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

==== 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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