DOI: 10.5281/zenodo.14044847
title: Global Cooling and Decoupling on Mountain Glaciers - Observations and Future Estimation.

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The attached datasets are the result of work under consideration for publishing.

The datasets provide metadata, summary statistics and estimates of air temperature cooling and decoupling on the mountain glaciers of the world.  This work was funded by the EU Horizon 2020 Marie Sklodowska-Curie Actions Grant 101026058 under the project name 'TEMPEST' (wwww.tempestglacier.com).

The following files are described:

1) GLACIER_DECOUPLING_OBSERVATIONS_DATABASE.mat

2) FUTURE_DECOUPLING_ESTIMATES_DATABASE.mat

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1) GLACIER_DECOUPLING_OBSERVATIONS_DATABASE.mat is a Matlab data structure with the following order:

DATABASE - > [GLACIER CODENAME] -> [VARIABLE_LIST]

where the glacier codename is a three letter code for a given glacier with a two number year identifier following an underscore, where multiple years on a single glacier exist (e.g. 'ARO_22' for Arolla Glacier in 2022).

Within each glacier location sub-structure exists the data of interest. The Variable list is as follows:

NAME: Name of Glacier (year if parenthesis if applicable)CODE: Codename of glacierGLACIER_LAT: Mean latitude of glacier location in decimal degreesGLACIER_LON: Mean longitude of glacier location in decimal degreesGLACIER_ELE: Mean elevation of glacier location in metres above sea levelUTC: UTC time zone of glacier locationDATE_START: Start date of observation period in Matlab datetime formatDATE_END: End date of observation period in Matlab datetime formatAWS_SUM: Total number of 'AWS' obervsation points on the glacierAWS_TOTAL_OBS: Total number of valid hourly observations across all stationsAWS_NAME: Name of AWS station(s)AWS_LAT: Latitude of AWS location in decimal degreesAWS_LON: Longitude of AWS location in decimal degreesAWS_ELE: Elevation of AWS location in metres above sea level (at the time of observation start)AWS_FPL: The flowpath length of the AWS location in metres. FPL is taken direct from the literature where available, or calculated from Matlab TopoToolBox functions (Schwanghart and Kuhn, 2010) where unavailableAWS_SHIELD: A cell array indicating whether the temperature measurements are artificially aspirated or notAWS_SLP: Mean slope of AWS location in degrees, extracted from the ASTER GDEM grid cellAWS_ASP: Mean aspect of AWS location in degrees, extracted from the ASTER GDEM grid cellAWS_DEB: Presence of supraglacial debris at AWS location (1 = yes, 0 = no)AWS_TPI: The topographic position index of the AWS location in metres, where negative values indicate higher surrounding terrain in a 1 km search windowAWS_dCEN: Distance of the AWS location to the centreline of the glacier, in metresAWS_dEDGE: Distance of the AWS location to a lateral side of the glacier or the terminus, in metresAWS_RGH: Roughness elements of surrounding DEM pixels at the AWS location, corresponding to 2d standard deviation filter (Riley et. al. 1999)AWS_OFF_ELE: Elevation of Off-glacier AWS used to derive TaAmb (see below), in metres above sea levelTA_GLA_MEAN: Mean on-glacier air temperature (TaGla) in °C for the date range stated in DATE_START:DATE_ENDTA_OFF_MEAN: Mean off-glacier air temperature (TaOFF) in °C for the date range stated in DATE_START:DATE_ENDTA_AMB_MEAN: Mean ambient air temperature (TaAmb) in °C for the date range stated in DATE_START:DATE_END, derived from the extrapolation of TaOFF using a locally-derived lapse rateTLR_MEAN: Mean air temperature lapse rate in °C m^-1, following the glaciological convention where negative lapse rates imply a decrease of air temperature with increasing elevationRH_MEAN: Mean relative humidity in % from the off-glacier AWS, estimated for the on-glacier AWS locations given a lapse rate of the dew point temperature, subsequently converted Q_MEAN: Mean specific humidity in g kg^-1 for the AWS locations, following the approach for relative humidityTE_MEAN: Mean equivalent temperature in °C, calculated following Matthews et al (2022) using TaAmb and specific humiditySWIN_MEAN: Mean incoming shortwave radiation at the AWS location in Wm^-2, where available (NaN when not available)LWIN_MEAN: Mean incoming longwave radiation at the AWS location in Wm^-2, where available (NaN when not available)WIND_ID: ID of AWS station (column of AWS_NAME) where wind data are available and validWIND_SPD_MEAN: Mean wind speed in m s^-1 for those AWS indicated by WIND_IDWIND_DIR_MEAN: Angular mean wind direction in degrees for those AWS indicated by WIND_IDWIND_DC: Directional consistency (unitless) of wind direction for those AWS indicated by WIND_IDWIND_UWI: Up-glacier wind index (unitless) of wind direction relative to down-glacier flow direction at the AWS locations indicated by WIND_ID, following Shaw et al. (2023), whereby a value of -1 (+1) suggests that the mean wind direction is flowing precisely down-(up-)glacierSNOW_ALB: Mean snow albedo (unitless) for the AWS location, derived from the closest pixel of the daily MODIS MOD10A1 product, extracted for the date range stated in DATE_START:DATE_END from the NASA APPears websiteSNOW_SCA: As SNOW_ALB, but for the fractional snow covered area of the given pixel (unitless)AWS_K: The estimated k parameter of decoupling (unitless), derived from the ratio of TaAmb and TaGla for each AWS location, given a bisquare robust regression fit in MatlabAWS_R2: The R-squared value of the fit for k, used to exclude calculations of k with low confidenceAWS_K_UNC: The uncertainty estiamte of the derived k value, considering the maximum difference of regression k-estimates with randomly perturbed uncertainties of the TLR (for TaAmb) and TaGla

 

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2) FUTURE_DECOUPLING_ESTIMATES_DATABASE.mat is a Matlab data structure with the following order:

FUTURE_K -> [VARIABLE_LIST]

The variables presented are based upon estimates of decoupling and cooling over mountain glaciers given future evolution of climate from a pessimistic SSP5-8.5 CMIP6 ensemble. Decoupling ('k') and cooling are estimated based upon either no change of glacier geometry ('static') or the evolution of glacier length and thickness based upon the model results of Rounce et al. (2023).

The Variable list is as follows:

RGI_ID: The ID number from the Randolph Glacier Inventory v.6 (Pfeffer et al., 2014) for each glacier considered in this analysis. Note that this excludes ice sheets, ice caps and large periphery glaciers of Antarctica and Greenland (n = 187412)REGION: A number corresponding to the region of each glacier analysedLAT: The mean latitude of each glacier in decimal degreesLON: The mean longitude of each glacier in decimal degreesELE: The mean elevation of each glacier in metres above sea levelYEARS: The years (2000-2099) considered in the analysisLENGTH: The length change of each glacier and each year (n,y), as calculated from Rounce et al. (2023), in metresTA: The elevation-adjusted mean air temperature in °C for each glacier in each year (n,y), derived from ensemble mean bias-corrected CMIP6 estimates of an SSP5-8.5 scenarioQ: The elevation-adjusted mean specific humidity in g kg^-1 for each glacier in each year (n,y), derived from ensemble mean bias-corrected CMIP6 estimates of an SSP5-8.5 scenarioFF: The elevation-adjusted mean specific humidity in m s^-1 for each glacier in each year (n,y), derived from ensemble mean bias-corrected CMIP6 estimates of an SSP5-8.5 scenarioK_STATIC: The mean decoupling value for each glacier and each year (n,y) calculated from a multi-linear regression model, based upon observations in database 1) above and considering no change in glacier geometryK_DYNAMIC: The mean decoupling value for each glacier and each year (n,y) calculated from a multi-linear regression model, based upon observations in database 1) above and where glacier geometry is updated based upon the model results of Rounce et al. (2023)COOLING_STATIC: Future evolution of glacier cooling in °C per glacier and year (n,y), defined as (TA x K) - TA, where no change in glacier geometry occursCOOLING_DYNAMIC: Future evolution of glacier cooling in °C per glacier and year (n,y), defined as (TA x K) - TA, where glacier geometry is updated based upon the model results of Rounce et al. (2023)

 

 

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cited works

Pfeffer, W. T., Arendt, A. A., Bliss, A., Bolch, T., Cogley, J. G., Gardner, A. S., Hagen, J., Hock, R., Kaser, G., Kienholz, C., Miles, E. S., Moholdt, G., Rastner, P., Raup, B. H., Paul, F., Radic, V., Mo, N., Rich, J., Sharp, M. J., & The Randolph Consortium, I. (2014). The Randolph Glacier Inventory : a globally complete inventory of glaciers. Journal of Glaciology, 60(221), 537–552. https://doi.org/10.3189/2014JoG13J176

Riley, S.J., De Gloria, S.D. and Elliot, R. (1999) A Terrain Ruggedness Index That Quantifies Topographic Heterogeneity. Intermountain Journal of Sciences 

Rounce, D., Hock, R., Maussion, F., Hugonnet, R., Kochtitzky, W., Huss, M., Berthier, E., Brinkerhoff, D., Compagno, L., Copland, L., Farinotti, D., Menounos, B., & McNabb, R. (2023). Global glacier change in the 21st century: Every increase in temperature matters David. Science, 83(January), 78–83.

Schwanghart, W., & Kuhn, N. J. (2010). TopoToolbox: A set of Matlab functions for topographic analysis. Environmental Modelling and Software, 25(6), 770–781. https://doi.org/10.1016/j.envsoft.2009.12.002

Shaw, T. E., Buri, P., Mccarthy, M., Miles, E. S., Ayala, Á., & Pellicciotti, F. (2023). The Decaying Near-Surface Boundary Layer of a Retreating Alpine Glacier. Geophysical Research Letters, 50, 1–12. https://doi.org/10.1029/2023GL103043