Articles | Volume 17, issue 3
https://doi.org/10.5194/gmd-17-975-2024
© 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-17-975-2024
© 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
| 
06 Feb 2024
Development and technical paper |
| 06 Feb 2024
| 06 Feb 2024
The utility of simulated ocean chlorophyll observations: a case study with the Chlorophyll Observation Simulator Package (version 1) in CESMv2.2
Genevieve L. Clow
CORRESPONDING AUTHOR
Department of Atmospheric and Oceanic Sciences, University of Colorado, Boulder, Boulder, Colorado, USA
Institute of Arctic and Alpine Research, University of Colorado, Boulder, Boulder, Colorado, USA
Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, Boulder, Colorado, USA
Nicole S. Lovenduski
Department of Atmospheric and Oceanic Sciences, University of Colorado, Boulder, Boulder, Colorado, USA
Institute of Arctic and Alpine Research, University of Colorado, Boulder, Boulder, Colorado, USA
Michael N. Levy
Climate and Global Dynamics Laboratory, National Center for Atmospheric Research, Boulder, Colorado, USA
Keith Lindsay
Climate and Global Dynamics Laboratory, National Center for Atmospheric Research, Boulder, Colorado, USA
Jennifer E. Kay
Department of Atmospheric and Oceanic Sciences, University of Colorado, Boulder, Boulder, Colorado, USA
Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, Boulder, Colorado, USA
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Cara Nissen, Alison R. Gray, Nicole S. Lovenduski, Mathew Maltrud, and LeAnn Conlon
EGUsphere, https://doi.org/10.5194/egusphere-2026-4623, https://doi.org/10.5194/egusphere-2026-4623, 2026
This preprint is open for discussion and under review for Ocean Science (OS).
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Monthly 1°x1° gridded products of surface seawater partial pressure of carbon dioxide (pCO2) are routinely used to detect change in Southern Ocean carbon cycling, but we do not know the sampling error due to incomplete observational coverage at the monthly 1°x1° scale. Using synthetic observations from an ocean model, we estimate the pCO2 sampling error to be 13–14 μatm and negative in sign. Our findings imply that oceanic CO2 uptake derived from available pCO2 observations is biased high.
Amanda R. Fay, Dustin Carroll, Galen A. McKinley, Nicole S. Lovenduski, Dimitris Menemenlis, and Hong Zhang
EGUsphere, https://doi.org/10.5194/egusphere-2026-2870, https://doi.org/10.5194/egusphere-2026-2870, 2026
This preprint is open for discussion and under review for Biogeosciences (BG).
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The ocean helps slow climate change by absorbing carbon dioxide, but our research shows this process weakens quickly when we reduce emissions. Using a sophisticated model, we found that the ocean’s carbon uptake drops within just a few years of mitigation. This change is most visible in the North Atlantic and Southern Ocean. To accurately track our progress in addressing climate change, we must focus global monitoring efforts on these specific, sensitive regions of the open sea.
Skyler Kern, Mary E. McGuinn, Katherine M. Smith, Nadia Pinardi, Kyle E. Niemeyer, Nicole S. Lovenduski, and Peter E. Hamlington
Geosci. Model Dev., 19, 5601–5622, https://doi.org/10.5194/gmd-19-5601-2026, https://doi.org/10.5194/gmd-19-5601-2026, 2026
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The parameters that control a model's behavior determine its ability to represent a system. In this work, multiple cases test how to estimate the parameters of a model with components corresponding to both the physics and the chemical and biological processes (i.e. the biogeochemistry) of the ocean. While demonstrating how to approach this problem type, the results show estimating both sets of parameters simultaneously is better than estimating the physics then the biogeochemistry separately.
Paulo Ceppi, Alejandro Bodas-Salcedo, Mark D. Zelinka, Timothy Andrews, Florent Brient, Robin Chadwick, Jonathan M. Gregory, Yen-Ting Hwang, Sarah M. Kang, Jennifer E. Kay, Thorsten Mauritsen, Tomoo Ogura, George Tselioudis, Masahiro Watanabe, Mark J. Webb, and Allison A. Wing
EGUsphere, https://doi.org/10.5194/egusphere-2026-398, https://doi.org/10.5194/egusphere-2026-398, 2026
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Clouds constitute a key uncertainty for climate change projections. The Cloud Feedback Model Intercomparison Project (CFMIP) aims to address this challenge by evaluating and understanding clouds and their impacts on atmospheric circulation, precipitation, and climate sensitivity. The present paper describes the CFMIP experiment protocol for the Coupled Model Intercomparison Project phase 7 (CMIP7), and discusses the accompanying science questions and opportunities for progress.
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., 18, 8217–8234, https://doi.org/10.5194/gmd-18-8217-2025, https://doi.org/10.5194/gmd-18-8217-2025, 2025
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We have developed a new feature in the atmosphere and ocean components of the Community Earth System Model version 2 by implementing 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, and it 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.
Ash Gilbert, Jennifer E. Kay, and Penny Rowe
Geosci. Model Dev., 18, 7185–7197, https://doi.org/10.5194/gmd-18-7185-2025, https://doi.org/10.5194/gmd-18-7185-2025, 2025
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We developed a novel methodology for assessing whether a new physics parameterization should be added to a climate model based on its effect across a hierarchy of model dynamical constraints. Our study used this model hierarchy to evaluate the effect of a new cloud radiation parameterization on longwave radiation and determined that the parameterization should be added to climate radiation models, but its effect is not large enough to be a priority.
Jonah K. Shaw, Dustin J. Swales, Sergio DeSouza-Machado, David D. Turner, Jennifer E. Kay, and David P. Schneider
Geosci. Model Dev., 18, 4935–4950, https://doi.org/10.5194/gmd-18-4935-2025, https://doi.org/10.5194/gmd-18-4935-2025, 2025
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Satellites have observed Earth's emissions of infrared radiation since the 1970s. Because infrared wavelengths interact with the atmosphere in distinct ways, these observations contain information about Earth and the atmosphere. We present a tool that runs within Earth system models and produces output that can be directly compared with satellite measurements of infrared radiation. We then use this tool for climate model evaluation, climate change detection, and satellite mission design.
Malik J. Jordan, Emily F. Klee, Peter E. Hamlington, Nicole S. Lovenduski, and Kyle E. Niemeyer
EGUsphere, https://doi.org/10.5194/egusphere-2025-2901, https://doi.org/10.5194/egusphere-2025-2901, 2025
Preprint archived
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We developed a method to simplify complex ocean biogeochemical models so they can run faster in computer simulations without losing important details. By adapting techniques from combustion science, we created smaller versions of a large ocean model that still accurately represent key changes in ocean biology and chemistry. This work helps make detailed ocean simulations more efficient, supporting better understanding of ocean health and climate.
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
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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.
Megan Thompson-Munson, Jennifer E. Kay, and Bradley R. Markle
The Cryosphere, 18, 3333–3350, https://doi.org/10.5194/tc-18-3333-2024, https://doi.org/10.5194/tc-18-3333-2024, 2024
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The upper layers of the Greenland Ice Sheet are absorbent and can store meltwater that would otherwise flow into the ocean and raise sea level. The amount of meltwater that the ice sheet can store changes when the air temperature changes. We use a model to show that warming and cooling have opposite but unequal effects. Warming has a stronger effect than cooling, which highlights the vulnerability of the Greenland Ice Sheet to modern climate change.
Leah Bertrand, Jennifer E. Kay, John Haynes, and Gijs de Boer
Earth Syst. Sci. Data, 16, 1301–1316, https://doi.org/10.5194/essd-16-1301-2024, https://doi.org/10.5194/essd-16-1301-2024, 2024
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The vertical structure of clouds has a major impact on global energy flows, air circulation, and the hydrologic cycle. Two satellite instruments, CloudSat radar and CALIPSO lidar, have taken complementary measurements of cloud vertical structure for over a decade. Here, we present the 3S-GEOPROF-COMB product, a globally gridded satellite data product combining CloudSat and CALIPSO observations of cloud vertical structure.
Marika M. Holland, Cecile Hannay, John Fasullo, Alexandra Jahn, Jennifer E. Kay, Michael Mills, Isla R. Simpson, William Wieder, Peter Lawrence, Erik Kluzek, and David Bailey
Geosci. Model Dev., 17, 1585–1602, https://doi.org/10.5194/gmd-17-1585-2024, https://doi.org/10.5194/gmd-17-1585-2024, 2024
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Climate evolves in response to changing forcings, as prescribed in simulations. Models and forcings are updated over time to reflect new understanding. This makes it difficult to attribute simulation differences to either model or forcing changes. Here we present new simulations which enable the separation of model structure and forcing influence between two widely used simulation sets. Results indicate a strong influence of aerosol emission uncertainty on historical climate.
Skyler Kern, Mary E. McGuinn, Katherine M. Smith, Nadia Pinardi, Kyle E. Niemeyer, Nicole S. Lovenduski, and Peter E. Hamlington
Geosci. Model Dev., 17, 621–649, https://doi.org/10.5194/gmd-17-621-2024, https://doi.org/10.5194/gmd-17-621-2024, 2024
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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.
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
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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.
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
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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.
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Luke Gregor, Judith Hauck, Corinne Le Quéré, Ingrid T. Luijkx, Are Olsen, Glen P. Peters, Wouter Peters, Julia Pongratz, Clemens Schwingshackl, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Simone R. Alin, Ramdane Alkama, Almut Arneth, Vivek K. Arora, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Henry C. Bittig, Laurent Bopp, Frédéric Chevallier, Louise P. Chini, Margot Cronin, Wiley Evans, Stefanie Falk, Richard A. Feely, Thomas Gasser, Marion Gehlen, Thanos Gkritzalis, Lucas Gloege, Giacomo Grassi, Nicolas Gruber, Özgür Gürses, Ian Harris, Matthew Hefner, Richard A. Houghton, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Atul K. Jain, Annika Jersild, Koji Kadono, Etsushi Kato, Daniel Kennedy, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Peter Landschützer, Nathalie Lefèvre, Keith Lindsay, Junjie Liu, Zhu Liu, Gregg Marland, Nicolas Mayot, Matthew J. McGrath, Nicolas Metzl, Natalie M. Monacci, David R. Munro, Shin-Ichiro Nakaoka, Yosuke Niwa, Kevin O'Brien, Tsuneo Ono, Paul I. Palmer, Naiqing Pan, Denis Pierrot, Katie Pocock, Benjamin Poulter, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Carmen Rodriguez, Thais M. Rosan, Jörg Schwinger, Roland Séférian, Jamie D. Shutler, Ingunn Skjelvan, Tobias Steinhoff, Qing Sun, Adrienne J. Sutton, Colm Sweeney, Shintaro Takao, Toste Tanhua, Pieter P. Tans, Xiangjun Tian, Hanqin Tian, Bronte Tilbrook, Hiroyuki Tsujino, Francesco Tubiello, Guido R. van der Werf, Anthony P. Walker, Rik Wanninkhof, Chris Whitehead, Anna Willstrand Wranne, Rebecca Wright, Wenping Yuan, Chao Yue, Xu Yue, Sönke Zaehle, Jiye Zeng, and Bo Zheng
Earth Syst. Sci. Data, 14, 4811–4900, https://doi.org/10.5194/essd-14-4811-2022, https://doi.org/10.5194/essd-14-4811-2022, 2022
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The Global Carbon Budget 2022 describes the datasets and methodology used to quantify the anthropogenic emissions of carbon dioxide (CO2) and their partitioning among the atmosphere, the land ecosystems, and the ocean. These living datasets are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
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
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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.
Charles D. Koven, Vivek K. Arora, Patricia Cadule, Rosie A. Fisher, Chris D. Jones, David M. Lawrence, Jared Lewis, Keith Lindsay, Sabine Mathesius, Malte Meinshausen, Michael Mills, Zebedee Nicholls, Benjamin M. Sanderson, Roland Séférian, Neil C. Swart, William R. Wieder, and Kirsten Zickfeld
Earth Syst. Dynam., 13, 885–909, https://doi.org/10.5194/esd-13-885-2022, https://doi.org/10.5194/esd-13-885-2022, 2022
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We explore the long-term dynamics of Earth's climate and carbon cycles under a pair of contrasting scenarios to the year 2300 using six models that include both climate and carbon cycle dynamics. One scenario assumes very high emissions, while the second assumes a peak in emissions, followed by rapid declines to net negative emissions. We show that the models generally agree that warming is roughly proportional to carbon emissions but that many other aspects of the model projections differ.
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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.
Satellite observations of chlorophyll allow us to study marine phytoplankton on a global scale;...
