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  • 1
    Online Resource
    Online Resource
    Advances in operational permafrost monitoring on Svalbard and in Norway
    IOP Publishing ; 2022
    In:  Environmental Research Letters Vol. 17, No. 9 ( 2022-09-01), p. 095012-
    Details
    In: Environmental Research Letters, IOP Publishing, Vol. 17, No. 9 ( 2022-09-01), p. 095012-
    Abstract: The cryosphere web portal maintained by the Norwegian Meteorological Institute (MET Norway), https://cryo.met.no , provides access to the latest operational data and the current state of sea ice, snow, and permafrost in Norway, the Arctic, and the Antarctic. We present the latest addition to this portal: the operational permafrost monitoring at MET Norway and methods for visualising real-time permafrost temperature data. The latest permafrost temperatures are compared to the climatology generated from the station’s data record, including median, confidence intervals, extremes, and trends. There are additional operational weather stations with extended measurement programs at these locations. The collocated monitoring offers daily updated data for studying and monitoring the current state, trends, and the effects of, e.g. extreme climate events on permafrost temperatures. Ground temperature rates obtained from the long-term records in the warmer permafrost found in Norway are typically 0.1 ∘ C–0.2 ∘ C per decade. In contrast, in the colder permafrost of the High Arctic on Svalbard, a warming of up to 0.7 ∘ C per decade is apparent. The operational monitoring provides information faster than ever before, potentially assisting in the early detection of, e.g. record high active layer thickness and pronounced permafrost temperature increases. It may also become an important cornerstone of early warning systems for natural hazards associated with permafrost warming and degradation. Currently, data are submitted manually to the international Global Terrestrial Network for Permafrost and are scheduled for integration with World Meteorological Organisation (WMO) operational services through the WMO Global Cryosphere Watch.
    Type of Medium: Online Resource
    ISSN: 1748-9326
    URL: Article
    DOI: 10.1088/1748-9326/ac8e1c
    Language: Unknown
    Publisher: IOP Publishing
    Publication Date: 2022
    detail.hit.zdb_id: 2255379-4
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  • 2
    Online Resource
    Online Resource
    Version 2 of the EUMETSAT OSI SAF and ESA CCI sea-ice concentration climate data records
    Copernicus GmbH ; 2019
    In:  The Cryosphere Vol. 13, No. 1 ( 2019-01-09), p. 49-78
    Details
    In: The Cryosphere, Copernicus GmbH, Vol. 13, No. 1 ( 2019-01-09), p. 49-78
    Abstract: Abstract. 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 & SSM/I & SSMIS or AMSR-E & AMSR2), in the imaging frequency channels (37 GHz and either 6 or 19 GHz), in their horizontal resolution (25 or 50 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.
    Type of Medium: Online Resource
    ISSN: 1994-0424
    URL: Article
    DOI: 10.5194/tc-13-49-2019
    Language: English
    Publisher: Copernicus GmbH
    Publication Date: 2019
    detail.hit.zdb_id: 2393169-3
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  • 3
    Online Resource
    Online Resource
    Satellite passive microwave sea-ice concentration data set intercomparison: closed ice and ship-based observations
    Copernicus GmbH ; 2019
    In:  The Cryosphere Vol. 13, No. 12 ( 2019-12-10), p. 3261-3307
    Details
    In: The Cryosphere, Copernicus GmbH, Vol. 13, No. 12 ( 2019-12-10), p. 3261-3307
    Abstract: 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 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.
    Type of Medium: Online Resource
    ISSN: 1994-0424
    URL: Article
    DOI: 10.5194/tc-13-3261-2019
    Language: English
    Publisher: Copernicus GmbH
    Publication Date: 2019
    detail.hit.zdb_id: 2393169-3
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  • 4
    Online Resource
    Online Resource
    Improving short-term sea ice concentration forecasts using deep learning
    Copernicus GmbH ; 2024
    In:  The Cryosphere Vol. 18, No. 4 ( 2024-04-30), p. 2161-2176
    Details
    In: The Cryosphere, Copernicus GmbH, Vol. 18, No. 4 ( 2024-04-30), p. 2161-2176
    Abstract: Abstract. Reliable short-term sea ice forecasts are needed to support maritime operations in polar regions. While sea ice forecasts produced by physically based models still have limited accuracy, statistical post-processing techniques can be applied to reduce forecast errors. In this study, post-processing methods based on supervised machine learning have been developed for improving the skill of sea ice concentration forecasts from the TOPAZ4 prediction system for lead times from 1 to 10 d. The deep learning models use predictors from TOPAZ4 sea ice forecasts, weather forecasts, and sea ice concentration observations. Predicting the sea ice concentration for the next 10 d takes about 4 min (including data preparation), which is reasonable in an operational context. On average, the forecasts from the deep learning models have a root mean square error 41 % lower than TOPAZ4 forecasts and 29 % lower than forecasts based on persistence of sea ice concentration observations. They also significantly improve the forecasts for the location of the ice edges, with similar improvements as for the root mean square error. Furthermore, the impact of different types of predictors (observations, sea ice, and weather forecasts) on the predictions has been evaluated. Sea ice observations are the most important type of predictors, and the weather forecasts have a much stronger impact on the predictions than sea ice forecasts.
    Type of Medium: Online Resource
    ISSN: 1994-0424
    URL: Article
    URL: Article
    DOI: 10.5194/tc-18-2161-2024
    DOI: 10.5194/tc-18-2161-2024-supplement
    Language: English
    Publisher: Copernicus GmbH
    Publication Date: 2024
    detail.hit.zdb_id: 2393169-3
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