Search

Long-term monitoring of snow cover is crucial for climatic and hydrological studies. The utility of long-term snow-cover products lies in their ability to record the real states of the earth's surface. Although a long-term, consistent snow product derived from the ESA CCI + (Climate Change Initiative) AVHRR GAC (Advanced Very High Resolution Radiometer global area coverage) dataset dating back to the 1980s has been generated and released, its accuracy and consistency have not been extensively evaluated. Here, we extensively validate the AVHRR GAC snow-cover extent dataset for the mountainous Hindu Kush Himalayan (HKH) region due to its high importance for climate change impact and adaptation studies. The sensor-to-sensor consistency was first investigated using a snow dataset based on long-term in situ stations (1982–2013). Also, this includes a study on the dependence of AVHRR snow-cover accuracy related to snow depth. Furthermore, in order to increase the spatial coverage of validation and explore the influences of land-cover type, elevation, slope, aspect, and topographical variability in the accuracy of AVHRR snow extent, a comparison with Landsat Thematic Mapper (TM) data was included. Finally, the performance of the AVHRR GAC snow-cover dataset was also compared to the MODIS (MOD10A1 V006) product. Our analysis shows an overall accuracy of 94 % in comparison with in situ station data, which is the same with MOD10A1 V006. Using a ±3 d temporal filter caused a slight decrease in accuracy (from 94 % to 92 %). Validation against Landsat TM data over the area with a wide range of conditions (i.e., elevation, topography, and land cover) indicated overall root mean square errors (RMSEs) of about 13.27 % and 16 % and overall biases of about -5.83 % and -7.13 % for the AVHRR GAC raw and gap-filled snow datasets, respectively. It can be concluded that the here validated AVHRR GAC snow-cover climatology is a highly valuable and powerful dataset to assess environmental changes in the HKH region due to its good quality, unique temporal coverage (1982–2019), and inter-sensor/satellite consistency. [ABSTRACT FROM AUTHOR]

AVHRR Global Area Coverage (GAC) data provide daily global coverage of the Earth, which are widely used for global environmental and climate studies. However, their geolocation accuracy has not been comprehensively evaluated due to the difficulty caused by onboard resampling and the resulting coarse resolution, which hampers their usefulness in various applications. In this study, a correlation-based patch matching method (CPMM) was proposed to characterize and quantify the geo-location accuracy at the sub-pixel level for satellite data with coarse resolution, such as the AVHRR GAC dataset. This method is neither limited to landmarks nor suffers from errors caused by false detection due to the effect of mixed pixels caused by a coarse spatial resolution, and it thus enables a more robust and comprehensive geometric assessment than existing approaches. Data of NOAA-17, MetOp-A and MetOp-B satellites were selected to test the geocoding accuracy. The three satellites predominately present west shifts in the across-track direction, with average values of -1.69 , -1.9 , -2.56 km and standard deviations of 1.32, 1.1, 2.19 km for NOAA-17, MetOp-A, and MetOp-B, respectively. The large shifts and uncertainties are partly induced by the larger satellite zenith angles (SatZs) and partly due to the terrain effect, which is related to SatZ and becomes apparent in the case of large SatZs. It is thus suggested that GAC data with SatZs less than 40 ∘ should be preferred in applications. The along-track geolocation accuracy is clearly improved compared to the across-track direction, with average shifts of -0.7 , -0.02 and 0.96 km and standard deviations of 1.01, 0.79 and 1.70 km for NOAA-17, MetOp-A and MetOp-B, respectively. The data can be accessed from 10.5676/DWD/ESA_Cloud_cci/AVHRR-AM/V002 (Stengel et al., 2017) and 10.5067/MODIS/MOD13A1.006 (Didan, 2015). [ABSTRACT FROM AUTHOR]

AVHRR GAC (Global Area Coverage) data provide daily global coverage of the Earth, which are widely used for global environmental and climate studies. However, their geolocation accuracy has not been comprehensively evaluated due to the difficulty caused by onboard resampling and the resulting coarse resolution, which hampers their usefulness in various applications. In this study, a Correlation-based Patch Matching Method (CPMM) was proposed to characterize and quantify the AVHRR GAC geo-location accuracy at the subpixel level. This method is not limited to landmarks and not suffer from errors caused by false detection due to the effect of mixed pixels, thus enables a more robust and comprehensive geometric assessment. Data of NOAA-17, MetOp-A, and MetOp-B satellites were selected to test the geocoding accuracy. The three satellites predominately present West shifts in the across-track direction, with average values of −1.69 km, −1.9 km, −2.56 km and standard deviations of 1.32 km, 1.1 km, 2.19 km for NOAA-17, MetOp-A, and MetOp-B, respectively. The large shifts and uncertainties are partly induced by the larger satellite zenith angles (SatZ) and partly due to the terrain effect, which is related to SatZ and becomes apparent in the case of large SatZ. It is thus suggested that GAC data with SatZ less than 40° should be preferred in applications. The along-track geolocation accuracy is clearly improved compared to the across-track direction, with average shifts of −0.7 km, −0.02 km, 0.96 km and standard deviations of 1.01 km, 0.79 km, 1.70 km for NOAA-17, MetOp-A, and MetOp-B, respectively. [ABSTRACT FROM AUTHOR]

Albedo feedback is an important driver of glacier melt over bare-ice surfaces. Light-absorbing impurities strongly enhance glacier melt rates but their abundance, composition and variations in space and time are subject to considerable uncertainties and ongoing scientific debates. In this study, we assess the temporal evolution of shortwave broadband albedo derived from 15 end-of-summer Landsat scenes for the bare-ice areas of 39 large glaciers in the western and southern Swiss Alps. Trends in bare-ice albedo crucially depend on the spatial scale considered. No significant negative temporal trend in bare-ice albedo was found on a regional to glacier-wide scale. However, at higher spatial scales, certain areas of bare ice, including the lowermost elevations and margins of the ablation zones, revealed significant darkening over the study period 1999 to 2016. A total glacier area of 13.5 km 2 (equivalent to about 12 % of the average end-of-summer bare-ice area in the study area) exhibited albedo trends significant at the 95 % confidence level or higher. Most of this area was affected by a negative albedo trend of about - 0.05 decade -1. Generally, bare-ice albedo exhibits a strong interannual variability, caused by a complex interplay of meteorological conditions prior to the acquisition of the data, local glacier characteristics and the date of the investigated satellite imagery. Although a darkening of glacier ice was found to be present over only a limited region, we emphasize that due to the recent and projected growth of bare-ice areas and prolongation of the ablation season in the region, the albedo feedback will considerably enhance the rate of glacier mass loss in the Swiss Alps in the near future. [ABSTRACT FROM AUTHOR]

Dark (low albedo) surface ice on the Greenland Ice Sheet enhances melting and subsequent runoff, a major mass loss contributor during the ablation season. The accumulation of both biological (e.g., glacier ice algae) and abiotic (e.g., mineral dust) light‐absorbing particulates are important darkening factors, that are potentially influenced by the duration of snow‐free, bare ice (a phenological factor), and other geo‐topographical factors such as elevation, slope, aspect and the distance from the ice margin. Here, we present the first medium‐resolution (30 m) analysis of the phenological and geo‐topographical controls on the distribution of dark ice in SE and SW Greenland from statistical analysis of data derived from a harmonized satellite albedo product and ArcticDEM. The duration of bare ice primarily controls the distribution of dark surface ice, allowing for algae growth on inland ice surfaces in particular, whereas geo‐topographical factors are only secondary controls. Plain Language Summary: Meltwater that runs off the Greenland Ice Sheet is currently the largest single glaciological contributor to global sea level rise. Recent darkening of the surface ice has contributed significantly to increased melt rates. We used medium resolution (30m) satellite data to link ice surface darkness (albedo) to the topography (elevation, slope, and aspect) of the ice sheet surface, the distance from the ice margin, and the duration of bare or snow‐free surface ice. Our analysis shows that the duration of bare ice or snow‐free conditions was the most significant factor in driving the darkening of ice. We also found that the impact of topographic factors on the ice darkening varied depending on whether abiotic or biological processes were driving the darkening. This information helps us to better explain where the ice sheet is melting fastest and to predict the loss of ice mass, particularly considering the expected increase in the area of bare, snow‐free ice in our warming climate. Key Points: The duration of snow‐free, bare ice is the primary control on the distribution of dark iceLonger durations of bare ice (median = 40 days) are prerequisites for ice to become darkSurface slope and aspect influence darkening by modulating the duration of bare ice and glacier ice algae growth [ABSTRACT FROM AUTHOR]

Albedo is an important parameter in the energy balance of bare-ice surfaces and modulates glacier melt rates. The prolongation of the ablation period enforces the albedo feedback and highlights the need for profound knowledge on impacts of bare-ice albedo on glacier mass balance. In this study, we assess the mass balance sensitivity of 12 Swiss glaciers with abundant long-term in-situ data on changes in bare-ice albedo. We use pixel-based bare-ice albedo derived from Landsat 8. A distributed mass-balance model is applied to the period 1997–2016 and experiments are performed to assess the impact of albedo changes on glacier mass balance. Our results indicate that glacier-wide mass-balance sensitivities to changes in bare-ice albedo correlate strongly with mean annual mass balances (r2 = 0.81). Large alpine glaciers react more sensitively to bare-ice albedo changes due to their ablation areas being situated at lower elevations. We find average sensitivities of glacier-wide mass balance of −0.14 m w.e. a−1 per 0.1 albedo decrease. Although this value is considerably smaller than sensitivity to air temperature change, we stress the importance of the enhanced albedo feedback that will be amplified due to atmospheric warming and a suspected darkening of glacier surface in the near future. [ABSTRACT FROM AUTHOR]

Surface albedo partitions the amount of energy received by glacier surfaces from shortwave fluxes and modulates the energy available for melt processes. The ice-albedo feedback, influenced by the contamination of bare-ice surfaces with light-absorbing impurities, plays a major role in the melting of mountain glaciers in a warming climate. However, little is known about the spatial and temporal distribution and variability of bare-ice glacier surface albedo under changing conditions. In this study, we focus on two mountain glaciers located in the western Swiss Alps and perform a cross-comparison of different albedo products. We take advantage of high spectral and spatial resolution (284 bands, 2 m) imaging spectrometer data from the Airborne Prism Experiment (APEX) and investigate the applicability and potential of Sentinel-2 and Landsat 8 data to derive broadband albedo products. The performance of shortwave broadband albedo retrievals is tested and we assess the reliability of published narrow-to-broadband conversion algorithms. The resulting albedo products from the three sensors and different algorithms are further cross-compared. Moreover, the impact of the anisotropy correction is analysed depending on different surface types. While degradation of the spectral resolution impacted glacier-wide mean albedo by about 5%, reducing the spatial resolution resulted in changes of less than 1%. However, in any case, coarser spatial resolution was no longer able to represent small-scale variability of albedo on glacier surfaces. We discuss the implications when using Sentinel-2 and Landsat 8 to map dynamic glaciological processes and to monitor glacier surface albedo on larger spatial and more frequent temporal scales. [ABSTRACT FROM AUTHOR]

Glacier surfaces are not only composed of ice or snow but are heterogeneous mixtures of different materials. The occurrence and dynamics of light-absorbing impurities affect ice surface characteristics and strongly influence glacier melt processes. However, our understanding of the spatial distribution of impurities and their impact on ice surface characteristics and the glacier's energy budget is still limited. We use imaging spectroscopy in combination with in-situ experiments to assess the composition of ice surface materials and their respective impact on surface albedo and glacier melt rates. Spectroscopy data were acquired in August 2013 using the Airborne Prism EXperiment (APEX) imaging spectrometer and were used to map the abundances of six predominant surface materials on Glacier de la Plaine Morte, Swiss Alps. A pixel-based classification revealed that about 10% of the ice surface is covered with snow, water or debris. The remaining 90% of the surface can be divided into three types of glacier ice, namely ~ 7% dirty ice, ~ 43% pure ice and ~ 39% bright ice. Spatially distributed spectral albedo derived from APEX reflectance data in combination with in-situ multi-angular spectroscopic measurements was used to analyse albedo patterns present on the glacier surface. About 85% of all pixels exhibit a low albedo between 0.1 and 0.4 (mean albedo 0.29 ± 0.12), indicating that Glacier de la Plaine Morte is covered with a significant amount of light-absorbing impurities, resulting in a strong ice-albedo feedback during the ablation season. Using a pixel-based albedo map instead of a constant albedo for ice (0.34) as input for a mass balance model revealed that the glacier-wide total ablation remained similar (10% difference). However, the large local variations in mass balance can only be reproduced using the pixel-based albedo derived from APEX, emphasizing the need to quantify spatial albedo differences as an important input for glacier mass balance models. [ABSTRACT FROM AUTHOR]

The routing and storage of meltwater and the configuration of drainage systems in glaciers exert a profound influence on glacier behaviour. However, little is known about the hydrological systems of cold glaciers, which form a significant proportion of the total glacier population in the climate sensitive region of the High Arctic. Using glacio-speleological techniques, we obtained direct access to explore and survey three conduit systems and one moulin within the tongue area of Tellbreen, a small cold-based valley glacier in central Spitsbergen. More than 600 m of conduits were surveyed and mapped in plan, profile and cross-section view to analyse the configuration of the drainage system. The investigations revealed that cold-based glaciers can exhibit a dendritic drainage network with supra-, en- and subglacial components formed most likely by cut-and-closure processes as well as surface-to-bed drainage via moulins. Furthermore, we observed that water is stored within the glacier and released gradually via subglacial conduits during the winter months, forming a large and active icing in the proglacial area. The presence of supra-, en- and subglacial components, the surface-to-bed moulin and the dendritic subglacial drainage network suggest that existing models and understanding of the hydrology of cold glaciers needs to be re-evaluated, mostly concerning the different possible pathways and processes that form the hydrological system. [ABSTRACT FROM AUTHOR]

The Tien Shan and Pamir mountains host over 28,000 glaciers providing essential water resources for increasing water demand in Central Asia. A disequilibrium between glaciers and climate affects meltwater release to Central Asian rivers, challenging the region's water availability. Previous research has neglected temporal variability. We present glacier mass balance estimates based on transient snowline and geodetic surveys with unprecedented spatiotemporal resolution from 1999/00 to 2017/18. Our results reveal spatiotemporal heterogeneity characterized by two mass balance clusters: (a) positive, low variability, and (b) negative, high variability. This translates into variable glacial meltwater release (≈1-16%) of annual river runoff for two watersheds. Our study reveals more complex climate forcingrunoff responses and importance of glacial meltwater variability for the region than suggested previously. Plain Language Summary Glaciers in Central Asia act as water towers for millions of people by storing and releasing water in response to climate. Monitoring glaciers is difficult due to their often very remote locations. Satellite remote sensing has emerged as a powerful method but a drawback is their (semi-)decadal resolution for glacier mass change surveys. We present a methodology, combining multiyear elevation change maps with frequent snowline observations to estimate mass changes and variability at annual scale, which allows us identifying so far unrecognized regions of contrasting trends for the Tien Shan and Pamir mountains. These "hot spots" reveal a far more complex climate-glacier interplay than previously known. The additional meltwater released from the retreating glaciers varies considerably and contributes to the river flow for warm dry years by twice as much as for cold wet years. Our findings will help to better understand the impact of climate change on Central Asian glaciers and their meltwater release. [ABSTRACT FROM AUTHOR]

Albedo plays a key role in regulating the absorption of solar radiation within ice surfaces and hence strongly regulates the production of meltwater. A combination of Landsat and Sentinel 2 data provides the longest continuous medium resolution (10–30 m) earth surface observatory records. An albedo product (harmonized satellite albedo, hereafter HSA) has already been developed and validated for the Greenland Ice Sheet (GrIS), using harmonized Landsat 4–8 and Sentinel 2 datasets. In this paper, the HSA was validated for various Arctic and alpine glaciers and ice caps using in situ measurements. We determine the optimal spatial window size in point-to-pixel analysis, the best practices in evaluating remote sensing algorithms with groundtruth data, and cross sensor comparison of the Landsat 9 (L9) and Landsat 8 (L8) data. The impact of the spatial window size on measured ice surface homogeneity and albedo validation was analysed at both local and regional scales. Homogeneity statistics calculated from the grey-level co-occurrence matrix (GLCM) suggest that the ice surface becomes more homogeneous as the image resolution becomes coarser. The optimal spatial window size was found to be 90 m, based on maximizing the statistical and graphical measures while minimizing the root mean square error and bias. HSAs generally agree closely with in situ albedo measurements (e.g. Pearson’s R ranges from 0.68 to 0.92) across various Arctic and alpine glaciers and ice caps. Cross sensor differences between L9 and L8 are minor, and we suggest that no harmonization is necessary to add L9 to our HSA product. [ABSTRACT FROM AUTHOR]

Climate change over High Mountain Asia (HMA, including the Tibetan Plateau) is investigated over the period 1979–2014 and in future projections following the four Shared Socioeconomic Pathways: SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5. The skill of 26 Coupled Model Intercomparison Project phase 6 (CMIP6) models is estimated for near-surface air temperature, snow cover extent and total precipitation, and 10 of them are used to describe their projections until 2100. Similarly to previous CMIP models, this new generation of general circulation models (GCMs) shows a mean cold bias over this area reaching - 1.9 [ - 8.2 to 2.9] ∘ C (90 % confidence interval) in comparison with the Climate Research Unit (CRU) observational dataset, associated with a snow cover mean overestimation of 12 % [ - 13 % to 43 %], corresponding to a relative bias of 52 % [ - 53 % to 183 %] in comparison with the NOAA Climate Data Record (CDR) satellite dataset. The temperature and snow cover model biases are more pronounced in winter. Simulated precipitation rates are overestimated by 1.5 [0.3 to 2.9] mm d -1 , corresponding to a relative bias of 143 % [31 % to 281 %], but this might be an apparent bias caused by the undercatch of solid precipitation in the APHRODITE (Asian Precipitation-Highly-Resolved Observational Data Integration Towards Evaluation of Water Resources) observational reference. For most models, the cold surface bias is associated with an overestimation of snow cover extent, but this relationship does not hold for all models, suggesting that the processes of the origin of the biases can differ from one model to another. A significant correlation between snow cover bias and surface elevation is found, and to a lesser extent between temperature bias and surface elevation, highlighting the model weaknesses at high elevation. The models with the best performance for temperature are not necessarily the most skillful for the other variables, and there is no clear relationship between model resolution and model skill. This highlights the need for a better understanding of the physical processes driving the climate in this complex topographic area, as well as for further parameterization developments adapted to such areas. A dependency of the simulated past trends on the model biases is found for some variables and seasons; however, some highly biased models fall within the range of observed trends, suggesting that model bias is not a robust criterion to discard models in trend analysis. The HMA median warming simulated over 2081–2100 with respect to 1995–2014 ranges from 1.9 [1.2 to 2.7] ∘ C for SSP1-2.6 to 6.5 [4.9 to 9.0] ∘ C for SSP5-8.5. This general warming is associated with a relative median snow cover extent decrease from - 9.4 % [ - 16.4 % to - 5.0 %] to - 32.2 % [ - 49.1 % to - 25.0 %] and a relative median precipitation increase from 8.5 % [4.8 % to 18.2 %] to 24.9 % [14.4 % to 48.1 %] by the end of the century in these respective scenarios. The warming is 11 % higher over HMA than over the other Northern Hemisphere continental surfaces, excluding the Arctic area. Seasonal temperature, snow cover and precipitation changes over HMA show a linear relationship with the global surface air temperature (GSAT), except for summer snow cover which shows a slower decrease at strong levels of GSAT. [ABSTRACT FROM AUTHOR]

Climate change over High Mountain Asia (HMA, including the Tibetan Plateau) is investigated over the period 1979-2014 and in future projections following the four shared socioeconomic pathways SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5. The skill of 26 CMIP6 models is estimated for near-surface air temperature, snow cover extent and total precipitation, and 10 of them are used to describe their projections until 2100. Similarly to previous CMIP models, this new generation of GCMs shows a mean cold bias over this area reaching -1.9 [-8.2 to 2.9] °C (90 % confidence interval) in comparison with the CRU observational dataset, associated with a snow cover mean overestimation of 12 [-13 to 43] %, corresponding to a relative bias of 52 [-53 to 183] % in comparison with the NOAA CDR satellite dataset. The temperature and snow cover model biases are more pronounced in winter. Simulated precipitation rates are overestimated by 1.5 [0.3 to 2.9] mm day-1, corresponding to a relative bias of 143 [31 to 281] %, but this might be an apparent bias caused by the undercatch of solid precipitation in the APHRODITE observational reference. For most models, the cold surface bias is associated with an overestimation of snow cover extent, but this relationship does not hold for all models, suggesting that the processes of the origin of the biases can differ from one model to another one. A significant correlation between snow cover bias and surface elevation is found, and to a lesser extent between temperature bias and surface elevation, highlighting the model weaknesses at high elevation. The models performing the best for temperature are not necessarily the most skillful for the other variables, and there is no clear relationship between model resolution and model skill. This highlights the need for a better understanding of the physical processes driving the climate in this complex topographic area, as well as for further parameterization developments adapted to such areas. A dependency of the simulated past trends to the model biases is found for some variables and seasons, however, some highly biased models fall within the range of observed trends suggesting that model bias is not a robust criterion to discard models in trend analysis. The HMA median warming simulated over 2081-2100 with respect to 1995-2014 ranges from 1.9 [1.2 to 2.7] °C for SSP1-2.6 to 6.5 [4.9 to 9.0] °C for SSP5-8.5. This general warming is associated with a relative median snow cover extent decrease from -9.4 [-16.4 to -5.0] % to -32.2 [-49.1 to -25.0] % and a relative median precipitation increase from 8.5 [4.8 to 18.2] % to 24.9 [14.4 to 48.1] % by the end of the century in these respective scenarios. The warming is 11 % higher over HMA than over the other Northern Hemisphere continental surfaces excluding the Arctic area. Seasonal temperature, snow cover and precipitation changes over HMA show a linear relationship with the Global Surface Air Temperature (GSAT), except for summer snow cover that shows a slower decrease at strong levels of GSAT. [ABSTRACT FROM AUTHOR]

Although worldwide inventories of glacier area have been coordinated internationally for several decades, a similar effort for glacier ice thicknesses was only initiated in 2013. Here, we present the third version of the Glacier Thickness Database (GlaThiDa v3), which includes 3 854 279 thickness measurements distributed over roughly 3000 glaciers worldwide. Overall, 14 % of global glacier area is now within 1 km of a thickness measurement (located on the same glacier) – a significant improvement over GlaThiDa v2, which covered only 6 % of global glacier area and only 1100 glaciers. Improvements in measurement coverage increase the robustness of numerical interpolations and model extrapolations, resulting in better estimates of regional to global glacier volumes and their potential contributions to sea-level rise. In this paper, we summarize the sources and compilation of glacier thickness data and the spatial and temporal coverage of the resulting database. In addition, we detail our use of open-source metadata formats and software tools to describe the data, validate the data format and content against this metadata description, and track changes to the data following modern data management best practices. Archived versions of GlaThiDa are available from the World Glacier Monitoring Service (e.g., v3.1.0, from which this paper was generated: 10.5904/wgms-glathida-2020-10;), while the development version is available on GitLab (https://gitlab.com/wgms/glathida , last access: 9 November 2020). [ABSTRACT FROM AUTHOR]

Although worldwide inventories of glacier area have been coordinated internationally for several decades, a similar effort for glacier ice thicknesses was only initiated in 2013. Here, we present the third version of the Glacier Thickness Database (GlaThiDa v3), which includes 3 854 279 thickness measurements distributed over more than 3000 glaciers worldwide. Overall, 14 % of global glacier area is now within 1 km of a thickness measurement - a significant improvement over GlaThiDa v2, which covered only 6 % of global glacier area, and only 1100 glaciers. In this paper, we summarize the sources and compilation of glacier thickness data and the spatial and temporal coverage of the resulting database. In addition, we detail our use of open-source metadata formats and software tools to describe the data, validate the data against this description, and track changes to the data following modern data management best-practices. Archived versions GlaThiDa are available from the World Glacier Monitoring Service (https://doi.org/10.5904/wgms-glathida-2019-03; GlaThiDa Consortium (2019)) and the development version (v3.1.0-rc1, from which this manuscript draft is generated) is available on GitLab (https://gitlab.com/wgms/glathida/-/tree/v3.1.0-rc1). [ABSTRACT FROM AUTHOR]

The Tien Shan and Pamir, hosting more than 25'000 glaciers, act as important water towers for the dry, continental lowlands of Central Asia. Most glacier mass balance assessments focus on region-wide, decadal mass changes and only a few address seasonal or annual glacier mass balances. The regional annual variability is thus far poorly understood. However, it is essentially needed to improve our understanding of glacier runoff contribution. Here, multi-annual glacier mass changes derived from remote sensing were complemented with seasonal to annual mass balance series inferred from modelling closely constrained with repeated transient snowline observations on a regional scale. To derive annual time series, transient snowline observations were used to calibrate an accumulation and temperature-index melt model for each year and glacier individually. The modelled decadal mass balance was constrained with glacier specific geodetic mass balances calculated from satellite stereo images. We calculated geodetic mass loss from 2004 to 2012 and annual surface mass balance time series from 2000 to 2017 for the Tien Shan and Pamir. A geodetic mass loss of -0.52 ± 0.53 m w.e. yr-1 was calculated for the Tien Shan and -0.24 ± 0.43 m w.e. yr-1 for the Pamir from 2004 to 2012. First results from transient snowline-constrained modelling confirmed the decadal mass loss ranging from -0.36 ± 0.32 to -0.44 to ± 0.32 m w.e. yr-1 for 2000 to 2017 for the subranges Kyrgyz Ala-Too (Tien Shan), the Pamir-Alay (Pamir) and the Akshiirak range (Tien Shan). Lower rates of mass loss was encountered for the early years of the study period and exceptionally negative balances were found for most glaciers in 2008. Our results show a significant trend to increased mass loss for the Pamir-Alay and the Kyrgyz Ala-Too, accompanied with a moderate increase of the interannual variability with time. Annual variability is similar for the Kyrgyz Ala-Too and Pamir-Alay and is considerably smaller for the Akshiirak massif. For Akshiirak, the interannual variability seems to have increased in recent years, however, no significant trends in mass balance were observed. Our time series, annually tied to transient snowline observations and linked to the geodetic estimates at the decadal scale, enable the analysis of yearly mass balance variability for glaciers in a region with few in situ measurements. Moreover, they deliver important baseline data for in depth analysis of the glacier response to climate fluctuations and enable to analyse the importance of glacier melt contribution to the hydrological cycle with an enhanced temporal resolution. [ABSTRACT FROM AUTHOR]

Large numbers of small valley glaciers on Svalbard were thicker and more extensive during the Little Ice Age (LIA), demonstrated by prominent ice-cored moraines up to several kilometres beyond present-day margins. The majority of these glaciers have since experienced a long period of strongly negative mass balance during the 20th century and are now largely frozen to their beds, indicating they are likely to have undergone a thermal transition from a polythermal to a cold-based regime. We present evidence for such a switch by reconstructing the former flow dynamics and thermal regime of Tellbreen, a small cold-based valley glacier in central Spitsbergen, based on its basal sequence and glaciological structures. Within the basal sequence, the underlying matrix-supported diamict is interpreted as saturated subglacial traction till which has frozen at the bed, indicating that the thermal switch has resulted in a cessation of subglacial sediment deformation due to freezing of the former deforming layer. This is overlain by debris-poor dispersed facies ice, interpreted to have formed through strain-induced metamorphism of englacial ice. The sequential development of structures includes arcuate fracture traces, interpreted as shear planes formed in a compressive/transpressive stress regime; and fracture traces, interpreted as healed extensional crevasses. The formation of these sediment/ice facies and structures is indicative of dynamic, warm-based flow, most likely during the LIA when the glacier was significantly thicker. [ABSTRACT FROM AUTHOR]

Ice surface albedo is a primary modulator of melt and runoff, yet our understanding of how reflectance varies over time across the Greenland Ice Sheet remains poor. This is due to a disconnect between point or transect scale albedo sampling and the coarser spatial, spectral and/or temporal resolutions of available satellite products. Here, we present time-series of bare-ice surface reflectance data that span a range of length scales, from the 500 m for Moderate Resolution Imaging Spectrometer's MOD10A1 product, to 10 m for Sentinel-2 imagery, 0.1 m spot measurements from ground-based field spectrometry, and 2.5 cm from uncrewed aerial drone imagery. Our results reveal broad similarities in seasonal patterns in bare-ice reflectance, but further analysis identifies short-term dynamics in reflectance distribution that are unique to each dataset. Using these distributions, we demonstrate that areal mean reflectance is the primary control on local ablation rates, and that the spatial distribution of specific ice types and impurities is secondary. Given the rapid changes in mean reflectance observed in the datasets presented, we propose that albedo parameterizations can be improved by (i) quantitative assessment of the representativeness of time-averaged reflectance data products, and, (ii) using temporally-resolved functions to describe the variability in impurity distribution at daily time-scales. We conclude that the regional melt model performance may not be optimally improved by increased spatial resolution and the incorporation of sub-pixel heterogeneity, but instead, should focus on the temporal dynamics of bare-ice albedo. [ABSTRACT FROM AUTHOR]

Seasonal snow is an important component of the global climate system. It is highly variable in space and time and sensitive to short term synoptic scale processes and long term climate-induced changes to temperature and precipitation. Current snow products derived from different algorithms applied to various satellite data show significant discrepancies in extent and snow mass, a potential source for biases in climate monitoring and modelling. The recently launched ESA CCI+ Programme addresses seasonal snow as one of 9 Essential Climate Variables to be derived from satellite data. In the snow_cci project, scheduled for 2018 to 2021 in its first phase, reliable fully validated processing lines are developed and implemented. These tools are used to generate homogeneous multi-sensor time series for the main parameters of global snow cover focusing on snow extent and snow water equivalent. Using GCOS guidelines, the requirements for these parameters are assessed and consolidated using workshops and questionnaires addressing users dealing with different climate applications. Snow extent product generation applies algorithms for fractional snow extent and cloud screening, generating consistent daily products for snow on the surface (viewable snow) and snow on the surface corrected for forest masking (snow on ground) with global coverage. Input data are medium resolution optical satellite imagery (AVHRR-2/3, AATSR, MODIS, VIIRS, SLSTR/OLCI) from 1981 to present. An iterative development cycle is implemented including homogenisation of the snow extent products from different sensors by minimizing the bias. Independent validation of the snow products is performed using high resolution snow maps from Landsat and Sentinel-2 acquired across different seasons and climate zones around the globe from 1985 onwards as well as in-situ snow data following snow community agreed validation protocols. Global time series of daily snow water equivalent (SWE) products are generated from passive microwave data from SMMR, SSM/I, and AMSR from 1978 onwards combined with in-situ snow depth measurements. Long-term stability and quality of the product is assessed using independent snow survey data and by intercomparison with the snow information from global land process models.The usability of the snow_cci products is ensured through the Climate Research Group, which performs case studies related to long term trends of seasonal snow, evaluation of CMIP-6 and other snow-focused climate model experiments, and of the simulation of Arctic hydrological regimes.In this presentation, we will summarize the requirements and product specification for the snow extent and SWE products, with a focus on climate applications. We will present an overview of the algorithms and systems for generation of the time series. Demonstration products for daily global snow extent and snow water equivalent products from different sensors will be presented along with first results of the multi-sensor consistency and validation activities and inter-comparisons with snow products from other sources. [ABSTRACT FROM AUTHOR]

Girls on Ice Switzerland provides tuition-free field-based short courses about alpine climate change for young women (15-17 years) from diverse backgrounds. Next to scientific contents, mountaineering skills and art concepts are taught. Girls on Ice Switzerland does not only intend to transfer scientific knowledge (i), but also aims on a general understanding of scientific concepts (ii), on a mediation of nature experiences (iii) and on an enhanced self-confidence and self-evaluation (iv). A combination of inquire-based teaching, experiential learning, and the tangibility of climate change science in the alpine environment provide a unique teaching environment. This particular framework allows to (a) communicate science to non- and potential not-yet-peers, to (b) facilitate insights into the scientific work through hands-on experiences, and to (c) enhance the participants' general interest in science. Between 2017 and 2020, Girls on Ice Switzerland is funded by a science communication (Agora) project of the Swiss National Science Foundation and organises three to four courses. Within the Agora project, the Evaluation Office of the Zurich University of Teacher Education performs an evaluation including pre- and post-inquiry of the participants. Through both quantitative and qualitative methods, the evaluation focuses on the aims of the program, the surrounding theoretical framework, and the conclusions drawn from the previous evaluation(s). Evaluated aspects include (i) scientific knowledge, (ii) idea of science and knowledge about scientific professions, (iii) closeness to nature and (iv) self-assessment and self-efficiency. The first-year and second-year's course evaluation showed, that the programme overall reached its initially set aims. In our experience, the formal evaluation helps to optimize the science communication by demanding a clear set of goals for different characteristics of the program. The results of the questionnaires make it possible to track the success and to make according changes to the program for the following years. In addition, we use another, rather informal tool for the optimization of the science communication. After each programme, we have an instructor debriefing where we take the time to discuss how the program performed overall and what could be adapted and improved. Here the basis for optimization are the instructors and their perception of how things went. Thus, comments and interactions with the participants will shape how the instructors will perceive the success of the program. [ABSTRACT FROM AUTHOR]