ALBERT

Description: The ongoing glacier shrinkage in the Alps requires frequent updates of glacier outlines to provide an accurate database for monitoring, modelling purposes (e.g. determination of run-off, mass balance, or future glacier extent), and other applications. With the launch of the first Sentinel-2 (S2) satellite in 2015, it became possible to create a consistent, Alpine-wide glacier inventory with an unprecedented spatial resolution of 10 m. The first S2 images from August 2015 already provided excellent mapping conditions for most glacierized regions in the Alps and were used as a base for the compilation of a new Alpine-wide glacier inventory in a collaborative team effort. In all countries, glacier outlines from the latest national inventories have been used as a guide to compile an update consistent with the respective previous interpretation. The automated mapping of clean glacier ice was straightforward using the band ratio method, but the numerous debris-covered glaciers required intense manual editing. Cloud cover over many glaciers in Italy required also including S2 scenes from 2016. The outline uncertainty was determined with digitizing of 14 glaciers several times by all participants. Topographic information for all glaciers was obtained from the ALOS AW3D30 digital elevation model (DEM). Overall, we derived a total glacier area of 1806±60 km2 when considering 4395 glaciers 〉0.01 km2. This is 14 % (−1.2 % a−1) less than the 2100 km2 derived from Landsat in 2003 and indicates an unabated continuation of glacier shrinkage in the Alps since the mid-1980s. It is a lower-bound estimate, as due to the higher spatial resolution of S2 many small glaciers were additionally mapped or increased in size compared to 2003. Median elevations peak around 3000 m a.s.l., with a high variability that depends on location and aspect. The uncertainty assessment revealed locally strong differences in interpretation of debris-covered glaciers, resulting in limitations for change assessment when using glacier extents digitized by different analysts. The inventory is available at https://doi.org/10.1594/PANGAEA.909133 (Paul et al., 2019).

Description: The question of how to derive and present uncertainty information in climate data records (CDRs) has received sustained attention within the European Space Agency Climate Change Initiative (CCI), a programme to generate CDRs addressing a range of essential climate variables (ECVs) from satellite data. Here, we review the nature, mathematics, practicalities, and communication of uncertainty information in CDRs from Earth observations. This review paper argues that CDRs derived from satellite-based Earth observation (EO) should include rigorous uncertainty information to support the application of the data in contexts such as policy, climate modelling, and numerical weather prediction reanalysis. Uncertainty, error, and quality are distinct concepts, and the case is made that CDR products should follow international metrological norms for presenting quantified uncertainty. As a baseline for good practice, total standard uncertainty should be quantified per datum in a CDR, meaning that uncertainty estimates should clearly discriminate more and less certain data. In this case, flags for data quality should not duplicate uncertainty information, but instead describe complementary information (such as the confidence in the uncertainty estimate provided or indicators of conditions violating the retrieval assumptions). The paper discusses the many sources of error in CDRs, noting that different errors may be correlated across a wide range of timescales and space scales. Error effects that contribute negligibly to the total uncertainty in a single-satellite measurement can be the dominant sources of uncertainty in a CDR on the large space scales and long timescales that are highly relevant for some climate applications. For this reason, identifying and characterizing the relevant sources of uncertainty for CDRs is particularly challenging. The characterization of uncertainty caused by a given error effect involves assessing the magnitude of the effect, the shape of the error distribution, and the propagation of the uncertainty to the geophysical variable in the CDR accounting for its error correlation properties. Uncertainty estimates can and should be validated as part of CDR validation when possible. These principles are quite general, but the approach to providing uncertainty information appropriate to different ECVs is varied, as confirmed by a brief review across different ECVs in the CCI. User requirements for uncertainty information can conflict with each other, and a variety of solutions and compromises are possible. The concept of an ensemble CDR as a simple means of communicating rigorous uncertainty information to users is discussed. Our review concludes by providing eight concrete recommendations for good practice in providing and communicating uncertainty in EO-based climate data records.

Description: The glaciers on the Antarctic Peninsula (AP) potentially make a large contribution to sea level rise. However, this contribution has been difficult to estimate since no complete glacier inventory (outlines, attributes, separation from the ice sheet) is available. This work fills the gap and presents a new glacier inventory of the AP north of 70° S, based on digitally combining preexisting data sets with geographic information system (GIS) techniques. Rock outcrops have been removed from the glacier basin outlines of Cook et al. (2014) by intersection with the latest layer of the Antarctic Digital Database (Burton-Johnson et al., 2016). Glacier-specific topographic parameters (e.g., mean elevation, slope and aspect) as well as hypsometry have been calculated from the DEM of Cook et al. (2012). We also assigned connectivity levels to all glaciers following the concept by Rastner et al. (2012). Moreover, the bedrock data set of Huss and Farinotti (2014) enabled us to add ice thickness and volume for each glacier. The new inventory is available from the Global Land Ice Measurements from Space (GLIMS) database (doi:10.7265/N5V98602) and consists of 1589 glaciers covering an area of 95 273 km2, slightly more than the 89 720 km2 covered by glaciers surrounding the Greenland Ice Sheet. Hence, compared to the preexisting data set of Cook et al. (2014), this data set covers a smaller area and one glacier less due to the intersection with the rock outcrop data set. The total estimated ice volume is 34 590 km3, of which one-third is below sea level. The hypsometric curve has a bimodal shape due to the unique topography of the AP, which consists mainly of ice caps with outlet glaciers. Most of the glacierized area is located at 200–500 m a.s.l., with a secondary maximum at 1500–1900 m. Approximately 63 % of the area is drained by marine-terminating glaciers, and ice-shelf tributary glaciers cover 35 % of the area. This combination indicates a high sensitivity of the glaciers to climate change for several reasons: (1) only slightly rising equilibrium-line altitudes would expose huge additional areas to ablation, (2) rising ocean temperatures increase melting of marine terminating glaciers, and (3) ice shelves have a buttressing effect on their feeding glaciers and their collapse would alter glacier dynamics and strongly enhance ice loss (Rott et al., 2011). The new inventory should facilitate modeling of the related effects using approaches tailored to glaciers for a more accurate determination of their future evolution and contribution to sea level rise.

Description: Climate data records (CDRs) derived from Earth observation (EO) should include rigorous uncertainty information, to support application of the data in policy, climate modelling and numerical weather prediction reanalysis. Uncertainty, error and quality are distinct concepts, and CDR products should follow international norms for presenting quantified uncertainty. Ideally, uncertainty should be quantified per datum in a CDR, and the uncertainty estimates should be able to discriminate more and less certain data with confidence. In this case, flags for data quality should not duplicate uncertainty information, but instead describe complementary information (such as the confidence held in the uncertainty estimate provided, or indicators of conditions violating retrieval assumptions). Errors have many sources and some are correlated across a wide range of time and space scales. Error effects that contribute negligibly to the total uncertainty in a single satellite measurement can be the dominant sources of uncertainty in a CDR on large space and long time scales that are highly relevant for some climate applications. For this reason, identifying and characterizing the relevant sources of uncertainty for CDRs is particularly challenging. Characterisation of uncertainty caused by a given error effect involves assessing the magnitude of the effect, the shape of the error distribution, and the propagation of the uncertainty to the geophysical variable in the CDR accounting for its error correlation properties. Uncertainty estimates can and should be validated as part of CDR validation, where possible. These principles are quite general, but the form of uncertainty information appropriate to different essential climate variables (ECVs) is highly variable, as confirmed by a quick review of the different approaches to uncertainty taken across different ECVs in the European Space Agency’s Climate Change Initiative. User requirements for uncertainty information can conflict with each other, and again a variety of solutions and compromises are possible. The concept of an ensemble CDR as a simple means of communicating rigorous uncertainty information to users is discussed. Our review concludes by providing eight recommendations for good practice in providing and communicating uncertainty in EO-based climate data records.

Description: The glaciers on the Antarctic Peninsula (AP) potentially make a large contribution to sea level rise. However, this contribution has been difficult to estimate, as no complete glacier inventory (outlines, attributes, separation from the ice sheet) has been available. This work fills the gap and presents a new glacier inventory of the AP north of 70° S, based on digitally combining pre-existing datasets with GIS techniques. Rock outcrops have been removed from the glacier basin outlines of Cook et al. (2014) by digital intersection with the latest layer of the Antarctic Digital Database (Burton-Johnson et al., 2016). Glacier-specific topographic parameters (e.g. mean elevation, slope and aspect) as well as hypsometry have been calculated from the Digital Elevation Model (DEM) of Cook et al. (2012). We also assigned connectivity levels to all glaciers following the concept by Rastner et al. (2012). Moreover, the bedrock dataset of Huss and Farinotti (2014) enabled us to add ice thickness and volume for each glacier. The new inventory is available from the GLIMS database (doi:10.7265/N5V98602) and consists of 1589 glaciers covering an area of 95 273 km2, slightly more than the 90 000 km2 covered by glaciers surrounding the Greenland Ice Sheet. The total ice volume is 34 590 km3, of which 1/3 is below sea level. The hypsometric curve has a bimodal shape due to the unique topography of the AP, which consists mainly of ice caps with outlet glaciers. Most of the glacierized area is located at 200–500 m a.s.l. with a secondary maximum at 1500–1900 m. Approximately 63 % of the area is drained by marine-terminating glaciers, and ice shelf tributary glaciers cover 35 % of the area. This combination indicates a high sensitivity of the glaciers to climate change for several reasons: (1) only slightly rising equilibrium line altitudes would expose huge additional areas to ablation, (2) rising ocean temperatures increase melting of marine terminating glaciers, and (3) ice shelves have a buttressing effect on their feeding glaciers and their collapse would alter glacier dynamics and strongly enhance ice loss (Rott et al., 2011). The new inventory should facilitate modelling of the related effects using approaches tailored to glaciers for a more accurate determination of their future evolution and contribution to sea level rise.

Description: Comprehensive assessments of global glacier mass changes based on a variety of observations and prevailing methodologies have been published at multi-annual intervals. For the years in between, the glaciological method provides annual observations of specific mass changes but is suspected to not be representative at the regional to global scales due to uneven glacier distribution with respect to the full sample. Here, we present a simple approach to estimate and correct for this bias in the glaciological sample and, hence, to provide an ad hoc estimate of global glacier mass changes and corresponding sea-level equivalents for the latest years, i.e. 2016/17 and 2017/18.

Description: Comprehensive assessments of global glacier mass changes based on a variety of observations and prevailing methodologies have been published at multi-annual intervals. For the years in between, the glaciological method provides annual observations of specific mass changes but is suspected to not be representative at the regional to global scales due to uneven glacier distribution with respect to the full sample. Here, we present a simple approach to estimate and correct for this bias in the glaciological sample and, hence, to provide an ad hoc estimate of global glacier mass changes and corresponding sea-level equivalents for the latest years, i.e. about -300±250 Gt in 2016/17 and -500±200 Gt in 2017/18.

Description: Among glacier instabilities, collapses of large parts of low-angle glaciers are a striking, exceptional phenomenon. So far, merely the 2002 collapse of Kolka Glacier in the Caucasus Mountains and the 2016 twin detachments of the Aru glaciers in western Tibet have been well documented. Here we report on the previously unnoticed collapse of an unnamed cirque glacier in the Central Andes of Argentina in March 2007. Although of much smaller ice volume, the 4.5 ± 1 × 106 m3 collapse of Leñas glacier in the Andes is similar to the Caucasus and Tibet ones in that the resulting ice avalanche travelled a total distance of ∼ 2 km over a surprisingly low angle of reach (∼ 5°).

Description: Among glacier instabilities, collapses of large parts of low-angle glaciers are a striking, exceptional phenomenon. So far, merely the 2002 collapse of Kolka Glacier in the Caucasus Mountains and the 2016 twin detachments of the Aru glaciers in western Tibet have been well documented. Here we report on the previously unnoticed collapse of an unnamed cirque glacier in the Central Andes of Argentina in March 2007. Although of much smaller ice volume, this 4.2±0.6×106 m3 collapse in the Andes is similar to the Caucasus and Tibet ones in that the resulting ice avalanche travelled a total distance of ∼2 km over a surprisingly low angle of reach (∼5∘).

Description: Knowledge about the coverage and characteristics of glaciers in High Mountain Asia (HMA) is still incomplete and heterogeneous. However, several applications, such as modelling of past or future glacier development, run-off, or glacier volume, rely on the existence and accessibility of complete datasets. In particular, precise outlines of glacier extent are required to spatially constrain glacier-specific calculations such as length, area, and volume changes or flow velocities. As a contribution to the Randolph Glacier Inventory (RGI) and the Global Land Ice Measurements from Space (GLIMS) glacier database, we have produced a homogeneous inventory of the Pamir and the Karakoram mountain ranges using 28 Landsat TM and ETM+ scenes acquired around the year 2000. We applied a standardized method of automated digital glacier mapping and manual correction using coherence images from the Advanced Land Observing Satellite 1 (ALOS-1) Phased Array type L-band Synthetic Aperture Radar 1 (PALSAR-1) as an additional source of information; we then (i) separated the glacier complexes into individual glaciers using drainage divides derived by watershed analysis from the ASTER global digital elevation model version 2 (GDEM2) and (ii) separately delineated all debris-covered areas. Assessment of uncertainties was performed for debris-covered and clean-ice glacier parts using the buffer method and independent multiple digitizing of three glaciers representing key challenges such as shadows and debris cover. Indeed, along with seasonal snow at high elevations, shadow and debris cover represent the largest uncertainties in our final dataset. In total, we mapped more than 27 800 glaciers 〉0.02 km2 covering an area of 35 520±1948 km2 and an elevation range from 2260 to 8600 m. Regional median glacier elevations vary from 4150 m (Pamir Alai) to almost 5400 m (Karakoram), which is largely due to differences in temperature and precipitation. Supraglacial debris covers an area of 3587±662 km2, i.e. 10 % of the total glacierized area. Larger glaciers have a higher share in debris-covered area (up to 〉20 %), making it an important factor to be considered in subsequent applications (https://doi.org/10.1594/PANGAEA.894707).