Articles | Volume 10, issue 3
https://doi.org/10.5194/os-10-323-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/os-10-323-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| Highlight paper
| 
09 May 2014
Research article | Highlight paper |
| 09 May 2014
Adapting to life: ocean biogeochemical modelling and adaptive remeshing
Applied Modelling and Computation Group, Department of Earth Science and Engineering, Imperial College London, SW7 2AZ, UK
E. E. Popova
National Oceanography Centre, Southampton, University of Southampton Waterfront Campus, European Way, Southampton, SO14 3ZH, UK
D. A. Ham
Applied Mathematics and Mathematical Physics, Department of Mathematics, Imperial College London, SW7 2AZ, UK
M. D. Piggott
Applied Modelling and Computation Group, Department of Earth Science and Engineering, Imperial College London, SW7 2AZ, UK
Grantham Institute for Climate Change, Imperial College London, SW7 2AZ, UK
M. Srokosz
National Oceanography Centre, Southampton, University of Southampton Waterfront Campus, European Way, Southampton, SO14 3ZH, UK
Related authors
Samuel D. Parkinson, Simon W. Funke, Jon Hill, Matthew D. Piggott, and Peter A. Allison
Geosci. Model Dev., 10, 1051–1068, https://doi.org/10.5194/gmd-10-1051-2017, https://doi.org/10.5194/gmd-10-1051-2017, 2017
A. S. Candy, A. Avdis, J. Hill, G. J. Gorman, and M. D. Piggott
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmdd-7-5993-2014, https://doi.org/10.5194/gmdd-7-5993-2014, 2014
Revised manuscript has not been submitted
S. D. Parkinson, J. Hill, M. D. Piggott, and P. A. Allison
Geosci. Model Dev., 7, 1945–1960, https://doi.org/10.5194/gmd-7-1945-2014, https://doi.org/10.5194/gmd-7-1945-2014, 2014
Davor Dundovic, Joseph G. Wallwork, Stephan C. Kramer, Fabien Gillet-Chaulet, Regine Hock, and Matthew D. Piggott
EGUsphere, https://doi.org/10.5194/egusphere-2024-2649, https://doi.org/10.5194/egusphere-2024-2649, 2024
Short summary
Short summary
Accurate numerical studies of glaciers often require high-resolution simulations, which often prove too demanding even for modern computers. In this paper we develop a method that identifies whether different parts of a glacier require high or low resolution based on its physical features, such as its thickness and velocity. We show that by doing so we can achieve a more optimal simulation accuracy for the available computing resources compared to uniform resolution simulations.
Reuben W. Nixon-Hill, Daniel Shapero, Colin J. Cotter, and David A. Ham
Geosci. Model Dev., 17, 5369–5386, https://doi.org/10.5194/gmd-17-5369-2024, https://doi.org/10.5194/gmd-17-5369-2024, 2024
Short summary
Short summary
Scientists often use models to study complex processes, like the movement of ice sheets, and compare them to measurements for estimating quantities that are hard to measure. We highlight an approach that ensures accurate results from point data sources (e.g. height measurements) by evaluating the numerical solution at true point locations. This method improves accuracy, aids communication between scientists, and is well-suited for integration with specialised software that automates processes.
Sia Ghelichkhan, Angus Gibson, D. Rhodri Davies, Stephan C. Kramer, and David A. Ham
Geosci. Model Dev., 17, 5057–5086, https://doi.org/10.5194/gmd-17-5057-2024, https://doi.org/10.5194/gmd-17-5057-2024, 2024
Short summary
Short summary
We introduce the Geoscientific ADjoint Optimisation PlaTform (G-ADOPT), designed for inverse modelling of Earth system processes, with an initial focus on mantle dynamics. G-ADOPT is built upon Firedrake, Dolfin-Adjoint and the Rapid Optimisation Library, which work together to optimise models using an adjoint method, aligning them with seismic and geologic datasets. We demonstrate G-ADOPT's ability to reconstruct mantle evolution and thus be a powerful tool in geosciences.
Shuaib Rasheed, Simon C. Warder, Yves Plancherel, and Matthew D. Piggott
Nat. Hazards Earth Syst. Sci., 24, 737–755, https://doi.org/10.5194/nhess-24-737-2024, https://doi.org/10.5194/nhess-24-737-2024, 2024
Short summary
Short summary
Here we use a high-resolution bathymetry dataset of the Maldives archipelago, as well as corresponding high numerical model resolution, to carry out a scenario-based tsunami hazard assessment for the entire Maldives archipelago to investigate the potential impact of plausible far-field tsunamis across the Indian Ocean at the island scale. The results indicate that several factors contribute to mitigating and amplifying tsunami waves at the island scale.
Jennifer R. Shadrick, Dylan H. Rood, Martin D. Hurst, Matthew D. Piggott, Klaus M. Wilcken, and Alexander J. Seal
Earth Surf. Dynam., 11, 429–450, https://doi.org/10.5194/esurf-11-429-2023, https://doi.org/10.5194/esurf-11-429-2023, 2023
Short summary
Short summary
This study uses a coastal evolution model to interpret cosmogenic beryllium-10 concentrations and topographic data and, in turn, quantify long-term cliff retreat rates for four chalk sites on the south coast of England. By using a process-based model, clear distinctions between intertidal weathering rates have been recognised between chalk and sandstone rock coast sites, advocating the use of process-based models to interpret the long-term behaviour of rock coasts.
Mariana C. A. Clare, Tim W. B. Leijnse, Robert T. McCall, Ferdinand L. M. Diermanse, Colin J. Cotter, and Matthew D. Piggott
Nat. Hazards Earth Syst. Sci., 22, 2491–2515, https://doi.org/10.5194/nhess-22-2491-2022, https://doi.org/10.5194/nhess-22-2491-2022, 2022
Short summary
Short summary
Assessing uncertainty is computationally expensive because it requires multiple runs of expensive models. We take the novel approach of assessing uncertainty from coastal flooding using a multilevel multifidelity (MLMF) method which combines the efficiency of less accurate models with the accuracy of more expensive models at different resolutions. This significantly reduces the computational cost but maintains accuracy, making previously unfeasible real-world studies possible.
Jennifer R. Shadrick, Martin D. Hurst, Matthew D. Piggott, Bethany G. Hebditch, Alexander J. Seal, Klaus M. Wilcken, and Dylan H. Rood
Earth Surf. Dynam., 9, 1505–1529, https://doi.org/10.5194/esurf-9-1505-2021, https://doi.org/10.5194/esurf-9-1505-2021, 2021
Short summary
Short summary
Here we use topographic and 10Be concentration data to optimise a coastal evolution model. Cliff retreat rates are calculated for two UK sites for the past 8000 years and, for the first time, highlight a strong link between the rate of sea level rise and long-term cliff retreat rates. This method enables us to study past cliff response to sea level rise and so to greatly improve forecasts of future responses to accelerations in sea level rise that will result from climate change.
Shuaib Rasheed, Simon C. Warder, Yves Plancherel, and Matthew D. Piggott
Ocean Sci., 17, 319–334, https://doi.org/10.5194/os-17-319-2021, https://doi.org/10.5194/os-17-319-2021, 2021
Short summary
Short summary
Environmental issues arising due to coastal modification and future sea level scenarios are a major environmental hazard facing the Maldives today. Here, we carry out high-resolution tidal modelling of a Maldivian atoll for the first time and show that coastal modification in the island scale is capable of driving large-scale change in the wider atoll basin in a short time, comparable to that of long-term sea level rise scenarios and on par with observations.
Jessica Cartwright, Christopher J. Banks, and Meric Srokosz
The Cryosphere, 14, 1909–1917, https://doi.org/10.5194/tc-14-1909-2020, https://doi.org/10.5194/tc-14-1909-2020, 2020
Short summary
Short summary
This study uses reflected GPS signals to measure ice at the South Pole itself for the first time. These measurements are essential to understand the interaction of the ice with the Earth’s physical systems. Orbital constraints mean that satellites are usually unable to measure in the vicinity of the South Pole itself. This is overcome here by using data obtained by UK TechDemoSat-1. Data are processed to obtain the height of glacial ice across the Greenland and Antarctic ice sheets.
Thomas H. Gibson, Lawrence Mitchell, David A. Ham, and Colin J. Cotter
Geosci. Model Dev., 13, 735–761, https://doi.org/10.5194/gmd-13-735-2020, https://doi.org/10.5194/gmd-13-735-2020, 2020
Short summary
Short summary
Galerkin finite element discretizations for atmospheric modeling often require the solution of ill-conditioned, saddle point equations which can be efficiently solved using a hybridized method. By extending Firedrake's domain-specific abstraction, we provide a mechanism for the rapid implementation of hybridization methods for a wide class of methods. In this paper, we show that hybridization is an effective alternative to traditional block solvers for simulating geophysical flows.
Tuomas Kärnä, Stephan C. Kramer, Lawrence Mitchell, David A. Ham, Matthew D. Piggott, and António M. Baptista
Geosci. Model Dev., 11, 4359–4382, https://doi.org/10.5194/gmd-11-4359-2018, https://doi.org/10.5194/gmd-11-4359-2018, 2018
Short summary
Short summary
Unstructured meshes are attractive for coastal ocean modeling, as they allow more accurate representation of complex coastal topography. Unstructured mesh models are, however, often perceived as slow and inaccurate. We present a novel discontinuous Galerkin ocean model: Thetis. We demonstrate that the model is able to simulate baroclinic ocean flows with high accuracy on a triangular prismatic mesh. This work paves the way for highly accurate and efficient three-dimensional coastal ocean models.
Samuel D. Parkinson, Simon W. Funke, Jon Hill, Matthew D. Piggott, and Peter A. Allison
Geosci. Model Dev., 10, 1051–1068, https://doi.org/10.5194/gmd-10-1051-2017, https://doi.org/10.5194/gmd-10-1051-2017, 2017
Gheorghe-Teodor Bercea, Andrew T. T. McRae, David A. Ham, Lawrence Mitchell, Florian Rathgeber, Luigi Nardi, Fabio Luporini, and Paul H. J. Kelly
Geosci. Model Dev., 9, 3803–3815, https://doi.org/10.5194/gmd-9-3803-2016, https://doi.org/10.5194/gmd-9-3803-2016, 2016
Short summary
Short summary
Unstructured meshes offer flexibility but are perceived as slow. Some applications, including atmosphere or ocean simulations, admit an extruded mesh: the horizontal mesh may be unstructured, but the vertical dimension can be traversed in a structured way. By extending the Firedrake automated simulation framework to this case, we show that an extruded mesh can be traversed as fast as a structured mesh. This paves the way for highly efficient unstructured mesh models of the ocean and atmosphere.
C. T. Jacobs and M. D. Piggott
Geosci. Model Dev., 8, 533–547, https://doi.org/10.5194/gmd-8-533-2015, https://doi.org/10.5194/gmd-8-533-2015, 2015
A. S. Candy, A. Avdis, J. Hill, G. J. Gorman, and M. D. Piggott
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmdd-7-5993-2014, https://doi.org/10.5194/gmdd-7-5993-2014, 2014
Revised manuscript has not been submitted
S. D. Parkinson, J. Hill, M. D. Piggott, and P. A. Allison
Geosci. Model Dev., 7, 1945–1960, https://doi.org/10.5194/gmd-7-1945-2014, https://doi.org/10.5194/gmd-7-1945-2014, 2014
M. E. Rognes, D. A. Ham, C. J. Cotter, and A. T. T. McRae
Geosci. Model Dev., 6, 2099–2119, https://doi.org/10.5194/gmd-6-2099-2013, https://doi.org/10.5194/gmd-6-2099-2013, 2013
Cited articles
Anderson, T. R. and Pondaven, P.: Non-redfield carbon and nitrogen cycling in the Sargasso Sea: pelagic imbalances and export flux, Deep Sea Res.-Pt. I, 50, 573–591, https://doi.org/10.1016/S0967-0637(03)00034-7, 2003.
Berline, L., Brankart, J.-M., Brasseur, P., Ourmières, Y., and Verron, J.: Improving the physics of a coupled physical–biogeochemical model of the North Atlantic through data assimilation: Impact on the ecosystem, J. Mar. Syst., 64, 153–172, https://doi.org/10.1016/j.jmarsys.2006.03.007, 2007.
Burchard, H. and Beckers, J.-M.: Non-uniform adaptive vertical grids in one-dimensional numerical ocean models, Ocean Model., 6, 51–81, https://doi.org/10.1016/S1463-5003(02)00060-4, 2004.
Burchard, H. and Bolding, K.: Comparative Analysis of Four Second-Moment Turbulence Closure Models for the Oceanic Mixed Layer, J. Phys. Oceanogr., 31, 1943–1968, https://doi.org/10.1175/1520-0485(2001)031<1943:CAOFSM>2.0.CO;2, 2001.
Denman, K. and Miyake, M.: Upper Layer Modification at Ocean Station Papa: Observations and Simulation, J. Phys. Oceanogr., 3, 185–196, 1973.
Farrell, P., Piggott, M., Pain, C., Gorman, G., and Wilson, C.: Conservative interpolation between unstructured meshes via supermesh construction, Comput. Method. Appl. M., 198, 2632–2642, https://doi.org/10.1016/j.cma.2009.03.004, 2009.
Ford, R., Pain, C. C., Piggott, M. D., Goddard, A. J. H., de Oliveira, C. R. E., and Umpleby, A. P.: A Nonhydrostatic Finite-Element Model for Three-Dimensional Stratified Oceanic Flows. Part I: Model Formulation, Mon. Weather Rev., 132, 2816–2831, https://doi.org/10.1175/MWR2824.1, 2004.
Friedrichs, M. A., Hood, R. R., and Wiggert, J. D.: Ecosystem model complexity versus physical forcing: Quantification of their relative impact with assimilated Arabian Sea data, Deep Sea Res.-Pt. II, 53, 576–600, https://doi.org/10.1016/j.dsr2.2006.01.026, 2006.
Gaspar, P., Grégoris, Y., Lefevre, J.-M., and Goris, Y. G. R. I.: A Simple Eddy Kinetic Energy Model for Simulations of the Oceanic Vertical Mixing: Tests at Station Papa and Long-Term Upper Ocean Study Site, J. Geophys. Res., 95, 16179–16193, https://doi.org/10.1029/JC095iC09p16179, 1990.
Hanert, E., Deleersnijder, E., and Legat, V.: An adaptive finite element water column model using the Mellor-Yamada level 2.5 turbulence closure scheme, Ocean Model., 12, 205–223, https://doi.org/10.1016/j.ocemod.2005.05.003, 2006.
Hiester, H., Piggott, M., and Allison, P.: The impact of mesh adaptivity on the gravity current front speed in a two-dimensional lock-exchange, Ocean Model., 38, 1–21, https://doi.org/10.1016/j.ocemod.2011.01.003, 2011.
Hill, J., Piggott, M., Ham, D., Popova, E., and Srokosz, M.: On the performance of a generic length scale turbulence model within an adaptive finite element ocean model, Ocean Model., 56, 1–15, https://doi.org/10.1016/j.ocemod.2012.07.003, 2012.
Hofmeister, R., Burchard, H., and Beckers, J.-M.: Non-uniform adaptive vertical grids for 3D numerical ocean models, Ocean Model., 33, 70–86, https://doi.org/10.1016/j.ocemod.2009.12.003, 2010.
Ji, R., Davis, C., Chen, C., and Beardsley, R.: Influence of local and external processes on the annual nitrogen cycle and primary productivity on Georges Bank: A 3-D biological–physical modeling study, J. Mar. Syst., 73, 31–47, https://doi.org/10.1016/j.jmarsys.2007.08.002, 2008.
Kettle, H. and Merchant, C. J.: Modeling ocean primary production: Sensitivity to spectral resolution of attenuation and absorption of light, Prog. Oceanogr., 78, 135–146, https://doi.org/10.1016/j.pocean.2008.04.002, 2008.
Khangaonkar, T., Sackmann, B., Long, W., Mohamedali, T., and Roberts, M.: Simulation of annual biogeochemical cycles of nutrient balance, phytoplankton bloom(s), and DO in Puget Sound using an unstructured grid model, Ocean Dynam., 62, 1353–1379, https://doi.org/10.1007/s10236-012-0562-4, 2012.
Kleypas, J. A. and Doney, S. C.: Nutrients, Chlorophyll, Primary Production and related Biogeochemical Properties in the Ocean Mixed Layer, Tech. Rep. TN-447+STR, NCAR, available at: http://rda.ucar.edu/datasets/ds259.0/ (last access: 8 May 2014), 2001.
Large, W. G. and Yeager, S. G.: Diurnal to decadal global forcing for ocean and sea-ice models: The data sets and flux climatologies, Tech. Rep. Technical Report TN-460+STR, NCAR, 2004.
Lévy, M., Iovino, D., Resplandy, L., Klein, P., Madec, G., Tréguier, A.-M., Masson, S., and Takahashi, K.: Large-scale impacts of submesoscale dynamics on phytoplankton: Local and remote effects, Ocean Model., 43-44, 77–93, https://doi.org/10.1016/j.ocemod.2011.12.003, 2012.
Loseille, A. and Alauzet, F.: Continuous Mesh Framework Part II: Validations and Applications, SIAM J. Numer. Anal., 49, 61–86, https://doi.org/10.1137/10078654X, 2011.
Luo, L., Wang, J., Schwab, D. J., Vanderploeg, H., Leshkevich, G., Bai, X., Hu, H., and Wang, D.: Simulating the 1998 spring bloom in Lake Michigan using a coupled physical-biological model, J. Geophys. Res.-Oceans, 117, C10011, https://doi.org/10.1029/2012JC008216, 2012.
Madec, G.: NEMO ocean engine, Note du Pole de modélisation, Institut Pierre-Simon Laplace (IPSL), France, No. 27 ISSN No 1288-1619, 2008.
McGillicuddy, D. J., Anderson, L. A., Doney, S. C., and Maltrud, M. E.: Eddy-driven sources and sinks of nutrients in the upper ocean: Results from a 0.1° resolution model of the North Atlantic, Global Biogeochem. Cy., 17, 1035, https://doi.org/10.1029/2002GB001987, 2003.
Menendez, A. N., Badano, N. D., Lopolito, M. F., and Re, M.: Water quality assessment for a coastal zone through numerical modeling, J. Appl. Water Eng. Res., 1, 8–16, https://doi.org/10.1080/23249676.2013.827892, 2013.
Oschlies, A.: Can eddies make ocean deserts bloom?, Global Biogeochem. Cy., 16, 1106, https://doi.org/10.1029/2001GB001830, 2002.
Oschlies, A. and Garçon, V.: An eddy-permitting coupled physical-biological model of the North Atlantic: 1. Sensitivity to advection numerics and mixed layer physics, Global Biogeochem. Cy., 13, 135–160, https://doi.org/10.1029/98GB02811, 1999.
Pain, C. C., Umpleby, A. P., de Oliveira, C. R. E., and Goddard, A. J. H.: Tetrahedral mesh optimisation and adaptivity for steady-state and transient finite element calculations, Comput. Method Appl. M., 190, 3771–3796, https://doi.org/10.1016/S0045-7825(00)00294-2, 2001.
Pain, C., Piggott, M., Goddard, A., Fang, F., Gorman, G., Marshall, D., Eaton, M., Power, P., and de Oliveira, C.: Three-dimensional unstructured mesh ocean modelling, Ocean Model., 10, 5–33, https://doi.org/10.1016/j.ocemod.2004.07.005, the Second International Workshop on Unstructured Mesh Numerical Modelling of Coastal, Shelf and Ocean Flows, 2005.
Pichelina, É., Fortina, M., and Boivinb, S.: Étude numérique d'estimations d'erreur a posteriori, Revue Européenne des Éléments, 9, 467–486, 2000.
Piggott, M. D., Pain, C. C., Gorman, G. J., Power, P. W., and Goddard, A. J. H.: h, r, and hr adaptivity with applications in numerical ocean modelling, Ocean Model., 10, 95–113, https://doi.org/10.1016/j.ocemod.2004.07.007, 2005.
Piggott, M., Pain, C., Gorman, G., Marshall, D., Marshall, D. P., and Killworth, P. D.: Unstructured adaptive meshes for ocean modeling, in: Ocean Modeling in an Eddying Regime. Geophysical Monograph Series, 177, https://doi.org/10.1029/177GM22, 2008a.
Piggott, M. D., Gorman, G. J., Pain, C. C., Allison, P. A., Candy, A. S., Martin, B. T., and Wells, M. R.: A new computational framework for multi-scale ocean modelling based on adapting unstructured meshes, Int. J. Numer. Meth. Fl., 56, 1003–1015, https://doi.org/10.1002/fld.1663, 2008b.
Piggott, M. D., Farrell, P. E., Wilson, C. R., Gorman, G. J., and Pain, C. C.: Anisotropic mesh adaptivity for multi-scale ocean modelling, Philos. T. Roy. Soc. A, 367, 4591–4611, https://doi.org/10.1098/rsta.2009.0155, 2009.
Popova, E. E., Coward, A. C., Nurser, G. A., de Cuevas, B., Fasham, M. J. R., and Anderson, T. R.: Mechanisms controlling primary and new production in a global ecosystem model – Part I: Validation of the biological simulation, Ocean Sci., 2, 249–266, https://doi.org/10.5194/os-2-249-2006, 2006.
Ueckermann, M. P. and Lermusiaux, P. F. J.: High-order schemes for 2D unsteady biogeochemical ocean models, Ocean Dynam., 60, 1415–1445, https://doi.org/10.1007/s10236-010-0351-x, 2010.
Umlauf, L. and Burchard, H.: A generic length-scale equation for geophysical turbulence models, J. Mar. Res., 61, 235–265, https://doi.org/10.1357/002224003322005087, 2003.
Uppala, S. M., Kållberg, P. W., Simmons, A. J., Andrae, U., Bechtold, V. D. C., Fiorino, M., Gibson, J. K., Haseler, J., Hernandez, A., Kelly, G. A., Li, X., Onogi, K., Saarinen, S., Sokka, N., Allan, R. P., Andersson, E., Arpe, K., Balmaseda, M. A., Beljaars, A. C. M., Berg, L. V. D., Bidlot, J., Bormann, N., Caires, S., Chevallier, F., Dethof, A., Dragosavac, M., Fisher, M., Fuentes, M., Hagemann, S., Hólm, E., Hoskins, B. J., Isaksen, L., Janssen, P. A. E. M., Jenne, R., Mcnally, A. P., Mahfouf, J.-F., Morcrette, J.-J., Rayner, N. A., Saunders, R. W., Simon, P., Sterl, A., Trenberth, K. E., Untch, A., Vasiljevic, D., Viterbo, P., and Woollen, J.: The ERA-40 re-analysis, Q. J. Roy. Meteorol. Soc., 131, 2961–3012, https://doi.org/10.1256/qj.04.176, 2005.
Weber, L., Völker, C., Oschlies, A., and Burchard, H.: Iron profiles and speciation of the upper water column at the Bermuda Atlantic Time-series Study site: a model based sensitivity study, Biogeosciences, 4, 689–706, https://doi.org/10.5194/bg-4-689-2007, 2007.
