Skill assessment of three earth system models with common marine biogeochemistry

Séférian, Roland; Bopp, Laurent; Gehlen, Marion; Orr, James C.; Éthé, Christian; Cadule, Patricia; Aumont, Olivier; Mélia, David Salas y; Voldoire, Aurore; Madec, Gurvan

doi:10.1007/s00382-012-1362-8

Cited by 116 publications

(94 citation statements)

References 104 publications

(148 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This indicates that eddy-permitting spatial resolution of the ocean model alone is insufficient to solve the coupled models deficiencies with respect to [O 2 ] dynamics. Other model-data discrepancies, particularly simulated oxygen increases within the ocean interior at high latitudes, are likely to be related to persistent errors in model physical mixing and deep ocean ventilation, as reported for historical CMIP5 experiments of IPSL-CM5A and CNRM-CM5.1 (Séférian et al, 2012). In addition, the inability of models to capture [O 2 ] dynamics in ice-covered high-latitude areas can be attributed to uncertainties in the underlying sea-ice models related to the growth and melting of seasonal ice (e.g., .…”

Section: Discussionmentioning

confidence: 89%

Detecting an external influence on recent changes in oceanic oxygen using an optimal fingerprinting method

et al. 2013

View full text Add to dashboard Cite

Ocean deoxygenation has been observed in all major ocean basins over the past 50 yr. Although this signal is largely consistent with oxygen changes expected from anthropogenic climate change, the contribution of external forcing to recent deoxygenation trends relative to natural internal variability is yet to be established. Here we conduct a formal optimal fingerprinting analysis to investigate if external forcing has had a detectable influence on observed dissolved oxygen concentration ([O₂]) changes between ∼1970 and ∼1992 using simulations from two Earth System Models (MPI-ESM-LR and HadGEM2-ES). We detect a response to external forcing at a 90% confidence level and find that observed [O₂] changes are inconsistent with internal variability as simulated by models. This result is robust in the global ocean for depth-averaged (1-D) zonal mean patterns of [O₂] change in both models. Further analysis with the MPI-ESM-LR model shows similar positive detection results for depth-resolved (2-D) zonal mean [O₂] changes globally and for the Pacific Ocean individually. Observed oxygen changes in the Atlantic Ocean are indistinguishable from natural internal variability. Simulations from both models consistently underestimate the amplitude of historical [O₂] changes in response to external forcing, suggesting that model projections for future ocean deoxygenation may also be underestimated

show abstract

Section: Discussionmentioning

confidence: 89%

Detecting an external influence on recent changes in oceanic oxygen using an optimal fingerprinting method

et al. 2013

View full text Add to dashboard Cite

show abstract

“…the inclusion of fire management) for E LUC can lead to significantly different estimates Aumont and Bopp (2006) No change CCSM-BEC Doney et al (2009) No change; small differences in the mean flux are caused by a change in how global and annual means were computed MICOM-HAMOCC (NorESM-OC) Assmann et al (2010) l,m Revised light penetration formulation and parameters for ecosystem module, revised salinity restoring scheme enforcing salt conservation, new scheme enforcing global freshwater balance, and model grid changed from displaced pole to tripolar MPIOM-HAMOCC Ilyina et al (2013) No change NEMO-PISCES (CNRM) Séférian et al (2013) Zaehle et al (2010) and Friend (2010). i See also Ito and Inatomi (2012).…”

Section: Other Published E Luc Methodsmentioning

confidence: 99%

Global Carbon Budget 2015

Quéré

Moriarty

Andrew

et al. 2015

Earth Syst. Sci. Data

668

485

View full text Add to dashboard Cite

Abstract. Accurate assessment of anthropogenic carbon dioxide (CO 2 ) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere is important to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe data sets and a methodology to quantify all major components of the global carbon budget, including their uncertainties, based on the combination of a range of data, algorithms, statistics, and model estimates and their interpretation by a broad scientific community. We discuss changes compared to previous estimates as well as consistency within and among components, alongside methodology and data limitations. CO 2 emissions from fossil fuels and industry (E FF ) are based on energy statistics and cement production data, while emissions from land-use change (E LUC ), mainly deforestation, are based on combined evidence from land-cover-change data, fire activity associated with deforestation, and models. The global atmospheric CO 2 concentration is measured directly and its rate of growth (G ATM ) is computed from the annual changes in concentration. The mean ocean CO 2 sink (S OCEAN ) is based on observations from the 1990s, while the annual anomalies and trends are estimated with ocean models. The variability in S OCEAN is evaluated with data products based on surveys of ocean CO 2 measurements. The global residual terrestrial CO 2 sink (S LAND ) is estimated by the difference of the other terms of the global carbon budget and compared to results of independent dynamic global vegetation models forced by observed climate, CO 2 , and land-cover change (some including nitrogen-carbon interactions). We compare the mean land and ocean fluxes and their variability to estimates from three atmospheric inverse methods for three broad latitude bands. All uncertainties are reported as ±1σ , reflecting the current capacity to characterise the annual estimates of each component of the global carbon budget. For the last decade available (2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014), E FF was 9.0 ± 0.5 GtC yr −1 , E LUC was 0.9 ± 0.5 GtC yr −1 , G ATM was 4.4 ± 0.1 GtC yr −1 , S OCEAN was 2.6 ± 0.5 GtC yr −1 , and S LAND was 3.0 ± 0.8 GtC yr −1 . For the year 2014 alone, E FF grew to 9.8 ± 0.5 GtC yr −1 , 0.6 % above 2013, continuing the growth trend in these emissions, albeit at a slower rate compared to the average growth of 2.2 % yr −1 that took place during 2005-2014. Also, for 2014, E LUC was 1.1 ± 0.5 GtC yr −1 , G ATM was 3.9 ± 0.2 GtC yr −1 , S OCEAN was 2.9 ± 0.5 GtC yr −1 , and S LAND was 4.1 ± 0.9 GtC yr −1 . G ATM was lower in 2014 compared to the past decade (2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014), reflecting a larger S LAND for that year. The global atmospheric CO 2 concentration reached 397.15 ± 0.10 ppm averaged over 2014. For 2015, preliminary data indicate that the growth in E FF will be near or slightly below zero, with a projection of −0.6 [range of −1.6 to +0....

show abstract

“…The DOC from rivers is assumed to be labile and is directly converted to DIC upon its delivery to the ocean. Inputs of dissolved Fe, NO 3 , PO 4 , and SiO 4 are computed from the sum of DIC and DOC river input using a constant set of ratios for C:N:P:Si:Fe as computed from Meybeck [1982] for C:N, from Takahashi et al [1985] for N:P, from de Baar and de Jong [2001] for Fe:C, and from Treguer et al [1995] for Si:C. More complete descriptions of the whole ocean-sea ice and marine biogeochemistry component are provided by Voldoire et al [2013] and Seferian et al [2013], respectively.…”

Section: A16 Model 17mentioning

confidence: 99%

Net primary productivity estimates and environmental variables in the Arctic Ocean: An assessment of coupled physical-biogeochemical models

Lee

Matrai

Friedrichs

et al. 2016

J. Geophys. Res. Oceans

Self Cite

View full text Add to dashboard Cite

The relative skill of 21 regional and global biogeochemical models was assessed in terms of how well the models reproduced observed net primary productivity (NPP) and environmental variables such as nitrate concentration (NO3), mixed layer depth (MLD), euphotic layer depth (Zeu), and sea ice concentration, by comparing results against a newly updated, quality‐controlled in situ NPP database for the Arctic Ocean (1959–2011). The models broadly captured the spatial features of integrated NPP (iNPP) on a pan‐Arctic scale. Most models underestimated iNPP by varying degrees in spite of overestimating surface NO3, MLD, and Zeu throughout the regions. Among the models, iNPP exhibited little difference over sea ice condition (ice‐free versus ice‐influenced) and bottom depth (shelf versus deep ocean). The models performed relatively well for the most recent decade and toward the end of Arctic summer. In the Barents and Greenland Seas, regional model skill of surface NO3 was best associated with how well MLD was reproduced. Regionally, iNPP was relatively well simulated in the Beaufort Sea and the central Arctic Basin, where in situ NPP is low and nutrients are mostly depleted. Models performed less well at simulating iNPP in the Greenland and Chukchi Seas, despite the higher model skill in MLD and sea ice concentration, respectively. iNPP model skill was constrained by different factors in different Arctic Ocean regions. Our study suggests that better parameterization of biological and ecological microbial rates (phytoplankton growth and zooplankton grazing) are needed for improved Arctic Ocean biogeochemical modeling.

show abstract

Skill assessment of three earth system models with common marine biogeochemistry

Cited by 116 publications

References 104 publications

Detecting an external influence on recent changes in oceanic oxygen using an optimal fingerprinting method

Detecting an external influence on recent changes in oceanic oxygen using an optimal fingerprinting method

Global Carbon Budget 2015

Net primary productivity estimates and environmental variables in the Arctic Ocean: An assessment of coupled physical-biogeochemical models

Contact Info

Product

Resources

About