1 km Arctic regional land ice areas, daily, with cloud mask, geotiff at https://github.com/AdrienWehrle/SICE/tree/master/masks

The study aimed at exploring, developing and validating different approaches to retrieve snow thickness over sea ice; to develop a new prototype processor; and to produce and validate an experimental dataset of snow thickness over the Arctic.

Climate change is one of the greatest societal challenges of the 21st century. The dominant source of global warming is the increase of anthropogenic greenhouse gases in the Earth`s atmosphere. atmosphere. The two most important of those species are carbon dioxide (CO2) and methane (CH4). Together they account for ~82% of the anthropogenic radiative forcing. However, uncertainties in our knowledge of the budgets of these gases, which are determined by their sources and sinks, as well as inadequately understood feedback mechanisms, limit the accuracy of current climate change projections from the local to the global scale. To reliably predict the climate of our planet, and to guide political conventions on greenhouse gas avoidance, adequate knowledge of the sources and sinks of these greenhouse gases, their feedbacks, and the quantification of natural versus anthropogenic fluxes is mandatory. Wetland emissions of methane constitute the largest single source of methane to the atmosphere, even when considering all anthropogenic emissions, and are the most uncertain part of the budget. After the tropics, the largest distribution of wetlands is in the Arctic. The Arctic is warming twice as fast as compared to the global average, making climate changess polar effects more intense than anywhere else in the world. The Arctic accounts for nearly 50% of all organic carbon stored in the planetss soil but rising temperatures and thawing permafrost threatens its stability. The main objectives and tasks of MethaneCAMP are to: Collaborate and coordinate with the AMPAC (Artic Methane and Permafrost Challenge) initiative and forming AMPAC network aiming to contribute to bottom:up and top-down estimates of changes in methane emissions in the Arctic. Prepare a high-latitude-focused assessment of current atmospheric CH4 retrievals from medium spatial resolution and high spatial resolution instruments. Identify the improvement potential for high-latitude retrievals of CH4, test and validate these improvements and synthesize the potential of joint strategies. Analyse the changes in the Arctic CH4 with specific focus on i) quantifying longer:term trends, ii) identifying hot spots directly from observations, and iii) studying the apportionment between biogenic and anthropogenic CH4 sources by employing multi-scale Arctic CH4 observations in inverse modelling.

Develop and validate different approaches to retrieve snow thickness over the sea ice, to develop a new prototype processor, and to produce and validate an experimental dataset of snow thickness over the Arctic.

1 km Greenland, daily, with cloud mask, geotiff at https://github.com/AdrienWehrle/SICE/tree/master/masks

Satellite LRM, SAR, SARIN polar altimeter product generated from Cryosat data by DTU Space

Arctic sea surface salinity retrieved from SMOS, spatial resolution 0.25 deg (EASE grid 2.0), temporal resolution 9-day maps generated daily. The product contains the following data: i) sea surface salinity (p.s.u), ii) sea surface salinity uncertainty (p.s.u), and iii) sea surface salinity anomaly (p.s.u): difference between sea surface salinity provided by SSS field and the annual sea surface salinity provided by WOA 2018 A5B7. Product version 3.1. The product will be freely distributed at the BEC webpage http://bec.icm.csic.es and at the project webpage https://arcticsalinity.argans.co.uk.

Using new techniques to measure pan-Arctic sea ice thickness from the satellite radar altimeter Cryosat-2 during summer months

Prior and posterior uncertainties of sea ice volume (SIV, columns 4-6) and snow volume (SNV, columns 7-9) respectively for three regions in km3. Column 1 indicates observation, column 2 indicates uncertainty range ("product" refers to uncertainty specification provided with product), column 3 indicates uncertainty range of additional hypothetical snow product ("-" means no snow product is used). In each of columns 4-9 the lowest uncertainty range is highlighted in bold face font. The two bottom rows give estimates for the uncertainty due to model error, i.e. the residual uncertainty with optimal control vector.

Point product containing a cloud of elevations with an associated uncertainty in geo spatial units. The thematic point product is published on a monthly basis once the Uncertainty calculation is complete.