Marine phytoplankton have a crucial role in the modulation of marine-based food webs1, fishery yields2 and the global drawdown of atmospheric carbon dioxide3. However, owing to sparse measurements before satellite monitoring in the twenty-first century, the long-term response of planktonic stocks to climate forcing is unknown. Here, using a continuous, multi-century record of subarctic Atlantic marine productivity, we show that a marked 10 ± 7% decline in net primary productivity has occurred across this highly productive ocean basin over the past two centuries. We support this conclusion by the application of a marine-productivity proxy, established using the signal of the planktonic-derived aerosol methanesulfonic acid, which is commonly identified across an array of Greenlandic ice cores. Using contemporaneous satellite-era observations, we demonstrate the use of this signal as a robust and high-resolution proxy for past variations in spatially integrated marine productivity. We show that the initiation of declining subarctic Atlantic productivity broadly coincides with the onset of Arctic surface warming4, and that productivity strongly covaries with regional sea-surface temperatures and basin-wide gyre circulation strength over recent decades. Taken together, our results suggest that the decline in industrial-era productivity may be evidence of the predicted5 collapse of northern Atlantic planktonic stocks in response to a weakened Atlantic Meridional Overturning Circulation6,7,8. Continued weakening of this Atlantic Meridional Overturning Circulation, as projected for the twenty-first century9,10, may therefore result in further productivity declines across this globally relevant region.
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Previously unpublished ice core [MSA] data used in this study (D4, NGT-B16, NGT-B18, NGT-B20, NGT-B21 and NGT–B26) are available via the National Oceanic and Atmospheric Administration (NOAA) Paleoclimatology Data Archive (https://www.ncdc.noaa.gov/paleo/study/26691). TUNU, Summit2010, 20D and GC [MSA] data are available via the NSF Arctic Data Center (http://arcticdata.io). NGRIP [MSA] data are from ref. 15. GRIP93a data are from ref. 18. All [MSA] data are available upon request from the corresponding author. Source Data for Figs. 1, 2, 4 are available in the online version of this paper. Availability of CPR plankton-abundance data is made possible by the Marine Biological Association (https://www.cprsurvey.org/; https://doi.org/10.7487/2018.29.1.1109 and https://doi.org/10.7487/2018.53.1.1118). Ocean productivity data are publicly available from https://www.science.oregonstate.edu/ocean.productivity/. Ocean [DMSSW] data can be accessed via https://saga.pmel.noaa.gov/dms/. The NCEP/NCAR reanalysis data can be downloaded from https://www.ready.noaa.gov/HYSPLIT.php.
Code used for [MSA] signal extraction, Monte Carlo correlation analysis, HYSPLIT analysis and CPR analysis is available from the corresponding author upon request. Code for SiZer change-point analysis was modified after a previous publication4 and is available from the corresponding author upon request. The HYSPLIT source code can be downloaded from https://www.ready.noaa.gov/HYSPLIT.php.
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We thank D. Thornalley, R. Ji and Ø. Paasche for their feedback on early versions of the study, as well as H. Hátún, D. G. Boyce and D. Johns for contributing oceanographic and planktonic data shown in the study; M. Legrand for sharing the GRIP93a ice core [MSA] data; the students and staff of the DRI ice core group for assistance in the field and laboratory; the National Science Foundation (NSF) Ice Core Facility (formerly NICL), A. York, M. Bingham, M. Hatch and Milton Academy students for the Greenland Central (GC) ice core sampling, processing and [MSA] analysis support. Funding for retrieval and analysis of the GC ice core was provided by the US NSF Arctic System Science Program (award OPP-1205196 to S.B.D. and OPP-1205008 to M.J.E.). M.B.O. acknowledges support from the US Department of Defense Office of Naval Research – National Defense Science and Engineering Graduate (NDSEG) fellowship, and an Ocean Outlook Fellowship to the Bjerknes Centre for Climate Research. M.B.O. and S.B.D. further acknowledge institutional support from the Woods Hole Oceanographic Institution (WHOI). H.F. acknowledges support of the tracer glaciological work along the North Greenland Traverse (NGT) 1993–95 by the German Research Foundation (DFG) and the long-term support of ice core science at the University of Bern by the Swiss National Science Foundation (SNF). Collection and analysis of the D4, TUNU and Summit2010 cores were supported by NSF grants 0221515, 0909541 and 1204176 to J.R.M. E.S.S. acknowledges NASA grant NNX15AF31G.
Nature thanks John Dunne, Rafel Simo and the other anonymous reviewer(s) for their contribution to the peer review of this work.
The authors declare no competing interests.
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data figures and tables
a, Monthly integrated NPP (g C) across the subarctic Atlantic (50–65° N, 60–10° W, see also yellow boxed region in Fig. 3a) for the SeaWiFS-VGPM and MODIS-VGPM NPP products12, as well as an independently derived SeaWiFS-carbon-based productivity model57 (CbPM) (Supplementary Information) and MODIS-CbPM NPP products. b, c, SeaWiFS- and MODIS-derived NPP mean seasonality (±2σ; n = 20 years; January 1998–December 2017) for the VGPM (b) and CbPM (c) datasets. Note that four months—November–December–January–February—experience partial polar darkness over the subarctic Atlantic latitude bands (50–65° N), leading to systematic underestimates of productivity during these months. d, e, VGPM-based (d) and CbPM-based (e) linear regressions with ship-based [DMSSW] measurements (similar to Fig. 3b; see Methods) using the MODIS and SeaWiFS NPP datasets. Shaded bands show the 95% confidence interval of the regression. Regression values (r2) represent the squared Pearson product–moment coefficients.
Extended Data Fig. 2 Seasonal representativeness of subarctic Atlantic VGPM NPP satellite-era trends and sensitivity to satellite sensor used.
a, Comparison of summertime-integrated (JJA) subarctic Atlantic VGPM NPP yields for three different sensor estimates: a SeaWiFS-dominant estimate (red; 1998–2007 NPP estimates derived from the SeaWiFS sensor and 2008–2017 NPP estimates from the MODIS sensor), a MODIS-dominant estimate (blue; SeaWiFS-based data for 1998–2002 and MODIS-based data for 2003–2017), and the composite stack (dark grey; comprising the average of SeaWiFS- and MODIS-derived summertime VGPM NPP estimates over their period of common annual overlap, 2003–2007). b, Differential linear trend analysis of the composite summertime subarctic Atlantic NPP time series from a. No decadal-scale linear trends were found to be significant at P < 0.05, using a two-sided Student’s t-test with n − 2 degrees of freedom (where n represents the varying trend length in years). c, d, EOF1 (c) and PC1 (d) of summertime VGPM NPP using the MODIS-dominant dataset, similar to Fig. 3c, d, respectively. e, f, As in c, d, but showing EOF results from the SeaWiFS-dominant summertime VGPM NPP dataset. g–l, As in a–f, but showing annually integrated VGPM NPP estimates. All regression values (r) represent Pearson product–moment coefficients.
a, b, Data availability (ad 1958–2016) by CPR standard region (Methods) for annually derived (a; ≥8 out of 12 months per year of data required) and summertime-derived (b; ≥4 out of 6 months per year during April–May–June–July–August–September) data. c, d, Three approaches for compositing time series of CPR-based planktonic abundance, for both annual (c) and summertime (d) data42 (see Supplementary Information). AWA, area-weighted averaging; ISD, inverse-squared distance-based data infilling; EOF, EOF-based data infilling. Thin dashed lines show standardized (z-score units relative to ad 1958-2016) raw annual relative abundances of plankton; bold lines show adjusted relative abundances, to correct for potential long-term biases in the volume of water sampled by CPR devices53,54. Note that the annual area-weighted averaged series is reproduced from Fig. 4a.
Extended Data Fig. 4 HYSPLIT-computed summertime (JJA) airmass transport probability densities for each ice core site.
Site-specific JJA marine-airmass transport density maps, representing the relative probability of an oceanic airmass passing through a given atmospheric column before its arrival at each site. All marine-airmass transport density maps are computed over the period ad 1 January 1948 to 31 December 2013 (that is, 6,121 JJA trajectories per site) and normalized on a 0–1 relative scale with 1 and 0 indicating the most and least probable airmass trajectory grid point, respectively. Sites are shown anticlockwise from most southerly (20D; top left) to most northerly (NGT-B21; top right) situated on the GrIS.
Extended Data Fig. 5 HYSPLIT-computed summertime (JJA) airmass median elevation maps for each ice core site.
Site-specific median atmospheric altitudes (m above sea level) for all ocean-situated JJA hourly trajectory locations over the period ad 1948–2013. All maps were computed over the period ad 1 January 1948 to 31 December 2013 (that is, 6,121 JJA trajectories per site). Sites are shown anticlockwise from most southerly (20D; top left) to most northerly (NGT-B21; top right) situated on the GrIS.
Top, the 12 Greenland [MSA] records from Fig. 1a annually averaged across the two airmass-trajectory factor analysis groupings from Fig. 1b (r = 0.63, P < 0.0001); the r value represents the Pearson product–moment coefficient. All records have been standardized (z units) relative to their period of common overlap (ad 1821–1985). The shaded bands show ±1 s.e.m. about the stack means. The grey line shows the composite (12-site) mean. Bottom, [MSA] record availability over time.
a, Reproduced from Fig. 3b. b, As in a, but for globally regressed values. The diameter of the circles represents the root of the relative weighting (inverse standard error of the [DMSSW] measurement) used in the weighted least squares (WLS) regressions (nWLS,subarctic = 184; nWLS,global = 2,219 degrees of freedom). Grey circles represent points used in the ordinary least squares (OLS) regression (nOLS,subarctic = 222; nOLS,global = 3,043). c, Probability density of global r values over i = 1, 2, …, 10,000 degrees-of-freedom preserving (that is, ni = 184) bootstrap WLS regressions (Supplementary Information). d–f, Linear regression analyses of subarctic Atlantic [DMSSW] versus CPR-based abundances of diatoms (d), dinoflagellates (e) and coccolithophores (f). In all regressions, the coloured or grey shaded regions denote the 95% confidence intervals around the regression parameters for the WLS or OLS regressions, respectively. All WLS and OLS regressions were significant at P < 0.005 and P < 0.05, respectively, assuming a two-tailed Student’s t-distribution with a t-statistic representing n − 2 degrees of freedom. Regression values (r) represent Pearson product–moment coefficients.
a, Correlation matrix (Pearson product–moment coefficients, r) of planktonic and observed-climatic indices from Fig. 4a. Integers represent n, the years of overlap between paired series. Bold n values represent significance at the 90% confidence level (P < 0.1; assuming a two-tailed Student’s t-distribution with a t-statistic representing n − 2 degrees of freedom). Bold n values with an asterisk represent significance using a Monte Carlo-based Fourier phase-randomization procedure, a more stringent test to account for serial correlation (and hence varying degrees of freedom) among paired series (Methods). All 10-year low-pass-filtered (bottom left of diagonal), and linearly detrended 10-year low-pass-filtered (top right of diagonal), series convolved using a Gaussian filter. Paired series with less than 20 years of overlap are denoted missing with an ‘x’. b, WLS model of 5-year smoothed [MSA] PC1 and summertime NPP PC1 (n = 12 years; r2 = 0.72; P = 0.04 after adjusting for reduced degrees of freedom; Methods). The regression weights are the inverse 1σ of [MSA] PC1 values. Histogram distributions denote the range of industrial-era onset and satellite-era [MSA] PC1 values following 10,000 bootstrap tests (distributions normalized to their maximum). The shaded band shows the 95% confidence interval of the WLS regression parameters. The corresponding 95% confidence ranges of NPP rates (g C per JJA) over the industrial-era onset and satellite era are shown to the right. The analysis suggests an average decline of approximately 14 ± 11% (mean ± 2σ) in summertime-integrated NPP yields since the industrial-era onset.
a, b, Annual SST linear trends in the ERSST v.530 (a) and HadISST v.1.158 (b) reanalyses. Grid points exhibiting a 147-year (ad 1870–2016) cooling trend within the subarctic Atlantic (50–65° N; 60–10° W; bold blue outline) are outlined by black isopleths and defined to encompass the Atlantic warming hole59. c, ERSST and HadISST anomalies (mean-centred relative to ad 1870–2016; n = 147 years) for the Northern Hemisphere (top; ±σ), the Atlantic warming hole (middle; ±σ) and the difference between warming hole and Northern Hemisphere SSTs, representing the AMOC index as approximately defined previously6,7 (bottom; ±σ). Bold AMOC time series are 10-year (Butterworth) low-pass-filtered data.
The Supplementary Information contains details and discussion regarding several analyses conducted in support of the main-study conclusions. Specifically, it contains the following seven sections (including three Supplementary Tables): 1. Marine airmass source attribution and covariation in Greenland [MSA] records; 2. Comparison of data infilling methodologies for [MSA]-PC1 computation; 3. Satellite-derived net primary productivity (NPP) products and temporal trends (A.D. 1998 – 2017); 4. Processing of Continuous Plankton Recorder survey data; 5. Comparison of [MSA]-PC1 to regional sea ice behavior; 6. NOAA Global Surface Seawater DMS Database measurements; 7. Defining the subarctic Atlantic SST “warming hole” and calculation of the observational AMOC index.
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Osman, M.B., Das, S.B., Trusel, L.D. et al. Industrial-era decline in subarctic Atlantic productivity. Nature 569, 551–555 (2019). https://doi.org/10.1038/s41586-019-1181-8
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