Satellite passive microwave sea-ice concentration data set inter-comparison for Arctic summer conditions

Author(s):
Kern, S.; Lavergne, T.; Notz, D.; Toudal Pedersen, L.; Tonboe, R.
Publication title: Cryosphere
2020
 | Volume: 14 | Issue: 7
2020
DOI: 10.5194/tc-14-2469-2020
Abstract:
We report on results of a systematic inter-comparison of 10 global sea-ice concentration (SIC) data products at 12.5 to 50.0 km grid resolution from s… We report on results of a systematic inter-comparison of 10 global sea-ice concentration (SIC) data products at 12.5 to 50.0 km grid resolution from satellite passive microwave (PMW) observations for the Arctic during summer. The products are compared against SIC and net ice surface fraction (ISF) - SIC minus the per-grid-cell melt pond fraction (MPF) on sea ice - as derived from MODerate resolution Imaging Spectroradiometer (MODIS) satellite observations and observed from ice-going vessels. Like in Kern et al. (2019), we group the 10 products based on the concept of the SIC retrieval used. Group I consists of products of the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Ocean and Sea Ice Satellite Application Facility (OSI SAF) and European Space Agency (ESA) Climate Change Initiative (CCI) algorithms. Group II consists of products derived with the Comiso bootstrap algorithm and the National Oceanographic and Atmospheric Administration (NOAA) National Snow and Ice Data Center (NSIDC) SIC climate data record (CDR). Group III consists of Arctic Radiation and Turbulence Interaction Study (ARTIST) Sea Ice (ASI) and National Aeronautics and Space Administration (NASA) Team (NT) algorithm products, and group IV consists of products of the enhanced NASA Team algorithm (NT2). We find widespread positive and negative differences between PMW and MODIS SIC with magnitudes frequently reaching up to 20 %-25 % for groups I and III and up to 30 %-35 % for groups II and IV. On a pan-Arctic scale these differences may cancel out: Arctic average SIC from group I products agrees with MODIS within 2 %-5 % accuracy during the entire melt period from May through September. Group II and IV products overestimate MODIS Arctic average SIC by 5 %-10 %. Out of group III, ASI is similar to group I products while NT SIC underestimates MODIS Arctic average SIC by 5 %-10 %. These differences, when translated into the impact computing Arctic sea-ice area (SIA), match well with the differences in SIA between the four groups reported for the summer months by Kern et al. (2019). MODIS ISF is systematically overestimated by all products; NT provides the smallest overestimations (up to 25 %) and group II and IV products the largest overestimations (up to 45 %). The spatial distribution of the observed overestimation of MODIS ISF agrees reasonably well with the spatial distribution of the MODIS MPF and we find a robust linear relationship between PMW SIC and MODIS ISF for group I and III products during peak melt, i.e. July and August. We discuss different cases taking into account the expected influence of ice surface properties other than melt ponds, i.e. wet snow and coarse-grained snow/refrozen surface, on brightness temperatures and their ratios used as input to the SIC retrieval algorithms. Based on this discussion we identify the mismatch between the actually observed surface properties and those represented by the ice tie points as the most likely reason for (i) the observed differences between PMW SIC and MODIS ISF and for (ii) the often surprisingly small difference between PMW and MODIS SIC in areas of high melt pond fraction. We conclude that all 10 SIC products are highly inaccurate during summer melt. We hypothesize that the unknown number of melt pond signatures likely included in the ice tie points plays an important role - particularly for groups I and II - and recommend conducting further research in this field. © Author(s) 2020. more
Climate Data Records Cryosphere OSI-SAF Validation

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

Author(s):
Kern, S.; Lavergne, T.; Notz, D.; Toudal Pedersen, L.; Tage Tonboe, R.; Saldo, R.; Macdonald Sørensen, A.
Publication title: Cryosphere
2019
 | Volume: 13 | Issue: 12
2019
DOI: 10.5194/tc-13-3261-2019
Abstract:
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 bot… 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 selfoptimizing 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 C1.0% and C3.5 %, while their biases in the Antarctic range from-0:2% to C0.9 %. Group III product biases are different for the Arctic, C0.9% (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. © 2019 Royal Society of Chemistry. All rights reserved. more
Climate Data Records Cryosphere OSI-SAF Validation

Satellite Remote Sensing of Surface Winds, Waves, and Currents: Where are we Now?

Author(s):
Hauser, D.; Abdalla, S.; Ardhuin, F.; Bidlot, J.-R.; Bourassa, M.; Cotton, D.; Gommenginger, C.; Evers-King, H.; Johnsen, H.; Knaff, J.; Lavender, S.; Mouche, A.; Reul, N.; Sampson, C.; Steele, E.C.C.; Stoffelen, A.
Publication title: Surveys in Geophysics
2023
2023
DOI: 10.1007/s10712-023-09771-2
Abstract:
This review paper reports on the state-of-the-art concerning observations of surface winds, waves, and currents from space and their use for scientifi… This review paper reports on the state-of-the-art concerning observations of surface winds, waves, and currents from space and their use for scientific research and subsequent applications. The development of observations of sea state parameters from space dates back to the 1970s, with a significant increase in the number and diversity of space missions since the 1990s. Sensors used to monitor the sea-state parameters from space are mainly based on microwave techniques. They are either specifically designed to monitor surface parameters or are used for their abilities to provide opportunistic measurements complementary to their primary purpose. The principles on which is based on the estimation of the sea surface parameters are first described, including the performance and limitations of each method. Numerous examples and references on the use of these observations for scientific and operational applications are then given. The richness and diversity of these applications are linked to the importance of knowledge of the sea state in many fields. Firstly, surface wind, waves, and currents are significant factors influencing exchanges at the air/sea interface, impacting oceanic and atmospheric boundary layers, contributing to sea level rise at the coasts, and interacting with the sea-ice formation or destruction in the polar zones. Secondly, ocean surface currents combined with wind- and wave- induced drift contribute to the transport of heat, salt, and pollutants. Waves and surface currents also impact sediment transport and erosion in coastal areas. For operational applications, observations of surface parameters are necessary on the one hand to constrain the numerical solutions of predictive models (numerical wave, oceanic, or atmospheric models), and on the other hand to validate their results. In turn, these predictive models are used to guarantee safe, efficient, and successful offshore operations, including the commercial shipping and energy sector, as well as tourism and coastal activities. Long-time series of global sea-state observations are also becoming increasingly important to analyze the impact of climate change on our environment. All these aspects are recalled in the article, relating to both historical and contemporary activities in these fields. © 2023, The Author(s). more
Atmosphere Climate Data Records NWP-SAF OSI-SAF Software for CDR development Validation

Satellite signature of the instantaneous wind response to mesoscale oceanic thermal structures

Author(s):
Meroni, Agostino N.; Desbiolles, Fabien; Pasquero, Claudia
Publication title: Quarterly Journal of the Royal Meteorological Society
2023
 | Volume: 149 | Issue: 757
2023
DOI: 10.1002/qj.4561
Abstract:
The thermal air–sea interaction mechanism that modulates the atmospheric mixing due to sea-surface temperature (SST) variability is studied with long-… The thermal air–sea interaction mechanism that modulates the atmospheric mixing due to sea-surface temperature (SST) variability is studied with long-term consistent satellite records. Statistical analyses of daily and instantaneous wind and SST data are performed over the major western boundary currents (WBCs). This wind–SST coupling, which is mediated by atmospheric mixing, is found to be very relevant on daily, and even shorter, time scales. Co-located and simultaneous SST and surface wind fields (from Advanced Very High Resolution Radiometer and Advanced Scatterometer data) reveal that the atmosphere responds instantaneously to the presence of SST structures with a larger coupling coefficient with respect to daily and monthly time-averaged fields. The coupling strength varies seasonally over WBCs in the Northern Hemisphere, with wintertime coupling being the lowest. Reanalysis data show that this behaviour is related to the seasonality of the air–sea temperature difference over the region of interest. Over the Northern Hemisphere WBCs, dry and cold continental air masses drive very unstable conditions, associated with very weak thermal air–sea coupling. more
Application Atmosphere Climate Data Records OSI-SAF Ocean

Satellite remote sensing of regional and seasonal Arctic cooling showing a multi-decadal trend towards brighter and more liquid clouds

Author(s):
Lelli, L.; Vountas, M.; Khosravi, N.; Burrows, J.P.
Publication title: Atmospheric Chemistry and Physics
2023
 | Volume: 23 | Issue: 4
2023
DOI: 10.5194/acp-23-2579-2023
Abstract:
Two decades of measurements of spectral reflectance of solar radiation at the top of the atmosphere and a complementary record of cloud properties fro… Two decades of measurements of spectral reflectance of solar radiation at the top of the atmosphere and a complementary record of cloud properties from satellite passive remote sensing have been analyzed for their pan-Arctic, regional, and seasonal changes. The pan-Arctic loss of brightness, which is explained by the retreat of sea ice during the current warming period, is not compensated by a corresponding increase in cloud cover. A systematic change in the thermodynamic phase of clouds has taken place, shifting towards the liquid phase at the expense of the ice phase. Without significantly changing the total cloud optical thickness or the mass of condensed water in the atmosphere, liquid water content has increased, resulting in positive trends in liquid cloud optical thickness and albedo. This leads to a cooling trend by clouds being superimposed on top of the pan-Arctic amplified warming, induced by the anthropogenic release of greenhouse gases, the ice-albedo feedback, and related effects. Except over the permanent and parts of the marginal sea ice zone around the Arctic Circle, the rate of surface cooling by clouds has increased, both in spring (-32 % in total radiative forcing for the whole Arctic) and in summer (-14 %). The magnitude of this effect depends on both the underlying surface type and changes in the regional Arctic climate. Copyright: © 2023 Luca Lelli et al. more
Application Atmosphere Climate Data Records EUM-Secr

Satellite-based time-series of sea-surface temperature since 1980 for climate applications

Author(s):
Embury, O; Merchant, CJ; Good, SA; Rayner, NA; Hoyer, JL; Atkinson, C; Block, T; Alerskans, E; Pearson, KJ; Worsfold, M; Mccarroll, N; Donlon, C
Publication title: SCIENTIFIC DATA
2024
 | Volume: 11 | Issue: 1
2024
DOI: 10.1038/s41597-024-03147-w
Abstract:
A 42-year climate data record of global sea surface temperature (SST) covering 1980 to 2021 has been produced from satellite observations, with a high… A 42-year climate data record of global sea surface temperature (SST) covering 1980 to 2021 has been produced from satellite observations, with a high degree of independence from in situ measurements. Observations from twenty infrared and two microwave radiometers are used, and are adjusted for their differing times of day of measurement to avoid aliasing and ensure observational stability. A total of 1.5 x 1013 locations are processed, yielding 1.4 x 1012 SST observations deemed to be suitable for climate applications. The corresponding observation density varies from less than 1 km-2 yr-1 in 1980 to over 100 km-2 yr-1 after 2007. Data are provided at their native resolution, averaged on a global 0.05 degrees latitude-longitude grid (single-sensor with gaps), and as a daily, merged, gap-free, SST analysis at 0.05 degrees. The data include the satellite-based SSTs, the corresponding time-and-depth standardised estimates, their standard uncertainty and quality flags. Accuracy, spatial coverage and length of record are all improved relative to a previous version, and the timeseries is routinely extended in time using consistent methods. more
Application Climate Data Records Cryosphere OSI-SAF Ocean

Sea surface wind retrieval from CFOSAT Rotating fan-beam scatterometer in ocean cyclones

Author(s):
Ji, W.; Fang, Z.; Liu, D.; Yu, R.; Feng, D.
Publication title: International Journal of Remote Sensing
2024
 | Volume: 45 | Issue: 22
2024
DOI: 10.1080/01431161.2024.2402007
Abstract:
Rotating fan-beam scatterometer (RFSCAT) is a radar scatterometer system for sea surface wind vector measurement. Compared with other available scatte… Rotating fan-beam scatterometer (RFSCAT) is a radar scatterometer system for sea surface wind vector measurement. Compared with other available scatterometers, RFSCAT can provide more combination of azimuth angles and incidence angles for a single WVC (wind vector cell), this observation mechanism is more conducive to the sea surface wind direction retrieval. In this paper, the NSCAT-4DS GMF (geophysical model function) with SST (sea surface temperature) correction, and the MSS (Multiple Solution Scheme) combination with a 2DVAR (2-dimensional variational analysis) are adopted to retrieve the sea surface winds from RFSCAT on the CFOSAT (Chinese-French Oceanography Satellite). The retrieved RFSCAT sea surface winds and ASCAT (advanced scatterometer) sea surface winds are compared and tested, and the feasibility of the RFSCAT measuring sea surface winds under high wind speeds is analysed. The results show that the RMSE of the RFSCAT sea surface wind speed using improved algorithm has decreased by 0.292 m s−1, the correlation coefficient has increased by 0.032, and the residual standard deviation has decreased by 0.194 m s−1. The RMSE of RFSCAT sea surface wind direction has decreased by 5.950°, the correlation coefficient has increased by 0.002, and the residual standard deviation has decreased by 5.567°. It is shown that the changes in winds RMSE using pre-improved and improved algorithms have statistical significance. In a word, the spaceborne Ku-band rotating fan-beam scatterometer can capture the winds structure of ocean cyclones. Although there may be high wind speeds saturation phenomena, the detecting sea surface winds at high wind speeds show preferable performance. © 2024 Informa UK Limited, trading as Taylor & Francis Group. more
Atmosphere Climate Data Records OSI-SAF Ocean Validation

Scientific Developments and the EPS-SG Scatterometer

Author(s):
Stoffelen, Ad; Aaboe, Signe; Calvet, Jean-Christophe; Cotton, James; De Chiara, Giovanna; Saldana, Julia Figa; Mouche, Alexis Aurelien; Portabella, Marcos; Scipal, Klaus; Wagner, Wolfgang
Publication title: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
2017
 | Volume: 10 | Issue: 5
2017
DOI: 10.1109/JSTARS.2017.2696424
Abstract:
The second-generation exploitation of meteorological satellite polar system (EPS-SG) C-band-wavelength scatterometer instrument (called SCA), planned … The second-generation exploitation of meteorological satellite polar system (EPS-SG) C-band-wavelength scatterometer instrument (called SCA), planned for launch in 2022, has a direct heritage from the successful advanced scatterometer (ASCAT) flown on the current EPS satellites. In addition, SCA will represent three major innovations with respect to ASCAT, namely: 1) Cross polarization and horizontal copolarization; 2) a nominal spatial resolution of 25 km; and 3) 20% greater spatial coverage than ASCAT. The associated expected science and application benefits that led the SCA design are discussed with respect to ocean, land, and sea ice applications for near-real time, climate monitoring, and research purposes. Moreover, an option to implement an ocean Doppler capability to retrieve the ocean motion vector is briefly discussed as well. In conclusion, the SCA instrument innovations are well set to provide timely benefits in all the main application areas of the scatterometer (winds, soil moisture, sea ice) and can be expected to contribute to new and more sophisticated meteorological, oceanographic, land, sea ice, and climate services in the forthcoming SCA era. more
Application Atmosphere Climate Data Records Land OSI-SAF

Sea ice cover in the Copernicus Arctic Regional Reanalysis

Author(s):
Batrak, Y.; Cheng, B.; Kallio-Myers, V.
Publication title: Cryosphere
2024
 | Volume: 18 | Issue: 3
2024
DOI: 10.5194/tc-18-1157-2024
Abstract:
The Copernicus Arctic Regional Reanalysis (CARRA) is a novel regional high-resolution atmospheric reanalysis product that covers a considerable part o… The Copernicus Arctic Regional Reanalysis (CARRA) is a novel regional high-resolution atmospheric reanalysis product that covers a considerable part of the European Arctic including substantial amounts of ice-covered areas. Sea ice in CARRA is modelled by means of a one-dimensional thermodynamic sea ice parameterisation scheme, which also explicitly resolves the evolution of the snow layer over sea ice. In the present study, we assess the representation of sea ice cover in CARRA and validate it against a wide set of satellite products and observations from ice mass balance buoys. We show that CARRA adequately represents general interannual trends towards thinner and warmer ice in the Arctic. Compared to ERA5, sea ice in CARRA shows a reduced warm bias in the ice surface temperature. The strongest improvement was observed for winter months over the central Arctic and the Greenland and Barents seas where a 4.91ĝ€¯°C median ice surface temperature error in ERA5 is reduced to 1.88ĝ€¯°C in CARRA on average. Over Baffin Bay, intercomparisons suggest the presence of a cold winter-time ice surface temperature bias in CARRA. No improvement over ERA5 was found in the ice surface albedo with spring-time errors in CARRA being up to 0.08 higher on average than those in ERA5 when computed against the CLARA-A2 satellite retrieval product. Summer-time ice surface albedos are comparable in CARRA and ERA5. Sea ice thickness and snow depth in CARRA adequately resolve the annual cycle of sea ice cover in the Arctic and bring added value compared to ERA5. However, limitations of CARRA indicate potential benefits of utilising more advanced approaches for representing sea ice cover in next-generation reanalyses. © Copyright: more
Application Atmosphere CM-SAF Climate Data Records Terrestrial

Seasonal Arctic sea ice forecasting with probabilistic deep learning

Author(s):
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.; Shuckburgh, E.
Publication title: Nature Communications
2021
 | Volume: 12 | Issue: 1
2021
DOI: 10.1038/s41467-021-25257-4
Abstract:
Anthropogenic warming has led to an unprecedented year-round reduction in Arctic sea ice extent. This has far-reaching consequences for indigenous and… Anthropogenic warming has led to an unprecedented year-round reduction in Arctic sea ice extent. This has far-reaching consequences for indigenous and local communities, polar ecosystems, and global climate, motivating the need for accurate seasonal sea ice forecasts. While physics-based dynamical models can successfully forecast sea ice concentration several weeks ahead, they struggle to outperform simple statistical benchmarks at longer lead times. We present a probabilistic, deep learning sea ice forecasting system, IceNet. The system has been trained on climate simulations and observational data to forecast the next 6 months of monthly-averaged sea ice concentration maps. We show that IceNet advances the range of accurate sea ice forecasts, outperforming a state-of-the-art dynamical model in seasonal forecasts of summer sea ice, particularly for extreme sea ice events. This step-change in sea ice forecasting ability brings us closer to conservation tools that mitigate risks associated with rapid sea ice loss. © 2021, The Author(s). more
Application Climate Data Records Cryosphere OSI-SAF
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