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

==== 8.5.2.2 Implications for Near-Term Water Cycle Projections ====

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Adapting water resource management in the face of climate change will greatly benefit from improved prediction of land surface hydrology at the decadal time scale. Climate predictions ( [[IPCC:Wg1:Chapter:Chapter-1#1.4.4|Section 1.4.4]] ) differ from climate projections by constraining the initial state of the slow components of the climate system (i.e., the ocean, the cryosphere and the terrestrial hydrology) as well as volcanic aerosols and ozone depleting substances with observations. Anthropogenic and natural radiative forcing and low-frequency modes of variability (e.g., AMV and PDV, Annex IV.2.7 and IV.2.6) suggest the possible predictability of climate in the first decade or so of the 21st century, in addition to the projected response to the anthropogenic forcing (Sections 4.2.3 and 4.4.1.3).

In AR5, decadal prediction of precipitation over some land areas showed improved skill due to specified radiative forcing, with almost no added value from ocean initialization. Since AR5, more studies have been devoted to understanding the potential or effective water cycle predictability related to ocean multi-decadal variability. Decadal hindcast experiments based on large ensembles highlight increasing skill scores in annual mean precipitation three to seven years ahead, at least over the Sahel and Europe ( [[#Yeager--2018|Yeager et al., 2018]] ). There is relatively high predictability of the AMV impacts over the Mediterranean basin, Central Asia and the Americas (from the USA to northern South America) during boreal summer, but in boreal winter the signal-to-noise ratio shows only weak predictability over land ( [[#Yamamoto--2016|Yamamoto and Palter, 2016]] ; [[#Ruprich-Robert--2017|Ruprich-Robert et al., 2017]] ). The link between South Asian summer monsoon changes and the AMOC and the decadal variability in the Pacific Ocean open the possibility of increased predictability for the near future (Kushnir et al. , 2017; X. Huang et al. , 2020b; Sandeep et al., 2020) .

The additional skill associated with the initialization of the cryosphere and the land surface has received limited attention. However, there is observational evidence that oceanic decadal variations can propagate into the atmosphere and, consequently accumulate into terrestrial land surface reservoirs(e.g., [[#Bonnet--2017|Bonnet et al., 2017]] ) and vegetation (e.g., [[#Zeng--1999|Zeng et al., 1999]] ). This land surface memory, like in soil moisture ( [[#Alessandri--2008|Alessandri and Navarra, 2008]] ; [[#Catalano--2016|Catalano et al., 2016]] ) or snow( [[#Loranty--2014|Loranty et al., 2014]] ), may also contribute to the decadal predictability of the terrestrial component of the water cycle, but remains difficult to assess given the limitations of observational records. Vegetation initialization seems to generate as much noise as signal and does not necessarily translate into improved skill in early decadal predictions based on ESMs ( [[#Weiss--2014|Weiss et al., 2014]] ).

Decadal hydrological predictability in an idealized setting has also been investigated through offline land surface hindcast experiments, driven by observed atmospheric forcing and/or initial conditions, suggesting the potential for skilful predictions for terrestrial water storage, deep soil moisture, and groundwater ( [[#Yuan--2018|Yuan and Zhu, 2018]] ). Yet, a real-world assessment is hampered by the lack of observations and is only feasible when multi-decadal records of satellite estimates of terrestrial water storage, snow mass or soil moisture are available.

In summary, there is ''high confidence'' that the water cycle changes that have already emerged from internal variability will become more pronounced in near-term (2021–2040) projections. However, there is ''low confidence'' in decadal predictions of precipitation changes, particularly over most land areas, because internal variability remains difficult to predict and can offset or amplify the forced water cycle response.

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