Determination of the Siberian Carbon Balance by “Top-Down” and “Bottom-Up” Approaches - I. The Global Context: The Carbon Cycle in the Climate System 1. Overview of the carbon cycle 2. Fundamental scientific questions 3. Prognostic comprehensive models of the oceanic carbon system and of the terrestrial biosphere 4. Coupled models of the carbon cycle - climate system Martin Heimann Max-Planck-Institute for Biogeochemistry PF 100164, D-07701 Jena, Germany www.bgc-jena.mpg.de/~martin.heimann martin.heimann@bgc-jena.mpg.de
History of the Atmospheric CO 2 Concentration
The Global Carbon Cycle in the Climate System
Global CO 2 Budget [PgC a -1 ] 1980s 1990s 1. IPCC TAR [Prentice et al., 2001] Atmospheric increase +3.3 ± 0.1 +3.2 ± 0.1 Fossil fuel and cement production +5.4 ± 0.3 +6.3 ± 0.4 Ocean –1.9 ± 0.6 –1.7 ± 0.5 Land (Net) –0.2 ± 0.7 –1.4 ± 0.7 Changes in landuse +1.7 (+0.6 to +2.5) - Residual (Land) –1.9 (–3.8 to +0.3) - 2. Ocean correction [LeQueré et al., 2003] Ocean –1.8 ± 0.8 –1.9 ± 0.7 Land (Net) –0.3 ± 0.9 –1.2 ± 0.8 3. FAO Statistics + Model [Houghton, 2002] Changes in landuse +2.0 (+0.9 to +2.8) +2.2 (+1.4 to +3.0) Residual (Land) -2.3 (–4.0 to –0.3) -3.4 (–5.0 to –1.8) 4. Remote sensing + Model [De Fries et al., 2002] Changes in landuse +0.6 (+0.3 to +0.8) +0.9 (+0.5 to +1.4) Residual (Land) -0.9 (-3.0 to 0) -2.1 (-3.4 to -0.9) Units: PgC a -1 , positive: flux into atmosphere
Basic Questions on Terrestrial and Oceanic Sinks Mechanisms - which process is responsible for the CO 2 uptake? z Stability - how stable are these sinks? z Permanence - how permanently is the carbon stacked away? z Vulnerability - how vulnerable are the sink processes, e.g. with z respect to climate feedbacks or anthropogenic impacts? Attribution - can we uniquely attribute the sinks to a particular z driver? Spatio-temporal quantification - can we accurately quantify the sinks z in time and space?
Tools Observation network (eddy covariance flux towers, atmospheric z measurements, tracer measurements, inventories, remote sensing, etc.) Process studies ( in situ , manipulative, etc.) z Top-down methods z Bottom-up methods (process models, statistical extrapolations) z Data assimilation z Prognostic coupled carbon cycle - climate system models z
Carbon Flows in Terrestrial Biosphere
Modelled Processes in a Terrestrial Dynamical Vegetation Model (DGVM)
Characteristics of Terrestrial Dynamical Vegetation/Carbon Cycle Models State variables: Biomass in different pools (above-ground, below-ground, z woody, herbaceous, soil pools), leaf area, fractional vegetation cover by different functional vegetation types, nutrients (N, P), temperature and water in different vertical levels in canopy and soil Plant physiology (e.g. photosynthesis) z Land surface water and energy balance z Biology (e.g. growth, allocation, phenology, senescence, diversity, nutrient z interactions): based on empirical relations and optimization principles Current developments: inclusion of age structure z System of non-linear ordinary first order differential equations z Grid-based (typical 0.5° lat-lon) with no interaction between grid cells z Example: LPJ Model: Sitch et al., GCB, 2003
Coupled Carbon Cycle - Climate Model Simulations CO 2 Atmosphere Radiative forcing Emissions Atmosphere GCM Land surface parameters Climate Vegetation Ocean Ocean GCM Soils Carbon Carbon Cycle Model Climate Model
Climate Change Emissions [PgC a -1 ] Scenarios with Coupled Climate- Carbon Cycle Models 1000ppm CO 2 Concentration 750ppm Hadley IPSL Globally averaged near surface temperature +5°C Cox et al. 2001, Dufrene et al., 2001 +3°C
Simulated CO 2 - Uptake by Land and Ocean Cox et al. 2001, Dufresne et al., 2001 Friedlingstein et al., in press, 2003
Simulated Changes in Terrestrial Carbon Pools 1860-2100 in Hadley Model Cox et al. 2001
Feedback Analysis Hadley Model
Quantitative Feedback Analysis Friedlingstein et al., 2003
Conclusions I Global carbon cycle behaves up to now almost like a linear system z in response to the anthropogenic perturbation First coupled climate model simulations demonstrate drastically z different, but potentially significant positive feedbacks between carbon cycle and physical climate system Non-linear effects become apparent during early 21st century - z reduce CO 2 sinks on land and ocean Need for the development of comprehensive monitoring strategy z for: Early detection significant carbon cycle modifications y (e.g. permafrost decline) Model evaluation y Political motivation: corroboration of Kyoto target compliance y
Determination of the Siberian Carbon Balance by “Top-Down” and “Bottom-Up” Approaches - II. Estimating Regional Carbon Balances 1. Methodologies 2. Top-down 3. Bottom-up 4. Outlook
Terrestrial Carbon Observing System - Siberia (TCOS-Siberia, EVK2-CT 2001-00131) 2002-2004 Principal Investigators: MPI BGC Jena, Germany (Heimann, coordination) z LSCE, Saclay, France (Ciais) z IUP, University of Heidelberg, Germany (Levin) z RUG, Groningen, Netherlands (Meijer) z UNITUS, Viterbo, Italy (Valentini) z University of Amsterdam, The Netherlands (Dolman) z IPEE-RAS, Moscow, Russia (Varlagin) z IFOR-RAS, Krasnojarsk, Russia (Shibistova) z IBPC-RAS, Yakutsk, Russia (Maximov) z PIG-RAS, Cherskii, Russia (Zimov) z UNI.BIAL, Bialystok, Poland (Chilmonczyk) z UNI.FB.FBS, Ceske Budejovice, Czech Republic (Santruckova) z
Methodology of CarboEurope Project (IP FP6)
Top-Down Approach: Inverse Modeling of Atmospheric CO 2 Ingredients: Observations at atmospheric station network + observation errors z 3d atmospheric transport model z A priori source/sink distribution + error covariances z Additional constraints z Inversion method z
Global Atmospheric Background Station Network (2001) Globalview (95) AEROCARB (20) TCOS-Siberia (8)
Near Surface Annual Mean CO 2 Concentration Induced by Fossil Fuel CO 2 Emissions (5.4 PgC a -1 ) [GFDL Model Simulation] 60 30 lat 0 -30 -60 0 90 180 270 360 lon 0 2 4 6 8 10 12 ppmv
Continental Monitoring Locations of TCOS-Siberia Aircraft profiles EUROSIBERIAN CARBONFLUX AEROCARB TCOS Siberia Surface sampling NOAA-CMDL (flasks) NOAA, MISU (continuous) Tall Towers Zotino/Bor (60N, 90E)
Continental Measurements: Terrestrial Carbon Observing System - Siberia Profiling and sampling of lower troposphere (up to 3000m) by light aircraft Direct CO 2 flux measurements in key ecosystems by means of eddy covariance technique
Typical Daytime Summer Profiles Zotino, 60°N, 90°E
Observed Seasonal Cycle of CO 2 in Lower Troposphere above Zotino, 60°N, 90°E ppm Year
Seasonal Cycle of CO 2 at Zotino (60°N,90°E) Zotino, free Troposphere Zotino, PBL (>~3km) ppm North Atlantic Surface, 60°N (ICE, STM, NOAA-CMDL)
Atmospheric Model Nesting Meteorology CO 2 Transport model CO 2 Source Global ECMWF TM3 TURC B,I B,I Mesoscale 1/2° REMO 0.5° TURC REMO 1/6° Mesoscale 1/6° Canopy-CBL Model local
Concentration of CO 2 in PBL (~300m) REMO 0.5° Regional Model Simulation
Footprint Analysis TCOS - Siberia Sites ( dC/dQ, relative units) Z=2500m Z=500m Main monitoring sites
Monthly Mean W-E Composite Concentration Gradient at 60°N, July 1998 REMO (0.5°x0.5°x19L), TM3 (4°x5°x19L) Composite Gradients at 60N, TM3, REMO, July 4 2 3000m 0 m p p 250m -2 -4 -20 0 20 40 60 80 100 120 Longitude
Top-Down Approach: Inverse Modeling of Atmospheric CO 2 Ingredients: Observations at atmospheric station network + observation errors z 3d atmospheric transport model z A priori source/sink distribution + error covariances z Additional constraints z Inversion method z
Inverse Modeling Strategy - Bayesian Approach
Atmospheric Inversion Problem is Ill-Defined Space of Sources Space of Concentrations Q( x ,t) C( x ,t) Observations Null Space Transport Model T “Optimal” Solution Solutions Compatible with Observations+Uncertainty Concentration Uncertainty A Priori Source A Priori Source Uncertainty
Computational Aspects: Efficient Computation of Sensitivities dC i /dQ j - Use of Adjoint Atmospheric Transport Model Example: global, 10-year inversion of surface sources: Number of monthly observations: z ~ 10yr x 12 months x 100 stations =~ 12’000 data points Number of unknown monthly source values (4°x5° global grid): z ~ 72 x 48 x 12 months x 10 yr =~ 400’000 source values Dimension of transport matrix: 12’000 x 400’000 z
Net CO 2 Sources (Average 1995-2000) Fossil+Ocean+Land gC m -2 a -1 Ocean+Land Long-term observation stations NOAA/CMDL Rödenbeck et al., ACP, 2003
Net CO 2 Sources (Average 1995- 2000) Fossil+Ocean+Land gC m -2 a -1 Ocean+Land Long-term observation stations NOAA/CMDL Rödenbeck et al., ACP, 2003
Temporal Variability of Natural CO 2 Sources Estimated from Atmospheric Measurements by Inverse Modeling Results of different set-ups (No. of observation stations, a priori sources, etc.) Rödenbeck et al., ACPD, 2003
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