Editing IPCC:AR6/SRCCL/Chapter-3 (section)

== 3.5 Future projections ==

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=== 3.5.1 Future projections of desertification ===

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Assessing the impact of climate change on future desertification is difficult as several environmental and anthropogenic variables interact to determine its dynamics. The majority of modelling studies regarding the future evolution of desertification rely on the analysis of specific climate change scenarios and Global Climate Models (GCMs) and their effect on a few processes or drivers that trigger desertification (Cross-Chapter Box 1 in Chapter 1).

With regards to climate impacts, the analysis of global and regional climate models concludes that under all representative concentration pathways (RCPs) potential evapotranspiration (PET) would increase worldwide as a consequence of increasing surface temperatures and surface water vapour deficit (Sherwood and Fu 2014 <sup>[[#fn:r850|850]]</sup> ). Consequently, there would be associated changes in aridity indices that depend on this variable ( ''high agreement, robust evidence'' ) (Cook et al. 2014a <sup>[[#fn:r851|851]]</sup> ; Dai 2011 <sup>[[#fn:r852|852]]</sup> ; Dominguez et al. 2010 <sup>[[#fn:r853|853]]</sup> ; Feng and Fu 2013 <sup>[[#fn:r854|854]]</sup> ; Ficklin et al. 2016 <sup>[[#fn:r855|855]]</sup> ; Fu et al. 2016 <sup>[[#fn:r856|856]]</sup> ; Greve and Seneviratne 1999 <sup>[[#fn:r857|857]]</sup> ; Koutroulis 2019 <sup>[[#fn:r858|858]]</sup> ; Scheff and Frierson 2015 <sup>[[#fn:r859|859]]</sup> ). Due to the large increase in PET and decrease in precipitation over some subtropical land areas, aridity index will decrease in some drylands (Zhao and Dai 2015 <sup>[[#fn:r860|860]]</sup> ), with one model estimating approximately 10% increase in hyper-arid areas globally (Zeng and Yoon 2009 <sup>[[#fn:r861|861]]</sup> ). Increases in PET are projected to continue due to climate change (Cook et al. 2014a <sup>[[#fn:r862|862]]</sup> ; Fu et al. 2016 <sup>[[#fn:r863|863]]</sup> ; Lin et al. 2015 <sup>[[#fn:r864|864]]</sup> ; Scheff and Frierson 2015 <sup>[[#fn:r865|865]]</sup> ). However, as noted in Sections 3.1.1 and 3.2.1, these PET calculations use assumptions that are not valid in an environment with changing CO <sub>2</sub> . Evidence from precipitation, runoff or photosynthetic uptake of CO <sub>2</sub> suggest that a future warmer world will be less arid (Roderick et al. 2015 <sup>[[#fn:r866|866]]</sup> ). Observations in recent decades indicate that the Hadley cell has expanded poleward in both hemispheres (Fu et al. 2006 <sup>[[#fn:r867|867]]</sup> ; Hu and Fu 2007 <sup>[[#fn:r868|868]]</sup> ; Johanson et al. 2009 <sup>[[#fn:r869|869]]</sup> ; Seidel and Randel 2007 <sup>[[#fn:r870|870]]</sup> ), and under all RCPs would continue expanding (Johanson et al. 2009 <sup>[[#fn:r871|871]]</sup> ; Lu et al. 2007 <sup>[[#fn:r872|872]]</sup> ). This expansion leads to the poleward extension of subtropical dry zones and hence an expansion in drylands on the poleward edge (Scheff and Frierson 2012 <sup>[[#fn:r873|873]]</sup> ). Overall, this suggests that while aridity will increase in some places ( ''high confidence'' ), there is insufficient evidence to suggest a global change in dryland aridity ( ''medium confidence'' ).

Regional modelling studies confirm the outcomes of Global Climate Models (Africa: Terink et al. 2013 <sup>[[#fn:r874|874]]</sup> ; China: Yin et al. 2015 <sup>[[#fn:r875|875]]</sup> ; Brazil: Marengo and Bernasconi 2015 <sup>[[#fn:r876|876]]</sup> ; Cook et al. 2012 <sup>[[#fn:r877|877]]</sup> ; Greece: Nastos et al. 2013 <sup>[[#fn:r878|878]]</sup> ; Italy: Coppola and Giorgi 2009 <sup>[[#fn:r879|879]]</sup> ). According to the IPCC AR5 (IPCC 2013) <sup>[[#fn:r880|880]]</sup> , decreases in soil moisture are detected in the Mediterranean, southwest USA and southern African regions. This is in line with alterations in the Hadley circulation and higher surface temperatures. This surface drying will continue to the end of this century under the RCP8.5 scenario ( ''high confidence'' ). Ramarao et al. (2015) <sup>[[#fn:r881|881]]</sup> showed that a future climate projection based on RCP4.5 scenario indicated the possibility for detecting the summer-time soil drying signal over the Indian region during the 21st century in response to climate change. The IPCC Special Report on Global Warming of 1.5°C (SR15) (Chapter 3; Hoegh-Guldberg et al. 2018 <sup>[[#fn:r882|882]]</sup> ) concluded with ‘ ''medium confidence'' ’ that global warming by more than 1.5°C increases considerably the risk of aridity for the Mediterranean area and southern Africa. Miao et al. (2015b) <sup>[[#fn:r883|883]]</sup> showed an acceleration of desertification trends under the RCP8.5 scenario in the middle and northern part of Central Asia and some parts of north-western China. It is also useful to consider the effects of the dynamic–thermodynamical feedback of the climate. Schewe and Levermann (2017) <sup>[[#fn:r884|884]]</sup> show increases of up to 300% in the Central Sahel rainfall by the end of the century due to an expansion of the West African monsoon. Warming could trigger an intensification of monsoonal precipitation due to increases in ocean moisture availability.

The impacts of climate change on dust storm activity are not yet comprehensively studied and represent an important knowledge gap. Currently, GCMs are unable to capture recent observed dust emission and transport (Evan 2018 <sup>[[#fn:r885|885]]</sup> ; Evan et al. 2014 <sup>[[#fn:r886|886]]</sup> ), limiting confidence in future projections. Literature suggests that climate change decreases wind erosion/dust emission overall, with regional variation ( ''low confidence'' ). Mahowald et al. (2006) <sup>[[#fn:r887|887]]</sup> and Mahowald (2007) <sup>[[#fn:r888|888]]</sup> found that climate change led to a decrease in desert dust source areas globally using CMIP3 GCMs. Wang et al. (2009) <sup>[[#fn:r889|889]]</sup> found a decrease in sand dune movement by 2039 (increasing thereafter) when assessing future wind-erosion-driven desertification in arid and semi-arid China using a range of SRES scenarios and HadCM3 simulations. Dust activity in the Southern Great Plains in the USA was projected to increase, while in the Northern Great Plains it was projected to decrease under RCP8.5 climate change scenario (Pu and Ginoux 2017 <sup>[[#fn:r890|890]]</sup> ). Evan et al. (2016) <sup>[[#fn:r891|891]]</sup> project a decrease in African dust emission associated with a slowdown of the tropical circulation in the high CO <sub>2</sub> RCP8.5 scenario.

Global estimates of the impact of climate change on soil salinisation show that under the IS92a emissions scenario (a scenario prepared in 1992 that contains ‘business as usual’ assumptions) (Leggett et al. 1992 <sup>[[#fn:r892|892]]</sup> ) the area at risk of salinisation would increase in the future ( ''limited evidence, high agreement'' ) (Schofield and Kirkby 2003 <sup>[[#fn:r893|893]]</sup> ). Climate change has an influence on soil salinisation that induces further land degradation through several mechanisms that vary in their level of complexity. However, only a few examples can be found to illustrate this range of impacts, including the effect of groundwater table depletion (Rengasamy 2006 <sup>[[#fn:r894|894]]</sup> ) and irrigation management (Sivakumar 2007 <sup>[[#fn:r895|895]]</sup> ), salt migration in coastal aquifers with decreasing water tables (Sherif and Singh 1999 <sup>[[#fn:r896|896]]</sup> ) (Section 4.10.7), and surface hydrology and vegetation that affect wetlands and favour salinisation (Nielsen and Brock 2009 <sup>[[#fn:r897|897]]</sup> ).

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==== 3.5.1.1 Future vulnerability and risk of desertification ====

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Following the conceptual framework developed in the Special Report on extreme events (SREX) (IPCC 2012 <sup>[[#fn:r898|898]]</sup> ), future risks are assessed by examining changes in exposure (that is, presence of people; livelihoods; species or ecosystems; environmental functions, service, and resources; infrastructure; or economic, social or cultural assets; see Glossary), changes in vulnerability (that is, propensity or predisposition to be adversely affected; see Glossary) and changes in the nature and magnitude of hazards (that is, potential occurrence of a natural or human-induced physical event that causes damage; see Glossary). Climate change is expected to further exacerbate the vulnerability of dryland ecosystems to desertification by increasing PET globally (Sherwood and Fu 2014 <sup>[[#fn:r899|899]]</sup> ). Temperature increases between 2°C and 4°C are projected in drylands by the end of the 21st century under RCP4.5 and RCP8.5 scenarios, respectively (IPCC 2013 <sup>[[#fn:r900|900]]</sup> ). An assessment by Carrão et al. 2017 <sup>[[#fn:r901|901]]</sup> showed an increase in drought hazards by late-century (2071–2099) compared to a baseline (1971–2000) under high RCPs in drylands around the Mediterranean, south-eastern Africa, and southern Australia. In Latin America, Morales et al. (2011) <sup>[[#fn:r902|902]]</sup> indicated that areas affected by drought will increase significantly by 2100 under SRES scenarios A2 and B2. The countries expected to be affected include Guatemala, El Salvador, Honduras and Nicaragua. In CMIP5 scenarios, Mediterranean types of climate are projected to become drier (Alessandri et al. 2014 <sup>[[#fn:r903|903]]</sup> ; Polade et al. 2017 <sup>[[#fn:r904|904]]</sup> ), with the equatorward margins being potentially replaced by arid climate types (Alessandri et al. 2014 <sup>[[#fn:r905|905]]</sup> ). Globally, climate change is predicted to intensify the occurrence and severity of droughts ( ''medium confidence'' ) (Dai 2013 <sup>[[#fn:r906|906]]</sup> ; Sheffield and Wood 2008 <sup>[[#fn:r907|907]]</sup> ; Swann et al. 2016 <sup>[[#fn:r908|908]]</sup> ; Wang 2005 <sup>[[#fn:r909|909]]</sup> ; Zhao and Dai 2015 <sup>[[#fn:r910|910]]</sup> ; Carrão et al. 2017 <sup>[[#fn:r911|911]]</sup> ; Naumann et al. 2018 <sup>[[#fn:r912|912]]</sup> ) (Section 2.2). Ukkola et al. (2018) <sup>[[#fn:r913|913]]</sup> showed large discrepancies between CMIP5 models for all types of droughts, limiting the confidence that can be assigned to projections of drought.

Drylands are characterised by high climatic variability. Climate impacts on desertification are not only defined by projected trends in mean temperature and precipitation values but are also strongly dependent on changes in climate variability and extremes (Reyer et al. 2013 <sup>[[#fn:r914|914]]</sup> ). The responses of ecosystems depend on diverse vegetation types. Drier ecosystems are more sensitive to changes in precipitation and temperature (Li et al. 2018 <sup>[[#fn:r915|915]]</sup> ; Seddon et al. 2016 <sup>[[#fn:r916|916]]</sup> ; You et al. 2018 <sup>[[#fn:r917|917]]</sup> ), increasing vulnerability to desertification. It has also been reported that areas with high variability in precipitation tend to have lower livestock densities and that those societies that have a strong dependence on livestock that graze natural forage are especially affected (Sloat et al. 2018 <sup>[[#fn:r918|918]]</sup> ). Social vulnerability in drylands increases as a consequence of climate change that threatens the viability of pastoral food systems (Dougill et al. 2010 <sup>[[#fn:r919|919]]</sup> ; López-i-Gelats et al. 2016 <sup>[[#fn:r920|920]]</sup> ). Social drivers can also play an important role with regards to future vulnerability (Máñez Costa et al. 2011 <sup>[[#fn:r921|921]]</sup> ). In the arid region of north-western China, Liu et al. (2016b) <sup>[[#fn:r922|922]]</sup> estimated that under RCP4.5 areas of increased vulnerability to climate change and desertification will surpass those with decreased vulnerability.

Using an ensemble of global climate, integrated assessment and impact models, Byers et al. (2018) <sup>[[#fn:r923|923]]</sup> investigated 14 impact indicators at different levels of global mean temperature change and socio-economic development. The indicators cover water, energy and land sectors. Of particular relevance to desertification are the water (e.g., water stress, drought intensity) and the land (e.g., habitat degradation) indicators. Under shared socio-economic pathway SSP2 (‘Middle of the Road’) at 1.5°C, 2°C and 3°C of global warming, the numbers of dryland populations exposed (vulnerable) to various impacts related to water, energy and land sectors (e.g., water stress, drought intensity, habitat degradation) are projected to reach 951 (178) million, 1152 (220) million and 1285 (277) million, respectively. While at global warming of 2°C, under SSP1 (‘Sustainability’), the exposed (vulnerable) dryland population is 974 (35) million, and under SSP3 (‘Fragmented World’) it is 1267 (522) million. Steady increases in the exposed and vulnerable populations are seen for increasing global mean temperatures. However much larger differences are seen in the vulnerable population under different SSPs. Around half the vulnerable population is in South Asia, followed by Central Asia, West Africa and East Asia.

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=== 3.5.2 Future projections of impacts ===

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Future climate change is expected to increase the potential for increased soil erosion by water in dryland areas ( ''medium confidence'' ). Yang et al. (2003) <sup>[[#fn:r924|924]]</sup> use a Revised Universal Soil Loss Equation (RUSLE) model to study global soil erosion under historical, present and future conditions of both cropland and climate. Soil erosion potential has increased by about 17%, and climate change will increase this further in the future. In northern Iran, under the SRES A2 emission scenario the mean erosion potential is projected to grow by 45%, comparing the period 1991–2010 with 2031–2050 (Zare et al. 2016 <sup>[[#fn:r925|925]]</sup> ).

A strong decrease in precipitation for almost all parts of Turkey was projected for the period 2021–2050 compared to 1971–2000 using Regional Climate Model, RegCM4.4 of the International Centre for Theoretical Physics (ICTP) under RCP4.5 and RCP8.5 scenarios (Türkeş et al. 2019 <sup>[[#fn:r926|926]]</sup> ). The projected changes in precipitation distribution can lead to more extreme precipitation events and prolonged droughts, increasing Turkey’s vulnerability to soil erosion. In Portugal, a study comparing wet and dry catchments under A1B and B1 emission scenarios showed an increase in erosion in dry catchments (Serpa et al. 2015 <sup>[[#fn:r927|927]]</sup> ). In Morocco an increase in sediment load is projected as a consequence of reduced precipitation (Simonneaux et al. 2015 <sup>[[#fn:r928|928]]</sup> ). WGII AR5 concluded the impact of increases in heavy rainfall and temperature on soil erosion will be modulated by soil management practices, rainfall seasonality and land cover (Jiménez Cisneros et al. 2014 <sup>[[#fn:r929|929]]</sup> ). Ravi et al. (2010) <sup>[[#fn:r930|930]]</sup> predicted an increase in hydrologic and aeolian soil erosion processes as a consequence of droughts in drylands. However, there are some studies that indicate that soil erosion will be reduced in Spain (Zabaleta et al. 2013 <sup>[[#fn:r931|931]]</sup> ), Greece (Nerantzaki et al. 2015 <sup>[[#fn:r932|932]]</sup> ) and Australia (Klik and Eitzinger 2010 <sup>[[#fn:r933|933]]</sup> ), while others project changes in erosion as a consequence of the expansion of croplands (Borrelli et al. 2017 <sup>[[#fn:r934|934]]</sup> ).

Potential dryland expansion implies lower carbon sequestration and higher risk of desertification (Huang et al. 2017 <sup>[[#fn:r935|935]]</sup> ), with severe impacts on land usability and threats to food security. At the level of biomes (global-scale zones, generally defined by the type of plant life that they support in response to average rainfall and temperature patterns; see Glossary), soil carbon uptake is determined mostly by weather variability. The area of the land in which dryness controls CO <sub>2</sub> exchange has risen by 6% since 1948 and is projected to expand by at least another 8% by 2050. In these regions net carbon uptake is about 27% lower than elsewhere (Yi et al. 2014 <sup>[[#fn:r936|936]]</sup> ). Potential losses of soil carbon are projected to range from 9% to 12% of the total carbon stock in the 0–20 cm layer of soils in southern European Russia by end of this century (Ivanov et al. 2018 <sup>[[#fn:r937|937]]</sup> ).

Desertification under climate change will threaten biodiversity in drylands ( ''medium confidence'' ). Rodriguez-Caballero et al. (2018) <sup>[[#fn:r938|938]]</sup> analysed the cover of biological soil crusts under current and future environmental conditions utilising an environmental niche modelling approach. Their results suggest that biological soil crusts currently cover approximately 1600 Mha in drylands. Under RCP scenarios 2.6 to 8.5, 25–40% of this cover will be lost by 2070 with climate and land use contributing equally. The predicted loss is expected to substantially reduce the contribution of biological soil crusts to nitrogen cycling (6.7–9.9 TgN yr− <sup>1</sup> ) and carbon cycling (0.16–0.24 PgC yr− <sup>1</sup> ) (Rodriguez-Caballero et al. 2018 <sup>[[#fn:r939|939]]</sup> ). A study in Colorado Plateau, USA showed that changes in climate in drylands may damage the biocrust communities by promoting rapid mortality of foundational species (Rutherford et al. 2017 <sup>[[#fn:r940|940]]</sup> ), while in the Southern California deserts climate change-driven extreme heat and drought may surpass the survival thresholds of some desert species (Bachelet et al. 2016 <sup>[[#fn:r941|941]]</sup> ). In semi-arid Mediterranean shrublands in eastern Spain, plant species richness and plant cover could be reduced by climate change and soil erosion (García-Fayos and Bochet 2009 <sup>[[#fn:r942|942]]</sup> ). The main drivers of species extinctions are land-use change, habitat pollution, over-exploitation, and species invasion, while climate change is indirectly linked to species extinctions (Settele et al. 2014 <sup>[[#fn:r943|943]]</sup> ). Malcolm et al. (2006) <sup>[[#fn:r944|944]]</sup> found that more than 2000 plant species located within dryland biodiversity hotspots could become extinct within 100 years, starting 2004 (within the Cape Floristic Region, Mediterranean Basin and southwest Australia). Furthermore, it is suggested that land use and climate change could cause the loss of 17% of species within shrublands and 8% within hot deserts by 2050 ( ''low confidence'' ) (van Vuuren et al. 2006 <sup>[[#fn:r945|945]]</sup> ). A study in the semi-arid Chinese Altai Mountains showed that mammal species richness will decline, rates of species turnover will increase, and more than 50% of their current ranges will be lost (Ye et al. 2018 <sup>[[#fn:r946|946]]</sup> ).

Changing climate and land use have resulted in higher aridity and more droughts in some drylands, with the rising role of precipitation, wind and evaporation on desertification (Fischlin et al. 2007 <sup>[[#fn:r947|947]]</sup> ). In a 2°C world, annual water discharge is projected to decline, and heatwaves are projected to pose risk to food production by 2070 (Waha et al. 2017 <sup>[[#fn:r948|948]]</sup> ). However, Betts et al. (2018) <sup>[[#fn:r949|949]]</sup> found a mixed response of water availability (runoff) in dryland catchments to global temperature increases from 1.5°C to 2°C. The forecasts for Sub-Saharan Africa suggest that higher temperatures, increase in the number of heatwaves, and increasing aridity, will affect the rainfed agricultural systems (Serdeczny et al. 2017 <sup>[[#fn:r950|950]]</sup> ). A study by Wang et al. (2009) <sup>[[#fn:r951|951]]</sup> in arid and semi-arid China showed decreased livestock productivity and grain yields from 2040 to 2099, threatening food security. In Central Asia, projections indicate a decrease in crop yields, and negative impacts of prolonged heat waves on population health (Reyer et al. 2017 <sup>[[#fn:r952|952]]</sup> ) (Section 3.7.2). World Bank (2009) <sup>[[#fn:r953|953]]</sup> projected that, without the carbon fertilisation effect, climate change will reduce the mean yields for 11 major global crops – millet, field pea, sugar beet, sweet potato, wheat, rice, maize, soybean, groundnut, sunflower and rapeseed – by 15% in Sub-Saharan Africa, 11% in Middle East and North Africa, 18% in South Asia, and 6% in Latin America and the Caribbean by 2046–2055, compared to 1996–2005. A separate meta-analysis suggested a similar reduction in yields in Africa and South Asia due to climate change by 2050 (Knox et al. 2012 <sup>[[#fn:r954|954]]</sup> ). Schlenker and Lobell (2010) <sup>[[#fn:r955|955]]</sup> estimated that in sub-Saharan Africa, crop production may be reduced by 17–22% due to climate change by 2050. At the local level, climate change impacts on crop yields vary by location (Section 5.2.2). Negative impacts of climate change on agricultural productivity contribute to higher food prices. The imbalance between supply and demand for agricultural products is projected to increase agricultural prices in the range of 31% for rice, to 100% for maize by 2050 (Nelson et al. 2010 <sup>[[#fn:r956|956]]</sup> ), and cereal prices in the range between a 32% increase and a 16% decrease by 2030 (Hertel et al. 2010 <sup>[[#fn:r957|957]]</sup> ). In southern European Russia, it is projected that the yields of grain crops will decline by 5–10% by 2050 due to the higher intensity and coverage of droughts (Ivanov et al. 2018 <sup>[[#fn:r958|958]]</sup> ).

Climate change can have strong impacts on poverty in drylands ( ''medium confidence'' ) (Hallegatte and Rozenberg 2017 <sup>[[#fn:r959|959]]</sup> ; Hertel and Lobell 2014 <sup>[[#fn:r960|960]]</sup> ). Globally, Hallegatte et al. (2015) <sup>[[#fn:r961|961]]</sup> project that without rapid and inclusive progress on eradicating multidimensional poverty, climate change could increase the number of the people living in poverty by between 35 million and 122 million people by 2030. Although these numbers are global and not specific to drylands, the highest impacts in terms of the share of the national populations being affected are projected to be in the drylands areas of the Sahel region, eastern Africa and South Asia (Stephane Hallegatte et al. 2015 <sup>[[#fn:r962|962]]</sup> ). The impacts of climate change on poverty vary depending on whether the household is a net agricultural buyer or seller. Modelling results showed that poverty rates would increase by about one-third among the urban households and non-agricultural self-employed in Malawi, Uganda, Zambia and Bangladesh due to high agricultural prices and low agricultural productivity under climate change (Hertel et al. 2010) <sup>[[#fn:r963|963]]</sup> . On the contrary, modelled poverty rates fell substantially among agricultural households in Chile, Indonesia, the Philippines and Thailand, because higher prices compensated for productivity losses (Hertel et al. 2010 <sup>[[#fn:r964|964]]</sup> ).

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