Forests harbour around 60% of the world’s biodiversity and around half of its terrestrial carbon, so there is an urgent need to predict how forests will respond to increased atmospheric CO2, logging and land-use change. This project will collate millions of pre-existing field measurements of trees from national forest inventories into a coherent, user-friendly database and use this data in the development and parameterization of models fire at global scales.
The terrestrial carbon levels are determined by the inputs and outputs of carbon to and from ecosystems. Carbon is input to ecosystems from atmospheric CO2 via photosynthesis and becomes tied up in plant matter before eventually returning to the atmosphere via respiration or burning. There is mounting evidence of the importance of burning. Fire accounts for around 10% of carbon losses from ecosystems, and has a much larger indirect impact by reducing global forest area by 50%; and fire is thought to drive much of the interannual variation in the net flux of carbon between the atmosphere and ecosystems. To date, a lack of data has imposed a severe limit on the ability to produce reliable models of fire at global scales, especially because parameterizing terms to describe interannual variation requires long-term data covering multiple years.
Dr Cyril Crevoisier has recently derived a new, simple way to extract reliable fire severity information from infra-red satellite data. Two novel aspects of this new technique make it particularly valuable. First, unlike previous methods, the technique does not rely on Bayesian parameter estimation techniques constrained by strong regional priors – rather, the estimates follow simply and directly from the observational data within each separate grid-cell, making the effective sample size much greater than was possible previously. Second, global infra-red data from are available from 1979 to present: measurement periods of this length are rare for any satellite data.
The first aim of the collaboration is to extend Cyril’s technique, which has only been applied to 5 years of data so far, to 25-year section of past infra-red data. This represents a significant, but manageable, computational challenge in data retrieval, storage, filtering, and error checking. The resultant gridded, 25-year, monthly time-scale map of fire fluxes will be an interesting scientific result in itself, and a valuable resource for a variety of global analyses. The second aim is to combine the new fire data with global climate data for the same period to parameterize a new global model of fire. This model will predict the magnitude of ecosystem carbon loss due to fire given the weather over previous months. The model will be parameterized using likelihood and / or Bayesian methods in conjunction with MCMC sampling. The model will be placed within one of the current generation of Earth system models (the GFDL model, one of the 14 Earth system models included in the IPCC 2001 report), and will hopefully be adopted by other Earth system models, all of which currently use fire models that are much less constrained by data than this new model will be. An additional, long-term aim of the collaboration is to build analysis capability in advance of the new sources of infra-red satellite data that are set to become available in the near future. In principle, these more detailed infra-red data could be used to measure CO2 global fluxes directly.
- Cyril Crevoisier (École Polytechnique / CNRS, France)