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Geoscientific Model Development
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GMD | Articles | Volume 12, issue 5
Geosci. Model Dev., 12, 1965–1990, 2019
https://doi.org/10.5194/gmd-12-1965-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-12-1965-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Geosci. Model Dev., 12, 1965–1990, 2019
https://doi.org/10.5194/gmd-12-1965-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-12-1965-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Development and technical paper 15 May 2019
Development and technical paper | 15 May 2019
CSIB v1 (Canadian Sea-ice Biogeochemistry): a sea-ice biogeochemical model for the NEMO community ocean modelling framework
Hakase Hayashida et al.
Related authors
Jonathan P. D. Abbatt, W. Richard Leaitch, Amir A. Aliabadi, Allan K. Bertram, Jean-Pierre Blanchet, Aude Boivin-Rioux, Heiko Bozem, Julia Burkart, Rachel Y. W. Chang, Joannie Charette, Jai P. Chaubey, Robert J. Christensen, Ana Cirisan, Douglas B. Collins, Betty Croft, Joelle Dionne, Greg J. Evans, Christopher G. Fletcher, Martí Galí, Roghayeh Ghahremaninezhad, Eric Girard, Wanmin Gong, Michel Gosselin, Margaux Gourdal, Sarah J. Hanna, Hakase Hayashida, Andreas B. Herber, Sareh Hesaraki, Peter Hoor, Lin Huang, Rachel Hussherr, Victoria E. Irish, Setigui A. Keita, John K. Kodros, Franziska Köllner, Felicia Kolonjari, Daniel Kunkel, Luis A. Ladino, Kathy Law, Maurice Levasseur, Quentin Libois, John Liggio, Martine Lizotte, Katrina M. Macdonald, Rashed Mahmood, Randall V. Martin, Ryan H. Mason, Lisa A. Miller, Alexander Moravek, Eric Mortenson, Emma L. Mungall, Jennifer G. Murphy, Maryam Namazi, Ann-Lise Norman, Norman T. O'Neill, Jeffrey R. Pierce, Lynn M. Russell, Johannes Schneider, Hannes Schulz, Sangeeta Sharma, Meng Si, Ralf M. Staebler, Nadja S. Steiner, Jennie L. Thomas, Knut von Salzen, Jeremy J. B. Wentzell, Megan D. Willis, Gregory R. Wentworth, Jun-Wei Xu, and Jacqueline D. Yakobi-Hancock
Atmos. Chem. Phys., 19, 2527–2560, https://doi.org/10.5194/acp-19-2527-2019, https://doi.org/10.5194/acp-19-2527-2019, 2019
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The Arctic is experiencing considerable environmental change with climate warming, illustrated by the dramatic decrease in sea-ice extent. It is important to understand both the natural and perturbed Arctic systems to gain a better understanding of how they will change in the future. This paper summarizes new insights into the relationships between Arctic aerosol particles and climate, as learned over the past five or so years by a large Canadian research consortium, NETCARE.
Hakase Hayashida, Nadja Steiner, Adam Monahan, Virginie Galindo, Martine Lizotte, and Maurice Levasseur
Biogeosciences, 14, 3129–3155, https://doi.org/10.5194/bg-14-3129-2017, https://doi.org/10.5194/bg-14-3129-2017, 2017
Short summary
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In remote regions, cloud conditions may be strongly influenced by oceanic source of dimethylsulfide (DMS) produced by plankton and bacteria. In the Arctic, sea ice provides an additional source of these aerosols. The results of this study highlight the importance of taking into account both the sea-ice sulfur cycle and ecosystem in the flux estimates of oceanic DMS near the ice margins and identify key uncertainties in processes and rates that would be better constrained by new observations.
Clark Pennelly and Paul G. Myers
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-111, https://doi.org/10.5194/gmd-2020-111, 2020
Preprint under review for GMD
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A high-resolution ocean simulation was carried out within the Labrador Sea, a region where low-resolution climate simulations may misrepresent. We show that small scale eddies, and their associated transport, are better resolved at higher resolution than at lower resolution. These eddies transport important properties to the interior of the Labrador Sea, impacting the stratification and reducing the convection extent to be far more accurate when compared to what observations suggest.
Vivek K. Arora, Anna Katavouta, Richard G. Williams, Chris D. Jones, Victor Brovkin, Pierre Friedlingstein, Jörg Schwinger, Laurent Bopp, Olivier Boucher, Patricia Cadule, Matthew A. Chamberlain, James R. Christian, Christine Delire, Rosie A. Fisher, Tomohiro Hajima, Tatiana Ilyina, Emilie Joetzjer, Michio Kawamiya, Charles Koven, John Krasting, Rachel M. Law, David M. Lawrence, Andrew Lenton, Keith Lindsay, Julia Pongratz, Thomas Raddatz, Roland Séférian, Kaoru Tachiiri, Jerry F. Tjiputra, Andy Wiltshire, Tongwen Wu, and Tilo Ziehn
Biogeosciences Discuss., https://doi.org/10.5194/bg-2019-473, https://doi.org/10.5194/bg-2019-473, 2019
Revised manuscript accepted for BG
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Since the pre-industrial period, land and ocean have taken up about half of the carbon emitted into the atmosphere by humans. Comparison of different earth system models with carbon cycle allows to assess how carbon uptake by land and ocean differs among models. This yields an estimate of uncertainty of our understanding of how land and ocean respond to increasing atmospheric CO2. This manuscript summarizes results from two such model intercomparisons projects.
Neil C. Swart, Jason N. S. Cole, Viatcheslav V. Kharin, Mike Lazare, John F. Scinocca, Nathan P. Gillett, James Anstey, Vivek Arora, James R. Christian, Sarah Hanna, Yanjun Jiao, Warren G. Lee, Fouad Majaess, Oleg A. Saenko, Christian Seiler, Clint Seinen, Andrew Shao, Michael Sigmond, Larry Solheim, Knut von Salzen, Duo Yang, and Barbara Winter
Geosci. Model Dev., 12, 4823–4873, https://doi.org/10.5194/gmd-12-4823-2019, https://doi.org/10.5194/gmd-12-4823-2019, 2019
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The Canadian Earth System Model version 5 (CanESM5) is a global model developed to simulate historical climate change and variability, to make centennial-scale projections of future climate, and to produce initialized seasonal and decadal predictions. This paper describes the model components and quantifies the model performance. CanESM5 simulations contribute to the Coupled Model Intercomparison Project phase 6 (CMIP6) and will be employed for climate science applications in Canada.
Carsten Abraham, Amber M. Holdsworth, and Adam H. Monahan
Nonlin. Processes Geophys., 26, 401–427, https://doi.org/10.5194/npg-26-401-2019, https://doi.org/10.5194/npg-26-401-2019, 2019
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Atmospheric stably stratified boundary layers display transitions between regimes of sustained and intermittent turbulence. These transitions are not well represented in numerical weather prediction and climate models. A prototype explicitly stochastic turbulence parameterization simulating regime dynamics is presented and tested in an idealized model. Results demonstrate that the approach can improve the regime representation in models.
Fei Lu, Nils Weitzel, and Adam H. Monahan
Nonlin. Processes Geophys., 26, 227–250, https://doi.org/10.5194/npg-26-227-2019, https://doi.org/10.5194/npg-26-227-2019, 2019
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ll-posedness of the inverse problem and sparse noisy data are two major challenges in the modeling of high-dimensional spatiotemporal processes. We present a Bayesian inference method with a strongly regularized posterior to overcome these challenges, enabling joint state-parameter estimation and quantifying uncertainty in the estimation. We demonstrate the method on a physically motivated nonlinear stochastic partial differential equation arising from paleoclimate construction.
Laura C. Gillard, Xianmin Hu, and Paul G. Myers
The Cryosphere Discuss., https://doi.org/10.5194/tc-2019-84, https://doi.org/10.5194/tc-2019-84, 2019
Revised manuscript accepted for TC
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Greenland's glaciers in contact with the ocean drain the majority of the ice sheet (GrIS). Deep troughs along the shelf branch into fjords, connecting glaciers with ocean waters. The heat from the ocean entering deep troughs may then accelerate the mass loss. Onshore heat transport through troughs was investigated with an ocean model. Processes that drive the delivery of ocean heat respond differently by region to increasing GrIS meltwater, mean circulation and filtering out of storms.
Katja Fennel, Simone Alin, Leticia Barbero, Wiley Evans, Timothée Bourgeois, Sarah Cooley, John Dunne, Richard A. Feely, Jose Martin Hernandez-Ayon, Xinping Hu, Steven Lohrenz, Frank Muller-Karger, Raymond Najjar, Lisa Robbins, Elizabeth Shadwick, Samantha Siedlecki, Nadja Steiner, Adrienne Sutton, Daniela Turk, Penny Vlahos, and Zhaohui Aleck Wang
Biogeosciences, 16, 1281–1304, https://doi.org/10.5194/bg-16-1281-2019, https://doi.org/10.5194/bg-16-1281-2019, 2019
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We review and synthesize available information on coastal ocean carbon fluxes around North America (NA). There is overwhelming evidence, compiled and discussed here, that the NA coastal margins act as a sink. Our synthesis shows the great diversity in processes driving carbon fluxes in different coastal regions, highlights remaining gaps in observations and models, and discusses current and anticipated future trends with respect to carbon fluxes and acidification.
Jonathan P. D. Abbatt, W. Richard Leaitch, Amir A. Aliabadi, Allan K. Bertram, Jean-Pierre Blanchet, Aude Boivin-Rioux, Heiko Bozem, Julia Burkart, Rachel Y. W. Chang, Joannie Charette, Jai P. Chaubey, Robert J. Christensen, Ana Cirisan, Douglas B. Collins, Betty Croft, Joelle Dionne, Greg J. Evans, Christopher G. Fletcher, Martí Galí, Roghayeh Ghahremaninezhad, Eric Girard, Wanmin Gong, Michel Gosselin, Margaux Gourdal, Sarah J. Hanna, Hakase Hayashida, Andreas B. Herber, Sareh Hesaraki, Peter Hoor, Lin Huang, Rachel Hussherr, Victoria E. Irish, Setigui A. Keita, John K. Kodros, Franziska Köllner, Felicia Kolonjari, Daniel Kunkel, Luis A. Ladino, Kathy Law, Maurice Levasseur, Quentin Libois, John Liggio, Martine Lizotte, Katrina M. Macdonald, Rashed Mahmood, Randall V. Martin, Ryan H. Mason, Lisa A. Miller, Alexander Moravek, Eric Mortenson, Emma L. Mungall, Jennifer G. Murphy, Maryam Namazi, Ann-Lise Norman, Norman T. O'Neill, Jeffrey R. Pierce, Lynn M. Russell, Johannes Schneider, Hannes Schulz, Sangeeta Sharma, Meng Si, Ralf M. Staebler, Nadja S. Steiner, Jennie L. Thomas, Knut von Salzen, Jeremy J. B. Wentzell, Megan D. Willis, Gregory R. Wentworth, Jun-Wei Xu, and Jacqueline D. Yakobi-Hancock
Atmos. Chem. Phys., 19, 2527–2560, https://doi.org/10.5194/acp-19-2527-2019, https://doi.org/10.5194/acp-19-2527-2019, 2019
Short summary
Short summary
The Arctic is experiencing considerable environmental change with climate warming, illustrated by the dramatic decrease in sea-ice extent. It is important to understand both the natural and perturbed Arctic systems to gain a better understanding of how they will change in the future. This paper summarizes new insights into the relationships between Arctic aerosol particles and climate, as learned over the past five or so years by a large Canadian research consortium, NETCARE.
Gerald M. Lohmann and Adam H. Monahan
Atmos. Meas. Tech., 11, 3131–3144, https://doi.org/10.5194/amt-11-3131-2018, https://doi.org/10.5194/amt-11-3131-2018, 2018
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Using high-resolution surface irradiance data with original temporal resolutions between 0.01 s and 1 s from six different locations in the Northern Hemisphere, we characterize the changes in representation of temporal variability resulting from time averaging. Our results indicate that a temporal averaging time scale of around 1 s marks a transition in representing single-point irradiance variability, such that longer averages result in substantial underestimates of variability.
Adam H. Monahan
Nonlin. Processes Geophys., 25, 335–353, https://doi.org/10.5194/npg-25-335-2018, https://doi.org/10.5194/npg-25-335-2018, 2018
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Bivariate probability density functions (pdfs) of wind speed characterize the relationship between speeds at two different locations or times. This study develops such pdfs of wind speed from distributions of the components, following a well-established approach for univariate distributions. The ability of these models to characterize example observed datasets is assessed. The mathematical complexity of these models suggests further extensions of this line of reasoning may not be practical.
Xianmin Hu, Jingfan Sun, Ting On Chan, and Paul G. Myers
The Cryosphere, 12, 1233–1247, https://doi.org/10.5194/tc-12-1233-2018, https://doi.org/10.5194/tc-12-1233-2018, 2018
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We evaluated the sea ice thickness simulation in the Canadian Arctic Archipelago region using 1/4 and 1/12 degree NEMO LIM2 configurations. Model resolution dose not play a significant role. Relatively smaller thermodynamic contribution in the winter season is found in the thick ice covered areas, with larger contributions in the thin ice covered regions. No significant trend in winter maximum ice volume is found in the northern CAA and Baffin Bay but a decline is simulated within Parry Channel.
Jacoba Mol, Helmuth Thomas, Paul G. Myers, Xianmin Hu, and Alfonso Mucci
Biogeosciences, 15, 1011–1027, https://doi.org/10.5194/bg-15-1011-2018, https://doi.org/10.5194/bg-15-1011-2018, 2018
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In the fall of 2014, the upwelling of water from the deep Canada Basin brought water onto the shallower Mackenzie Shelf in the Beaufort Sea. This increased the concentration of CO2 in water on the shelf, which alters pH and changes the transfer of CO2 between the ocean and atmosphere. These findings were a combined result of water sampling for CO2 parameters and the use of a computer model that simulates water movement in the ocean.
Hakase Hayashida, Nadja Steiner, Adam Monahan, Virginie Galindo, Martine Lizotte, and Maurice Levasseur
Biogeosciences, 14, 3129–3155, https://doi.org/10.5194/bg-14-3129-2017, https://doi.org/10.5194/bg-14-3129-2017, 2017
Short summary
Short summary
In remote regions, cloud conditions may be strongly influenced by oceanic source of dimethylsulfide (DMS) produced by plankton and bacteria. In the Arctic, sea ice provides an additional source of these aerosols. The results of this study highlight the importance of taking into account both the sea-ice sulfur cycle and ecosystem in the flux estimates of oceanic DMS near the ice margins and identify key uncertainties in processes and rates that would be better constrained by new observations.
Jan-Erik Tesdal, James R. Christian, Adam H. Monahan, and Knut von Salzen
Atmos. Chem. Phys., 16, 10847–10864, https://doi.org/10.5194/acp-16-10847-2016, https://doi.org/10.5194/acp-16-10847-2016, 2016
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A global atmosphere model with explicit representation of aerosol processes is used to assess uncertainties in the climate impact of ocean DMS efflux and the role of spatial and temporal variability of the DMS flux in the effect on climate. The radiative effect of sulfate is nearly linearly related to global total DMS flux. Removing the spatial or temporal variability of DMS flux changes the global radiation budget, but the effect is of second-order importance relative to the global mean flux.
Gerald M. Lohmann, Adam H. Monahan, and Detlev Heinemann
Atmos. Chem. Phys., 16, 6365–6379, https://doi.org/10.5194/acp-16-6365-2016, https://doi.org/10.5194/acp-16-6365-2016, 2016
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Increasing numbers of photovoltaic (PV) power systems call for the characterization of irradiance variability with very high spatiotemporal resolution. We use 1 Hz irradiance data recorded by as many as 99 pyranometers and show mixed sky conditions to differ substantially from clear and overcast skies. For example, the probabilities of strong fluctuations and their respective spatial autocorrelation structures are appreciably distinct under mixed conditions.
Related subject area
Cryosphere
Description and validation of the ice-sheet model Yelmo (version 1.0)
Evaluating integrated surface/subsurface permafrost thermal hydrology models in ATS (v0.88) against observations from a polygonal tundra site
Extended enthalpy formulations in the ice flow model ISSM version 4.17: discontinuous conductivity and anisotropic SUPG
SICOPOLIS-AD v1: an open-source adjoint modeling framework for ice sheet simulation enabled by the algorithmic differentiation tool OpenAD
On the calculation of normalized viscous–plastic sea ice stresses
Modelling thermomechanical ice deformation using an implicit pseudo-transient method (FastICE v1.0) based on graphical processing units (GPUs)
The Community Firn Model (CFM) v1.0
Version 1 of a sea ice module for the physics-based, detailed, multi-layer SNOWPACK model
A module to convert spectral to narrowband snow albedo for use in climate models: SNOWBAL v1.2
Simulating the Early Holocene demise of the Laurentide Ice Sheet with BISICLES (public trunk revision 3298)
On the discretization of the ice thickness distribution in the NEMO3.6-LIM3 global ocean–sea ice model
Using Arctic ice mass balance buoys for evaluation of modelled ice energy fluxes
Scientific workflows applied to the coupling of a continuum (Elmer v8.3) and a discrete element (HiDEM v1.0) ice dynamic model
A rapidly converging initialisation method to simulate the present-day Greenland ice sheet using the GRISLI ice sheet model (version 1.3)
Assessment of sub-shelf melting parameterisations using the ocean–ice-sheet coupled model NEMO(v3.6)–Elmer/Ice(v8.3)
LIVVkit 2.1: automated and extensible ice sheet model validation
ATAT 1.1, the Automated Timing Accordance Tool for comparing ice-sheet model output with geochronological data
The Open Global Glacier Model (OGGM) v1.1
Description and evaluation of the Community Ice Sheet Model (CISM) v2.1
Implementation and performance of adaptive mesh refinement in the Ice Sheet System Model (ISSM v4.14)
A continuum model (PSUMEL1) of ice mélange and its role during retreat of the Antarctic Ice Sheet
ESM-SnowMIP: assessing snow models and quantifying snow-related climate feedbacks
The GRISLI ice sheet model (version 2.0): calibration and validation for multi-millennial changes of the Antarctic ice sheet
CVPM 1.1: a flexible heat-transfer modeling system for permafrost
Dynamically coupling full Stokes and shallow shelf approximation for marine ice sheet flow using Elmer/Ice (v8.3)
The NASA Eulerian Snow on Sea Ice Model (NESOSIM) v1.0: initial model development and analysis
MPAS-Albany Land Ice (MALI): a variable-resolution ice sheet model for Earth system modeling using Voronoi grids
BrAHMs V1.0: a fast, physically based subglacial hydrology model for continental-scale application
SHAKTI: Subglacial Hydrology and Kinetic, Transient Interactions v1.0
SMRT: an active–passive microwave radiative transfer model for snow with multiple microstructure and scattering formulations (v1.0)
PIC v1.3: comprehensive R package for computing permafrost indices with daily weather observations and atmospheric forcing over the Qinghai–Tibet Plateau
Numerical experiments on vapor diffusion in polar snow and firn and its impact on isotopes using the multi-layer energy balance model Crocus in SURFEX v8.0
Implementation of higher-order vertical finite elements in ISSM v4.13 for improved ice sheet flow modeling over paleoclimate timescales
Intercomparison of Antarctic ice-shelf, ocean, and sea-ice interactions simulated by MetROMS-iceshelf and FESOM 1.4
The sea ice model component of HadGEM3-GC3.1
A JavaScript API for the Ice Sheet System Model (ISSM) 4.11: towards an online interactive model for the cryosphere community
Implementation of a physically based water percolation routine in the Crocus/SURFEX (V7.3) snowpack model
Evaluating the performance of coupled snow–soil models in SURFEXv8 to simulate the permafrost thermal regime at a high Arctic site
Sea-ice evaluation of NEMO-Nordic 1.0: a NEMO–LIM3.6-based ocean–sea-ice model setup for the North Sea and Baltic Sea
A vertical representation of soil carbon in the JULES land surface scheme (vn4.3_permafrost) with a focus on permafrost regions
An ice sheet model validation framework for the Greenland ice sheet
Optimal numerical solvers for transient simulations of ice flow using the Ice Sheet System Model (ISSM versions 4.2.5 and 4.11)
An approach to computing discrete adjoints for MPI-parallelized models applied to Ice Sheet System Model 4.11
The CMIP6 Sea-Ice Model Intercomparison Project (SIMIP): understanding sea ice through climate-model simulations
Comparison of adjoint and nudging methods to initialise ice sheet model basal conditions
Experimental design for three interrelated marine ice sheet and ocean model intercomparison projects: MISMIP v. 3 (MISMIP +), ISOMIP v. 2 (ISOMIP +) and MISOMIP v. 1 (MISOMIP1)
Performance and applicability of a 2.5-D ice-flow model in the vicinity of a dome
An optimized treatment for algorithmic differentiation of an important glaciological fixed-point problem
Large ensemble modeling of the last deglacial retreat of the West Antarctic Ice Sheet: comparison of simple and advanced statistical techniques
ICESHEET 1.0: a program to produce paleo-ice sheet reconstructions with minimal assumptions
Alexander Robinson, Jorge Alvarez-Solas, Marisa Montoya, Heiko Goelzer, Ralf Greve, and Catherine Ritz
Geosci. Model Dev., 13, 2805–2823, https://doi.org/10.5194/gmd-13-2805-2020, https://doi.org/10.5194/gmd-13-2805-2020, 2020
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Here we describe Yelmo v1.0, an intuitive and state-of-the-art hybrid ice sheet model. The model design and physics are described, and benchmark simulations are provided to validate its performance. Yelmo is a versatile ice sheet model that can be applied to a wide variety of problems.
Ahmad Jan, Ethan T. Coon, and Scott L. Painter
Geosci. Model Dev., 13, 2259–2276, https://doi.org/10.5194/gmd-13-2259-2020, https://doi.org/10.5194/gmd-13-2259-2020, 2020
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Computer simulations are important tools for understanding the response of Arctic permafrost to a warming climate. To build confidence in an emerging class of permafrost simulators, we evaluated the Advanced Terrestrial Simulator against field observations from a frozen tundra site near Utqiaġvik (formerly Barrow), Alaska. The 3-year simulations agree well with observations of snow depth, summer water table, soil temperature at multiple locations, and spatially averaged evaporation.
Martin Rückamp, Angelika Humbert, Thomas Kleiner, Mathieu Morlighem, and Helene Seroussi
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-78, https://doi.org/10.5194/gmd-2020-78, 2020
Revised manuscript accepted for GMD
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We present extended enthalpy formulations within the finite-element Ice Sheet System Model that show a better performance to earlier implementations. A first experiment indicates that the treatment of the discontinuous conductivities at the CTS with a geometric mean produce accurate results even when applied to coarse vertical resolutions. In a second experiment, we propose a novel SUPG formulation. that circumvents the problem of thin elements. This method provides accurate and stable results.
Liz C. Logan, Sri Hari Krishna Narayanan, Ralf Greve, and Patrick Heimbach
Geosci. Model Dev., 13, 1845–1864, https://doi.org/10.5194/gmd-13-1845-2020, https://doi.org/10.5194/gmd-13-1845-2020, 2020
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A new capability has been developed for the ice sheet model SICOPOLIS (SImulation COde for POLythermal Ice Sheets) that enables the generation of derivative code, such as tangent linear or adjoint models, by means of algorithmic differentiation. It relies on the source transformation algorithmic (AD) differentiation tool OpenAD. The reverse mode of AD provides the adjoint model, SICOPOLIS-AD, which may be applied for comprehensive sensitivity analyses as well as gradient-based optimization.
Jean-François Lemieux and Frédéric Dupont
Geosci. Model Dev., 13, 1763–1769, https://doi.org/10.5194/gmd-13-1763-2020, https://doi.org/10.5194/gmd-13-1763-2020, 2020
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Sea ice dynamics plays an important role in shaping the sea cover in polar regions. Winds and ocean currents exert large stresses on the sea ice cover. This can lead to the formation of long cracks and ridges, which strongly impact the exchange of heat, momentum and moisture between the atmosphere and the ocean. It is therefore crucial for a sea ice model to be able to represent these features. This article describes how internal sea ice stresses should be diagnosed from model simulations.
Ludovic Räss, Aleksandar Licul, Frédéric Herman, Yury Y. Podladchikov, and Jenny Suckale
Geosci. Model Dev., 13, 955–976, https://doi.org/10.5194/gmd-13-955-2020, https://doi.org/10.5194/gmd-13-955-2020, 2020
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Accurate predictions of future sea level rise require numerical models that predict rapidly deforming ice. Localised ice deformation can be captured numerically only with high temporal and spatial resolution. This paper’s goal is to propose a parallel FastICE solver for modelling ice deformation. Our model is particularly useful for improving our process-based understanding of localised ice deformation. Our solver reaches a parallel efficiency of 99 % on GPU-based supercomputers.
C. Max Stevens, Vincent Verjans, Jessica M.D. Lundin, Emma C. Kahle, Annika N. Horlings, Brita I. Horlings, and Edwin D. Waddington
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2019-361, https://doi.org/10.5194/gmd-2019-361, 2020
Revised manuscript accepted for GMD
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Understanding processes in snow (firn), including compaction and air flow, is important for calculating how much mass the ice sheets are losing and for interpreting climate records from ice cores. We have developed the open-source 'Community Firn Model' to simulate these processes. We use it to compare 13 different firn-compaction equations and found that they do not agree to within 10%. We also show that including firn compaction in firn-air model improves the match with data from ice cores.
Nander Wever, Leonard Rossmann, Nina Maaß, Katherine C. Leonard, Lars Kaleschke, Marcel Nicolaus, and Michael Lehning
Geosci. Model Dev., 13, 99–119, https://doi.org/10.5194/gmd-13-99-2020, https://doi.org/10.5194/gmd-13-99-2020, 2020
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Sea ice is an important component of the global climate system. The presence of a snow layer covering sea ice can impact ice mass and energy budgets. The detailed, physics-based, multi-layer snow model SNOWPACK was modified to simulate the snow–sea-ice system, providing simulations of the snow microstructure, water percolation and flooding, and superimposed ice formation. The model is applied to in situ measurements from snow and ice mass-balance buoys installed in the Antarctic Weddell Sea.
Christiaan T. van Dalum, Willem Jan van de Berg, Quentin Libois, Ghislain Picard, and Michiel R. van den Broeke
Geosci. Model Dev., 12, 5157–5175, https://doi.org/10.5194/gmd-12-5157-2019, https://doi.org/10.5194/gmd-12-5157-2019, 2019
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Climate models are often limited to relatively simple snow albedo schemes. Therefore, we have developed the SNOWBAL module to couple a climate model with a physically based wavelength dependent snow albedo model. Using SNOWBAL v1.2 to couple the snow albedo model TARTES with the regional climate model RACMO2 indicates a potential performance gain for the Greenland ice sheet.
Ilkka S. O. Matero, Lauren J. Gregoire, and Ruza F. Ivanovic
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2019-304, https://doi.org/10.5194/gmd-2019-304, 2019
Revised manuscript accepted for GMD
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Eight thousand years ago, the N. hemisphere cooled by several degrees for a century, due to the collapse of an ice sheet in N. America that released large amounts of meltwater to the North Atlantic and slowed down its circulation. We numerically model the ice sheet to understand its evolution during this event. Our results match data thanks to good ice dynamics, but depend mostly on surface melt and snowfall. Further work will help us understand how past and future ice melt affects climate.
François Massonnet, Antoine Barthélemy, Koffi Worou, Thierry Fichefet, Martin Vancoppenolle, Clément Rousset, and Eduardo Moreno-Chamarro
Geosci. Model Dev., 12, 3745–3758, https://doi.org/10.5194/gmd-12-3745-2019, https://doi.org/10.5194/gmd-12-3745-2019, 2019
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Sea ice thickness varies considerably on spatial scales of several meters. However, contemporary climate models cannot resolve such scales yet. This is why sea ice models used in climate models include an ice thickness distribution (ITD) to account for this unresolved variability. Here, we explore with the ocean–sea ice model NEMO3.6-LIM3 the sensitivity of simulated mean Arctic and Antarctic sea ice states to the way the ITD is discretized.
Alex West, Mat Collins, and Ed Blockley
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2019-113, https://doi.org/10.5194/gmd-2019-113, 2019
Preprint under review for GMD
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This study presents a way of estimating energy fluxes within sea ice from ice mass balance buoys (devices measuring ice elevation and temperature). It is shown how the resulting dataset can be used to evaluate a coupled climate model (HadGEM2-ES), with biases in the energy fluxes seen to be consistent with biases in the sea ice state and surface radiation. This method has potential to improve sea ice model evaluation, so as to better understand spread in model simulations of sea ice state.
Shahbaz Memon, Dorothée Vallot, Thomas Zwinger, Jan Åström, Helmut Neukirchen, Morris Riedel, and Matthias Book
Geosci. Model Dev., 12, 3001–3015, https://doi.org/10.5194/gmd-12-3001-2019, https://doi.org/10.5194/gmd-12-3001-2019, 2019
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Scientific workflows enable complex scientific computational scenarios, which include data intensive scenarios, parametric executions, and interactive simulations. In this article, we applied the UNICORE workflow management system to automate a formerly hard-coded coupling of a glacier flow model and a calving model, which contain many tasks and dependencies, ranging from pre-processing and data management to repetitive executions on heterogeneous high-performance computing (HPC) resources.
Sébastien Le clec'h, Aurélien Quiquet, Sylvie Charbit, Christophe Dumas, Masa Kageyama, and Catherine Ritz
Geosci. Model Dev., 12, 2481–2499, https://doi.org/10.5194/gmd-12-2481-2019, https://doi.org/10.5194/gmd-12-2481-2019, 2019
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To provide reliable projections of the ice-sheet contribution to future sea-level rise, ice sheet models must be able to simulate the observed ice sheet present-day state. Using a low computational iterative minimisation procedure, based on the adjustment of the basal drag coefficient, we rapidly minimise the errors between the simulated and the observed Greenland ice thickness and ice velocity, and we succeed in stabilising the simulated Greenland ice sheet state under present-day conditions.
Lionel Favier, Nicolas C. Jourdain, Adrian Jenkins, Nacho Merino, Gaël Durand, Olivier Gagliardini, Fabien Gillet-Chaulet, and Pierre Mathiot
Geosci. Model Dev., 12, 2255–2283, https://doi.org/10.5194/gmd-12-2255-2019, https://doi.org/10.5194/gmd-12-2255-2019, 2019
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The melting at the base of floating ice shelves is the main driver of the Antarctic ice sheet current retreat. Here, we use an ideal set-up to assess a wide range of melting parameterisations depending on oceanic properties with regard to a new ocean–ice-sheet coupled model, published here for the first time. A parameterisation that depends quadratically on thermal forcing in both a local and a non-local way yields the best results and needs to be further assessed with more realistic set-ups.
Katherine J. Evans, Joseph H. Kennedy, Dan Lu, Mary M. Forrester, Stephen Price, Jeremy Fyke, Andrew R. Bennett, Matthew J. Hoffman, Irina Tezaur, Charles S. Zender, and Miren Vizcaíno
Geosci. Model Dev., 12, 1067–1086, https://doi.org/10.5194/gmd-12-1067-2019, https://doi.org/10.5194/gmd-12-1067-2019, 2019
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A robust validation of ice sheet models is presented using LIVVkit, version 2.1. It targets ice sheet and coupled Earth system models, and handles datasets and operations that require high-performance computing and storage. We apply LIVVkit to a Greenland ice sheet simulation to show the degree to which it captures the surface mass balance. LIVVkit identifies a positive bias due to insufficient melting compared to observations that is focused largely around Greenland's southwest region.
Jeremy C. Ely, Chris D. Clark, David Small, and Richard C. A. Hindmarsh
Geosci. Model Dev., 12, 933–953, https://doi.org/10.5194/gmd-12-933-2019, https://doi.org/10.5194/gmd-12-933-2019, 2019
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During the last 2.6 million years, the Earth's climate has cycled between cold glacials and warm interglacials, causing the growth and retreat of ice sheets. These ice sheets can be independently reconstructed using numerical models or from dated evidence that they leave behind (e.g. sediments, boulders). Here, we present a tool for comparing numerical model simulations with dated ice-sheet material. We demonstrate the utility of this tool by applying it to the last British–Irish ice sheet.
Fabien Maussion, Anton Butenko, Nicolas Champollion, Matthias Dusch, Julia Eis, Kévin Fourteau, Philipp Gregor, Alexander H. Jarosch, Johannes Landmann, Felix Oesterle, Beatriz Recinos, Timo Rothenpieler, Anouk Vlug, Christian T. Wild, and Ben Marzeion
Geosci. Model Dev., 12, 909–931, https://doi.org/10.5194/gmd-12-909-2019, https://doi.org/10.5194/gmd-12-909-2019, 2019
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Mountain glaciers are one of the few remaining subsystems of the global climate system for which no globally applicable community-driven model exists. Here we present the Open Global Glacier Model (OGGM; www.oggm.org), developed to provide a modular and open-source numerical model framework for simulating past and future change of any glacier in the world.
William H. Lipscomb, Stephen F. Price, Matthew J. Hoffman, Gunter R. Leguy, Andrew R. Bennett, Sarah L. Bradley, Katherine J. Evans, Jeremy G. Fyke, Joseph H. Kennedy, Mauro Perego, Douglas M. Ranken, William J. Sacks, Andrew G. Salinger, Lauren J. Vargo, and Patrick H. Worley
Geosci. Model Dev., 12, 387–424, https://doi.org/10.5194/gmd-12-387-2019, https://doi.org/10.5194/gmd-12-387-2019, 2019
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This paper describes the Community Ice Sheet Model (CISM) version 2.1. CISM solves equations for ice flow, heat conduction, surface melting, and other processes such as basal sliding and iceberg calving. It can be used for ice-sheet-only simulations or as the ice sheet component of the Community Earth System Model. Model solutions have been verified for standard test problems. CISM can efficiently simulate the whole Greenland ice sheet, with results that are broadly consistent with observations.
Thiago Dias dos Santos, Mathieu Morlighem, Hélène Seroussi, Philippe Remy Bernard Devloo, and Jefferson Cardia Simões
Geosci. Model Dev., 12, 215–232, https://doi.org/10.5194/gmd-12-215-2019, https://doi.org/10.5194/gmd-12-215-2019, 2019
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The reduction of numerical errors in ice sheet modeling increases the results' accuracy reliability. We improve numerical accuracy by better capturing grounding line dynamics, while maintaining a low computational cost. We implement an adaptive mesh refinement (AMR) technique in the Ice Sheet System Model and compare AMR simulations with uniformly refined meshes. Our results show that the computational time with AMR is significantly shorter than for uniformly refined meshes for a given accuracy.
David Pollard, Robert M. DeConto, and Richard B. Alley
Geosci. Model Dev., 11, 5149–5172, https://doi.org/10.5194/gmd-11-5149-2018, https://doi.org/10.5194/gmd-11-5149-2018, 2018
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Around the margins of ice sheets in contact with the ocean, calving of icebergs can generate large amounts of floating ice debris called "mélange". In major Greenland fjords, mélange significantly slows down ice flow from upstream. Our study applies numerical models to past and possible future episodes of rapid Antarctic Ice Sheet retreat. We find that, due to larger spatial scales, Antarctic mélange does not significantly impede flow or slow ice retreat and associated sea level rise.
Gerhard Krinner, Chris Derksen, Richard Essery, Mark Flanner, Stefan Hagemann, Martyn Clark, Alex Hall, Helmut Rott, Claire Brutel-Vuilmet, Hyungjun Kim, Cécile B. Ménard, Lawrence Mudryk, Chad Thackeray, Libo Wang, Gabriele Arduini, Gianpaolo Balsamo, Paul Bartlett, Julia Boike, Aaron Boone, Frédérique Chéruy, Jeanne Colin, Matthias Cuntz, Yongjiu Dai, Bertrand Decharme, Jeff Derry, Agnès Ducharne, Emanuel Dutra, Xing Fang, Charles Fierz, Josephine Ghattas, Yeugeniy Gusev, Vanessa Haverd, Anna Kontu, Matthieu Lafaysse, Rachel Law, Dave Lawrence, Weiping Li, Thomas Marke, Danny Marks, Martin Ménégoz, Olga Nasonova, Tomoko Nitta, Masashi Niwano, John Pomeroy, Mark S. Raleigh, Gerd Schaedler, Vladimir Semenov, Tanya G. Smirnova, Tobias Stacke, Ulrich Strasser, Sean Svenson, Dmitry Turkov, Tao Wang, Nander Wever, Hua Yuan, Wenyan Zhou, and Dan Zhu
Geosci. Model Dev., 11, 5027–5049, https://doi.org/10.5194/gmd-11-5027-2018, https://doi.org/10.5194/gmd-11-5027-2018, 2018
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This paper provides an overview of a coordinated international experiment to determine the strengths and weaknesses in how climate models treat snow. The models will be assessed at point locations using high-quality reference measurements and globally using satellite-derived datasets. How well climate models simulate snow-related processes is important because changing snow cover is an important part of the global climate system and provides an important freshwater resource for human use.
Aurélien Quiquet, Christophe Dumas, Catherine Ritz, Vincent Peyaud, and Didier M. Roche
Geosci. Model Dev., 11, 5003–5025, https://doi.org/10.5194/gmd-11-5003-2018, https://doi.org/10.5194/gmd-11-5003-2018, 2018
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This paper presents the GRISLI (Grenoble ice sheet and land ice) model in its newest revision. We present the recent model improvements from its original version (Ritz et al., 2001), together with a discussion of the model performance in reproducing the present-day Antarctic ice sheet geometry and the grounding line advances and retreats during the last 400 000 years. We show that GRISLI is a computationally cheap model, able to reproduce the large-scale behaviour of ice sheets.
Gary D. Clow
Geosci. Model Dev., 11, 4889–4908, https://doi.org/10.5194/gmd-11-4889-2018, https://doi.org/10.5194/gmd-11-4889-2018, 2018
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CVPM is a modular heat-transfer modeling system designed for scientific and engineering studies in permafrost terrain, and as an educational tool. CVPM implements the heat-transfer equations in both Cartesian and cylindrical coordinates. To accommodate a diversity of geologic settings, a variety of materials can be specified within the model domain. CVPM can be used over a broad range of depth, temperature, porosity, water saturation, and solute conditions on either Earth or Mars.
Eef C. H. van Dongen, Nina Kirchner, Martin B. van Gijzen, Roderik S. W. van de Wal, Thomas Zwinger, Gong Cheng, Per Lötstedt, and Lina von Sydow
Geosci. Model Dev., 11, 4563–4576, https://doi.org/10.5194/gmd-11-4563-2018, https://doi.org/10.5194/gmd-11-4563-2018, 2018
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Ice flow forced by gravity is governed by the full Stokes (FS) equations, which are computationally expensive to solve. Therefore, approximations to the FS equations are used, especially when modeling an ice sheet on long time spans. Here, we report a combination of an approximation with the FS equations that allows simulating the dynamics of ice sheets over long time spans without introducing artifacts caused by application of approximations in parts of the domain where they are not valid.
Alek A. Petty, Melinda Webster, Linette Boisvert, and Thorsten Markus
Geosci. Model Dev., 11, 4577–4602, https://doi.org/10.5194/gmd-11-4577-2018, https://doi.org/10.5194/gmd-11-4577-2018, 2018
Matthew J. Hoffman, Mauro Perego, Stephen F. Price, William H. Lipscomb, Tong Zhang, Douglas Jacobsen, Irina Tezaur, Andrew G. Salinger, Raymond Tuminaro, and Luca Bertagna
Geosci. Model Dev., 11, 3747–3780, https://doi.org/10.5194/gmd-11-3747-2018, https://doi.org/10.5194/gmd-11-3747-2018, 2018
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MPAS-Albany Land Ice (MALI) is a new variable-resolution land ice model that uses unstructured grids on a plane or sphere. MALI is built for Earth system modeling on high-performance computing platforms using existing software libraries. MALI simulates the evolution of ice thickness, velocity, and temperature, and it includes schemes for simulating iceberg calving and the flow of water beneath ice sheets and its effect on ice sliding. The model is demonstrated for the Antarctic ice sheet.
Mark Kavanagh and Lev Tarasov
Geosci. Model Dev., 11, 3497–3513, https://doi.org/10.5194/gmd-11-3497-2018, https://doi.org/10.5194/gmd-11-3497-2018, 2018
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We present and validate BrAHMs (BAsal Hydrology Model): a new
physically based basal hydrology model, which captures the two main
types of subglacial drainage systems (high-pressure distributed systems and
low-pressure channelized systems). BrAHMs is designed for continental
glacial cycle scale contexts, for which computational speed is
essential. This speed is accomplished, in part, by numerical methods
novel to basal hydrology contexts.
Aleah Sommers, Harihar Rajaram, and Mathieu Morlighem
Geosci. Model Dev., 11, 2955–2974, https://doi.org/10.5194/gmd-11-2955-2018, https://doi.org/10.5194/gmd-11-2955-2018, 2018
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Meltwater drainage beneath glaciers and ice sheets influences how fast they move and is complicated and constantly changing. Most models distinguish between
fastand
slowdrainage with different equations for each system. The SHAKTI model allows for the ice–water drainage arrangement to transition naturally between different types of flow. This model can be used to understand how drainage affects glacier speeds and the associated ice loss to further inform predictions of sea level rise.
Ghislain Picard, Melody Sandells, and Henning Löwe
Geosci. Model Dev., 11, 2763–2788, https://doi.org/10.5194/gmd-11-2763-2018, https://doi.org/10.5194/gmd-11-2763-2018, 2018
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The Snow Microwave Radiative Transfer (SMRT) is a novel model developed to calculate how microwaves are scattered and emitted by snow. The model is built from separate, interconnecting modules to make it easy to compare different aspects of the theory. SMRT is the first model to allow a choice of how to represent the microstructure of the snow, which is extremely important, and has been used to unite multiple previous studies. This model will ultimately be used to observe snow from space.
Lihui Luo, Zhongqiong Zhang, Wei Ma, Shuhua Yi, and Yanli Zhuang
Geosci. Model Dev., 11, 2475–2491, https://doi.org/10.5194/gmd-11-2475-2018, https://doi.org/10.5194/gmd-11-2475-2018, 2018
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Based on the current situation of permafrost modeling in the Qinghai–Tibet Plateau (QTP), a software PIC was developed to evaluate the temporal–spatial change trends of permafrost, which allows us to automatically compute permafrost indices with daily weather and atmospheric forcing datasets. The main features include computing, visualization, and statistics. The software will serve engineering applications and can be used to assess the impact of climate change on permafrost over the QTP.
Alexandra Touzeau, Amaëlle Landais, Samuel Morin, Laurent Arnaud, and Ghislain Picard
Geosci. Model Dev., 11, 2393–2418, https://doi.org/10.5194/gmd-11-2393-2018, https://doi.org/10.5194/gmd-11-2393-2018, 2018
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We introduced a new module of water vapor diffusion into the snowpack model Crocus. Vapor transport locally modifies the density of snow layers, possibly influencing compaction. It also affects the original isotopic signature of snow layers. We also introduced water isotopes (𝛿18O) in the model. Over 10 years, the modeled attenuation of isotopic variations due to vapor diffusion is 7–18 % lower than the observations. Thus, other processes are required to explain the total attenuation.
Joshua K. Cuzzone, Mathieu Morlighem, Eric Larour, Nicole Schlegel, and Helene Seroussi
Geosci. Model Dev., 11, 1683–1694, https://doi.org/10.5194/gmd-11-1683-2018, https://doi.org/10.5194/gmd-11-1683-2018, 2018
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This paper details the implementation of higher-order vertical finite elements in the Ice Sheet System Model (ISSM). When using higher-order vertical finite elements, fewer vertical layers are needed to accurately capture the thermal structure in an ice sheet versus a conventional linear vertical interpolation, therefore greatly improving model runtime speeds, particularly in higher-order stress balance ice sheet models. The implications for paleoclimate ice sheet simulations are discussed.
Kaitlin A. Naughten, Katrin J. Meissner, Benjamin K. Galton-Fenzi, Matthew H. England, Ralph Timmermann, Hartmut H. Hellmer, Tore Hattermann, and Jens B. Debernard
Geosci. Model Dev., 11, 1257–1292, https://doi.org/10.5194/gmd-11-1257-2018, https://doi.org/10.5194/gmd-11-1257-2018, 2018
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MetROMS and FESOM are two ocean/sea-ice models which resolve Antarctic ice-shelf cavities and consider thermodynamics at the ice-shelf base. We simulate the period 1992–2016 with both models, and with two options for resolution in FESOM, and compare output from the three simulations. Ice-shelf melt rates, sub-ice-shelf circulation, continental shelf water masses, and sea-ice processes are compared and evaluated against available observations.
Jeff K. Ridley, Edward W. Blockley, Ann B. Keen, Jamie G. L. Rae, Alex E. West, and David Schroeder
Geosci. Model Dev., 11, 713–723, https://doi.org/10.5194/gmd-11-713-2018, https://doi.org/10.5194/gmd-11-713-2018, 2018
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The sea ice component of the Met Office coupled climate model, HadGEM3-GC3.1, is presented and evaluated. We determine that the mean state of the sea ice is well reproduced for the Arctic; however, a warm sea surface temperature bias over the Southern Ocean results in a low Antarctic sea ice cover.
Eric Larour, Daniel Cheng, Gilberto Perez, Justin Quinn, Mathieu Morlighem, Bao Duong, Lan Nguyen, Kit Petrie, Silva Harounian, Daria Halkides, and Wayne Hayes
Geosci. Model Dev., 10, 4393–4403, https://doi.org/10.5194/gmd-10-4393-2017, https://doi.org/10.5194/gmd-10-4393-2017, 2017
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This work presents a new way of carrying out simulations using the C++ based Ice Sheet System Model (ISSM) within a web page. This allows for a new generation of websites that can rely on the entire code of a climate model, without compromising or simplifying the physics implemented in such a model. We believe this approach will enable better education/outreach websites as well as improve access to complex climate models without compromising their integrity.
Christopher J. L. D'Amboise, Karsten Müller, Laurent Oxarango, Samuel Morin, and Thomas V. Schuler
Geosci. Model Dev., 10, 3547–3566, https://doi.org/10.5194/gmd-10-3547-2017, https://doi.org/10.5194/gmd-10-3547-2017, 2017
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We present a new water percolation routine added to the Crocus model. The new routine is physically based, describing motion of water through a layered snowpack considering capillary-driven and gravity flow. We tested the routine on two data sets. Wet-snow layers were able to reach higher saturations than the empirical routine. Meaningful applicability is limited until new and better parameterizations of water retention are developed, and feedbacks are adjusted to handle higher saturations.
Mathieu Barrere, Florent Domine, Bertrand Decharme, Samuel Morin, Vincent Vionnet, and Matthieu Lafaysse
Geosci. Model Dev., 10, 3461–3479, https://doi.org/10.5194/gmd-10-3461-2017, https://doi.org/10.5194/gmd-10-3461-2017, 2017
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Global warming projections still suffer from a limited representation of the permafrost–carbon feedback. This study assesses the capacity of snow-soil coupled models to simulate the permafrost thermal regime at Bylot Island, a high Arctic site. Significant flaws are found in the description of Arctic snow properties, resulting in erroneous heat transfers between the soil and the snow in simulations. Improved snow schemes are needed to accurately predict the future of permafrost.
Per Pemberton, Ulrike Löptien, Robinson Hordoir, Anders Höglund, Semjon Schimanke, Lars Axell, and Jari Haapala
Geosci. Model Dev., 10, 3105–3123, https://doi.org/10.5194/gmd-10-3105-2017, https://doi.org/10.5194/gmd-10-3105-2017, 2017
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The Baltic Sea is seasonally ice covered with intense wintertime ship traffic and a sensitive ecosystem. Understanding the sea-ice pack is important for climate effect studies and forecasting. A NEMO-LIM3.6-based model setup for the North Sea/Baltic Sea is introduced, including a method for ice in the coastal zone. We evaluate different sea-ice parameters and overall find that the model agrees well with the observation though deformed ice is more challenging to capture.
Eleanor J. Burke, Sarah E. Chadburn, and Altug Ekici
Geosci. Model Dev., 10, 959–975, https://doi.org/10.5194/gmd-10-959-2017, https://doi.org/10.5194/gmd-10-959-2017, 2017
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There is a large amount of relatively inert organic carbon locked into permafrost soils. In a warming climate the permafrost will thaw and this organic carbon will become vulnerable to decomposition. This process is not typically included within Earth system models (ESMs). This paper describes the development of a vertically resolved soil organic carbon decomposition model which, in the future, can be included within the UKESM to quantify the response of the climate to permafrost carbon loss.
Stephen F. Price, Matthew J. Hoffman, Jennifer A. Bonin, Ian M. Howat, Thomas Neumann, Jack Saba, Irina Tezaur, Jeffrey Guerber, Don P. Chambers, Katherine J. Evans, Joseph H. Kennedy, Jan Lenaerts, William H. Lipscomb, Mauro Perego, Andrew G. Salinger, Raymond S. Tuminaro, Michiel R. van den Broeke, and Sophie M. J. Nowicki
Geosci. Model Dev., 10, 255–270, https://doi.org/10.5194/gmd-10-255-2017, https://doi.org/10.5194/gmd-10-255-2017, 2017
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We introduce the Cryospheric Model Comparison Tool (CmCt) and propose qualitative and quantitative metrics for evaluating ice sheet model simulations against observations. Greenland simulations using the Community Ice Sheet Model are compared to gravimetry and altimetry observations from 2003 to 2013. We show that the CmCt can be used to score simulations of increasing complexity relative to observations of dynamic change in Greenland over the past decade.
Feras Habbal, Eric Larour, Mathieu Morlighem, Helene Seroussi, Christopher P. Borstad, and Eric Rignot
Geosci. Model Dev., 10, 155–168, https://doi.org/10.5194/gmd-10-155-2017, https://doi.org/10.5194/gmd-10-155-2017, 2017
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This work presents the results from testing a suite of numerical solvers on a standard ice sheet benchmark test. We note the relevance of this test to practical simulations and identify the fastest solvers for the transient simulation. The highlighted solvers show significant speed-ups in relation to the default solver (~1.5–100 times faster) and enable a new capability for solving massive, high-resolution models that are critical for improving projections of ice sheets and sea-level change.
Eric Larour, Jean Utke, Anton Bovin, Mathieu Morlighem, and Gilberto Perez
Geosci. Model Dev., 9, 3907–3918, https://doi.org/10.5194/gmd-9-3907-2016, https://doi.org/10.5194/gmd-9-3907-2016, 2016
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We present an approach to derive the adjoint state of the C++ coded Ice Sheet System Model. The approach enables data assimilation of observations to improve projections of polar ice sheet mass balance and contribution to sea-level rise. It is applicable to other Earth science frameworks relying on C++ and parallel computing, is non-intrusive, and enables computation of transient adjoints for any type of physics, hence providing insights into the sensitivities of any model to its inputs.
Dirk Notz, Alexandra Jahn, Marika Holland, Elizabeth Hunke, François Massonnet, Julienne Stroeve, Bruno Tremblay, and Martin Vancoppenolle
Geosci. Model Dev., 9, 3427–3446, https://doi.org/10.5194/gmd-9-3427-2016, https://doi.org/10.5194/gmd-9-3427-2016, 2016
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The large-scale evolution of sea ice is both an indicator and a driver of climate changes. Hence, a realistic simulation of sea ice is key for a realistic simulation of the climate system of our planet. To assess and to improve the realism of sea-ice simulations, we present here a new protocol for climate-model output that allows for an in-depth analysis of the simulated evolution of sea ice.
Cyrille Mosbeux, Fabien Gillet-Chaulet, and Olivier Gagliardini
Geosci. Model Dev., 9, 2549–2562, https://doi.org/10.5194/gmd-9-2549-2016, https://doi.org/10.5194/gmd-9-2549-2016, 2016
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Model projections of ice sheet contribution to 21st century sea level rise are greatly affected by initial conditions. Solutions have been developed to infer the friction of the ice on its bedrock using observed surface velocities. A drawback of these methods is that remaining uncertainties, especially in the bedrock elevation, lead to non-physical ice flux divergence anomalies. Here, we compare two different solutions able to infer both bedrock friction and elevation with good performance.
Xylar S. Asay-Davis, Stephen L. Cornford, Gaël Durand, Benjamin K. Galton-Fenzi, Rupert M. Gladstone, G. Hilmar Gudmundsson, Tore Hattermann, David M. Holland, Denise Holland, Paul R. Holland, Daniel F. Martin, Pierre Mathiot, Frank Pattyn, and Hélène Seroussi
Geosci. Model Dev., 9, 2471–2497, https://doi.org/10.5194/gmd-9-2471-2016, https://doi.org/10.5194/gmd-9-2471-2016, 2016
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Coupled ice sheet–ocean models capable of simulating moving grounding lines are just becoming available. Such models have a broad range of potential applications in studying the dynamics of ice sheets and glaciers, including assessing their contributions to sea level change. Here we describe the idealized experiments that make up three interrelated Model Intercomparison Projects (MIPs) for marine ice sheet models and regional ocean circulation models incorporating ice shelf cavities.
Olivier Passalacqua, Olivier Gagliardini, Frédéric Parrenin, Joe Todd, Fabien Gillet-Chaulet, and Catherine Ritz
Geosci. Model Dev., 9, 2301–2313, https://doi.org/10.5194/gmd-9-2301-2016, https://doi.org/10.5194/gmd-9-2301-2016, 2016
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In ice-flow modelling, computing in 3-D requires a lot of resources, but 2-D models lack physical likelihood when the flow is diverging. That is why 2-D models accounting for the divergence, so-called 2.5-D models, are an interesting trade-off. However, the applicability of these 2.5-D models has never been systematically examined. We show that these models are ineffective in the case of highly diverging flows, but also for varying temperature, which was not suspected.
Daniel N. Goldberg, Sri Hari Krishna Narayanan, Laurent Hascoet, and Jean Utke
Geosci. Model Dev., 9, 1891–1904, https://doi.org/10.5194/gmd-9-1891-2016, https://doi.org/10.5194/gmd-9-1891-2016, 2016
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Geophysical adjoint models are powerful tools, allowing sensitivity studies that are not possible otherwise, and enabling optimized fit of models to observing data sets. The complexity involved requires the use of algorithmic differentiation (AD) software, but AD adjoint calculation for ice models can be slow, with prohibitive memory requirements. In this paper, we present a method to improve the performance of ice model adjoint generation, in terms of timing, memory load, and accuracy.
David Pollard, Won Chang, Murali Haran, Patrick Applegate, and Robert DeConto
Geosci. Model Dev., 9, 1697–1723, https://doi.org/10.5194/gmd-9-1697-2016, https://doi.org/10.5194/gmd-9-1697-2016, 2016
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Computer modeling of variations of the Antarctic Ice Sheet help to
understand the ice sheet's sensitivity to climate change. We apply
a numerical model to its retreat over the last 20 000 years, from its
maximum glacial extent to modern. An ensemble of 625 simulations is performed
with systematic combinations of uncertain model parameter values. Results are
analyzed using (1) simple averaging, and (2) advanced statistical techniques,
and reasonable agreement is found between the two.
Evan J. Gowan, Paul Tregoning, Anthony Purcell, James Lea, Oscar J. Fransner, Riko Noormets, and J. A. Dowdeswell
Geosci. Model Dev., 9, 1673–1682, https://doi.org/10.5194/gmd-9-1673-2016, https://doi.org/10.5194/gmd-9-1673-2016, 2016
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We present a program that can create paleo-ice sheet reconstructions, using an assumed basal shear stress, margin location and basal topography as input. This allows for the quick determination of relatively realistic past ice sheet configurations without reliance on highly uncertain factors such as climate and ice dynamics. This is ideal for modelling Earth deformation due to the loading of ice sheets. The subsequent ice sheet configurations can be used as an input for climate modelling.
Cited articles
Abraham, C., Steiner, N., Monahan, A., and Michel, C.: Effects of
subgrid-scale
snow thickness variability on radiative transfer in sea ice, J.
Geophys. Res.-Oceans, 120, 5597–5614, https://doi.org/10.1002/2015JC010741, 2015. a
Amante, C. and Eakins, B. W.: ETOPO1 1 arc-minute global relief model:
procedures, data sources and analysis, US Department of Commerce, National
Oceanic and Atmospheric Administration, National Environmental Satellite,
Data, and Information Service, National Geophysical Data Center, Marine
Geology and Geophysics Division Colorado, Boulder, Colorado, USA, 2009. a
Arora, V. K., Scinocca, J. F., Boer, G. J., Christian, J. R., Denman, K. L.,
Flato, G. M., Kharin, V. V., Lee, W. G., and Merryfield, W. J.: Carbon
emission limits required to satisfy future representative concentration
pathways of greenhouse gases, Geophys. Res. Lett., 38, L05805,
https://doi.org/10.1029/2010GL046270, 2011. a
Arrigo, K. R.: Sea Ice Ecosystems, Annu. Rev. Mar. Sci., 6,
439–467, https://doi.org/10.1146/annurev-marine-010213-135103, 2014. a
Arrigo, K. R., Sullivan, C. W., and Kremer, J. N.: A bio-optical model of
Antarctic sea ice, J. Geophys. Res.-Oceans, 96,
10581–10592, https://doi.org/10.1029/91JC00455,
1991. a
Assmy, P., Ehn, J. K.,
Fernández-Méndez, M., Hop, H.,
Katlein, C., Sundfjord, A., Bluhm, K., Daase, M., Engel, A., Fransson, A.,
Granskog, M. A., Hudson, S. R., Kristiansen, S., Nicolaus, M., Peeken, I.,
Renner, A. H. H., Spreen, G., Tatarek, A., and Wiktor, J.: Floating
Ice-Algal Aggregates below Melting Arctic Sea Ice, PLoS ONE, 8,
e76599, https://doi.org/10.1371/journal.pone.0076599, 2013. a
Assmy, P., Fernández-Méndez, M., Duarte, P., Meyer, A., Randelhoff,
A.,
Mundy, C. J., Olsen, L. M., Kauko, H. M., Bailey, A., Chierici, M., Cohen,
L., Doulgeris, A. P., Ehn, J. K., Fransson, A., Gerland, S., Hop, H., Hudson,
S. R., Hughes, N., Itkin, P., Johnsen, G., King, J. A., Koch, B. P., Koenig,
Z., Kwasniewski, S., Laney, S. R., Nicolaus, M., Pavlov, A. K., Polashenski,
C. M., Provost, C., Rösel, A., Sandbu, M., Spreen, G., Smedsrud, L. H.,
Sundfjord, A., Taskjelle, T., Tatarek, A., Wiktor, J., Wagner, P. M., Wold,
A., Steen, H., and Granskog, M. A.: Leads in Arctic pack ice enable early
phytoplankton blooms below snow-covered sea ice, Sci. Rep., 7,
40850, https://doi.org/10.1038/srep40850,
2017. a
Aumont, O., Ethé, C., Tagliabue, A., Bopp, L., and Gehlen, M.: PISCES-v2:
an ocean biogeochemical model for carbon and ecosystem studies, Geosci. Model
Dev., 8, 2465–2513, https://doi.org/10.5194/gmd-8-2465-2015, 2015. a, b
Balmaseda, M. A., Mogensen, K., and Weaver, A. T.: Evaluation of the ECMWF
ocean reanalysis system ORAS4, Q. J. Roy.
Meteor. Soc., 139, 1132–1161, https://doi.org/10.1002/qj.2063,
2013. a
Bouillon, S., Maqueda, M. A. M., Legat, V., and Fichefet, T.: An
elastic–viscous–plastic sea ice model formulated on
Arakawa B and C grids, Ocean Model., 27, 174–184,
2009. a
Brown, Z. W., Lowry, K. E., Palmer, M. A., van Dijken, G. L., Mills, M. M.,
Pickart, R. S., and Arrigo, K. R.: Characterizing the subsurface chlorophyll
a maximum in the Chukchi Sea and Canada Basin, Deep-Sea Res. Pt. II,
118, 88–104, https://doi.org/10.1016/j.dsr2.2015.02.010, 2015. a, b
Crank, J. and Nicolson, P.: A practical method for numerical evaluation of
solutions of partial differential equations of the heat-conduction type,
Adv. Comput. Math., 6, 207–226, https://doi.org/10.1007/BF02127704, 1996. a
Dai, A. and Trenberth, K. E.: Estimates of Freshwater Discharge from
Continents: Latitudinal and Seasonal Variations, J.
Hydrometeorol., 3, 660–687,
https://doi.org/10.1175/1525-7541(2002)003<0660:EOFDFC>2.0.CO;2,
2002. a
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P.,
Kobayashi,
S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P.,
Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C.,
Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B.,
Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P., Köhler,
M., Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J.-J.,
Park, B.-K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J.-N., and
Vitart, F.: The ERA-Interim reanalysis: configuration and performance of
the data assimilation system, Q. J. Roy. Meteor.
Soc., 137, 553–597, https://doi.org/10.1002/qj.828,
2011. a
Dupont, F., Higginson, S., Bourdallé-Badie, R., Lu, Y., Roy, F., Smith,
G. C., Lemieux, J.-F., Garric, G., and Davidson, F.: A high-resolution ocean
and sea-ice modelling system for the Arctic and North Atlantic oceans,
Geosci. Model Dev., 8, 1577–1594, https://doi.org/10.5194/gmd-8-1577-2015,
2015. a
Eppley, R. W.: Temperature and phytoplankton growth in the sea, Fish. Bull.,
70, 1063–1085, 1972. a
Fichefet, T. and Maqueda, M. A. M.: Sensitivity of a global sea ice model to
the treatment of ice thermodynamics and dynamics, J. Geophys.
Res.-Oceans, 102, 12609–12646, https://doi.org/10.1029/97JC00480,
1997. a
Flato, G. M. and Brown, R. D.: Variability and climate sensitivity of
landfast
Arctic sea ice, J. Geophys. Res.-Oceans, 101,
25767–25777, https://doi.org/10.1029/96JC02431,
1996. a
Garcia, H. E., Locarnini, R. A., Boyer, T. P., Antonov, J. I., Baranova,
O. K.,
Zweng, M. M., Reagan, J. R., and Johnson, D. R.: World Ocean Atlas 2013,
Volume 3: Dissolved Oxygen, Apparent Oxygen Utilization, and
Oxygen Saturation, edited by: Levitus, S. and Mishonov, A., NOAA Atlas
NESDIS 75, National Oceanographic Data Center, Maryland, USA, 27 pp., 2014. a
Garrison, D. L., Ackley, S. F., and Buck, K. R.: A physical mechanism for
establishing algal populations in frazil ice, Nature, 306, 363–365,
https://doi.org/10.1038/306363a0,
1983. a
Hashimoto, S., Horimoto, N., Yamaguchi, Y., Ishimaru, T., and Saino, T.:
Relationship between net and gross primary production in the Sagami Bay,
Japan, Limnol. Oceanogr., 50, 1830–1835, 2005. a
Hayashida, H.: CSIB v1 source code (Version v1), Zenodo,
https://doi.org/10.5281/zenodo.1435254, 2018b. a
Hu, X. and Myers, P. G.: Changes to the Canadian Arctic Archipelago
Sea
Ice and Freshwater Fluxes in the Twenty-First Century under the
Intergovernmental Panel on Climate Change A1B Climate Scenario,
Atmos.-Ocean, 52, 331–350, https://doi.org/10.1080/07055900.2014.942592,
2014. a
Jin, M., Deal, C. J., Wang, J., Shin, K.-H., Tanaka, N., Whitledge, T. E.,
Lee,
S. H., and Gradinger, R. R.: Controls of the landfast ice–ocean
ecosystem offshore Barrow, Alaska, Ann. Glaciol., 44, 63–72,
2006. a
Jin, M., Deal, C., Lee, S. H., Elliott, S., Hunke, E., Maltrud, M., and
Jeffery, N.: Investigation of Arctic sea ice and ocean primary production
for the period 1992–2007 using a 3-D global
ice–ocean ecosystem model, Deep-Sea Res. Pt. II, 81–84, 28–35,
https://doi.org/10.1016/j.dsr2.2011.06.003, 2012. a, b, c, d, e, f, g, h, i, j, k, l
Jin, M., Popova, E. E., Zhang, J., Ji, R., Pendleton, D., Varpe, Ø., Yool,
A., and Lee, Y. J.: Ecosystem model intercomparison of under-ice and total
primary production in the Arctic Ocean, J. Geophys. Res.-Oceans, 121,
934–948, https://doi.org/10.1002/2015JC011183, 2015. a
Jin, M., Deal, C., Maslowski, W., Matrai, P., Roberts, A., Osinski, R., Lee,
Y. J., Frants, M., Elliott, S., Jeffery, N., Hunke, E., and Wang, S.: Effects
of Model Resolution and Ocean Mixing on Forced Ice-Ocean
Physical and Biogeochemical Simulations Using Global and Regional
System Models, J. Geophys. Res.-Oceans, 123, 358–377,
https://doi.org/10.1002/2017JC013365,
2018. a, b, c, d
Kauko, H. M., Taskjelle, T., Assmy, P., Pavlov, A. K., Mundy, C. J., Duarte,
P., Fernández-Méndez, M., Olsen, L. M., Hudson, S. R., Johnsen, G.,
Elliott, A., Wang, F., and Granskog, M. A.: Windows in Arctic sea ice:
Light transmission and ice algae in a refrozen lead, J. Geophys.
Res.-Biogeo., 122, 2016JG003626, https://doi.org/10.1002/2016JG003626,
2017. a
Lauvset, S. K., Key, R. M., Olsen, A., van Heuven, S., Velo, A., Lin, X.,
Schirnick, C., Kozyr, A., Tanhua, T., Hoppema, M., Jutterström, S.,
Steinfeldt, R., Jeansson, E., Ishii, M., Perez, F. F., Suzuki, T., and
Watelet, S.: A new global interior ocean mapped climatology: the
Lavoie, D., Denman, K., and Michel, C.: Modeling ice algal growth and decline
in a seasonally ice-covered region of the Arctic (Resolute Passage,
Canadian Archipelago), J. Geophys. Res., 110, C11009,
https://doi.org/10.1029/2005JC002922, 2005. a, b, c
Legendre, L., Ackley, S. F., Dieckmann, G. S., Gulliksen, B., Horner, R.,
Hoshiai, T., Melnikov, I. A., Reeburgh, W. S., Spindler, M., and Sullivan,
C. W.: Ecology of sea ice biota, Polar Biol., 12, 429–444,
https://doi.org/10.1007/BF00243114,
1992. a, b, c
Leu, E., Mundy, C. J., Assmy, P., Campbell, K., Gabrielsen, T. M., Gosselin,
M., Juul-Pedersen, T., and Gradinger, R.: Arctic spring awakening
– Steering principles behind the phenology of vernal ice algal
blooms, Prog. Oceanogr., 139, 151–170,
https://doi.org/10.1016/j.pocean.2015.07.012,
2015. a
Loose, B., McGillis, W. R., Schlosser, P., Perovich, D., and Takahashi, T.:
Effects of freezing, growth, and ice cover on gas transport processes in
laboratory seawater experiments, Geophys. Res. Lett., 36, L05603,
https://doi.org/10.1029/2008GL036318,
2009. a
Maykut, G. A. and Untersteiner, N.: Some results from a time-dependent
thermodynamic model of sea ice, J. Geophys. Res., 76,
1550–1575, https://doi.org/10.1029/JC076i006p01550,
1971. a
Melnikov, I. A., Kolosova, E. G., Welch, H. E., and Zhitina, L. S.: Sea ice
biological communities and nutrient dynamics in the Canada Basin of the
Arctic Ocean, Deep-Sea Res. Pt. I,
49, 1623–1649, https://doi.org/10.1016/S0967-0637(02)00042-0,
2002. a
Miller, L. A., Fripiat, F., Else, B. G., Bowman, J. S., Brown, K. A.,
Collins,
R. E., Ewert, M., Fransson, A., Gosselin, M., Lannuzel, D., Meiners, K. M.,
Michel, C., Nishioka, J., Nomura, D., Papadimitriou, S., Russell, L. M.,
Sørensen, L. L., Thomas, D. N., Tison, J.-L., van Leeuwe, M. A.,
Vancoppenolle, M., Wolff, E. W., and Zhou, J.: Methods for biogeochemical
studies of sea ice: The state of the art, caveats, and recommendations,
Elementa, 3, 38,
https://doi.org/10.12952/journal.elementa.000038,
2015. a
Morel, A.: Optical modeling of the upper ocean in relation to its biogenous
matter content (case I waters), J. Geophys. Res.-Oceans,
93, 10749–10768, https://doi.org/10.1029/JC093iC09p10749,
1988. a
Mortenson, E., Hayashida, H., Steiner, N., Monahan, A., Blais, M., Gale,
M. A.,
Galindo, V., Gosselin, M., Hu, X., Lavoie, D., and Mundy, C. J.: A
model-based analysis of physical and biological controls on ice algal and
pelagic primary production in Resolute Passage, Elem. Sci. Anth., 5, 39,
https://doi.org/10.1525/elementa.229,
2017. a, b, c, d, e, f
Olsen, L. M., Laney, S. R., Duarte, P., Kauko, H. M.,
Fernández-Méndez,
M., Mundy, C. J., Rösel, A., Meyer, A., Itkin, P., Cohen, L., Peeken, I.,
Tatarek, A., Róźańska-Pluta, M., Wiktor, J., Taskjelle, T.,
Pavlov, A. K., Hudson, S. R., Granskog, M. A., Hop, H., and Assmy, P.: The
seeding of ice algal blooms in Arctic pack ice: The multiyear ice seed
repository hypothesis, J. Geophys. Res.-Biogeo., 122,
2016JG003668, https://doi.org/10.1002/2016JG003668,
2017. a
Pabi, S., van Dijken, G. L., and Arrigo, K. R.: Primary production in the
Arctic Ocean, 1998–2006, J. Geophys. Res.-Oceans, 113, C08005,
https://doi.org/10.1029/2007JC004578, 2008. a
Popova, E. E., Yool, A., Coward, A. C., Dupont, F., Deal, C., Elliott, S.,
Hunke, E., Jin, M., Steele, M., and Zhang, J.: What controls primary
production in the Arctic Ocean? Results from an intercomparison of five
general circulation models with biogeochemistry, J. Geophys.
Res.-Oceans, 117, C00D12, https://doi.org/10.1029/2011JC007112,
2012. a, b
Prather, M. J.: Numerical advection by conservation of second-order moments,
J. Geophys. Res.-Atmos., 91, 6671–6681,
https://doi.org/10.1029/JD091iD06p06671,
1986. a
Rampal, P., Weiss, J., Marsan, D., and Bourgoin, M.: Arctic sea ice velocity
field: General circulation and turbulent-like fluctuations, J.
Geophys. Res.-Oceans, 114, C10014, https://doi.org/10.1029/2008JC005227,
2009. a
Rampal, P., Bouillon, S., Bergh, J., and Ólason, E.: Arctic sea-ice
diffusion from observed and simulated Lagrangian trajectories, The
Cryosphere, 10, 1513–1527, https://doi.org/10.5194/tc-10-1513-2016, 2016. a
Rebreanu, L., Vanderborght, J.-P., and Chou, L.: The diffusion coefficient of
dissolved silica revisited, Mar. Chem., 112, 230–233,
https://doi.org/10.1016/j.marchem.2008.08.004,
2008. a
Roy, F., Chevallier, M., Smith, G. C., Dupont, F., Garric, G., Lemieux,
J.-F.,
Lu, Y., and Davidson, F.: Arctic sea ice and freshwater sensitivity to the
treatment of the atmosphere-ice-ocean surface layer: THE ARCTIC
AIR-ICE-OCEAN SURFACE LAYER, J. Geophys. Res.-Oceans, 120, 4392–4417, https://doi.org/10.1002/2014JC010677, 2015. a
Sakshaug, E.: Primary and Secondary Production in the Arctic Seas,
in:
The Organic Carbon Cycle in the Arctic Ocean, edited by: Stein, R.
and MacDonald, R. W., Springer Berlin Heidelberg, 57–81,
https://doi.org/10.1007/978-3-642-18912-8_3,
2004. a
Sakshaug, E., Bricaud, A., Dandonneau, Y., Falkowski, P. G., Kiefer, D. A.,
Legendre, L., Morel, A., Parslow, J., and Takahashi, M.: Parameters of
photosynthesis: definitions, theory and interpretation of results, J.
Plankton Res., 19, 1637–1670, https://doi.org/10.1093/plankt/19.11.1637,
1997. a
Schweiger, A., Lindsay, R., Zhang, J., Steele, M., Stern, H., and Kwok, R.:
Uncertainty in modeled Arctic sea ice volume, J. Geophys.
Res.-Oceans, 116, C00D06, https://doi.org/10.1029/2011JC007084,
2011. a
Steele, M., Morley, R., and Ermold, W.: PHC: A Global Ocean
Hydrography with a High-Quality Arctic Ocean, J. Climate,
14, 2079–2087, https://doi.org/10.1175/1520-0442(2001)014<2079:PAGOHW>2.0.CO;2,
2001. a
Stefels, J., Steinke, M., Turner, S., Malin, G., and Belviso, S.:
Environmental
constraints on the production and removal of the climatically active gas
dimethylsulphide (DMS) and implications for ecosystem modelling,
Biogeochemistry, 83, 245–275, https://doi.org/10.1007/s10533-007-9091-5,
2007. a
Steiner, N. S., Sou, T., Deal, C., Jackson, J. M., Jin, M., Popova, E.,
Williams, W., and Yool, A.: The Future of the Subsurface Chlorophyll-a
Maximum in the Canada Basin - A Model Intercomparison, J.
Geophys. Res.-Oceans, 120, 387–409, https://doi.org/10.1002/2015JC011232, 2015. a
Steiner, N. S., et al.: Using models to assess and understand trends in
Arctic Ocean phytoplankton production from 1979–2015, in preparation, 2019. a
Tedesco, L., Miettunen, E., An, B. W., Happala, J., and Kaartokallio, H.:
Long-term mesoscale variability of modelled sea-ice primary production in the
northern Baltic Sea, Elem. Sci. Anth., 5, 29, https://doi.org/10.1525/elementa.223,
2017. a
Thorndike, A. S.: Diffusion of sea ice, J. Geophys. Res.-Oceans, 91,
7691–7696, https://doi.org/10.1029/JC091iC06p07691, 1986. a
Tsujino, H., Urakawa, S., Nakano, H., Small, R. J., Kim, W. M., Yeager,
S. G.,
Danabasoglu, G., Suzuki, T., Bamber, J. L., Bentsen, M., Böning, C. W.,
Bozec, A., Chassignet, E. P., Curchitser, E., Boeira Dias, F., Durack, P. J.,
Griffies, S. M., Harada, Y., Ilicak, M., Josey, S. A., Kobayashi, C.,
Kobayashi, S., Komuro, Y., Large, W. G., Le Sommer, J., Marsland, S. J.,
Masina, S., Scheinert, M., Tomita, H., Valdivieso, M., and Yamazaki, D.:
JRA-55 based surface dataset for driving ocean–sea-ice models
(JRA55-do), Ocean Model., 130, 79–139,
https://doi.org/10.1016/j.ocemod.2018.07.002,
2018. a
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. Meteor. Soc., 131,
2961–3012, https://doi.org/10.1256/qj.04.176,
2005. a
Vancoppenolle, M. and Tedesco, L.: Numerical models of sea ice
biogeochemistry, in: Sea Ice, Wiley-Blackwell, 3 edn., 492–515,
https://doi.org/10.1002/9781118778371.ch20,
2016. a
Vancoppenolle, M., Goosse, H., de Montety, A., Fichefet, T., Tremblay, B.,
and
Tison, J.-L.: Modeling brine and nutrient dynamics in Antarctic sea ice:
The case of dissolved silica, J. Geophys. Res., 115,
C02005, https://doi.org/10.1029/2009JC005369, 2010. a
Vancoppenolle, M., Bouillon, S., Fichefet, T., Goosse, H., Lecomte, O.,
Maqueda, M. A. M., and Madec, G.: The Louvain-la-Neuve sea ice model,
Tech. rep., Université catholique de Louvain, available at:
http://www.climate.be/users/lecomte/LIM3_users_guide_2012.pdf (last
access: 13 May 2019), 2012. a, b, c, d
Vancoppenolle, M., Meiners, K. M., Michel, C., Bopp, L., Brabant, F., Carnat,
G., Delille, B., Lannuzel, D., Madec, G., Moreau, S., Tison, J.-L., and
van der Merwe, P.: Role of sea ice in global biogeochemical cycles: emerging
views and challenges, Quaternary Sci. Rev., 79, 207–230,
https://doi.org/10.1016/j.quascirev.2013.04.011,
2013. a
Wang, Z., Lu, Y., Wright, D. G., and Dupont, F.: Sea ice sensitivity to the
parameterisation of open water area, J. Oper. Oceanogr., 3,
3–9, https://doi.org/10.1080/1755876X.2010.11020113,
2010.
a
Warren, S. G., Rigor, I. G., Untersteiner, N., Radionov, V. F., Bryazgin,
N. N., Aleksandrov, Y. I., and Colony, R.: Snow Depth on Arctic Sea
Ice, J. Climate, 12, 1814–1829, https://doi.org/10.1175/1520-0442(1999)012<1814:SDOASI>2.0.CO;2,
1999. a
Watanabe, E.: Linkages among halocline variability, shelf-basin interaction,
and wind regimes in the Beaufort Sea demonstrated in pan-Arctic Ocean
modeling framework, Ocean Model., 71, 43–53,
https://doi.org/10.1016/j.ocemod.2012.12.010,
2013. a
Watanabe, E., Onodera, J., Harada, N., Aita, M. N., Ishida, A., and Kishi, M.
J.: Wind-driven interannual variability of sea ice algal production in the
western Arctic Chukchi Borderland, Biogeosciences, 12, 6147–6168,
https://doi.org/10.5194/bg-12-6147-2015, 2015. a, b, c
Wolf-Gladrow, D. A., Zeebe, R. E., Klaas, C., Körtzinger, A., and
Dickson,
A. G.: Total alkalinity: The explicit conservative expression and its
application to biogeochemical processes, Mar. Chem., 106, 287–300,
https://doi.org/10.1016/j.marchem.2007.01.006,
2007. a
Zeebe, R. E., Eicken, H., Robinson, D. H., Wolf-Gladrow, D., and Dieckmann,
G. S.: Modeling the heating and melting of sea ice through light absorption
by microalgae, J. Geophys. Res.-Oceans, 101, 1163–1181,
https://doi.org/10.1029/95JC02687,
1996. a, b, c
Zhang, J. and Rothrock, D. A.: Modeling Global Sea Ice with a
Thickness
and Enthalpy Distribution Model in Generalized Curvilinear
Coordinates, Mon. Weather Rev., 131, 845–861,
https://doi.org/10.1175/1520-0493(2003)131<0845:MGSIWA>2.0.CO;2,
2003. a
Zhang, J., Spitz, Y. H., Steele, M., Ashjian, C., Campbell, R., Berline, L.,
and Matrai, P.: Modeling the impact of declining sea ice on the Arctic
marine planktonic ecosystem, J. Geophys. Res.-Oceans, 115,
C10015, https://doi.org/10.1029/2009JC005387,
2010. a, b
Search articles
Short summary
Ice algae, the primary producer in sea ice, play a fundamental role in shaping marine ecosystems and biogeochemical cycling of key elements in polar regions. In this study, we developed a process-based numerical model component representing sea-ice biogeochemistry for a sea ice–ocean coupled general circulation model. The model developed can be used to simulate the projected changes in sea-ice ecosystems and biogeochemistry in response to on-going rapid decline of the Arctic.
Ice algae, the primary producer in sea ice, play a fundamental role in shaping marine ecosystems...
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