Finding the Fingerprint of Anthropogenic Climate Change in Marine Phytoplankton Abundance

Elsworth, Geneviève; Lovenduski, Nicole S.; McKinnon, Karen A.; Krumhardt, Kristen M.; Brady, Riley X.

doi:10.1007/s40641-020-00156-w

Cited by 16 publications

(10 citation statements)

References 77 publications

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“…The ESA OC-CCI v3.1 dataset also contains two large El Niño events in both 1997/1998 and 2015/2016; the ESA OC-CCI v2.0 dataset only contains the former. Events such as El Niño can have significant effects on trend estimates, and thus it is important to have as long a record length as possible, so as to minimize their effect on trend estimates 33 . To assess the effect of record length we have included a comparison of trends detected over the periods of September 1997–December 2016 and September 1997–December 2013 with the longest period used here ending in December 2018 with the same methodology (see Text S2 and Figure S2 in supporting information).…”

Section: Discussionmentioning

confidence: 99%

Regional surface chlorophyll trends and uncertainties in the global ocean

Hammond

Beaulieu

Henson

et al. 2020

Sci Rep

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Changes in marine primary productivity are key to determine how climate change might impact marine ecosystems and fisheries. Satellite ocean color sensors provide coverage of global ocean chlorophyll with a combined record length of ~ 20 years. Coupled physical–biogeochemical models can inform on expected changes and are used here to constrain observational trend estimates and their uncertainty. We produce estimates of ocean surface chlorophyll trends, by using Coupled Model Intercomparison Project (CMIP5) models to form priors as a “first guess”, which are then updated using satellite observations in a Bayesian spatio-temporal model. Regional chlorophyll trends are found to be significantly different from zero in 18/23 regions, in the range ± 1.8% year−1. A global average of these regional trends shows a net positive trend of 0.08 ± 0.35% year−1, highlighting the importance of considering chlorophyll changes at a regional level. We compare these results with estimates obtained with the commonly used “vague” prior, representing no independent knowledge; coupled model priors are shown to slightly reduce trend magnitude and uncertainties in most regions. The statistical model used here provides a robust framework for making best use of all available information and can be applied to improve understanding of global change.

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Section: Discussionmentioning

confidence: 99%

Regional surface chlorophyll trends and uncertainties in the global ocean

Hammond

Beaulieu

Henson

et al. 2020

Sci Rep

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“…Observed historical trends in global mean surface ocean temperature, pH, and subsurface oxygen were compared with the multi-model mean of the CMIP6 ensemble over the corresponding years of historical simulations (Table 3). Global observations of historical trends in euphotic-zone nitrate concentrations and integrated primary production were deemed insufficiently robust, given the associated interannual-decadal variability, to be assessed in the models (Elsworth et al 2020). The observed 1901-2012 SST warming of +0.06 • C per decade is well reproduced in the CMIP6 ensemble, although SST warming between 1979 and 2012 is warm-biased in the multi-model mean.…”

Section: Comparison With Historical Global Trendsmentioning

confidence: 99%

Twenty-first century ocean warming, acidification, deoxygenation, and upper-ocean nutrient and primary production decline from CMIP6 model projections

et al. 2020

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Abstract. Anthropogenic climate change is projected to lead to ocean warming, acidification, deoxygenation, reductions in near-surface nutrients, and changes to primary production, all of which are expected to affect marine ecosystems. Here we assess projections of these drivers of environmental change over the twenty-first century from Earth system models (ESMs) participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) that were forced under the CMIP6 Shared Socioeconomic Pathways (SSPs). Projections are compared to those from the previous generation (CMIP5) forced under the Representative Concentration Pathways (RCPs). A total of 10 CMIP5 and 13 CMIP6 models are used in the two multi-model ensembles. Under the high-emission scenario SSP5-8.5, the multi-model global mean change (2080–2099 mean values relative to 1870–1899) ± the inter-model SD in sea surface temperature, surface pH, subsurface (100–600 m) oxygen concentration, euphotic (0–100 m) nitrate concentration, and depth-integrated primary production is +3.47±0.78 ∘C, -0.44±0.005, -13.27±5.28, -1.06±0.45 mmol m−3 and -2.99±9.11 %, respectively. Under the low-emission, high-mitigation scenario SSP1-2.6, the corresponding global changes are +1.42±0.32 ∘C, -0.16±0.002, -6.36±2.92, -0.52±0.23 mmol m−3, and -0.56±4.12 %. Projected exposure of the marine ecosystem to these drivers of ocean change depends largely on the extent of future emissions, consistent with previous studies. The ESMs in CMIP6 generally project greater warming, acidification, deoxygenation, and nitrate reductions but lesser primary production declines than those from CMIP5 under comparable radiative forcing. The increased projected ocean warming results from a general increase in the climate sensitivity of CMIP6 models relative to those of CMIP5. This enhanced warming increases upper-ocean stratification in CMIP6 projections, which contributes to greater reductions in upper-ocean nitrate and subsurface oxygen ventilation. The greater surface acidification in CMIP6 is primarily a consequence of the SSPs having higher associated atmospheric CO2 concentrations than their RCP analogues for the same radiative forcing. We find no consistent reduction in inter-model uncertainties, and even an increase in net primary production inter-model uncertainties in CMIP6, as compared to CMIP5.

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“…(2017) and additionally described in Elsworth et al. (2020), with slight modifications to the approach. We model chlorophyll concentration as:

X^{i, t} = β_{0}^{i} goodbreak+ β_{S}^{i, m (t)} goodbreak+ β_{F}^{t} goodbreak+ β_{E N S O}^{i, m (t)} M_{E N S O}^{t} goodbreak+ β_{P D O}^{i, m (t)} M_{P D O}^{t} goodbreak+ ϵ^{i, t},

where X is the chlorophyll concentration at the location i and time t , and m(t) indicates the month associated with time t .…”

Section: Creating a Synthetic Ensemble Of The Observational Recordmentioning

confidence: 99%

“…The synthetic ensemble can be used to illustrate how variable phasing in climate modes can produce different trends over the observational period, both at a discrete location and across the full spatial grid as in Elsworth et al. (2020). For example, Figure 6 illustrates the temporal evolution of two synthetic ensemble members generated from the HOT dataset.…”

Section: Implications For the Interpretation Of Observational Recordsmentioning

confidence: 99%

Alternate History: A Synthetic Ensemble of Ocean Chlorophyll Concentrations

Elsworth

Lovenduski

McKinnon

2021

Global Biogeochemical Cycles

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The ocean biosphere strongly influences biogeochemical cycling, carbon export, and air-sea carbon flux. Although phytoplankton constitute a relatively small reservoir of carbon, their ability to photosynthetically fix carbon from the atmosphere enhances the ocean's role as a carbon sink, allowing the ocean to store 45 times more carbon than the atmosphere (Friedlingstein et al., 2019). The efficiency and strength of the ocean biological pump can influence atmospheric carbon dioxide concentrations; in the absence of the ocean biosphere, atmospheric carbon dioxide concentrations would increase by approximately 50% of preindustrial values (McKinley et al., 2017).Internal climate variability plays an important role in the abundance and distribution of phytoplankton in the global ocean. Modes of internal climate variability, such as the El Niño Southern Oscillation (ENSO), the Southern Annular Mode (SAM), and the North Atlantic Oscillation (NAO), alter the physical and chemical environment for, and thus the abundance of, phytoplankton on timescales ranging from interannual to multi-decadal (

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Finding the Fingerprint of Anthropogenic Climate Change in Marine Phytoplankton Abundance

Cited by 16 publications

References 77 publications

Regional surface chlorophyll trends and uncertainties in the global ocean

Regional surface chlorophyll trends and uncertainties in the global ocean

Twenty-first century ocean warming, acidification, deoxygenation, and upper-ocean nutrient and primary production decline from CMIP6 model projections

Alternate History: A Synthetic Ensemble of Ocean Chlorophyll Concentrations

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