Scientific Steering Group (SSG)
Participants of SSG-23, Pune, India
The Scientific Steering Group (SSG) is appointed by the Joint Scientific Committee (JSC) of the World Climate Research Programme (WCRP) and reports to the JSC. Members are selected to cover areas of climate science relevant to CLIVAR while striving to take into account geographic and gender balance.
The SSG oversees the implementation of the CLIVAR activities and ensures that the overall project is responsive to advances in the field and the evolution of the current scientific understanding. The SSG ensures the interaction of CLIVAR with the other WCRP core projects and activities, the WCRP Grand Challenges and other relevant programs outside of WCRP.
Membership of the SSG is rotated; initial terms of service are for three years with possibility of a two-year extension. The CLIVAR SSG meets annually jointly with representatives of each of the CLIVAR panels (typically panel chairs) and invited guests.
SSG Terms of Reference
- Formulate the CLIVAR research programme on climate variability and predictability, based on coupled ocean-atmosphere models, guided by the analysis of observations including paleoclimatic reconstructions, as required to understand the phenomena and predict climate variations.
- Organise an observing programme that would fulfil the data requirements of CLIVAR, taking into account the development of the operational Global Climate and Global Ocean Observing Systems and possible contributions from national research projects.
- Provide scientific guidance for the implementation of CLIVAR, using advice of experts and expert groups as necessary.
- Ensure the exchange and analysis of CLIVAR data and the dissemination of scientific results.
- Establish scientific liaison with relevant organisations and existing programmes, as appropriate.
- Advise the Joint Scientific Committee of the World Climate Research Programme of progress achieved in the implementation of CLIVAR and scientific advances in the understanding of climate variability and predictability.