A global Carbon Cycle Data Assimilation System CCDAS

A global Carbon Cycle Data Assimilation System (CCDAS) and its link to CAMELS • Marko Scholze 1, Peter Rayner 2, Wolfgang Knorr 3, • Thomas Kaminski 4, Ralf Giering 4 & Heinrich Widmann 3 • 1 st Carbo. Europe Integration Workshop, Potsdam, 2004 1 2 3 4 Fast. Opt

QUEST c • QUEST is a newly, NERC funded directed programme (5 years). • QUEST aims to achieve a better qualitative and quantitative understanding of large-scale processes and interactions in the Earth System, especially the interactions among biological, physical and chemical processes in the atmosphere, ocean and land their implications for human activities. • QUEST mainly focuses on: (1) the contemporary carbon cycle and its interactions with climate and atmospheric chemistry; (2) the natural regulation of atmospheric composition on glacial-interglacial and longer time scales; and (3) the implications of global environmental changes for the sustainable use of resources. • QUEST consists of a core team, strategic activities, fellowships, and collaborative grants. • QUEST website: http: //quest. bris. ac. uk

CAMELS Carbon Assimilation and Modelling of the European Land Surface CAMELS an EU Framework V Project (Part of the Carbo. Europe Cluster) CAMELS c

CAMELS PARTICIPANTS (the “Jockeys”) q q q q q Hadley Centre, Met Office, UK – Coordinator: Peter Cox LSCE, France MPI-BGC, Germany UNITUS, Italy ALTERRA, Netherlands European Forestry Institute, Finland CEH, UK IES/JRC, EC Fast. Opt, Germany

CAMELS AND INVERSE MODELLING • CAMELS Goals and General Strategy: Combining Inverse and Forward Model Strategies (material by Peter Cox, Hadley Centre) • Carbon Cycle Data Assimilation and Calculation of Uncertainties (CCDAS consortium)

CAMELS Goals • Best estimates and uncertainty bounds for the contemporary and historical land carbon sinks in Europe and elsewhere, isolating the effects of direct landmanagement. • A prototype carbon cycle data assimilation system (CCDAS) exploiting existing data sources (e. g. flux measurements, carbon inventory data, satellite products) and the latest terrestrial ecosystem models (TEMs), in order to produce operational estimates of “Kyoto sinks“.

CAMELS Motivating Science Questions • Where are the current carbon sources and sinks located on the land how do European sinks compare with other large continental areas? • Why do these sources and sinks exist, i. e. what are the relative contributions of CO 2 fertilisation, nitrogen deposition, climate variability, land management and landuse change? • How could we make optimal use of existing data sources and the latest models to produce operational estimates of the European land carbon sink?

CAMELS Inverse Modelling Method : Use atmospheric transport model to infer CO 2 sources and sinks most consistent with atmospheric CO 2 measurements. Pros : a) Large-scale; b) Data based (transparency). Cons : a) Uncertain (network too sparse); b) not constrained by ecophysiological understanding; c) net CO 2 flux only (cannot isolate land management).

CAMELS Forward Modelling Method : Build “bottom-up” process-based models of land ocean carbon uptake. Advantages : a) Include physical and ecophysiological constraints; b) Can isolate land-management effects; c) can be used predictively (not just monitoring). Disadvantages : a) Uncertain (gaps in process understanding); b) Do not make optimal use of large-scale observational constraints.

CAMELS The Case for Data-Model Fusion • Mechanistic Models are needed to separate contributions to the land carbon sink (e. g. as required by KP). • Large-scale data constraints (from CO 2 and remote-sensing) are required to provide best estimates and error bars at regional and national scales. • Data-Model Fusion = ecophysiological constraints from forward modelling + large-scale CO 2 constraints from inverse modelling

CAMELS Flow Diagram

Combined ‘top-down’/’bottom-up’ Method CCDAS – Carbon Cycle Data Assimilation System Misfit 1 Misfit to observations Forward Modeling: Parameters –> Misfit CO 2 station concentration Atmospheric Transport Model: TM 2 Fluxes Biosphere Model: BETHY Model parameter Inverse Modeling: Parameter optimization

CCDAS set-up 2 -stage-assimilation: 1. AVHRR data (Knorr, 2000) 2. Atm. CO 2 data Background fluxes: 1. Fossil emissions (Marland et al. , 2001 und Andres et al. , 1996) 2. Ocean CO 2 (Takahashi et al. , 1999 und Le Quéré et al. , 2000) 3. Land-use (Houghton et al. , 1990) Transport Model TM 2 (Heimann, 1995)

BETHY (Biosphere Energy-Transfer-Hydrology Scheme) lat, lon = 2 deg • • GPP: C 3 photosynthesis – Farquhar et al. (1980) C 4 photosynthesis – Collatz et al. (1992) stomata – Knorr (1997) Plant respiration: maintenance resp. = f(Nleaf, T) – Farquhar, Ryan (1991) growth resp. ~ NPP – Ryan (1991) Soil respiration: fast/slow pool resp. , temperature (Q 10 formulation) and moisture dependent Carbon balance: average NPP = b average soil resp. (at each grid point) t=1 h t=1 day soil b<1: source b>1: sink

Calibration Step Flow of information in CCDAS. Oval boxes represent the various quantities. Rectangular boxes denote mappings between these fields.

Methodology Minimize cost function such as (Bayesian form): where is a model mapping parameters is a set of observations error covariance matrix need of to observable quantities (adjoint of the model)

Calculation of uncertainties • Error covariance of parameters = inverse Hessian • Covariance (uncertainties) of prognostic quantities • Adjoint, Hessian, and Jacobian code generated automatically from model code by TAF

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

Data fit

Seasonal cycle Barrow Niwot Ridge observed seasonal cycle optimised modeled seasonal cycle

Global Growth Rate Atmospheric CO 2 growth rate Calculated as: observed growth rate optimised modeled growth rate

Parameters I • 3 PFT specific parameters (Jmax, Jmax/Vmax and b) • 18 global parameters • 57 parameters in all plus 1 initial value (offset) Param Initial Predicted Prior unc. (%) Unc. Reduction (%) fautleaf c-cost Q 10 (slow) t (fast) 0. 4 1. 25 1. 5 0. 24 1. 27 1. 35 1. 62 2. 5 0. 5 70 75 39 1 72 78 b (Tr. Ev) b (Tr. Dec) b (Tmp. Dec) b (Ev. Cn) b (Dec. Cn) b (C 4 Gr) b (Crop) 1. 0 1. 44 0. 35 2. 48 0. 92 0. 73 1. 56 3. 36 25 25 78 95 62 95 91 90 1

Parameters II Relative Error Reduction

Carbon Balance Euroflux (1 -26) and other eddy covariance sites* net carbon flux 1980 -2000 g. C / (m 2 year) *from Valentini et al. (2000) and others latitude N

Uncertainty in net flux Uncertainty in net carbon flux 1980 -200 g. C / (m 2 year)

Uncertainty in prior net flux Uncertainty in net carbon flux from prior values 1980 -2000 g. C / (m 2 year)

NEP anomalies: global and tropical global flux anomalies tropical (20 S to 20 N) flux anomalies

IAV and processes Major El Niño events Major La Niña event Post Pinatubo period

Interannual Variability I Normalized CO 2 flux and ENSO Lag correlation (low-pass filtered) ENSO and terr. biosph. CO 2: Correlations seems strong with a maximum at ~4 months lag, for both El Niño and La Niña states.

Interannual Variabiliy II Lagged correlation on grid-cell basis at 99% significance correlation coefficient

Low-resolution CCDAS • A fully functional low resolution version of CCDAS, BETHY runs on the TM 2 grid (appr. 10° x 7. 8°) • 506 vegetation points compared to 8776 (high-res. ) • About a factor of 20 faster than high-res. Version -> ideal for developing, testing and debugging • On a global scale results are comparable (can be used for preoptimising)

Conclusions • CCDAS with 58 parameters can fit 20 years of CO 2 concentration data; ~15 directions can be resolved • Terr. biosphere response to climate fluctuations dominated by El Nino. • A tool to test model with uncertain parameters and to deliver a posterior uncertainties on parameters and prognostics.

Future • • Explore more parameter configurations. Include missing processes (e. g. fire). Upgrade transport model and extend data. Include more data constraints (eddy fluxes, isotopes, high frequency data, satellites) -> scaling issue. • Projections of prognostics and uncertainties into future. • Extend approach to a prognostic ocean carbon cycle model.