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4. BIOPERIANT12: a mesoscale resolving coupled physics-biogeochemical model for the Southern Ocean.

Author

Chang, Nicolette, Nicholson, Sarah-Anne, Plessis, Marcel du, Lebehot, Alice D., Mashifane, Thulwaneng, Moalusi, Tumelo C., Mongwe, N. Precious, and Monteiro, Pedro M. S.

Subjects
OCEAN dynamics, MIXING height (Atmospheric chemistry), PARTIAL pressure, CLOUDINESS, CARBON dioxide
Abstract

We present BIOPERIANT12, a regional model configuration of the Southern Ocean (SO) at a mesoscale-resolving 1/12°. This is a stable, ocean–ice–biogeochemical configuration derived from the Nucleus for European Modelling of the Ocean (NEMO) modelling platform. It is specifically designed to investigate questions related to the mean state, seasonal cycle variability and mesoscale processes in the mixed layer and within the upper ocean (<1000 m). In particular, the focus is on understanding processes behind carbon and heat exchange, systematic errors in biogeochemistry and assumptions underlying the parameters chosen to represent these SO processes. The dynamics of the ocean model play a large role in driving ocean biogeochemistry and we show that over the chosen period of analysis 2000–2009 that the simulated dynamics in the upper ocean provide a stable mean state, as compared to observation-based datasets (themselves subject to biases such as sparsity of data, cloud cover, etc.), and through which the characteristics of variability can be described. Using ocean biomes to delineate the major regions of the SO, the model demonstrates a useful representation of ocean biogeochemistry and partial pressure of carbon dioxide (pCO2). In addition to a reasonable model mean state performance, through model–data metrics BIOPERIANT12 highlights several pathways for improving Southern Ocean model simulations such as the representation of temporal variability and the overestimation of biological biomass. [ABSTRACT FROM AUTHOR]

Published
2024
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5. The elevated Curie temperature and half-metallicity in the ferromagnetic semiconductor La$_{x}$Eu$_{1-x}$O

Author

Monteiro, Pedro M. S., Baker, Peter J., Hine, Nicholas D. M., Steinke, Nina-J., Ionescu, Adrian, Cooper, Joshaniel F. K., Barnes, Crispin H. W., Kinane, Christian J., Salman, Zaher, Wildes, Andrew R., Prokscha, Thomas, and Langridge, Sean

Subjects
Condensed Matter - Strongly Correlated Electrons
Abstract

Here we study the effect of La doping in EuO thin films using SQUID magnetometry, muon spin rotation ($\mu$SR), polarized neutron reflectivity (PNR), and density functional theory (DFT). The $\mu$SR data shows that the La$_{0.15}$Eu$_{0.85}$O is homogeneously magnetically ordered up to its elevated $T_{\rm C}$. It is concluded that bound magnetic polaron behavior does not explain the increase in $T_{\rm C}$ and an RKKY-like interaction is consistent with the $\mu$SR data. The estimation of the magnetic moment by DFT simulations concurs with the results obtained by PNR, showing a reduction of the magnetic moment per La$_{x}$Eu$_{1-x}$O for increasing lanthanum doping. This reduction of the magnetic moment is explained by the reduction of the number of Eu-4$f$ electrons present in all the magnetic interactions in EuO films. Finally, we show that an upwards shift of the Fermi energy with La or Gd doping gives rise to half-metallicity for doping levels as high as 3.2 %., Comment: 7 pages, 11 figures

Published
2015
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6. Spatially homogeneous ferromagnetism below the enhanced Curie temperature in EuO_{1-x} thin films

Author

Monteiro, Pedro M. S., Baker, Peter J., Ionescu, Adrian, Barnes, Crispin H. W., Salman, Zaher, Suter, Andreas, Prokscha, Thomas, and Langridge, Sean

Subjects
Condensed Matter - Strongly Correlated Electrons, Condensed Matter - Materials Science, Condensed Matter - Other Condensed Matter
Abstract

We have used low energy implanted muons as a volume sensitive probe of the magnetic properties of EuO_{1-x} thin films. We find that static and homogeneous magnetic order persists up to the elevated T_C in the doped samples and the muon signal displays the double dome feature also observed in the sample magnetization. Our results appear incompatible with either the magnetic phase separation or bound magnetic polaron descriptions previously suggested to explain the elevated T_C, but are compatible with an RKKY-like interaction mediating magnetic interactions above 69 K., Comment: 5 pages, 10 pages of supplementary information, 11 figures

Published
2013
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7. Magnetisation dynamics in exchange coupled spring systems with perpendicular anisotropy

Author

Monteiro, Pedro M. S. and Schmool, D. S.

Subjects
Condensed Matter - Mesoscale and Nanoscale Physics, Condensed Matter - Other Condensed Matter, Physics - Computational Physics
Abstract

Magnetisation dynamics in exchange spring magnets have been studied using simulations of the FePt/Fe bilayer system. The FePt hard layer exhibits strong perpendicular magnetocrystalline anisotropy, while the soft (Fe) layer has negligible magnetocrystalline anisotropy. The variation of the local spin orientation in the Fe layer is determined by the competition of the exchange coupling interaction with the hard layer and the magnetostatic energy which favours in-plane magnetisation. Dynamics were studied by monitoring the response of the Fe layer magnetisation after the abrupt application of a magnetic field which causes the systems to realign via precessional motion. This precessional motion allows us to obtain the frequency spectrum and hence examine the dynamical magnetisation motion. Since the rotation of the spins in the soft layer does not have a well defined magnetic anisotropy, the system does not present the usual frequency field characteristics for a thin film. Additionally we obtain multi-peaked resonance spectra for the application of magnetic fields perpendicular to the film plane, though we discount the existence of spin wave modes and propose that this arises due to variations in the local effective field across the Fe layer. The dynamic response is only considered in the Fe layer, with the FePt layer held fixed in the perpendicular orientation., Comment: 8 pages, 14 figures, v2: references added and improved introduction

Published
2009
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8. Storm‐Driven pCO2 Feedback Weakens the Response of Air‐Sea CO2 Fluxes in the Sub‐Antarctic Southern Ocean.

Author

Toolsee, Tesha, Nicholson, Sarah‐Anne, and Monteiro, Pedro M. S.

Subjects
OCEAN zoning, WINTER storms, OCEAN, AUTONOMOUS robots, PARTIAL pressure, GLIDERS (Aeronautics)
Abstract

The sub‐seasonal CO2 flux (FCO2) variability across the Southern Ocean is poorly understood due to sparse observations at the required temporal and spatial scales. Twinned surface and profiling gliders experiments were used to investigate how storms influence FCO2 through the air‐sea gradient in partial pressure of CO2 (ΔpCO2) in the sub‐Antarctic zone. Winter‐spring storms caused ΔpCO2 to weaken (by 22–37 μatm) due to mixing/entrainment and weaker stratification. This weakening in ΔpCO2 was in phase with the increase in wind stress resulting in a reduction of the storm‐driven CO2 uptake by 6%–27%. During summer, stronger stratification explained the weaker sensitivity of ΔpCO2 to storms, instead temperature changes dominated the ΔpCO2 variability. These results highlight the importance of observing synoptic‐scale variability in ΔpCO2, the absence of which may propagate significant biases to the mean annual FCO2 estimates from large‐scale observing programmes and reconstructions. Plain Language Summary: The sub‐Antarctic zone of the Southern Ocean is a region that mostly experiences carbon dioxide (CO2) uptake because of its low temperature, strong winds and lower CO2 content. The wind can influence the CO2 uptake through two pathways: the speed of CO2 transfer between the air‐sea interface (kw) and the difference in CO2 concentration in the surface ocean and overlying atmosphere (ΔpCO2). Using autonomous robots that can measure hourly air and water conditions simultaneously, we show that not resolving ΔpCO2 during a storm event can lead to overestimating the CO2 uptake. This is particularly important during winter and spring when the ocean's surface layers are less stratified. The warmer temperatures during summer meant a more stratified surface layer resulting in a weaker and delayed impact of storms on the ΔpCO2. This study shows that the various annual CO2 uptake estimation methods used by the research community should not neglect ΔpCO2 responses during storms. Key Points: Hourly glider observations show that the impact of storms on both kw and ΔpCO2 simultaneously modulates the ocean CO2 uptake variabilityWinter‐spring storms weaken ΔpCO2 through enhanced entrainment and mixing, partially counteracting the increase in CO2 uptake due to kw aloneBy not accounting for the storm‐linked positive feedback in ΔpCO2, the cumulative seasonal CO2 uptake was found to be overestimated by ∼6% [ABSTRACT FROM AUTHOR]

Published
2024
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9. The Southern Ocean Carbon Cycle 1985–2018: Mean, Seasonal Cycle, Trends, and Storage

Author

Hauck, Judith, Gregor, Luke, Nissen, Cara, Patara, Lavinia, Hague, Mark, Mongwe, Precious, Bushinsky, Seth, Doney, Scott C., Gruber, Nicolas, Le Quéré, Corinne, Manizza, Manfredi, Mazloff, Matthew, Monteiro, Pedro M. S., Terhaar, Jens, Hauck, Judith, Gregor, Luke, Nissen, Cara, Patara, Lavinia, Hague, Mark, Mongwe, Precious, Bushinsky, Seth, Doney, Scott C., Gruber, Nicolas, Le Quéré, Corinne, Manizza, Manfredi, Mazloff, Matthew, Monteiro, Pedro M. S., and Terhaar, Jens

Abstract

We assess the Southern Ocean CO2 uptake (1985–2018) using data sets gathered in the REgional Carbon Cycle Assessment and Processes Project Phase 2. The Southern Ocean acted as a sink for CO2 with close agreement between simulation results from global ocean biogeochemistry models (GOBMs, 0.75 ± 0.28 PgC yr−1) and pCO2-observation-based products (0.73 ± 0.07 PgC yr−1). This sink is only half that reported by RECCAP1 for the same region and timeframe. The present-day net uptake is to first order a response to rising atmospheric CO2, driving large amounts of anthropogenic CO2 (Cant) into the ocean, thereby overcompensating the loss of natural CO2 to the atmosphere. An apparent knowledge gap is the increase of the sink since 2000, with pCO2-products suggesting a growth that is more than twice as strong and uncertain as that of GOBMs (0.26 ± 0.06 and 0.11 ± 0.03 Pg C yr−1 decade−1, respectively). This is despite nearly identical pCO2 trends in GOBMs and pCO2-products when both products are compared only at the locations where pCO2 was measured. Seasonal analyses revealed agreement in driving processes in winter with uncertainty in the magnitude of outgassing, whereas discrepancies are more fundamental in summer, when GOBMs exhibit difficulties in simulating the effects of the non-thermal processes of biology and mixing/circulation. Ocean interior accumulation of Cant points to an underestimate of Cant uptake and storage in GOBMs. Future work needs to link surface fluxes and interior ocean transport, build long overdue systematic observation networks and push toward better process understanding of drivers of the carbon cycle. Key Points: - Ocean models and machine learning estimates agree on the mean Southern Ocean CO2 sink, but the trend since 2000 differs by a factor of two - REgional Carbon Cycle Assessment and Processes Project Phase 2 estimates a 50% smaller Southern Ocean CO2 sink for the same region and timeframe as RECCAP1 - Large model spread in summer and winter

Published
2023
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10. Southern Ocean phytoplankton dynamics and carbon export: insights from a seasonal cycle approach

Author

Thomalla, Sandy J., Du Plessis, Marcel, Fauchereau, Nicolas, Giddy, Isabelle, Gregor, Luke, Henson, Stephanie, Joubert, Warren R., Little, Hazel, Monteiro, Pedro M. S., Mtshali, Thato, Nicholson, Sarah, Ryan-Keogh, Thomas J., Swart, Sebastiaan, Thomalla, Sandy J., Du Plessis, Marcel, Fauchereau, Nicolas, Giddy, Isabelle, Gregor, Luke, Henson, Stephanie, Joubert, Warren R., Little, Hazel, Monteiro, Pedro M. S., Mtshali, Thato, Nicholson, Sarah, Ryan-Keogh, Thomas J., and Swart, Sebastiaan

Abstract

Quantifying the strength and efficiency of the Southern Ocean biological carbon pump (BCP) and its response to predicted changes in the Earth's climate is fundamental to our ability to predict long-term changes in the global carbon cycle and, by extension, the impact of continued anthropogenic perturbation of atmospheric CO2. There is little agreement, however, in climate model projections of the sensitivity of the Southern Ocean BCP to climate change, with a lack of consensus in even the direction of predicted change, highlighting a gap in our understanding of a major planetary carbon flux. In this review, we summarize relevant research that highlights the important role of fine-scale dynamics (both temporal and spatial) that link physical forcing mechanisms to biogeochemical responses that impact the characteristics of the seasonal cycle of phytoplankton and by extension the BCP. This approach highlights the potential for integrating autonomous and remote sensing observations of fine scale dynamics to derive regionally optimized biogeochemical parameterizations for Southern Ocean models. Ongoing development in both the observational and modelling fields will generate new insights into Southern Ocean ecosystem function for improved predictions of the sensitivity of the Southern Ocean BCP to climate change. This article is part of a discussion meeting issue 'Heat and carbon uptake in the Southern Ocean: the state of the art and future priorities'.

Published
2023

11. Building a carbon dioxide removal science--policy partnership for southern Africa.

Author

Monteiro, Pedro M. S. and Midgley, Guy F.

Subjects
*CARBON dioxide, *CARBON pricing, *GREENHOUSE gases, *ATMOSPHERIC carbon dioxide, *CLIMATE change mitigation, *CARBON sequestration, *CLIMATE change
Abstract

The article focuses on the importance of establishing a regionally focused and coordinated carbon dioxide removal (CDR) science-policy platform in southern Africa. The topics include the significance of CDR in steering the planet towards a safe climate, the challenges and opportunities associated with CDR interventions, and the need for governance mechanisms, technologies, and scientific capabilities to support CDR development.

Published
2023
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12. Supplementary figures for ‘Southern Ocean phytoplankton dynamics and carbon export: insights from a seasonal cycle approach’

Author

Thomalla, Sandy J., Du Plessis, Marcel, Fauchereau, Nicolas, Giddy, Isabelle, Gregor, Luke, Henson, Stephanie, Joubert, Warren R., Little, Hazel, Monteiro, Pedro M. S., Mtshali, Thato, Nicholson, Sarah, Ryan-Keogh, Thomas J., and Swart, Sebastiaan

Abstract

Quantifying the strength and efficiency of the Southern Ocean biological carbon pump (BCP) and its response to predicted changes in the Earth's climate is fundamental to our ability to predict long-term changes in the global carbon cycle and, by extension, the impact of continued anthropogenic perturbation of atmospheric CO2. There is little agreement, however, in climate model projections of the sensitivity of the Southern Ocean BCP to climate change, with a lack of consensus in even the direction of predicted change, highlighting a gap in our understanding of a major planetary carbon flux. In this review, we summarize relevant research that highlights the important role of fine-scale dynamics (both temporal and spatial) that link physical forcing mechanisms to biogeochemical responses that impact the characteristics of the seasonal cycle of phytoplankton and by extension the BCP. This approach highlights the potential for integrating autonomous and remote sensing observations of fine scale dynamics to derive regionally optimized biogeochemical parameterizations for Southern Ocean models. Ongoing development in both the observational and modelling fields will generate new insights into Southern Ocean ecosystem function for improved predictions of the sensitivity of the Southern Ocean BCP to climate change.This article is part of the discussion meeting issue 'Heat and carbon uptake in the Southern Ocean: the state of the art and future priorities'.

Published
2023
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14. The sensitivity of pCO(2) reconstructions to sampling scales across a Southern Ocean sub-domain: a semi-idealized ocean sampling simulation approach

Author

Djeutchouang, Laique M., Chang, Nicolette, Gregor, Luke, Vichi, Marcello, Monteiro, Pedro M. S., Djeutchouang, Laique M., Chang, Nicolette, Gregor, Luke, Vichi, Marcello, and Monteiro, Pedro M. S.

Abstract

The Southern Ocean is a complex system yet is sparsely sampled in both space and time. These factors raise questions about the confidence in present sampling strategies and associated machine learning (ML) reconstructions. Previous studies have not yielded a clear understanding of the origin of uncertainties and biases for the reconstructions of the partial pressure of carbon dioxide (pCO(2)) at the surface ocean (pCO(2)(ocean)). We examine these questions through a series of semi-idealized observing system simulation experiments (OSSEs) using a high-resolution (+/- 10 km) coupled physical and biogeochemical model (NEMO-PISCES, Nucleus for European Modelling of the Ocean, Pelagic Interactions Scheme for Carbon and Ecosystem Studies). Here we choose 1 year of the model sub-domain of 10 degrees of latitude (40-50 degrees S) by 20 degrees of longitude (10 degrees W-10 degrees E). This domain is crossed by the sub-Antarctic front and thus includes both the sub-Antarctic zone and the polar frontal zone in the south-east Atlantic Ocean, which are the two most sampled sub-regions of the Southern Ocean. We show that while this sub-domain is small relative to the Southern Ocean scales, it is representative of the scales of variability we aim to examine. The OSSEs simulated the observational scales of pCO(2)(ocean) in ways that are comparable to existing ocean CO2 observing platforms (ships, Wave Gliders, carbon floats, Saildrones) in terms of their temporal sampling scales and not necessarily their spatial ones. The pCO(2) reconstructions were carried out using a two-member ensemble approach that consisted of two machine learning (ML) methods, (1) the feed-forward neural network and (2) the gradient boosting machines. The baseline data were from the ship-based simulations mimicking ship-based observations from the Surface Ocean CO2 Atlas (SOCAT). For each of the sampling-scale scenarios, we applied the two-member ensemble method to reconstruct the full sub-domain pCO(2)(ocea

Published
2022
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16. Global Carbon Budget 2016

Author

Quéré, Corinne Le, Andrew, Robbie M, Canadell, Josep G, Sitch, Stephen, Korsbakken, Jan Ivar, Peters, Glen P, Manning, Andrew C, Boden, Thomas A, Tans, Pieter P, Houghton, Richard A, Keeling, Ralph F, Alin, Simone, Andrews, Oliver D, Anthoni, Peter, Barbero, Leticia, Bopp, Laurent, Chevallier, Frédéric, Chini, Louise P, Ciais, Philippe, Currie, Kim, Delire, Christine, Doney, Scott C, Friedlingstein, Pierre, Gkritzalis, Thanos, Harris, Ian, Hauck, Judith, Haverd, Vanessa, Hoppema, Mario, Goldewijk, Kees Klein, Jain, Atul K, Kato, Etsushi, Koertzinger, Arne, Landschuetzer, Peter, Lefèvre, Nathalie, Lenton, Andrew, Lienert, Sebastian, Lombardozzi, Danica, Melton, Joe R, Metzl, Nicolas, Millero, Frank, Monteiro, Pedro M. S, Munro, David R, Nabel, Julia E. M. S, Nakaoka, Shin-ichiro, O’Brien, Kevin, Olsen, Are, Omar, Abdirahman M, Ono, Tsuneo, Pierrot, Denis, Poulter, Benjamin, Roedenbeck, Christian, Salisbury, Joe, Schuster, Ute, Schwinger, Joerg, Séférian, Roland, Skjelvan, Ingunn, Stocker, Benjamin D, Sutton, Adrienne J, Takahashi, Taro, Tian, Hanqin, Tilbrook, Bronte, van der Laan-Luijkx, Ingrid T, van der Werf, Guido R, Viovy, Nicolas, Walker, Anthony P, Wiltshire, Andrew J, and Zaehle, Soenke

Subjects
Meteorology And Climatology
Abstract

Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere the global carbon budget 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 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 and consistency within and among components, alongside methodology and data limitations. CO2 emissions from fossil fuels and industry (EFF) are based on energy statistics and cement production data, respectively, while emissions from land-use change (ELUC), mainly deforestation, are based on combined evidence from land-cover change data, fire activity associated with deforestation, and models. The global atmospheric CO2 concentration is measured directly and its rate of growth (GATM) is computed from the annual changes in concentration. The mean ocean CO2 sink (SOCEAN) is based on observations from the 1990s, while the annual anomalies and trends are estimated with ocean models. The variability in SOCEAN is evaluated with data products based on surveys of ocean CO2 measurements. The global residual terrestrial CO2 sink (SLAND) is estimated by the difference of the other terms of the global carbon budget and compared to results of independent dynamic global vegetation models. 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 +/- 1(sigma), reflecting the current capacity to characterize the annual estimates of each component of the global carbon budget. For the last decade available (2006-2015), EFF was 9.3+/-0.5 GtC/yr, ELUC 1.0+/-0.5 GtC/yr,GATM 4.5+/-0.1 GtC/yr, SOCEAN 2.6+/-0.5 GtC/yr, and SLAND 3.1+/-0.9 GtC/yr. For year 2015 alone, the growth in EFF was approximately zero and emissions remained at 9.9+/-0.5 GtC/yr, showing a slowdown in growth of these emissions compared to the average growth of 1.8/yr that took place during 2006-2015.Also, for 2015, ELUC was 1.3+/-0.5 GtC/yr, GATM was 6.3+/-0.2 GtC/yr, SOCEAN was 3.0+/-0.5 GtC/yr, and SLAND was 1.9+/-0.9 GtC/yr. GATM was higher in 2015 compared to the past decade (2006-2015), reflecting a smaller SLAND for that year. The global atmospheric CO2 concentration reached 399.4+/-0.1 ppm averaged over 2015. For 2016, preliminary data indicate the continuation of low growth in EFF with +0.2% (range of -1.0 to +1.8% ) based on national emissions projections for China and USA, and projections of gross domestic product corrected for recent changes in the carbon intensity of the economy for the rest of the world. In spite of the low growth of EFF in 2016, the growth rate in atmospheric CO2 concentration is expected to be relatively high because of the persistence of the smaller residual terrestrial sink (SLAND) in response to El Nino conditions of 2015-2016. From this projection of EFF and assumed constant ELUC for 2016, cumulative emissions of CO2 will reach 565+/-55 GtC (2075+/-205 GtCO2) for 1870-2016, about 75% from EFF and 25% from ELUC. This living data update documents changes in the methods and data sets used in this new carbon budget compared with previous publications of this data set.

Published
2016
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18. Storms drive outgassing of CO2 in the subpolar Southern Ocean.

Author

Nicholson, Sarah-Anne, Whitt, Daniel B., Fer, Ilker, du Plessis, Marcel D., Lebéhot, Alice D., Swart, Sebastiaan, Sutton, Adrienne J., and Monteiro, Pedro M. S.

Subjects
STORMS, OUTGASSING, FRONTS (Meteorology), OCEAN turbulence, OCEANIC mixing, NORTH Atlantic oscillation, OCEAN, MIXING height (Atmospheric chemistry)
Abstract

The subpolar Southern Ocean is a critical region where CO2 outgassing influences the global mean air-sea CO2 flux (FCO2). However, the processes controlling the outgassing remain elusive. We show, using a multi-glider dataset combining FCO2 and ocean turbulence, that the air-sea gradient of CO2 (∆pCO2) is modulated by synoptic storm-driven ocean variability (20 µatm, 1–10 days) through two processes. Ekman transport explains 60% of the variability, and entrainment drives strong episodic CO2 outgassing events of 2–4 mol m−2 yr−1. Extrapolation across the subpolar Southern Ocean using a process model shows how ocean fronts spatially modulate synoptic variability in ∆pCO2 (6 µatm2 average) and how spatial variations in stratification influence synoptic entrainment of deeper carbon into the mixed layer (3.5 mol m−2 yr−1 average). These results not only constrain aliased-driven uncertainties in FCO2 but also the effects of synoptic variability on slower seasonal or longer ocean physics-carbon dynamics. Storms dominate the subpolar Southern Ocean, where upwelling CO2 drives outgassing that impacts global CO2 budget, yet how storms modify this outgassing is unknown. Here, the authors present coupled atmosphere-ocean observations to show how storm-driven ocean mixing and circulation cause substantial CO2 variability and outgassing. [ABSTRACT FROM AUTHOR]

Published
2022
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19. The Southern Ocean carbon sink 1985-2018: first results of the RECCAP2 project

Author

Hauck, Judith, Gregor, Luke, Nissen, Cara, Mortenson, Eric, Bushinsky, Seth, Doney, Scott, Gruber, Nicolas, Lenton, Andrew, LeQuere, Corinne, Mazloff, Matt, Monteiro, Pedro M. S., Patara, Lavinia, Hauck, Judith, Gregor, Luke, Nissen, Cara, Mortenson, Eric, Bushinsky, Seth, Doney, Scott, Gruber, Nicolas, Lenton, Andrew, LeQuere, Corinne, Mazloff, Matt, Monteiro, Pedro M. S., and Patara, Lavinia

Published
2021

20. Integrated ocean carbon research: a summary of ocean carbon research, and vision of coordinated ocean carbon research and observations for the next decade

Author

Arico, S, Arrieta, Jesus M., Bakker, D. C. E., Boyd, P. W., Cotrim da Cunha, L., Chai, L, Dai, F, Gruber, N., Isensee, K, Ishii, M., Jiao, N., Lauvset, S. K., McKinley, Galen A., Monteiro, Pedro M. S., Robinson, C, Sabine, C., Sanders, R., Schoo, K, Schuster, U., Shutler, J, Thomas, H., Wanninkhof, R., Watson, A. J., Bopp, L, Bracco, A, Cai, A, Fay, Amanda, Feely, RA, Gregor, Luke, Hauck, Judith, Heinze, Christoph, Henson, Stephanie, Hwang, J., Post, J., Suntharalingam, P., Telszewski, M, Tilbrook, B., Valsala, V, Rojas Aldana, A, Arico, S, Arrieta, Jesus M., Bakker, D. C. E., Boyd, P. W., Cotrim da Cunha, L., Chai, L, Dai, F, Gruber, N., Isensee, K, Ishii, M., Jiao, N., Lauvset, S. K., McKinley, Galen A., Monteiro, Pedro M. S., Robinson, C, Sabine, C., Sanders, R., Schoo, K, Schuster, U., Shutler, J, Thomas, H., Wanninkhof, R., Watson, A. J., Bopp, L, Bracco, A, Cai, A, Fay, Amanda, Feely, RA, Gregor, Luke, Hauck, Judith, Heinze, Christoph, Henson, Stephanie, Hwang, J., Post, J., Suntharalingam, P., Telszewski, M, Tilbrook, B., Valsala, V, and Rojas Aldana, A

Published
2021

21. Quantifying the ocean carbon sink for 1994-2007: Combined evidence from multiple methods

Author

McKinley, Galen A., Cross, Jessica, DeVries, Tim, Hauck, Judith, Fay, Amanda R, Landschützer, Peter, Laruelle, Goulven, Lovenduski, Nicole, Monteiro, Pedro M. S., Najjar, Raymond G., Resplandy, Laure, Rödenbeck, Christian, Sabine, Christopher, Sutton, Adrienne J., Wanninkhof, Rik, Williams, Nancy, McKinley, Galen A., Cross, Jessica, DeVries, Tim, Hauck, Judith, Fay, Amanda R, Landschützer, Peter, Laruelle, Goulven, Lovenduski, Nicole, Monteiro, Pedro M. S., Najjar, Raymond G., Resplandy, Laure, Rödenbeck, Christian, Sabine, Christopher, Sutton, Adrienne J., Wanninkhof, Rik, and Williams, Nancy

Abstract

By means of a variety of international observing and modeling efforts, the ocean carbon community has developed several independent estimates for ocean carbon uptake. In this presentation, we report on the synthesis effort we are undertaking under the auspices of an Ocean Carbon and Biogeochemistry Working Group. Our initial goal for this working group is to determine the best estimate for the net and anthropogenic carbon sink from 1994-2007, and then to infer the total magnitude of the poorly quantified fluxes that constitute their difference. Estimates for the net, or contemporary, ocean carbon uptake are derived from surface ocean pCO2 data interpolated to global coverage. From 4 of these products, we find Fnet = -1.7 PgC/yr for 1994-2007. Estimates for uptake of anthropogenic carbon comes from (1) interior observations of dissolved inorganic carbon and other tracers, (2) an ocean model constrained with observations, and (3) a suite of nine free-running ocean hindcast models in which the natural carbon cycle is assumed to be in a long-term steady state. Fant = -2.3 PgC/yr from the mean of these approaches. The difference between these two estimates is -0.6 PgC/yr, and acts as a quantitative constraint on the sum of the additional fluxes. As coastal zones and the Arctic are additional net carbon sinks, the sum of outgassed river-derived carbon, skin temperature effects on air-sea CO2 exchange, and non-steady state natural carbon fluxes in the open ocean can be no larger than a few tenths of PgC/yr. Our presentation details the uncertainties and assumptions made in deriving these estimates, and suggests paths forward to further reduce uncertainties.

Published
2020

23. Ocean Climate Observing Requirements in Support of Climate Research and Climate Information

Author

Stammer, Detlef, Bracco, Annalisa, Achutarao, Krishna, Beal, Lisa, Bindoff, Nathaniel L., Braconnot, Pascale, Cai, Wenju, Chen, Dake, Collins, Matthew, Danabasoglu, Gokhan, Dewitte, Boris, Farneti, Riccardo, Fox-kemper, Baylor, Fyfe, John, Griffies, Stephen M., Jayne, Steven R., Lazar, Alban, Lengaigne, Matthieu, Lin, Xiaopei, Marsland, Simon, Minobe, Shoshiro, Monteiro, Pedro M. S., Robinson, Walter, Roxy, Mathew Koll, Rykaczewski, Ryan R., Speich, Sabrina, Smith, Inga J., Solomon, Amy, Storto, Andrea, Takahashi, Ken, Toniazzo, Thomas, Vialard, Jerome, Stammer, Detlef, Bracco, Annalisa, Achutarao, Krishna, Beal, Lisa, Bindoff, Nathaniel L., Braconnot, Pascale, Cai, Wenju, Chen, Dake, Collins, Matthew, Danabasoglu, Gokhan, Dewitte, Boris, Farneti, Riccardo, Fox-kemper, Baylor, Fyfe, John, Griffies, Stephen M., Jayne, Steven R., Lazar, Alban, Lengaigne, Matthieu, Lin, Xiaopei, Marsland, Simon, Minobe, Shoshiro, Monteiro, Pedro M. S., Robinson, Walter, Roxy, Mathew Koll, Rykaczewski, Ryan R., Speich, Sabrina, Smith, Inga J., Solomon, Amy, Storto, Andrea, Takahashi, Ken, Toniazzo, Thomas, and Vialard, Jerome

Abstract

Natural variability and change of the Earth's climate have significant global societal impacts. With its large heat and carbon capacity and relatively slow dynamics, the ocean plays an integral role in climate, and provides an important source of predictability at seasonal and longer timescales. In addition, the ocean provides the slowly evolving lower boundary to the atmosphere, driving, and modifying atmospheric weather. Understanding and monitoring ocean climate variability and change, to constrain and initialize models as well as identify model biases for improved climate hindcasting and prediction, requires a scale-sensitive, and long-term observing system. A climate observing system has requirements that significantly differ from, and sometimes are orthogonal to, those of other applications. In general terms, they can be summarized by the simultaneous need for both large spatial and long temporal coverage, and by the accuracy and stability required for detecting the local climate signals. This paper reviews the requirements of a climate observing system in terms of space and time scales, and revisits the question of which parameters such a system should encompass to meet future strategic goals of the World Climate Research Program (WCRP), with emphasis on ocean and sea-ice covered areas. It considers global as well as regional aspects that should be accounted for in designing observing systems in individual basins. Furthermore, the paper discusses which data-driven products are required to meet WCRP research and modeling needs, and ways to obtain them through data synthesis and assimilation approaches. Finally, it addresses the need for scientific capacity building and international collaboration in support of the collection of high-quality measurements over the large spatial scales and long time-scales required for climate research, bridging the scientific rational to the required resources for implementation.

Published
2019
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24. The IPCC Assessment Report Six Working Group 1 report and southern Africa: Reasons to take action.

Author

Engelbrecht, Francois A. and Monteiro, Pedro M. S.

Subjects
*EARTH system science, *PHYSICAL sciences, *CARBON cycle, *ATMOSPHERIC carbon dioxide, *CARBON emissions, *CLIMATE change mitigation, *TROPICAL cyclones
Abstract

The article focuses on the Intergovernmental Panel on Climate Change (IPCC) Assessment Report Six (AR6) Working Group I (WG1) report focus on the assessment of the global climate-carbon system with implications for adaptation and mitigation action in southern Africa. It mentions climate change attribution science is capable of quantifying the role of human influence in the occurrence of individual weather events.

Published
2021
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25. Interannual drivers of the seasonal cycle of CO2 in the Southern Ocean

Author

Gregor, Luke, Kok, Schalk, and Monteiro, Pedro M. S.

Abstract

Resolving and understanding the drivers of variability of CO2 in the Southern Ocean and its potential climate feedback is one of the major scientific challenges of the ocean-climate community. Here we use a regional approach on empirical estimates of pCO2 to understand the role that seasonal variability has in long-term CO2 changes in the Southern Ocean. Machine learning has become the preferred empirical modelling tool to interpolate time- and location-restricted ship measurements of pCO2. In this study we use an ensemble of three machine-learning products: support vector regression (SVR) and random forest regression (RFR) from Gregor et al. (2017), and the self-organising-map feed-forward neural network (SOM-FFN) method from Landschützer et al. (2016). The interpolated estimates of ΔpCO2 are separated into nine regions in the Southern Ocean defined by basin (Indian, Pacific, and Atlantic) and biomes (as defined by Fay and McKinley, 2014a). The regional approach shows that, while there is good agreement in the overall trend of the products, there are periods and regions where the confidence in estimated ΔpCO2 is low due to disagreement between the products. The regional breakdown of the data highlighted the seasonal decoupling of the modes for summer and winter interannual variability. Winter interannual variability had a longer mode of variability compared to summer, which varied on a 4–6-year timescale. We separate the analysis of the ΔpCO2 and its drivers into summer and winter. We find that understanding the variability of ΔpCO2 and its drivers on shorter timescales is critical to resolving the long-term variability of ΔpCO2. Results show that ΔpCO2 is rarely driven by thermodynamics during winter, but rather by mixing and stratification due to the stronger correlation of ΔpCO2 variability with mixed layer depth. Summer pCO2 variability is consistent with chlorophyll a variability, where higher concentrations of chlorophyll a correspond with lower pCO2 concentrations. In regions of low chlorophyll a concentrations, wind stress and sea surface temperature emerged as stronger drivers of ΔpCO2. In summary we propose that sub-decadal variability is explained by summer drivers, while winter variability contributes to the long-term changes associated with the SAM. This approach is a useful framework to assess the drivers of ΔpCO2 but would greatly benefit from improved estimates of ΔpCO2 and a longer time series.

Published
2018

26. Global Carbon Budget 2017

Author

Le Quéré, Corinne, Andrew, Robbie M., Friedlingstein, Pierre, Sitch, Stephen, Pongratz, Julia, Manning, Andrew C., Korsbakken, Jan Ivar, Peters, Glen P., Canadell, Josep G., Jackson, Robert B., Boden, Thomas A., Tans, Pieter P., Andrews, Oliver D., Arora, Vivek, Bakker, Dorothee C. E., Barbero, Leticia, Becker, Meike, Betts, Richard, Bopp, Laurent, Chevallier, Frédéric, Chini, Louise P., Ciais, Philippe, Cosca, Catherine E., Cross, Jessica, Currie, Kim, Gasser, Thomas, Harris, Ian, Hauck, Judith, Haverd, Vanessa, Houghton, Richard A., Hunt, Christopher W., Hurtt, George, Ilyina, Tatiana, Jain, Atul K., Kato, Etsushi, Kautz, Markus, Keeling, Ralph F., Klein Goldewijk, Kees, Körtzinger, Arne, Landschützer, Peter, Lefèvre, Nathalie, Lenton, Andrew, Lienert, Sebastian, Lima, Ivan D., Lombardozzi, Danica, Metzl, Nicolas, Millero, Frank J., Monteiro, Pedro M. S., Munro, David R., Nabel, Julia E. M. S., Nakaoka, Shin-ichiro, Nojiri, Yukihiro, Padin, X. Antonio, Peregon, Anna, Pfeil, Benjamin, Pierrot, Denis, Poulter, Benjamin, Rehder, Gregor, Reimer, Janet, Rödenbeck, Christian, Schwinger, Jörg, Séférian, Roland, Skjelvan, Ingunn, Stocker, Benjamin D., Tian, Hanqin, Tilbrook, Bronte, Tubiello, Francesco, van der Laan-Luijkx, Ingrid T., Van Der Werf, Guido R., Van Heuven, Steven M. A. C., Viovy, Nicolas, Vuichard, Nicolas, Walker, Anthony P., Watson, Andrew J., Wiltshire, Andrew J., Zaehle, Sönke, Zhu, Dan, Tyndall Centre for Climate Change Research, University of East Anglia [Norwich] (UEA), Center for International Climate and Environmental Research [Oslo] (CICERO), University of Oslo (UiO), College of Engineering, Mathematics and Physical Sciences, University of Exeter, College of Life and Environmental Sciences, University of Exeter, Max Planck Institute for Meteorology (MPI-M), Max-Planck-Gesellschaft, Global Carbon Project, CSIRO Marine and Atmospheric Research, Department of Earth System Science [Stanford] (ESS), Stanford EARTH, Stanford University-Stanford University, Climate Change Science Institute [Oak Ridge] (CCSI), Oak Ridge National Laboratory [Oak Ridge] (ORNL), UT-Battelle, LLC-UT-Battelle, LLC, ESRL Chemical Sciences Division [Boulder] (CSD), NOAA Earth System Research Laboratory (ESRL), National Oceanic and Atmospheric Administration (NOAA)-National Oceanic and Atmospheric Administration (NOAA), Canadian Centre for Climate Modelling and Analysis (CCCma), Environment and Climate Change Canada, Cooperative Institute for Marine and Atmospheric Studies, Rosenstiel School for Marine and Atmospheric Science (CIMAS), Rosenstiel School of Marine and Atmospheric Science (RSMAS), University of Miami [Coral Gables]-University of Miami [Coral Gables], NOAA Atlantic Oceanographic and Meteorological Laboratory (AOML), National Oceanic and Atmospheric Administration (NOAA), Bjerknes Centre for Climate Research (BCCR), Department of Biological Sciences [Bergen] (BIO / UiB), University of Bergen (UiB)-University of Bergen (UiB), Geophysical Institute [Bergen] (GFI / BiU), University of Bergen (UiB), Laboratoire de Météorologie Dynamique (UMR 8539) (LMD), Institut national des sciences de l'Univers (INSU - CNRS)-École polytechnique (X)-École des Ponts ParisTech (ENPC)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Département des Géosciences - ENS Paris, École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Modélisation INVerse pour les mesures atmosphériques et SATellitaires (SATINV), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Department of Geographical Sciences, University of Maryland [College Park], University of Maryland System-University of Maryland System, ICOS-ATC (ICOS-ATC), NOAA Pacific Marine Environmental Laboratory [Seattle] (PMEL), National Institute of Water and Atmospheric Research [Wellington] (NIWA), International Institute for Applied Systems Analysis [Laxenburg] (IIASA), Climatic Research Unit, Alfred-Wegener-Institut, Helmholtz-Zentrum für Polar- und Meeresforschung (AWI), Commonwealth Scientific and Industrial Research Organisation (CSIRO), Woods Hole Oceanographic Institution (WHOI), Ocean Process Analysis Laboratory, University of New Hampshire (UNH), Department of Atmospheric Sciences [Urbana], University of Illinois at Urbana-Champaign [Urbana], University of Illinois System-University of Illinois System, The Institute of Applied Energy (IAE), Karlsruher Institut für Technologie (KIT), University of California [San Diego] (UC San Diego), University of California, PBL Netherlands Environmental Assessment Agency, Christian-Albrechts-Universität zu Kiel (CAU), Austral, Boréal et Carbone (ABC), Laboratoire d'Océanographie et du Climat : Expérimentations et Approches Numériques (LOCEAN), Institut Pierre-Simon-Laplace (IPSL (FR_636)), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP)-Institut de Recherche pour le Développement (IRD)-Muséum national d'Histoire naturelle (MNHN)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Institut Pierre-Simon-Laplace (IPSL (FR_636)), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP)-Institut de Recherche pour le Développement (IRD)-Muséum national d'Histoire naturelle (MNHN)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU), CISRO Oceans and Atmosphere, Antarctic Climate & Ecosystem Cooperative Research Centre, University of Tasmania [Hobart, Australia] (UTAS), Climate and Environmental Physics [Bern] (CEP), Physikalisches Institut [Bern], Universität Bern [Bern]-Universität Bern [Bern], Oeschger Centre for Climate Change Research (OCCR), University of Bern, National Center for Atmospheric Research [Boulder] (NCAR), Cycles biogéochimiques marins : processus et perturbations (CYBIOM), Department of Ocean Sciences, University of Miami [Coral Gables], Instituto de Engenharia de Sistemas e Computadores Investigação e Desenvolvimento em Lisboa (INESC-ID), Instituto Superior Técnico, Universidade Técnica de Lisboa (IST)-Instituto de Engenharia de Sistemas e Computadores (INESC), University of Wisconsin Whitewater, National Institute for Environmental Studies (NIES), Montana State University (MSU), Max-Planck-Institut für Biogeochemie (MPI-BGC), Groupe d'étude de l'atmosphère météorologique (CNRM-GAME), Institut national des sciences de l'Univers (INSU - CNRS)-Météo France-Centre National de la Recherche Scientifique (CNRS), Shandong Agricultural University (SDAU), Antarctic Climate and Ecosystems Cooperative Research Centre (ACE-CRC), Wageningen University and Research [Wageningen] (WUR), Faculty of Earth and Life Sciences [Amsterdam] (FALW), Vrije Universiteit Amsterdam [Amsterdam] (VU), Modélisation des Surfaces et Interfaces Continentales (MOSAIC), NASA Ames Research Center (ARC), Biogeochemical Systems Department [Jena], Max Planck Institute for Biogeochemistry (MPI-BGC), Max-Planck-Gesellschaft-Max-Planck-Gesellschaft, and Huazhong University of Science and Technology [Wuhan] (HUST)

Subjects
[PHYS.PHYS.PHYS-GEO-PH]Physics [physics]/Physics [physics]/Geophysics [physics.geo-ph]
Abstract

International audience; Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere – the "global carbon budget" – 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 methodology to quantify the five major components of the global carbon budget and their uncertainties. CO2 emissions from fossil fuels and industry (EFF) are based on energy statistics and cement production data, respectively, while emissions from land-use change (ELUC), mainly deforestation, are based on land-cover change data and bookkeeping models. The global atmospheric CO2 concentration is measured directly and its rate of growth (GATM) is computed from the annual changes in concentration. The ocean CO2 sink (SOCEAN) and terrestrial CO2 sink (SLAND) are estimated with global process models constrained by observations. The resulting carbon budget imbalance (BIM), the difference between the estimated total emissions and the estimated changes in the atmosphere, ocean, and terrestrial biosphere, is a measure of imperfect data and understanding of the contemporary carbon cycle. All uncertainties are reported as ±1σ. For the last decade available (2007–2016), EFF was 9.4 ± 0.5 GtC yr−1, ELUC 1.3 ± 0.7 GtC yr−1, GATM 4.7 ± 0.1 GtC yr−1, SOCEAN 2.4 ± 0.5 GtC yr−1, and SLAND 3.0 ± 0.8 GtC yr−1, with a budget imbalance BIM of 0.6 GtC yr−1 indicating overestimated emissions and/or underestimated sinks. For year 2016 alone, the growth in EFF was approximately zero and emissions remained at 9.9 ± 0.5 GtC yr−1. Also for 2016, ELUC was 1.3 ± 0.7 GtC yr−1, GATM was 6.1 ± 0.2 GtC yr−1, SOCEAN was 2.6 ± 0.5 GtC yr−1, and SLAND was 2.7 ± 1.0 GtC yr−1, with a small BIM of −0.3 GtC. GATM continued to be higher in 2016 compared to the past decade (2007–2016), reflecting in part the high fossil emissions and the small SLAND consistent with El Niño conditions. The global atmospheric CO2 concentration reached 402.8 ± 0.1 ppm averaged over 2016. For 2017, preliminary data for the first 6–9 months indicate a renewed growth in EFF of +2.0 % (range of 0.8 to 3.0 %) based on national emissions projections for China, USA, and India, and projections of gross domestic product (GDP) corrected for recent changes in the carbon intensity of the economy for the rest of the world. This living data update documents changes in the methods and data sets used in this new global carbon budget compared with previous publications of this data set (Le Quéré et al., 2016, 2015b, a, 2014, 2013). All results presented here can be downloaded from https://doi.org/10.18160/GCP-2017 (GCP, 2017).

Published
2018
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27. Ocean Climate Observing Requirements in Support of Climate Research and Climate Information

Author

Stammer, Detlef, primary, Bracco, Annalisa, additional, AchutaRao, Krishna, additional, Beal, Lisa, additional, Bindoff, Nathaniel L., additional, Braconnot, Pascale, additional, Cai, Wenju, additional, Chen, Dake, additional, Collins, Matthew, additional, Danabasoglu, Gokhan, additional, Dewitte, Boris, additional, Farneti, Riccardo, additional, Fox-Kemper, Baylor, additional, Fyfe, John, additional, Griffies, Stephen M., additional, Jayne, Steven R., additional, Lazar, Alban, additional, Lengaigne, Matthieu, additional, Lin, Xiaopei, additional, Marsland, Simon, additional, Minobe, Shoshiro, additional, Monteiro, Pedro M. S., additional, Robinson, Walter, additional, Roxy, Mathew Koll, additional, Rykaczewski, Ryan R., additional, Speich, Sabrina, additional, Smith, Inga J., additional, Solomon, Amy, additional, Storto, Andrea, additional, Takahashi, Ken, additional, Toniazzo, Thomas, additional, and Vialard, Jerome, additional

Published
2019
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28. The seasonal cycle of pCO(2) and CO2 fluxes in the Southern Ocean: diagnosing anomalies in CMIP5 Earth system models

Author

Mongwe, N. Precious, Vichi, Marcello, Monteiro, Pedro M. S., Mongwe, N. Precious, Vichi, Marcello, and Monteiro, Pedro M. S.

Abstract

The Southern Ocean forms an important component of the Earth system as a major sink of CO2 and heat. Recent studies based on the Coupled Model Intercomparison Project version 5 (CMIP5) Earth system models (ESMs) show that CMIP5 models disagree on the phasing of the seasonal cycle of the CO2 flux (FCO2) and compare poorly with available observation products for the Southern Ocean. Because the seasonal cycle is the dominant mode of CO2 variability in the Southern Ocean, its simulation is a rigorous test for models and their long-term projections. Here we examine the competing roles of temperature and dissolved inorganic carbon (DIC) as drivers of the seasonal cycle of pCO(2) in the Southern Ocean to explain the mechanistic basis for the seasonal biases in CMIP5 models. We find that despite significant differences in the spatial characteristics of the mean annual fluxes, the intra-model homogeneity in the seasonal cycle of FCO2 is greater than observational products. FCO2 biases in CMIP5 models can be grouped into two main categories, i.e., group-SST and group-DIC. Group-SST models show an exaggeration of the seasonal rates of change of sea surface temperature (SST) in autumn and spring during the cooling and warming peaks. These higher-than-observed rates of change of SST tip the control of the seasonal cycle of pCO(2) and FCO2 towards SST and result in a divergence between the observed and modeled seasonal cycles, particularly in the Sub-Antarctic Zone. While almost all analyzed models (9 out of 10) show these SST-driven biases, 3 out of 10 (namely NorESM1-ME, HadGEM-ES and MPI-ESM, collectively the group-DIC models) compensate for the solubility bias because of their overly exaggerated primary production, such that biologically driven DIC changes mainly regulate the seasonal cycle of FCO2.

Published
2018
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29. Empirical methods for the estimation of Southern Ocean CO2: support vector and random forest regression

Author

Gregor, Luke, Kok, Schalk, Monteiro, Pedro M S, Department of Oceanography, and Faculty of Science

Subjects
South Africa, National Health Programs, Practice Guidelines as Topic, Humans, Mass Casualty Incidents, Disaster Planning, Burns, Societies, Medical
Abstract

The Southern Ocean accounts for 40 % of oceanic CO2 uptake, but the estimates are bound by large uncertainties due to a paucity in observations. Gap-filling empirical methods have been used to good effect to approximate pCO2 from satellite observable variables in other parts of the ocean, but many of these methods are not in agreement in the Southern Ocean. In this study we propose two additional methods that perform well in the Southern Ocean: support vector regression (SVR) and random forest regression (RFR). The methods are used to estimate ΔpCO2 in the Southern Ocean based on SOCAT v3, achieving similar trends to the SOM-FFN method by LandschYtzer et al. (2014). Results show that the SOM-FFN and RFR approaches have RMSEs of similar magnitude (14.84 and 16.45 µatm, where 1 atm = 101 325 Pa) where the SVR method has a larger RMSE (24.40 µatm). However, the larger errors for SVR and RFR are, in part, due to an increase in coastal observations from SOCAT v2 to v3, where the SOM-FFN method used v2 data. The success of both SOM-FFN and RFR depends on the ability to adapt to different modes of variability. The SOM-FFN achieves this by having independent regression models for each cluster, while this flexibility is intrinsic to the RFR method. Analyses of the estimates shows that the SVR and RFR's respective sensitivity and robustness to outliers define the outcome significantly. Further analyses on the methods were performed by using a synthetic dataset to assess the following: which method (RFR or SVR) has the best performance? What is the effect of using time, latitude and longitude as proxy variables on ΔpCO2? What is the impact of the sampling bias in the SOCAT v3 dataset on the estimates? We find that while RFR is indeed better than SVR, the ensemble of the two methods outperforms either one, due to complementary strengths and weaknesses of the methods. Results also show that for the RFR and SVR implementations, it is better to include coordinates as proxy variables as RMSE scores are lowered and the phasing of the seasonal cycle is more accurate. Lastly, we show that there is only a weak bias due to undersampling. The synthetic data provide a useful framework to test methods in regions of sparse data coverage and show potential as a useful tool to evaluate methods in future studies.

Published
2017
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30. Storm‐Driven pCO2Feedback Weakens the Response of Air‐Sea CO2Fluxes in the Sub‐Antarctic Southern Ocean

Author

Toolsee, Tesha, Nicholson, Sarah‐Anne, and Monteiro, Pedro M. S.

Abstract

The sub‐seasonal CO2flux (FCO2) variability across the Southern Ocean is poorly understood due to sparse observations at the required temporal and spatial scales. Twinned surface and profiling gliders experiments were used to investigate how storms influence FCO2through the air‐sea gradient in partial pressure of CO2(ΔpCO2) in the sub‐Antarctic zone. Winter‐spring storms caused ΔpCO2to weaken (by 22–37 μatm) due to mixing/entrainment and weaker stratification. This weakening in ΔpCO2was in phase with the increase in wind stress resulting in a reduction of the storm‐driven CO2uptake by 6%–27%. During summer, stronger stratification explained the weaker sensitivity of ΔpCO2to storms, instead temperature changes dominated the ΔpCO2variability. These results highlight the importance of observing synoptic‐scale variability in ΔpCO2, the absence of which may propagate significant biases to the mean annual FCO2estimates from large‐scale observing programmes and reconstructions. The sub‐Antarctic zone of the Southern Ocean is a region that mostly experiences carbon dioxide (CO2) uptake because of its low temperature, strong winds and lower CO2content. The wind can influence the CO2uptake through two pathways: the speed of CO2transfer between the air‐sea interface (kw) and the difference in CO2concentration in the surface ocean and overlying atmosphere (ΔpCO2). Using autonomous robots that can measure hourly air and water conditions simultaneously, we show that not resolving ΔpCO2during a storm event can lead to overestimating the CO2uptake. This is particularly important during winter and spring when the ocean's surface layers are less stratified. The warmer temperatures during summer meant a more stratified surface layer resulting in a weaker and delayed impact of storms on the ΔpCO2. This study shows that the various annual CO2uptake estimation methods used by the research community should not neglect ΔpCO2responses during storms. Hourly glider observations show that the impact of storms on both kwand ΔpCO2simultaneously modulates the ocean CO2uptake variabilityWinter‐spring storms weaken ΔpCO2through enhanced entrainment and mixing, partially counteracting the increase in CO2uptake due to kwaloneBy not accounting for the storm‐linked positive feedback in ΔpCO2, the cumulative seasonal CO2uptake was found to be overestimated by ∼6% Hourly glider observations show that the impact of storms on both kwand ΔpCO2simultaneously modulates the ocean CO2uptake variability Winter‐spring storms weaken ΔpCO2through enhanced entrainment and mixing, partially counteracting the increase in CO2uptake due to kwalone By not accounting for the storm‐linked positive feedback in ΔpCO2, the cumulative seasonal CO2uptake was found to be overestimated by ∼6%

Published
2024
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36. Global Carbon Budget 2017

Author

Le Quéré, Corinne, Andrew, Robbie M., Friedlingstein, Pierre, Sitch, Stephen, Pongratz, Julia, Manning, Andrew C., Korsbakken, Jan Ivar, Peters, Glen P., Canadell, Josep G., Jackson, Robert B., Boden, Thomas A., Tans, Pieter P., Andrews, Oliver D., Arora, Vivek K., Bakker, Dorothee C. E., Barbero, Leticia, Becker, Meike, Betts, Richard A., Bopp, Laurent, Chevallier, Frédéric, Chini, Louise P., Ciais, Philippe, Cosca, Catherine E., Cross, Jessica, Currie, Kim, Gasser, Thomas, Harris, Ian, Hauck, Judith, Haverd, Vanessa, Houghton, Richard A., Hunt, Christopher W., Hurtt, George, Ilyina, Tatiana, Jain, Atul K., Kato, Etsushi, Kautz, Markus, Keeling, Ralph F., Klein Goldewijk, Kees, Körtzinger, Arne, Landschützer, Peter, Lefèvre, Nathalie, Lenton, Andrew, Lienert, Sebastian, Lima, Ivan, Lombardozzi, Danica, Metzl, Nicolas, Millero, Frank, Monteiro, Pedro M. S., Munro, David R., Nabel, Julia E. M. S., Nakaoka, Shin-ichiro, Nojiri, Yukihiro, Padín, X. Antoni, Peregon, Anna, Pfeil, Benjamin, Pierrot, Denis, Poulter, Benjamin, Rehder, Gregor, Reimer, Janet, Rödenbeck, Christian, Schwinger, Jörg, Séférian, Roland, Skjelvan, Ingunn, Stocker, Benjamin D., Tian, Hanqin, Tilbrook, Bronte, van der Laan-Luijkx, Ingrid T., van der Werf, Guido R., van Heuven, Steven, Viovy, Nicolas, Vuichard, Nicolas, Walker, Anthony P., Watson, Andrew J., Wiltshire, Andrew J., Zaehle, Sönke, Zhu, Dan, Le Quéré, Corinne, Andrew, Robbie M., Friedlingstein, Pierre, Sitch, Stephen, Pongratz, Julia, Manning, Andrew C., Korsbakken, Jan Ivar, Peters, Glen P., Canadell, Josep G., Jackson, Robert B., Boden, Thomas A., Tans, Pieter P., Andrews, Oliver D., Arora, Vivek K., Bakker, Dorothee C. E., Barbero, Leticia, Becker, Meike, Betts, Richard A., Bopp, Laurent, Chevallier, Frédéric, Chini, Louise P., Ciais, Philippe, Cosca, Catherine E., Cross, Jessica, Currie, Kim, Gasser, Thomas, Harris, Ian, Hauck, Judith, Haverd, Vanessa, Houghton, Richard A., Hunt, Christopher W., Hurtt, George, Ilyina, Tatiana, Jain, Atul K., Kato, Etsushi, Kautz, Markus, Keeling, Ralph F., Klein Goldewijk, Kees, Körtzinger, Arne, Landschützer, Peter, Lefèvre, Nathalie, Lenton, Andrew, Lienert, Sebastian, Lima, Ivan, Lombardozzi, Danica, Metzl, Nicolas, Millero, Frank, Monteiro, Pedro M. S., Munro, David R., Nabel, Julia E. M. S., Nakaoka, Shin-ichiro, Nojiri, Yukihiro, Padín, X. Antoni, Peregon, Anna, Pfeil, Benjamin, Pierrot, Denis, Poulter, Benjamin, Rehder, Gregor, Reimer, Janet, Rödenbeck, Christian, Schwinger, Jörg, Séférian, Roland, Skjelvan, Ingunn, Stocker, Benjamin D., Tian, Hanqin, Tilbrook, Bronte, van der Laan-Luijkx, Ingrid T., van der Werf, Guido R., van Heuven, Steven, Viovy, Nicolas, Vuichard, Nicolas, Walker, Anthony P., Watson, Andrew J., Wiltshire, Andrew J., Zaehle, Sönke, and Zhu, Dan

Published
2017

37. A multi-decade record of high-quality fCO2 data in version 3 of the Surface Ocean CO2 Atlas (SOCAT)

Author

Bakker, Dorothee C. E., Pfeil, Benjamin, Landa, Camilla S., Metzl, Nicolas, O'Brien, Kevin M., Olsen, Are, Smith, Karl M., Cosca, Catherine E., Harasawa, Sumiko, Jones, Stephen D., Nakaoka, Shin-Ichiro, Nojiri, Yukihiro, Schuster, Ute, Steinhoff, Tobias, Sweeney, Colm, Takahashi, Taro, Tilbrook, Bronte, Wada, Chisato, Wanninkhof, Rik H., Alin, Simone R., Balestrini, Carlos F., Barbero, Leticia, Bates, Nicholas R., Bianchi, Alejandro A., Bonou, Frédéric, Boutin, Jacqueline, Bozec, Yann, Burger, Eugene F., Cai, Wei-Jun, Castle, Robert D., Chen, Liqi, Chierici, Melissa, Currie, Kim, Evans, Wiley, Featherstone, Charles, Feely, Richard A., Fransson, Agneta, Goyet, Catherine, Greenwood, Naomi, Gregor, Luke, Hankin, Steven, Hardman-Mountford, Nick J., Harlay, Jérôme, Hauck, Judith, Hoppema, Mario, Humphreys, Matthew P., Hunt, Christopher W., Huss, Betty, Ibánhez, J. Severino P., Johannessen, Truls, Keeling, Ralph F., Kitidis, Vassilis, Körtzinger, Arne, Kozyr, Alex, Krasakopoulou, Evangelia, Kuwata, Akira, Landschützer, Peter, Lauvset, Siv K., Lefèvre, Nathalie, Lo Monaco, Claire, Manke, Ansley B., Mathis, Jeremy T., Merlivat, Liliane, Millero, Frank J., Monteiro, Pedro M. S., Munro, David R., Murata, Akihiko, Newberger, Timothy, Omar, Abdirahman M., Ono, Tsuneo, Paterson, Kristina, Pearce, David, Pierrot, Denis, Robbins, Lisa L., Saito, Shu, Salisbury, Joseph E., Schlitzer, Reiner, Schneider, Bernd, Schweitzer, Roland, Sieger, Rainer, Skjelvan, Ingunn, Sullivan, Kevin F., Sutherland, Stewart C., Sutton, Adrienne J., Tadokoro, Kazuaki, Telszewski, Maciej, Tuma, Matthias, van Heuven, Steven M. A. C., Vandemark, Doug, Ward, Brian, Watson, Andrew J., Xu, Suqing, Centre for Ocean and Atmospheric, school of Environmental Sciences, University of East Anglia [Norwich] (UEA), University of Bergen (UiB), Bjerknes Centre for Climate Research (BCCR), Department of Biological Sciences [Bergen] (BIO / UiB), University of Bergen (UiB)-University of Bergen (UiB), Équipe CO2 (E-CO2), Laboratoire d'Océanographie et du Climat : Expérimentations et Approches Numériques (LOCEAN), Muséum national d'Histoire naturelle (MNHN)-Institut de Recherche pour le Développement (IRD)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut Pierre-Simon-Laplace (IPSL (FR_636)), École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris Diderot - Paris 7 (UPD7)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris Diderot - Paris 7 (UPD7)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Muséum national d'Histoire naturelle (MNHN)-Institut de Recherche pour le Développement (IRD)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut Pierre-Simon-Laplace (IPSL (FR_636)), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris Diderot - Paris 7 (UPD7)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), NOAA Pacific Marine Environmental Laboratory [Seattle] (PMEL), National Oceanic and Atmospheric Administration (NOAA), Joint Institute for the Study of the Atmosphere and Ocean (JISAO), University of Washington [Seattle], National Institute for Environmental Studies (NIES), University of Exeter, Helmholtz Centre for Ocean Research [Kiel] (GEOMAR), NOAA Earth System Research Laboratory (ESRL), Lamont-Doherty Earth Observatory (LDEO), Columbia University [New York], CSIRO Marine and Atmospheric Research (CSIRO-MAR), Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), NOAA Atlantic Oceanographic and Meteorological Laboratory (AOML), Departamento de Oceanografia, Servicio de Hidrografía Naval, Cooperative Institute for Marine and Atmospheric Studies, Rosenstiel School for Marine and Atmospheric Science (CIMAS), Rosenstiel School of Marine and Atmospheric Science (RSMAS), University of Miami [Coral Gables]-University of Miami [Coral Gables], Ocean and Earth Science [Southampton], University of Southampton-National Oceanography Centre (NOC), Departmento de Engenharia de Produção, Centro de Estudos e Ensaios em Risco e Modelagem Ambiental, Universidade Federal de Pernambuco [Recife] (UFPE), Interactions et Processus au sein de la couche de Surface Océanique (IPSO), Adaptation et diversité en milieu marin (AD2M), Station biologique de Roscoff [Roscoff] (SBR), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), School of Marine Science and Policy, University of Delaware [Newark], The Third Institute of Oceanography SOA, Department of Marine Sciences, University of Gothenburg (GU), National Institute of Water and Atmospheric Research [Wellington] (NIWA), Norwegian Polar Institute, Institut de Modélisation et d'Analyses en géo-environnement et santé - Espace Développement (IMAGES-Espace DEV), UMR 228 Espace-Dev, Espace pour le développement, Institut de Recherche pour le Développement (IRD)-Université de Perpignan Via Domitia (UPVD)-Avignon Université (AU)-Université de La Réunion (UR)-Université de Montpellier (UM)-Université de Guyane (UG)-Université des Antilles (UA)-Institut de Recherche pour le Développement (IRD)-Université de Perpignan Via Domitia (UPVD)-Avignon Université (AU)-Université de La Réunion (UR)-Université de Montpellier (UM)-Université de Guyane (UG)-Université des Antilles (UA), Centre for Environment, Fisheries and Aquaculture Science [Lowestoft] (CEFAS), Ocean Systems and Climate Group, CSIR, CSIRO Oceans and Atmosphere, CISRO Oceans and Atmosphere, University of Hawai‘i [Mānoa] (UHM), Alfred-Wegener-Institut, Helmholtz-Zentrum für Polar- und Meeresforschung (AWI), Ocean Process Analysis Laboratory, University of New Hampshire (UNH), IRD Lago Sul, Brazil, University of California [San Diego] (UC San Diego), University of California (UC), Plymouth Marine Laboratory (PML), Oak Ridge National Laboratory [Oak Ridge] (ORNL), UT-Battelle, LLC, University of the Aegean, Tohoku National Fisheries Research Institute, National Fisheries Research Institute, Max-Planck-Institut für Meteorologie (MPI-M), Max-Planck-Gesellschaft, Geophysical Institute [Bergen] (GFI / BiU), Austral, Boréal et Carbone (ABC), Department of Ocean Sciences, University of Miami [Coral Gables], Department of Atmospheric and Oceanic Sciences [Boulder] (ATOC), University of Colorado [Boulder], Institute of Arctic Alpine Research [University of Colorado Boulder] (INSTAAR), Japan Agency for Marine-Earth Science and Technology (JAMSTEC), Cooperative Institute for Research in Environmental Sciences (CIRES), University of Colorado [Boulder]-National Oceanic and Atmospheric Administration (NOAA), National Research Institute for Fisheries Science,Japan Fisheries Research and Education Agency, Université Paris Diderot - Paris 7 (UPD7), United States Geological Survey [Reston] (USGS), Japan Meteorological Agency (JMA), Ocean Process Analysis Laboratory (OPAL), Leibniz Institute for Baltic Sea Research Warnemünde, Weathertop consulting LLC, International Ocean Carbon Coordination Project, WCRP Joint planning staff, World Meteorological Organization (WCRP), Royal Netherlands Institute for Sea Research (NIOZ), AirSea Laboratory, School of Physics and Ryan Institute, National University of Ireland [Galway] (NUI Galway), Institut de Recherche pour le Développement (IRD)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Muséum national d'Histoire naturelle (MNHN)-Institut Pierre-Simon-Laplace (IPSL (FR_636)), École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris Diderot - Paris 7 (UPD7)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris Diderot - Paris 7 (UPD7)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Muséum national d'Histoire naturelle (MNHN)-Institut Pierre-Simon-Laplace (IPSL (FR_636)), University of Leeds, College of Life and Environmental Sciences [Exeter], Met Eireann, CSIRO Wealth from Oceans National Research Flagship and Antarctic Climate and Ecosystems CRC, Helmholtz Zentrum für Umweltforschung = Helmholtz Centre for Environmental Research (UFZ), Bermuda Institute of Ocean Sciences (BIOS), Centre de résonance magnétique des systèmes biologiques (CRMSB), Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB), CHImie Marine (CHIM), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Station biologique de Roscoff [Roscoff] (SBR), Department of Chemistry, Computer Science Department (UBC-Computer Science), University of British Columbia (UBC), Laboratoire de Biophysique et Dynamique des Systèmes Intégrés (BDSI), Université de Perpignan Via Domitia (UPVD), Oceans and Atmosphere Flagship (CSIRO), CSIRO Oceans and Atmosphere Flagship, Department of Oceanography (DOCEAN), Federal University of Pernambuco [Recife], University of California, Plymouth Marine Laboratory, Christian-Albrechts-Universität zu Kiel (CAU), Department of Civil and Environmental Engineering [Berkeley] (CEE), University of California [Berkeley], University of California-University of California, University of Wisconsin Whitewater, National Institute of Advanced Industrial Science and Technology (AIST), Department of Computer Science [Royal Holloway], Royal Holloway [University of London] (RHUL), Cooperative Institute for Marine and Atmospheric Studies (CIMAS), Max Planck Institute for Chemical Ecology, School of Physics [NUI Galway], School of Environmental Sciences [Norwich], College of Life and Environmental Sciences, University of Exeter, Université de Guyane (UG)-Université des Antilles (UA)-Institut de Recherche pour le Développement (IRD)-Université de Perpignan Via Domitia (UPVD)-Avignon Université (AU)-Université de La Réunion (UR)-Université de Montpellier (UM)-Université de Guyane (UG)-Université des Antilles (UA)-Institut de Recherche pour le Développement (IRD)-Université de Perpignan Via Domitia (UPVD)-Avignon Université (AU)-Université de La Réunion (UR)-Université de Montpellier (UM), Institute of Arctic and Alpine Research (INSTAAR), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS), University of California [Berkeley] (UC Berkeley), and University of California (UC)-University of California (UC)

Subjects
lcsh:GE1-350, lcsh:Geology, [SDU]Sciences of the Universe [physics], lcsh:QE1-996.5, [SDU.STU]Sciences of the Universe [physics]/Earth Sciences, [PHYS.PHYS.PHYS-GEO-PH]Physics [physics]/Physics [physics]/Geophysics [physics.geo-ph], lcsh:Environmental sciences, ComputingMilieux_MISCELLANEOUS, [SDU.STU.OC]Sciences of the Universe [physics]/Earth Sciences/Oceanography
Abstract

The Surface Ocean CO2 Atlas (SOCAT) is a synthesis of quality-controlled fCO2 (fugacity of carbon dioxide) values for the global surface oceans and coastal seas with regular updates. Version 3 of SOCAT has 14.7 million fCO2 values from 3646 data sets covering the years 1957 to 2014. This latest version has an additional 4.6 million fCO2 values relative to version 2 and extends the record from 2011 to 2014. Version 3 also significantly increases the data availability for 2005 to 2013. SOCAT has an average of approximately 1.2 million surface water fCO2 values per year for the years 2006 to 2012. Quality and documentation of the data has improved. A new feature is the data set quality control (QC) flag of E for data from alternative sensors and platforms. The accuracy of surface water fCO2 has been defined for all data set QC flags. Automated range checking has been carried out for all data sets during their upload into SOCAT. The upgrade of the interactive Data Set Viewer (previously known as the Cruise Data Viewer) allows better interrogation of the SOCAT data collection and rapid creation of high-quality figures for scientific presentations. Automated data upload has been launched for version 4 and will enable more frequent SOCAT releases in the future. High-profile scientific applications of SOCAT include quantification of the ocean sink for atmospheric carbon dioxide and its long-term variation, detection of ocean acidification, as well as evaluation of coupled-climate and ocean-only biogeochemical models. Users of SOCAT data products are urged to acknowledge the contribution of data providers, as stated in the SOCAT Fair Data Use Statement. This ESSD (Earth System Science Data) "living data" publication documents the methods and data sets used for the assembly of this new version of the SOCAT data collection and compares these with those used for earlier versions of the data collection (Pfeil et al., 2013; Sabine et al., 2013; Bakker et al., 2014). Individual data set files, included in the synthesis product, can be downloaded here: doi:10.1594/PANGAEA.849770. The gridded products are available here: doi:10.3334/CDIAC/OTG.SOCAT_V3_GRID.

Published
2016
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38. Intraseasonal variability linked to sampling alias in air-sea CO2 fluxes in the Southern Ocean

Author

Monteiro, Pedro M. S., Gregor, Luke, Lévy, Marina, Maenner, Stacy, Sabine, Christopher L., Swart, Sebastiaan, Processus de couplage à Petite Echelle, Ecosystèmes et Prédateurs Supérieurs (PEPS), Laboratoire d'Océanographie et du Climat : Expérimentations et Approches Numériques (LOCEAN), Muséum national d'Histoire naturelle (MNHN)-Institut de Recherche pour le Développement (IRD)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut Pierre-Simon-Laplace (IPSL (FR_636)), École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris Diderot - Paris 7 (UPD7)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris Diderot - Paris 7 (UPD7)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Muséum national d'Histoire naturelle (MNHN)-Institut de Recherche pour le Développement (IRD)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut Pierre-Simon-Laplace (IPSL (FR_636)), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris Diderot - Paris 7 (UPD7)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Department of Oceanography [Cape Town], University of Cape Town, Institut de Recherche pour le Développement (IRD)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Muséum national d'Histoire naturelle (MNHN)-Institut Pierre-Simon-Laplace (IPSL (FR_636)), École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris Diderot - Paris 7 (UPD7)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris Diderot - Paris 7 (UPD7)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Muséum national d'Histoire naturelle (MNHN)-Institut Pierre-Simon-Laplace (IPSL (FR_636))

Subjects
[SDU.STU.GP]Sciences of the Universe [physics]/Earth Sciences/Geophysics [physics.geo-ph], ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2015
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39. Elevated curie temperature and half-metallicity in the ferromagnetic semiconductor LaxEu1−xO

Author

Monteiro, Pedro M. S., Baker, Peter J. (Peter James), Hine, Nicholas, Steinke, Nina-J., Ionescu, Adrian M., Cooper, Joshaniel F. K., Barnes, Crispin H. W., Kinane, Christian J., Salman, Zaher, Wildes, Andrew R., Prokscha, Thomas, and Langridge, Sean

Subjects
Condensed Matter::Materials Science, Condensed Matter::Superconductivity, Condensed Matter::Strongly Correlated Electrons, QC
Abstract

Here we study the effect of La doping in EuO thin films using SQUID magnetometry, muon spin rotation (µSR), polarized neutron reflectivity (PNR), and density functional theory (DFT). The µSR data shows that the La0.15Eu0.85O is homogeneously magnetically ordered up to its elevated TC. It is concluded that bound magnetic polaron behavior does not explain the increase in TC and an RKKY-like interaction is consistent with the µSR data. The estimation of the magnetic moment by DFT simulations concurs with the results obtained by PNR, showing a reduction of the magnetic moment per LaxEu1−xO for increasing lanthanum doping. This reduction of the magnetic moment is explained by the reduction of the number of Eu-4f electrons present in all the magnetic interactions in EuO films. Finally, we show that an upwards shift of the Fermi energy with La or Gd doping gives rise to half metallicity for doping levels as high as 3.2%.

Published
2015

40. Global Carbon Budget 2016

Author

Le Quéré, Corinne, Andrew, Robbie M., Canadell, Josep G., Sitch, Stephen, Korsbakken, Jan Ivar, Peters, Glen P., Manning, Andrew C., Boden, Thomas A., Tans, Pieter P., Houghton, Richard A., Keeling, Ralph F., Alin, Simone, Andrews, Oliver D., Anthoni, Peter, Barbero, Leticia, Bopp, Laurent, Chevallier, Frédéric, Chini, Louise P., Ciais, Philippe, Currie, Kim, Delire, Christine, Doney, Scott C., Friedlingstein, Pierre, Gkritzalis, Thanos, Harris, Ian, Hauck, Judith, Haverd, Vanessa, Hoppema, Mario, Klein Goldewijk, Kees, Jain, Atul K., Kato, Etsushi, Körtzinger, Arne, Landschützer, Peter, Lefèvre, Nathalie, Lenton, Andrew, Lienert, Sebastian, Lombardozzi, Danica, Melton, Joe R., Metzl, Nicolas, Millero, Frank, Monteiro, Pedro M. S., Munro, David R., Nabel, Julia E. M. S., Nakaoka, Shin-ichiro, O'Brien, Kevin, Olsen, Are, Omar, Abdirahman M., Ono, Tsuneo, Pierrot, Denis, Poulter, Benjamin, Rödenbeck, Christian, Salisbury, Joe, Schuster, Ute, Schwinger, Jörg, Séférian, Roland, Skjelvan, Ingunn, Stocker, Benjamin D., Sutton, Adrienne J., Takahashi, Taro, Tian, Hanqin, Tilbrook, Bronte, van der Laan-Luijkx, Ingrid T., van der Werf, Guido R., Viovy, Nicolas, Walker, Anthony P., Wiltshire, Andrew J., Zaehle, Sönke, Le Quéré, Corinne, Andrew, Robbie M., Canadell, Josep G., Sitch, Stephen, Korsbakken, Jan Ivar, Peters, Glen P., Manning, Andrew C., Boden, Thomas A., Tans, Pieter P., Houghton, Richard A., Keeling, Ralph F., Alin, Simone, Andrews, Oliver D., Anthoni, Peter, Barbero, Leticia, Bopp, Laurent, Chevallier, Frédéric, Chini, Louise P., Ciais, Philippe, Currie, Kim, Delire, Christine, Doney, Scott C., Friedlingstein, Pierre, Gkritzalis, Thanos, Harris, Ian, Hauck, Judith, Haverd, Vanessa, Hoppema, Mario, Klein Goldewijk, Kees, Jain, Atul K., Kato, Etsushi, Körtzinger, Arne, Landschützer, Peter, Lefèvre, Nathalie, Lenton, Andrew, Lienert, Sebastian, Lombardozzi, Danica, Melton, Joe R., Metzl, Nicolas, Millero, Frank, Monteiro, Pedro M. S., Munro, David R., Nabel, Julia E. M. S., Nakaoka, Shin-ichiro, O'Brien, Kevin, Olsen, Are, Omar, Abdirahman M., Ono, Tsuneo, Pierrot, Denis, Poulter, Benjamin, Rödenbeck, Christian, Salisbury, Joe, Schuster, Ute, Schwinger, Jörg, Séférian, Roland, Skjelvan, Ingunn, Stocker, Benjamin D., Sutton, Adrienne J., Takahashi, Taro, Tian, Hanqin, Tilbrook, Bronte, van der Laan-Luijkx, Ingrid T., van der Werf, Guido R., Viovy, Nicolas, Walker, Anthony P., Wiltshire, Andrew J., and Zaehle, Sönke

Published
2016
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41. Global Carbon Budget 2016

Author

Le Quéré, Corinne, primary, Andrew, Robbie M., additional, Canadell, Josep G., additional, Sitch, Stephen, additional, Korsbakken, Jan Ivar, additional, Peters, Glen P., additional, Manning, Andrew C., additional, Boden, Thomas A., additional, Tans, Pieter P., additional, Houghton, Richard A., additional, Keeling, Ralph F., additional, Alin, Simone, additional, Andrews, Oliver D., additional, Anthoni, Peter, additional, Barbero, Leticia, additional, Bopp, Laurent, additional, Chevallier, Frédéric, additional, Chini, Louise P., additional, Ciais, Philippe, additional, Currie, Kim, additional, Delire, Christine, additional, Doney, Scott C., additional, Friedlingstein, Pierre, additional, Gkritzalis, Thanos, additional, Harris, Ian, additional, Hauck, Judith, additional, Haverd, Vanessa, additional, Hoppema, Mario, additional, Klein Goldewijk, Kees, additional, Jain, Atul K., additional, Kato, Etsushi, additional, Körtzinger, Arne, additional, Landschützer, Peter, additional, Lefèvre, Nathalie, additional, Lenton, Andrew, additional, Lienert, Sebastian, additional, Lombardozzi, Danica, additional, Melton, Joe R., additional, Metzl, Nicolas, additional, Millero, Frank, additional, Monteiro, Pedro M. S., additional, Munro, David R., additional, Nabel, Julia E. M. S., additional, Nakaoka, Shin-ichiro, additional, O'Brien, Kevin, additional, Olsen, Are, additional, Omar, Abdirahman M., additional, Ono, Tsuneo, additional, Pierrot, Denis, additional, Poulter, Benjamin, additional, Rödenbeck, Christian, additional, Salisbury, Joe, additional, Schuster, Ute, additional, Schwinger, Jörg, additional, Séférian, Roland, additional, Skjelvan, Ingunn, additional, Stocker, Benjamin D., additional, Sutton, Adrienne J., additional, Takahashi, Taro, additional, Tian, Hanqin, additional, Tilbrook, Bronte, additional, van der Laan-Luijkx, Ingrid T., additional, van der Werf, Guido R., additional, Viovy, Nicolas, additional, Walker, Anthony P., additional, Wiltshire, Andrew J., additional, and Zaehle, Sönke, additional

Published
2016
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42. A multi-decade record of high-quality &lt;i&gt;f&lt;/i&gt;CO&lt;sub&gt;2&lt;/sub&gt; data in version 3 of the Surface Ocean CO&lt;sub&gt;2&lt;/sub&gt; Atlas (SOCAT)

Author

Bakker, Dorothee C. E., primary, Pfeil, Benjamin, additional, Landa, Camilla S., additional, Metzl, Nicolas, additional, O'Brien, Kevin M., additional, Olsen, Are, additional, Smith, Karl, additional, Cosca, Cathy, additional, Harasawa, Sumiko, additional, Jones, Stephen D., additional, Nakaoka, Shin-ichiro, additional, Nojiri, Yukihiro, additional, Schuster, Ute, additional, Steinhoff, Tobias, additional, Sweeney, Colm, additional, Takahashi, Taro, additional, Tilbrook, Bronte, additional, Wada, Chisato, additional, Wanninkhof, Rik, additional, Alin, Simone R., additional, Balestrini, Carlos F., additional, Barbero, Leticia, additional, Bates, Nicholas R., additional, Bianchi, Alejandro A., additional, Bonou, Frédéric, additional, Boutin, Jacqueline, additional, Bozec, Yann, additional, Burger, Eugene F., additional, Cai, Wei-Jun, additional, Castle, Robert D., additional, Chen, Liqi, additional, Chierici, Melissa, additional, Currie, Kim, additional, Evans, Wiley, additional, Featherstone, Charles, additional, Feely, Richard A., additional, Fransson, Agneta, additional, Goyet, Catherine, additional, Greenwood, Naomi, additional, Gregor, Luke, additional, Hankin, Steven, additional, Hardman-Mountford, Nick J., additional, Harlay, Jérôme, additional, Hauck, Judith, additional, Hoppema, Mario, additional, Humphreys, Matthew P., additional, Hunt, Christopher W., additional, Huss, Betty, additional, Ibánhez, J. Severino P., additional, Johannessen, Truls, additional, Keeling, Ralph, additional, Kitidis, Vassilis, additional, Körtzinger, Arne, additional, Kozyr, Alex, additional, Krasakopoulou, Evangelia, additional, Kuwata, Akira, additional, Landschützer, Peter, additional, Lauvset, Siv K., additional, Lefèvre, Nathalie, additional, Lo Monaco, Claire, additional, Manke, Ansley, additional, Mathis, Jeremy T., additional, Merlivat, Liliane, additional, Millero, Frank J., additional, Monteiro, Pedro M. S., additional, Munro, David R., additional, Murata, Akihiko, additional, Newberger, Timothy, additional, Omar, Abdirahman M., additional, Ono, Tsuneo, additional, Paterson, Kristina, additional, Pearce, David, additional, Pierrot, Denis, additional, Robbins, Lisa L., additional, Saito, Shu, additional, Salisbury, Joe, additional, Schlitzer, Reiner, additional, Schneider, Bernd, additional, Schweitzer, Roland, additional, Sieger, Rainer, additional, Skjelvan, Ingunn, additional, Sullivan, Kevin F., additional, Sutherland, Stewart C., additional, Sutton, Adrienne J., additional, Tadokoro, Kazuaki, additional, Telszewski, Maciej, additional, Tuma, Matthias, additional, van Heuven, Steven M. A. C., additional, Vandemark, Doug, additional, Ward, Brian, additional, Watson, Andrew J., additional, and Xu, Suqing, additional

Published
2016
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44. Is the southern Benguela a significantregional sink of CO2?

Author

Gregor, Luke, Monteiro, Pedro M S, Department of Oceanography, and Faculty of Science

Abstract

This study was undertaken to characterise the seasonal cycle of air–sea fluxes of carbon dioxide (CO2 ) in the southern Benguela upwelling system off the South African west coast. Samples were collected from six monthly cross-shelf cruises in the St. Helena Bay region during 2010. CO2 fluxes were calculated from pCO2 derived from total alkalinity and dissolved inorganic carbon and scatterometer-based winds. Notwithstanding that it is one of the most biologically productive eastern boundary upwelling systems in the global ocean, the southern Benguela was found to be a very small net annual CO2 sink of -1.4 ± 0.6 mol C/m2 per year (1.7 Mt C/year). Regional primary productivity was offset by nearly equal rates of sediment and sub-thermocline remineralisation flux of CO2 , which is recirculated to surface waters by upwelling. The juxtaposition of the strong, narrow near-shore out-gassing region and the larger, weaker offshore sink resulted in the shelf area being a weak CO2 sink in all seasons but autumn (-5.8, 1.4 and -3.4 mmol C/m2 per day for summer, autumn and winter, respectively).

Published
2013

45. The seasonal cycle of pCO2 and CO2 fluxes in the Southern Ocean: diagnosing anomalies in CMIP5 Earth system models.

Author

Mongwe, N. Precious, Vichi, Marcello, and Monteiro, Pedro M. S.

Subjects
EARTH system science, CARBON dioxide analysis, OCEAN temperature, COMPUTER simulation, HEAT transfer
Abstract

The Southern Ocean forms an important component of the Earth system as a major sink of CO2 and heat. Recent studies based on the Coupled Model Intercomparison Project version 5 (CMIP5) Earth system models (ESMs) show that CMIP5 models disagree on the phasing of the seasonal cycle of the CO2 flux (FCO2/ and compare poorly with available observation products for the Southern Ocean. Because the seasonal cycle is the dominant mode of CO2 variability in the Southern Ocean, its simulation is a rigorous test for models and their long-term projections. Here we examine the competing roles of temperature and dissolved inorganic carbon (DIC) as drivers of the seasonal cycle of pCO2 in the Southern Ocean to explain the mechanistic basis for the seasonal biases in CMIP5 models. We find that despite significant differences in the spatial characteristics of the mean annual fluxes, the intra-model homogeneity in the seasonal cycle of FCO2 is greater than observational products. FCO2 biases in CMIP5 models can be grouped into two main categories, i.e., group-SST and group-DIC. Group-SST models show an exaggeration of the seasonal rates of change of sea surface temperature (SST) in autumn and spring during the cooling and warming peaks. These higher-than-observed rates of change of SST tip the control of the seasonal cycle of pCO2 and FCO2 towards SST and result in a divergence between the observed and modeled seasonal cycles, particularly in the Sub-Antarctic Zone. While almost all analyzed models (9 out of 10) show these SST-driven biases, 3 out of 10 (namely NorESM1-ME, HadGEM-ES and MPI-ESM, collectively the group-DIC models) compensate for the solubility bias because of their overly exaggerated primary production, such that biologically driven DIC changes mainly regulate the seasonal cycle of FCO2. [ABSTRACT FROM AUTHOR]

Published
2018
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46. Interannual drivers of the seasonal cycle of CO2 in the Southern Ocean.

Author

Gregor, Luke, Kok, Schalk, and Monteiro, Pedro M. S.

Subjects
CARBON dioxide, CARBON cycle, CLIMATE change, MACHINE learning, RANDOM forest algorithms
Abstract

Resolving and understanding the drivers of variability of CO2 in the Southern Ocean and its potential climate feedback is one of the major scientific challenges of the ocean-climate community. Here we use a regional approach on empirical estimates of pCO2 to understand the role that seasonal variability has in long-term CO2 changes in the Southern Ocean. Machine learning has become the preferred empirical modelling tool to interpolate time- and locationrestricted ship measurements of pCO2. In this study we use an ensemble of three machine-learning products: support vector regression (SVR) and random forest regression (RFR) from Gregor et al. (2017), and the self-organising-map feedforward neural network (SOM-FFN) method from Landschützer et al. (2016). The interpolated estimates of ΔpCO2 are separated into nine regions in the Southern Ocean defined by basin (Indian, Pacific, and Atlantic) and biomes (as defined by Fay and McKinley, 2014a). The regional approach shows that, while there is good agreement in the overall trend of the products, there are periods and regions where the confidence in estimated ΔpCO2 is low due to disagreement between the products. The regional breakdown of the data highlighted the seasonal decoupling of the modes for summer and winter interannual variability. Winter interannual variability had a longer mode of variability compared to summer, which varied on a 4-6-year timescale. We separate the analysis of the ΔpCO2 and its drivers into summer and winter. We find that understanding the variability of ΔpCO2 and its drivers on shorter timescales is critical to resolving the long-term variability of ΔpCO2. Results show that ΔpCO2 is rarely driven by thermodynamics during winter, but rather by mixing and stratification due to the stronger correlation of ΔpCO2 variability with mixed layer depth. Summer pCO2 variability is consistent with chlorophyll a variability, where higher concentrations of chlorophyll a correspond with lower pCO2 concentrations. In regions of low chlorophyll a concentrations, wind stress and sea surface temperature emerged as stronger drivers of ΔpCO2. In summary we propose that sub-decadal variability is explained by summer drivers, while winter variability contributes to the long-term changes associated with the SAM. This approach is a useful framework to assess the drivers of ΔpCO2 but would greatly benefit from improved estimates of ΔpCO2 and a longer time series. [ABSTRACT FROM AUTHOR]

Published
2018
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47. Empirical methods for the estimation of Southern Ocean CO2: support vector and random forest regression.

Author

Gregor, Luke, Kok, Schalk, and Monteiro, Pedro M. S.

Subjects
RANDOM forest algorithms, SUPPORT vector machines, CARBON dioxide, REGRESSION analysis
Abstract

The Southern Ocean accounts for 40% of oceanic CO2 uptake, but the estimates are bound by large uncertainties due to a paucity in observations. Gap-filling empirical methods have been used to good effect to approximate pCO2 from satellite observable variables in other parts of the ocean, but many of these methods are not in agreement in the Southern Ocean. In this study we propose two additional methods that perform well in the Southern Ocean: support vector regression (SVR) and random forest regression (RFR). The methods are used to estimate ΔpCO2 in the Southern Ocean based on SOCAT v3, achieving similar trends to the SOM-FFN method by Landschützer et al. (2014). Results show that the SOM-FFN and RFR approaches have RMSEs of similar magnitude (14.84 and 16.45 µatm, where 1 atm=101 325 Pa) where the SVR method has a larger RMSE (24.40 µatm). However, the larger errors for SVR and RFR are, in part, due to an increase in coastal observations from SOCAT v2 to v3, where the SOM-FFN method used v2 data. The success of both SOM-FFN and RFR depends on the ability to adapt to different modes of variability. The SOM-FFN achieves this by having independent regression models for each cluster, while this flexibility is intrinsic to the RFR method. Analyses of the estimates shows that the SVR and RFR's respective sensitivity and robustness to outliers define the outcome significantly. Further analyses on the methods were performed by using a synthetic dataset to assess the following: which method (RFR or SVR) has the best performance? What is the effect of using time, latitude and longitude as proxy variables on ΔpCO2? What is the impact of the sampling bias in the SOCAT v3 dataset on the estimates? We find that while RFR is indeed better than SVR, the ensemble of the two methods outperforms either one, due to complementary strengths and weaknesses of the methods. Results also show that for the RFR and SVR implementations, it is better to include coordinates as proxy variables as RMSE scores are lowered and the phasing of the seasonal cycle is more accurate. Lastly, we show that there is only a weak bias due to undersampling. The synthetic data provide a useful framework to test methods in regions of sparse data coverage and show potential as a useful tool to evaluate methods in future studies. [ABSTRACT FROM AUTHOR]

Published
2017
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48. Interannual drivers of the seasonal cycle of CO2 fluxes in the Southern Ocean.

Author

Gregor, Luke, Kok, Schalk, and Monteiro, Pedro M. S.

Subjects
CARBON cycle, SEASONAL temperature variations, CARBON dioxide, SURFACE temperature, MACHINE learning, MARINE ecology
Abstract

Machine learning methods (support vector regression and random forest regression) were used to map gridded estimates of ΔpCO2 in the Southern Ocean from SOCAT v3 data. A low (1°  × monthly) and high (0.25° × 16-day) resolution implementation of each of these methods as well as the SOM-FFN method of Landschützer et al. (2014) were added to a five member ensemble. The ensemble mean ΔpCO2 was used to calculate FCO2 (air-sea CO2 flux). Data was separated into nine domains defined by basin (Indian, Pacific and Atlantic) and biomes defined by Fay and McKinley (2014). The regional approach showed large zonal asymmetry in ΔpCO2 and FCO2 estimates. Importantly, there was a seasonal decoupling of the modes summer and winter interannual variability. Winter trends had a larger 10 year mode of variability compared to summer trends, which had a shorter 4-6 year mode. To understand this variability of FCO2, we separately assessed changes in summer and winter ΔpCO2 and the drivers thereof. The dominant winter changes were driven by wind stress variability. Summer variability correlated well with chlorophyll-a variability where the latter had high concentrations. In regions of low chlorophyll-a concentrations, wind stress and sea surface temperature were lower order drivers of ΔpCO2. [ABSTRACT FROM AUTHOR]

Published
2017
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49. Mechanisms of the Sea–Air CO2 Flux Seasonal Cycle biases in CMIP5 Earth Systems Models in the Southern Ocean.

Author

Precious Mongwe, N., Vichi, Marcello, and Monteiro, Pedro M. S.

Subjects
ATMOSPHERIC carbon dioxide, CARBON dioxide in seawater, CARBON cycle, CARBON dioxide solubility, CLIMATE change, MARINE ecology
Abstract

The Southern Ocean forms a key component of the global carbon cycle. Recent studies, however, show that CMIP5 Earth System Models (ESM) disagree on the representation of the seasonal cycle of the CO2 flux (FCO2) and compare poorly to observations in the Southern Ocean. This model-observations bias has important implications on the ability of ESMs to predict century scale CO2 sink and related climate feedbacks. In this study, we used a specialized diagnostic analysis on 10 CMIP5 models in the Southern Ocean to discriminate the role of the major drivers, namely the temperature control and the concentration of dissolved inorganic carbon (DIC). Our analysis shows that the FCO2 biases in CMIP5 models cluster in two major groups. Group A models (MPI-ESM-MR, NorESM2 and HadGEM-ES) are characterized by exaggerated primary production such that biologically driven DIC changes mainly regulate the seasonal cycle of FCO2. Group-B (CMCC-CESM, GFDL-ESM2M, IPSL-CM5A-MR, MRI-ESM, CanESM2, CNRS-CERFACS) overestimates the role of temperature and thus the change in CO2 solubility becomes a dominant driver of FCO2 variability. While CMIP5 models mostly show a singular dominant influence of these two extremes, observations show a modest influence of both, with a dominance of DIC regulation. We found that CMIP5 models overestimate cooling and warming rates during autumn and spring with respect to observations. Because of this, the role of solubility is overestimated, particularly during these seasons (autumn and spring) in group B models, to the extent of contradicting the biological CO2 uptake during spring. Group A does not show this solubility driven bias due to the overestimation of DIC draw down. This finding strongly implies that the inability of the CMIP5 ESMs to resolve CO2 biological uptake during spring might be crucially related to the sensitivity of the pCO2 to temperature in addition to underestimated biological CO2 uptake. [ABSTRACT FROM AUTHOR]

Published
2017
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