scholarly journals Impact of Large-Scale Afforestation on Surface Temperature: A Case Study in the Kubuqi Desert, Inner Mongolia Based on the WRF Model

Forests ◽  
2019 ◽  
Vol 10 (5) ◽  
pp. 368
Author(s):  
Liming Wang ◽  
Xuhui Lee ◽  
Duole Feng ◽  
Congsheng Fu ◽  
Zhongwang Wei ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Inner Mongolia ◽  
Large Scale ◽  
Wrf Model ◽  
Bowen Ratio ◽  
Bare Soil ◽  
Surface Temperature Change ◽  
Mean Values ◽  
Temperature Changes ◽  
Atmospheric Feedback

Afforestation activities in the Kubuqi Desert, Inner Mongolia, China, have substantially increased tree and shrub coverage in this region. In this study, the response of the surface temperature to afforestation is simulated with the Weather Research and Forecasting model. The surface temperature changes are decomposed into contributions from the intrinsic surface biophysical effect and atmospheric feedback, using the theory of intrinsic biophysical mechanism. The effect of afforestation on the surface temperature is 1.34 K, −0.48 K, 2.09 K and 0.22 K for the summer daytime, the summer nighttime, the winter daytime and the winter nighttime, respectively, for the grid cells that have experienced conversion from bare soil to shrub. The corresponding domain mean values are 0.15 K, −0.2 K, 0.67 K, and 0.06 K. The seasonal variation of surface temperature change is mainly caused by changes in roughness and Bowen ratio. In the daytime, the surface temperature changes are dominated by the biophysical effect, with albedo change being the main biophysical factor. In the nighttime, the biophysical effect (mainly associated with roughness change) and the atmospheric feedback (mainly associated with change in the background air temperature) contribute similar amounts to the surface temperature changes. We conclude that the atmospheric feedback can amplify the influence of the surface biophysical effect, especially in the nighttime.

scholarly journals Satellite-Observed Surface Temperature Changes after the 2004 Taylor Complex Fire in Alaska

2011 ◽  
Vol 15 (11) ◽  
pp. 1-14 ◽  
Author(s):  
Jianjun Ge
Keyword(s):  
Surface Temperature ◽  
Boreal Forest ◽  
Land Surface ◽  
Large Scale ◽  
Climate Models ◽  
Primary Data ◽  
Burned Area ◽  
Area Index ◽  
Surface Temperature Change ◽  
Temperature Changes

Abstract Land-use and land-cover change has been recognized as a key component in global climate change. In the boreal forest ecosystem, fires often cause significant changes in vegetation structure and surface biophysical characteristics, which in turn dramatically change energy and water balances of land surface. Several studies have characterized fire-induced changes in surface energy balance in boreal ecosystem based on site observations. This study provides satellite-observed impacts of a large fire on surface climate in Alaska’s boreal forest. A land surface temperature (LST) product from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) is used as the primary data. Five years after fire, surface temperature over the burned area increased by an average of 2.0°C in the May–August period. The increase reached a maximum of 3.2°C in the year immediately following the fire. The warm anomaly decreased slightly after the second year but remained until the fifth year of the study. These changes in surface temperature may directly affect surface net radiation and thus partition of surface available energy. By documenting continuous and synoptic surface temperature changes over multiple years, this paper demonstrates the value of Earth Observing System (EOS) observations for land–climate interaction research. The observed characteristics of surface temperature change as well as changes in key surface biophysical parameters such as albedo and leaf area index (LAI) can be used in the next generation of climate models to improve the representation of large-scale ecosystem disturbances.

scholarly journals Cities Exacerbate Climate Warming

Urban Science ◽  
2021 ◽  
Vol 5 (1) ◽  
pp. 27
Author(s):  
Lahouari Bounoua ◽  
Kurtis Thome ◽  
Joseph Nigro
Keyword(s):  
Surface Temperature ◽  
Land Surface ◽  
Large Scale ◽  
Climate Models ◽  
Warming Trend ◽  
Climate Mitigation ◽  
Temperature Changes ◽  
Surface Warming ◽  
The Us

Urbanization is a complex land transformation not explicitly resolved within large-scale climate models. Long-term timeseries of high-resolution satellite data are essential to characterize urbanization within land surface models and to assess its contribution to surface temperature changes. The potential for additional surface warming from urbanization-induced land use change is investigated and decoupled from that due to change in climate over the continental US using a decadal timescale. We show that, aggregated over the US, the summer mean urban-induced surface temperature increased by 0.15 °C, with a warming of 0.24 °C in cities built in vegetated areas and a cooling of 0.25 °C in cities built in non-vegetated arid areas. This temperature change is comparable in magnitude to the 0.13 °C/decade global warming trend observed over the last 50 years caused by increased CO2. We also show that the effect of urban-induced change on surface temperature is felt above and beyond that of the CO2 effect. Our results suggest that climate mitigation policies must consider urbanization feedback to put a limit on the worldwide mean temperature increase.

scholarly journals The Nonradiative Effect Dominates Local Surface Temperature Change Caused by Afforestation in China

2019 ◽  
Vol 32 (14) ◽  
pp. 4445-4471 ◽  
Author(s):  
Jun Ge ◽  
Weidong Guo ◽  
Andrew J. Pitman ◽  
Martin G. De Kauwe ◽  
Xuelong Chen ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Temperature Change ◽  
Large Scale ◽  
Radiative Effect ◽  
Local Surface ◽  
Local Conditions ◽  
Surface Temperature Change ◽  
Open Land ◽  
Biophysical Mechanism ◽  
Local Surface Temperature

AbstractChina is several decades into large-scale afforestation programs to help address significant ecological and environmental degradation, with further afforestation planned for the future. However, the biophysical impact of afforestation on local surface temperature remains poorly understood, particularly in midlatitude regions where the importance of the radiative effect driven by albedo and the nonradiative effect driven by energy partitioning is uncertain. To examine this issue, we investigated the local impact of afforestation by comparing adjacent forest and open land pixels using satellite observations between 2001 and 2012. We attributed local surface temperature change between adjacent forest and open land to radiative and nonradiative effects over China based on the Intrinsic Biophysical Mechanism (IBM) method. Our results reveal that forest causes warming of 0.23°C (±0.21°C) through the radiative effect and cooling of −0.74°C (±0.50°C) through the nonradiative effect on local surface temperature compared with open land. The nonradiative effect explains about 79% (±16%) of local surface temperature change between adjacent forest and open land. The contribution of the nonradiative effect varies with forest and open land types. The largest cooling is achieved by replacing grasslands or rain-fed croplands with evergreen tree types. Conversely, converting irrigated croplands to deciduous broadleaf forest leads to warming. This provides new guidance on afforestation strategies, including how these should be informed by local conditions to avoid amplifying climate-related warming.

How are the coastal breezes affected by changes in the land surface? Analysis from a case study using WRF

2021 ◽  
Author(s):  
Roberto Mulero-Martinez ◽  
Carlos Román-Cascón ◽  
Marie Lothon ◽  
Fabienne Lohou ◽  
Carlos Yagüe ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Surface Temperature ◽  
Land Surface ◽  
Wrf Model ◽  
Sea Breeze ◽  
Sea Breezes ◽  
Temperature Changes ◽  
Precipitation Regimes ◽  
Thermally Driven

<p>Sea breezes are common and recurrent thermally-driven wind circulations formed in coastal areas under conditions of weak synoptic forcing. The different heat capacity between the land and the sea causes the thermal contrast needed for their formation. Therefore, the temperature changes at the surface of both the sea and the land influence the breezes characteristics. In this work, we investigate how sensitive are the sea breezes to changes in land cover and soil moisture, which may have a direct impact on the surface temperature inland. This is done through the design of different sensitivity experiments performed with the Weather Research and Forecasting (WRF) model, where we tested the effect of the land use and soil moisture modification. This was done through the simulation of a typical sea-breeze case study in the coastal area of the southwest of the Iberian Peninsula (Gulf of Cádiz). The differences among the experiments are compared spatially and confronted with observations from different meteorological towers at the coast and inland. A special emphasis is made on the changes observed in the area of the National Park of Doñana. This area is characterised by large shallow marshes with varying seasonal status and extensive rice crops. Thus, contrasting conditions of the surface are typically observed, which also depend on the previous hydrological conditions. Preliminary results highlight the importance of the correct representation of the surface inland to obtain a proper simulation of the sea-breeze system. Besides, new lines of research emerge to analyse the impacts caused by other potential modifications in the surface conditions of the land and the ocean (e.g., global change, urbanization, crop modification, changes in precipitation regimes or sea surface temperature, etc).</p>

scholarly journals An Intercomparison of GCM and RCM Dynamical Downscaling for Characterizing the Hydroclimatology of California and Nevada

2018 ◽  
Vol 19 (9) ◽  
pp. 1485-1506 ◽  
Author(s):  
Zexuan Xu ◽  
Alan M. Rhoades ◽  
Hans Johansen ◽  
Paul A. Ullrich ◽  
William D. Collins
Keyword(s):  
Surface Temperature ◽  
Large Scale ◽  
Snow Water Equivalent ◽  
Dynamical Downscaling ◽  
Wrf Model ◽  
Horizontal Resolution ◽  
Total Precipitation ◽  
Microphysics Scheme ◽  
Reanalysis Dataset ◽  
Simulated Surface

Abstract Dynamical downscaling is a widely used technique to properly capture regional surface heterogeneities that shape the local hydroclimatology. However, in the context of dynamical downscaling, the impacts on simulation fidelity have not been comprehensively evaluated across many user-specified factors, including the refinements of model horizontal resolution, large-scale forcing datasets, and dynamical cores. Two global-to-regional downscaling methods are used to assess these: specifically, the variable-resolution Community Earth System Model (VR-CESM) and the Weather Research and Forecasting (WRF) Model with horizontal resolutions of 28, 14, and 7 km. The modeling strategies are assessed by comparing the VR-CESM and WRF simulations with consistent physical parameterizations and grid domains. Two groups of WRF Models are driven by either the NCEP reanalysis dataset (WRF_NCEP) or VR-CESM7 results (WRF_VRCESM) to evaluate the effects of large-scale forcing datasets. The simulated hydroclimatologies are compared with reference datasets for key properties including total precipitation, snow cover, snow water equivalent (SWE), and surface temperature. The large-scale forcing datasets are critical to the WRF simulations of total precipitation but not surface temperature, controlled by the wind field and atmospheric moisture transport at the ocean boundary. No significant benefit is found in the regional average simulated hydroclimatology by increasing horizontal resolution refinement from 28 to 7 km, probably due to the systematic biases from the diagnostic treatment of rainfall and snowfall in the microphysics scheme. The choice of dynamical core has little impact on total precipitation but significantly determines simulated surface temperature, which is affected by the snow-albedo feedback in winter and soil moisture estimations in summer.

scholarly journals Global Land Surface Temperature Change (2003–2017) and Its Relationship with Climate Drivers: AIRS, MODIS, and ERA5-Land Based Analysis

2020 ◽  
Vol 13 (1) ◽  
pp. 44
Author(s):  
Jiang Liu ◽  
Daniel Fiifi Tawia Hagan ◽  
Yi Liu
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Large Scale ◽  
Critical Role ◽  
Longwave Radiation ◽  
Reanalysis Data ◽  
Surface Solar Radiation ◽  
Surface Temperature Change ◽  
Spatio Temporal

Land surface temperature (LST) plays a critical role in the water cycle and energy balance at global and regional scales. Large-scale LST estimates can be obtained from satellite observations and reanalysis data. In this study, we first investigate the long-term changes of LST during 2003–2017 on a per-pixel basis using three different datasets derived from (i) the Atmospheric Infrared Sounder (AIRS) onboard Aqua satellite, (ii) the Moderate Resolution Imaging Spectroradiometer (MODIS) also aboard Aqua, and (iii) the recently released ERA5-Land reanalysis data. It was found that the spatio-temporal patterns of these data agree very well. All three products globally showed an uptrend in the annual average LST during 2003–2017 but with considerable spatial variations. The strongest increase was found over the region north of 45° N, particularly over Asian Russia, whereas a slight decrease was observed over Australia. The regression analysis indicated that precipitation (P), incoming surface solar radiation (SW↓), and incoming surface longwave radiation (LW↓) can together explain the inter-annual LST variations over most regions, except over tropical forests, where the inter-annual LST variation is low. Spatially, the LST changes during 2003–2017 over the region north of 45° N were mainly influenced by LW↓, while P and SW↓ played a more important role over other regions. A detailed look at Asian Russia and the Amazon rainforest at a monthly time scale showed that warming in Asian Russia is dominated by LST increases in February–April, which are closely related with the simultaneously increasing LW↓ and clouds. Over the southern Amazon, the most apparent LST increase is found in the dry season (August–September), primarily affected by decreasing P. In addition, increasing SW↓ associated with decreasing atmospheric aerosols was another factor found to cause LST increases. This study shows a high level of consistency among LST trends derived from satellite and reanalysis products, thus providing more robust characteristics of the spatio-temporal LST changes during 2003–2017. Furthermore, the major climatic drivers of LST changes during 2003–2017 were identified over different regions, which might help us predict the LST in response to changing climate in the future.

scholarly journals Parameters That Influence Corrosion Detection in Reinforced Concrete Based on Eddy Current Thermography

2020 ◽  
Vol 2020 ◽  
pp. 1-9 ◽  
Author(s):  
Lu Liu ◽  
Dan Zheng ◽  
Jianting Zhou ◽  
Juan Yang ◽  
Hong Zhang
Keyword(s):  
Reinforced Concrete ◽  
Surface Temperature ◽  
Temperature Change ◽  
Eddy Current ◽  
Steel Corrosion ◽  
Concrete Cover ◽  
Quantitative Detection ◽  
Surface Temperature Change ◽  
Temperature Changes ◽  
Steel Bar

This study introduces an eddy current thermography technique that can be used to detect and evaluate steel corrosion in a reinforced concrete structure. The rate of surface temperature changes in reinforced concrete is proposed as a means to characterize the degree of steel bar corrosion. The rate of surface temperature changes increased gradually with an increase in the corrosion degree. The influence of structural parameters on the rate of the temperature change was analyzed in detail. The results indicated that the rate of surface temperature change increased with a decrease in the concrete cover depth and with an increase in the humidity of the concrete, and this was affected by the diameter of the internal steel bar. Concrete cover was the most significant factor that affected the rate of the surface temperature change, except for the corrosion degree. The variations in the surface temperature of reinforced concrete can be explained using the law of electromagnetic induction and the electrochemical property change of corroded steel bar. This research provides a reliable basis for real-world applications and is helpful to understand the application scope of eddy current thermography technology for the quantitative detection of steel corrosion.

scholarly journals A Simulated Climatology of Spectrally Decomposed Atmospheric Infrared Radiation

2013 ◽  
Vol 26 (5) ◽  
pp. 1702-1715 ◽  
Author(s):  
Yi Huang
Keyword(s):  
Water Vapor ◽  
Surface Temperature ◽  
Temperature Change ◽  
Infrared Radiation ◽  
Longwave Radiation ◽  
Co2 Absorption ◽  
Tropospheric Temperature ◽  
Surface Temperature Change ◽  
Temperature Changes ◽  
Radiation Spectra

Abstract A simulation experiment is conducted to inquire into the mean climate state and likely trends in atmospheric infrared radiation spectra. Upwelling and downwelling spectra at five vertical levels from the surface to the top of the atmosphere (TOA) are rigorously calculated from a climate-model-simulated atmosphere for a 25-yr period. Tracing the longwave radiation flux vertically and spectrally renders a dissection of the greenhouse effect of the earth atmosphere and its change due to climate forcings and feedbacks. The results show that the total outgoing longwave radiation (OLR) at the TOA may be conserved due to 1) compensating temperature and opacity effects and 2) contrasting temperature changes in troposphere and stratosphere. The tightly coupled tropospheric temperature and opacity effects reduce the overall tropospheric contribution to OLR change to be comparable to the overall stratospheric contribution, which suggests that transient OLR change is constrained by the relative strengths of stratospheric and tropospheric temperature changes. The total OLR energy, however, is redistributed across its spectrum. The earliest detectable global climate change signal lies in the CO2 absorption bands, which results from stratospheric cooling and the CO2 opacity effect. This signal can be detected much sooner than surface temperature change and is little affected by achievable instrument accuracy. In contrast, both tropospheric temperature and opacity effects increase downwelling longwave radiation (DLR), which makes DLR a verifiable aspect of global warming. The time it takes to detect surface DLR change roughly equals that of surface temperature change. Measuring downwelling radiances at strong water vapor lines at the tropopause can particularly help monitor stratospheric water vapor.

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