Post on 06-Apr-2019
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
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Terminology …
NEE = Net Ecosystem ExchangeGPP = Gross Primary ProductivityTER= Terrestrial Ecosystem Respiration
Schulze, E.D. et al. (2000) Science, 289, 2058‐2059.
UNCERTAIN
UNCERTAIN
Highly UNCERTAIN
UNCERTAIN
Uncertain biosphere‐atmosphere fluxes
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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.
How can we infer regional/global ecosystem‐atmosphere fluxes?
Can we constrain the dynamics of land‐surface processes?
Functional dependencies
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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?
Functional dependencies
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CO2 fluxes reflect a series of complex interactions:
; ; . ; … ;
; ; ;
; ; : , , , …
Fluxnet‐Canada
Ameriflux
LBA
CarboAfricaAfriflux
Carboeurope/NECCTCOS
AsiafluxKoFlux
Ozflux
ChinafluxUSCCC
FLUXNET: fluxdata.org
In situ
Flux monitoring: Covarying (explanatory) variables
Ecosystem‐atmosphere Metrologyexchanges of GHGs Vegetation type
Phenology
“Think Globally, Fit Locally”
Training mapping algorithm
Validating
Title from Saul et al. (2003) Journal of Machine Learning Research, 4, 119‐155,
In situ Global
Flux monitoring: Empirical “upscaling” Covarying (explanatory) variables
Ecosystem‐atmosphere Metrologyexchanges of GHGs Vegetation type
Phenology
Temperature: CRU‐PIKPrecipitation: GPCPFPAR: harmonized AVHRR, SeaWIFS, MERIS productVegetation map: SYNMAP
(Partly) overcomes site‐pecularities, point‐to‐grid scale mismatch and representativeness
“Think Globally, Fit Locally”
Application
Title from Saul et al. (2003) Journal of Machine Learning Research, 4, 119‐155,
Global estimation of ecosystem‐atmosphere fluxes • gross primary productivity, GPP• terrestrial ecosystem respiration, TER, • (in principle: net ecosystem exchange, NEE)• latent energy, LE• sensible heat, H
Upscaling of fluxes Jung et al. (2011) Journal of Geophysical Research, 116, G00J07
See also
Beer et al. (2010) Science, 329, 834‐838
Jung et al. (2010) Nature, 467, 951‐954.
Ensemble median map
GPP [gC m‐2 yr‐1]
Global estimation of terrestrial gross primary productivity (GPP)
Global total: 123 +‐8 Pg/yr
Light‐use eff. ignores C4 veg (> 20 Pg)
Beer et al. (2010) Science, 329, 834‐838
Model treeensembles
ANN
Semi‐empirical
Water‐use
Light‐use eff.
Machine learning
PFT+Clim
Upscaling of fluxes
Ensemble median map
GPP [gC m‐2 yr‐1]
Global estimation of terrestrial gross primary productivity (GPP)
Global total: 123 +‐8 Pg/yr
Light‐use eff. ignores C4 veg (> 20 Pg)
Beer et al. (2010) Science, 329, 834‐838
Model treeensembles
ANN
Semi‐empirical
Water‐use
Light‐use eff.
Machine learning
PFT+Clim
Upscaling of fluxes
Latitudinal patterns of GPP as model constraint
Process models:CLM‐CNLPJ‐DGVMLPJmLSDGVMORCHIDEE
All 1° resolution orhigher
Beer et al. (2010) Science,329, 834‐838
Upscaling of fluxes
Rationale: An “upscaled field” is more than a unit‐transformed vegetation index:
Upscaling of fluxes
Comparing interannual variability of EVI and upscaled GPP (different upscaling approach)
Sensible heat (H)
Evap. fraction HLELE
GPP
Water‐use effic. (GPP/AET)
Energy‐flux related patterns Carbon‐flux related patterns
Upscaling of fluxesInsights to “Global Ecosystem Properties”
Forest carbon stocks (i.e. above ground living biomass) in the tropicsSaatchi 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!
Upscaling of static variables is established:
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
Future integration of EO and C‐cycle studies
Solberg et al. (2010) IEEE Trans. Geosc. Rem. Sens.
… 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
Future integration of EO and C‐cycle studies
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, … )
Space for improvement
Good agreement between the upscaledGPP and Fs
Final remarks
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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