Editing IPCC:AR6/WGI/Chapter-7 (section)

==== 7.3.4.1 Land Use ====

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Land-use forcing is defined as those changes in land-surface properties directly caused by human activity rather than by climate processes (see also [[IPCC:Wg1:Chapter:Chapter-2#2.2.7|Section 2.2.7]] ). Land-use change affects the surface albedo. For example, deforestation typically replaces darker forested areas with brighter cropland, and thus imposes a negative radiative forcing on climate, while afforestation and reforestation can have the opposite effect. Precise changes depend on the nature of the forest, crops and underlying soil. Land-use change also affects the amount of water transpired by vegetation ( [[#Devaraju--2015|Devaraju et al., 2015]] ). Irrigation of land directly affects evaporation ( [[#Sherwood--2018|Sherwood et al., 2018]] ), causing a global increase of 32,500 m <sup>3</sup> s <sup>−1</sup> due to human activity. Changes in evaporation and transpiration affect the latent heat budget, but do not directly affect the top-of-atmosphere (TOA) radiative fluxes. The lifetime of water vapour is so short that the effect of changes in evaporation on the greenhouse contribution of water vapour are negligible ( [[#Sherwood--2018|Sherwood et al., 2018]] ). However, evaporation can affect the ERF through adjustments, particularly through changes in low-cloud amounts. Land management affects the emissions or removal of GHGs from the atmosphere (such as CO <sub>2</sub> , CH <sub>4</sub> , N <sub>2</sub> O). These emissions changes have the greatest effect on climate ( [[#Ward--2014|Ward et al., 2014]] ), however they are already included in GHG inventories. Land-use change also affects the emissions of dust and biogenic volatile organic compounds (BVOCs), which form aerosols and affect the atmospheric concentrations of ozone and methane (Section 6.2.2). The effects of land use on surface temperature and hydrology were recently assessed in SRCCL ( [[#Jia--2019|Jia et al., 2019]] ).

Using the definition of ERF from ( [[#7.1|Section 7.1]] , the adjustment in land-surface temperature is excluded from the definition of ERF, but changes in vegetation and snow cover (resulting from land-use change) are included ( [[#Boisier--2013|Boisier et al., 2013]] ). Land-use change in the mid-latitudes induces a substantial amplifying adjustment in snow cover. Few climate model studies have attempted to quantify the ERF of land-use change. T. [[#Andrews--2017|]] [[#Andrews--2017|Andrews et al. (2017)]] calculated a very large surface albedo ERF (–0.47 W m <sup>–2</sup> ) from 1860 to 2005 in the HadGEM2-ES model, although they did not separate out the surface albedo change from snow cover change. HadGEM2-ES is known to overestimate the amount of boreal trees and shrubs in the unperturbed state ( [[#Collins--2011|Collins et al., 2011]] ) so will tend to overestimate the ERF associated with land-use change. The increases in dust in HadGEM2-ES contributed an extra –0.25 W m <sup>–2</sup> , whereas cloud cover changes added a small positive adjustment (0.15 W m <sup>–2</sup> ) consistent with a reduction in transpiration. A multi-model quantification of land-use forcing in CMIP6 models (excluding one outlier) ( [[#Smith--2020b|Smith et al., 2020b]] ) found an IRF of –0.15 ± 0.12 W m <sup>–2</sup> (1850–2014), and an ERF (correcting for land-surface temperature change) of –0.11 ± 0.09 W m <sup>–2</sup> . This shows a small positive adjustment term (mainly from a reduction in cloud cover). CMIP5 models show an IRF of –0.11 [–0.16 to –0.04] W m <sup>–2</sup> (1850–2000) after excluding unrealistic models ( [[#Lejeune--2020|Lejeune et al., 2020]] ).

The contribution of land-use change to albedo changes has recently been investigated using MODIS and AVHRR to attribute surface albedo to geographically specific land-cover types ( [[#Ghimire--2014|Ghimire et al., 2014]] ). When combined with a historical land-use map ( [[#Hurtt--2011|Hurtt et al., 2011]] ) this gives a SARF of –0.15 ± 0.01 W m <sup>–2</sup> for the period 1700–2005, of which approximately –0.12 W m <sup>–2</sup> is from 1850. This study accounted for correlations between vegetation type and snow cover, but not the adjustment in snow cover identified in T. [[#Andrews--2017|]] [[#Andrews--2017|Andrews et al. (2017)]] .

The indirect contributions of land-use change through biogenic emissions is very uncertain. Decreases in BVOCs reduce ozone and methane ( [[#Unger--2014|Unger, 2014]] ), but also reduce the formation of organic aerosols and their effects on clouds ( [[#Scott--2017|Scott et al., 2017]] ). Adjustments through changes in aerosols and chemistry are model dependent ( [[#Zhu--2019b|Zhu et al., 2019b]] ; [[#Zhu--2020|Zhu and Penner, 2020]] ), and it is not yet possible to make an assessment based on a limited number of studies.

The contribution of irrigation (mainly to low-cloud amount) is assessed as –0.05 <sup></sup> [–0.1 to 0.05] W m <sup>–2</sup> for the historical period ( [[#Sherwood--2018|Sherwood et al., 2018]] ).

Because the CMIP5 and CMIP6 modelling studies are in agreement with [[#Ghimire--2014|Ghimire et al. (2014)]] , that study is used as the assessed albedo ERF. Adding the irrigation effect to this gives an overall assessment of the ERF from land-use change of –0.20 ± 0.10 W m <sup>–2</sup> ( ''medium confidence'' ). Changes in ERF since 2014 are assumed to be small compared to the uncertainty, so this ERF applies to the period 1750–2019. The uncertainty range includes uncertainties in the adjustments.

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