Editing IPCC:AR6/SRCCL/Chapter-2 (section)

==== 2.5.1.1 Impacts of global historical land cover changes on climate ====

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'''''At the global level'''''

The contribution of anthropogenic land cover changes to the net global warming throughout the 20th century has been derived from few model-based estimates that account simultaneously for biogeochemical and biophysical effects of land on climate (Table 2.4). The simulated net change in mean global annual surface air temperature, averaged over all the simulations, is a small warming of 0.078 ± 0.093°C, ranging from small cooling simulated by two models (–0.05°C and –0.02°C respectively in Brovkin et al. (2004) <sup>[[#fn:r988|988]]</sup> and Simmons and Matthews (2016) <sup>[[#fn:r989|989]]</sup> , to larger warming simulated by three models (&gt;+0.14°C; Shevliakova et al. 2013 <sup>[[#fn:r990|990]]</sup> ; Pongratz et al. 2010 <sup>[[#fn:r991|991]]</sup> ; Matthews et al. 2004 <sup>[[#fn:r992|992]]</sup> ). When starting from the Holocene period, He et al. (2014) <sup>[[#fn:r993|993]]</sup> estimated an even larger net warming effect of anthropogenic land cover changes (+0.72°C).

This net small warming signal results from the competing effects of biophysical cooling ( ''medium confidence'' ) and biogeochemical warming ( ''very high confidence'' ) (Figure 2.14 <sup>[[#fn:1|1]]</sup> ). The global biophysical cooling alone has been estimated by a larger range of climate models and is –0.10 ± 0.14°C; it ranges from –0.57°C to +0.06°C (e.g., Zhang et al. 2013a <sup>[[#fn:r994|994]]</sup> ; Hua and Chen 2013 <sup>[[#fn:r995|995]]</sup> ; Jones et al. 2013b <sup>[[#fn:r996|996]]</sup> ; Simmons and Matthews 2016 <sup>[[#fn:r997|997]]</sup> ) (Table A2.1). This cooling is essentially dominated by increases in surface albedo: historical land cover changes have generally led to a dominant brightening of land as discussed in AR5 (Myhre et al. 2013 <sup>[[#fn:r998|998]]</sup> ). Reduced incoming longwave radiation at the surface from reduced evapotranspiration and thus less water vapour in the atmosphere has also been reported as a potential contributor to this cooling (Claussen et al. 2001 <sup>[[#fn:r999|999]]</sup> ). The cooling is, however, dampened by decreases in turbulent fluxes, leading to decreased loss of heat and water vapour from the land through convective processes. Those non-radiative processes are well-known to often oppose the albedo- induced surface temperature changes (e.g., Davin and de Noblet- Ducoudre (2010) <sup>[[#fn:r1000|1000]]</sup> , Boisier et al. (2012) <sup>[[#fn:r1001|1001]]</sup> ).

Historical land cover changes have contributed to the increase in atmospheric CO <sub>2</sub> content (Section 2.3) and thus to global warming (biogeochemical effect, ''very high confidence'' ). The global mean biogeochemical warming has been calculated from observation- based estimates (+0.25 ± 0.10°C) (e.g., Li et al. (2017a) <sup>[[#fn:r1002|1002]]</sup> , Avitabile et al. (2016) <sup>[[#fn:r1003|1003]]</sup> , Carvalhais et al. (2014) <sup>[[#fn:r1004|1004]]</sup> , Le Quéré et al. (2015) <sup>[[#fn:r1005|1005]]</sup> ), or estimated from DGVMs (+0.24 ± 0.12°C) (Peng et al. 2017 <sup>[[#fn:r1006|1006]]</sup> ; Arneth et al. 2017 <sup>[[#fn:r1007|1007]]</sup> ; Pugh et al. 2015 <sup>[[#fn:r1008|1008]]</sup> ; Hansis et al. 2015 <sup>[[#fn:r1009|1009]]</sup> ) and global climate models (+0.20 ± 0.05°C) (Pongratz et al. 2010 <sup>[[#fn:r1010|1010]]</sup> ; Brovkin et al. 2004 <sup>[[#fn:r1011|1011]]</sup> ; Matthews et al. 2004 <sup>[[#fn:r1012|1012]]</sup> ; Simmons and Matthews 2016 <sup>[[#fn:r1013|1013]]</sup> ).

The magnitude of these simulated biogeochemical effects may, however, be underestimated as they do not account for a number of processes such as land management, nitrogen/phosphorus cycles, changes in the emissions of CH <sub>4</sub> , N <sub>2</sub> O and non-GHG emissions from land (Ward et al. 2014 <sup>[[#fn:r1014|1014]]</sup> ; Arneth et al. 2017 <sup>[[#fn:r1015|1015]]</sup> ; Cleveland et al. 2015 <sup>[[#fn:r1016|1016]]</sup> ; Pongratz et al. 2018 <sup>[[#fn:r1017|1017]]</sup> ). Two studies have accounted for those compounds and found a global net positive radiative forcing in response to historical anthropogenic land cover changes, indicating a net surface warming (Mahowald et al. 2017 <sup>[[#fn:r1018|1018]]</sup> ; Ward et al. 2014 <sup>[[#fn:r1019|1019]]</sup> ). However, first the estimated biophysical radiative forcing in those studies only accounts for changes in albedo and not for changes in turbulent fluxes. Secondly, the combined estimates also depend on other several key modelling estimates such as climate sensitivity, CO <sub>2</sub> fertilisation caused by land use emissions, possible synergistic effects, validity of radiative forcing concept for land forcing. The comparison with the other above-mentioned modelling studies is thus difficult.

In addition, most of those estimates do not account for the evolution of natural vegetation in unmanaged areas, while observations and numerical studies have reported a greening of the land in boreal regions resulting from both extended growing season and poleward migration of tree lines (Lloyd et al. 2003 <sup>[[#fn:r1020|1020]]</sup> ; Lucht et al. 1995 <sup>[[#fn:r1021|1021]]</sup> ; Section 2.2). This greening enhances global warming via a reduction of surface albedo (winter darkening of the land through the snow- albedo feedbacks; e.g., Forzieri et al. 2017 <sup>[[#fn:r1022|1022]]</sup> ). At the same time, cooling occurs due to increased evapotranspiration during the growing season, along with enhanced photosynthesis, in essence, increased CO <sub>2</sub> sink (Qian et al. 2010 <sup>[[#fn:r1023|1023]]</sup> ). When feedbacks from the poleward migration of treeline are accounted for, together with the biophysical effects of historical anthropogenic land cover change, the biophysical annual cooling (about –0.20°C to –0.22°C on land, –0.06°C globally) is significantly dampened by the warming (about +0.13°C) resulting from the movements of natural vegetation (Strengers et al. 2010 <sup>[[#fn:r1024|1024]]</sup> ). Accounting simultaneously for both anthropogenic and natural land cover changes reduces the cooling impacts of historical land cover change in this specific study.

'''''At the regional level'''''

The global and annual estimates reported above mask out very contrasted regional and seasonal differences. Biogeochemical effects of anthropogenic land cover change on temperature follow the spatial patterns of GHG-driven climate change with stronger warming over land than ocean, and stronger warming in northern high latitudes than in the tropics and equatorial regions (Arctic amplification). Biophysical effects on the contrary are much stronger where land cover has been modified than in their surroundings (see Section 2.5.4 for a discussion on non-local effects). Very contrasted regional temperature changes can thus result, depending on whether biophysical processes dampen or exacerbate biogeochemical impacts.

Figure 2.15 compares, for seven climate models, the biophysical effects of historical anthropogenic land cover change in North America and Eurasia (essentially cooling) to the regional warming resulting from the increased atmospheric CO <sub>2</sub> content since pre- industrial times (De Noblet-Ducoudré et al. 2012 <sup>[[#fn:r1025|1025]]</sup> ; comparing 1973–2002 to 1871–1900). It shows a dominant biophysical cooling effect of changes in land cover, at all seasons, as large as the regional footprint of anthropogenic global warming. Averaged over all agricultural areas of the world (Pongratz et al. 2010 <sup>[[#fn:r1026|1026]]</sup> ) reported a 20th century biophysical cooling of –0.10°C, and Strengers et al. (2010) <sup>[[#fn:r1027|1027]]</sup> reported a land induced cooling as large as –1.5°C in western Russia and eastern China between 1871 and 2007. There is thus ''medium confidence'' that anthropogenic land cover change has dampened warming in many regions of the world over the historical period.

Very few studies have explored the effects of historical land cover changes on seasonal climate. There is, however, evidence that the seasonal magnitude and sign of those effects at the regional level are strongly related to soil-moisture/evapotranspiration and snow regimes, particularly in temperate and boreal latitudes (Teuling et al. 2010 <sup>[[#fn:r1028|1028]]</sup> ; Pitman and de Noblet-Ducoudré 2012 <sup>[[#fn:r1029|1029]]</sup> ; Alkama and Cescatti 2016 <sup>[[#fn:r1030|1030]]</sup> ). Quesada et al. (2017a) <sup>[[#fn:r1031|1031]]</sup> showed that atmospheric circulation changes can be significantly strengthened in winter for tropical and temperate regions. However, the lack of studies underlines the need for a more systematic assessment of seasonal, regional and other- than-mean-temperature metrics in the future.

'''''Effects on extremes'''''

The effect of historical deforestation on extreme temperature trends is intertwined with the effect of other climate forcings, thus making it difficult to quantify based on observations. Based on results from four climate models, the impact of historical anthropogenic land cover change on temperature and precipitation extremes was found to be locally as important as changes arising from increases in atmospheric CO <sub>2</sub> and sea-surface temperatures, but with a lack of model agreement on the sign of changes (Pitman et al. 2012 <sup>[[#fn:r1032|1032]]</sup> ). In some regions, the impact of land cover change masks or amplifies the effect of increased CO <sub>2</sub> on extremes (Avila et al. 2012 <sup>[[#fn:r1033|1033]]</sup> ; Christidis et al. 2013 <sup>[[#fn:r1034|1034]]</sup> ). Using an observational constraint for the local biophysical effect of land cover change applied to a set of CMIP5 climate models, Lejeune et al. (2018) <sup>[[#fn:r1035|1035]]</sup> found that historical deforestation increased extreme hot temperatures in northern mid-latitudes. The results also indicate a stronger impact on the warmest temperatures compared to mean temperatures. Findell et al. (2017) <sup>[[#fn:r1036|1036]]</sup> reached similar conclusions, although using only a single climate model. Importantly, the climate models involved in these three studies did not consider the effect of management changes, which have been shown to be important, as discussed in Section 2.5.2.

Based on the studies discussed above, there is limited evidence but high agreement that land cover change affects local temperature extremes more than mean values. Observational studies assessing the role of land cover on temperature extremes are still very limited (Zaitchik et al. 2006 <sup>[[#fn:r1037|1037]]</sup> ; Renaud and Rebetez 2008 <sup>[[#fn:r1038|1038]]</sup> ), but suggest that trees dampen seasonal and diurnal temperature variations at all latitudes, and even more so in temperate regions compared to short vegetation (Chen et al. 2018 <sup>[[#fn:r1039|1039]]</sup> ; Duveiller et al. 2018 <sup>[[#fn:r1040|1040]]</sup> ; Li et al. 2015a <sup>[[#fn:r1041|1041]]</sup> ; Lee et al. 2011 <sup>[[#fn:r1042|1042]]</sup> ). Furthermore, trees also locally dampen the amplitude of heat extremes (Renaud and Rebetez 2008 <sup>[[#fn:r1043|1043]]</sup> ; Zaitchik et al. 2006 <sup>[[#fn:r1044|1044]]</sup> ) although this result depends on the forest type, coniferous trees providing less cooling effect than broadleaf trees (Renaud et al. 2011 <sup>[[#fn:r1045|1045]]</sup> ; Renaud and Rebetez 2008 <sup>[[#fn:r1046|1046]]</sup> ).

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<span id="table-2.4"></span>
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'''Table 2.4'''

<span id="change-in-mean-global-annual-surface-air-temperature-resulting-from-anthropogenic-land-cover-change-over-the-historical-period."></span>
'''Change in mean global annual surface air temperature resulting from anthropogenic land cover change over the historical period.'''

This historical period varies from one simulation to another (middle column).

<!-- TABLE -->
{| class="wikitable"
|-

Reference of the study

Time period

Mean global annual change in surface air temperature (°C)

|-

Simmons and Matthews (2016)

1750–2000

–0.02

|-

Shevliakova et al. (2013)

1861–2005

+0.17

|-

Pongratz et al. (2010)

1900–2000

+0.14

|-

Matthews et al. (2004)

1700–2000

+0.15

|-

Brovkin et al. (2004)

1850–2000

–0.05

|-

Mean ± standard deviation

0.078 ± 0.093

|}
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<span id="figure-2.14"></span>
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'''Figure 2.14'''

<span id="changes-in-mean-global-annual-surface-air-temperature-ºc-in-response-to-historical-and-future-anthropogenic-land-cover-changes-as-estimated-from-a-range-of-studies.-see-table-a2.1-in-the-appendix-for-detailed-information.-temperature-changes-resulting-from-biophysical-processes-e.g.-changes-in-physical-land-surface-characteristics-such-as-albedo-evapotranspiration-and-roughness-length"></span>
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'''Changes in mean global annual surface air temperature (ºC) in response to historical and future anthropogenic land cover changes as estimated from a range of studies. See Table A2.1 in the Appendix for detailed information. Temperature changes resulting from biophysical processes (e.g., changes in physical land surface characteristics such as albedo, evapotranspiration and roughness length) […]'''
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[[File:08e4c0dfc6985de239e57e8cfecc576b Figure-2.14-1024x724.jpg]]

Changes in mean global annual surface air temperature (ºC) in response to historical and future anthropogenic land cover changes as estimated from a range of studies. See Table A2.1 in the Appendix for detailed information. Temperature changes resulting from biophysical processes (e.g., changes in physical land surface characteristics such as albedo, evapotranspiration and roughness length) are illustrated using blue symbols and temperature changes resulting from biogeochemical processes (e.g., changes in atmospheric CO <sub>2</sub> composition) use red symbols. Future changes are shown for three distinct scenarios: RCP8.5, RCP4.5 and RCP2.6. The markers ‘filled circle’, ‘filled cross’ and ‘filled triangle down’ represent estimates from global climate models, DGVMs and observations respectively. When the number of estimates is sufficiently large, box plots are overlaid; they show the ensemble minimum, first quartile (25th percentile), median, third quartile (75th percentile), and the ensemble maximum. Scatter points beyond the box plot are the outliers. Details about how temperature change is estimated from DGVMs and observations is provided in the Appendix. Numbers on the right-hand side give the mean and the range of simulated mean global annual warming from various climate models

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<span id="figure-2.15"></span>
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'''Figure 2.15'''

<span id="simulated-changes-in-mean-surface-air-temperature-ºc-between-the-pre-industrial-period-18701900-and-the-present-day-19722002-for-all-seasons-and-for-a-north-america-and-b-eurasia.source-de-noblet-ducoudré-et-al.-2012.-brown-boxes-are-the-changes-simulated-in-response-to-increased-atmospheric-ghg-content-between-both-time-periods-and-subsequent-changes-in-sea-surface"></span>
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'''Simulated changes in mean surface air temperature (ºC) between the pre-industrial period (1870–1900) and the present-day (1972–2002) for all seasons and for (A) North America and (B) Eurasia.Source: De Noblet-Ducoudré et al. (2012). Brown boxes are the changes simulated in response to increased atmospheric GHG content between both time periods and subsequent changes in sea-surface […]'''
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[[File:f4afc1705c5c633841db1ca2f70ea59f Figure-2.15-1024x481.jpg]]

Simulated changes in mean surface air temperature (ºC) between the pre-industrial period (1870–1900) and the present-day (1972–2002) for all seasons and for (A) North America and (B) Eurasia.Source: De Noblet-Ducoudré et al. (2012) <sup>[[#fn:r1047|1047]]</sup> . Brown boxes are the changes simulated in response to increased atmospheric GHG content between both time periods and subsequent changes in sea-surface temperature and sea-ice extent (SST/CO <sub>2</sub> ). The CO <sub>2</sub> changes accounted for include emissions from all sources, including land use. Blue boxes are the changes simulated in response to the biophysical effects of historical land cover changes. The box-and-whisker plots have been drawn using results from seven climate models and ensembles of 10 simulations per model and time period. The bottom and top of each grey box are the 25th and 75th percentiles, and the horizontal line within each box is the 50th percentile (the median). The whiskers (straight lines) indicate the ensemble maximum and minimum values. Seasons are respectively December-January-February (DJF), March-April-May (MAM), June-July-August (JJA) and September-October-November (SON). North America and Eurasia are extended regions where land-use changes are the largest between the two time periods considered (their contours can be found in Figure 1 of De Noblet-Ducoudré et al. (2012).

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