Unmixing the regional climate response to recent historical land cover changes in Europe

Author(s):  
Bo Huang ◽  
Xiangping Hu ◽  
Geir-Arne Fuglstad ◽  
Xu Zhou ◽  
Wenwu Zhao ◽  
...  

<p>Land cover changes (LCCs) influence the regional climate because they alter biophysical mechanisms like evapotranspiration, albedo, and surface roughness. Previous research mainly assessed the regional climate implications of individual land cover transitions, such as the effects of historical forest clearance or idealized large-scale scenarios of deforestation/afforestation, but the combined effects from the mix of recent historical land cover changes in Europe have not been explored. In this study, we use a combination of high resolution land cover data with a regional climate model (the Weather Research and Forecasting model, WRF, v3.9.1) to quantify the effects on surface temperature of land cover changes between 1992 and 2015. Unlike many previous studies that had to use one unrealistic large-scale simulation for each LCC to single out its climate effects, our analysis simultaneously considers the effects of the mix of historical land cover changes in Europe and introduces a new method to disentangle the individual contributions. This approach, based on a ridge statistical regression, does not require an explicit consideration of the different components of the surface energy budget, and directly shows the temperature changes from each land transition.</p><p>            From 1992 to 2015, around 70 Mha of land transitions occurred in Europe. Approximately 25 Mha of agricultural land was left abandoned, which was only partially compensated by cropland expansion (about 20 Mha). Declines in agricultural land mostly occurred in favor of forests (15 Mha) and urban settlements (8 Mha). Relative to 1992, we find that the land covers of 2015 are associated with an average temperature cooling of -0.12±0.20 °C, with seasonal and spatial variations. At a continental level, the mean cooling is mainly driven by agriculture abandonment (cropland-to-forest transitions). Idealized simulations where cropland transitions to other land classes are excluded result in a mean warming of +0.10±0.19 °C, especially during summer. Conversions to urban land always resulted in warming effects, whereas the local temperature response to forest gains and losses shows opposite signs from the western and central part of the domain (where forests have cooling effects) to the eastern part (where forests are associated to warming). Gradients in soil moisture and local climate conditions are the main drivers of these differences. Our findings are a first attempt to quantify the regional climate response to historical LCC in Europe, and our method allows to unmix the temperature signal of a grid cell to the underlying LCCs (i.e., temperature impact per land transition). Further developing biophysical implications from LCCs for their ultimate consideration in land use planning can improve synergies for climate change adaptation and mitigation.</p><p> </p>

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Xiangzheng Deng ◽  
Chunhong Zhao ◽  
Haiming Yan

There have been tremendous changes in the global land use pattern in the past 50 years, which has directly or indirectly exerted significant influence on the global climate change. Quantitative analysis for the impacts of land use and land cover changes (LUCC) on surface climate is one of the core scientific issues to quantitatively analyze the impacts of LUCC on the climate so as to scientifically understand the influence of human activities on the climate change. This paper comprehensively analyzed the primary scientific issues about the impacts of LUCC on the regional climate and reviewed the progress in relevant researches. Firstly, it introduced the influence mechanism of LUCC on the regional climate and reviewed the progress in the researches on the biogeophysical process and biogeochemical process. Then the model simulation of effects of LUCC on the regional climate was introduced, and the development from the global climate model to the regional climate model and the integration of the improved land surface model and the regional climate model were reviewed in detail. Finally, this paper discussed the application of the regional climate models in the development and management of agricultural land and urban land.


2021 ◽  
Author(s):  
Wolfgang Obermeier ◽  

<p>The quantification of the net carbon flux from land use and land cover changes (f<sub>LULCC</sub>) is essential to understand the global carbon cycle, and consequently, to support climate change mitigation. However, large-scale f<sub>LULCC</sub> is not directly measurable, and can only be inferred by models, such as semi-empirical bookkeeping models, and process-based dynamic global vegetation models (DGVMs). By definition, f<sub>LULCC</sub> estimates between these two model types are not directly comparable. For example, transient DGVM-based f<sub>LULCC</sub> of the annual global carbon budget includes the so-called Loss of Additional Sink Capacity (LASC). The latter accounts for environmental impacts on the land carbon storage capacities of managed land compared to potential vegetation which is not included in bookkeeping models. Additionally, estimates of transient DGVM-based f<sub>LULCC</sub> differ from bookkeeping model estimates, since they depend on arbitrarily chosen simulation time periods and the timing of land use and land cover changes within the historic period (which includes different accumulation periods for legacy effects). However, DGVMs enable a f<sub>LULCC</sub> approximation independent of the timing of land use and land cover changes and their legacy effects by simulations run under constant pre-industrial or present-day environmental forcings.</p><p>In this study, we analyze these different DGVM-derived f<sub>LULCC</sub> definitions, under transiently changing environmental conditions and fixed pre-industrial and fixed present-day conditions, within 18 regions for twelve DGVMs and quantify their differences as well as climate- and CO<sub>2</sub>-induced components. The multi model mean under transient conditions reveals a global f<sub>LULCC</sub> of 2.0±0.6 PgC yr<sup>-1</sup> for 2009-2018, with ~40% stemming from the LASC (0.8±0.3 PgC yr<sup>-1</sup>). Within the industrial period (1850 onward), cumulative f<sub>LULCC</sub> reached 189±56 PgC with 40±15 PgC from the LASC.</p><p>Regional hotspots of high LASC values exist in the USA, China, Brazil, Equatorial Africa and Southeast Asia, which we mainly relate to deforestation for cropland. Distinct negative LASC estimates were observed in Europe (early reforestation) and from 2000 onward in the Ukraine (recultivation of post-Soviet abandoned agricultural land). Negative LASC estimates indicate that fLULCC estimates in these regions are lower in transient DGVM simulations compared to bookkeeping-approaches. By unraveling the spatio-temporal variability of the different DGVM-derived f<sub>LULCC</sub> estimates, our study calls for a harmonized attribution of model-derived f<sub>LULCC</sub>. We propose an approach that bridges bookkeeping and DGVM approaches for f<sub>LULCC</sub> estimation by adopting a mean DGVM-ensemble LASC for a defined reference period.</p>


2020 ◽  
Author(s):  
Astrid Manciu ◽  
Andreas Krause ◽  
Anja Rammig ◽  
Benjamin Quesada

<p>Deforestation in Colombia has drastically increased in recent years. At the same time, droughts and floods are affecting the country more frequently due to climate change. Analyzing the impacts and interactions of deforestation and global warming is challenging due to the terrain’s complexity and the high climate variability along with the severe lack of regional climate modelling.</p><p>Here, we quantify the impact of historical anthropogenic global warming (CC) and land cover changes (LCC) on precipitation, temperature and the surface energy balance in Colombia by running the Weather Research and Forecasting model WRF v3.9.1.1. across different land cover and climate scenarios during the study period 2009-2011 for Colombia.</p><p>We find that precipitation is increased by CC with a stronger effect over forests. LCC implies a small reduction of precipitation which is strongly enhanced above deforested areas. LCC is found to be a strong driver of regional precipitation changes representing up to 25% and 60% of the CC effects magnitude in Coastal Caribbean and Andean regions, respectively. CC causes a temperature increase across the whole domain, in particular with increasing altitude. Surprisingly however, WRF simulates a slight cooling after deforestation which is not in line with almost all observations and modelling studies regarding biophysical effects of tropical deforestation. This apparent bias is further investigated across different WRF schemes and parameters because of its great importance for climate studies using WRF with default parametrization in tropical contexts.</p><p> </p>


2020 ◽  
Author(s):  
Getaneh Haile Shoddo

Abstract Development initiatives like the recent increase in large-scale investment agriculture have made a significant impact on the forest. In the name of development, the land is often given to investors often in long-term leases and at bargain prices. Research on deforestation has been mostly restricted to poverty and population growth as the driving forces for tropical deforestation; however, explanations emphasizing market factors such as increases in large-scale investment agriculture as a cause of deforestation have only been carried out in a small number of areas. The aim of this study is to explore the effects of agricultural land expansion in changing land use and land use cover changes using remote sensing/GIS tools in Sheka zone southwester Ethiopia from 1995 to 2015. The results showed that expansion of investment agriculture has a clear impact on both the local people and the forest ecosystem. The conversion of forestland to investment agriculture has caused varied and extensive environmental degradation to the Sheka forest. The Land Use and Land Cover changes in the Sheka zone are discussed based on underlying socioeconomic factors.


2020 ◽  
Vol 125 (24) ◽  
Author(s):  
Clara Orbe ◽  
David Rind ◽  
Jeffrey Jonas ◽  
Larissa Nazarenko ◽  
Greg Faluvegi ◽  
...  

2020 ◽  
Author(s):  
Bo Huang ◽  
Francesco Cherubini ◽  
Xiangping Hu ◽  
Geir-Arne Fuglstad ◽  
Xu Zhou ◽  
...  

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