Snow height on sea ice and sea ice drift from autonomous measurements from buoy 2015S22, deployed during the Norwegian Young sea ICE cruise (N-ICE 2015) project

Snow height was measured by the Snow Depth Buoy 2015S22, an autonomous platform, drifting on Arctic sea ice, deployed during the Norwegian Young sea ICE cruise (N-ICE 2015) project. The resulting time series describes the evolution of snow depth as a function of place and time between 2015-03-01 and 2015-05-06 in sample intervals of 1 hour. The Snow Depth Buoy consists of four independent sonar measurements representing the area (approx. 10 m**2) around the buoy. The buoy was installed on first year ice. In addition to snow depth, geographic position (GPS), barometric pressure, air temperature, and ice surface temperature were measured. Negative values of snow depth occur if surface ablation continues into the sea ice. Thus, these measurements describe the position of the sea ice surface relative to the original snow-ice interface. Differences between single sensors indicate small-scale variability of the snow pack around the buoy. The data set has been processed, including the removal of obvious inconsistencies (missing values). Records without any snow depth may still be used for sea ice drift analyses.

Snow height on sea ice and sea ice drift from autonomous measurements from buoy 2015S26, deployed during the Norwegian Young sea ICE cruise (N-ICE 2015) project

Snow height was measured by the Snow Depth Buoy 2015S26, an autonomous platform, drifting on Arctic sea ice, deployed during the Norwegian Young sea ICE cruise (N-ICE 2015) project. The resulting time series describes the evolution of snow depth as a function of place and time between 2015-01-24 and 2015-02-21 in sample intervals of 1 hour. The Snow Depth Buoy consists of four independent sonar measurements representing the area (approx. 10 m**2) around the buoy. The buoy was installed on first year ice. In addition to snow depth, geographic position (GPS), barometric pressure, air temperature, and ice surface temperature were measured. Negative values of snow depth occur if surface ablation continues into the sea ice. Thus, these measurements describe the position of the sea ice surface relative to the original snow-ice interface. Differences between single sensors indicate small-scale variability of the snow pack around the buoy. The data set has been processed, including the removal of obvious inconsistencies (missing values). Records without any snow depth may still be used for sea ice drift analyses.

Maps of subsoil temperature and active layer depth of Yakutian ASSR (Autonomous Soviet Socialist Republic of the Soviet Union)

Based on the map of landscapes and permafrost conditions in Yakutia (Merzlotno-landshaftnaya karta Yakutskoi0 ASSR, Gosgeodeziya SSSR, 1991), rasterized maps of permafrost temperature and active-layer thickness of Yakutia, East Siberia were derived. The mean and standard deviation at 0.5-degree grid cell size are estimated by assigning a probability density function at 0.001-degree spatial resolution. Spatial pattern of both variables are dominated by a climatic gradient from north to south, and by mountains and the soil type distribution. Uncertainties are highest in mountains and in the sporadic permafrost zone in the south. The maps are best suited as a benchmark for land surface models which include a permafrost module.

Snow height on sea ice and sea ice drift from autonomous measurements from buoy 2015S18, deployed during POLARSTERN cruise ANT-XXX/2 (PS89)

Snow height was measured by the Snow Depth Buoy 2015S18, an autonomous platform, drifting on Antarctic sea ice, deployed during POLARSTERN cruise ANT-XXX/2 (PS89). The resulting time series describes the evolution of snow depth as a function of place and time between 2015-01-03 and 2015-01-18 in sample intervals of 1 hour. The Snow Depth Buoy consists of four independent sonar measurements representing the area (approx. 10 m**2) around the buoy. The buoy was installed on first year ice. In addition to snow depth, geographic position (GPS), barometric pressure, air temperature, and ice surface temperature were measured. Negative values of snow depth occur if surface ablation continues into the sea ice. Thus, these measurements describe the position of the sea ice surface relative to the original snow-ice interface. Differences between single sensors indicate small-scale variability of the snow pack around the buoy. The data set has been processed, including the removal of obvious inconsistencies (missing values). Records without any snow depth may still be used for sea ice drift analyses.

Properties of seawater (pH total scale) from a Satlantic SeaFET instrument mounted on the continuous surface water sampling system during the Tara Oceans expedition 2009-2013

The Tara Oceans Expedition (2009-2013) sampled the world oceans on board a 36 m long schooner, collecting environmental data and organisms from viruses to planktonic metazoans for later analyses using modern sequencing and state-of-the-art imaging technologies. Tara Oceans Data are particularly suited to study the genetic, morphological and functional diversity of plankton. The present data set provides continuous pH measurements made during 2013 expedition with a Satlantic SeaFET instrument that was connected to the flowthrough system. Data calibration was performed according to Bresnahan et al. (2014) (using spectrophotometric pH measurements on discrete samples (Clayton and Byrne 1993). pH_internal values were taken to calibrate the data (rather than pH_external) because of the better calibration coefficient (there was no trend associated with it). The equations of Clayton and Byrne (1993) was used to compute pH from the measured absorbance values at the temperature of measurement. The data was converted to in situ temperature using the "CO2-sys" program which can be downloaded from http://cdiac.ornl.gov/ftp/co2sys/.

Properties of seawater (partial pressure of carbon dioxide (pCO2)) from a ProOceanus CO2-Pro sensor mounted on the continuous surface water sampling system during the Tara Oceans expedition 2009-2013

The Tara Oceans Expedition (2009-2013) sampled the world oceans on board a 36 m long schooner, collecting environmental data and organisms from viruses to planktonic metazoans for later analyses using modern sequencing and state-of-the-art imaging technologies. Tara Oceans Data are particularly suited to study the genetic, morphological and functional diversity of plankton. The present data set provides continuous measurements of partial pressure of carbon dioxide (pCO2), using a ProOceanus CO2-Pro instrument mounted on the flowthrough system. This automatic sensor is fitted with an equilibrator made of gas permeable silicone membrane and an internal detection loop with a non-dispersive infrared detector of PPSystems SBA-4 CO2 analyzer. A zero-CO2 baseline is provided for the subsequent measurements circulating the internal gas through a CO2 absorption chamber containing soda lime or Ascarite. The frequency of this automatic zero point calibration was set to be 24 hours. All data recorded during zeroing processes were discarded with the 15-minute data after each calibration. The output of CO2-Pro is the mole fraction of CO2 in the measured water and the pCO2 is obtained using the measured total pressure of the internal wet gas. The fugacity of CO2 (fCO2) in the surface seawater, whose difference with the atmospheric CO2 fugacity is proportional to the air-sea CO2 fluxes, is obtained by correcting the pCO2 for non-ideal CO2 gas concentration according to Weiss (1974). The fCO2 computed using CO2-Pro measurements was corrected to the sea surface condition by considering the temperature effect on fCO2 (Takahashi et al., 1993). The surface seawater observations that were initially estimated with a 15 seconds frequency were averaged every 5-min cycle. The performance of CO2-Pro was adjusted by comparing the sensor outputs against the thermodynamic carbonate calculation of pCO2 using the carbonic system constants of Millero et al. (2006) from the determinations of total inorganic carbon (CT ) and total alkalinity (AT ) in discrete samples collected at sea surface. AT was determined using an automated open cell potentiometric titration (Haraldsson et al. 1997). CT was determined with an automated coulometric titration (Johnson et al. 1985; 1987), using the MIDSOMMA system (Mintrop, 2005). fCO2 data are flagged according to the WOCE guidelines following Pierrot et al. (2009) identifying recommended values and questionable measurements giving additional information about the reasons of the questionability.

Snow height on sea ice and sea ice drift from autonomous measurements from buoy 2014S13, deployed during the CryoVEx2014 field campaign

Snow height was measured by the Snow Depth Buoy 2014S13, an autonomous platform, drifting on Arctic sea ice, deployed during the CryoVEx2014 field campaign. The resulting time series describes the evolution of snow height as a function of place and time between 2014-03-30 and 2014-07-20 in sample intervals of 1 hour. The Snow Depth Buoy consists of four independent sonar measurements representing the area (approx. 10 m**2) around the buoy. The buoy was installed on multi year ice. In addition to snow height, geographic position (GPS), barometric pressure, air temperature, and ice surface temperature were measured. Negative values of snow height occur if surface ablation continues into the sea ice. Thus, these measurements describe the position of the sea ice surface relative to the original snow-ice interface. Differences between single sensors indicate small-scale variability of the snow pack around the buoy. The data set has been processed, including the removal of obvious inconsistencies (missing values). Records without any snow height may still be used for sea ice drift analyses. Note: This data set contains only relative changes in snow height, because no initial readings of absolute snow height are available.

Solar radiation over and under sea ice at ROV station PS86/081-1 during POLARSTERN cruise PS86 (AURORA) in July 2014

The observed changes in physical properties of sea ice such as decreased thickness and increased melt pond cover severely impact the energy budget of Arctic sea ice. Increased light transmission leads to increased deposition of solar energy in the upper ocean and thus plays a crucial role for amount and timing of sea-ice-melt and under-ice primary production. Recent developments in underwater technology provide new opportunities to study light transmission below the largely inaccessible underside of sea ice. We measured spectral under-ice radiance and irradiance using the new Nereid Under-Ice (NUI) underwater robotic vehicle, during a cruise of the R/V Polarstern to 83°N 6°W in the Arctic Ocean in July 2014. NUI is a next generation hybrid remotely operated vehicle (H-ROV) designed for both remotely piloted and autonomous surveys underneath land-fast and moving sea ice. Here we present results from one of the first comprehensive scientific dives of NUI employing its interdisciplinary sensor suite. We combine under-ice optical measurements with three dimensional under-ice topography (multibeam sonar) and aerial images of the surface conditions. We investigate the influence of spatially varying ice-thickness and surface properties on the spatial variability of light transmittance during summer. Our results show that surface properties such as melt ponds dominate the spatial distribution of the under-ice light field on small scales (<1000 m**2), while sea ice-thickness is the most important predictor for light transmission on larger scales. In addition, we propose the use of an algorithm to obtain histograms of light transmission from distributions of sea ice thickness and surface albedo.

Snow height on sea ice and sea ice drift from autonomous measurements from buoy 2013S1, deployed during Antarctic Fast Ice Network 2013 (AFIN 2013)

Snow height was measured by the Snow Depth Buoy 2013S1, an autonomous platform, installed close to Neumayer III Base, Antarctic during Antarctic Fast Ice Network 2013 (AFIN 2013). The resulting time series describes the evolution of snow height as a function of place and time between 2013-02-11 and 2013-04-29 in sample intervals of 1 hour. The Snow Depth Buoy consists of four independent sonar measurements representing the area (approx. 10 m**2) around the buoy. The buoy was installed on the ice shelf. In addition to snow height, geographic position (GPS), barometric pressure, air temperature, and ice surface temperature were measured. Negative values of snow height occur if surface ablation continues into the sea ice. Thus, these measurements describe the position of the sea ice surface relative to the original snow-ice interface. Differences between single sensors indicate small-scale variability of the snow pack around the buoy. The data set has been processed, including the removal of obvious inconsistencies (missing values). Records without any snow height may still be used for sea ice drift analyses. Note: This data set contains only relative changes in snow height, because no initial readings of absolute snow height are available.

Snow height on sea ice and sea ice drift from autonomous measurements from buoy 2013S3, deployed at the Barneo ice camp 2013

Snow height was measured by the Snow Depth Buoy 2013S3, an autonomous platform, drifting on Arctic sea ice. This buoy was deployed at the Barneo ice camp 2013. The resulting time series describes the evolution of snow height as a function of place and time between 2013-04-09 and 2013-06-13 in sample intervals of 1 hour. The Snow Depth Buoy consists of four independent sonar measurements representing the area (approx. 10 m**2) around the buoy. The buoy was installed on multi year ice. In addition to snow height, geographic position (GPS), barometric pressure, air temperature, and ice surface temperature were measured. Negative values of snow height occur if surface ablation continues into the sea ice. Thus, these measurements describe the position of the sea ice surface relative to the original snow-ice interface. Differences between single sensors indicate small-scale variability of the snow pack around the buoy. The data set has been processed, including the removal of obvious inconsistencies (missing values). Records without any snow height may still be used for sea ice drift analyses.

Snow height on sea ice and sea ice drift from autonomous measurements from buoy 2013S4, deployed during SIZONet 2013

Snow height was measured by the Snow Depth Buoy 2013S4, an autonomous platform, installed on land-fast sea ice off Barrow, Alaska during SIZONet 2013. The resulting time series describes the evolution of snow height as a function of place and time between 2013-04-09 and 2013-06-28 in sample intervals of 1 hour. The Snow Depth Buoy consists of four independent sonar measurements representing the area (approx. 10 m**2) around the buoy. The buoy was installed on land-fast sea ice. In addition to snow height, geographic position (GPS), barometric pressure, air temperature, and ice surface temperature were measured. Negative values of snow height occur if surface ablation continues into the sea ice. Thus, these measurements describe the position of the sea ice surface relative to the original snow-ice interface. Differences between single sensors indicate small-scale variability of the snow pack around the buoy. The data set has been processed, including the removal of obvious inconsistencies (missing values). Records without any snow height may still be used for sea ice drift analyses. Note: This data set contains only relative changes in snow height, because no initial readings of absolute snow height are available.