scholarly journals Distinct roles of land cover in regulating spatial variabilities of temperature responses to radiative effects of aerosols and clouds

2021 ◽  
Author(s):  
Linyi Wei ◽  
Yong Wang ◽  
Shu Liu ◽  
Guang J. Zhang ◽  
Bin Wang
Keyword(s):  
Land Cover ◽  
Surface Temperature ◽  
Land Surface ◽  
Western Europe ◽  
Temperature Response ◽  
The United States ◽  
Surface Energy Budget ◽  
Radiative Effects ◽  
Anthropogenic Aerosol ◽  
Temperature Responses

Abstract Surface temperature responses to aerosol and cloud radiative perturbations are complicated by the underlying land surface processes. To disentangle this complexity, this study investigates the role of land surfaces in the radiative effects of aerosols and clouds on surface temperature from a terrestrial surface energy budget perspective using the National Center for Atmospheric Research (NCAR) Community Earth System Model version 1.2.1 (CESM1.2.1). It is found that land cover enhances the spatial variation of the temperature response to aerosol direct radiative effects (DRE) and cloud radiative effects (CRE) during daytime and nighttime respectively while it reduces that of the temperature response to CRE during the daytime by collocation of local surface climate sensitivity and aerosol DRE and CRE. With identical anthropogenic aerosol emissions over eight major emission regions in the past, present and future projections including Brazil, China, East Africa, India, Indonesia, South Africa, the United States and Western Europe, local temperature responses to aerosol DRE (CRE) are more strongly regulated by land cover in the daytime (nighttime).

Decomposing local and remote surface temperature impacts of Asian aerosols

2021 ◽  
Author(s):  
Joonas Merikanto ◽  
Kalle Nordling ◽  
Petri Räisänen ◽  
Jouni Räisänen ◽  
Declan O'Donnell ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Energy Transport ◽  
East Asian ◽  
Temperature Response ◽  
Longwave Radiation ◽  
Shortwave Radiation ◽  
Anthropogenic Aerosols ◽  
Anthropogenic Aerosol ◽  
Clear Sky ◽  
Temperature Responses

<p>We investigate how a regionally confined radiative forcing of South and East Asian aerosols translate into local and remote surface temperature responses across the globe. To do so, we carry out equilibrium climate simulations with and without modern day South and East Asian anthropogenic aerosols in two climate models with independent development histories (ECHAM6.1 and NorESM1).  We run the models with the same anthropogenic aerosol representations via MACv2-SP (a simple plume implementation of the 2<sup>nd</sup> version of the Max Planck Institute Aerosol Climatology). This leads to a near identical change in instantaneous direct and indirect aerosol forcing due to removal of Asian aerosols in the two models. We then robustly decompose and compare the energetic pathways that give rise to the global and regional surface temperature effects in the models by a novel temperature response decomposition method, which translated the changes in atmospheric and surface energy fluxes into surface temperature responses by using a concept of planetary emissivity.  </p><p>We find that the removal of South and East Asian anthropogenic aerosols leads to strong local warming  response from increased clear-sky shortwave radiation over the region, combined with opposing warming and cooling responses due to changes in cloud longwave and shortwave radiation. However, the local warming response is strongly modulated by the changes in horizontal atmospheric energy transport. Atmospheric energy transport and changes in clear-sky longwave radiation redistribute the surface temperature responses efficiently across the Northern hemisphere, and to a lesser extent also over the Southern hemisphere. The model-mean global surface temperature response to Asian anthropogenic aerosol removal is 0.26±0.04 °C (0.22±0.03 for ECHAM6.1 and 0.30±0.03 °C for NorESM1) of warming. Model-to-model differences in global surface temperature response mainly arise from differences in longwave cloud (0.01±0.01 for ECHAM6.1 and 0.05±0.01 °C for NorESM1) and shortwave cloud (0.03±0.03 for ECHAM6.1 and 0.07±0.02 °C for NorESM1) responses. The differences in cloud responses between the models also dominate the differences in regional temperature responses. In both models, the Northern hemispheric surface warming amplifies towards the Arctic, where the total temperature response is highly seasonal and modulated by seasonal changes in oceanic heat exchange and clear-sky longwave radiation.</p><p>We estimate that under a strong Asian aerosol mitigation policy tied with strong greenhouse gas mitigation (Shared Socioeconomic Pathway 1-1.9) the Asian aerosol reductions can add around 8 years’ worth of current day global warming during the next few decades.</p>

scholarly journals SPACE BASED SPATIO-TEMPORAL ASSESSMENT OF LAND SURFACE TEMPERATURE IN KARUNAGAPPALLY MUNICIPALITY, A FAST GROWING CITY IN THE WESTERN COAST OF INDIA

2018 ◽  
Vol XLII-5 ◽  
pp. 773-779
Author(s):  
S. Satheendran S. ◽  
S. Chandran S. ◽  
A. Varghese
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Urban Population ◽  
The United States ◽  
Google Earth ◽  
Land Use Land Cover ◽  
Rapid Urbanization

<p><strong>Abstract.</strong> Urbanization is the process by which towns and cities are formed and become larger as more and more people begin living and working in central areas. According to 2001 census, the urban population of the country was 286.11 million, living in 5161 towns, which constitutes 27.81% of the total country’s population. However, the same as per 2011 census has risen to 377.16 million viz. 32.16% of the total country’s population and the number of towns has gone up to 7935. The rate of urban growth in the country is very high as compared to developed countries, and the large cities are becoming larger mostly due to continuous migration of population to these cities. India’s current urban population exceeds the whole population of the United States, the world’s third largest country. By 2050, over half of India’s population is expected to be urban dwellers. This creates enormous pressure on existing urban infrastructure.</p><p>Urbanization trend in the State of Kerala shows marked peculiarities. The main reason for urban population growth is the increase in the number of urban areas and urbanization of the peripheral areas of the existing major urban centers. However, unlike the other parts of the country the Urbanization in Kerala is not limited to the designated cities and towns. The difference between rural and urban agglomerations is very negligible as far as Kerala is concerned. The Kerala society by and large can be termed as urbanized. Kerala has been witnessing rapid urbanization since 1980.</p><p>The present study, is an attempt to analyses the extent of land use/ land cover changes in the Municipality over the years from 2012 to 2017 and land surface variation over the years from 2000 to 2017.The land use/ land cover pattern of 2012 to 2017 was extracted from High resolution images of the study area were downloaded from Google Earth API and the Land Surface Temperature changes were analyzed from the thermal bands of the Landsat Imageries.</p>

scholarly journals Separating the Impact of Individual Land Surface Properties on the Terrestrial Surface Energy Budget in both the Coupled and Uncoupled Land–Atmosphere System

2019 ◽  
Vol 32 (18) ◽  
pp. 5725-5744 ◽  
Author(s):  
Marysa M. Laguë ◽  
Gordon B. Bonan ◽  
Abigail L. S. Swann
Keyword(s):  
Surface Energy ◽  
Surface Temperature ◽  
Surface Properties ◽  
Energy Budget ◽  
Land Surface ◽  
Temperature Response ◽  
Surface Energy Budget ◽  
Evaporative Resistance ◽  
Atmospheric Feedbacks ◽  
The Impact

Abstract Changes in the land surface can drive large responses in the atmosphere on local, regional, and global scales. Surface properties control the partitioning of energy within the surface energy budget to fluxes of shortwave and longwave radiation, sensible and latent heat, and ground heat storage. Changes in surface energy fluxes can impact the atmosphere across scales through changes in temperature, cloud cover, and large-scale atmospheric circulation. We test the sensitivity of the atmosphere to global changes in three land surface properties: albedo, evaporative resistance, and surface roughness. We show the impact of changing these surface properties differs drastically between simulations run with an offline land model, compared to coupled land–atmosphere simulations that allow for atmospheric feedbacks associated with land–atmosphere coupling. Atmospheric feedbacks play a critical role in defining the temperature response to changes in albedo and evaporative resistance, particularly in the extratropics. More than 50% of the surface temperature response to changing albedo comes from atmospheric feedbacks in over 80% of land areas. In some regions, cloud feedbacks in response to increased evaporative resistance result in nearly 1 K of additional surface warming. In contrast, the magnitude of surface temperature responses to changes in vegetation height are comparable between offline and coupled simulations. We improve our fundamental understanding of how and why changes in vegetation cover drive responses in the atmosphere, and develop understanding of the role of individual land surface properties in controlling climate across spatial scales—critical to understanding the effects of land-use change on Earth’s climate.

scholarly journals Cooling Effect of Different Land Cover Types: A Case Study in Xi’an and Xianyang, China

2021 ◽  
Vol 13 (3) ◽  
pp. 1099
Author(s):  
Yuhe Ma ◽  
Mudan Zhao ◽  
Jianbo Li ◽  
Jian Wang ◽  
Lifa Hu
Keyword(s):  
Land Cover ◽  
Surface Temperature ◽  
Urban Heat Island ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Cooling Effect ◽  
Heat Island ◽  
Land Cover Types ◽  
Island Effect ◽  
Urban Heat

One of the climate problems caused by rapid urbanization is the urban heat island effect, which directly threatens the human survival environment. In general, some land cover types, such as vegetation and water, are generally considered to alleviate the urban heat island effect, because these landscapes can significantly reduce the temperature of the surrounding environment, known as the cold island effect. However, this phenomenon varies over different geographical locations, climates, and other environmental factors. Therefore, how to reasonably configure these land cover types with the cooling effect from the perspective of urban planning is a great challenge, and it is necessary to find the regularity of this effect by designing experiments in more cities. In this study, land cover (LC) classification and land surface temperature (LST) of Xi’an, Xianyang and its surrounding areas were obtained by Landsat-8 images. The land types with cooling effect were identified and their ideal configuration was discussed through grid analysis, distance analysis, landscape index analysis and correlation analysis. The results showed that an obvious cooling effect occurred in both woodland and water at different spatial scales. The cooling distance of woodland is 330 m, much more than that of water (180 m), but the land surface temperature around water decreased more than that around the woodland within the cooling distance. In the specific urban planning cases, woodland can be designed with a complex shape, high tree planting density and large planting areas while water bodies with large patch areas to cool the densely built-up areas. The results of this study have utility for researchers, urban planners and urban designers seeking how to efficiently and reasonably rearrange landscapes with cooling effect and in urban land design, which is of great significance to improve urban heat island problem.

Plot-scale retrieval of land surface temperature and emissivity estimation

2021 ◽  
Author(s):  
Gitanjali Thakur ◽  
Stan Schymanski ◽  
Kaniska Mallick ◽  
Ivonne Trebs
Keyword(s):  
Land Cover ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Satellite Data ◽  
Land Surface ◽  
Longwave Radiation ◽  
Landscape Scale ◽  
The Earth ◽  
Downwelling Longwave Radiation

&lt;p&gt;The surface energy balance (SEB) is defined as the balance between incoming energy from the sun and outgoing energy from the Earth&amp;#8217;s surface. All components of the SEB depend on land surface temperature (LST). Therefore, LST is an important state variable that controls the energy and water exchange between the Earth&amp;#8217;s surface and the atmosphere. LST can be estimated radiometrically, based on the infrared radiance emanating from the surface. At the landscape scale, LST is derived from thermal radiation measured using&amp;#160; satellites.&amp;#160; At the plot scale, eddy covariance flux towers commonly record downwelling and upwelling longwave radiation, which can be inverted to retrieve LST&amp;#160; using the grey body equation :&lt;br&gt;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160; R&lt;sub&gt;lup&lt;/sub&gt; = &amp;#949;&amp;#963; T&lt;sub&gt;s&lt;/sub&gt;&lt;sup&gt;4&lt;/sup&gt; + (1 &amp;#8722; &amp;#949;) R&lt;sub&gt; ldw&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160; &lt;/sub&gt;(1)&lt;br&gt;where R&lt;sub&gt;lup&lt;/sub&gt; is the upwelling longwave radiation, R&lt;sub&gt;ldw&lt;/sub&gt; is the downwelling longwave radiation, &amp;#949; is the surface emissivity, &lt;em&gt;T&lt;sub&gt;s&lt;/sub&gt;&amp;#160; &lt;/em&gt;is the surface temperature and &amp;#963;&amp;#160; is the Stefan-Boltzmann constant. The first term is the temperature-dependent part, while the second represents reflected longwave radiation. Since in the past downwelling longwave radiation was not measured routinely using flux towers, it is an established practice to only use upwelling longwave radiation for the retrieval of plot-scale LST, essentially neglecting the reflected part and shortening Eq. 1 to:&lt;br&gt;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160; R&lt;sub&gt;lup&lt;/sub&gt; = &amp;#949;&amp;#963; T&lt;sub&gt;s&lt;/sub&gt;&lt;sup&gt;4 &lt;/sup&gt;&amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160;&amp;#160; (2)&lt;br&gt;Despite&amp;#160; widespread availability of downwelling longwave radiation measurements, it is still common to use the short equation (Eq. 2) for in-situ LST retrieval. This prompts the question if ignoring the downwelling longwave radiation introduces a bias in LST estimations from tower measurements. Another associated question is how to obtain the correct &amp;#949; needed for in-situ LST retrievals using tower-based measurements.&lt;br&gt;The current work addresses these two important science questions using observed fluxes at eddy covariance towers for different land cover types. Additionally, uncertainty in retrieved LST and emissivity due to uncertainty in input fluxes was quantified using SOBOL-based uncertainty analysis (SALib). Using landscape-scale emissivity obtained from satellite data (MODIS), we found that the LST&amp;#160; obtained using the complete equation (Eq. 1) is 0.5 to 1.5 K lower than the short equation (Eq. 2). Also, plot-scale emissivity was estimated using observed sensible heat flux and surface-air temperature differences. Plot-scale emissivity obtained using the complete equation was generally between 0.8 to 0.98 while the short equation gave values between 0.9 to 0.98, for all land cover types. Despite additional input data for the complete equation, the uncertainty in plot-scale LST was not greater than if the short equation was used. Landscape-scale daytime LST obtained from satellite data (MODIS TERRA) were strongly correlated with our plot-scale estimates, but on average higher by 0.5 to 9 K, regardless of the equation used. However, for most sites, the correspondence between MODIS TERRA LST and retrieved plot-scale LST estimates increased significantly if plot-scale emissivity was used instead of the landscape-scale emissivity obtained from satellite data.&lt;/p&gt;

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