Over the last decade, the Greenland Ice Sheet has shown rapid change, characterised by rapid thinning along the margins, accelerating outlet glaciers and overall increasing mass loss. The state of the Greenland Ice Sheet is of global importance and has consequently been included in the CCI Programme, described by several Essential Climate Variables (ECVs).

The objective of the Greenland Ice Sheet project is to maximise the impact of ESA satellite data on climate research, by analysing data from international Earth Observation missions such as ERS, Envisat, CryoSat, GRACE and the Sentinel series of satellites.

This project produces data products of the following five ECV parameters, which are important in characterising the Greenland Ice Sheet:

During the project, a new R&D parameter will be produced:

The production of the following ECV parameter has been suspended:

The CCI ESA data will be supplemented with partner agency data from missions such as Landsat and GRACE in order to provide consistent, long-term time series of these five parameters.

All data is made available to the public in a transparent format. We believe that the data sets produced are of great societal importance, particularly due to the connection between ice sheets’ changes and future global sea level changes.

The Need for Ice Sheet Data from Satellites

There is a global interest in understanding the dynamics of ice sheets and their response to climate changes. This need has emerged from a need to understand the consequences of present and future changes in ice sheet mass in order to predict their contribution to the global and regional sea level change (when the ice sheets melt, sea level will drop in the vicinity of the ice sheets). One of the uncertainties in predicting future sea level is that the ice sheet flow models have not yet been developed at a sufficient level of detail to take the effects of fast-flowing ice streams into account. Furthermore, the physical processes at the base of an ice sheet and their relation to basal hydrology have not yet been fully addressed and implemented into models. The issue of basal conditions and their relation to fast-flowing ice streams is a critical point in understanding the ice sheet response to global warming.

Numerical models of the ice sheet are inherently complex. Model simulations require large computer resources and the capacity of the computing systems implies constraints on the possible space and time resolution. This leads to the following situation:

The ice sheet modelling community is generally a diverse and scattered community working with various models of different complexity, different datasets, and resolutions, with a focus on different goals. Ice flow modellers have been working independently with individually developed models, but in recent years, community ice flow models are being developed, and research groups are forming around these models. A number of these models are being coupled to climate models, mostly offline, but progress is made in fully coupled climate and ice sheet model systems. The purpose of these coupled modelling efforts has mainly been to investigate the evolution of the ice sheets in the past or into the future, in particular, to understand the contribution to the global sea level, and secondary to include feedback from ice sheets in coupled climate models.

The international research community is relatively unorganised in regard to a formalized program of long-term monitoring of the Greenland Ice Sheet (GrIS) changes. In spite of the immediate interest in GrIS mass changes, the reporting of such changes is mainly found in scientific publications, but a few systematic monitoring programs are formalized.

Users of the Ice Sheets Data

Users of the ice_sheets_cci data products can generally be divided into the following groups:

The direct users of the ice sheets data products are thus a relatively broad group covering several scientific communities. They are working with different approaches and at different levels. However, for all groups, it is often a significant problem to collect relevant data from various sources and transform them into a standard format.