ICOS Science Conference 2022: Day 1, Session 6

1. Investigation of the Suess Effect in the Southern Indian
Ocean over the last two decades (1998-2021)
coraline.leseurre@locean.ipsl.fr
Coraline Leseurre
Gilles Reverdin, Claire Waelbroeck,
Claire Lo Monaco, Nicolas Metzl,
Catherine Pierre, Virginie Racapé,
Jérôme Demange, Jonathan Fin,
Claude Mignon

2. Background
• Increase of CO2
anth emission (13C-depleted)
2

3. Background
• Increase of CO2
anth emission (13C-depleted) → decrease in δ13C-CO2 = Suess effect
3

4. Background
• Increase of CO2
anth emission (13C-depleted) → decrease in δ13C-CO2 = Suess effect
• Ocean absorbs ≈25% of this CO2 → Suess effect in the ocean
4

5. Why Southern Ocean?
Climatological mean annual sea-air CO2 flux (reference year 2000)
Takahashi et al. (2009); Eide et al. (2017)
Gruber et al. (2019)
5
Sink of natural and anthropogenic CO2
atm
→Formation of Intermediate and Mode
Waters between 30°S and 50°S

6. Why Southern Ocean?
Sink of natural and anthropogenic CO2
atm
→Formation of Intermediate and Mode
Waters between 30°S and 50°S
Climatological mean annual sea-air CO2 flux (reference year 2000)
Column inventory of anthropogenic CO2 in the ocean
1994 – 2007
Takahashi et al. (2009); Eide et al. (2017)
Gruber et al. (2019)
6
Depth-integrated 13C Suess effect (‰ m)

7. Previous work on fCO2 (and pH) changes Leseurre et al. (2022)
7

8. Previous work on fCO2 (and pH) changes Leseurre et al. (2022)
8
Major result: Accumulation of Anthropogenic CO2 → Should detect the Suess effect

9. OISO : Océan Indien Service d’Observation
Marion Dufresne
• 1 to 2 oceanographic cruises per year
• Since 1998
• Southern Indian Ocean
• Surface sampling (underway)
• Water column sampling (station)
➢ Temporal monitoring of oceanic CO2 parameters
9

10. OISO : Océan Indien Service d’Observation
Marion Dufresne
• 1 to 2 oceanographic cruises per year
• Since 1998
• Southern Indian Ocean
• Surface sampling (underway)
• Water column sampling (station)
➢ Temporal monitoring of oceanic CO2 parameters
10
Bakker et al. (2016, 2022)

11. Objectives
Marion Dufresne
11
SAF
PF
STF
Our study area
Describe new summer δ13CDIC observation
obtained in the surface layer of
Southern Indian Ocean
to investigate the Suess effect (1998-2021)
Madagascar
Antarctica
South Indian Ocean

12. Standard 2 Standard 1
H3PO4 3%
Apollo
12
6 Samples
Gas extraction system (Apollo SciTech)
→ acidification of the sea water sample
→ conversion of DIC to CO2
→ CO2 extraction
Standard 3
PC 1
CO2-free
Clean Air
CO2 trap
New Lab’ Measurements Leseurre et al. (in prep)

13. Standard 2 Standard 1
H3PO4 3%
Apollo
PC 1
13
6 Samples
Cavity ring-down spectroscopy (CRDS-PICARRO)
→ Determination of mixing ratios of CO2 in air ([CO2])
→ And its stable carbon isotopic composition
(δ13C-CO2)
Standard 3
PC 2
Picarro
CO2 trap
CO2-free
Clean Air
New Lab’ Measurements Leseurre et al. (in prep)
Precision: 0.12‰
2 µmol kg-1

14. Dataset in the Southern Indian Ocean between 1998 and 2021 Leseurre et al. (in prep)
14
• Variable signal (depending on the sampling localisation and time)
• Difficult to estimate regional trends
→ Estimate the trends for each station and gridded boxes
Summer Mixed Layer (≈ 0-150m)

15. Summer trends of surface δ13CDIC between 1998 and 2021
O6
O7
O8
O9
O10
O11
O12
A3
Western track
Eastern
track
Black : from surface
data along the track
Orange : from station
mixed layer data
Leseurre et al. (in prep)
15

16. O6
O7
O8
O9
O10
O11
O12
A3
Western track
Eastern
track
Black : from surface
data along the track
Orange : from station
mixed layer data
• The 2 datasets are generally in good agreement
• δ13CDIC ↘ -0.011 (±0.006) ‰ yr-1
Leseurre et al. (in prep)
16
Summer trends of surface δ13CDIC between 1998 and 2021

17. O6
O7
O8
O9
O10
O11
O12
A3
Western track
Eastern
track
Black : from surface
data along the track
Orange : from station
mixed layer data
• The 2 datasets are generally in good agreement
• δ13CDIC ↘ -0.011 (±0.006) ‰ yr-1
• 2 times slower than atmospheric trend: -0.024 ‰ yr-1 (Hawaï)
Leseurre et al. (in prep)
17
Summer trends of surface δ13CDIC between 1998 and 2021

18. O6
O7
O8
O9
O10
O11
O12
A3
Western track
Eastern
track
Black : from surface
data along the track
Orange : from station
mixed layer data
• The 2 datasets are generally in good agreement
• δ13CDIC ↘ -0.011 (±0.006) ‰ yr-1
• 2 times slower than atmospheric trend: -0.024 ‰ yr-1 (Hawaï)
• Faster ↘ in the northern part of the area
Leseurre et al. (in prep)
18
Summer trends of surface δ13CDIC between 1998 and 2021

19. O6
O7
O8
O9
O10
O11
O12
A3
Western track
Eastern
track
Black : from surface
data along the track
Orange : from station
mixed layer data
• The 2 datasets are generally in good agreement
• δ13CDIC ↘ -0.011 (±0.006) ‰ yr-1
• 2 times slower than atmospheric trend: -0.024 ‰ yr-1 (Hawaï)
• Faster ↘ in the northern part of the area
Leseurre et al. (in prep)
19
Summer trends of surface δ13CDIC between 1998 and 2021
Are these changes related to
Suess Effect or to natural variability?

20. extended Multi-Linear Regression eMLR
Friis et al. (2005); Racapé et al. (2013)
Leseurre et al. (in prep)
→ To separate natural and anthropogenic signal for DIC and δ13CDIC
• eMLR estimated between: 1998-2000 (time 1) and 2019-2021 (time 2)
• Need independent parameters for the description of the system
• Best statistical results for summer mixed layer : Temperature and [NO3]
∆𝑌𝑒𝑀𝐿𝑅
= 𝑏0,𝑡2 − 𝑏0,𝑡1 + 𝑏1,𝑡2 − 𝑏1,𝑡1 𝑿𝟏,𝒕𝒊 + … + 𝑏𝑛,𝑡2 − 𝑏𝑛,𝑡1 𝑿𝒏,𝒕𝒊
regression constants
regression coefficients for Xn parameters
Y = DIC or δ13CDIC
20

21. Which predictive parameter for eMLR?
• Temperature → Characterizes water masses
• Nitrate → Characterizes the organic carbon pump (related to NO3 consumption)
Leseurre et al. (in prep)
21
Summer Mixed Layer (≈ 0-150m)

22. Results of eMLR in summer surface layer Leseurre et al. (in prep)
22

23. Results of eMLR in summer surface layer Leseurre et al. (in prep)
• Strong linear relationship between accumulation of
anthropogenic CO2 (ΔDICeMLR) and oceanic Suess
effect (Δδ13CDIC
eMLR)
23

24. Leseurre et al. (in prep)
• Strong linear relationship between accumulation of
anthropogenic CO2 (ΔDICeMLR) and oceanic Suess
effect (Δδ13CDIC
eMLR)
• Variable in regional space
24
Results of eMLR in summer surface layer

25. Leseurre et al. (in prep)
• Strong linear relationship between accumulation of
anthropogenic CO2 (ΔDICeMLR) and oceanic Suess
effect (Δδ13CDIC
eMLR)
• Variable in regional space
• Maximum change in the northern part of the area
25
Results of eMLR in summer surface layer

26. Leseurre et al. (in prep)
• Strong linear relationship between accumulation of
anthropogenic CO2 (ΔDICeMLR) and oceanic Suess
effect (Δδ13CDIC
eMLR)
• Variable in regional space
• Maximum change in the northern part of the area
• Caveat: some anomalies
26
Related to strong productivity in
summer 1998 north-east of Kerguelen
Results of eMLR in summer surface layer

27. Leseurre et al. (in prep)
ΔDIC
µmol kg-1 yr-1
Δδ13CDIC
‰ yr-1
Anthropogenic change from eMLR calculation +0.8 (±0.2) -0.011 (±0.005)
27
Results of eMLR in summer surface layer

28. Leseurre et al. (in prep)
ΔDIC
µmol kg-1 yr-1
Δδ13CDIC
‰ yr-1
Anthropogenic change from eMLR calculation +0.8 (±0.2) -0.011 (±0.005)
Anthropogenic change from TrOCA calculation +0.7 (±0.4)
28
• ΔDICeMLR (surface layers) ≈ ΔDICanth (subsurface)
Results of eMLR in summer surface layer

29. Leseurre et al. (in prep)
ΔDIC
µmol kg-1 yr-1
Δδ13CDIC
‰ yr-1
Anthropogenic change from eMLR calculation +0.8 (±0.2) -0.011 (±0.005)
Anthropogenic change from TrOCA calculation +0.7 (±0.4)
Observed change +1.0 (±0.4) -0.011 (±0.006)
29
• ΔDICeMLR (surface layer) ≈ ΔDICanth (subsurface)
• Rates of change deduced from eMLR are similar to
rates deduced from regional trends based on time-
series observation (1998-2021)
Results of eMLR in summer surface layer

30. Leseurre et al. (in prep)
ΔDIC
µmol kg-1 yr-1
Δδ13CDIC
‰ yr-1
Anthropogenic change from eMLR calculation +0.8 (±0.2) -0.011 (±0.005)
Anthropogenic change from TrOCA calculation +0.7 (±0.4)
Observed change +1.0 (±0.4) -0.011 (±0.006)
→ Small impact of natural variability on decadal scale
30
• ΔDICeMLR (surface layer) ≈ ΔDICanth (subsurface)
• Rates of change deduced from eMLR are similar to
rates deduced from regional trends based on time-
series observation (1998-2021)
Results of eMLR in summer surface layers

31. Leseurre et al. (in prep)
• Method eMLR → to detect the Suess effect of δ13CDIC
• eMLR in summer mixed layer with Temperature and [NO3] showed:
➢ Anthropogenic changes for both DIC and δ13CDIC
➢ Small impact of natural variability on decadal scale
➢ Strong linear relationship between anthropogenic CO2 and the Suess effect
• Over the period 1998-2021 in the surface layer of Southern Indian Ocean:
➢ Increase in Cant of +0.8 µmol kg-1 yr-1
➢ Suess effect of -0.011 ‰ yr-1
31
Conclusion

32. Investigation of the Suess Effect in the Southern Indian
Ocean over the last two decades (1998-2021)
coraline.leseurre@locean.ipsl.fr
Coraline Leseurre
Looking for a Post Doc’ on in situ ocean carbon cycle
Thank you for your attention