Version 2 of the EUMETSAT OSI SAF and ESA CCI sea-ice concentration climate data records

We introduce the OSI-450, the SICCI-25km and the SICCI-50km climate data records of gridded global sea-ice concentration. These three records are derived from passive microwave satellite data and offer three distinct advantages compared to existing records: first, all three records provide quantitative information on uncertainty and possibly applied filtering at every grid point and every time step. Second, they are based on dynamic tie points, which capture the time evolution of surface characteristics of the ice cover and accommodate potential calibration differences between satellite missions. Third, they are produced in the context of sustained services offering committed extension, documentation, traceability, and user support. The three records differ in the underlying satellite data (SMMR &amp; SSM/I &amp; SSMIS or AMSR-E &amp; AMSR2), in the imaging frequency channels (37&thinsp;GHz and either 6 or 19&thinsp;GHz), in their horizontal resolution (25 or 50&thinsp;km), and in the time period they cover. We introduce the underlying algorithms and provide an evaluation. We find that all three records compare well with independent estimates of sea-ice concentration both in regions with very high sea-ice concentration and in regions with very low sea-ice concentration. We hence trust that these records will prove helpful for a better understanding of the evolution of the Earth's sea-ice cover.</p

Satellite passive microwave sea-ice concentration data set intercomparison: closed ice and ship-based observations

We report on results of a systematic intercomparison of 10 global sea-ice concentration (SIC) data products at 12.5 to 50.0 km grid resolution for both the Arctic and the Antarctic. The products are compared with each other with respect to differences in SIC, sea-ice area (SIA), and sea-ice extent (SIE), and they are compared against a global wintertime near-100 % reference SIC data set for closed pack ice conditions and against global year-round ship-based visual observations of the sea-ice cover. We can group the products based on the concept of their SIC retrieval algorithms. Group I consists of data sets using the self-optimizing EUMETSAT OSI SAF and ESA CCI algorithms. Group II includes data using the Comiso bootstrap algorithm and the NOAA NSIDC sea-ice concentration climate data record (CDR). The standard NASA Team and the ARTIST Sea Ice (ASI) algorithms are put into group III, and NASA Team 2 is the only element of group IV. The three CDRs of group I (SICCI-25km, SICCI-50km, and OSI-450) are biased low compared to a 100 % reference SIC data set with biases of - 0.4 % to -1.0 % (Arctic) and -0.3 % to -1.1 % (Antarctic). Products of group II appear to be mostly biased high in the Arctic by between +1.0 % and +3.5 %, while their biases in the Antarctic range from -0.2 % to +0.9 %. Group III product biases are different for the Arctic, +0.9 % (NASA Team) and -3.7 % (ASI), but similar for the Antarctic, -5.4 % and -5.6 %, respectively. The standard deviation is smaller in the Arctic for the quoted group I products (1.9 % to 2.9 %) and Antarctic (2.5 % to 3.1 %) than for group II and III products: 3.6 % to 5.0 % for the Arctic and 4.0 % to 6.5 % for the Antarctic. We refer to the paper to understand why we could not give values for group IV here. We discuss the impact of truncating the SIC distribution, as naturally retrieved by the algorithms around the 100 % sea-ice concentration end. We show that evaluation studies of such truncated SIC products can result in misleading statistics and favour data sets that systematically overestimate SIC. We describe a method to reconstruct the non-truncated distribution of SIC before the evaluation is performed. On the basis of this evaluation, we open a discussion about the overestimation of SIC in data products, with far-reaching consequences for surface heat flux estimations in winter. We also document inconsistencies in the behaviour of the weather filters used in products of group II, and we suggest advancing studies about the influence of these weather filters on SIA and SIE time series and their trends

Comparisons of passive microwave remote sensing sea ice concentrations with ship-based visual observations during the CHINARE Arctic summer cruises of 2010-2018

In order to apply satellite data to guiding navigation in the Arctic more effectively, the sea ice concentrations (SIC) derived from passive microwave (PM) products were compared with ship-based visual observations (OBS) collected during the Chinese National Arctic Research Expeditions (CHINARE). A total of 3 667 observations were collected in the Arctic summers of 2010, 2012, 2014, 2016, and 2018. PM SIC were derived from the NASA-Team (NT), Bootstrap (BT) and Climate Data Record (CDR) algorithms based on the SSMIS sensor, as well as the BT, enhanced NASA-Team (NT2) and ARTIST Sea Ice (ASI) algorithms based on AMSR-E/AMSR-2 sensors. The daily arithmetic average of PM SIC values and the daily weighted average of OBS SIC values were used for the comparisons. The correlation coefficients (CC), biases and root mean square deviations (RMSD) between PM SIC and OBS SIC were compared in terms of the overall trend, and under mild/normal/severe ice conditions. Using the OBS data, the influences of floe size and ice thickness on the SIC retrieval of different PM products were evaluated by calculating the daily weighted average of floe size code and ice thickness. Our results show that CC values range from 0.89 (AMSR-E/AMSR-2 NT2) to 0.95 (SSMIS NT), biases range from -3.96% (SSMIS NT) to 12.05% (AMSR-E/AMSR-2 NT2), and RMSD values range from 10.81% (SSMIS NT) to 20.15% (AMSR-E/AMSR-2 NT2). Floe size has a significant influence on the SIC retrievals of the PM products, and most of the PM products tend to underestimate SIC under smaller floe size conditions and overestimate SIC under larger floe size conditions. Ice thickness thicker than 30 cm does not have a significant influence on the SIC retrieval of PM products. Overall, the best (worst) agreement occurs between OBS SIC and SSMIS NT (AMSR-E/AMSR-2 NT2) SIC in the Arctic summer.Peer reviewe

Predicting Sea Ice Concentration with Calibrated Uncertainty Quantification using Passive Microwave and Reanalysis Data

The adoption of deep learning (DL) techniques in the domain of remote sensing, and specifically sea ice concentration (SIC) prediction, using passive microwave (PM) data and atmospheric climate data has seen a growing interest. Given these predictions, it has been called upon to accompany predictions with their uncertainty, as a means to enhance quality and trustworthiness of results, which can be used in various climate applications in modelling and policy. Though, studies regarding uncertainty quantification (UQ) for SIC prediction has seen little interest. Within DL, there exists a subset of methodologies that work alongside prediction methodologies to effectively quantify uncertainty present within the model, as well as the uncertainty inherent in the data. Among these techniques include Bayesian Neural Networks (BNN's), and heteroscedastic neural networks (HNN's), where the former is used to measure model (epistemic) uncertainty and the latter data (aleatoric) uncertainty. For predicting SIC, and quantifying model and data uncertainty, we propose the use of a combined methodology using a heteroscedastic Bayesian neural network (HBNN) which follows the architecture of a multilayer perceptron (MLP) using PM and atmospheric data. Additionally, we explore the notion of calibration, and related methodologies as a means to evaluate the quality of uncertainties. The advantage of the proposed approach is its data driven nature for prediction and UQ, which is flexible to the context of the given data, such as in space or time. From the results of UQ, it was found that uncertainties vary throughout the seasonal ice cycle, where the months that coincide with melt-onset in the region are susceptible to the highest uncertainties. Additionally, within the study region, uncertainties were scattered, where highest uncertainties were found in areas near or in the marginal ice zone. It was also found that the inclusion of TB's in the feature space are most necessary to produce quality estimates of SIC, and the inclusion of atmospheric variables as input contributed to reduce uncertainty. Finally, when analyzing the effects of calibration on the model, it was found to yield quality and trustworthy predictions of uncertainty

Remote Sensing of Environmental Changes in Cold Regions

This Special Issue gathers papers reporting recent advances in the remote sensing of cold regions. It includes contributions presenting improvements in modeling microwave emissions from snow, assessment of satellite-based sea ice concentration products, satellite monitoring of ice jam and glacier lake outburst floods, satellite mapping of snow depth and soil freeze/thaw states, near-nadir interferometric imaging of surface water bodies, and remote sensing-based assessment of high arctic lake environment and vegetation recovery from wildfire disturbances in Alaska. A comprehensive review is presented to summarize the achievements, challenges, and opportunities of cold land remote sensing

Retrieval of sea ice parameters using fusion of high resolution model and remote sensing data

This thesis discusses the retrieval of sea ice parameters using the combination of remote sensing data and a sea ice model for the region of the Baffin Bay, Hudson Bay, Labrador Sea and the Gulf of St. Lawrence. The Los Alamos sea ice model, CICE, which is used as a module for coupled global ice-ocean models, was used for this work. The model was implemented with a 7-category thickness distribution, open boundaries and a variable coefficient for ice-ocean heat flux. A slab ocean mixed-layer model based on density criteria was used for the standalone regional implementation of the model. The model estimates of ice concentration were validated using seasonal means, and anomalies. A combined optimal interpolation and nudging scheme was implemented to assimilate Sea Surface Temperature (SST) and ice concentration from Advanced very-high-resolution radiometer (AVHRR) and Advanced Microwave Scanning Radiometer for EOS (AMSR-E) respectively. The inclusion of the variable drag coefficient required updates of ice volume and dependent tracers corresponding to the updates in the ice concentration estimates. The sea ice variables of thickness, freeboard, level ice draft and keel depth were compared with the estimates derived from Soil Moisture and Ocean Salinity (SMOS), CryoSat2, and a ULS instrument respectively. The assimilated model provided better estimates of ice concentration, thickness, freeboard and level ice draft. The model estimated ice thickness compared well with the thin ice thickness estimated from the SMOS data, except during March, when there is significant ice extent. The reason for this discrepancy could be attributed to the absence of mixed layer heat flux forcing in the model and also the effect of snow and the onset of melt that alters the observation. Field measurements were also used for the comparison of model estimates. The measurements from the Upward Looking Sonar (ULS) instrument located at Makkovick Bank were used to estimate the level ice draft and keel depth. The observations from ULS along with model estimates were used to determine the coefficient that relates the sail and keel measurements. The level ice draft showed a good match with the values extracted from the ULS data, while the sail to keel relationship coefficient seems to vary between a value of 3 during January and February and a value of 7 from March to May. Further studies have to be conducted to understand these variations. The ice concentration estimates from the assimilated model were compared with the ice concentration estimates derived from the images that were obtained during a field survey along the Labrador coast. The results of the ice concentration derived from the images showed a good match with the model values. The results were also compared with the estimates from Canadian Ice Service (CIS) ice charts and Advanced Microwave Scanning Radiometer-Earth observation (AMSR-E)

Microwave Indices from Active and Passive Sensors for Remote Sensing Applications

Past research has comprehensively assessed the capabilities of satellite sensors operating at microwave frequencies, both active (SAR, scatterometers) and passive (radiometers), for the remote sensing of Earth’s surface. Besides brightness temperature and backscattering coefficient, microwave indices, defined as a combination of data collected at different frequencies and polarizations, revealed a good sensitivity to hydrological cycle parameters such as surface soil moisture, vegetation water content, and snow depth and its water equivalent. The differences between microwave backscattering and emission at more frequencies and polarizations have been well established in relation to these parameters, enabling operational retrieval algorithms based on microwave indices to be developed. This Special Issue aims at providing an overview of microwave signal capabilities in estimating the main land parameters of the hydrological cycle, e.g., soil moisture, vegetation water content, and snow water equivalent, on both local and global scales, with a particular focus on the applications of microwave indices

Measurement, Knowledge, and Representation: A Sociological Study of Arctic Sea-Ice Science

Satellite-derived observations of Arctic sea ice are instrumental in contemporary sea-ice research. Through the production and dissemination of data products, these observations shape our understanding of Arctic sea-ice conditions, knowledge of which is essential for informing policy responses, decision-making, and action in the face of unprecedented climate change. However, due to the complex, dynamic, and indeterminate nature of sea ice and various scientific and technological challenges involved in its observation, measurement, and representation, the accuracy to which these products depict Arctic sea ice is limited. Moreover, the methodologies used to acquire, process, and report satellite data vary between scientific institutions, resulting in inconsistent estimates of key sea-ice parameters. Informed by social constructivist arguments developed within science and technology studies and critical cartography, this thesis contends that satellite-derived sea-ice data products represent a particular way of observing, interpreting, and classifying complex geophysical conditions that is socially and culturally contingent. This raises important questions about how sea-ice knowledge is constructed through the interactions between sea ice, sensing technologies, and social practices. Accordingly, this thesis integrates ethnographic and visual methodologies to critically explore how dynamic and indeterminate geophysical data are acquired, processed, and reported in Arctic sea-ice science. By examining sea-ice data products in terms of their underlying practices and technologies, institutional settings, and the broader socio-cultural, political, and historical contexts in which they are embedded, this thesis provides insights into the sociological nature of contemporary sea-ice research. It concludes that greater recognition of the social contingencies shaping how sea-ice data products are generated and disseminated is needed to foster more democratic and socially responsible forms of scientific knowledge. The findings presented in this thesis may provide valuable starting points for critically examining how sea-ice science may be made more equitable and enriched or improved by alternative perspectives