Permafrost is a phenomenon of the subsurface thermal state and is defined as ground at or below the freezing point of water for two or more years.
Permafrost underlies approximately one quarter of the terrestrial Northern Hemisphere. From borehole temperature data and active layer depth measurements we know that over the past three decades permafrost has been warming, and continues to warm across the circumpolar North. Changing permafrost interacts with ecosystems and climate on various spatial and temporal scales. Environmental changes accelerate the microbial breakdown of organic carbon and the release of the greenhouse gases carbon dioxide and methane which can accelerate climate change. Monitoring across scales is required in order to quantify the changes of variations in this ECV.
Permafrost cannot be directly detected from space, but many surface features of permafrost terrains and typical periglacial landforms are observable with a variety of EO sensors ranging from very high to medium resolution in various wavelengths. In addition, landscape dynamics associated with permafrost changes and geophysical variables relevant for characterising the state of permafrost, such as land surface temperature or snow-water equivalent, can be observed with space-based Earth Observation. Permafrost_CCI will provide for different epochs consistent global maps of the parameters permafrost temperature and active layer thickness based on Earth Observation records ingested into a permafrost model scheme.
The ultimate objective of Permafrost_CCI is to develop and deliver permafrost maps as ECV products primarily derived from satellite measurements. The required associated parameters by GCOS for the ECV Permafrost are “Depth of active layer (m)” and “Permafrost temperature (K)”. Algorithms have been identified which can provide these parameters ingesting a set of global satellite data products (Land Surface Temperature (LST), Snow Water Equivalent (SWE), and landcover) in a permafrost model scheme that computes the ground thermal regime. In Permafrost_CCI we will strongly rely on data products from recent, ongoing and future ESA projects (e.g. LST_CCI, Snow_CCI), which offer consistency over several satellite generations. Validation and evaluation efforts comprise comparison to in-situ measurements of subsurface properties (active layer depth,active layer and permafrost temperatures, organic layer thickness, liquid water content in the active layer and permafrost) and surface properties (vegetation cover, snow depth, surface and air temperatures) as well as rock glacier inventories, local permafrost maps and geophysical survey measurements.
About the project
Permafrost_CCI will use the transient permafrost model CryoGrid 2, which was developed at the University of Oslo and has recently been demonstrated for North Siberia. Remotely sensed data sets of Land Surface Temperature (LST), Snow Water Equivalent (SWE) and landcover will be ingested in a permafrost model that computes the ground thermal regime over time for the production of consistent permafrost maps and active layer thickness at several epochs. We envision a target spatial scale between 10 and 1km and a temporal resolution of 1 month, which meets the requirements of the climate modelling community. Ensemble runs will be performed in order to take the subgrid variability into account and facilitate computing a permafrost probability for each pixel. In addition, ensemble methods can provide a measure of uncertainty, which will be developed and implemented together with users. The performance of the transient algorithm crucially depends on the representation of the ground properties, in particular ice and organic contents. We will compile a new ground stratigraphy product which is tailored to also suit the requirements of the global climate modelling community, thereby addressing a major shortcoming of the permafrost representation in climate models. In addition, the utilisation of freeze/thaw datasets for estimation of permafrost extent and temperature will be considered for round robin activities and especially evaluation of uncertainties in the permafrost transition zone. This approach is purely based on satellite measurements, but with comparably low spatial resolution and accuracy.
Within Permafrost_CCI we develop a hierarchical fully automatic processing scheme for the Permafrost ECVs. We will develop a modular production system for depth of active layer and permafrost temperature. The modular design allows extension of the system to support additional/new input data sources. Due to the very large data volumes to be processed, the algorithms will be implemented in a modern way to support distributed processing on multi-core cluster systems and production of intermediate products and of selected spatial tiles. The developed processing system is sustainable in the sense that it can later be exploited outside CCI e.g. within the C3S services.
At least four user case studies will be selected to demonstrate the value and impact of CCI Permafrost products for different aspects of climate research. The user case studies will consider climate models and local scale information from in-situ data and high resolution land cover maps.
Special emphasis is placed on validation via international and national permafrost monitoring networks and in cooperation with the permafrost community enhancing confidence in the validity of the CCI+Permafrost new maps of potential permafrost extent. We are currently assembling and adapting ground data from the Global Terrestrial Network for Permafrost GTN-P (WMO/GCOS) managed by the International Permafrost Association IPA [https://gtnp.arcticportal.org]. The validation and evaluation efforts will also innovatively consider high-mountain permafrost regions, using in-situ observations of ground temperatures, changes in subsurface ice and unfrozen water content, and velocities of permafrost creep, provided by national data-services such as PERMOS in Switzerland (http://www.permos.ch). The PERMOS data and the ESA GlobPermafrost rock glacier inventory will support the validation of CCI+ Permafrost products in mountain areas, where the CCI+ Permafrost products contain the highest uncertainties. This optimized and standardized validation data set will be supplied within the CCI+ Climate Research Data Package (CRDP) and will thus be publicly available for validation also for the broader climate science community.