The Cryosphere Observing System for GEOSS

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Background
In recent years, the cryosphere has received increasing attention from the climate science community, national and international policy makers, the media, and the general public. There are a variety of reasons for this, including:
  • The cryosphere is an inherent component of the Earth Climate System and is probably the most under-sampled element within it.
  • The cryosphere, particularly glaciers, the Greenland ice sheet, permafrost, and Arctic sea-ice, is expected to undergo dramatic changes associated with the climate change. The stability of the cryosphere is therefore a high priority issue for Earth Science with many practical implications.
  • Through several feedbacks it has a large effect on the predictability of weather and climate, and knowledge of the cryosphere is therefore vital at many levels of decision-making.
  • It plays an important role in generating and mediating the conditions for a possible abrupt climate change.
  • It is one of the factors of largest uncertainty among contributors to mean sea level rise.
  • It is an important source for fresh water resources for many countries.
  • In polar regions sea-ice critically affects the pathways and hence patterns of world sea-borne trade, and strongly influences fishing activity.
  • The cryosphere provides many of the most useful indicators of long-term climate change.
The GEO/GEOSS Cryosphere Community of Practice (CP) is required to create a framework for improved coordination of cryospheric observations conducted by research, long-term scientific monitoring, and operational programmes, and to generate the data and information needed for both operational services and research. In the polar regions, the cost of in situ observations is very high, and satellite monitoring is challenging. Therefore there is a particularly strong need for a close coordination of observations serving the various user communities and nations. There is also a need to strengthen national and international institutional structures responsible for cryospheric observations, and to increase resources for ensuring the transition of research-based cryosphere observing projects to sustained observations. The likelihood of achieving such goals will be significantly enhanced through the development of a comprehensive, coordinated, integrated and coherent approach as proposed by the IGOS Cryosphere Theme, and planned for implementation by the Cryosphere Community of Practice.

The Cryosphere Observing System, CryOS, includes more than simply measurements of snow and ice properties. It must include the following five components:
  • Satellite remote sensing instruments,
  • Networks of ground-based instrumentation,
  • Aircraft-based measurements,
  • Modelling, assimilation, and reanalysis systems, and
  • Data management system.
Satellite instruments are essential for delivering sustained, consistent observations of the global cryosphere. No one all-encompassing sensor exists; rather, the combination and synthesis of data from different yet complementary sensors is essential, and underlines the critical importance of maintaining key synergetic elements of the system. Equally important are surface and airborne observations, in that they provide key data that cannot currently be measured from space, more detailed information in critical areas, and observations with which to calibrate and validate satellite retrievals. Satellites in turn are a key to extending local in situ measurements. CryOS needs to foster the evaluation of the cryosphere in models, to disentangle the role of the cryosphere in climate and its predictability as simulated by climate models, and to stimulate improvements in the parameterization of cryospheric processes. The data and information management component must facilitate the flow of data and information in cryospheric research, long-term scientific monitoring, and operational monitoring. However, it must go beyond the traditional metadata service or web portal by encouraging the development of tools to combine all types of data, including model fields, from diverse and distributed data centers.