# What fluxes are telling us so far ? A naïve reanalysis of CO2 fluxes over the past 18 years

Virginie Moreaux, Paul Berbigier, Daniel Berveiller, Jean-Marc Bonnefond, Christophe Chipeaux, Nicolas Delpierre, Olivier Darsonville, Eric Dufrene, André Granier, Richard Joffre, et al.

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## To cite this version:

Virginie Moreaux, Paul Berbigier, Daniel Berveiller, Jean-Marc Bonnefond, Christophe Chipeaux, et al.. What fluxes are telling us so far ? A naïve reanalysis of CO2 fluxes over the past 18 years. Assemblée Générale ICOS France, Dec 2018, Paris, France. 21 p. hal-02790285

HAL Id: hal-02790285

https://hal.inrae.fr/hal-02790285

Submitted on 5 Jun 2020

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Distributed under a Creative Commons Attribution 4.0 International License
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At half the way: what is still to be achieved ?
# Sites with minimal management
Data filtered & processed using
homogenized protocol
(EddyPRO)

* **Barbeau**: old growth mixed broadleaved forest (11°C, 690mm)

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* **Le Bray**: coniferous Atlantic forest (13°C, 950mm)

* **Laqueuille**: extensive grassland (7°C, 1050 mm)

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* **Puechabon**: old-growth evergreen Quercus coppice (14°C, 910 mm)
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Time series analysed

F<sub>CO2</sub>

Puechabon

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GPP

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R<sub>ECO</sub>

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F<sub>CO2</sub>

Le Bray

<figure><img src="image_12.png" /></figure>

GPP

<figure><img src="image_13.png" /></figure>

R<sub>ECO</sub>

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Barbeau

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Laqueuille

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# What fluxes are telling us so far ?

## A naïve reanalysis of CO₂ fluxes over the past 18 years

Berbigier P., Berveiller D., J.-M. Bonnefond, Chipeaux C., Delpierre N., Darsonville O., Dufrene E., A. Granier Joffre R., Klump K., Lafont S., Limousin J-M., Longdoz B., Loustau D., Ourcival J.-M., Piquemal K., Pontailler J.-Y., Rambal S., Soussana J.-F.

Moreaux V.
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⇒ 1. High frequency classification approach: Random Forest analysis (Breiman, 2001)

⇒ 2. Across frequency domain: Cospectra analysis with wavelet theory

Torrence C & Compo GP, 1998

Stoy et al. 2005, 2009

Vargas et al. 2010, 2011

Fares et al. 2013

⇒ 3. Inferential statistics (linear/non linear regression analysis)

⇒ 4. Low frequency trends. Detection, attribution.
# 1. Classification of environmental factors : ecosystem photosynthesis (GPP)

* Random forest analysis at 1/2h time scale

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# 1. Classification of environmental factors : [ecosystem respiration (R<sub>ECO</sub>)](#)

* Random forest analysis at 1/2h time scale

Puechabon

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Le Bray

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Barbeau

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Laqueuille

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## 2. Continuous time series analysis

### Wavelet analysis: scalogram and average cross-coherence graphs

* Appropriate to nonstationary and heteroscedastic time series

* Single and cross-spectra in time or frequency domains

* Assess synchrony and phasing (advance/delay between signals at given frequencies)
Cross correlograms of GPP, SW↓ and Soil Water (REW)

Barbeau

<figure><img src="image_30.png" /></figure>

Average cross wavelet power

Puechabon

<figure><img src="image_31.png" /></figure>

Average cross wavelet power

Le Bray

<figure><img src="image_32.png" /></figure>

Average cross wavelet power

GPP - SW↓

<figure><img src="image_33.png" /></figure>

Average cross wavelet power

<figure><img src="image_34.png" /></figure>

Average cross wavelet power

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Average cross wavelet power

GPP - REW
<figure><img src="image_36.png" /><figcaption>Temperate coniferous forest (FR-LBr)</figcaption></figure>

<figure><img src="image_37.png" /><figcaption>Extensive grassland (FR-Laq)</figcaption></figure>

<figure><img src="image_38.png" /><figcaption>Mediterranean evergreen broadleaf forest (FR-Pue)</figcaption></figure>

<figure><img src="image_39.png" /></figure>
Selected scalograms: GPP - R<sub>ECO</sub>

Temperate deciduous broadleaf forest (FR-Fon)

<figure><img src="image_40.png" /><figcaption>cross-wavelet coherence, GPP over Reco</figcaption></figure>

Extensive grassland (FR-Laq)

<figure><img src="image_41.png" /><figcaption>cross-wavelet coherence, GPP over Reco</figcaption></figure>

Temperate coniferous forest (FR-Bra)

<figure><img src="image_42.png" /><figcaption>cross-wavelet coherence, GPP over Reco</figcaption></figure>

Mediterranean evergreen broadleaf forest (FR-Pue)

<figure><img src="image_43.png" /><figcaption>cross-wavelet coherence, GPP over Reco</figcaption></figure>
3. Regression analysis: GPP response to environmental parameters: PPFD↓

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Similar response of ecosystem photosynthesis/LAI to PPFD among sites and between years.
3. Regression analysis: GPP response to environmental
parameters: PPFD↓

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The response of ecosystem photosynthesis/LAI to PPFD x VPD
is similar among sites.
## 3. Regression analysis: R<sub>ECO</sub> response to temperature

<figure><img src="image_48.png" /></figure>

Same response of ecosystem respiration to temperature among sites and between years.
3. Time series re-analysis: naive conclusions

Large similarities among all sites - years.

- Photosynthesis correlated with:

$$ \text{SW} \downarrow > \text{Air VPD} > \left\{ \begin{array}{l} \text{Air Temperature (Fr-Laq FR-LBr)} \\ \text{Soil water (Fr-Fon, Fr-Pue)} \end{array} \right. $$

- Respiration correlated with:

Temperature > Air VPD > soil water content
4. Low frequency changes

<figure><img src="image_49.png" /></figure>

FR-Fon

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FR-Pue

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FR-LBr

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4. Low frequency changes: are they significant ?

Longterm trend analysis: **Example of Barbeau: FR-Fon**

after Baldocchi et al. 2018

Standard deviation of NEE

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Linear regression
slope for the
trend

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4. Low frequency changes: are they significant ?

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Temporal trends across sites: significant but not consistent
* Climate drivers of CO₂ exchanges are strikingly similar among a range of ecosystems

* SW↓, Tair, Soil Water, air water vapour saturation deficit

* Respiration is coupled more tightly with GPP in ecosystems with lesser biomass and soil carbon stocks

* Faster transfer of C from foliage to soil

* Larger fraction of autotrophic respiration

* Cumulative effects of drifting variables (e.g. CO2) are barely visible.

* Uncertainty and lack of temporal consistency still too large

* Confounding effects (growth, age,...) are dominant

* Obtained time series so far:

* numerical analysis of fluxes data say little about ecosystem functioning

* long for scientists but short for the ecosystems !
And few thoughts for future research

From naive statistical correlations to causal attribution of biogeochemical fluxes:

* Transform ecosystem stations, « Flux towers » into terrestrial biogeochemical observatories where :

* Monitoring of environmental drivers completed (Ozone, Ndeposition, ...)

* Fluxes measurements can be better ascribed to processes

* In-depth, knowledge-guided time series investigations

* Develop plant growth processes modelling !!
  Plant growth drives photosynthesis !
  But what is driving plant growth ?
# Acknowledgements

## Sites :

* Berbigier P., J.-M. Bonnefond, Chipeaux C., Loustau D.

* Berveiller D., Delpierre N., Dufrene E., Pontailler J.-Y.,

* Darsonville O., Falcimagne R., Klump K., Soussana J.-F.

* Cuntz M., Granier A., Gross P., Lily J.-B., Longdoz B.

* Joffre R., Limousin J-M., Ourcival J.-M., Piquemal K., Rambal S.

* Buysse P., Cellier P., Loubet B.

* Brut A., Ceschia E., Tallec T.

## Data analysis:

* Moreaux V., Brut A., Delpierre N., Dufrene E., Klump K., Lafont S., Limousin J.-M., Longdoz B., Loubet B., Loustau D., Tallec T.

## CESEC project: Cross-comparison of Reco and GPP in response to environmental parameters: synthesis over French forest ecosystems (ADEME)

## RINGO project: Long term trends and variability on carbon fluxes: uncertainties and detection ability of heterogeneous network. (H2020 / INFRAIA, TASK 3.5)