及寒潮过程,并对高温日数与寒潮日数存在严重的低估。

PRELIMINARY STUDY OF INDOOR FEATURES OF TEMPERATURE AND HUMIDITY FOR ROOMS FACING NORTH-SOUTH IN WINTER IN A LOW-LATITUDE PLATEAU CITY

Analyzing observations of wintertime air temperature in both indoor and outdoor surroundings in Kunming,a city lying in low latitudes,characteristics of temperature and humidity have been studied for the interior of rooms facing north-south under differnet weather conditions ,Signicant warming effect has been identified in terms of lowest and daily-mean indoor temperature in the area of Kumming.The heating amplitude ranges from 7.7℃ to 10.0℃ and from 4.6℃ to 5.8℃ for the interior part of rooms facing the south and from 4.6℃to 7.0℃and from 1.3℃ to 4.4℃ for the interior part of rooms facing the north,respectively for the two elements,The highest air temperature is higher indoor than outdoor for rooms facing the south,but otherwise is usually true for rooms facing the north,Additional findings point out that buildings not only help maintain relatively warm indoor temperature but delay its variation.The diurnal cycle of temperature indoor is smaller and ranges by 40%-48% for south-facing rooms,and by 20%-30% for north-facing rooms,than outdoor,and the highest temperature is about 2 hours hate inside the room than outside.It shows how inertly indoor temperature varies.The work also finds that relative humidity is less indoor in southward rooms than in northward ones and difference is the largest on fine days but the smallest when it is overcast.For the diurnal variation,the indoor relative humidity is large at nighttime with small amplitude but small during daytime with large amplitude.The above-presented results can be served as scientific foundation for more research no climate in low-latitude cities and rational design of urban architectures.

Dynamicity of Land Use/Land Cover(LULC):An analysis from peri-urban and rural neighbourhoods of Durgapur Municipal Corporation(DMC)in India

The availability of better economic possibilities and well-connected transportation networks has attracted people to migrate to peri-urban and rural neighbourhoods,changing the landscape of regions outside the city and fostering the growth of physical infrastructure.Using multi-temporal satellite images,the dynamics of Land Use/Land Cover(LULC)changes,the impact of urban growth on LULC changes,and regional environmental implications were investigated in the peri-urban and rural neighbourhoods of Durgapur Municipal Corporation in India.The study used different case studies to highlight the study area’s heterogeneity,as the phenomenon of change is not consistent.Landsat TM and OLI-TIRS satellite images in 1991,2001,2011,and 2021 were used to analyse the changes in LULC types.We used the relative deviation(RD),annual change intensity(ACI),uniform intensity(UI)to show the dynamicity of LULC types(agriculture land;built-up land;fallow land;vegetated land;mining area;and water bodies)during 1991-2021.This study also applied the Decision-Making Trial and Evaluation Laboratory(DEMATEL)to measure environmental sensitivity zones and find out the causes of LULC changes.According to LULC statistics,agriculture land,built-up land,and mining area increased by 51.7,95.46,and 24.79 km^(2),respectively,from 1991 to 2021.The results also suggested that built-up land and mining area had the greatest land surface temperature(LST),whereas water bodies and vegetated land showed the lowest LST.Moreover,this study looked at the relationships among LST,spectral indices(Normalized Differenced Built-up Index(NDBI),Normalized Difference Vegetation Index(NDVI),and Normalized Difference Water Index(NDWI)),and environmental sensitivity.The results showed that all of the spectral indices have the strongest association with LST,indicating that built-up land had a far stronger influence on the LST.The spectral indices indicated that the decreasing trends of vegetated land and water bodies were 4.26 and 0.43 km^(2)/a,respectively,during 1991-2021.In summary,this study can help the policy-makers to predict the increasing rate of temperature and the causes for the temperature increase with the rapid expansion of built-up land,thus making effective peri-urban planning decisions.

Spatial Variation in CO_(2) Concentration Improves the Simulated Surface Air Temperature Increase in the Northern Hemisphere

The increasing concentration of atmospheric CO_(2) since the Industrial Revolution has affected surface air temperature.However,the impact of the spatial distribution of atmospheric CO_(2) concentration on surface air temperature biases remains highly unclear.By incorporating the spatial distribution of satellite-derived atmospheric CO_(2) concentration in the Beijing Normal University Earth System Model,this study investigated the increase in surface air temperature since the Industrial Revolution in the Northern Hemisphere(NH) under historical conditions from 1976-2005.In comparison with the increase in surface temperature simulated using a uniform distribution of CO_(2),simulation with a nonuniform distribution of CO_(2)produced better agreement with the Climatic Research Unit(CRU) data in the NH under the historical condition relative to the baseline over the period 1901-30.Hemispheric June-July-August(JJA) surface air temperature increased by 1.28℃ ±0.29℃ in simulations with a uniform distribution of CO_(2),by 1.00℃±0.24℃ in simulations with a non-uniform distribution of CO_(2),and by 0.24℃ in the CRU data.The decrease in downward shortwave radiation in the non-uniform CO_(2) simulation was primarily attributable to reduced warming in Eurasia,combined with feedbacks resulting from increased leaf area index(LAI) and latent heat fluxes.These effects were more pronounced in the non-uniform CO_(2)simulation compared to the uniform CO_(2) simulation.Results indicate that consideration of the spatial distribution of CO_(2)concentration can reduce the overestimated increase in surface air temperature simulated by Earth system models.