the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Estimating Marine Carbon Uptake in the Northeast Pacific Using a Neural Network Approach
Roberta C. Hamme
Debby Ianson
Peter Landschützer
Mohamed M. M. Ahmed
Neil C. Swart
Paul A. Covert
Abstract. The global ocean takes up nearly a quarter of anthropogenic CO2 emissions annually, but the variability of this uptake at regional scales remains poorly understood. Here we use a neural network approach to interpolate sparse observations, creating a monthly gridded seawater partial pressure of CO2 (pCO2) data product from January 1998 to December 2019, at 1/12° × 1/12° spatial resolution, in the Northeast Pacific open ocean. The data product (ANN-NEP; NCEI Record ID: BGSH2HNRP) was created from pCO2 observations within the 2021 version of the Surface Ocean CO2 Atlas (SOCAT), and a range of predictor variables acting as proxies for processes affecting pCO2 to create non-linear relationships to interpolate observations at a spatial resolution four times greater than leading global products and with better overall performance. In moving to a higher resolution, we show that the internal division of training data is the most important parameter for reducing overfitting. Using our pCO2 product, wind speed, and atmospheric CO2, we evaluate air-sea CO2 flux variability. On sub-decadal to decadal timescales, we find that the upwelling strength of the subpolar Alaskan Gyre, driven by large-scale atmospheric forcing, acts as the primary control on air-sea CO2 flux variability (r2 = 0.93, p < 0.01). In the northern part of our study region, divergence with atmospheric CO2 is enhanced by increased local wind stress curl, enhancing upwelling and entrainment of naturally CO2-rich subsurface waters, leading to decade-long intervals of strong winter outgassing. During recent Pacific marine heatwaves from 2013 on, we find enhanced atmospheric CO2 uptake (by as much as 45 %) due to limited wintertime entrainment. Our product estimates long-term surface ocean pCO2 increase at a rate below the atmospheric trend (1.4 ± 0.1 μatm yr−1) with the slowest increase in the center of the subpolar gyre where there is strong interaction with subsurface waters. This mismatch suggests the Northeast Pacific Ocean sink for atmospheric CO2 may be increasing.
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Patrick J. Duke et al.
Status: open (until 05 Jul 2023)
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RC1: 'Comment on egusphere-2023-870', Marine Fourrier, 02 Jun 2023
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General comments
This paper by Duke and co-authors describes a neural network approach to interpolate observations into a gridded pCO2 data product over the period 1998-2019 at 1/12° resolution specifically in the Northeast Pacific. After presenting the specific circulation and hydrographic features of their area, the authors detail the methodological choices they made in developing the NN and advise the reader on specific training steps to improve performance. They then go on to discuss the wider scientific applications of such a product.
Overall, the paper is very well written, nicely structured and easy to read. The figures could be larger to improve readability, but figures and legends are still clear. The level of detail in the methodological and NN development sections is much appreciated, as it is rare to find such detail in oceanographic research papers. In particular, the decision not to normalise all your inputs and to use dynamic provinces is very interesting. Also all the developments you have done on the internal division data ratio, which could be applied to many other uses.
A few specific comments remain, as detailed below:
Specific comments- It would be nice to further highlight the potential uses of your data product and/or your method outside the traditional straightforward pCO2/FCO2 observing community (i.e. modellers, determination of climate indices, ...).
- Have you considered comparing your FCO2 with other products: e.g. SeaFlux (Fay et al., 2021)?
- End of section 2.1 and Table 1: there seems to be some confusion here about what you have used for what. You mention the xCO2 data produced by NOAA, but in Table 1 you cite Lanschutzer? As in the next sentence you mention the pCO2 climatology by the same authors, restructuring the sentences if this is not a confusion would be very useful.
- Table 1: for SST you kept the 1/20° resolution and did not aggregate to 1/12°, unlike the others?
- Figure 2c: Is your validation data set representative enough? It does not cover your whole range?
- End of section 2.5: You average the outputs of the 10 NN. You do this directly, but have you tried to give the median +/std as this is also useful information.
- Line 210: "12.9 +:-1.1 µatm" : I'm not sure where this number comes from.
- Line 332: flux densities as high as 3.6, but your figures end at a maximum of 3?
- Lines 394-395: provide some insight into how.
- In this section you compare an atmospheric xCO2 with an increase in oceanic pCO2. Can't you convert the atmospheric xCO2 to pCO2 to compare things in the same ranges/units?
pCO2ATM = [PT - (RH/100) × PH2O] × xCO2ATM
where PH2O is the water vapour pressure at atmospheric temperature for xCO2ATM (in atm) calculated according to Dickson et al. (2007), RH is the relative humidity (in %) and PT is the total atmospheric pressure (in atm). If some of these are missing, you can get them from products (SeaFlux mentioned above). Otherwise you are comparing things that are not directly comparable. - Line 437: detail how you got the vertical velocities with Ekman pumping
Technical corrections- Abstract: For readers not familiar with your particular area, it would be better to introduce the fact that your area is a sink earlier.
- Line 58: "The estimated long-term trend in surface ocean pCO2 appears to be increasing at less than the atmospheric rate." Less what? rate, amount of increase? Rewrite to clarify: "at a slower rate" or "less than the atmospheric rate of increase".
- Line 119: fCO2 has been converted to pCO2: how, give equation.
- Figure 3: Add some metrics to the graphs (from the text)
- Line 150: "changing the internal division ratio", what did you end up using (see where you give more details).
- Line 424: "could be partially tied to if" missing word/rewording
- Figure 7: the black line is not really black.
- Supplementary Figure 1 a&b: useful to superimpose bar charts of the whole dataset behind to further demonstrate how your subsampled data is representative.
Citation: https://doi.org/10.5194/egusphere-2023-870-RC1
Patrick J. Duke et al.
Patrick J. Duke et al.
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