Develop and validate an advanced system for climate monitoring and prediction for the support of a sustainable development and protection of the environment in Europe. The system will focus on global warming and the consequences thereof. The European cryosphere (the Euro-Arctic region and high-mountain areas with seasonal snow, including Greenland) will be the focus of the main indicator system. Snow and ice variables are extracted and processed by advanced sensor technology and algorithms and applied in regional climate models and statistical models in order to predict changes and run scenario analyses. Project partners with national operational responsibilities have committed themselves with assistance from the industrial partners in the consortium to make EuroClim an operational long-term monitoring system if the prototype system is a technical and cost-effective success.
The main goal is achieved through seven sub-objectives
To determine current and future climate-change user needs and requirements
The user needs and requirements have to be determined and understood in full depth in order to develop a technical solution that will be attractive to the whole European user community. The professional user community for climate change information is mainly climate research laboratories, national authorities and international authorities. The European citizen is also a very important user group requiring concise and easily understandable information, e.g., for clarifying frequent misleading information in the media. The media itself is also expected to be an important user group of the system. Examples of the type of questions the system will answer are: What will the sea level be within 50-100 years with the predicted warming? Which areas will then be flooded? How frequent will flooding and hurricanes occur? How strong will they be? Can Northern Europe experience a regional cooling due to changes in the North Atlantic ocean circulation?
To develop a concept and the technology for generic, scalable and distributed processing and storage of environmental geographical data
There are several environmental problems that require large-scale spatial monitoring, typically from the national to global scale. A class of such systems is long-term monitoring and management systems, which acquire, process, store data and analyse long time-series of data in order to estimate trends and make predictions for future development. The project will develop a generic system concept for this class that integrates distributed system components in a consistent manner creating a virtual monolithic system with a unified user-interface. The system includes a truly distributed solution to storing and processing acquired environmental data as well as background geographical information. The project aims at a high performance information system with sensible information at any scale, ranging from the European (or global) level to regional or local level. The distributed approach ensures direct access to the most up-to-date sources of information for authorities, researchers and the general public. Finally, currently no solution for an integrated web-based retrieval on meta, vector, and raster data exists. The project will evaluate relevant international standards such as CORBA and OGC Web Map in order to reach its objectives
To develop methodology for integrated analysis and storage of multi-sensor, multi-resolution and multi-temporal point and spatial data
There is a tremendous multinational effort to develop sensor technology for environmental monitoring, in particular satellite-based sensors. Algorithms for analysis of single-sensor data have been developed for decades, however, the development of algorithms utilising simultaneous observations with several types of sensors (multi-sensor) have not been developed very far, including data on various scales from point-measurements on the surface to high, medium and low-resolution satellite data. The situation is the same for multi-temporal data, time series of such data. The project aims at developing a consistent and unifying mathematical framework for these types of data. Generic algorithms and data management facilities for high-volume multi-data will be built upon this framework.
To improve the accuracy of algorithms for retrieval of cryospheric variables from earth-observation data of seasonal snow, glaciers and sea ice
When using the cryosphere as indicator of climate change, important variables include: sea-ice coverage and thickness; land seasonal snow cover, wetness and spectral reflectance; and localisation of glacier\'s equilibrium line altitude. The project aims at improving algorithms for extraction of these variables by combining data from different sensor types and ancillary data. Higher accuracy will result in higher accuracy in climate scenario analysis and thereby in the predictions. The research includes development of a bi-directional spectral reflectance model for snow incorporating temporal changes due to metamorphism and impurities, precise sub-pixel snow-cover mapping, new methods of mapping sea-ice thickness using passive microwaves, optical and dual polarisation radar data in combination, and finally, use of multipolarisation radar data for glacier mass balance monitoring.
To improve simulations of a high-resolution state-of-the-art climate model
We will develop and make experiments with a high-resolution model of the ocean circulation in the North Atlantic with embedded sea ice dynamics. Further, we wish to simulate future climate based on atmospheric re-analysis experiments. To do this, we are going to combine remotely-sensed and field observations as well as the output of reduced energy balance models. Further, we also will investigate the transformation from statistical properties of external forcing fields to statistical properties of the resulting output variables and identify key dependencies. The combined expertise within geophysics, satellite remote sensing, statistics, and computer science in the consortium tied to close collaboration with climate model experts is expected to improve the quality of today's climate model simulations.
To develop new statistical tools for trend estimation, scenario analysis and uncertainty assessment
The massive amounts of remote sensing data for processing call for efficient computational-statistical methods for extracting relevant information. Furthermore, uncertainty estimates are important for testing hypotheses about the climate and for future policy making. Statistical and computational methods will be developed and improved for efficient estimation of trends and indicators of climatic change from multiple-scale remote sensing data. We will also develop a framework for assessing uncertainty and generating scenarios.
To initiate an operational monitoring and prediction service
A successful outcome of the development and validation of the monitoring and prediction system is intended to directly lead to an operational application of the system. Towards the end of the project three project partners with national monitoring responsibilities will, if the system is technically and economically successful, make the system operational. Other organisations should also be attracted and committed for using the system network via the user-partners and with the support of the commercially oriented partners. Thus, we aim at getting an almost complete and consistent coverage in Europe for using the system on an operational basis.