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

===== 8.5.1.1.3 Land surface processes =====

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Land surface processes determine the partitioning of net surface radiation into sensible, latent and ground heat fluxes, the partitioning of precipitation into evapotranspiration and runoff, and the net terrestrial carbon flux at the Earth’s surface. They are relevant for simulating the terrestrial water cycle responses to climate change, as well as the response to land use change (FAQ 8.1). Even basic land surface properties such as albedo ( [[#Terray--2018|Terray et al., 2018]] ) or the ratio of transpiration to total evaporation ( [[#Chang--2018|Chang et al., 2018]] ) still need to be improved in state-of-the-art coupled GCMs. Runoff sensitivities are also not well constrained in these models, which display a large spread for the present-day climate, influencing simulated changes under global warming ( [[#Lehner--2019|Lehner et al., 2019]] ). Earth System Models (ESMs) incorporate some combined biophysical and biogeochemical processes to a limited extent, and many relevant processes about how plants and soils interactively respond to climate changes are yet to be considered (e.g., Y. [[#Liu--2020|]] [[#Liu--2020|]] [[#Liu--2020|Liu et al., 2020]] ). Consequently, land surface processes and their atmospheric coupling contribute to the range in water cycle projections ( [[#Jia--2019|Jia et al., 2019]] ).

Since AR5, development of new and existing processes in land surface models (LSMs) have been evaluated. These include soil freezing and permafrost (Vergnes et al. , 2014; Chadburn et al. , 2015; K. Yang et al. , 2018; Gao et al. , 2019) , soil and snow hydrology ( [[#Brunke--2016|Brunke et al., 2016]] ; [[#Decharme--2016|Decharme et al., 2016]] ), glaciers ( [[#Shannon--2019|Shannon et al., 2019]] ), surface waters and rivers ( [[#Decharme--2012|Decharme et al., 2012]] ), as well as vegetation ( [[#Bartlett--2015|Bartlett and Verseghy, 2015]] ; [[#Betts--2015|Betts et al., 2015]] ; [[#Knauer--2015|Knauer et al., 2015]] ; [[#Tang--2015|Tang et al., 2015]] ) and the representation of hydraulic gradients throughout the soil–plant–atmosphere continuum (Bonan et al., 2014). Such land surface model developments have led to significant improvements in global offline hydrological simulations driven by observed atmospheric forcings (e.g., [[#Li--2017|]] [[#Li--2017|]] [[#Li--2017|]] [[#Li--2017|C. Li et al., 2017]] ; [[#Decharme--2019|Decharme et al., 2019]] ).

Progress in the representation of land surface heterogeneity has been made, in the form of improved mapping of root zone storage capacity (Wang-Erlandsson et al., 2016), improved vegetation stand, disturbance and fire dynamics (F. [[#Li--2013|]] [[#Li--2013|Li et al., 2013]] ; [[#Fisher--2018|Fisher et al., 2018]] ; [[#Haverd--2018|Haverd et al., 2018]] ; [[#Yue--2018|Yue et al., 2018]] ; [[#Zou--2019|Zou et al., 2019]] ), better representation of urban surfaces (Box 10.3), and the explicit representation of inland water bodies ( [[#Gu--2015|Gu et al., 2015]] ; [[#Verseghy--2017|Verseghy and MacKay, 2017]] ). The representation of realistic snow and vegetation cover significantly affects the simulation of the land surface energy and water budgets at multiple time scales (Loranty et al., 2014; [[#Bartlett--2015|Bartlett and Verseghy, 2015]] ; [[#Thackeray--2015|Thackeray et al., 2015]] ; [[#Qiu--2016|Qiu et al., 2016]] ; [[#Thackeray--2016|Thackeray and Fletcher, 2016]] ; L. [[#Wang--2016|]] [[#Wang--2016|]] [[#Wang--2016|Wang et al., 2016]] ; [[#Alessandri--2017|Alessandri et al., 2017]] ). Groundwater remains inadequately represented in many models, which limits our current understanding of the two-way interactions between groundwater and the rest of the hydrologic cycle (R.G. Taylor et al. , 2013a; Leng et al. , 2014; Vergnes et al. , 2014; Pokhrel et al. , 2015; [[#Maxwell--2016|Maxwell and Condon, 2016]] ; [[#Collins--2017|Collins, 2017]] ; Scanlon et al. , 2018; Condon et al. , 2020) . Land management exerts an increasing influence on the water cycle ( [[#Abbott--2019|Abbott et al., 2019]] ) whose representation in the current-generation climate models is generally incomplete ( [[IPCC:Wg1:Chapter:Chapter-10#10.3.3.7.2|Section 10.3.3.7.2]] ).

Aside from land surface models (LSMs), global hydrological models (GHMs) have been further developed for off-line simulations of the hydrological impacts of both climate change and water management (Jiménez Cisneros et al. , 2014; Schewe et al. , 2014; Döll et al. , 2016, 2018; Pokhrel et al. , 2016, 2017; Veldkamp et al. , 2018) . GHMs can equal or outweigh the contribution of GCMs to uncertainties in hydrological projections at the regional scale (Giuntoli et al., 2015). Historical GHM simulations are currently not sufficient to improve regional water cycle projections, due to modelling uncertainties in both the driving GCMs and land surface hydrology (Pechlivanidis et al. , 2017; Samaniego et al. , 2017; Hattermann et al. , 2018; Krysanova et al. , 2018) . Biophysical vegetation processes are still not accounted for in many GHMs, which may lead to inadequate projections of terrestrial runoff and water resources. However, hydrological models that do simulate these effects often disagree ( [[#Prudhomme--2014|Prudhomme et al., 2014]] ), so do not necessarily provide the added value of a more sophisticated representation of vegetation processes and land surface conditions ( [[#Döll--2016|Döll et al., 2016]] ).

Since AR5, there has been increasing recognition of the need to better understand the role of land–atmosphere coupling and related feedbacks (Joetzjer et al. , 2014; Berg et al. , 2016; Catalano et al. , 2016; [[#Berg--2018a|Berg and Sheffield, 2018a]] ; Santanello et al. , 2018) . This has led to the development of dedicated field campaigns (Song et al., 2016; [[#Phillips--2017|Phillips et al., 2017]] ; [[#Dirmeyer--2018|Dirmeyer et al., 2018]] ), remotely sensed observations (Ferguson and Wood, 2011; [[#Roundy--2017|Roundy and Santanello, 2017]] ), and tailored diagnostics (Tawfik et al., 2015a, b; [[#Miralles--2016|Miralles et al., 2016]] , 2019; [[#Dirmeyer--2017|Dirmeyer and Halder, 2017]] ). Dynamic vegetation models have been introduced in global ESMs but they need further evaluation (Medlyn et al., 2015; [[#Prentice--2015|Prentice et al., 2015]] ; [[#Cantú--2018|Cantú et al., 2018]] ; [[#Franks--2018|Franks et al., 2018]] ) to provide valuable information on potential vegetation feedbacks. Plant migration and mortality, increased disturbances from wild fires, insects and extreme events, interactive nitrogen cycle, or the impact of increased levels of tropospheric ozone are often ignored or poorly represented in the current-generation of ESMs (Bonan and Doney, 2018; [[#Fisher--2018|Fisher et al., 2018]] ).

The physiological response of plants to increasing atmospheric CO <sub>2</sub> is generally accounted for, but only using empirical models of stomatal conductance that are characterized by a single critical parameter of intrinsic water-use efficiency (Franks et al., 2017, 2018). This reflects a lack of structural diversity and caution about the consensus of the photosynthesis response to increasing CO <sub>2</sub> ( [[#Knauer--2015|Knauer et al., 2015]] ; [[#Huang--2016|Huang et al., 2016]] ), which has implications for the ability of the current-generation models to account for uncertainty in future evapotranspiration changes. Most CMIP5 models underestimate the ratio of plant transpiration to total terrestrial evapotranspiration, which may suggest that they also underestimate the impact of plant physiology on the water cycle (Lian et al., 2018). Plant hydraulics are not explicitly considered in many land surface models, which may lead to an underestimation of the influence of the increasing atmospheric moisture stress on plant transpiration under climate change (Massmann et al. , 2019; Grossiord et al. , 2020; Y. Liu et al. , 2020) . Most ESMs underestimate the water use efficiency measured at many sites and, consequently overestimate the ratio of evapotranspiration to precipitation (J. [[#Li--2018|]] [[#Li--2018|]] [[#Li--2018|Li et al., 2018]] ).

In summary, since AR5 substantial advances have been made in the representation of land surface processes in current-generation Earth System Models (ESMs). Offline hydrological models allow the application of bias-adjusted atmospheric forcings, but there is ''low confidence'' of an improved response compared to coupled climate models, given their inherent limitations (Box 10.2). While improvements in the representation of complex land surface feedbacks relevant to the water cycle are needed, there is currently ''low confidence'' that they will systematically improve the reliability of water cycle projections.

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