The Multiple Snow Data Assimilation System (MuSA v1.0)

Accurate knowledge of the seasonal snow distribution is vital in several domains including ecology, water resources management, and tourism. Current spaceborne sensors provide a useful but incomplete description of the snowpack. Many studies suggest that the assimilation of remotely sensed products in physically based snowpack models is a promising path forward to estimate the spatial distribution of snow water equivalent (SWE). However, to date there is no standalone, open-source, community-driven project dedicated to snow data assimilation, which makes it difficult to compare existing algorithms and fragments development efforts. Here we introduce a new data assimilation toolbox, the Multiple Snow Data Assimilation System (MuSA), to help fill this gap. MuSA was developed to fuse remotely sensed information that is available at different timescales with the energy and mass balance Flexible Snow Model (FSM2). MuSA was designed to be user-friendly and scalable. It enables assimilation of different state variables such as the snow depth, SWE, snow surface temperature, binary or fractional snow-covered area, and snow albedo and could be easily upgraded to assimilate other variables such as liquid water content or snow density in the future. MuSA allows the joint assimilation of an arbitrary number of these variables, through the generation of an ensemble of FSM2 simulations. The characteristics of the ensemble (i.e., the number of particles and their prior covariance) may be controlled by the user, and it is generated by perturbing the meteorological forcing of FSM2. The observational variables may be assimilated using different algorithms including particle filters and smoothers as well as ensemble Kalman filters and smoothers along with their iterative variants. We demonstrate the wide capabilities of MuSA through two snow data assimilation experiments. First, 5 m resolution snow depth maps derived from drone surveys are assimilated in a distributed fashion in the Izas catchment (central Pyrenees). Furthermore, we conducted a joint-assimilation experiment, fusing MODIS land surface temperature and fractional snow-covered area with FSM2 in a single-cell experiment. In light of these experiments, we discuss the pros and cons of the assimilation algorithms, including their computational cost.</p

Snow-vegetation-atmosphere interactions in alpine tundra

The interannual variability of snow cover in alpine areas is increasing, which may affect the tightly coupled cycles of carbon and water through snow-vegetation-atmosphere interactions across a range of spatio-temporal scales. To explore the role of snow cover for the land-atmosphere exchange of CO2 and water vapor in alpine tundra ecosystems, we combined three years (2019&ndash;2021) of continuous eddy covariance flux measurements of net ecosystem exchange of CO2 (NEE) and evapotranspiration (ET) from the Finse site in alpine Norway (1210 m a.s.l.) with a ground-based ecosystem-type classification and satellite imagery from Sentinel-2, Landsat 8, and MODIS. While the snow conditions in 2019 and 2021 can be described as site-typical, 2020 features an extreme snow accumulation associated with a strong negative phase of the Scandinavian Pattern of the synoptic atmospheric circulation during spring. This extreme snow accumulation caused a one-month delay in melt-out date, which falls on the 92nd-percentile in the distribution of yearly melt-out dates in the period 2001&ndash;2021. The melt-out dates follow a consistent fine-scale spatial relationship with ecosystem types across years. Mountain and lichen heathlands melt out more heterogeneously than fens and flood plains, while late snowbeds melt out up to one month later than the other ecosystem types. While the summertime average Normalized Difference Vegetation Index (NDVI) was reduced considerably during the extreme snow year 2020, it reached the same maximum as in the other years for all but one the ecosystem type (late snowbeds), indicating that the delayed onset of vegetation growth is compensated to the same maximum productivity. Eddy covariance estimates of NEE and ET are gap-filled separately for two wind sectors using a random forest regression model to account for complex and nonlinear ecohydrological interactions. While the two wind sectors differ markedly in vegetation composition and flux magnitudes, their flux response is controlled by the same drivers as estimated by the predictor importance of the random forest model as well as the high correlation of flux magnitudes (correlation coefficient r = 0.92 for NEE and r = 0.89 for ET) between both areas. The one-month delay of the start of the snow-free season in 2020 reduced the total annual ET by 50 % compared to 2019 and 2021, and reduced the growing season carbon assimilation to turn the ecosystem from a moderate annual carbon sink (&minus;31 to &minus;6 gC m&minus;2 yr&minus;1) to a source (34 to 20 gC m&minus;2 yr&minus;1). These results underpin the strong dependence of ecosystem structure and functioning on snow dynamics, whose anomalies can result in important ecological extreme events for alpine ecosystems.</p

Circumpolar mapping of permafrost temperature and thaw depth in the ESA Permafrost CCI project

Permafrost is an Essential Climate Variable (ECV) within the Global Climate Observing System (GCOS), which is characterized by subsurface temperatures and the depth of the seasonal thaw layer. Complementing ground-based monitoring networks, the Permafrost CCI project funded by the European Space Agency (ESA) 2018-2021 will establish Earth Observation (EO) based products for the permafrost ECV spanning the last two decades. Since ground temperature and thaw depth cannot be directly observed from space-borne sensors, we will ingest a variety of satellite and reanalysis data in a ground thermal model, which allows to quantitatively characterize the changing permafrost systems in Arctic and High-Mountain areas. As recently demonstrated for the Lena River Delta in Northern Siberia, the algorithm uses remotely sensed data sets of Land Surface Temperature (LST), Snow Water Equivalent (SWE) and landcover to drive the transient permafrost model CryoGrid 2, which yields ground temperature at various depths, in addition to thaw depth. For the circumpolar CCI product, we aim for a spatial resolution of 1km, and ensemble runs will be performed for each pixel to represent the subgrid variability of snow and land cover. The performance of the transient algorithm crucially depends on the correct representation of ground properties, in particular ice and organic contents. Therefore, the project will compile a new subsurface stratigraphy product which also holds great potential for improving Earth System Model results in permafrost environments. We present simulation runs for various permafrost regions and characterize the accuracy and ability to reproduce trends against ground-based data. Finally, we evaluate the feasibility of future “permafrost reanalysis” products, exploiting the information content of various satellite products to deliver the best possible estimate for the permafrost thermal state over a range of spatial scales

Applying the Eddy Covariance Method Under Difficult Conditions.

We assess how reliable the Eddy-Covariance (EC) method is in estimating surface fluxes under the difficult conditions that occur in the high Arctic. Emphasis is placed on stable stratification and the breakdown of EC assumptions that may occur in such a regime. To investigate these difficulties we developed an EC processing module from scratch, providing an extensive and transparent overview of the EC method. Raw data was obtained from an open path EC system located in the Bayelva catchment near Ny Ålesund (79&deg;N), Svalbard, Norway. Our flux estimates are in reasonable agreement with those found from the standardized EC package TK2. Strong relative non-stationarity represents the greatest hindrance to data quality at Bayelva, occurring for 11% of the data period. Overall, average relative flux uncertainties were found to be 20% for both the sensible (SH) and latent heat (LH) flux. Under stable stratification these uncertainties were considerably higher, 27% on average. Through Ogive classification we found that the traditional 30 minute SH and LH fluxes converged (resolved the turbulent cospectrum) 70% of the time. Here too the stable regime stands out, with low convergence fractions of 41% and 48% for LH and SH, respectively. To our knowledge it is the first time such an analysis has been carried out in the Arctic. Concluding, while usually successful for neutral and unstable conditions, the traditional 30 minute flux averaging period is, more often than not, poorly suited for the stable regime. We attribute this to the observed and predicted shift in cospectral peaks towards lower periods under stable stratification, along with an erosion of the cospectral gap. An apparently simple fix of reducing the averaging period is not generally a valid solution. The required reduction could introduce unacceptable levels of flux uncertainty

Ensemble-based retrospective analysis of the seasonal snowpack

This thesis presents a satellite-based modeling framework that can estimate how much snow was stored in the terrain. These estimates can help guide climate analysis and prediction. Accurate quantification of Earth’s snow mass is a long-standing problem to which a solution is direly needed with ongoing climate change. Snow plays an essential role in the climate system and snowmelt is a vital source of freshwater for a quarter of the world’s population. The framework combines satellite imagery and historic weather data to remotely estimate snow mass by leveraging enhanced ensemble-based data assimilation algorithms. The result is a retrospective analysis (reanalysis) of the snow mass that can be obtained for any location on Earth. So far, this framework has been successfully implemented in three different environments: Svalbard, the Californian Sierra Nevada, and the Swiss Alps. In the future, snow reanalyses could be used to train algorithms to predict snow mass in near real time. They may also help validate and subsequently improve climate models. Ultimately this would allow us to make even more informed future projections of the possible fate of the environment that sustains us

Evaluating satellite retrieved fractional snow-covered area at a high-Arctic site using terrestrial photography

The seasonal snow-cover is one of the most rapidly varying natural surface features on Earth. It strongly modulates the terrestrial water, energy, and carbon balance. Fractional snow-covered area (fSCA) is an essential snow variable that can be retrieved from multispectral satellite imagery. In this study, we evaluate fSCA retrievals from multiple sensors that are currently in polar orbit: the operational land imager (OLI) on-board Landsat 8, the multispectral instrument (MSI) on-board the Sentinel-2 satellites, and the moderate resolution imaging spectroradiometer (MODIS) on-board Terra and Aqua. We consider several retrieval algorithms that fall into three classes: thresholding of the normalized difference snow index (NDSI), regression on the NDSI, and spectral unmixing. We conduct the evaluation at a high-Arctic site in Svalbard, Norway, by comparing satellite retrieved fSCA to coincident high-resolution snow-cover maps obtained from a terrestrial automatic camera system. For the lower resolution MODIS retrievals, the regression-based retrievals outperformed the unmixing-based retrievals for all metrics but the bias. For the higher resolution sensors (OLI and MSI), retrievals based on NDSI thresholding overestimated the fSCA due to the mixed pixel problem whereas spectral unmixing retrievals provided the most reliable estimates across the board. We therefore encourage the operationalization of spectral unmixing retrievals of fSCA from both OLI and MSI

TopoCLIM: rapid topography-based downscaling of regional climate model output in complex terrain v1.1

This study describes and evaluates a new down-scaling scheme that specifically addresses the need for hillslope-scale atmospheric-forcing time series for modelling the local impact of regional climate change projections on the land surface in complex terrain. The method has a global scope in that it does not rely directly on surface observations and is able to generate the full suite of model forcing variables required for hydrological and land surface modelling in hourly time steps. It achieves this by utilizing the previously published TopoSCALE scheme to generate synthetic observations of the current climate at the hillslope scale while accounting for a broad range of surface-atmosphere interactions. These synthetic observations are then used to debias (downscale) CORDEX climate variables using the quantile mapping method. A further temporal disaggregation step produces sub-daily fields. This approach has the advantages of other empirical-statistical methods, including speed of use, while it avoids the need for ground data, which are often limited. It is therefore a suitable method for a wide range of remote regions where ground data is absent, incomplete, or not of sufficient length. The approach is evaluated using a network of high-elevation stations across the Swiss Alps, and a test application in which the impacts of climate change on Alpine snow cover are modelled