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  • Surface meteorological data collected at the following British Antarctic Survey stations in Antarctica: Adelaide Island (1962-1976); Deception Island (1959-1967); Faraday/Argentine Islands (1946-1995); Fossil Bluff (1961-2005); Grytviken (1959-1981); Halley (1957 to 2013); Rothera (1976 to 2013); Signy (1956 to 2000). The following meteorological parameters are included in the files: Sea Level Pressure (hPa); Station Level Pressure (hPa); Temperature (Deg C); Wind Speed (knots); Wind Direction (Degrees). Observations were recorded every 3 or six hours for the first part of the record and then at hourly intervals in the later part when electronic measuring systems were introduced in the 1980s and 1990s.

  • High-resolution hindcasts (1979-2019) of summer climate over Antarctica using the UK Met Office Unified Model (MetUM) and HIRHAM5 were conducted at the British Antarctic Survey and Danish Meteorological Institute, respectively. The hindcasts are conducted for summer 1979-2018, i.e., from December 1979 to February 2019, for December, January, February (DJF). This dataset consists of near-surface temperature output from these hindcasts at a temporal resolution of every 3 hrs. The hindcasts are contributions to the COordinated Regional Downscaling EXperiment (CORDEX) project. Both models are run over Antarctic CORDEX domains, which encompass all of Antarctica and some of the surrounding ocean, at a horizontal grid spacing of around 12 km. The near-surface temperatures are used to estimate regional surface melt "potential" over Antarctic ice shelves as a function of summertime temperature extremes and identify regions of potentially enhanced "hotspots" of melt potential based on the occurrence (and magnitude) of various temperatures. Funding was provided by the European Union''s Horizon 2020 research and innovation framework programme under Grant agreement no. 101003590 (PolarRES)

  • Meteorological data collected on Larsen Ice Shelf including pressure, temperature, wind speed and direction.

  • High-resolution simulations of near-surface (1.5 m) temperature and (10 m) zonal and meridional winds over the Brunt Ice Shelf in the Antarctic for the year 2015 were conducted using the atmosphere-only Met Office Unified Model by the British Antarctic Survey, Cambridge, UK. The datasets produced were necessary to place point meteorological measurements from the various automatic weather stations on the Brunt Ice Shelf into a wider spatial context by identifying spatial temperature gradients and investigating how such gradients may have affected the homogeneity of the composite Halley temperature record. The work formed part of the core science undertaken at the British Antarctic Survey.

  • Precipitation and near-surface temperature data from the Coupled Model Intercomparison Project phase 5 (CMIP5 models) are statistically downscaled to create these gridded datasets over the Rio Santa River Basin (in the Cordillera Blanca; d02) and the Vilcanota-Urubamba region (d03) at 4 km horizontal resolution, from 2019-2100. The bias-corrected WRF data found in the related dataset are used as the observational truth for the historical period 1980-2018, and the data are downscaled using an empirical quantile mapping technique. Two representative concentration pathways (RCP) have been downscaled, RCP 4.5 and RCP 8.5, from 30 CMIP5 models. The daily total precipitation and daily minimum and maximum temperature at 2 m are downscaled, and the daily average and monthly average temperatures are calculated using the hourly temperature (not archived due to space constraints). The potential evapotranspiration is estimated from the downscaled precipitation and temperature data, using the Hargreaves equation. These data were corrected as part of the PEGASUS (Producing EnerGy and preventing hAzards from SUrface water Storage in Peru) and Peru GROWS (Peruvian Glacier Retreat and its Impact on Water Security) projects. The datasets were created to assess future climate in the Peruvian Andes, as a basis to determine future climate in the region, and as an input for glaciological and hydrological models. The data were created on the JASMIN supercomputer. The creation of this data was conducted under the Peru GROWS and PEGASUS projects, which were both funded by NERC (grants NE/S013296/1 and NE/S013318/1, respectively) and CONCYTEC through the Newton-Paulet Fund. The Peruvian part of the Peru GROWS project was conducted within the framework of the call E031-2018-01-NERC "Glacier Research Circles", through its executing unit FONDECYT (Contract No. 08-2019-FONDECYT).

  • These 21 Last Interglacial (LIG) summer surface air temperature (SSAT) observations were compiled to assess LIG Arctic sea ice (Guarino et al 2020). Twenty of the observations were also previously used in the IPCC-AR5 report. Each observation is thought to be of summer LIG air temperature anomaly relative to present day and is located in the circum-Arctic region. All sites are from north of 51N. There are 7 terrestrial based temperature records; 8 lacustrine records; 2 marine pollen-based records; and 3 ice core records included in the original compilation. This compilation includes 1 additional ice core record. This work was funded by NERC standard research grant nos. NE/P013279/1 and NE/P009271/1.

  • Temperature and precipitation data from the Weather Research and Forecasting model are bias-corrected against observations to create these bias-corrected gridded datasets over the Rio Santa River Basin (in the Cordillera Blanca) at 4 km horizontal resolution (d02), the Vilcanota-Urubamba region at 4 km horizontal resolution (d03) and the upper region of the Rio Santa River Basin at 800 m horizontal resolution (d04). The raw WRF data can be found in the related dataset. Full details of the bias-correction can be found in Fyffe et al., (2021). These data were corrected as part of the PEGASUS (Producing EnerGy and preventing hAzards from SUrface water Storage in Peru) and Peru GROWS (Peruvian Glacier Retreat and its Impact on Water Security) projects. The datasets were created to assess past climate in the Peruvian Andes, as a basis to determine future climate in the region, and as an input for glaciological and hydrological models. The data were created using the British Antarctic Survey high performance computer. The creation of this data was conducted under the Peru GROWS and PEGASUS projects, which were both funded by NERC (grants NE/S013296/1 and NE/S013318/1, respectively) and CONCYTEC through the Newton-Paulet Fund. The Peruvian part of the Peru GROWS project was conducted within the framework of the call E031-2018-01-NERC "Glacier Research Circles", through its executing unit FONDECYT (Contract No. 08-2019-FONDECYT).

  • High-resolution simulation of summer climate over West Antarctica using the Polar-optimised version of the Weather Research and Forecasting (WRF) model conducted at British Antarctic Survey, Cambridge, UK. Runs are conducted for summer (January-centred) 1980-2015, i.e. from December 1979 to February 2015, for December, January and February (DJF). Experiments were carried out for the NERC West Antarctic Grant (NE/K00445X/1) during 2014-2017. The project is aimed at understanding the variability and climatology over the West Antarctic ice sheet and ice shelves as well as to project the future change over the twenty-first century. The model outer domain encompasses the West Antarctic ice sheet and a large part of the surrounding ocean at 45 km horizontal grid spacing, and the nested (one-way) inner domain covers the Amundsen Sea Embayment at 15 km grid spacing. The model uses vertical eta coordinates with both domains have a model top of 50 hPa, and 30 vertical levels.

  • Based on the bias-corrected WRF data and the statistically downscaled CMIP5 data (see related datasets), six climate change detection indices are calculated, based on the Expert Team on Climate Change Detection and Indices (ETCCDI). Each index is calculated for the control period (1980-2018) from the bias-corrected WRF data, and the future (2019-2100) for each of the 30 CMIP5 models. Six of the ETCCDI climate indices are calculated here (taken from Zhang (2011)): the simple precipitation intensity index describing the total annual precipitation on wet days; the annual total precipitation falling on days where precipitation is above the 95th percentile of the 1980-2018 period; the number of dry days (precipitation under 1 mm) in a year (a variation on "continuous dry days" given in Zhang (2011); the annual average monthly maximum temperature; the warm spell duration index describing the annual count of days with at least 6 consecutive days above the 90th percentile of daily maximum temperature from 1980-2018; the number of frost days (minimum daily temperature below 0 deg C). These data were corrected as part of the PEGASUS (Producing EnerGy and preventing hAzards from SUrface water Storage in Peru) and Peru GROWS (Peruvian Glacier Retreat and its Impact on Water Security) projects. The datasets were created to assess future climate in the Peruvian Andes. The data were created on the JASMIN supercomputer. The creation of this data was conducted under the Peru GROWS and PEGASUS projects, which were both funded by NERC (grants NE/S013296/1 and NE/S013318/1, respectively) and CONCYTEC through the Newton-Paulet Fund. The Peruvian part of the Peru GROWS project was conducted within the framework of the call E031-2018-01-NERC "Glacier Research Circles", through its executing unit FONDECYT (Contract No. 08-2019-FONDECYT).

  • Global monthly outputs of orography, surface air temperature and water stable isotopes (d18O) were run by the isotope-enabled atmosphere/ocean coupled model HadCM3 for the last interglacial (128 ka). An ensemble of ten idealised Antarctic Ice Sheet (AIS) simulations were processed, included a pre-industrial and a last interglacial control simulations. The eight other simulations used changed topography of the AIS relative to Dome C to ensure the preservation of the atmospheric pathways. The simulations were run 100 years and the last 50 years were used for the analyses. This work was funding through the European Research Council under the Horizon 2020 research and innovation programme (grant agreement No 742224, WACSWAIN) and NERC grant NE/P009271/1.