Editing IPCC:AR6/WGI/Chapter-3 (section)

==== 3.3.2.3 Precipitation ====

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AR5 concluded that there was ''medium confidence'' that human influence had contributed to large-scale precipitation changes over land since 1950, including an increase in the Northern Hemisphere mid- to high latitudes. Moreover, AR5 concluded that observational uncertainties and challenges in precipitation modelling precluded a more confident assessment ( [[#Bindoff--2013|Bindoff et al., 2013]] ). Overall, it found that large-scale features of mean precipitation in CMIP5 models were in modest agreement with observations, but there were systematic errors in the tropics ( [[#Flato--2013|Flato et al., 2013]] ).

Since AR5, X. [[#Li--2016b|Li et al. (2016b)]] found that CMIP5 models simulate the large scale patterns of annual mean land precipitation and seasonality well, as well as reproducing qualitatively the observed zonal mean land precipitation trends for the period 1948–2005: models capture the drying trends in the tropics and at 45°S and the wetting trend in the Northern Hemisphere mid- to high latitudes, but the amplitudes of the changes are much smaller than observed. Land precipitation was found to show enhanced seasonality in observations ( [[#Chou--2013|Chou et al., 2013]] ), qualitatively consistent with the simulated response to anthropogenic forcing ( [[#Dwyer--2014|Dwyer et al., 2014]] ). However, models do not appear to reproduce the zonal mean trends in the magnitude of the seasonal cycle over the period 1948–2005, nor the two-dimensional distributions of trends of annual precipitation and seasonality over land, but differences may be explainable by internal variability (X. [[#Li--2016|]] [[#Li--2016|Li et al., 2016]] b). However, observed trends in seasonality depend on data set used (X. [[#Li--2016|]] [[#Li--2016|Li et al., 2016]] b; [[#Marvel--2017|Marvel et al., 2017]] ), and [[#Marvel--2017|Marvel et al. (2017)]] found that observed changes in the annual cycle phase are consistent with model estimates of forced changes. These phase changes are mainly characterized by earlier onset of the wet season on the equatorward flanks of the extratropical storm tracks, particularly in the Southern Hemisphere. Box 8.2 assesses regional changes in water cycle seasonality.

The CMIP5 models have also been shown to adequately simulate the mean and interannual variability of the global monsoon ( [[#3.3.3.2|Section 3.3.3.2]] ), but maintain the double ITCZ bias in the equatorial Pacific ( [[#Lee--2014|Lee and Wang, 2014]] ; [[#Tian--2015|Tian, 2015]] ; [[#Ni--2018|Ni and Hsu, 2018]] ). Despite the ITCZ bias, CMIP5 models have been used to detect in reanalysis a southward shift in the ITCZ prior to 1975, followed by a northward shift in the ITCZ after 1975, in response to forced changes in inter-hemispheric temperature contrast (Sections 3.3.1.1 and 8.3.2.1, and Figure 8.11; [[#Bonfils--2020|Bonfils et al., 2020]] ; [[#Friedman--2020|Friedman et al., 2020]] ). CMIP5 models perform better than CMIP3 models, in particular regarding the global monsoon domain and intensity ( [[#Lee--2014|Lee and Wang, 2014]] ).

In observations at time scales less than a day intermittent rainfall fluctuations dominate variability, but CMIP5 models systematically underestimate them ( [[#Covey--2018|Covey et al., 2018]] ). Moreover, as noted in previous generation models, CMIP5 models produce rainfall too early in the day ( [[#Covey--2016|Covey et al., 2016]] ). Also, models overpredict precipitation frequency but have weaker intensity, although comparison with observed datasets is complex as there are large differences in intensity among them ( [[#Herold--2016|Herold et al., 2016]] ; [[#Pendergrass--2017|Pendergrass and Deser, 2017]] ; [[#Trenberth--2017|Trenberth et al., 2017]] ). Regarding trends in precipitation intensity, models have also been shown to reproduce the compensation between increasing heavy precipitation and decreasing light to moderate rainfall ( [[#Thackeray--2018b|Thackeray et al., 2018b]] ), a characteristic found in the observational record ( [[#Gu--2018|Gu and Adler, 2018]] ). Regional model performance is further assessed in [[IPCC:Wg1:Chapter:Chapter-8|Chapter 8]] and the Atlas, while precipitation extremes are considered in Chapter 11.

The simulation of annual mean rainfall patterns in the CMIP6 models reveals minor improvements compared to those in CMIP5 models (Figure 3.13). The persistent biases include the double ITCZ in the tropical Pacific (seen as bands of excessive rainfall on both sides of the equatorial Pacific in Figure 3.13b,d) and the southward-shifted ITCZ in the equatorial Atlantic, which have been linked to the meridional pattern of SST bias (S. [[#Zhou--2020|]] [[#Zhou--2020|Zhou et al., 2020]] ) and the reduced sensitivity of precipitation to local SST ( [[#Good--2021|Good et al., 2021]] ). [[#Tian--2020|Tian and Dong (2020)]] also found that all three generations of CMIP models share similar systematic annual mean precipitation errors in the tropics, but that the double ITCZ bias is slightly reduced in CMIP6 models in comparison to CMIP3 and CMIP5 models. They also found some improvement in the overly intense Indian ocean ITCZ and the too dry South American continent except over the Andes. [[#Fiedler--2020|Fiedler et al. (2020)]] identified improvements in the tropical mean spatial correlations and root mean square error of the climatology as well as in the day-to-day variability, but found little change across CMIP phases in the double ITCZ bias and diurnal cycle. The CMIP6 models reproduce better the domain and intensity of the global monsoon (see [[#3.3.3.2|Section 3.3.3.2]] ). Moreover, CMIP6 models better represent the storm tracks ( [[#Priestley--2020|Priestley et al., 2020]] ; also ( [[#3.3.3.3|Section 3.3.3.3]] ), thereby reducing the precipitation biases in the North Atlantic and mid-latitudes of the Southern Hemisphere (Figure 3.13b,d). As a result, pattern correlations between simulated and observed annual mean precipitation range between 0.80 and 0.92 for CMIP6 models, compared to a range of 0.79 to 0.88 for CMIP5 ( [[#Bock--2020|Bock et al., 2020]] ). This relative improvement may be related to increased model resolution, as found when comparing biases in the mean of the HighResMIP models with the mean of the corresponding lower-resolution versions of the same models (see Figure 3.13e,f), particularly in the tropics and extratropical storm tracks. Consistent with this, a recent study using several coupled models showed that increasing the atmospheric resolution leads to a strong decrease in the precipitation bias in the tropical Atlantic ITCZ (see further discussion in [[#3.8.2.2|Section 3.8.2.2]] ; [[#Vannière--2019|Vannière et al., 2019]] ). Based on these results we assess that despite some improvements, CMIP6 models still have deficiencies in simulating precipitation patterns, particularly over the tropical ocean ( ''high confidence'' ).

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[[File:0cec6193260a8480eb69f4fad725ce73 IPCC_AR6_WGI_Figure_3_13.png]]

Figure 3.13 | '''Annual-mean precipitation rate (mm day''' '''–1''' ''') for the period 1995–2014. (a)''' Multi-model (ensemble) mean constructed with one realization of the CMIP6 historical experiment from each model. '''(b)''' Multi-model mean bias, defined as the difference between the CMIP6 multi-model mean and the precipitation analysis from the Global Precipitation Climatology Project (GPCP) version 2.3 ( [[#Adler--2003|Adler et al., 2003]] ). '''(c)''' Multi-model mean of the root mean square error calculated over all months separately and averaged with respect to the precipitation analysis from GPCP version 2.3. '''(d)''' Multi-model mean bias, calculated as the difference between the CMIP6 multi-model mean and the precipitation analysis from GPCP version 2.3. Also shown is the multi-model mean bias as the difference between the multi-model mean of '''(e)''' high resolution and '''(f)''' low-resolution simulations of four HighResMIP models and the precipitation analyses from GPCP version 2.3. Uncertainty is represented using the advanced approach. No overlay indicates regions with robust signal, where ≥66% of models show change greater than the variability threshold and ≥80% of all models agree on sign of change; diagonal lines indicate regions with no change or no robust signal, where &lt;66% of models show a change greater than the variability threshold; crossed lines indicate regions with conflicting signal, where ≥66% of models show change greater than the variability threshold and &lt;80% of all models agree on the sign of change. For more information on the advanced approach, please refer to the Cross-Chapter Box Atlas.1. Dots in panel (e) mark areas where the bias in high resolution versions of the HighResMIP models is not lower in at least three out of four models than in the corresponding low-resolution versions. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).

Recent studies comparing observations and CMIP5 simulations have shown that tropical volcanic eruptions induce a significant reduction in global precipitation, particularly over the wet tropics, including the global monsoon regions ( [[#Iles--2014|Iles and Hegerl, 2014]] ; [[#Paik--2017|Paik and Min, 2017]] ; [[#Paik--2020a|Paik et al., 2020a]] ). Reconstructions and modelling studies also suggest a distinct remote influence of volcanic forcing such that large volcanoes erupting in one hemisphere can enhance monsoon precipitation in the other hemisphere (F. [[#Liu--2016|]] [[#Liu--2016|Liu et al., 2016]] ; [[#Zuo--2019|Zuo et al., 2019]] ). The climatic effect of volcanic eruptions is further assessed in Cross-Chapter Box 4.1.

An intensification of the wet–dry zonal mean patterns, consisting of the wet tropical and mid-latitude bands becoming wetter, and the dry subtopics becoming drier is expected in response to greenhouse gas and ozone changes (Section 8.2.2.2). However, detecting these changes is complicated by model errors in locating the main features of rainfall patterns. To deal with this issue, [[#Marvel--2013|Marvel and Bonfils (2013)]] identified in each CMIP5 historical simulation the latitudinal peaks and troughs of the rainfall latitudinal patterns, measured the amplification and shift of these patterns in a pattern-based fingerprinting study, and found that the simultaneous amplification and shift in zonal precipitation patterns are detectable in Global Precipitation Climatology Project (GPCP) observations over the 1979–2012 period. Similarly, [[#Bonfils--2020|Bonfils et al. (2020)]] found that the intensification of wet–dry zonal patterns identified in CMIP5 historical simulations is detectable in reanalyses over the 1950–2014 period (see also Figure 8.11).

Based on long-term island precipitation records, [[#Polson--2016|Polson et al. (2016)]] identified significant increases in precipitation in the tropics and decreases in the subtropics, which are consistent with those simulated by the CMIP5 models. Moreover, results from [[#Polson--2017|Polson and Hegerl (2017)]] give support to an intensification of the water cycle according to the wet-gets-wetter, dry-gets-drier paradigm over tropical land areas as well. Other studies suggest that this paradigm does not necessarily hold over dry regions where moisture is limited (see also Section 8.2.2.1; [[#Greve--2014|Greve et al., 2014]] ; [[#Kumar--2015|Kumar et al., 2015]] ). [[#Polson--2017|Polson and Hegerl (2017)]] explained this discrepancy by taking into account the seasonal and interannual movement of the regions ( [[#Allan--2014|Allan, 2014]] ). A follow-up study using CMIP6 models also found that the observed strengthening contrast of precipitation over wet and dry regions was detectable, although the increase was significantly larger in observations than in the multi-model mean. The change was attributed to a combination of anthropogenic and natural forcings, with anthropogenic forcings detectable in multi-signal analyses (Figure 3.14; [[#Schurer--2020|Schurer et al., 2020]] ).

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[[File:8946db0b175de9fa41f10aaefd9df3d4 IPCC_AR6_WGI_Figure_3_14.png]]

Figure 3.14 | '''Wet (a) and dry (b) region tropical mean (30°S–30°N) annual precipitation anomalies.''' Observed data are shown with black lines (GPCP), ERA5 reanalysis is shown in grey, single model simulations are shown with light blue/red lines (CMIP6), and multi-model mean results are shown with dark blue/red lines (CMIP6). Wet and dry region annual anomalies are calculated as the running mean over 12 months relative to a 1988–2020 base period. The regions are defined as the wettest third and driest third of the surface area, calculated for the observations and for each model separately for each season (following [[#Polson--2017|Polson and Hegerl, 2017]] ). Scaling factors '''(c, d)''' are calculated for the combination of the wet and dry region mean, where the observations, reanalysis and all the model simulations are first standardized using the mean standard deviation of the pre-industrial control simulations. Two total least squares regression methods are used: noise in variables (following [[#Polson--2017|Polson and Hegerl, 2017]] ) which estimates a best estimate and a 5–95% confidence interval using the pre-industrial controls (circle and thick green line) and the pre-industrial controls with double the variance (thin green line); and a bootstrap method ( [[#DelSole--2019|DelSole et al., 2019]] ) (5–95% confidence interval shown with a purple line and best estimate with a circle). Panel (c) shows results for GPCP and panel (d) for ERA5. Figure is adapted from [[#Schurer--2020|Schurer et al. (2020)]] . Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).

Global land precipitation has ''likely'' increased since the middle of the 20th century ( ''medium confidence'' ), while there is ''low confidence'' in trends in land data prior to 1950 and over the ocean during the satellite era due to disagreement between datasets ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.1.3.4|Section 2.3.1.3.4]] ). Figure 3.15a shows the time evolution of the global mean land precipitation since 1950, as well as the trend during the period. [[#Adler--2017|Adler et al. (2017)]] found no significant trend in the global mean precipitation during the satellite era, consistent with model simulations ( [[#Wu--2013|Wu et al., 2013]] ) and physical understanding of the energy budget (Section 8.2.1). This has been suggested to be due to the negative effect of anthropogenic sulphate aerosol that opposed the positive influence of rising global mean temperatures due to greenhouse gases ( [[#Salzmann--2016|Salzmann, 2016]] ; [[#Richardson--2018|Richardson et al., 2018]] ). The precipitation change expected from ocean warming is also partly offset by the fast atmospheric adjustment to increasing greenhouse gases (Section 8.2.1). Over the ocean, the negligible trend may be due to the cancelling effects of CO <sub>2</sub> and aerosols ( [[#Richardson--2018|Richardson et al., 2018]] ).

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[[File:6965d17c0b5e1e5bdf038a871bbd85eb IPCC_AR6_WGI_Figure_3_15.png]]

Figure 3.15 | '''Observed and simulated time series of anomalies in zonal average annual mean precipitation. (a), (c–f)''' Evolution of global and zonal average annual mean precipitation (mm day <sup>–1</sup> ) over areas of land where there are observations, expressed relative to the base period of 1961–1990, simulated by CMIP6 models (one ensemble member per model) forced with both anthropogenic and natural forcings (brown) and natural forcings only (green). Multi-model means are shown in thick solid lines and shading shows the 5–95% confidence interval of the individual model simulations. The data is smoothed using a low pass filter. Observations from three different datasets are included: gridded values derived from Global Historical Climatology Network (GHCN version 2) station data, updated from [[#Zhang--2007|Zhang et al. (2007)]] , data from the Global Precipitation Climatology Product (GPCP L3 version 2.3, [[#Adler--2003|Adler et al. (2003)]] ) and from the Climate Research Unit (CRU TS4.02, [[#Harris--2014|Harris et al. (2014)]] ). Also plotted are boxplots showing interquartile and 5–95% ranges of simulated trends over the period for simulations forced with both anthropogenic and natural forcings (brown) and natural forcings only (blue). Observed trends for each observational product are shown as horizontal lines. Panel (b) shows annual mean precipitation rate (mm day <sup>–1</sup> ) of GHCN version 2 for the years 1950–2014 over land areas used to compute the plots. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).

A gridpoint based analysis of annual precipitation trends over land regions since 1901 ( [[#Knutson--2018|Knutson and Zeng, 2018]] ) comparing observed and simulated trends found that detectable anthropogenic increasing trends have occurred prominently over many mid- to high-latitude regions of the Northern Hemisphere and subtropics of the Southern Hemisphere. The observed trends in many cases are significantly stronger than modelled in the CMIP5 historical runs for the 1901–2010 period (though not for 1951–2010), which may be due to disagreement between observed datasets ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.1.3.4|Section 2.3.1.3.4]] ), and/or suggest possible deficiencies in models.

The observed precipitation increase in the Northern Hemisphere high latitudes over the period 1966–2005 was attributed to anthropogenic forcing by a study using CMIP5 models ( [[#Wan--2015|Wan et al., 2015]] ) supporting the AR5 assessment. Initial results from CMIP6 also support the role of anthropogenic forcing in the precipitation increase observed in Northern Hemisphere high latitudes (see Figure 3.15c): the observed positive trend detected for the band 60°N–90°N can only be reproduced when anthropogenic forcing is included, although models tend to simulate overall a larger positive trend. A similar positive trend, but less significant, is also detected between 30°N–60°N, while in the southern mid-latitudes no trend is simulated (see Figure 3.15d, f).

For the Southern Hemisphere extratropics, [[#Solman--2016|Solman and Orlanski (2016)]] found that the observed summertime rainfall increase over high latitudes and decrease over mid-latitudes over the period 1979–2010 are quasi-zonally symmetric and related to changes in eddy activity. The latter were in turn found to be associated with the poleward shift of the westerlies due mostly to ozone depletion. Positive rainfall trends in the subtropics, particularly over south-eastern South America (see also Section 10.4.2.2) and northern and central Australia, have been also attributed to stratospheric ozone depletion ( [[#Kang--2011|Kang et al., 2011]] ; [[#Gonzalez--2014|Gonzalez et al., 2014]] ) and greenhouse gases ( [[#Vera--2015|Vera and Díaz, 2015]] ; [[#Saurral--2019|Saurral et al., 2019]] ). During austral winter, wetting at high latitudes and drying at mid-latitudes are not zonally homogenous, due to both changes in eddy activity and increased lower troposphere humidity. [[#Solman--2016|Solman and Orlanski (2016)]] associated these climate changes with increases in greenhouse gas concentration levels. Recently, [[#Blazquez--2017|Blazquez and Solman (2017)]] have shown that CMIP5 models represent very well the dynamical forcing and the frequency of frontal precipitation in the Southern Hemisphere winter extratropics, but the amount of precipitation due to fronts is overestimated. Chapters 10 and 11 validate in more detail the simulation of fronts in climate models (Sections 10.3.3.4.4 and 11.7.2.3).

Over the ocean, observations show coherent large-scale patterns of fresh ocean regions becoming fresher and salty ocean regions saltier across the globe, which has been related through modelling studies to changes in precipitation minus evaporation and is consistent with the wet-gets-wetter, dry-gets-drier paradigm (see Sections 3.5.2.2 and 8.2.2.1; [[#Durack--2012|Durack et al., 2012]] , 2013; [[#Skliris--2014|Skliris et al., 2014]] ; [[#Durack--2015|Durack, 2015]] ; [[#Hegerl--2015|Hegerl et al., 2015]] ; [[#Levang--2015|Levang and Schmitt, 2015]] ; [[#Zika--2015|Zika et al., 2015]] ; [[#Grist--2016|Grist et al., 2016]] ; [[#Cheng--2020|Cheng et al., 2020]] ).

Overall, studies published since AR5 provide further evidence of an anthropogenic influence on precipitation, and therefore we now assess that it is ''likely'' that human influence has contributed to large-scale precipitation changes observed since the mid-20th century. New attribution studies strengthen previous findings of a detectable increase in mid to high latitude land precipitation over the Northern Hemisphere ( ''high confidence'' ). There is ''medium confidence'' that human influence has contributed to a strengthening of the zonal mean wet tropics-dry subtropics contrast, and that tropical rainfall changes follow the wet-gets-wetter, dry-gets-drier paradigm. There is also ''medium confidence'' that ozone depletion has increased precipitation over the southern high latitudes and decreased it over southern mid-latitudes during austral summer. Owing to observational uncertainties and inconsistent results between studies, we conclude that there is ''low confidence'' in the attribution of changes in the seasonality of precipitation.

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