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CR2.8

EDI
Cryospheric Data Science and Artificial Intelligence: Opportunities and Challenges

Machine learning, artificial intelligence and big data approaches have recently emerged as key tools in understanding the cryosphere. These approaches are being increasingly applied to answer long standing questions in cryospheric science, including those relating to remote sensing, forecasting, and improving process understanding across Antarctic, Arctic and Alpine regions. In doing so, data science and AI techniques are being used to gain insight into system complexity, analyse data on unprecedented temporal and spatial scales, and explore much wider parameter spaces than were previously possible.
In this session we invite submissions that utilise data science and/or AI techniques that address research questions relating to glaciology, sea ice, permafrost and/or polar climate science. Approaches used may include (but are not limited to) machine learning, artificial intelligence, big data processing/automation techniques, advanced statistics, and innovative software/computing solutions. These could be applied to any (or combinations) of data sources including remote sensing, numerical model output and field/lab observations. We particularly invite contributions that apply techniques and approaches that reveal new insights into cryospheric research problems that would not otherwise be achievable using traditional methods, and those that discuss how or if approaches can be applied or adapted to other areas of cryospheric science. Given the rapid development of this field by a diverse group of international researchers, we convene this session to help foster future collaboration amongst session contributors, attendees, and international stakeholders and help address the most challenging questions in cryospheric science.

Co-organized by CL5.1/ESSI1/GI2/OS1
Convener: James Lea | Co-conveners: Amber Leeson, Celia A. BaumhoerECSECS, Michel Tsamados
Presentations
| Fri, 27 May, 14:05–16:40 (CEST)
 
Room N2

Fri, 27 May, 13:20–14:50

Chairpersons: Celia A. Baumhoer, Stephen Brough

14:05–14:08
Introduction

14:08–14:18
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EGU22-12785
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ECS
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solicited
William Gregory

Over the past four decades, the inexorable growth in technology and subsequently the availability of Earth-observation and model data has been unprecedented. Hidden within these data are the fingerprints of the physical processes that govern climate variability over a wide range of spatial and temporal scales, and it is the task of the climate scientist to separate these patterns from noise. Given the wealth of data now at our disposal, machine learning methods are becoming the tools of choice in climate science for a variety of applications ranging from data assimilation, to sea ice feature detection from space. This talk summarises recent developments in the application of machine learning methods to the study of polar climate, with particular focus on Arctic sea ice. Supervised learning techniques including Gaussian process regression, and unsupervised learning techniques including cluster analysis and complex networks, are applied to various problems facing the polar climate community at present, where each application can be considered an individual component of the larger sea ice prediction problem. These applications include: seasonal sea ice forecasting, improving spatio-temporal data coverage in the presence of sparse satellite observations, and illuminating the spatio-temporal connectivity between climatological processes.

How to cite: Gregory, W.: Machine learning tools for pattern recognition in polar climate science, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12785, https://doi.org/10.5194/egusphere-egu22-12785, 2022.

14:18–14:24
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EGU22-2726
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ECS
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Highlight
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On-site presentation
Nora Gourmelon et al.

The temporal variability of glacier calving front positions provides essential information about the state of marine-terminating glaciers. These positions can be extracted from Synthetic Aperture Radar (SAR) images throughout the year. To automate this extraction, we apply deep learning techniques that segment the SAR images into different classes: glacier; ocean including ice-melange and sea-ice covered ocean; rock outcrop; and regions with no information like areas outside the SAR swath, layover regions and SAR shadow. The calving front position can be derived from these regions during post-processing.   
A downside of deep learning is that hyper-parameters need to be tuned manually. For this tuning, expert knowledge and experience in deep learning are required. Furthermore, the fine-tuning process takes up much time, and the researcher needs to have programming skills.
    
In the biomedical imaging domain, a deep learning framework [1] has become increasingly popular for image segmentation. The nnU-Net can be used out-of-the-box. It automatically adapts the U-Net, the state-of-the-art architecture for image segmentation, to different datasets and segmentation tasks. Hence, no more manual tuning is required. The framework outperforms specialized deep learning pipelines in a multitude of public biomedical segmentation competitions.   
We apply the nnU-Net to the task of glacier segmentation, investigating whether the framework is also beneficial in the domain of remote sensing. Therefore, we train and test the nnU-Net on CaFFe (https://github.com/Nora-Go/CaFFe), a benchmark dataset for automatic calving front detection on SAR images. CaFFe comprises geocoded, orthorectified imagery acquired by the satellite missions RADARSAT-1, ERS-1/2, ALOS PALSAR, TerraSAR-X, TanDEM-X, Envisat, and Sentinel-1, covering the period 1995 - 2020. The ground range resolution varies between 7 and 20 m2. The nnU-Net learns from the multi-class "zones" labels provided with the dataset. We adopt the post-processing scheme from Gourmelon et al. [2] to extract the front from the segmented landscape regions. The test set includes images from the Mapple Glacier located on the Antarctic Peninsula and the Columbia Glacier in Alaska. The nnU-Net's calving front predictions for the Mapple Glacier lie close to the ground truth with just 125 m mean distance error. As the Columbia Glacier shows several calving front sections, its segmentation is more difficult than that of the laterally constrained Mapple Glacier. This complexity of the calving fronts is also reflected in the results: Predictions for the Columbia Glacier show a mean distance error of 635 m. Concludingly, the results demonstrate that the nnU-Net holds considerable potential for the remote sensing domain, especially for glacier segmentation.
    
[1] Isensee, F., Jaeger, P.F., Kohl, S.A.A. et al. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat Methods 18, 203–211 (2021). https://doi.org/10.1038/s41592-020-01008-z 

[2] Gourmelon, N., Seehaus, T., Braun, M., Maier, A., Christlein, V.: Calving Fronts and Where to Find Them: A Benchmark Dataset and Methodology for Automatic Glacier Calving Front Extraction from SAR Imagery, In Prep.

How to cite: Gourmelon, N., Seehaus, T., Braun, M., Maier, A., and Christlein, V.: Dissecting Glaciers - Can an Automated Bio-Medical Image Segmentation Tool also Segment Glaciers?, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2726, https://doi.org/10.5194/egusphere-egu22-2726, 2022.

14:24–14:30
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EGU22-6948
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ECS
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On-site presentation
Josefine Umlauft et al.

The cryosphere is a highly active and dynamic environment that rapidly responds to changing climatic conditions. In particular, the physical processes behind glacial dynamics are poorly understood because they remain challenging to observe. Glacial dynamics are strongly intermittent in time and heterogeneous in space. Thus, monitoring with high spatio-temporal resolution is essential.

In course of the RESOLVE (‘High-resolution imaging in subsurface geophysics : development of a multi-instrument platform for interdisciplinary research’) project, continuous seismic observations were obtained using a dense seismic network (100 nodes, Ø 700 m) installed on Glacier d’Argentière (French Alpes) during May in 2018. This unique data set offers the chance to study targeted processes and dynamics within the cryosphere on a local scale in detail.

 

To identify seismic signatures of ice beds in the presence of melt-induced microseismic noise, we applied the supervised ML technique gradient tree boosting. The approach has been proven suitable to directly observe the physical state of a tectonic fault. Transferred to glacial settings, seismic surface records could therefore reveal frictional properties of the ice bed, offering completely new means to study the subglacial environment and basal sliding, which is difficult to access with conventional approaches.

We built our ML model as follows: Statistical properties of the continuous seismic records (variance, kurtosis and quantile ranges), meteorological data and a seismic source catalogue obtained using beamforming (matched field processing) serve as features which we fit to measures of the GPS displacement rate of Glacier d’Argentière (labels). Our preliminary results suggest that seismic source activity at the bottom of the glacier strongly correlates with surface displacement rates and hence, is directly linked to basal motion. By ranking the importance of our input features, we have learned that other than for reasonably long monitoring time series along tectonic faults, statistical properties of seismic observations only do not suffice in glacial environments to estimate surface displacement. Additional beamforming features however, are a rich archive that enhance the ML model performance considerably and allow to directly observe ice dynamics.

How to cite: Umlauft, J., Roux, P., Lecointre, A., Gimbert, F., Nanni, U., Walpersdorf, A., Rouet-LeDuc, B., Hulbert, C., Trugman, D., and Johnson, P.: Mapping Glacier Basal Sliding with Beamforming and Artificial Intelligence, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6948, https://doi.org/10.5194/egusphere-egu22-6948, 2022.

14:30–14:36
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EGU22-1294
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ECS
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On-site presentation
Veronica Tollenaar et al.

The vast majority of the Antarctic ice sheet is covered with snow that compacts under its own weight and transforms into ice below the surface. However, in some areas, this typically blue-colored ice is directly exposed at the surface. These so-called "blue ice areas" represent islands of negative surface mass balance through sublimation and/or melt. Moreover, blue ice areas expose old ice that is easily accessible in large quantities at the surface, and some areas contain ice that extends beyond the time scales of classic deep-drilling ice cores.

Observation and modeling efforts suggest that the location of blue ice areas is related to a specific combination of topographic and meteorological factors. In the literature, these factors are described as (i) enhanced katabatic winds that erode snow, due to an increase of the surface slope or a tunneling effect of topography, (ii) the increased albedo of blue ice (with respect to snow), which enhances ablative processes, and (iii) the presence of nunataks (mountains protruding the ice) that act as barriers to the ice flow upstream, and prevent deposition of blowing snow on the lee side of the mountain. However, it remains largely unknown which role the physical processes play in creating and/or maintaining  blue ice at the surface of the ice sheet.

Here, we study how a combination of environmental and topographic factors lead to the observation of blue ice. We also quantify the relevance of the single processes and build an interpretable model aiming at not only predicting blue ice presence, but also explaining why it is there. To do so, data is fed into a convolutional neural network, a machine learning algorithm which uses the spatial context of the data to generate a prediction on the presence of blue ice areas. More specifically, we use a U-Net architecture that through convolutions and linked up-convolutions allows to obtain a semantic segmentation (i.e., a pixel-level map) of the input data. Ground reference data is obtained from existing products of blue ice area outlines that are based on multispectral observations. These products contain considerable uncertainties, as (i) the horizontal change from snow to ice is gradual and a single threshold in this transition is not applicable uniformly over the continent, and (ii) the blue ice area extent is known to vary seasonally. Therefore, we train our deep learning model with a loss function with increasing weight towards the center of blue ice areas.

Our first results indicate that the neural network predicts the location of blue ice relatively well, and that surface elevation data plays an important role in determining the location of blue ice. In our ongoing work, we analyze both the predictions and the neural network itself to quantify which factors posses predictive capacity to explain the location of blue ice. Eventually this information may allow us to answer the simple yet important question of why blue ice areas are located where they are, with potentially important implications for their role as paleoclimate archives and for their evolution under changing climatic conditions.

How to cite: Tollenaar, V., Zekollari, H., Tuia, D., Kellenberger, B., Rußwurm, M., Lhermitte, S., and Pattyn, F.: What determines the location of Antarctic blue ice areas? A deep learning approach, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1294, https://doi.org/10.5194/egusphere-egu22-1294, 2022.

14:36–14:42
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EGU22-3446
Andreas Dietz and Celia Baumhoer and the AI-CORE Team

Artificial Intelligence for Cold Regions (AI-CORE) is a collaborative approach for applying Artificial Intelligence (AI) methods in the field of remote sensing of the cryosphere. Several research institutes (German Aerospace Center, Alfred-Wegener-Institute, Technical University Dresden) bundled their expertise to jointly develop AI-based solutions for pressing geoscientific questions in cryosphere research. The project addresses four geoscientific use cases such as the change pattern identification of outlet glaciers in Greenland, the object identification in permafrost areas, the detection of calving fronts in Antarctica and the firn-line detection on glaciers. Within this presentation, the four AI-based final approaches for each addressed use case will be presented and exemplary results will be shown. Further on, the implementation of all developed AI-methods in three different computer centers was realized and the lessons learned from implementing several ready-to-use AI-tools in different processing infrastructures will be discussed. Finally, a best-practice example for sharing AI-implementations between different institutes is provided along with opportunities and challenges faced during the present project duration.

How to cite: Dietz, A. and Baumhoer, C. and the AI-CORE Team: The AI-CORE Project - Artificial Intelligence for Cold Regions, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3446, https://doi.org/10.5194/egusphere-egu22-3446, 2022.

14:42–14:50
Questions

Fri, 27 May, 15:10–16:40

Chairpersons: Celia A. Baumhoer, Stephen Brough

15:10–15:16
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EGU22-5612
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ECS
Yueli Chen et al.

In the terrestrial cryosphere, freeze/thaw (FT) state transition plays an important and measurable role for climatic, hydrological, ecological, and biogeochemical processes in permafrost landscapes.

Satellite active and passive microwave remote sensing has shown its principal capacity to provide effective monitoring of landscape FT dynamics. Many algorithms have been developed and evaluated over time in this scope. With the advancement of data science and artificial intelligence methods, the potential of better understanding the cryosphere is emerging.

This work is dedicated to exploring an effective approach to retrieve FT state based on microwave remote sensing data using machine learning methods, which is expected to fill in some hidden blind spots in the deterministic algorithms. Time series of remote sensing data will be created as training data. In the initial stage, the work aims to test the feasibility and establish the basic neural network based on fewer training factors. In the advanced stage, we will improve the model in terms of structure, such as adding more complex dense layers and testing optimizers, and in terms of discipline, such as introducing more influencing factors for training. Related parameters, for example, land cover types, will be included in the analysis to improve the method and understanding of FT-related processes.

How to cite: Chen, Y., Wang, L., Bernier, M., and Ludwig, R.: Retrieving freeze/thaw-cycles using Machine Learning approach in Nunavik (Québec, Canada), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5612, https://doi.org/10.5194/egusphere-egu22-5612, 2022.

15:16–15:22
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EGU22-10637
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ECS
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Virtual presentation
Manu Tom et al.

Climate change intensifies glacier melt which effectively leads to the formation of numerous new glacial lakes in the overdeepenings of former glacier beds. Additionally, the area of many existing glacial lakes is increasing. More than one thousand glacial lakes have emerged in Switzerland since the Little Ice Age, and hundreds of lakes are expected to form in the 21st century. Rapid deglaciation and formation of new lakes severely affect downstream ecosystem services, hydropower production and high-alpine hazard situations. Day by day, glacier lake inventories for high-alpine terrains are increasingly becoming available to the research community. However, a high-frequency mapping and monitoring of these lakes are necessary to assess hazards and to estimate Glacial Lake Outburst Flood (GLOF) risks, especially for lakes with high seasonal variations. One way to achieve this goal is to leverage the possibilities of satellite-based remote sensing, using optical and Synthetic Aperture Radar (SAR) satellite sensors and deep learning.

There are several challenges to be tackled. Mapping glacial lakes using satellite sensors is difficult, due to the very small area of a great majority of these lakes. The inability of the optical sensors (e.g. Sentinel-2) to sense through clouds creates another bottleneck. Further challenges include cast and cloud shadows, and increased levels of lake and atmospheric turbidity. Radar sensors (e.g. Sentinel-1 SAR) are unaffected by cloud obstruction. However, handling cast shadows and natural backscattering variations from water surfaces are hurdles in SAR-based monitoring. Due to these sensor-specific limitations, optical sensors provide generally less ambiguous but temporally irregular information, while SAR data provides lower classification accuracy but without cloud gaps.

We propose a deep learning-based SAR-optical satellite data fusion pipeline that merges the complementary information from both sensors. We put forward to use Sentinel-1 SAR and Sentinel-2 L2A imagery as input to a deep network with a Convolutional Neural Network (CNN) backbone. The proposed pipeline performs a fusion of information from the two input branches that feed heterogeneous satellite data. A shared block learns embeddings (feature representation) invariant to the input satellite type, which are then fused to guide the identification of glacial lakes. Our ultimate aim is to produce geolocated maps of the target regions where the proposed bottom-up, data-driven methodology will classify each pixel either as lake or background.

This work is part of two major projects: ESA AlpGlacier project that targets mapping and monitoring of the glacial lakes in the Swiss (and European) Alps, and the UNESCO (Adaptation Fund) GLOFCA project that aims to reduce the vulnerabilities of populations in the Central Asian countries (Kazakhstan, Tajikistan, Uzbekistan, and Kyrgyzstan) from GLOFs in a changing climate. As part of the GLOFCA project, we are developing a python-based analytical toolbox for the local authorities, which incorporates the proposed deep learning-based pipeline for mapping and monitoring the glacial lakes in the target regions in Central Asia.

How to cite: Tom, M., Frey, H., and Odermatt, D.: A deep learning approach for mapping and monitoring glacial lakes from space, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10637, https://doi.org/10.5194/egusphere-egu22-10637, 2022.

15:22–15:28
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EGU22-2904
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ECS
Saurabh Kaushik et al.

The Himalayan glacierized region has experienced a substantial rise in number and area of glacial lakes in the past two decades. These glacial lakes directly influence glacier melt, velocity, geometry, and thus overall response of the glacier to climate change. The sudden release of water from these glacial lakes poses a severe threat to downstream communities and infrastructure. Thereby, regular monitoring and modelling of these lakes bear significance in order to understand regional climate change, and mitigating the anticipated impact of glacial lake outburst flood. Here, we proposed an automated scheme for Himalayan glacial lake extent mapping using multisource remote sensing data and a state-of-the-art deep learning technique. A combination of multisource remote sensing data [Synthetic Aperture Radar (SAR) coherence, thermal, visible, near-infrared, shortwave infrared, Advanced Land Observing Satellite (ALOS) DEM, surface slope and Normalised Difference Water Index (NDWI)] is used as input to a fully connected feed-forward Convolutional Neural Network (CNN). The CNN is trained on 660 images (300×300×10) collected from 11 sites spread across Himalaya. The CNN architecture is designed for choosing optimum size, number of hidden layers, convolutional layers, filters, and other hypermeters using hit and trial method. The model performance is evaluated over 3 different sites of Eastern Himalaya, representing heterogenous landscapes. The novelty of the presented automated scheme lies in its spatio-temporal transferability over the large geographical region (~8477, 10336 and 6013 km2). The future work involves Intra-annual lake extent mapping across High-Mountain Asian region in an automated fashion.

Keywords: Glacial Lake, convolutional neural network, semantic segmentation, remote sensing, Himalaya, SAR and climate change

How to cite: Kaushik, S., Singh, T., Joshi, P. K., and Dietz, A. J.: Automated mapping of Eastern Himalayan glacial lakes using deep learning and multisource remote sensing data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2904, https://doi.org/10.5194/egusphere-egu22-2904, 2022.

15:28–15:33
Questions

15:33–15:39
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EGU22-3701
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ECS
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Highlight
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Virtual presentation
Matteo Guidicelli et al.

The scarcity and limited accuracy of snow and precipitation observation and estimation in high-mountain regions reduce our understanding of climatic-cryospheric processes. Thus, we compared the snow water equivalent (SWE) from winter mass balance observations of 95 glaciers distributed over the Alps, Canada, Central Asia and Scandinavia, with the cumulative gridded precipitation data from the ERA-5 and the MERRA-2 reanalysis products. We propose a machine learning model to downscale the gridded precipitation from the reanalyses to the altitude of the glaciers. The machine learning model is a gradient boosting regressor (GBR), which combines several meteorological variables from the reanalyses (air temperature and relative humidity are also downscaled to the altitude of the glaciers) and topographical parameters. Among the most important variables selected by the GBR model, are the downscaled relative humidity and the downscaled air temperature. These GBR-derived estimates are evaluated against the winter mass balance observations 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 estimates downscaled by the GBR show lower biases and higher correlations with the winter mass balance observations than downscaled estimates derived with a lapse-rate-based approach. Finally, the GBR estimates are used to derive SWE trends between 1981 and 2021 at high-altitudes. The trends obtained from the GBRs are more enhanced than those obtained from the gridded precipitation of the reanalyses. When the data is regrouped regionwide, significant trends are only observed for the Alps (positive) and for Scandinavia (negative), while significant positive or negative trends are observed in all the regions when looking locally at single glaciers and specific elevations. Positive (negative) SWE trends are typically observed at higher (lower) elevations, where the impact of rising temperatures is less (more) dominating.

How to cite: Guidicelli, M., Gabella, M., Huss, M., and Salzmann, N.: Snow accumulation over the world's glaciers (1981-2021) inferred from climate reanalyses and machine learning, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3701, https://doi.org/10.5194/egusphere-egu22-3701, 2022.

15:39–15:45
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EGU22-12882
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ECS
Joel Perez Ferrer et al.

We have recently applied an objective mapping type approach to merge observations from multiple altimeters, both for enhancing the temporal/spatial resolution of freeboard samples and for analyzing crossovers between satellites (Gregory et al, 2021). This mapping provides optimal interpolation of proximal observations to a location in space and time based on the covariance of the observations and a priori understanding of their spatiotemporal correlation length scales. This offers a best linear estimator and error field for the observation (radar freeboard or snow depth), which can be used to better constrain pan-Arctic uncertainties. 

 

In addition we will explore here a newly developed inverse modelling framework  to synchronously retrieve the snow and ice thickness from bias corrected or calibrated radar freeboards from multiple satellite retrievals. The radar equations expressed in section can be rearranged to formulate the joint forward model at gridded level relating measured radar freeboards from multiple satellites (and airborne data) to the underlying snow and ice thickness. In doing so we have also introduced a penetration factor correction term for OIB radar freeboard measurements. To solve this inverse model problem for  and  we use the following two methodologies inspired from Earth Sciences applications (i.e. seismology):  

 

Space ‘uncorrelated’ inverse modelling. The method is called `space uncorrelated' inverse modelling as the algorithm is applied locally, for small distinct regions in the Arctic Ocean, multiple times, until the entire Arctic ocean is covered. To sample the parameter space  we use the publicly available Neighbourhoud Algorithm (NA) developed originally for seismic tomography of Earth’s interior and recently by us to a sea ice dynamic inversion problem (Hoerton et al, 2019).   

 

Space ‘correlated inverse modelling. For the second method of inverse modelling, we used what we call a `space correlated' approach. Here the main algorithm is applied over the entire Arctic region, aiming to retrieve the desired parameters at once. In contrast with the previous approach, in this method we take into account positional correlations for the physical parameters when we are solving the inverse problem, the output being a map of the Arctic composed of a dynamically generated a tiling in terms of Voronoi cells. In that way, regions with less accurate observations will be more coarsely resolved while highly sampled regions will be provided on a finer grid with a smaller uncertainty. The main algorithm used here to calculate the posterior solution is called `reverse jump Monte Carlo Markov Chain' (hereafter referred to as rj-MCMC) and its concept was designed by Peter Green in 1999 (Green, 1995). Bodin and Sambridge (2009) adapted this algorithm for seismic inversion, which is the basis of the algorithm used in this study.  

 

How to cite: Perez Ferrer, J., Tsamados, M., Fox, M., Suciu, T., Heorton, H., and Nab, C.: Inverse modelling techniques for snow and ice thickness retrievals from satellite altimetry  , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12882, https://doi.org/10.5194/egusphere-egu22-12882, 2022.

15:45–15:51
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EGU22-5317
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ECS
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Virtual presentation
Ritu Anilkumar et al.

The last few years have seen an increasing number of studies modeling glacier evolution using deep learning. Most of these techniques have focussed on artificial neural networks (ANN) that are capable of providing a regressed value of mass balance using topographic and meteorological input features. The large number of parameters in an ANN demands a large dataset for training the parameter values. This is relatively difficult to achieve for regions with a sparse in-situ data measurement set up such as the Himalayas. For example, of the 14326 point mass balance measurements obtained from the Fluctuations of Glaciers database for the period of 1950-2020 for glaciers between 60S and 60N, a mere 362 points over four glaciers exist for the Himalayan region. These are insufficient to train complex neural network architectures over the region. We attempt to overcome this data hurdle by using transfer learning. Here, the parameters are first trained over the 9584 points in the Alps following which the weights were used for retraining for the Himalayan data points. Fourteen meteorological from the ERA5Land monthly averaged reanalysis data were used as input features for the study. A 70-30 split of the training and testing set was maintained to ensure the authenticity of the accuracy estimates via independent testing. Estimates are assessed on a glacier scale in the temporal domain to assess the feasibility of using deep learning to fill temporal gaps in data. Our method is also compared with other machine learning algorithms such as random forest-based regression and support vector-based regression and we observe that the complexity of the dataset is better represented by the neural network architecture. With an overall normalized root mean squared loss consistently less than 0.09, our results suggest the capability of deep learning to fill the temporal data gaps over the glaciers and potentially reduce the spatial gap on a regional scale.

How to cite: Anilkumar, R., Bharti, R., and Chutia, D.: Point Mass Balance Regression using Deep Neural Networks: A Transfer Learning Approach, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5317, https://doi.org/10.5194/egusphere-egu22-5317, 2022.

15:51–15:56
Questions

15:56–16:02
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EGU22-9753
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ECS
Mathias Jensen et al.

In recent years, the importance of dynamics in the Arctic Ocean have proven itself with respect to climate monitoring and modelling. Data used for creating models often include temperature & salinity profiles. Such profiles in the Arctic region are sparse and acquiring new data is expensive and time-consuming. Thus, efficient methods of interpolation are necessary to expand regional data. In this project, 3D temperature & salinity profiles are reconstructed using 2D surface measurements from ships, floats and satellites. The technique is based on a stacked Long Short-Term Memory (LSTM) neural network. The goal is to be able to reconstruct the profiles using remotely sensed data.

How to cite: Jensen, M., Bang-Hansen, C., Andersen, O. B., Ludwigsen, C. B., and Ehrhorn, M.: Using LSTM on surface data to reconstruct 3D Temperature & Salinity profiles in the Arctic Ocean, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9753, https://doi.org/10.5194/egusphere-egu22-9753, 2022.

16:02–16:08
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EGU22-5910
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ECS
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Virtual presentation
Tobias Finn et al.

In this talk, we propose to use neural networks in a hybrid modelling setup to learn sub-grid-scale dynamics of sea-ice that cannot be resolved by geophysical models. The multifractal and stochastic nature of the sea-ice dynamics create significant obstacles to represent such dynamics with neural networks. Here, we will introduce and screen specific neural network architectures that might be suited for this kind of task. To prove our concept, we perform idealised twin experiments in a simplified Maxwell-Elasto-Brittle sea-ice model which includes only sea-ice dynamics within a channel-like setup. In our experiments, we use high-resolution runs as proxy for the reality, and we train neural networks to correct errors of low-resolution forecast runs.

Since we perform the two kind of runs on different grids, we need to define a projection operator from high- to low-resolution. In practice, we compare the low-resolution forecasted state at a given time to the projected state of the high resolution run at the same time. Using a catalogue of these forecasted and projected states, we will learn and screen different neural network architectures with supervised training in an offline learning setting. Together with this simplified training, the screening helps us to select appropriate architectures for the representation of multifractality and stochasticity within the sea-ice dynamics. As a next step, these screened architectures have to be scaled to larger and more complex sea-ice models like neXtSIM.

How to cite: Finn, T., Durand, C., Farchi, A., Bocquet, M., Chen, Y., Carrassi, A., and Dansereau, V.: Learning and screening of neural networks architectures for sub-grid-scale parametrizations of sea-ice dynamics from idealised twin experiments, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5910, https://doi.org/10.5194/egusphere-egu22-5910, 2022.

16:08–16:14
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EGU22-10386
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ECS
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On-site presentation
Matteo Sangiorgio et al.

Machine Learning (ML) has become an increasingly popular tool to model the evolution of sea ice in the Arctic region. ML tools produce highly accurate and computationally efficient forecasts on specific tasks. Yet, they generally lack physical interpretability and do not support the understanding of system dynamics and interdependencies among target variables and driving factors.

Here, we present a 2-step framework to model Arctic sea ice dynamics with the aim of balancing high performance and accuracy typical of ML and result interpretability. We first use time series clustering to obtain homogeneous subregions of sea ice spatiotemporal variability. Then, we run an advanced feature selection algorithm, called Wrapper for Quasi Equally Informative Subset Selection (W-QEISS), to process the sea ice time series barycentric of each cluster. W-QEISS identifies neural predictors (i.e., extreme learning machines) of the future evolution of the sea ice based on past values and returns the most relevant set of input variables to describe such evolution.

Monthly output from the Pan-Arctic Ice-Ocean Modeling and Assimilation System (PIOMAS)  from 1978 to 2020 is used for the entire Arctic region. Sea ice thickness represents the target of our analysis, while sea ice concentration, snow depth, sea surface temperature and salinity are considered as candidate drivers.

Results show that autoregressive terms have a key role in the short term (with lag time 1 and 2 months) as well as the long term (i.e., in the previous year); salinity along the Siberian coast is frequently selected as a key driver, especially with a one-year lag; the effect of sea surface temperature is stronger in the clusters with thinner ice; snow depth is relevant only in the short term.

The proposed framework is an efficient support tool to better understand the physical process driving the evolution of sea ice in the Arctic region.

How to cite: Sangiorgio, M., Bianco, E., Iovino, D., Materia, S., and Castelletti, A.: Arctic sea ice dynamics forecasting through interpretable machine learning, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10386, https://doi.org/10.5194/egusphere-egu22-10386, 2022.

16:14–16:20
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EGU22-8945
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ECS
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Virtual presentation
Julia Kaltenborn et al.

Ice lead analysis is an essential task for evaluating climate change processes in the Arctic. Ice leads are narrow cracks in the sea-ice, which build a complex network. While detecting and modeling ice leads has been performed in numerous ways based on airborne images, the dynamics of ice leads over time remain hidden and largely unexplored. These dynamics could be analyzed by interpreting the ice leads as more than just airborne images, but as what they really are: a dynamic network. The lead’s start, end, and intersection points can be considered nodes, and the leads themselves as edges of a network. As the nodes and edges change over time, the ice lead network is constantly evolving. This new network perspective on ice leads could be of great interest for the cryospheric science community since it opens the door to new methods. For example, adapting common link prediction methods might make data-driven ice lead forecasting and tracking feasible.
To reveal the hidden dynamics of ice leads, we performed a spatio-temporal and network analysis of ice lead networks. The networks used and presented here are based on daily ice lead observations from Moderate Resolution Imaging Spectroradiometer (MODIS) between 2002 and 2020 by Hoffman et al. [1].
The spatio-temporal analysis of the ice leads exhibits seasonal, annual, and overall trends in the ice lead dynamics. We found that the number of ice leads is decreasing, and the number of width and length outliers is increasing overall. The network analysis of the ice lead graphs reveals unique network characteristics that diverge from those present in common real-world networks. Most notably, current network science methods (1) exploit the information that is embedded into the connections of the network, e.g., in connection clusters, while (2) nodes remain relatively fixed over time. Ice lead networks, however, (1) embed their relevant information spatially, e.g., in spatial clusters, and (2) shift and change drastically. These differences require improvements and modifications on common graph classification and link prediction methods such as Preferential Attachment and EvolveGCN on the domain of ice lead dynamic networks.
This work is a call for extending existing network analysis toolkits to include a new class of real-world dynamic networks. Utilizing network science techniques will hopefully further our understanding of ice leads and thus of Arctic processes that are key to climate change mitigation and adaptation.

Acknowledgments

We would like to thank Prof. Gunnar Spreen, who provided us insights into ice lead detection and possible challenges connected to the project idea. Furthermore, we would like to thank Shenyang Huang and Asst. Prof. David Rolnick for their valuable feedback and support. J.K. was supported in part by the DeepMind scholarship, the Mitacs Globalink Graduate Fellowship, and the German Academic Scholarship Foundation.

References

[1] Jay P Hoffman, Steven A Ackerman, Yinghui Liu, and Jeffrey R Key. 2019. The detection and characterization of Arctic sea ice leads with satellite imagers. Remote Sensing 11, 5 (2019), 521.

How to cite: Kaltenborn, J., Ramesh, V., and Wright, T.: Ice Lead Network Analysis, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8945, https://doi.org/10.5194/egusphere-egu22-8945, 2022.

16:20–16:28
Questions

16:28–16:40
Overall Session Discussion & Wrap up