Articles | Volume 9, issue 4
Geosci. Model Dev., 9, 1455–1476, 2016
https://doi.org/10.5194/gmd-9-1455-2016
Geosci. Model Dev., 9, 1455–1476, 2016
https://doi.org/10.5194/gmd-9-1455-2016

Model description paper 19 Apr 2016

Model description paper | 19 Apr 2016

A stochastic, Lagrangian model of sinking biogenic aggregates in the ocean (SLAMS 1.0): model formulation, validation and sensitivity

Tinna Jokulsdottir and David Archer

Related authors

A model of mercury cycling and isotopic fractionation in the ocean
David E. Archer and Joel D. Blum
Biogeosciences, 15, 6297–6313, https://doi.org/10.5194/bg-15-6297-2018,https://doi.org/10.5194/bg-15-6297-2018, 2018
Short summary
A model of the methane cycle, permafrost, and hydrology of the Siberian continental margin
D. Archer
Biogeosciences, 12, 2953–2974, https://doi.org/10.5194/bg-12-2953-2015,https://doi.org/10.5194/bg-12-2953-2015, 2015
Short summary
Modeling the impediment of methane ebullition bubbles by seasonal lake ice
S. Greene, K. M. Walter Anthony, D. Archer, A. Sepulveda-Jauregui, and K. Martinez-Cruz
Biogeosciences, 11, 6791–6811, https://doi.org/10.5194/bg-11-6791-2014,https://doi.org/10.5194/bg-11-6791-2014, 2014
Short summary

Related subject area

Biogeosciences
Calibrating soybean parameters in JULES 5.0 from the US-Ne2/3 FLUXNET sites and the SoyFACE-O3 experiment
Felix Leung, Karina Williams, Stephen Sitch, Amos P. K. Tai, Andy Wiltshire, Jemma Gornall, Elizabeth A. Ainsworth, Timothy Arkebauer, and David Scoby
Geosci. Model Dev., 13, 6201–6213, https://doi.org/10.5194/gmd-13-6201-2020,https://doi.org/10.5194/gmd-13-6201-2020, 2020
Short summary
Modeling long-term fire impact on ecosystem characteristics and surface energy using a process-based vegetation–fire model SSiB4/TRIFFID-Fire v1.0
Huilin Huang, Yongkang Xue, Fang Li, and Ye Liu
Geosci. Model Dev., 13, 6029–6050, https://doi.org/10.5194/gmd-13-6029-2020,https://doi.org/10.5194/gmd-13-6029-2020, 2020
Short summary
Energy, water and carbon exchanges in managed forest ecosystems: description, sensitivity analysis and evaluation of the INRAE GO+ model, version 3.0
Virginie Moreaux, Simon Martel, Alexandre Bosc, Delphine Picart, David Achat, Christophe Moisy, Raphael Aussenac, Christophe Chipeaux, Jean-Marc Bonnefond, Soisick Figuères, Pierre Trichet, Rémi Vezy, Vincent Badeau, Bernard Longdoz, André Granier, Olivier Roupsard, Manuel Nicolas, Kim Pilegaard, Giorgio Matteucci, Claudy Jolivet, Andrew T. Black, Olivier Picard, and Denis Loustau
Geosci. Model Dev., 13, 5973–6009, https://doi.org/10.5194/gmd-13-5973-2020,https://doi.org/10.5194/gmd-13-5973-2020, 2020
Short summary
Improving Yasso15 soil carbon model estimates with ensemble adjustment Kalman filter state data assimilation
Toni Viskari, Maisa Laine, Liisa Kulmala, Jarmo Mäkelä, Istem Fer, and Jari Liski
Geosci. Model Dev., 13, 5959–5971, https://doi.org/10.5194/gmd-13-5959-2020,https://doi.org/10.5194/gmd-13-5959-2020, 2020
Short summary
Oceanic and atmospheric methane cycling in the cGENIE Earth system model – release v0.9.14
Christopher T. Reinhard, Stephanie L. Olson, Sandra Kirtland Turner, Cecily Pälike, Yoshiki Kanzaki, and Andy Ridgwell
Geosci. Model Dev., 13, 5687–5706, https://doi.org/10.5194/gmd-13-5687-2020,https://doi.org/10.5194/gmd-13-5687-2020, 2020
Short summary

Cited articles

Alldredge, A. and Gotschalk, C.: In situ settling behavior of marine snow, Limnol. Oceanogr., 33, 339–351, 1988.
Alldredge, A. and McGillivary, P.: The attachment probabilities of marine snow and their implications for particle coagulation in the ocean, Deep-Sea Res., 38, 431–443, 1991.
Alldredge, A. and Silver, M. W.: Characteristics, Dynamics and Significance of Marine Snow, Prog. Oceanogr., 20, 41–82, 1988.
Alldredge, A., Granata, G. C., Gotschalk, C. C., and Dickey, T. D.: The physical strength of marine snow and its implications for particle disaggregation in the ocean, Limnol. Oceanogr., 35, 1415–1428, 1990.
Alldredge, A., Gotschalk, C., Passow, U., and Riebesell, U.: Mass aggregation of diatom blooms: Insights from a mesocosm study, Deep-Sea Res. Pt. II, 42, 9–27, 1995.
Download
Short summary
To better understand what controls the flux of organic and inorganic material down the water column we developed a numerical model that simulates coagulation, settling and bio-chemical transformation of particles in the ocean. To simulate the many types of material the particles constitute, we took a Lagrangian approach. Our results suggest the flux is most sensitive to environmental change in polar regions. We found that zooplankton are the biggest unknown when predicting the flux.