CMUG Integration Meeting Jun 2014
CMUG Integration Meeting Jun 2014
Date: 2 to 4 June 2014
Location: Met Office, Exeter, UK
Please ask the authors' permission before reproducing any part of these presentations.
Monday 2 June
Keynote presentations
Keynote 1: Integration of Remote Sensing in IRI's Climate and Environmental Monitoring Activities for Food Security, Human Health and Disaster Management Pietro Ceccato, Environmental Monitoring Program, IRI
Keynote 2: The importance of observations for understanding the role of glaciers in the Earth climate system Michael Zemp, Director World Glacier Monitoring Service, University of Zurich
Keynote 3: Satellite data sets for climate prediction and services (SPECS) Francisco Doblas-Reyes, Head of Climate Forecasting Unit, IC3
Keynote 4: Observational needs for global carbon cycle modelling Chris Jones, Head of Earth System and Mitigation Science, Met Office
CCI ECV Phase 1 results & Phase 2 plans
Soil Moisture Eva Haas
Land Cover Andy Hartley
Fire Emilio Chuvieco
Aerosol Thomas Holzer-Popp
GHG Michael Buchwitz
Cloud Rainer Hollmann
Ozone Melanie Coldewey-Egbers
Glaciers Liss Andreassen
Ice Sheets Rene Forsburg
SST Nick Rayner
Sea Ice Stein Sandven
Ocean Colour Shubha Sathyendranath
Sea Level Michael Ablain
Tuesday 3 June
CMUG assessment of ECVs
Introduction to CMUG assessments Roger Saunders
Ocean colour assessment David Ford
Ozone and SSH assessments Serge Planton
Ozone, GHG and aerosol assessments Rossana Dragani
Cloud assessments Mark Ringer
Land cover, Fire and Soil moisture Alex Loew
Discussion of ECVs
Discussion of Phase 1 results, Phase 2 plans and the development of research Roger Saunders, Met Office
Applications for ECVs
Datasets for evaluating climate models and their projections: Obs4MIPs Robert Ferraro, NASA-JPL
How observations will inform CMIP6 Cath Senior, Met Office
The QA4ECV project better climate information for research Folkert Boersma, KNMI
Using satellite data to understand uncertainties in reanalyses: UERRA Richard Renshaw, Met Office
CORDEX progress and the need of high-resolution observational datasets Grigory Nikulin, SMHI