The Carbon Cycle Data Assimilation System CCDAS - JULES

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CCDAS two-step procedure for

inferring diagnostics and prognostics

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1 st derivative (gradient) of J( p ) to model parameters p : −∂J( p ) ∂p yields direction of steepest descent. Gradient Method cost function J( p ) 2 nd derivative (Hessian) of J( p ): ∂ 2 J( p ) ∂ p 2 yields curvature of J. Approximates covariance of parameters. Model parameter space ( p ) Figure from Tarantola, 1987

CCDAS two-step procedure for inferring diagnostics and prognostics

Page 1 and 2: The Carbon Cycle Data Assimilation
Page 3 and 4: Carbon Cycle Data Assimilation Syst
Page 5: • Terrestrial biosphere model BET
Page 9 and 10: examples: Posterior uncertainties o
Page 11 and 12: CCDAS prognostic mode here hindcast
Page 13 and 14: CCDAS extension to include remotely
Page 15 and 16: CCDAS extension to include a progno
Page 17 and 18: CCDAS extension to include a model
Page 19: Summary • CCDAS tests a given com

The Carbon Cycle Data Assimilation System CCDAS - JULES

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