Wetland maps including open water extent dynamics based on ENVISAT ASAR WS for Siberia, 2007 and 2008, links to GeoTIFFs

Wetlands store large amounts of carbon, and depending on their status and type, they release specific amounts of methane gas to the atmosphere. The connection between wetland type and methane emission has been investigated in various studies and utilized in climate change monitoring and modelling. For improved estimation of methane emissions, land surface models require information such as the wetland fraction and its dynamics over large areas. Existing datasets of wetland dynamics present the total amount of wetland (fraction) for each model grid cell, but do not discriminate the different wetland types like permanent lakes, periodically inundated areas or peatlands. Wetland types differently influence methane fluxes and thus their contribution to the total wetland fraction should be quantified. Especially wetlands of permafrost regions are expected to have a strong impact on future climate due to soil thawing. In this study ENIVSAT ASAR Wide Swath data was tested for operational monitoring of the distribution of areas with a long-term SW near 1 (hSW) in northern Russia (SW = degree of saturation with water, 1 = saturated), which is a specific characteristic of peatlands. For the whole northern Russia, areas with hSW were delineated and discriminated from dynamic and open water bodies for the years 2007 and 2008. The area identified with this method amounts to approximately 300,000 km**2 in northern Siberia in 2007. It overlaps with zones of high carbon storage. Comparison with a range of related datasets (static and dynamic) showed that hSW represents not only peatlands but also temporary wetlands associated with post-forest fire conditions in permafrost regions. Annual long-term monitoring of change in boreal and tundra environments is possible with the presented approach. Sentinel-1, the successor of ENVISAT ASAR, will provide data that may allow continuous monitoring of these wetland dynamics in the future complementing global observations of wetland fraction.

Composition of airborne material from yellow snow fallen in the Russian North

Large amounts of dust responsible for bright colors of atmospheric precipitation in the temperate, subpolar and polar zones of the northern hemisphere have been rarely observed. In the twentieth century and in the beginning of the twenty first century in the Northern European Russia such events were not registered up to March 25-26, 2008. At that time in some parts of the Arkhangel'sk region, Komi Republic, and Nenets Autonomous Area atmospheric precipitation as sleet and rain responsible for sand- and saffron colors of ice crust formation on the snow surface was observed. During detailed mineralogical, geochemical, pollen, diatom and meteorological investigations it was established that semidesert and steppe regions of the Northwest Kazakhstan, Volgograd and Astrakhan' regions, and Kalmykia are the main sources of the yellow dust.

Spring snowmelt and midwinter thaw and refreeze north of 60°N based on Seawinds QuikScat 2000-2009

The scatterometer SeaWinds on QuikSCAT provided regular measurements at Ku-band from 1999 to 2009. Although it was designed for ocean applications, it has been frequently used for the assessment of seasonal snowmelt patterns aside from other terrestrial applications such as ice cap monitoring, phenology and urban mapping. This paper discusses general data characteristics of SeaWinds and reviews relevant change detection algorithms. Depending on the complexity of the method, parameters such as long-term noise and multiple event analyses were incorporated. Temporal averaging is a commonly accepted preprocessing step with consideration of diurnal, multi-day or seasonal averages.

Autonomous recording current meter measurements at the Håkon Mosby mud volcano (LOOME)

The deployment of LOOME was performed by lowering the LOOME frame by winch, followed by positioning of the surface sensors across the most active site by ROV. The frame was placed on an inactive slab of hydrates, eastwards and adjacent to the hot spot. To the frame autonomous recording current meter was mounted, recording physical oceanography variables approximately two to three meter above the seafloor.

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.