Editing IPCC:AR6/WGI/Chapter-2 (section)

==== 2.3.1.3 Global Hydrological Cycle ====

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This section focuses on large-scale changes in a subset of components of the hydrological cycle (Cross-Chapter Box 2.2). [[IPCC:Wg1:Chapter:Chapter-8|Chapter 8]] undertakes a holistic assessment of changes in the hydrological cycle integrating observations, modelling and theoretical understanding, while ( [[IPCC:Wg1:Chapter:Chapter-11|Chapter 11]] assesses hydrological cycle extremes such as droughts and floods.

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===== 2.3.1.3.1 Paleo perspective of the global hydrological cycle =====

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The AR5 assessed large-scale indicators of terrestrial paleo hydroclimate, including as part of its assessment of paleo floods and droughts, but did not assess proxy evidence for paleo hydroclimate indicators over continental and larger scales. The paleoclimate evidence assessed in AR5 was broadly consistent with global hydroclimate scaling with temperature: warmer periods were wetter (e.g., the Pliocene; increased precipitation) with colder periods being drier (e.g., the LGM; decreased precipitation).

Substantial limitations exist in reconstructing the global hydrological cycle prior to the Quaternary, particularly during the Eocene, due to the lack of high-resolution proxy records and their sparsity. Spatial heterogeneity complicates identification of wetting and drying signals during the PETM and the EECO, with paleo data and model simulations suggesting an intensified global hydrological cycle ( [[#Carmichael--2016|Carmichael et al., 2016]] , 2017; [[#Hyland--2017|Hyland et al., 2017]] ; [[#West--2020|West et al., 2020]] ), in particular an increased specific humidity ( [[#Winnick--2015|Winnick et al., 2015]] ; [[#van%20Dijk--2020|van Dijk et al., 2020]] ). Conditions wetter than present were inferred for the MPWP (Cross Chapter Box 2.4), with intensified Asian monsoons ( [[#An--2015|An et al., 2015]] ) but with nevertheless drier conditions over tropical and subtropical SH locations ( [[#Pontes--2020|Pontes et al., 2020]] ). A new global reconstruction of hydroclimate proxies for the LIG points to stronger boreal precipitation compared to 1850–1900 over high latitudes and especially over monsoon areas, with a more heterogeneous signal for the SH ( [[#Scussolini--2019|Scussolini et al., 2019]] ). This heterogeneity is also present in the tropics, characterized by large zonal differences in precipitation change due to the variations in the intensity of Walker circulation ( [[#2.3.1.4.1|Section 2.3.1.4.1]] ). Available records indicate reduced global vegetation cover and abundant atmospheric dust deposition during the LGM (increased aridity), particularly over the tropics and high latitudes ( [[#Lamy--2014|Lamy et al., 2014]] ; [[#Újvári--2017|Újvári et al., 2017]] ). This agrees with models and moisture-sensitive proxies, suggesting an overall decrease in global precipitation during the LGM relative to recent decades, albeit with regional-scale heterogeneity ( [[#Cao--2019|Cao et al., 2019]] ). Despite lower global precipitation amounts, research since AR5 has identified a wetting of mid-latitudes during the LGM ( [[#Putnam--2017|Putnam and Broecker, 2017]] ; [[#Lowry--2018|Lowry and Morrill, 2018]] ; [[#Morrill--2018|Morrill et al., 2018]] ), thereby complicating the characterization of the LGM as a relatively ‘dry’ period. Low evaporation rates and increased top-soil moisture during the LGM may have contributed to elevated levels of large closed-basin lakes located in the 30°–45° latitudinal belts ( [[#Putnam--2017|Putnam and Broecker, 2017]] ; [[#Scheff--2017|Scheff et al., 2017]] ), such as the south-west United States (e.g., [[#Ibarra--2018|Ibarra et al., 2018]] ), southern Australia ( [[#Petherick--2013|Petherick et al., 2013]] ; [[#Fitzsimmons--2015|Fitzsimmons et al., 2015]] ; [[#Sniderman--2019|Sniderman et al., 2019]] ) and Patagonia (e.g., [[#Quade--2017|Quade and Kaplan, 2017]] ).

New analyses suggest that during the Holocene, the NH mid-latitudes became increasingly wet, in phase with the strength of the latitudinal temperature and insolation gradients ( [[#Shuman--2016|Shuman and Marsicek, 2016]] ; [[#Routson--2019|Routson et al., 2019]] ). Nevertheless, there was also considerable spatial heterogeneity and variability on centennial to millennial timescales ( [[#Newby--2014|Newby et al., 2014]] ; [[#Shuman--2016|Shuman and Marsicek, 2016]] ; H. [[#Zhang--2018|]] [[#Zhang--2018|]] [[#Zhang--2018|]] [[#Zhang--2018|Zhang et al., 2018]] ; [[#Liefert--2020|Liefert and Shuman, 2020]] ). The NH tropics and many regions of the SH deep tropics experienced wetting up until the early to mid-Holocene but drying thereafter ( [[#Shanahan--2015|Shanahan et al., 2015]] ; [[#Nash--2016|Nash et al., 2016]] ; [[#Muñoz--2017|Muñoz et al., 2017]] ; [[#Quade--2018|Quade et al., 2018]] ). ''Evidence'' for the SH is ''limited'' , with a wetting trend during the Holocene in low latitudes of South America ( [[#Kanner--2013|Kanner et al., 2013]] ; [[#Mollier-Vogel--2013|Mollier-Vogel et al., 2013]] ) and parts of the African tropics ( [[#Schefuß--2011|Schefuß et al., 2011]] ; [[#Chevalier--2015|Chevalier and Chase, 2015]] ) but a drying tendency over southern Australia and New Zealand ( [[#van%20den%20Bos--2018|van den Bos et al., 2018]] ; [[#Barr--2019|Barr et al., 2019]] ) and South America ( [[#Quade--2017|Quade and Kaplan, 2017]] ; [[#Moreno--2018|Moreno et al., 2018]] ).

For the CE, new proxy records have led to the creation of continental drought atlases ( [[#Cook--2015|Cook et al., 2015]] ; [[#Palmer--2015|Palmer et al., 2015]] ; [[#Stahle--2016|Stahle et al., 2016]] ; [[#Morales--2020|Morales et al., 2020]] ) and millennial reanalyses ( [[#Steiger--2018|Steiger et al., 2018]] ; [[#Tardif--2019|Tardif et al., 2019]] ). These reconstructions highlighted the occurrence of multi-decadal regional mega-droughts in the NH before 1600 CE, particularly during 800–1200 CE, with a predominance of wet periods after 1700 CE ( [[#Cook--2015|Cook et al., 2015]] ; [[#Rodysill--2018|Rodysill et al., 2018]] ; [[#Shuman--2018|Shuman et al., 2018]] ). In the SH, much of South America and the African tropics experienced a reduction of precipitation during 900–1200 CE and a wetting peak during 1500–1800 CE ( [[#Tierney--2015|Tierney et al., 2015]] ; [[#Nash--2016|Nash et al., 2016]] ; [[#Fletcher--2018|Fletcher et al., 2018]] ; [[#Lüning--2018|Lüning et al., 2018]] ; [[#Campos--2019|Campos et al., 2019]] ), with an opposite pattern in southern subtropical Africa ( [[#Woodborne--2015|Woodborne et al., 2015]] ; [[#Lüning--2018|Lüning et al., 2018]] ). Large multi-decadal variability was documented over Australia and New Zealand during the 800–1300 CE period, followed by a well-defined wet period during 1500–1800 CE ( [[#Barr--2014|Barr et al., 2014]] ; [[#Evans--2019|Evans et al., 2019]] ).

To summarize, since AR5 there has been considerable progress in detecting the variations of the global hydrological cycle prior to the instrumental period. There are indications from multiple sources of a wetting trend during the Holocene, particularly for the NH and parts of the SH tropics ( ''medium confidence'' ). Hydroclimate during the CE is dominated by regional variability, generally precluding definitive statements on changes at continental and larger scales, with a general reduction of mega-drought occurrences over the last about 500 years ( ''medium confidence'' ). Availability of proxy data for assessing Holocene hydroclimate variability is biased towards the NH, with ''medium evidence'' but ''low agreement'' for the assessment of SH changes.

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<span id="surface-humidity"></span>
===== 2.3.1.3.2 Surface humidity =====

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The AR5 reported ''very likely'' widespread increases in near-surface air specific humidity since the 1970s, abating from around 2000 to 2012 ( ''medium confidence'' ). This abatement resulted in a recent decline in relative humidity over the land.

Near surface humidity has been monitored using in-situ data (e.g., NOCSv2.0; [[#Berry--2011|Berry and Kent, 2011]] ), satellite-derived estimations (e.g., HOAPS3, [[#Liman--2018|Liman et al., 2018]] ; J-OFURO3, [[#Tomita--2019|Tomita et al., 2019]] ), global gridded products such as HadISDH ( [[#Willett--2014|Willett et al., 2014]] , 2020), and reanalyses (e.g., ERA5, JRA-55 and 20CRv3). In-situ based humidity products suffer from uncertainties over poorly sampled regions particularly in the SH ( [[#Berry--2011|Berry and Kent, 2011]] ; [[#Kent--2014|Kent et al., 2014]] ; [[#Willett--2014|Willett et al., 2014]] ). There is general consensus in the inter-annual variability and sign of trends implying ''high confidence'' in increasing specific humidity since the 1970s and decreasing relative humidity since 2000, particularly over land ( [[#Simmons--2010|Simmons et al., 2010]] ; [[#Willett--2014|Willett et al., 2014]] , 2020). Since 2012, specific humidity over land and ocean has remained well above the 1973–2019 average and reached record or near-record values (Figure 2.13b), with the strong 2015–2016 El Niño event boosting surface moisture levels ( [[#Byrne--2018|Byrne and O’Gorman, 2018]] ). The abatement from around 2000 to 2012 reported in AR5 has not persisted. This is consistent with increases in total column water vapour ( [[#2.3.1.3.3|Section 2.3.1.3.3]] ) and a resumption of rapid warming in surface temperatures ( [[#2.3.1.1.3|Section 2.3.1.1.3]] ). The global averaged relative humidity however has remained depressed since 2000 (Figure 2.13d; [[#Simmons--2010|Simmons et al., 2010]] ; [[#Willett--2014|Willett et al., 2014]] , 2020; [[#Dunn--2017|Dunn et al., 2017]] ; [[#Vicente-Serrano--2018|Vicente-Serrano et al., 2018]] ).

Since 1973, increases in specific humidity have been widespread and significant across the majority of the land and ocean regions where observations are available (Figure 2.13a). In contrast, trends in relative humidity show distinct spatial patterns with generally increasing trends over the higher latitudes and the tropics and generally decreasing trends over the sub-tropics and mid-latitudes, particularly over land areas (Figure 2.13c). Near-surface specific humidity over the oceans has increased since the 1970s according to several in-situ, satellite and reanalysis data records ( [[#Kent--2014|Kent et al., 2014]] ; [[#Robertson--2020|Robertson et al., 2020]] ; [[#Willett--2020|Willett et al., 2020]] ). According to the HadISDH product, increases in specific humidity and decreases in relative humidity are significant particularly over the NH mid-latitudes (Figure 2.13a,c). Poor data coverage over the SH south of 20°S does not allow for the robust assessment of trends. Sources of uncertainty include the initial measurement accuracy, homogenization over land, observational height at ships and instrument bias adjustment over ocean, and sparse spatio-temporal sampling ( [[#Prytherch--2015|Prytherch et al., 2015]] ; [[#Roberts--2019|Roberts et al., 2019]] ; [[#Willett--2020|Willett et al., 2020]] ).

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[[File:fac3527940cee5a3edb1f1f7ea1185f3 IPCC_AR6_WGI_Figure_2_13.png]]

'''Figure 2.13''' '''|''' '''Changes in surface humidity. (a)''' Trends in surface specific humidity over 1973–2019. Trends are calculated using OLS regression with significance assessed following AR(1) adjustment after [[#Santer--2008|Santer et al. (2008)]] ; ‘×’ marks denote non-significant trends). '''(b)''' Global average surface specific humidity annual anomalies (1981–2010 base period). '''(c)''' as (a) but for the relative humidity. '''(d)''' as (b) but for the global average surface relative humidity annual anomalies. Further details on data sources and processing are available in the chapter data table (Table 2.SM.1).

In summary, observations since the 1970s show a ''very likely'' increase in near surface specific humidity over both land and oceans. A ''very likely'' decrease in relative humidity has occurred over much of the global land area since 2000, particularly over mid-latitude regions of the NH, with increases at northern high latitudes.

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<span id="total-column-water-vapour-tcwv"></span>

===== 2.3.1.3.3 Total column water vapour (TCWV) =====

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The AR5 concluded that total column water vapour (TCWV) ''very likely'' increased since the 1970s, at a rate that was overall consistent with the Clausius-Clapeyron relationship (about 7% per °C) given the observed increase in atmospheric temperature.

Records prior to the instigation of quasi-global coverage by radiosondes require the use of statistical relationships to infer TCWV from historical SST observations or the evaluation of centennial-scale reanalysis products ( [[#Smith--2015|Smith and Arkin, 2015]] ). These approaches reveal two periods of positive trends, one from 1910 to 1940 and the other from 1975 onwards ( [[#Zhang--2013|Zhang et al., 2013]] ; [[#Mieruch--2014|Mieruch et al., 2014]] ; [[#Shi--2018|Shi et al., 2018]] ), concurrent with periods of positive SST trends (Figure 2.11). Potential sources of errors in the SST-based estimation of TCWV include both uncertainties in historical SST and uncertainties in the parameters that define the relationship between the variables ( [[#Smith--2015|Smith and Arkin, 2015]] ). Trends based on 20CRv2c, ERA-20C and ERA-20CM indicate an increase in TCWV over much of the global ocean since the beginning of the 20th century, particularly over the tropics ( [[#Bordi--2015|Bordi et al., 2015]] ; [[#Smith--2015|Smith and Arkin, 2015]] ; [[#Poli--2016|Poli et al., 2016]] ). TCWV trends estimated since the middle of the 20th century from radiosonde observations show significant increases over North America and large portions of Eurasia, while decreases are restricted to Australia, eastern Asia and the Mediterranean region (Y. [[#Zhang--2018|]] [[#Zhang--2018|]] [[#Zhang--2018|]] [[#Zhang--2018|Zhang et al., 2018]] ). Overall, there is a significant increase in TCWV over global land areas since 1979 ( [[#Chen--2016|Chen and Liu, 2016]] ).

Since the late 1970s a range of satellite missions permit a quasi-global assessment of TCWV. Several satellite products provide water vapour retrievals based upon distinct spectral domains, in addition to products from radiosondes, reanalyses and GNSS radio occultation. The GEWEX Water Vapour Assessment (G-VAP) provided an intercomparison of several TCWV data records, with global coverage but limited timespan ( [[#Schröder--2018|Schröder et al., 2018]] ). The various global products generally exhibit a positive trend since 1979 (Figure 2.14; [[#Allan--2014|Allan et al., 2014]] ; [[#Mieruch--2014|Mieruch et al., 2014]] ; [[#Schröder--2016|Schröder et al., 2016]] ; J. [[#Wang--2016|]] [[#Wang--2016|]] [[#Wang--2016|]] [[#Wang--2016|]] [[#Wang--2016|]] [[#Wang--2016|Wang et al., 2016]] ), most evident over the tropics ( [[#Gu--2013|Gu and Adler, 2013]] ; [[#Chen--2016|Chen and Liu, 2016]] ; [[#Mears--2018|Mears et al., 2018]] ; [[#Wang--2020|Wang and Liu, 2020]] ; [[#Salamalikis--2021|Salamalikis et al., 2021]] ). The existence of apparent breakpoints in several products, which are generally coincident with changes in the observing system, lead to trend estimates that are not in line with theoretical expectations imposed by the Clausius-Clapeyron relationship ( [[#Schröder--2019|Schröder et al., 2019]] ), although other factors such as regional moisture divergence/convergence could account for the observed TCWV-temperature scaling. Substantial potential inhomogeneities affect trend estimates based on satellite, reanalysis and merged products in particular over Central Africa, the Sahara and central South America ( [[#Schröder--2016|Schröder et al., 2016]] , 2019; J. [[#Wang--2016|]] [[#Wang--2016|]] [[#Wang--2016|]] [[#Wang--2016|]] [[#Wang--2016|]] [[#Wang--2016|Wang et al., 2016]] ). Moreover, data gaps in observations from ground-based GNSS receivers and radiosondes lead to ''low confidence'' in TCWV estimation in these regions.

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[[File:100dd2195115423d233bb4cf4b6a71ee IPCC_AR6_WGI_Figure_2_14.png]]

'''Figure 2.1''' '''4 |''' '''Time series of global mean total column water vapour annual anomalies (mm) relative to a 1988–2008 base period.''' Further details on data sources and processing are available in the chapter data table (Table 2.SM.1).

In summary, positive trends in global total column water vapour are ''very likely'' since 1979 when globally representative direct observations began, although uncertainties associated with changes in the observing system imply ''medium confidence'' in estimation of the trend magnitudes. ''Low confidence'' in longer-term trends arises from uncertainties in the SST-TCWV relationship and current centennial scale reanalyses, particularly during the first half of the 20th century.

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<span id="global-precipitation"></span>

===== 2.3.1.3.4 Global precipitation =====

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The AR5 concluded that there was ''low confidence'' in precipitation change averaged over global land areas prior to 1950, and ''medium confidence'' thereafter with no significant global trends. There was a ''likely'' overall increase in precipitation in the well-sampled NH mid-latitudes, with ''high confidence'' after 1951.

In situ precipitation records over land extend back for centuries in a few locations, and to the early to mid-20th century quasi-globally. Datasets differ in their input data, completeness of records, period covered, and the gridding procedures applied, which, given spatial clustering and the small spatial scales of precipitation, results in differences in global and regional estimates of precipitation changes (Q. [[#Sun--2018|]] [[#Sun--2018|Sun et al., 2018]] ; [[#Nogueira--2020|Nogueira, 2020]] ). The spatial variability of observed long-term trends (1901–2019) based on GPCC V2020 and CRU TS 4.04 (Figure 2.15a,b) indicates significant increases in precipitation mainly over eastern North America, northern Eurasia, southern South America and north-western Australia. Decreases are strongest across tropical western and equatorial Africa and southern Asia. The temporal evolution of global annual land precipitation anomalies exhibits little consistency between GPCC V2020, CRU TS 4.04 and GHCNv4 datasets, especially prior to 1950, that is associated with limitations in data coverage (Figure 2.15c; [[#Wu--2013|Wu et al., 2013]] ; [[#Shen--2014|Shen et al., 2014]] ; [[#Gu--2015|Gu and Adler, 2015]] ). These disagreements between datasets prior to the 1950s result in differences in trend estimates over global land (Table 2.6). A qualitative consistency in decadal and interdecadal variations between the products is only observed since the 1950s, with primarily positive land precipitation anomalies during the 1950s, 1970s and during 2000 to 2019 (Figure 2.15c).

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'''Table 2.''' '''6 |''' '''Globally averaged trend estimates over land and 90% confidence''' '''intervals for annual precipitation for each time series in Figure 2.15c over three periods all ending in 2019.''' Trends are calculated using OLS regression with significance assessed after [[#Santer--2008|Santer et al. (2008)]] . Further details on data sources and processing are available in the chapter data table (Table 2.SM.1).

{| class="wikitable"
|-
| rowspan="2"| '''Dataset'''

| colspan="3"| '''Trends in annual precipitation (mm yr''' <sup>–1</sup> '''per decade)'''

|-
| '''1901–2019'''

| '''1960–2019'''

| '''1980–2019'''

|-
| GPCCv2020

| 1.01 <sup>a</sup> ± 0.99

| 1.67 ± 3.23

| 5.60 ± 6.38

|-
| CRU TS 4.04

| 0.57 ± 2.08

| 0.17 ± 3.12

| 5.75 <sup>a</sup> ± 5.09

|-
| GHCNv4

| 3.19 <sup>a</sup> ± 1.48

| 5.03 <sup>a</sup> ± 4.87

| 11.06 <sup>a</sup> ± 9.17

|-
| GPCPv2.3

|
| 5.41 <sup>a</sup> ± 5.20

|}

<sup>a</sup> Trend values significant at the 10% level.

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[[File:c2addba488a0d6d769c525d313a70c08 IPCC_AR6_WGI_Figure_2_15.png]]

'''Figure 2.15''' '''|''' '''Changes in observed precipitation. (a, b)''' Spatial variability of observed precipitation trends over land for 1901–2019 for two global in-situ products. Trends are calculated using OLS regression with significance assessed following AR(1) adjustment after [[#Santer--2008|Santer et al. (2008)]] (‘×’ marks denote non-significant trends). '''(c)''' Annual time series and decadal means from 1891 to date relative to a 1981–2010 climatology (note that different products commence at distinct times). '''(d, e)''' as (a, b), but for the periods starting in 1980. '''(f)''' is for the same period for the globally complete merged GPCP v2.3 product. Further details on data sources and processing are available in the chapter data table (Table 2.SM.1).

Several satellite-based precipitation datasets improve the representation of the spatio-temporal changes since the late 20th century. Some of these are based exclusively on satellite data (e.g., CMORPH, [[#Joyce--2004|Joyce et al., 2004]] ; GSMaP, [[#Okamoto--2005|Okamoto et al., 2005]] ), with others being combinations of in situ observations, reanalyses and satellite retrievals (e.g., CMAP, [[#Xie--1997|Xie and Arkin, 1997]] ; TRMM 3B43 V7, [[#Huffman--2007|Huffman et al., 2007]] ; PERSIANN-CDR, [[#Ashouri--2015|Ashouri et al., 2015]] ; CHIRPS, [[#Funk--2015|Funk et al., 2015]] ; GPCP V2.3, [[#Adler--2018|Adler et al., 2018]] ). These can be affected by systematic and random uncertainties due to inhomogeneities in the satellite-derived precipitation and station data and the uncertainties of blending algorithms ( [[#Hegerl--2015|Hegerl et al., 2015]] ; Q. [[#Sun--2018|]] [[#Sun--2018|Sun et al., 2018]] ). The spatial coverage of these products is near-global, with available estimations formally covering 60°S–60°N with decreasing quality from low to high latitudes, depending on the sensors and algorithms used ( [[#Hu--2019|Hu et al., 2019]] ). A detailed description of the most relevant satellite products is provided in section 10.2.1.1.

Recent trends (1980–2019) for GPCC V2020, CRU TS 4.04 and GPCP V2.3 show significant increases in land precipitation over tropical Africa, the eastern portions of Europe and North America, central Asia and the Maritime Continent (Figure 2.14d–f). Significant decreases are observed over central South America, western North America, northern Africa and the Middle East. A detailed assessment of the recent regional precipitation trends using the same datasets can be found in the Atlas. Global trends for 1980–2019 show a general increase in annual precipitation over land, which is particularly marked for CRU TS 4.04 and GHCNv4 (Table 2.6). These changes have been accompanied by a strengthening of precipitation seasonality over tropical land areas, although with broad spread between different satellite-based (GPCP, MSWEP_V1.2, PERSIANN-CDR) and in situ gridded datasets (GPCC, CRU TS; [[#Chou--2013|Chou et al., 2013]] ; [[#Li--2016|Li et al., 2016]] ; [[#Tan--2020|Tan et al., 2020]] ). Increasing trends since 1980, in contrast to longer-term declining trends since 1901, are particularly evident over much of Africa, while more widespread negative trends were observed over much of southern South America in the more recent period ( [[IPCC:Wg1:Chapter:Atlas|Atlas]] 7.2; [[#Knutson--2018|Knutson and Zeng, 2018]] ). A faster recent increase in precipitation over global land is inferred comparing the precipitation trends over 1960–2019 with 1980–2019 (Table 2.6). Over the global ocean, the comparison between precipitation datasets is compromised by the different measurement periods, as well as the spatial coverage of the available products ( [[#Adler--2017|Adler et al., 2017]] ; [[#Nguyen--2018|Nguyen et al., 2018]] ; [[#Jaber--2020|Jaber and Abu-Allaban, 2020]] ; [[#Nogueira--2020|Nogueira, 2020]] ), limiting the ability to assess the sign and magnitude of precipitation trends. The GPCPv2.3 database ( [[#Adler--2017|Adler et al., 2017]] , 2018) exhibits an increase of 2.94 mm yr <sup>–1</sup> per decade over 1980–2019, principally due to the trends over the Indian ocean and in the tropical western Pacific (Figure 2.15f). The regional patterns of recent trends are consistent with the documented increase in precipitation over tropical wet regions and the decrease over dry areas, estimated through GPCP v2.2 data ( [[#Liu--2013|Liu and Allan, 2013]] ; [[#Trammell--2015|Trammell et al., 2015]] ; [[#Kao--2017|Kao et al., 2017]] ; [[#Polson--2017|Polson and Hegerl, 2017]] ).

In summary, globally averaged land precipitation has ''likely'' increased since the middle of the <sup></sup> 20th century ( ''medium confidence'' ), with ''low confidence'' in trends prior to 1950. A faster increase in global land precipitation was observed since the 1980s ( ''medium confidence'' ), with large interannual variability and regional heterogeneity. Over the global ocean there is ''low confidence'' in the estimates of precipitation trends, linked to uncertainties in satellite retrievals, merging procedures and limited in situ observations.

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<span id="precipitation-minus-evaporation"></span>

===== 2.3.1.3.5 Precipitation minus evaporation =====

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The AR5 concluded that the pattern of precipitation minus evaporation (P–E) over the ocean had been enhanced since the 1950s ( ''medium confidence'' ). Saline surface waters had become saltier, while the relatively fresh surface waters had become fresher. The inferred changes in P–E were consistent with the observed increased TCWV, although uncertainties in the available products prevented identifying robust trends.

Estimating global-scale trends in P–E using direct observations alone is challenging due to limited evaporation measurements and inhomogeneities in satellite-derived precipitation and evaporation datasets ( [[#Hegerl--2015|Hegerl et al., 2015]] ; [[#López--2017|López et al., 2017]] ). Hence, the assessment of global P–E trends is generally performed using reanalyses, although changes in the observing system imply considerable uncertainty ( [[#Skliris--2014|Skliris et al., 2014]] ). Since the second half of the <sup></sup> 20th century, several reanalyses and observational datasets have shown increases in P–E over global land, although 75% of land areas exhibit no significant changes and both internal variability and observational uncertainty are substantial ( [[#Greve--2014|Greve et al., 2014]] ; [[#Robertson--2016|Robertson et al., 2016]] ). The recently released ERA5 ( [[#Hersbach--2020|Hersbach et al., 2020]] ) showed improvements in the representation of tropical precipitation, although it overestimates global precipitation trends in comparison to ERA-Interim and GPCP ( [[#Nogueira--2020|Nogueira, 2020]] ), and suffers from temporal changes in the annual balance between precipitation and evaporation ( [[#Hersbach--2020|Hersbach et al., 2020]] ). The spatial pattern of P–E trends over 1980–2019 (Figure 2.16a) are largely consistent with the trends in the GPCP v2.3 precipitation dataset (Figure 2.15f and [[#2.3.1.3.4|Section 2.3.1.3.4]] ) and agrees in sign with the trends from other reanalyses such as JRA-55 and MERRA-2 (L. [[#Yu--2020|]] [[#Yu--2020|Yu et al., 2020]] ).

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[[File:065e14ccefd8d7dafb150d10c9e869fc IPCC_AR6_WGI_Figure_2_16.png]]

'''Figure 2.''' '''16 |''' '''Changes in precipitation minus evaporation. (a)''' Trends in precipitation minus evaporation (P–E) between 1980 and 2019. Trends are calculated using OLS regression with significance assessed following AR(1) adjustment after [[#Santer--2008|Santer et al. (2008)]] (‘×’ marks denote non-significant trends). Time series of '''(b)''' global, '''(c)''' land-only and '''(d)''' ocean-only average annual P–E (mm day <sup>–1</sup> ). Further details on data sources and processing are available in the chapter data table (Table 2.SM.1).

A variety of reanalysis products exhibit diverse temporal evolutions of P–E (Figure 2.16b–d). Globally MERRA-2, ERA20C and ERA20CM exhibit little change whereas JRA-55, ERA5 and 20CRv3 all imply long-term changes (Figure 2.16d). A potential limitation in estimating P–E from some reanalysis products is readily apparent when considering the temporal evolution of global P–E from CFSR and MERRA (Figure 2.16d) which both exhibit strong discontinuities over the global ocean in the late 1990s. Over global land as a whole, precipitation exceeds evaporation (P–E &gt;0) for all the reanalysis products (Figure 2.16c), with decreasing trends in P–E for ERA5 and JRA-55 and increasing trends for MERRA-2 and CFSR. The P–E over the global ocean is negative (evaporation exceeding precipitation) for most reanalyses (Figure 2.16d), with declining trends in ERA5 and MERRA-2 dominated by trends in evaporation ( [[#Bosilovich--2017|Bosilovich et al., 2017]] ; [[#Hersbach--2020|Hersbach et al., 2020]] ) (Figure 2.16d). The recent increase in ocean evaporation was also documented for several reanalyses ( [[#Craig--2017|Craig et al., 2017]] ) and in satellite data ( [[#Andersson--2011|Andersson et al., 2011]] ; [[#Robertson--2014|Robertson et al., 2014]] ), although with considerable differences between available estimates ( [[#Chandanpurkar--2017|Chandanpurkar et al., 2017]] ; L. [[#Yu--2020|]] [[#Yu--2020|Yu et al., 2020]] ). An alternative indirect approach to estimate P–E changes is based on near-surface ocean salinity ( [[#2.3.3.2|Section 2.3.3.2]] ), which is partially driven by the freshwater flux at the ocean surface. The near-surface salinity trends are more spatially coherent compared to those revealed by P–E estimates from reanalyses, with an intensification of the water cycle over oceans, especially in subtropical regions ( [[#Durack--2012|Durack et al., 2012]] ; [[#Skliris--2014|Skliris et al., 2014]] ; L. [[#Yu--2020|]] [[#Yu--2020|Yu et al., 2020]] ). However, the precise rate of water cycle intensification implied by salinity trends is sensitive to methodological choices (e.g., [[#Skliris--2016|Skliris et al., 2016]] ; [[#Zika--2018|Zika et al., 2018]] ).

In conclusion, observational uncertainty yields ''low confidence'' in globally averaged trends in P–E over the 20th century, with a spatial pattern dominated by precipitation changes over land and by evaporation increases over the ocean. Different reanalyses disagree on the sign of long-term changes in the global mean P–E.

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===== 2.3.1.3.6 Streamflow =====

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The AR5 concluded that there was ''low confidence'' in a positive trend in global river discharge during the <sup></sup> 20th century. It noted that many of the largest rivers with long term streamflow records have been impacted by non-climatic human influences such as dam construction or land-use change.

River discharge is monitored widely, although gaps remain at a subcontinental scale over central Asia and Africa ( [[#Wei--2020|Wei et al., 2020]] ). Substantial recent efforts have been made to generate new global streamflow datasets, consolidating observations from many stream gauges to create streamflow indices ( [[#Do--2018|Do et al., 2018]] ; [[#Gudmundsson--2018|Gudmundsson et al., 2018]] ) and gridded products using neural networks ( [[#Barbarossa--2018|Barbarossa et al., 2018]] ) or combinations between observations and reanalyses ( [[#Suzuki--2018|Suzuki et al., 2018]] ; [[#Ghiggi--2019|Ghiggi et al., 2019]] ).

Human intervention on river discharge linked to increases in evapotranspiration and some reduction of intra-annual streamflow variability ( [[#Jaramillo--2015|Jaramillo and Destouni, 2015]] ; [[#Chai--2020|Chai et al., 2020]] ) might affect the detection of trends in extreme daily streamflow events ( [[#Do--2017|Do et al., 2017]] ; [[#Gudmundsson--2019|Gudmundsson et al., 2019]] ). However, these activities have a minor impact on annual streamflow compared to climate variations ( [[#Dai--2009|Dai et al., 2009]] ; [[#Alkama--2013|Alkama et al., 2013]] ). Available global studies post-1950 generally concur that there have been more rivers experiencing decreases than increases in runoff ( [[#Do--2017|Do et al., 2017]] ; [[#Su--2018|Su et al., 2018]] ; [[#Gudmundsson--2019|Gudmundsson et al., 2019]] ; X. [[#Shi--2019|]] [[#Shi--2019|Shi et al., 2019]] ). Most of the rivers have not experienced statistically significant changes in streamflow, and when globally aggregated there is no significant change ( [[#Dai--2017|Dai and Zhao, 2017]] ). Global streamflow variability is strongly modulated by ENSO and PDV, with below-normal global streamflow as a response to El Niño events and vice-versa during La Niña episodes ( [[#Dai--2016|Dai, 2016]] ; [[#Liang--2016|Liang et al., 2016]] ; [[#Kim--2019|Kim, 2019]] ). The response of streamflow to changes in precipitation associated with ENSO and PDV has heterogeneous regional patterns at subcontinental scales (Section 8.3.2.9.1). No significant trends are found for reanalysis-based discharge estimates over 1993 to 2015 ( [[#Chandanpurkar--2017|Chandanpurkar et al., 2017]] ). Uncertainties in global streamflow trends arise predominantly from changes in instrumentation, gauge restoration, recalibration of rating curves, flow regulation or channel engineering ( [[#Alkama--2011|Alkama et al., 2011]] ; [[#Gudmundsson--2018|Gudmundsson et al., 2018]] ; [[#Ghiggi--2019|Ghiggi et al., 2019]] ).

In summary, the sign of global streamflow trends remains uncertain, with slightly more globally gauged rivers experiencing significantly decreasing flows than significantly increasing flows since the 1950s ( ''low confidence'' ).

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