Clarifying the role of radiative mechanisms in the spatio-temporal changes of land surface temperature across the Horn of Africa

Abera, Temesgen Alemayehu; Heiskanen, Janne; Pellikka, Petri; Rautiainen, Miina; Maeda, Eduardo Eiji

doi:10.1016/j.rse.2018.11.024

Cited by 44 publications

(38 citation statements)

References 45 publications

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“…The albedo trend had a negligible impact on LST trend. This result agrees with previous studies in tropical regions, which showed a minor role of albedo on the magnitude and sign of LST compared to LE (e.g., Abera et al, 2019; Bright et al, 2017; Li et al, 2015; Pielke et al, 1998). For example, an increase in albedo was reported in areas of increased LST during dry period in arid parts of EA (Abera, Heiskanen, Pellikka, Adhikari, 2020), indicating minor influence of albedo on LST since an increase in albedo is expected to reduce LST.…”

Section: Discussionsupporting

confidence: 93%

“…LE had the highest contribution (33%) in explaining the overall daytime LST trends (warming and cooling). This is likely related to the stronger sensitivity of LST to LE flux in water-limited arid and semiarid environment ( Figure 8; Abera, Heiskanen, Pellikka, Adhikari, 2020;Small & Kurc, 2003) as well as due to the radiative heating of the surface, which is largely balanced by latent heat flux through evapotranspiration during daytime (Abera et al, 2019;Oke & Cleugh, 1987).…”

Section: Surface Biophysical Drivers Of Lst Trendmentioning

confidence: 99%

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Land Surface Temperature Trend and Its Drivers in East Africa

et al. 2020

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Land surface temperature (LST) is affected by surface-atmosphere interaction. Yet, the degree to which surface and atmospheric factors impact the magnitude of LST trend is not well established. Here, we used surface energy balance, boosted regression tree model, and satellite observation and reanalysis data to unravel the effects of surface factors (albedo, sensible heat, latent heat, and ground heat) as well as incoming radiation (shortwave and longwave) on LST trends in East Africa (EA). Our result showed that 11% of EA was affected by significant (p < 0.05) daytime annual LST trends, which exhibited both cooling of −0.19 K year −1 (mainly in South Sudan and Sudan) and warming of 0.22 K year −1 (mainly in Somalia and Kenya). The nighttime LST trends affected a large part of EA (31%) and were dominated by significant warming trend (0.06 K year −1). Influenced by contrasting daytime and nighttime LST trends, the diurnal LST range reduced in 15% of EA. The modeling result showed that latent heat flux (32%), incoming longwave radiation (30%), and shortwave radiation (23%) were stronger in explaining daytime LST trend. The effects of surface factors were stronger in both cooling and warming trends, whereas atmospheric factors had stronger control only on surface cooling trends. These results indicate the differential control of surface and atmospheric factors on warming and cooling trends, highlighting the importance of considering both factors for accurate evaluation of the LST trends in the future.

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Section: Discussionsupporting

confidence: 93%

Section: Surface Biophysical Drivers Of Lst Trendmentioning

confidence: 99%

Land Surface Temperature Trend and Its Drivers in East Africa

et al. 2020

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“…Poor rainfall areas such as Gash Barka, Northern Red Sea, and Anseba regions were the most vulnerable regions to both meteorological and agricultural drought conditions; moreover, rainfall is becoming erratic and below average because of climate variability [51]. Sea-surface effects due to adjacency to the Red Sea, and an overall increase in temperature and albedo, desertification, and deforestation have all contributed to the effect of the drought on crop production and anomalies in vegetation greenness [40,47,52]. Thus, there is a further need to understand and characterize dynamics of the vegetation and land cover changes in relation to drought patterns to build a base for local drought predictions in association with climate models and downscaled multi-model projections [53].…”

Section: Discussionmentioning

confidence: 99%

Spatio-Temporal Analysis of Vegetation Dynamics as a Response to Climate Variability and Drought Patterns in the Semiarid Region, Eritrea

et al. 2019

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There is a growing concern over change in vegetation dynamics and drought patterns with the increasing climate variability and warming trends in Africa, particularly in the semiarid regions of East Africa. Here, several geospatial techniques and datasets were used to analyze the spatio-temporal vegetation dynamics in response to climate (precipitation and temperature) and drought in Eritrea from 2000 to 2017. A pixel-based trend analysis was performed, and a Pearson correlation coefficient was computed between vegetation indices and climate variables. In addition, vegetation condition index (VCI) and standard precipitation index (SPI) classifications were used to assess drought patterns in the country. The results demonstrated that there was a decreasing NDVI (Normalized Difference Vegetation Index) slope at both annual and seasonal time scales. In the study area, 57.1% of the pixels showed a decreasing annual NDVI trend, while the significance was higher in South-Western Eritrea. In most of the agro-ecological zones, the shrublands and croplands showed decreasing NDVI trends. About 87.16% of the study area had a positive correlation between growing season NDVI and precipitation (39.34%, p < 0.05). The Gash Barka region of the country showed the strongest and most significant correlations between NDVI and precipitation values. The specific drought assessments based on VCI and SPI summarized that Eritrea had been exposed to recurrent droughts of moderate to extreme conditions during the last 18 years. Based on the correlation analysis and drought patterns, this study confirms that low precipitation was mainly attributed to the slowly declining vegetation trends and increased drought conditions in the semi-arid region. Therefore, immediate action is needed to minimize the negative impact of climate variability and increasing aridity in vegetation and ecosystem services.

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“…But in the tropical climate, the warming effect is often reported in the deforestation processes. For example, Abera, et al [53] found the net warming effect is 1.3 K when the land cover converted from forest to cropland. Although the albedo change decrease the land surface temperature by 0.12 K, the dominant influence of non-radiative mechanisms (roughness change and evapotranspiration change) is about 10 times higher than that of radiative forcing.…”

Section: Warming Effect In the Daytimementioning

confidence: 99%

“…Although the albedo change decrease the land surface temperature by 0.12 K, the dominant influence of non-radiative mechanisms (roughness change and evapotranspiration change) is about 10 times higher than that of radiative forcing. The study site of Abera, et al [53] is the Horn of Africa, which is close to the Indian Ocean. The precipitation in the forest area can reach 1500 mm, about five times higher than that in the Kubuqi Desert.…”

Section: Warming Effect In the Daytimementioning

confidence: 99%

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

Wang

Lee

Feng

et al. 2019

Forests

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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.

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Clarifying the role of radiative mechanisms in the spatio-temporal changes of land surface temperature across the Horn of Africa

Cited by 44 publications

References 45 publications

Land Surface Temperature Trend and Its Drivers in East Africa

Land Surface Temperature Trend and Its Drivers in East Africa

Spatio-Temporal Analysis of Vegetation Dynamics as a Response to Climate Variability and Drought Patterns in the Semiarid Region, Eritrea

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

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