This dataset presents ERA5 daily downscaled surface air temperature and snowfall from two sites next to the first ice core drilled from Cordillera Darwin, Chile. These records were presented in Tetzner et al. (2025) to study regional-to-local environmental conditions at the firn core site. These local surface air temperature and snowfall estimations suggest the icefield has been progressively exposed to surface melt conditions, but not enough to produce significant melt at the firn core site. This dataset was created with the support of the Dieter R Tetzner''s National Geographic Early Career Grant 2019.

Temperature, pressure, wind speed and wind direction from two automatic weather stations on the Brunt Ice Shelf that operated during 2015.

Microclimate data collected hourly at Anchorage Island, for 15 climatic variables via automatic data loggers, from 2001-2009. Data is not available across the entire temporal range for all variables. NERC funded under the British Antarctic Survey National Capability programme, Polar Science for Planet Earth.

Microclimate data collected hourly at Coal Nunatak, for 10 climatic variables via automatic data loggers, 2006-2019. Data is not available across the entire temporal range for all variables. NERC funded under the British Antarctic Survey National Capability programme, Polar Science for Planet Earth. **Please be advised to use Version 2.0 of this dataset, which has undergone additional quality control, found here: https://data.bas.ac.uk/metadata.php?id=GB/NERC/BAS/PDC/01598**

Meteorological variables (wind speed, air temperature and wind direction) were collected using two wind towers. Photogrammetric data were collected using a pole-mounted digital camera and DJI Phantom 3 UAV. LiDAR data collected via terrestrial and airborne laser scanning. Fieldwork carried out at Hintereisferner glacier, in the Oetztal Alps region, Tyrol, Austria, from 1-15 August 2018 by Joshua Chambers, Thomas Smith and Mark Smith. Terrestrial laser scan (TLS) data collected by Rudolf Sailer. Airborne laser scan (ALS) data originally from Open Data Austria, see Sailer et al. (2012). One wind tower recorded for the entire study duration, the second was moved to different plots every ~4 days. Photogrammetric data were collected on 8, 10, 11, 12 and 13 August. TLS scans were split into upper- and lower-glacier, and completed on 3, 7, 12 and 16 August. Data were used to examine the relations between glacier aerodynamic roughness and sampling resolution, and to develop a correction factor for roughness derived from coarser resolution data. Fieldwork was funded by an INTERACT Transnational Access grant awarded to Mark Smith under the European Union H2020 Grant Agreement No. 730938. Joshua Chambers is supported by a NERC PhD studentship (NE/L002574/1). Ivana Stiperski was funded by Austrian Science Fund (FWF) grant T781-N32.

Meteorological variables (wind speed, air temperature and wind direction) were collected using two wind towers. Photogrammetric data were collected using a pole-mounted digital camera and DJI Phantom 3 UAV. LiDAR data collected via terrestrial and airborne laser scanning. Fieldwork carried out at Hintereisferner glacier, in the Oetztal Alps region, Tyrol, Austria, from 1-15 August 2018 by Joshua Chambers, Thomas Smith and Mark Smith. Terrestrial laser scan (TLS) data collected by Rudolf Sailer. Airborne laser scan (ALS) data originally from Open Data Austria, see Sailer et al. (2012). One wind tower recorded for the entire study duration, the second was moved to different plots every ~4 days. Photogrammetric data were collected on 8, 10, 11, 12 and 13 August. TLS scans were split into upper- and lower-glacier, and completed on 3, 7, 12 and 16 August. Data were used to examine the relations between glacier aerodynamic roughness and sampling resolution, and to develop a correction factor for roughness derived from coarser resolution data. Fieldwork was funded by an INTERACT Transnational Access grant awarded to Mark Smith under the European Union H2020 Grant Agreement No. 730938. Joshua Chambers is supported by a NERC PhD studentship (NE/L002574/1). Ivana Stiperski was funded by Austrian Science Fund (FWF) grant T781-N32. ***** PLEASE BE ADVISED TO USE VERSION 2.0 DATA ***** The VERSION 2.0 data set (see ''Related Data Set Metadata'' link below) provides corrected glacier aerodynamic roughness calculated using the new model outlined in Chambers et al.

The data consists of 30 minute observations recorded by an automatic weather station (iWS 18) in Cabinet Inlet on Larsen C Ice Shelf on the Antarctic Peninsula. The iWS consists of a custom-built weather station unit, assembled at the Institute of Marine and Atmospheric research Utrecht (IMAU). There are sensors for air temperature, surface air pressure, relative humidity, as well as a gps, an acoustic snow height sensor, an ARGOS communication antenna, and three Lithium batteries that fuel the unit when solar radiation is absent. The unit is complemented by a propeller-vane Young anemometer measuring wind direction and speed. Additionally, all radiation fluxes are measured with a Kipp and Zonen CNR4 radiometer. This dataset runs from November 2014 to January 2017. Funded was provided by the NERC grant NE/L005409/1. ***** PLEASE BE ADVISED TO USE VERSION 2.0 DATA ***** The VERSION 2.0 data set (see ''Related Data Set Metadata'' link below) has an additional 10 months of measurements.

Microclimate data reported hourly from 2017-2024 on the Backslope behind the British base at Signy Island, South Orkney Islands. Data comprises 15 climatic variables via an automatic data logger and is a continuation of a previous dataset from a logger at Jane Col on Signy Island. Data are not available for the entire temporal range for all variables. Data are collected as part of long-term monitoring of microclimate across the Southern Ocean. This dataset is supported by NERC funding under the British Antarctic Survey National Capability Programme, Polar Science for Planet Earth

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).

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).