This CD-ROM set contains the volume 1 Snow, Ice, and Oceans data collection. The data covers a 24 month period, 1987-1988, and all but one are mapped to a common spatial resolution and grid (1 degree x 1 degree). Temporal resolution for most datasets is monthly; however, a few are at a finer resolution (e.g., 6-hourly). This dataset contains data covering: *Global snow depth *Sea surface temperature *Weekly Northern Hemisphere snow cover *NMC/ECMWF Reanalysis sea ice

The Meteosat Second Generation (MSG) satellites, operated by EUMETSAT (The European Organisation for the Exploitation of Meteorological Satellites), provide almost continuous imagery to meteorologists and researchers in Europe and around the world. These include visible, infra-red, water vapour, High Resolution Visible (HRV) images and derived cloud top height, cloud top temperature, fog, snow detection and volcanic ash products. These images are available for a range of geographical areas. This dataset contains RGB 'False Colour' 321 product images from MSG satellites over Europe and the North Atlantic. Imagery available from March 2005 onwards at a frequency of 15 minutes (some are hourly) and are at least 24 hours old. RGB images are composite images generated by combining two or more channels and displaying in colour. The naming convention describes which channel is assigned to the red, green and blue colours. For example RGB 321 means that channel 3 (1.6 micron) is on the red, channel 2 (0.8 micron) is on the green and channel 1 (0.6 micron) is on the blue. This combination can then highlight different physical features through the differing amounts of red, green and blue and hence give a unique colour to that feature. In this case, turquoise clouds contain ice crystals, whilst white clouds are water clouds (inc. fog). Vegetation creates a green signal and sandy areas are pink. Snow covered ground is turquoise. Note: a change in product can be seen from a change to software implemented on 25/11/2013 where the scaling and gamma correction of the R, G, and B channels were tuned to give an improved image, in effect lightening the brighter colours in the image image. The geographic extent for images within this datasets is available via the linked documentation 'MSG satellite imagery product geographic area details'. Each MSG imagery product area can be referenced from the third and fourth character of the image product name giving in the filename. E.g. for EEAO11 the corresponding geographic details can be found under the entry for area code 'AO' (i.e West Africa).

The Meteosat Second Generation (MSG) satellites, operated by EUMETSAT (The European Organisation for the Exploitation of Meteorological Satellites), provide almost continuous imagery to meteorologists and researchers in Europe and around the world. These include visible, infra-red, water vapour, High Resolution Visible (HRV) images and derived cloud top height, cloud top temperature, fog, snow detection and volcanic ash products. These images are available for a range of geographical areas. This dataset contains cloud and snow mask images from MSG satellites UKV domain area. Imagery available from March 2005 onwards at a frequency of 15 minutes (some are hourly) and are at least 24 hours old. The geographic extent for images within this datasets is available via the linked documentation 'MSG satellite imagery product geographic area details'. Each MSG imagery product area can be referenced from the third and fourth character of the image product name giving in the filename. E.g. for EEAO11 the corresponding geographic details can be found under the entry for area code 'AO' (i.e West Africa).

The Meteosat Second Generation (MSG) satellites, operated by EUMETSAT (The European Organisation for the Exploitation of Meteorological Satellites), provide almost continuous imagery to meteorologists and researchers in Europe and around the world. These include visible, infra-red, water vapour, High Resolution Visible (HRV) images and derived cloud top height, cloud top temperature, fog, snow detection and volcanic ash products. These images are available for a range of geographical areas. This dataset contains RGB 'False Colour' 321 product images from MSG satellites over the tropics. Imagery available from March 2005 onwards at a frequency of 15 minutes (some are hourly) and are at least 24 hours old. RGB images are composite images generated by combining two or more channels and displaying in colour. The naming convention describes which channel is assigned to the red, green and blue colours. For example RGB 321 means that channel 3 (1.6 micron) is on the red, channel 2 (0.8 micron) is on the green and channel 1 (0.6 micron) is on the blue. This combination can then highlight different physical features through the differing amounts of red, green and blue and hence give a unique colour to that feature. In this case, turquoise clouds contain ice crystals, whilst white clouds are water clouds (inc. fog). Vegetation creates a green signal and sandy areas are pink. Snow covered ground is turquoise. Note: a change in product can be seen from a change to software implemented on 25/11/2013 where the scaling and gamma correction of the R, G, and B channels were tuned to give an improved image, in effect lightening the brighter colours in the image image. The geographic extent for images within this datasets is available via the linked documentation 'MSG satellite imagery product geographic area details'. Each MSG imagery product area can be referenced from the third and fourth character of the image product name giving in the filename. E.g. for EEAO11 the corresponding geographic details can be found under the entry for area code 'AO' (i.e West Africa).

Meteosat Second Generation is operated by The European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) and provides almost continuous images to meteorologists and researchers in Europe and around the world. This includes visible, infra-red, water vapour, High Resolution Visible (HRV) images and the derived cloud top height, cloud top temperature, fog, snow detection, and volcanic ash products. These images are available for a range of geographical areas. Images are available from March 2005 onwards at a frequency of 15 minutes (some are hourly) and are at least 24 hours old. This dataset contains RGB 'False Colour' 321 Product images from the MSG satellites over the Great Rift Valley, Africa. RGB images are composite images generated by combining two or more channels and displaying in colour. The naming convention describes which channel is assigned to the red, green and blue colours. For example RGB 321 means that channel 3 (1.6 micron) is on the red, channel 2 (0.8 micron) is on the green and channel 1 (0.6 micron) is on the blue. This combination can then highlight different physical features through the differing amounts of red, green and blue and hence give a unique colour to that feature. In this case, turquoise clouds contain ice crystals, whilst white clouds are water clouds (inc. fog). Vegetation creates a green signal and sandy areas are pink. Snow covered ground is turquoise. Note: a change in product can be seen from a change to software implemented on 25/11/2013 where the scaling and gamma correction of the R, G, and B channels were tuned to give an improved image, in effect lightening the brighter colours in the image image. The geographic extent for images within this datasets is available via the linked documentation 'MSG satellite imagery product geographic area details'. Each MSG imagery product area can be referenced from the third and fourth character of the image product name giving in the filename. E.g. for EEAO11 the corresponding geographic details can be found under the entry for area code 'AO' (i.e West Africa).

The International Satellite Land Surface Climatology Project, Initiative II (ISLSCP II) is a follow on project from The International Satellite Land Surface Climatology Project (ISLSCP). ISLSCP II had the lead role in addressing land-atmosphere interactions - process modelling, data retrieval algorithms, field experiment design and execution, and the development of global data sets. The ISLSCP II dataset contains comprehensive data over the 10 year period from 1986 to 1995, from the International Satellite Land Surface Climatology Project (ISLSCP). This dataset contains: *Sea Ice extent *Sea surface temperature *Snow cover The data are mapped to consistent grids (0.5 x 0.5 degrees for topography, 1 x 1 degrees for meteorological parameters). Some data have a grid size of 0.25 x 0.25 degrees. The temporal resolution for most data sets is monthly (however a few are at finer resolution - 3 hourly). This dataset is public.

The Meteosat Second Generation (MSG) satellites, operated by EUMETSAT (The European Organisation for the Exploitation of Meteorological Satellites), provide almost continuous imagery to meteorologists and researchers in Europe and around the world. These include visible, infra-red, water vapour, High Resolution Visible (HRV) images and derived cloud top height, cloud top temperature, fog, snow detection and volcanic ash products. These images are available for a range of geographical areas. This dataset contains RGB 'False Colour' 321 product images from MSG satellites over UKV domain area. Imagery available from March 2005 onwards at a frequency of 15 minutes (some are hourly) and are at least 24 hours old. RGB images are composite images generated by combining two or more channels and displaying in colour. The naming convention describes which channel is assigned to the red, green and blue colours. For example RGB 321 means that channel 3 (1.6 micron) is on the red, channel 2 (0.8 micron) is on the green and channel 1 (0.6 micron) is on the blue. This combination can then highlight different physical features through the differing amounts of red, green and blue and hence give a unique colour to that feature. In this case, turquoise clouds contain ice crystals, whilst white clouds are water clouds (inc. fog). Vegetation creates a green signal and sandy areas are pink. Snow covered ground is turquoise. The geographic extent for images within this datasets is available via the linked documentation 'MSG satellite imagery product geographic area details'. Each MSG imagery product area can be referenced from the third and fourth character of the image product name giving in the filename. E.g. for EEAO11 the corresponding geographic details can be found under the entry for area code 'AO' (i.e West Africa).

This dataset contains Daily Snow Cover Fraction (snow on ground) from MODIS, produced by the Snow project of the ESA Climate Change Initiative programme. Snow cover fraction on ground (SCFG) indicates the area of snow observed from space on land surfaces, in forested areas corrected for the transmissivity of the forest canopy. The SCFG is given in percentage (%) per pixel. The global SCFG product is available at about 1 km pixel size for all land areas, excluding Antarctica and Greenland ice sheets. The coastal zones of Greenland are included. The SCFG time series provides daily products for the period 2000 – 2020. The SCFG product is based on Moderate resolution Imaging Spectroradiometer (MODIS) data on-board the Terra satellite. The retrieval method of the Snow_cci SCFG product from MODIS data has been further developed and improved based on the ESA GlobSnow approach described by Metsämäki et al. (2015) and complemented with a pre-classification module developed by ENVEO. For the SCFG product generation from MODIS, multiple reflective and emissive spectral bands are used. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm version 2.0 (SCDA2.0) (Metsämäki et al., 2015). All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 0.55 µm and 1.6 µm, and an emissive band centred at about 11 µm. The Snow_cci snow cover mapping algorithm is a two-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the snow_cci SCFG retrieval method is applied. The main differences of the Snow_cci snow cover mapping algorithm compared to the GlobSnow algorithm described in Metsämäki et al. (2015) are (i) improvements of the cloud screening approach applicable on a global scale, (ii) the pre-classification of snow free areas on global land areas, (iii) the usage of spatially variable background reflectance and forest reflectance maps instead of global constant values for snow free land and forest, (iv) the update of the constant value for wet snow based on analyses of spatially distributed reflectance time series of MODIS data, and (v) the update of the global forest canopy transmissivity based on forest density from Hansen et al. (2013) and forest type layers from Land Cover CCI (Defourny, 2019) to assure in forested areas consistency of the SCFG and the SCFV CRDP v2.0 from MODIS data (https://catalogue.ceda.ac.uk/uuid/ebe625b6f77945a68bda0ab7c78dd76b) using the same retrieval approach. Improvements of the Snow_cci SCFG version 2.0 compared to the Snow_cci version 1.0 include (i) the utilisation of an updated background reflectance map derived from statistical analyses of an extended MODIS time series, (ii) an update of the forest canopy transmissivity map, and (iii) an update of the constant reflectance value for wet snow based on the analysis of time series of the MODIS reflectance at 0.55 µm. Permanent snow and ice, and water areas are masked based on the Land Cover CCI data set of the year 2000. Both classes were separately aggregated to the pixel spacing of the SCFG product. Water areas are masked if more than 30 percent of the pixel is classified as water, permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map. The product uncertainty for observed land pixels is provided as unbiased root mean square error (RMSE) per pixel in the ancillary variable. The SCFG product is aimed to serve the needs for users working in the cryosphere and climate research and monitoring activities, including the detection of variability and trends, climate modelling and aspects of hydrology, meteorology, and biology. ENVEO is responsible for the SCFG product development and generation from MODIS data, SYKE supported the development. There are a few days without any MODIS acquisitions in the years 2000, 2001, 2002, 2003, 2008, 2016 and 2018. On several days in the years 2000 to 2006, and on a few days in the years 2012, 2015 and 2016, the acquired MODIS data have either only limited coverage, or some of the MODIS data were corrupted during the download process. For these days, the SCFG products are available but have data gaps.

This dataset contains Daily Snow Cover Fraction of viewable snow from the MODIS satellite instruments, produced by the Snow project of the ESA Climate Change Initiative programme. Snow cover fraction viewable (SCFV) indicates the area of snow viewable from space over all land surfaces. In forested areas this refers to snow viewable on top of the forest canopy. The SCFV is given in percentage (%) per pixel. The global SCFV product is available at about 1 km pixel size for all land areas, excluding Antarctica and Greenland ice sheets. The coastal zones of Greenland are included. The SCFV time series provides daily products for the period 2000 – 2020. The SCFV product is based on Moderate resolution Imaging Spectroradiometer (MODIS) data on-board the Terra satellite. The retrieval method of the Snow_cci SCFV product from MODIS data has been further developed and improved based on the ESA GlobSnow approach described by Metsämäki et al. (2015) and complemented with a pre-classification module developed by ENVEO. For the SCFV product generation from MODIS, multiple reflective and emissive spectral bands are used. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm version 2.0 (SCDA2.0) (Metsämäki et al., 2015). All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 0.55 µm and 1.6 µm, and an emissive band centred at about 11 µm. The snow_cci snow cover mapping algorithm is a two-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the Snow_cci SCFV retrieval method is applied. The main differences of the Snow_cci snow cover mapping algorithm compared to the GlobSnow algorithm described in Metsämäki et al. (2015) are (i) improvements of the cloud screening approach applicable on a global scale, (ii) the pre-classification of snow free areas on global land areas, (iii) the adaptation of the retrieval method using of a spatially variable ground reflectance instead of global constant values for snow free land, (iv) the update of the constant value for wet snow based on analyses of spatially distributed reflectance time series of MODIS data to assure in forested areas consistency of the SCFV and the SCFG CRDP v2.0 from MODIS data (https://catalogue.ceda.ac.uk/uuid/ebe625b6f77945a68bda0ab7c78dd76b) using the same retrieval approach. Improvements of the Snow_cci SCFV version 2.0 compared to the Snow_cci version 1.0 include (i) the utilisation of an updated ground reflectance map derived from statistical analyses of an extended MODIS time series, (ii) an update of the forest mask used for the transmissivity estimation, and (iii) an update of the constant reflectance value for wet snow based on the analysis of time series of the MODIS reflectance at 0.55 µm. Permanent snow and ice, and water areas are masked based on the Land Cover CCI data set of the year 2000. Both classes were separately aggregated to the pixel spacing of the SCFV product. Water areas are masked if more than 30 percent of the pixel is classified as water, permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map. The product uncertainty for observed land pixels is provided as unbiased root mean square error (RMSE) per pixel in the ancillary variable. The SCFV product is aimed to serve the needs for users working in the cryosphere and climate research and monitoring activities, including the detection of variability and trends, climate modelling and aspects of hydrology, meteorology, and biology. ENVEO is responsible for the SCFV product development and generation from MODIS data, SYKE supported the development. There are a few days without any MODIS acquisitions in the years 2000, 2001, 2002, 2003, 2008, 2016 and 2018. On several days in the years 2000 to 2006, and on a few days in the years 2012, 2015 and 2016, the acquired MODIS data have either only limited coverage, or some of the MODIS data were corrupted during the download process. For these days, the SCFV products are available but have data gaps.

In situ meteorological forcing and evaluation data, and bias-corrected reanalysis forcing data for cold regions modelling at ten sites: one maritime (Sapporo, Japan), one arctic (Sodankylä, Finland), three boreal (Old Aspen, Old Jack Pine and Old Black Spruce, Saskatchewan, Canada) and five mid-latitude alpine (Col de Porte, France; Reynolds Mountain East, Idaho, USA, Senator Beck and Swamp Angel, Colorado, USA; Weissfluhjoch, Switzerland). The long-term datasets are the reference sites chosen for evaluating models participating in the Earth System Model-Snow Model Intercomparison Project (ESM-SnowMIP). Periods covered by the in situ data span from 1994 to 2016 with the period of available data varying by location from between 7 and 20 years of hourly meteorological data, with evaluation data (snow depth, snow water equivalent, albedo, soil temperature and surface temperature) available at varying temporal intervals. 30-year (1980-2010) time-series have been extracted from a global gridded surface meteorology dataset (Global Soil Wetness Project Phase 3) for the grid cells containing the reference sites, interpolated to one-hour timesteps and bias corrected.