Time series satellite observation-based estimates of land to ocean flow of carbon from - PowerPoint Presentation

Slide1

Time series satellite observation-based estimates of land to ocean flow of carbon from the Amazon

Richard P. Sims, Thomas M. Holding, Peter E. Land, Chris Perry, Jean-Francois Piolle, Jamie D. Shutler

(Friedlingstein et al.

2020)

~0.78 Pg C y

-1

?

The temporal and spatial scale of carbonate

system measurements

in rivers makes quantifying the flux difficult.

Can we create datasets that allow us to calculate the riverine inorganic carbon flux for a major river like the Amazon?

How can Earth observations

help us determine the riverine carbon flux? The Coriolis Ocean dataset for Reanalysis(CORA)

v5.2 SSS, 1990-2020 (Szekely et al., 2019)

We can derive gridded total alkalinity (TA) fields using

linear equations from the literature (Land et al. 2019).

TA=58.1*SSS + 265

(

Lefèvre

et al.

2010)

Decadal

TA

records from satellites

.

Same approach

for

dissolved inorganic carbon (DIC).

DIC=49.48*SSS + 226.8

DIC algorithm

(

Ternon

et al. 2000)In prep ESSD (Sims et al. 2022).Uncertainty of 59 μmol kg-1CORA v5.2 SSS, 1990-2020 (Szekely et al., 2019)

= Velocity(ms

-1

) x cross sectional area(m2)

= Discharge of water (m3s-1) x DIC (kg m-3)

Use gauge data for this

F

ixed value

From our dataset

Discharge of water (m

3

s

-1)Transport of DIC (kg s-1)

Which DIC values do we use?

Rivers don’t flow like

this.

Plume

is defined as SSS <35

(

Hu

et al

. 2004).

Radii 24

Radii 20

Radii 15

Radii 10

Radii 5

Radii 1

Determine how much of the plume is in each

radii.

Area of the grid cell

x Depth of the plume (Coles et al. 2013)x DIC concentration

x Scaling factor for conservative mixing

Sum of

riverine =

DIC

in a

cell

Averaging the

DIC in all the cells which fall in each

radii give 24 separate estimates for plume DIC.

Which of the 24

DIC should we pick?

Conclusions

Decadal carbonate system datasets from remote sensing products.Quantified Amazon DIC outflow (include uncertainty)

. High seasonal variability in DIC outflow.Higher DIC outflow than observation based methods (observation bias?).20% global river flow from Amazon (Moura et al. 2016).

Inorganic outflow ~0.06±0.01 Pg C y-1.

Thankyou for listening

Any questions?

Knowledge gaps

1 Year - investigate advances using satellite observed surface flows (SWOT due for launch in 2022; work can begin with simulated datasets).5 Year - begin using SWOT and investigate potential of geostationary observations from GLIMR (GLIMR due for launch in 2026).

10 big rivers make up about half of all river flow.