Ecosystem‐atmosphere interactions and states of the (terrestrial) biosphere
Miguel Mahecha, Markus Reichstein,
Nuno Carvalhais, Martin Jung, Enrico Tomelleri
Max Planck Institute for Biogeochemistry, Jena, Germany
Oct. 31, 2011
Interlinked land surface processes State‐of‐the‐art concept of relevant land surface dynamics (incl. biogeophysics, biogeochemistry, and biogeo‐
graphy)
Bonan, G. (2008) Science, 320, 1444‐1449
C fluxes
Terminology …
UNCERTAIN
NEE = Net Ecosystem Exchange
GPP = Gross Primary Productivity
TER= Terrestrial Ecosystem Respiration
Schulze, E.D. et al. (2000) Science, 289, 2058‐2059.
UNCERTAIN
Highly UNCERTAIN
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UNCERTAIN
Uncertain biosphere‐atmosphere fluxes Gaps in state‐of‐the‐art knowledge    Uncertainty on future effect global change
Terrestrial ecosystems: C sink
C source
Figure redrawn after: Friedlingstein et al. (2006) Climate–Carbon Cycle Feedback Analysis: Results from the C4MIP Model Intercomparison. J. Climate, 19, 3337–3353.
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Functional dependencies
CO2 fluxes reflect a series of complex interactions:
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How can we infer regional/global ecosystem‐atmosphere fluxes? Can we constrain the dynamics of land‐surface processes?
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Functional dependencies
CO2 fluxes reflect a series of complex interactions:
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:
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;
,
… ;
;
,…
FLUXNET: fluxdata.org Fluxnet‐Canada
Carboeurope/NECC
TCOS
How can we infer regional/global ecosystem‐atmosphere fluxes? Can we constrain the dynamics of land‐surface processes?
Ameriflux
LBA
Chinaflux
USCCC
CarboAfrica
Afriflux
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Asiaflux
KoFlux
Ozflux
“Think Globally, Fit Locally”
Title from Saul et al. (2003) Journal of Machine Learning Research, 4, 119‐155,
In situ Flux monitoring:
Ecosystem‐atmosphere
exchanges of GHGs Covarying (explanatory) variables
Training mapping algorithm
Validating
Metrology
Vegetation type Phenology “Think Globally, Fit Locally”
Title from Saul et al. (2003) Journal of Machine Learning Research, 4, 119‐155,
In situ Global
Flux monitoring: Empirical “upscaling”
Covarying (explanatory) variables
Application
Ecosystem‐atmosphere
exchanges of GHGs (Partly) overcomes site‐
pecularities, point‐to‐grid scale mismatch and representativeness
Metrology
Vegetation type Phenology Temperature: CRU‐PIK
Precipitation: GPCP
FPAR: harmonized AVHRR, SeaWIFS, MERIS product
Vegetation map: SYNMAP Upscaling of fluxes
Jung et al. (2011) Journal of Geophysical Research, 116, G00J07
Global estimation of ecosystem‐atmosphere fluxes See also • gross primary productivity, GPP
• terrestrial ecosystem respiration, TER, Beer et al. (2010) Science, 329, 834‐838
• (in principle: net ecosystem exchange, NEE)
• latent energy, LE
Jung et al. (2010) Nature, 467, 951‐954. • sensible heat, H
Upscaling of fluxes
Global estimation of terrestrial gross primary productivity (GPP)
Ensemble median map
Global total: 123 +‐8 Pg/yr
Machine learning
PFT+Clim
Water‐use Model tree
ensembles
ANN
Light‐use eff.
GPP [gC m‐2 yr‐1]
Semi‐empirical
Beer et al. (2010) Science, 329, 834‐838
Light‐use eff. ignores C4 veg (> 20 Pg)
Upscaling of fluxes
Global estimation of terrestrial gross primary productivity (GPP)
Ensemble median map
Global total: 123 +‐8 Pg/yr
Machine learning
PFT+Clim
Water‐use Model tree
ensembles
ANN
Light‐use eff.
GPP [gC m‐2 yr‐1]
Semi‐empirical
Beer et al. (2010) Science, 329, 834‐838
Light‐use eff. ignores C4 veg (> 20 Pg)
Upscaling of fluxes
Latitudinal patterns of GPP as model constraint
Process models:
CLM‐CN
LPJ‐DGVM
LPJmL
SDGVM
ORCHIDEE
All 1° resolution or
higher
Beer et al. (2010) Science,
329, 834‐838
Upscaling of fluxes
Rationale: An “upscaled field” is more than a unit‐transformed vegetation index:
Comparing interannual variability of EVI and upscaled GPP (different upscaling approach)
Upscaling of fluxes
Insights to “Global Ecosystem Properties”
Energy‐flux related patterns
Sensible heat (H)
LE
Evap. fraction LE  H
Carbon‐flux related patterns
GPP
Water‐use effic. (GPP/AET)
Upscaling of static variables is established:
Forest carbon stocks (i.e. above ground living biomass) in the tropics
Saatchi et al. (2011) PNAS, 108, 9899–9904
“Geoscience Laser Altimeter System (GLAS), onboard … ICESat in combination with other remote sensing data bases and ground data” … “4079 in situ inventory plots and satellite light detection and ranging (Lidar) samples of forest structure to estimate carbon storage, plus optical and microwave imagery (1‐km resolution)”
Ideally we would have time‐series of these data!
Future integration of EO and C‐cycle studies
Integrating more physically relevant variables (i.e. for reducing uncertainties in TER)
 Describing terrestrial C cycle requires more than vegetation indices
 Water cycle variables should be considered (soil moisture, interception, LST, … ).
(… e.g. also to estimate dissolved organic matter losses)
Acknowledging changes in physiognomic states‐of‐the biosphere (for full C balance)
 Land use and land cover change
 C losses via fire,
 Changes in stand structure due to wind throw,  Insect outbreaks Solberg et al. (2010) IEEE Trans. Geosc. Rem. Sens. Future integration of EO and C‐cycle studies
… extension to other GHGs
Most importantly: fluxes of CH4 would require considering water‐related variables, i.e
• Soil moisture, e.g. Liu et al. (2011) Hydrol. Earth Syst. Sci., 15, 425–436
• Wetland extend, e.g. Prigent et al. (2007) J. Geophys. Res., 112, D12107
• LST
Papa et al. (2011) J. Geophys. Res., 115, D12111
Space for improvement
Seeking supporting lines of evidence with better interpretable remote sensing indicators
Frankenberg et al. (2011) GRL, 38, L17706
Solar induced chlorophyll fluorescence
(FLEX satellite mission, … )
Good agreement between the upscaled
GPP and Fs
Final remarks
 Considering water‐variables for inferring C‐fluxes (e.g. CH4)
 C‐ cycle operates from seconds to centennial scales… …  Warranting consistency with previous missions
…  Maximal mission extension
 Full transparency on data uncertainty, critical for upscaling, model‐data fusion
 Desirable to establish links between sentinel data streams and in‐situ monitoring networks Thank you
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