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Observations from space provide unique information about the climate system and climate change. It is increasingly clear that the observations are critical, and a coordinated effort is needed to ensure that long and reliable records are available and preserved. To address this need, the European Space Agency (ESA) has initiated the Climate Change Initiative to improve data products related to climate from long-term monitoring from space and to make these products available for all. The Global Climate Observation System (GCOS) has identified a list of Essential Climate Variables (ECVs), essential factors in the climate system. The ESA Climate Change Initiative (CCI) program has initiated programs devoted to 13 of these ECVs, one of which is ice sheets.

The state of the ice sheets is thought to be a major factor determining the pace of sea-level change.  The goal of the ice_sheets_cci project is to set up a longterm and reliable production of a set of key parameters from ice sheets derived from available and future satellite observations. The selected key parameters are:

  • Surface Elevation Change (SEC);
  • Ice Velocity (IV);
  • Grounding Line Location (GLL);
  • Calving Front Location (CFL); not for currnet Antarctic
  • Gravimetric Mass Balance (GMB)

The project focuses on the Antarctic ice sheet including its outlet glaciers. Similar methods are implemented for Greenland ice sheet.

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 of ice sheet mass in order to predict their contribution to the global and regional sea level change (when the ice sheets melts, sea level will drop in 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 the relation to basal hydrology has 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:

  • Large-scale ice sheet models are presently running on a lower resolution than available satellite data, e.g. surface elevation and velocity. Thus they are not using the full capacity of satellite based data in validations. These models generally need long time series to understand the effect of large scale changes in climate and precipitation.
  • To understand the processes controlling changes in ice flow and outlet glaciers, it is necessary to have access to high-resolution observations. Recently new higher-order models, sometimes nested in lower order models, have been developed to address this issue.

The ice sheet modeling community is generally a diverse and scattered community working with various models of different complexity, different datasets, different resolutions, with focus on different goals. Ice flow modelers 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 off-line, but progress is made in fully coupled climate and ice sheet model systems. The purpose of these coupled modeling 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 feedbacks from ice sheets in coupled climate models.

The international research community is relatively un-organized in regards of 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 are 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:

  • Ice sheet modelers who are using the ECV parameters to validate and/or initialize their models, e.g. comparing modeled and observed SEC, CFL or GLL, or using the ECV parameters to constrain model parameters, e.g. constrain basal drag and ice viscosity by fitting modeled and observed IV.
  • Remote sensing scientists who are deriving volume and mass changes from satellite observations.
  • Surface mass balance modelers, who are interpreting satellite observed volume and mass changes, e.g. deriving mass change from observed volume changes by using firn densification models, or comparing observed mass loss with estimates from surface mass balance models based on climate models and observations.
  • Climate and Ocean modelers, who are interested in the ice sheet component of the climate system and its interactions with other parts of the climate system, e.g. freshwater fluxes from ice sheet on shorter timescales or orographic forcing of wind patterns on longer timescales.
  • Authorities and organizations who are interested in monitoring of the ice sheets for political or practical decisions, for example hydro-power plant planning and maintenance, and information on iceberg production from calving glaciers, of specific interest to future oil- and gas exploration off Greenland.

The direct users of the ice_sheets_cci 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 to transform them into a standard format.