Deriving glacier outlines from satellite data has become increasingly popular in the past decade. In particular when glacier outlines are used as a base for change assessment, it is important to know how accurate they are. Calculating the accuracy correctly is challenging, as appropriate reference data (e.g. from higher-resolution sensors) are seldom available. Moreover, after the required manual correction of the raw outlines (e.g. for debris cover), such a comparison would only reveal the accuracy of the analyst rather than of the algorithm applied. Here we compare outlines for clean and debris-covered glaciers, as derived from single and multiple digitizing by different or the same analysts on very high- (1 m) and medium-resolution (30 m) remote-sensing data, against each other and to glacier outlines derived from automated classification of Landsat Thematic Mapper data. Results show a high variability in the interpretation of debris-covered glacier parts, largely independent of the spatial resolution (area differences were up to 30%), and an overall good agreement for clean ice with sufficient contrast to the surrounding terrain (differences ∼5%). The differences of the automatically derived outlines from a reference value are as small as the standard deviation of the manual digitizations from several analysts. Based on these results, we conclude that automated mapping of clean ice is preferable to manual digitization and recommend using the latter method only for required corrections of incorrectly mapped glacier parts (e.g. debris cover, shadow).

Global sea ice products are produced from the Earth Observing System (EOS) Moderate Resolution Imaging Spectroradiometer (MODIS) onboard both the Terra and Aqua satellites. Daily sea ice extent and ice surface temperature (IST) products are available at 1- and 4-km resolution. Validation activities during the "cold period" (when meltwater is generally not present) in the Northern Hemisphere, defined here as October through May, have been undertaken to assess the accuracy of the 1-km resolution MODIS IST algorithm and product. Validation was also done at the South Pole station in Antarctica. In the Arctic Ocean, near-surface air temperatures from the National Oceanic and Atmospheric Administration (NOAA) National Ocean Service (NOS) Center for Operational Oceanographic Products and Services (CO-OPS) Alaska tide stations and from drifting buoys from the North Pole Environmental Observatory (NPEO) buoy program were compared with MODIS-derived ISTs. Using the standard MODIS sea ice product, which utilizes the MODIS cloud mask, results show a bias (mean error) of -2.1 K and a root mean square (RMS) error of 3.7 K. The negative bias means that the satellite retrieval is less than the air temperature. With the bias removed, the RMS error is 3.0 K. When additional visual cloud screening is performed to eliminate MODIS pixels thought to be contaminated by fog, results improved, with a subset of the larger dataset showing a bias of -0.9 K and an RMS error of 1.6 K. Uncertainties would be reduced in the Arctic Ocean dataset if the skin temperature of the sea ice were reported instead of the near-surface air temperatures. With the bias removed, the RMS error for the Arctic Ocean dataset is 1.3 K. Results from the South Pole station in Antarctica show that under clear skies as determined using lidar measurements, the MODIS ISTs are also very close to those of the near-surface air temperatures with a bias of -1.2 K and an RMS error of 1.7 K. With the bias removed, the RMS error for the South Pole dataset is 1.2 K. Thus, the accuracy (RMS error) of the IST measurement is 1.2-1.3 K. It is not possible to obtain an accurate IST from MODIS in the presence of even very thin clouds or fog, and this is the main limitation of the MODIS ice surface temperature product. MODIS sea ice products may be ordered from the National Snow and Ice Data Center in Boulder, CO.

A joint US Air Force/National Aeronautics and Space Administration (NASA) blended global snow product that uses Earth Observation System Moderate Resolution Imaging Spectroradiometer (MODIS), Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) and Quick Scatterometer (QuikSCAT or QSCAT) data has been developed. Existing snow products derived from these sensors have been blended into a single, global, daily, user-friendly product by using a newly developed Air Force Weather Agency (AFWA)/NASA Snow Algorithm (ANSA). This initial blended snow product uses minimal modelling to expeditiously yield improved snow products, which include, or will include, snow-cover extent, fractional snow cover, snow water equivalent (SWE), onset of snowmelt and identification of actively melting snow cover. The blended snow products are currently 25-km resolution. These products are validated with data from the lower Great Lakes region of the USA, from Colorado obtained during the Cold Land Processes Experiment (CLPX), and from Finland. The AMSR-E product is especially useful in detecting snow through clouds; however, passive microwave data miss snow in those regions where the snow cover is thin, along the margins of the continental snowline, and on the lee side of the Rocky Mountains, for instance. In these regions, the MODIS product can map shallow snow cover under cloud-free conditions. The confidence for mapping snow-cover extent is greater with the MODIS product than with the microwave product when cloud-free MODIS observations are available. Therefore, the MODIS product is used as the default for detecting snow cover. The passive microwave product is used as the default only in those areas where MODIS data are not applicable due to the presence of clouds and darkness. The AMSR-E snow product is used in association with the difference between ascending and descending satellite passes or diurnal-amplitude variations (DAV) to detect the onset of melt, and a QSCAT product will be used to map areas of snow that are actively melting.

Black carbon (BC) concentrations observed in 22 snowpits sampled in the northwest sector of the Greenland ice sheet in April 2014 have allowed us to identify a strong and widespread BC aerosol deposition event, which was dated to have accumulated in the pits from two snow storms between 27 July and 2 August 2013. This event comprises a significant portion (57% on average across all pits) of total BC deposition over 10 months (July 2013 to April 2014). Here we link this deposition event to forest fires burning in Canada during summer 2013 using modeling and remote sensing tools. Aerosols were detected by both the Cloud‐Aerosol Lidar with Orthogonal Polarization (on board CALIPSO) and Moderate Resolution Imaging Spectroradiometer (Aqua) instruments during transport between Canada and Greenland. We use high‐resolution regional chemical transport modeling (WRF‐Chem) combined with high‐resolution fire emissions (FINNv1.5) to study aerosol emissions, transport, and deposition during this event. The model captures the timing of the BC deposition event and shows that fires in Canada were the main source of deposited BC. However, the model underpredicts BC deposition compared to measurements at all sites by a factor of 2–100. Underprediction of modeled BC deposition originates from uncertainties in fire emissions and model treatment of wet removal of aerosols. Improvements in model descriptions of precipitation scavenging and emissions from wildfires are needed to correctly predict deposition, which is critical for determining the climate impacts of aerosols that originate from fires.