As identified by previous work, there is an evident need for the homogenization and harmonization of climate reconstructions using a common set of sampling and methods of analysis. Networking creates added value and is invaluable to advance the continuity, scope and quality of research.
There is a strong need to connect climate modellers with climate scientists studying past change. In this field, research has been carried out separately within the existing national and European research communities. Integration of climate datasets with climate models will allow
(i) the identification of key knowledge gaps in the distribution and quality of past climate data;
(ii) recognize the limitations of models for specific applications in order to provide guidance to users; and
(iii) identify the causes of model limitations to provide guidance for developers to reduce uncertainties in future prediction.
The linkages between proxy data collection and interpretation, and their application in modelling experiments is a key aspect of the project and will form a significant part of this Action. The aim is to explore different approaches to modelling experiments which could be used to inform model quality in the context of the data collected.
In this framework, this COST Action is expected to produce innovative results. Significant synergies will arise from the observational data (range, magnitude and impact of past change) and methodologies (cross-fertilization between palaeoclimatic reconstruction and the need of the modelling community to best exploit these datasets). The past climate science community will benefit from the modelling community recommendations, addressing the required data issues and respond to the research needs. In turn, the modelling community will be supported to develop major insights into the mechanisms and impacts of abrupt and extreme change.
This Action will specifically stimulate the much needed interaction between the modelling and palaeo communities by structuring a series of workshops where this Action will identify specific goals, such as producing better integrated model/data comparisons for key scenarios.