Articles | Volume 17, issue 7
https://doi.org/10.5194/tc-17-2965-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-17-2965-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
21 Jul 2023
Research article |
| 21 Jul 2023
| 21 Jul 2023
Deep learning subgrid-scale parametrisations for short-term forecasting of sea-ice dynamics with a Maxwell elasto-brittle rheology
Tobias Sebastian Finn
CORRESPONDING AUTHOR
CEREA, École des Ponts and EDF R&D, Île-de-France, France
Charlotte Durand
CEREA, École des Ponts and EDF R&D, Île-de-France, France
Alban Farchi
CEREA, École des Ponts and EDF R&D, Île-de-France, France
Marc Bocquet
CEREA, École des Ponts and EDF R&D, Île-de-France, France
Yumeng Chen
Dept. of Meteorology and NCEO, University of Reading, Reading, United Kingdom
Alberto Carrassi
Dept. of Meteorology and NCEO, University of Reading, Reading, United Kingdom
Dept. of Physics and Astronomy “Augusto Righi”, University of Bologna, Bologna, Italy
Véronique Dansereau
Laboratoire 3SR, Grenoble INP, CNRS, Université Grenoble Alpes, Grenoble, France
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Joffrey Dumont Le Brazidec, Marc Bocquet, Olivier Saunier, and Yelva Roustan
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Joffrey Dumont Le Brazidec, Marc Bocquet, Olivier Saunier, and Yelva Roustan
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Yumeng Chen, Konrad Simon, and Jörn Behrens
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Mesh adaptivity can reduce overall model error by only refining meshes in specific areas where it us necessary in the runtime. Here we suggest a way to integrate mesh adaptivity into an existing Earth system model, ECHAM6, without having to redesign the implementation from scratch. We show that while the additional computational effort is manageable, the error can be reduced compared to a low-resolution standard model using an idealized test and relatively realistic dust transport tests.
Einar Ólason, Pierre Rampal, and Véronique Dansereau
The Cryosphere, 15, 1053–1064, https://doi.org/10.5194/tc-15-1053-2021, https://doi.org/10.5194/tc-15-1053-2021, 2021
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We analyse the fractal properties observed in the pattern of the long, narrow openings that form in Arctic sea ice known as leads. We use statistical tools to explore the fractal properties of the lead fraction observed in satellite data and show that our sea-ice model neXtSIM displays the same behaviour. Building on this result we then show that the pattern of heat loss from ocean to atmosphere in the model displays similar fractal properties, stemming from the fractal properties of the leads.
Tobias Sebastian Finn, Gernot Geppert, and Felix Ament
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2020-672, https://doi.org/10.5194/hess-2020-672, 2021
Revised manuscript not accepted
Short summary
Short summary
Through the lens of recent developments in hydrological modelling and data assimilation, we hourly update the soil moisture with ensemble data assimilation and sparse 2-metre-temperature observations in a coupled limited area model system. In idealized experiments, we improve the soil moisture analysis by coupled data assimilation across the atmosphere-land interface. We conclude that we can merge the separated assimilation cycles for the atmosphere and land surface into one single cycle.
Colin Grudzien, Marc Bocquet, and Alberto Carrassi
Geosci. Model Dev., 13, 1903–1924, https://doi.org/10.5194/gmd-13-1903-2020, https://doi.org/10.5194/gmd-13-1903-2020, 2020
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All scales of a dynamical physical process cannot be resolved accurately in a multiscale, geophysical model. The behavior of unresolved scales of motion are often parametrized by a random process to emulate their effects on the dynamically resolved variables, and this results in a random–dynamical model. We study how the choice of a numerical discretization of such a system affects the model forecast and estimation statistics, when the random–dynamical model is unbiased in its parametrization.
Francine Schevenhoven, Frank Selten, Alberto Carrassi, and Noel Keenlyside
Earth Syst. Dynam., 10, 789–807, https://doi.org/10.5194/esd-10-789-2019, https://doi.org/10.5194/esd-10-789-2019, 2019
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Weather and climate predictions potentially improve by dynamically combining different models into a
supermodel. A crucial step is to train the supermodel on the basis of observations. Here, we apply two different training methods to the global atmosphere–ocean–land model SPEEDO. We demonstrate that both training methods yield climate and weather predictions of superior quality compared to the individual models. Supermodel predictions can also outperform the commonly used multi-model mean.
Thomas Lauvaux, Liza I. Díaz-Isaac, Marc Bocquet, and Nicolas Bousserez
Atmos. Chem. Phys., 19, 12007–12024, https://doi.org/10.5194/acp-19-12007-2019, https://doi.org/10.5194/acp-19-12007-2019, 2019
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A small-size ensemble of mesoscale simulations has been filtered to characterize the spatial structures of transport errors in atmospheric CO2 mixing ratios. The extracted error structures in in situ and column CO2 show similar length scales compared to other meteorological variables, including seasonality, which could be used as proxies in regional inversion systems.
Pierre Rampal, Véronique Dansereau, Einar Olason, Sylvain Bouillon, Timothy Williams, Anton Korosov, and Abdoulaye Samaké
The Cryosphere, 13, 2457–2474, https://doi.org/10.5194/tc-13-2457-2019, https://doi.org/10.5194/tc-13-2457-2019, 2019
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Ali Aydoğdu, Alberto Carrassi, Colin T. Guider, Chris K. R. T Jones, and Pierre Rampal
Nonlin. Processes Geophys., 26, 175–193, https://doi.org/10.5194/npg-26-175-2019, https://doi.org/10.5194/npg-26-175-2019, 2019
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Computational models involving adaptive meshes can both evolve dynamically and be remeshed. Remeshing means that the state vector dimension changes in time and across ensemble members, making the ensemble Kalman filter (EnKF) unsuitable for assimilation of observational data. We develop a modification in which analysis is performed on a fixed uniform grid onto which the ensemble is mapped, with resolution relating to the remeshing criteria. The approach is successfully tested on two 1-D models.
Marc Bocquet, Julien Brajard, Alberto Carrassi, and Laurent Bertino
Nonlin. Processes Geophys., 26, 143–162, https://doi.org/10.5194/npg-26-143-2019, https://doi.org/10.5194/npg-26-143-2019, 2019
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This paper describes an innovative way to use data assimilation to infer the dynamics of a physical system from its observation only. The method can operate with noisy and partial observation of the physical system. It acts as a deep learning technique specialised to dynamical models without the need for machine learning tools. The method is successfully tested on chaotic dynamical systems: the Lorenz-63, Lorenz-96, and Kuramoto–Sivashinski models and a two-scale Lorenz model.
Julien Brajard, Alberto Carrassi, Marc Bocquet, and Laurent Bertino
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2019-136, https://doi.org/10.5194/gmd-2019-136, 2019
Revised manuscript not accepted
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We explore the possibility of combining data assimilation with machine learning. We introduce a new hybrid method for a two-fold scope: (i) emulating hidden, possibly chaotic, dynamics and (ii) predicting its future states. Numerical experiments have been carried out using the chaotic Lorenz 96 model, proving both the convergence of the hybrid method and its statistical skills including short-term forecasting and emulation of the long-term dynamics.
Liza I. Díaz-Isaac, Thomas Lauvaux, Marc Bocquet, and Kenneth J. Davis
Atmos. Chem. Phys., 19, 5695–5718, https://doi.org/10.5194/acp-19-5695-2019, https://doi.org/10.5194/acp-19-5695-2019, 2019
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We demonstrate that transport model errors, one of the main contributors to the uncertainty in regional CO2 inversions, can be represented by a small-size ensemble carefully calibrated with meteorological data. Our results also confirm transport model errors represent a significant fraction of the model–data mismatch in CO2 mole fractions and hence in regional inverse CO2 fluxes.
Alban Farchi and Marc Bocquet
Nonlin. Processes Geophys., 25, 765–807, https://doi.org/10.5194/npg-25-765-2018, https://doi.org/10.5194/npg-25-765-2018, 2018
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Data assimilation looks for an optimal way to learn from observations of a dynamical system to improve the quality of its predictions. The goal is to filter out the noise (both observation and model noise) to retrieve the true signal. Among all possible methods, particle filters are promising; the method is fast and elegant, and it allows for a Bayesian analysis. In this review paper, we discuss implementation techniques for (local) particle filters in high-dimensional systems.
Colin Grudzien, Alberto Carrassi, and Marc Bocquet
Nonlin. Processes Geophys., 25, 633–648, https://doi.org/10.5194/npg-25-633-2018, https://doi.org/10.5194/npg-25-633-2018, 2018
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Using the framework Lyapunov vectors, we analyze the asymptotic properties of ensemble based Kalman filters and how these are influenced by dynamical chaos, especially in the context of random model errors and small ensemble sizes. Particularly, we show a novel derivation of the evolution of forecast uncertainty for ensemble-based Kalman filters with weakly-nonlinear error growth, and discuss its impact for filter design in geophysical models.
Olivier Pannekoucke, Marc Bocquet, and Richard Ménard
Nonlin. Processes Geophys., 25, 481–495, https://doi.org/10.5194/npg-25-481-2018, https://doi.org/10.5194/npg-25-481-2018, 2018
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The forecast of weather prediction uncertainty is a real challenge and is crucial for risk management. However, uncertainty prediction is beyond the capacity of supercomputers, and improvements of the technology may not solve this issue. A new uncertainty prediction method is introduced which takes advantage of fluid equations to predict simple quantities which approximate real uncertainty but at a low numerical cost. A proof of concept is shown by an academic model derived from fluid dynamics.
Anthony Fillion, Marc Bocquet, and Serge Gratton
Nonlin. Processes Geophys., 25, 315–334, https://doi.org/10.5194/npg-25-315-2018, https://doi.org/10.5194/npg-25-315-2018, 2018
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This study generalizes a paper by Pires et al. (1996) to state-of-the-art data assimilation techniques, such as the iterative ensemble Kalman smoother (IEnKS). We show that the longer the time window over which observations are assimilated, the better the accuracy of the IEnKS. Beyond a critical time length that we estimate, we show that this accuracy finally degrades. We show that the use of the quasi-static minimizations but generalized to the IEnKS yields a significantly improved accuracy.
Matthias Rabatel, Pierre Rampal, Alberto Carrassi, Laurent Bertino, and Christopher K. R. T. Jones
The Cryosphere, 12, 935–953, https://doi.org/10.5194/tc-12-935-2018, https://doi.org/10.5194/tc-12-935-2018, 2018
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Large deviations still exist between sea ice forecasts and observations because of both missing physics in models and uncertainties on model inputs. We investigate how the new sea ice model neXtSIM is sensitive to uncertainties in the winds. We highlight and quantify the role of the internal forces in the ice on this sensitivity and show that neXtSIM is better at predicting sea ice drift than a free-drift (without internal forces) ice model and is a skilful tool for search and rescue operations.
Véronique Dansereau, Jérôme Weiss, Pierre Saramito, Philippe Lattes, and Edmond Coche
The Cryosphere, 11, 2033–2058, https://doi.org/10.5194/tc-11-2033-2017, https://doi.org/10.5194/tc-11-2033-2017, 2017
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A new mechanical framework is used to model the drift of sea ice in a narrow channel between Greenland and Ellesmere Island. It is able to reproduce its main features : curved cracks, ice “bridges” that stop the flow of ice for several months of the year and some thick, strongly localized ridged ice. The simulations suggest that a mechanical weakening of the sea ice cover can shorten the lifespan of ice bridges and result in an increased export of ice through the narrow channels of the Arctic.
Véronique Dansereau, Jérôme Weiss, Pierre Saramito, and Philippe Lattes
The Cryosphere, 10, 1339–1359, https://doi.org/10.5194/tc-10-1339-2016, https://doi.org/10.5194/tc-10-1339-2016, 2016
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In this paper we present a new mechanical modelling framework for the deformation of sea ice on regional and larger scales named Maxwell elasto-brittle. The model successfully reproduces the formation of narrow, oriented leads which concentrate the deformation within the damaged, i.e., fractured, ice as well as the intermittency of the damaging process, and hence represents a relevant contribution to the ongoing development of operational modelling platforms, regional and global climate models.
J.-M. Haussaire and M. Bocquet
Geosci. Model Dev., 9, 393–412, https://doi.org/10.5194/gmd-9-393-2016, https://doi.org/10.5194/gmd-9-393-2016, 2016
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The focus is on the development of low-order models of atmospheric transport and chemistry and their use for data assimilation purposes. A new low-order coupled chemistry meteorology model is developed. It consists of the Lorenz40-variable model used as a wind field coupled with a simple ozone photochemistry module. Advanced ensemble variational methods are applied to this model to obtain insights on the use of data assimilation with coupled models, in an offline mode or in an online mode.
M. Bocquet, P. N. Raanes, and A. Hannart
Nonlin. Processes Geophys., 22, 645–662, https://doi.org/10.5194/npg-22-645-2015, https://doi.org/10.5194/npg-22-645-2015, 2015
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The popular data assimilation technique known as the ensemble Kalman filter (EnKF) suffers from sampling errors due to the limited size of the ensemble. This deficiency is usually cured by inflating the sampled error covariances and by using localization. This paper further develops and discusses the finite-size EnKF, or EnKF-N, a variant of the EnKF that does not require inflation. It expands the use of the EnKF-N to a wider range of dynamical regimes.
M. Bocquet, H. Elbern, H. Eskes, M. Hirtl, R. Žabkar, G. R. Carmichael, J. Flemming, A. Inness, M. Pagowski, J. L. Pérez Camaño, P. E. Saide, R. San Jose, M. Sofiev, J. Vira, A. Baklanov, C. Carnevale, G. Grell, and C. Seigneur
Atmos. Chem. Phys., 15, 5325–5358, https://doi.org/10.5194/acp-15-5325-2015, https://doi.org/10.5194/acp-15-5325-2015, 2015
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Data assimilation is used in atmospheric chemistry models to improve air quality forecasts, construct re-analyses of concentrations, and perform inverse modeling. Coupled chemistry meteorology models (CCMM) are atmospheric chemistry models that simulate meteorological processes and chemical transformations jointly. We review here the current status of data assimilation in atmospheric chemistry models, with a particular focus on future prospects for data assimilation in CCMM.
Y. Wang, K. N. Sartelet, M. Bocquet, P. Chazette, M. Sicard, G. D'Amico, J. F. Léon, L. Alados-Arboledas, A. Amodeo, P. Augustin, J. Bach, L. Belegante, I. Binietoglou, X. Bush, A. Comerón, H. Delbarre, D. García-Vízcaino, J. L. Guerrero-Rascado, M. Hervo, M. Iarlori, P. Kokkalis, D. Lange, F. Molero, N. Montoux, A. Muñoz, C. Muñoz, D. Nicolae, A. Papayannis, G. Pappalardo, J. Preissler, V. Rizi, F. Rocadenbosch, K. Sellegri, F. Wagner, and F. Dulac
Atmos. Chem. Phys., 14, 12031–12053, https://doi.org/10.5194/acp-14-12031-2014, https://doi.org/10.5194/acp-14-12031-2014, 2014
Y. Wang, K. N. Sartelet, M. Bocquet, and P. Chazette
Atmos. Chem. Phys., 14, 3511–3532, https://doi.org/10.5194/acp-14-3511-2014, https://doi.org/10.5194/acp-14-3511-2014, 2014
O. Saunier, A. Mathieu, D. Didier, M. Tombette, D. Quélo, V. Winiarek, and M. Bocquet
Atmos. Chem. Phys., 13, 11403–11421, https://doi.org/10.5194/acp-13-11403-2013, https://doi.org/10.5194/acp-13-11403-2013, 2013
M. Bocquet and P. Sakov
Nonlin. Processes Geophys., 20, 803–818, https://doi.org/10.5194/npg-20-803-2013, https://doi.org/10.5194/npg-20-803-2013, 2013
M. R. Koohkan, M. Bocquet, Y. Roustan, Y. Kim, and C. Seigneur
Atmos. Chem. Phys., 13, 5887–5905, https://doi.org/10.5194/acp-13-5887-2013, https://doi.org/10.5194/acp-13-5887-2013, 2013
Y. Wang, K. N. Sartelet, M. Bocquet, and P. Chazette
Atmos. Chem. Phys., 13, 269–283, https://doi.org/10.5194/acp-13-269-2013, https://doi.org/10.5194/acp-13-269-2013, 2013
Related subject area
Discipline: Sea ice | Subject: Numerical Modelling
A hybrid ice-mélange model based on particle and continuum methods
Combining observational data and numerical models to obtain a seamless high temporal resolution seasonal cycle of snow and ice mass balance at the MOSAiC Central Observatory
How many parameters are needed to represent polar sea ice surface patterns and heterogeneity?
Exploring non-Gaussian sea ice characteristics via observing system simulation experiments
Past and future of the Arctic sea ice in High-Resolution Model Intercomparison Project (HighResMIP) climate models
Data-driven surrogate modeling of high-resolution sea-ice thickness in the Arctic
Using Icepack to reproduce ice mass balance buoy observations in landfast ice: improvements from the mushy-layer thermodynamics
Understanding the influence of ocean waves on Arctic sea ice simulation: a modeling study with an atmosphere–ocean–wave–sea ice coupled model
Sea ice cover in the Copernicus Arctic Regional Reanalysis
Smoothed particle hydrodynamics implementation of the standard viscous–plastic sea-ice model and validation in simple idealized experiments
Phase-field models of floe fracture in sea ice
The effect of partial dissolution on sea-ice chemical transport: a combined model–observational study using poly- and perfluoroalkylated substances (PFASs)
Impact of atmospheric forcing uncertainties on Arctic and Antarctic sea ice simulations in CMIP6 OMIP models
Arctic sea ice mass balance in a new coupled ice–ocean model using a brittle rheology framework
Wave-triggered breakup in the marginal ice zone generates lognormal floe size distributions: a simulation study
Exploring the capabilities of electrical resistivity tomography to study subsea permafrost
Sea ice floe size: its impact on pan-Arctic and local ice mass and required model complexity
A probabilistic seabed–ice keel interaction model
The effect of changing sea ice on wave climate trends along Alaska's central Beaufort Sea coast
Arctic sea ice anomalies during the MOSAiC winter 2019/20
Edge displacement scores
Toward a method for downscaling sea ice pressure for navigation purposes
The Arctic Ocean Observation Operator for 6.9 GHz (ARC3O) – Part 1: How to obtain sea ice brightness temperatures at 6.9 GHz from climate model output
The Arctic Ocean Observation Operator for 6.9 GHz (ARC3O) – Part 2: Development and evaluation
Feature-based comparison of sea ice deformation in lead-permitting sea ice simulations
Wave energy attenuation in fields of colliding ice floes – Part 1: Discrete-element modelling of dissipation due to ice–water drag
Validation of the sea ice surface albedo scheme of the regional climate model HIRHAM–NAOSIM using aircraft measurements during the ACLOUD/PASCAL campaigns
Simulating intersection angles between conjugate faults in sea ice with different viscous–plastic rheologies
IcePAC – a probabilistic tool to study sea ice spatio-temporal dynamics: application to the Hudson Bay area
New insight from CryoSat-2 sea ice thickness for sea ice modelling
Investigating future changes in the volume budget of the Arctic sea ice in a coupled climate model
Medium-range predictability of early summer sea ice thickness distribution in the East Siberian Sea based on the TOPAZ4 ice–ocean data assimilation system
Saskia Kahl, Carolin Mehlmann, and Dirk Notz
The Cryosphere, 19, 129–141, https://doi.org/10.5194/tc-19-129-2025, https://doi.org/10.5194/tc-19-129-2025, 2025
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Ice mélange, a mixture of sea ice and icebergs, can impact sea-ice–ocean interactions. But climate models do not yet represent it due to computational limits. To address this shortcoming and include ice mélange into climate models, we suggest representing icebergs as particles. We integrate their feedback into mathematical equations used to model the sea-ice motion in climate models. The setup is computationally efficient due to the iceberg particle usage and enables a realistic representation.
Polona Itkin and Glen E. Liston
EGUsphere, https://doi.org/10.5194/egusphere-2024-3402, https://doi.org/10.5194/egusphere-2024-3402, 2024
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The MOSAiC project provided a year of observations of Arctic snow and sea ice, though some data were interrupted, especially during summer melt onset. We developed a data-model fusion system to produce continuous, high-resolution time series of snow and sea ice parameters. On all three analyzed three ice types snow redistribution correlated with sea ice deformation and level ice thickness was governed by the thinnest fraction of snow cover.
Joseph Fogarty, Elie Bou-Zeid, Mitchell Bushuk, and Linette Boisvert
The Cryosphere, 18, 4335–4354, https://doi.org/10.5194/tc-18-4335-2024, https://doi.org/10.5194/tc-18-4335-2024, 2024
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We hypothesize that using a broad set of surface characterization metrics for polar sea ice surfaces will lead to more accurate representations in general circulation models. However, the first step is to identify the minimum set of metrics required. We show via numerical simulations that sea ice surface patterns can play a crucial role in determining boundary layer structures. We then statistically analyze a set of high-resolution sea ice surface images to obtain this minimal set of parameters.
Christopher Riedel and Jeffrey Anderson
The Cryosphere, 18, 2875–2896, https://doi.org/10.5194/tc-18-2875-2024, https://doi.org/10.5194/tc-18-2875-2024, 2024
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Accurate sea ice conditions are crucial for quality sea ice projections, which have been connected to rapid warming over the Arctic. Knowing which observations to assimilate into models will help produce more accurate sea ice conditions. We found that not assimilating sea ice concentration led to more accurate sea ice states. The methods typically used to assimilate observations in our models apply assumptions to variables that are not well suited for sea ice because they are bounded variables.
Julia Selivanova, Doroteaciro Iovino, and Francesco Cocetta
The Cryosphere, 18, 2739–2763, https://doi.org/10.5194/tc-18-2739-2024, https://doi.org/10.5194/tc-18-2739-2024, 2024
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Climate models show differences in sea ice representation in comparison to observations. Increasing the model resolution is a recognized way to improve model realism and obtain more reliable future projections. We find no strong impact of resolution on sea ice representation; it rather depends on the analysed variable and the model used. By 2050, the marginal ice zone (MIZ) becomes a dominant feature of the Arctic ice cover, suggesting a shift to a new regime similar to that in Antarctica.
Charlotte Durand, Tobias Sebastian Finn, Alban Farchi, Marc Bocquet, Guillaume Boutin, and Einar Ólason
The Cryosphere, 18, 1791–1815, https://doi.org/10.5194/tc-18-1791-2024, https://doi.org/10.5194/tc-18-1791-2024, 2024
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This paper focuses on predicting Arctic-wide sea-ice thickness using surrogate modeling with deep learning. The model has a predictive power of 12 h up to 6 months. For this forecast horizon, persistence and daily climatology are systematically outperformed, a result of learned thermodynamics and advection. Consequently, surrogate modeling with deep learning proves to be effective at capturing the complex behavior of sea ice.
Mathieu Plante, Jean-François Lemieux, L. Bruno Tremblay, Adrienne Tivy, Joey Angnatok, François Roy, Gregory Smith, Frédéric Dupont, and Adrian K. Turner
The Cryosphere, 18, 1685–1708, https://doi.org/10.5194/tc-18-1685-2024, https://doi.org/10.5194/tc-18-1685-2024, 2024
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We use a sea ice model to reproduce ice growth observations from two buoys deployed on coastal sea ice and analyze the improvements brought by new physics that represent the presence of saline liquid water in the ice interior. We find that the new physics with default parameters degrade the model performance, with overly rapid ice growth and overly early snow flooding on top of the ice. The performance is largely improved by simple modifications to the ice growth and snow-flooding algorithms.
Chao-Yuan Yang, Jiping Liu, and Dake Chen
The Cryosphere, 18, 1215–1239, https://doi.org/10.5194/tc-18-1215-2024, https://doi.org/10.5194/tc-18-1215-2024, 2024
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We present a new atmosphere–ocean–wave–sea ice coupled model to study the influences of ocean waves on Arctic sea ice simulation. Our results show (1) smaller ice-floe size with wave breaking increases ice melt, (2) the responses in the atmosphere and ocean to smaller floe size partially reduce the effect of the enhanced ice melt, (3) the limited oceanic energy is a strong constraint for ice melt enhancement, and (4) ocean waves can indirectly affect sea ice through the atmosphere and the ocean.
Yurii Batrak, Bin Cheng, and Viivi Kallio-Myers
The Cryosphere, 18, 1157–1183, https://doi.org/10.5194/tc-18-1157-2024, https://doi.org/10.5194/tc-18-1157-2024, 2024
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Atmospheric reanalyses provide consistent series of atmospheric and surface parameters in a convenient gridded form. In this paper, we study the quality of sea ice in a recently released regional reanalysis and assess its added value compared to a global reanalysis. We show that the regional reanalysis, having a more complex sea ice model, gives an improved representation of sea ice, although there are limitations indicating potential benefits in using more advanced approaches in the future.
Oreste Marquis, Bruno Tremblay, Jean-François Lemieux, and Mohammed Islam
The Cryosphere, 18, 1013–1032, https://doi.org/10.5194/tc-18-1013-2024, https://doi.org/10.5194/tc-18-1013-2024, 2024
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We developed a standard viscous–plastic sea-ice model based on the numerical framework called smoothed particle hydrodynamics. The model conforms to the theory within an error of 1 % in an idealized ridging experiment, and it is able to simulate stable ice arches. However, the method creates a dispersive plastic wave speed. The framework is efficient to simulate fractures and can take full advantage of parallelization, making it a good candidate to investigate sea-ice material properties.
Huy Dinh, Dimitrios Giannakis, Joanna Slawinska, and Georg Stadler
The Cryosphere, 17, 3883–3893, https://doi.org/10.5194/tc-17-3883-2023, https://doi.org/10.5194/tc-17-3883-2023, 2023
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We develop a numerical method to simulate the fracture in kilometer-sized chunks of floating ice in the ocean. Our approach uses a mathematical model that balances deformation energy against the energy required for fracture. We study the strength of ice chunks that contain random impurities due to prior damage or refreezing and what types of fractures are likely to occur. Our model shows that crack direction critically depends on the orientation of impurities relative to surrounding forces.
Max Thomas, Briana Cate, Jack Garnett, Inga J. Smith, Martin Vancoppenolle, and Crispin Halsall
The Cryosphere, 17, 3193–3201, https://doi.org/10.5194/tc-17-3193-2023, https://doi.org/10.5194/tc-17-3193-2023, 2023
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A recent study showed that pollutants can be enriched in growing sea ice beyond what we would expect from a perfectly dissolved chemical. We hypothesise that this effect is caused by the specific properties of the pollutants working in combination with fluid moving through the sea ice. To test our hypothesis, we replicate this behaviour in a sea-ice model and show that this type of modelling can be applied to predicting the transport of chemicals with complex behaviour in sea ice.
Xia Lin, François Massonnet, Thierry Fichefet, and Martin Vancoppenolle
The Cryosphere, 17, 1935–1965, https://doi.org/10.5194/tc-17-1935-2023, https://doi.org/10.5194/tc-17-1935-2023, 2023
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This study provides clues on how improved atmospheric reanalysis products influence sea ice simulations in ocean–sea ice models. The summer ice concentration simulation in both hemispheres can be improved with changed surface heat fluxes. The winter Antarctic ice concentration and the Arctic drift speed near the ice edge and the ice velocity direction simulations are improved with changed wind stress. The radiation fluxes and winds in atmospheric reanalyses are crucial for sea ice simulations.
Guillaume Boutin, Einar Ólason, Pierre Rampal, Heather Regan, Camille Lique, Claude Talandier, Laurent Brodeau, and Robert Ricker
The Cryosphere, 17, 617–638, https://doi.org/10.5194/tc-17-617-2023, https://doi.org/10.5194/tc-17-617-2023, 2023
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Sea ice cover in the Arctic is full of cracks, which we call leads. We suspect that these leads play a role for atmosphere–ocean interactions in polar regions, but their importance remains challenging to estimate. We use a new ocean–sea ice model with an original way of representing sea ice dynamics to estimate their impact on winter sea ice production. This model successfully represents sea ice evolution from 2000 to 2018, and we find that about 30 % of ice production takes place in leads.
Nicolas Guillaume Alexandre Mokus and Fabien Montiel
The Cryosphere, 16, 4447–4472, https://doi.org/10.5194/tc-16-4447-2022, https://doi.org/10.5194/tc-16-4447-2022, 2022
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On the fringes of polar oceans, sea ice is easily broken by waves. As small pieces of ice, or floes, are more easily melted by the warming waters than a continuous ice cover, it is important to incorporate these floe sizes in climate models. These models simulate climate evolution at the century scale and are built by combining specialised modules. We study the statistical distribution of floe sizes under the impact of waves to better understand how to connect sea ice modules to wave modules.
Mauricio Arboleda-Zapata, Michael Angelopoulos, Pier Paul Overduin, Guido Grosse, Benjamin M. Jones, and Jens Tronicke
The Cryosphere, 16, 4423–4445, https://doi.org/10.5194/tc-16-4423-2022, https://doi.org/10.5194/tc-16-4423-2022, 2022
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We demonstrate how we can reliably estimate the thawed–frozen permafrost interface with its associated uncertainties in subsea permafrost environments using 2D electrical resistivity tomography (ERT) data. In addition, we show how further analyses considering 1D inversion and sensitivity assessments can help quantify and better understand 2D ERT inversion results. Our results illustrate the capabilities of the ERT method to get insights into the development of the subsea permafrost.
Adam William Bateson, Daniel L. Feltham, David Schröder, Yanan Wang, Byongjun Hwang, Jeff K. Ridley, and Yevgeny Aksenov
The Cryosphere, 16, 2565–2593, https://doi.org/10.5194/tc-16-2565-2022, https://doi.org/10.5194/tc-16-2565-2022, 2022
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Numerical models are used to understand the mechanisms that drive the evolution of the Arctic sea ice cover. The sea ice cover is formed of pieces of ice called floes. Several recent studies have proposed variable floe size models to replace the standard model assumption of a fixed floe size. In this study we show the need to include floe fragmentation processes in these variable floe size models and demonstrate that model design can determine the impact of floe size on size ice evolution.
Frédéric Dupont, Dany Dumont, Jean-François Lemieux, Elie Dumas-Lefebvre, and Alain Caya
The Cryosphere, 16, 1963–1977, https://doi.org/10.5194/tc-16-1963-2022, https://doi.org/10.5194/tc-16-1963-2022, 2022
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In some shallow seas, grounded ice ridges contribute to stabilizing and maintaining a landfast ice cover. A scheme has already proposed where the keel thickness varies linearly with the mean thickness. Here, we extend the approach by taking into account the ice thickness and bathymetry distributions. The probabilistic approach shows a reasonably good agreement with observations and previous grounding scheme while potentially offering more physical insights into the formation of landfast ice.
Kees Nederhoff, Li Erikson, Anita Engelstad, Peter Bieniek, and Jeremy Kasper
The Cryosphere, 16, 1609–1629, https://doi.org/10.5194/tc-16-1609-2022, https://doi.org/10.5194/tc-16-1609-2022, 2022
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Diminishing sea ice is impacting waves across the Arctic region. Recent work shows the effect of the sea ice on offshore waves; however, effects within the nearshore are less known. This study characterizes the wave climate in the central Beaufort Sea coast of Alaska. We show that the reduction of sea ice correlates strongly with increases in the average and extreme waves. However, found trends deviate from offshore, since part of the increase in energy is dissipated before reaching the shore.
Klaus Dethloff, Wieslaw Maslowski, Stefan Hendricks, Younjoo J. Lee, Helge F. Goessling, Thomas Krumpen, Christian Haas, Dörthe Handorf, Robert Ricker, Vladimir Bessonov, John J. Cassano, Jaclyn Clement Kinney, Robert Osinski, Markus Rex, Annette Rinke, Julia Sokolova, and Anja Sommerfeld
The Cryosphere, 16, 981–1005, https://doi.org/10.5194/tc-16-981-2022, https://doi.org/10.5194/tc-16-981-2022, 2022
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Sea ice thickness anomalies during the MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate) winter in January, February and March 2020 were simulated with the coupled Regional Arctic climate System Model (RASM) and compared with CryoSat-2/SMOS satellite data. Hindcast and ensemble simulations indicate that the sea ice anomalies are driven by nonlinear interactions between ice growth processes and wind-driven sea-ice transports, with dynamics playing a dominant role.
Arne Melsom
The Cryosphere, 15, 3785–3796, https://doi.org/10.5194/tc-15-3785-2021, https://doi.org/10.5194/tc-15-3785-2021, 2021
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This study presents new methods to assess how well observations of sea ice expansion are reproduced by results from models. The aim is to provide information about the quality of forecasts for changes in the sea ice extent to operators in or near ice-infested waters. A test using 2 years of model results demonstrates the practical applicability and usefulness of the methods that are presented.
Jean-François Lemieux, L. Bruno Tremblay, and Mathieu Plante
The Cryosphere, 14, 3465–3478, https://doi.org/10.5194/tc-14-3465-2020, https://doi.org/10.5194/tc-14-3465-2020, 2020
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Sea ice pressure poses great risk for navigation; it can lead to ship besetting and damages. Sea ice forecasting systems can predict the evolution of pressure. However, these systems have low spatial resolution (a few km) compared to the dimensions of ships. We study the downscaling of pressure from the km-scale to scales relevant for navigation. We find that the pressure applied on a ship beset in heavy ice conditions can be markedly larger than the pressure predicted by the forecasting system.
Clara Burgard, Dirk Notz, Leif T. Pedersen, and Rasmus T. Tonboe
The Cryosphere, 14, 2369–2386, https://doi.org/10.5194/tc-14-2369-2020, https://doi.org/10.5194/tc-14-2369-2020, 2020
Short summary
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The high disagreement between observations of Arctic sea ice makes it difficult to evaluate climate models with observations. We investigate the possibility of translating the model state into what a satellite could observe. We find that we do not need complex information about the vertical distribution of temperature and salinity inside the ice but instead are able to assume simplified distributions to reasonably translate the simulated sea ice into satellite
language.
Clara Burgard, Dirk Notz, Leif T. Pedersen, and Rasmus T. Tonboe
The Cryosphere, 14, 2387–2407, https://doi.org/10.5194/tc-14-2387-2020, https://doi.org/10.5194/tc-14-2387-2020, 2020
Short summary
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The high disagreement between observations of Arctic sea ice inhibits the evaluation of climate models with observations. We develop a tool that translates the simulated Arctic Ocean state into what a satellite could observe from space in the form of brightness temperatures, a measure for the radiation emitted by the surface. We find that the simulated brightness temperatures compare well with the observed brightness temperatures. This tool brings a new perspective for climate model evaluation.
Nils Hutter and Martin Losch
The Cryosphere, 14, 93–113, https://doi.org/10.5194/tc-14-93-2020, https://doi.org/10.5194/tc-14-93-2020, 2020
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Sea ice is composed of a multitude of floes that constantly deform due to wind and ocean currents and thereby form leads and pressure ridges. These features are visible in the ice as stripes of open-ocean or high-piled ice. High-resolution sea ice models start to resolve these deformation features. In this paper we present two simulations that agree with satellite data according to a new evaluation metric that detects deformation features and compares their spatial and temporal characteristics.
Agnieszka Herman, Sukun Cheng, and Hayley H. Shen
The Cryosphere, 13, 2887–2900, https://doi.org/10.5194/tc-13-2887-2019, https://doi.org/10.5194/tc-13-2887-2019, 2019
Short summary
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Sea ice interactions with waves are extensively studied in recent years, but mechanisms leading to wave energy attenuation in sea ice remain poorly understood. Close to the ice edge, processes contributing to dissipation include collisions between ice floes and turbulence generated under the ice due to velocity differences between ice and water. This paper analyses details of those processes both theoretically and by means of a numerical model.
Evelyn Jäkel, Johannes Stapf, Manfred Wendisch, Marcel Nicolaus, Wolfgang Dorn, and Annette Rinke
The Cryosphere, 13, 1695–1708, https://doi.org/10.5194/tc-13-1695-2019, https://doi.org/10.5194/tc-13-1695-2019, 2019
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The sea ice surface albedo parameterization of a coupled regional climate model was validated against aircraft measurements performed in May–June 2017 north of Svalbard. The albedo parameterization was run offline from the model using the measured parameters surface temperature and snow depth to calculate the surface albedo and the individual fractions of the ice surface subtypes. An adjustment of the variables and additionally accounting for cloud cover reduced the root-mean-squared error.
Damien Ringeisen, Martin Losch, L. Bruno Tremblay, and Nils Hutter
The Cryosphere, 13, 1167–1186, https://doi.org/10.5194/tc-13-1167-2019, https://doi.org/10.5194/tc-13-1167-2019, 2019
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We study the creation of fracture in sea ice plastic models. To do this, we compress an ideal piece of ice of 8 km by 25 km. We use two different mathematical expressions defining the resistance of ice. We find that the most common one is unable to model the fracture correctly, while the other gives better results but brings instabilities. The results are often in opposition with ice granular nature (e.g., sand) and call for changes in ice modeling.
Charles Gignac, Monique Bernier, and Karem Chokmani
The Cryosphere, 13, 451–468, https://doi.org/10.5194/tc-13-451-2019, https://doi.org/10.5194/tc-13-451-2019, 2019
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The IcePAC tool is made to estimate the probabilities of specific sea ice conditions based on historical sea ice concentration time series from the EUMETSAT OSI-409 product (12.5 km grid), modelled using the beta distribution and used to build event probability maps, which have been unavailable until now. Compared to the Canadian ice service atlas, IcePAC showed promising results in the Hudson Bay, paving the way for its usage in other regions of the cryosphere to inform stakeholders' decisions.
David Schröder, Danny L. Feltham, Michel Tsamados, Andy Ridout, and Rachel Tilling
The Cryosphere, 13, 125–139, https://doi.org/10.5194/tc-13-125-2019, https://doi.org/10.5194/tc-13-125-2019, 2019
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This paper uses sea ice thickness data (CryoSat-2) to identify and correct shortcomings in simulating winter ice growth in the widely used sea ice model CICE. Adding a model of snow drift and using a different scheme for calculating the ice conductivity improve model results. Sensitivity studies demonstrate that atmospheric winter conditions have little impact on winter ice growth, and the fate of Arctic summer sea ice is largely controlled by atmospheric conditions during the melting season.
Ann Keen and Ed Blockley
The Cryosphere, 12, 2855–2868, https://doi.org/10.5194/tc-12-2855-2018, https://doi.org/10.5194/tc-12-2855-2018, 2018
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As the climate warms during the 21st century, our model shows extra melting at the top and the base of the Arctic sea ice. The reducing ice cover affects the impact these processes have on the sea ice volume budget, where the largest individual change is a reduction in the amount of growth at the base of existing ice. Using different forcing scenarios we show that, for this model, changes in the volume budget depend on the evolving ice area but not on the speed at which the ice area declines.
Takuya Nakanowatari, Jun Inoue, Kazutoshi Sato, Laurent Bertino, Jiping Xie, Mio Matsueda, Akio Yamagami, Takeshi Sugimura, Hironori Yabuki, and Natsuhiko Otsuka
The Cryosphere, 12, 2005–2020, https://doi.org/10.5194/tc-12-2005-2018, https://doi.org/10.5194/tc-12-2005-2018, 2018
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Medium-range predictability of early summer sea ice thickness in the East Siberian Sea was examined, based on TOPAZ4 forecast data. Statistical examination indicates that the estimate drops abruptly at 4 days, which is related to dynamical process controlled by synoptic-scale atmospheric fluctuations such as an Arctic cyclone. For longer lead times (> 4 days), the thermodynamic melting process takes over, which represents most of the remaining prediction.
Cited articles
Amitrano, D., Grasso, J.-R., and Hantz, D.: From Diffuse to Localised Damage
through Elastic Interaction, Geophys. Res. Lett., 26, 2109–2112,
https://doi.org/10.1029/1999GL900388, 1999. a
Andersson, T. R., Hosking, J. S., Pérez-Ortiz, M., Paige, B., Elliott,
A., Russell, C., Law, S., Jones, D. C., Wilkinson, J., Phillips, T., Byrne,
J., Tietsche, S., Sarojini, B. B., Blanchard-Wrigglesworth, E., Aksenov,
Y., Downie, R., and Shuckburgh, E.: Seasonal Arctic Sea Ice Forecasting
with Probabilistic Deep Learning, Nat. Commun., 12, 5124,
https://doi.org/10.1038/s41467-021-25257-4, 2021. a
Ayzel, G., Scheffer, T., and Heistermann, M.: RainNet v1.0: a convolutional neural network for radar-based precipitation nowcasting, Geosci. Model Dev., 13, 2631–2644, https://doi.org/10.5194/gmd-13-2631-2020, 2020. a
Ba, J. L., Kiros, J. R., and Hinton, G. E.: Layer Normalization,
arXiv [preprint], https://doi.org/10.48550/arXiv.1607.06450, 2016. a
Bachlechner, T., Majumder, B. P., Mao, H. H., Cottrell, G. W., and McAuley, J.:
ReZero Is All You Need: Fast Convergence at Large Depth,
arXiv [preprint], https://doi.org/10.48550/arXiv.2003.04887, 2020. a
Beck, A. and Kurz, M.: A Perspective on Machine Learning Methods in Turbulence
Modeling, GAMM-Mitteilungen, 44, e202100002, https://doi.org/10.1002/gamm.202100002,
2021. a
Beucler, T., Ebert-Uphoff, I., Rasp, S., Pritchard, M., and Gentine, P.:
Machine Learning for Clouds and Climate (Invited Chapter for
the AGU Geophysical Monograph Series “Clouds and Climate”) [preprint],
https://doi.org/10.1002/essoar.10506925.1, 2021. a
Bloom, S. C., Takacs, L. L., da Silva, A. M., and Ledvina, D.: Data
Assimilation Using Incremental Analysis Updates, Mon. Weather Rev.,
124, 1256–1271, https://doi.org/10.1175/1520-0493(1996)124<1256:DAUIAU>2.0.CO;2, 1996. a
Bocquet, M., Brajard, J., Carrassi, A., and Bertino, L.: Data assimilation as a learning tool to infer ordinary differential equation representations of dynamical models, Nonlin. Processes Geophys., 26, 143–162, https://doi.org/10.5194/npg-26-143-2019, 2019. a, b
Bocquet, M., Brajard, J., Carrassi, A., and Bertino, L.: Bayesian Inference of
Chaotic Dynamics by Merging Data Assimilation, Machine Learning and
Expectation-Maximization, Foundations of Data Science, 2, 55,
https://doi.org/10.3934/fods.2020004, 2020. a
Bouchat, A., Hutter, N., Chanut, J., Dupont, F., Dukhovskoy, D., Garric, G.,
Lee, Y. J., Lemieux, J.-F., Lique, C., Losch, M., Maslowski, W., Myers,
P. G., Ólason, E., Rampal, P., Rasmussen, T., Talandier, C., Tremblay,
B., and Wang, Q.: Sea Ice Rheology Experiment (SIREx): 1. Scaling
and Statistical Properties of Sea-Ice Deformation Fields, J.
Geophys. Res.-Oceans, 127, e2021JC017667,
https://doi.org/10.1029/2021JC017667, 2022. a, b
Boutin, G., Williams, T., Horvat, C., and Brodeau, L.: Modelling the Arctic
Wave-Affected Marginal Ice Zone: A Comparison with ICESat-2 Observations,
Philos. T. R. Soc. A, 380, 20210262, https://doi.org/10.1098/rsta.2021.0262, 2022. a
Brajard, J., Carrassi, A., Bocquet, M., and Bertino, L.: Combining Data
Assimilation and Machine Learning to Emulate a Dynamical Model from Sparse
and Noisy Observations: A Case Study with the Lorenz 96 Model,
J. Comput. Sci., 44, 101171,
https://doi.org/10.1016/j.jocs.2020.101171, 2020. a
Brajard, J., Carrassi, A., Bocquet, M., and Bertino, L.: Combining Data
Assimilation and Machine Learning to Infer Unresolved Scale Parametrization,
Philos. T. R. Soc. A, 379, 20200 086, https://doi.org/10.1098/rsta.2020.0086, 2021. a
Breiman, L.: Statistical Modeling: The Two Cultures (with Comments and
a Rejoinder by the Author), Stat. Sci., 16, 199–231,
https://doi.org/10.1214/ss/1009213726, 2001. a
Brenowitz, N. D. and Bretherton, C. S.: Prognostic Validation of a Neural
Network Unified Physics Parameterization, Geophys. Res. Lett., 45,
6289–6298, https://doi.org/10.1029/2018GL078510, 2018. a
Carrassi, A., Weber, R. J. T., Guemas, V., Doblas-Reyes, F. J., Asif, M., and Volpi, D.: Full-field and anomaly initialization using a low-order climate model: a comparison and proposals for advanced formulations, Nonlin. Processes Geophys., 21, 521–537, https://doi.org/10.5194/npg-21-521-2014, 2014. a
Cheng, Y., Giometto, M. G., Kauffmann, P., Lin, L., Cao, C., Zupnick, C., Li,
H., Li, Q., Huang, Y., Abernathey, R., and Gentine, P.: Deep Learning for
Subgrid-Scale Turbulence Modeling in Large-Eddy Simulations of the
Convective Atmospheric Boundary Layer, J. Adv. Model.
Earth Sy., 14, e2021MS002 847, https://doi.org/10.1029/2021MS002847, 2022. a
Cipolla, R., Gal, Y., and Kendall, A.: Multi-Task Learning Using
Uncertainty to Weigh Losses for Scene Geometry and Semantics,
in: 2018 IEEE/CVF Conference on Computer Vision and Pattern
Recognition, 7482–7491, IEEE, Salt Lake City, UT, USA,
https://doi.org/10.1109/CVPR.2018.00781, 2018. a, b
Dansereau, V., Weiss, J., and Saramito, P.: A Continuum
Viscous-Elastic-Brittle, Finite Element DG Model for the Fracture
and Drift of Sea Ice, in: Challenges and Innovations in
Geomechanics, edited by: Barla, M., Di Donna, A., and Sterpi, D., Lecture
Notes in Civil Engineering, 125–139, Springer International
Publishing, Cham, https://doi.org/10.1007/978-3-030-64514-4_8, 2021. a
De, S. and Smith, S. L.: Batch Normalization Biases Residual Blocks Towards
the Identity Function in Deep Networks, arXiv [preprint],
https://doi.org/10.48550/arXiv.2002.10444, 2020. a
Falcon, W. and The PyTorch Lightning team: PyTorch Lightning, Zenodo [code], https://doi.org/10.5281/zenodo.3530844, 2019. a
Farchi, A., Bocquet, M., Laloyaux, P., Bonavita, M., and Malartic, Q.: A
Comparison of Combined Data Assimilation and Machine Learning Methods for
Offline and Online Model Error Correction, J. Comput. Sci.,
55, 101468, https://doi.org/10.1016/j.jocs.2021.101468, 2021a. a, b, c, d
Farchi, A., Laloyaux, P., Bonavita, M., and Bocquet, M.: Using Machine Learning
to Correct Model Error in Data Assimilation and Forecast Applications,
Q. J. Roy. Meteorol. Soc., 147, 3067–3084,
https://doi.org/10.1002/qj.4116, 2021b. a
Finn, T.: cerea-daml/hybrid_nn_meb_model: Preprint submission (Preprint), Zenodo [code], https://doi.org/10.5281/zenodo.7997435, 2023. a
Fisher, R. A.: Frequency Distribution of the Values of the
Correlation Coefficient in Samples from an Indefinitely Large
Population, Biometrika, 10, 507–521, https://doi.org/10.2307/2331838, 1915. a
Geer, A. J.: Learning Earth System Models from Observations: Machine Learning
or Data Assimilation?, Philos. T. R. Soc. A, 379, 20200089,
https://doi.org/10.1098/rsta.2020.0089, 2021. a
Gentine, P., Pritchard, M., Rasp, S., Reinaudi, G., and Yacalis, G.: Could
Machine Learning Break the Convection Parameterization Deadlock?,
Geophys. Res. Lett., 45, 5742–5751, https://doi.org/10.1029/2018GL078202,
2018. a
Girard, L., Bouillon, S., Weiss, J., Amitrano, D., Fichefet, T., and Legat, V.:
A New Modeling Framework for Sea-Ice Mechanics Based on Elasto-Brittle
Rheology, Ann. Glaciol., 52, 123–132,
https://doi.org/10.3189/172756411795931499, 2011. a
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D.,
Ozair, S., Courville, A., and Bengio, Y.: Generative Adversarial Nets,
in: Advances in Neural Information Processing Systems 27, edited by:
Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N. D., and Weinberger,
K. Q., 2672–2680, Curran Associates, Inc., ISBN 9781510800410, 2014. a
Guillaumin, A. P. and Zanna, L.: Stochastic-Deep Learning Parameterization
of Ocean Momentum Forcing, J. Adv. Model. Earth Sy.,
13, e2021MS002534, https://doi.org/10.1029/2021MS002534, 2021. a
Hendrycks, D. and Gimpel, K.: Gaussian Error Linear Units (GELUs),
arXiv [preprint], https://doi.org/10.48550/arXiv.1606.08415, 2020. a, b
Hodson, T. O.: Root-mean-square error (RMSE) or mean absolute error (MAE): when to use them or not, Geosci. Model Dev., 15, 5481–5487, https://doi.org/10.5194/gmd-15-5481-2022, 2022. a
Horvat, C. and Roach, L. A.: WIFF1.0: a hybrid machine-learning-based parameterization of wave-induced sea ice floe fracture, Geosci. Model Dev., 15, 803–814, https://doi.org/10.5194/gmd-15-803-2022, 2022. a
Houghton, J. T., Ding, Y., Griggs, D. J., Noguer, M., van der
Linden, P. J., Dai, X., Maskell, K., and Johnson, C. A.: Climate
Change 2001: The Scientific Basis: Contribution of Working Group I to the
Third Assessment Report of the Intergovernmental Panel on Climate
Change, Cambridge University Press, ISBN 0-521-01495-6, 2001 a
Irrgang, C., Boers, N., Sonnewald, M., Barnes, E. A., Kadow, C., Staneva, J.,
and Saynisch-Wagner, J.: Towards Neural Earth System Modelling by
Integrating Artificial Intelligence in Earth System Science, Nature
Machine Intelligence, 3, 667–674, https://doi.org/10.1038/s42256-021-00374-3, 2021. a
Kaplan, J., McCandlish, S., Henighan, T., Brown, T. B., Chess, B., Child, R.,
Gray, S., Radford, A., Wu, J., and Amodei, D.: Scaling Laws for Neural
Language Models, arXiv [preprint], https://doi.org/10.48550/arXiv.2001.08361, 2020. a
Kingma, D. P. and Ba, J.: Adam: A Method for Stochastic Optimization,
arXiv [preprint], https://doi.org/10.48550/arXiv.1412.6980, 2017. a
Korosov, A., Rampal, P., Ying, Y., Ólason, E., and Williams, T.: Towards improving short-term sea ice predictability using deformation observations, The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2022-46, in review, 2022. a
Lee, S., Seo, Y., Lee, K., Abbeel, P., and Shin, J.: Addressing Distribution
Shift in Online Reinforcement Learning with Offline Datasets, Offline Reinforcement Learning Workshop at Neural Information Processing Systems, https://offline-rl-neurips.github.io/pdf/13.pdf (last access: 17 July 2023), 2021. a
Levine, S., Kumar, A., Tucker, G., and Fu, J.: Offline Reinforcement
Learning: Tutorial, Review, and Perspectives on Open
Problems, arXiv [preprint], https://doi.org/10.48550/arXiv.2005.01643, 2020. a
Liu, Q., Zhang, R., Wang, Y., Yan, H., and Hong, M.: Daily Prediction of
the Arctic Sea Ice Concentration Using Reanalysis Data Based on a
Convolutional LSTM Network, J. Mar. Sci. Eng., 9,
330, https://doi.org/10.3390/jmse9030330, 2021. a
Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., and Xie, S.: A
ConvNet for the 2020s, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA, 18–24 June 2022, 11966–11976,
https://doi.org/10.1109/CVPR52688.2022.01167, 2022. a, b, c, d, e, f, g, h, i, j
Murphy, K. P.: Machine Learning: A Probabilistic Perspective, Adaptive
Computation and Machine Learning Series, MIT Press, Cambridge, MA, ISBN 978-0-262-01802-9,
2012. a
Norton, R. M.: The Double Exponential Distribution: Using Calculus to
Find a Maximum Likelihood Estimator, Am. Stat., 38,
135–136, https://doi.org/10.2307/2683252, 1984. a
Odena, A., Dumoulin, V., and Olah, C.: Deconvolution and Checkerboard
Artifacts, Distill, 1, e3, https://doi.org/10.23915/distill.00003, 2016. a, b
Ólason, E., Rampal, P., and Dansereau, V.: On the statistical properties of sea-ice lead fraction and heat fluxes in the Arctic, The Cryosphere, 15, 1053–1064, https://doi.org/10.5194/tc-15-1053-2021, 2021. a
Ólason, E., Boutin, G., Korosov, A., Rampal, P., Williams, T., Kimmritz,
M., Dansereau, V., and Samaké, A.: A New Brittle Rheology and
Numerical Framework for Large-Scale Sea-Ice Models, J.
Adv. Model. Earth Sy., 14, e2021MS002685,
https://doi.org/10.1029/2021MS002685, 2022. a, b, c, d
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen,
T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E.,
DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L.,
Bai, J., and Chintala, S.: PyTorch: An Imperative Style,
High-Performance Deep Learning Library, in: Advances in Neural
Information Processing Systems 32, edited by: Wallach, H., Larochelle, H.,
Beygelzimer, A., Alché-Buc, F., Fox, E., and Garnett, R.,
Curran Associates, Inc., 8024–8035, ISBN 9781713807933, 2019. a
Prudencio, R. F., Maximo, M. R. O. A., and Colombini, E. L.: A Survey on
Offline Reinforcement Learning: Taxonomy, Review, and Open
Problems, arXiv [preprint], https://doi.org/10.48550/arXiv.2203.01387, 2022. a
Rabatel, M., Rampal, P., Carrassi, A., Bertino, L., and Jones, C. K. R. T.: Impact of rheology on probabilistic forecasts of sea ice trajectories: application for search and rescue operations in the Arctic, The Cryosphere, 12, 935–953, https://doi.org/10.5194/tc-12-935-2018, 2018. a
Rampal, P., Bouillon, S., Ólason, E., and Morlighem, M.: neXtSIM: a new Lagrangian sea ice model, The Cryosphere, 10, 1055–1073, https://doi.org/10.5194/tc-10-1055-2016, 2016. a, b, c
Rampal, P., Dansereau, V., Olason, E., Bouillon, S., Williams, T., Korosov, A., and Samaké, A.: On the multi-fractal scaling properties of sea ice deformation, The Cryosphere, 13, 2457–2474, https://doi.org/10.5194/tc-13-2457-2019, 2019. a
Rasp, S.: Coupled online learning as a way to tackle instabilities and biases in neural network parameterizations: general algorithms and Lorenz 96 case study (v1.0), Geosci. Model Dev., 13, 2185–2196, https://doi.org/10.5194/gmd-13-2185-2020, 2020. a
Rasp, S., Pritchard, M. S., and Gentine, P.: Deep Learning to Represent Subgrid
Processes in Climate Models, P. Natl. Acad. Sci. USA,
115, 9684–9689, https://doi.org/10.1073/pnas.1810286115, 2018. a
Ravuri, S., Lenc, K., Willson, M., Kangin, D., Lam, R., Mirowski, P.,
Fitzsimons, M., Athanassiadou, M., Kashem, S., Madge, S., Prudden, R.,
Mandhane, A., Clark, A., Brock, A., Simonyan, K., Hadsell, R., Robinson, N.,
Clancy, E., Arribas, A., and Mohamed, S.: Skilful Precipitation Nowcasting
Using Deep Generative Models of Radar, Nature, 597, 672–677,
https://doi.org/10.1038/s41586-021-03854-z, 2021. a
Ronneberger, O., Fischer, P., and Brox, T.: U-Net: Convolutional
Networks for Biomedical Image Segmentation, arXiv [preprint], https://doi.org/10.48550/arXiv.1505.04597,
2015. a, b, c, d
Rybkin, O., Daniilidis, K., and Levine, S.: Simple and Effective VAE
Training with Calibrated Decoders, arXiv [preprint], https://doi.org/10.48550/arXiv.2006.13202,
2020. a
Saramito, P.: Efficient C++ Finite Element Computing with Rheolef, CNRS-CCSD ed., https://cel.hal.science/cel-00573970 (last access: 17 July 2023), 2020. a
Schweiger, A. J. and Zhang, J.: Accuracy of Short-Term Sea Ice Drift Forecasts
Using a Coupled Ice-Ocean Model, J. Geophys. Res.-Oceans,
120, 7827–7841, https://doi.org/10.1002/2015JC011273, 2015. a
Seifert, A. and Rasp, S.: Potential and Limitations of Machine Learning
for Modeling Warm-Rain Cloud Microphysical Processes, J. Adv.
Model. Earth Sy., 12, e2020MS002301, https://doi.org/10.1029/2020MS002301,
2020. a
Simonyan, K., Vedaldi, A., and Zisserman, A.: Deep Inside Convolutional
Networks: Visualising Image Classification Models and Saliency
Maps, arXiv [preprint], https://doi.org/10.48550/arXiv.1312.6034, 2013. a
Smilkov, D., Thorat, N., Kim, B., Viégas, F., and Wattenberg, M.:
SmoothGrad: Removing Noise by Adding Noise, arXiv [preprint],
https://doi.org/10.48550/arXiv.1706.03825, 2017.
a
Sohl-Dickstein, J., Weiss, E. A., Maheswaranathan, N., and Ganguli, S.: Deep
Unsupervised Learning Using Nonequilibrium Thermodynamics, arXiv [preprint],
https://doi.org/10.48550/arXiv.1503.03585, 2015. a
Springenberg, J. T., Dosovitskiy, A., Brox, T., and Riedmiller, M.: Striving
for Simplicity: The All Convolutional Net, arXiv [preprint],
https://doi.org/10.48550/arXiv.1412.6806, 2015. a, b
Stockdale, T. N.: Coupled Ocean–Atmosphere Forecasts in the
Presence of Climate Drift, Mon. Weather Rev., 125, 809–818,
https://doi.org/10.1175/1520-0493(1997)125<0809:COAFIT>2.0.CO;2, 1997. a
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D.,
Vanhoucke, V., and Rabinovich, A.: Going Deeper with Convolutions,
arXiv [preprint],
https://doi.org/10.48550/arXiv.1409.4842, 2014. a, b
Tang, C.: Numerical Simulation of Progressive Rock Failure and Associated
Seismicity, Int. J. Rock Mech. Min. Sci., 34,
249–261, https://doi.org/10.1016/S0148-9062(96)00039-3, 1997. a
Tomczak, J. M.: Deep Generative Modeling, Springer International
Publishing, Cham, https://doi.org/10.1007/978-3-030-93158-2, 2022. a
Wilchinsky, A. V. and Feltham, D. L.: Modelling the Rheology of Sea Ice as a
Collection of Diamond-Shaped Floes, J. Non-Newtonian Fluid,
138, 22–32, https://doi.org/10.1016/j.jnnfm.2006.05.001, 2006. a
Wilchinsky, A. V. and Feltham, D. L.: Modeling Coulombic Failure of Sea Ice
with Leads, J. Geophys. Res.-Oceans, 116, C08040,
https://doi.org/10.1029/2011JC007071, 2011. a
Yadan, O.: Hydra – A Framework for Elegantly Configuring Complex
Applications, GitHub [code], https://github.com/facebookresearch/hydra (last access: 19 July 2023), 2019. a
Zanna, L. and Bolton, T.: Data-Driven Equation Discovery of Ocean
Mesoscale Closures, Geophys. Res. Lett., 47, e2020GL088376,
https://doi.org/10.1029/2020GL088376, 2020. a
Short summary
We combine deep learning with a regional sea-ice model to correct model errors in the sea-ice dynamics of low-resolution forecasts towards high-resolution simulations. The combined model improves the forecast by up to 75 % and thereby surpasses the performance of persistence. As the error connection can additionally be used to analyse the shortcomings of the forecasts, this study highlights the potential of combined modelling for short-term sea-ice forecasting.
We combine deep learning with a regional sea-ice model to correct model errors in the sea-ice...
