Southern ocean sea level anomaly in the sea ice-covered sector from multimission satellite observations

Despite its central role in the global climate, the Southern Ocean circulation is still one of the least understood ocean circulation systems of the planet. One major constraint to our understanding of this region is the challenge of observing ocean circulation in the seasonally sea ice sector of the Southern Ocean. Here, we present a new Sea Level Anomaly (SLA) product, focusing on the subpolar Southern Ocean and including its sea ice covered parts from 2013 to 2019. Combining observations from multiple satellites, including Cryosat-2, Sentinel-3A, and SARAL/AltiKa, processed with state-of-the-art algorithms, allows an improvement in spatial and temporal resolution compared with previous products. Validation is made by comparing our estimate with existing SLA products, cross-comparing estimates from individual satellites in the sea ice zones, and comparing the time series of the product with a Bottom Pressure Recorder in the Drake Passage.

Measurement(s)	Sea Level Anomaly
Technology Type(s)	Satellite Altimetry
Sample Characteristic - Location	Southern Ocean

Improving the Estimation of Water Level over Freshwater Ice Cover using Altimetry Satellite Active and Passive Observations

Owing to its temporal resolution of 10-day and its polar orbit allowing several crossings over large lakes, the US National Aeronautics and Space Administration (NASA) and the French Centre National d’Etudes Spatiales (CNES) missions including Topex/Poseidon, Jason-1/2/3 demonstrated strong capabilities for the continuous and long-term monitoring (starting in 1992) of large and medium-sized water bodies. However, the presence of heterogeneous targets in the altimeter footprint, such as ice cover in boreal areas, remains a major issue to obtain estimates of water level over subarctic lakes of similar accuracy as over other inland water bodies using satellite altimetry (i.e., R ≥ 0.9 and RMSE ≤ 10 to 20 cm when compared to in-situ water stages). In this study, we aim to automatically identify the Jason-2 altimetry measurements corresponding to open water, ice and transition (water-ice) to improve the estimations of water level during freeze and thaw periods using only the point measurements of open water. Four Canadian lakes were selected to analyze active (waveform parameters) and passive (brightness temperature) microwave data acquired by the Jason-2 radar altimetry mission: Great Slave Lake, Lake Athabasca, Lake Winnipeg, and Lake of the Woods. To determine lake surface states, backscattering coefficient and peakiness at Ku-band derived from the radar altimeter waveform and brightness temperature at 18.7 and 37 GHz measured by the microwave radiometer contained in the geophysical data records (GDR) of Jason-2 were used in two different unsupervised classification techniques to define the thresholds of discrimination between open water and ice measurements. K-means technique provided better results than hierarchical clustering based upon silhouette criteria and the Calinski-Harabz index. Thresholds of discrimination between ice and water were validated with the Normalized Difference Snow Index (NDSI) snow cover products of the MODIS satellite. The use of open water threshold resulted in improved water level estimation compared to in situ water stages, especially in the presence of ice. For the four lakes, the Pearson coefficient (r) increased on average from about 0.8 without the use of the thresholds to more than 0.90. The unbiased RMSE were generally lower than 20 cm when the threshold of open water was used and more than 22 cm over smaller lakes, without using the thresholds.

Arctic Ocean Sea Level Record from the Complete Radar Altimetry Era: 1991–2018

In recent years, there has been a large focus on the Arctic due to the rapid changes of the region. Arctic sea level determination is challenging due to the seasonal to permanent sea-ice cover, lack of regional coverage of satellites, satellite instruments ability to measure ice, insufficient geophysical models, residual orbit errors, challenging retracking of satellite altimeter data. We present the European Space Agency (ESA) Climate Change Initiative (CCI) Technical University of Denmark (DTU)/Technischen Universität München (TUM) sea level anomaly (SLA) record based on radar satellite altimetry data in the Arctic Ocean from the European Remote Sensing satellite number 1 (ERS-1) (1991) to CryoSat-2 (2018). We use updated geophysical corrections and a combination of altimeter data: Reprocessing of Altimeter Product for ERS (REAPER) (ERS-1), ALES+ retracker (ERS-2, Envisat), combination of Radar Altimetry Database System (RADS) and DTUs in-house retracker LARS (CryoSat-2). Furthermore, this study focuses on the transition between conventional and Synthetic Aperture Radar (SAR) altimeter data to make a smooth time series regarding the measurement method. We find a sea level rise of 1.54 mm/year from September 1991 to September 2018 with a 95% confidence interval from 1.16 to 1.81 mm/year. ERS-1 data is troublesome and when ignoring this satellite the SLA trend becomes 2.22 mm/year with a 95% confidence interval within 1.67–2.54 mm/year. Evaluating the SLA trends in 5 year intervals show a clear steepening of the SLA trend around 2004. The sea level anomaly record is validated against tide gauges and show good results. Additionally, the time series is split and evaluated in space and time.

A Study of the Technology Used to Distinguish Sea Ice and Seawater on the Haiyang-2A/B (HY-2A/B) Altimeter Data

When the Haiyang-2B (HY-2B) was launched into space to form a star network with the Haiyang-2A (HY-2A), it provided new data sources for the sea ice research of the Earth’s polar regions. The ability of altimeter echoes to distinguish sea ice and sea water is usable in operational ice charting. In this research study, the level 1B (L1B) data of HY-2A/B altimeter from November 2018 was used to analyze the altimeter waveforms from the polar regions. The Suboptimal Maximum Likelihood Estimation (SMLE) and Offset Center of Gravity (OCOG) tracking packages could maintain the waveform characteristics of diffused and quasi-specular surfaces by comparison. Also, they could be utilized to distinguish sea ice from seawater in the polar regions. It was determined that the types of echoes obtained from the seawater were diffuse. Also, some “ocean-like” waveform data had existed for the old ice formations in the Arctic regions during the study period. The types of echoes obtained from Arctic sea ice were found to be mainly quasi-specular. In the present study, three methods (Threshold segmentation, K-nearest-neighbor (KNN), and Lib-Support Vector machine (LIBSVM)) with four waveform parameters (Automatic Gain Control (AGC) and Pulse Peaking (PP) values of the Ku and C Bands) were adopted to distinguish between the sea ice and seawater areas. The accuracy rate of the separation results for the LIBSVM except band Ku from HY-2B ALT was found to be less than 40% in Antarctic. Meanwhile, the other two methods were observed to have maintained the waveforms correctly at accuracy rates of approximately 80% in Antarctic and the Arctic. In addition, the observed distinguishing errors were located in the regions of the old ice of the Arctic region. In addition, due to the summer melting processes, the large number of ice floes and the snow cover had made it difficult to distinguish the seawater and sea ice in the Antarctic regions.

A New Retracking Algorithm for Retrieving Sea Ice Freeboard from CryoSat-2 Radar Altimeter Data during Winter–Spring Transition

A new method called Bézier curve fitting (BCF) for approximating CryoSat-2 (CS-2) SAR-mode waveform is developed to optimize the retrieval of surface elevation of both sea ice and leads for the period of late winter/early spring. We found that the best results are achieved when the retracking points are fixed on positions at which the rise of the fitted Bézier curve reaches 70% of its peak in case of leads, and 50% in case of sea ice. In order to evaluate the proposed retracking algorithm, we compare it to other empirically-based methods currently reported in the literature, namely the threshold first-maximum retracker algorithm (TFMRA) and the European Space Agency (ESA) CS-2 in-depth Level-2 algorithm (L2I). The results of the retracking procedure for the different algorithms are validated using data of the Operation Ice Bridge (OIB) airborne mission. For two OIB campaign periods in March 2015 and April 2016, the mean absolute differences between freeboard values retrieved from CS-2 and OIB data were 9.22 and 7.79 cm when using the BCF method, 10.41 cm and 8.16 cm for TFMRA, and 10.01 cm and 8.42 cm for L2I. This suggests that the sea ice freeboard data can be obtained with a higher accuracy when using the proposed BCF method instead of the TFMRA or the CS-2 L2I algorithm.

Retrieving Sea Level and Freeboard in the Arctic: A Review of Current Radar Altimetry Methodologies and Future Perspectives

Spaceborne radar altimeters record echo waveforms over all Earth surfaces, but their interpretation and quantitative exploitation over the Arctic Ocean is particularly challenging. Radar returns may be from all ocean, all sea ice, or a mixture of the two, so the first task is the determination of which surface and then an interpretation of the signal to give range. Subsequently, corrections have to be applied for various surface and atmospheric effects before making a comparison with a reference level. This paper discusses the drivers for improved altimetry in the Arctic and then reviews the various approaches that have been used to achieve the initial classification and subsequent retracking over these diverse surfaces, showing examples from both LRM (low resolution mode) and SAR (synthetic aperture radar) altimeters. The review then discusses the issues concerning corrections, including the choices between using other remote-sensing measurements and using those from models or climatology. The paper finishes with some perspectives on future developments, incorporating secondary frequency, interferometric SAR and opportunities for fusion with measurements from laser altimetry or from the SMOS salinity sensor, and provides a full list of relevant abbreviations.

High interannual variability of sea ice thickness in the Arctic region

Possible future changes in Arctic sea ice cover and thickness, and consequent changes in the ice-albedo feedback, represent one of the largest uncertainties in the prediction of future temperature rise. Knowledge of the natural variability of sea ice thickness is therefore critical for its representation in global climate models. Numerical simulations suggest that Arctic ice thickness varies primarily on decadal timescales owing to changes in wind and ocean stresses on the ice, but observations have been unable to provide a synoptic view of sea ice thickness, which is required to validate the model results. Here we use an eight-year time-series of Arctic ice thickness, derived from satellite altimeter measurements of ice freeboard, to determine the mean thickness field and its variability from 65 degrees N to 81.5 degrees N. Our data reveal a high-frequency interannual variability in mean Arctic ice thickness that is dominated by changes in the amount of summer melt, rather than by changes in circulation. Our results suggest that a continued increase in melt season length would lead to further thinning of Arctic sea ice.