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

==== 10.2.2.3 Data Scarcity ====

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Data scarcity arises largely due to the lack of maintenance of observing stations, inaccessibility of the data held in national networks, and uneven spatial distribution of stations that lead to a low density in many regions. This is particularly problematic when trying to assess regional climate change, for which a high density of observational data is desirable. Although in several regions numerous stations provide (monthly) data covering more than 100 years for both temperature and precipitation ( [[#GCOS--2015|GCOS, 2015]] ), large areas of the world remain sparsely covered. The post-1990 decline in the total number of stations contributing to the Global Precipitation Climatology Centre (GPCC) monthly product may be related to delays in data acquisition and not paucity of data ( [[#GCOS--2015|GCOS, 2015]] ). This is because GPCC is the result of a single time scale, single Essential Climate Variable (ECV) and single data collection centre. There is no similar drop-off of the rainfall reports in the Global Historical Climatology Network Daily database (GHCNd, [[#Menne--2012|Menne et al., 2012]] ) or the Integrated Surface Database (ISD) at the sub-daily time scale.

[[#Kidd--2017|Kidd et al. (2017)]] made some assumptions about GPCC-available gauges and indicated that only 1.6% of Earth’s surface lies within 10 km of a rain gauge, and many areas of the world are beyond 100 km from the nearest rain gauge. Data scarcity is especially critical over Africa ( [[#Nikulin--2012|Nikulin et al., 2012]] ; [[#Dike--2018|Dike et al., 2018]] ) but the apparent data scarcity could be due to reasons other than actual paucity of data, as stated earlier. For instance, over South Africa, the number of weather stations collecting daily temperature used in the fourth version of the Climatic Research Unit Temperature dataset (CRUTEM4, [[#Osborn--2014|Osborn and Jones, 2014]] ) has significantly declined since 1980 ( [[#Archer--2018|Archer et al., 2018]] ). Although CRUTEM4 has now been replaced by CRUTEM5 ( [[#Osborn--2021|Osborn et al., 2021]] ) it has yet to take advantage of the significant international efforts to curate and make available improved global holdings ( [[#Rennie--2014|Rennie et al., 2014]] ) which increased the global available station count for monthly mean temperatures. This includes additional stations from many African countries. The apparent decline in stations since the 1980s could also be due to countries not contributing their data to the SYNOP/CLIMAT networks for reasons other than having non-operational stations.

Even in Europe, precipitation station density in the widely used E-OBS gridded dataset varies largely in space and time across regions ( [[#Prein--2017|Prein and Gobiet, 2017]] ). This variability is partly due to the reluctance of some data owners to share their data with an international effort. Regardless of the reason, low station density is a major source of uncertainty ( [[#Isotta--2015|Isotta et al., 2015]] ). [[#Kirchengast--2014|Kirchengast et al. (2014)]] and [[#O--2019|O and Foelsche (2019)]] found that at least 2 to 5 (12) stations are required for capturing the area-averaged precipitation amount of heavy summer precipitation events on a daily (hourly) basis with a normalized root-mean-square error of less than 20%. Like the E-OBS dataset, gridded daily temperature and precipitation datasets are being developed for other regions of the world. Examples include south-east Asia (SA-OBS, [[#Van%20den%20Besselaar--2017|Van den Besselaar et al., 2017]] ), and Latin America and West Africa (ICA&amp;D, Van den [[#Besselaar--2015|Besselaar et al., 2015]] ). Despite the uneven distribution of stations in space and time, the value in these initiatives is illustrated by the large number of studies in which the data product is used. This is the case, for instance, in the work of [[#Condom--2020|Condom et al. (2020)]] over the Andes, a region with prominent data scarcity, and the African Monsoon Multidisciplinary Analyses project over West Africa (AMMA; e.g., [[#Lebel--2009|Lebel and Ali, 2009]] ). There have been efforts to reduce data scarcity through initiatives such as the International Surface Temperature Initiative (ISTI, [[#Thorne--2011|Thorne et al., 2011]] ), GHCND, and the Expanding Met Office Hadley Centre ISD with quality-controlled, sub-daily station data from 1931 (HadISD, [[#Dunn--2016|Dunn et al., 2016]] ).

Data scarcity arising from changing coverage in observation station networks results in substantial problems for climate monitoring (e.g., trend analysis of extreme events requires high temporal and spatial resolutions) or model evaluation ( [[#10.3.3.1|Section 10.3.3.1]] ). It is ''virtually certain'' that the scarcity and decline of observational availability in some regions (but not necessarily globally), increase the uncertainty of the long-term global temperature and precipitation estimates. As an example, [[#Lin--2019|Lin and Huybers (2019)]] found that changes in the number of rain gauges after 1975 resulted in spurious trends in extremes of Indian rainfall in a 0.25° gridded dataset spanning the 20th century. In fact, the number of stations used to construct the gridded dataset dropped by half after 1990, leading to inhomogeneity and spurious trends ( [[#10.6.3|Section 10.6.3]] ). Over the southern part of the Mediterranean, which is an area sparsely covered by meteorological stations, data scarcity can lead to large uncertainties in the different gridded datasets and strongly affect model evaluation ( [[#10.6.4|Section 10.6.4]] ). Satellite observations can compensate the ground-based precipitation radar data sparsity to prevent an oversight of significant climate change signals ( [[#Yokoyama--2019|Yokoyama et al., 2019]] ).

There are techniques for estimating and reconstructing missing data. The methods depend on the variable of interest, the temporal resolution (e.g., daily or monthly), and the type of climate (wet or dry), among others. There has been very little evaluation of the performance of classical and data mining methods (e.g., [[#Sattari--2017|Sattari et al., 2017]] ). The classical methods include the arithmetic mean, inverse distance weighting method, multiple regression analysis, multiple imputation, and single best estimator, while the data-mining methods include multilayer perceptron artificial neural network, support vector machine, adaptive neuro-fuzzy inference system, gene expression programming method, and K-nearest neighbour. Crowd-sourced data (individuals contribute their own data points to create a dataset for others to use) could play a role in minimizing data scarcity ( [[#10.2.4|Section 10.2.4]] ).

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