The eMLR approach and a sensitivity analysis.

Toste Tanhua

Arne Körtzinger

Karsten Friis

Darryn W. Waugh

Douglas W.R. Wallace

extended Multiple Linear Regression

(eMLR)RpapaaDIC nn .....110

This eMLR is based on potential temperaturealkalinitysilicatenitrate

AOU

)(5.0746.0 3NOAlkAOUDICDICbio

Two methods to calculate thedecadal change in DIC

Sensetivity of the eMLR determined with Monte Carlo

analysis

We used the M60/5 dataas the „historic“ data set, and created a „modern“ data set by adding Cant.

To this we added noiseand biases.

Testing the sensetivity of the eMLR

Adding 2 noise temperature 0.005°Calkalinity 4.2 mol kg-1 DIC 1.4 mol kg-1

nitrate 0.2 mol kg-1

silicate 0.2 mol kg-1

AOU 0.6 mol kg-1

Adding bias5 mol kg-1 too high Alk5 mol kg-1 too low AOU5 % too high Si

Same thing for the Western BasineMLR seems to bewater mass sensitive,i.e. ideally one eMLR for each water massshould be done, but this is impractical.

The systematic errors plotted as a section

The eMLR should be performed on the data set with the „best“ data set.

We also introduced a 20 random noise.....

Can we extend the decadal change in Cant to the full anthropogenic period?

Cant is increasing exponentially, i.e. the Transient Steady State assumption is valid.

0

20 )()(),(

c

rctctrc

Small sensitivity to non-exponential growth of Cant, and to different buffer factors for the carbonate system.

Another way of calculating Cant.

The eMLR approach compares well with a tracer based approach.