Editing IPCC:AR6/WGI/Chapter-10 (section)

==== 10.6.3.3 Observational Issues for India ====

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India has one of the oldest rain-gauge networks in the world, leading to the production of numerous observational products (reviewed in [[#Khouider--2020|Khouider et al., 2020]] ). Gridded gauge-based products dating back to the 19th century reveal pronounced decadal variability ( [[#Sontakke--2008|Sontakke et al., 2008]] ). Trends for India over the whole 20th century are inconclusive ( [[#Knutson--2018|Knutson and Zeng, 2018]] ), although declining over central and northern areas ( [[#Roxy--2015|Roxy et al., 2015]] ). Assessment of multiple observational datasets covering the Indian summer monsoon reveals significant declining rainfall over the second half of the 20th century ( [[IPCC:Wg1:Chapter:Chapter-8#8.3.2.4.1|Section 8.3.2.4.1]] and Figure 10.19c,d). A subsequent recovery has been noted since the early 2000s ( [[#Jin--2017|Jin and Wang, 2017]] ).

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[[File:6a7505023aeaa709cd7d8681f24af85e IPCC_AR6_WGI_Figure_10_19.png]]

'''Figure 10.''' '''19 |''' '''Changes in the Indian summer monsoon in the historical and future periods.''' Observational uncertainty demonstrated by a snapshot of rain-gauge density (% of 0.05° subgrid boxes containing at least one gauge) in the APHRO-MA 0.5° daily precipitation dataset for June to September 1956. '''(b)''' Multi-model ensemble (MME) mean bias of 34 CMIP6 models for June to September precipitation (mm day <sup>–1</sup> ) compared to CRU TS observations for the 1985–2010 period. '''(c)''' Maps of rainfall trends (mm day <sup>–1</sup> per decade) in CRU TS observations (1950–2000), the CMIP6 MME-mean of SSP5-8.5 future projections for 2015–2100 (34 models), the CMIP6 hist-GHG and hist-aer runs, both measured over 1950 to 2000. '''(d)''' Low-pass filtered time series of June to September precipitation anomalies (%, relative to 1995–2014 baseline) averaged over the central India box shown in panel (b). The averaging region (20°N–28°N, 76°E–87°E) follows other works ( [[#Bollasina--2011|Bollasina et al., 2011]] ; [[#Jin--2017|Jin and Wang, 2017]] ; [[#Huang--2020b|Huang et al., 2020b]] ). Time series are shown for CRU TS (brown), GPCC (dark blue), REGEN (green), APHRO-MA (light brown) observational estimates and the IITM all-India rainfall product (light blue) in comparison with the CMIP6 mean of 13 models for the all-forcings historical (pink) the aerosol-only (hist-aer, grey) and greenhouse gas-only (hist-GHG, blue). Dark red and blue lines show low-pass filtered MME-mean change in the CMIP6 historical/SSP5-8.5 (34 models) and CMIP5 historical/RCP8.5 (41 models) experiments for future projections to 2100. The filter is the same as that used in Figure 10.11 (d). To the right, box-and-whisker plots show the 2081–2100 change averaged over the CMIP5 (blue) and CMIP6 (dark red) ensembles. Note that some models exceed the plotting range (CMIP5: GISS-E2-R-CC, GISS-E2-R, IPSL-CM5B-LRl and CMIP6: CanESM5-CanOE, CanESM5 and GISS-E2-1-G). '''(e)''' Precipitation linear trend (% per decade) over Central India for historical 1950–2000 (left) and future 2015–2100 (right) periods in Indian Monsoon rainfall in observed estimates (black crosses), the CMIP5 historical-RCP8.5 simulations (blue), the CMIP6 ensemble (dark red) for historical all-forcings experiment and SSP5-8.5 future projection, the CMIP6 hist-GHG (light blue triangles), hist-aer (grey triangles) and historical all-forcings (same sample as for hist-aer and hist-GHG, pink circles). Ensemble means are also shown. Box-and-whisker plots show the trend distribution of the three coupled and the d4PDF atmosphere-only (for past period only) SMILEs used throughout (Chapter 10 and follow the methodology used in Figure 10.6. '''(f)''' Example spread of trends (mm day <sup>–1</sup> per decade) for the period 2016–2045 in RCP8.5 SMILE experiments of the MPI-ESM model, showing the difference between the three driest and three wettest trends among ensemble members over central India. All trends are estimated using ordinary least-squares regression. Further details on data sources and processing are available in the chapter data table (Table 10.SM.11).

Observational products containing critical inhomogeneities in gauge distribution and reporting over time are acknowledged as suitable for mesoscale analysis ( [[#Rajeevan--2009|Rajeevan and Bhate, 2009]] ), while use for climate trends requires consistent reporting over time from quality-controlled gauges (e.g., about 2000 gauges since the 1950s in [[#Rajeevan--2006|Rajeevan et al., 2006]] ). A newer 0.25°-gridded product covering 1901 onwards ( [[#Pai--2014|Pai et al., 2014]] , 2015), based on Shepard’s interpolation method for irregularly-spaced stations ( [[#Shepard--1968|Shepard, 1968]] ), shows increased intensity of daily rainfall and extremes over some regions, especially in the late-20th century. However, changes to the inputted gauges may have introduced an artificial jump in extreme rainfall since 1975 over central India ( [[#10.2.2.3|Section 10.2.2.3]] ; [[#Lin--2019|Lin and Huybers, 2019]] ). They suggest that this method may have masked declines in mean rainfall and highlight the need for availability of raw gauge data to allow transparent assessments. [[#Khouider--2020|Khouider et al. (2020)]] have successfully tested a probabilistic interpolation method for India to overcome problems inherent in algorithms based on inverse-distance weighting when applied to data-sparse regions. An example snapshot of the uneven distribution of rain gauges in a common observational product is shown in Figure 10.19a.

The uncertainty among local and international observational products for India can pose challenges when evaluating climate models (as in [[#10.2.2.6|Section 10.2.2.6]] ; [[#Prakash--2015|Prakash et al., 2015]] ). For the seasonal mean summer monsoon rainfall, [[#Collins--2013a|Collins et al. (2013a)]] found large biases separating many CMIP5 models from the available observational products. However, for seasonal mean variability, the spread across observational products was larger than across the CMIP5 ensemble.

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