Scaling Global Warming Impacts on Ocean Ecosystems: Lessons From a Suite of Earth System Models

Bahl, Alexis; Gnanadesikan, Anand; Pradal, Marie-Aude

doi:10.3389/fmars.2020.00698

Cited by 13 publications

(10 citation statements)

References 82 publications

(122 reference statements)

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“…The difficulty of attributing recently observed changes in NPP to climate change and even of detecting long-term trends (Wernand et al, 2013;Beaulieu et al, 2013;Boyce et al, 2010;Henson et al, 2016) make ESMs essential for anticipating anthropogenic changes in NPP. In this regard, it is key to understand the origins and consequences of ESM biases and/or projection uncertainties.…”

Section: Discussionmentioning

confidence: 99%

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Oceanic primary production decline halved in eddy-resolving simulations of global warming

Couespel

Lévy

Bopp³

2021

Biogeosciences

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Abstract. The decline in ocean primary production is one of the most alarming consequences of anthropogenic climate change. This decline could indeed lead to a decrease in marine biomass and fish catch, as highlighted by recent policy-relevant reports. Because of computational constraints, current Earth system models used to project ocean primary production under global warming scenarios have to parameterize flows occurring below the resolution of their computational grid (typically 1∘). To overcome these computational constraints, we use an ocean biogeochemical model in an idealized configuration representing a mid-latitude double-gyre circulation and perform global warming simulations under an increasing horizontal resolution (from 1 to 1/27∘) and under a large range of parameter values for the eddy parameterization employed in the coarse-resolution configuration. In line with projections from Earth system models, all our simulations project a marked decline in net primary production in response to the global warming forcing. Whereas this decline is only weakly sensitive to the eddy parameters in the eddy-parameterized coarse 1∘ resolution simulations, the simulated decline in primary production in the subpolar gyre is halved at the finest eddy-resolving resolution (−12 % at 1/27∘ vs. −26 % at 1∘) at the end of the 70-year-long global warming simulations. This difference stems from the high sensitivity of the sub-surface nutrient transport to model resolution. Although being only one piece of a much broader and more complicated response of the ocean to climate change, our results call for improved representation of the role of eddies in nutrient transport below the seasonal mixed layer to better constrain the future evolution of marine biomass and fish catch potential.

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Section: Discussionmentioning

confidence: 99%

“…Current projections of NPP decline are based on eddyparameterized models and thus are potentially biased by their inadequate representation of sub-grid processes (Bahl et al, 2020). Here, we assess the difference that eddy resolution makes to shaping the response of NPP to future global warming.…”

Section: Introductionmentioning

confidence: 99%

Oceanic primary production decline halved in eddy-resolving simulations of global warming

Couespel

Lévy

Bopp³

2021

Biogeosciences

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“…The three model runs consist of a standard historical pre-industrial model spin-up (BLING -PI Control), a similar case to the first but in which the carbon dioxide concentration is 4 times higher (BLING -4×CO 2 ), and a case similar to the historical spin-up except that the horizontal mixing parameter is 3 times higher (BLING -3×Mixing). These model runs are described in greater detail in Gnanadesikan et al (2013), Gnanadesikan (2014), andBahl et al (2020). With the standard historical model essentially serving as a form of a "control", the two remaining cases allow us to examine if changes in the physical circulation could result in changes to the apparent relationships.…”

Section: Tracers Of Phytoplankton With Allometric Zooplankton (Topaz)mentioning

confidence: 99%

Using neural network ensembles to separate ocean biogeochemical and physical drivers of phytoplankton biogeography in Earth system models

2022

Self Cite

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Abstract. Earth system models (ESMs) are useful tools for predicting and understanding past and future aspects of the climate system. However, the biological and physical parameters used in ESMs can have wide variations in their estimates. Even small changes in these parameters can yield unexpected results without a clear explanation of how a particular outcome was reached. The standard method for estimating ESM sensitivity is to compare spatiotemporal distributions of variables from different runs of a single ESM. However, a potential pitfall of this method is that ESM output could match observational patterns because of compensating errors. For example, if a model predicts overly weak upwelling and low nutrient concentrations, it might compensate for this by allowing phytoplankton to have a high sensitivity to nutrients. Recently, we demonstrated that neural network ensembles (NNEs) are capable of extracting relationships between predictor and target variables within ocean biogeochemical models. Being able to view the relationships between variables, along with spatiotemporal distributions, allows for a more mechanistically based examination of ESM outputs. Here, we investigated whether we could apply NNEs to help us determine why different ESMs produce different spatiotemporal distributions of phytoplankton biomass. We tested this using three cases. The first and second case used different runs of the same ESM, except that the physical circulations differed between them in the first case, while the biological equations differed between them in the second. Our results indicated that the NNEs were capable of extracting the relationships between variables for different runs of a single ESM, allowing us to distinguish between differences due to changes in circulation (which do not change relationships) from changes in biogeochemical formulation (which do change relationships). In the third case, we applied NNEs to two different ESMs. The results of the third case highlighted the capability of NNEs to contrast the apparent relationships of different ESMs and some of the challenges it presents. Although applied specifically to the ocean components of an ESM, our study demonstrates that Earth system modelers can use NNEs to separate the contributions of different components of ESMs. Specifically, this allows modelers to compare the apparent relationships across different ESMs and observational datasets.

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“…The three model runs consisted of: a standard historical preindustrial model spin-up (BLING -PI Control), a similar case to the first but where the carbon dioxide concentration 170 was four times higher (BLING -4x CO2), and a case similar to the historical spin-up except that the horizontal mixing parameter was three times higher (BLING -3x Mixing). These model runs are described in greater detail in Gnanadesikan et al (2013), Gnanadesikan (2014), andBahl et al (2020). With the standard historical model essentially serving as a form of a "control," the two remaining cases allowed us to examine if changes in the physical circulation would result in changes to the apparent relationships.…”

Section: Case 1 -Same Esm: Identical Biological Equations Different Physical Circulationsmentioning

confidence: 99%

Using Neural Network Ensembles to Separate Biogeochemical and Physical Components in Earth System Models

Holder

Gnanadesikan

Pradal

2021

Preprint

Self Cite

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Abstract. Earth system models (ESMs) are useful tools for predicting and understanding past and future aspects of the climate system. However, the biological and physical parameters used in ESMs can have wide variations in their estimates. Even small changes in these parameters can yield unexpected results without a clear explanation of how a particular outcome was reached. The standard method for estimating ESM sensitivity is to compare spatiotemporal distributions of variables from different runs of a single ESM. However, a potential pitfall of this method is that ESM output could match observational patterns because of compensating errors. For example, if a model predicts overly weak upwelling and low nutrient concentrations, it may compensate for this by allowing phytoplankton to have a high sensitivity to nutrients. Recently, it has been demonstrated that neural network ensembles (NNEs) are capable of extracting relationships between predictor and target variables within ocean biogeochemical models. Being able to view the relationships between variables, along with spatiotemporal distributions, allows for a more mechanistically based examination of ESM outputs. Here, we investigated whether we could apply NNEs to help us determine why different ESMs produce different results. We tested this using three cases. The first and second case use different runs of the same ESM, except the physical circulations differ between them in the first case while the biological equations differ between them in the second. Our results indicate that the NNEs were capable of extracting the relationships between variables, allowing us to distinguish between differences due to changes in circulation (which do not change relationships) from changes in biogeochemical formulation (which do change relationships).

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Scaling Global Warming Impacts on Ocean Ecosystems: Lessons From a Suite of Earth System Models

Cited by 13 publications

References 82 publications

Oceanic primary production decline halved in eddy-resolving simulations of global warming

Oceanic primary production decline halved in eddy-resolving simulations of global warming

Using neural network ensembles to separate ocean biogeochemical and physical drivers of phytoplankton biogeography in Earth system models

Using Neural Network Ensembles to Separate Biogeochemical and Physical Components in Earth System Models

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