Editing IPCC:AR6/WGI/Chapter-8 (section)

=== 8.6.2 Abrupt Water Cycle Responses to Changes in the Land Surface ===

<div id="h2-21-siblings" class="h2-siblings"></div>

Changes in the land surface, including vegetation cover and dust emissions, can trigger abrupt changes in the water cycle. Plants regulate the exchange of water and energy between the land surface and the atmosphere ( [[#8.2.3.3|Section 8.2.3.3]] ), such that sudden shifts in plant functions, types, or biomes can trigger feedbacks that have the potential to cause abrupt changes in the regional water cycle. Dust emissions, from either climatic or land use changes, affect the radiation budget and can regionally exacerbate dry extremes. Below, we assess the likelihood of abrupt changes in the water cycle for the well-studied regions of the Amazon and the Sahel, and the potential for dust emissions to amplify drought and aridity.

<div id="8.6.2.1" class="h3-container"></div>

<span id="amazon-deforestation-and-drying"></span>
==== 8.6.2.1 Amazon Deforestation and Drying ====

<div id="h3-50-siblings" class="h3-siblings"></div>

The Amazon forest plays an active role in driving atmospheric moisture transport and generating precipitation in the South American region ( SRCCL; Drumond et al. , 2014; Poveda et al. , 2014; Yin et al. , 2014; Staal et al. , 2018, 2020; Agudelo et al. , 2019; Espinoza et al. , 2019) . This close association between the land surface and the water cycle makes the Amazon a potential hotspot for abrupt change ( [[#Torres--2014|Torres and Marengo, 2014]] ). Both deforestation and drying are projected to increase by 2100, resulting in a worst-case scenario of up to a 50% loss in forest cover by 2050 (Soares-Filho et al. , 2006; Boisier et al. , 2015; ter Steege et al. , 2015; Gomes et al. , 2019) . Deforestation in the Amazon also raises the probability of catastrophic fires ( [[#Brando--2014|Brando et al., 2014]] ). The combination of deforestation, drier conditions, and increased fire can push the rainforest ecosystem past a tipping point, beyond which there is rapid land surface degradation, a sharp reduction in atmospheric moisture recycling, an increase in the fraction of precipitation that runs off, and a further shift towards a drier climate (Staal et al. , 2015; Boers et al. , 2017; Zemp et al. , 2017; Ruiz-Vásquez et al. , 2020) . A rapid drop in precipitation has a direct impact on river flows, driving basin-scale shifts from a regulated to unregulated state ( [[#Salazar--2018|Salazar et al., 2018]] ). Regional climate modeling experiments confirm that increased deforestation leads to a drier climate, although not all models show a true tipping point, at least under present-day climatic conditions ( [[#Lejeune--2015|Lejeune et al., 2015]] ; [[#Spracklen--2015|Spracklen and Garcia-Carreras, 2015]] ).

In AR5, some simulations using a coupled climate–carbon cycle model exhibited an abrupt dieback of the Amazon forest in future climate scenarios ( [[#Oyama--2003|Oyama and Nobre, 2003]] ; [[#Cox--2004|Cox et al., 2004]] ; [[#Malhi--2008|Malhi et al., 2008]] ).However, subsequent work demonstrated that abrupt Amazon dieback does not occur consistently across, or even within, Earth system models ( [[#Lambert--2013|Lambert et al., 2013]] ; [[#Boulton--2017|Boulton et al., 2017]] ). The occurrence of dieback is highly dependent on both how dry the simulated climate is in the present day ( [[#Malhi--2009|Malhi et al., 2009]] ) as well as the representation of forest structure and competitive dynamics ( [[#Levine--2016|Levine et al., 2016]] ). Models with a low diversity of plant characteristics and types have a higher tendency for abrupt change ( [[#Sakschewski--2016|Sakschewski et al., 2016]] ). Abrupt shifts and ecosystem disruptions can occur on the sub-regional level ( [[#Pires--2013|Pires and Costa, 2013]] ), highlighting the need for higher-resolution modelling studies. Since AR5, CMIP6 projections suggest that a tipping point in the Amazon system may be crossed on a local or regional scale ( [[#Staal--2020|Staal et al., 2020]] ) but continue to be highly dependent on model biases in precipitation and the simulation of the land surface. Consequently, the timing, and probability, of an abrupt shift remains difficult to ascertain.

In summary, while there is a strong theoretical expectation that Amazon drying and deforestation can cause a rapid change in the regional water cycle, currently there is ''limited'' model ''evidence'' to verify this response, hence there is ''low confidence'' that such a change will occur by 2100.

<div id="8.6.2.2" class="h3-container"></div>

<span id="greening-of-the-sahara-and-the-sahel"></span>
==== 8.6.2.2 Greening of the Sahara and the Sahel ====

<div id="h3-51-siblings" class="h3-siblings"></div>

Greening of the Sahara and Sahel regions in North Africa, in response to an increase in precipitation, has long been considered an amplifying mechanism that can lead to abrupt change. Although the high surface albedo of the desert stabilizes the energy balance of the system ( [[#Charney--1975|Charney, 1975]] ), greening can induce strong, positive feedbacks between the land surface and precipitation that can shift the region into a ‘Green Sahara’ state. The fact that the transition phase between a Desert Sahara and Green Sahara is not theoretically stable (Brovkin et al., 1998) creates a tipping point and allows for the possibility of an abrupt shift between dry and wet climate regimes. Paleoclimate reconstructions provide evidence of past Green Sahara states (DeMenocal and Tierney, 2012), under which rainfall rates increased by an order of magnitude (Tierney et al., 2017), leading to a vegetated landscape (Jolly et al., 1998) with large lake basins (Gasse, 2000; [[#Drake--2006|Drake and Bristow, 2006]] ). The underlying driver of the Green Sahara is the periodic increase in summer insolation associated with the orbital precession cycle (Kutzbach, 1981). In this sense, Green Saharas are not direct analogues for a response to anthropogenic greenhouse gas emissions (GHGs), as these past states were forced by natural, seasonal changes in solar radiation. However, the climate dynamics of Green Sahara periods (which have global impacts, [[#Pausata--2020|Pausata et al., 2020]] ), and the speed of the transitions between Desert Saharas and Green Saharas, are relevant for future projections.

Since AR5, paleoclimatic studies have improved our view of the timing, spatial extent, and speed of transitions associated with the early Holocene (11,000–5,000 years ago) Green Sahara. Observed transitions into and out of Green Sahara states are always faster than the underlying forcing, in agreement with theoretical considerations ( ''high confidence'' ) (Tierney and DeMenocal, 2013; [[#Shanahan--2015|Shanahan et al., 2015]] ; [[#Tierney--2017|Tierney et al., 2017]] ). However, there is ''low confidence'' in the duration of the transition because sedimentary records cannot typically resolve changes on decadal to multi-decadal time scales (Tierney and DeMenocal, 2013). Both paleoclimate data and modelling experiments suggest that the timing and speed of the transition was spatially heterogeneous ( ''high confidence'' ), with northern Saharan locations becoming drier thousands of years before more equatorial locations (Shanahan et al., 2015; [[#Tierney--2017|Tierney et al., 2017]] ; [[#Dallmeyer--2020|Dallmeyer et al., 2020]] ). These observations are consistent with theoretical studies suggesting that spatial heterogeneity and diversity in ecosystems can mitigate the probability of catastrophic change (Van Nes and Scheffer, 2005; [[#Bathiany--2013|Bathiany et al., 2013]] ). Conversely, low ecosystem diversity can produce local or regional ‘hot spots’ of abrupt change such as those seen in some paleoclimate records (Claussen et al., 2013).

CMIP5 and CMIP6 models ''',''' some of which include dynamic vegetation schemes, cannot simulate the magnitude, nor the spatial extent, of greening and precipitation change associated with the last Green Sahara under standard mid-Holocene (6,000 years ago) boundary conditions ( ''high confidence'' ) (Figure 3.11; Harrison et al. , 2014; Tierney et al. , 2017; Brierley et al. , 2020). This result remains unchanged since AR4 ( [[#Jansen--2007|Jansen et al., 2007]] ). This may be due to climatological biases in the models ( [[#Harrison--2015|Harrison et al., 2015]] ) or could imply that the strength of the feedbacks between vegetation and the water cycle in the models is too weak (Hopcroft et al., 2017). To date, climate models still only produce the amount and spatial extent of rainfall that is needed to sustain a Green Sahara if they are given prescribed changes in the land surface, such as albedo, soil moisture, vegetation cover and/or dust emissions (Pausata et al., 2016; [[#Skinner--2016|Skinner and Poulsen, 2016]] ; [[#Tierney--2017|Tierney et al., 2017]] ).

Some climate model simulations suggest that under future high-emissions scenarios, CO <sub>2</sub> radiative forcing causes rapid greening in the Sahel and Sahara regions via precipitation change (Claussen et al., 2003; [[#Drijfhout--2015|Drijfhout et al., 2015]] ). For example, in the BNU-ESM RCP8.5 simulation, the change is abrupt with the percentage of bare soil dropping from 45% to 15%, and percentage of tree cover rising from 50% to 75%, within 10 years (2050–2060; Drijfhout et al., 2015). However, other modelling results suggest that this may be a short-lived response to CO <sub>2</sub> fertilization (Bathiany et al., 2014).

In summary, given outstanding uncertainties in how well the current generation of climate models capture land surface feedbacks in the Sahel and Sahara, there is ''low confidence'' that an abrupt change to a greener state will occur in these regions before 2100 or 2300.

<div id="8.6.2.3" class="h3-container"></div>

<span id="amplification-of-drought-by-dust"></span>
==== 8.6.2.3 Amplification of Drought by Dust ====

<div id="h3-52-siblings" class="h3-siblings"></div>

Mineral dust aerosols in the climate system originate from both semi-permanent and transient sources ( [[#Prospero--2002|Prospero et al., 2002]] ; [[#Ginoux--2012|Ginoux et al., 2012]] ). The former are typically arid regions where significant alluvial sediments have accumulated over time, while the latter are often associated with natural (e.g., droughts, wildfires) and anthropogenic (e.g., land use change, desertification) disturbances. Modern-day dust emissions are dominated by natural sources ( [[#Ginoux--2012|Ginoux et al., 2012]] ), although human emissions may contribute 10–60% of the global atmospheric dust load ( [[#Webb--2018|Webb and Pierre, 2018]] ). Paleo-dust records suggest that human factors (land use change and landscape disturbance) may have doubled global dust emissions between 1750 and the last quarter of the 20th century (Section 2.2.6; [[#Hooper--2018|Hooper and Marx, 2018]] ).

Dust aerosols influence the climate system and hydrologic cycle through both direct impacts on radiation (absorbing and scattering longwave and shortwave) and via indirect effects on cloud and precipitation processes (Box 8.1; [[#Choobari--2014|Choobari et al., 2014]] ; [[#Kok--2018|Kok et al., 2018]] ; [[#Schepanski--2018|Schepanski, 2018]] ). The capacity of dust aerosols to suppress precipitation by reducing humidity and energy availability, and increasing stability in the atmosphere ( [[#Cook--2013|Cook et al., 2013]] ; [[#Huang--2014|Huang et al., 2014]] ) can drive positive feedbacks (see also Section 6.3.6). Thus there is strong potential for dust to contribute to abrupt changes in the water cycle, especially in semi-arid regions where wind erosion is highly sensitive to vegetation cover and drought variability ( [[#Yu--2015|Yu et al., 2015]] ). One such event occurred over the Central USA during the 1930s: the Dust Bowl drought, an iconic event characterized by widespread land degradation and historically unprecedented levels of dust storm activity ( [[#Hansen--2004|Hansen and Libecap, 2004]] ; [[#Lee--2015|Lee and Gill, 2015]] ). While initialized by warm sea surface temperatures in the North Atlantic, modeling work indicates that land cover changes and resulting dust emissions contributed to the severity and spatial extent of the drought by further suppressing precipitation ( [[#Cook--2009|Cook et al., 2009]] ; [[#Hu--2018|Hu et al., 2018]] ; [[#Cowan--2020|Cowan et al., 2020]] ). There is also increasing evidence that dust aerosol feedbacks are necessary to explain the magnitude of rainfall increase during the mid-Holocene Green Sahara ( [[#Pausata--2016|Pausata et al., 2016]] ; [[#Tierney--2017|Tierney et al., 2017]] ).

The importance of dust aerosol feedbacks in future abrupt climate events, like droughts or rapid aridification, is unclear. In part, this is because the response of dust aerosol emissions and loading levels in the atmosphere to climate change is highly uncertain (Tegen and [[#Schepanski--2018|Schepanski, 2018]] ; [[#Webb--2018|Webb and Pierre, 2018]] ). This difficulty in predicting future dust responses is rooted in the fact that emissions depend on both changes to the land surface (e.g., land use/land cover change, aridification, ecological responses to climate change) and the state of the atmosphere (Tegen and [[#Schepanski--2018|Schepanski, 2018]] ). While there is some evidence that global dust aerosol concentrations in the future will increase ( [[#Allen--2016|Allen et al., 2016]] ; Tegen and [[#Schepanski--2018|Schepanski, 2018]] ), it is highly dependent on changes in precipitation patterns and atmospheric circulation (see the SRCCL, [[IPCC:Wg1:Chapter:Chapter-2#2.4.1|Section 2.4.1]] ), and it is not clear what the radiative impact will be ( [[#Allen--2016|Allen et al., 2016]] ; [[#Kok--2018|Kok et al., 2018]] ).

In summary, due to ''limited evidence'' , there is ''low confidence'' regarding the role of dust in abrupt climate change events over the next century.

<div id="8.6.3" class="h2-container"></div>

<span id="abrupt-water-cycle-responses-to-initiation-or-termination-of-solar-radiation-modification"></span>