Martin De Kauwe

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NATURE CLIMATE CHANGE | VOL 5 | JUNE 2015 | www.nature.com/natureclimatechange The response of the terrestrial biosphere to increasing atmospheric CO2 concentration (Ca) is a major uncertainty in models projecting future climate change, because of the critical feedback between terrestrial ecosystem carbon (C) cycling and the atmosphere1–3. Current Earth(More)
The ESA GLOBCARBON project aims to generate fully calibrated estimates of at-land products quasiindependent of the original Earth Observation source for use in Dynamic Global Vegetation Models, a central component of the IGBP-IHDP-WCRP Global Carbon Cycle Joint Project. The service features global estimates of: burned area, fAPAR, LAI and vegetation growth(More)
[1] Remotely sensed, multiannual data sets of shortwave radiative surface fluxes are now available for assimilation into land surface schemes (LSSs) of climate and/or numerical weather prediction models. The RAMI4PILPS suite of virtual experiments assesses the accuracy and consistency of the radiative transfer formulations that provide the magnitudes of(More)
Martin G. De Kauwe, Yan-Shih Lin, Ian J. Wright, Belinda E. Medlyn, Kristine Y. Crous, David S. Ellsworth, Vincent Maire, I. Colin Prentice, Owen K. Atkin, Alistair Rogers, € Ulo Niinemets, Shawn P. Serbin, Patrick Meir, Johan Uddling, Henrique F. Togashi, Lasse Tarvainen, Lasantha K. Weerasinghe, Bradley J. Evans, F. Yoko Ishida and Tomas F. Domingues(More)
Stomatal conductance (gs) affects the fluxes of carbon, energy and water between the vegetated land surface and the atmosphere. We test an implementation of an optimal stomatal conductance model within the Community Atmosphere Biosphere Land Exchange (CABLE) land surface model (LSM). In common with many LSMs, CABLE does not differentiate between gs model(More)
This paper explores the potential to improve spatial estimates of key carbon fluxes by combining Earth Observation data with a simple ecosystem model. Spatial estimates of Leaf Area Index from MODIS at the kilometre scale over a coniferous forest site in Oregon are assimilated into an ecosystem model with an Ensemble Kalman filter. Results show that(More)
This paper details recent progress in the assimilation of top of canopy reflectance data into an ecosystem model to constrain estimates of net carbon flux. Previous work has demonstrated the feasibility of such an approach but its application at wide spatial scales remains relatively un explored. Two aspects of this are examined in this paper: the(More)
Ecosystem models are valuable tools for understanding the growth of vegetation, its response to climatic change and its role in the cycling of greenhouse gasses. Data Assimilation (DA) of synoptic coverage Earth Observation (EO) data into ecosystem models provides a statistically optimal mechanism for constraining the model state vector trajectory both(More)
Understanding the spatial and temporal variation in carbon fluxes is essential to constrain models that predict climate change. However, our current knowledge of spatial and temporal patterns is uncertain, particularly over land. The European Space Agency (ESA) GLOBCARBON project was initiated to generate fully calibrated estimates of at-land products(More)
Data assimilation techniques such as the ensemble Kalman filter and the sequential Metropolis-Hastings algorithm provide a means of integrating satellite data with ecosystemmodels to optimally adjust their temporal trajectory. To some extent these methods can compensate for poor model parameterisations but a preferable scenario is to calibrate the model(More)
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