Articles | Volume 16, issue 16
https://doi.org/10.5194/gmd-16-4639-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-4639-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Methods for assessment of models
| 
18 Aug 2023
Methods for assessment of models |
| 18 Aug 2023
| 18 Aug 2023
Using Probability Density Functions to Evaluate Models (PDFEM, v1.0) to compare a biogeochemical model with satellite-derived chlorophyll
Plymouth Marine Laboratory, Prospect Place, Plymouth PL1 3DH, UK
Christopher L. Follett
Department of Earth, Ocean and Ecological Sciences, University of Liverpool, Liverpool L69 3GP
Department of Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
Jacob Bien
Data Sciences and Operations, University of Southern California, Los Angeles, California, USA
Stephanie Dutkiewicz
Department of Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
Sangwon Hyun
Department of Statistics, University of California Santa Cruz, Santa Cruz, California, USA
Gemma Kulk
Plymouth Marine Laboratory, Prospect Place, Plymouth PL1 3DH, UK
Gael L. Forget
Department of Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
Christian Müller
Department of Statistics, LMU/HMGU Munich, Munich, Germany
Marie-Fanny Racault
School of Environmental Sciences, University of East Anglia, Norwich NR4 7TJ, UK
Christopher N. Hill
Department of Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
Thomas Jackson
Plymouth Marine Laboratory, Prospect Place, Plymouth PL1 3DH, UK
Shubha Sathyendranath
Plymouth Marine Laboratory, Prospect Place, Plymouth PL1 3DH, UK
National Centre for Earth Observation, Plymouth Marine Laboratory, Plymouth PL1 3DH, UK
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Ocean Sci., 19, 703–727, https://doi.org/10.5194/os-19-703-2023, https://doi.org/10.5194/os-19-703-2023, 2023
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We present a remote sensing algorithm to estimate the size distribution of particles suspended in natural near-surface ocean water using ocean color data. The algorithm can be used to estimate the abundance and carbon content of phytoplankton, photosynthesizing microorganisms that are at the basis of the marine food web and play an important role in Earth’s carbon cycle and climate. A merged, multi-sensor satellite data set and the model scientific code are provided.
Kristofer Döös, Bror Jönsson, and Joakim Kjellsson
Geosci. Model Dev., 10, 1733–1749, https://doi.org/10.5194/gmd-10-1733-2017, https://doi.org/10.5194/gmd-10-1733-2017, 2017
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The TRACMASS trajectory code with corresponding schemes has been improved and become more accurate and user friendly over the years. An outcome of the present study is that we strongly recommend the use of the
time-dependentTRACMASS scheme. We would also like to dissuade the use of the more primitive
stepwise-stationaryscheme, since the velocity fields remain stationary for longer periods, creating abrupt discontinuities in the velocity fields and yielding inaccurate solutions.
B. F. Jonsson, S. Doney, J. Dunne, and M. L. Bender
Biogeosciences, 12, 681–695, https://doi.org/10.5194/bg-12-681-2015, https://doi.org/10.5194/bg-12-681-2015, 2015
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We compare how two global circulation models simulate biological production over the year with observations. Note that models simulate the range of biological production and biomass well but fail with regard to timing and regional structures. This is probably because the physics in the models are wrong, especially vertical processes such as mixed layer dynamics.
Tihomir S. Kostadinov, Lisl Robertson Lain, Christina Eunjin Kong, Xiaodong Zhang, Stéphane Maritorena, Stewart Bernard, Hubert Loisel, Daniel S. F. Jorge, Ekaterina Kochetkova, Shovonlal Roy, Bror Jonsson, Victor Martinez-Vicente, and Shubha Sathyendranath
Ocean Sci., 19, 703–727, https://doi.org/10.5194/os-19-703-2023, https://doi.org/10.5194/os-19-703-2023, 2023
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We present a remote sensing algorithm to estimate the size distribution of particles suspended in natural near-surface ocean water using ocean color data. The algorithm can be used to estimate the abundance and carbon content of phytoplankton, photosynthesizing microorganisms that are at the basis of the marine food web and play an important role in Earth’s carbon cycle and climate. A merged, multi-sensor satellite data set and the model scientific code are provided.
André Valente, Shubha Sathyendranath, Vanda Brotas, Steve Groom, Michael Grant, Thomas Jackson, Andrei Chuprin, Malcolm Taberner, Ruth Airs, David Antoine, Robert Arnone, William M. Balch, Kathryn Barker, Ray Barlow, Simon Bélanger, Jean-François Berthon, Şükrü Beşiktepe, Yngve Borsheim, Astrid Bracher, Vittorio Brando, Robert J. W. Brewin, Elisabetta Canuti, Francisco P. Chavez, Andrés Cianca, Hervé Claustre, Lesley Clementson, Richard Crout, Afonso Ferreira, Scott Freeman, Robert Frouin, Carlos García-Soto, Stuart W. Gibb, Ralf Goericke, Richard Gould, Nathalie Guillocheau, Stanford B. Hooker, Chuamin Hu, Mati Kahru, Milton Kampel, Holger Klein, Susanne Kratzer, Raphael Kudela, Jesus Ledesma, Steven Lohrenz, Hubert Loisel, Antonio Mannino, Victor Martinez-Vicente, Patricia Matrai, David McKee, Brian G. Mitchell, Tiffany Moisan, Enrique Montes, Frank Muller-Karger, Aimee Neeley, Michael Novak, Leonie O'Dowd, Michael Ondrusek, Trevor Platt, Alex J. Poulton, Michel Repecaud, Rüdiger Röttgers, Thomas Schroeder, Timothy Smyth, Denise Smythe-Wright, Heidi M. Sosik, Crystal Thomas, Rob Thomas, Gavin Tilstone, Andreia Tracana, Michael Twardowski, Vincenzo Vellucci, Kenneth Voss, Jeremy Werdell, Marcel Wernand, Bozena Wojtasiewicz, Simon Wright, and Giuseppe Zibordi
Earth Syst. Sci. Data, 14, 5737–5770, https://doi.org/10.5194/essd-14-5737-2022, https://doi.org/10.5194/essd-14-5737-2022, 2022
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Rachael N. C. Sanders, Daniel C. Jones, Simon A. Josey, Bablu Sinha, and Gael Forget
Ocean Sci., 18, 953–978, https://doi.org/10.5194/os-18-953-2022, https://doi.org/10.5194/os-18-953-2022, 2022
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In 2015, record low temperatures were observed in the North Atlantic. Using an ocean model, we show that surface heat loss in December 2013 caused 75 % of the initial cooling before this "cold blob" was trapped below the surface. The following summer, the cold blob re-emerged due to a strong temperature difference between the surface ocean and below, driving vertical diffusion of heat. Lower than average surface warming then led to the coldest temperature anomalies in August 2015.
Ehud Strobach, Andrea Molod, Donifan Barahona, Atanas Trayanov, Dimitris Menemenlis, and Gael Forget
Geosci. Model Dev., 15, 2309–2324, https://doi.org/10.5194/gmd-15-2309-2022, https://doi.org/10.5194/gmd-15-2309-2022, 2022
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The Green's functions methodology offers a systematic, easy-to-implement, computationally cheap, scalable, and extendable method to tune uncertain parameters in models accounting for the dependent response of the model to a change in various parameters. Herein, we successfully show for the first time that long-term errors in earth system models can be considerably reduced using Green's functions methodology. The method can be easily applied to any model containing uncertain parameters.
Axel Peytavin, Bruno Sainte-Rose, Gael Forget, and Jean-Michel Campin
Geosci. Model Dev., 14, 4769–4780, https://doi.org/10.5194/gmd-14-4769-2021, https://doi.org/10.5194/gmd-14-4769-2021, 2021
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We present a new algorithm developed at The Ocean Cleanup to update ocean plastic models based on measurements from the field to improve future cleaning operations. Prepared in collaboration with MIT researchers, this initial study presents its use in several analytical and real test cases in which two observers in a flow field record regular observations to update a plastic forecast. We demonstrate this improves the prediction, even with inaccurate knowledge of the water flows driving plastic.
Paul J. Tréguer, Jill N. Sutton, Mark Brzezinski, Matthew A. Charette, Timothy Devries, Stephanie Dutkiewicz, Claudia Ehlert, Jon Hawkings, Aude Leynaert, Su Mei Liu, Natalia Llopis Monferrer, María López-Acosta, Manuel Maldonado, Shaily Rahman, Lihua Ran, and Olivier Rouxel
Biogeosciences, 18, 1269–1289, https://doi.org/10.5194/bg-18-1269-2021, https://doi.org/10.5194/bg-18-1269-2021, 2021
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Silicon is the second most abundant element of the Earth's crust. In this review, we show that silicon inputs and outputs, to and from the world ocean, are 57 % and 37 % higher, respectively, than previous estimates. These changes are significant, modifying factors such as the geochemical residence time of silicon, which is now about 8000 years and 2 times faster than previously assumed. We also update the total biogenic silica pelagic production and provide an estimate for sponge production.
Stephanie Dutkiewicz, Pedro Cermeno, Oliver Jahn, Michael J. Follows, Anna E. Hickman, Darcy A. A. Taniguchi, and Ben A. Ward
Biogeosciences, 17, 609–634, https://doi.org/10.5194/bg-17-609-2020, https://doi.org/10.5194/bg-17-609-2020, 2020
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Phytoplankton are an essential component of the marine food web and earth's carbon cycle. We use observations, ecological theory and a unique trait-based ecosystem model to explain controls on patterns of marine phytoplankton biodiversity. We find that different dimensions of diversity (size classes, biogeochemical functional groups, thermal norms) are controlled by a disparate combination of mechanisms. This may explain why previous studies of phytoplankton diversity had conflicting results.
Svetlana N. Losa, Stephanie Dutkiewicz, Martin Losch, Julia Oelker, Mariana A. Soppa, Scarlett Trimborn, Hongyan Xi, and Astrid Bracher
Biogeosciences Discuss., https://doi.org/10.5194/bg-2019-289, https://doi.org/10.5194/bg-2019-289, 2019
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This study highlights recent advances and challenges of applying coupled physical-biogeochemical modeling for investigating the distribution of the key phytoplankton groups in the Southern Ocean. By leveraging satellite and in situ observations we define numerical ecological model requirements in the phytoplankton trait specification and level of physiological and morphological differentiation for capturing and explaining the observed biogeography of diatoms, coccolithophores and Phaeocystis.
Rachel E. O'Brien, Kelsey J. Ridley, Manjula R. Canagaratna, John T. Jayne, Philip L. Croteau, Douglas R. Worsnop, Sri Hapsari Budisulistiorini, Jason D. Surratt, Christopher L. Follett, Daniel J. Repeta, and Jesse H. Kroll
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Heather A. Bouman, Trevor Platt, Martina Doblin, Francisco G. Figueiras, Kristinn Gudmundsson, Hafsteinn G. Gudfinnsson, Bangqin Huang, Anna Hickman, Michael Hiscock, Thomas Jackson, Vivian A. Lutz, Frédéric Mélin, Francisco Rey, Pierre Pepin, Valeria Segura, Gavin H. Tilstone, Virginie van Dongen-Vogels, and Shubha Sathyendranath
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Stephanie Dutkiewicz, Anna E. Hickman, and Oliver Jahn
Biogeosciences, 15, 613–630, https://doi.org/10.5194/bg-15-613-2018, https://doi.org/10.5194/bg-15-613-2018, 2018
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This study provides a demonstration that a biogeochemical/ecosystem/optical computer model which explicitly captures how light is radiated at the surface of the ocean and can be used as a laboratory to explore products (such as Chl a) that are derived from satellite measurements of ocean colour. It explores uncertainties that arise from data input used to derive the algorithms for the products, and issues arising from the interplay between optically important constituents in the ocean.
Kristofer Döös, Bror Jönsson, and Joakim Kjellsson
Geosci. Model Dev., 10, 1733–1749, https://doi.org/10.5194/gmd-10-1733-2017, https://doi.org/10.5194/gmd-10-1733-2017, 2017
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The TRACMASS trajectory code with corresponding schemes has been improved and become more accurate and user friendly over the years. An outcome of the present study is that we strongly recommend the use of the
time-dependentTRACMASS scheme. We would also like to dissuade the use of the more primitive
stepwise-stationaryscheme, since the velocity fields remain stationary for longer periods, creating abrupt discontinuities in the velocity fields and yielding inaccurate solutions.
Corinne Le Quéré, Erik T. Buitenhuis, Róisín Moriarty, Séverine Alvain, Olivier Aumont, Laurent Bopp, Sophie Chollet, Clare Enright, Daniel J. Franklin, Richard J. Geider, Sandy P. Harrison, Andrew G. Hirst, Stuart Larsen, Louis Legendre, Trevor Platt, I. Colin Prentice, Richard B. Rivkin, Sévrine Sailley, Shubha Sathyendranath, Nick Stephens, Meike Vogt, and Sergio M. Vallina
Biogeosciences, 13, 4111–4133, https://doi.org/10.5194/bg-13-4111-2016, https://doi.org/10.5194/bg-13-4111-2016, 2016
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We present a global biogeochemical model which incorporates ecosystem dynamics based on the representation of ten plankton functional types, and use the model to assess the relative roles of iron vs. grazing in determining phytoplankton biomass in the Southern Ocean. Our results suggest that observed low phytoplankton biomass in the Southern Ocean during summer is primarily explained by the dynamics of the Southern Ocean zooplankton community, despite iron limitation of phytoplankton growth.
G. Forget, D. Ferreira, and X. Liang
Ocean Sci., 11, 839–853, https://doi.org/10.5194/os-11-839-2015, https://doi.org/10.5194/os-11-839-2015, 2015
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Results from the ECCO v4 ocean state estimate identify the constraint of fitting Argo profiles as an effective observational basis for inverse estimation of regional turbulent transport rates. The estimated parameters' geography is physically plausible and exhibits close connections with the observed upper-ocean stratification. They yield a clear reduction in the model drift away from observations over multi-century-long simulations, including for independent biochemistry variables.
G. Forget, J.-M. Campin, P. Heimbach, C. N. Hill, R. M. Ponte, and C. Wunsch
Geosci. Model Dev., 8, 3071–3104, https://doi.org/10.5194/gmd-8-3071-2015, https://doi.org/10.5194/gmd-8-3071-2015, 2015
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The ECCO v4 non-linear inverse modeling framework and its reference solution are made publicly available. The inverse estimate of ocean physics and atmospheric forcing yields a dynamically consistent and global state estimate without unidentified sources of heat and salt that closely fits in situ and satellite data. Any user can reproduce it accurately. Parametric and external model uncertainties are of comparable magnitudes and generally exceed structural model uncertainties.
B. F. Jonsson, S. Doney, J. Dunne, and M. L. Bender
Biogeosciences, 12, 681–695, https://doi.org/10.5194/bg-12-681-2015, https://doi.org/10.5194/bg-12-681-2015, 2015
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We compare how two global circulation models simulate biological production over the year with observations. Note that models simulate the range of biological production and biomass well but fail with regard to timing and regional structures. This is probably because the physics in the models are wrong, especially vertical processes such as mixed layer dynamics.
Related subject area
Oceanography
Intercomparisons of Tracker v1.1 and four other ocean particle-tracking software packages in the Regional Ocean Modeling System
CAR36, a regional high-resolution ocean forecasting system for improving drift and beaching of Sargassum in the Caribbean archipelago
Implementation of additional spectral wave field exchanges in a three-dimensional wave–current coupled WAVEWATCH-III (version 6.07) and CROCO (version 1.2) configuration: assessment of their implications for macro-tidal coastal hydrodynamics
Comparison of 4-dimensional variational and ensemble optimal interpolation data assimilation systems using a Regional Ocean Modeling System (v3.4) configuration of the eddy-dominated East Australian Current system
LOCATE v1.0: numerical modelling of floating marine debris dispersion in coastal regions using Parcels v2.4.2
New insights into the South China Sea throughflow and water budget seasonal cycle: evaluation and analysis of a high-resolution configuration of the ocean model SYMPHONIE version 2.4
MQGeometry-1.0: a multi-layer quasi-geostrophic solver on non-rectangular geometries
Parameter estimation for ocean background vertical diffusivity coefficients in the Community Earth System Model (v1.2.1) and its impact on El Niño–Southern Oscillation forecasts
Great Lakes wave forecast system on high-resolution unstructured meshes
Impact of increased resolution on Arctic Ocean simulations in Ocean Model Intercomparison Project phase 2 (OMIP-2)
StraitFlux – Precise computations of Water Strait fluxes on various Modelling Grids
A high-resolution physical–biogeochemical model for marine resource applications in the northwest Atlantic (MOM6-COBALT-NWA12 v1.0)
A flexible z-layers approach for the accurate representation of free surface flows in a coastal ocean model (SHYFEM v. 7_5_71)
Implementation and assessment of a model including mixotrophs and the carbonate cycle (Eco3M_MIX-CarbOx v1.0) in a highly dynamic Mediterranean coastal environment (Bay of Marseille, France) – Part 1: Evolution of ecosystem composition under limited light and nutrient conditions
Ocean wave tracing v.1: a numerical solver of the wave ray equations for ocean waves on variable currents at arbitrary depths
Design and evaluation of an efficient high-precision ocean surface wave model with a multiscale grid system (MSG_Wav1.0)
Evaluation of the CMCC global eddying ocean model for the Ocean Model Intercomparison Project (OMIP2)
Barents-2.5km v2.0: an operational data-assimilative coupled ocean and sea ice ensemble prediction model for the Barents Sea and Svalbard
Open-ocean tides simulated by ICON-O, version icon-2.6.6
Comparison of the Coastal and Regional Ocean Community Model (CROCO) and NCAR-LES in Non-hydrostatic Simulations
Data assimilation sensitivity experiments in the East Auckland Current system using 4D-Var
Using the COAsT Python package to develop a standardised validation workflow for ocean physics models
Improving Antarctic Bottom Water precursors in NEMO for climate applications
Formulation, optimization, and sensitivity of NitrOMZv1.0, a biogeochemical model of the nitrogen cycle in oceanic oxygen minimum zones
Waves in SKRIPS: WAVEWATCH III coupling implementation and a case study of Tropical Cyclone Mekunu
Adding sea ice effects to a global operational model (NEMO v3.6) for forecasting total water level: approach and impact
DELWAVE 1.0: Deep-learning surrogate model of surface wave climate in the Adriatic Basin
Enhanced ocean wave modeling by including effect of breaking under both deep- and shallow-water conditions
An internal solitary wave forecasting model in the northern South China Sea (ISWFM-NSCS)
The 3D biogeochemical marine mercury cycling model MERCY v2.0 – linking atmospheric Hg to methylmercury in fish
Global seamless tidal simulation using a 3D unstructured-grid model (SCHISM v5.10.0)
Arctic Ocean simulations in the CMIP6 Ocean Model Intercomparison Project (OMIP)
ChemicalDrift 1.0: an open-source Lagrangian chemical-fate and transport model for organic aquatic pollutants
The Met Office operational wave forecasting system: the evolution of the regional and global models
4DVarNet-SSH: end-to-end learning of variational interpolation schemes for nadir and wide-swath satellite altimetry
Development and validation of a global 1∕32° surface-wave–tide–circulation coupled ocean model: FIO-COM32
Reproducible and relocatable regional ocean modelling: fundamentals and practices
Barotropic tides in MPAS-Ocean (E3SM V2): impact of ice shelf cavities
Using the two-way nesting technique AGRIF with MARS3D V11.2 to improve hydrodynamics and estimate environmental indicators
Multidecadal and climatological surface current simulations for the southwestern Indian Ocean at 1∕50° resolution
The tidal effects in the Finite-volumE Sea ice–Ocean Model (FESOM2.1): a comparison between parameterised tidal mixing and explicit tidal forcing
HIDRA2: deep-learning ensemble sea level and storm tide forecasting in the presence of seiches – the case of the northern Adriatic
Moana Ocean Hindcast – a > 25-year simulation for New Zealand waters using the Regional Ocean Modeling System (ROMS) v3.9 model
A nonhydrostatic oceanic regional model, ORCTM v1, for internal solitary wave simulation
How does 4DVar data assimilation affect the vertical representation of mesoscale eddies? A case study with observing system simulation experiments (OSSEs) using ROMS v3.9
An ensemble Kalman filter-based ocean data assimilation system improved by adaptive observation error inflation (AOEI)
GULF18, a high-resolution NEMO-based tidal ocean model of the Arabian/Persian Gulf
The Baltic Sea Model Intercomparison Project (BMIP) – a platform for model development, evaluation, and uncertainty assessment
An ensemble Kalman filter system with the Stony Brook Parallel Ocean Model v1.0
Wind work at the air-sea interface: a modeling study in anticipation of future space missions
Jilian Xiong and Parker MacCready
Geosci. Model Dev., 17, 3341–3356, https://doi.org/10.5194/gmd-17-3341-2024, https://doi.org/10.5194/gmd-17-3341-2024, 2024
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The new offline particle tracking package, Tracker v1.1, is introduced to the Regional Ocean Modeling System, featuring an efficient nearest-neighbor algorithm to enhance particle-tracking speed. Its performance was evaluated against four other tracking packages and passive dye. Despite unique features, all packages yield comparable results. Running multiple packages within the same circulation model allows comparison of their performance and ease of use.
Sylvain Cailleau, Laurent Bessières, Léonel Chiendje, Flavie Dubost, Guillaume Reffray, Jean-Michel Lellouche, Simon van Gennip, Charly Régnier, Marie Drevillon, Marc Tressol, Matthieu Clavier, Julien Temple-Boyer, and Léo Berline
Geosci. Model Dev., 17, 3157–3173, https://doi.org/10.5194/gmd-17-3157-2024, https://doi.org/10.5194/gmd-17-3157-2024, 2024
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In order to improve Sargassum drift forecasting in the Caribbean area, drift models can be forced by higher-resolution ocean currents. To this goal a 3 km resolution regional ocean model has been developed. Its assessment is presented with a particular focus on the reproduction of fine structures representing key features of the Caribbean region dynamics and Sargassum transport. The simulated propagation of a North Brazil Current eddy and its dissipation was found to be quite realistic.
Gaetano Porcile, Anne-Claire Bennis, Martial Boutet, Sophie Le Bot, Franck Dumas, and Swen Jullien
Geosci. Model Dev., 17, 2829–2853, https://doi.org/10.5194/gmd-17-2829-2024, https://doi.org/10.5194/gmd-17-2829-2024, 2024
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Here a new method of modelling the interaction between ocean currents and waves is presented. We developed an advanced coupling of two models, one for ocean currents and one for waves. In previous couplings, some wave-related calculations were based on simplified assumptions. Our method uses more complex calculations to better represent wave–current interactions. We tested it in a macro-tidal coastal area and found that it significantly improves the model accuracy, especially during storms.
Colette Gabrielle Kerry, Moninya Roughan, Shane Keating, David Gwyther, Gary Brassington, Adil Siripatana, and Joao Marcos A. C. Souza
Geosci. Model Dev., 17, 2359–2386, https://doi.org/10.5194/gmd-17-2359-2024, https://doi.org/10.5194/gmd-17-2359-2024, 2024
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Ocean forecasting relies on the combination of numerical models and ocean observations through data assimilation (DA). Here we assess the performance of two DA systems in a dynamic western boundary current, the East Australian Current, across a common modelling and observational framework. We show that the more advanced, time-dependent method outperforms the time-independent method for forecast horizons of 5 d. This advocates the use of advanced methods for highly variable oceanic regions.
Ivan Hernandez, Leidy M. Castro-Rosero, Manuel Espino, and Jose M. Alsina Torrent
Geosci. Model Dev., 17, 2221–2245, https://doi.org/10.5194/gmd-17-2221-2024, https://doi.org/10.5194/gmd-17-2221-2024, 2024
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The LOCATE numerical model was developed to conduct Lagrangian simulations of the transport and dispersion of marine debris at coastal scales. High-resolution hydrodynamic data and a beaching module that used particle distance to the shore for land–water boundary detection were used on a realistic debris discharge scenario comparing hydrodynamic data at various resolutions. Coastal processes and complex geometric structures were resolved when using nested grids and distance-to-shore beaching.
Ngoc B. Trinh, Marine Herrmann, Caroline Ulses, Patrick Marsaleix, Thomas Duhaut, Thai To Duy, Claude Estournel, and R. Kipp Shearman
Geosci. Model Dev., 17, 1831–1867, https://doi.org/10.5194/gmd-17-1831-2024, https://doi.org/10.5194/gmd-17-1831-2024, 2024
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A high-resolution model was built to study the South China Sea (SCS) water, heat, and salt budgets. Model performance is demonstrated by comparison with observations and simulations. Important discards are observed if calculating offline, instead of online, lateral inflows and outflows of water, heat, and salt. The SCS mainly receives water from the Luzon Strait and releases it through the Mindoro, Taiwan, and Karimata straits. SCS surface interocean water exchanges are driven by monsoon winds.
Louis Thiry, Long Li, Guillaume Roullet, and Etienne Mémin
Geosci. Model Dev., 17, 1749–1764, https://doi.org/10.5194/gmd-17-1749-2024, https://doi.org/10.5194/gmd-17-1749-2024, 2024
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We present a new way of solving the quasi-geostrophic (QG) equations, a simple set of equations describing ocean dynamics. Our method is solely based on the numerical methods used to solve the equations and requires no parameter tuning. Moreover, it can handle non-rectangular geometries, opening the way to study QG equations on realistic domains. We release a PyTorch implementation to ease future machine-learning developments on top of the presented method.
Zheqi Shen, Yihao Chen, Xiaojing Li, and Xunshu Song
Geosci. Model Dev., 17, 1651–1665, https://doi.org/10.5194/gmd-17-1651-2024, https://doi.org/10.5194/gmd-17-1651-2024, 2024
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Parameter estimation is the process that optimizes model parameters using observations, which could reduce model errors and improve forecasting. In this study, we conducted parameter estimation experiments using the CESM and the ensemble adjustment Kalman filter. The obtained initial conditions and parameters are used to perform ensemble forecast experiments for ENSO forecasting. The results revealed that parameter estimation could reduce analysis errors and improve ENSO forecast skills.
Ali Abdolali, Saeideh Banihashemi, Jose Henrique Alves, Aron Roland, Tyler J. Hesser, Mary Anderson Bryant, and Jane McKee Smith
Geosci. Model Dev., 17, 1023–1039, https://doi.org/10.5194/gmd-17-1023-2024, https://doi.org/10.5194/gmd-17-1023-2024, 2024
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This article presents an overview of the development and implementation of Great Lake Wave Unstructured (GLWUv2.0), including the core model and workflow design and development. The validation was conducted against in situ data for the re-forecasted duration for summer and wintertime (ice season). The article describes the limitations and challenges encountered in the operational environment and the path forward for the next generation of wave forecast systems in enclosed basins like the GL.
Qiang Wang, Qi Shu, Alexandra Bozec, Eric P. Chassignet, Pier Giuseppe Fogli, Baylor Fox-Kemper, Andy McC. Hogg, Doroteaciro Iovino, Andrew E. Kiss, Nikolay Koldunov, Julien Le Sommer, Yiwen Li, Pengfei Lin, Hailong Liu, Igor Polyakov, Patrick Scholz, Dmitry Sidorenko, Shizhu Wang, and Xiaobiao Xu
Geosci. Model Dev., 17, 347–379, https://doi.org/10.5194/gmd-17-347-2024, https://doi.org/10.5194/gmd-17-347-2024, 2024
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Increasing resolution improves model skills in simulating the Arctic Ocean, but other factors such as parameterizations and numerics are at least of the same importance for obtaining reliable simulations.
Susanna Winkelbauer, Michael Mayer, and Leopold Haimberger
EGUsphere, https://doi.org/10.5194/egusphere-2023-2883, https://doi.org/10.5194/egusphere-2023-2883, 2024
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Oceanic transports shape the global climate, but the evaluation and validation of this key quantity based on reanalysis and model data is complicated by the distortion of the used modelling grids and the large number of different grid-types. We present two new methods that allow to calculate oceanic fluxes of volume, heat, salinity and ice through almost arbitrary sections for various models and reanalyses, independent of the used modelling grids.
Andrew C. Ross, Charles A. Stock, Alistair Adcroft, Enrique Curchitser, Robert Hallberg, Matthew J. Harrison, Katherine Hedstrom, Niki Zadeh, Michael Alexander, Wenhao Chen, Elizabeth J. Drenkard, Hubert du Pontavice, Raphael Dussin, Fabian Gomez, Jasmin G. John, Dujuan Kang, Diane Lavoie, Laure Resplandy, Alizée Roobaert, Vincent Saba, Sang-Ik Shin, Samantha Siedlecki, and James Simkins
Geosci. Model Dev., 16, 6943–6985, https://doi.org/10.5194/gmd-16-6943-2023, https://doi.org/10.5194/gmd-16-6943-2023, 2023
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We evaluate a model for northwest Atlantic Ocean dynamics and biogeochemistry that balances high resolution with computational economy by building on the new regional features in the MOM6 ocean model and COBALT biogeochemical model. We test the model's ability to simulate impactful historical variability and find that the model simulates the mean state and variability of most features well, which suggests the model can provide information to inform living-marine-resource applications.
Luca Arpaia, Christian Ferrarin, Marco Bajo, and Georg Umgiesser
Geosci. Model Dev., 16, 6899–6919, https://doi.org/10.5194/gmd-16-6899-2023, https://doi.org/10.5194/gmd-16-6899-2023, 2023
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We propose a discrete multilayer shallow water model based on z-layers which, thanks to the insertion and removal of surface layers, can deal with an arbitrarily large tidal oscillation independently of the vertical resolution. The algorithm is based on a two-step procedure used in numerical simulations with moving boundaries (grid movement followed by a grid topology change, that is, the insertion/removal of surface layers), which avoids the appearance of very thin surface layers.
Lucille Barré, Frédéric Diaz, Thibaut Wagener, France Van Wambeke, Camille Mazoyer, Christophe Yohia, and Christel Pinazo
Geosci. Model Dev., 16, 6701–6739, https://doi.org/10.5194/gmd-16-6701-2023, https://doi.org/10.5194/gmd-16-6701-2023, 2023
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While several studies have shown that mixotrophs play a crucial role in the carbon cycle, the impact of environmental forcings on their dynamics remains poorly investigated. Using a biogeochemical model that considers mixotrophs, we study the impact of light and nutrient concentration on the ecosystem composition in a highly dynamic Mediterranean coastal area: the Bay of Marseille. We show that mixotrophs cope better with oligotrophic conditions compared to strict auto- and heterotrophs.
Trygve Halsne, Kai Håkon Christensen, Gaute Hope, and Øyvind Breivik
Geosci. Model Dev., 16, 6515–6530, https://doi.org/10.5194/gmd-16-6515-2023, https://doi.org/10.5194/gmd-16-6515-2023, 2023
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Surface waves that propagate in oceanic or coastal environments get influenced by their surroundings. Changes in the ambient current or the depth profile affect the wave propagation path, and the change in wave direction is called refraction. Some analytical solutions to the governing equations exist under ideal conditions, but for realistic situations, the equations must be solved numerically. Here we present such a numerical solver under an open-source license.
Jiangyu Li, Shaoqing Zhang, Qingxiang Liu, Xiaolin Yu, and Zhiwei Zhang
Geosci. Model Dev., 16, 6393–6412, https://doi.org/10.5194/gmd-16-6393-2023, https://doi.org/10.5194/gmd-16-6393-2023, 2023
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Ocean surface waves play an important role in the air–sea interface but are rarely activated in high-resolution Earth system simulations due to their expensive computational costs. To alleviate this situation, this paper designs a new wave modeling framework with a multiscale grid system. Evaluations of a series of numerical experiments show that it has good feasibility and applicability in the WAVEWATCH III model, WW3, and can achieve the goals of efficient and high-precision wave simulation.
Doroteaciro Iovino, Pier Giuseppe Fogli, and Simona Masina
Geosci. Model Dev., 16, 6127–6159, https://doi.org/10.5194/gmd-16-6127-2023, https://doi.org/10.5194/gmd-16-6127-2023, 2023
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This paper describes the model performance of three global ocean–sea ice configurations, from non-eddying (1°) to eddy-rich (1/16°) resolutions. Model simulations are obtained following the Ocean Model Intercomparison Project phase 2 (OMIP2) protocol. We compare key global climate variables across the three models and against observations, emphasizing the relative advantages and disadvantages of running forced ocean–sea ice models at higher resolution.
Johannes Röhrs, Yvonne Gusdal, Edel S. U. Rikardsen, Marina Durán Moro, Jostein Brændshøi, Nils Melsom Kristensen, Sindre Fritzner, Keguang Wang, Ann Kristin Sperrevik, Martina Idžanović, Thomas Lavergne, Jens Boldingh Debernard, and Kai H. Christensen
Geosci. Model Dev., 16, 5401–5426, https://doi.org/10.5194/gmd-16-5401-2023, https://doi.org/10.5194/gmd-16-5401-2023, 2023
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A model to predict ocean currents, temperature, and sea ice is presented, covering the Barents Sea and northern Norway. To quantify forecast uncertainties, the model calculates ensemble forecasts with 24 realizations of ocean and ice conditions. Observations from satellites, buoys, and ships are ingested by the model. The model forecasts are compared with observations, and we show that the ocean model has skill in predicting sea surface temperatures.
Jin-Song von Storch, Eileen Hertwig, Veit Lüschow, Nils Brüggemann, Helmuth Haak, Peter Korn, and Vikram Singh
Geosci. Model Dev., 16, 5179–5196, https://doi.org/10.5194/gmd-16-5179-2023, https://doi.org/10.5194/gmd-16-5179-2023, 2023
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The new ocean general circulation model ICON-O is developed for running experiments at kilometer scales and beyond. One targeted application is to simulate internal tides crucial for ocean mixing. To ensure their realism, which is difficult to assess, we evaluate the barotropic tides that generate internal tides. We show that ICON-O is able to realistically simulate the major aspects of the observed barotropic tides and discuss the aspects that impact the quality of the simulated tides.
Xiaoyu Fan, Baylor Fox-Kemper, Nobuhiro Suzuki, Qing Li, Patrick Marchesiello, Francis Auclair, Peter P. Sullivan, and Paul S. Hall
EGUsphere, https://doi.org/10.5194/egusphere-2023-1657, https://doi.org/10.5194/egusphere-2023-1657, 2023
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Simulations of the oceanic turbulent boundary layer using the nonhydrostatic CROCO ROMS and NCAR-LES models are compared. CROCO and the NCAR-LES are similarly accurate, but CROCO’s additional features (e.g., nesting and realism) and its compressible turbulence formulation carry additional costs.
Rafael Santana, Helen Macdonald, Joanne O'Callaghan, Brian Powell, Sarah Wakes, and Sutara H. Suanda
Geosci. Model Dev., 16, 3675–3698, https://doi.org/10.5194/gmd-16-3675-2023, https://doi.org/10.5194/gmd-16-3675-2023, 2023
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We show the importance of assimilating subsurface temperature and velocity data in a model of the East Auckland Current. Assimilation of velocity increased the representation of large oceanic vortexes. Assimilation of temperature is needed to correctly simulate temperatures around 100 m depth, which is the most difficult region to simulate in ocean models. Our simulations showed improved results in comparison to the US Navy global model and highlight the importance of regional models.
David Byrne, Jeff Polton, Enda O'Dea, and Joanne Williams
Geosci. Model Dev., 16, 3749–3764, https://doi.org/10.5194/gmd-16-3749-2023, https://doi.org/10.5194/gmd-16-3749-2023, 2023
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Validation is a crucial step during the development of models for ocean simulation. The purpose of validation is to assess how accurate a model is. It is most commonly done by comparing output from a model to actual observations. In this paper, we introduce and demonstrate usage of the COAsT Python package to standardise the validation process for physical ocean models. We also discuss our five guiding principles for standardised validation.
Katherine Hutchinson, Julie Deshayes, Christian Éthé, Clément Rousset, Casimir de Lavergne, Martin Vancoppenolle, Nicolas C. Jourdain, and Pierre Mathiot
Geosci. Model Dev., 16, 3629–3650, https://doi.org/10.5194/gmd-16-3629-2023, https://doi.org/10.5194/gmd-16-3629-2023, 2023
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Bottom Water constitutes the lower half of the ocean’s overturning system and is primarily formed in the Weddell and Ross Sea in the Antarctic due to interactions between the atmosphere, ocean, sea ice and ice shelves. Here we use a global ocean 1° resolution model with explicit representation of the three large ice shelves important for the formation of the parent waters of Bottom Water. We find doing so reduces salt biases, improves water mass realism and gives realistic ice shelf melt rates.
Daniele Bianchi, Daniel McCoy, and Simon Yang
Geosci. Model Dev., 16, 3581–3609, https://doi.org/10.5194/gmd-16-3581-2023, https://doi.org/10.5194/gmd-16-3581-2023, 2023
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We present NitrOMZ, a new model of the oceanic nitrogen cycle that simulates chemical transformations within oxygen minimum zones (OMZs). We describe the model formulation and its implementation in a one-dimensional representation of the water column before evaluating its ability to reproduce observations in the eastern tropical South Pacific. We conclude by describing the model sensitivity to parameter choices and environmental factors and its application to nitrogen cycling in the ocean.
Rui Sun, Alison Cobb, Ana B. Villas Bôas, Sabique Langodan, Aneesh C. Subramanian, Matthew R. Mazloff, Bruce D. Cornuelle, Arthur J. Miller, Raju Pathak, and Ibrahim Hoteit
Geosci. Model Dev., 16, 3435–3458, https://doi.org/10.5194/gmd-16-3435-2023, https://doi.org/10.5194/gmd-16-3435-2023, 2023
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In this work, we integrated the WAVEWATCH III model into the regional coupled model SKRIPS. We then performed a case study using the newly implemented model to study Tropical Cyclone Mekunu, which occurred in the Arabian Sea. We found that the coupled model better simulates the cyclone than the uncoupled model, but the impact of waves on the cyclone is not significant. However, the waves change the sea surface temperature and mixed layer, especially in the cold waves produced due to the cyclone.
Pengcheng Wang and Natacha B. Bernier
Geosci. Model Dev., 16, 3335–3354, https://doi.org/10.5194/gmd-16-3335-2023, https://doi.org/10.5194/gmd-16-3335-2023, 2023
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Effects of sea ice are typically neglected in operational flood forecast systems. In this work, we capture these effects via the addition of a parameterized ice–ocean stress. The parameterization takes advantage of forecast fields from an advanced ice–ocean model and features a novel, consistent representation of the tidal relative ice–ocean velocity. The new parameterization leads to improved forecasts of tides and storm surges in polar regions. Associated physical processes are discussed.
Peter Mlakar, Antonio Ricchi, Sandro Carniel, Davide Bonaldo, and Matjaž Ličer
EGUsphere, https://doi.org/10.5194/egusphere-2023-718, https://doi.org/10.5194/egusphere-2023-718, 2023
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We propose a new point-prediction DEep Learning WAVe Emulating model (DELWAVE) which successfully emulates the ocean wave model (SWAN) over synoptic to climate timescales. Compared to control climatology over all wind directions, the mismatch between DELWAVE and SWAN is generally small compared to the difference between scenario and control conditions, suggesting that the noise introduced by surrogate modeling is substantially weaker than the climate change signal.
Yue Xu and Xiping Yu
Geosci. Model Dev., 16, 2811–2831, https://doi.org/10.5194/gmd-16-2811-2023, https://doi.org/10.5194/gmd-16-2811-2023, 2023
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An accurate description of the wind energy input into ocean waves is crucial to ocean wave modeling, and a physics-based consideration of the effect of wave breaking is absolutely necessary to obtain such an accurate description, particularly under extreme conditions. This study evaluates the performance of a recently improved formula, taking into account not only the effect of breaking but also the effect of airflow separation on the leeside of steep wave crests in a reasonably consistent way.
Yankun Gong, Xueen Chen, Jiexin Xu, Jieshuo Xie, Zhiwu Chen, Yinghui He, and Shuqun Cai
Geosci. Model Dev., 16, 2851–2871, https://doi.org/10.5194/gmd-16-2851-2023, https://doi.org/10.5194/gmd-16-2851-2023, 2023
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Internal solitary waves (ISWs) play crucial roles in mass transport and ocean mixing in the northern South China Sea. Massive numerical investigations have been conducted in this region, but there was no systematic evaluation of a three-dimensional model about precisely simulating ISWs. Here, an ISW forecasting model is employed to evaluate the roles of resolution, tidal forcing and stratification in accurately reproducing wave properties via comparison to field and remote-sensing observations.
Johannes Bieser, David J. Amptmeijer, Ute Daewel, Joachim Kuss, Anne L. Soerensen, and Corinna Schrum
Geosci. Model Dev., 16, 2649–2688, https://doi.org/10.5194/gmd-16-2649-2023, https://doi.org/10.5194/gmd-16-2649-2023, 2023
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MERCY is a 3D model to study mercury (Hg) cycling in the ocean. Hg is a highly harmful pollutant regulated by the UN Minamata Convention on Mercury due to widespread human emissions. These emissions eventually reach the oceans, where Hg transforms into the even more toxic and bioaccumulative pollutant methylmercury. MERCY predicts the fate of Hg in the ocean and its buildup in the food chain. It is the first model to consider Hg accumulation in fish, a major source of Hg exposure for humans.
Y. Joseph Zhang, Tomas Fernandez-Montblanc, William Pringle, Hao-Cheng Yu, Linlin Cui, and Saeed Moghimi
Geosci. Model Dev., 16, 2565–2581, https://doi.org/10.5194/gmd-16-2565-2023, https://doi.org/10.5194/gmd-16-2565-2023, 2023
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Simulating global ocean from deep basins to coastal areas is a daunting task but is important for disaster mitigation efforts. We present a new 3D global ocean model on flexible mesh to study both tidal and nontidal processes and total water prediction. We demonstrate the potential for
seamlesssimulation, on a single mesh, from the global ocean to a few estuaries along the US West Coast. The model can serve as the backbone of a global tide surge and compound flooding forecasting framework.
Qi Shu, Qiang Wang, Chuncheng Guo, Zhenya Song, Shizhu Wang, Yan He, and Fangli Qiao
Geosci. Model Dev., 16, 2539–2563, https://doi.org/10.5194/gmd-16-2539-2023, https://doi.org/10.5194/gmd-16-2539-2023, 2023
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Ocean models are often used for scientific studies on the Arctic Ocean. Here the Arctic Ocean simulations by state-of-the-art global ocean–sea-ice models participating in the Ocean Model Intercomparison Project (OMIP) were evaluated. The simulations on Arctic Ocean hydrography, freshwater content, stratification, sea surface height, and gateway transports were assessed and the common biases were detected. The simulations forced by different atmospheric forcing were also evaluated.
Manuel Aghito, Loris Calgaro, Knut-Frode Dagestad, Christian Ferrarin, Antonio Marcomini, Øyvind Breivik, and Lars Robert Hole
Geosci. Model Dev., 16, 2477–2494, https://doi.org/10.5194/gmd-16-2477-2023, https://doi.org/10.5194/gmd-16-2477-2023, 2023
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The newly developed ChemicalDrift model can simulate the transport and fate of chemicals in the ocean and in coastal regions. The model combines ocean physics, including transport due to currents, turbulence due to surface winds and the sinking of particles to the sea floor, with ocean chemistry, such as the partitioning, the degradation and the evaporation of chemicals. The model will be utilized for risk assessment of ocean and sea-floor contamination from pollutants emitted from shipping.
Nieves G. Valiente, Andrew Saulter, Breogan Gomez, Christopher Bunney, Jian-Guo Li, Tamzin Palmer, and Christine Pequignet
Geosci. Model Dev., 16, 2515–2538, https://doi.org/10.5194/gmd-16-2515-2023, https://doi.org/10.5194/gmd-16-2515-2023, 2023
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We document the Met Office operational global and regional wave models which provide wave forecasts up to 7 d ahead. Our models present coarser resolution offshore to higher resolution near the coastline. The increased resolution led to replication of the extremes but to some overestimation during modal conditions. If currents are included, wave directions and long period swells near the coast are significantly improved. New developments focus on the optimisation of the models with resolution.
Maxime Beauchamp, Quentin Febvre, Hugo Georgenthum, and Ronan Fablet
Geosci. Model Dev., 16, 2119–2147, https://doi.org/10.5194/gmd-16-2119-2023, https://doi.org/10.5194/gmd-16-2119-2023, 2023
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4DVarNet is a learning-based method based on traditional data assimilation (DA). This new class of algorithms can be used to provide efficient reconstructions of a dynamical system based on single observations. We provide a 4DVarNet application to sea surface height reconstructions based on nadir and future Surface Water and Ocean and Topography data. It outperforms other methods, from optimal interpolation to sophisticated DA algorithms. This work is part of on-going AI Chair Oceanix projects.
Bin Xiao, Fangli Qiao, Qi Shu, Xunqiang Yin, Guansuo Wang, and Shihong Wang
Geosci. Model Dev., 16, 1755–1777, https://doi.org/10.5194/gmd-16-1755-2023, https://doi.org/10.5194/gmd-16-1755-2023, 2023
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A new global surface-wave–tide–circulation coupled ocean model (FIO-COM32) with a resolution of 1/32° × 1/32° is developed and validated. Both the promotion of the horizontal resolution and included physical processes are shown to be important contributors to the significant improvements in FIO-COM32 simulations. It is time to merge these separated model components (surface waves, tidal currents and ocean circulation) and start a new generation of ocean model development.
Jeff Polton, James Harle, Jason Holt, Anna Katavouta, Dale Partridge, Jenny Jardine, Sarah Wakelin, Julia Rulent, Anthony Wise, Katherine Hutchinson, David Byrne, Diego Bruciaferri, Enda O'Dea, Michela De Dominicis, Pierre Mathiot, Andrew Coward, Andrew Yool, Julien Palmiéri, Gennadi Lessin, Claudia Gabriela Mayorga-Adame, Valérie Le Guennec, Alex Arnold, and Clément Rousset
Geosci. Model Dev., 16, 1481–1510, https://doi.org/10.5194/gmd-16-1481-2023, https://doi.org/10.5194/gmd-16-1481-2023, 2023
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The aim is to increase the capacity of the modelling community to respond to societally important questions that require ocean modelling. The concept of reproducibility for regional ocean modelling is developed: advocating methods for reproducible workflows and standardised methods of assessment. Then, targeting the NEMO framework, we give practical advice and worked examples, highlighting key considerations that will the expedite development cycle and upskill the user community.
Nairita Pal, Kristin N. Barton, Mark R. Petersen, Steven R. Brus, Darren Engwirda, Brian K. Arbic, Andrew F. Roberts, Joannes J. Westerink, and Damrongsak Wirasaet
Geosci. Model Dev., 16, 1297–1314, https://doi.org/10.5194/gmd-16-1297-2023, https://doi.org/10.5194/gmd-16-1297-2023, 2023
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Understanding tides is essential to accurately predict ocean currents. Over the next several decades coastal processes such as flooding and erosion will be severely impacted due to climate change. Tides affect currents along the coastal regions the most. In this paper we show the results of implementing tides in a global ocean model known as MPAS–Ocean. We also show how Antarctic ice shelf cavities affect global tides. Our work points towards future research with tide–ice interactions.
Sébastien Petton, Valérie Garnier, Matthieu Caillaud, Laurent Debreu, and Franck Dumas
Geosci. Model Dev., 16, 1191–1211, https://doi.org/10.5194/gmd-16-1191-2023, https://doi.org/10.5194/gmd-16-1191-2023, 2023
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The nesting AGRIF library is implemented in the MARS3D hydrodynamic model, a semi-implicit, free-surface numerical model which uses a time scheme as an alternating-direction implicit (ADI) algorithm. Two applications at the regional and coastal scale are introduced. We compare the two-nesting approach to the classic offline one-way approach, based on an in situ dataset. This method is an efficient means to significantly improve the physical hydrodynamics and unravel ecological challenges.
Noam S. Vogt-Vincent and Helen L. Johnson
Geosci. Model Dev., 16, 1163–1178, https://doi.org/10.5194/gmd-16-1163-2023, https://doi.org/10.5194/gmd-16-1163-2023, 2023
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Ocean currents transport things over large distances across the ocean surface. Predicting this transport is key for tackling many environmental problems, such as marine plastic pollution and coral reef resilience. However, doing this requires a good understanding ocean currents, which is currently lacking. Here, we present and validate state-of-the-art simulations for surface currents in the southwestern Indian Ocean, which will support future marine dispersal studies across this region.
Pengyang Song, Dmitry Sidorenko, Patrick Scholz, Maik Thomas, and Gerrit Lohmann
Geosci. Model Dev., 16, 383–405, https://doi.org/10.5194/gmd-16-383-2023, https://doi.org/10.5194/gmd-16-383-2023, 2023
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Tides have essential effects on the ocean and climate. Most previous research applies parameterised tidal mixing to discuss their effects in models. By comparing the effect of a tidal mixing parameterisation and tidal forcing on the ocean state, we assess the advantages and disadvantages of the two methods. Our results show that tidal mixing in the North Pacific Ocean strongly affects the global thermohaline circulation. We also list some effects that are not considered in the parameterisation.
Marko Rus, Anja Fettich, Matej Kristan, and Matjaž Ličer
Geosci. Model Dev., 16, 271–288, https://doi.org/10.5194/gmd-16-271-2023, https://doi.org/10.5194/gmd-16-271-2023, 2023
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We propose a new fast and reliable deep-learning architecture HIDRA2 for sea level and storm surge modeling. HIDRA2 features new feature encoders and a fusion-regression block. We test HIDRA2 on Adriatic storm surges, which depend on an interaction between tides and seiches. We demonstrate that HIDRA2 learns to effectively mimic the timing and amplitude of Adriatic seiches. This is essential for reliable HIDRA2 predictions of total storm surge sea levels.
Joao Marcos Azevedo Correia de Souza, Sutara H. Suanda, Phellipe P. Couto, Robert O. Smith, Colette Kerry, and Moninya Roughan
Geosci. Model Dev., 16, 211–231, https://doi.org/10.5194/gmd-16-211-2023, https://doi.org/10.5194/gmd-16-211-2023, 2023
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The current paper describes the configuration and evaluation of the Moana Ocean Hindcast, a > 25-year simulation of the ocean state around New Zealand using the Regional Ocean Modeling System v3.9. This is the first open-access, long-term, continuous, realistic ocean simulation for this region and provides information for improving the understanding of the ocean processes that affect the New Zealand exclusive economic zone.
Hao Huang, Pengyang Song, Shi Qiu, Jiaqi Guo, and Xueen Chen
Geosci. Model Dev., 16, 109–133, https://doi.org/10.5194/gmd-16-109-2023, https://doi.org/10.5194/gmd-16-109-2023, 2023
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The Oceanic Regional Circulation and Tide Model (ORCTM) is developed to reproduce internal solitary wave dynamics. The three-dimensional nonlinear momentum equations are involved with the nonhydrostatic pressure obtained via solving the Poisson equation. The validation experimental results agree with the internal wave theories and observations, demonstrating that the ORCTM can successfully describe the life cycle of nonlinear internal solitary waves under different oceanic environments.
David E. Gwyther, Shane R. Keating, Colette Kerry, and Moninya Roughan
Geosci. Model Dev., 16, 157–178, https://doi.org/10.5194/gmd-16-157-2023, https://doi.org/10.5194/gmd-16-157-2023, 2023
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Ocean eddies are important for weather, climate, biology, navigation, and search and rescue. Since eddies change rapidly, models that incorporate or assimilate observations are required to produce accurate eddy timings and locations, yet the model accuracy is rarely assessed below the surface. We use a unique type of ocean model experiment to assess three-dimensional eddy structure in the East Australian Current and explore two pathways in which this subsurface structure is being degraded.
Shun Ohishi, Takemasa Miyoshi, and Misako Kachi
Geosci. Model Dev., 15, 9057–9073, https://doi.org/10.5194/gmd-15-9057-2022, https://doi.org/10.5194/gmd-15-9057-2022, 2022
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An adaptive observation error inflation (AOEI) method was proposed for atmospheric data assimilation to mitigate erroneous analysis updates caused by large observation-minus-forecast differences for satellite brightness temperature around clear- and cloudy-sky boundaries. This study implemented the AOEI with an ocean data assimilation system, leading to an improvement of analysis accuracy and dynamical balance around the frontal regions with large meridional temperature differences.
Diego Bruciaferri, Marina Tonani, Isabella Ascione, Fahad Al Senafi, Enda O'Dea, Helene T. Hewitt, and Andrew Saulter
Geosci. Model Dev., 15, 8705–8730, https://doi.org/10.5194/gmd-15-8705-2022, https://doi.org/10.5194/gmd-15-8705-2022, 2022
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More accurate predictions of the Gulf's ocean dynamics are needed. We investigate the impact on the predictive skills of a numerical shelf sea model of the Gulf after changing a few key aspects. Increasing the lateral and vertical resolution and optimising the vertical coordinate system to best represent the leading physical processes at stake significantly improve the accuracy of the simulated dynamics. Additional work may be needed to get real benefit from using a more realistic bathymetry.
Matthias Gröger, Manja Placke, H. E. Markus Meier, Florian Börgel, Sandra-Esther Brunnabend, Cyril Dutheil, Ulf Gräwe, Magnus Hieronymus, Thomas Neumann, Hagen Radtke, Semjon Schimanke, Jian Su, and Germo Väli
Geosci. Model Dev., 15, 8613–8638, https://doi.org/10.5194/gmd-15-8613-2022, https://doi.org/10.5194/gmd-15-8613-2022, 2022
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Comparisons of oceanographic climate data from different models often suffer from different model setups, forcing fields, and output of variables. This paper provides a protocol to harmonize these elements to set up multidecadal simulations for the Baltic Sea, a marginal sea in Europe. First results are shown from six different model simulations from four different model platforms. Topical studies for upwelling, marine heat waves, and stratification are also assessed.
Shun Ohishi, Tsutomu Hihara, Hidenori Aiki, Joji Ishizaka, Yasumasa Miyazawa, Misako Kachi, and Takemasa Miyoshi
Geosci. Model Dev., 15, 8395–8410, https://doi.org/10.5194/gmd-15-8395-2022, https://doi.org/10.5194/gmd-15-8395-2022, 2022
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We develop an ensemble-Kalman-filter-based regional ocean data assimilation system in which satellite and in situ observations are assimilated at a daily frequency. We find the best setting for dynamical balance and accuracy based on sensitivity experiments focused on how to inflate the ensemble spread and how to apply the analysis update to the model evolution. This study has a broader impact on more general data assimilation systems in which the initial shocks are a significant issue.
Hector S. Torres, Patrice Klein, Jinbo Wang, Alexander Wineteer, Bo Qiu, Andrew F. Thompson, Lionel Renault, Ernesto Rodriguez, Dimitris Menemenlis, Andrea Molod, Christopher N. Hill, Ehud Strobach, Hong Zhang, Mar Flexas, and Dragana Perkovic-Martin
Geosci. Model Dev., 15, 8041–8058, https://doi.org/10.5194/gmd-15-8041-2022, https://doi.org/10.5194/gmd-15-8041-2022, 2022
Short summary
Short summary
Wind work at the air-sea interface is the scalar product of winds and currents and is the transfer of kinetic energy between the ocean and the atmosphere. Using a new global coupled ocean-atmosphere simulation performed at kilometer resolution, we show that all scales of winds and currents impact the ocean dynamics at spatial and temporal scales. The consequential interplay of surface winds and currents in the numerical simulation motivates the need for a winds and currents satellite mission.
Cited articles
Aas, E.: Two-stream irradiance model for deep waters, Appl. Optics, 26,
2095–2101, 1987. a
Bailey, S. W. and Werdell, P. J.: A multi-sensor approach for the on-orbit
validation of ocean color satellite data products, Remote Sens.
Environ., 102, 12–23, https://doi.org/10.1016/j.rse.2006.01.015, 2006. a
Beaugrand, G., Reid, P. C., Ibañez, F., and Planque, B.: Biodiversity of
North Atlantic and North Sea calanoid copepods, Mar. Ecol. Prog.
Ser., 204, 299–303, 2000. a
Cael, B., Bisson, K., and Follett, C. L.: Can rates of ocean primary production
and biological carbon export be related through their probability
distributions?, Global Biogeochem. Cy., 32, 954–970, 2018. a
Campbell, J. W.: The lognormal distribution as a model for bio‐optical
variability in the sea, J. Geophys. Res.-Oceans, 100,
13237–13254, https://doi.org/10.1029/95jc00458, 1995. a
Campin, J.-M., Heimbach, P., Losch, M., Forget, G., Edhill3, Adcroft, A.,
Amolod, Menemenlis, D., Dfer22, Hill, C., Jahn, O., Scott, J.,
Stephdut, Mazloff, M., Baylor Fox-Kemper, Antnguyen13, Doddridge, E.,
Fenty, I., Bates, M., AndrewEichmann-NOAA, Smith, T., Martin, T.,
Lauderdale, J., Abernathey, R., Samarkhatiwala, Hongandyan, Deremble, B.,
Dngoldberg, Bourgault, P., and Dussin, R.: MITgcm/MITgcm: mid 2020 version, Zenodo [data set],
https://doi.org/10.5281/zenodo.3967889, 2020. a
de Mora, L., Butenschön, M., and Allen, J. I.: The assessment of a global marine ecosystem model on the basis of emergent properties and ecosystem function: a case study with ERSEM, Geosci. Model Dev., 9, 59–76, https://doi.org/10.5194/gmd-9-59-2016, 2016. a
Doney, S. C., Lima, I., Lindsay, K., Moore, J. K., Dutkiewicz, S., Friedrichs,
M. A., and Matear, R. J.: Marine Biogeochemical Modeling: Recent Advances and
Future Challenges, Oceanography, 14, 93–107,
https://doi.org/10.5670/oceanog.2001.10, 2001. a
Doney, S. C., Lima, I., Moore, J. K., Lindsay, K., Behrenfeld, M. J.,
Westberry, T. K., Mahowald, N., Glover, D. M., and Takahashi, T.: Skill
metrics for confronting global upper ocean ecosystem-biogeochemistry models
against field and remote sensing data, J. Marine Syst., 76,
95–112, https://doi.org/10.1016/j.jmarsys.2008.05.015, 2009. a, b
Dutkiewicz, S.: Climate Change Simulation output files (1995–2100), Harvard Dataverse [data set],
https://doi.org/10.7910/DVN/08OJUV, 2018. a, b
Dutkiewicz, S., Hickman, A. E., Jahn, O., Gregg, W. W., Mouw, C. B., and Follows, M. J.: Capturing optically important constituents and properties in a marine biogeochemical and ecosystem model, Biogeosciences, 12, 4447–4481, https://doi.org/10.5194/bg-12-4447-2015, 2015. a, b, c
Dutkiewicz, S., Hickman, A. E., and Jahn, O.: Modelling ocean-colour-derived chlorophyll a, Biogeosciences, 15, 613–630, https://doi.org/10.5194/bg-15-613-2018, 2018. a, b, c, d
Dutkiewicz, S., Hickman, A. E., Jahn, O., Henson, S., Beaulieu, C., and Monier,
E.: Ocean colour signature of climate change, Nat. Commun., 10, 578,
https://doi.org/10.1038/s41467-019-08457-x, 2019. a, b, c, d
Dutkiewicz, S., Baird, M., Chai, F., Ciavatta, S., Edwards, C. A., Evers-King,
H., Friedrichs, M. A. M., Frolov, S., Gehlen, M., Henson, S., Hickman, A.,
Ibrahim, A., Jahn, O., Jones, E., Kaufman, D. E., Mélin, F., Mouw, C.,
Muhling, B., Rousseaux, C., Shulman, I., Stock, C. A., Werdell, P. J., and
Wiggert, J. D.: Synergy between Ocean Colour and Biogeochemical/Ecosystem
Models., Tech. Rep., 19, IOCCG, https://doi.org/10.25607/OBP-711, 2020a. a
Dutkiewicz, S., Cermeno, P., Jahn, O., Follows, M. J., Hickman, A. E., Taniguchi, D. A. A., and Ward, B. A.: Dimensions of marine phytoplankton diversity, Biogeosciences, 17, 609–634, https://doi.org/10.5194/bg-17-609-2020, 2020b. a, b
Dutkiewicz, S., Boyd, P. W., and Riebesell, U.: Exploring biogeochemical and
ecological redundancy in phytoplankton communities in the global ocean,
Glob. Change Biol., 27, 1196–1213, https://doi.org/10.1111/gcb.15493, 2021. a, b, c
Edwards, P. N.: History of climate modeling, WIRES
Clim. Change, 2, 128–139, https://doi.org/10.1002/wcc.95, 2011. a
Follett, C. L., Dutkiewicz, S., Forget, G., Cael, B. B., and Follows, M. J.:
Moving Ecological and Biogeochemical Transitions Across the North Pacific,
Limnol. Oceanogr., 66, 2442–2454, https://doi.org/10.1002/lno.11763, 2021. a
Forget, G. and Ferreira, D.: Global ocean heat transport dominated by heat
export from the tropical Pacific, Nat. Geosci., 12, 351–354,
https://doi.org/10.1038/s41561-019-0333-7, 2019. a, b
Forget, G. and Ponte, R. M.: The partition of regional sea level variability,
Prog. Oceanogr., 137, 173–195, https://doi.org/10.1016/j.pocean.2015.06.002,
2015. a, b, c
Forget, G. and Wunsch, C.: Estimated Global Hydrographic Variability, J.
Phys. Oceanogr., 37, 1997–2008, https://doi.org/10.1175/jpo3072.1, 2007. a
Forget, G., Campin, J.-M., Heimbach, P., Hill, C. N., Ponte, R. M., and Wunsch, C.: ECCO version 4: an integrated framework for non-linear inverse modeling and global ocean state estimation, Geosci. Model Dev., 8, 3071–3104, https://doi.org/10.5194/gmd-8-3071-2015, 2015. a, b, c
Frogner, C., Zhang, C., Mobahi, H., Araya-Polo, M., and Poggio, T.: Learning
with a Wasserstein Loss, in: Proceedings of the 28th International Conference
on Neural Information Processing Systems – Volume 2, NIPS'15,
MIT Press, Cambridge, MA, USA, 2053–2061, https://doi.org/10.48550/arXiv.1506.05439, 2015. a
Geider, R. J., MacIntyre, H. L., and Kana, T. M.: A dynamic regulatory model
of phytoplanktonic acclimation to light, nutrients, and temperature,
Limnol. Oceanogr., 43, 679–694, https://doi.org/10.4319/lo.1998.43.4.0679,
1998. a
Gordon, H. R.: Irradiance attenuation coefficient in a stratified ocean: a
local property of the medium, Appl. Optics, 19, 2092,
https://doi.org/10.1364/ao.19.002092, 1980. a
Hague, M. and Vichi, M.: A Link Between CMIP5 Phytoplankton Phenology and Sea
Ice in the Atlantic Southern Ocean, Geophys. Res. Lett., 45,
6566–6575, https://doi.org/10.1029/2018gl078061, 2018. a
Hansen, P. J., Bjørnsen, P. K., and Hansen, B. W.: Zooplankton grazing and
growth: Scaling within the 2-2,-µm body size range, Limnol.
Oceanogr., 42, 687–704, https://doi.org/10.4319/lo.1997.42.4.0687, 1997. a
Henson, S. A., Dunne, J. P., and Sarmiento, J. L.: Decadal variability in
North Atlantic phytoplankton blooms, J. Geophys. Res.-Oceans, 114, C04013, https://doi.org/10.1029/2008JC005139, 2009. a
Holling, C. S.: Some Characteristics of Simple Types of Predation and
Parasitism, Can. Entomol., 91, 385–398,
https://doi.org/10.4039/Ent91385-7, 1959. a
Jackson, T., Sathyendranath, S., and Mélin, F.: An improved optical
classification scheme for the Ocean Colour Essential Climate Variable and its
applications, Remote Sens. Environ., 203, 152–161,
https://doi.org/10.1016/j.rse.2017.03.036, 2017. a
Jolliff, J. K., Kindle, J. C., Shulman, I., Penta, B., Friedrichs, M. A.,
Helber, R., and Arnone, R. A.: Summary diagrams for coupled
hydrodynamic-ecosystem model skill assessment, J. Marine Syst., 76,
64–82, https://doi.org/10.1016/j.jmarsys.2008.05.014, 2009. a
Jönsson, B.: Example code for processing, statistics, and analysis of data
stored as histograms, Zenodo [code and data set], https://doi.org/10.5281/zenodo.6683849, 2022. a
Jönsson, B. F. and Salisbury, J. E.: Episodicity in phytoplankton dynamics
in a coastal region, Geophys. Res. Lett., 43, 5821–5828,
https://doi.org/10.1002/2016GL068683, 2016. a, b
Jonsson, B. F., Doney, S., Dunne, J., and Bender, M. L.: Evaluating Southern Ocean biological production in two ocean biogeochemical models on daily to seasonal timescales using satellite chlorophyll and
Jönsson, B. F., Sathyendranath, S., and Platt, T.: Trends in Winter Light
Environment Over the Arctic Ocean: A Perspective From Two Decades of Ocean
Color Data, Geophys. Res. Lett., 47, e2020GL089037,
https://doi.org/10.1029/2020GL089037, 2020a. a
Jönsson, B. F., Sathyendranath, S., and Platt, T.: Trends in Winter Light
Environment Over the Arctic Ocean: A Perspective From Two Decades of Ocean
Color Data, Geophys. Res. Lett., 47, e2020GL089037,
https://doi.org/10.1029/2020GL089037, 2020b. a
Jönsson, B. F., Salisbury, J., Atwood, E. C., Sathyendranath, S., and
Mahadevan, A.: Dominant timescales of variability in global satellite
chlorophyll and SST revealed with a MOving Standard deviation Saturation
(MOSS) approach, Remote Sens. Environ., 286, 113404,
https://doi.org/10.1016/j.rse.2022.113404, 2023. a
Kiørboe, T.: A Mechanistic Approach to Plankton Ecology, Princeton
University Press, https://doi.org/10.1515/9780691190310, 2019. a
Lee, Z. and Hu, C.: Global distribution of Case-1 waters: An analysis from
SeaWiFS measurements, Remote Sens. Environ., 101, 270–276,
https://doi.org/10.1016/j.rse.2005.11.008, 2006. a
Lee, Z., Carder, K. L., and Arnone, R. A.: Deriving inherent optical properties
from water color: a multiband quasi-analytical algorithm for optically deep
waters, Appl. Optics, 41, 5755–5772, 2002. a
Levina, E. and Bickel, P.: The Earth Mover's distance is the Mallows distance: some insights from statistics, in: Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001, Vancouver, BC, Canada, 2001, vol. 2, 251–256, https://doi.org/10.1109/ICCV.2001.937632, 2001. a
Mayner, W., Louf, R., and Braun, V.: pyemd: 0.2.0, Zenodo [code], https://doi.org/10.5281/zenodo.33535,
2015. a
Mélin, F., Vantrepotte, V., Chuprin, A., Grant, M., Jackson, T., and
Sathyendranath, S.: Assessing the fitness-for-purpose of satellite
multi-mission ocean color climate data records: A protocol applied to
OC-CCI chlorophyll-a data, Remote Sens. Environ., 203, 139–151,
https://doi.org/10.1016/j.rse.2017.03.039, 2017. a
Monge, G.: Mémoire sur la théorie des déblais et des remblais, De
l'Imprimerie Royale, https://www.worldcat.org/title/memoire-sur-la-theorie-des-deblais-et-des-remblais/oclc/51928110 (last access: 7 July 2023), 1781. a
Moore, T. S., Campbell, J. W., and Dowell, M. D.: A class-based approach to
characterizing and mapping the uncertainty of the MODIS ocean chlorophyll
product, Remote Sens. Environ., 113, 2424–2430,
https://doi.org/10.1016/j.rse.2009.07.016, 2009. a
Moore, T. S., Dowell, M. D., and Franz, B. A.: Detection of coccolithophore
blooms in ocean color satellite imagery: A generalized approach for use with
multiple sensors, Remote Sens. Environ., 117, 249–263,
https://doi.org/10.1016/j.rse.2011.10.001, 2012. a
Morel, A. and Prieur, L.: Analysis of variations in ocean color1: Ocean color
analysis, Limnol. Oceanogr., 22, 709–722,
https://doi.org/10.4319/lo.1977.22.4.0709, 1977. a
Oliver, E. C., Benthuysen, J. A., Darmaraki, S., Donat, M. G., Hobday, A. J.,
Holbrook, N. J., Schlegel, R. W., and Gupta, A. S.: Marine Heatwaves, Annu.
Rev. Mar. Sci., 13, 313–342,
https://doi.org/10.1146/annurev-marine-032720-095144, 2021. a
O'Reilly, J. E., Maritorena, S., Mitchell, B. G., Siegel, D. A., Carder, K. L.,
Garver, S. A., Kahru, M., and McClain, C.: Ocean color chlorophyll algorithms
for SeaWiFS, J. Geophys. Res.-Oceans, 103, 24937–24953,
https://doi.org/10.1029/98JC02160, 1998. a, b
Orlova, D. Y., Zimmerman, N., Meehan, S., Meehan, C., Waters, J., Ghosn, E.
E. B., Filatenkov, A., Kolyagin, G. A., Gernez, Y., Tsuda, S., Moore, W.,
Moss, R. B., Herzenberg, L. A., and Walther, G.: Earth Mover's Distance
(EMD): A True Metric for Comparing Biomarker Expression Levels in Cell
Populations, PLOS ONE, 11, e0151859,
https://doi.org/10.1371/journal.pone.0151859, 2016. a
Pele, O. and Werman, M.: A Linear Time Histogram Metric for Improved SIFT
Matching, in: Computer Vision – ECCV 2008, ECCV 2008, Lecture Notes in Computer Science, vol. 5304, edited by: Forsyth, D., Torr, P., and Zisserman, A., Springer, Berlin, Heidelberg, https://doi.org/10.1007/978-3-540-88690-7_37, 2008. a
Pele, O. and Werman, M.: Fast and Robust Earth Mover's Distances, in: 2009 IEEE 12th International Conference on Computer Vision, Kyoto, Japan, 2009, 460–467, https://doi.org/10.1109/ICCV.2009.5459199,
2009. a
Raspaud, M., Hoese, D., Lahtinen, P., Dybbroe, A., Finkensieper, S., Roberts,
W., Rasmussen, L. O., Proud, S., Joro, S., Daruwala, R., Holl, G., Jasmin,
T., BENR0, Leppelt, T., Egede, U., R.K.Garcia, Itkin, M., LTMeyer,
Sigurdsson, E., Radar, S., Division, N., Aspenes, T., Hazbottles,
ColinDuff, Joleenf, , Cody, Clementi, L., Honnorat, M., Schulz, H.,
Hatt, B., and Valentino, A.: pytroll/satpy: Version 0.16.0, Zenodo [code],
https://doi.org/10.5281/zenodo.3250583, 2019. a
Reygondeau, G., Longhurst, A., Martinez, E., Beaugrand, G., Antoine, D., and
Maury, O.: Dynamic biogeochemical provinces in the global ocean, Global
Biogeochem. Cy., 27, 1046–1058, https://doi.org/10.1002/gbc.20089, 2013. a, b
Rubner, Y., Tomasi, C., and Guibas, L. J.: Earth mover's distance as a metric
for image retrieval, Int. J. Comput. Vision, 40, 99–121,
https://doi.org/10.1023/A:1026543900054, 2000. a
Sathyendranath, S. and Platt, T.: Remote sensing of ocean chlorophyll:
consequence of nonuniform pigment profile, Appl. Optics, 28, 490–495,
https://doi.org/10.1364/ao.28.000490, 1989. a
Sathyendranath, S., Brewin, R., Brockmann, C., Brotas, V., Calton, B., Chuprin,
A., Cipollini, P., Couto, A., Dingle, J., Doerffer, R., Donlon, C., Dowell,
M., Farman, A., Grant, M., Groom, S., Horseman, A., Jackson, T., Krasemann,
H., Lavender, S., Martinez-Vicente, V., Mazeran, C., Mélin, F., Moore,
T., Müller, D., Regner, P., Roy, S., Steele, C., Steinmetz, F., Swinton,
J., Taberner, M., Thompson, A., Valente, A., Zühlke, M., Brando, V.,
Feng, H., Feldman, G., Franz, B., Frouin, R., Gould, R., Hooker, S., Kahru,
M., Kratzer, S., Mitchell, B., Muller-Karger, F., Sosik, H., Voss, K.,
Werdell, J., and Platt, T.: An Ocean-Colour Time Series for Use in Climate
Studies: The Experience of the Ocean-Colour Climate Change Initiative
(OC-CCI), Sensors, 19, 4285, https://doi.org/10.3390/s19194285, 2019. a, b
Sathyendranath, S., Jackson, T., Brockmann, C., Brotas, V., Calton, B.,
Chuprin, A., Clements, O., Cipollini, P., Danne, O., Dingle, J., Donlon, C.,
Grant, M., Groom, S., Krasemann, H., Lavender, S., Mazeran, C., Mélin,
F., Moore, T. S., Müller, D., Regner, P., Steinmetz, F., Steele, C.,
Swinton, J., Valente, A., Zühlke, M., Feldman, G., Franz, B., Frouin, R.,
Werdell, J., and Platt, T.: ESA Ocean Colour Climate Change Initiative
(Ocean_Colour_cci): Version 4.2 Data, CEDA Archive [data set],
https://doi.org/10.5285/D62F7F801CB54C749D20E736D4A1039F, 2020. a, b
Schartau, M., Landry, M. R., and Armstrong, R. A.: Density estimation of
plankton size spectra: A reanalysis of IronEx II data, J. Plankton
Res., 32, 1167–1184, https://doi.org/10.1093/plankt/fbq072, 2010. a
Séférian, R., Berthet, S., Yool, A., Palmiéri, J., Bopp, L.,
Tagliabue, A., Kwiatkowski, L., Aumont, O., Christian, J., Dunne, J., and
et al.: Tracking Improvement in Simulated Marine Biogeochemistry Between
CMIP5 and CMIP6, Current Climate Change Reports, 6, 95–119,
https://doi.org/10.1007/s40641-020-00160-0, 2020. a
Sommer, U., Charalampous, E., Genitsaris, S., and Moustaka-Gouni, M.:
Benefits, costs and taxonomic distribution of marine phytoplankton body
size, J. Plankton Res., 39, 494–508, https://doi.org/10.1093/plankt/fbw071, 2017. a
Steinmetz, F., Deschamps, P.-Y., and Ramon, D.: Atmospheric correction in
presence of sun glint: application to MERIS, Opt. Express, 19, 9783,
https://doi.org/10.1364/oe.19.009783, 2011. a
Stow, C. A., Jolliff, J., McGillicuddy, D. J., Doney, S. C., Allen, J. I.,
Friedrichs, M. A., Rose, K. A., and Wallhead, P.: Skill assessment for
coupled biological/physical models of marine systems, J. Marine
Syst., 76, 4–15, https://doi.org/10.1016/j.jmarsys.2008.03.011, 2009. a
Taylor, K. E.: Summarizing multiple aspects of model performance in a single
diagram, J. Geophys. Res.-Atmos., 106, 7183–7192,
https://doi.org/10.1029/2000jd900719, 2001. a, b, c
Valente, A., Sathyendranath, S., Brotas, V., Groom, S., Grant, M., Taberner, M., Antoine, D., Arnone, R., Balch, W. M., Barker, K., Barlow, R., Bélanger, S., Berthon, J.-F., Beşiktepe, Ş., Borsheim, Y., Bracher, A., Brando, V., Canuti, E., Chavez, F., Cianca, A., Claustre, H., Clementson, L., Crout, R., Frouin, R., García-Soto, C., Gibb, S. W., Gould, R., Hooker, S. B., Kahru, M., Kampel, M., Klein, H., Kratzer, S., Kudela, R., Ledesma, J., Loisel, H., Matrai, P., McKee, D., Mitchell, B. G., Moisan, T., Muller-Karger, F., O'Dowd, L., Ondrusek, M., Platt, T., Poulton, A. J., Repecaud, M., Schroeder, T., Smyth, T., Smythe-Wright, D., Sosik, H. M., Twardowski, M., Vellucci, V., Voss, K., Werdell, J., Wernand, M., Wright, S., and Zibordi, G.: A compilation of global bio-optical in situ data for ocean-colour satellite applications – version two, Earth Syst. Sci. Data, 11, 1037–1068, https://doi.org/10.5194/essd-11-1037-2019, 2019a. a
Valente, A., Sathyendranath, S., Brotas, V., Groom, S., Grant, M., Taberner,
M., Antoine, D., Arnone, R., Balch, W. M., Barker, K., Barlow, R. G.,
Bélanger, S., Berthon, J.-F., Besiktepe, S., Borsheim, K. Y., Bracher,
A., Brando, V. E., Canuti, E., Chavez, F. P., Cianca, A., Claustre, H.,
Clementson, L., Crout, R., Frouin, R., García-Soto, C., Gibb, S. W.,
Gould, R., Hooker, S. B., Kahru, M., Kampel, M., Klein, H., Kratzer, S.,
Kudela, R. M., Ledesma, S., Loisel, H., Matrai, P. A., McKee, D., Mitchell,
B. G., Moisan, T., Muller-Karger, F. E., O'Dowd, L., Ondrusek, M., Platt, T.,
Poulton, A. J., Repecaud, M., Schroeder, T., Smyth, T. J., Smythe-Wright, D.,
Sosik, H., Twardowski, M. S., Vellucci, V., Voss, K., Werdell, P. J.,
Wernand, M. R., Wright, S., and Zibordi, G.: A compilation of global
bio-optical in situ data for ocean-colour satellite applications – version
two, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.898188, 2019b. a
Vaserstein, L. N.: Markov Processes over Denumerable Products of Spaces,
Describing Large Systems of Automata, Probl. Peredachi Inf., 5, 64–72, https://www.mathnet.ru/php/archive.phtml?wshow=paper&jrnid=ppi&paperid=1811&option_lang=eng (last access: 7 July 2023), 1969.
a
Vichi, M., Allen, J. I., Masina, S., and Hardman‐Mountford, N. J.: The
emergence of ocean biogeochemical provinces: A quantitative assessment and a
diagnostic for model evaluation, Global Biogeochem. Cy., 25, GB2005,
https://doi.org/10.1029/2010gb003867, 2011. a
Short summary
While biogeochemical models and satellite-derived ocean color data provide unprecedented information, it is problematic to compare them. Here, we present a new approach based on comparing probability density distributions of model and satellite properties to assess model skills. We also introduce Earth mover's distances as a novel and powerful metric to quantify the misfit between models and observations. We find that how 3D chlorophyll fields are aggregated can be a significant source of error.
While biogeochemical models and satellite-derived ocean color data provide unprecedented...
