B1. Summary
B2. Objectives
B3. Contribution to the programme/key action objectives
B4. Innovation
B5. Project and work plan
B5.1 Introduction
B5.2 Project Planning
B5.3.1 Work package list
B5.3.2 Deliverables list
B5.3.2 Deliverables list
B5.4 Justification of cost
B6. References
CLIVRE proposes an integrated regional approach to study climate variability and change and their effects on socio-economic activities. Inter-disciplinary efforts will join climatologists, impact researcher, economists and sociologists. The climatologists and impact researchers will identify the sensitive areas from the standpoint of the climate variability and change and the economists and sociologist will identify the key regions in the sustainable development perspective. People's perception and understanding of climate and climate change issues will equally be studied. Mutual interaction between climate/environmental and socio/economical analyses will lead to a number of sensitive regions for which a coherent diagnosis and prediction system will be built to help the elaboration of sustainable development scenarios.
The main result of CLIVRE will be an improved capability for environmental change assessment related to both climate variability and socio-economic problems in the sensitive regions as well as an improved knowledge for the decision-making process which leads to the creation of policies related to adaptation and mitigation to climate changes. The deliverables of CLIVRE will take the form of reports consisting of information for scientists, general public and recommandations for policy makers, data sets in a coupled climate-environmental/socio-economical data base, new methodologies regarding diagnosis and prediction of climate driven environmental change in the sustainable development perspective.
B2. Background and scientific objectives
An important objective of the Fifth Framework Programme is to develop the scientific , technological and socio-economic basis and tools necessary for the study and understanding of changes in the environment ( Key action 2 of the Energy, Environment and Sustainable Development Programme).
The primary scientific aims of CLIVRE are:
1. to develop an integrated environmental approach in which climate variability and abrupt change analysis and socio-economical impact studies interact in the sustainable development perspective
2. to build a regional framework for diagnosis and prediction of climate driven environmental change in the sensitive areas defined by the integrated climatological and socio-economical approach.
These are the objectives of RTD priority 2.1.4 open under the second call of the Energy, Environment and Sustainable Development Programme. However, they cannot be separated from the climate change prediction and scenarios studies and the need for environmental observations which are covered by RTD priorities 2.1.3 and 2.4.1. Therefore CLIVRE needs also to address activities in these fields.
The specific CLIVRE objectives are:
to identify the sensitive regions to climate variability and climate abrupt change using both climate/environmental and socio-economical criteria;
to identify and interact with the potential users of project results.
B3. Contribution to the programme/key action objectives
CLIVRE is addressing both environmental and societal-driven issues related to climate variability and change effects spelled out under 1.1.4-2.1.4 and 1.1.4-2.1.3 priorities. Also, CLIVRE will necessarily make better use of existing observation (1.1.4-2.4.1).
There are many scientific results concearning global climate variability and change. A scientific challange is to project the global signals on the regional scale and to identify the sensitive regions in the sustainable development perspective. Climate variability analyses and the downscaling of global change signals on the regional level have been made and then used by the impact researcher for certain areas but the question of their relevance to socio-economic activities has not been interactively addressed as . The sociologist, economists and policy makers are usually situated at the end of one-way knowledge transfer when dealing with climate impacts. CLIVRE will build upon these results using a new approach. Climate and related socio-economic analysis will be connected in a feedback loop. The new elements brought by the proposed project are:
- the interaction between climatologists, impact researchers, sociologist, and economists in defining sensitive areas which will allow a mutual adjustment between climate and socio-economical issues related to climate variability and climate abrupt change;
- the methodology for diagnosis and prediction of climate driven environmental change in sensitive areas
- the assessment of economical benefits of climate diagnosis and predictions in the sensitive areas;
- the improved communication between climatologists, impact researcher and individual private and public end-users;
- estimates of people's perception and understanding of climate and climate changes as a basis for improvements in the decision-making processes leading to climate policies which will be understood and acceptable by the public.
B5.1 Introduction
B5.2 Project Planning
B5.3.1 Work package list
B5.3.2 Deliverables list
B5.3.2 Deliverables list
B5.4 Justification of cost
The integrated multidisciplinary approach within CLIVRE is based on the concept of "sensitive area". In defining the sensitive regions both climate and socio-economic criteria will be take into account. The climate fluctuations relevant to socio-economic activities rose from natural sources of variability and from climate change impacts. The dominant modes of climate variability in Europe such as North Atlantic Oscillation (NAO), Eastern Atlantic pattern are large scale phenomena (Barnston and Livezey, 1987) but their effects on climate conditions are regionally different. For instance, many studies shown that thermal and precipitation anomalies have opposite sign in the northern and southern Europe when NAO pahses occurr in winter (Van Loon and Rogers, 1978; Hurrell, 1995). However, a more detailed picture of NAO effects on regional level is still needed. The effects of Eastern Atlantic pattern, ... are even less studied. Therefore, the analysis of regional climate fluctuations to identify the sensitive areas to the dominant mode of climate variability in Europe will contribute to a better understanding of large-scale/regional scale physical mechanisms controlling the regional climate variability. Furthermore, the socio-economic activities would benefit in a more effective way from an accurate identification of sensitive areas to severe climate fluctuations. This is the usual one-way knowledge flow but the key societal issues will only be solved if in addition to developing climate knowledge the socio-economic context is also analyzed and taken into account. In our proposed project, climate and socio-economic analysis will be coupled to identify the sensitive areas. In this case, the definition of sensitive areas will follow a mutual adjustment process between climate and socio-economical criteria.
The assessment of climate predictability in the sensitive areas will be used to identify the climatic variables which could give predictive information for socio-economic activities. The predictive potential of large scale phenomena which influence the sensitive areas will be used in building prediction models using linear techniques such as multiple regression of canonical correllation analysis (CCA) modes (Von Storch, 1995). Also, hindcasts experiments will be performed using an adapted version of Barnett and Preisendorfer (1978) analog prediction technique based on the climate state vector approach. Socio-economic criteria will be used to assess the practical benefits of climatic outlooks. A mutual adjustment will be made in order to build an optim methodology from both climatic and socio-economic point of view.
Climate change signals which are superimposed on natural climate variability could be important components of climate fluctuations. Climate change scenarios will help to identify the sensitive areas to abrupt climate change. For this purpose, the combined vectors of large-scale parameters and regional scale parameters will be used in the multivariate statistical models and these models will be verified againts the observed data sets and used to improve the statistical downscaling models in order to obtain more reliable regional climate change estimates from the GCM simulations (Von Storch, 1995). Different GCMs may be used to assess the uncertainty level of the results. Several scenarios of regional climate change have been already developed using various climate models. Building upon these results, new versions of climate models will be used as an updated input for downscaling procedure.Consequently, a measure of the uncertainty of the impact of such changes upon various economical domains (e.g agriculture) can be also assessed. Sensitive areas from the standpoint of socio-economical development will be also taken into account by climatologists and impact researcher in their analysis and the models for downscaling will be re-built for a better coupling of socio-economical and climate/environmental aspects. Socio-economic change scenarios will be elaborated following the climate chane scenarios.
The results of climate variability, predictability and change studies for the different European areas will be synthetised in a regionally-orientated methodology for diagnosis and prediction of climate fluctuations in the sustainable development perspective using regional databases consisting of coupled climate/socio-economic indices. Communication with private and public end-users will be established by organising meetings, editing reports and building an interactive website dedicated to the project.
Figure 1. The components of the proposed project
diagram
FIGURE 2- FLOW DIAGRAM OF CLIMATE AND HUMAN SYSTEMS AND THEIR LINKS
The flow diagram (figure 1) identifies the key relationships (links) between climate systems and human systems and their relationships to the decision-making processes which are related to the creation of climate change policies. Studies of human (people's) perception of climate and climate changes as well as the role of values, attitudes and motivation among the population of the sensitive areas selected will be evaluated as part of WP4.
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1-1 |
1-2 |
2-1 |
2-2 |
3-1 |
3-2 |
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1.identifying the sensitive areas to natural climate variability; identifying relationship between large scale and regional climate and the large scale phenomena which influnence climate conditions in the sensitive areas; |
x |
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2.socio-economic analyses for the identified areas; mutual adjustment between climate and socio-economic criteria; the final selection of sensitive area to both natural climate fluctuations and socio-economic criteria. |
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x
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3. building of optimal downscaling models for climate change scenarios in the sensitive areas identified before; regionally-orientated socio-economic change scenarios folowing climate change projections on regional level. |
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4. comparison between the results obtained with different global climate model outputs and the assassment of the level of uncertainaty; socio-economic recommandation for decision makers in the sustainable development perspective; |
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5.hincasts experiments with linear and analog prediction models in the sensitive areas; the assessment of socio-economic benefits of climate outlooks in the sensitive areas; |
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6. development of the optimal predictive regionally-orientated methodology from both climatic and socio-economic point of view; the assessment of public awerness regarding climate variability and change issues. |
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(1-1=first half of the year 1, 2=year 2…)
B5.3 Work packages and deliverables
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Workpackage No. |
Worckpackage title |
Lead Contractor No |
Person-months |
Start month |
End month |
Deliverable |
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WP1 |
Sensitive regions to natural climate fluctuations in the sustainable development perspective |
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?72 |
0 |
12 |
D1, D4,D8 |
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WP2 |
Sensitive regions to abrupt climate change in the sustainable development perspective |
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?72 |
13 |
24 |
D2,D3,D8 |
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WP3 |
Climate predictability in the sensitive regions |
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?72 |
25 |
36 |
D5,D6,D8 |
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WP4 |
Socio-economic aspects of climate variability and change in the sensitive regions |
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?72 |
0 |
36 |
D4,D7,D8,D9 |
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Delivera-ble No |
Deliverable title |
Delivery date |
Nature |
Dissemination level |
|
D1 |
Sensitive areas to natural climate fluctuations and socio-economic constraints |
12 |
Re |
Pu |
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D2 |
Evaluation report on performance of different GCMs in the sensitive areas |
18 |
Re |
Pu |
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D3 |
Climate change scenarios for the sensitive areas |
24 |
Re |
Pu |
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D4 |
Coupled climate/socio-economic database consisting of regional indices |
30 |
Da |
Pu |
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D5 |
Regionally orientated methodology for climate prediction |
32 |
Me |
Pu |
|
D6 |
Assessment of socio-economic benefits due to climate diagnosis and prediction in the sensitive areas; |
34 |
Re |
Pu |
| D7 | Regionally-orientated methodology for diagnosis and prediction of coupled climate-socio-economic changes. | 34 | Me | Pu |
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D8 |
Dissemination of information through an interactive website |
34 |
Re |
Pu |
|
D9 |
Recommandations for regional strategies related to climate variability and change impact |
8 |
Me |
Pu |
B5.3.3 Detailed workpackage description
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Work package description Sensitive regions to climate fluctuations in the sustainable development perspective |
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Work package number |
WP1 |
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Start date or starting event |
0 |
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Lead contractor number |
? |
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Person months per partner |
4/1 |
?/2 |
4/3 |
/4 |
/5 |
/6 |
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Objectives
Input to the workpackage: observational data from the national meteorological services and global data sets such as air surface temperature and precipitation and NCEP/NCAR re-analyses, socio-economic indicators. |
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Description of work Spatial and temporal climate variability patterns will be regionally identified using multivariate statistics such as Empirical Orthogonal Function (EOF) decomposition. The linkages between large scale variability modes (North Atlantic Oscillatin, Eastern Atlantic pattern) and local climate fluctuations will be identified using canonical correlation analysis (CCA). The significant CCA pairs will be used as input for WP2 and WP3. EOF and CCA techniques will be also used to build coupled climate/socio-economic indicies for regionally orientated data bases in WP4. |
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Deliverables
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Milestones and expected results
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Work package description Climate (variability and) change in the sensitive regions |
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Work package number |
WP2 |
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Start date or starting event |
13 |
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Lead contractor number |
3 |
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Person months per partner |
10/1 |
?/2 |
22/3 |
?/4 |
?/5 |
?/6 |
?/7 |
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Objectives
Input to the workpacket: observational data from national meteorological services, GCM simulations from the CMPI project, available global climate model outputs provided by CLIVAR and EUROCLIVAR modelling community, the results from WP1 and WP4. |
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Description of work Large scale predictors for relevant predictands will be used to build statistical models. Multivariate statistical methods including the canonical correlation analysis (CCA), singular value decomposition (SVD) and stepwise multiple regression will be used to relate combined vectors of large scale predictors as well as local/regional predictands. The statistical relationship is then applied to the anomalies of the large-scale parameters simulated by GCMs (general circulation models) under various perturbed climate in order to estimate the local/regional climate changes. More than one GCM will be tested in order to assess the level of the uncertainty of the estimated changes. Only the cases with large-scale parameters well simulated by GCMs will be considered useful for climate change estimation. These estimations will be done for the selected sensitive regions. |
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Deliverables
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Milestones and expected results
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Work package description Climate predictability in the sensitive region |
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Work package number |
WP3 |
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Start date or starting event |
0 |
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Lead contractor number |
? |
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Person months per partner |
10/1 |
?/2 |
8/3 |
?/4 |
?/5 |
?/6 |
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Objectives
Input to the workpacket: selected observational data and indicies from WP1 and WP4, results from WP2. |
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Description of work Significant canaonical correlation analysis (CCA) modes representing the linkages between large scale phenomena and local climate fluctuations will be used to build predictive models for certain local variables. The analog prediction technique based on large scale and local climate state vectors will be aslo used for developing extended-range forecasting models. The skill of the models in forecasting climate variables will be evaluated using the results from the hindcast experiments. Socio-economic benefits will be assessed for the models with large skills which are statistically significant. A mutual adjustement will be made in order to build the optimal predictive methodology using both climate and socio-economic criteria. |
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Deliverables
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Milestones and expected results
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( new elements to be added in WP4?)
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Work package description Regionally-orientated system for diagnosis and prediction of coupled climate/socio-economic changes |
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Work package number |
WP4 |
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Start date or starting event |
0 |
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Lead contractor number |
1 |
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Person months per partner |
18/1 |
?/2 |
2/3 |
?/4 | ?/5 | ?/6 |
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Objectives
Input to the Work package: selected data, indicies and results from WP1, WP2, WP3. |
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Description of work The multidisciplinary approach within CLIVRE has in view creating databases of environmental, economic and social indicies and developing regional strategies based on statistical modelling in sensitive areas. That would create a basis for cooperation between climatologists, environmental and agricultural economists and sociologists involved in research; it would also extend the applicability area of models to various research fields and would consider the environmental impact on, for instance, rural – urban relationship at regional level. The next step would be to present a diagnosis of the sensitive areas using a multidimensional approach (climate and socio-economic criteria) and develop a forecasting analysis in the mentioned areas. These would be done with the help of linear and non-linear statistical models. |
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Deliverables
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Milestones and expected results Development of the database 0-6 Definition of joint socio-economical/environmental sensitive areas 6-13 Diagnosis & forecasting modelling in the joint sensitive areas 29-31 Development of regional strategies 32-34 Recommendations for decision makers and information for general public. 35-36 |
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B5.4 Justification of cost (to be updated)
References (to be updated)
Barnett, T.P. and R. Preisendorfer, 1978: Multifield Analog Prediction of Short Term Climate Fluctuations Using a Climate State Vector. J. Atmos. Sci., 35, 10, 1771-1787.
Barnston, A.G. and R.E. Livezey,1987: Classification, seasonality and persistence of low frequency atmospheric circulation p atterns. Mon. Wea. Rev., 115, 1083-1126.
Global Warming. The Greenpeace Report, Jeremy Leggett (ed.), 1990, Oxford University Press, 545 pp. ?
Hurrell, J.W., 1995: Decadal Trends in the North Atlantic Oscillation: Regional Temperatures and Precipitation, Science, 269, 676-679.
von Storch, H., 1995: Spatial patterns: EOFs and CCA. Analysis of climate variability: Application of statistical techniques, H. Von Storch and A. Navara, Eds., Springer-Verlag, 227-258.
Van Loon, H., and J.C. Rogers, 1978: The seesaw in winter temperatures between Greenlandand northern Europe. Part I: General Description. Mon.Wea.Rev., 106, 296-310.
Preisendorfer, R. W., 1988: Principal component analysis in meteorology and oceanography. Elsevier, 425 pp.