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

=== 2.5.2 Impacts of specific land use changes ===

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==== 2.5.2.1 Impacts of deforestation and forestation ====

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Deforestation or forestation <sup>[[#fn:2|2]]</sup> , wherever it occurs, triggers simultaneously warming and cooling of the surface and of the atmosphere via changes in its various characteristics (Pitman 2003 <sup>[[#fn:r1076|1076]]</sup> ; Strengers et al. 2010 <sup>[[#fn:r1077|1077]]</sup> ; Bonan 2008 <sup>[[#fn:r1078|1078]]</sup> ). Following deforestation, warming results from (i) the release of  CO <sub>2</sub> and other GHGs in the atmosphere (biogeochemical impact) and subsequent increase in incoming infrared radiation at surface (greenhouse effect), (ii) a decrease in the total loss of energy through turbulent fluxes (latent and sensible heat fluxes) resulting from reduced surface roughness, (iii) an increased incoming solar radiation following reduced cloudiness that often (but not always) accompanies the decreased total evapotranspiration. Cooling occurs in response to (iv) increased surface albedo that reduces the amount of absorbed solar radiation, (v) reduced incoming infrared radiation triggered by the decreased evapotranspiration and subsequent decrease in atmospheric water vapour. Points ii–v are referred to as biophysical effects. Deforestation and forestation also alter rainfall and winds (horizontal as well as vertical, as will be further discussed below).

The literature that discusses the effects of forestation on climate is more limited than for deforestation, but they reveal a similar climatic response with opposite sign, as further discussed below. For each latitudinal band (tropical, temperate and boreal) we look at how very large-scale deforestation or forestation impacts on the global mean climate, followed by an examination of the large-scale changes in the specific latitudinal band, and finally more regionally focused analysis. Large-scale idealised deforestation or forestation experiments are often carried out with global or regional climate models as they allow us to understand and measure how sensitive climate is to very large changes

in land cover (similar to the instant doubling of CO <sub>2</sub> in climate models to calculate the climatic sensitivity to GHGs). Details of the model-based studies discussed below can be found in Table A2.2 in the Appendix.

''Global and regional impacts of deforestation/forestation in tropical regions''

A pan-tropical deforestation would lead to the net release of CO <sub>2</sub> from land, and thus to mean global annual warming, with model-based estimates of biogeochemical effects ranging from +0.19 to +1.06°C, with a mean value of +0.53 ± 0.32°C (Ganopolski et al. 2001 <sup>[[#fn:r1079|1079]]</sup> ; Snyder et al. 2004 <sup>[[#fn:r1080|1080]]</sup> ; Devaraju et al. 2015a <sup>[[#fn:r1081|1081]]</sup> ; Longobardi et al. 2016 <sup>[[#fn:r1082|1082]]</sup> ; Perugini et al. 2017 <sup>[[#fn:r1083|1083]]</sup> ). There is, however, ''no agreement'' between models on the magnitude and sign of the biophysical effect of such changes at the global scale (the range spans from –0.5°C to +0.7°C with a mean value of +0.1 ± 0.27°C) (e.g., Devaraju et al. (2015b) <sup>[[#fn:r1084|1084]]</sup> , Snyder (2010) <sup>[[#fn:r1085|1085]]</sup> , Longobardi et al. (2016a) <sup>[[#fn:r1086|1086]]</sup> ) (Figure 2.17). This is the result of many compensation effects in action: increased surface albedo following deforestation, decreased atmospheric water vapour content due to less tropical evapotranspiration, and decreased loss of energy from tropical land in the form of latent and sensible heat fluxes.

There is, however, ''high confidence'' that such large land cover change would lead to a mean biophysical warming when averaged over the deforested land. A mean warming of +0.61 ± 0.48°C is found over the entire tropics. On the other hand, biophysical regional cooling and global warming is expected from forestation (Wang et al. 2014b <sup>[[#fn:r1087|1087]]</sup> ; Bathiany et al. 2010 <sup>[[#fn:r1088|1088]]</sup> ).

Large-scale deforestation (whether pan-tropical or imposed at the sub-continent level, e.g., the Amazon) results in significant mean rainfall decrease (Lawrence and Vandecar 2015 <sup>[[#fn:r1089|1089]]</sup> ; Lejeune et al. 2015 <sup>[[#fn:r1090|1090]]</sup> ; Perugini et al. 2017 <sup>[[#fn:r1091|1091]]</sup> ). In their review, Perugini et al. (2017) <sup>[[#fn:r1092|1092]]</sup> reported an average simulated decrease of –288 ± 75 mm yr <sup>–1</sup> (95% confidence interval). Inversely large-scale forestation increases tropical rainfall by 41 ± 21 mm yr <sup>–1</sup> . The magnitude of the change in precipitation strongly depends on the type of land cover conversion. For instance, conversion of tropical forest to bare soil causes larger reductions in regional precipitation than conversion to pasture (respectively –470 ± 60 mm yr <sup>–1</sup> and –220 ± 100 mm yr <sup>–1</sup> ). Biogeochemical effects in response to pan-tropical deforestation, particularly CO <sub>2</sub> release, are generally not taken into account in those studies, but could intensify the hydrological cycle and thus precipitation (Kendra Gotangco Castillo and Gurney 2013 <sup>[[#fn:r1093|1093]]</sup> ).

Specific model-based deforestation studies have been carried out for Africa (Hagos et al. 2014 <sup>[[#fn:r1094|1094]]</sup> ; Boone et al. 2016 <sup>[[#fn:r1095|1095]]</sup> ; Xue et al. 2016 <sup>[[#fn:r1096|1096]]</sup> ; Nogherotto et al. 2013 <sup>[[#fn:r1097|1097]]</sup> ; Hartley et al. 2016 <sup>[[#fn:r1098|1098]]</sup> ; Klein et al. 2017 <sup>[[#fn:r1099|1099]]</sup> ; Abiodun et al. 2012 <sup>[[#fn:r1100|1100]]</sup> ), southern America (Butt et al. 2011 <sup>[[#fn:r1101|1101]]</sup> ; Wu et al. 2017 <sup>[[#fn:r1102|1102]]</sup> ; Spracklen and Garcia-Carreras 2015 <sup>[[#fn:r1103|1103]]</sup> ; Lejeune et al. 2015) and Southeast Asia (Ma et al. 2013b <sup>[[#fn:r1104|1104]]</sup> ; Werth and Avissar 2005 <sup>[[#fn:r1105|1105]]</sup> ; Mabuchi et al. 2005 <sup>[[#fn:r1106|1106]]</sup> ; Tölle et al. 2017 <sup>[[#fn:r1107|1107]]</sup> ). All found decreases in evapotranspiration following deforestation ( ''high agreement'' ), resulting in surface warming, despite the competing effect from increased surface albedo ( ''high agreement'' ). Changes in thermal gradients between deforested and adjacent regions, between land and ocean, affect horizontal surface winds ( ''high agreement'' ) and thus modify the areas where rain falls, as discussed in Section 2.5.4. An increase in the land-sea thermal contrast has been found in many studies as surface friction is reduced by deforestation, thus increasing the monsoon flow in Africa and South America (Wu et al. 2017 <sup>[[#fn:r1108|1108]]</sup> ).

Observation-based estimates all agree that deforestation increases local land-surface and ambient air temperatures in the tropics, while forestation has the reverse effect ( ''very high confidence'' ) (Prevedello et al. 2019 <sup>[[#fn:r1109|1109]]</sup> ; Schultz et al. 2017 <sup>[[#fn:r1110|1110]]</sup> ; Li et al. 2015b <sup>[[#fn:r1111|1111]]</sup> ; Alkama and Cescatti 2016 <sup>[[#fn:r1112|1112]]</sup> ). There is very ''high confidence'' that forests are cooler than any shorter vegetation (crops, grasses, bare soil) during daytime due to larger transpiration rates, and there is ''high confidence'' that the amplitude of the diurnal cycle is smaller in the presence of forests.

Large-scale forestation scenarios of West Africa (Abiodun et al. 2012 <sup>[[#fn:r1113|1113]]</sup> ), eastern China (Ma et al. 2013a <sup>[[#fn:r1114|1114]]</sup> ) or the Saharan and Australian deserts (Ornstein et al. 2009 <sup>[[#fn:r1115|1115]]</sup> ; Kemena et al. 2017 <sup>[[#fn:r1116|1116]]</sup> ) all concluded that regional surface cooling is simulated wherever trees are grown (–2.5°C in the Sahel, –1°C in the savanna area of West Africa, up to –8°C in the western Sahara and –1.21°C over land in eastern China) while cooling of the ambient air is smaller (–0.16°C). In the case of savanna forestation, this decrease entirely compensates the GHG-induced future warming (+1°C following the SRESA1B scenario).WestAfrican countries thus have the potential to reduce, or even totally cancel in some places, the GHG-induced warming in the deforested regions (Abiodun et al. 2012 <sup>[[#fn:r1117|1117]]</sup> ). However, this is compensated by enhanced warming in adjacent countries (non-local effect).

''Global and regional impacts of deforestation/forestation in temperate regions.''

As for the tropics, model-based experiments show that large- scale temperate deforestation would induce a small mean global annual warming through the net release of CO <sub>2</sub> into the atmosphere (ranging from +0.10 to +0.40°C with a mean value of +0.20 ± 0.13°C) (Figure 2.17), whereas there is less agreement on the sign of the mean global annual temperature change resulting from biophysical processes: estimates range from –0.5°C to +0.18°C with a mean value of –0.13 ± 0.22°C. There is also very ''low agreement'' on the mean annual temperature change in the temperate zone (–0.4 ± 0.62°C; Phillips et al. 2007 <sup>[[#fn:r1118|1118]]</sup> ; Snyder et al. 2004 <sup>[[#fn:r1119|1119]]</sup> ; Longobardi et al. 2016a <sup>[[#fn:r1120|1120]]</sup> ; Devaraju et al. 2015a <sup>[[#fn:r1121|1121]]</sup> , 2018 <sup>[[#fn:r1122|1122]]</sup> ). There is ''medium agreement'' on a global and latitudinal biophysical warming in response to forestation (Laguë and Swann 2016 <sup>[[#fn:r1123|1123]]</sup> ; Swann et al. 2012 <sup>[[#fn:r1124|1124]]</sup> ; Gibbard et al. 2005 <sup>[[#fn:r1125|1125]]</sup> ; Wang et al. 2014b <sup>[[#fn:r1126|1126]]</sup> ) (Figure 2.17), but this is based on a smaller number of studies.

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'''Figure 2.17'''

<span id="changes-in-mean-annual-surface-air-temperature-ºc-in-response-to-idealised-large-scale-deforestation-circles-or-forestation-crosses.estimated-from-a-range-of-studies-see-table-a2.2-in-the-appendix-for-detailed-information-and-references-to-the-studies.-temperature-changes-resulting-from-biophysical-processes-e.g.-changes-in-physical-land-surface-characteristics-such-as-albedo-evapotranspiration-and"></span>
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'''Changes in mean annual surface air temperature (ºC) in response to idealised large-scale deforestation (circles) or forestation (crosses).Estimated from a range of studies (see Table A2.2 in the Appendix for detailed information and references to the studies). Temperature changes resulting from biophysical processes (e.g., changes in physical land surface characteristics such as albedo, evapotranspiration, and […]'''
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[[File:1124de0ee2732fc48f4e1c95cb2e0d83 Figure-2.17-1024x680.jpg]]

Changes in mean annual surface air temperature (ºC) in response to idealised large-scale deforestation (circles) or forestation (crosses).Estimated from a range of studies (see Table A2.2 in the Appendix for detailed information and references to the studies). 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 orange symbols. Small blue and orange circles and crosses are model-based estimates of changes in temperature averaged globally. Large circles are estimates averaged only over the latitudinal band where deforestation is imposed.

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The lack of agreement at the annual scale among the climate models is, however, masking rising agreement regarding seasonal impacts of deforestation at those latitudes. There is ''high agreement'' that temperate deforestation leads to summer warming and winter cooling (Bright et al. 2017 <sup>[[#fn:r1127|1127]]</sup> ; Zhao and Jackson 2014 <sup>[[#fn:r1128|1128]]</sup> ; Gálos et al. 2011 <sup>[[#fn:r1129|1129]]</sup> , 2013 <sup>[[#fn:r1130|1130]]</sup> ; Wickham etal.2013 <sup>[[#fn:r1131|1131]]</sup> ;Ahlswede and Thomas 2017 <sup>[[#fn:r1132|1132]]</sup> ; Anderson-Teixeira et al. 2012 <sup>[[#fn:r1133|1133]]</sup> ; Anderson et al. 2011 <sup>[[#fn:r1134|1134]]</sup> ; Chen et al. 2012 <sup>[[#fn:r1135|1135]]</sup> ; Strandberg and Kjellström 2018 <sup>[[#fn:r1136|1136]]</sup> ). The winter cooling is driven by the increased surface albedo, amplified by the snow-albedo feedback. In some models, and when deforestation is simulated for very large areas, the cooling is further amplified by high latitude changes in sea-ice and snow extent (polar amplification). Summer warming occurs because the latent and sensible heat fluxes that take energy out of the surface diminish with the smaller roughness length and lower evapotranspiration efficiency of low vegetation, as compared to tree canopies (Davin and de Noblet-Ducoudre 2010 <sup>[[#fn:r1137|1137]]</sup> ; Anav et al. 2010 <sup>[[#fn:r1138|1138]]</sup> ). Conversely, there is ''high agreement'' that forestation in North America or in Europe cools surface climate during summer time, especially in regions where water availability can support large evapotranspiration rates. In temperate regions with water deficits, the simulated change in evapotranspiration following forestation will be insignificant, while the decreased surface albedo will favour surface warming.

Observation-based estimates confirm the existence of a seasonal pattern of response to deforestation, with colder winters any time there is snow on the ground and in any place where soils are brighter than the trees, and warmer summers (Schultz et al. 2017 <sup>[[#fn:r1139|1139]]</sup> ; Wickham et al. 2014 <sup>[[#fn:r1140|1140]]</sup> ; Juang et al. 2007 <sup>[[#fn:r1141|1141]]</sup> ; Tang et al. 2018 <sup>[[#fn:r1142|1142]]</sup> ; Peng et al. 2014 <sup>[[#fn:r1143|1143]]</sup> ; Zhang et al. 2014b <sup>[[#fn:r1144|1144]]</sup> ; Prevedello et al. 2019 <sup>[[#fn:r1145|1145]]</sup> ; Li et al. 2015b <sup>[[#fn:r1146|1146]]</sup> ; Alkama and Cescatti 2016 <sup>[[#fn:r1147|1147]]</sup> ). In contrast, forestation induces cooler summers wherever trees have access to sufficient soil moisture to transpire. The magnitude of the cooling depends on the wetness of the area of concern (Wickham et al. 2013) as well as on the original and targeted species and varieties implicated in the vegetation conversion (Peng et al. 2014 <sup>[[#fn:r1148|1148]]</sup> ; Juang et al. 2007 <sup>[[#fn:r1149|1149]]</sup> ).

There is also ''high confidence'' from observation-based estimates that mean annual daytime temperatures are warmer following deforestation, while night-time temperatures are cooler (Schultz et al. 2017 <sup>[[#fn:r1150|1150]]</sup> ; Wickham et al. 2014 <sup>[[#fn:r1151|1151]]</sup> ; Juang et al. 2007 <sup>[[#fn:r1152|1152]]</sup> ; Tang et al. 2018 <sup>[[#fn:r1153|1153]]</sup> ; Prevedello et al. 2019 <sup>[[#fn:r1154|1154]]</sup> ; Peng et al. 2014 <sup>[[#fn:r1155|1155]]</sup> ; Zhang et al. 2014b <sup>[[#fn:r1156|1156]]</sup> ; Li et al. 2015b <sup>[[#fn:r1157|1157]]</sup> ; Alkama and Cescatti 2016 <sup>[[#fn:r1158|1158]]</sup> ). Deforestation then increases the amplitude of diurnal temperature variations while forestation reduces it ( ''high confidence'' ). Two main reasons have been put forward to explain why nights are warmer in forested areas: their larger capacity to store heat and the existence of a nocturnal temperature inversion bringing warmer air from the higher atmospheric levels down to the surface.

In addition to those seasonal and diurnal fluctuations, Lejeune et al. (2018) <sup>[[#fn:r1159|1159]]</sup> found systematic warming of the hottest summer days following historical deforestation in the northern mid-latitudes, and this echoes Strandberg and Kjellström (2018) <sup>[[#fn:r1160|1160]]</sup> who argue that the August 2003 and July 2010 heatwaves could have been largely mitigated if Europe had been largely forested.

In a combined modelling of large-scale forestation of western Europe and climate change scenario (SRES A2), Gálos et al. (2013) <sup>[[#fn:r1161|1161]]</sup> found relatively small dampening potential of additional forest on ambient air temperature at the end of the 21st century when compared to the beginning (the cooling resulting from land cover changes is –0.5°C whereas the GHG-induced warming exceeds 2.5°C). Influence on rainfall was, however, much larger and significant. Projected annual rainfall decreases following warming were cancelled in Germany and significantly reduced in both France and Ukraine through forestation. In addition, forestation decreased the number of warming-induced dry days but increased the number of extreme precipitation events.

The net impact of forestation, combining both biophysical and biogeochemical effects, has been tested in the warmer world predicted by RCP 8.5 scenario (Sonntag et al. 2016 <sup>[[#fn:r1162|1162]]</sup> , 2018 <sup>[[#fn:r1163|1163]]</sup> ). The cooling effect from the addition of 8 Mkm2 of forests following the land use RCP 4.5 scenario was too small (–0.27°C annually) to dampen the RCP 8.5 warming. However, it reached about –1°C in some temperate regions and –2.5°C in boreal ones. This is accompanied by a reduction in the number of extremely warm days.

''Global and regional impacts of deforestation/forestation in boreal regions''

Consistent with what we have previously discussed for temperate and tropical regions, large-scale boreal deforestation induces a biogeochemical warming of +0.11 ± 0.09°C (Figure 2.17). But contrary to those other latitudinal bands, the biophysical effect is a consistent cooling across all models (–0.55 ± 0.29°C when averaged globally). It is also significantly larger than the biogeochemical warming (e.g., Dass et al. (2013) <sup>[[#fn:r1164|1164]]</sup> , Longobardi et al. (2016a) <sup>[[#fn:r1165|1165]]</sup> , Devaraju et al. (2015a) <sup>[[#fn:r1166|1166]]</sup> , Bathiany et al. (2010) <sup>[[#fn:r1167|1167]]</sup> , Devaraju et al. (2018) <sup>[[#fn:r1168|1168]]</sup> ) and is driven by the increased albedo, enhanced by the snow-albedo feedback as well as by an increase in sea-ice extent in the Arctic. Over boreal lands, the cooling is as large as –1.8 ± 1.2°C. However, this means that annual cooling masks a seasonal contrast, as discussed in Strandberg and Kjellström (2018) <sup>[[#fn:r1169|1169]]</sup> and Gao et al. (2014) <sup>[[#fn:r1170|1170]]</sup> : during summer time, following the removal of forest, the decreased evapotranspiration results in a significant summer warming that outweighs the effect of an increased albedo effect.

The same observation-based estimates (as discussed in the previous subsection) show similar patterns for the temperate latitudes: seasonal and daily contrasts. Schultz et al. (2017) <sup>[[#fn:r1171|1171]]</sup> , however, found that mean annual night-time changes are as large as daytime ones in those regions (mean annual nocturnal cooling of –1.4 ± 0.10°C, balanced by mean annual daytime warming of 1.4 ± 0.04°C). This contrasts with both temperate and tropical regions where daytime changes are always larger than the night-time ones.

Arora and Montenegro (2011) <sup>[[#fn:r1172|1172]]</sup> combined large-scale forestation and climate change scenario (SRES A2): forestation of either 50% or 100% of the total agricultural area was gradually prescribed between years 2011 and 2060 everywhere. In addition, boreal, temperate and tropical forestation have been tested separately. Both biophysical and biogeochemical effects were accounted for. The net simulated impact of forestation was a cooling varying from –0.04°C to –0.45°C, depending on the location and magnitude of the additional forest cover. It was, however, quite marginal compared to the large global warming resulting from anthropogenic GHG emissions (+3°C at the end of the 21st century). In their experiment, forestation in boreal regions led to biophysical warming and biogeochemical cooling that compensated each other, whereas forestation in the tropics led to both biophysical and biogeochemical cooling. The authors concluded that tropical forestation is three times more effective at cooling down climate than boreal or temperate forestation.

''Conclusion''

In conclusion, planting trees will always result in capturing more atmospheric CO <sub>2</sub> , and thus will mean annual cooling of the globe ( ''very high confidence'' ). At the regional level, however, the magnitude and sign of the local temperature change depends on (i) where forestation occurs, (ii) its magnitude, (iii) the level of warming under which the land cover change is applied, and (iv) the land conversion type. This is because the background climatic conditions (e.g., precipitation and snow regimes, mean annual temperature) within which the land cover changes occur vary across regions (Pitman et al. 2011 <sup>[[#fn:r1173|1173]]</sup> ; Montenegro et al. 2009 <sup>[[#fn:r1174|1174]]</sup> ; Juang et al. 2007 <sup>[[#fn:r1175|1175]]</sup> ; Wickham et al. 2014 <sup>[[#fn:r1176|1176]]</sup> ; Hagos et al. 2014 <sup>[[#fn:r1177|1177]]</sup> ; Voldoire 2006 <sup>[[#fn:r1178|1178]]</sup> ; Feddema et al. 2005 <sup>[[#fn:r1179|1179]]</sup> ; Strandberg and Kjellström 2018 <sup>[[#fn:r1180|1180]]</sup> ). In addition, there is ''high confidence'' that estimates of the influence of any land cover or land use change on surface temperature from the sole consideration of the albedo and the CO <sub>2</sub> effects is incorrect as changes in turbulent fluxes (i.e., latent and sensible heat fluxes) are large contributors to local temperature change (Bright et al. 2017 <sup>[[#fn:r1181|1181]]</sup> ).

There is ''high confidence'' that, in boreal and temperate latitudes, the presence of forest cools temperature in warmer locations and seasons (provided that the soil is not dry), whereas it warms temperature in colder locations and seasons (provided the soil is brighter than the trees or covered with snow). In the humid tropics, forestation increases evapotranspiration year-round and thus decreases temperature ( ''high confidence'' ). In tropical areas with a strong seasonality of rainfall, forestation will also increase evapotranspiration year-round, unless the soil becomes too dry. In all regions there is medium confidence that the diurnal temperature range decreases with increasing forest cover, with potentially reduced extreme values of temperature.

Although there is not enough literature yet that rigorously compares both biophysical and biogeochemical effects of realistic scenarios of forestation, there is ''high confidence'' that, at the local scale (that is where the forest change occurs), biophysical effects on surface temperature are far more important than the effects resulting from the changes in emitted CO <sub>2</sub> .

What is lacking in the literature today is an estimate of the impacts that natural disturbances in forests will have on local climates and on the build-up of atmospheric CO <sub>2</sub> (O’Halloran et al. 2012 <sup>[[#fn:r1182|1182]]</sup> ), illustrated with many examples that changes in albedo following disturbances can result in radiative forcing changes opposite to, and as large as, the ones resulting from the associated changes in the net release of CO <sub>2</sub> by land. The resulting climate effects depend on the duration of the perturbation and of the following recovery of vegetation.

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==== 2.5.2.2 Impacts of changes in land management ====

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There have been little changes in net cropland area over the past 50 years (at the global scale) compared to continuous changes in land management (Erb et al. 2017 <sup>[[#fn:r1183|1183]]</sup> ). Similarly, in Europe, change in forest management has resulted in a very significant anthropogenic land change. Management affects water, energy and GHG fluxes exchanged between the land and the atmosphere, and thus affects temperature and rainfall, sometimes to the same extent as changes in land cover do (as discussed in Luyssaert et al. (2014) <sup>[[#fn:r1184|1184]]</sup> ).

The effects of irrigation, which is a practice that has been substantially studied, including one attempt to manage solar radiation via increases in cropland albedo (geoengineering the land) are assessed, along with a discussion of recent findings on the effects of forest management on local climate, although there is not enough literature yet on this topic to carry out a thorough assessment. The effects of urbanisation on climate are assessed in a specific cross-chapter box within this chapter (Cross-Chapter Box 4 in this chapter).

There are a number of other practices that exist whose importance for climate mitigation has been examined (some are reported in Section 2.6 and Chapter 6). There is, however, not enough literature available for assessing their biophysical effect on climate. Few papers are generally found per agricultural practice, for example, Jeong et al. (2014b) <sup>[[#fn:r1185|1185]]</sup> for double cropping, Bagley et al. (2017) <sup>[[#fn:r1186|1186]]</sup> for the timing of the growing season and Erb et al. (2017) <sup>[[#fn:r1187|1187]]</sup> for a review of 10 management practices.

Similarly, there are very few studies that have examined how choosing species varieties and harvesting strategies in forest management impacts on climate through biophysical effects, and how those effects compare to the consequences of the chosen strategies on the net CO <sub>2</sub> sink of the managed forest. The modelling studies highlight the existence of competing effects, for example, between the capacity of certain species to store more carbon than others (thus inducing cooling) while at the same time reducing the total evapotranspiration loss and absorbing more solar radiation via lower albedo (thus inducing warming) (Naudts et al. 2016a <sup>[[#fn:r1188|1188]]</sup> ; Luyssaert et al. 2018 <sup>[[#fn:r1189|1189]]</sup> ).

''Irrigation''

There is substantial literature on the effects of irrigation on local, regional and global climate as this is a major land management issue. There is very ''high confidence'' that irrigation increases total evapotranspiration, increases the total amount of water vapour in the atmosphere and decreases mean surface daytime temperature within the irrigated area and during the time of irrigation (Bonfils and Lobell 2007 <sup>[[#fn:r1190|1190]]</sup> ; Alter et al. 2015 <sup>[[#fn:r1191|1191]]</sup> ; Chen and Jeong 2018 <sup>[[#fn:r1192|1192]]</sup> ; Christy et al. 2006 <sup>[[#fn:r1193|1193]]</sup> ; Im and Eltahir 2014 <sup>[[#fn:r1194|1194]]</sup> ; Im et al. 2014 <sup>[[#fn:r1195|1195]]</sup> ; Mueller et al. 2015 <sup>[[#fn:r1196|1196]]</sup> ). Decreases in maximum daytime temperature can locally be as large as –3°C to –8°C (Cook et al. 2015 <sup>[[#fn:r1197|1197]]</sup> ; Han and Yang 2013 <sup>[[#fn:r1198|1198]]</sup> ; Huber et al. 2014 <sup>[[#fn:r1199|1199]]</sup> ; Alter et al. 2015 <sup>[[#fn:r1200|1200]]</sup> ; Im et al. 2014 <sup>[[#fn:r1201|1201]]</sup> ). Estimates of the contribution of irrigation to past historical trends in ambient air temperature vary between –0.07°C and –0.014°C/decade in northern China (Han and Yang 2013 <sup>[[#fn:r1202|1202]]</sup> ; Chen and Jeong 2018 <sup>[[#fn:r1203|1203]]</sup> ) while being quite larger in California, USA (–0.14°C to –0.25°C/decade) (Bonfils and Lobell 2007 <sup>[[#fn:r1204|1204]]</sup> ). Surface cooling results from increased energy being taken up from the land via larger evapotranspiration rates. In addition, there is growing evidence from modelling studies that such cooling can locally mitigate the effect of heatwaves (Thiery et al. 2017 <sup>[[#fn:r1205|1205]]</sup> ; Mueller et al. 2015 <sup>[[#fn:r1206|1206]]</sup> ).

There is ''no agreement'' on changes in night-time temperatures, as discussed in Chen and Jeong (2018) <sup>[[#fn:r1207|1207]]</sup> who summarised the findings from observations in many regions of the world (India, China, North America and eastern Africa) (Figure 2.18). Where night-time warming is found (Chen and Jeong 2018 <sup>[[#fn:r1208|1208]]</sup> ; Christy et al. 2006 <sup>[[#fn:r1209|1209]]</sup> ), two explanations are put forward, (i) an increase in incoming longwave radiation in response to increased atmospheric water vapour content (greenhouse effect), and (ii) an increased storage of heat in the soil during daytime. Because of the larger heat capacity of moister soil, heat is then released to the atmosphere at night.

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<span id="figure-2.18"></span>
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'''Figure 2.18'''

<span id="global-map-of-areas-equipped-for-irrigation-colours-expressed-as-a-percentage-of-total-area-or-irrigation-fraction.-source-siebert-et-al.-2013.-numbered-boxes-show-regions-where-irrigation-causes-cooling-down-arrow-of-surface-mean-tmean-maximum-tmax-or-minimum-tmin-temperature-or-else-no-significant-effect-right-arrow-or-where-the-effect-is"></span>
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'''Global map of areas equipped for irrigation (colours), expressed as a percentage of total area, or irrigation fraction. Source: Siebert et al. (2013). Numbered boxes show regions where irrigation causes cooling (down arrow) of surface mean (Tmean), maximum (Tmax) or minimum (Tmin) temperature, or else no significant effect (right arrow) or where the effect is […]'''
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[[File:8a2d4154a588ac50461beb56b94e25c9 Figure-2.18-724x1024.jpg]]

Global map of areas equipped for irrigation (colours), expressed as a percentage of total area, or irrigation fraction. Source: Siebert et al. (2013) <sup>[[#fn:r1210|1210]]</sup> . Numbered boxes show regions where irrigation causes cooling (down arrow) of surface mean (Tmean), maximum (Tmax) or minimum (Tmin) temperature, or else no significant effect (right arrow) or where the effect is uncertain (question mark), based on observational studies as reviewed in Chen and Jeong (2018) <sup>[[#fn:r1211|1211]]</sup> . Tmax refers to the warmest daily temperature while Tmin to the coldest one, which generally occurs at night (Alter et al. 2015 <sup>[[#fn:r1212|1212]]</sup> ; Han and Yang 2013 <sup>[[#fn:r1213|1213]]</sup> ; Roy et al. 2007 <sup>[[#fn:r1214|1214]]</sup> ; Shi et al. 2013 <sup>[[#fn:r1215|1215]]</sup> ; Bonfils and Lobell 2007 <sup>[[#fn:r1216|1216]]</sup> ; Lobell et al. 2008 <sup>[[#fn:r1217|1217]]</sup> ; Lobell and Bonfils 2008 <sup>[[#fn:r1218|1218]]</sup> ; Christy et al. 2006 <sup>[[#fn:r1219|1219]]</sup> ; Mahmood et al. 2006 <sup>[[#fn:r1220|1220]]</sup> ; Mueller et al. 2015 <sup>[[#fn:r1221|1221]]</sup> ).

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There is ''robust evidence'' from modelling studies that implementing irrigation enhances rainfall, although there is very ''low confidence'' on where this increase occurs. When irrigation occurs in Sahelian Africa during the monsoon period, rainfall is decreased over irrigated areas ( ''high agreement'' ), increased in the southwest if the crops are located in western Africa (Alter et al. 2015 <sup>[[#fn:r1222|1222]]</sup> ) and increased in the east/northeast when crops are located further east in Sudan (Im and Eltahir 2014 <sup>[[#fn:r1223|1223]]</sup> ; Im et al. 2014 <sup>[[#fn:r1224|1224]]</sup> ) The cooler irrigated surfaces in the Sahel, because of their greater evapotranspiration, inhibit convection and create an anomalous descending motion over crops that suppresses rainfall but influences the circulation of monsoon winds. Irrigation in India occurs prior to the start of the monsoon season and the resulting land cooling decreases the land-sea temperature contrast. This can delay the onset of the Indian monsoon and decrease its intensity (Niyogi et al. 2010 <sup>[[#fn:r1225|1225]]</sup> ; Guimberteau et al. 2012 <sup>[[#fn:r1226|1226]]</sup> ). Results from a modelling study by De Vrese et al. (2016) <sup>[[#fn:r1227|1227]]</sup> suggest that part of the excess rainfall triggered by Indian irrigation falls westward, in the horn of Africa. The theory behind those local and downwind changes in rainfall support the findings from the models, but we do not yet have sufficient literature to robustly assess the magnitude and exact location of the expected changes driven by irrigation.

''Cropland albedo''

Various methods have been proposed to increase surface albedo in cropland and thus reduce local surface temperature ( ''high confidence'' ): choose ‘brighter’ crop varieties (Ridgwell et al. 2009 <sup>[[#fn:r1228|1228]]</sup> ; Crook et al. 2015 <sup>[[#fn:r1229|1229]]</sup> ; Hirsch et al. 2017 <sup>[[#fn:r1230|1230]]</sup> ; Singarayer et al. 2009 <sup>[[#fn:r1231|1231]]</sup> ; Singarayer and Davies-Barnard 2012 <sup>[[#fn:r1232|1232]]</sup> ), abandon tillage (Lobell et al. 2006 <sup>[[#fn:r1233|1233]]</sup> ; Davin et al. 2014 <sup>[[#fn:r1234|1234]]</sup> ), include cover crops into rotation in areas where soils are darker than vegetation (Carrer et al. 2018 <sup>[[#fn:r1235|1235]]</sup> ; Kaye and Quemada 2017 <sup>[[#fn:r1236|1236]]</sup> ) or use greenhouses (as in Campra et al. (2008) <sup>[[#fn:r1237|1237]]</sup> ). See Seneviratne et al. (2018) <sup>[[#fn:r1238|1238]]</sup> for a review.

Whatever the solution chosen, the induced reduction in absorbed solar radiation cools the land – more specifically during the hottest summer days ( ''low confidence'' ) (Davin et al. 2014 <sup>[[#fn:r1239|1239]]</sup> ; Wilhelm et al. 2015 <sup>[[#fn:r1240|1240]]</sup> ; Figure 2.19). Changes in temperature are essentially local and seasonal (limited to crop growth season) or sub-seasonal (when resulting from inclusion of cover crop or tillage suppression). Such management action on incoming solar radiation thus holds the potential to counteract warming in cultivated areas during crop growing season.

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<span id="figure-2.19"></span>
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'''Figure 2.19'''

<span id="change-in-summer-julyaugust-daily-maximum-temperature-ºc-resulting-from-increased-surface-albedo-in-unploughed-versus-ploughed-land-in-a-southern-and-b-northern-europe-during-the-period-19862009.-changes-are-simulated-for-different-quantiles-of-the-daily-maximum-temperature-distribution-where-q1-represents-the-coolest-1-and-q99-the-warmest-1-of-summer-days."></span>
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'''Change in summer (July–August) daily maximum temperature (ºC) resulting from increased surface albedo in unploughed versus ploughed land, in (A) southern, and (B) northern Europe, during the period 1986–2009. Changes are simulated for different quantiles of the daily maximum temperature distribution, where Q1 represents the coolest 1% and Q99 the warmest 1% of summer days. […]'''
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[[File:08d354f66f961d81b064ddf459156770 Figure-2.19-1024x398.jpg]]

Change in summer (July–August) daily maximum temperature (ºC) resulting from increased surface albedo in unploughed versus ploughed land, in (A) southern, and (B) northern Europe, during the period 1986–2009. Changes are simulated for different quantiles of the daily maximum temperature distribution, where Q1 represents the coolest 1% and Q99 the warmest 1% of summer days. Only grid cells with more than 60% of their area in cropland are included. The dashed bars represent the standard deviation calculated across all days and grid points. SE refers to southern Europe (below 45ºN) and NE to northern Europe (above 45ºN). (Davin et al., 2014)

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Introducing cover crops into a rotation can also have a warming effect in areas where vegetation has a darker albedo than soil, or in winter during snow periods if the cover crops or their residues are tall enough to overtop the snow cover (Kaye and Quemada 2017 <sup>[[#fn:r1241|1241]]</sup> ; Lombardozzi et al. 2018 <sup>[[#fn:r1242|1242]]</sup> ). In addition, evapotranspiration greater than that of bare soil during this transitional period reduces soil temperature (Ceschia et al. 2017 <sup>[[#fn:r1243|1243]]</sup> ). Such management strategy can have another substantial mitigation effect as it allows carbon to be stored in the soil and to reduce both direct and indirect N <sub>2</sub> O emissions (Basche et al. 2014 <sup>[[#fn:r1244|1244]]</sup> ; Kaye and Quemada 2017 <sup>[[#fn:r1245|1245]]</sup> ), in particular if fertilisation of the subsequent crop is reduced (Constantin et al. 2010 <sup>[[#fn:r1246|1246]]</sup> , 2011 <sup>[[#fn:r1247|1247]]</sup> ). The use of cover crops thus substantially improves the GHG budget of croplands (Kaye and Quemada 2017 <sup>[[#fn:r1248|1248]]</sup> ; Tribouillois et al. 2018 <sup>[[#fn:r1249|1249]]</sup> ). More discussion on the role of management practices for mitigation can be found in Section 2.6 and Chapter 6.

Only a handful of modelling studies have looked at effects other than changes in atmospheric temperature in response to increased cropland albedo. Seneviratne et al. (2018) <sup>[[#fn:r1250|1250]]</sup> have found significant changes in rainfall following an idealised increase in cropland albedo, especially within the Asian monsoon regions. The benefits of cooler temperature on production, resulting from increased albedo, is cancelled out by decreases in rainfall that are harmful for crop productivity. The rarity of a concomitant evaluation of albedo management impact on crop productivity prevents us from providing a robust assessment of this practice in terms of both climate mitigation and food security.

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