The Biophysical Impacts of Idealized Afforestation on Surface Temperature in China: Local and Nonlocal Effects

Abstract

Afforestation can impact surface temperature through local and nonlocal biophysical effects. However, the local and nonlocal effects of afforestation in China have rarely been explicitly investigated. In this study, we separate the local and nonlocal effects of idealized afforestation in China based on a checkerboard method and the regional Weather Research and Forecasting (WRF) Model. Two checkerboard pattern–like afforestation simulations (AFF1/4 and AFF3/4) with regularly spaced afforested and unaltered grid cells are performed; afforestation is implemented in one out of every four grid cells in AFF1/4 and in three out of every four grid cells in AFF3/4. The mechanisms for the local and nonlocal effects are examined through the decomposition of the surface energy balance. The results show that the local effects dominate surface temperature responses to afforestation in China, with a cooling effect of approximately −1.00°C for AFF1/4 and AFF3/4. In contrast, the nonlocal effects warm the land surface by 0.14°C for AFF1/4 and 0.41°C for AFF3/4. The local cooling effects mainly result from 1) enhanced sensible and latent heat fluxes and 2) decreases in downward shortwave radiation due to increased low cloud cover fractions. The nonlocal warming effects mainly result from atmospheric feedbacks, including 1) increases in downward shortwave radiation due to decreased low cloud cover fractions and 2) increases in downward longwave radiation due to increased middle and high cloud cover fractions. This study highlights that, despite the unexpected nonlocal warming effect, afforestation in China still has great potential in mitigating climate warming through biophysical processes.

An Evaluation of CMIP6 Models in Representing the Biophysical Effects of Deforestation With Satellite‐Based Observations

“…Therefore, although differences between simulations and observations may be mostly attributed to the model deficiency, these differences may also include uncertainties arising from the construction of observed deforestation effects. Last, it should be emphasized that, since both simulated and observed deforestation effects are obtained based on the space‐for‐time method, the deforestation effect evaluated here is local; the atmospheric feedback to deforestation or the nonlocal effects of deforestation (e.g., clouds and large‐scale circulations; Chen & Dirmeyer, 2020; Chen et al., 2022; Winckler et al., 2019) is not fully considered. Therefore, whether ESMs can reasonably represent land‒atmosphere coupling and thereby the nonlocal effect remains unknown.…”

“…Unlike studies that focus on deforested regions and compare the climate before and after deforestation (Alkama & Cescatti, 2016; Boysen et al., 2020; Pitman et al., 2009), the space‐for‐time approach can extract the deforestation signal in a broader space since it does not require deforestation to truly occur. However, only local effects can be obtained by the method, whereas nonlocal effects due to large‐scale atmospheric feedbacks to deforestation are mostly neglected (Chen & Dirmeyer, 2020; Chen et al., 2022; Winckler et al., 2019).…”

Heeft landgebruik effect op ons weer? : Literatuuronderzoek relatie landgebruik en klimaat op regionale en lokale schaal: De Kleine Waterkringloop

“…Daarnaast hebben satellietwaarnemingen een bias voor condities zonder wolkenvorming, doordat het zicht van de satellieten wordt geblokkeerd door wolken. Aangezien wolken de energiebalans sterk kunnen beïnvloeden (Chen et al, 2022a), zal de daadwerkelijke jaarlijkse energiebalans afwijken van deze waarden.…”

Coupled weather-biosphere model (WRF-VPRM) response to extreme land cover scenarios over Ganga basin: a cross-component analysis

“…Analysis of such extreme scenarios by adopting cross component analysis (Coupling of weather/climate model with biosphere models) strategies using regional climate models is not new. Similar idealized scenarios have been already tested at both global and regional scales [26,27]. Land Use and Climate Across Scales (LUCAS) [28,29] is another such initiative which aims to understand the biophysical impacts of such extreme scenarios using Regional Climate Models.…”