Global Cooling and Decoupling on Mountain Glaciers - Observations and Future Estimation.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 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). %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 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' observation 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 et al., 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 elevationBIAS: The mean difference in TA_GLA - TA_AMB in °C.RH_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 humidityTE_MEAN_UP: An upper estimate for the mean equivalent temperature in °C, given the uncertainty of the TLR.TE_MEAN_LO: A lower estimate for the mean equivalent temperature in °C, given the uncertainty of the TLR.SWIN_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)FFera: The mean wind speed derived from ERA5Land, for the pixel over the AWS. Values are in m s^1, but are notably slow (a common issue from ERA5 datasets). 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 estimate of the derived k value, considering the maximum difference of regression k-estimates with randomly perturbed uncertainties of the TLR (for TaAmb) and TaGla %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 2) HISTORICAL_DECOUPLING_ESTIMATES_DATABASE.mat is a Matlab data structure with the following order: HISTORICAL_K -> [VARIABLE_LIST] The variables presented are based upon estimates of decoupling and cooling over mountain glaciers given a mean ERA5-Land climatology for the period 2000-2022. 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 = 186,792)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 levelTA: The elevation-adjusted mean air temperature in °C for each glacier from the nearest ERA5-Land pixelQ: The elevation-adjusted mean specific humidity in g kg^-1 for each glacier from the nearest ERA5-Land pixelFF: The mean wind speed in m s^-1 for each glacier from the nearest ERA5-Land pixel. The value was multiplied by 2.5 to best match off-glacier wind speed observations close to glaciersLEN: The length of each glacier in metres, given by the RGIv6 inventoryK: The calculated mean decoupling value 'k' per glacier (unitless)K_CI: The 95% confidence for k (unitless)COOL: The mean value of cooling per glacier in °C, expressed as (TA x K) - TA, then averaged per glacierCOOL_CI: The 95% confidence interval of mean cooling per glacier in °C %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 3) 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 middle of the road (SSP2-4.5) and a pessimistic (SSP5-8.5) CMIP6 ensembles. 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 = 186,792)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 analysis AREA_245: The area change of each glacier and each year (n,y), as calculated from Rounce et al. (2023) for the ensemble mean of SSP2-4.5 (km^2)TA_245: 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 SSP2-4.5 scenarioQ_245: 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 SSP2-4.5 scenarioFF_245: 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 SSP2-4.5 scenarioK_STATIC_245: 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 geometry. The future climate is given by the ensemble mean of an SSP2-4.5 CMIP6 scenarioK_DYNAMIC_245: 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) for the ensemble mean of an SSP2-4.5 CMIP6 scenarioK_DYNAMIC_245_CI: The 95% confidence interval for the mean decoupling value for each glacier and each year (n,y,CI) 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) for the ensemble mean of an SSP2-4.5 CMIP6 scenarioCOOLING_STATIC_245: 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 occurs. The future climate is given by the ensemble mean of an SSP2-4.5 CMIP6 scenarioCOOLING_DYNAMIC_245: 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) for the ensemble mean of an SSP2-4.5 CMIP6 scenarioCOOLING_DYNAMIC_245_CI: The 95% confidence interval for glacier cooling in °C per glacier and year (n,y,CI), defined as (TA x K) - TA, where glacier geometry is updated based upon the model results of Rounce et al. (2023) for the ensemble mean of an SSP2-4.5 CMIP6 scenario AREA_585: The area change of each glacier and each year (n,y), as calculated from Rounce et al. (2023) for the ensemble mean of SSP5-8.5 (km^2)TA_585: 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_585: 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_585: 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_585: 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 geometry. The future climate is given by the ensemble mean of an SSP5-8.5 CMIP6 scenarioK_DYNAMIC_585: 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) for the ensemble mean of an SSP5-8.5 CMIP6 scenarioK_DYNAMIC_585_CI: The 95% confidence interval for the mean decoupling value for each glacier and each year (n,y,CI) 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) for the ensemble mean of an SSP5-8.5 CMIP6 scenarioCOOLING_STATIC_585: 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 occurs. The future climate is given by the ensemble mean of an SSP5-8.5 CMIP6 scenarioCOOLING_DYNAMIC_585: 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) for the ensemble mean of an SSP5-8.5 CMIP6 scenarioCOOLING_DYNAMIC_585_CI: The 95% confidence interval for glacier cooling in °C per glacier and year (n,y,CI), defined as (TA x K) - TA, where glacier geometry is updated based upon the model results of Rounce et al. (2023) for the ensemble mean of an SSP5-8.5 CMIP6 scenario %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 4) Make_Plots_GlobalCooling_OBSERVATIONS.m A Matlab script to call in the data structure of 1) and plot Figures 1 & 2 of the paper*. * The zip folders for 'FUNCTIONS' and 'GIS' should be unzipped and placed in the same file locations (unless code is adapted for different directories). %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 5) Make_Plots_GlobalCooling_ESTIMATES.m A Matlab script to call in the data structures of 2) and 3) and plot Figures 3 & 4 of the paper*. * The zip folders for 'FUNCTIONS' and 'GIS' should be unzipped and placed in the same file locations (unless code is adapted for different directories). %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 6) METADATA_TABLE_ON-GLACIER_TA_DATA.csv A comma separated values table of the metadata provided as Table S1 of the paper's Supplementary Information Section. This table provides an overview of the compiled on-glacier data in 1) and the key metadata information, including the RGI ID number of the glacier (RGI v.6) and year of observation, latitude and longitude (°), the number of on-glacier AWS, the period of observation during the summer/dry season months and whether other meteorological data were available (1 = yes). A reference for the data is provided, which, in the absence of a data description paper (or paper providing the data), is the most relevant reference concerning the data known to the authors. Where available online, the link to the hosted data is provided. A reference list from the table is provided in 7). %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 7) METADATA_TABLE_REFERENCES.txt A reference list from the metadata table as given in 6).所附数据集为待发表研究成果的配套数据。本数据集包含全球山地冰川气温冷却与脱耦现象的元数据、统计汇总结果及估算值。本研究由欧盟地平线2020计划玛丽·居里行动（EU Horizon 2020 Marie Sklodowska-Curie Actions）资助，资助编号为101026058，项目名称为“TEMPEST”（官网：www.tempestglacier.com）。 1) **GLACIER_DECOUPLING_OBSERVATIONS_DATABASE.mat**：为Matlab数据结构，组织形式如下： DATABASE -> [冰川代码名] -> [变量列表] 其中冰川代码名为3位字母标识，后跟下划线加两位数字的年份标识符，用于区分同一冰川的不同年度观测（例如“ARO_22”代表2022年的阿罗拉冰川）。 每个冰川子结构下包含目标观测数据，变量列表如下： NAME：冰川名称（若适用可在括号中标注年份） CODE：冰川代码名 GLACIER_LAT：冰川位置的平均纬度（十进制度） GLACIER_LON：冰川位置的平均经度（十进制度） GLACIER_ELE：冰川位置的平均海拔（米，以海平面为基准） UTC：冰川位置的UTC时区 DATE_START：观测时段起始日期，采用Matlab日期时间格式 DATE_END：观测时段结束日期，采用Matlab日期时间格式 AWS_SUM：冰川上自动气象站（Automatic Weather Station, AWS）的总数量 AWS_TOTAL_OBS：所有站点的有效逐小时观测总次数 AWS_NAME：自动气象站站点名称 AWS_LAT：自动气象站位置的纬度（十进制度） AWS_LON：自动气象站位置的经度（十进制度） AWS_ELE：自动气象站位置的观测起始海拔（米，以海平面为基准） AWS_FPL：自动气象站位置的冰面流径长度（米）。若文献中有直接记录则直接采用，否则通过Matlab TopoToolBox工具包（Schwanghart等，2010）计算得到 AWS_SHIELD：单元格数组，用于标识气温测量是否采用人工抽气方式 AWS_SLP：自动气象站位置的平均坡度（度），从ASTER全球数字高程模型（ASTER Global Digital Elevation Model, ASTER GDEM）栅格中提取 AWS_ASP：自动气象站位置的平均坡向（度），从ASTER GDEM栅格中提取 AWS_DEB：自动气象站位置是否存在冰表碎屑（1=是，0=否） AWS_TPI：自动气象站位置的地形位置指数（米），负值代表在1公里搜索窗口内周围地形更高 AWS_dCEN：自动气象站位置到冰川中心线的距离（米） AWS_dEDGE：自动气象站位置到冰川侧缘或末端的距离（米） AWS_RGH：自动气象站位置周围数字高程模型（DEM）像素的粗糙度参数，对应2维标准差滤波方法（Riley等，1999） AWS_OFF_ELE：用于推导背景气温（TaAmb，详见下文）的冰川外自动气象站的海拔（米，以海平面为基准） TA_GLA_MEAN：DATE_START至DATE_END时段内，冰川表面平均气温（TaGla，单位：°C） TA_OFF_MEAN：DATE_START至DATE_END时段内，冰川外平均气温（TaOFF，单位：°C） TA_AMB_MEAN：DATE_START至DATE_END时段内，背景平均气温（TaAmb，单位：°C），通过冰川外气温结合局地气温直减率外推得到 TLR_MEAN：平均气温直减率（°C·m⁻¹），采用冰川学通用惯例，负值代表气温随海拔升高而降低 BIAS：TA_GLA与TA_AMB的平均差值（°C），即TA_GLA - TA_AMB的均值 RH_MEAN：冰川外自动气象站观测的平均相对湿度（%），通过露点气温直减率估算得到冰川上自动气象站位置的相对湿度 Q_MEAN：自动气象站位置的平均比湿（g·kg⁻¹），计算方法与相对湿度一致 TE_MEAN：平均等效气温（°C），通过Matthews等（2022）的方法，结合背景气温与比湿计算得到 TE_MEAN_UP：平均等效气温的上限估算值（°C），考虑了气温直减率的不确定性 TE_MEAN_LO：平均等效气温的下限估算值（°C），考虑了气温直减率的不确定性 SWIN_MEAN：自动气象站位置的平均入射短波辐射（W·m⁻²），若无数据则记为非数值（NaN） LWIN_MEAN：自动气象站位置的平均入射长波辐射（W·m⁻²），若无数据则记为非数值（NaN） FFera：从ERA5-Land再分析数据中提取的自动气象站对应像素的平均风速（m·s⁻¹），该数值普遍偏小（ERA5数据集的常见问题） WIND_ID：具备有效风速观测数据的自动气象站站点（对应AWS_NAME的列索引） WIND_SPD_MEAN：WIND_ID对应的自动气象站的平均风速（m·s⁻¹） WIND_DIR_MEAN：WIND_ID对应的自动气象站的平均风向（度，角度平均） WIND_DC：WIND_ID对应的自动气象站的风向一致性（无单位） WIND_UWI：WIND_ID对应的自动气象站的上游风向指数（无单位），参考Shaw等（2023）的方法，取值为-1（+1）代表平均风向恰好沿冰川向下（向上）流动 SNOW_ALB：自动气象站位置的平均雪反照率（无单位），从NASA APPears数据平台提取DATE_START至DATE_END时段内的MODIS MOD10A1产品（中分辨率成像光谱仪MOD10A1, MODIS MOD10A1）最近像素值计算得到 SNOW_SCA：与SNOW_ALB计算方法一致，对应像素的积雪覆盖分数（无单位） AWS_K：脱耦系数k的估算值（无单位），通过每个自动气象站位置的背景气温与冰川表面气温的比值，结合Matlab中的双权稳健回归拟合得到 AWS_R2：脱耦系数k拟合的决定系数R²，用于剔除置信度较低的k值计算结果 AWS_K_UNC：脱耦系数k的不确定性估算值，考虑了背景气温直减率（用于计算TaAmb）与冰川表面气温的随机扰动不确定性后，回归得到的k估计值的最大差值 2) **HISTORICAL_DECOUPLING_ESTIMATES_DATABASE.mat**：为Matlab数据结构，组织形式如下： HISTORICAL_K -> [变量列表] 本数据集基于2000-2022年ERA5-Land再分析数据的平均态，估算全球山地冰川的脱耦与冷却现象。变量列表如下： RGI_ID：本次分析中每条冰川对应的兰德冰川目录v.6（Randolph Glacier Inventory v.6, RGIv6）编号。注：本数据集未包含南极洲、格陵兰的冰盖、冰帽及大型外围冰川（共186792条冰川除外） REGION：对应每条冰川所属区域的编号 LAT：每条冰川的平均纬度（十进制度） LON：每条冰川的平均经度（十进制度） ELE：每条冰川的平均海拔（米，以海平面为基准） TA：每条冰川的海拔校正后平均气温（°C），来自最近的ERA5-Land像素 Q：每条冰川的海拔校正后平均比湿（g·kg⁻¹），来自最近的ERA5-Land像素 FF：每条冰川的平均风速（m·s⁻¹），来自最近的ERA5-Land像素，该数值乘以2.5以匹配冰川附近的冰川外风速观测结果 LEN：每条冰川的长度（米），来自RGIv6目录 K：每条冰川的平均脱耦系数k（无单位） K_CI：脱耦系数k的95%置信区间（无单位） COOL：每条冰川的平均冷却值（°C），计算公式为(TA × K) - TA，再按冰川平均得到 COOL_CI：每条冰川平均冷却值的95%置信区间（°C） 3) **FUTURE_DECOUPLING_ESTIMATES_DATABASE.mat**：为Matlab数据结构，组织形式如下： FUTURE_K -> [变量列表] 本数据集基于共享社会经济路径2-4.5（SSP2-4.5）与共享社会经济路径5-8.5（SSP5-8.5）下耦合模式比较计划第六阶段（Coupled Model Intercomparison Project Phase 6, CMIP6）集合模式的未来气候演化结果，估算全球山地冰川的脱耦与冷却现象。脱耦系数（k）与冷却值的估算采用两种情景模式：一是冰川几何形态无变化（静态），二是基于Rounce等（2023）的模型结果更新冰川长度与厚度。变量列表如下： #### SSP2-4.5情景部分 RGI_ID：本次分析中每条冰川对应的RGIv6编号，注：本数据集未包含南极洲、格陵兰的冰盖、冰帽及大型外围冰川（共186792条冰川除外） REGION：对应每条冰川所属区域的编号 LAT：每条冰川的平均纬度（十进制度） LON：每条冰川的平均经度（十进制度） ELE：每条冰川的平均海拔（米，以海平面为基准） YEARS：本次分析涵盖的年份（2000-2099） AREA_245：每条冰川每年的面积变化（n,y），基于Rounce等（2023）的SSP2-4.5情景集合平均结果计算得到（单位：km²） TA_245：每条冰川每年的海拔校正后平均气温（°C，n,y），来自SSP2-4.5情景下CMIP6集合平均的偏差校正结果 Q_245：每条冰川每年的海拔校正后平均比湿（g·kg⁻¹，n,y），来自SSP2-4.5情景下CMIP6集合平均的偏差校正结果 FF_245：每条冰川每年的海拔校正后平均风速（m·s⁻¹，n,y），来自SSP2-4.5情景下CMIP6集合平均的偏差校正结果 K_STATIC_245：每条冰川每年的平均脱耦系数k（n,y），通过多元线性回归模型计算得到，基于数据集1）的观测结果，且假设冰川几何形态无变化。未来气候采用SSP2-4.5情景下CMIP6集合平均结果 K_DYNAMIC_245：每条冰川每年的平均脱耦系数k（n,y），通过多元线性回归模型计算得到，基于数据集1）的观测结果，且冰川几何形态基于Rounce等（2023）的SSP2-4.5情景集合平均模型结果更新 K_DYNAMIC_245_CI：每条冰川每年的平均脱耦系数k的95%置信区间（n,y,CI），通过多元线性回归模型计算得到，基于数据集1）的观测结果，且冰川几何形态基于Rounce等（2023）的SSP2-4.5情景集合平均模型结果更新 COOLING_STATIC_245：每条冰川每年的冰川冷却值未来演化（°C，n,y），计算公式为(TA × K) - TA，假设冰川几何形态无变化。未来气候采用SSP2-4.5情景下CMIP6集合平均结果 COOLING_DYNAMIC_245：每条冰川每年的冰川冷却值未来演化（°C，n,y），计算公式为(TA × K) - TA，冰川几何形态基于Rounce等（2023）的SSP2-4.5情景集合平均模型结果更新 COOLING_DYNAMIC_245_CI：每条冰川每年的冰川冷却值的95%置信区间（°C，n,y,CI），计算公式为(TA × K) - TA，冰川几何形态基于Rounce等（2023）的SSP2-4.5情景集合平均模型结果更新 #### SSP5-8.5情景部分 AREA_585：每条冰川每年的面积变化（n,y），基于Rounce等（2023）的SSP5-8.5情景集合平均结果计算得到（单位：km²） TA_585：每条冰川每年的海拔校正后平均气温（°C，n,y），来自SSP5-8.5情景下CMIP6集合平均的偏差校正结果 Q_585：每条冰川每年的海拔校正后平均比湿（g·kg⁻¹，n,y），来自SSP5-8.5情景下CMIP6集合平均的偏差校正结果 FF_585：每条冰川每年的海拔校正后平均风速（m·s⁻¹，n,y），来自SSP5-8.5情景下CMIP6集合平均的偏差校正结果 K_STATIC_585：每条冰川每年的平均脱耦系数k（n,y），通过多元线性回归模型计算得到，基于数据集1）的观测结果，且假设冰川几何形态无变化。未来气候采用SSP5-8.5情景下CMIP6集合平均结果 K_DYNAMIC_585：每条冰川每年的平均脱耦系数k（n,y），通过多元线性回归模型计算得到，基于数据集1）的观测结果，且冰川几何形态基于Rounce等（2023）的SSP5-8.5情景集合平均模型结果更新 K_DYNAMIC_585_CI：每条冰川每年的平均脱耦系数k的95%置信区间（n,y,CI），通过多元线性回归模型计算得到，基于数据集1）的观测结果，且冰川几何形态基于Rounce等（2023）的SSP5-8.5情景集合平均模型结果更新 COOLING_STATIC_585：每条冰川每年的冰川冷却值未来演化（°C，n,y），计算公式为(TA × K) - TA，假设冰川几何形态无变化。未来气候采用SSP5-8.5情景下CMIP6集合平均结果 COOLING_DYNAMIC_585：每条冰川每年的冰川冷却值未来演化（°C，n,y），计算公式为(TA × K) - TA，冰川几何形态基于Rounce等（2023）的SSP5-8.5情景集合平均模型结果更新 COOLING_DYNAMIC_585_CI：每条冰川每年的冰川冷却值的95%置信区间（°C，n,y,CI），计算公式为(TA × K) - TA，冰川几何形态基于Rounce等（2023）的SSP5-8.5情景集合平均模型结果更新 4) **Make_Plots_GlobalCooling_OBSERVATIONS.m**：用于调用数据集1）的数据结构，绘制论文中的图1与图2*。 * 需将“FUNCTIONS”与“GIS”压缩包解压至相同目录下（若需修改目录路径可调整代码）。 5) **Make_Plots_GlobalCooling_ESTIMATES.m**：用于调用数据集2）与3）的数据结构，绘制论文中的图3与图4*。 * 需将“FUNCTIONS”与“GIS”压缩包解压至相同目录下（若需修改目录路径可调整代码）。 6) **METADATA_TABLE_ON-GLACIER_TA_DATA.csv**：逗号分隔值表格，对应论文补充材料中的表S1。本表格汇总了数据集1）中的冰川原位观测数据，并提供关键元数据信息，包括冰川的RGIv6编号、观测年份、经纬度（°）、原位自动气象站数量、夏季/旱季观测时段，以及是否具备其他气象数据（1=是）。表格同时提供数据来源参考文献，在暂无专门数据描述论文的情况下，采用作者已知的与该数据最相关的文献。若数据可在线获取，还会提供托管数据的链接。本表格的参考文献列表见7）。 7) **METADATA_TABLE_REFERENCES.txt**：包含6）中元数据表格对应的参考文献列表。