Post on 27-Dec-2015
A global Carbon Cycle Data Assimilation System (CCDAS) and
its link to CAMELS
Marko Scholze1, Peter Rayner2, Wolfgang Knorr3, Thomas Kaminski4, Ralf Giering4 & Heinrich
Widmann3
1st CarboEurope Integration Workshop, Potsdam, 20042
FastOpt431
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 and 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 c
Carbon Assimilation and Modelling of the European Land Surface
an EU Framework V Project (Part of the CarboEurope Cluster)
CAMELS
CAMELS
CAMELS PARTICIPANTS (the “Jockeys”)
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 FastOpt, Germany
CAMELS
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
CAMELS
CAMELS Goals
• Best estimates and uncertainty bounds for the contemporary and historical land carbon sinks in Europe and elsewhere, isolating the effects of direct land-management.
• 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
CAMELS Motivating Science Questions
• Where are the current carbon sources and sinks located on the land and 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 CO2 fertilisation, nitrogen deposition, climate variability, land management and land-use 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?
Inverse Modelling
Method : Use atmospheric transport model to infer CO2 sources and sinks most consistent with atmospheric CO2 measurements.
Pros : a) Large-scale; b) Data based (transparency).
Cons : a) Uncertain (network too sparse); b) not constrained by ecophysiological understanding; c) net CO2 flux only (cannot isolate land management).
CAMELS
CAMELS
Forward Modelling
Method : Build “bottom-up” process-based models of land and 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 CO2 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 CO2 constraints from
inversemodelling
CAMELS Flow Diagram
CAMELS
Combined ‘top-down’/’bottom-up’ MethodCCDAS – Carbon Cycle Data Assimilation
System
CO2 stationconcentration
Biosphere Model:BETHY
Atmospheric Transport Model: TM2
Misfit to observations
Model parameter
Fluxes
Misfit 1 Forward Modeling:
Parameters –> Misfit
Inverse Modeling:
Parameter optimization
CCDAS set-up
2-stage-assimilation:
1. AVHRR data(Knorr, 2000)
2. Atm. CO2 data
Background fluxes:1. Fossil emissions (Marland et al., 2001 und Andres et al., 1996)2. Ocean CO2 (Takahashi et al., 1999 und Le Quéré et al., 2000)3. Land-use (Houghton et al., 1990)
Transport Model TM2 (Heimann, 1995)
BETHY(Biosphere Energy-Transfer-Hydrology
Scheme)
• GPP:C3 photosynthesis – Farquhar et al. (1980)C4 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 (Q10 formulation) and soil moisture dependent
• Carbon balance:average NPP = average soil resp. (at each grid point)
<1: source>1: sink
t=1h
t=1h
t=1day
lat, lon = 2 deg
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):
DpMDpMpp pppJ D
T
pT
)()()( 2
1
2
1 10
10 0
-- C C
where- is a model mapping parameters to observable quantities- is a set of observations- error covariance matrixC
DM
p
need of (adjoint of the model)Jp
Calculation of uncertainties
• Error covariance of parameters1
2
2
ji,
p pJ
C = inverse Hessian
T
pX p)p(X
p)p(X
CC
• Covariance (uncertainties) of prognostic quantities
• Adjoint, Hessian, and Jacobian code generated automatically from model code by TAF
cost function J (p) p
Figure from Tarantola, 1987
Gradient Method
1st derivative (gradient) ofJ (p) to model parameters p:
yields direction of steepest descent.
p
p
ppJ
)(
Model parameter space (p)p
2nd derivative (Hessian)of J (p):
yields curvature of J.Approximates covariance ofparameters.
p
22 ppJ
)(
Data fit
Seasonal cycle
Barrow Niwot Ridge
observed seasonal cycle
optimised modeled seasonal cycle
Global Growth Rate
Calculated as:
observed growth rate
optimised modeled growth rate
Atmospheric CO2 growth rate
MLOSPOGLOB CCC 75.025.0
Parameters I
• 3 PFT specific parameters (Jmax, Jmax/Vmax and )
• 18 global parameters• 57 parameters in all plus 1 initial value (offset)
Param InitialPredicted
Prior unc. (%) Unc. Reduction (%)
fautleafc-costQ10 (slow)
(fast)
0.41.251.51.5
0.241.271.351.62
2.50.57075
3917278
(TrEv)(TrDec) (TmpDec) (EvCn) (DecCn) (C4Gr) (Crop)
1.01.01.01.01.01.01.0
1.440.352.480.920.731.563.36
25252525252525
7895629591901
Parameters II
Relative Error Reduction
Carbon Balance
latitude N*from Valentini et al. (2000) and others
Euroflux (1-26) and othereddy covariance sites*
net carbon flux 1980-2000gC / (m2 year)
Uncertainty in net flux
Uncertainty in net carbon flux 1980-200gC / (m2 year)
Uncertainty in prior net flux
Uncertainty in net carbon flux from prior values 1980-2000gC / (m2 year)
NEP anomalies: global and tropical
global flux anomalies
tropical (20S to 20N) flux anomalies
IAV and processes
Major El Niño events
Major La Niña event
Post Pinatubo period
Interannual Variability I
Normalized CO2 flux and ENSO
Lag correlation(low-pass filtered)
ENSO and terr. biosph. CO2: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 TM2 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 pre-optimising)
Conclusions
• CCDAS with 58 parameters can fit 20 years of CO2 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.