ALBERT

Description: The most practical way to get a spatially broad and continuous measurements of the surface temperature in the data-sparse cryosphere is by satellite remote sensing. The uncertainties in satellite-derived LSTs must be understood to develop internally-consistent decade-scale land-surface temperature (LST) records needed for climate studies. In this work we assess satellite-derived "clear-sky" LST products from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and LSTs derived from the Enhanced Thematic Mapper Plus (ETM+) over snow and ice on Greenland. When possible, we compare satellite-derived LSTs with in-situ air-temperature observations from Greenland Climate Network (GC-Net) automatic-weather stations (AWS). We find that MODIS, ASTER and ETM+ provide reliable and consistent LSTs under clear-sky conditions and relatively-flat terrain over snow and ice targets over a range of temperatures from -40 to 0 C. The satellite-derived LSTs agree within a relative RMS uncertainty of approx.0.5 C. The good agreement among the LSTs derived from the various satellite instruments is especially notable since different spectral channels and different retrieval algorithms are used to calculate LST from the raw satellite data. The AWS record in-situ data at a "point" while the satellite instruments record data over an area varying in size from: 57 X 57 m (ETM+), 90 X 90 m (ASTER), or to 1 X 1 km (MODIS). Surface topography and other factors contribute to variability of LST within a pixel, thus the AWS measurements may not be representative of the LST of the pixel. Without more information on the local spatial patterns of LST, the AWS LST cannot be considered valid ground truth for the satellite measurements, with RMS uncertainty approx.2 C. Despite the relatively large AWS-derived uncertainty, we find LST data are characterized by high accuracy but have uncertain absolute precision.

Description: Surface temperatures on the Greenland Ice Sheet have been studied on the ground, using automatic weather station (AWS) data from the Greenland-Climate Network (GC-Net), and from analysis of satellite sensor data. Using Advanced Very High Frequency Radiometer (AVHRR) weekly surface temperature maps, warming of the surface of the Greenland Ice Sheet has been documented since 1981. We extended and refined this record using higher-resolution Moderate-Resolution Imaging Spectroradiometer (MODIS) data from March 2000 to the present. We developed a daily and monthly climate-data record (CDR) of the "clear-sky" surface temperature of the Greenland Ice Sheet using an ice-surface temperature (1ST) algorithm developed for use with MODIS data. Validation of this CDR is ongoing. MODIS Terra swath data are projected onto a polar stereographic grid at 6.25-km resolution to develop binary, gridded daily and mean-monthly 1ST maps. Each monthly map also has a color-coded image map that is available to download. Also included with the monthly maps is an accompanying map showing number of days in the month that were used to calculate the mean-monthly 1ST. This is important because no 1ST decision is made by the algorithm for cells that are considered cloudy by the internal cloud mask, so a sufficient number of days must be available to produce a mean 1ST for each grid cell. Validation of the CDR consists of several facets: 1) comparisons between ISTs and in-situ measurements; 2) comparisons between ISTs and AWS data; and 3) comparisons of ISTs with surface temperatures derived from other satellite instruments such as the Thermal Emission and Reflection Radiometer (ASTER) and Enhanced Thematic Mapper Plus (ETM+). Previous work shows that Terra MODIS ISTs are about 3 C lower than in-situ temperatures measured at Summit Camp, during the winter of 2008-09 under clear skies. In this work we begin to compare surface temperatures derived from AWS data with ISTs from the MODIS CDR.

Description: The accelerating loss of mass from the Greenland ice sheet is a major contribution to current sea level rise. Increased melt water runoff is responsible for half of Greenlands mass loss increase. Surface melt has been increasing in extent and intensity, setting a record for surface area melt and runoff in 2012. The mechanisms and timescales involved in allowing surface melt water to reach the ocean where it can contribute to sea level rise are poorly understood. The potential capacity to store this water in liquid or frozen form in the firn (multi-year snow layer) is significant, and could delay its sea-level contribution. Here we describe direct observation of water within a perennial firn aquifer persisting throughout the winter in the southern ice sheet,where snow accumulation and melt rates are high. This represents a previously unknown storagemode for water within the ice sheet. Ice cores, groundairborne radar and a regional climatemodel are used to estimate aquifer area (70 plue or minus 10 x 10(exp 3) square kilometers ) and water table depth (5-50 m). The perennial firn aquifer represents a new glacier facies to be considered 29 in future ice sheet mass 30 and energy budget calculations.

Description: The Advanced Very High Resolution (AVHRR) Polar Pathfinder Data (APP) provides the first long time series of consistent, calibrated surface albedo and surface temperature data for the polar regions. Validations of these products have consisted of individual studies that analyzed algorithm performance for limited regions and or time periods. This paper reports on comparisons made between the APP-derived surface albedo and that measured at fourteen automatic weather stations (AWS) around the Greenland ice sheet from January 1997 to August 1998. Results show that satellite-derived surface albedo values are on average 10% less than those measured by the AWS stations. However, the station measurements tend to be biased high by about 4% and thus the differences in absolute albedo may be less (e.g. 6%). In regions of the ice sheet where the albedo variability is small, such as the dry snow facies, the APP albedo uncertainty exceeds the natural variability. Further work is needed to improve the absolute accuracy of the APP-derived surface albedo. Even so, the data provide temporally and spatially consistent estimates of the Greenland ice sheet albedo.

Description: Enhanced melting along with surface-temperature increases measured using infrared satellite data, have been documented for the Greenland Ice Sheet. Recently we developed a climate-quality data record of ice-surface temperature (IST) of the Greenland Ice Sheet using the Moderate-Resolution Imaging Spectroradiometer (MODIS) IST product -- http://modis-snow-ice.gsfc.nasa.gov.Using daily and mean-monthly MODIS IST maps from the data record we show maximum extent of melt for the ice sheet and its six major drainage basins for a 12-year period extending from March of 2000 through December of 2011. The duration of the melt season on the ice sheet varies in different drainage basins with some basins melting progressively earlier over the study period. Some (but not all) of the basins also show a progressively-longer duration of melt. The short time of the study period (approx 12 years) precludes an evaluation of statistically-significant trends. However the dataset provides valuable information on natural variability of IST, and on the ability of the MODIS instrument to capture changes in IST and melt conditions in different drainage basins of the ice sheet.

Description: Surface temperatures on the Greenland Ice Sheet have been studied on the ground, using automatic weather station (AWS) data from the Greenland-Climate Network (GC-Net), and from analysis of satellite sensor data. Using Advanced Very High Frequency Radiometer (AVHRR) weekly surface temperature maps, warming of the surface of the Greenland Ice Sheet has been documented since 1981. We extended and refined this record using higher-resolution Moderate-Resolution Imaging Spectroradiometer (MODIS) data from March 2000 to the present. We developed a daily and monthly climate-data record (CDR) of the "clear-sky" surface temperature of the Greenland Ice Sheet using an ice-surface temperature (1ST) algorithm developed for use with MODIS data. Validation of this CDR is ongoing. MODIS Terra swath data are projected onto a polar stereographic grid at 6.25-km resolution to develop binary, gridded daily and mean-monthly 1ST maps. Each monthly map also has a color-coded image map that is available to download. Also included with the monthly maps is an accompanying map showing number of days in the month that were used to calculate the mean-monthly 1ST. This is important because no 1ST decision is made by the algorithm for cells that are considered cloudy by the internal cloud mask, so a sufficient number of days must be available to produce a mean 1ST for each grid cell. Validation of the CDR consists of several facets: 1) comparisons between ISTs and in-situ measurements; 2) comparisons between ISTs and AWS data; and 3) comparisons of ISTs with surface temperatures derived from other satellite instruments such as the Thermal Emission and Reflection Radiometer (ASTER) and Enhanced Thematic Mapper Plus (ETM+). Previous work shows that Terra MODIS ISTs are about 3 C lower than in-situ temperatures measured at Summit Camp, during the winter of 2008-09 under clear skies. In this work we begin to compare surface temperatures derived from AWS data with ISTs from the MODIS CDR. The Greenland Ice Sheet 1ST CDR will be useful for monitoring surface-temperature trends and can be used as input or for validation of climate models. The CDR can be extended into the future using MODIS Terra, Aqua and NPOESS Preparatory Project Visible Infrared Imager Radiometer Suite (VII RS) data.

Description: The Greenland Ice Sheet (GrIS) mass loss has been accelerating at a rate of about 20 ± 10 Gt/yr2 since the end of the 1990's, with around 60 % of this mass loss directly attributed to enhanced surface meltwater runoff. However, in the climate and glaciology communities, different approaches exist on how to model the different surface mass balance (SMB) components using: (1) complex physically-based climate models which are computationally expensive; (2) intermediate complexity energy balance models; (3) simple and fast positive degree day models which base their inferences on statistical principles and are computationally highly efficient. Additionally, many of these models compute the SMB components based on different spatial and temporal resolutions, with different forcing fields as well as different ice sheet topographies and extents, making inter-comparison difficult. In the GrIS SMB model intercomparison project (GrSMBMIP) we address these issues by forcing each model with the same data (i.e., the ERA-Interim reanalysis) except for two global models for which this forcing is limited to the oceanic conditions, and at the same time by interpolating all modelled results onto a common ice sheet mask at 1 km horizontal resolution for the common period 1980–2012. The SMB outputs from 13 models are then compared over the GrIS to (1) SMB estimates using a combination of gravimetric remote sensing data from GRACE and measured ice discharge, (2) ice cores, snow pits, in-situ SMB observations, and (3) remotely sensed bare ice extent from MODerate-resolution Imaging Spectroradiometer (MODIS). Our results reveal that the mean GrIS SMB of all 13 models has been positive between 1980 and 2012 with an average of 340 ± Gt/yr, but has decreased at an average rate of −7.3 Gt/yr2 (with a significance of 96 %), mainly driven by an increase of 8.0 Gt/yr2 (with a significance of 98 %) in meltwater runoff. Spatially, the largest spread among models can be found around the margins of the ice sheet, highlighting the need for accurate representation of the GrIS ablation zone extent and processes driving the surface melt. In addition, a higher density of in-situ SMB observations is required, especially in the south-east accumulation zone, where the model spread can reach 2 mWE/yr due to large discrepancies in modelled snowfall accumulation. Overall, polar regional climate models (RCMs) perform the best compared to observations, in particular for simulating precipitation patterns. However, other simpler and faster models have biases of same order than RCMs with observations and remain then useful tools for long-term simulations. Finally, it is interesting to note that the ensemble mean of the 13 models produces the best estimate of the present day SMB relative to observations, suggesting that biases are not systematic among models.