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

== 10.2 Using Observations for Constructing Regional Climate Information ==

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Considerable challenges (and opportunities) remain in using observations for climate monitoring, for evaluating and improving climate models ( [[#10.3.1|Section 10.3.1]] ), for constructing reanalyses and post-processing model outputs, and therefore, ultimately, for increasing our confidence in the attribution of past climate changes and in future climate projections at the regional scale. While an assessment of large-scale observations can be found in [[IPCC:Wg1:Chapter:Chapter-2|Chapter 2]] (Cross-Chapter Box 2.2 and [[IPCC:Wg1:Chapter:Chapter-2#2.3|Section 2.3]] ), this section discusses the specific aspects of the observations at regional scale and over the typological regions considered in the regional chapters ( [[#10.1.5|Section 10.1.5]] ). This section focuses on land regions and does not consider the specific requirements of ocean observations (see [[IPCC:Wg1:Chapter:Chapter-9|Chapter 9]] and SROCC ( [[#IPCC--2019b|IPCC, 2019b]] ) for more information on this aspect).

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=== 10.2.1 Observation Types and Their Use at Regional Scale ===

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==== 10.2.1.1 In Situ and Remote-sensing Data ====

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Surface or in situ observations can come from a variety of networks: climate reference networks, mesoscale weather and supersite observation networks, citizen science networks, among others, all with their strengths and weaknesses ( [[#McPherson--2013|McPherson, 2013]] ; [[#Thorne--2018|Thorne et al., 2018]] ). Supersite observatories are surface and atmospheric boundary layer observing networks that measure a large number of atmospheric and soil variables at least hourly over a decade or more, ideally located in rural areas ( [[#Ackerman--2003|Ackerman and Stokes, 2003]] ; [[#Haeffelin--2005|Haeffelin et al., 2005]] ; [[#Xie--2010|Xie et al., 2010]] ; [[#Chiriaco--2018|Chiriaco et al., 2018]] ). Adequate calibration of instruments, quality control and homogenization are essential in these sites. They produce valuable data needed to diagnose processes and changes in regional and local climate. Many climate datasets have been developed from in situ station observations, at different spatial scales and temporal frequencies (Annex I: Observational Products). These include sub-daily ( [[#Dumitrescu--2016|Dumitrescu et al., 2016]] ; [[#Blenkinsop--2017|Blenkinsop et al., 2017]] ), daily ( [[#Chen--2008|Chen et al., 2008]] ; Camera et al., 2014; [[#Journée--2015|Journée et al., 2015]] ; [[#Funk--2015|Funk et al., 2015]] ; [[#Aalto--2016|Aalto et al., 2016]] ; [[#Beck--2017a|Beck et al., 2017a]] , b; [[#Schneider--2017|Schneider et al., 2017]] ) or monthly time scales ( [[#Cuervo-Robayo--2014|Cuervo-Robayo et al., 2014]] ; [[#Aryee--2018|Aryee et al., 2018]] ). Sub-daily data is useful for estimating storm surge ( [[#Mori--2014|Mori et al., 2014]] ) or river discharge ( [[#Shrestha--2015|Shrestha et al., 2015]] ), daily data for carbon-stock dynamics ( [[#Haga--2020|Haga et al., 2020]] ) or tourism ( [[#Watanabe--2018|Watanabe et al., 2018]] ), and monthly data for beach morphology ( [[#Bennett--2019|Bennett et al., 2019]] ).

Satellite products provide a valuable complement to in situ measurements, particularly over regions where in situ measurements are unavailable. They have been discussed in earlier chapters (e.g., Chapters 2 and 8) for large-scale assessment. Currently 54 essential climate variables (ECVs; [[#Bojinski--2014|Bojinski et al., 2014]] ) are defined by the Global Climate Observing System (GCOS) program, and passed on, for example, to NASA programmes through the Decadal Survey, to the Copernicus Climate Change Service of the European Union, to the ESA Climate Change Initiative ESA-CCI, as well as to the international collaborations with geostationary Earth orbit (GEO) satellites. Their observations are valuable ( ''high confidence'' ) for regional applications since they provide multi-channel images at very high spatiotemporal resolutions, typically 16 channels, 1–2 km, every 10 to 15 minutes. The advanced geostationary satellites are: Himawari-8 and 9 ( [[#Kurihara--2016|Kurihara et al., 2016]] ), GOES-East and GOES-17 ( [[#Goodman--2018|Goodman et al., 2018]] ), Meteosat-10 and 11 ( [[#Schmetz--2002|Schmetz et al., 2002]] ) and FY-4 ( [[#Cao--2014|Cao et al., 2014]] ). Geostationary satellite networks or constellations form an essential component of the Global Observation System ( https://www.wmo.int/pages/prog/www/OSY/GOS.html ), providing measurements not only for various cloud properties and moisture but also for air quality, land and ocean surface conditions, and lightning.

Low Earth orbit (LEO) satellites, with orbits typically at 400–700 km, provide advanced measurements of the Earth’s surface. Sun-synchronous polar orbiters can also cover the polar regions, which cannot be observed with GEO satellites. Examples of LEO observations for land surface monitoring are NASA’s Landsat ( [[#Wulder--2016|Wulder et al., 2016]] ), ESA’s Soil Moisture Ocean Salinity Earth Explorer (SMOS) mission ( [[#Kerr--2012|Kerr et al., 2012]] ), the Sentinel missions of the Copernicus programme, and JAXA’s ALOS-2 ( [[#Ohki--2019|Ohki et al., 2019]] ), providing high spatial resolution land surface images. Many kinds of data are accumulated for land use and land cover studies, targeting aspects like urban footprint ( [[#Florczyk--2019|Florczyk et al., 2019]] ), land-cover data (Global Land 30; CCI-LC: [[#ESA--2021|ESA, 2021]] ; [[#Chen--2018|Chen and Chen, 2018]] ), land surfacetemperature data (Landsat, [[#Parastatidis--2017|Parastatidis et al., 2017]] ), and surface albedo ( [[#Chrysoulakis--2019|Chrysoulakis et al., 2019]] ).

Availability of active sensors on LEO satellites enables measurement of microphysical properties of aerosol, cloud and precipitation, which can advance regional climate studies and process evaluation studies to improve regional climate models ( ''high confidence'' ). An example is the polar-orbiting ‘afternoon-train’ satellite constellation (known as the A-train), incorporating Aqua, CALIPSO, Cloudsat, PARASOL, Glory and Aura satellites. Vertical profiling observations from Cloudsat (with a W-band cloud radar) and CALIPSO (with a cloud lidar) led to considerable advances in measurements of cloud microphysics ( [[#Stephens--2018|Stephens et al., 2018]] ). Precipitation and its extremes are essential concerns of regional climate studies. The GPM (65°N–65°S, 2014–present) and the preceding TRMM (36.5°N–36.5°S, 1997–2015) with Ku-/Ka-band precipitation radars have provided three-dimensional measurements of precipitation with about 5 km resolution and sub-daily sampling ( [[#Skofronick-Jackson--2017|Skofronick-Jackson et al., 2017]] ). Their non-sun-synchronous observation works to cross-calibrate the constellation satellites to produce global high-resolution mapped products of precipitation, such as Integrated Multi-satellitE Retrievals for GPM (IMERG; [[#Huffman--2007|Huffman et al., 2007]] ) and the Global Satellite Mapping of Precipitation (GSMaP; [[#Kubota--2007|Kubota et al., 2007]] ), with hourly sampling at about 11 km resolution. The CPC MORPHing technique (CMORPH) has provided 30 min interval global precipitation with about 8 km coverage since 2002 ( [[#Joyce--2004|Joyce et al., 2004]] ). Precipitation estimations from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) is a sub-daily to daily rainfall product that covers 50°S to 50°N globally with 25 km resolution from 2000 to the present ( [[#Nguyen--2019|Nguyen et al., 2019]] ), and is used for semi-global-scale precipitation coverage ( [[#Benestad--2018|Benestad, 2018]] ). TRMM/GPM observations have enabled estimates to be obtained for global four-dimensional convective heating ( [[#Shige--2009|Shige et al., 2009]] ; [[#Tao--2016|Tao et al., 2016]] ; [[#Takayabu--2020|Takayabu and Tao, 2020]] ).

The use of these data has enhanced our understanding of precipitation processes at regional scale ( ''high confidence'' ), such as diurnal cycles in a large river valley (H. [[#Chen--2012|]] [[#Chen--2012|Chen et al., 2012]] ), and in coastal ( [[#Hassim--2016|Hassim et al., 2016]] ; [[#Yokoi--2017|Yokoi et al., 2017]] ) and mountainous regions ( [[#Hirose--2017|Hirose et al., 2017]] ). Three-dimensional observations revealed the contrasts in regional characteristics of rainfall extremes in monsoon regions and continental dry regions ( [[#Sohn--2013|Sohn et al., 2013]] ; [[#Hamada--2018|Hamada and Takayabu, 2018]] ). Satellite measurements are also used to evaluate climate model performance, as well as to develop new parametrizations. As a demonstration of the utility of these products in studying model bias, a subtropical cumulus congestus regime has been identified that may be implicated in the unrealistic double Inter-tropical Convergence Zone (ITCZ) found in some climate models ( [[#Takayabu--2010|Takayabu et al., 2010]] ; [[#Hirota--2011|Hirota et al., 2011]] , 2014). Another example is a parametrization of a land surface model that was developed specifically for a certain soil type. By assimilating satellite brightness temperature observations with their LDAS-UT scheme, [[#Yang--2007|Yang et al. (2007)]] successfully optimized a land surface model for the Tibetan Plateau.

For application at a regional scale, it is important to consider variations in the spatiotemporal resolution of the satellite products. A simple concatenation of data in time can show artificial jumps that are artefacts of changes in calibration and processing algorithms, or related to satellite orbital stability or changing performance of the instruments ( [[#Wielicki--2013|Wielicki et al., 2013]] ; [[#Barrett--2014|Barrett et al., 2014]] ). Recalibration and cross-calibration are then prerequisites for obtaining homogeneous time series of measurements across different or successive satellites that can then be used to produce long series that are valid as climate data records ( [[#Kanemaru--2017|Kanemaru et al., 2017]] ; [[#Merchant--2017|Merchant et al., 2017]] ). Scale representativeness is also an issue in utilizing soil observations ( [[#Taylor--2012|Taylor et al., 2012]] , 2013). Although a variety of technologies to measure soil moisture at the point scale exist ( [[#Dobriyal--2012|Dobriyal et al., 2012]] ), its spatial representativeness is less than 1 m <sup>2</sup> ( [[#Ochsner--2013|Ochsner et al., 2013]] ; L. [[#Liu--2016|]] [[#Liu--2016|Liu et al., 2016]] ). Therefore, to be able to use in situ soil moisture for validating coarser-scale data from satellites or models, networks of point-scale measurements are used ( [[#Crow--2015|Crow et al., 2015]] ; [[#Polcher--2016|Polcher et al., 2016]] ). Smaller networks are typically of the size of a single climate model gridcell or a satellite pixel and are suitable for monitoring watersheds, while small numbers of those representing larger areas (&gt;100 km <sup>2</sup> ) are emerging ( [[#Ochsner--2013|Ochsner et al., 2013]] ).

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==== 10.2.1.2 Derived Products ====

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Derived observational products are created from raw datasets collected from surface stations, remote-sensing instruments, or research vessels, which are converted into meaningful physical quantities by applying a suitable measurement theory, using either statistical interpolation techniques ( [[#10.2.2.4|Section 10.2.2.4]] ) or numerical atmospheric and land surface models ( [[#Bosilovich--2015|Bosilovich et al., 2015]] ).

Most global observational datasets are available at coarse temporal and spatial resolution, and do not include all available station data from a particular region, due to data availability problems. Therefore, efforts have been made to develop regional or country-scale datasets (Annex I). Radar and satellite remote sensing are resources that can provide a valuable complement to direct measurements at regional scale. Examples for precipitation have been described already, some of which have been released to the community ( [[#Dinku--2014|Dinku et al., 2014]] ; [[#Oyler--2015|Oyler et al., 2015]] ; [[#Manz--2016|Manz et al., 2016]] ; [[#Dietzsch--2017|Dietzsch et al., 2017]] ; [[#Yang--2017|Yang et al., 2017]] ; [[#Bližňák--2018|Bližňák et al., 2018]] ; [[#Krähenmann--2018|Krähenmann et al., 2018]] ; [[#Panziera--2018|Panziera et al., 2018]] ; [[#Shen--2018|Shen et al., 2018]] ). However, some of these datasets are limited by their short record, varying between one ( [[#Shen--2018|Shen et al., 2018]] ) and 64 years ( [[#Oyler--2015|Oyler et al., 2015]] ).

Reanalysis products are numerical climate simulations that use data assimilation to incorporate as many irregular observations as possible. These products encompass many physical and dynamical processes. They generate a coherent estimate of the state of the climate system on uniform grids either at global ( [[#Chaudhuri--2013|Chaudhuri et al., 2013]] ; [[#Balsamo--2015|Balsamo et al., 2015]] ), regional ( [[#Chaney--2014|Chaney et al., 2014]] ; [[#Maidment--2014|Maidment et al., 2014]] ; [[#Dahlgren--2016|Dahlgren et al., 2016]] ; [[#Langodan--2017|Langodan et al., 2017]] ; [[#Attada--2018|Attada et al., 2018]] ; [[#Mahmood--2018|Mahmood et al., 2018]] ) or country scales ( [[#Rostkier-Edelstein--2014|Rostkier-Edelstein et al., 2014]] ; Krähenmannet al., 2018; [[#Mahmood--2018|Mahmood et al., 2018]] ).

Reanalyses incorporate an increasing volume of observations from a growing number of sources over time, which sometimes presents a difficulty for trend analysis. However, regional reanalyses are valuable for regional climate assessments, since they can employ high-resolution model simulations due to their limited spatial domain. Their accuracy is also better than global reanalyses since they are often developed over regions with a high density of observational data (sometimes not freely available for all regions) to be assimilated into the model (e.g., [[#Yamada--2012|Yamada et al., 2012]] ). Regional reanalyses can assimilate locally dense and high-frequency observations, such as from local observation networks ( [[#Mahmood--2018|Mahmood et al., 2018]] ; [[#Su--2019|Su et al., 2019]] ) and radar precipitation ( [[#Wahl--2017|Wahl et al., 2017]] ) in addition to the observations assimilated by global reanalyses. In some regional reanalyses, satellite-derived high-resolution sea ice ( [[#Bromwich--2016|Bromwich et al., 2016]] , 2018) and sea surface temperature ( [[#Su--2019|Su et al., 2019]] ) are also applied as lower boundary conditions. The periods of regional reanalyses are limited by the availability of the observations for assimilation and by the global reanalyses needed as lateral boundary conditions. Most regional reanalyses cover the past 10 to 30 years. There are also regional reanalysis activities that use conventional observations only, which produce consistent datasets over 60 years to capture precipitation trends, extremes and changes ( [[#Fukui--2018|Fukui et al., 2018]] ). Existing regional reanalyses cover North America ( [[#Mesinger--2006|Mesinger et al., 2006]] ), Europe ( [[#Dahlgren--2016|Dahlgren et al., 2016]] ; [[#Jermey--2016|Jermey and Renshaw, 2016]] ; [[#Kaspar--2020|Kaspar et al., 2020]] ), the Arctic ( [[#Bromwich--2016|Bromwich et al., 2016]] , 2018), South Asia ( [[#Mahmood--2018|Mahmood et al., 2018]] ), and Australia ( [[#Su--2019|Su et al., 2019]] ). A project for regional reanalysis covering Japan has also started ( [[#Fukui--2018|Fukui et al., 2018]] ), where grid spacing is between 5 and 32 km, although cumulus parametrizations are still needed to compute sub-grid scale cumulus convection. Recently, reanalyses using convection-permitting regional models have been published (e.g., [[#Wahl--2017|Wahl et al., 2017]] , for central Europe).

The data assimilation schemes used in regional reanalyses are often relatively simple methods, specifically nudging ( [[#Kaspar--2020|Kaspar et al., 2020]] ) and 3DVAR ( [[#Mesinger--2006|Mesinger et al., 2006]] ; [[#Bromwich--2016|Bromwich et al., 2016]] ; [[#Dahlgren--2016|Dahlgren et al., 2016]] ), rather than the more complex schemes implemented in state-of-the-art global reanalysis systems. This is partly due to limitations of computational resources. Recently, a number of regional reanalyses using more sophisticated methods, such as 4DVAR and Ensemble Kalman filter, have been published ( [[#Jermey--2016|Jermey and Renshaw, 2016]] ; [[#Fukui--2018|Fukui et al., 2018]] ; [[#Mahmood--2018|Mahmood et al., 2018]] ; [[#Su--2019|Su et al., 2019]] ). The regional reanalyses also incorporate uncertainties due to deficiencies of the models, data assimilation schemes and observations. To estimate uncertainties, some regional reanalyses apply data assimilation using ensemble forecasts ( [[#Bach--2016|Bach et al., 2016]] ). Another approach compares multiple regional reanalyses produced with different systems covering the same domain, which represents the uncertainties better than single reanalysis systems with ensemble data assimilation schemes ( [[#Kaiser-Weiss--2019|Kaiser-Weiss et al., 2019]] ).

The regional reanalyses represent the frequencies of extremes and the distributions of precipitation, surface air temperature, and surface wind better than global reanalyses ( ''high confidence'' ). This is due to the use of high-resolution regional climate models (RCMs), as indicated by different regional climate modelling studies ( [[#Mesinger--2006|Mesinger et al., 2006]] ; [[#Bollmeyer--2015|Bollmeyer et al., 2015]] ; [[#Bromwich--2016|Bromwich et al., 2016]] , 2018; [[#Dahlgren--2016|Dahlgren et al., 2016]] ; [[#Jermey--2016|Jermey and Renshaw, 2016]] ; [[#Fukui--2018|Fukui et al., 2018]] ; [[#Su--2019|Su et al., 2019]] ). Regional reanalyses, however, retain uncertainties due to deficiencies in the physical parametrization used in RCMs and by the use of relatively simple data assimilation algorithms ( [[#Bromwich--2016|Bromwich et al., 2016]] ; [[#Jermey--2016|Jermey and Renshaw, 2016]] ; [[#Su--2019|Su et al., 2019]] ). Regional reanalyses can provide estimates that are more consistent with observations than dynamical downscaling approaches, due to the assimilation of additional local observations ( ''high confidence'' ) ( [[#Bollmeyer--2015|Bollmeyer et al., 2015]] ; [[#Fukui--2018|Fukui et al., 2018]] ).

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=== 10.2.2 Challenges for Regional Climate Change Assessment ===

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==== 10.2.2.1 Quality Control ====

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The usefulness of any observational dataset is conditioned by the availability and outcome of a quality control (QC) process. The objective of the QC is to verify that data are representative of the measured variable and to what degree the value could be contaminated by unrelated or conflicting factors ( [[#WMO--2017a|WMO, 2017a]] ). Data quality assessment is key for ensuring that the data are credible and to establish trusted relationships between the data provider and the users ( [[#Nightingale--2019|Nightingale et al., 2019]] ). QC is performed for all relevant global climate datasets (e.g., [[#Menne--2018|Menne et al., 2018]] ). For instance, QC informs users that old reanalysis datasets can be inconsistent in the long term because they assimilated inhomogeneous observations over the reanalyses period ( [[#Kobayashi--2015|Kobayashi et al., 2015]] ). As a consequence, the evaluation against independent observations suggests that reanalyses should not be automatically regarded as climate-quality products for monitoring long-term trends at the regional level ( [[#Manzanas--2014|Manzanas et al., 2014]] ; [[#Torralba--2017|Torralba et al., 2017]] ). QC needs to be systematically carried out by the institutions responsible for handling the data (e.g., [[#Cao--2016b|Cao et al., 2016b]] ).

The QC procedure depends strongly on the specific nature of the dataset. It focuses on aspects such as the correct identification of sensor, time and location, detection of unfeasible or inconsistent data, error estimation, assessment of the adequacy of the uncertainty information and the adequacy of the documentation (e.g., [[#Heaney--2016|Heaney et al., 2016]] ). QC principles also apply to model data ( [[#Tapiador--2017|Tapiador et al., 2017]] ). An important piece of information provided is the representativeness error ( [[#10.2.1.1|Section 10.2.1.1]] ; [[#Gervais--2014|Gervais et al., 2014]] ). When problems in the data representativeness are identified, observational datasets are provided with a quality mask ( [[#Contractor--2020|Contractor et al., 2020]] ), or the problematic dataare either removed or corrected ( [[#Ashcroft--2018|Ashcroft et al., 2018]] ). These are factors often taken into account in constructing regional climate information ( [[#Kotlarski--2019|Kotlarski et al., 2019]] ).

Quality-controlled data are now produced widely at the regional level, as in the case of sub-daily precipitation records in the United Kingdom ( [[#Blenkinsop--2017|Blenkinsop et al., 2017]] ) and the USA ( [[#Nelson--2016|Nelson et al., 2016]] ). However, many more datasets and variables lack the same level of scrutiny ( [[#Alexander--2016|Alexander, 2016]] ). Quality-controlled, high-resolution observational datasets are especially needed at regional and local scales to assess models as their resolution increases ( [[#Di%20Luca--2016|Di Luca et al., 2016]] ; [[#Zittis--2017|Zittis and Hadjinicolaou, 2017]] ), although the awareness and appropriate use of the QC information is challenging ( [[#Tapiador--2017|Tapiador et al., 2017]] ) when generating regional climate information ( ''high confidence'' ).

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==== 10.2.2.2 Homogenization ====

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Homogenization aims to make data spatially and temporally ‘homogeneous’. Changes in a homogeneous time series are solely due to large-scale climatic changes (whether forced or due to internal variability). Station data are influenced by factors that act at regional scales, from the mesoscale and local scale down to the microscale ( [[#WMO--2019|WMO, 2019]] ). Station time series contain inhomogeneities such as artificial jumps or trends, which hamper assessments of regional long-term trends. Typical reasons for this are the urbanization of a station’s surroundings, which can lead to warming ( [[#Hamdi--2010|Hamdi, 2010]] ; [[#Hansen--2010|Hansen et al., 2010]] ; [[#Adachi--2012|Adachi et al., 2012]] ; [[#Jones--2016|Jones, 2016]] ; Y. [[#Sun--2016|]] [[#Sun--2016|Sun et al., 2016]] ), or relocations outside of the urban area, which could lead to cooling ( [[#Tuomenvirta--2001|Tuomenvirta, 2001]] ; [[#Yan--2010|Yan et al., 2010]] ; [[#Xu--2013|Xu et al., 2013]] ; [[#Dienst--2017|Dienst et al., 2017]] , 2019). Another potential source of inhomogeneity is a change in measurement methods that affect most instruments of an observational network over a limited time span, such as the transition to Stevenson screens ( [[#Parker--1994|Parker, 1994]] ; [[#Böhm--2010|Böhm et al., 2010]] ; [[#Brunet--2011|Brunet et al., 2011]] ; [[#Auchmann--2012|Auchmann and Brönnimann, 2012]] ) or to automatic weather stations ( [[#WMO--2017b|WMO, 2017b]] ).

The above examples have been selected as they are present in many stations and without going through homogenization they could potentially have influenced global land warming estimates ( [[IPCC:Wg1:Chapter:Chapter-1#1.5.1|Section 1.5.1]] ). Single-break inhomogeneities tend to have a magnitude comparable to global climate change ( [[#Tuomenvirta--2001|Tuomenvirta, 2001]] ; [[#Venema--2012|Venema et al., 2012]] ) and are thus important for analyses of small regions. Also station records in national networks often have similar changes, making them important for national climate change estimates, but many of these influences are averaged out at the global scale ( [[#Jones--2016|Jones, 2016]] ).

The main approach to reduce the influence of inhomogeneities in station observations is statistical homogenization by comparing the data from a candidate station with those of neighbouring reference stations in conjunction with the use of metadata ( [[#Trewin--2010|Trewin, 2010]] ). This is a challenging task because both reference and candidate records normally have multiple inhomogeneities. Three challenges should be considered. First, most of our understanding of statistical homogenization stems from the homogenization of temperature observations from dense networks. Recent studies suggest that our ability to remove biases quickly diminishes for sparse networks ( [[#Gubler--2017|Gubler et al., 2017]] ; [[#Lindau--2018a|Lindau and Venema, 2018a]] ). This affects early instrumental data and observations that are not strongly correlated between stations, such as wind and humidity ( [[#Chimani--2018|Chimani et al., 2018]] ).

Second, in addition to systematic errors, homogenized data also suffer from random errors, introduced by the homogenization process. These errors are largest at the station level but are also present in network-averaged signals ( [[#Lindau--2018b|Lindau and Venema, 2018b]] ). These errors are determined by the break time series, as well as the noise series and the performance of the homogenization method, are spatially correlated, and have an impact on activities such as interpolation and statistical post-processing of climate simulations ( [[#10.2.3.1|Section 10.2.3.1]] ). Third, the above discussion pertains to the homogenization of monthly and annual means. Homogenization of daily variability around the mean is more difficult. For daily data, specific correction methods are used ( [[#Della-Marta--2006|Della-Marta and Wanner, 2006]] ; [[#Mestre--2011|Mestre et al., 2011]] ; [[#Trewin--2013|Trewin, 2013]] ; [[#Zhou--2021|]] [[#Zhou--2021|C. Zhou et al., 2021]] ) that are able to improve the homogeneity of test cases, although recent independent validation efforts were not able to show much improvement ( [[#Chimani--2018|Chimani et al., 2018]] ). The difference with homogenization methods of monthly and annual means may stem from assumptions on the nature of inhomogeneities for daily data, which are not yet well understood ( [[#Chimani--2018|Chimani et al., 2018]] ).

It is ''virtually certain'' that statistical homogenization methods reduce the uncertainties of long-term estimates. Considering a decomposition of the long-term warming error into a bias and a noise uncertainty around the bias, the (trend) bias especially will be reduced, but also most of the noise uncertainty. This conclusion is based on our understanding of the causes of inhomogeneities and their statistical nature combined with the design principles of statistical homogenization methods, as well as on analytical ( [[#Lindau--2018b|Lindau and Venema, 2018b]] ), numerical ( [[#Venema--2012|Venema et al., 2012]] ; [[#Williams--2012|Williams et al., 2012]] ) and empirical validation studies ( [[#Hausfather--2016|Hausfather et al., 2016]] ; [[#Gubler--2017|Gubler et al., 2017]] ; [[#Killick--2020|Killick et al., 2020]] ).

The above section is about the homogenization of land stations. Satellite data has its own issues and methods for homogenization ( [[#Brinckmann--2013|Brinckmann et al., 2013]] ; [[#Huang--2015|Huang et al., 2015]] ; [[#Brogniez--2016|Brogniez et al., 2016]] ). The homogenization of radiosonde data and land station data use similar methods ( [[#Haimberger--2012|Haimberger et al., 2012]] ; [[#Jovanovic--2017|Jovanovic et al., 2017]] ).

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==== 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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<span id="gridding"></span>
==== 10.2.2.4 Gridding ====

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Derived gridded datasets require merging data from different sources of observations and/or reanalysis data on a regular grid ( [[#10.2.1.2|Section 10.2.1.2]] ; e.g., [[#Xie--1997|Xie and Arkin, 1997]] ). However, in situ observations are distributed irregularly, especially over sparsely populated areas. This leads to an interpolation challenge. Gridded products of climate variables, including temperature and precipitation, are strongly affected ( ''high confidence'' ) by the interpolation method over complex orography and data scarce regions ( [[#Hofstra--2008|Hofstra et al., 2008]] ; [[#Herrera--2016|Herrera et al., 2016]] ).

There are two main approaches to produce gridded datasets: (i) based on in situ observations only, and (ii) combining in situ observations with remote-sensing data and/or reanalysis data. The first approach has been widely employed in regions with high station density using interpolation techniques, such as inverse-distance weighting, optimal interpolation, and kriging ( [[#Chen--2008|Chen et al., 2008]] ; [[#Haylock--2008|Haylock et al., 2008]] ; [[#Frei--2014|Frei, 2014]] ; [[#Isotta--2014|Isotta et al., 2014]] ; Masson and [[#Frei--2014|Frei, 2014]] ; [[#Hiebl--2016|Hiebl and Frei, 2016]] ; [[#Nguyen-Xuan--2016|Nguyen-Xuan et al., 2016]] ). The second approach has been mainly applied in data-sparse regions with low station density, using simple bias adjustment, quantile mapping, and kriging techniques with in situ observations, remote-sensing and reanalysis data ( [[#Cheema--2012|Cheema and Bastiaanssen, 2012]] ; [[#Erdin--2012|Erdin et al., 2012]] ; Dinku et al., 2014; [[#Abera--2016|Abera et al., 2016]] ; [[#Krähenmann--2018|Krähenmann et al., 2018]] ).

Gridding of station data is affected by uncertainties stemming from measurement errors, inhomogeneities, the distribution of the underlying stations and the interpolation error, with station density being the dominant factor ( [[#Herrera--2019|Herrera et al., 2019]] ). Uncertainty due to interpolation is typically small for temperature but substantial for precipitation and its derivatives, such as drought indices ( [[#Chubb--2015|Chubb et al., 2015]] ; [[#Hellwig--2018|Hellwig et al., 2018]] ). The largest uncertainties typically occur in sparsely sampled mountain areas ( [[#10.2.2.5|Section 10.2.2.5]] ). Interpolation generally give rise to smoothing effects, such as low variability of the derived dataset with respect to the in situ observations ( [[#Chen--2019|Chen et al., 2019]] ). As a result, the effective resolution of gridded data is typically much lower than its nominal resolution. For instance, a 5 km gridded precipitation dataset for the European Alps has an effective resolution of about 10 to 25 km ( [[#Isotta--2014|Isotta et al., 2014]] ). In an example for precipitation in Spain, the effective resolution converged to the nominal resolution only when at least 6 to 7 stations were inside the gridcell ( [[#Herrera--2019|Herrera et al., 2019]] ). To account for the smoothing errors, new stochastic ensemble observation datasets have been introduced ( [[#Von%20Clarmann--2014|Von Clarmann, 2014]] ).

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<span id="observations-in-mountain-areas"></span>
==== 10.2.2.5 Observations in Mountain Areas ====

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Spatiotemporal variability of meteorological parameters observed over mountainous areas is often large, indicating strong control exerted by local topography on meteorological parameters ( [[#Gultepe--2014|Gultepe et al., 2014]] ). Difficult access, harsh climatic conditions as well as instrumental issues make meteorological measurements extremely challenging at higher elevations ( [[#Azam--2018|Azam et al., 2018]] ; [[#Beniston--2018|Beniston et al., 2018]] ). Measurements of wind speed, temperature, relative humidity and radiative fluxes are critical for climate model evaluation, but difficult to handle due to their point-scale representativeness and small-scale spatiotemporal variability over mountainous terrain, and often need adjustment ( [[#Gultepe--2015|Gultepe, 2015]] ). High-altitude (&gt;3000 metres) permanent meteorological stations are limited and current knowledge is mainly based on valley-bottom or low-elevation meteorological stations ( [[#Qin--2009|Qin et al., 2009]] ; [[#Lawrimore--2011|Lawrimore et al., 2011]] ; [[#Gultepe--2015|Gultepe, 2015]] ; [[#Condom--2020|Condom et al., 2020]] ), which, generally do not represent the higher elevation climate ( [[#Immerzeel--2015|Immerzeel et al., 2015]] ; [[#Shea--2015|Shea et al., 2015]] ).

Measuring precipitation amounts, especially of solid precipitation, in mountainous areas is particularly challenging due to the presence of orographic barriers, strong vertical and horizontal precipitation rate variability, and the difficulty in finding representative sites for precipitation measurements ( [[#Barry--2012|Barry, 2012]] ). However, the precipitation amounts can be indirectly estimated by the observed point mass balances at glacier accumulation areas representing net snow accumulation ( [[#Haimberger--2012|Haimberger et al., 2012]] ; [[#Immerzeel--2015|Immerzeel et al., 2015]] ; [[#Sakai--2015|Sakai et al., 2015]] ; [[#Azam--2018|Azam et al., 2018]] ). There is ''very high confidence'' that precipitation measurements, especially solid precipitation, in mountainous areas are strongly affected by the gauge location and setup. Precipitation measurements are also affected by the type of measurement method, presence/absence of shielding, presence/absence of a heating system and operating meteorological conditions ( [[#Nitu--2018|Nitu et al., 2018]] ). Solid precipitation measurements may have errors ranging from 20% to 50%, largely due to under-catch in windy, icing and riming conditions ( [[#Rasmussen--2012|Rasmussen et al., 2012]] ), and therefore require corrections by applying transfer functions developed mainly from collected wind speed and temperature data ( [[#Kochendorfer--2017|Kochendorfer et al., 2017]] ). The latest Solid Precipitation Intercomparison Experiment (SPICE) report recommends measurements of wind speed, wind direction and temperature as the minimum standard ancillary data for solid precipitation monitoring ( [[#Nitu--2018|Nitu et al., 2018]] ).

Recent advances in remote-sensing methods provide an alternative, but they also have limitations over mountainous areas. Different versions of the Tropical Rainfall Measuring Mission (TRMM) products were found to perform differently over mountainous areas ( [[#Zulkafli--2014|Zulkafli et al., 2014]] ). Orographic heavy rainfall associated with Typhoon Morakot in 2009 was severely underestimated in all microwave products including TRMM 3B42 ( [[#Shige--2013|Shige et al., 2013]] ). The underestimation has been mitigated in the Global Satellite Mapping of Precipitation (GSMaP) product by considering the orographic effects ( [[#Shige--2013|Shige et al., 2013]] ). Studies have suggested a high accuracy of passive optical satellite (e.g., MODIS, Landsat) snow products under clear skies when compared with the field observations. However, cloud masking and sub-pixel cloud heterogeneity in these snow-cover products considerably restrict their applications ( [[#Kahn--2011|Kahn et al., 2011]] ; [[#Brun--2015|Brun et al., 2015]] ; [[#Tang--2017|Tang et al., 2017]] ; [[#Stillinger--2019|Stillinger et al., 2019]] ). Gridded datasets (e.g., CRU, GPCC Full Data Product, GPCC Monitoring Product, ERA-Interim, ERA5, ERA5-land, MERRA-2, MERRA-2 bias adjusted, PERSIANN-CDR) are of paramount importance, yet they often lack enough in situ observations to improve the temporal and spatial distribution of meteorological parameters over complex mountain terrain ( [[#Zandler--2019|Zandler et al., 2019]] ).

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<span id="structural-uncertainty"></span>
==== 10.2.2.6 Structural Uncertainty ====

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Beyond climate monitoring, the quality and availability of multiple observational reference datasets play a central role in model evaluation. In fact, when using observations for model evaluation, there are multiple examples where inter-observational uncertainty is as large as the inter-model variability. This has been shown for various aspects of the Indian monsoon ( [[#10.6.3|Section 10.6.3]] ; [[#Collins--2013a|Collins et al., 2013a]] ) and for precipitation uncertainties over Africa ( [[#10.6.4|Section 10.6.4]] ; [[#Nikulin--2012|Nikulin et al., 2012]] ; [[#Sylla--2013|Sylla et al., 2013]] ; [[#Dosio--2015|Dosio et al., 2015]] ; [[#Bador--2020|Bador et al., 2020]] ) and Europe ( [[#Prein--2017|Prein and Gobiet, 2017]] ). [[#Kotlarski--2019|Kotlarski et al. (2019)]] compared three high-resolution observational temperature and precipitation datasets (E-OBS, a compilation of national/regional high-resolution gridded datasets, and the EURO4M-MESAN 0.22° reanalysis based on a high-resolution limited-area model) with five EURO-CORDEX RCMs driven by ERA-Interim. Generally, the differences between RCMs are larger than those between observation datasets, but for individual regions and performance metrics, observational uncertainty can dominate. They also showed that the choice of reference dataset can have an influence on the RCM performance score. Over the high mountain Asia region and East Asia, differences among gridded precipitation datasets can generate significant uncertainties in deriving precipitation characteristics (J. [[#Kim--2015|]] [[#Kim--2015|Kim et al., 2015]] ; [[#Kim--2016|Kim and Park, 2016]] ; [[#Guo--2017|Guo et al., 2017]] ). Over western North America, observational uncertainty induces differences in multi-decadal precipitation trends ( [[#Lehner--2018|Lehner et al., 2018]] ). Taking a very different perspective, the agreement between model simulations may be used to estimate the uncertainty and quality of observations ( [[#Massonnet--2016|Massonnet et al., 2016]] ). There is ''high confidence'' that an ensemble of multiple observational references at a regional scale is fundamental for model performance assessment. The uncertainties vary according to region, season, and statistical properties (Cross-Chapter Box 10.2).

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<span id="other-uses-of-observations-at-regional-scale"></span>
=== 10.2.3 Other Uses of Observations at Regional Scale ===

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<span id="observations-for-calibrating-statistical-methods"></span>
==== 10.2.3.1 Observations for Calibrating Statistical Methods ====

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Statistical downscaling, bias adjustment and weather generators are post-processing methods used to derive climate information from climate simulations. They all require observational data for calibration as well as evaluation ( [[#10.3.3.1|Section 10.3.3.1]] ). Typically, the so-called perfect prognosis methods use quasi-observations for the predictors (i.e., reanalyses) and actual observations for the predictands (the surface variables of interest). By contrast, bias adjustment methods use observations only for the predictands. Weather generators typically require only observed predictands, although some are conditioned on observed predictors as well. Very often these methods are based on daily data, because of user needs, but also because of the limited availability of sub-daily observations and the limited ability of climate models to realistically simulate sub-daily weather ( [[#Iizumi--2012|Iizumi et al., 2012]] ). Some methods are calibrated on the monthly scale, but some of the generated time series are then further disaggregated to the daily scale (e.g., [[#Thober--2014|Thober et al., 2014]] ). A few methods, mainly weather generators, represent sub-daily weather ( [[#Mezghani--2009|Mezghani and Hingray, 2009]] ; [[#Kaczmarska--2014|Kaczmarska et al., 2014]] ). Many methods simulate temperature and precipitation only, although some also represent wind, radiation and other variables. The limited availability of high quality and long observational records typically restricts these applications to a few cases ( [[#Verfaillie--2017|Verfaillie et al., 2017]] ; [[#Pryor--2019|Pryor and Hahmann, 2019]] ). Overall, there is ''high confidence'' that limited availability of station observations, including variables beyond temperature and precipitation as well as sub-daily data, limit the use of statistical modelling of regional climate.

All the limitations and challenges of observational data discussed in [[#10.2.2|Section 10.2.2]] also apply to its use for post-processing of climate model data. High quality and long observational data series are particularly relevant to quantify uncertainties. Different reanalyses present significant discrepancies when used as key predictor variables at the daily scale and may even affect the downscaled climate change signal ( [[#Brands--2012|Brands et al., 2012]] ; [[#Dayon--2015|Dayon et al., 2015]] ; [[#Manzanas--2015|Manzanas et al., 2015]] ; [[#Horton--2019|Horton and Brönnimann, 2019]] ). There is ''high confidence'' that reanalysis uncertainties limit the quality of statistical downscaling in some regions, although no assessment has been made for the most recent reanalysis products.

An important issue for bias adjustment is the correct representation of the required spatial scale. Ideally, bias adjustment is calibrated against area-averaged data of the same spatial scale as the climate model output. Hence, high-quality observed gridded datasets with an effective resolution close to the nominal model resolution are required. Driven by the need to also generate regional-scale information in station-sparse regions, researchers have considered derived datasets that blend in situ and remote-sensing data to produce high-resolution observations to be used as predictands (Sections 10.2.1.2 and 10.2.2.4; [[#Haiden--2011|Haiden et al., 2011]] ; [[#Wilby--2013|Wilby and Yu, 2013]] ).

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<span id="observation-for-paleoclimate-data-assimilation"></span>
==== 10.2.3.2 Observation for Paleoclimate Data Assimilation ====

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Following some early concept studies, the first practical applications of paleoclimate data assimilation over past centuries used only selected data to reconstruct past climate changes for analysis of a specific process or case ( [[#Widmann--2010|Widmann et al., 2010]] ). Recently, assimilation of multiple series from various data sources, including tree rings, ice cores, lake cores, corals, and bivalves, has allowed production of reconstructions that can be widely shared and applied to multiple purposes, as with modern reanalyses ( [[#Hakim--2016|Hakim et al., 2016]] ; [[#Franke--2017|Franke et al., 2017]] ; Steiger et al., 2018; [[#Tardif--2019|Tardif et al., 2019]] ). Most of these paleo-reanalyses are global but there are products using regional models or targeted at specific regions such as Europe, East Africa and the Indian Ocean ( [[#Fallah--2018|Fallah et al., 2018]] ; [[#Klein--2018|Klein and Goosse, 2018]] ).

Paleo-reanalyses are enabling a new range of applications and have already provided useful information on seasonal-to-multi-decadal climate variability over past millennia. They are useful tools to study the co-variance between variables at interannual-to-centennial time scales and at regional to global spatial scales. In particular, they have highlighted the processes that can be responsible for changes in continental hydrology at multi-decadal time scales ( [[#Franke--2017|Franke et al., 2017]] ; [[#Klein--2018|Klein and Goosse, 2018]] ; Steiger et al., 2018). Paleo-reanalyses have confirmed a large contribution of internal variability in past changes at regional scale during the pre-industrial period, superimposed on a weak common signal due to forcing changes ( [[#Goosse--2012|Goosse et al., 2012]] ) and the absence of a globallycoherent warm period in the common era before the recent warming ( [[#Neukom--2019|Neukom et al., 2019]] ). Reconstructions of the atmospheric state obtained in the reanalysis also provide ''robust evidence'' of a local enhancement of warming or cooling conditions due to changes in atmospheric circulation, such as for the warm conditions in some European regions around 950–1250 CE, the cooling observed in 1809/1810, or the cold and rainy 1816 summer in Europe (Cross-Chapter Box 4.1; [[#Goosse--2012|Goosse et al., 2012]] ; [[#Hakim--2016|Hakim et al., 2016]] ; [[#Franke--2017|Franke et al., 2017]] ; [[#Schurer--2019|Schurer et al., 2019]] ).

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<span id="outlook-for-improving-observational-data-for-regional-climates"></span>
=== 10.2.4 Outlook for Improving Observational Data for Regional Climates ===

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An encouraging development for understanding climate variations over the past 250 years or so at the global and regional scale lies in the field of data rescue, in which hitherto hidden archives of meteorological data are brought to the forefront (Sections 1.5.1.1 and 2.5). Surface observations from data rescue projects may then be assimilated to derive long-term high-resolution gridded surface regional reanalysis ( [[#Devers--2020|Devers et al., 2020]] ). Global extended reanalyses such as 20CR ( [[#Compo--2011|Compo et al., 2011]] ), ERA-20C ( [[#Poli--2016a|Poli et al., 2016a]] , b) or CERA-20C ( [[#Laloyaux--2018|Laloyaux et al., 2018]] ) may be further downscaled to quantify the variability of past climate at the regional scale ( [[#Caillouet--2016|Caillouet et al., 2016]] , 2019).

One of the main scientific challenges related to high-resolution regional climate modelling is dealing with the representation of fine-scale processes (e.g., [[#Yano--2018|Yano et al., 2018]] ) in observational datasets. Additionally, reliable observation networks following WMO standards have a very sparse geographical representation. Hence, regional climate models have started to use high-resolution data combined with crowdsourced observations ( [[#Zheng--2018|Zheng et al., 2018]] ). Recent efforts have led to the production of homogeneously processed long-term datasets for regional climate model evaluation ( [[#Goudenhoofdt--2016|Goudenhoofdt and Delobbe, 2016]] ; [[#Humphrey--2017|Humphrey et al., 2017]] ; [[#Yang--2019|Yang and Ng, 2019]] ). While they are far less reliable and accurate than professional observations, crowdsourced data are abundantly available and can give spatial representations at very high resolution. This technological trend could prove very useful ( ''high confidence'' ), and the regional climate community is making efforts to understand the extent to which these data sources can be exploited, at least as a complement to traditional datasets ( [[#Overeem--2013|Overeem et al., 2013]] ; [[#Meier--2017|Meier et al., 2017]] ; [[#Uijlenhoet--2018|Uijlenhoet et al., 2018]] ; [[#de%20Vos--2019|de Vos et al., 2019]] ; [[#Langendijk--2019b|Langendijk et al., 2019b]] ).

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<span id="using-models-for-constructing-regional-climate-information"></span>