Prediction of monthly Arctic sea ice concentrations using satellite and reanalysis data based on convolutional neural networks

Changes in Arctic sea ice affect atmospheric circulation, ocean current, and polar ecosystems. There have been unprecedented decreases in the amount of Arctic sea ice due to global warming. In this study, a novel 1-month sea ice concentration (SIC) prediction model is proposed, with eight predictors using a deep-learning approach, convolutional neural networks (CNNs). This monthly SIC prediction model based on CNNs is shown to perform better predictions (mean absolute error - MAE - of 2.28 %, anomaly correlation coefficient - ACC - of 0.98, root-mean-square error - RMSE - of 5.76 %, normalized RMSE - nRMSE - of 16.15 %, and NSE - Nash-Sutcliffe efficiency - of 0.97) than a random-forest-based (RF-based) model (MAE of 2.45 %, ACC of 0.98, RMSE of 6.61 %, nRMSE of 18.64 %, and NSE of 0.96) and the persistence model based on the monthly trend (MAE of 4.31 %, ACC of 0.95, RMSE of 10.54 %, nRMSE of 29.17 %, and NSE of 0.89) through hindcast validations. The spatio-temporal analysis also confirmed the superiority of the CNN model. The CNN model showed good SIC prediction results in extreme cases that recorded unforeseen sea ice plummets in 2007 and 2012 with RMSEs of less than 5.0 %. This study also examined the importance of the input variables through a sensitivity analysis. In both the CNN and RF models, the variables of past SICs were identified as the most sensitive factor in predicting SICs. For both models, the SIC-related variables generally contributed more to predict SICs over ice-covered areas, while other meteorological and oceanographic variables were more sensitive to the prediction of SICs in marginal ice zones. The proposed 1-month SIC prediction model provides valuable information which can be used in various applications, such as Arctic shipping-route planning, management of the fishing industry, and long-term sea ice forecasting and dynamics

An investigation into the interaction between waves and ice

This thesis is submitted in the partial fulfillment of the requirements for the degree of Doctor of Philosophy at the University of Oslo. It represents work that has been carried out between 2015 and 2018, under the supervision of Pr. Atle Jensen, Dr. Graig Sutherland, and Dr. Kai H. Christensen, in collaboration with Pr. Aleksey Marchenko and Pr. Brian Ward. The work presented was carried at the University of Oslo and the University Center in Svalbard. Financial support for the work was provided by the Norwegian Research Council under the Petromaks 2 scheme, through the project WOICE (Experiments on Waves in Oil and Ice), NFR Grant number 233901. The thesis consists of an introduction, and a selection of 7 publications. The introduction presents the scientific context in which the work was undertaken, the methodology used, the results obtained, as well as some personal thoughts about unsuccessful directions encountered during the project and possible future work. I certify that this dissertation is mine and that the results presented are the result of the work of our research group, to which I brought significant contribution

A dataset of direct observations of sea ice drift and waves in ice

Variability in sea ice conditions, combined with strong couplings to the atmosphere and the ocean, lead to a broad range of complex sea ice dynamics. More in-situ measurements are needed to better identify the phenomena and mechanisms that govern sea ice growth, drift, and breakup. To this end, we have gathered a dataset of in-situ observations of sea ice drift and waves in ice. A total of 15 deployments were performed over a period of 5 years in both the Arctic and Antarctic, involving 72 instruments. These provide both GPS drift tracks, and measurements of waves in ice. The data can, in turn, be used for tuning sea ice drift models, investigating waves damping by sea ice, and helping calibrate other sea ice measurement techniques, such as satellite based observations

CryoSat-2 Significant Wave Height in Polar Oceans Derived Using a Semi-Analytical Model of Synthetic Aperture Radar 2011–2019

This paper documents the retrieval of significant ocean surface wave heights in the Arctic Ocean from CryoSat-2 data. We use a semi-analytical model for an idealised synthetic aperture satellite radar or pulse-limited radar altimeter echo power. We develop a processing methodology that specifically considers both the Synthetic Aperture and Pulse Limited modes of the radar that change close to the sea ice edge within the Arctic Ocean. All CryoSat-2 echoes to date were matched by our idealised echo revealing wave heights over the period 2011–2019. Our retrieved data were contrasted to existing processing of CryoSat-2 data and wave model data, showing the improved fidelity and accuracy of the semi-analytical echo power model and the newly developed processing methods. We contrasted our data to in situ wave buoy measurements, showing improved data retrievals in seasonal sea ice covered seas. We have shown the importance of directly considering the correct satellite mode of operation in the Arctic Ocean where SAR is the dominant operating mode. Our new data are of specific use for wave model validation close to the sea ice edge and is available at the link in the data availability statement

Variability of Arctic Sea Ice: The View from Space, An 18-year Record

A recently compiled 18-year record (1979 to 1996) of sea ice concentrations derived from four passive-microwave satellite instruments has allowed the quantification of a variety of measures of Arctic sea ice variability. Earlier maps generated using data through August 1987 have been updated to 18-year summaries of the annual range of sea ice distributions, the interannual variability of average monthly sea ice distributions, the frequency of sea ice coverage over the 18 years, the length of the sea ice season, and trends in the length of the sea ice season. Linear least squares trends over the 18-year record show the sea ice season to have lengthened over some sizeable regions, especially in the Bering Sea, Baffin Bay, Davis Strait, the Labrador Sea, and the Gulf of St. Lawrence, but to have shortened over a much larger area, including the Sea of Okhotsk, the Greenland Sea, the Barents Sea, and all the seas along the north coast of Russia. The area with trends showing sea ice seasons shortening by over 0.5 days/year is 7 500 000 km², over 2.5 times the area experiencing a lengthening of the sea ice season by over 0.5 days/year. Neither the shortening nor the lengthening, however, is uniform or monotonic over the 18-year record. Instead, the ice cover exhibits widespread interannual variability, not just in the length of the sea ice season but for each month-a fact well illustrated by the monthly average September ice coverage, which was at its lowest extent in 1995 but at its second highest one year later, in the final year of the record. The maps of ice frequency and ice variability can help identify how anomalous individual years are. In some cases, they can help forestall unnecessary concern over seemingly unusual conditions which, upon examination of the maps, are found to fall well within the observed variability. Grâce à un dossier compilant 18 années d'étude (de 1979 à 1996) sur les concentrations de glace marine mesurées par quatre instruments à hyperfréquences passives portés sur des satellites, on a pu quantifier diverses mesures de la variabilité de la glace marine dans l'Arctique. Les premières cartes créées à l'aide de données allant jusqu'en août 1987 ont été mises à jour sous forme de résumés (portant sur une période de18 ans) de la superficie annuelle des distributions de glace marine, de la variabilité interannuelle de la moyenne mensuelle des distributions de glace marine, de la fréquence de la couverture de glace marine au cours des 18 années, de la durée de la saison de glace marine et des tendances dans cette même durée. Une analyse des tendances, par la méthode linéaire des moindres carrés, enregistrées sur 18 ans montre que la saison de glace marine est devenue plus longue dans certaines régions assez vastes, surtout dans la mer de Béring, la baie de Baffin, le détroit de Davis, la mer du Labrador et le golfe du Saint-Laurent, mais qu'elle a raccourci dans une zone bien plus étendue, qui comprend la mer d'Okhotsk, la mer de Norvège, la mer de Barents et toutes les eaux longeant la côte nord de Russie. La région où se manifestent les tendances au raccourcissement de la saison de glace marine de 0,5 jour/an s'étend sur 7 500 000 km², soit plus de 2,5 fois l'étendue où se manifeste une extension de la saison de glace marine de 0,5 jour/an. Mais ni le raccourcissement ni l'extension ne sont uniformes ou monotones au cours des 18 années d'études. La couverture de glace affiche, au contraire, une variabilité interannuelle généralisée, non seulement dans la longueur de la saison de glace marine, mais pour chaque mois - fait qu'illustre bien la moyenne mensuelle de la couverture de glace pour le mois de septembre, moyenne qui était à son minimum en 1995, mais à son maximum de second rang un an plus tard, durant l'année finale de l'enregistrement. Les cartes de fréquence de la glace et de variabilité de la glace peuvent montrer les anomalies d'une année à l'autre. Dans certains cas, ces cartes peuvent aider à prévenir d'inutiles soucis quant aux conditions apparemment inhabituelles qui, si l'on étudie les cartes, se situent parfaitement dans la fourchette de variabilité observée

A Parameterization Scheme for Wind Wave Modules that Includes the Sea Ice Thickness in the Marginal Ice Zone

The global wave model WAVEWATCH III®; works well in open water. To simulate the propagation and attenuation of waves through ice-covered water, existing simulations have considered the influence of sea ice by adding the sea ice concentration in the wind wave module; however, they simply suppose that the wind cannot penetrate the ice layer and ignore the possibility of wind forcing waves below the ice cover. To improve the simulation performance of wind wave modules in the marginal ice zone (MIZ), this study proposes a parameterization scheme by directly including the sea ice thickness. Instead of scaling the wind input with the fraction of open water, this new scheme allows partial wind input in ice-covered areas based on the ice thickness. Compared with observations in the Barents Sea in 2016, the new scheme appears to improve the modeled waves in the high-frequency band. Sensitivity experiments with and without wind wave modules show that wind waves can play an important role in areas with low sea ice concentration in the MIZ.acceptedVersio

Physics of arctic landfast sea ice and implications on the cryosphere : An overview

Landfast sea ice (LFSI) is a critical component of the Arctic sea ice cover, and is changing as a result of Arctic amplification of climate change. Located in coastal areas, LFSI is of great significance to the physical and ecological systems of the Arctic shelf and in local indigenous communities. We present an overview of the physics of Arctic LFSI and the associated implications on the cryosphere. LFSI is kept in place by four fasten mechanisms. The evolution of LFSI is mostly determined by thermodynamic processes, and can therefore be used as an indicator of local climate change. We also present the dynamic processes that are active prior to the formation of LFSI, and those that are involved in LFSI freeze-up and breakup. Season length, thickness and extent of Arctic LFSI are decreasing and showing different trends in different seas, and therefore, causing environmental and climatic impacts. An improved coordination of Arctic LFSI observation is needed with a unified and systematic observation network supported by cooperation between scientists and indigenous communities, as well as a better application of remote sensing data to acquire detailed LFSI cryosphere physical parameters, hence revolving both its annual cycle and long-term changes. Integrated investigations combining in situ measurements, satellite remote sensing and numerical modeling are needed to improve our understanding of the physical mechanisms of LFSI seasonal changes and their impacts on the environment and climate.Peer reviewe

A weekly Arctic sea-ice thickness data record from merged CryoSat-2 and SMOS satellite data

Sea-ice thickness on a global scale is derived from different satellite sensors using independent retrieval methods. Due to the sensor and orbit characteristics, such satellite retrievals differ in spatial and temporal resolution as well as in the sensitivity to certain sea-ice types and thickness ranges. Satellite altimeters, such as CryoSat-2 (CS2), sense the height of the ice surface above the sea level, which can be converted into sea-ice thickness. Relative uncertainties associated with this method are large over thin ice regimes. Another retrieval method is based on the evaluation of surface brightness temperature (TB) in L-band microwave frequencies (1.4 GHz) with a thickness-dependent emission model, as measured by the Soil Moisture and Ocean Salinity (SMOS) satellite. While the radiometer-based method looses sensitivity for thick sea ice (> 1 m), relative uncertainties over thin ice are significantly smaller than for the altimetry-based retrievals. In addition, the SMOS product provides global sea-ice coverage on a daily basis unlike the altimeter data. This study presents the first merged product of complementary weekly Arctic sea-ice thickness data records from the CS2 altimeter and SMOS radiometer. We use two merging approaches: a weighted mean (WM) and an optimal interpolation (OI) scheme. While the weighted mean leaves gaps between CS2 orbits, OI is used to produce weekly Arctic-wide sea-ice thickness fields. The benefit of the data merging is shown by a comparison with airborne electromagnetic (AEM) induction sounding measurements. When compared to airborne thickness data in the Barents Sea, the merged product has a root mean square deviation (RMSD) of about 0.7 m less than the CS2 product and therefore demonstrates the capability to enhance the CS2 product in thin ice regimes. However, in mixed first-year (FYI) and multiyear (MYI) ice regimes as in the Beaufort Sea, the CS2 retrieval shows the lowest bias