ID10: Changes in Snow Cover in Mountainous Regions of the Earth
Climate change clearly affects the amount and distribution of snow in mountains in space and time, although this relationship is not simple. For example, several studies show that the response of mountain snow cover to climate change (i) is not a simple effect of temperature change, and depends on: (ii) geographical location (climate zone), (iii) latitude, and (iv) regional atmospheric influences (e.g. interaction with synoptic-scale atmospheric flows). However, the observational capacity and the process understanding of these interactions varies across mountain regions. The aim of this session is to bring together the knowledge and experience of researchers from different mountain regions of the world working on climate change-induced changes in snow cover, to present the current state of understanding and to identify research gaps. Given the general importance of snow for ecology, the economy and for human life in general, researchers from different and interdisciplinary fields are encouraged to contribute.
Abstract ID 162 | Date: 2022-09-13 13:30 – 13:40 | Type: Oral Presentation | Place: SOWI – Seminar room U3 |
Kotlarski, Sven (1); Steger, Christian R. (2); Bülow, Katharina (3); Teichmann, Claas (3)
1: Federal Office of Meteorology and Climatology MeteoSwiss, Zurich, Switzerland
2: Institute for Atmospheric and Climate Science, ETH Zurich, Switzerland
3: GERICS Climate Service Center Germany, Hamburg, Germany
Keywords: Snow Cover, Climate, Regional Climate Model, Cordex
Large parts of the world, including many mountain regions, have experienced a decline of surface snow cover during the last decades as a response to global warming. With climate projections indicating a progression of this warming trend, surface snow cover is generally expected to further decrease. One way to quantify the future snow cover evolution is to force dedicated physical snow pack models at local, regional or even continental scale with post-processed output from global or regional climate models. The latter, however, do incorporate interactive snow cover models as part of their land surface parameterization schemes, thus enabling the analysis of past and future snow cover variability and trends in the climate models themselves. With the advantage of being fully and interactively coupled to the simulated atmospheric state, these schemes are typically of a simplified nature and generally operate at the rather coarse grid cell resolution of the embedding climate model. However, past works have revealed the general usability of climate model-simulated snow cover.
In this contribution we exploit the most recent regional climate model ensemble for Europe as provided by the EURO-CORDEX initiative at a spatial resolution of 12 km for deriving estimates of the future snow cover evolution over Europe, including a number of high mountain ranges. A dedicated validation exercise compares several simulated snow indicators in the historical period against a range of reference datasets and mostly reveals a satisfying performance of the simulations. On a European scale mean annual snow cover duration, for instance, is well captured by the ensemble mean of the models. In regions with complex topography, winter snow water equivalent can however be distinctively overestimated and certain grid cells reveal unrealistic glaciation (i.e. year-round snow coverage). These shortcomings can be partly attributed to inaccuracies in the simulated atmospheric forcing, i.e. to biases in air temperature and/or precipitation. Snow cover scenarios for the 21st century show a distinct reduction in snow cover duration for low elevation regions, with the magnitude of this decrease depending, amongst other factors, on the emission scenario considered. Projected decreases in snow cover are typically less pronounced for medium to high-elevation regions. These results generally confirm regional assessments obtained by driving snow pack models with climate model output in an offline mode and, thereby, highlight the potential of climate model-simulated snow cover as an additional or alternative data source.
Abstract ID 721 | Date: 2022-09-13 13:40 – 13:50 | Type: Oral Presentation | Place: SOWI – Seminar room U3 |
Guidicelli, Matteo (1); Treichler, Désirée (2); Salzmann, Nadine (3,4)
1: Department of Geosciences, University of Fribourg, Fribourg, Switzerland
2: Institute of Geosciences, University of Oslo, Oslo, Norway
3: WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland
4: Climate Change, Extremes and Natural Hazards in Alpine Regions Research Center CERC, Davos, Switzerland
Keywords: Snow Accumulation, Glaciers, Reanalysis, Machine Learning, Icesat-2
Snow regimes in high-mountain regions are changing in response to atmospheric warming. However, the scarcity and limited accuracy of observations of snow and precipitation at high elevation reduce our understanding of cryosphere-climate linkage. Here, we compare the winter mass balance of 95 glaciers distributed over the Alps, Western Canada, Central Asia and Scandinavia, with the total precipitation from the ERA-5 and the MERRA-2 reanalysis products during the snow accumulation seasons from 1981 until today. We propose a machine learning model to adjust the precipitation of reanalysis products to the elevation of the glaciers, thus deriving snow water equivalent (SWE) estimates over glaciers uncovered by ground observations and/or filling observational gaps. We use a gradient boosting regressor (GBR), which combines several meteorological variables from the reanalyses (e.g. air temperature, relative humidity) with topographical parameters. These GBR-derived estimates are evaluated against the winter mass balance data by means of a leave-one-glacier-out cross-validation (site-independent GBR) and a leave-one-season-out cross-validation (season-independent GBR). The GBRs allowed to reduce (increase) the bias (correlation) between the precipitation of the original reanalyses and the winter mass balance data of the glaciers (from an overall RMSEs (CORR) of 946 mm (0.74) and 793 mm (0.81) of MERRA-2 and ERA-5, to 443 mm (0.85) and 422 mm (0.86) of the site-independent GBRs, and 287 mm (0.94) and 272 mm (0.95) of the season-independent GBRs). Finally, the GBR models are used to derive SWE trends on glaciers between 1981 and 2021. The resulting trends are more pronounced than those obtained from the total precipitation of the original reanalyses. On a regional scale, significant 41-year SWE trends are observed in the Alps (MERRA-2 season-independent GBR: +0.4 %/year) and in Western Canada (ERA-5 season-independent GBR: +0.2 %/year), while significant positive/negative trends are observed in all the regions for single glaciers or specific elevations. Negative (positive) SWE trends are typically observed at lower (higher) elevations, where the impact of rising temperatures is more (less) dominant. However, denser ground-based or improved remote sensing observations would enable to further evaluate and develop the presented methods. In future research, we thus aim at exploiting snow depth observations derived from the novel ICESat-2 satellite laser altimeter to improve the reliability of machine learning model-based snow estimates in high-mountain observation-scarce regions.
Abstract ID 337 | Date: 2022-09-13 13:50 – 14:00 | Type: Oral Presentation | Place: SOWI – Seminar room U3 |
Schellander, Harald; Winkler, Michael
ZAMG – Zentralanstalt für Meteorologie und Geodynamik, Austria
Keywords: Trend, Swe, Modeling, Alpine, Change
The snowpack in mountainous regions plays a key role in the water cycle, by storing water during the winter season, and releasing its content as runoff in the spring and summer months. The mass of a snowpack can be measured in terms of snow water equivalent (SWE), which describes the equivalent amount of water stored in the snowpack. It is directly connected to snow depth (HS) by the bulk snow density.
Due to its obvious importance, SWE trends and their relationship to climate change have already been the goal of a number of studies mostly on a global and hemispherical scale and, e.g., the Fifth IPCC assessment report states a high confidence for a general and continuing decline of SWE over the past decades. For the European Alps, however, only few studies have been conducted. They show a similar decrease, but at higher elevations and at smaller scales the spatial distribution and temporal evolution of SWE is still not well understood and highly uncertain. This is due to generally sparse and short SWE measurements in the Alps and considerable difficulties for satellite- and model-based techniques due to the complex topography.
Recent modelling efforts led to the ∆SNOW model, a semi-empirical approach to derive daily SWE exclusively from consecutive HS series, of which there are many in the Alps of excellent quality and length, and which have recently been published and are freely available. In the first part of this contribution, improvements of the ∆SNOW model are presented. For example, the model's restriction to temporally fixed density parameters is eliminated. This will extend the usability for the conversion of snow depths to SWE to long-lasting snowpacks of high Alpine areas and lowlands. The improved ∆SNOW model is then used to estimate SWE at more than 1500 stations with continuous, daily HS records in and around the Alps from flatlands to very high Alpine regions with 130 stations above 2000 m. We also show first results of a trend analysis of seasonal mean and peak SWE of this very large number of stations in the Alps. The high station density and large elevation range covered opens the opportunity for gaining new insights in the spatial and elevational distribution of SWE changes in the Alps at an unprecedented local scale.
Abstract ID 308 | Date: 2022-09-13 14:00 – 14:10 | Type: Oral Presentation | Place: SOWI – Seminar room U3 |
Rhoades, Alan M. (1); Hatchett, Benjamin J. (2); Risser, Mark D. (1); Collins, William D. (1,3); Bambach, Nicolas E. (4); Huning, Laurie S. (5,6); Mccrary, Rachel M. (7); Siirila-Woodburn, Erica R. (1); Ullrich, Paul A. (1,4); Wehner, Michael F. (1); Zarzycki, Colin M. (8); Jones, Andrew D. (1,4)
1: Lawrence Berkeley National Laboratory, United States of America
2: Desert Research Institute, United States of America
3: University of California, Berkeley, United States of America
4: University of California, Davis, United States of America
5: California State University, Long Beach, United States of America
6: University of California, Irvine, United States of America
7: National Center for Atmospheric Research, United States of America
8: Penn State University, United States of America
Keywords: Snow, Runoff Efficiency, Climate Change, Earth System Models, American Cordillera
Societies and ecosystems that reside within and downstream of mountains rely on seasonal snowmelt to satisfy their water demands. Anthropogenic climate change has reduced mountain snowpacks worldwide, altering snowmelt magnitude and timing. Here, the global warming level leading to widespread and persistent mountain snowpack decline, termed low-to-no snow, is estimated for the world's most latitudinally contiguous mountain range, the American Cordillera. These estimates are derived from a recent high-resolution Earth system model ensemble (HighResMIP) that include six simulations covering 1950-2100, under a high-emissions scenario, and using atmospheric model intercomparison project (AMIP) protocols. We show a combination of dynamical, thermodynamical, and hypsometric factors results in an asymmetric emergence of low-to-no snow conditions within the midlatitudes of the American Cordillera. Low-to-no snow emergence occurs approximately 20 years earlier in the Southern Hemisphere, at a third of the local warming level, and coincides with runoff efficiency declines in both dry and wet years. Prevention of a low-to-no snow future in either hemisphere requires the level of global warming to be held to, at most, +2.5 deg C.
Abstract ID 321 | Date: 2022-09-13 14:10 – 14:20 | Type: Oral Presentation | Place: SOWI – Seminar room U3 |
Siirila-Woodburn, Erica (1); Rhoades, Alan (1); Hatchett, Benjamin (2); Huning, Laurie (3); Szinai, Julia (1,4); Tague, Christina (5); Nico, Peter (1); Feldman, Daniel (1); Jones, Andrew (1,4); Collins, William (1); Kaatz, Laurna (6); Maina, Fadji (7); Dennedy-Frank, P. James (1)
1: Earth and Environmental Sciences Area, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
2: Western Regional Climate Center (WRCC), Reno, NV, USA
3: Department of Civil and Environmental Engineering, University of California, Irvine, Irvine, CA, USA
4: Energy and Resources Group, University of California, Berkeley, Berkeley, CA, USA
5: Bren School of Environmental Science & Management, University of California, Santa Barbara, Santa Barbara, CA, USA
6: Denver Water, Denver, CO, USA
7: NASA Goddard Space Flight Center, Greenbelt, MD, USA
Keywords: Snowpack Loss, Water Partitioning, Climate Change, Water Cycle
Water management in the western United States (WUS), like many places globally, has a long-held reliance on snowpack, but anthropogenic climate change is decreasing seasonal snowpacks worldwide, posing substantial, potentially even catastrophic consequences on water resources. We present a synthesis of 21st century WUS snowpack projections and discuss the trickle-down impacts on the greater hydrologic cycle to better constrain the timeline of impending water failures. Through a new definition of low-to-no snow and a framework to contextualize the sequencing of snow drought years, results show that across the WUS, snow water equivalent is expected to decline ~25% by 2050, with losses comparable to historical trends. Using this framework, models suggest low-to-no snow will become persistent in ~35–60 years in the WUS if greenhouse gas emissions continue unabated.
These changes have potentially outsized impacts on the integrated hydrologic cycle. Many approaches struggle to infer the specific impacts given competing factors such as increased evapotranspiration, altered vegetation composition, and changes in wildfire behavior in a warmer world. Coupled atmosphere-through-bedrock models driven by high performance computing are powerful tools to disentangle non-linear and co-evolving processes across the critical zone in montane environments. Examples of recent advancements will be discussed, with a focus on the fundamental physical drivers of change. These include the role in which large precipitation events (including rain-on-snow events typical of atmospheric rivers) play on groundwater, as well as how atmospheric changes in future nearly snow-free years result in more ephemeral streams. These snow and hydrodynamic changes undermine conventional water management practices. However, proactive implementation of soft and hard adaptation strategies provide a potential avenue to build resilience to extreme, episodic and, eventually, persistent low-to-no snow conditions.
Abstract ID 124 | Date: 2022-09-13 14:20 – 14:30 | Type: Oral Presentation | Place: SOWI – Seminar room U3 |
Lalande, Mickaël (1); Ménégoz, Martin (1); Krinner, Gerhard (1); Ottlé, Catherine (2)
1: Univ. Grenoble Alpes, CNRS, IRD, G-INP, IGE, 38000 Grenoble, France
2: LSCE-IPSL (CNRS-CEA-UVSQ), Université Paris-Saclay, Gif-sur-Yvette, France
Keywords: High Mountain Asia, Cmip6, Snow Cover Parameterization, Mountainous Area, General Circulation Models
With an average elevation of 4000 m, High Mountain Asia (HMA) and the Tibetan Plateau (TP) are hosting the third largest reservoir of glaciers and snow after the two polar ice caps, and trigger strong rates of orographic precipitation. Climate studies over HMA are related to serious challenges concerning the exposure of human infrastructures to natural hazards and the water resources for agriculture, drinking water and hydroelectricity to whom several hundred million inhabitants are depending. However, temperature, precipitation, and snow cover in this region are poorly described in global climate models because their coarse resolution is not adapted to the complex topography of this area. Since the first CMIP exercises, a cold model bias has been identified in HMA, however, its attribution is not obvious and may be different from one model to another. Our study focuses on a multi-model comparison of 27 CMIP6 models over 1979-2014. A cold bias is still present in near-surface air temperature over HMA reaching an annual value of -2.0 °C (± 3.2 °C), associated with an over-extended relative snow cover extent of 53 % (± 62 %), and a relative excess of precipitation of 139 % (± 38 %). Precipitation biases are uncertain because of the undercatch of solid precipitation in observations. Higher-resolution models do not systematically perform better than the coarse-gridded ones, suggesting that the development of more realistic physical parameterizations over complex topography areas is still needed. We implemented new snow cover parameterizations taking into account the sub-grid topography in the IPSL general circulation model. This model shows a strong cold bias and an excess of snow cover over HMA. These new parameterizations were calibrated over HMA using a high-resolution snow reanalysis and compared to a deep learning algorithm. Preliminary results show improvements in simulated snow cover and reduced cold bias over HMA. The residual biases suggest that other factors must be involved (e.g., tropospheric cold bias, precipitation biases, aerosol forcing uncertainties, orographic drag), and raise the potential limitation of the implemented parameterizations over permanent snow areas. Nevertheless, taking into account the sub-grid topography in the snow cover parameterization is essential to properly represent snow cover dynamics over mountainous areas.
Abstract ID 487 | Date: 2022-09-13 14:30 – 14:40 | Type: Oral Presentation | Place: SOWI – Seminar room U3 |
Cullen, Nicolas (1); Sirguey, Pascal (1); Redpath, Todd (1); Vargo, Lauren (2); Anderson, Brian (2); Zammit, Christian (3)
1: University of Otago, New Zealand
2: Victoria University of Wellington, New Zealand
3: National Institute of Water and Atmospheric Research (NIWA), New Zealand
Keywords: Seasonal Snow, Remote Sensing, Snow Modelling
The snow that falls in mountain regions in New Zealand is a major source of freshwater, but we still struggle to reliably estimate the amount of snow stored in our high alpine regions, how much water can be expected downstream and when it will be released. Mountain rivers in both the North and South Islands of New Zealand feed our largest hydro-electric power schemes, and provide critical water for irrigation, especially during drought. The recognition that the snow contribution to water resources is changing has prompted new efforts to better understand the climate processes governing seasonal snow. Recently, focus has been on developing a regional-scale, real time operational snow cover product with MODIS imagery, supplemented with the acquisition of high spatial and temporal resolution satellite imagery to detect detailed changes in snow variability. These remote sensing approaches, combined with relatively sparse ground based observations, provide important validation and calibration to hydrological and advanced physics-based snow modelling. Review of the progress and challenges of the current remote sensing and snow and hydrological modelling efforts to characterise seasonal snow in New Zealand will be discussed, including projections of the contribution the snowpack makes to the water cycle in key catchments. The end goal of these efforts is to ensure a set of discrete quantitative hydrologic "storylines" are produced for use in decision making by government, communities and industry.
Abstract ID 670 | Date: 2022-09-13 16:00 – 16:10 | Type: Oral Presentation | Place: SOWI – Seminar room U3 |
Azizi, Abdul Haseeb; Akhtar, Fazlullah; Tischbein, Bernhard
Center for Development Research (ZEF), University of Bonn, Germany
Keywords: Data-Scarcity, Climate Change, Snowcover, Snowmelt, Afghanistan
Snowcover is the principal source of streamflow, groundwater recharge, irrigation and environmental flows in the Hindukush Himalaya (HKH) region of Afghanistan. For estimation of water availability as a essential information for water management, it is important to investigate the spatiotemporal variation in the snow cover area (SCA). Therefore, the objective of this study was to assess the spatiotemporal variation in snow cover in the Upper Kabul River Basin (i.e. Panjshir Watershed) which constitutes around 4.87% land area of the Kabul River Basin. For this purpose, the improved Moderate Resolution Imaging Spectroradiometer (MODIS) product (i.e. MOYDGL06) was used which covers a period of 2003 – 2018. The results indicate that usually, the snow accumulates from September to February; the annual maximum SCA during the study period was found to be 3519 km2 in February. From March onward, as the temperature rise, the snowmelt starts until the end of August when the SCA is least (i.e. 11 km2), mostly in the high elevation zones. The mean annual SCA during this period was 1876 ± 35.7 km2 which accounts for about 52.8% of the total area of the target region. The results of this study highlight the MODIS snow cover products' capability to assess the spatiotemporal dynamics of snow cover over the mountainous areas of complex river basins. Moreover, using satellite products in data-scarce regions for water availability estimations is the best feasible alternative. The detailed estimation of SCA both in space and time will help the long-term planning and development initiatives in the field of water resources management.
Abstract ID 178 | Date: 2022-09-13 16:10 – 16:20 | Type: Oral Presentation | Place: SOWI – Seminar room U3 |
Hydrometeorological Research Institute, Uzhydromet, Uzbekistan., Uzbekistan
Keywords: Seasonal Snowline, Snow Cover, Modsnow, Mountain River Basin, Modis, Cloud Cover, Dem And Remote Sensing.
Adkham Mamaraimov1, Bakhriddin Nishonov1, Akmal Gafurov1, Ukhtam Adkhamov2, and Abror Gafurov2
1 Hydrometeorological Research Institute (HMRI), Uzhydromet, Uzbekistan.
2 German Research Centre for Geosciences (GFZ Potsdam), Germany.
Corresponding author: Adkham Mamaraimov (firstname.lastname@example.org)
In many parts of the world, water availability highly depends on snowmelt and glacier melt, which is formed in mountainous river basins. The snowmelt and snow cover dynamics are characterized by the seasonal snowline data that can be used to improve hydrological forecasts and for the climate changes related studies in mountain areas of Uzbekistan. However, snowline data based on traditional method are insufficient to represent large remote mountain areas with highly heterogeneous topography.
Nowadays, it is possible to obtain spatially distributed snow cover data for high-altitudes using the remote sensing methods. Thus, we used Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover data in this study. The MODIS snow cover data was processed by the MODSNOW-Tool, which includes cloud elimination. With this, daily spatially distributed snow cover maps were prepared for the Pskem River Basin from 2000 to 2018. The daily cloud-free snow cover maps were used to assess daily snow elevation in the basin, which was used to study variation of snowline over the past 18 years.
The results showed that the analysis of trends in the dynamics of the monthly average minimum snowline shows that a statistically significant period of positive trends is especially in the spring season, as well as in the first month of summer and the negative trend was observed in the autumn season, respectively. The average temperature was positively correlated (R=0.88) with the monthly average minimum snowline, which was significant at the confidence level of 0.95. The correlation between the minimum snowline and the total precipitation was negative (R=-0.68) and not significant. The increasing trend (decrease of snowline) shows that snow in spring is increasing because winter precipitation is increasing or winter temperature is decreasing. More snow in winter means more snow fraction and lower snowline elevation in spring. The decreasing trend of snowline in autumn can be explained by increasing temperature in these months, which leads to decrease of precipitation falling as snow.
The main advantage of the daily minimum snowline information based on remote sensing data is to monitor remote mountain rivers. Using the remote sensing snow cover, understanding of hydrological processes in remote areas can be assured.
Abstract ID 512 | Date: 2022-09-13 16:20 – 16:30 | Type: Oral Presentation | Place: SOWI – Seminar room U3 |
Albalat, Anna; Trapero, Laura; Pons, Marc
Andorra Research + Innovation, Andorra
Keywords: Indicators, Climate Change, Snow Depth, Adaptation, Ski Resorts.
Andorra is characterised by a high mountain climate marked by the geographical and topographical environment with a great local variety. This mountain climate is influenced by the Mediterranean climate with a subcontinental tendency. This high variability in turn has a marked influence on the temporal and spatial evolution of the snow cover. The ski resorts of Andorra have had daily measurements of temperature, precipitation, snow cover and recent snow since the 1980s (Arcalís, Pal, Arinsal, Pas de la Casa, Grau Roig, Soldeu).
This paper focuses on the observations of snow depth and recent snow recorded by the observers of the ski resorts and characterise the vulnerability of snow depth at different altitudes, being more relevant the areas below 1500m according to the last report of the OPCC (Pyrenean Observatory of Climate Change) on climate change in the Pyrenees. In this case, Andorra's ski resorts are located at altitudes between 1600m and 2500m. Based on these series, different indicators have been developed to characterise the evolution of the snow cover in the ski resorts in a regional and simplified manner for monitoring purposes and to provide an orientated tool for decision-making.
In a first approximation, the ski seasons have been objectively categorised (surplus, normal, deficit) and the analysis of the temporal evolution of the indicators has been completed. In the most southerly resorts, there has been a notable decrease in the number of days with a snow depth of more than one metre. The results presented are useful for understanding the effect of climate change in recent years and the planning of adaptation and mitigation measures in winter conditions.
Abstract ID 700 | Date: 2022-09-13 16:30 – 16:40 | Type: Oral Presentation | Place: SOWI – Seminar room U3 |
Ayala, Álvaro; Schauwecker, Simone; Macdonell, Shelley
Centro de Estudios Avanzados en Zonas Áridas (CEAZA), La Serena, Chile
Keywords: Snow, Runoff, Andes, Dry Regions
Snowmelt is the primary source of water for streams and groundwater in the western side of the Desert Andes (20-32°S). This high-elevation region (with peaks > 6000 m a.s.l.) is characterized by scarce precipitation concentrated in winter, and warm and dry summers. While complex spatial patterns of winter snow accumulation are shaped by strong winds and the rough topography, the dry air and intense radiation favour sublimation in spring and summer. These conditions likely restrict snowmelt to specific sites with available snow and sufficient energy for melt. In this work, we identify sites dominated by snowmelt and quantify the physical processes that explain their location using a combination of i) field data, ii) satellite-derived indices of snow presence, iii) retrospective snow water equivalent reconstructions, and iv) numerical simulations with a process-based snow evolution model. As study site we select La Laguna River basin, a high-elevation (~3000-6000 m a.s.l., 513 km2) catchment in north-central Chile. Our results show that snow tends to accumulate on south-east oriented slopes that are protected from the dominant western winds. These sites remain snow-covered during more than 40% of the melt season, and their meteorological conditions favour melt over sublimation. On the other hand, there is little snow accumulation on the north-west oriented slopes, and strong winds and solar exposition yield large sublimation amounts (>70% of the total accumulation) that largely reduce snowmelt contribution to runoff. In recent years, the current drought in north-central Chile might have enhanced sublimation-favourable conditions and reduced snowmelt runoff contribution. We show that south-east slopes near to valley bottoms produce the largest runoff contribution and we suggest that a detailed monitoring of these sites would improve our understanding of the hydrology and ecosystems of the Desert Andes.
Abstract ID 470 | Date: 2022-09-13 16:40 – 16:50 | Type: Oral Presentation | Place: SOWI – Seminar room U3 |
Nagler, Thomas (1); Schwaizer, Gabriele (1); Mölg, Nico (1); Keuris, Lars (1); Hetzenecker, Markus (1); Felbauer, Lucia (1); Metsaemaeki, Sari (2); Marin, Carlo (3); Premier, Valentina (3); Trofaier, Anna (4)
1: ENVEO Environmental Earth Observation IT GmbH
2: Finnish Environment Institute
3: Eurac Research
4: European Space Agency
Keywords: Snow Cover, Climate Data Record, Satellite, Climate Change, Cryosphere
Seasonal snow is an important component of the global climate system. It is highly variable in space and time, being sensitive to synoptic scale processes and long-term climate-induced changes related to temperature and precipitation. Current snow products derived from various satellite sensors by means of different retrieval algorithms show substantial differences in area extent and snow mass, a source of uncertainty for cryosphere monitoring and climate model validation. The ESA Climate Change Initiative (CCI+) Programme addresses seasonal snow as one of nine Essential Climate Variables derived from satellite data. Here we report on CCI activities concerned with the snow cover extent at regional to global scale.
In the first phase of the Snow-CCI project (2018 – 2021), reliable and fully validated processing lines for the generation of snow climate data records were developed and implemented. Homogeneous multi-sensor time series of daily snow extent were generated. Using GCOS guidelines, the product requirements for these parameters were assessed and consolidated in the frame of workshops and by direct interaction with users who are concerned with different climate applications. Consistent daily products on fractional snow extent at global coverage are provided for snow viewable from space (viewable snow) and for snow on the surface corrected for forest masking (snow on ground). Input data are medium resolution optical satellite images from the MODIS and Sentinel-3 SLSTR sensors, extending from 2000 to present. For the Snow-CCI Climate Research Data Package version 2 an iterative development cycle was implemented in order to improve and homogenise the snow extent products from different sensors. Independent validation of the snow extent products is performed using high resolution snow maps from Landsat and Sentinel-2 data acquired across different seasons and climate zones around the globe from 2000 onwards, as well as using in-situ snow data, following protocols developed by the snow community.
We will present an overview of the algorithms and systems for generation of snow products that are available at the ESA Open data portal. The 20 years timeseries from MODIS (starting in 2000, 1 km pixel spacing) and from Sentinel-3 SLSTR (from 2020 onwards) will be presented along with the results of the multi-sensor consistency and validation activities and of inter-comparisons with snow products from other sources, focussing on different mountain regions of the Earth.
Abstract ID 809 | Date: 2022-09-13 16:50 – 17:00 | Type: Oral Presentation | Place: SOWI – Seminar room U3 |
Schöner, Wolfgang (1); Ma, Lijuan (2); Marshall, Shawn (3)
1: University of Graz, Austria
2: China Meteorological Administration, China
3: Environment and Climate Change Canada, Canada
Keywords: Snow, Global Assessment, Climatology, Trend, Harmonization
Snow is an essential feature of mountainous regions worldwide and contributes to mountainous regions acting as water towers and providing a critical water supply for downstream areas. In addition, water from snowmelt is essential for power generation, irrigation, water supply, groundwater recharge and aquatic ecosystems. Climate change is clearly altering the amount and distribution of snow cover in the mountains in space and time. However, the relationship between climate change, or more precisely atmospheric variables, and snowpack responses is not simple and straightforward. Snow on the ground can subsequently be redistributed through numerous mechanisms. Ablation (loss of snow cover), on the other hand, is a complex process chain of surface-atmosphere interactions that reduces snow deposited on the ground by melting or sublimation. The conclusions from the existing studies show that despite the great importance of snow in mountain regions, an inventory of snow cover in mountains on a global scale is still lacking. Even regional inventories are strongly limited to a few well-monitored mountain regions such as the US Rockies and the European Alps.
The Joint Bodyl on the Status of Mountain Snowcover therefore has multiple motivations and objectives:
1. the primary research objective is to provide robust information on changes in mountain snowpack at the global scale over the past decades, based on the compilation and standardisation of existing data (sources) at sufficiently high resolution
2. in addition to compiling and analysing existing data series by examining spatial and temporal trends in snowpack properties and derived indicators, this initiative also aims to better understand the processes of accumulation and ablation based on existing modelling and observational studies.
3 Another important objective of the initiative is to provide open access to snow data for the research community and to contribute to operational capacity building with respect to understanding changes in mountain snowpack and its climate, water and environmental impacts and responses.
The Joint Body is structured into 4 main activities (work packages):
WP1: Quality control and homogenisation of mountain snow data for use in climatology and hydrology.
WP2: Status of multidecadal changes of snow cover in mountain regions of the world
WP3: Processes of snow accumulation
WP4: Snow ablation processes – research gaps in mountain snow modelling
The presentation will introduce the topics of the Joint Body and outline how snow researchers can and should be involved and how participants can benefit from this bottom-up initiative.