Satellite observations show widespread increasing trends of leaf area index (LAI), known as the Earth greening. However, the biophysical impacts of this greening on land surface temperature (LST) remain unclear. Here, we quantify the biophysical impacts of Earth greening on LST from 2000 to 2014 and disentangle the contributions of different factors using a physically based attribution model. We find that 93% of the global vegetated area shows negative sensitivity of LST to LAI increase at the annual scale, especially for semiarid woody vegetation. Further considering the LAI trends ( P ≤ 0.1), 30% of the global vegetated area is cooled by these trends and 5% is warmed. Aerodynamic resistance is the dominant factor in controlling Earth greening’s biophysical impacts: The increase in LAI produces a decrease in aerodynamic resistance, thereby favoring increased turbulent heat transfer between the land and the atmosphere, especially latent heat flux.
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Sensitivities and Responses of Land Surface Temperature to Deforestation-Induced Biophysical Changes in Two Global Earth System Models
Abstract While the significance of quantifying the biophysical effects of deforestation is rarely disputed, the sensitivities of land surface temperature (LST) to deforestation-induced changes in different biophysical factors (e.g., albedo, aerodynamic resistance, and surface resistance) and the relative importance of those biophysical changes remain elusive. Based on the subgrid-scale outputs from two global Earth system models (ESMs, i.e., the Geophysical Fluid Dynamics Laboratory Earth System Model and the Community Earth System Model) and an improved attribution framework, the sensitivities and responses of LST to deforestation are examined. Both models show that changes in aerodynamic resistance are the most important factor responsible for LST changes, with other factors such as albedo and surface resistance playing secondary but important roles. However, the magnitude of the contributions from different biophysical factors to LST changes is quite different for the two ESMs. We find that the differences between the two models in terms of the sensitivities are smaller than those of the corresponding biophysical changes, indicating that the dissimilarity between the two models in terms of LST responses to deforestation is more related to the magnitude of biophysical changes. It is the first time that the attribution of subgrid surface temperature variability is comprehensively compared based on simulations with two commonly used global ESMs. This study yields new insights into the similarity and dissimilarity in terms of how the biophysical processes are represented in different ESMs and further improves our understanding of how deforestation impacts on the local surface climate.
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- Award ID(s):
- 1832959
- NSF-PAR ID:
- 10201182
- Date Published:
- Journal Name:
- Journal of Climate
- Volume:
- 33
- Issue:
- 19
- ISSN:
- 0894-8755
- Page Range / eLocation ID:
- 8381 to 8399
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Land surface temperature (LST) responds to land‐use/land‐cover change (LULCC), which modifies surface properties that control the surface energy balance (SEB). Quantifying changes in LST due to individual perturbations caused by LULCC is an attribution problem. Most attribution methods are based on the first‐order Taylor series expansion (FOTSE) of a linearized SEB equation. The accuracy of these methods is affected by the use of FOTSE at two places. The first is to linearize the SEB equation and to obtain an analytical solution for LST (the LST model), and the second is to obtain LST changes as the linear sum of concurrent changes in multiple factors (the attribution model). In this study, we systematically assess the importance of non‐linear effects lost in these linearization processes using the second‐order Taylor series expansion (SOTSE). Results show that while the SOTSE LST model outperforms the FOTSE LST model, the order of Taylor series expansion in the LST model does not significantly influence the attribution of LST changes. However, the SOTSE attribution model is considerably more accurate than the FOTSE attribution model, especially when the magnitude of perturbations is large. Results suggest that contributions from higher‐order and cross‐order terms in the attribution model can be as large as 50%. Sensitivity analysis further shows that non‐linear effects associated with changing surface resistance for LULCC scenarios with large perturbations (e.g., deforestation and urbanization) are particularly strong. In conclusion, we recommend using the FOTSE LST model and the SOTSE attribution model.
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Abstract The two-resistance mechanism (TRM) attribution method, which was designed to analyze the urban–rural contrast of temperature, is improved to study the urban–rural contrast of heat stress. The improved method can be applied to diagnosing any heat stress index that is a function of temperature and humidity. As an example, in this study we use it to analyze the summertime urban–rural contrast of simplified wet bulb globe temperature (SWBGT) simulated by the Geophysical Fluid Dynamics Laboratory land model coupled with an urban canopy model. We find that the urban–rural contrast of SWBGT is primarily caused by the lack of evapotranspiration in urban areas during the daytime and the release of heat storage during the nighttime, with the urban–rural differences in aerodynamic features playing either positive or negative roles depending on the background climate. Compared to the magnitude of the urban–rural contrast of temperature, the magnitude of the urban–rural contrast of SWBGT is damped due to the moisture deficits in urban areas. We further find that the urban–rural contrast of 2-m air temperature/SWBGT is fundamentally different from that of canopy air temperature/SWBGT. Turbulent mixing in the surface layer leads to much smaller urban–rural contrasts of 2-m air temperature/SWBGT than their canopy air counterparts. Significance Statement Heat leads to serious public health concerns, but urban and rural areas have different levels of heat stress. Our study explains the magnitude and pattern of the simulated urban–rural contrast in heat stress at the global scale and improves an attribution method to quantify which biophysical processes are mostly responsible for the simulated urban–rural contrast in heat stress. We highlight two well-known causes of higher heat stress in cities: the lack of evapotranspiration and the stronger release of heat storage. Meanwhile, we draw attention to the vegetation types in rural areas, which determine the urban–rural difference in surface roughness and significantly affect the urban–rural difference in heat stress. Last, we find the urban–rural contrasts of 2-m air temperature/SWBGT are largely reduced relative to their canopy air counterparts due to the turbulent mixing effect.more » « less
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Abstract While the signs of the sensitivities of surface temperature (
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1175/JCLI-D-19-0725.1
