Articles | Volume 17, issue 13
https://doi.org/10.5194/gmd-17-5191-2024
© 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-17-5191-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
08 Jul 2024
Model description paper |
| 08 Jul 2024
| 08 Jul 2024
Linking global terrestrial and ocean biogeochemistry with process-based, coupled freshwater algae–nutrient–solid dynamics in LM3-FANSY v1.0
Minjin Lee
CORRESPONDING AUTHOR
Program in Atmospheric and Oceanic Sciences, Princeton University, Princeton, NJ 08540, USA
Charles A. Stock
NOAA/Geophysical Fluid Dynamics Laboratory, Princeton, NJ 08540, USA
John P. Dunne
NOAA/Geophysical Fluid Dynamics Laboratory, Princeton, NJ 08540, USA
Elena Shevliakova
NOAA/Geophysical Fluid Dynamics Laboratory, Princeton, NJ 08540, USA
Related authors
M. Lee, S. Malyshev, E. Shevliakova, P. C. D. Milly, and P. R. Jaffé
Biogeosciences, 11, 5809–5826, https://doi.org/10.5194/bg-11-5809-2014, https://doi.org/10.5194/bg-11-5809-2014, 2014
Vivek Seelanki, Wei Cheng, Phyllis J. Stabeno, Albert J. Hermann, Elizabeth J. Drenkard, Charles A. Stock, and Katherine Hedstrom
EGUsphere, https://doi.org/10.5194/egusphere-2025-1229, https://doi.org/10.5194/egusphere-2025-1229, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
Both physical and ecosystem properties of the ocean are rapidly changing. These changes anticipating ecosystem responses to environmental change and effectively managing marine. The model-based predictions and their performance in the historical states of the ocean must be carefully evaluated against observations. In this study a coupled ocean and sea-ice simulation during 1993–2018 using observations. We focus on the Bering Sea shelf, which is the largest productive ecosystem in the U.S.
Mathieu A. Poupon, Laure Resplandy, Jessica Garwood, Charles Stock, Niki Zadeh, and Jessica Y. Luo
Ocean Sci., 21, 851–875, https://doi.org/10.5194/os-21-851-2025, https://doi.org/10.5194/os-21-851-2025, 2025
Short summary
Short summary
Zooplankton diel vertical migration (DVM) shapes ocean biogeochemical cycles. We present a new DVM model that reproduces migration depths observed in the North Atlantic Ocean. We show that chlorophyll shading contributes to reducing zooplankton migration depth and mainly controls its spatial and temporal variability. Thus, high chlorophyll concentrations may limit carbon sequestration caused by zooplankton migration despite the general abundance of zooplankton migration in these environments.
Minki Hong, Nathaniel Chaney, Sergey Malyshev, Enrico Zorzetto, Anthony Preucil, and Elena Shevliakova
Geosci. Model Dev., 18, 2275–2301, https://doi.org/10.5194/gmd-18-2275-2025, https://doi.org/10.5194/gmd-18-2275-2025, 2025
Short summary
Short summary
This study shows the significance of groundwater in resolving the coupled terrestrial water–energy cycle. LM4-SHARC (soil–hillslope aquifer–river continuum) describes the hillslope groundwater using its emergent properties, yielding noticeable improvements in soil moisture/temperature and groundwater discharge predictions. The implications of groundwater-mediated hydrologic interactions between hillslopes and streams need further exploration in the Earth system modeling community.
Enrico Zorzetto, Paul Ginoux, Sergey Malyshev, and Elena Shevliakova
The Cryosphere, 19, 1313–1334, https://doi.org/10.5194/tc-19-1313-2025, https://doi.org/10.5194/tc-19-1313-2025, 2025
Short summary
Short summary
Light-absorbing particle (LAP) deposition on snow leads to a darkening of the snow surface and can thus accelerate snow melt. Understanding the extent to which different types of LAPs contribute to snow melt is important to both predict changes in water availability and improve global climate model predictions. Here, we extend a recently developed snow model to account for the deposition of LAPs in the snowpack and evaluate the effect of snow darkening on accelerating snow melt.
Tomohiro Hajima, Michio Kawamiya, Akihiko Ito, Kaoru Tachiiri, Chris D. Jones, Vivek Arora, Victor Brovkin, Roland Séférian, Spencer Liddicoat, Pierre Friedlingstein, and Elena Shevliakova
Biogeosciences, 22, 1447–1473, https://doi.org/10.5194/bg-22-1447-2025, https://doi.org/10.5194/bg-22-1447-2025, 2025
Short summary
Short summary
This study analyzes atmospheric CO2 concentrations and global carbon budgets simulated by multiple Earth system models, using several types of simulations (CO2 concentration- and emission-driven experiments). We successfully identified problems with regard to the global carbon budget in each model. We also found urgent issues with regard to land use change CO2 emissions that should be solved in the latest generation of models.
Paul J. Durack, Karl E. Taylor, Peter J. Gleckler, Gerald A. Meehl, Bryan N. Lawrence, Curt Covey, Ronald J. Stouffer, Guillaume Levavasseur, Atef Ben-Nasser, Sebastien Denvil, Martina Stockhause, Jonathan M. Gregory, Martin Juckes, Sasha K. Ames, Fabrizio Antonio, David C. Bader, John P. Dunne, Daniel Ellis, Veronika Eyring, Sandro L. Fiore, Sylvie Joussaume, Philip Kershaw, Jean-Francois Lamarque, Michael Lautenschlager, Jiwoo Lee, Chris F. Mauzey, Matthew Mizielinski, Paola Nassisi, Alessandra Nuzzo, Eleanor O’Rourke, Jeffrey Painter, Gerald L. Potter, Sven Rodriguez, and Dean N. Williams
EGUsphere, https://doi.org/10.5194/egusphere-2024-3729, https://doi.org/10.5194/egusphere-2024-3729, 2025
Short summary
Short summary
CMIP6 was the most expansive and ambitious Model Intercomparison Project (MIP), the latest in a history, extending four decades. CMIP engaged a growing community focused on improving climate understanding, and quantifying and attributing observed climate change being experienced today. The project's profound impact is due to the combining the latest climate science and technology, enabling the latest-generation climate simulations and increasing community attention in every successive phase.
Enhui Liao, Laure Resplandy, Fan Yang, Yangyang Zhao, Sam Ditkovsky, Manon Malsang, Jenna Pearson, Andrew C. Ross, Robert Hallberg, and Charles Stock
EGUsphere, https://doi.org/10.5194/egusphere-2024-3646, https://doi.org/10.5194/egusphere-2024-3646, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
We introduce a regional ocean model of the northern Indian Ocean, a region central to the livelihoods and economies of countries that comprise about one-third of the world’s population. The model successfully represents the key physical and biogeochemical features of the region and is well suited for physical and biogeochemical studies on timescales ranging from weeks to decades, in addition to supporting marine resource applications and management in the northern Indian Ocean.
John Patrick Dunne, Helene T. Hewitt, Julie Arblaster, Frédéric Bonou, Olivier Boucher, Tereza Cavazos, Paul J. Durack, Birgit Hassler, Martin Juckes, Tomoki Miyakawa, Matthew Mizielinski, Vaishali Naik, Zebedee Nicholls, Eleanor O’Rourke, Robert Pincus, Benjamin M. Sanderson, Isla R. Simpson, and Karl E. Taylor
EGUsphere, https://doi.org/10.5194/egusphere-2024-3874, https://doi.org/10.5194/egusphere-2024-3874, 2024
Short summary
Short summary
This manuscript provides the motivation and experimental design for the seventh phase of the Coupled Model Intercomparison Project (CMIP7) to coordinate community based efforts to answer key and timely climate science questions and facilitate delivery of relevant multi-model simulations for: prediction and projection, characterization, attribution and process understanding; vulnerability, impacts and adaptations analysis; national and international climate assessments; and society at large.
Yona Silvy, Thomas L. Frölicher, Jens Terhaar, Fortunat Joos, Friedrich A. Burger, Fabrice Lacroix, Myles Allen, Raffaele Bernardello, Laurent Bopp, Victor Brovkin, Jonathan R. Buzan, Patricia Cadule, Martin Dix, John Dunne, Pierre Friedlingstein, Goran Georgievski, Tomohiro Hajima, Stuart Jenkins, Michio Kawamiya, Nancy Y. Kiang, Vladimir Lapin, Donghyun Lee, Paul Lerner, Nadine Mengis, Estela A. Monteiro, David Paynter, Glen P. Peters, Anastasia Romanou, Jörg Schwinger, Sarah Sparrow, Eric Stofferahn, Jerry Tjiputra, Etienne Tourigny, and Tilo Ziehn
Earth Syst. Dynam., 15, 1591–1628, https://doi.org/10.5194/esd-15-1591-2024, https://doi.org/10.5194/esd-15-1591-2024, 2024
Short summary
Short summary
The adaptive emission reduction approach is applied with Earth system models to generate temperature stabilization simulations. These simulations provide compatible emission pathways and budgets for a given warming level, uncovering uncertainty ranges previously missing in the Coupled Model Intercomparison Project scenarios. These target-based emission-driven simulations offer a more coherent assessment across models for studying both the carbon cycle and its impacts under climate stabilization.
Elizabeth J. Drenkard, Charles A. Stock, Andrew C. Ross, Yi-Cheng Teng, Theresa Morrison, Wei Cheng, Alistair Adcroft, Enrique Curchitser, Raphael Dussin, Robert Hallberg, Claudine Hauri, Katherine Hedstrom, Albert Hermann, Michael G. Jacox, Kelly A. Kearney, Remi Pages, Darren J. Pilcher, Mercedes Pozo Buil, Vivek Seelanki, and Niki Zadeh
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-195, https://doi.org/10.5194/gmd-2024-195, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
We made a new regional ocean model to assist fisheries and ecosystem managers make decisions in the Northeast Pacific Ocean (NEP). We found that the model did well simulating past ocean conditions like temperature, and nutrient and oxygen levels, and can even reproduce metrics used by and important to ecosystem managers.
Andrew C. Ross, Charles A. Stock, Vimal Koul, Thomas L. Delworth, Feiyu Lu, Andrew Wittenberg, and Michael A. Alexander
Ocean Sci., 20, 1631–1656, https://doi.org/10.5194/os-20-1631-2024, https://doi.org/10.5194/os-20-1631-2024, 2024
Short summary
Short summary
In this paper, we use a high-resolution regional ocean model to downscale seasonal ocean forecasts from the Seamless System for Prediction and EArth System Research (SPEAR) model of the Geophysical Fluid Dynamics Laboratory (GFDL). We find that the downscaled model has significantly higher prediction skill in many cases.
Benjamin M. Sanderson, Ben B. B. Booth, John Dunne, Veronika Eyring, Rosie A. Fisher, Pierre Friedlingstein, Matthew J. Gidden, Tomohiro Hajima, Chris D. Jones, Colin G. Jones, Andrew King, Charles D. Koven, David M. Lawrence, Jason Lowe, Nadine Mengis, Glen P. Peters, Joeri Rogelj, Chris Smith, Abigail C. Snyder, Isla R. Simpson, Abigail L. S. Swann, Claudia Tebaldi, Tatiana Ilyina, Carl-Friedrich Schleussner, Roland Séférian, Bjørn H. Samset, Detlef van Vuuren, and Sönke Zaehle
Geosci. Model Dev., 17, 8141–8172, https://doi.org/10.5194/gmd-17-8141-2024, https://doi.org/10.5194/gmd-17-8141-2024, 2024
Short summary
Short summary
We discuss how, in order to provide more relevant guidance for climate policy, coordinated climate experiments should adopt a greater focus on simulations where Earth system models are provided with carbon emissions from fossil fuels together with land use change instructions, rather than past approaches that have largely focused on experiments with prescribed atmospheric carbon dioxide concentrations. We discuss how these goals might be achieved in coordinated climate modeling experiments.
Enrico Zorzetto, Sergey Malyshev, Paul Ginoux, and Elena Shevliakova
Geosci. Model Dev., 17, 7219–7244, https://doi.org/10.5194/gmd-17-7219-2024, https://doi.org/10.5194/gmd-17-7219-2024, 2024
Short summary
Short summary
We describe a new snow scheme developed for use in global climate models, which simulates the interactions of snowpack with vegetation, atmosphere, and soil. We test the new snow model over a set of sites where in situ observations are available. We find that when compared to a simpler snow model, this model improves predictions of seasonal snow and of soil temperature under the snowpack, important variables for simulating both the hydrological cycle and the global climate system.
Amali A. Amali, Clemens Schwingshackl, Akihiko Ito, Alina Barbu, Christine Delire, Daniele Peano, David M. Lawrence, David Wårlind, Eddy Robertson, Edouard L. Davin, Elena Shevliakova, Ian N. Harman, Nicolas Vuichard, Paul A. Miller, Peter J. Lawrence, Tilo Ziehn, Tomohiro Hajima, Victor Brovkin, Yanwu Zhang, Vivek K. Arora, and Julia Pongratz
EGUsphere, https://doi.org/10.5194/egusphere-2024-2460, https://doi.org/10.5194/egusphere-2024-2460, 2024
Short summary
Short summary
Our study explored the impact of anthropogenic land-use change (LUC) on climate dynamics, focusing on biogeophysical (BGP) and biogeochemical (BGC) effects using data from the CMIP6-LUMIP project. We found that LUC-induced carbon emissions contribute to a BGC warming of 0.20 °C, with BGC effects dominating globally over BGP effects, which show regional variability. Our findings highlight discrepancies in model simulations and emphasise the need for improved representations of LUC processes.
Mathilde Dugenne, Marco Corrales-Ugalde, Jessica Y. Luo, Rainer Kiko, Todd D. O'Brien, Jean-Olivier Irisson, Fabien Lombard, Lars Stemmann, Charles Stock, Clarissa R. Anderson, Marcel Babin, Nagib Bhairy, Sophie Bonnet, Francois Carlotti, Astrid Cornils, E. Taylor Crockford, Patrick Daniel, Corinne Desnos, Laetitia Drago, Amanda Elineau, Alexis Fischer, Nina Grandrémy, Pierre-Luc Grondin, Lionel Guidi, Cecile Guieu, Helena Hauss, Kendra Hayashi, Jenny A. Huggett, Laetitia Jalabert, Lee Karp-Boss, Kasia M. Kenitz, Raphael M. Kudela, Magali Lescot, Claudie Marec, Andrew McDonnell, Zoe Mériguet, Barbara Niehoff, Margaux Noyon, Thelma Panaïotis, Emily Peacock, Marc Picheral, Emilie Riquier, Collin Roesler, Jean-Baptiste Romagnan, Heidi M. Sosik, Gretchen Spencer, Jan Taucher, Chloé Tilliette, and Marion Vilain
Earth Syst. Sci. Data, 16, 2971–2999, https://doi.org/10.5194/essd-16-2971-2024, https://doi.org/10.5194/essd-16-2971-2024, 2024
Short summary
Short summary
Plankton and particles influence carbon cycling and energy flow in marine ecosystems. We used three types of novel plankton imaging systems to obtain size measurements from a range of plankton and particle sizes and across all major oceans. Data were compiled and cross-calibrated from many thousands of images, showing seasonal and spatial changes in particle size structure in different ocean basins. These datasets form the first release of the Pelagic Size Structure database (PSSdb).
Katja Frieler, Jan Volkholz, Stefan Lange, Jacob Schewe, Matthias Mengel, María del Rocío Rivas López, Christian Otto, Christopher P. O. Reyer, Dirk Nikolaus Karger, Johanna T. Malle, Simon Treu, Christoph Menz, Julia L. Blanchard, Cheryl S. Harrison, Colleen M. Petrik, Tyler D. Eddy, Kelly Ortega-Cisneros, Camilla Novaglio, Yannick Rousseau, Reg A. Watson, Charles Stock, Xiao Liu, Ryan Heneghan, Derek Tittensor, Olivier Maury, Matthias Büchner, Thomas Vogt, Tingting Wang, Fubao Sun, Inga J. Sauer, Johannes Koch, Inne Vanderkelen, Jonas Jägermeyr, Christoph Müller, Sam Rabin, Jochen Klar, Iliusi D. Vega del Valle, Gitta Lasslop, Sarah Chadburn, Eleanor Burke, Angela Gallego-Sala, Noah Smith, Jinfeng Chang, Stijn Hantson, Chantelle Burton, Anne Gädeke, Fang Li, Simon N. Gosling, Hannes Müller Schmied, Fred Hattermann, Jida Wang, Fangfang Yao, Thomas Hickler, Rafael Marcé, Don Pierson, Wim Thiery, Daniel Mercado-Bettín, Robert Ladwig, Ana Isabel Ayala-Zamora, Matthew Forrest, and Michel Bechtold
Geosci. Model Dev., 17, 1–51, https://doi.org/10.5194/gmd-17-1-2024, https://doi.org/10.5194/gmd-17-1-2024, 2024
Short summary
Short summary
Our paper provides an overview of all observational climate-related and socioeconomic forcing data used as input for the impact model evaluation and impact attribution experiments within the third round of the Inter-Sectoral Impact Model Intercomparison Project. The experiments are designed to test our understanding of observed changes in natural and human systems and to quantify to what degree these changes have already been induced by climate change.
Andrew C. Ross, Charles A. Stock, Alistair Adcroft, Enrique Curchitser, Robert Hallberg, Matthew J. Harrison, Katherine Hedstrom, Niki Zadeh, Michael Alexander, Wenhao Chen, Elizabeth J. Drenkard, Hubert du Pontavice, Raphael Dussin, Fabian Gomez, Jasmin G. John, Dujuan Kang, Diane Lavoie, Laure Resplandy, Alizée Roobaert, Vincent Saba, Sang-Ik Shin, Samantha Siedlecki, and James Simkins
Geosci. Model Dev., 16, 6943–6985, https://doi.org/10.5194/gmd-16-6943-2023, https://doi.org/10.5194/gmd-16-6943-2023, 2023
Short summary
Short summary
We evaluate a model for northwest Atlantic Ocean dynamics and biogeochemistry that balances high resolution with computational economy by building on the new regional features in the MOM6 ocean model and COBALT biogeochemical model. We test the model's ability to simulate impactful historical variability and find that the model simulates the mean state and variability of most features well, which suggests the model can provide information to inform living-marine-resource applications.
Weiyi Tang, Bess B. Ward, Michael Beman, Laura Bristow, Darren Clark, Sarah Fawcett, Claudia Frey, François Fripiat, Gerhard J. Herndl, Mhlangabezi Mdutyana, Fabien Paulot, Xuefeng Peng, Alyson E. Santoro, Takuhei Shiozaki, Eva Sintes, Charles Stock, Xin Sun, Xianhui S. Wan, Min N. Xu, and Yao Zhang
Earth Syst. Sci. Data, 15, 5039–5077, https://doi.org/10.5194/essd-15-5039-2023, https://doi.org/10.5194/essd-15-5039-2023, 2023
Short summary
Short summary
Nitrification and nitrifiers play an important role in marine nitrogen and carbon cycles by converting ammonium to nitrite and nitrate. Nitrification could affect microbial community structure, marine productivity, and the production of nitrous oxide – a powerful greenhouse gas. We introduce the newly constructed database of nitrification and nitrifiers in the marine water column and guide future research efforts in field observations and model development of nitrification.
Jonathan D. Sharp, Andrea J. Fassbender, Brendan R. Carter, Gregory C. Johnson, Cristina Schultz, and John P. Dunne
Earth Syst. Sci. Data, 15, 4481–4518, https://doi.org/10.5194/essd-15-4481-2023, https://doi.org/10.5194/essd-15-4481-2023, 2023
Short summary
Short summary
Dissolved oxygen content is a critical metric of ocean health. Recently, expanding fleets of autonomous platforms that measure oxygen in the ocean have produced a wealth of new data. We leverage machine learning to take advantage of this growing global dataset, producing a gridded data product of ocean interior dissolved oxygen at monthly resolution over nearly 2 decades. This work provides novel information for investigations of spatial, seasonal, and interannual variability in ocean oxygen.
Fabian A. Gomez, Sang-Ki Lee, Charles A. Stock, Andrew C. Ross, Laure Resplandy, Samantha A. Siedlecki, Filippos Tagklis, and Joseph E. Salisbury
Earth Syst. Sci. Data, 15, 2223–2234, https://doi.org/10.5194/essd-15-2223-2023, https://doi.org/10.5194/essd-15-2223-2023, 2023
Short summary
Short summary
We present a river chemistry and discharge dataset for 140 rivers in the United States, which integrates information from the Water Quality Database of the US Geological Survey (USGS), the USGS’s Surface-Water Monthly Statistics for the Nation, and the U.S. Army Corps of Engineers. This dataset includes dissolved inorganic carbon and alkalinity, two key properties to characterize the carbonate system, as well as nutrient concentrations, such as nitrate, phosphate, and silica.
Enrico Zorzetto, Sergey Malyshev, Nathaniel Chaney, David Paynter, Raymond Menzel, and Elena Shevliakova
Geosci. Model Dev., 16, 1937–1960, https://doi.org/10.5194/gmd-16-1937-2023, https://doi.org/10.5194/gmd-16-1937-2023, 2023
Short summary
Short summary
In this paper we develop a methodology to model the spatial distribution of solar radiation received by land over mountainous terrain. The approach is designed to be used in Earth system models, where coarse grid cells hinder the description of fine-scale land–atmosphere interactions. We adopt a clustering algorithm to partition the land domain into a set of homogeneous sub-grid
tiles, and for each tile we evaluate solar radiation received by land based on terrain properties.
Alban Planchat, Lester Kwiatkowski, Laurent Bopp, Olivier Torres, James R. Christian, Momme Butenschön, Tomas Lovato, Roland Séférian, Matthew A. Chamberlain, Olivier Aumont, Michio Watanabe, Akitomo Yamamoto, Andrew Yool, Tatiana Ilyina, Hiroyuki Tsujino, Kristen M. Krumhardt, Jörg Schwinger, Jerry Tjiputra, John P. Dunne, and Charles Stock
Biogeosciences, 20, 1195–1257, https://doi.org/10.5194/bg-20-1195-2023, https://doi.org/10.5194/bg-20-1195-2023, 2023
Short summary
Short summary
Ocean alkalinity is critical to the uptake of atmospheric carbon and acidification in surface waters. We review the representation of alkalinity and the associated calcium carbonate cycle in Earth system models. While many parameterizations remain present in the latest generation of models, there is a general improvement in the simulated alkalinity distribution. This improvement is related to an increase in the export of biotic calcium carbonate, which closer resembles observations.
Zun Yin, Kirsten L. Findell, Paul Dirmeyer, Elena Shevliakova, Sergey Malyshev, Khaled Ghannam, Nina Raoult, and Zhihong Tan
Hydrol. Earth Syst. Sci., 27, 861–872, https://doi.org/10.5194/hess-27-861-2023, https://doi.org/10.5194/hess-27-861-2023, 2023
Short summary
Short summary
Land–atmosphere (L–A) interactions typically focus on daytime processes connecting the land state with the overlying atmospheric boundary layer. However, much prior L–A work used monthly or daily means due to the lack of daytime-only data products. Here we show that monthly smoothing can significantly obscure the L–A coupling signal, and including nighttime information can mute or mask the daytime processes of interest. We propose diagnosing L–A coupling within models or archiving subdaily data.
Sian Kou-Giesbrecht, Sergey Malyshev, Isabel Martínez Cano, Stephen W. Pacala, Elena Shevliakova, Thomas A. Bytnerowicz, and Duncan N. L. Menge
Biogeosciences, 18, 4143–4183, https://doi.org/10.5194/bg-18-4143-2021, https://doi.org/10.5194/bg-18-4143-2021, 2021
Short summary
Short summary
Representing biological nitrogen fixation (BNF) is an important challenge for land models. We present a novel representation of BNF and updated nitrogen cycling in a land model. It includes a representation of asymbiotic BNF by soil microbes and the competitive dynamics between nitrogen-fixing and non-fixing plants. It improves estimations of major carbon and nitrogen pools and fluxes and their temporal dynamics in comparison to previous representations of BNF in land models.
George C. Hurtt, Louise Chini, Ritvik Sahajpal, Steve Frolking, Benjamin L. Bodirsky, Katherine Calvin, Jonathan C. Doelman, Justin Fisk, Shinichiro Fujimori, Kees Klein Goldewijk, Tomoko Hasegawa, Peter Havlik, Andreas Heinimann, Florian Humpenöder, Johan Jungclaus, Jed O. Kaplan, Jennifer Kennedy, Tamás Krisztin, David Lawrence, Peter Lawrence, Lei Ma, Ole Mertz, Julia Pongratz, Alexander Popp, Benjamin Poulter, Keywan Riahi, Elena Shevliakova, Elke Stehfest, Peter Thornton, Francesco N. Tubiello, Detlef P. van Vuuren, and Xin Zhang
Geosci. Model Dev., 13, 5425–5464, https://doi.org/10.5194/gmd-13-5425-2020, https://doi.org/10.5194/gmd-13-5425-2020, 2020
Short summary
Short summary
To estimate the effects of human land use activities on the carbon–climate system, a new set of global gridded land use forcing datasets was developed to link historical land use data to eight future scenarios in a standard format required by climate models. This new generation of land use harmonization (LUH2) includes updated inputs, higher spatial resolution, more detailed land use transitions, and the addition of important agricultural management layers; it will be used for CMIP6 simulations.
Claudine Hauri, Cristina Schultz, Katherine Hedstrom, Seth Danielson, Brita Irving, Scott C. Doney, Raphael Dussin, Enrique N. Curchitser, David F. Hill, and Charles A. Stock
Biogeosciences, 17, 3837–3857, https://doi.org/10.5194/bg-17-3837-2020, https://doi.org/10.5194/bg-17-3837-2020, 2020
Short summary
Short summary
The coastal ecosystem of the Gulf of Alaska (GOA) is especially vulnerable to the effects of ocean acidification and climate change. To improve our conceptual understanding of the system, we developed a new regional biogeochemical model setup for the GOA. Model output suggests that bottom water is seasonally high in CO2 between June and January. Such extensive periods of reoccurring high CO2 may be harmful to ocean acidification-sensitive organisms.
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.
Thomas L. Frölicher, Luca Ramseyer, Christoph C. Raible, Keith B. Rodgers, and John Dunne
Biogeosciences, 17, 2061–2083, https://doi.org/10.5194/bg-17-2061-2020, https://doi.org/10.5194/bg-17-2061-2020, 2020
Short summary
Short summary
Climate variations can have profound impacts on marine ecosystems. Here we show that on global scales marine ecosystem drivers such as temperature, pH, O2 and NPP are potentially predictable 3 (at the surface) and more than 10 years (subsurface) in advance. However, there are distinct regional differences in the potential predictability of these drivers. Our study suggests that physical–biogeochemical forecast systems have considerable potential for use in marine resource management.
Bing Pu, Paul Ginoux, Huan Guo, N. Christina Hsu, John Kimball, Beatrice Marticorena, Sergey Malyshev, Vaishali Naik, Norman T. O'Neill, Carlos Pérez García-Pando, Juliette Paireau, Joseph M. Prospero, Elena Shevliakova, and Ming Zhao
Atmos. Chem. Phys., 20, 55–81, https://doi.org/10.5194/acp-20-55-2020, https://doi.org/10.5194/acp-20-55-2020, 2020
Short summary
Short summary
Dust emission initiates when surface wind velocities exceed a threshold depending on soil and surface characteristics and varying spatially and temporally. Climate models widely use wind erosion thresholds. The climatological monthly global distribution of the wind erosion threshold, Vthreshold, is retrieved using satellite and reanalysis products and improves the simulation of dust frequency, magnitude, and the seasonal cycle in the Geophysical Fluid Dynamics Laboratory land–atmosphere model.
Katja Fennel, Simone Alin, Leticia Barbero, Wiley Evans, Timothée Bourgeois, Sarah Cooley, John Dunne, Richard A. Feely, Jose Martin Hernandez-Ayon, Xinping Hu, Steven Lohrenz, Frank Muller-Karger, Raymond Najjar, Lisa Robbins, Elizabeth Shadwick, Samantha Siedlecki, Nadja Steiner, Adrienne Sutton, Daniela Turk, Penny Vlahos, and Zhaohui Aleck Wang
Biogeosciences, 16, 1281–1304, https://doi.org/10.5194/bg-16-1281-2019, https://doi.org/10.5194/bg-16-1281-2019, 2019
Short summary
Short summary
We review and synthesize available information on coastal ocean carbon fluxes around North America (NA). There is overwhelming evidence, compiled and discussed here, that the NA coastal margins act as a sink. Our synthesis shows the great diversity in processes driving carbon fluxes in different coastal regions, highlights remaining gaps in observations and models, and discusses current and anticipated future trends with respect to carbon fluxes and acidification.
Fabien Paulot, Sergey Malyshev, Tran Nguyen, John D. Crounse, Elena Shevliakova, and Larry W. Horowitz
Atmos. Chem. Phys., 18, 17963–17978, https://doi.org/10.5194/acp-18-17963-2018, https://doi.org/10.5194/acp-18-17963-2018, 2018
Nathaniel W. Chaney, Marjolein H. J. Van Huijgevoort, Elena Shevliakova, Sergey Malyshev, Paul C. D. Milly, Paul P. G. Gauthier, and Benjamin N. Sulman
Hydrol. Earth Syst. Sci., 22, 3311–3330, https://doi.org/10.5194/hess-22-3311-2018, https://doi.org/10.5194/hess-22-3311-2018, 2018
Short summary
Short summary
The petabytes of existing global environmental data provide an invaluable asset to improve the characterization of land heterogeneity in Earth system models. This study introduces a clustering algorithm that summarizes a domain's heterogeneity through spatially interconnected clusters. A series of land model simulations in central California using this approach illustrate the critical role that multi-scale heterogeneity can have on the macroscale water, energy, and carbon cycles.
Derek P. Tittensor, Tyler D. Eddy, Heike K. Lotze, Eric D. Galbraith, William Cheung, Manuel Barange, Julia L. Blanchard, Laurent Bopp, Andrea Bryndum-Buchholz, Matthias Büchner, Catherine Bulman, David A. Carozza, Villy Christensen, Marta Coll, John P. Dunne, Jose A. Fernandes, Elizabeth A. Fulton, Alistair J. Hobday, Veronika Huber, Simon Jennings, Miranda Jones, Patrick Lehodey, Jason S. Link, Steve Mackinson, Olivier Maury, Susa Niiranen, Ricardo Oliveros-Ramos, Tilla Roy, Jacob Schewe, Yunne-Jai Shin, Tiago Silva, Charles A. Stock, Jeroen Steenbeek, Philip J. Underwood, Jan Volkholz, James R. Watson, and Nicola D. Walker
Geosci. Model Dev., 11, 1421–1442, https://doi.org/10.5194/gmd-11-1421-2018, https://doi.org/10.5194/gmd-11-1421-2018, 2018
Short summary
Short summary
Model intercomparison studies in the climate and Earth sciences communities have been crucial for strengthening future projections. Given the speed and magnitude of anthropogenic change in the marine environment, the time is ripe for similar comparisons among models of fisheries and marine ecosystems. We describe the Fisheries and Marine Ecosystem Model Intercomparison Project, which brings together the marine ecosystem modelling community to inform long-term projections of marine ecosystems.
Sam S. Rabin, Daniel S. Ward, Sergey L. Malyshev, Brian I. Magi, Elena Shevliakova, and Stephen W. Pacala
Geosci. Model Dev., 11, 815–842, https://doi.org/10.5194/gmd-11-815-2018, https://doi.org/10.5194/gmd-11-815-2018, 2018
Short summary
Short summary
This paper describes a new fire model that for the first time simulates how fire is used on cropland and pasture in the modern day, as imposed using a recently developed dataset. A non-agricultural fire module is fit algorithmically against non-agricultural burned area. Fitting improves performance and the general global pattern of fire is represented, but some gaps remain. The novel separation of agricultural burning from other fire may necessitate new design thinking in the future.
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.
James C. Orr, Raymond G. Najjar, Olivier Aumont, Laurent Bopp, John L. Bullister, Gokhan Danabasoglu, Scott C. Doney, John P. Dunne, Jean-Claude Dutay, Heather Graven, Stephen M. Griffies, Jasmin G. John, Fortunat Joos, Ingeborg Levin, Keith Lindsay, Richard J. Matear, Galen A. McKinley, Anne Mouchet, Andreas Oschlies, Anastasia Romanou, Reiner Schlitzer, Alessandro Tagliabue, Toste Tanhua, and Andrew Yool
Geosci. Model Dev., 10, 2169–2199, https://doi.org/10.5194/gmd-10-2169-2017, https://doi.org/10.5194/gmd-10-2169-2017, 2017
Short summary
Short summary
The Ocean Model Intercomparison Project (OMIP) is a model comparison effort under Phase 6 of the Coupled Model Intercomparison Project (CMIP6). Its physical component is described elsewhere in this special issue. Here we describe its ocean biogeochemical component (OMIP-BGC), detailing simulation protocols and analysis diagnostics. Simulations focus on ocean carbon, other biogeochemical tracers, air-sea exchange of CO2 and related gases, and chemical tracers used to evaluate modeled circulation.
David M. Lawrence, George C. Hurtt, Almut Arneth, Victor Brovkin, Kate V. Calvin, Andrew D. Jones, Chris D. Jones, Peter J. Lawrence, Nathalie de Noblet-Ducoudré, Julia Pongratz, Sonia I. Seneviratne, and Elena Shevliakova
Geosci. Model Dev., 9, 2973–2998, https://doi.org/10.5194/gmd-9-2973-2016, https://doi.org/10.5194/gmd-9-2973-2016, 2016
Short summary
Short summary
Human land-use activities have resulted in large changes to the Earth's surface, with resulting implications for climate. In the future, land-use activities are likely to expand and intensify further to meet growing demands for food, fiber, and energy. The goal of LUMIP is to take the next steps in land-use change science, and enable, coordinate, and ultimately address the most important land-use science questions in more depth and sophistication than possible in a multi-model context to date.
Chris D. Jones, Vivek Arora, Pierre Friedlingstein, Laurent Bopp, Victor Brovkin, John Dunne, Heather Graven, Forrest Hoffman, Tatiana Ilyina, Jasmin G. John, Martin Jung, Michio Kawamiya, Charlie Koven, Julia Pongratz, Thomas Raddatz, James T. Randerson, and Sönke Zaehle
Geosci. Model Dev., 9, 2853–2880, https://doi.org/10.5194/gmd-9-2853-2016, https://doi.org/10.5194/gmd-9-2853-2016, 2016
Short summary
Short summary
How the carbon cycle interacts with climate will affect future climate change and how society plans emissions reductions to achieve climate targets. The Coupled Climate Carbon Cycle Model Intercomparison Project (C4MIP) is an endorsed activity of CMIP6 and aims to quantify these interactions and feedbacks in state-of-the-art climate models. This paper lays out the experimental protocol for modelling groups to follow to contribute to C4MIP. It is a contribution to the CMIP6 GMD Special Issue.
Charlotte Laufkötter, Meike Vogt, Nicolas Gruber, Olivier Aumont, Laurent Bopp, Scott C. Doney, John P. Dunne, Judith Hauck, Jasmin G. John, Ivan D. Lima, Roland Seferian, and Christoph Völker
Biogeosciences, 13, 4023–4047, https://doi.org/10.5194/bg-13-4023-2016, https://doi.org/10.5194/bg-13-4023-2016, 2016
Short summary
Short summary
We compare future projections in marine export production, generated by four ecosystem models under IPCC's high-emission scenario RCP8.5. While all models project decreases in export, they differ strongly regarding the drivers. The formation of sinking particles of organic matter is the most uncertain process with models not agreeing on either magnitude or the direction of change. Changes in diatom concentration are a strong driver for export in some models but of low significance in others.
Roland Séférian, Marion Gehlen, Laurent Bopp, Laure Resplandy, James C. Orr, Olivier Marti, John P. Dunne, James R. Christian, Scott C. Doney, Tatiana Ilyina, Keith Lindsay, Paul R. Halloran, Christoph Heinze, Joachim Segschneider, Jerry Tjiputra, Olivier Aumont, and Anastasia Romanou
Geosci. Model Dev., 9, 1827–1851, https://doi.org/10.5194/gmd-9-1827-2016, https://doi.org/10.5194/gmd-9-1827-2016, 2016
Short summary
Short summary
This paper explores how the large diversity in spin-up protocols used for ocean biogeochemistry in CMIP5 models contributed to inter-model differences in modeled fields. We show that a link between spin-up duration and skill-score metrics emerges from both individual IPSL-CM5A-LR's results and an ensemble of CMIP5 models. Our study suggests that differences in spin-up protocols constitute a source of inter-model uncertainty which would require more attention in future intercomparison exercises.
S. Sedigh Marvasti, A. Gnanadesikan, A. A. Bidokhti, J. P. Dunne, and S. Ghader
Biogeosciences, 13, 1049–1069, https://doi.org/10.5194/bg-13-1049-2016, https://doi.org/10.5194/bg-13-1049-2016, 2016
Short summary
Short summary
This study examines challenges in modeling phytoplankton blooms in Northwestern Arabian Sea and Gulf of Oman. Blooms in the region show strong modulation both by seasons and in the wintertime by eddies. However getting both of these correct is a challenge in a set of state-of-the-art global Earth System models. It is argued that maintaining a sharp pycnocline may be the key for preventing the wintertime bloom from being too strong and for allowing eddies to modulate upward mixing of nutrients.
C. Laufkötter, M. Vogt, N. Gruber, M. Aita-Noguchi, O. Aumont, L. Bopp, E. Buitenhuis, S. C. Doney, J. Dunne, T. Hashioka, J. Hauck, T. Hirata, J. John, C. Le Quéré, I. D. Lima, H. Nakano, R. Seferian, I. Totterdell, M. Vichi, and C. Völker
Biogeosciences, 12, 6955–6984, https://doi.org/10.5194/bg-12-6955-2015, https://doi.org/10.5194/bg-12-6955-2015, 2015
Short summary
Short summary
We analyze changes in marine net primary production (NPP) and its drivers for the 21st century in 9 marine ecosystem models under the RCP8.5 scenario. NPP decreases in 5 models and increases in 1 model; 3 models show no significant trend. The main drivers include stronger nutrient limitation, but in many models warming-induced increases in phytoplankton growth outbalance the nutrient effect. Temperature-driven increases in grazing and other loss processes cause a net decrease in biomass and NPP.
S. S. Rabin, B. I. Magi, E. Shevliakova, and S. W. Pacala
Biogeosciences, 12, 6591–6604, https://doi.org/10.5194/bg-12-6591-2015, https://doi.org/10.5194/bg-12-6591-2015, 2015
Short summary
Short summary
People worldwide use fire to manage agriculture, but often also suppress fire in the landscape surrounding their fields. Here, we estimate the net result of these effects of cropland and pasture on fire at a regional, monthly level. Pasture is shown, for the first time, to contribute strongly to global patterns of burning. Our results could be used to improve representations of burning in global vegetation and climate models, improving our understanding of how people affect the Earth system.
E. S. Weng, S. Malyshev, J. W. Lichstein, C. E. Farrior, R. Dybzinski, T. Zhang, E. Shevliakova, and S. W. Pacala
Biogeosciences, 12, 2655–2694, https://doi.org/10.5194/bg-12-2655-2015, https://doi.org/10.5194/bg-12-2655-2015, 2015
Short summary
Short summary
We present a model, LM3-PPA, which simulates vegetation dynamics and biogeochemical processes by explicitly scaling from individual plants to ecosystems using the perfect plasticity approximation. It includes height-structured competition for light- and root-allocation-dependent competition for belowground resources. Because of the tractability of the PPA, the coupled LM3-PPA model is able to retain computational tractability, as well as close linkages to mathematically tractable special cases.
B. F. Jonsson, S. Doney, J. Dunne, and M. L. Bender
Biogeosciences, 12, 681–695, https://doi.org/10.5194/bg-12-681-2015, https://doi.org/10.5194/bg-12-681-2015, 2015
Short summary
Short summary
We compare how two global circulation models simulate biological production over the year with observations. Note that models simulate the range of biological production and biomass well but fail with regard to timing and regional structures. This is probably because the physics in the models are wrong, especially vertical processes such as mixed layer dynamics.
C. D. Nevison, M. Manizza, R. F. Keeling, M. Kahru, L. Bopp, J. Dunne, J. Tiputra, T. Ilyina, and B. G. Mitchell
Biogeosciences, 12, 193–208, https://doi.org/10.5194/bg-12-193-2015, https://doi.org/10.5194/bg-12-193-2015, 2015
Short summary
Short summary
The observed seasonal cycles in atmospheric potential oxygen (APO) at five surface monitoring sites are compared to those inferred from the air-sea O2 fluxes of six ocean biogeochemistry models. The simulated air-sea fluxes are translated into APO seasonal cycles using a matrix method that takes into account atmospheric transport model (ATM) uncertainty among 13 different ATMs. Net primary production (NPP), estimated from satellite ocean color data, is also compared to model output.
C. A. Stock, J. P. Dunne, and J. G. John
Biogeosciences, 11, 7125–7135, https://doi.org/10.5194/bg-11-7125-2014, https://doi.org/10.5194/bg-11-7125-2014, 2014
Short summary
Short summary
Climate change projections suggest large regional ocean productivity shifts for mesozooplankton, an important food resource for fish, which are amplified relative to changes in phytoplankton production. Amplification is attributed to changes in planktonic food web dynamics under global warming. Results have implications for regional economies and food security. Improved understanding of the response of plankton food webs to climate change is essential to refine amplification estimates.
M. Gehlen, R. Séférian, D. O. B. Jones, T. Roy, R. Roth, J. Barry, L. Bopp, S. C. Doney, J. P. Dunne, C. Heinze, F. Joos, J. C. Orr, L. Resplandy, J. Segschneider, and J. Tjiputra
Biogeosciences, 11, 6955–6967, https://doi.org/10.5194/bg-11-6955-2014, https://doi.org/10.5194/bg-11-6955-2014, 2014
Short summary
Short summary
This study evaluates potential impacts of pH reductions on North Atlantic deep-sea ecosystems in response to latest IPCC scenarios.Multi-model projections of pH changes over the seafloor are analysed with reference to a critical threshold based on palaeo-oceanographic studies, contemporary observations and model results. By 2100 under the most severe IPCC CO2 scenario, pH reductions occur over ~23% of deep-sea canyons and ~8% of seamounts – including seamounts proposed as marine protected areas.
M. Lee, S. Malyshev, E. Shevliakova, P. C. D. Milly, and P. R. Jaffé
Biogeosciences, 11, 5809–5826, https://doi.org/10.5194/bg-11-5809-2014, https://doi.org/10.5194/bg-11-5809-2014, 2014
Z. M. Subin, P. C. D. Milly, B. N. Sulman, S. Malyshev, and E. Shevliakova
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hessd-11-8443-2014, https://doi.org/10.5194/hessd-11-8443-2014, 2014
Preprint withdrawn
Short summary
Short summary
We provide a framework for including the effects of fine-scale hydrology on biogeochemistry in Earth-system models (ESMs). We simulate a representative hillslope in each ESM grid cell. While including the hillslope does not change the average hydrology, it causes greater vegetation and soil carbon to accumulate in lowlands. This is important for understanding how soil carbon might be affected by climate change, particularly in wetlands.
K. E. O. Todd-Brown, J. T. Randerson, F. Hopkins, V. Arora, T. Hajima, C. Jones, E. Shevliakova, J. Tjiputra, E. Volodin, T. Wu, Q. Zhang, and S. D. Allison
Biogeosciences, 11, 2341–2356, https://doi.org/10.5194/bg-11-2341-2014, https://doi.org/10.5194/bg-11-2341-2014, 2014
M. Ishii, R. A. Feely, K. B. Rodgers, G.-H. Park, R. Wanninkhof, D. Sasano, H. Sugimoto, C. E. Cosca, S. Nakaoka, M. Telszewski, Y. Nojiri, S. E. Mikaloff Fletcher, Y. Niwa, P. K. Patra, V. Valsala, H. Nakano, I. Lima, S. C. Doney, E. T. Buitenhuis, O. Aumont, J. P. Dunne, A. Lenton, and T. Takahashi
Biogeosciences, 11, 709–734, https://doi.org/10.5194/bg-11-709-2014, https://doi.org/10.5194/bg-11-709-2014, 2014
L. Bopp, L. Resplandy, J. C. Orr, S. C. Doney, J. P. Dunne, M. Gehlen, P. Halloran, C. Heinze, T. Ilyina, R. Séférian, J. Tjiputra, and M. Vichi
Biogeosciences, 10, 6225–6245, https://doi.org/10.5194/bg-10-6225-2013, https://doi.org/10.5194/bg-10-6225-2013, 2013
C. Beaulieu, S. A. Henson, Jorge L. Sarmiento, J. P. Dunne, S. C. Doney, R. R. Rykaczewski, and L. Bopp
Biogeosciences, 10, 2711–2724, https://doi.org/10.5194/bg-10-2711-2013, https://doi.org/10.5194/bg-10-2711-2013, 2013
V. Cocco, F. Joos, M. Steinacher, T. L. Frölicher, L. Bopp, J. Dunne, M. Gehlen, C. Heinze, J. Orr, A. Oschlies, B. Schneider, J. Segschneider, and J. Tjiputra
Biogeosciences, 10, 1849–1868, https://doi.org/10.5194/bg-10-1849-2013, https://doi.org/10.5194/bg-10-1849-2013, 2013
J. G. John, A. M. Fiore, V. Naik, L. W. Horowitz, and J. P. Dunne
Atmos. Chem. Phys., 12, 12021–12036, https://doi.org/10.5194/acp-12-12021-2012, https://doi.org/10.5194/acp-12-12021-2012, 2012
Related subject area
Climate and Earth system modeling
A method for quantifying uncertainty in spatially interpolated meteorological data with application to daily maximum air temperature
Baseline Climate Variables for Earth System Modelling
PaleoSTeHM v1.0: a modern, scalable spatiotemporal hierarchical modeling framework for paleo-environmental data
The Tropical Basin Interaction Model Intercomparison Project (TBIMIP)
ZEMBA v1.0: an energy and moisture balance climate model to investigate Quaternary climate
Development and evaluation of a new 4DEnVar-based weakly coupled ocean data assimilation system in E3SMv2
TemDeep: a self-supervised framework for temporal downscaling of atmospheric fields at arbitrary time resolutions
The ensemble consistency test: from CESM to MPAS and beyond
Presentation, calibration and testing of the DCESS II Earth system model of intermediate complexity (version 1.0)
Synthesizing global carbon–nitrogen coupling effects – the MAGICC coupled carbon–nitrogen cycle model v1.0
Historical trends and controlling factors of isoprene emissions in CMIP6 Earth system models
Investigating carbon and nitrogen conservation in reported CMIP6 Earth system model data
From weather data to river runoff: using spatiotemporal convolutional networks for discharge forecasting
A Fortran–Python interface for integrating machine learning parameterization into earth system models
A rapid-application emissions-to-impacts tool for scenario assessment: Probabilistic Regional Impacts from Model patterns and Emissions (PRIME)
The DOE E3SM version 2.1: overview and assessment of the impacts of parameterized ocean submesoscales
WRF-ELM v1.0: a regional climate model to study land–atmosphere interactions over heterogeneous land use regions
Modeling commercial-scale CO2 storage in the gas hydrate stability zone with PFLOTRAN v6.0
DiuSST: a conceptual model of diurnal warm layers for idealized atmospheric simulations with interactive sea surface temperature
High-Resolution Model Intercomparison Project phase 2 (HighResMIP2) towards CMIP7
T&C-CROP: representing mechanistic crop growth with a terrestrial biosphere model (T&C, v1.5) – model formulation and validation
An updated non-intrusive, multi-scale, and flexible coupling interface in WRF 4.6.0
Monitoring and benchmarking Earth system model simulations with ESMValTool v2.12.0
The Earth Science Box Modeling Toolkit (ESBMTK 0.14.0.11): a Python library for research and teaching
CropSuite v1.0 – a comprehensive open-source crop suitability model considering climate variability for climate impact assessment
ICON ComIn – the ICON Community Interface (ComIn version 0.1.0, with ICON version 2024.01-01)
Using feature importance as an exploratory data analysis tool on Earth system models
A new metrics framework for quantifying and intercomparing atmospheric rivers in observations, reanalyses, and climate models
The real challenges for climate and weather modelling on its way to sustained exascale performance: a case study using ICON (v2.6.6)
Improving the representation of major Indian crops in the Community Land Model version 5.0 (CLM5) using site-scale crop data
Evaluation of CORDEX ERA5-forced NARCliM2.0 regional climate models over Australia using the Weather Research and Forecasting (WRF) model version 4.1.2
Design, evaluation, and future projections of the NARCliM2.0 CORDEX-CMIP6 Australasia regional climate ensemble
The Detection and Attribution Model Intercomparison Project (DAMIP v2.0) contribution to CMIP7
Amending the algorithm of aerosol–radiation interactions in WRF-Chem (v4.4)
The very-high-resolution configuration of the EC-Earth global model for HighResMIP
GOSI9: UK Global Ocean and Sea Ice configurations
Decomposition of skill scores for conditional verification: impact of Atlantic Multidecadal Oscillation phases on the predictability of decadal temperature forecasts
Virtual Integration of Satellite and In-situ Observation Networks (VISION) v1.0: In-Situ Observations Simulator (ISO_simulator)
Climate model downscaling in central Asia: a dynamical and a neural network approach
Advanced climate model evaluation with ESMValTool v2.11.0 using parallel, out-of-core, and distributed computing
Multi-year simulations at kilometre scale with the Integrated Forecasting System coupled to FESOM2.5 and NEMOv3.4
Subsurface hydrological controls on the short-term effects of hurricanes on nitrate–nitrogen runoff loading: a case study of Hurricane Ida using the Energy Exascale Earth System Model (E3SM) Land Model (v2.1)
CARIB12: a regional Community Earth System Model/Modular Ocean Model 6 configuration of the Caribbean Sea
Process-based modeling framework for sustainable irrigation management at the regional scale: Integrating rice production, water use, and greenhouse gas emissions
A regional physical-biogeochemical ocean model for marine resource applications in the Northeast Pacific (MOM6-COBALT-NEP10k v1.0)
Architectural insights into and training methodology optimization of Pangu-Weather
Evaluation of global fire simulations in CMIP6 Earth system models
Evaluating downscaled products with expected hydroclimatic co-variances
Software sustainability of global impact models
fair-calibrate v1.4.1: calibration, constraining, and validation of the FaIR simple climate model for reliable future climate projections
Conor T. Doherty, Weile Wang, Hirofumi Hashimoto, and Ian G. Brosnan
Geosci. Model Dev., 18, 3003–3016, https://doi.org/10.5194/gmd-18-3003-2025, https://doi.org/10.5194/gmd-18-3003-2025, 2025
Short summary
Short summary
We present, analyze, and validate a methodology for quantifying uncertainty in gridded meteorological data products produced by spatial interpolation. In a validation case study using daily maximum near-surface air temperature (Tmax), the method works well and produces predictive distributions with closely matching theoretical versus actual coverage levels. Application of the method reveals that the magnitude of uncertainty in interpolated Tmax varies significantly in both space and time.
Martin Juckes, Karl E. Taylor, Fabrizio Antonio, David Brayshaw, Carlo Buontempo, Jian Cao, Paul J. Durack, Michio Kawamiya, Hyungjun Kim, Tomas Lovato, Chloe Mackallah, Matthew Mizielinski, Alessandra Nuzzo, Martina Stockhause, Daniele Visioni, Jeremy Walton, Briony Turner, Eleanor O'Rourke, and Beth Dingley
Geosci. Model Dev., 18, 2639–2663, https://doi.org/10.5194/gmd-18-2639-2025, https://doi.org/10.5194/gmd-18-2639-2025, 2025
Short summary
Short summary
The Baseline Climate Variables for Earth System Modelling (ESM-BCVs) are defined as a list of 135 variables which have high utility for the evaluation and exploitation of climate simulations. The list reflects the most frequently used variables from Earth system models based on an assessment of data publication and download records from the largest archive of global climate projects.
Yucheng Lin, Robert E. Kopp, Alexander Reedy, Matteo Turilli, Shantenu Jha, and Erica L. Ashe
Geosci. Model Dev., 18, 2609–2637, https://doi.org/10.5194/gmd-18-2609-2025, https://doi.org/10.5194/gmd-18-2609-2025, 2025
Short summary
Short summary
PaleoSTeHM v1.0 is a state-of-the-art framework designed to reconstruct past environmental conditions using geological data. Built on modern machine learning techniques, it efficiently handles the sparse and noisy nature of paleo-records, allowing scientists to make accurate and scalable inferences about past environmental change. By using flexible statistical models, PaleoSTeHM separates different sources of uncertainty, improving the precision of historical climate reconstructions.
Ingo Richter, Ping Chang, Ping-Gin Chiu, Gokhan Danabasoglu, Takeshi Doi, Dietmar Dommenget, Guillaume Gastineau, Zoe E. Gillett, Aixue Hu, Takahito Kataoka, Noel S. Keenlyside, Fred Kucharski, Yuko M. Okumura, Wonsun Park, Malte F. Stuecker, Andréa S. Taschetto, Chunzai Wang, Stephen G. Yeager, and Sang-Wook Yeh
Geosci. Model Dev., 18, 2587–2608, https://doi.org/10.5194/gmd-18-2587-2025, https://doi.org/10.5194/gmd-18-2587-2025, 2025
Short summary
Short summary
Tropical ocean basins influence each other through multiple pathways and mechanisms, referred to here as tropical basin interaction (TBI). Many researchers have examined TBI using comprehensive climate models but have obtained conflicting results. This may be partly due to differences in experiment protocols and partly due to systematic model errors. The Tropical Basin Interaction Model Intercomparison Project (TBIMIP) aims to address this problem by designing a set of TBI experiments that will be performed by multiple models.
Daniel F. J. Gunning, Kerim H. Nisancioglu, Emilie Capron, and Roderik S. W. van de Wal
Geosci. Model Dev., 18, 2479–2508, https://doi.org/10.5194/gmd-18-2479-2025, https://doi.org/10.5194/gmd-18-2479-2025, 2025
Short summary
Short summary
This work documents the first results from ZEMBA: an energy balance model of the climate system. The model is a computationally efficient tool designed to study the response of climate to changes in the Earth's orbit. We demonstrate that ZEMBA reproduces many features of the Earth's climate for both the pre-industrial period and the Earth's most recent cold extreme – the Last Glacial Maximum. We intend to develop ZEMBA further and investigate the glacial cycles of the last 2.5 million years.
Pengfei Shi, L. Ruby Leung, and Bin Wang
Geosci. Model Dev., 18, 2443–2460, https://doi.org/10.5194/gmd-18-2443-2025, https://doi.org/10.5194/gmd-18-2443-2025, 2025
Short summary
Short summary
Improving climate predictions has significant socio-economic impacts. In this study, we develop and apply a new weakly coupled ocean data assimilation (WCODA) system to a coupled climate model. The WCODA system improves simulations of ocean temperature and salinity across many global regions. This system is meant to advance our understanding of the ocean's role in climate predictability.
Liwen Wang, Qian Li, Qi Lv, Xuan Peng, and Wei You
Geosci. Model Dev., 18, 2427–2442, https://doi.org/10.5194/gmd-18-2427-2025, https://doi.org/10.5194/gmd-18-2427-2025, 2025
Short summary
Short summary
Our research presents a novel deep learning approach called "TemDeep" for downscaling atmospheric variables at arbitrary time resolutions based on temporal coherence. Results show that our method can accurately recover evolution details superior to other methods, reaching 53.7 % in the restoration rate. Our findings are important for advancing weather forecasting models and enabling more precise and reliable predictions to support disaster preparedness, agriculture, and sustainable development.
Teo Price-Broncucia, Allison Baker, Dorit Hammerling, Michael Duda, and Rebecca Morrison
Geosci. Model Dev., 18, 2349–2372, https://doi.org/10.5194/gmd-18-2349-2025, https://doi.org/10.5194/gmd-18-2349-2025, 2025
Short summary
Short summary
The ensemble consistency test (ECT) and its ultrafast variant (UF-ECT) have become powerful tools in the development community for the identification of unwanted changes in the Community Earth System Model (CESM). We develop a generalized setup framework to enable easy adoption of the ECT approach for other model developers and communities. This framework specifies test parameters to accurately characterize model variability and balance test sensitivity and computational cost.
Esteban Fernández Villanueva and Gary Shaffer
Geosci. Model Dev., 18, 2161–2192, https://doi.org/10.5194/gmd-18-2161-2025, https://doi.org/10.5194/gmd-18-2161-2025, 2025
Short summary
Short summary
We describe, calibrate and test the Danish Center for Earth System Science (DCESS) II model, a new, broad, adaptable and fast Earth system model. DCESS II is designed for global simulations over timescales of years to millions of years using limited computer resources like a personal computer. With its flexibility and comprehensive treatment of the global carbon cycle, DCESS II is a useful, computationally friendly tool for simulations of past climates as well as for future Earth system projections.
Gang Tang, Zebedee Nicholls, Alexander Norton, Sönke Zaehle, and Malte Meinshausen
Geosci. Model Dev., 18, 2193–2230, https://doi.org/10.5194/gmd-18-2193-2025, https://doi.org/10.5194/gmd-18-2193-2025, 2025
Short summary
Short summary
We studied carbon–nitrogen coupling in Earth system models by developing a global carbon–nitrogen cycle model (CNit v1.0) within the widely used emulator MAGICC. CNit effectively reproduced the global carbon–nitrogen cycle dynamics observed in complex models. Our results show persistent nitrogen limitations on plant growth (net primary production) from 1850 to 2100, suggesting that nitrogen deficiency may constrain future land carbon sequestration.
Ngoc Thi Nhu Do, Kengo Sudo, Akihiko Ito, Louisa K. Emmons, Vaishali Naik, Kostas Tsigaridis, Øyvind Seland, Gerd A. Folberth, and Douglas I. Kelley
Geosci. Model Dev., 18, 2079–2109, https://doi.org/10.5194/gmd-18-2079-2025, https://doi.org/10.5194/gmd-18-2079-2025, 2025
Short summary
Short summary
Understanding historical isoprene emission changes is important for predicting future climate, but trends and their controlling factors remain uncertain. This study shows that long-term isoprene trends vary among Earth system models mainly due to partially incorporating CO2 effects and land cover changes rather than to climate. Future models that refine these factors’ effects on isoprene emissions, along with long-term observations, are essential for better understanding plant–climate interactions.
Gang Tang, Zebedee Nicholls, Chris Jones, Thomas Gasser, Alexander Norton, Tilo Ziehn, Alejandro Romero-Prieto, and Malte Meinshausen
Geosci. Model Dev., 18, 2111–2136, https://doi.org/10.5194/gmd-18-2111-2025, https://doi.org/10.5194/gmd-18-2111-2025, 2025
Short summary
Short summary
We analyzed carbon and nitrogen mass conservation in data from various Earth system models. Our findings reveal significant discrepancies between flux and pool size data, where cumulative imbalances can reach hundreds of gigatons of carbon or nitrogen. These imbalances appear primarily due to missing or inconsistently reported fluxes – especially for land-use and fire emissions. To enhance data quality, we recommend that future climate data protocols address this issue at the reporting stage.
Florian Börgel, Sven Karsten, Karoline Rummel, and Ulf Gräwe
Geosci. Model Dev., 18, 2005–2019, https://doi.org/10.5194/gmd-18-2005-2025, https://doi.org/10.5194/gmd-18-2005-2025, 2025
Short summary
Short summary
Forecasting river runoff, which is crucial for managing water resources and understanding climate impacts, can be challenging. This study introduces a new method using convolutional long short-term memory (ConvLSTM) networks, a machine learning model that processes spatial and temporal data. Focusing on the Baltic Sea region, our model uses weather data as input to predict daily river runoff for 97 rivers.
Tao Zhang, Cyril Morcrette, Meng Zhang, Wuyin Lin, Shaocheng Xie, Ye Liu, Kwinten Van Weverberg, and Joana Rodrigues
Geosci. Model Dev., 18, 1917–1928, https://doi.org/10.5194/gmd-18-1917-2025, https://doi.org/10.5194/gmd-18-1917-2025, 2025
Short summary
Short summary
Earth system models (ESMs) struggle with the uncertainties associated with parameterizing subgrid physics. Machine learning (ML) algorithms offer a solution by learning the important relationships and features from high-resolution models. To incorporate ML parameterizations into ESMs, we develop a Fortran–Python interface that allows for calling Python functions within Fortran-based ESMs. Through two case studies, this interface demonstrates its feasibility, modularity, and effectiveness.
Camilla Mathison, Eleanor J. Burke, Gregory Munday, Chris D. Jones, Chris J. Smith, Norman J. Steinert, Andy J. Wiltshire, Chris Huntingford, Eszter Kovacs, Laila K. Gohar, Rebecca M. Varney, and Douglas McNeall
Geosci. Model Dev., 18, 1785–1808, https://doi.org/10.5194/gmd-18-1785-2025, https://doi.org/10.5194/gmd-18-1785-2025, 2025
Short summary
Short summary
We present PRIME (Probabilistic Regional Impacts from Model patterns and Emissions), which is designed to take new emissions scenarios and rapidly provide regional impact information. PRIME allows large ensembles to be run on multi-centennial timescales, including the analysis of many important variables for impact assessments. Our evaluation shows that PRIME reproduces the climate response for known scenarios, providing confidence in using PRIME for novel scenarios.
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.
Huilin Huang, Yun Qian, Gautam Bisht, Jiali Wang, Tirthankar Chakraborty, Dalei Hao, Jianfeng Li, Travis Thurber, Balwinder Singh, Zhao Yang, Ye Liu, Pengfei Xue, William J. Sacks, Ethan Coon, and Robert Hetland
Geosci. Model Dev., 18, 1427–1443, https://doi.org/10.5194/gmd-18-1427-2025, https://doi.org/10.5194/gmd-18-1427-2025, 2025
Short summary
Short summary
We integrate the E3SM Land Model (ELM) with the WRF model through the Lightweight Infrastructure for Land Atmosphere Coupling (LILAC) Earth System Modeling Framework (ESMF). This framework includes a top-level driver, LILAC, for variable communication between WRF and ELM and ESMF caps for ELM initialization, execution, and finalization. The LILAC–ESMF framework maintains the integrity of the ELM's source code structure and facilitates the transfer of future ELM model developments to WRF-ELM.
Michael Nole, Jonah Bartrand, Fawz Naim, and Glenn Hammond
Geosci. Model Dev., 18, 1413–1425, https://doi.org/10.5194/gmd-18-1413-2025, https://doi.org/10.5194/gmd-18-1413-2025, 2025
Short summary
Short summary
Safe carbon dioxide (CO2) storage is likely to be critical for mitigating some of the most severe effects of climate change. We present a simulation framework for modeling CO2 storage beneath the seafloor, where CO2 can form a solid. This can aid in permanent CO2 storage for long periods of time. Our models show what a commercial-scale CO2 injection would look like in a marine environment. We discuss what would need to be considered when designing a subsea CO2 injection.
Reyk Börner, Jan O. Haerter, and Romain Fiévet
Geosci. Model Dev., 18, 1333–1356, https://doi.org/10.5194/gmd-18-1333-2025, https://doi.org/10.5194/gmd-18-1333-2025, 2025
Short summary
Short summary
The daily cycle of sea surface temperature (SST) impacts clouds above the ocean and could influence the clustering of thunderstorms linked to extreme rainfall and hurricanes. However, daily SST variability is often poorly represented in modeling studies of how clouds cluster. We present a simple, wind-responsive model of upper-ocean temperature for use in atmospheric simulations. Evaluating the model against observations, we show that it performs significantly better than common slab models.
Malcolm J. Roberts, Kevin A. Reed, Qing Bao, Joseph J. Barsugli, Suzana J. Camargo, Louis-Philippe Caron, Ping Chang, Cheng-Ta Chen, Hannah M. Christensen, Gokhan Danabasoglu, Ivy Frenger, Neven S. Fučkar, Shabeh ul Hasson, Helene T. Hewitt, Huanping Huang, Daehyun Kim, Chihiro Kodama, Michael Lai, Lai-Yung Ruby Leung, Ryo Mizuta, Paulo Nobre, Pablo Ortega, Dominique Paquin, Christopher D. Roberts, Enrico Scoccimarro, Jon Seddon, Anne Marie Treguier, Chia-Ying Tu, Paul A. Ullrich, Pier Luigi Vidale, Michael F. Wehner, Colin M. Zarzycki, Bosong Zhang, Wei Zhang, and Ming Zhao
Geosci. Model Dev., 18, 1307–1332, https://doi.org/10.5194/gmd-18-1307-2025, https://doi.org/10.5194/gmd-18-1307-2025, 2025
Short summary
Short summary
HighResMIP2 is a model intercomparison project focusing on high-resolution global climate models, that is, those with grid spacings of 25 km or less in the atmosphere and ocean, using simulations of decades to a century in length. We are proposing an update of our simulation protocol to make the models more applicable to key questions for climate variability and hazard in present-day and future projections and to build links with other communities to provide more robust climate information.
Jordi Buckley Paules, Simone Fatichi, Bonnie Warring, and Athanasios Paschalis
Geosci. Model Dev., 18, 1287–1305, https://doi.org/10.5194/gmd-18-1287-2025, https://doi.org/10.5194/gmd-18-1287-2025, 2025
Short summary
Short summary
We present and validate enhancements to the process-based T&C model aimed at improving its representation of crop growth and management practices. The updated model, T&C-CROP, enables applications such as analysing the hydrological and carbon storage impacts of land use transitions (e.g. conversions between crops, forests, and pastures) and optimizing irrigation and fertilization strategies in response to climate change.
Sébastien Masson, Swen Jullien, Eric Maisonnave, David Gill, Guillaume Samson, Mathieu Le Corre, and Lionel Renault
Geosci. Model Dev., 18, 1241–1263, https://doi.org/10.5194/gmd-18-1241-2025, https://doi.org/10.5194/gmd-18-1241-2025, 2025
Short summary
Short summary
This article details a new feature we implemented in the popular regional atmospheric model WRF. This feature allows for data exchange between WRF and any other model (e.g. an ocean model) using the coupling library Ocean–Atmosphere–Sea–Ice–Soil Model Coupling Toolkit (OASIS3-MCT). This coupling interface is designed to be non-intrusive, flexible and modular. It also offers the possibility of taking into account the nested zooms used in WRF or in the models with which it is coupled.
Axel Lauer, Lisa Bock, Birgit Hassler, Patrick Jöckel, Lukas Ruhe, and Manuel Schlund
Geosci. Model Dev., 18, 1169–1188, https://doi.org/10.5194/gmd-18-1169-2025, https://doi.org/10.5194/gmd-18-1169-2025, 2025
Short summary
Short summary
Earth system models are important tools to improve our understanding of current climate and to project climate change. Thus, it is crucial to understand possible shortcomings in the models. New features of the ESMValTool software package allow one to compare and visualize a model's performance with respect to reproducing observations in the context of other climate models in an easy and user-friendly way. We aim to help model developers assess and monitor climate simulations more efficiently.
Ulrich G. Wortmann, Tina Tsan, Mahrukh Niazi, Irene A. Ma, Ruben Navasardyan, Magnus-Roland Marun, Bernardo S. Chede, Jingwen Zhong, and Morgan Wolfe
Geosci. Model Dev., 18, 1155–1167, https://doi.org/10.5194/gmd-18-1155-2025, https://doi.org/10.5194/gmd-18-1155-2025, 2025
Short summary
Short summary
The Earth Science Box Modeling Toolkit (ESBMTK) is a user-friendly Python library that simplifies the creation of models to study earth system processes, such as the carbon cycle and ocean chemistry. It enhances learning by emphasizing concepts over programming and is accessible to students and researchers alike. By automating complex calculations and promoting code clarity, ESBMTK accelerates model development while improving reproducibility and the usability of scientific research.
Florian Zabel, Matthias Knüttel, and Benjamin Poschlod
Geosci. Model Dev., 18, 1067–1087, https://doi.org/10.5194/gmd-18-1067-2025, https://doi.org/10.5194/gmd-18-1067-2025, 2025
Short summary
Short summary
CropSuite is a new open-source crop suitability model. It provides a GUI and a wide range of options, including a spatial downscaling of climate data. We apply CropSuite to 48 staple and opportunity crops at a 1 km spatial resolution in Africa. We find that climate variability significantly impacts suitable areas but also affects optimal sowing dates and multiple cropping potential. The results provide valuable information for climate impact assessments, adaptation, and land-use planning.
Kerstin Hartung, Bastian Kern, Nils-Arne Dreier, Jörn Geisbüsch, Mahnoosh Haghighatnasab, Patrick Jöckel, Astrid Kerkweg, Wilton Jaciel Loch, Florian Prill, and Daniel Rieger
Geosci. Model Dev., 18, 1001–1015, https://doi.org/10.5194/gmd-18-1001-2025, https://doi.org/10.5194/gmd-18-1001-2025, 2025
Short summary
Short summary
The ICOsahedral Non-hydrostatic (ICON) model system Community Interface (ComIn) library supports connecting third-party modules to the ICON model. Third-party modules can range from simple diagnostic Python scripts to full chemistry models. ComIn offers a low barrier for code extensions to ICON, provides multi-language support (Fortran, C/C++, and Python), and reduces the migration effort in response to new ICON releases. This paper presents the ComIn design principles and a range of use cases.
Daniel Ries, Katherine Goode, Kellie McClernon, and Benjamin Hillman
Geosci. Model Dev., 18, 1041–1065, https://doi.org/10.5194/gmd-18-1041-2025, https://doi.org/10.5194/gmd-18-1041-2025, 2025
Short summary
Short summary
Machine learning has advanced research in the climate science domain, but its models are difficult to understand. In order to understand the impacts and consequences of climate interventions such as stratospheric aerosol injection, complex models are often necessary. We use a case study to illustrate how we can understand the inner workings of a complex model. We present this technique as an exploratory tool that can be used to quickly discover and assess relationships in complex climate data.
Bo Dong, Paul Ullrich, Jiwoo Lee, Peter Gleckler, Kristin Chang, and Travis A. O'Brien
Geosci. Model Dev., 18, 961–976, https://doi.org/10.5194/gmd-18-961-2025, https://doi.org/10.5194/gmd-18-961-2025, 2025
Short summary
Short summary
A metrics package designed for easy analysis of atmospheric river (AR) characteristics and statistics is presented. The tool is efficient for diagnosing systematic AR bias in climate models and useful for evaluating new AR characteristics in model simulations. In climate models, landfalling AR precipitation shows dry biases globally, and AR tracks are farther poleward (equatorward) in the North and South Atlantic (South Pacific and Indian Ocean).
Panagiotis Adamidis, Erik Pfister, Hendryk Bockelmann, Dominik Zobel, Jens-Olaf Beismann, and Marek Jacob
Geosci. Model Dev., 18, 905–919, https://doi.org/10.5194/gmd-18-905-2025, https://doi.org/10.5194/gmd-18-905-2025, 2025
Short summary
Short summary
In this paper, we investigated performance indicators of the climate model ICON (ICOsahedral Nonhydrostatic) on different compute architectures to answer the question of how to generate high-resolution climate simulations. Evidently, it is not enough to use more computing units of the conventionally used architectures; higher memory throughput is the most promising approach. More potential can be gained from single-node optimization rather than simply increasing the number of compute nodes.
Kangari Narender Reddy, Somnath Baidya Roy, Sam S. Rabin, Danica L. Lombardozzi, Gudimetla Venkateswara Varma, Ruchira Biswas, and Devavat Chiru Naik
Geosci. Model Dev., 18, 763–785, https://doi.org/10.5194/gmd-18-763-2025, https://doi.org/10.5194/gmd-18-763-2025, 2025
Short summary
Short summary
The study aimed to improve the representation of wheat and rice in a land model for the Indian region. The modified model performed significantly better than the default model in simulating crop phenology, yield, and carbon, water, and energy fluxes compared to observations. The study highlights the need for global land models to use region-specific crop parameters for accurately simulating vegetation processes and land surface processes.
Giovanni Di Virgilio, Fei Ji, Eugene Tam, Jason P. Evans, Jatin Kala, Julia Andrys, Christopher Thomas, Dipayan Choudhury, Carlos Rocha, Yue Li, and Matthew L. Riley
Geosci. Model Dev., 18, 703–724, https://doi.org/10.5194/gmd-18-703-2025, https://doi.org/10.5194/gmd-18-703-2025, 2025
Short summary
Short summary
We evaluate the skill in simulating the Australian climate of some of the latest generation of regional climate models. We show when and where the models simulate this climate with high skill versus model limitations. We show how new models perform relative to the previous-generation models, assessing how model design features may underlie key performance improvements. This work is of national and international relevance as it can help guide the use and interpretation of climate projections.
Giovanni Di Virgilio, Jason P. Evans, Fei Ji, Eugene Tam, Jatin Kala, Julia Andrys, Christopher Thomas, Dipayan Choudhury, Carlos Rocha, Stephen White, Yue Li, Moutassem El Rafei, Rishav Goyal, Matthew L. Riley, and Jyothi Lingala
Geosci. Model Dev., 18, 671–702, https://doi.org/10.5194/gmd-18-671-2025, https://doi.org/10.5194/gmd-18-671-2025, 2025
Short summary
Short summary
We introduce new climate models that simulate Australia’s future climate at regional scales, including at an unprecedented resolution of 4 km for 1950–2100. We describe the model design process used to create these new climate models. We show how the new models perform relative to previous-generation models and compare their climate projections. This work is of national and international relevance as it can help guide climate model design and the use and interpretation of climate projections.
Nathan P. Gillett, Isla R. Simpson, Gabi Hegerl, Reto Knutti, Dann Mitchell, Aurélien Ribes, Hideo Shiogama, Dáithí Stone, Claudia Tebaldi, Piotr Wolski, Wenxia Zhang, and Vivek K. Arora
EGUsphere, https://doi.org/10.5194/egusphere-2024-4086, https://doi.org/10.5194/egusphere-2024-4086, 2025
Short summary
Short summary
Climate model simulations of the response to human and natural influences together, natural climate influences alone, and greenhouse gases alone, among others, are key to quantifying human influence on the climate. The last set of such coordinated simulations underpinned key findings in the last Intergovernmental Panel on Climate Change (IPCC) report. Here we propose a new set of such simulations to be used in the next generation of attribution studies, and to underpin the next IPCC report.
Jiawang Feng, Chun Zhao, Qiuyan Du, Zining Yang, and Chen Jin
Geosci. Model Dev., 18, 585–603, https://doi.org/10.5194/gmd-18-585-2025, https://doi.org/10.5194/gmd-18-585-2025, 2025
Short summary
Short summary
In this study, we improved the calculation of how aerosols in the air interact with radiation in WRF-Chem. The original model used a simplified method, but we developed a more accurate approach. We found that this method significantly changes the properties of the estimated aerosols and their effects on radiation, especially for dust aerosols. It also impacts the simulated weather conditions. Our work highlights the importance of correctly representing aerosol–radiation interactions in models.
Eduardo Moreno-Chamarro, Thomas Arsouze, Mario Acosta, Pierre-Antoine Bretonnière, Miguel Castrillo, Eric Ferrer, Amanda Frigola, Daria Kuznetsova, Eneko Martin-Martinez, Pablo Ortega, and Sergi Palomas
Geosci. Model Dev., 18, 461–482, https://doi.org/10.5194/gmd-18-461-2025, https://doi.org/10.5194/gmd-18-461-2025, 2025
Short summary
Short summary
We present the high-resolution model version of the EC-Earth global climate model to contribute to HighResMIP. The combined model resolution is about 10–15 km in both the ocean and atmosphere, which makes it one of the finest ever used to complete historical and scenario simulations. This model is compared with two lower-resolution versions, with a 100 km and a 25 km grid. The three models are compared with observations to study the improvements thanks to the increased resolution.
Catherine Guiavarc'h, David Storkey, Adam T. Blaker, Ed Blockley, Alex Megann, Helene Hewitt, Michael J. Bell, Daley Calvert, Dan Copsey, Bablu Sinha, Sophia Moreton, Pierre Mathiot, and Bo An
Geosci. Model Dev., 18, 377–403, https://doi.org/10.5194/gmd-18-377-2025, https://doi.org/10.5194/gmd-18-377-2025, 2025
Short summary
Short summary
The Global Ocean and Sea Ice configuration version 9 (GOSI9) is the new UK hierarchy of model configurations based on the Nucleus for European Modelling of the Ocean (NEMO) and available at three resolutions. It will be used for various applications, e.g. weather forecasting and climate prediction. It improves upon the previous version by reducing global temperature and salinity biases and enhancing the representation of Arctic sea ice and the Antarctic Circumpolar Current.
Andy Richling, Jens Grieger, and Henning W. Rust
Geosci. Model Dev., 18, 361–375, https://doi.org/10.5194/gmd-18-361-2025, https://doi.org/10.5194/gmd-18-361-2025, 2025
Short summary
Short summary
The performance of weather and climate prediction systems is variable in time and space. It is of interest how this performance varies in different situations. We provide a decomposition of a skill score (a measure of forecast performance) as a tool for detailed assessment of performance variability to support model development or forecast improvement. The framework is exemplified with decadal forecasts to assess the impact of different ocean states in the North Atlantic on temperature forecast.
Maria R. Russo, Sadie L. Bartholomew, David Hassell, Alex M. Mason, Erica Neininger, A. James Perman, David A. J. Sproson, Duncan Watson-Parris, and Nathan Luke Abraham
Geosci. Model Dev., 18, 181–191, https://doi.org/10.5194/gmd-18-181-2025, https://doi.org/10.5194/gmd-18-181-2025, 2025
Short summary
Short summary
Observational data and modelling capabilities have expanded in recent years, but there are still barriers preventing these two data sources from being used in synergy. Proper comparison requires generating, storing, and handling a large amount of data. This work describes the first step in the development of a new set of software tools, the VISION toolkit, which can enable the easy and efficient integration of observational and model data required for model evaluation.
Bijan Fallah, Masoud Rostami, Emmanuele Russo, Paula Harder, Christoph Menz, Peter Hoffmann, Iulii Didovets, and Fred F. Hattermann
Geosci. Model Dev., 18, 161–180, https://doi.org/10.5194/gmd-18-161-2025, https://doi.org/10.5194/gmd-18-161-2025, 2025
Short summary
Short summary
We tried to contribute to a local climate change impact study in central Asia, a region that is water-scarce and vulnerable to global climate change. We use regional models and machine learning to produce reliable local data from global climate models. We find that regional models show more realistic and detailed changes in heavy precipitation than global climate models. Our work can help assess the future risks of extreme events and plan adaptation strategies in central Asia.
Manuel Schlund, Bouwe Andela, Jörg Benke, Ruth Comer, Birgit Hassler, Emma Hogan, Peter Kalverla, Axel Lauer, Bill Little, Saskia Loosveldt Tomas, Francesco Nattino, Patrick Peglar, Valeriu Predoi, Stef Smeets, Stephen Worsley, Martin Yeo, and Klaus Zimmermann
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-236, https://doi.org/10.5194/gmd-2024-236, 2025
Revised manuscript accepted for GMD
Short summary
Short summary
The Earth System Model Evaluation Tool (ESMValTool) is a community diagnostics and performance metrics tool for the evaluation of Earth system models. Here, we describe recent significant improvements of ESMValTool’s computational efficiency including parallel, out-of-core, and distributed computing. Evaluations with the enhanced version of ESMValTool are faster, use less computational resources, and can handle input data larger than the available memory.
Thomas Rackow, Xabier Pedruzo-Bagazgoitia, Tobias Becker, Sebastian Milinski, Irina Sandu, Razvan Aguridan, Peter Bechtold, Sebastian Beyer, Jean Bidlot, Souhail Boussetta, Willem Deconinck, Michail Diamantakis, Peter Dueben, Emanuel Dutra, Richard Forbes, Rohit Ghosh, Helge F. Goessling, Ioan Hadade, Jan Hegewald, Thomas Jung, Sarah Keeley, Lukas Kluft, Nikolay Koldunov, Aleksei Koldunov, Tobias Kölling, Josh Kousal, Christian Kühnlein, Pedro Maciel, Kristian Mogensen, Tiago Quintino, Inna Polichtchouk, Balthasar Reuter, Domokos Sármány, Patrick Scholz, Dmitry Sidorenko, Jan Streffing, Birgit Sützl, Daisuke Takasuka, Steffen Tietsche, Mirco Valentini, Benoît Vannière, Nils Wedi, Lorenzo Zampieri, and Florian Ziemen
Geosci. Model Dev., 18, 33–69, https://doi.org/10.5194/gmd-18-33-2025, https://doi.org/10.5194/gmd-18-33-2025, 2025
Short summary
Short summary
Detailed global climate model simulations have been created based on a numerical weather prediction model, offering more accurate spatial detail down to the scale of individual cities ("kilometre-scale") and a better understanding of climate phenomena such as atmospheric storms, whirls in the ocean, and cracks in sea ice. The new model aims to provide globally consistent information on local climate change with greater precision, benefiting environmental planning and local impact modelling.
Yilin Fang, Hoang Viet Tran, and L. Ruby Leung
Geosci. Model Dev., 18, 19–32, https://doi.org/10.5194/gmd-18-19-2025, https://doi.org/10.5194/gmd-18-19-2025, 2025
Short summary
Short summary
Hurricanes may worsen water quality in the lower Mississippi River basin (LMRB) by increasing nutrient runoff. We found that runoff parameterizations greatly affect nitrate–nitrogen runoff simulated using an Earth system land model. Our simulations predicted increased nitrogen runoff in the LMRB during Hurricane Ida in 2021, albeit less pronounced than the observations, indicating areas for model improvement to better understand and manage nutrient runoff loss during hurricanes in the region.
Giovanni Seijo-Ellis, Donata Giglio, Gustavo Marques, and Frank Bryan
Geosci. Model Dev., 17, 8989–9021, https://doi.org/10.5194/gmd-17-8989-2024, https://doi.org/10.5194/gmd-17-8989-2024, 2024
Short summary
Short summary
A CESM–MOM6 regional configuration of the Caribbean Sea was developed in response to the rising need for high-resolution models for climate impact studies. The configuration is validated for the period 2000–2020 and improves significant errors in a low-resolution model. Oceanic properties are well represented. Patterns of freshwater associated with the Amazon River are well captured, and the mean flows of ocean waters across multiple passages in the Caribbean Sea agree with observations.
Yan Bo, Hao Liang, Tao Li, and Feng Zhou
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-212, https://doi.org/10.5194/gmd-2024-212, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
This study proposed an advancing framework for modeling regional rice production, water use, and greenhouse gas emissions. The framework integrated a process-based soil-crop model with key physiological effects, a novel model upscaling method, and the NSGA-II multi-objective optimization algorithm at a parallel computing platform. The framework provides a valuable tool for irrigation optimization to deliver co-benefits of ensuring food production, reducing water use and greenhouse gas emissions.
Elizabeth J. Drenkard, Charles A. Stock, Andrew C. Ross, Yi-Cheng Teng, Theresa Morrison, Wei Cheng, Alistair Adcroft, Enrique Curchitser, Raphael Dussin, Robert Hallberg, Claudine Hauri, Katherine Hedstrom, Albert Hermann, Michael G. Jacox, Kelly A. Kearney, Remi Pages, Darren J. Pilcher, Mercedes Pozo Buil, Vivek Seelanki, and Niki Zadeh
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-195, https://doi.org/10.5194/gmd-2024-195, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
We made a new regional ocean model to assist fisheries and ecosystem managers make decisions in the Northeast Pacific Ocean (NEP). We found that the model did well simulating past ocean conditions like temperature, and nutrient and oxygen levels, and can even reproduce metrics used by and important to ecosystem managers.
Deifilia To, Julian Quinting, Gholam Ali Hoshyaripour, Markus Götz, Achim Streit, and Charlotte Debus
Geosci. Model Dev., 17, 8873–8884, https://doi.org/10.5194/gmd-17-8873-2024, https://doi.org/10.5194/gmd-17-8873-2024, 2024
Short summary
Short summary
Pangu-Weather is a breakthrough machine learning model in medium-range weather forecasting that considers 3D atmospheric information. We show that using a simpler 2D framework improves robustness, speeds up training, and reduces computational needs by 20 %–30 %. We introduce a training procedure that varies the importance of atmospheric variables over time to speed up training convergence. Decreasing computational demand increases the accessibility of training and working with the model.
Fang Li, Xiang Song, Sandy P. Harrison, Jennifer R. Marlon, Zhongda Lin, L. Ruby Leung, Jörg Schwinger, Virginie Marécal, Shiyu Wang, Daniel S. Ward, Xiao Dong, Hanna Lee, Lars Nieradzik, Sam S. Rabin, and Roland Séférian
Geosci. Model Dev., 17, 8751–8771, https://doi.org/10.5194/gmd-17-8751-2024, https://doi.org/10.5194/gmd-17-8751-2024, 2024
Short summary
Short summary
This study provides the first comprehensive assessment of historical fire simulations from 19 Earth system models in phase 6 of the Coupled Model Intercomparison Project (CMIP6). Most models reproduce global totals, spatial patterns, seasonality, and regional historical changes well but fail to simulate the recent decline in global burned area and underestimate the fire response to climate variability. CMIP6 simulations address three critical issues of phase-5 models.
Seung H. Baek, Paul A. Ullrich, Bo Dong, and Jiwoo Lee
Geosci. Model Dev., 17, 8665–8681, https://doi.org/10.5194/gmd-17-8665-2024, https://doi.org/10.5194/gmd-17-8665-2024, 2024
Short summary
Short summary
We evaluate downscaled products by examining locally relevant co-variances during precipitation events. Common statistical downscaling techniques preserve expected co-variances during convective precipitation (a stationary phenomenon). However, they dampen future intensification of frontal precipitation (a non-stationary phenomenon) captured in global climate models and dynamical downscaling. Our study quantifies a ramification of the stationarity assumption underlying statistical downscaling.
Emmanuel Nyenah, Petra Döll, Daniel S. Katz, and Robert Reinecke
Geosci. Model Dev., 17, 8593–8611, https://doi.org/10.5194/gmd-17-8593-2024, https://doi.org/10.5194/gmd-17-8593-2024, 2024
Short summary
Short summary
Research software is vital for scientific progress but is often developed by scientists with limited skills, time, and funding, leading to challenges in usability and maintenance. Our study across 10 sectors shows strengths in version control, open-source licensing, and documentation while emphasizing the need for containerization and code quality. We recommend workshops; code quality metrics; funding; and following the findable, accessible, interoperable, and reusable (FAIR) standards.
Chris Smith, Donald P. Cummins, Hege-Beate Fredriksen, Zebedee Nicholls, Malte Meinshausen, Myles Allen, Stuart Jenkins, Nicholas Leach, Camilla Mathison, and Antti-Ilari Partanen
Geosci. Model Dev., 17, 8569–8592, https://doi.org/10.5194/gmd-17-8569-2024, https://doi.org/10.5194/gmd-17-8569-2024, 2024
Short summary
Short summary
Climate projections are only useful if the underlying models that produce them are well calibrated and can reproduce observed climate change. We formalise a software package that calibrates the open-source FaIR simple climate model to full-complexity Earth system models. Observations, including historical warming, and assessments of key climate variables such as that of climate sensitivity are used to constrain the model output.
Cited articles
Adcroft, A., Anderson, W., Balaji, V., Blanton, C., Bushuk, M., Dufour, C. O., Dunne, J. P., Griffies, S. M., Hallberg, R., Harrison, M. J., Held, I. M., Jansen, M. F., John, J. G., Krasting, J. P., Langenhorst, A. R., Legg, S., Liang, Z., McHugh, C., Radhakrishnan, A., Reichl, B. G., Rosati, T., Samuels, B. L., Shao, A., Stouffer, R. Winton, M., Wittenberg, A. T., Xiang, B., Zadeh, N., and Zhang, R..: The GFDL global ocean and sea ice model OM4.0: Model description and simulation features, J. Adv. Model. Earth Sy., 11, 3167–3211, https://doi.org/10.1029/2019MS001726, 2019.
Anderson, D. M., Glibert, P. M., and Burkholder, J. M.: Harmful algal blooms and eutrophication: Nutrient sources, composition, and consequences, Estuaries, 25, 704–726, https://doi.org/10.1007/BF02804901, 2002.
Anderson, D. M., Fensin, E., Gobler, C. J., Hoeglund, A. E., Hubbard, K. A., Kulis, D. M., Landsberg, J. H., Lefebvre, K. A., Provoost, P., Richlen, M. L., Smith, J. L., Solow, A. R., and Trainer, V, L.: Marine harmful algal blooms (HABs) in the United States: History, current status and future trends, Harmful Algae, 102, 1568–9883, https://doi.org/10.1016/j.hal.2021.101975, 2021.
Beusen, A. H. W. and Planbureau voor de Leefomgeving (PBL): Global riverine nitrogen (N) and phosphorus (P) input, retention and export during the 20th century, V2, DANS Data Station Physical and Technical Sciences [data set], https://doi.org/10.17026/dans-zgs-9k9m, 2016.
Beusen, A. H. W., Dekkers, A. L. M., Bouwman, A. F., Ludwig, W., and Harrison, J.: Estimation of global river transport of sediments and associated particulate C, N, and P, Global Biogeochem. Cy., 19, GB4S05, https://doi.org/10.1029/2005GB002453, 2005.
Beusen, A. H. W., Van Beek, L. P. H., Bouwman, A. F., Mogollón, J. M., and Middelburg, J. J.: Coupling global models for hydrology and nutrient loading to simulate nitrogen and phosphorus retention in surface water – description of IMAGE–GNM and analysis of performance, Geosci. Model Dev., 8, 4045–4067, https://doi.org/10.5194/gmd-8-4045-2015, 2015.
Beusen, A. H. W., Bouwman, A. F., Van Beek, L. P. H., Mogollón, J. M., and Middelburg, J. J.: Global riverine N and P transport to ocean increased during the 20th century despite increased retention along the aquatic continuum, Biogeosciences, 13, 2441–2451, https://doi.org/10.5194/bg-13-2441-2016, 2016.
Bian, Z., Pan, S., Wang, Z., Yao, Y., Xu, R., Shi, H., Kalin, L., Anderson, C., Justic, D., Lohrenz, S., and Tian, H.: A Century-Long Trajectory of Phosphorus Loading and Export From Mississippi River Basin to the Gulf of Mexico: Contributions of Multiple Environmental Changes, Global Biogeochem. Cy., 36, e2022GB007347, https://doi.org/10.1029/2022GB007347, 2022.
Billen, G., Garnier, J., and Hanset, P.: Modelling phytoplankton development in whole drainage networks: The riverstrahler model applied to the Seine River system, Hydrobiologia, 289, 119–137, https://doi.org/10.1007/978-94-017-2670-2_11, 1994.
Bouwman, A. F., Pawłowski, M., Liu, C., Beusen, A. H. W., Shumway, S. E., Glibert, P. M., and Overbeek, C. C.: Global hindcasts and future projections of coastal nitrogen and phosphorus loads due to shellfish and seaweed aquaculture, Rev. Fish Sci., 19, 331–357, https://doi.org/10.1080/10641262.2011.603849, 2011.
Bouwman, A. F., Beusen, A. H. W., Overbeek, C. C., Bureau, D. P., Pawlowski, M., and Glibert, P. M.: Hindcasts and future projections of global inland and coastal nitrogen and phosphorus loads due to finfish aquaculture, Rev. Fish Sci., 21, 112–156, https://doi.org/10.1080/10641262.2013.790340, 2013a.
Bouwman, A. F., Klein Goldewijk, K., Van der Hoek, K. W., Beusen, A. H. W., Van Vuuren, D. P., Willems, W. J., Rufino, M. C., and Stehfest, E.: Exploring global changes in nitrogen and phosphorus cycles in agriculture induced by livestock production over the 1900–2050 period, P. Natl. Acad. Sci. USA, 110, 20882–20887, https://doi.org/10.1073/pnas.1012878108, 2013b.
Bowie, G. L., Mills, W. B., Porcella, D. B., Campbell, C. L., Pagenkopf, J. R., Rupp, G. L., Johnson, K.M., Chan, P. W. H., Gherini, S. A., and Chamberlin, C. E.: Rates, Constants, and Kinetic Formulations in Surface Water Quality Modeling, U.S. Envir. Prot. Agency, ORD, Athens, GA, ERL, EPA/600/3-85/040, https://nepis.epa.gov/Exe/ZyPDF.cgi/9100R3IW.PDF?Dockey=9100R3IW.PDF (last access: 20 June 2024), 1985.
Boyer, E. W., Howarth, R. W., Galloway, J. N., Dentener, F. J., Green, P. A., and Vorosmarty, C. J.: Riverine nitrogen export from the continents to the coasts, Global Biogeochem. Cy., 20, GB1S91, https://doi.org/10.1029/2005GB002537, 2006.
Cerdan, O., Govers, G., Le Bissonnais, Y., Van Oost, K., Poesen, J., Saby, N., Gobin, A., Vacca, A., Quinton, J., Auerswald, K., Klik, A., Kwaad, F. J. P. M., Raclot, D., Ionita, I., Rejman, J., Rousseva, S., Muxart, T., Roxo, M. J., and Dostal, T.: Rates and spatial variations of soil erosion in Europe: A study based on erosion plot data, Geomorphology, 122, 167–177, https://doi.org/10.1016/j.geomorph.2010.06.011, 2010.
Chapra, S. C.: Surface Water-Quality Modeling, Waveland Press, Long Grove, IL, USA, ISBN 1-57766-605-4, 1997.
Chapra, S. C., Pelletier, G. J., and Tao, H. QUAL2K: A Modeling Framework for Simulating River and Stream Water Quality, Version 2.11: Documentation and Users Manual, Civil and Environmental Engineering Dept., Tufts University, Medford, MA, https://csdms.colorado.edu/csdms_wiki/images/Q2KDocv2_11b8.pdf (last access: 23 June 2024), 2008.
Cordell, D., Drangert, J., and White, S.: The story of phosphorus: Global food security and food for thought, Glob. Environ. Change, 19, 292–305, https://doi.org/10.1016/j.gloenvcha.2008.10.009, 2009.
Danielson, J. J. and Gesch, D. B.: Global multi-resolution terrain elevation data 2010 (GMTED2010): U.S. Geological Survey Open-File Report 2011–1073, https://pubs.usgs.gov/of/2011/1073/pdf/of2011-1073.pdf (last access: 20 June 2024), 2011.
Dentener, F., Stevenson, D., Ellingsen, K., Noije, T. v., Schultz, M., Amann, M., Atherton, C., Bell, N., Bergmann, D., Bey, I., Bouwman, L., Butler, T., Cofala, J., Collins, B., Drevet, J., Doherty, R., Eickhout, B., Eskes, H., Fiore, A., Gauss, M., Hauglustaine, D., Horowitz, L., Isaksen, I. S. A., Josse, B., Lawrence, M., Krol, M., Lamarque, J. F., Montanaro, V.,Müller, J. F., Peuch, V. H., Pitari, G., Pyle, J., Rast, S., Rodriguez, J., Sanderson, M., Savage, N. H., Shindell, D., Strahan, S., Szopa, S., Sudo, K., Dingenen, R. V., Wild, O., and Zeng, G.: The global atmospheric environment for the next generation, Environ. Sci. Technol., 40, 3586–3594, https://doi.org/10.1021/es0523845, 2006.
Diaz, R. J. and Rosenberg, R.: Spreading dead zones and consequences for marine ecosystems, Science, 321, 926–929, 2008.
Di Toro, D. M.: Optics of turbid estuarine waters: approximations and applications, Water Res., 12, 1059–1068, 1978.
Di Toro, D. M.: Sediment Flux Modeling. Wiley-Interscience, New York, NY, USA, ISBN 978-0-471-13535-7, 2001.
Dumont, E., Harrison, J. A., Kroeze, C., Bakker, E. J., and Seitzinger, S. P.: Global distribution and sources of dissolved inorganic nitrogen export to the coastal zone: Results from a spatially explicit, global model, Global Biogeochem. Cy., 19, GB4S02, https://doi.org/10.1029/2005GB002488, 2005.
Dunne, J. P., Armstrong, R. A., Gnanadesikan, A., and Sarmiento, J. L.: Empirical and mechanistic models for the particle export ratio, Global Biogeochem. Cy., 19, GB4026, https://doi.org/10.1029/2004GB002390, 2005.
Durack, P. J., Taylor, K. E., and those preparing forcing datasets: CMIP6 Forcing Datasets Summary, https://docs.google.com/document/d/1pU9IiJvPJwRvIgVaSDdJ4O0Jeorv_2ekEtted34K9cA/edit, last access: 20 June 2024.
Environment and Natural Resources Department: Wastewater as a resource, European Investment Bank, https://www.eib.org/en/publications/wastewater-as-a-resource (last access: 20 June 2024), 2022.
Eppley, R. W.: Temperature and phytoplankton growth in the sea, Fish. Bull., 70, 1063–1085, 1972.
Ferguson, R. I. and Church, M.: A simple universal equation for grain settling velocity, J. Sediment. Res., 74, 933–937, 2004.
Fowler, D., Coyle, M., Skiba, U., Sutton, M. A., Cape, J. N., Reis, S., Sheppard, L. J., Jenkins, A., Grizzetti, B., Galloway, J. N., Vitousek, P., Leach, A., Bouwman, A. F., Butterbach-Bahl, K., Dentener, F., Stevenson, D., Amann, M., and Voss, M.: The global nitrogen cycle in the twenty-first century, Philos. T. Roy. Soc. B, 368, 20130164, https://doi.org/10.1098/rstb.2013.0164, 2013.
Frost, B. W. and Franzen, N. C.: Grazing and iron limitation in the control of phytoplankton stock and nutrient concentration: a chemostat analogue of the Pacific equatorial upwelling zone, Mar. Ecol. Prog. Ser., 83, 291–303, 1992.
Galloway, J. N., Dentener, F. J., Capone, D. G., Boyer, E. W., Howarth, R. W., Seitzinger, S. P., Asner, G. P., Cleveland, C. C., Green, P. A., Holland, E. A., Karl, D. M., Michaels, A. F., Porter, J. H., Townsend, A. R., and Vöosmarty, C. J.: Nitrogen cycles: past, present, and future, Biogeochemistry, 70, 153–226, https://doi.org/10.1007/s10533-004-0370-0, 2004.
Garnier, J., Nemery, J., Billen, G., and Thery, S.: Nutrient dynamics and control of eutrophication in the Marne River system: modelling the role of exchangeable phosphorus, J. Hydrol., 304, 397–412, https://doi.org/10.1016/j.jhydrol.2004.07.040, 2005.
Geider, R. J., MacIntyre, H. L., and Kana, T. M.: Dynamic model of phytoplankton growth and acclimation: responses of the balanced growth rate and the chlorophyll a:carbon ratio to light, nutrient-limitation and temperature, Mar. Ecol. Prog. Ser., 148, 187–200, 1997.
Gerber, S., Hedin, L. O., Oppenheimer, M., Pacala, S. W., and Shevliakova, E.: Nitrogen cycling and feedbacks in a global dynamic land model, Glob. Biogeochem. Cy., 24, GB1001, https://doi.org/10.1029/2008GB003336, 2010.
Glibert, P. M. and Terlizzi, D. E.: Cooccurrence of elevated urea levels and dinoflagellate blooms in temperate estuarine aquaculture ponds, Appl. Environ. Microbiol., 65, 5594–5596, https://doi.org/10.1128/AEM.65.12.5594-5596.1999, 1999.
Glibert, P. M., Magnien, R., Lomas, M. W., Alexander, J., Fan, C., Haramoto, E., Trice, M., and Kana, T. M.: Harmful algal blooms in the Chesapeake and Coastal Bays of Maryland, USA: Comparison of 1997, 1998, and 1999 events, Estuaries, 24, 875–883, https://doi.org/10.2307/1353178, 2001.
Glibert, P. M., Mayorga, E., and Seitzinger, S.: Prorocentrum minimum tracks anthropogenic nitrogen and phosphorus inputs on a global basis: Application of spatially explicit nutrient export models, Harmful Algae, 8, 33–38, https://doi.org/10.1016/j.hal.2008.08.023, 2008.
Green, P. A., Vörösmarty, C. J., Meybeck, M., Galloway, J. N., Peterson, B. J., and Boyer, E. W.: Pre-industrial and contemporary fluxes of nitrogen through rivers: a global assessment based on typology, Biogeochemistry, 68, 71–105, https://doi.org/10.1023/B:BIOG.0000025742.82155.92, 2004.
Harrison, J. A., Caraco, N., and Seitzinger, S. P.: Global patterns and sources of dissolved organic matter export to the coastal zone: Results from a spatially explicit, global model, Global Biogeochem. Cy., 19, GB4S04, https://doi.org/10.1029/2005GB002480, 2005.
Harrison, J. A., Bouwman, A. F., Mayorga, E., and Seitzinger, S.: Magnitudes and sources of dissolved inorganic phosphorus inputs to surface fresh waters and the coastal zone: A new global model, Global Biogeochem. Cy., 24, GB1003, https://doi.org/10.1029/2009GB003590, 2010.
Harrison, J. A., Beusen, A. H. W., Fink, G., Tang, T., Strokal, M., Bouwman, A. F., Metson, G. S., and Vilmin, L.: Modeling phosphorus in rivers at the global scale: recent successes, remaining challenges, and near-term opportunities, Curr. Opin. Environ. Sustain., 36, 68–77, https://doi.org/10.1016/j.cosust.2018.10.010, 2019.
Hartmann, J., Moosdorf, N., Lauerwald, R., Hinderer, M., and West, A. J.: Global chemical weathering and associated P-release – The role of lithology, temperature and soil properties, Chem. Geol., 363, 145–163, https://doi.org/10.1016/j.chemgeo.2013.10.025, 2014.
Hatono, M. and Yoshimura, K.: Development of a global sediment dynamics model, Prog. Earth Planet. Sci., 7, 59, https://doi.org/10.1186/s40645-020-00368-6, 2020.
He, B., Kanae, S., Oki, T., Hirabayashi, Y., Yamashiki, Y., and Takara, K.: Assessment of global nitrogen pollution in rivers using an integrated biogeochemical modeling framework, Water Res., 45, 2573–2586, https://doi.org/10.1016/j.watres.2011.02.011, 2011.
Hegglin, M. I., Kinnison, D., and Lamarque, J. F.: input4MIPs, ESGF [data set], https://aims2.llnl.gov/search/input4mips, last access: 20 June 2024.
Heisler, J., Glibert, P. M., Burkholder, J. M., Anderson, D. M., Cochlan, W., Dennison, W. C., Dortch, Q., Gobler, C. J., Heil, C. A., Humphries, E., Lewitus, A., Magnien, R., Marshallm, H. G., Sellner, K., Stockwell, D. A., Stoecker, D. K., and Suddleson, M.: Eutrophication and harmful algal blooms: A scientific consensus, Harmful Algae, 8, 3–13, https://doi.org/10.1016/j.hal.2008.08.006, 2008.
Howarth, R. W. and Marino, R.: Nitrogen as the limiting nutrient for eutrophication in coastal marine ecosystems: Evolving views over three decades, Limnol. Oceanogr., 51, 364–376, https://doi.org/10.4319/lo.2006.51.1_part_2.0364, 2006.
Hurtt, G. C., Chini, L., Sahajpal, R., Frolking, S., Bodirsky, B. L., Calvin, K., Doelman, J. C., Fisk, J., Fujimori, S., Klein Goldewijk, K., Hasegawa, T., Havlik, P., Heinimann, A., Humpenöder, F., Jungclaus, J., Kaplan, J. O., Kennedy, J., Krisztin, T., Lawrence, D., Lawrence, P., Ma, L., Mertz, O., Pongratz, J., Popp, A., Poulter, B., Riahi, K., Shevliakova, E., Stehfest, E., Thornton, P., Tubiello, F. N., van Vuuren, D. P., and Zhang, X.: Harmonization of global land use change and management for the period 850–2100 (LUH2) for CMIP6, Geosci. Model Dev., 13, 5425–5464, https://doi.org/10.5194/gmd-13-5425-2020, 2020a.
Hurtt, G. C., Chini, L., Sahajpal, R., Frolking, S., Bodirsky, B. L., Calvin, K., Doelman, J. C., Fisk, J., Fujimori, S., Klein Goldewijk, K., Hasegawa, T., Havlik, P., Heinimann, A., Humpenöder, F., Jungclaus, J., Kaplan, J. O., Kennedy, J., Krisztin, T., Lawrence, D., Lawrence, P., Ma, L., Mertz, O., Pongratz, J., Popp, A., Poulter, B., Riahi, K., Shevliakova, E., Stehfest, E., Thornton, P., Tubiello, F. N., van Vuuren, D. P., and Zhang, X.: Land Use Harmonization 2, Global Ecology Laboratory, University of Maryland [data set], https://luh.umd.edu/data.shtml (last access: 20 June 2024), 2020b.
Kaushal, S., Groffman, P. M., Band, L. E., Shields, C. A., Morgan, R. P., Palmer, M. A., Belt, K. T., Swan, C. M., Findlay, S. E. G., and Fisher, G. T.: Interaction between urbanization and climate variability amplifies watershed nitrate export in Maryland, Environ. Sci. Technol., 42, 5872–5878, https://doi.org/10.1021/es800264f, 2008.
Kemp, W. M., Boynton, W. R., Adolf, J. E., Boesch, D. F., Boicourt, W. C., Brush, G., Cornwell, J. C., Fisher, T. R., Glibert, P.M., Hagy, J. D., Harding, L. W., Houde, E. D., Kimmel, D. G., Miller, W. D., Newell, R. I. E., Roman, M. R., Smith, E. M., and Stevenson, J. C.: Eutrophication of Chesapeake Bay: historical trends and ecological interactions, Mar. Ecol. Prog. Ser., 303, 1–29, https://doi.org/10.3354/MEPS303001, 2005.
Lacoul, P. and Freedman, B.: Environmental influences on aquatic plants in freshwater ecosystems, Environ. Rev., 14, 89–136, https://doi.org/10.1139/a06-001, 2006.
Lee, C.: Nutrient and pesticide data collected from the USGS National Water Quality Network and previous networks, 1950–2021, U.S. Geological Survey, https://doi.org/10.5066/P948Z0VZ, 2022.
Lee, C. J., Murphy, J. C., Crawford, C. G., and Deacon, J. R.: Methods for computing water-quality loads at sites in the U.S. Geological Survey National Water Quality Network (ver. 1.3, August 2021): U.S. Geological Survey Open-File Report 2017–1120, 20 pp., https://doi.org/10.3133/ofr20171120, 2017.
Lee, C. J., Hirsch, R. M., and Crawford, C. G.: An evaluation of methods for computing annual water-quality loads: U.S. Geological Survey Scientific Investigations Report 2019–5084, 59 pp., https://doi.org/10.3133/sir20195084, 2019.
Lee, M.: minjinl/LM3-FANSY: LM3-FANSY v1.0 (v1.0), Zenodo [code and data set], https://doi.org/10.5281/zenodo.10962725, Lee, 2024.
Lee, M., Malyshev, S., Shevliakova, E., Milly, P. C. D., and Jaffé, P. R.: Capturing interactions between nitrogen and hydrological cycles under historical climate and land use: Susquehanna watershed analysis with the GFDL land model LM3-TAN, Biogeosciences, 11, 5809–5826, https://doi.org/10.5194/bg-11-5809-2014, 2014.
Lee, M., Shevliakova, E., Malyshev, S., Milly, P. C. D., and Jaffé, P. R.: Climate variability and extremes, interacting with nitrogen storage, amplify eutrophication risk, Geophys. Res. Lett., 43, 7520–7528, https://doi.org/10.1002/2016GL069254, 2016.
Lee, M., Shevliakova, E., Stock, C. A., Malyshev, S., and Milly, P. C. D.: Prominence of the tropics in the recent rise of global nitrogen pollution, Nat. Commun., 10, 1437, https://doi.org/10.1038/s41467-019-09468-4, 2019.
Lee, M., Stock, C. A., Shevliakova, E., Malyshev, S., and Milly, P. C. D.: Globally prevalent land nitrogen memory amplifies water pollution following drought years, Environ. Res. Lett., 16, 014049, https://doi.org/10.1088/1748-9326/abd1a0, 2021.
Leong, S. C. Y., Murata, A., Nagashima, Y., and Taguchi, S.: Variability in toxicity of the dinoflagellate Alexandrium tamarense in response to different nitrogen sources and concentrations, Toxicon, 43, 407–415, https://doi.org/10.1016/j.toxicon.2004.01.015, 2004.
Liu, X., Stock, C. A., Dunne, J. P., Lee, M., Shevliakova, E., Malyshev, S., and Milly, P. C. D.: Simulated global coastal ecosystem responses to a half-century increase in river nitrogen loads, Geophys. Res. Lett., 48, e2021GL094367, https://doi.org/10.1029/2021GL094367, 2021.
Maavara, T., Parsons, C. T., Ridenour, C., Stojanovic, S., Dürr, H. H., Powley, H. R., and Van Cappellen, P.: Global phosphorus retention by river damming, Proc. Natl. Acad. Sci. USA, 112, 15603–15608, https://doi.org/10.1073/pnas.1511797112, 2015.
Mayorga, E., Seitzinger, S. P., Harrison, J. A., Dumont, E., Beusen, A. H. W., Bouwman, A. F. Fekete, B. M., Kroeze, C., and Van Drecht, G.: Global Nutrient Export from WaterSheds 2 (NEWS 2): Model development and implementation, Environ. Model. Softw., 25, 837–853, https://doi.org/10.1016/j.envsoft.2010.01.007, 2010.
Meinshausen, M., Vogel, E., Nauels, A., Lorbacher, K., Meinshausen, N., Etheridge, D. M., Fraser, P. J., Montzka, S. A., Rayner, P. J., Trudinger, C. M., Krummel, P. B., Beyerle, U., Canadell, J. G., Daniel, J. S., Enting, I. G., Law, R. M., Lunder, C. R., O'Doherty, S., Prinn, R. G., Reimann, S., Rubino, M., Velders, G. J. M., Vollmer, M. K., Wang, R. H. J., and Weiss, R.: Historical greenhouse gas concentrations for climate modelling (CMIP6), Geosci. Model Dev., 10, 2057–2116, https://doi.org/10.5194/gmd-10-2057-2017, 2017a.
Meinshausen, M., Vogel, E., Nauels, A., Lorbacher, K., Meinshausen, N., Etheridge, D. M., Fraser, P. J., Montzka, S. A., Rayner, P. J., Trudinger, C. M., Krummel, P. B., Beyerle, U., Canadell, J. G., Daniel, J. S., Enting, I. G., Law, R. M., Lunder, C. R., O'Doherty, S., Prinn, R. G., Reimann, S., Rubino, M., Velders, G. J. M., Vollmer, M. K., Wang, R. H. J., and Weiss, R.: input4MIPs, ESGF [data set], https://aims2.llnl.gov/search/input4mips (last access: 20 June 2024), 2017b.
McGechan, M. B. and Lewis, D. R.: SW – Soil and Water: Sorption of Phosphorus by Soil, Part 1: Principles, Equations and Models, Biosyst. Eng., 82, 1–24, https://doi.org/10.1006/bioe.2002.0054, 2002.
McLaughlin, C. J., Smith, C. A., Buddemeier, R. W., Bartley, J. D., and Maxwell, B. A.: Rivers, runoff, and reefs, Glob. Planet. Change, 39, 191–199, https://doi.org/10.1016/S0921-8181(03)00024-9, 2003.
Meinshausen, M., Vogel, E., Nauels, A., Lorbacher, K., Meinshausen, N., Etheridge, D. M., Fraser, P. J., Montzka, S. A., Rayner, P. J., Trudinger, C. M., Krummel, P. B., Beyerle, U., Canadell, J. G., Daniel, J. S., Enting, I. G., Law, R. M., Lunder, C. R., O'Doherty, S., Prinn, R. G., Reimann, S., Rubino, M., Velders, G. J. M., Vollmer, M. K., Wang, R. H. J., and Weiss, R.: Historical greenhouse gas concentrations for climate modelling (CMIP6), Geosci. Model Dev., 10, 2057–2116, https://doi.org/10.5194/gmd-10-2057-2017, 2017.
Meybeck, M. and Ragu, A.: Presenting the GEMS-GLORI, a compendium of world river discharge to the oceans, Freshwater Contamination, 243, 3–14, 1997.
Meybeck, M. and Ragu, A.: GEMS-GLORI world river discharge database, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.804574, 2012.
Milly, P. C. D., Malyshev, S., Shevliakova, E., Dunne, K. A., Findell, K. L., Gleeson, T., Liang, Z., Phillipps, P., Stouffer, R. J., and Swenson, S.: An enhanced model of land water and energy for global hydrologic and earth-system studies, J. Hydrometeorol., 15, 1739–1761, https://doi.org/10.1175/JHM-D-13-0162.1, 2014.
Morée, A. L., Beusen, A. H. W., Bouwman, A. F., and Willems, W. J.: Exploring global nitrogen and phosphorus flows in urban wastes during the twentieth century, Global Biogeochem. Cy., 27, 836–846, https://doi.org/10.1002/gbc.20072, 2013.
Nemery, J.: Origine et devenir du phosphore dans le continuum aquatique de la Seine, des petits basins à l'estuaire. Ro^le du phosphore échangeable sur l'eutrophisation, PhD thesis, University of Paris, France, 258 pp., 2003.
Paerl, H. W., Otten, T. G., and Kudela, R.: Mitigating the expansion of harmful algal blooms across the freshwater-to-marine continuum, Environ. Sci. Technol., 52, 5519–5529, https://doi.org/10.1021/acs.est.7b05950, 2018.
Parsons, M. L. and Dortch, Q.: Sedimentological evidence of an increase in Pseudo-nitzschia (Bacillariophyceae) abundance in response to coastal eutrophication, Limnol. Oceanogr., 47, 551–558, https://doi.org/10.4319/lo.2002.47.2.0551, 2002.
Pelletier, G. J., Chapra, S. C., and Tao, H.: QUAL2Kw – A framework for modeling water quality in streams and rivers using a genetic algorithm for calibration, Environ. Modell. Softw., 21, 419–425, https://doi.org/10.1016/j.envsoft.2005.07.002, 2006.
Pelletier, J. D.: A spatially distributed model for the long-term suspended sediment discharge and delivery ratio of drainage basins, J. Geophys. Res., 117, F02028, https://doi.org/10.1029/2011JF002129, 2012.
Poesen, J., Boardman, J., Wilcox, B., and Valentin, C.: Water erosion monitoring and experimentation for global change studies, J. Soil Water Conserv., 51, 386–390, 1996.
Post, W. M., Pastor, J., Zinke, P. J., and Stangenberger, A. G.: Global patterns of soil nitrogen storage, Nature, 317, 613–616, 1985.
Redfield, A. C., Ketchum, B. H., and Richards, F. A.: The Influence of Organisms on the Composition of Seawater, in: The Sea, edited by: Hill, M. N., Wiley-Interscience, NY, 27–46, 1963.
Renschler, C. S. and Harbor, J.: Soil erosion assessment tools from point to regional scales – The role of geomorphologists in land management research and implementation, Geomorphology, 47, 189–209, https://doi.org/10.1016/S0169-555X(02)00082-X, 2002.
Restrepo, J. D., Zapata, P., Díaz, J. M., Garzón-Ferreira, J., and García, C. B.: Fluvial fluxes into the Caribbean Sea and their impact on coastal ecosystems: The Magdalena River, Colombia, Glob. Planet. Change, 50, 33–49, https://doi.org/10.1016/j.gloplacha.2005.09.002, 2006.
Riley, G. A.: Oceanography of Long Island sound 1952–54, Bull. Bingham. Oceanog. Collection., 15, 15–16, 1956.
Sayers, M. J., Grimm, A. G., Shuchman, R. A., Deines, A. M., Bunnell, D. B., Raymer, Z. B., Rogers, M. W., Woelmer, W., Bennion, D. H., Brooks, C. N., Whitley, M. A., Warner, D. M., and Mychek-Londer, J.: A new method to generate a high-resolution global distribution map of lake chlorophyll, Int. J. Remote Sens., 36, 1942–1964, https://doi.org/10.1080/01431161.2015.1029099, 2015.
Seitzinger, S., Harrison, J. A., Böhlke, J. K., Bouwman, A. F., Lowrance, R., Peterson, B., Tobias, C., and Van Drecht G.: Denitrification across landscapes and waterscapes: a synthesis, Ecol. Appl., 6, 2064–2090, https://doi.org/10.1890/1051-0761(2006)016[2064:DALAWA]2.0.CO;2, 2006.
Seitzinger, S. P., Mayorga, E., Bouwman, A. F., Kroeze, C., Beusen, A. H. W., Billen, G., Van Drecht, G., Dumont, E., Fekete, B. M., Garnier, J., and Harrison, J. A.: Global river nutrient export: A scenario analysis of past and future trends, Global Biogeochem. Cycles, 24, GB0A08, https://doi.org/10.1029/2009GB003587, 2010.
Sheffield, J., Goteti, G., and Wood, E. F.: Development of a 50-year high resolution global dataset of meteorological forcings for land surface modeling, J. Climate, 19, 3088–3111, 2006a.
Sheffield, J., Goteti, G., and Wood, E. F.: Princeton Global Forcings, Princeton University [data set], https://hydrology.soton.ac.uk/data/pgf (last access: 20 June 2024), 2006b.
Shevliakova, E., Pacala, S. W., Malyshev, S., Hurtt, G. C., Milly, P. C. D., Caspersen, J. P., Sentman, L. T., Fisk, J. P., Wirth, C., and Crevoisier, C.: Carbon cycling under 300 years of land use changes: Importance of the secondary vegetation sink, Glob. Biogeochem. Cy., 23, GB2022, https://doi.org/10.1029/2007GB003176, 2009.
Sipler, R. E. and Bronk, D. A.: Dynamics of Dissolved Organic Nitrogen, in: Biogeochemistry of Marine Dissolved Organic Matter, edited by: Hansell, D. A. and Carlson, C. A., Academic Press, Kidlington, Oxford, UK, 128–233, ISBN 978-0-12-405940-5, 2014.
Smith, V. H.: Eutrophication of freshwater and coastal marine ecosystems a global problem, Environ. Sci. Pollut. Res., 10, 126–139, https://doi.org/10.1065/espr2002.12.142, 2003.
Smith, S. V., Swaney, D. P., Talaue-Mcmanus, L., Bartley, J. D., Sandhei, P. T., McLaughlin, C. J., Dupra, V. C., Crossland, C. J., Buddemeier, R. W., Maxwell, B. A., and Wulff, F.: Humans, Hydrology, and the Distribution of Inorganic Nutrient Loading to the Ocean, BioScience, 53, 234–245, https://doi.org/10.1641/0006-3568(2003)053[0235:HHATDO]2.0.CO;2, 2003.
Steele, J. H. and Henderson, E. W., The significance of interannual variability, in: Towards a Model of Ocean Biogeochemical Processes, edited by: Evans, G. T. and Fasham, M. J. R., Springer-Verlag, Berlin, Heidelberg, Germany, 227–260, ISBN 978-3-642-84604-5, 1992.
Stock, C. A., Dunne, J. P., and John, J. G.: Global-scale carbon and energy flows through the marine planktonic food web: An analysis with a coupled physical–biological model, Prog. Oceanogr., 120, 1–28, https://doi.org/10.1016/j.pocean.2013.07.001, 2014.
Syvitski, J. P. M., Vörösmarty, C. J., Kettner, A. J., and Green, P.: Impact of humans on the flux of terrestrial sediment to the global coastal ocean, Science, 308, 376–380, https://doi.org/10.1126/science.1109454, 2005.
Tan, Z., Leung, L. R., Li, H., Tesfa, T., Vanmaercke, M., Poesen, J., Zhang, X., Lu, H., and Hartmann, J.: A Global data analysis for representing sediment and particulate organic carbon yield in Earth System Models, Water Resour. Res., 53, 10674–10700, https://doi.org/10.1002/2017WR020806, 2017.
Thomas, S. C. and Martin, A. R.: Carbon Content of Tree Tissues: A Synthesis, Forests, 3, 332–352, https://doi.org/10.3390/f3020332, 2012.
Tian, H., Yang, Q., Najjar, R. G., Ren, W., Friedrichs, M. A. M., Hopkinson, C. S., and Pan, S.: Anthropogenic and climatic influences on carbon fluxes from eastern North America to the Atlantic Ocean: A process-based modeling study, J. Geophys. Res.-Biogeo., 120, 757–772, https://doi.org/10.1002/2014JG002760, 2015.
Tian, H., Xu, R., Pan, S., Yao, Y., Bian, Z., Cai, W.-J., Hopkinson, C. S., Justic, D., Lohrenz, S., Lu, C., Ren, W., and Yang, J.: Long-term trajectory of nitrogen loading and delivery from Mississippi River Basin to the Gulf of Mexico, Global Biogeochem. Cy., 34, e2019GB006475, https://doi.org/10.1029/2019GB006475, 2020.
Trainer, V. L., Cochlan, W. P., Erickson, A., Bill, B. D., Cox, F. H., Borchert, J. A., and Lefebvre, K. A.: Recent domoic acid closures of shellfish harvest areas in Washington State inland waterways, Harmful Algae, 6, 449–459, https://doi.org/10.1016/j.hal.2006.12.001, 2007.
Turowski, J. M., Rickenmann, D., and Dadson, S. J.: The partitioning of the total sediment load of a river into suspended load and bedload: A review of empirical data, Sedimentology, 57, 1126–1146, https://doi.org/10.1111/j.1365-3091.2009.01140.x, 2010.
Van Meter, K. J. and Van Cappellen, P., and Basu, N. B.: Legacy nitrogen may prevent achievement of water quality goals in the Gulf of Mexico, Science, 360, 427–430, https://doi.org/10.1126/science.aar4462, 2018.
Vilmin, L., Mogollón, J. M., Beusen, A. H. W., and Bouwman, A. F.: Forms and subannual variability of nitrogen and phosphorus loading to global river networks over the 20th century, Glob. Planet. Change, 163, 67–85, https://doi.org/10.1016/j.gloplacha.2018.02.007, 2018.
Vilmin, L., Mogollón, J. M., Beusen, A. H. W., van Hoek, W. J., Liu, X., Middelburg, J. J., and Bouwman, A. F.: Modeling process-based biogeochemical dynamics in surface fresh waters of large watersheds with the IMAGE-DGNM framework, J. Adv. Model. Earth Sy., 12, 1–19, https://doi.org/10.1029/2019MS001796, 2020.
Vilmin, L., Bouwman, A. F. , Beusen, A. H. W., van Hoek, W. J., and Mogollón, J. M.: Past anthropogenic activities offset dissolved inorganic phosphorus retention in the Mississippi River basin, Biogeochemistry, 161, 157–169, https://doi.org/10.1007/s10533-022-00973-1, 2022.
Vorosmarty, C. J., Meybeck, M., Fekete, B., Sharma, K., Green, P., and Syvitski, J. P. M.: Anthropogenic sediment retention: major global impact from registered river impoundments, Glob. Planet. Change, 39, 169–190, https://doi.org/10.1016/S0921-8181(03)00023-7, 2003.
Wade, A. J., Durand, P., Beaujouan, V., Wessel, W. W., Raat, K. J., Whitehead, P. G., Butterfield, D., Rankinen, K., and Lepisto, A.: A nitrogen model for European catchments: INCA, new model structure and equations, Hydrol. Earth Syst. Sci., 6, 559–582, https://doi.org/10.5194/hess-6-559-2002, 2002.
Yao, Y., Tian, H., Shi, H., Pan, S., Xu, R., Pan, N., and Canadell, J. G..: Increased global nitrous oxide emissions from streams and rivers in the Anthropocene, Nat. Clim. Change, 10, 138–142, https://doi.org/10.1038/s41558-019-0665-8, 2020.
Zhang, H., Lauerwald, R., Regnier, P., Ciais, P., Van Oost, K., Naipal, V., Guenet, B., and Yuan, W.: Estimating the lateral transfer of organic carbon through the European river network using a land surface model, Earth Syst. Dynam., 13, 1119–1144, https://doi.org/10.5194/esd-13-1119-2022, 2022.
Zhu, Z., Bi, J., Pan, Y., Ganguly, S., Anav, A., Xu, L., Samanta, A., Piao, S., Nemani, R. R., and Myneni, R. B.: Global Data Sets of Vegetation Leaf Area Index (LAI)3g and Fraction of Photosynthetically Active Radiation (FPAR)3g Derived from Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) for the Period 1981 to 2011, Remote Sens., 5, 927–948, https://doi.org/10.3390/rs5020927, 2013.
Short summary
Modeling global freshwater solid and nutrient loads, in both magnitude and form, is imperative for understanding emerging eutrophication problems. Such efforts, however, have been challenged by the difficulty of balancing details of freshwater biogeochemical processes with limited knowledge, input, and validation datasets. Here we develop a global freshwater model that resolves intertwined algae, solid, and nutrient dynamics and provide performance assessment against measurement-based estimates.
Modeling global freshwater solid and nutrient loads, in both magnitude and form, is imperative...
