International Collaboration on Data Assimilation in Terrestrial Carbon

Cycle Science

CARBON FUSIONwww.carbonfusion.org

The problem

Major uncertainties remain in the global C cycle (±1GtC yr-1 on land/ocean sink strength)

Bottom-up models (e.g. DGVMs) founder due to heterogeneity in process and difficulties in regional and global corroboration

Top-down models (e.g. inversion) use data so aggregated that it is difficult to generate detailed insights

The solution

Model-data fusion: Combine multiple data-sets with modelling Use appropriately designed models Take advantage of recent/planned satellite

launches Employ novel data assimilation approaches Use primary data rather than products to better

quantify uncertainty?

The partners - UK

CTCD – Centre for Terrestrial Carbon Dynamics

DARC – Data Assimilation Research Centre CLASSIC – Climate and Land-Surface Systems

Interactions Centre QUEST – Quantifying and Understanding the

Earth System UK Met Office ECMWF

Collaborators

Max-Planck-Institute for Biogeochemistry, Germany (Martin Heimann)

Laboratory for Climate Sciences and the Environment, France (Philippe Ciais, Peter Rayner)

Department of Atmospheric Science, Colorado State University, USA (Scott Denning)

Marine Biological Laboratory, USA (Edward Rastetter)

CSIRO Earth Observation Centre, Australia (Mike Raupach, Damian Barrett and Pep Canadell)

Objectives

1. to accelerate the development of DA in terrestrial C cycle science;

2. to foster international links and knowledge sharing;

3. to develop productive international collaborations;

4. to develop a global vision and strategy for research, in which the UK can fully participate and take a leading role.

Overarching science question

How can data assimilation contribute to improving the attribution of terrestrial C fluxes, and to reducing errors in estimating the current state of the C cycle?

Subsidiary questions

What are the relative advantages of different approaches to data assimilation?

What data and technical constraints are holding back the science?

How can we best incorporate remote sensing (especially long time-series for C-cycle re-analyses and C column content measurements) into DA schemes?

What is the potential for carbon-water connections in multiple-constraint data assimilation?

How can we assess errors and their propagation through DA schemes?

Proposed activities

1. Enhancing research capabilities by exchanges between participating labs

2. Conference and follow-on workshops for discussion and demonstration of current capabilities

3. A special issue focussing on the application of DA to the terrestrial C cycle

4. A research strategy document detailing a vision for the global community

5. A web-page to provide an interactive environment for C cycle research via model-data fusion

Today’s objectives

What is the current UK capability in model-data fusion for C cycle science?

What can we learn from other scientific communities on model-data fusion?

What are our current weaknesses and from whom can we learn?

What are the major issues to be addressed in the next 3-5 years?

What key outputs should we plan to produce?