Articles | Volume 17, issue 2
https://doi.org/10.5194/gmd-17-621-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-17-621-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
26 Jan 2024
Development and technical paper |
| 26 Jan 2024
| 26 Jan 2024
Computationally efficient parameter estimation for high-dimensional ocean biogeochemical models
Paul M. Rady Department of Mechanical Engineering, University of Colorado Boulder, Boulder, CO, USA
Mary E. McGuinn
Paul M. Rady Department of Mechanical Engineering, University of Colorado Boulder, Boulder, CO, USA
Katherine M. Smith
Los Alamos National Laboratory, Los Alamos, NM, USA
Nadia Pinardi
Department of Physics and Astronomy, University of Bologna, Bologna, Italy
Kyle E. Niemeyer
School of Mechanical, Industrial, and Manufacturing Engineering, Oregon State University, Corvallis, OR, USA
Nicole S. Lovenduski
Department of Atmospheric and Oceanic Sciences, Institute of Arctic and Alpine Research, University of Colorado Boulder, Boulder, CO, USA
Peter E. Hamlington
Paul M. Rady Department of Mechanical Engineering, University of Colorado Boulder, Boulder, CO, USA
Related authors
Katherine M. Smith, Skyler Kern, Peter E. Hamlington, Marco Zavatarelli, Nadia Pinardi, Emily F. Klee, and Kyle E. Niemeyer
Geosci. Model Dev., 14, 2419–2442, https://doi.org/10.5194/gmd-14-2419-2021, https://doi.org/10.5194/gmd-14-2419-2021, 2021
Short summary
Short summary
We present a newly developed reduced-order biogeochemical flux model that is complex and flexible enough to capture open-ocean ecosystem dynamics but reduced enough to incorporate into highly resolved numerical simulations with limited additional computational cost. The model provides improved correlations between model output and field data, indicating that significant improvements in the reproduction of real-world data can be achieved with a small number of variables.
Mahmud Hasan Ghani, Nadia Pinardi, Antonio Navarra, Lorenzo Mentaschi, Silvia Bianconcini, Francesco Maicu, and Francesco Trotta
EGUsphere, https://doi.org/10.5194/egusphere-2025-2867, https://doi.org/10.5194/egusphere-2025-2867, 2025
This preprint is open for discussion and under review for Ocean Science (OS).
Short summary
Short summary
Using the same SST and the same bulk formula, but different atmospheric reanalysis and analysis surface variable datasets, we show that higher resolution (ECMWF) dataset is crucial for evaluating the heat budget closure hypothesis in the Mediterranean Sea. For the first time, we investigate the impact of extreme heat loss events in the Mediterranean Sea in the long-term mean basin-averaged heat budget.
Paolo Oddo, Mario Adani, Francesco Carere, Andrea Cipollone, Anna Chiara Goglio, Eric Jansen, Ali Aydogdu, Francesca Mele, Italo Epicoco, Jenny Pistoia, Emanuela Clementi, Nadia Pinardi, and Simona Masina
EGUsphere, https://doi.org/10.5194/egusphere-2025-1553, https://doi.org/10.5194/egusphere-2025-1553, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
This study present a data assimilation scheme that combines ocean observational data with ocean model results to better understand the ocean and predict its future state. The method uses a variational approach focusing on the physical relationships between all the state vector variables errors. Testing in the Mediterranean Sea showed that a complex sea level operator based on a barotropic model works best.
Rita Lecci, Robyn Gwee, Kun Yan, Sanne Muis, Nadia Pinardi, Jun She, Martin Verlaan, Simona Masina, Wenshan Li, Hui Wang, Salvatore Causio, Antonio Novellino, Marco Alba, Etiënne Kras, Sandra Gaytan Aguilar, and Jan-Bart Calewaert
EGUsphere, https://doi.org/10.5194/egusphere-2025-1763, https://doi.org/10.5194/egusphere-2025-1763, 2025
This preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).
Short summary
Short summary
This study explored how sea level is changing along the China-Europe Sea Route. By combining satellite and in-situ observations with advanced modeling, the research identified ongoing sea level rise and an increasing frequency of extreme water level events in some regions. These findings underscore the importance of continued monitoring and provide useful knowledge to support long-term planning, coastal resilience, and informed decision-making.
Italo R. Lopes, Ivan Federico, Michalis Vousdoukas, Luisa Perini, Salvatore Causio, Giovanni Coppini, Maurilio Milella, Nadia Pinardi, and Lorenzo Mentaschi
EGUsphere, https://doi.org/10.5194/egusphere-2025-1695, https://doi.org/10.5194/egusphere-2025-1695, 2025
This preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).
Short summary
Short summary
We improved a computer model to simulate coastal flooding by including temporary barriers like sand dunes. We tested it where sand dunes are built seasonally to protect the shoreline for two real storms: one that broke through the dunes and another where dunes held strong. Our model showed how important it is to design these defenses carefully since even if a small part of a dune fails, a major flooding can happen. Overall, our work helps create better tools to manage and protect coastal areas.
Katherine M. Smith, Alice M. Barthel, LeAnn M. Conlon, Luke P. Van Roekel, Anthony Bartoletti, Jean-Christophe Golaz, Chengzhu Zhang, Carolyn Branecky Begeman, James J. Benedict, Gautam Bisht, Yan Feng, Walter Hannah, Bryce E. Harrop, Nicole Jeffery, Wuyin Lin, Po-Lun Ma, Mathew E. Maltrud, Mark R. Petersen, Balwinder Singh, Qi Tang, Teklu Tesfa, Jonathan D. Wolfe, Shaocheng Xie, Xue Zheng, Karthik Balaguru, Oluwayemi Garuba, Peter Gleckler, Aixue Hu, Jiwoo Lee, Ben Moore-Maley, and Ana C. Ordoñez
Geosci. Model Dev., 18, 1613–1633, https://doi.org/10.5194/gmd-18-1613-2025, https://doi.org/10.5194/gmd-18-1613-2025, 2025
Short summary
Short summary
Version 2.1 of the U.S. Department of Energy's Energy Exascale Earth System Model (E3SM) adds the Fox-Kemper et al. (2011) mixed-layer eddy parameterization, which restratifies the ocean surface layer through an overturning streamfunction. Results include surface layer bias reduction in temperature, salinity, and sea ice extent in the North Atlantic; a small strengthening of the Atlantic meridional overturning circulation; and improvements to many atmospheric climatological variables.
Seimur Shirinov, Ivan Federico, Simone Bonamano, Salvatore Causio, Nicolás Biocca, Viviana Piermattei, Daniele Piazzolla, Jacopo Alessandri, Lorenzo Mentaschi, Giovanni Coppini, Marco Marcelli, and Nadia Pinardi
EGUsphere, https://doi.org/10.5194/egusphere-2025-321, https://doi.org/10.5194/egusphere-2025-321, 2025
Short summary
Short summary
This study explores the impact of submerged vegetation on wave dynamics in vulnerable coastal regions. By incorporating measurements into a numerical model, we estimate the critical role of seagrass as a natural defense system. This research advances understanding of wave-vegetation interactions, achieving a more accurate representation of marine environments while supporting restoration efforts and emphasizing the need to preserve these ecosystems for resilience.
José A. Jiménez, Gundula Winter, Antonio Bonaduce, Michael Depuydt, Giulia Galluccio, Bart van den Hurk, H. E. Markus Meier, Nadia Pinardi, Lavinia G. Pomarico, and Natalia Vazquez Riveiros
State Planet, 3-slre1, 3, https://doi.org/10.5194/sp-3-slre1-3-2024, https://doi.org/10.5194/sp-3-slre1-3-2024, 2024
Short summary
Short summary
The Knowledge Hub on Sea Level Rise (SLR) has done a scoping study involving stakeholders from government and academia to identify gaps and needs in SLR information, impacts, and policies across Europe. Gaps in regional SLR projections and uncertainties were found, while concerns were raised about shoreline erosion and emerging problems like saltwater intrusion and ineffective adaptation plans. The need for improved communication to make better decisions on SLR adaptation was highlighted.
Nadia Pinardi, Bart van den Hurk, Michael Depuydt, Thorsten Kiefer, Petra Manderscheid, Lavinia Giulia Pomarico, and Kanika Singh
State Planet, 3-slre1, 2, https://doi.org/10.5194/sp-3-slre1-2-2024, https://doi.org/10.5194/sp-3-slre1-2-2024, 2024
Short summary
Short summary
The Knowledge Hub on Sea Level Rise (KH-SLR), a joint effort between JPI Climate and JPI Oceans, addresses the critical need for science-based information on sea level changes in Europe. The KH-SLR actively involves stakeholders through a co-design process discussing the impacts, adaptation planning, and policy requirements related to SLR in Europe. Its primary output is the KH Assessment Report (KH-AR), which is described in this volume.
Bart van den Hurk, Nadia Pinardi, Alexander Bisaro, Giulia Galluccio, José A. Jiménez, Kate Larkin, Angélique Melet, Lavinia Giulia Pomarico, Kristin Richter, Kanika Singh, Roderik van de Wal, and Gundula Winter
State Planet, 3-slre1, 1, https://doi.org/10.5194/sp-3-slre1-1-2024, https://doi.org/10.5194/sp-3-slre1-1-2024, 2024
Short summary
Short summary
The Summary for Policymakers compiles findings from “Sea Level Rise in Europe: 1st Assessment Report of the Knowledge Hub on Sea Level Rise”. It covers knowledge gaps, observations, projections, impacts, adaptation measures, decision-making principles, and governance challenges. It provides information for each European basin (Mediterranean, Black Sea, North Sea, Baltic Sea, Atlantic, and Arctic) and aims to assist policymakers in enhancing the preparedness of European coasts for sea level rise.
Joshua Coupe, Nicole S. Lovenduski, Luise S. Gleason, Michael N. Levy, Kristen Krumhardt, Keith Lindsay, Charles Bardeen, Clay Tabor, Cheryl Harrison, Kenneth G. MacLeod, Siddhartha Mitra, and Julio Sepúlveda
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-94, https://doi.org/10.5194/gmd-2024-94, 2024
Preprint under review for GMD
Short summary
Short summary
We develop a new feature in the atmosphere and ocean components of the Community Earth System Model version 2. We have implemented ultraviolet (UV) radiation inhibition of photosynthesis of four marine phytoplankton functional groups represented in the Marine Biogeochemistry Library. The new feature is tested with varying levels of UV radiation. The new feature will enable an analysis of an asteroid impact’s effect on the ozone layer and how that affects the base of the marine food web.
Cara Nissen, Nicole S. Lovenduski, Mathew Maltrud, Alison R. Gray, Yohei Takano, Kristen Falcinelli, Jade Sauvé, and Katherine Smith
Geosci. Model Dev., 17, 6415–6435, https://doi.org/10.5194/gmd-17-6415-2024, https://doi.org/10.5194/gmd-17-6415-2024, 2024
Short summary
Short summary
Autonomous profiling floats have provided unprecedented observational coverage of the global ocean, but uncertainties remain about whether their sampling frequency and density capture the true spatiotemporal variability of physical, biogeochemical, and biological properties. Here, we present the novel synthetic biogeochemical float capabilities of the Energy Exascale Earth System Model version 2 and demonstrate their utility as a test bed to address these uncertainties.
Bethany McDonagh, Emanuela Clementi, Anna Chiara Goglio, and Nadia Pinardi
Ocean Sci., 20, 1051–1066, https://doi.org/10.5194/os-20-1051-2024, https://doi.org/10.5194/os-20-1051-2024, 2024
Short summary
Short summary
Tides in the Mediterranean Sea are typically of low amplitude, but twin experiments with and without tides demonstrate that tides affect the circulation directly at scales away from those of the tides. Analysis of the energy changes due to tides shows that they enhance existing oscillations, and internal tides interact with other internal waves. Tides also increase the mixed layer depth and enhance deep water formation in key regions. Internal tides are widespread in the Mediterranean Sea.
Roberta Benincasa, Giovanni Liguori, Nadia Pinardi, and Hans von Storch
Ocean Sci., 20, 1003–1012, https://doi.org/10.5194/os-20-1003-2024, https://doi.org/10.5194/os-20-1003-2024, 2024
Short summary
Short summary
Ocean dynamics result from the interplay of internal processes and external inputs, primarily from the atmosphere. It is crucial to discern between these factors to gauge the ocean's intrinsic predictability and to be able to attribute a signal under study to either external factors or internal variability. Employing a simple analysis, we successfully characterized this variability in the Mediterranean Sea and compared it with the oceanic response induced by atmospheric conditions.
Genevieve L. Clow, Nicole S. Lovenduski, Michael N. Levy, Keith Lindsay, and Jennifer E. Kay
Geosci. Model Dev., 17, 975–995, https://doi.org/10.5194/gmd-17-975-2024, https://doi.org/10.5194/gmd-17-975-2024, 2024
Short summary
Short summary
Satellite observations of chlorophyll allow us to study marine phytoplankton on a global scale; yet some of these observations are missing due to clouds and other issues. To investigate the impact of missing data, we developed a satellite simulator for chlorophyll in an Earth system model. We found that missing data can impact the global mean chlorophyll by nearly 20 %. The simulated observations provide a more direct comparison to real-world data and can be used to improve model validation.
Geneviève W. Elsworth, Nicole S. Lovenduski, Kristen M. Krumhardt, Thomas M. Marchitto, and Sarah Schlunegger
Biogeosciences, 20, 4477–4490, https://doi.org/10.5194/bg-20-4477-2023, https://doi.org/10.5194/bg-20-4477-2023, 2023
Short summary
Short summary
Anthropogenic climate change will influence marine phytoplankton over the coming century. Here, we quantify the influence of anthropogenic climate change on marine phytoplankton internal variability using an Earth system model ensemble and identify a decline in global phytoplankton biomass variance with warming. Our results suggest that climate mitigation efforts that account for marine phytoplankton changes should also consider changes in phytoplankton variance driven by anthropogenic warming.
Giovanni Coppini, Emanuela Clementi, Gianpiero Cossarini, Stefano Salon, Gerasimos Korres, Michalis Ravdas, Rita Lecci, Jenny Pistoia, Anna Chiara Goglio, Massimiliano Drudi, Alessandro Grandi, Ali Aydogdu, Romain Escudier, Andrea Cipollone, Vladyslav Lyubartsev, Antonio Mariani, Sergio Cretì, Francesco Palermo, Matteo Scuro, Simona Masina, Nadia Pinardi, Antonio Navarra, Damiano Delrosso, Anna Teruzzi, Valeria Di Biagio, Giorgio Bolzon, Laura Feudale, Gianluca Coidessa, Carolina Amadio, Alberto Brosich, Arnau Miró, Eva Alvarez, Paolo Lazzari, Cosimo Solidoro, Charikleia Oikonomou, and Anna Zacharioudaki
Ocean Sci., 19, 1483–1516, https://doi.org/10.5194/os-19-1483-2023, https://doi.org/10.5194/os-19-1483-2023, 2023
Short summary
Short summary
The paper presents the Mediterranean Forecasting System evolution and performance developed in the framework of the Copernicus Marine Service.
István Dunkl, Nicole Lovenduski, Alessio Collalti, Vivek K. Arora, Tatiana Ilyina, and Victor Brovkin
Biogeosciences, 20, 3523–3538, https://doi.org/10.5194/bg-20-3523-2023, https://doi.org/10.5194/bg-20-3523-2023, 2023
Short summary
Short summary
Despite differences in the reproduction of gross primary productivity (GPP) by Earth system models (ESMs), ESMs have similar predictability of the global carbon cycle. We found that, although GPP variability originates from different regions and is driven by different climatic variables across the ESMs, the ESMs rely on the same mechanisms to predict their own GPP. This shows that the predictability of the carbon cycle is limited by our understanding of variability rather than predictability.
Umesh Pranavam Ayyappan Pillai, Nadia Pinardi, Ivan Federico, Salvatore Causio, Francesco Trotta, Silvia Unguendoli, and Andrea Valentini
Nat. Hazards Earth Syst. Sci., 22, 3413–3433, https://doi.org/10.5194/nhess-22-3413-2022, https://doi.org/10.5194/nhess-22-3413-2022, 2022
Short summary
Short summary
The study presents the application of high-resolution coastal modelling for wave hindcasting on the Emilia-Romagna coastal belt. The generated coastal databases which provide an understanding of the prevailing wind-wave characteristics can aid in predicting coastal impacts.
Julian Quick, Ryan N. King, Garrett Barter, and Peter E. Hamlington
Wind Energ. Sci., 7, 1941–1955, https://doi.org/10.5194/wes-7-1941-2022, https://doi.org/10.5194/wes-7-1941-2022, 2022
Short summary
Short summary
Wake steering is an emerging wind power plant control strategy where upstream turbines are intentionally yawed out of alignment with the incoming wind, thereby steering wakes away from downstream turbines. Trade-offs between the gains in power production and fatigue loads induced by this control strategy are the subject of continuing investigation. In this study, we present an optimization approach for efficiently exploring the trade-offs between power and loading during wake steering.
Stephen G. Yeager, Nan Rosenbloom, Anne A. Glanville, Xian Wu, Isla Simpson, Hui Li, Maria J. Molina, Kristen Krumhardt, Samuel Mogen, Keith Lindsay, Danica Lombardozzi, Will Wieder, Who M. Kim, Jadwiga H. Richter, Matthew Long, Gokhan Danabasoglu, David Bailey, Marika Holland, Nicole Lovenduski, Warren G. Strand, and Teagan King
Geosci. Model Dev., 15, 6451–6493, https://doi.org/10.5194/gmd-15-6451-2022, https://doi.org/10.5194/gmd-15-6451-2022, 2022
Short summary
Short summary
The Earth system changes over a range of time and space scales, and some of these changes are predictable in advance. Short-term weather forecasts are most familiar, but recent work has shown that it is possible to generate useful predictions several seasons or even a decade in advance. This study focuses on predictions over intermediate timescales (up to 24 months in advance) and shows that there is promising potential to forecast a variety of changes in the natural environment.
Giorgio Micaletto, Ivano Barletta, Silvia Mocavero, Ivan Federico, Italo Epicoco, Giorgia Verri, Giovanni Coppini, Pasquale Schiano, Giovanni Aloisio, and Nadia Pinardi
Geosci. Model Dev., 15, 6025–6046, https://doi.org/10.5194/gmd-15-6025-2022, https://doi.org/10.5194/gmd-15-6025-2022, 2022
Short summary
Short summary
The full exploitation of supercomputing architectures requires a deep revision of the current climate models. This paper presents the parallelization of the three-dimensional hydrodynamic model SHYFEM (System of HydrodYnamic Finite Element Modules). Optimized numerical libraries were used to partition the model domain and solve the sparse linear system of equations in parallel. The performance assessment demonstrates a good level of scalability with a realistic configuration used as a benchmark.
Katherine M. Smith, Skyler Kern, Peter E. Hamlington, Marco Zavatarelli, Nadia Pinardi, Emily F. Klee, and Kyle E. Niemeyer
Geosci. Model Dev., 14, 2419–2442, https://doi.org/10.5194/gmd-14-2419-2021, https://doi.org/10.5194/gmd-14-2419-2021, 2021
Short summary
Short summary
We present a newly developed reduced-order biogeochemical flux model that is complex and flexible enough to capture open-ocean ecosystem dynamics but reduced enough to incorporate into highly resolved numerical simulations with limited additional computational cost. The model provides improved correlations between model output and field data, indicating that significant improvements in the reproduction of real-world data can be achieved with a small number of variables.
Lester Kwiatkowski, Olivier Torres, Laurent Bopp, Olivier Aumont, Matthew Chamberlain, James R. Christian, John P. Dunne, Marion Gehlen, Tatiana Ilyina, Jasmin G. John, Andrew Lenton, Hongmei Li, Nicole S. Lovenduski, James C. Orr, Julien Palmieri, Yeray Santana-Falcón, Jörg Schwinger, Roland Séférian, Charles A. Stock, Alessandro Tagliabue, Yohei Takano, Jerry Tjiputra, Katsuya Toyama, Hiroyuki Tsujino, Michio Watanabe, Akitomo Yamamoto, Andrew Yool, and Tilo Ziehn
Biogeosciences, 17, 3439–3470, https://doi.org/10.5194/bg-17-3439-2020, https://doi.org/10.5194/bg-17-3439-2020, 2020
Short summary
Short summary
We assess 21st century projections of marine biogeochemistry in the CMIP6 Earth system models. These models represent the most up-to-date understanding of climate change. The models generally project greater surface ocean warming, acidification, subsurface deoxygenation, and euphotic nitrate reductions but lesser primary production declines than the previous generation of models. This has major implications for the impact of anthropogenic climate change on marine ecosystems.
Julian Quick, Jennifer King, Ryan N. King, Peter E. Hamlington, and Katherine Dykes
Wind Energ. Sci., 5, 413–426, https://doi.org/10.5194/wes-5-413-2020, https://doi.org/10.5194/wes-5-413-2020, 2020
Short summary
Short summary
We investigate the trade-offs in optimization of wake steering strategies, where upstream turbines are positioned to deflect wakes away from downstream turbines, with a probabilistic perspective. We identify inputs that are sensitive to uncertainty and demonstrate a realistic optimization under uncertainty for a wind power plant control strategy. Designing explicitly around uncertainty yielded control strategies that were generally less aggressive and more robust to the uncertain input.
Rolf Sander, Andreas Baumgaertner, David Cabrera-Perez, Franziska Frank, Sergey Gromov, Jens-Uwe Grooß, Hartwig Harder, Vincent Huijnen, Patrick Jöckel, Vlassis A. Karydis, Kyle E. Niemeyer, Andrea Pozzer, Hella Riede, Martin G. Schultz, Domenico Taraborrelli, and Sebastian Tauer
Geosci. Model Dev., 12, 1365–1385, https://doi.org/10.5194/gmd-12-1365-2019, https://doi.org/10.5194/gmd-12-1365-2019, 2019
Short summary
Short summary
We present the atmospheric chemistry box model CAABA/MECCA which
now includes a number of new features: skeletal mechanism
reduction, the MOM chemical mechanism for volatile organic
compounds, an option to include reactions from the Master
Chemical Mechanism (MCM) and other chemical mechanisms, updated
isotope tagging, improved and new photolysis modules, and the new
feature of coexisting multiple chemistry mechanisms.
CAABA/MECCA is a community model published under the GPL.
Riley X. Brady, Nicole S. Lovenduski, Michael A. Alexander, Michael Jacox, and Nicolas Gruber
Biogeosciences, 16, 329–346, https://doi.org/10.5194/bg-16-329-2019, https://doi.org/10.5194/bg-16-329-2019, 2019
Nicole S. Lovenduski, Stephen G. Yeager, Keith Lindsay, and Matthew C. Long
Earth Syst. Dynam., 10, 45–57, https://doi.org/10.5194/esd-10-45-2019, https://doi.org/10.5194/esd-10-45-2019, 2019
Short summary
Short summary
This paper shows that the absorption of carbon dioxide by the ocean is predictable several years in advance. This is important because fossil-fuel-derived carbon dioxide is largely responsible for anthropogenic global warming and because carbon dioxide emission management and global carbon cycle budgeting exercises can benefit from foreknowledge of ocean carbon absorption. The promising results from this new forecast system justify the need for additional oceanic observations.
Galen A. McKinley, Alexis L. Ritzer, and Nicole S. Lovenduski
Biogeosciences, 15, 6049–6066, https://doi.org/10.5194/bg-15-6049-2018, https://doi.org/10.5194/bg-15-6049-2018, 2018
Short summary
Short summary
Phytoplankton biomass changed significantly in the North Atlantic north of 40° N over 1998–2007. With a physical-ecosystem model, we show that biomass increases in the northwest are due to reduced vertical mixing that partially relieves light limitation of phytoplankton. To the east, these circulation changes lead to fewer nutrients being supplied horizontally from the west. Relationships between these biomass variations and atmosphere and ocean physics are not straightforward.
Ali Aydoğdu, Nadia Pinardi, Emin Özsoy, Gokhan Danabasoglu, Özgür Gürses, and Alicia Karspeck
Ocean Sci., 14, 999–1019, https://doi.org/10.5194/os-14-999-2018, https://doi.org/10.5194/os-14-999-2018, 2018
Short summary
Short summary
A 6-year simulation of the Turkish Straits System is presented. The simulation is performed by a model using unstructured triangular mesh and realistic atmospheric forcing. The dynamics and circulation of the Marmara Sea are analysed and the mean state of the system is discussed on annual averages. Volume fluxes computed throughout the simulation are presented and the response of the model to severe storms is shown. Finally, it was possible to assess the kinetic energy budget in the Marmara Sea.
Ali Aydoğdu, Timothy J. Hoar, Tomislava Vukicevic, Jeffrey L. Anderson, Nadia Pinardi, Alicia Karspeck, Jonathan Hendricks, Nancy Collins, Francesca Macchia, and Emin Özsoy
Nonlin. Processes Geophys., 25, 537–551, https://doi.org/10.5194/npg-25-537-2018, https://doi.org/10.5194/npg-25-537-2018, 2018
Short summary
Short summary
This study presents, to our knowledge, the first data assimilation experiments in the Sea of Marmara. We propose a FerryBox network for monitoring the state of the sea and show that assimilation of the temperature and salinity improves the forecasts in the basin. The flow of the Bosphorus helps to propagate the error reduction. The study can be taken as a step towards a marine forecasting system in the Sea of Marmara that will help to improve the forecasts in the adjacent Black and Aegean seas.
Amanda R. Fay, Nicole S. Lovenduski, Galen A. McKinley, David R. Munro, Colm Sweeney, Alison R. Gray, Peter Landschützer, Britton B. Stephens, Taro Takahashi, and Nancy Williams
Biogeosciences, 15, 3841–3855, https://doi.org/10.5194/bg-15-3841-2018, https://doi.org/10.5194/bg-15-3841-2018, 2018
Short summary
Short summary
The Southern Ocean is highly under-sampled and since this region dominates the ocean sink for CO2, understanding change is critical. Here we utilize available observations to evaluate how the seasonal cycle, variability, and trends in surface ocean carbon in the well-sampled Drake Passage region compare to that of the broader subpolar Southern Ocean. Results indicate that the Drake Passage is representative of the broader region; however, additional winter observations would improve comparisons.
Giuliana Turi, Michael Alexander, Nicole S. Lovenduski, Antonietta Capotondi, James Scott, Charles Stock, John Dunne, Jasmin John, and Michael Jacox
Ocean Sci., 14, 69–86, https://doi.org/10.5194/os-14-69-2018, https://doi.org/10.5194/os-14-69-2018, 2018
Short summary
Short summary
A high-resolution global model was used to study the influence of El Niño/La Niña events on the California Current System (CalCS). The mean surface oxygen (O2) response extends well offshore, where the pH response occurs within ~ 100 km of the coast. The surface O2 (pH) is primarily driven by temperature (upwelling) changes. Below 100 m, anomalously low O2 and low pH occurred during La Niña events near the coast, potentially stressing the ecosystem, but there are large variations between events.
Giorgia Verri, Nadia Pinardi, David Gochis, Joseph Tribbia, Antonio Navarra, Giovanni Coppini, and Tomislava Vukicevic
Nat. Hazards Earth Syst. Sci., 17, 1741–1761, https://doi.org/10.5194/nhess-17-1741-2017, https://doi.org/10.5194/nhess-17-1741-2017, 2017
Giovanni Coppini, Palmalisa Marra, Rita Lecci, Nadia Pinardi, Sergio Cretì, Mario Scalas, Luca Tedesco, Alessandro D'Anca, Leopoldo Fazioli, Antonio Olita, Giuseppe Turrisi, Cosimo Palazzo, Giovanni Aloisio, Sandro Fiore, Antonio Bonaduce, Yogesh Vittal Kumkar, Stefania Angela Ciliberti, Ivan Federico, Gianandrea Mannarini, Paola Agostini, Roberto Bonarelli, Sara Martinelli, Giorgia Verri, Letizia Lusito, Davide Rollo, Arturo Cavallo, Antonio Tumolo, Tony Monacizzo, Marco Spagnulo, Rorberto Sorgente, Andrea Cucco, Giovanni Quattrocchi, Marina Tonani, Massimiliano Drudi, Paola Nassisi, Laura Conte, Laura Panzera, Antonio Navarra, and Giancarlo Negro
Nat. Hazards Earth Syst. Sci., 17, 533–547, https://doi.org/10.5194/nhess-17-533-2017, https://doi.org/10.5194/nhess-17-533-2017, 2017
Short summary
Short summary
SeaConditions aims to support the users by providing the environmental information in due time and with adequate accuracy in the marine and coastal environments, enforcing users' sea situational awareness. SeaConditions consists of a web and mobile application for the provision of meteorological and oceanographic observation and forecasting products. The iOS/Android apps were downloaded by more than 105 000 users and more than 100 000 users have visited the web version (www.sea-conditions.com).
Ryan N. King, Katherine Dykes, Peter Graf, and Peter E. Hamlington
Wind Energ. Sci., 2, 115–131, https://doi.org/10.5194/wes-2-115-2017, https://doi.org/10.5194/wes-2-115-2017, 2017
Short summary
Short summary
This paper demonstrates optimization of wind turbine locations within a utility-scale wind plant using a nonlinear flow model and gradient-based optimization techniques made possible through the use of adjoints. This represents a groundbreaking improvement in model fidelity and optimization efficiency for wind energy applications. The optimized wind farms demonstrate significant improvements in annual energy production with turbine layouts that take advantage of nonlinear flow curvature effects.
Ivan Federico, Nadia Pinardi, Giovanni Coppini, Paolo Oddo, Rita Lecci, and Michele Mossa
Nat. Hazards Earth Syst. Sci., 17, 45–59, https://doi.org/10.5194/nhess-17-45-2017, https://doi.org/10.5194/nhess-17-45-2017, 2017
Short summary
Short summary
SANIFS (Southern Adriatic Northern Ionian coastal Forecasting System) is a coastal-ocean operational system based on the unstructured grid finite-element three-dimensional hydrodynamic SHYFEM model, which provides short-term forecasts. The operational chain is based on a downscaling approach starting from the large-scale system for the entire Mediterranean Basin (MFS, Mediterranean Forecasting System), which provides initial and boundary condition fields for the nested system.
Zhaoyi Wang, Andrea Storto, Nadia Pinardi, Guimei Liu, and Hui Wang
Nat. Hazards Earth Syst. Sci., 17, 17–30, https://doi.org/10.5194/nhess-17-17-2017, https://doi.org/10.5194/nhess-17-17-2017, 2017
Giovanni Coppini, Eric Jansen, Giuseppe Turrisi, Sergio Creti, Elena Yurievna Shchekinova, Nadia Pinardi, Rita Lecci, Ivano Carluccio, Yogesh Vittal Kumkar, Alessandro D'Anca, Gianandrea Mannarini, Sara Martinelli, Palmalisa Marra, Tommaso Capodiferro, and Tommaso Gismondi
Nat. Hazards Earth Syst. Sci., 16, 2713–2727, https://doi.org/10.5194/nhess-16-2713-2016, https://doi.org/10.5194/nhess-16-2713-2016, 2016
Short summary
Short summary
A new web-based and mobile Decision Support System (DSS) for Search-And-Rescue (SAR) at sea is presented, and its performance is evaluated using real case scenarios. The system, named OCEAN-SAR, is accessible via the website http://www.ocean-sar.com. OCEAN-SAR simulates drifting objects at sea, using as input ocean currents and wind. The performance of the service is evaluated by comparing simulations to data from the Italian Coast Guard pertaining to actual incidents in the Mediterranean Sea.
Nadia Pinardi, Vladyslav Lyubartsev, Nicola Cardellicchio, Claudio Caporale, Stefania Ciliberti, Giovanni Coppini, Francesca De Pascalis, Lorenzo Dialti, Ivan Federico, Marco Filippone, Alessandro Grandi, Matteo Guideri, Rita Lecci, Lamberto Lamberti, Giuliano Lorenzetti, Paolo Lusiani, Cosimo Damiano Macripo, Francesco Maicu, Michele Mossa, Diego Tartarini, Francesco Trotta, Georg Umgiesser, and Luca Zaggia
Nat. Hazards Earth Syst. Sci., 16, 2623–2639, https://doi.org/10.5194/nhess-16-2623-2016, https://doi.org/10.5194/nhess-16-2623-2016, 2016
Short summary
Short summary
A multiscale sampling experiment was carried out in the Gulf of Taranto (eastern Mediterranean) providing the first synoptic evidence of the large-scale circulation structure and associated mesoscale variability. The circulation is shown to be dominated by an anticyclonic gyre and upwelling areas at the gyre periphery.
Emanuela Fiori, Marco Zavatarelli, Nadia Pinardi, Cristina Mazziotti, and Carla Rita Ferrari
Nat. Hazards Earth Syst. Sci., 16, 2043–2054, https://doi.org/10.5194/nhess-16-2043-2016, https://doi.org/10.5194/nhess-16-2043-2016, 2016
Short summary
Short summary
This study shows the capability of the numerical model in reproducing the trophic index (TRIX) calculated from in situ data. The ecosystem simulations can represent an important support for monitoring activities, allowing the use of TRIX to be extended to larger areas where in situ sampling activities are difficult to implement. The model TRIX was calculated for the whole Adriatic Sea, showing trophic differences across the Adriatic Sea.
Svitlana Liubartseva, Giovanni Coppini, Nadia Pinardi, Michela De Dominicis, Rita Lecci, Giuseppe Turrisi, Sergio Cretì, Sara Martinelli, Paola Agostini, Palmalisa Marra, and Francesco Palermo
Nat. Hazards Earth Syst. Sci., 16, 2009–2020, https://doi.org/10.5194/nhess-16-2009-2016, https://doi.org/10.5194/nhess-16-2009-2016, 2016
Short summary
Short summary
An innovative fully operational 24/7 web-based decision support system, WITOIL (Where Is The Oil), has been developed to support oil pollution response. The system meets the real-time requirements in terms of performance and dynamic service delivery. Comprehensive computational resources and network bandwidth efficiently support the multi-user regime. The eight-language graphical user interface incorporates a great variety of user services, e.g., help and support, tooltips, and video tutorials.
Jenny Pistoia, Nadia Pinardi, Paolo Oddo, Matthew Collins, Gerasimos Korres, and Yann Drillet
Nat. Hazards Earth Syst. Sci., 16, 1807–1819, https://doi.org/10.5194/nhess-16-1807-2016, https://doi.org/10.5194/nhess-16-1807-2016, 2016
Short summary
Short summary
In this work we developed a new multi-model super-ensemble method to estimate sea surface temperature, an important product of ocean analysis systems. We find that ensemble size, quality, type of members and the training period length are all important elements of the MMSE methodology and require careful calibration. We show that with a rather limited but overconfident data set (with a low bias of the starting ensemble members) the RMSE analysis can be improved.
Gianandrea Mannarini, Giuseppe Turrisi, Alessandro D'Anca, Mario Scalas, Nadia Pinardi, Giovanni Coppini, Francesco Palermo, Ivano Carluccio, Matteo Scuro, Sergio Cretì, Rita Lecci, Paola Nassisi, and Luca Tedesco
Nat. Hazards Earth Syst. Sci., 16, 1791–1806, https://doi.org/10.5194/nhess-16-1791-2016, https://doi.org/10.5194/nhess-16-1791-2016, 2016
Short summary
Short summary
Safety and efficiency of navigation can be enhanced through a better situational awareness at sea. We designed and realized an operational infrastructure for providing the navigators with optimal routes through various devices: PC, tablets, and smartphones. Sea-state and wind forecasts are used as inputs. Both motor- and sailboat routes are addressed by VISIR.
Eric Jansen, Giovanni Coppini, and Nadia Pinardi
Nat. Hazards Earth Syst. Sci., 16, 1623–1628, https://doi.org/10.5194/nhess-16-1623-2016, https://doi.org/10.5194/nhess-16-1623-2016, 2016
Short summary
Short summary
In March 2014, a commercial airliner vanished without a trace. The main wreckage of the plane was never recovered, except for some small parts that washed up more than 17 months after the disappearance. In this paper we show a method to model the most likely trajectories of floating debris from the aircraft. The results show that the assumed area of the crash site is compatible with the recovered debris and predict that further debris may be found along the African east coast.
Jun She, Icarus Allen, Erik Buch, Alessandro Crise, Johnny A. Johannessen, Pierre-Yves Le Traon, Urmas Lips, Glenn Nolan, Nadia Pinardi, Jan H. Reißmann, John Siddorn, Emil Stanev, and Henning Wehde
Ocean Sci., 12, 953–976, https://doi.org/10.5194/os-12-953-2016, https://doi.org/10.5194/os-12-953-2016, 2016
Short summary
Short summary
This white paper addresses key scientific challenges and research priorities for the development of operational oceanography in Europe for the next 5–10 years. Knowledge gaps and deficiencies are identified in relation to common scientific challenges in four EuroGOOS knowledge areas: European ocean observations, modelling and forecasting technology, coastal operational oceanography, and operational ecology.
Natalie M. Freeman and Nicole S. Lovenduski
Earth Syst. Sci. Data, 8, 191–198, https://doi.org/10.5194/essd-8-191-2016, https://doi.org/10.5194/essd-8-191-2016, 2016
Short summary
Short summary
The Antarctic Polar Front (PF) is an important physical and biogeochemical divide in the Southern Ocean, delineating distinct zones of temperature, nutrients and biological communities. Our study learns from and advances previous efforts to locate the PF via satellite by avoiding cloud contamination and providing circumpolar realizations at high spatio-temporal resolution. These realizations are consistent with concurrent in situ PF locations and previously published climatological PF positions.
Gianandrea Mannarini, Nadia Pinardi, Giovanni Coppini, Paolo Oddo, and Alessandro Iafrati
Geosci. Model Dev., 9, 1597–1625, https://doi.org/10.5194/gmd-9-1597-2016, https://doi.org/10.5194/gmd-9-1597-2016, 2016
Short summary
Short summary
VISIR is a new numerical model for the computation of optimal ship routes from meteo-marine forecasts. VISIR offers the scientific community an open platform whereby various ideas and methods for ship route optimization can be shared, tested, and compared to each other.
Kristen M. Krumhardt, Nicole S. Lovenduski, Natalie M. Freeman, and Nicholas R. Bates
Biogeosciences, 13, 1163–1177, https://doi.org/10.5194/bg-13-1163-2016, https://doi.org/10.5194/bg-13-1163-2016, 2016
Short summary
Short summary
In this study, we combine phytoplankton pigment data with particulate inorganic carbon and chlorophyll measurements from the satellite record to assess recent trends in phytoplankton dynamics in the North Atlantic subtropical gyre, with a focus on coccolithophores. We show that coccolithophores in the North Atlantic have been increasing in abundance. Correlations suggest that they are responding positively to increasing inorganic carbon from anthropogenic inputs in the upper mixed layer.
N. S. Lovenduski, M. C. Long, and K. Lindsay
Biogeosciences, 12, 6321–6335, https://doi.org/10.5194/bg-12-6321-2015, https://doi.org/10.5194/bg-12-6321-2015, 2015
Short summary
Short summary
We investigate variability in surface ocean carbonate chemistry using output from a 1000-year control simulation of an Earth System Model. We find that the detection timescale for trends is strongly influenced by the variability. As the scientific community seeks to detect the anthropogenic influence on ocean carbonate chemistry, these results will aid the interpretation of trends calculated from spatially and temporally sparse observations.
P. Oddo, A. Bonaduce, N. Pinardi, and A. Guarnieri
Geosci. Model Dev., 7, 3001–3015, https://doi.org/10.5194/gmd-7-3001-2014, https://doi.org/10.5194/gmd-7-3001-2014, 2014
A. Guarnieri, A. J. Souza, N. Pinardi, and P. Traykovski
Ocean Sci. Discuss., https://doi.org/10.5194/osd-11-1391-2014, https://doi.org/10.5194/osd-11-1391-2014, 2014
Revised manuscript not accepted
M. De Dominicis, N. Pinardi, G. Zodiatis, and R. Lardner
Geosci. Model Dev., 6, 1851–1869, https://doi.org/10.5194/gmd-6-1851-2013, https://doi.org/10.5194/gmd-6-1851-2013, 2013
M. De Dominicis, N. Pinardi, G. Zodiatis, and R. Archetti
Geosci. Model Dev., 6, 1871–1888, https://doi.org/10.5194/gmd-6-1871-2013, https://doi.org/10.5194/gmd-6-1871-2013, 2013
A. Lenton, B. Tilbrook, R. M. Law, D. Bakker, S. C. Doney, N. Gruber, M. Ishii, M. Hoppema, N. S. Lovenduski, R. J. Matear, B. I. McNeil, N. Metzl, S. E. Mikaloff Fletcher, P. M. S. Monteiro, C. Rödenbeck, C. Sweeney, and T. Takahashi
Biogeosciences, 10, 4037–4054, https://doi.org/10.5194/bg-10-4037-2013, https://doi.org/10.5194/bg-10-4037-2013, 2013
Related subject area
Biogeosciences
ELM2.1-XGBfire1.0: improving wildfire prediction by integrating a machine learning fire model in a land surface model
Development and assessment of the physical–biogeochemical ocean regional model in the Northwest Pacific: NPRT v1.0 (ROMS v3.9–TOPAZ v2.0)
Estimation of above- and below-ground ecosystem parameters for DVM-DOS-TEM v0.7.0 using MADS v1.7.3
Alquimia v1.0: a generic interface to biogeochemical codes – a tool for interoperable development, prototyping and benchmarking for multiphysics simulators
Soil nitrous oxide emissions from global land ecosystems and their drivers within the LPJ-GUESS model (v4.1)
Parameterization toolbox for a physical–biogeochemical model compatible with FABM – a case study: the coupled 1D GOTM–ECOSMO E2E for the Sylt–Rømø Bight, North Sea
H2MV (v1.0): global physically constrained deep learning water cycle model with vegetation
NN-TOC v1: global prediction of total organic carbon in marine sediments using deep neural networks
China Wildfire Emission Dataset (ChinaWED v1) for the period 2012–2022
Process-based modeling of solar-induced chlorophyll fluorescence with VISIT-SIF version 1.0
Including the phosphorus cycle into the LPJ-GUESS dynamic global vegetation model (v4.1, r10994) – global patterns and temporal trends of N and P primary production limitation
A comprehensive land-surface vegetation model for multi-stream data assimilation, D&B v1.0
Sources of uncertainty in the SPITFIRE global fire model: development of LPJmL-SPITFIRE1.9 and directions for future improvements
Spatially varying parameters improve carbon cycle modeling in the Amazon rainforest with ORCHIDEE r8849
The unicellular NUM v.0.91: a trait-based plankton model evaluated in two contrasting biogeographic provinces
FESOM2.1-REcoM3-MEDUSA2: an ocean–sea ice–biogeochemistry model coupled to a sediment model
Satellite-based modeling of wetland methane emissions on a global scale (SatWetCH4 1.0)
Emulating grid-based forest carbon dynamics using machine learning: an LPJ-GUESS v4.1.1 application
Representing high-latitude deep carbon in the pre-industrial state of the ORCHIDEE-MICT land surface model (r8704)
Systematic underestimation of type-specific ecosystem process variability in the Community Land Model v5 over Europe
pyVPRM: A next-generation Vegetation Photosynthesis and Respiration Model for the post-MODIS era
Lambda-PFLOTRAN 1.0: a workflow for incorporating organic matter chemistry informed by ultra high resolution mass spectrometry into biogeochemical modeling
An improved model for air–sea exchange of elemental mercury in MITgcm-ECCOv4-Hg: the role of surfactants and waves
BOATSv2: new ecological and economic features improve simulations of high seas catch and effort
A dynamical process-based model for quantifying global agricultural ammonia emissions – AMmonia–CLIMate v1.0 (AMCLIM v1.0) – Part 1: Land module for simulating emissions from synthetic fertilizer use
Simulating the drought response of European tree species with the dynamic vegetation model LPJ-GUESS (v4.1, 97c552c5)
Simulating Ips typographus L. outbreak dynamics and their influence on carbon balance estimates with ORCHIDEE r8627
Biological nitrogen fixation of natural and agricultural vegetation simulated with LPJmL 5.7.9
BIOPERIANT12: a mesoscale resolving coupled physics-biogeochemical model for the Southern Ocean
Learning from conceptual models – a study of the emergence of cooperation towards resource protection in a social–ecological system
TROLL 4.0: representing water and carbon fluxes, leaf phenology and intraspecific trait variation in a mixed-species individual-based forest dynamics model – Part 1: Model description
The biogeochemical model Biome-BGCMuSo v6.2 provides plausible and accurate simulations of the carbon cycle in central European beech forests
TROLL 4.0: representing water and carbon fluxes, leaf phenology, and intraspecific trait variation in a mixed-species individual-based forest dynamics model – Part 2: Model evaluation for two Amazonian sites
DeepPhenoMem V1.0: deep learning modelling of canopy greenness dynamics accounting for multi-variate meteorological memory effects on vegetation phenology
Impacts of land-use change on biospheric carbon: an oriented benchmark using the ORCHIDEE land surface model
Implementing the iCORAL (version 1.0) coral reef CaCO3 production module in the iLOVECLIM climate model
Assimilation of carbonyl sulfide (COS) fluxes within the adjoint-based data assimilation system – Nanjing University Carbon Assimilation System (NUCAS v1.0)
Quantifying the role of ozone-caused damage to vegetation in the Earth system: a new parameterization scheme for photosynthetic and stomatal responses
Radiocarbon analysis reveals underestimation of soil organic carbon persistence in new-generation soil models
Exploring the potential of history matching for land surface model calibration
EAT v1.0.0: a 1D test bed for physical–biogeochemical data assimilation in natural waters
Using deep learning to integrate paleoclimate and global biogeochemistry over the Phanerozoic Eon
Modelling boreal forest's mineral soil and peat C dynamics with the Yasso07 model coupled with the Ricker moisture modifier
Dynamic ecosystem assembly and escaping the “fire trap” in the tropics: insights from FATES_15.0.0
In silico calculation of soil pH by SCEPTER v1.0
Simple process-led algorithms for simulating habitats (SPLASH v.2.0): robust calculations of water and energy fluxes
A global behavioural model of human fire use and management: WHAM! v1.0
Terrestrial Ecosystem Model in R (TEMIR) version 1.0: simulating ecophysiological responses of vegetation to atmospheric chemical and meteorological changes
biospheremetrics v1.0.2: an R package to calculate two complementary terrestrial biosphere integrity indicators – human colonization of the biosphere (BioCol) and risk of ecosystem destabilization (EcoRisk)
Modeling boreal forest soil dynamics with the microbially explicit soil model MIMICS+ (v1.0)
Ye Liu, Huilin Huang, Sing-Chun Wang, Tao Zhang, Donghui Xu, and Yang Chen
Geosci. Model Dev., 18, 4103–4117, https://doi.org/10.5194/gmd-18-4103-2025, https://doi.org/10.5194/gmd-18-4103-2025, 2025
Short summary
Short summary
This study integrates machine learning with a land surface model to improve wildfire predictions in North America. Traditional models struggle with accurately simulating burned areas due to simplified processes. By combining the predictive power of machine learning with a land model, our hybrid framework better captures fire dynamics. This approach enhances our understanding of wildfire behavior and aids in developing more effective climate and fire management strategies.
Daehyuk Kim, Hyun-Chae Jung, Jae-Hong Moon, and Na-Hyeon Lee
Geosci. Model Dev., 18, 3941–3964, https://doi.org/10.5194/gmd-18-3941-2025, https://doi.org/10.5194/gmd-18-3941-2025, 2025
Short summary
Short summary
Physical–biogeochemical ocean global models are required to analyze difficult oceanic environmental systems. To accurately understand the physical–biogeochemical processes at the regional scale, physical and biogeochemical models were coupled at a high resolution. The results successfully simulated the seasonal variations of chlorophyll and nutrients, particularly in the marginal seas, which were not captured by global models. The developed model is an important tool for studying physical–biogeochemical processes.
Elchin E. Jafarov, Hélène Genet, Velimir V. Vesselinov, Valeria Briones, Aiza Kabeer, Andrew L. Mullen, Benjamin Maglio, Tobey Carman, Ruth Rutter, Joy Clein, Chu-Chun Chang, Dogukan Teber, Trevor Smith, Joshua M. Rady, Christina Schädel, Jennifer D. Watts, Brendan M. Rogers, and Susan M. Natali
Geosci. Model Dev., 18, 3857–3875, https://doi.org/10.5194/gmd-18-3857-2025, https://doi.org/10.5194/gmd-18-3857-2025, 2025
Short summary
Short summary
This study improves how we tune ecosystem models to reflect carbon and nitrogen storage in Arctic soils. By comparing model outputs with data from a black spruce forest in Alaska, we developed a clearer, more efficient method of matching observations. This is a key step towards understanding how Arctic ecosystems may respond to warming and release carbon, helping make future climate predictions more reliable.
Sergi Molins, Benjamin J. Andre, Jeffrey N. Johnson, Glenn E. Hammond, Benjamin N. Sulman, Konstantin Lipnikov, Marcus S. Day, James J. Beisman, Daniil Svyatsky, Hang Deng, Peter C. Lichtner, Carl I. Steefel, and J. David Moulton
Geosci. Model Dev., 18, 3241–3263, https://doi.org/10.5194/gmd-18-3241-2025, https://doi.org/10.5194/gmd-18-3241-2025, 2025
Short summary
Short summary
Developing scientific software and making sure it functions properly requires a significant effort. As we advance our understanding of natural systems, however, there is the need to develop yet more complex models and codes. In this work, we present a piece of software that facilitates this work, specifically with regard to reactive processes. Existing tried-and-true codes are made available via this new interface, freeing up resources to focus on the new aspects of the problems at hand.
Jianyong Ma, Almut Arneth, Benjamin Smith, Peter Anthoni, Xu-Ri, Peter Eliasson, David Wårlind, Martin Wittenbrink, and Stefan Olin
Geosci. Model Dev., 18, 3131–3155, https://doi.org/10.5194/gmd-18-3131-2025, https://doi.org/10.5194/gmd-18-3131-2025, 2025
Short summary
Short summary
Nitrous oxide (N2O) is a powerful greenhouse gas mainly released from natural and agricultural soils. This study examines how global soil N2O emissions changed from 1961 to 2020 and identifies key factors driving these changes using an ecological model. The findings highlight croplands as the largest source, with factors like fertilizer use and climate change enhancing emissions. Rising CO2 levels, however, can partially mitigate N2O emissions through increased plant nitrogen uptake.
Hoa Nguyen, Ute Daewel, Neil Banas, and Corinna Schrum
Geosci. Model Dev., 18, 2961–2982, https://doi.org/10.5194/gmd-18-2961-2025, https://doi.org/10.5194/gmd-18-2961-2025, 2025
Short summary
Short summary
Parameterization is key in modeling to reproduce observations well but is often done manually. This study presents a particle-swarm-optimizer-based toolbox for marine ecosystem models, compatible with the Framework for Aquatic Biogeochemical Models, thus enhancing its reusability. Applied to the Sylt ecosystem, the toolbox effectively (1) identified multiple parameter sets that matched observations well, providing different insights into ecosystem dynamics, and (2) optimized model complexity.
Zavud Baghirov, Martin Jung, Markus Reichstein, Marco Körner, and Basil Kraft
Geosci. Model Dev., 18, 2921–2943, https://doi.org/10.5194/gmd-18-2921-2025, https://doi.org/10.5194/gmd-18-2921-2025, 2025
Short summary
Short summary
We use an innovative approach to studying the Earth's water cycle by integrating advanced machine learning techniques with a traditional water cycle model. Our model is designed to learn from observational data, with a particular emphasis on understanding the influence of vegetation on water movement. By closely aligning with real-world observations, our model offers new possibilities for enhancing our understanding of the water cycle and its interactions with vegetation.
Naveenkumar Parameswaran, Everardo González, Ewa Burwicz-Galerne, Malte Braack, and Klaus Wallmann
Geosci. Model Dev., 18, 2521–2544, https://doi.org/10.5194/gmd-18-2521-2025, https://doi.org/10.5194/gmd-18-2521-2025, 2025
Short summary
Short summary
Our research uses deep learning to predict organic carbon stocks in ocean sediments, which is crucial for understanding their role in the global carbon cycle. By analysing over 22 000 samples and various seafloor characteristics, our model gives more accurate results than traditional methods. We estimate that the top 10 cm of ocean sediments hold about 156 Pg of carbon. This work enhances carbon stock estimates and helps plan future sampling strategies to better understand oceanic carbon burial.
Zhengyang Lin, Ling Huang, Hanqin Tian, Anping Chen, and Xuhui Wang
Geosci. Model Dev., 18, 2509–2520, https://doi.org/10.5194/gmd-18-2509-2025, https://doi.org/10.5194/gmd-18-2509-2025, 2025
Short summary
Short summary
The China Wildfire Emission Dataset (ChinaWED v1) estimated wildfire emissions in China during 2012–2022 as 78.13 Tg CO2, 279.47 Gg CH4, and 6.26 Gg N2O annually. Agricultural fires dominated emissions, while forest and grassland emissions decreased. Seasonal peaks occurred in late spring, with hotspots in northeast, southwest, and east China. The model emphasizes the importance of using localized emission factors and high-resolution fire estimates for accurate assessments.
Tatsuya Miyauchi, Makoto Saito, Hibiki M. Noda, Akihiko Ito, Tomomichi Kato, and Tsuneo Matsunaga
Geosci. Model Dev., 18, 2329–2347, https://doi.org/10.5194/gmd-18-2329-2025, https://doi.org/10.5194/gmd-18-2329-2025, 2025
Short summary
Short summary
Solar-induced chlorophyll fluorescence (SIF) is an effective indicator for monitoring photosynthetic activity. This paper introduces VISIT-SIF, a biogeochemical model developed based on the Vegetation Integrative Simulator for Trace gases (VISIT) to represent satellite-observed SIF. Our simulations reproduced the global distribution and seasonal variations in observed SIF. VISIT-SIF helps to improve photosynthetic processes through a combination of biogeochemical modeling and observed SIF.
Mateus Dantas de Paula, Matthew Forrest, David Warlind, João Paulo Darela Filho, Katrin Fleischer, Anja Rammig, and Thomas Hickler
Geosci. Model Dev., 18, 2249–2274, https://doi.org/10.5194/gmd-18-2249-2025, https://doi.org/10.5194/gmd-18-2249-2025, 2025
Short summary
Short summary
Our study maps global nitrogen (N) and phosphorus (P) availability and how they changed from 1901 to 2018. We find that tropical regions are mostly P-limited, while temperate and boreal areas face N limitations. Over time, P limitation increased, especially in the tropics, while N limitation decreased. These shifts are key to understanding global plant growth and carbon storage, highlighting the importance of including P dynamics in ecosystem models.
Wolfgang Knorr, Matthew Williams, Tea Thum, Thomas Kaminski, Michael Voßbeck, Marko Scholze, Tristan Quaife, T. Luke Smallman, Susan C. Steele-Dunne, Mariette Vreugdenhil, Tim Green, Sönke Zaehle, Mika Aurela, Alexandre Bouvet, Emanuel Bueechi, Wouter Dorigo, Tarek S. El-Madany, Mirco Migliavacca, Marika Honkanen, Yann H. Kerr, Anna Kontu, Juha Lemmetyinen, Hannakaisa Lindqvist, Arnaud Mialon, Tuuli Miinalainen, Gaétan Pique, Amanda Ojasalo, Shaun Quegan, Peter J. Rayner, Pablo Reyes-Muñoz, Nemesio Rodríguez-Fernández, Mike Schwank, Jochem Verrelst, Songyan Zhu, Dirk Schüttemeyer, and Matthias Drusch
Geosci. Model Dev., 18, 2137–2159, https://doi.org/10.5194/gmd-18-2137-2025, https://doi.org/10.5194/gmd-18-2137-2025, 2025
Short summary
Short summary
When it comes to climate change, the land surface is where the vast majority of impacts happen. The task of monitoring those impacts across the globe is formidable and must necessarily rely on satellites – at a significant cost: the measurements are only indirect and require comprehensive physical understanding. We have created a comprehensive modelling system that we offer to the research community to explore how satellite data can be better exploited to help us capture the changes that happen on our lands.
Luke Oberhagemann, Maik Billing, Werner von Bloh, Markus Drüke, Matthew Forrest, Simon P. K. Bowring, Jessica Hetzer, Jaime Ribalaygua Batalla, and Kirsten Thonicke
Geosci. Model Dev., 18, 2021–2050, https://doi.org/10.5194/gmd-18-2021-2025, https://doi.org/10.5194/gmd-18-2021-2025, 2025
Short summary
Short summary
Under climate change, the conditions necessary for wildfires to form are occurring more frequently in many parts of the world. To help predict how wildfires will change in future, global fire models are being developed. We analyze and further develop one such model, SPITFIRE. Our work identifies and corrects sources of substantial bias in the model that are important to the global fire modelling field. With this analysis and these developments, we help to provide a basis for future improvements.
Lei Zhu, Philippe Ciais, Yitong Yao, Daniel Goll, Sebastiaan Luyssaert, Isabel Martínez Cano, Arthur Fendrich, Laurent Li, Hui Yang, Sassan Saatchi, and Wei Li
EGUsphere, https://doi.org/10.5194/egusphere-2025-397, https://doi.org/10.5194/egusphere-2025-397, 2025
Short summary
Short summary
This study enhances the accuracy of modeling the carbon dynamics of Amazon rainforest by optimizing key model parameters based on satellite data. Using spatially varying parameters for tree mortality and photosynthesis, we improved predictions of biomass, productivity, and tree mortality. Our findings highlight the critical role of wood density and water availability in forest processes, offering insights to refine global carbon cycle models.
Trine Frisbæk Hansen, Donald Eugene Canfield, Ken Haste Andersen, and Christian Jannik Bjerrum
Geosci. Model Dev., 18, 1895–1916, https://doi.org/10.5194/gmd-18-1895-2025, https://doi.org/10.5194/gmd-18-1895-2025, 2025
Short summary
Short summary
We describe and test the size-based Nutrient-Unicellular-Multicellular model, which defines unicellular plankton using a single set of parameters, on a eutrophic and oligotrophic ecosystem. The results demonstrate that both sites can be modeled with similar parameters and robust performance over a wide range of parameters. The study shows that the model is useful for non-experts and applicable for modeling ecosystems with limited data. It holds promise for evolutionary and deep-time climate models.
Ying Ye, Guy Munhoven, Peter Köhler, Martin Butzin, Judith Hauck, Özgür Gürses, and Christoph Völker
Geosci. Model Dev., 18, 977–1000, https://doi.org/10.5194/gmd-18-977-2025, https://doi.org/10.5194/gmd-18-977-2025, 2025
Short summary
Short summary
Many biogeochemistry models assume all material reaching the seafloor is remineralized and returned to solution, which is sufficient for studies on short-term climate change. Under long-term climate change, the carbon storage in sediments slows down carbon cycling and influences feedbacks in the atmosphere–ocean–sediment system. This paper describes the coupling of a sediment model to an ocean biogeochemistry model and presents results under the pre-industrial climate and under CO2 perturbation.
Juliette Bernard, Elodie Salmon, Marielle Saunois, Shushi Peng, Penélope Serrano-Ortiz, Antoine Berchet, Palingamoorthy Gnanamoorthy, Joachim Jansen, and Philippe Ciais
Geosci. Model Dev., 18, 863–883, https://doi.org/10.5194/gmd-18-863-2025, https://doi.org/10.5194/gmd-18-863-2025, 2025
Short summary
Short summary
Despite their importance, uncertainties remain in the evaluation of the drivers of temporal variability of methane emissions from wetlands on a global scale. Here, a simplified global model is developed, taking advantage of advances in remote-sensing data and in situ observations. The model reproduces the large spatial and temporal patterns of emissions, albeit with limitations in the tropics due to data scarcity. This model, while simple, can provide valuable insights into sensitivity analyses.
Carolina Natel, David Martin Belda, Peter Anthoni, Neele Haß, Sam Rabin, and Almut Arneth
EGUsphere, https://doi.org/10.5194/egusphere-2024-4064, https://doi.org/10.5194/egusphere-2024-4064, 2025
Short summary
Short summary
Complex models predict forest carbon responses to future climate change but are slow and computationally intensive, limiting large-scale analyses. We used machine learning to accelerate predictions from the LPJ-GUESS vegetation model. Our emulators, based on random forests and neural networks, achieved 97 % faster simulations. This approach enables rapid exploration of climate mitigation strategies and supports informed policy decisions.
Yi Xi, Philippe Ciais, Dan Zhu, Chunjing Qiu, Yuan Zhang, Shushi Peng, Gustaf Hugelius, Simon P. K. Bowring, Daniel S. Goll, and Ying-Ping Wang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-206, https://doi.org/10.5194/gmd-2024-206, 2025
Revised manuscript accepted for GMD
Short summary
Short summary
Including high-latitude deep carbon is critical for projecting future soil carbon emissions, yet it’s absent in most land surface models. Here we propose a new carbon accumulation protocol by integrating deep carbon from Yedoma deposits and representing the observed history of peat carbon formation in ORCHIDEE-MICT. Our results show an additional 157 PgC in present-day Yedoma deposits and a 1–5 m shallower peat depth, 43 % less passive soil carbon in peatlands against the convention protocol.
Christian Poppe Terán, Bibi S. Naz, Harry Vereecken, Roland Baatz, Rosie A. Fisher, and Harrie-Jan Hendricks Franssen
Geosci. Model Dev., 18, 287–317, https://doi.org/10.5194/gmd-18-287-2025, https://doi.org/10.5194/gmd-18-287-2025, 2025
Short summary
Short summary
Carbon and water exchanges between the atmosphere and the land surface contribute to water resource availability and climate change mitigation. Land surface models, like the Community Land Model version 5 (CLM5), simulate these. This study finds that CLM5 and other data sets underestimate the magnitudes of and variability in carbon and water exchanges for the most abundant plant functional types compared to observations. It provides essential insights for further research into these processes.
Theo Glauch, Julia Marshall, Christoph Gerbig, Santiago Botía, Michał Gałkowski, Sanam N. Vardag, and André Butz
EGUsphere, https://doi.org/10.5194/egusphere-2024-3692, https://doi.org/10.5194/egusphere-2024-3692, 2025
Short summary
Short summary
The Vegetation Photosynthesis and Respiration Model (VPRM) estimates carbon exchange between the atmosphere and biosphere by modeling gross primary production and respiration using satellite data and weather variables. Our new version, pyVPRM, supports diverse satellite products like Sentinel-2, MODIS, VIIRS and new land cover maps, enabling high spatial and temporal resolution. This improves flux estimates, especially in complex landscapes, and ensures continuity as MODIS nears decommissioning.
Katherine A. Muller, Peishi Jiang, Glenn Hammond, Tasneem Ahmadullah, Hyun-Seob Song, Ravi Kukkadapu, Nicholas Ward, Madison Bowe, Rosalie K. Chu, Qian Zhao, Vanessa A. Garayburu-Caruso, Alan Roebuck, and Xingyuan Chen
Geosci. Model Dev., 17, 8955–8968, https://doi.org/10.5194/gmd-17-8955-2024, https://doi.org/10.5194/gmd-17-8955-2024, 2024
Short summary
Short summary
The new Lambda-PFLOTRAN workflow incorporates organic matter chemistry into reaction networks to simulate aerobic respiration and biogeochemistry. Lambda-PFLOTRAN is a Python-based workflow in a Jupyter notebook interface that digests raw organic matter chemistry data via Fourier transform ion cyclotron resonance mass spectrometry, develops a representative reaction network, and completes a biogeochemical simulation with the open-source, parallel-reactive-flow, and transport code PFLOTRAN.
Ling Li, Peipei Wu, Peng Zhang, Shaojian Huang, and Yanxu Zhang
Geosci. Model Dev., 17, 8683–8695, https://doi.org/10.5194/gmd-17-8683-2024, https://doi.org/10.5194/gmd-17-8683-2024, 2024
Short summary
Short summary
In this study, we incorporate sea surfactants and wave-breaking processes into MITgcm-ECCOv4-Hg. The updated model shows increased fluxes in high-wind-speed and high-wave regions and vice versa, enhancing spatial heterogeneity. It shows that elemental mercury (Hg0) transfer velocity is more sensitive to wind speed. These findings may elucidate the discrepancies in previous estimations and offer insights into global Hg cycling.
Jerome Guiet, Daniele Bianchi, Kim J. N. Scherrer, Ryan F. Heneghan, and Eric D. Galbraith
Geosci. Model Dev., 17, 8421–8454, https://doi.org/10.5194/gmd-17-8421-2024, https://doi.org/10.5194/gmd-17-8421-2024, 2024
Short summary
Short summary
The BiOeconomic mArine Trophic Size-spectrum (BOATSv2) model dynamically simulates global commercial fish populations and their coupling with fishing activity, as emerging from environmental and economic drivers. New features, including separate pelagic and demersal populations, iron limitation, and spatial variation of fishing costs and management, improve the accuracy of high seas fisheries. The updated model code is available to simulate both historical and future scenarios.
Jize Jiang, David S. Stevenson, and Mark A. Sutton
Geosci. Model Dev., 17, 8181–8222, https://doi.org/10.5194/gmd-17-8181-2024, https://doi.org/10.5194/gmd-17-8181-2024, 2024
Short summary
Short summary
A special model called AMmonia–CLIMate (AMCLIM) has been developed to understand and calculate NH3 emissions from fertilizer use and also taking into account how the environment influences these NH3 emissions. It is estimated that about 17 % of applied N in fertilizers was lost due to NH3 emissions. Hot and dry conditions and regions with high-pH soils can expect higher NH3 emissions.
Benjamin Franklin Meyer, João Paulo Darela-Filho, Konstantin Gregor, Allan Buras, Qiao-Lin Gu, Andreas Krause, Daijun Liu, Phillip Papastefanou, Sijeh Asuk, Thorsten E. E. Grams, Christian S. Zang, and Anja Rammig
EGUsphere, https://doi.org/10.5194/egusphere-2024-3352, https://doi.org/10.5194/egusphere-2024-3352, 2024
Short summary
Short summary
Climate change has increased the likelihood of drought events across Europe, potentially threatening European forest carbon sink. Dynamic vegetation models with mechanistic plant hydraulic architecture are needed to model these developments. We evaluate the plant hydraulic architecture version of LPJ-GUESS and show it's capability at capturing species-specific evapotranspiration responses to drought and reproducing flux observations of both gross primary production and evapotranspiration.
Guillaume Marie, Jina Jeong, Hervé Jactel, Gunnar Petter, Maxime Cailleret, Matthew J. McGrath, Vladislav Bastrikov, Josefine Ghattas, Bertrand Guenet, Anne Sofie Lansø, Kim Naudts, Aude Valade, Chao Yue, and Sebastiaan Luyssaert
Geosci. Model Dev., 17, 8023–8047, https://doi.org/10.5194/gmd-17-8023-2024, https://doi.org/10.5194/gmd-17-8023-2024, 2024
Short summary
Short summary
This research looks at how climate change influences forests, and particularly how altered wind and insect activities could make forests emit instead of absorb carbon. We have updated a land surface model called ORCHIDEE to better examine the effect of bark beetles on forest health. Our findings suggest that sudden events, such as insect outbreaks, can dramatically affect carbon storage, offering crucial insights into tackling climate change.
Stephen Björn Wirth, Johanna Braun, Jens Heinke, Sebastian Ostberg, Susanne Rolinski, Sibyll Schaphoff, Fabian Stenzel, Werner von Bloh, Friedhelm Taube, and Christoph Müller
Geosci. Model Dev., 17, 7889–7914, https://doi.org/10.5194/gmd-17-7889-2024, https://doi.org/10.5194/gmd-17-7889-2024, 2024
Short summary
Short summary
We present a new approach to modelling biological nitrogen fixation (BNF) in the Lund–Potsdam–Jena managed Land dynamic global vegetation model. While in the original approach BNF depended on actual evapotranspiration, the new approach considers soil water content and temperature, vertical root distribution, the nitrogen (N) deficit and carbon (C) costs. The new approach improved simulated BNF compared to the scientific literature and the model ability to project future C and N cycle dynamics.
Nicolette Chang, Sarah-Anne Nicholson, Marcel du Plessis, Alice D. Lebehot, Thulwaneng Mashifane, Tumelo C. Moalusi, N. Precious Mongwe, and Pedro M. S. Monteiro
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-182, https://doi.org/10.5194/gmd-2024-182, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
Mesoscale features (10's to 100's of km) in the Southern Ocean (SO) are crucial for global heat and carbon transport, but often unresolved in models due to high computational costs. To address this source of uncertainty, we use a regional, NEMO model of the SO at 8 km resolution with coupled ocean, ice, and biogeochemistry, BIOPERIANT12. This serves as an experimental platform to explore physical-biogeochemical interactions, model parameters/formulations, and configuring future models.
Saeed Harati-Asl, Liliana Perez, and Roberto Molowny-Horas
Geosci. Model Dev., 17, 7423–7443, https://doi.org/10.5194/gmd-17-7423-2024, https://doi.org/10.5194/gmd-17-7423-2024, 2024
Short summary
Short summary
Social–ecological systems are the subject of many sustainability problems. Because of the complexity of these systems, we must be careful when intervening in them; otherwise we may cause irreversible damage. Using computer models, we can gain insight about these complex systems without harming them. In this paper we describe how we connected an ecological model of forest insect infestation with a social model of cooperation and simulated an intervention measure to save a forest from infestation.
Isabelle Maréchaux, Fabian Jörg Fischer, Sylvain Schmitt, and Jérôme Chave
EGUsphere, https://doi.org/10.5194/egusphere-2024-3104, https://doi.org/10.5194/egusphere-2024-3104, 2024
Short summary
Short summary
We describe TROLL 4.0, a simulator of forest dynamics that represents trees in a virtual space at one-meter resolution. Tree birth, growth, death and the underlying physiological processes such as carbon assimilation, water transpiration and leaf phenology depend on plant traits that are measured in the field for many individuals and species. The model is thus capable of jointly simulating forest structure, diversity and ecosystem functioning, a major challenge in modelling vegetation dynamics.
Katarína Merganičová, Ján Merganič, Laura Dobor, Roland Hollós, Zoltán Barcza, Dóra Hidy, Zuzana Sitková, Pavel Pavlenda, Hrvoje Marjanovic, Daniel Kurjak, Michal Bošel'a, Doroteja Bitunjac, Maša Zorana Ostrogović Sever, Jiří Novák, Peter Fleischer, and Tomáš Hlásny
Geosci. Model Dev., 17, 7317–7346, https://doi.org/10.5194/gmd-17-7317-2024, https://doi.org/10.5194/gmd-17-7317-2024, 2024
Short summary
Short summary
We developed a multi-objective calibration approach leading to robust parameter values aiming to strike a balance between their local precision and broad applicability. Using the Biome-BGCMuSo model, we tested the calibrated parameter sets for simulating European beech forest dynamics across large environmental gradients. Leveraging data from 87 plots and five European countries, the results demonstrated reasonable local accuracy and plausible large-scale productivity responses.
Sylvain Schmitt, Fabian Fischer, James Ball, Nicolas Barbier, Marion Boisseaux, Damien Bonal, Benoit Burban, Xiuzhi Chen, Géraldine Derroire, Jeremy Lichstein, Daniela Nemetschek, Natalia Restrepo-Coupe, Scott Saleska, Giacomo Sellan, Philippe Verley, Grégoire Vincent, Camille Ziegler, Jérôme Chave, and Isabelle Maréchaux
EGUsphere, https://doi.org/10.5194/egusphere-2024-3106, https://doi.org/10.5194/egusphere-2024-3106, 2024
Short summary
Short summary
We evaluate the capability of TROLL 4.0, a simulator of forest dynamics, to represent tropical forest structure, diversity and functioning in two Amazonian forests. Evaluation data include forest inventories, carbon and water fluxes between the forest and the atmosphere, and leaf area and canopy height from remote-sensing products. The model realistically predicts the structure and composition, and the seasonality of carbon and water fluxes at both sites.
Guohua Liu, Mirco Migliavacca, Christian Reimers, Basil Kraft, Markus Reichstein, Andrew D. Richardson, Lisa Wingate, Nicolas Delpierre, Hui Yang, and Alexander J. Winkler
Geosci. Model Dev., 17, 6683–6701, https://doi.org/10.5194/gmd-17-6683-2024, https://doi.org/10.5194/gmd-17-6683-2024, 2024
Short summary
Short summary
Our study employs long short-term memory (LSTM) networks to model canopy greenness and phenology, integrating meteorological memory effects. The LSTM model outperforms traditional methods, enhancing accuracy in predicting greenness dynamics and phenological transitions across plant functional types. Highlighting the importance of multi-variate meteorological memory effects, our research pioneers unlock the secrets of vegetation phenology responses to climate change with deep learning techniques.
Thi Lan Anh Dinh, Daniel Goll, Philippe Ciais, and Ronny Lauerwald
Geosci. Model Dev., 17, 6725–6744, https://doi.org/10.5194/gmd-17-6725-2024, https://doi.org/10.5194/gmd-17-6725-2024, 2024
Short summary
Short summary
The study assesses the performance of the dynamic global vegetation model (DGVM) ORCHIDEE in capturing the impact of land-use change on carbon stocks across Europe. Comparisons with observations reveal that the model accurately represents carbon fluxes and stocks. Despite the underestimations in certain land-use conversions, the model describes general trends in soil carbon response to land-use change, aligning with the site observations.
Nathaelle Bouttes, Lester Kwiatkowski, Manon Berger, Victor Brovkin, and Guy Munhoven
Geosci. Model Dev., 17, 6513–6528, https://doi.org/10.5194/gmd-17-6513-2024, https://doi.org/10.5194/gmd-17-6513-2024, 2024
Short summary
Short summary
Coral reefs are crucial for biodiversity, but they also play a role in the carbon cycle on long time scales of a few thousand years. To better simulate the future and past evolution of coral reefs and their effect on the global carbon cycle, hence on atmospheric CO2 concentration, it is necessary to include coral reefs within a climate model. Here we describe the inclusion of coral reef carbonate production in a carbon–climate model and its validation in comparison to existing modern data.
Huajie Zhu, Mousong Wu, Fei Jiang, Michael Vossbeck, Thomas Kaminski, Xiuli Xing, Jun Wang, Weimin Ju, and Jing M. Chen
Geosci. Model Dev., 17, 6337–6363, https://doi.org/10.5194/gmd-17-6337-2024, https://doi.org/10.5194/gmd-17-6337-2024, 2024
Short summary
Short summary
In this work, we developed the Nanjing University Carbon Assimilation System (NUCAS v1.0). Data assimilation experiments were conducted to demonstrate the robustness and investigate the feasibility and applicability of NUCAS. The assimilation of ecosystem carbonyl sulfide (COS) fluxes improved the model performance in gross primary productivity, evapotranspiration, and sensible heat, showing that COS provides constraints on parameters relevant to carbon-, water-, and energy-related processes.
Fang Li, Zhimin Zhou, Samuel Levis, Stephen Sitch, Felicity Hayes, Zhaozhong Feng, Peter B. Reich, Zhiyi Zhao, and Yanqing Zhou
Geosci. Model Dev., 17, 6173–6193, https://doi.org/10.5194/gmd-17-6173-2024, https://doi.org/10.5194/gmd-17-6173-2024, 2024
Short summary
Short summary
A new scheme is developed to model the surface ozone damage to vegetation in regional and global process-based models. Based on 4210 data points from ozone experiments, it accurately reproduces statistically significant linear or nonlinear photosynthetic and stomatal responses to ozone in observations for all vegetation types. It also enables models to implicitly capture the variability in plant ozone tolerance and the shift among species within a vegetation type.
Alexander S. Brunmayr, Frank Hagedorn, Margaux Moreno Duborgel, Luisa I. Minich, and Heather D. Graven
Geosci. Model Dev., 17, 5961–5985, https://doi.org/10.5194/gmd-17-5961-2024, https://doi.org/10.5194/gmd-17-5961-2024, 2024
Short summary
Short summary
A new generation of soil models promises to more accurately predict the carbon cycle in soils under climate change. However, measurements of 14C (the radioactive carbon isotope) in soils reveal that the new soil models face similar problems to the traditional models: they underestimate the residence time of carbon in soils and may therefore overestimate the net uptake of CO2 by the land ecosystem. Proposed solutions include restructuring the models and calibrating model parameters with 14C data.
Nina Raoult, Simon Beylat, James M. Salter, Frédéric Hourdin, Vladislav Bastrikov, Catherine Ottlé, and Philippe Peylin
Geosci. Model Dev., 17, 5779–5801, https://doi.org/10.5194/gmd-17-5779-2024, https://doi.org/10.5194/gmd-17-5779-2024, 2024
Short summary
Short summary
We use computer models to predict how the land surface will respond to climate change. However, these complex models do not always simulate what we observe in real life, limiting their effectiveness. To improve their accuracy, we use sophisticated statistical and computational techniques. We test a technique called history matching against more common approaches. This method adapts well to these models, helping us better understand how they work and therefore how to make them more realistic.
Jorn Bruggeman, Karsten Bolding, Lars Nerger, Anna Teruzzi, Simone Spada, Jozef Skákala, and Stefano Ciavatta
Geosci. Model Dev., 17, 5619–5639, https://doi.org/10.5194/gmd-17-5619-2024, https://doi.org/10.5194/gmd-17-5619-2024, 2024
Short summary
Short summary
To understand and predict the ocean’s capacity for carbon sequestration, its ability to supply food, and its response to climate change, we need the best possible estimate of its physical and biogeochemical properties. This is obtained through data assimilation which blends numerical models and observations. We present the Ensemble and Assimilation Tool (EAT), a flexible and efficient test bed that allows any scientist to explore and further develop the state of the art in data assimilation.
Dongyu Zheng, Andrew S. Merdith, Yves Goddéris, Yannick Donnadieu, Khushboo Gurung, and Benjamin J. W. Mills
Geosci. Model Dev., 17, 5413–5429, https://doi.org/10.5194/gmd-17-5413-2024, https://doi.org/10.5194/gmd-17-5413-2024, 2024
Short summary
Short summary
This study uses a deep learning method to upscale the time resolution of paleoclimate simulations to 1 million years. This improved resolution allows a climate-biogeochemical model to more accurately predict climate shifts. The method may be critical in developing new fully continuous methods that are able to be applied over a moving continental surface in deep time with high resolution at reasonable computational expense.
Boris Ťupek, Aleksi Lehtonen, Alla Yurova, Rose Abramoff, Bertrand Guenet, Elisa Bruni, Samuli Launiainen, Mikko Peltoniemi, Shoji Hashimoto, Xianglin Tian, Juha Heikkinen, Kari Minkkinen, and Raisa Mäkipää
Geosci. Model Dev., 17, 5349–5367, https://doi.org/10.5194/gmd-17-5349-2024, https://doi.org/10.5194/gmd-17-5349-2024, 2024
Short summary
Short summary
Updating the Yasso07 soil C model's dependency on decomposition with a hump-shaped Ricker moisture function improved modelled soil organic C (SOC) stocks in a catena of mineral and organic soils in boreal forest. The Ricker function, set to peak at a rate of 1 and calibrated against SOC and CO2 data using a Bayesian approach, showed a maximum in well-drained soils. Using SOC and CO2 data together with the moisture only from the topsoil humus was crucial for accurate model estimates.
Jacquelyn K. Shuman, Rosie A. Fisher, Charles Koven, Ryan Knox, Lara Kueppers, and Chonggang Xu
Geosci. Model Dev., 17, 4643–4671, https://doi.org/10.5194/gmd-17-4643-2024, https://doi.org/10.5194/gmd-17-4643-2024, 2024
Short summary
Short summary
We adapt a fire behavior and effects module for use in a size-structured vegetation demographic model to test how climate, fire regime, and fire-tolerance plant traits interact to determine the distribution of tropical forests and grasslands. Our model captures the connection between fire disturbance and plant fire-tolerance strategies in determining plant distribution and provides a useful tool for understanding the vulnerability of these areas under changing conditions across the tropics.
Yoshiki Kanzaki, Isabella Chiaravalloti, Shuang Zhang, Noah J. Planavsky, and Christopher T. Reinhard
Geosci. Model Dev., 17, 4515–4532, https://doi.org/10.5194/gmd-17-4515-2024, https://doi.org/10.5194/gmd-17-4515-2024, 2024
Short summary
Short summary
Soil pH is one of the most commonly measured agronomical and biogeochemical indices, mostly reflecting exchangeable acidity. Explicit simulation of both porewater and bulk soil pH is thus crucial to the accurate evaluation of alkalinity required to counteract soil acidification and the resulting capture of anthropogenic carbon dioxide through the enhanced weathering technique. This has been enabled by the updated reactive–transport SCEPTER code and newly developed framework to simulate soil pH.
David Sandoval, Iain Colin Prentice, and Rodolfo L. B. Nóbrega
Geosci. Model Dev., 17, 4229–4309, https://doi.org/10.5194/gmd-17-4229-2024, https://doi.org/10.5194/gmd-17-4229-2024, 2024
Short summary
Short summary
Numerous estimates of water and energy balances depend on empirical equations requiring site-specific calibration, posing risks of "the right answers for the wrong reasons". We introduce novel first-principles formulations to calculate key quantities without requiring local calibration, matching predictions from complex land surface models.
Oliver Perkins, Matthew Kasoar, Apostolos Voulgarakis, Cathy Smith, Jay Mistry, and James D. A. Millington
Geosci. Model Dev., 17, 3993–4016, https://doi.org/10.5194/gmd-17-3993-2024, https://doi.org/10.5194/gmd-17-3993-2024, 2024
Short summary
Short summary
Wildfire is often presented in the media as a danger to human life. Yet globally, millions of people’s livelihoods depend on using fire as a tool. So, patterns of fire emerge from interactions between humans, land use, and climate. This complexity means scientists cannot yet reliably say how fire will be impacted by climate change. So, we developed a new model that represents globally how people use and manage fire. The model reveals the extent and diversity of how humans live with and use fire.
Amos P. K. Tai, David H. Y. Yung, and Timothy Lam
Geosci. Model Dev., 17, 3733–3764, https://doi.org/10.5194/gmd-17-3733-2024, https://doi.org/10.5194/gmd-17-3733-2024, 2024
Short summary
Short summary
We have developed the Terrestrial Ecosystem Model in R (TEMIR), which simulates plant carbon and pollutant uptake and predicts their response to varying atmospheric conditions. This model is designed to couple with an atmospheric chemistry model so that questions related to plant–atmosphere interactions, such as the effects of climate change, rising CO2, and ozone pollution on forest carbon uptake, can be addressed. The model has been well validated with both ground and satellite observations.
Fabian Stenzel, Johanna Braun, Jannes Breier, Karlheinz Erb, Dieter Gerten, Jens Heinke, Sarah Matej, Sebastian Ostberg, Sibyll Schaphoff, and Wolfgang Lucht
Geosci. Model Dev., 17, 3235–3258, https://doi.org/10.5194/gmd-17-3235-2024, https://doi.org/10.5194/gmd-17-3235-2024, 2024
Short summary
Short summary
We provide an R package to compute two biosphere integrity metrics that can be applied to simulations of vegetation growth from the dynamic global vegetation model LPJmL. The pressure metric BioCol indicates that we humans modify and extract > 20 % of the potential preindustrial natural biomass production. The ecosystems state metric EcoRisk shows a high risk of ecosystem destabilization in many regions as a result of climate change and land, water, and fertilizer use.
Elin Ristorp Aas, Heleen A. de Wit, and Terje K. Berntsen
Geosci. Model Dev., 17, 2929–2959, https://doi.org/10.5194/gmd-17-2929-2024, https://doi.org/10.5194/gmd-17-2929-2024, 2024
Short summary
Short summary
By including microbial processes in soil models, we learn how the soil system interacts with its environment and responds to climate change. We present a soil process model, MIMICS+, which is able to reproduce carbon stocks found in boreal forest soils better than a conventional land model. With the model we also find that when adding nitrogen, the relationship between soil microbes changes notably. Coupling the model to a vegetation model will allow for further study of these mechanisms.
Cited articles
Adams, B. M., Eldred, M. S., Geraci, G., Hooper, R. W., Jakeman, J. D., Maupin, K. A., Monschke, J. A., Rushdi, A. A., Stephens, J. A., Swiler, L. P., Wildey, T. M., Bohnhoff, W. J., Dalbey, K. R., Ebeida, M. S., Eddy, J. P., Hough, P. D., Khalil, M., Kenneth, T. H., Ridway, E. M., Vigil, D. M., and Winokur, J. G.: Dakota, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis: Version 6.10 User’s Manual, Tech. Rep. SAND2014-4633, Sandia National Laboratory, https://doi.org/10.2172/1177077, 2019. a
Athias, V., Mazzega, P., and Jeandel, C.: Selecting a global optimization method to estimate the oceanic particle cycling rate constants, J. Mar. Res., 58, 675–707, 2000. a
Bagniewski, W., Fennel, K., Perry, M. J., and D'Asaro, E. A.: Optimizing models of the North Atlantic spring bloom using physical, chemical and bio-optical observations from a Lagrangian float, Biogeosciences, 8, 1291–1307, https://doi.org/10.5194/bg-8-1291-2011, 2011. a
Bianchi, D., Zavatarelli, M., Pinardi, N., Capozzi, R., Capotondi, L., Corselli, C., and Masina, S.: Simulations of ecosystem response during the sapropel S1 deposition event, Palaeogeogr. Palaeocl., 235, 265–287, https://doi.org/10.1016/J.PALAEO.2005.09.032, 2006. a
Blumberg, A. F. and Mellor, G. L.: A description of a three-dimensional coastal ocean circulation model, Costal and Estuarine Science, vol. 4, American Geophysical Union, 1987. a
Computational and Information Systems Laboratory: Cheyenne: HPE/SGI ICE XA System (University Community Computing), National Center for Atmospheric Research, Boulder, CO, https://doi.org/10.5065/D6RX99HX, 2019. a
Doney, S. C., Lima, I., Moore, J. K., Lindsay, K., Behrenfeld, M. J., Westberry, T. K., Mahowald, N., Glover, D. M., and Takahashi, T.: Skill metrics for confronting global upper ocean ecosystem-biogeochemistry models against field and remote sensing data, J. Mar. Syst., 76, 95–112, https://doi.org/10.1016/J.JMARSYS.2008.05.015, 2009. a
Fennel, K., Losch, M., Schroter, J., and Wenzeï, M.: Testing a marine ecosystem model: sensitivity analysis and parameter optimization, J. Mar. Syst., 28, 45–63, 2001. a
Friedrichs, M. A. M., Dusenberry, J. A., Anderson, L. A., Armstrong, R. A., Chai, F., Christian, J. R., Doney, S. C., Dunne, J., Fujii, M., Hood, R., McGillicuddy, D. J., Moore, J. K., Schartau, M., Spitz, Y. H., and Wiggert, J. D.: Assessment of skill and portability in regional marine biogeochemical models: Role of multiple planktonic groups, J. Geophys. Res., 112, C08001, https://doi.org/10.1029/2006JC003852, 2007. a, b
Henson, S. A., Yool, A., and Sanders, R.: Variability in efficiency of particulate organic carbon export: A model study, Global Biogeochem. Cy., 29, 33–45, https://doi.org/10.1002/2014GB004965, 2015. a
Hurtt, G. C. and Armstrong, R. A.: A pelagic ecosystem model calibrated with BATS data, Deep-Sea Res. Pt. II, 43, 653–683, https://doi.org/10.1016/0967-0645(96)00007-0, 1996. a
Hurtt, G. C. and Armstrong, R. A.: A pelagic ecosystem model calibrated with BATS and OWSI data, Deep-Sea Res. Pt. I, 46, 27–61, https://doi.org/10.1016/S0967-0637(98)00055-7, 1999. a, b
Karl, D. M. and Lukas, R.: The Hawaii Ocean Time-series (HOT) program: Background, rationale and field implementation, Deep-Sea Res. Pt. II, 43, 129–156, https://doi.org/10.1016/0967-0645(96)00005-7, 1996. a, b, c
Kern, S., McGuinn, M. E., Smith, K. M., Pinardi, N., Niemeyer, K. E., Lovenduski, N. S., and Hamlington, P. E.: skylerjk/BFM17-Opt: Coupling BFM17 + POM1D to DAKOTA for Optimization (Updated work flow), Zenodo [code], https://doi.org/10.5281/zenodo.10049146, 2023a. a
Kern, S., McGuinn, M. E., Smith, K. M., Pinardi, N., Niemeyer, K. E., Lovenduski, N. S., and Hamlington, P. E.: skylerjk/BFM17-SA-SinglePert: One-at-a-Time Sensitivity Analysis Code for BFM17, Zenodo [code], https://doi.org/10.5281/zenodo.7786746, 2023b. a
Kern, S., McGuinn, M. E., Smith, K. M., Pinardi, N., Niemeyer, K. E., Lovenduski, N. S., and Hamlington, P. E.: BFM17 Optimization Data and Plotting Scripts (Version 2), Zenodo [data set], https://doi.org/10.5281/zenodo.10049012, 2023c. a
Kidston, M., Matear, R., and Baird, M. E.: Parameter optimisation of a marine ecosystem model at two contrasting stations in the Sub-Antarctic Zone, Deep-Sea Res. Pt. II, 58, 2301–2315, https://doi.org/10.1016/J.DSR2.2011.05.018, 2011. a
Kim, H. H., Luo, Y.-W., Ducklow, H. W., Schofield, O. M., Steinberg, D. K., and Doney, S. C.: WAP-1D-VAR v1.0: development and evaluation of a one-dimensional variational data assimilation model for the marine ecosystem along the West Antarctic Peninsula, Geosci. Model Dev., 14, 4939–4975, https://doi.org/10.5194/gmd-14-4939-2021, 2021. a
Kriest, I.: Calibration of a simple and a complex model of global marine biogeochemistry, Biogeosciences, 14, 4965–4984, https://doi.org/10.5194/bg-14-4965-2017, 2017. a, b, c
Kriest, I., Sauerland, V., Khatiwala, S., Srivastav, A., and Oschlies, A.: Calibrating a global three-dimensional biogeochemical ocean model (MOPS-1.0), Geosci. Model Dev., 10, 127–154, https://doi.org/10.5194/gmd-10-127-2017, 2017. a, b, c
Kuhn, A. M. and Fennel, K.: Evaluating ecosystem model complexity for the northwest North Atlantic through surrogate-based optimization, Ocean Model., 142, 101437, https://doi.org/10.1016/J.OCEMOD.2019.101437, 2019. a
Kwon, E. Y. and Primeau, F.: Optimization and sensitivity study of a biogeochemistry ocean model using an implicit solver and in situ phosphate data, Global Biogeochem. Cy., 20, GB4009, https://doi.org/10.1029/2005GB002631, 2006. a
Kwon, E. Y. and Primeau, F.: Optimization and sensitivity of a global biogeochemistry ocean model using combined in situ DIC, alkalinity, and phosphate data, J. Geophys. Res.-Oceans, 113, 8011, https://doi.org/10.1029/2007JC004520, 2008. a
Long, M. C., Moore, J. K., Lindsay, K., Levy, M., Doney, S. C., Luo, J. Y., Krumhardt, K. M., Letscher, R. T., Grover, M., and Sylvester, Z. T.: Simulations With the Marine Biogeochemistry Library (MARBL), J. Adv. Model. Earth Sy., 13, e2021MS002647, https://doi.org/10.1029/2021MS002647, 2021. a
Mattern, J. P. and Edwards, C. A.: Simple parameter estimation for complex models – Testing evolutionary techniques on 3-dimensional biogeochemical ocean models, J. Mar. Syst., 165, 139–152, https://doi.org/10.1016/J.JMARSYS.2016.10.012, 2017. a
Mellor, G. L.: One-Dimensional, Ocean Surface Layer Modeling: A Problem and a Solution, J. Phys. Oceanogr., 31, 790–809, 2001. a
Mellor, G. L. and Yamada, T.: Development of a turbulence closure model for geophysical fluid problems, Rev. Geophys., 20, 851–875, https://doi.org/10.1029/RG020i004p00851, 1982. a, b
Meza, J. C., Oliva, R. A., Hough, P. D., and Williams, P. J.: OPT++: An Object-Oriented Toolkit for Nonlinear Optimization, ACM Trans. Math. Softw., 33, 1–27, https://doi.org/10.1145/1236463.1236467, 2007. a
Oliver, S., Cartis, C., Kriest, I., Tett, S. F. B., and Khatiwala, S.: A derivative-free optimisation method for global ocean biogeochemical models, Geosci. Model Dev., 15, 3537–3554, https://doi.org/10.5194/gmd-15-3537-2022, 2022. a
Prieß, M., Piwonski, J., Koziel, S., Oschlies, A., and Slawig, T.: Accelerated parameter identification in a 3D marine biogeochemical model using surrogate-based optimization, Ocean Model., 68, 22–36, https://doi.org/10.1016/j.ocemod.2013.04.003, 2013. a
Sauerland, V., Kriest, I., Oschlies, A., and Srivastav, A.: Multiobjective Calibration of a Global Biogeochemical Ocean Model Against Nutrients, Oxygen, and Oxygen Minimum Zones, J. Adv. Model. Earth Sy., 11, 1285–1308, https://doi.org/10.1029/2018MS001510, 2019. a, b
Schartau, M. and Oschlies, A.: Simultaneous data-based optimization of a 1D-ecosystem model at three locations in the North Atlantic: Part I-Method and parameter estimates, J. Mar. Res., 61, 765–793, 2003a. a
Schartau, M. and Oschlies, A.: Simultaneous data-based optimization of a 1D-ecosystem model at three locations in the North Atlantic: Part II – Standing stocks and nitrogen fluxes, J. Mar. Res., 61, 795–821, 2003b. a
Schartau, M., Oschlies, A., and Willebrand, J.: Parameter estimates of a zero-dimensional ecosystem model applying the adjoint method, Deep-Sea Res. Pt. II, 48, 1769–1800, https://doi.org/10.1016/S0967-0645(00)00161-2, 2001. a
Smith, K. M., Hamlington, P. E., and Fox-Kemper, B.: Effects of submesoscale turbulence on ocean tracers, J. Geophys. Res.-Oceans, 121, 908–933, https://doi.org/10.1002/2015JC011089, 2016. a
Smith, K. M., Hamlington, P. E., Niemeyer, K. E., Fox‐Kemper, B., and Lovenduski, N. S.: Effects of Langmuir Turbulence on Upper Ocean Carbonate Chemistry, J. Adv. Model. Earth Sy., 10, 2018MS001486, https://doi.org/10.1029/2018MS001486, 2018. a
Smith, K. M., Kern, S., Hamlington, P. E., Zavatarelli, M., Pinardi, N., Klee, E. F., and Niemeyer, K. E.: marco-zavatarelli/BFM17-56 BFM17-56, Zenodo [code], https://doi.org/10.5281/zenodo.3839984, 2020. a
Smith, K. M., Kern, S., Hamlington, P. E., Zavatarelli, M., Pinardi, N., Klee, E. F., and Niemeyer, K. E.: BFM17 v1.0: a reduced biogeochemical flux model for upper-ocean biophysical simulations, Geosci. Model Dev., 14, 2419–2442, https://doi.org/10.5194/gmd-14-2419-2021, 2021. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r, s, t, u, v, w, x, y, z, aa, ab, ac, ad
Spitz, Y., Moisan, J., and Abbott, M.: Configuring an ecosystem model using data from the Bermuda Atlantic Time Series (BATS), Deep-Sea Res. Pt. II, 48, 1733–1768, https://doi.org/10.1016/S0967-0645(00)00159-4, 2001. a
Spitz, Y. H., Moisan, J. R., Abbott, M. R., and Richman, J. G.: Data assimilation and a pelagic ecosystem model: parameterization using time series observations, J. Mar. Syst., 16, 51–68, https://doi.org/10.1016/S0924-7963(97)00099-7, 1998. a
Steinberg, D. K., Carlson, C. A., Bates, N. R., Johnson, R. J., Michaels, A. F., and Knap, A. H.: Overview of the US JGOFS Bermuda Atlantic Time-series Study (BATS): a decade-scale look at ocean biology and biogeochemistry, Deep-Sea Res. Pt. II, 48, 1405–1447, https://doi.org/10.1016/S0967-0645(00)00148-X, 2001. a, b, c
Vichi, M., Pinardi, N., Zavatarelli, M., Matteucci, G., Marcaccio, M., Bergamini, M. C., and Frascari, F.: One-dimensional ecosystem model tests in the Po Prodelta area (Northern Adriatic Sea), Environ. Modell. Softw., 13, 471–481, https://doi.org/10.1016/S1364-8152(98)00055-3, 1998. a, b
Vichi, M., Oddo, P., Zavatarelli, M., Coluccelli, A., Coppini, G., Celio, M., Fonda Umani, S., and Pinardi, N.: Calibration and validation of a one-dimensional complex marine biogeochemical flux model in different areas of the northern Adriatic shelf, Ann. Geophys., 21, 413–436, https://doi.org/10.5194/angeo-21-413-2003, 2003. a, b
Vichi, M., Pinardi, N., and Masina, S.: A generalized model of pelagic biogeochemistry for the global ocean ecosystem. Part I: Theory, J. Mar. Syst., 64, 89–109, https://doi.org/10.1016/j.jmarsys.2006.03.006, 2007. a, b
Ward, B. A., Friedrichs, M. A., Anderson, T. R., and Oschlies, A.: Parameter optimisation techniques and the problem of underdetermination in marine biogeochemical models, J. Mar. Syst., 81, 34–43, https://doi.org/10.1016/J.JMARSYS.2009.12.005, 2010. a
Ward, B. A., Schartau, M., Oschlies, A., Martin, A. P., Follows, M. J., and Anderson, T. R.: When is a biogeochemical model too complex? Objective model reduction and selection for North Atlantic time-series sites, Prog. Oceanogr., 116, 49–65, https://doi.org/10.1016/J.POCEAN.2013.06.002, 2013. a, b, c
Short summary
Computational models are used to simulate the behavior of marine ecosystems. The models often have unknown parameters that need to be calibrated to accurately represent observational data. Here, we propose a novel approach to simultaneously determine a large set of parameters for a one-dimensional model of a marine ecosystem in the surface ocean at two contrasting sites. By utilizing global and local optimization techniques, we estimate many parameters in a computationally efficient manner.
Computational models are used to simulate the behavior of marine ecosystems. The models often...
