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Temperature and heating-induced temperature difference measurements from SIMBA-type sea ice mass balance buoy 2022T97

Temperature and heating-induced temperature difference profiles were measured through the atmosphere, sea ice, and ocean using a SIMBA-type sea ice mass balance buoy equipped with a several meter long thermistor chain. The present dataset was recorded by SIMBA 2022T97 (original name NPOL_0803) installed on drifting sea ice in the Arctic Ocean during the expedition Kronprins Haakon AO22 in 2022. Data is available between 2022-08-06 10:38:00 and 2022-11-22 03:02:00. The thermistor chain was Variable 5 m long and included 241 sensors with a regular spacing of 2 cm. The resulting time series includes the evolution of temperature and temperature differences at 30 s and 120 s during a heating cycle of 120 s as a function of location, depth and time. The sampling intervals were usually between hourly and daily, but were most frequently configured to 6 hours for temperature, and 24 hours for temperature differences. In addition to temperatures and geographic location, barometric pressure, ~1 m air temperature, instrument tilt, and compass heading were measured. The present dataset was processed as follows: obvious inconsistencies (missing values) and unrealistic values of GPS position have been removed. This instrument was deployed as part of the project Arctic Passion.

Arctic PASSION - Polar Monthly Mean Ice Surface Temperature (AP-MMIST) for the time period 1982 to 2024

The Arctic PASSION Polar Monthly Mean IST data set (AP-MMIST) is a combined surface temperature product covering open ocean, marginal ice zone and closed sea ice areas, represented by Sea Surface Temperatures (SST), Marginal Ice Zone Temperatures (MIZT) and sea Ice Surface Temperatures (IST). Beside ocean and sea ice the data set also includes surface temperatures from the Greenland and Antarctic ice sheets. AP-MMIST has been jointly developed and produced by Arctic PASSION WP-1 and the Sea Ice Thematic Assembly Centre (Sea Ice TAC) under the Copernicus Climate Change Service (C3S - service contract: 2022/C3S2_312b_MOi_SC1). The AP-MMIST is a monthly averaged temperature product based on the C3S daily IST CDR and ICDR level 3 data. The daily mean C3S IST data set is a resampled and averaged daily mean IST product using Global Area Coverage - Advanced Very High-Resolution Radiometer (AVHRR) IST level 2 data as input. The level 2 and 3 CDR and ICDR data records are described in Algorithm Theoretical Baseline Document (Eastwood et al., 2023). The surface temperature retrieval algorithm used to produce the basic level 2 product is a traditional split window algorithm using two Thermal InfraRed (TIR) channels to compensate for atmosphere and angular emissivity dependency. This is described in the Algorithm Theoretical Baseline Document (Eastwood et al., 2023). The level 1 TIR input data set is the full data record from the AVHRR on-board NOAA satellite platforms since 1982, as well as AVHRR records on-board Metop satellites since 2006. The product output format is NetCDF with standard attributes, following CF convention to the degree possible. The monthly data are divided into 2 monthly files, one for each hemisphere, SH and NH.

Pan-Arctic Visualization of Landscape Change (2003-2022), Arctic PASSION Permafrost Service

This raster dataset, in Cloud Optimized GeoTIFF format (COG), provides information on land surface changes at the pan-arctic scale. Multispectral Landsat-5 TM, Landsat-7 ETM+, and Landsat-8 OLI imagery (cloud-cover less than 80%, months July and August) was used for detecting disturbance trends (associated with abrupt permafrost degradation) between 2003 and 2022. For each satellite image we calculated the Tasseled Cap multi-spectral index to translate the spectral reflectance signal to the semantic information Brightness, Greenness, and Wetness. In order to characterize change information, we calculated the linear trend of the Brightness, Greenness and Wetness over two decades on the individual pixel level. The final map product therefore contains information on the direction and magnitude of change for all three Tasseled Cap parameters in 30m spatial resolution across the pan-arctic permafrost domain. Features detected include coastal erosion, lake drainage, infrastructure expansion, and fires. The general processing methodology was developed by Fraser et al. 2014 and adapted and expanded by Nitze et al. 2016 and Nitze et al. 2018. Here we upscaled the processing to the circum-arctic permafrost region and the recent 20-year period from 2003 through 2022. The service covers the permafrost region up to 81° North: Alaska (USA), Canada, Greenland, Iceland, Norway, Sweden, Finland, Russia, Mongolia, and China. For Russia and China, regions not containing permafrost were excluded. The data has been processed in Google EarthEngine within the research projects ERC PETA-CARB, ESA CCI+ Permafrost, NSF Permafrost Discovery Gateway, and EU Arctic PASSION. The dataset is a contribution to the 'Panarctic requirements-driven Permafrost Service' of the Arctic PASSION project (see references). Changes in the Tasseled Cap indices Brightness, Greenness, and Wetness are displayed in the image bands red, green, and blue, respectively. Here, coastal erosion (a trend of a land surface transitioning to a water surface) is depicted in dark blue colors, while coastal accretion (a trend of a water surface transitioning to a land surface) is depicted in bright orange colors. Drained lakes appear in bright yellow or orange colors, depending on the soil conditions and vegetation regrowth. Fire scars are a further common feature, which can appear in different colors, depending on the time of the fire and pre-fire land cover. The data can be explored via the Arctic Landscape EXplorer (ALEX, see references) and is available as a public web map service (WMS, see references), both hosted by Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research.

Pan-Arctic Visualization of Landscape Change (2005-2024), Arctic PASSION Permafrost Service

This raster dataset, in Cloud Optimized GeoTIFF format (COG), provides information on land surface changes at the pan-arctic scale. Multispectral Landsat-5 TM, Landsat-7 ETM+, Landsat-8 OLI, and Landsat-9 OLI-2 imagery (cloud-cover less than 70%, months July and August) was used for detecting disturbance trends (associated with abrupt permafrost degradation) between 2005 and 2024. For each satellite image, we calculated the Tasseled Cap multi-spectral index to translate the spectral reflectance signal to the semantic information Brightness, Greenness, and Wetness. In order to characterize change information, we calculated the linear trend of Brightness, Greenness, and Wetness over two decades at the individual pixel level, based on annually aggregated data. The final map product therefore contains information on the direction and magnitude of change for all three Tasseled Cap parameters at 30 m spatial resolution across the pan-arctic permafrost domain. Features detected include coastal erosion, lake drainage, infrastructure expansion, and fires. The general processing methodology was developed by Fraser et al. (2014) and adapted and expanded by Nitze et al. (2016, 2018). Here, we upscaled the processing to the circum-arctic permafrost region and applied it to the recent 20-year period from 2005 through 2024. The service covers the permafrost region up to 81° North: Alaska (USA), Canada, Greenland, Iceland, Norway, Sweden, Finland, Russia, Mongolia, and China. For Russia and China, regions not containing permafrost were excluded. The data have been processed in Google Earth Engine as part of the research projects ERC PETA-CARB, ESA CCI+ Permafrost, NSF Permafrost Discovery Gateway, and EU Arctic PASSION. The dataset is a contribution to the 'Pan-Arctic Requirements-Driven Permafrost Service' of the Arctic PASSION project (see References). Changes in the Tasseled Cap indices – Brightness, Greenness, and Wetness – are displayed in the image bands red, green, and blue, respectively. Here, coastal erosion (a trend of a land surface transitioning to a water surface) is depicted in dark blue tones, while coastal accretion (a trend of a water surface transitioning to a land surface) is depicted in bright orange colors. Drained lakes are shown in bright yellow or orange colors, depending on the soil conditions and vegetation regrowth. Fire scars are a further common feature, appearing in different colors depending on the time of the fire and the pre-fire land cover. The data can be explored via the Arctic Landscape EXplorer (ALEX; see References) and are available as a public web map service (WMS; see References), both hosted by Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research.

Snow depth, sea ice thickness and interface temperatures derived from measurements of SIMBA buoys deployed in the Arctic Ocean and Southern Ocean between 2012 and 2023

This data collection, assembled as part of the Arctic PASSION project, encompasses 96 Snow and Ice Mass Balance Array (SIMBA) buoys that were deployed between 2012 and 2023 in the Arctic Ocean (predominantly the Transpolar Drift Stream) and the Southern Ocean (Weddell Sea/Atka Bay). SIMBAs are thermistor string type IMBs (Jackson et al., 2013) which measure the environmental temperature SIMBA-ET and a temperature change around the thermistors after a weak heating is applied to each sensor (SIMBA-HT). Based on a manual classification method, the SIMBA-ET and SIMBA-HT were processed to obtain snow depth and ice thickness (smoothed with a 3-day running mean), as well as the thermistor number, the vertical position Z relative to the snow-ice interface and the measured SIMBA-ET at each detected interface (atmosphere-snow, snow-ice and ice-ocean). To do this, we combined two derivatives of measured temperatures (the ET vertical gradient and HT rise ratio) to reduce the detection uncertainty of all interfaces considered. The snow and/or ice surface, consequentially the snow depth, is determined by the ET vertical gradient. Potential formation of snow ice is not explicitly considered in this data set, but may occur as depicted by vertical changes of the snow-ice interface position. The ice-ocean interface is usually determined using the HT rise ratio and serves as the lower limit for ice thickness. Overall, the accumulated error is 2 to 4 times the sensor spacing for both the snow depth and ice thickness. For interface temperatures, individual sensors in the chain measure with a temperature resolution of 0.0625°C, with the overall accuracy landing in the range of ± 2°C (Jackson et al., 2013). After the snow cover has melted, negative values for snow depth may indicate the onset of ice surface melt.

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