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

==== 8.5.3.2 Non-linearities in Land Surface Processes and Feedbacks ====

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Land surface responses and feedbacks represent a potential source of non-linearity for the water cycle response, at least at regional and local scales. The forced response of soil moisture and freshwater resources not only depends on precipitation, but also on evaporation ( [[#Laîné--2014|Laîné et al., 2014]] ), snowmelt ( [[#Thackeray--2016|Thackeray et al., 2016]] ), and runoff(X. [[#Zhang--2018|]] [[#Zhang--2018|]] [[#Zhang--2018|]] [[#Zhang--2018|]] [[#Zhang--2018|Zhang et al., 2018]] ) which are intrinsically non-linear processes depending on soil moisture or temperature thresholds. Bare ground evaporation is, for instance, usually estimated as a non-linear function of surface soil moisture ( [[#Jefferson--2015|Jefferson and Maxwell, 2015]] ). Plant transpiration requires more complex formulations with non-linear dependencies on multiple environmental factors including root-zone soil moisture and atmospheric CO <sub>2</sub> concentration ( [[#Franks--2017|Franks et al., 2017]] ). Globally, land surface evaporation is both energy and soil-moisture limited, but one of these limitations can become dominant depending on regions and seasons. Non-linearities may be particularly strong in transitional regimes where and when soil moisture limitation plays a major role ( [[#Berg--2018b|Berg and Sheffield, 2018b]] ).

Snowmelt is a nonl-inear process and projected changes in snowfall are also a non-linear combination of changes in total precipitation and in the fraction of solid precipitation. In cold regions, snowfall may first increase because of the increased water capacity of a warmer atmosphere and then decrease because snow falls as rain in an even warmer atmosphere. Such non-linearities can contribute to elevation, latitudinal and seasonal contrasts in the observed and projected retreat of the Northern Hemisphere (NH) snow cover ( [[#Shi--2015|Shi and Wang, 2015]] ; [[#Thackeray--2016|Thackeray et al., 2016]] ). Mountain glaciers also represent source of non-linear runoff responses since the annual runoff can first increase due to additional melting and then decrease as the glaciers shrink ( [[#Kraaijenbrink--2017|Kraaijenbrink et al., 2017]] ; [[#Shannon--2019|Shannon et al., 2019]] ). [[IPCC:Wg1:Chapter:Chapter-9#9.5.1.3|Section 9.5.1.3]] concludes with ''high confidence'' that the average annual runoff from glaciers will generally reach a peak at the latest by the end of the 21st century, and decline thereafter. This peak may have already occurred for small catchments with little ice cover, but tends to occur later in basins with large glaciers. Permafrost thawing is another mechanism which can trigger a non-linear hydrological response in the high latitudes of the NH( [[#Walvoord--2016|Walvoord and Kurylyk, 2016]] ), whose magnitude and potential abruptness is assessed in [[IPCC:Wg1:Chapter:Chapter-5#5.4.3.3|Section 5.4.3.3]] .

Land surface runoff and groundwater recharge are highly non-linear process, depending for instance on rainfall intensity, soil infiltration capacity, vertical profile of soil moisture and water table depth. A non-linear relationship between rainfall and groundwater recharge was observed in the tropics where intense seasonal rainfalls associated with internal climate variability contribute disproportionately to recharge (R.G. Taylor et al. , 2013a; Cuthbert et al. , 2019a) . Groundwater fluxes in arid regions are generally less responsive to climate variability than in humid regions, which can temporarily buffer climate change impacts on water resources or lead to a long, initially hidden, hydrological responses to global warming ( [[#Cuthbert--2019a|Cuthbert et al., 2019a]] ). Hydrological model simulations driven by individual and combined forcing show that decreased precipitation can cause larger deficits in soil moisture, streamflow and water table depth than other forcings, but also that these factors are not linearly cumulative when applied in combination ( [[#Hein--2019|Hein et al., 2019]] ). Surface runoff was found to scale only approximately with global warming ( [[#Tanaka--2017|Tanaka et al., 2017]] ). Significant non-linearities were found in the projected annual mean runoff response to global warming in CMIP5 projections, which could not be entirely explained by precipitation changes(X. [[#Zhang--2018|]] [[#Zhang--2018|]] [[#Zhang--2018|]] [[#Zhang--2018|]] [[#Zhang--2018|Zhang et al., 2018]] ). Similar non-linear behaviours are found in CMIP6 models over the Amazon, Yangtze, Niger, Euphrates and Mississippi river basins (Figure 8.26), highlighting the need to reassess the assumption of linearity when estimating regional water cycle changes.

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[[File:06341e23b271a4184c52705376bb9afe IPCC_AR6_WGI_Figure_8_26.png]]

'''Figure 8.26 |''' '''Rate of change in basin-scale annual mean runoff with increasing global warming levels.''' Relative changes (%) in basin-averaged annual mean runoff estimated as multi-model ensemble median from a variable subset of CMIP6 models for each SSP over nine major river basins: '''(a)''' Mississippi, '''(b)''' Danube, '''(c)''' Lena, '''(d)''' Amazon, '''(e)''' Euphrates, '''(f)''' Yangtze, '''(g)''' Niger, '''(h)''' Indus, and '''(i)''' Murray. The basin averages have been estimated after a first-order conservative remapping of the model outputs on the 0.5° by 0.5° river network of [[#Decharme--2019|Decharme et al. (2019)]] . The shaded area indicates the 5–95% confidence interval of the ensemble values across all SSPs. Note that the y-axis range differs across basins and is particularly large for Niger and Murray (panels g and i). The number of models considered is specified for each scenario in the legend located inside panel b. Further details on data sources and processing are available in the chapter data table (Table 8.SM.1).

Beyond changes in land surface water fluxes, non-linearities in the response of soil moisture and freshwater reservoirs have not been well documented in global climate projections but deserve further attention given the complex interactions between the water, energy and carbon cycles ( [[#Berg--2018a|Berg and Sheffield, 2018a]] ), the growing direct human influence on rivers and groundwater ( [[#Abbott--2019|Abbott et al., 2019]] ), and a possible offset between the linear components of changes in precipitation and evapotranspiration. Significant non-linearities were found in water scarcity projections, as seen by the stronger sensitivity to the first 2°C increase in global warming ( [[#Gosling--2016|Gosling and Arnell, 2016]] ).

In summary, there is both numerical and process-based evidence that terrestrial water cycle changes can be non-linear at the regional scale ( ''high confidence'' ). Non-linear regional responses of runoff, groundwater recharge and water scarcity have been documented based on both CMIP5 and CMIP6 models, and highlight the limitations of simple pattern-scaling techniques ( ''medium confidence'' ). Water resources fed by melting glaciers are particularly exposed to such non-linearities ( ''high co'' ''nfidence'' ).

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