Sea-ice loss amplifies summertime decadal CO2 increase in the western Arctic Ocean

Abstract

Rapid climate warming and sea-ice loss have induced major changes in the sea surface partial pressure of CO2 (\(p_{{\mathrm {CO}}_2}\)). However, the long-term trends in the western Arctic Ocean are unknown. Here we show that in 1994–2017, summer \(p_{{\mathrm {CO}}_2}\) in the Canada Basin increased at twice the rate of atmospheric increase. Warming and ice loss in the basin have strengthened the \(p_{{\mathrm {CO}}_2}\) seasonal amplitude, resulting in the rapid decadal increase. Consequently, the summer air–sea CO2 gradient has reduced rapidly, and may become near zero within two decades. In contrast, there was no significant \(p_{{\mathrm {CO}}_2}\) increase on the Chukchi Shelf, where strong and increasing biological uptake has held \(p_{{\mathrm {CO}}_2}\) low, and thus the CO2 sink has increased and may increase further due to the atmospheric CO2 increase. Our findings elucidate the contrasting physical and biological drivers controlling sea surface \(p_{{\mathrm {CO}}_2}\) variations and trends in response to climate change in the Arctic Ocean.

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Fig. 1: The spatial distribution of sea surface \(p_{{\mathrm {CO}}_2}\).
Fig. 2: Decadal change trends of sea surface \(p_{{\mathrm {CO}}_2}\) in the western Arctic Ocean.
Fig. 3: Schematic representation of recent environmental changes in the western Arctic during the ice-melt season.
Fig. 4: Simulation of sea surface \(p_{{\mathrm {CO}}_2}\) in the Chukchi Shelf and the Canada Basin.
Fig. 5: Sea-ice loss amplifying surface water \(p_{{\mathrm {CO}}_2}\) in the Canada Basin.

Data availability

Source Data for Extended Data Fig. 5 are provided with the paper. All the data are archived in publicly accessible databases, and the data sources are listed in the main text, Methods and Supplementary Information. The assembled \(p_{{\mathrm {CO}}_2}\) dataset used in this study is available in the Supplementary Information.

Code availability

The code used for \(p_{{\mathrm {CO}}_2}\) simulations is available in the Supplementary Information. R programming software was used to generate all the results. The saved simulation results are available on request from the corresponding author.

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Acknowledgements

We thank the contributors to the SOCAT v.5, LDEO, CHINARE, JAMSTEC, USGS, NSF Arctic Data Center and CDIAC datasets, as well as the research vessels and crews for collecting the data used in this study. We thank H. Wang for helpful discussion. This work was supported by the US NSF (grant nos ARC-0909330, PLR-1304337, OPP-1926158, PLR-1220032, PLR‐1504410, OPP−1735862 and PLR‐1723308), NOAA (grant nos NA09OAR4310078, NA150OAR4320064 and NA10NOS4000073), Interdisciplinary Research for Arctic Coastal Environments funded by US Deparment of Energy (DOE InteRFACE project), the National Natural Science Foundation of China (grant nos 41806222, 41630969, 41230529, 41476172 and 41706211), the Chinese Projects for Investigations and Assessments of the Arctic and Antarctic (grant no. CHINARE2017-2020), the National Key Research and Development Program of China (grant no. 2019YFA0607003), the Scientific Research Foundation of Third Institute of Oceanography, SOA (grant nos 2018005 and 2017029), the Bilateral Cooperation of Maritime Affairs (grant no. 2200207), Fujian science and technology innovation leader project 2016, and the Arctic Challenge for Sustainability (ArCS) Project funded by the Ministry of Education, Culture, Sports, Science and Technology of Japan (MEXT).

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Contributions

L.C. and W-J.C. organized the collaborative research on the Arctic Ocean Carbon Cycle and Acidification Project via CHINARE cruises. Z.O., W-J.C. and T.T. prepared the paper. Z.O., D.Q., B.C., Z.G. and H.S. executed the fieldwork. Z.O., W.Z., D.Q. and B.C. analysed the data. Z.O. did the model simulations. T.T., M.D.D., A.M., S.N., M.J. and L.L.R. contributed the data and materials. All authors contributed to the discussion and writing.

Corresponding author

Correspondence to Wei-Jun Cai.

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Extended data

Extended Data Fig. 1 The distribution of sea surface pCO2 at in situ temperature in the western Arctic Ocean.

All pCO2 data was measured by underway pCO2 systems except datasets in AOS1994, JOIS 1997, SHEBA 1998 and ODEN 2005 cruises, which were calculated from discrete samples. MR, NP, ODEN, XL, ML, PS, HY, RO, St. L, and Sikuliaq stand for the Research Vessel Mirai, Nathaniel B. Palmer, ODEN, Xuelong, Marcus G. Langseth, Polarstern, Healy, Ronald H. Brown, Louis S. St-Laurent, and Sikuliaq, respectively. A list of cruise information is provided in Supplementary Table 1. The white areas with dashed lines indicate monthly sea ice extent (ice concentration >15%) in September, which has the minimal sea ice extent (Nation Snow and Ice Data Center, http://nsidc.org/data/seaice_index/)53.

Extended Data Fig. 2 Monthly time series of the number of sea surface pCO2 measurements in the western Arctic Ocean.

(a) Canada Basin, (b) Chukchi Shelf, (c) Beaufort Sea, (d) Ice-covered region (north of 80ºN). For a given subregion, the number of pCO2 observations for each month could vary greatly among years depending on the number and timing of cruises, and the sea-ice conditions in a particular year. The number of pCO2 values substantially increased after 2007.

Extended Data Fig. 3 The changes in the seasonality of pCO2 and deseasonalized long-term trends.

We examined the seasonal variation of pCO2 by binning gridded (0.1° latitude × 0.25° longitude) values into Julian-Day for two periods: years prior to 2007 (ad) and 2007 to 2017 (e-h). We deseasonalized data to calculate monthly means of pCO2 following the method described in ref. 16. Briefly, we detrended pCO2 data first and then adjusted the monthly means by adding or subducting the anomaly with respect to the long-term summer mean (averaged over 1994-2017), assuming that the seasonal variations remained unchanged over years. The black and blue dots represent non- and seasonal adjusted monthly means of pCO2, respectively (il). The rates of change with standard error are noted.

Extended Data Fig. 4 The trends of CO2 air-sea gradient (∆pCO2).

The summer ∆pCO2 vary in the Chukchi Shelf, the Canada Basin, the Beaufort Sea and ice-covered region over the period of 1994 to 2017. The rates of change of ∆pCO2 were computed with monthly mean values (positive rates indicate decrease in ∆pCO2, while negative rates indicate increase in ∆pCO2). The dashed line indicates a complete air-sea gas equilibrium. ANOVA was performed for all regressions. Only ∆pCO2 in the Canada Basin shows a significant trend.

Extended Data Fig. 5 The long-term trends of sea surface sAlk, sDIC, Salinity, and Revelle Factor in the Canada Basin.

The discrete samples of sea surface (depth < 20 m) Alk and DIC were obtained from the Global Data Analysis Project version 2 database38 (grey dots). We calculated salinity normalized DIC (sDIC = DIC×S0/SSS) and Alk (sAlk = Alk×S0/SSS) and then averaged the data to calculate monthly means (black dots) for the linear regressions (a and b). The S0 is the reference salinity, i.e. the long-term mean of SSS. We also conducted a non-zero endmember salinity normalization39 for DIC and Alk (d and e; see Methods). The corresponding Revelle Factor was calculated in the CO2SYS program (c and f). The underway measurement of salinity was used for examining the long-term trend (g). We tested whether the slope significantly different from 0 by ANOVA. The rates of change with standard error are shown. Source data

Extended Data Fig. 6 Sea ice-loss amplifying the decrease in surface water Revelle Factor (RF) in the Canada Basin.

Black dots represent the initial condition for RF and DIC at -1.6 °C. The arrows indicate the processes of warming (red), CO2 uptake from the atmosphere (purple), dilution by ice meltwater (cyan). Sea ice reduction from 95% to ice-free is accompanied by a salinity decrease of 3.5 (Supplementary Table 4). The yellow shaded areas indicate the possible seasonal variations of RF, which are amplified by the synergistic effect of ice melt, warming and CO2 uptake. To estimate the change of RF, we allowed 2 °C and 3 °C warming, and 10 and 50 µmol kg−1 DIC perturbations due to air-sea CO2 exchange in 1990s and 2010s, respectively, which are consistent with the long-term warming rate of 0.5 °C per decade34 and the estimated increase in sDIC by 2.3-2.6 µmol kg−1 per year (Table 1 and Fig. 4d). Note that higher RF indicates lower acid-base buffer capacity.

Supplementary information

Supplementary Information

Supplementary Figs. 1–5, Tables 1–4 and references.

Reporting Summary

Supplementary Data 1

Assembled \(p_{{\mathrm {CO}}_2}\) dataset (1994–2017) used in this study.

Supplementary Software

The code used for \(p_{{\mathrm {CO}}_2}\) simulations for the Canada Basin.

Supplementary Data 2

The data used for \(p_{{\mathrm {CO}}_2}\) simulations for the Canada Basin.

Source data

Source Data Extended Data Fig. 5

DIC and Alk in the Canada Basin downloaded from GLODAPv2.

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Ouyang, Z., Qi, D., Chen, L. et al. Sea-ice loss amplifies summertime decadal CO2 increase in the western Arctic Ocean. Nat. Clim. Chang. 10, 678–684 (2020). https://doi.org/10.1038/s41558-020-0784-2

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