Articles | Volume 14, issue 9
https://doi.org/10.5194/gmd-14-5863-2021
© Author(s) 2021. 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-14-5863-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Review and perspective paper
| 
27 Sep 2021
Review and perspective paper |
| 27 Sep 2021
| 27 Sep 2021
Validation of terrestrial biogeochemistry in CMIP6 Earth system models: a review
Climate and Environment, Saint Francis Xavier University,
Antigonish, Canada
Environmental Sciences, Memorial University, St. John's, Canada
Andrew H. MacDougall
Climate and Environment, Saint Francis Xavier University,
Antigonish, Canada
Related authors
No articles found.
Benjamin Mark Sanderson, Victor Brovkin, Rosie Fisher, David Hohn, Tatiana Ilyina, Chris Jones, Torben Koenigk, Charles Koven, Hongmei Li, David Lawrence, Peter Lawrence, Spencer Liddicoat, Andrew Macdougall, Nadine Mengis, Zebedee Nicholls, Eleanor O'Rourke, Anastasia Romanou, Marit Sandstad, Jörg Schwinger, Roland Seferian, Lori Sentman, Isla Simpson, Chris Smith, Norman Steinert, Abigail Swann, Jerry Tjiputra, and Tilo Ziehn
EGUsphere, https://doi.org/10.5194/egusphere-2024-3356, https://doi.org/10.5194/egusphere-2024-3356, 2024
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
This study investigates how climate models warm in response to simplified carbon emissions trajectories, refining understanding of climate reversibility and commitment. Metrics are defined for warming response to cumulative emissions and for the cessation or ramp-down to net-zero and net-negative levels. Results indicate that previous concentration-driven experiments may have overstated zero emissions commitment due to emissions rates exceeding historical levels.
Makcim L. De Sisto and Andrew H. MacDougall
Biogeosciences, 21, 4853–4873, https://doi.org/10.5194/bg-21-4853-2024, https://doi.org/10.5194/bg-21-4853-2024, 2024
Short summary
Short summary
The remaining carbon budget (RCB) represents the allowable future CO2 emissions before a temperature target is reached. Understanding the uncertainty in the RCB is critical for effective climate regulation and policy-making. One major source of uncertainty is the representation of the carbon cycle in Earth system models. We assessed how nutrient limitation affects the estimation of the RCB. We found a reduction in the estimated RCB when nutrient limitation is taken into account.
Makcim L. De Sisto, Andrew H. MacDougall, Nadine Mengis, and Sophia Antoniello
Geosci. Model Dev., 16, 4113–4136, https://doi.org/10.5194/gmd-16-4113-2023, https://doi.org/10.5194/gmd-16-4113-2023, 2023
Short summary
Short summary
In this study, we developed a nitrogen and phosphorus cycle in an intermediate-complexity Earth system climate model. We found that the implementation of nutrient limitation in simulations has reduced the capacity of land to take up atmospheric carbon and has decreased the vegetation biomass, hence, improving the fidelity of the response of land to simulated atmospheric CO2 rise.
Francisco José Cuesta-Valero, Hugo Beltrami, Almudena García-García, Gerhard Krinner, Moritz Langer, Andrew H. MacDougall, Jan Nitzbon, Jian Peng, Karina von Schuckmann, Sonia I. Seneviratne, Wim Thiery, Inne Vanderkelen, and Tonghua Wu
Earth Syst. Dynam., 14, 609–627, https://doi.org/10.5194/esd-14-609-2023, https://doi.org/10.5194/esd-14-609-2023, 2023
Short summary
Short summary
Climate change is caused by the accumulated heat in the Earth system, with the land storing the second largest amount of this extra heat. Here, new estimates of continental heat storage are obtained, including changes in inland-water heat storage and permafrost heat storage in addition to changes in ground heat storage. We also argue that heat gains in all three components should be monitored independently of their magnitude due to heat-dependent processes affecting society and ecosystems.
Karina von Schuckmann, Audrey Minière, Flora Gues, Francisco José Cuesta-Valero, Gottfried Kirchengast, Susheel Adusumilli, Fiammetta Straneo, Michaël Ablain, Richard P. Allan, Paul M. Barker, Hugo Beltrami, Alejandro Blazquez, Tim Boyer, Lijing Cheng, John Church, Damien Desbruyeres, Han Dolman, Catia M. Domingues, Almudena García-García, Donata Giglio, John E. Gilson, Maximilian Gorfer, Leopold Haimberger, Maria Z. Hakuba, Stefan Hendricks, Shigeki Hosoda, Gregory C. Johnson, Rachel Killick, Brian King, Nicolas Kolodziejczyk, Anton Korosov, Gerhard Krinner, Mikael Kuusela, Felix W. Landerer, Moritz Langer, Thomas Lavergne, Isobel Lawrence, Yuehua Li, John Lyman, Florence Marti, Ben Marzeion, Michael Mayer, Andrew H. MacDougall, Trevor McDougall, Didier Paolo Monselesan, Jan Nitzbon, Inès Otosaka, Jian Peng, Sarah Purkey, Dean Roemmich, Kanako Sato, Katsunari Sato, Abhishek Savita, Axel Schweiger, Andrew Shepherd, Sonia I. Seneviratne, Leon Simons, Donald A. Slater, Thomas Slater, Andrea K. Steiner, Toshio Suga, Tanguy Szekely, Wim Thiery, Mary-Louise Timmermans, Inne Vanderkelen, Susan E. Wjiffels, Tonghua Wu, and Michael Zemp
Earth Syst. Sci. Data, 15, 1675–1709, https://doi.org/10.5194/essd-15-1675-2023, https://doi.org/10.5194/essd-15-1675-2023, 2023
Short summary
Short summary
Earth's climate is out of energy balance, and this study quantifies how much heat has consequently accumulated over the past decades (ocean: 89 %, land: 6 %, cryosphere: 4 %, atmosphere: 1 %). Since 1971, this accumulated heat reached record values at an increasing pace. The Earth heat inventory provides a comprehensive view on the status and expectation of global warming, and we call for an implementation of this global climate indicator into the Paris Agreement’s Global Stocktake.
Claude-Michel Nzotungicimpaye, Kirsten Zickfeld, Andrew H. MacDougall, Joe R. Melton, Claire C. Treat, Michael Eby, and Lance F. W. Lesack
Geosci. Model Dev., 14, 6215–6240, https://doi.org/10.5194/gmd-14-6215-2021, https://doi.org/10.5194/gmd-14-6215-2021, 2021
Short summary
Short summary
In this paper, we describe a new wetland methane model (WETMETH) developed for use in Earth system models. WETMETH consists of simple formulations to represent methane production and oxidation in wetlands. We also present an evaluation of the model performance as embedded in the University of Victoria Earth System Climate Model (UVic ESCM). WETMETH is capable of reproducing mean annual methane emissions consistent with present-day estimates from the regional to the global scale.
Andrew H. MacDougall
Biogeosciences, 18, 4937–4952, https://doi.org/10.5194/bg-18-4937-2021, https://doi.org/10.5194/bg-18-4937-2021, 2021
Short summary
Short summary
Permafrost soils hold about twice as much carbon as the atmosphere. As the Earth warms the organic matter in these soils will decay, releasing CO2 and CH4. It is expected that these soils will continue to release carbon to the atmosphere long after man-made emissions of greenhouse gases cease. Here we use a method employing hundreds of slightly varying model versions to estimate how much warming permafrost carbon will cause after human emissions of CO2 end.
Nadine Mengis, David P. Keller, Andrew H. MacDougall, Michael Eby, Nesha Wright, Katrin J. Meissner, Andreas Oschlies, Andreas Schmittner, Alexander J. MacIsaac, H. Damon Matthews, and Kirsten Zickfeld
Geosci. Model Dev., 13, 4183–4204, https://doi.org/10.5194/gmd-13-4183-2020, https://doi.org/10.5194/gmd-13-4183-2020, 2020
Short summary
Short summary
In this paper, we evaluate the newest version of the University of Victoria Earth System Climate Model (UVic ESCM 2.10). Combining recent model developments as a joint effort, this version is to be used in the next phase of model intercomparison and climate change studies. The UVic ESCM 2.10 is capable of reproducing changes in historical temperature and carbon fluxes well. Additionally, the model is able to reproduce the three-dimensional distribution of many ocean tracers.
Andrew H. MacDougall, Thomas L. Frölicher, Chris D. Jones, Joeri Rogelj, H. Damon Matthews, Kirsten Zickfeld, Vivek K. Arora, Noah J. Barrett, Victor Brovkin, Friedrich A. Burger, Micheal Eby, Alexey V. Eliseev, Tomohiro Hajima, Philip B. Holden, Aurich Jeltsch-Thömmes, Charles Koven, Nadine Mengis, Laurie Menviel, Martine Michou, Igor I. Mokhov, Akira Oka, Jörg Schwinger, Roland Séférian, Gary Shaffer, Andrei Sokolov, Kaoru Tachiiri, Jerry Tjiputra, Andrew Wiltshire, and Tilo Ziehn
Biogeosciences, 17, 2987–3016, https://doi.org/10.5194/bg-17-2987-2020, https://doi.org/10.5194/bg-17-2987-2020, 2020
Short summary
Short summary
The Zero Emissions Commitment (ZEC) is the change in global temperature expected to occur following the complete cessation of CO2 emissions. Here we use 18 climate models to assess the value of ZEC. For our experiment we find that ZEC 50 years after emissions cease is between −0.36 to +0.29 °C. The most likely value of ZEC is assessed to be close to zero. However, substantial continued warming for decades or centuries following cessation of CO2 emission cannot be ruled out.
Ignacio Hermoso de Mendoza, Hugo Beltrami, Andrew H. MacDougall, and Jean-Claude Mareschal
Geosci. Model Dev., 13, 1663–1683, https://doi.org/10.5194/gmd-13-1663-2020, https://doi.org/10.5194/gmd-13-1663-2020, 2020
Short summary
Short summary
We study the impact that the thickness of the subsurface and the geothermal gradient have in land models for climate simulations. To do this, we modify the Community Land Model version 4.5. In a scenario of rising atmospheric temperatures, the temperature of an insufficiently deep subsurface rises faster than it would in the real land. For the model, this produces faster permafrost thawing and increased emissions of land carbon to the atmosphere.
Chris D. Jones, Thomas L. Frölicher, Charles Koven, Andrew H. MacDougall, H. Damon Matthews, Kirsten Zickfeld, Joeri Rogelj, Katarzyna B. Tokarska, Nathan P. Gillett, Tatiana Ilyina, Malte Meinshausen, Nadine Mengis, Roland Séférian, Michael Eby, and Friedrich A. Burger
Geosci. Model Dev., 12, 4375–4385, https://doi.org/10.5194/gmd-12-4375-2019, https://doi.org/10.5194/gmd-12-4375-2019, 2019
Short summary
Short summary
Global warming is simply related to the total emission of CO2 allowing us to define a carbon budget. However, information on the Zero Emissions Commitment is a key missing link to assess remaining carbon budgets to achieve the climate targets of the Paris Agreement. It was therefore decided that a small targeted MIP activity to fill this knowledge gap would be extremely valuable. This article formalises the experimental design alongside the other CMIP6 documentation papers.
Andrew Hugh MacDougall
Geosci. Model Dev., 12, 597–611, https://doi.org/10.5194/gmd-12-597-2019, https://doi.org/10.5194/gmd-12-597-2019, 2019
Short summary
Short summary
The 1 % per year exponential change in CO2 concentration experiment is an idealized climate change scenario that has traditionally been used to facilitate comparison of different climate models and to create benchmark statistics. Here, we examine the limitations of this experiment for assessing the global carbon cycle and propose an alternative idealized experiment.
Andrew H. MacDougall and Reto Knutti
Biogeosciences, 13, 2123–2136, https://doi.org/10.5194/bg-13-2123-2016, https://doi.org/10.5194/bg-13-2123-2016, 2016
Short summary
Short summary
The soils of the permafrost region are estimated to hold 1100 to 1500 billion tonnes of carbon. As climate change progresses much of this permafrost is expected to thaw and the carbon within it decay. Here we conduct numerical experiments with a climate model to estimate with formal uncertainty bounds the release of carbon from permafrost soils. Our simulations suggest that the permafrost carbon will make a significant but not cataclysmic contribution to climate change over the next centuries.
Related subject area
Climate and Earth system modeling
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
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
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
ISOM 1.0: a fully mesoscale-resolving idealized Southern Ocean model and the diversity of multiscale eddy interactions
A computationally lightweight model for ensemble forecasting of environmental hazards: General TAMSAT-ALERT v1.2.1
Introducing the MESMER-M-TPv0.1.0 module: spatially explicit Earth system model emulation for monthly precipitation and temperature
The need for carbon-emissions-driven climate projections in CMIP7
Robust handling of extremes in quantile mapping – “Murder your darlings”
A protocol for model intercomparison of impacts of marine cloud brightening climate intervention
An extensible perturbed parameter ensemble for the Community Atmosphere Model version 6
Coupling the regional climate model ICON-CLM v2.6.6 to the Earth system model GCOAST-AHOI v2.0 using OASIS3-MCT v4.0
A fully coupled solid-particle microphysics scheme for stratospheric aerosol injections within the aerosol–chemistry–climate model SOCOL-AERv2
An improved representation of aerosol in the ECMWF IFS-COMPO 49R1 through the integration of EQSAM4Climv12 – a first attempt at simulating aerosol acidity
At-scale Model Output Statistics in mountain environments (AtsMOS v1.0)
Impact of ocean vertical-mixing parameterization on Arctic sea ice and upper-ocean properties using the NEMO-SI3 model
Bridging the gap: a new module for human water use in the Community Earth System Model version 2.2.1
Historical Trends and Controlling Factors of Isoprene Emissions in CMIP6 Earth System Models
Modeling Commercial-Scale CO2 Storage in the Gas Hydrate Stability Zone with PFLOTRAN v6.0
A new lightning scheme in the Canadian Atmospheric Model (CanAM5.1): implementation, evaluation, and projections of lightning and fire in future climates
Methane dynamics in the Baltic Sea: investigating concentration, flux, and isotopic composition patterns using the coupled physical–biogeochemical model BALTSEM-CH4 v1.0
Using feature importance as exploratory data analysis tool on earth system models
CropSuite – 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)
Split-explicit external mode solver in the finite volume sea ice–ocean model FESOM2
Applying double cropping and interactive irrigation in the North China Plain using WRF4.5
The sea ice component of GC5: coupling SI3 to HadGEM3 using conductive fluxes
CICE on a C-grid: new momentum, stress, and transport schemes for CICEv6.5
HyPhAICC v1.0: a hybrid physics–AI approach for probability fields advection shown through an application to cloud cover nowcasting
CICERO Simple Climate Model (CICERO-SCM v1.1.1) – an improved simple climate model with a parameter calibration tool
A non-intrusive, multi-scale, and flexible coupling interface in WRF
T&C-CROP: Representing mechanistic crop growth with a terrestrial biosphere model (T&C, v1.5): Model formulation and validation
Development of a plant carbon–nitrogen interface coupling framework in a coupled biophysical-ecosystem–biogeochemical model (SSiB5/TRIFFID/DayCent-SOM v1.0)
The Earth Science Box Modeling Toolkit (ESBMTK)
High Resolution Model Intercomparison Project phase 2 (HighResMIP2) towards CMIP7
Dynamical Madden–Julian Oscillation forecasts using an ensemble subseasonal-to-seasonal forecast system of the IAP-CAS model
Presentation, Calibration and Testing of the DCESS II Earth System Model of Intermediate Complexity (version 1.0)
Baseline Climate Variables for Earth System Modelling
The DOE E3SM Version 2.1: Overview and Assessment of the Impacts of Parameterized Ocean Submesoscales
Evaluation of atmospheric rivers in reanalyses and climate models in a new metrics framework
Implementation of a brittle sea ice rheology in an Eulerian, finite-difference, C-grid modeling framework: impact on the simulated deformation of sea ice in the Arctic
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.
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.
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.
Jingwei Xie, Xi Wang, Hailong Liu, Pengfei Lin, Jiangfeng Yu, Zipeng Yu, Junlin Wei, and Xiang Han
Geosci. Model Dev., 17, 8469–8493, https://doi.org/10.5194/gmd-17-8469-2024, https://doi.org/10.5194/gmd-17-8469-2024, 2024
Short summary
Short summary
We propose the concept of mesoscale ocean direct numerical simulation (MODNS), which should resolve the first baroclinic deformation radius and ensure the numerical dissipative effects do not directly contaminate the mesoscale motions. It can be a benchmark for testing mesoscale ocean large eddy simulation (MOLES) methods in ocean models. We build an idealized Southern Ocean model using MITgcm to generate a type of MODNS. We also illustrate the diversity of multiscale eddy interactions.
Emily Black, John Ellis, and Ross I. Maidment
Geosci. Model Dev., 17, 8353–8372, https://doi.org/10.5194/gmd-17-8353-2024, https://doi.org/10.5194/gmd-17-8353-2024, 2024
Short summary
Short summary
We present General TAMSAT-ALERT, a computationally lightweight and versatile tool for generating ensemble forecasts from time series data. General TAMSAT-ALERT is capable of combining multiple streams of monitoring and meteorological forecasting data into probabilistic hazard assessments. In this way, it complements existing systems and enhances their utility for actionable hazard assessment.
Sarah Schöngart, Lukas Gudmundsson, Mathias Hauser, Peter Pfleiderer, Quentin Lejeune, Shruti Nath, Sonia Isabelle Seneviratne, and Carl-Friedrich Schleussner
Geosci. Model Dev., 17, 8283–8320, https://doi.org/10.5194/gmd-17-8283-2024, https://doi.org/10.5194/gmd-17-8283-2024, 2024
Short summary
Short summary
Precipitation and temperature are two of the most impact-relevant climatic variables. Yet, projecting future precipitation and temperature data under different emission scenarios relies on complex models that are computationally expensive. In this study, we propose a method that allows us to generate monthly means of local precipitation and temperature at low computational costs. Our modelling framework is particularly useful for all downstream applications of climate model data.
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.
Peter Berg, Thomas Bosshard, Denica Bozhinova, Lars Bärring, Joakim Löw, Carolina Nilsson, Gustav Strandberg, Johan Södling, Johan Thuresson, Renate Wilcke, and Wei Yang
Geosci. Model Dev., 17, 8173–8179, https://doi.org/10.5194/gmd-17-8173-2024, https://doi.org/10.5194/gmd-17-8173-2024, 2024
Short summary
Short summary
When bias adjusting climate model data using quantile mapping, one needs to prescribe what to do at the tails of the distribution, where a larger data range is likely encountered outside of the calibration period. The end result is highly dependent on the method used. We show that, to avoid discontinuities in the time series, one needs to exclude data in the calibration range to also activate the extrapolation functionality in that time period.
Philip J. Rasch, Haruki Hirasawa, Mingxuan Wu, Sarah J. Doherty, Robert Wood, Hailong Wang, Andy Jones, James Haywood, and Hansi Singh
Geosci. Model Dev., 17, 7963–7994, https://doi.org/10.5194/gmd-17-7963-2024, https://doi.org/10.5194/gmd-17-7963-2024, 2024
Short summary
Short summary
We introduce a protocol to compare computer climate simulations to better understand a proposed strategy intended to counter warming and climate impacts from greenhouse gas increases. This slightly changes clouds in six ocean regions to reflect more sunlight and cool the Earth. Example changes in clouds and climate are shown for three climate models. Cloud changes differ between the models, but precipitation and surface temperature changes are similar when their cooling effects are made similar.
Trude Eidhammer, Andrew Gettelman, Katherine Thayer-Calder, Duncan Watson-Parris, Gregory Elsaesser, Hugh Morrison, Marcus van Lier-Walqui, Ci Song, and Daniel McCoy
Geosci. Model Dev., 17, 7835–7853, https://doi.org/10.5194/gmd-17-7835-2024, https://doi.org/10.5194/gmd-17-7835-2024, 2024
Short summary
Short summary
We describe a dataset where 45 parameters related to cloud processes in the Community Earth System Model version 2 (CESM2) Community Atmosphere Model version 6 (CAM6) are perturbed. Three sets of perturbed parameter ensembles (263 members) were created: current climate, preindustrial aerosol loading and future climate with sea surface temperature increased by 4 K.
Ha Thi Minh Ho-Hagemann, Vera Maurer, Stefan Poll, and Irina Fast
Geosci. Model Dev., 17, 7815–7834, https://doi.org/10.5194/gmd-17-7815-2024, https://doi.org/10.5194/gmd-17-7815-2024, 2024
Short summary
Short summary
The regional Earth system model GCOAST-AHOI v2.0 that includes the regional climate model ICON-CLM coupled to the ocean model NEMO and the hydrological discharge model HD via the OASIS3-MCT coupler can be a useful tool for conducting long-term regional climate simulations over the EURO-CORDEX domain. The new OASIS3-MCT coupling interface implemented in ICON-CLM makes it more flexible for coupling to an external ocean model and an external hydrological discharge model.
Sandro Vattioni, Rahel Weber, Aryeh Feinberg, Andrea Stenke, John A. Dykema, Beiping Luo, Georgios A. Kelesidis, Christian A. Bruun, Timofei Sukhodolov, Frank N. Keutsch, Thomas Peter, and Gabriel Chiodo
Geosci. Model Dev., 17, 7767–7793, https://doi.org/10.5194/gmd-17-7767-2024, https://doi.org/10.5194/gmd-17-7767-2024, 2024
Short summary
Short summary
We quantified impacts and efficiency of stratospheric solar climate intervention via solid particle injection. Microphysical interactions of solid particles with the sulfur cycle were interactively coupled to the heterogeneous chemistry scheme and the radiative transfer code of an aerosol–chemistry–climate model. Compared to injection of SO2 we only find a stronger cooling efficiency for solid particles when normalizing to the aerosol load but not when normalizing to the injection rate.
Samuel Rémy, Swen Metzger, Vincent Huijnen, Jason E. Williams, and Johannes Flemming
Geosci. Model Dev., 17, 7539–7567, https://doi.org/10.5194/gmd-17-7539-2024, https://doi.org/10.5194/gmd-17-7539-2024, 2024
Short summary
Short summary
In this paper we describe the development of the future operational cycle 49R1 of the IFS-COMPO system, used for operational forecasts of atmospheric composition in the CAMS project, and focus on the implementation of the thermodynamical model EQSAM4Clim version 12. The implementation of EQSAM4Clim significantly improves the simulated secondary inorganic aerosol surface concentration. The new aerosol and precipitation acidity diagnostics showed good agreement against observational datasets.
Maximillian Van Wyk de Vries, Tom Matthews, L. Baker Perry, Nirakar Thapa, and Rob Wilby
Geosci. Model Dev., 17, 7629–7643, https://doi.org/10.5194/gmd-17-7629-2024, https://doi.org/10.5194/gmd-17-7629-2024, 2024
Short summary
Short summary
This paper introduces the AtsMOS workflow, a new tool for improving weather forecasts in mountainous areas. By combining advanced statistical techniques with local weather data, AtsMOS can provide more accurate predictions of weather conditions. Using data from Mount Everest as an example, AtsMOS has shown promise in better forecasting hazardous weather conditions, making it a valuable tool for communities in mountainous regions and beyond.
Sofia Allende, Anne Marie Treguier, Camille Lique, Clément de Boyer Montégut, François Massonnet, Thierry Fichefet, and Antoine Barthélemy
Geosci. Model Dev., 17, 7445–7466, https://doi.org/10.5194/gmd-17-7445-2024, https://doi.org/10.5194/gmd-17-7445-2024, 2024
Short summary
Short summary
We study the parameters of the turbulent-kinetic-energy mixed-layer-penetration scheme in the NEMO model with regard to sea-ice-covered regions of the Arctic Ocean. This evaluation reveals the impact of these parameters on mixed-layer depth, sea surface temperature and salinity, and ocean stratification. Our findings demonstrate significant impacts on sea ice thickness and sea ice concentration, emphasizing the need for accurately representing ocean mixing to understand Arctic climate dynamics.
Sabin I. Taranu, David M. Lawrence, Yoshihide Wada, Ting Tang, Erik Kluzek, Sam Rabin, Yi Yao, Steven J. De Hertog, Inne Vanderkelen, and Wim Thiery
Geosci. Model Dev., 17, 7365–7399, https://doi.org/10.5194/gmd-17-7365-2024, https://doi.org/10.5194/gmd-17-7365-2024, 2024
Short summary
Short summary
In this study, we improved a climate model by adding the representation of water use sectors such as domestic, industry, and agriculture. This new feature helps us understand how water is used and supplied in various areas. We tested our model from 1971 to 2010 and found that it accurately identifies areas with water scarcity. By modelling the competition between sectors when water availability is limited, the model helps estimate the intensity and extent of individual sectors' water shortages.
Thi Nhu Ngoc Do, Kengo Sudo, Akihiko Ito, Louisa Emmons, Vaishali Naik, Kostas Tsigaridis, Øyvind Seland, Gerd A. Folberth, and Douglas I. Kelley
EGUsphere, https://doi.org/10.5194/egusphere-2024-2313, https://doi.org/10.5194/egusphere-2024-2313, 2024
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 climate. Future models that refine these factors’ effects on isoprene emissions, along with long-term observations, are essential for better understanding plant-climate interactions.
Michael Nole, Jonah Bartrand, Fawz Naim, and Glenn Hammond
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-162, https://doi.org/10.5194/gmd-2024-162, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
Safe carbon dioxide (CO2) storage is likely to be critical for mitigating some of the most dangerous 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 sub-sea CO2 injection.
Cynthia Whaley, Montana Etten-Bohm, Courtney Schumacher, Ayodeji Akingunola, Vivek Arora, Jason Cole, Michael Lazare, David Plummer, Knut von Salzen, and Barbara Winter
Geosci. Model Dev., 17, 7141–7155, https://doi.org/10.5194/gmd-17-7141-2024, https://doi.org/10.5194/gmd-17-7141-2024, 2024
Short summary
Short summary
This paper describes how lightning was added as a process in the Canadian Earth System Model in order to interactively respond to climate changes. As lightning is an important cause of global wildfires, this new model development allows for more realistic projections of how wildfires may change in the future, responding to a changing climate.
Erik Gustafsson, Bo G. Gustafsson, Martijn Hermans, Christoph Humborg, and Christian Stranne
Geosci. Model Dev., 17, 7157–7179, https://doi.org/10.5194/gmd-17-7157-2024, https://doi.org/10.5194/gmd-17-7157-2024, 2024
Short summary
Short summary
Methane (CH4) cycling in the Baltic Proper is studied through model simulations, enabling a first estimate of key CH4 fluxes. A preliminary budget identifies benthic CH4 release as the dominant source and two main sinks: CH4 oxidation in the water (92 % of sinks) and outgassing to the atmosphere (8 % of sinks). This study addresses CH4 emissions from coastal seas and is a first step toward understanding the relative importance of open-water outgassing compared with local coastal hotspots.
Daniel Ries, Katherine Goode, Kellie McClernon, and Benjamin Hillman
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-133, https://doi.org/10.5194/gmd-2024-133, 2024
Revised manuscript accepted for GMD
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.
Florian Zabel, Matthias Knüttel, and Benjamin Poschlod
EGUsphere, https://doi.org/10.5194/egusphere-2024-2526, https://doi.org/10.5194/egusphere-2024-2526, 2024
Short summary
Short summary
CropSuite is a fuzzy-logic based high resolution open-source crop suitability model considering the impact of climate variability. We apply CropSuite for 48 important staple and cash crops at 1 km spatial resolution for Africa. We find that climate variability significantly impacts on suitable areas, but also affects optimal sowing dates, and multiple cropping potentials. The results provide information that can be used 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. Discuss., https://doi.org/10.5194/gmd-2024-135, https://doi.org/10.5194/gmd-2024-135, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
The Icosahedral Nonhydrostatic (ICON) Model 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.
Tridib Banerjee, Patrick Scholz, Sergey Danilov, Knut Klingbeil, and Dmitry Sidorenko
Geosci. Model Dev., 17, 7051–7065, https://doi.org/10.5194/gmd-17-7051-2024, https://doi.org/10.5194/gmd-17-7051-2024, 2024
Short summary
Short summary
In this paper we propose a new alternative to one of the functionalities of the sea ice model FESOM2. The alternative we propose allows the model to capture and simulate fast changes in quantities like sea surface elevation more accurately. We also demonstrate that the new alternative is faster and more adept at taking advantages of highly parallelized computing infrastructure. We therefore show that this new alternative is a great addition to the sea ice model FESOM2.
Yuwen Fan, Zhao Yang, Min-Hui Lo, Jina Hur, and Eun-Soon Im
Geosci. Model Dev., 17, 6929–6947, https://doi.org/10.5194/gmd-17-6929-2024, https://doi.org/10.5194/gmd-17-6929-2024, 2024
Short summary
Short summary
Irrigated agriculture in the North China Plain (NCP) has a significant impact on the local climate. To better understand this impact, we developed a specialized model specifically for the NCP region. This model allows us to simulate the double-cropping vegetation and the dynamic irrigation practices that are commonly employed in the NCP. This model shows improved performance in capturing the general crop growth, such as crop stages, biomass, crop yield, and vegetation greenness.
Ed Blockley, Emma Fiedler, Jeff Ridley, Luke Roberts, Alex West, Dan Copsey, Daniel Feltham, Tim Graham, David Livings, Clement Rousset, David Schroeder, and Martin Vancoppenolle
Geosci. Model Dev., 17, 6799–6817, https://doi.org/10.5194/gmd-17-6799-2024, https://doi.org/10.5194/gmd-17-6799-2024, 2024
Short summary
Short summary
This paper documents the sea ice model component of the latest Met Office coupled model configuration, which will be used as the physical basis for UK contributions to CMIP7. Documentation of science options used in the configuration are given along with a brief model evaluation. This is the first UK configuration to use NEMO’s new SI3 sea ice model. We provide details on how SI3 was adapted to work with Met Office coupling methodology and documentation of coupling processes in the model.
Jean-François Lemieux, William H. Lipscomb, Anthony Craig, David A. Bailey, Elizabeth C. Hunke, Philippe Blain, Till A. S. Rasmussen, Mats Bentsen, Frédéric Dupont, David Hebert, and Richard Allard
Geosci. Model Dev., 17, 6703–6724, https://doi.org/10.5194/gmd-17-6703-2024, https://doi.org/10.5194/gmd-17-6703-2024, 2024
Short summary
Short summary
We present the latest version of the CICE model. It solves equations that describe the dynamics and the growth and melt of sea ice. To do so, the domain is divided into grid cells and variables are positioned at specific locations in the cells. A new implementation (C-grid) is presented, with the velocity located on cell edges. Compared to the previous B-grid, the C-grid allows for a natural coupling with some oceanic and atmospheric models. It also allows for ice transport in narrow channels.
Rachid El Montassir, Olivier Pannekoucke, and Corentin Lapeyre
Geosci. Model Dev., 17, 6657–6681, https://doi.org/10.5194/gmd-17-6657-2024, https://doi.org/10.5194/gmd-17-6657-2024, 2024
Short summary
Short summary
This study introduces a novel approach that combines physics and artificial intelligence (AI) for improved cloud cover forecasting. This approach outperforms traditional deep learning (DL) methods in producing realistic and physically consistent results while requiring less training data. This architecture provides a promising solution to overcome the limitations of classical AI methods and contributes to open up new possibilities for combining physical knowledge with deep learning models.
Marit Sandstad, Borgar Aamaas, Ane Nordlie Johansen, Marianne Tronstad Lund, Glen Philip Peters, Bjørn Hallvard Samset, Benjamin Mark Sanderson, and Ragnhild Bieltvedt Skeie
Geosci. Model Dev., 17, 6589–6625, https://doi.org/10.5194/gmd-17-6589-2024, https://doi.org/10.5194/gmd-17-6589-2024, 2024
Short summary
Short summary
The CICERO-SCM has existed as a Fortran model since 1999 that calculates the radiative forcing and concentrations from emissions and is an upwelling diffusion energy balance model of the ocean that calculates temperature change. In this paper, we describe an updated version ported to Python and publicly available at https://github.com/ciceroOslo/ciceroscm (https://doi.org/10.5281/zenodo.10548720). This version contains functionality for parallel runs and automatic calibration.
Sébastien Masson, Swen Jullien, Eric Maisonnave, David Gill, Guillaume Samson, Mathieu Le Corre, and Lionel Renault
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-140, https://doi.org/10.5194/gmd-2024-140, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
This article details a new feature we implemented in the most popular regional atmospheric model (WRF). This feature allows data to be exchanged 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.
Jordi Buckley Paules, Simone Fatichi, Bonnie Warring, and Athanasios Paschalis
EGUsphere, https://doi.org/10.5194/egusphere-2024-2072, https://doi.org/10.5194/egusphere-2024-2072, 2024
Short summary
Short summary
We outline and validate developments to the pre-existing process-based model T&C to better represent cropland processes. Foreseen applications of T&C-CROP include hydrological and carbon storage implications of land-use transitions involving crop, forest, and pasture conversion, as well as studies on optimal irrigation and fertilization under a changing climate.
Zheng Xiang, Yongkang Xue, Weidong Guo, Melannie D. Hartman, Ye Liu, and William J. Parton
Geosci. Model Dev., 17, 6437–6464, https://doi.org/10.5194/gmd-17-6437-2024, https://doi.org/10.5194/gmd-17-6437-2024, 2024
Short summary
Short summary
A process-based plant carbon (C)–nitrogen (N) interface coupling framework has been developed which mainly focuses on plant resistance and N-limitation effects on photosynthesis, plant respiration, and plant phenology. A dynamic C / N ratio is introduced to represent plant resistance and self-adjustment. The framework has been implemented in a coupled biophysical-ecosystem–biogeochemical model, and testing results show a general improvement in simulating plant properties with this framework.
Ulrich Georg Wortmann, Tina Tsan, Mahrukh Niazi, Ruben Navasardyan, Magnus-Roland Marun, Bernardo S. Chede, Jingwen Zhong, and Morgan Wolfe
EGUsphere, https://doi.org/10.5194/egusphere-2024-1864, https://doi.org/10.5194/egusphere-2024-1864, 2024
Short summary
Short summary
The Earth Science Box Modeling Toolkit (ESBMTK) is a Python library designed to separate model description from numerical implementation. This approach results in well-documented, easily readable, and maintainable model code, allowing students and researchers to concentrate on conceptual challenges rather than mathematical intricacies.
Malcolm John 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
EGUsphere, https://doi.org/10.5194/egusphere-2024-2582, https://doi.org/10.5194/egusphere-2024-2582, 2024
Short summary
Short summary
HighResMIP2 is a model intercomparison project focussing on high resolution global climate models, that is those with grid spacings of 25 km or less in atmosphere and ocean, using simulations of decades to a century or so 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.
Yangke Liu, Qing Bao, Bian He, Xiaofei Wu, Jing Yang, Yimin Liu, Guoxiong Wu, Tao Zhu, Siyuan Zhou, Yao Tang, Ankang Qu, Yalan Fan, Anling Liu, Dandan Chen, Zhaoming Luo, Xing Hu, and Tongwen Wu
Geosci. Model Dev., 17, 6249–6275, https://doi.org/10.5194/gmd-17-6249-2024, https://doi.org/10.5194/gmd-17-6249-2024, 2024
Short summary
Short summary
We give an overview of the Institute of Atmospheric Physics–Chinese Academy of Sciences subseasonal-to-seasonal ensemble forecasting system and Madden–Julian Oscillation forecast evaluation of the system. Compared to other S2S models, the IAP-CAS model has its benefits but also biases, i.e., underdispersive ensemble, overestimated amplitude, and faster propagation speed when forecasting MJO. We provide a reason for these biases and prospects for further improvement of this system in the future.
Esteban Fernández and Gary Shaffer
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-122, https://doi.org/10.5194/gmd-2024-122, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
Here we describe, calibrate and test DCESS II, a new, broad, adaptable and fast Earth System Model. DCESS II has been designed for global simulations over time scales 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 should prove to be a useful, computational-friendly tool for simulations of past climates as well as for future Earth System projections.
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
EGUsphere, https://doi.org/10.5194/egusphere-2024-2363, https://doi.org/10.5194/egusphere-2024-2363, 2024
Short summary
Short summary
The Baseline Climate Variables for Earth System Modelling (ESM-BCVs) are defined as a list of 132 variables which have high utility for the evaluation and exploitation of climate simulations. The list reflects the most heavily used variables from Earth System Models, based on an assessment of data publication and download records from the largest archive of global climate projects.
Katherine Smith, Alice M. Barthel, LeAnn M. Conlon, Luke P. Van Roekel, Anthony Bartoletti, Jean-Christophe Golez, Chengzhu Zhang, Carolyn Branecky Begeman, James J. Benedict, Gautum 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. Ordonez
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-149, https://doi.org/10.5194/gmd-2024-149, 2024
Revised manuscript accepted for GMD
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 biases reduction in temperature, salinity, and sea-ice extent in the North Atlantic, a small strengthening of the Atlantic Meridional Overturning Circulation, and improvements in many atmospheric climatological variables.
Bo Dong, Paul Ullrich, Jiwoo Lee, Peter Gleckler, Kristin Chang, and Travis O'Brien
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-142, https://doi.org/10.5194/gmd-2024-142, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
1. A metrics package designed for easy analysis of AR characteristics and statistics is presented. 2. The tool is efficient for diagnosing systematic AR bias in climate models, and useful for evaluating new AR characteristics in model simulations. 3. 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).
Laurent Brodeau, Pierre Rampal, Einar Ólason, and Véronique Dansereau
Geosci. Model Dev., 17, 6051–6082, https://doi.org/10.5194/gmd-17-6051-2024, https://doi.org/10.5194/gmd-17-6051-2024, 2024
Short summary
Short summary
A new brittle sea ice rheology, BBM, has been implemented into the sea ice component of NEMO. We describe how a new spatial discretization framework was introduced to achieve this. A set of idealized and realistic ocean and sea ice simulations of the Arctic have been performed using BBM and the standard viscous–plastic rheology of NEMO. When compared to satellite data, our simulations show that our implementation of BBM leads to a fairly good representation of sea ice deformations.
Cited articles
Achard, F., Beuchle, R., Mayaux, P., Stibig, H. J., Bodart, C., Brink, A.,
and Simonetti, D.: Determination of tropical deforestation rates and related
carbon losses from 1990 to 2010, Glob. Change Biol., 20, 2540–2554,
https://doi.org/10.1111/gcb.12605, 2014.
Amthor, J. S.: The McCree–de Wit–Penning de Vries–Thornley respiration
paradigms: 30 years later, Ann. Bot.-London, 86, 1–20, https://doi.org/10.1006/anbo.2000.1175, 2000.
Anav, A., Friedlingstein, P., Kidston, M., Bopp, L., Ciais, P., Cox, P.,
Jones, C., Jung, M., Myneni, R., and Zhu, Z.: Evaluating the land and ocean
components of the global carbon cycle in the CMIP5 earth system models, J.
Climate, 26, 6801–6843, https://doi.org/10.1175/JCLI-D-12-00417.1, 2013.
Anav, A., Friedlingstein, P., Beer, C., Ciais, P., Harper, A., Jones, C.,
and Zhao, M.: Spatiotemporal patterns of terrestrial gross primary
production: A review, Rev. Geophys., 53, 785–818, https://doi.org/10.1002/2015RG000483, 2015.
Anderson, T. R., Hawkins, E., and Jones, P. D.: CO2, the greenhouse
effect and global warming: from the pioneering work of Arrhenius and
Callendar to today's Earth System Models, Endeavour, 40, 178–187, https://doi.org/10.1016/j.endeavour.2016.07.002, 2016.
Arora, V. K. and Boer, G. J.: A parameterization of leaf phenology for the
terrestrial ecosystem component of climate models, Glob. Change Biol., 11,
39–59, https://doi.org/10.1111/j.1365-2486.2004.00890.x, 2005.
Arora, V. K. and Boer, G. J.: Uncertainties in the 20th century carbon
budget associated with land use change, Glob. Change Biol., 16, 3327–3348,
https://doi.org/10.1111/j.1365-2486.2010.02202.x, 2010.
Arora, V. K., Boer, G. J., Friedlingstein, P., Eby, M., Jones, C. D.,
Christian, J. R., Bonan, G., Bopp, L., Brovkin, V., Cadule, P., Hajima, T.,
Ilyina, T., Lindsay, K., Tjiputra, J. F., and Wu, T.: Carbon–concentration
and carbon–climate feedbacks in CMIP5 Earth system models, J. Climate, 26,
5289–5314, https://doi.org/10.1175/JCLI-D-12-00494.1, 2013.
Arora, V. K., Katavouta, A., Williams, R. G., Jones, C. D., Brovkin, V., Friedlingstein, P., Schwinger, J., Bopp, L., Boucher, O., Cadule, P., Chamberlain, M. A., Christian, J. R., Delire, C., Fisher, R. A., Hajima, T., Ilyina, T., Joetzjer, E., Kawamiya, M., Koven, C. D., Krasting, J. P., Law, R. M., Lawrence, D. M., Lenton, A., Lindsay, K., Pongratz, J., Raddatz, T., Séférian, R., Tachiiri, K., Tjiputra, J. F., Wiltshire, A., Wu, T., and Ziehn, T.: Carbon–concentration and carbon–climate feedbacks in CMIP6 models and their comparison to CMIP5 models, Biogeosciences, 17, 4173–4222, https://doi.org/10.5194/bg-17-4173-2020, 2020.
Avitabile, V., Herold, M., Heuvelink, G. B. M., Lewis, S. L., Phillips,
O. L., Asner, G. P., Armston, J., Ashton, P. S., Banin, L., Bayol, N.,
Berry, N. J., Boeckx, P., de Jong, B. H. J., DeVries, B., Girardin, C.
A. J., Kearsley, E., Lindsell, J. A., Lopez-Gonzalez, G., Lucas, R., Malhi,
Y., Morel, A., Mitchard, E. T. A., Nagy, L., Qie, L., Quinones, M. J., Ryan,
C. M., Ferry, S. J. F., Sunderland, T., Laurin, G. V., Gatti, R. C.,
Valentini, R., Verbeeck, H., Wijaya, A., and Willcock, S.: An integrated
pan-tropical biomass map using multiple reference datasets, Glob. Change
Biol., 22, 1406–1420, https://doi.org/10.1111/gcb.13139, 2016.
Baccini, A., Goetz, S. J., Walker, W. S., Laporte, N. T., Sun,
M., Sulla-Menashe, D., Hackler, J., Beck, P. S. A., Dubayah, R., Friedl, M.
A., Samanta, S., and Houghton, R. A.: Estimated carbon dioxide emissions
from tropical deforestation improved by carbon-density maps, Nat. Clim.
Change, 2, 182–185, https://doi.org/10.1038/nclimate1354, 2012.
Baret, F., Hagolle, O., Geiger, B., Bicheron, P., Miras, B., Huc, M.,
Berthelot, B., Niño, F., Weiss, M., Samain, O., Roujean, J. L., and
Leroy, M.: LAI, fAPAR and fCover CYCLOPES global products derived from
VEGETATION: Part 1: Principles of the algorithm, Remote Sens. Environ., 110,
275–286, https://doi.org/10.1016/j.rse.2007.02.018, 2007.
Batjes, N. H.: Harmonized soil property values for broad-scale modelling
(WISE30sec) with estimates of global soil carbon stocks, Geoderma, 269,
61–68, https://doi.org/10.1016/j.geoderma.2016.01.034, 2016.
Beer, C., Reichstein, M., Tomelleri, E., Ciais, P., Jung, M., Carvalhais,
N., Rödenbeck, C., Arain, M. A., Baldocchi, D., Bonan, G. B., Bondeau,
A., Cescatti, A., Lasslop, G., Lindroth, A., Lomas, M., Luyssaert, S.,
Margolis, H., Oleson, K. W., Roupsard, O., Veenendaal, E., Viovy, N.,
Williams, C., Woodward, F. I., and Papale, D.: Terrestrial gross carbon
dioxide uptake: global distribution and covariation with
climate, Science, 329, 834–838, https://doi.org/10.1126/science.1184984, 2010.
Blackard, J. A., Finco, M. V., Helmer, E. H., Holden, G. R., Hoppus, M.
L., Jacobs, D. M., Lister, A. J., Moisen, G. G., Nelson, M. D., Riemann,
R., Ruefenacht, B., Salajanu, D., Weyermann, D. L., Winterberger, K.
C., Brandeis, T. J., Czaplewski, R. L., McRoberts, R. E., Patterson, P.
L., and Tymcio, R. P.: Mapping U.S. forest biomass using nationwide forest
inventory data and moderate resolution information, Remote Sens.
Environ., 112, 1658–1677, https://doi.org/10.1016/j.rse.2007.08.021, 2008.
Bonan, G. B., Lombardozzi, D. L., Wieder, W. R., Oleson, K. W., Lawrence, D.
M., Hoffman, F. M., and Collier, N.: Model structure and climate data
uncertainty in historical simulations of the terrestrial carbon cycle
(1850–2014), Global Biogeochem. Cy., 33, 1310–1326, https://doi.org/10.1029/2019GB006175, 2019.
Bond-Lamberty, B., Bailey, V. L., Chen, M., Gough, C. M., and Vargas,
R.: Globally rising soil heterotrophic respiration over recent
decades, Nature, 560, 80–83, https://doi.org/10.1038/s41586-018-0358-x, 2018.
Boucher, O., Servonnat, J., Albright, A. L., Aumont, O., Balkanski,
Y., Bastrikov, V., Bekki, S., Bonnet, R., Bony, S., Bopp, L., Braconnot,
P., Brockmann, P., Cadule, P., Caubel, A., Cheruy, F., Codron, F., Cozic,
A., Cugnet, D., D'Andrea, F., Davini, P., de Lavergne, C., Denvil,
S., Deshayes, J., Devilliers, M., Ducharne, A., Dufresne, J.-L., Dupont,
E., Éthé, C., Fairhead, L., Falletti, L., Flavoni, S., Foujols,
M.-A., Gardoll, S., Gastineau, G., Ghattas, J., Grandpeix, J.-Y., Guenet,
B., Guez, L., Guilyardi, É., Guimberteau, M., Hauglustaine, D., Hourdin,
F., Idelkadi, A., Joussaume, S., Kageyama, M., Khodri, M., Krinner,
G., Lebas, N., Levavasseur, G., Lévy, C., Li, L., Lott, F., Lurton,
T., Luyssaert, S., Madec, G., Madeleine, J.-B., Maignan, F., Marchand,
M., Marti, O., Mellul, L., Meurdesoif, Y., Mignot, J., Musat, I., Ottlé,
C., Peylin, P., Planton, Y., Polcher, J., Rio, C., Rochetin, N., Rousset,
C., Sepulchre, P., Sima, A., Swingedouw, D., Thiéblemont, R., Khadre
Traore, A., Vancoppenolle, M., Vial, J., Vialard, J., Viovy, N.,
and Vuichard, N.: Presentation and evaluation of the IPSL-CM6A-LR climate
model, J. Adv. Model. Earth Sy., 12, e2019MS002010, https://doi.org/10.1029/2019MS002010, 2020.
Carvalhais, N., Forkel, M., Khomik, M., Bellarby, J., Jung, M., Migliavacca,
M., Mu, M., Saatchi, S., Santoro, M., Thurner, M., Weber, U., Ahrens,
B., Beer, C., Cescatti, A., Randerson, J. T., and Reichstein, M.: Global
covariation of carbon turnover times with climate in terrestrial
ecosystems, Nature, 514, 213–217, https://doi.org/10.1038/nature13731, 2014.
Chu, H., Baldocchi, D. D., John, R., Wolf, S., and Reichstein, M.: Fluxes
all of the time? A primer on the temporal representativeness of Fluxnet, J.
Geophys. Res.-Biogeo., 122, 289–307, https://doi.org/10.1002/2016JG003576, 2017.
Ciais, P., Sabine, C., Bala, G., Bopp, L., Brovkin, V., Canadell,
J., Chhabra, A., DeFries, R., Galloway, J., Heimann, M., Jones, C., Le
Quéré, C., Myneni, R. B., Piao, S., and Thornton, P.: Carbon and
Other Biogeochemical Cycles, in: Climate Change 2013: The Physical Science
Basis. Contribution of Working Group I to the Fifth Assessment Report of the
Intergovernmental Panel on Climate Change, edited by: Stocker, T. F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S. K., Boschung, J., Nauels, A., Xia, Y., Bex, V., and
Midgley, P. M., Cambridge University Press, Cambridge, United Kingdom
and New York, NY, USA, available at: https://www.ipcc.ch/site/assets/uploads/2018/02/WG1AR5_Chapter06_FINAL.pdf (last access: 1 April 2021), 2013.
Ciais, P., Tan, J., Wang, X., Roedenbeck, C., Chevallier, F., Piao,
S.-L., Moriarty, R., Broquet, G., Le Quéré, C., Canadell, J.
G., Peng, S., Poulter, B., Liu, Z., and Tans, P.: Five decades of northern
land carbon uptake revealed by the interhemispheric
CO2 gradient, Nature, 568, 221–225, https://doi.org/10.1038/s41586-019-1078-6, 2019.
Clark, D. B., Mercado, L. M., Sitch, S., Jones, C. D., Gedney, N., Best, M. J., Pryor, M., Rooney, G. G., Essery, R. L. H., Blyth, E., Boucher, O., Harding, R. J., Huntingford, C., and Cox, P. M.: The Joint UK Land Environment Simulator (JULES), model description – Part 2: Carbon fluxes and vegetation dynamics, Geosci. Model Dev., 4, 701–722, https://doi.org/10.5194/gmd-4-701-2011, 2011.
Collier, N., Hoffman, F. M., Lawrence, D. M., Keppel-Aleks, G., Koven, C.
D., Riley, W. J., Mu, M., and Randerson, J. T.: The International Land Model
Benchmarking (ILAMB) system: design, theory, and implementation, J. Adv. Model.
Earth Sy., 10, 2731–2754, https://doi.org/10.1029/2018MS001354,
2018.
Dai, M., Yin, Z., Meng, F., Liu, Q., and Cai, W. J.: Spatial distribution of
riverine DOC inputs to the ocean: an updated global synthesis, Curr. Opin. Env.
Sust., 4, 170–178, https://doi.org/10.1016/j.cosust.2012.03.003,
2012.
Danabasoglu, G., Lamarque, J. F., Bacmeister, J., Bailey, D. A., DuVivier,
A. K., Edwards, J., Emmons, L.K., Fasullo, J., Garcia, R., Gettelman, A.,
Hannay, C., Holland, M., Large, W., Lauritzen, P., Lawrence, D., Lenaerts,
J., Lindsay, K., Lipscomb, W., Mills M. J., Neale, R., Oleson, K.,
Otto-Bliesner, B., Phillips, A., Sacks, W., Tilmes, S., van Kampenhout, L.,
Vertenstein, M., Bertini, A., Dennis, J., Deser, C., Fischer, C.,
Fox-Kemper, B., Kay, J., Kinnison, D., Kushner, P., Larson, V., Long, M.,
Mickelson, S., Moore, J., Nienhouse, E., Polvani, L., Rasch, P., and Strand,
W.: The community earth system model version 2 (CESM2), J. Adv. Model. Earth
Sy., 12, e2019MS001916, https://doi.org/10.1029/2019MS001916, 2020.
Davies-Barnard, T., Meyerholt, J., Zaehle, S., Friedlingstein, P., Brovkin, V., Fan, Y., Fisher, R. A., Jones, C. D., Lee, H., Peano, D., Smith, B., Wårlind, D., and Wiltshire, A. J.: Nitrogen cycling in CMIP6 land surface models: progress and limitations, Biogeosciences, 17, 5129–5148, https://doi.org/10.5194/bg-17-5129-2020, 2020.
Defourny, P., Boettcher, M., Bontemps, S., Kirches, G., Lamarche, C.,
Peters, M., Santoro, M., and Schlerf, M.: Land cover CCI Product user guide
version 2, Technical report, European Space Agency, London, United Kingdom, 1–91, 2016.
de Kauwe, M. G., Disney, M. I., Quaife, T., Lewis, P., and Williams, M.: An
assessment of the MODIS Collection 5 leaf area index product for a region of
mixed coniferous forest, Remote Sens. Environ., 115, 767–780, https://doi.org/10.1016/j.rse.2010.11.004, 2011.
Delire, C., Séférian, R., Decharme, B., Alkama, R., Calvet, J. C.,
Carrer, D., Gibelin, A., Joetzjer, E., Morel, X., Rochner, M., and Tzanos,
D.: The global land carbon cycle simulated with ISBA-CTRIP: Improvements
over the last decade, J. Adv. Model. Earth Sy, 12, e2019MS001886, https://doi.org/10.1029/2019MS001886, 2020.
de Mora, L., Butenschön, M., and Allen, J. I.: How should sparse marine in situ measurements be compared to a continuous model: an example, Geosci. Model Dev., 6, 533–548, https://doi.org/10.5194/gmd-6-533-2013, 2013.
Desai, A. R., Richardson, A. D., Moffat, A. M., Kattge, J., Hollinger, D.
Y., Barr, A., and Stauch, V. J.: Cross-site evaluation of eddy covariance
GPP and RE decomposition techniques, Agr. Forest Meteorol., 148, 821–838,
https://doi.org/10.1016/j.agrformet.2007.11.012, 2008.
Deser, C., Lehner, F., Rodgers, K. B., Ault, T., Delworth, T. L., DiNezio,
P. N., and Ting, M.: Insights from Earth system model initial-condition large
ensembles and future prospects, Nat. Clim. Change, 10, 277–286, https://doi.org/10.1038/s41558-020-0731-2, 2020.
Dunne, J. P., Horowitz, L. W., Adcroft, A. J., Ginoux, P., Held, I. M.,
John, J. G., Krasting, J. P., Malyshev, S., Naik1, V., Paulot, F.,
Shevliakova, E., Stock, C. A., Zadeh, N., Balaji, V., Blanton, C., Dunne, K.
A., Dupuis, C., Durachta, J., Dussin, R., Gauthier, P. P. G., Griffies, S.
M., Guo, H., Hallberg, R. W., Harrison, M., He, J., Hurlin, W., McHugh, C.,
Menzel, R., Milly, P. C. D., Nikonov, S., Paynter, D. J., Ploshay, J.,
Radhakrishnan, A., Rand, K., Reichl, B. G., Robinson, T., Schwarzkopf, D.
M., Sentman, L. T., Underwood, S., Vahlenkamp, H., Winton, M., Wittenberg,
A. T., Wyman, B., Zeng, Y., and Zhao, M.: The GFDL Earth System Model
version 4.1 (GFDL-ESM 4.1): Overall coupled model description and simulation
characteristics, J. Adv. Model. Earth Sy., 12, e2019MS002015, https://doi.org/10.1029/2019MS002015, 2020.
Ehlers, I., Augusti, A., Betson, T. R., Nilsson, M. B., Marshall, J. D., and
Schleucher, J.: Detecting long-term metabolic shifts using isotopomers:
CO2-driven suppression of photorespiration in C3 plants over the 20th
century, P. Natl. Acad. Sci. USA, 112, 15585–15590, https://doi.org/10.1073/pnas.1504493112, 2015.
Erkkilä, K.-M., Ojala, A., Bastviken, D., Biermann, T., Heiskanen, J. J., Lindroth, A., Peltola, O., Rantakari, M., Vesala, T., and Mammarella, I.: Methane and carbon dioxide fluxes over a lake: comparison between eddy covariance, floating chambers and boundary layer method, Biogeosciences, 15, 429–445, https://doi.org/10.5194/bg-15-429-2018, 2018.
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E.: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization, Geosci. Model Dev., 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016, 2016a.
Eyring, V., Righi, M., Lauer, A., Evaldsson, M., Wenzel, S., Jones, C., Anav, A., Andrews, O., Cionni, I., Davin, E. L., Deser, C., Ehbrecht, C., Friedlingstein, P., Gleckler, P., Gottschaldt, K.-D., Hagemann, S., Juckes, M., Kindermann, S., Krasting, J., Kunert, D., Levine, R., Loew, A., Mäkelä, J., Martin, G., Mason, E., Phillips, A. S., Read, S., Rio, C., Roehrig, R., Senftleben, D., Sterl, A., van Ulft, L. H., Walton, J., Wang, S., and Williams, K. D.: ESMValTool (v1.0) – a community diagnostic and performance metrics tool for routine evaluation of Earth system models in CMIP, Geosci. Model Dev., 9, 1747–1802, https://doi.org/10.5194/gmd-9-1747-2016, 2016b.
Eyring, V., Cox, P. M., Flato, G. M., Gleckler, P. J., Abramowitz, G.,
Caldwell, P., Collins, W. D., Gier, B. K., Hall, A. D., Hoffman, F. M.,
Hurtt, G. C., Jahn, A., Jones, C. D., Klein, S. A., Krasting, J. P.,
Kwiatkowski, L., Lorenz, R., Maloney, E., Meehl, G. A., Pendergrass, A. G.,
Pincus, R., Ruane, A. C., Russell, J. L., Sanderson, B. M., Santer, B. D.,
Sherwood, S. C., Simpson, I. R., Stouffer, R. J., and Williamson, M. S.:
Taking climate model validation to the next level, Nat. Clim. Change, 9,
102–110, https://doi.org/10.1038/s41558-018-0355-y, 2019.
Eyring, V., Bock, L., Lauer, A., Righi, M., Schlund, M., Andela, B., Arnone, E., Bellprat, O., Brötz, B., Caron, L.-P., Carvalhais, N., Cionni, I., Cortesi, N., Crezee, B., Davin, E. L., Davini, P., Debeire, K., de Mora, L., Deser, C., Docquier, D., Earnshaw, P., Ehbrecht, C., Gier, B. K., Gonzalez-Reviriego, N., Goodman, P., Hagemann, S., Hardiman, S., Hassler, B., Hunter, A., Kadow, C., Kindermann, S., Koirala, S., Koldunov, N., Lejeune, Q., Lembo, V., Lovato, T., Lucarini, V., Massonnet, F., Müller, B., Pandde, A., Pérez-Zanón, N., Phillips, A., Predoi, V., Russell, J., Sellar, A., Serva, F., Stacke, T., Swaminathan, R., Torralba, V., Vegas-Regidor, J., von Hardenberg, J., Weigel, K., and Zimmermann, K.: Earth System Model Evaluation Tool (ESMValTool) v2.0 – an extended set of large-scale diagnostics for quasi-operational and comprehensive evaluation of Earth system models in CMIP, Geosci. Model Dev., 13, 3383–3438, https://doi.org/10.5194/gmd-13-3383-2020, 2020.
Fan, J., Chen, B., Wu, L., Zhang, F., Lu, X., and Xiang, Y.: Evaluation and
development of temperature-based empirical models for estimating daily
global solar radiation in humid regions, Energy, 144, 903–914, https://doi.org/10.1016/j.energy.2017.12.091, 2018.
Fan, N., Koirala, S., Reichstein, M., Thurner, M., Avitabile, V., Santoro, M., Ahrens, B., Weber, U., and Carvalhais, N.: Apparent ecosystem carbon turnover time: uncertainties and robust features, Earth Syst. Sci. Data, 12, 2517–2536, https://doi.org/10.5194/essd-12-2517-2020, 2020.
FAO.: Harmonized World Soil Database v 1.2, available at: http://www.fao.org/soils-portal/data-hub/soil-maps-and-databases/harmonized-world-soil-database-v12/en/ (last access: 29 January 2021),
2012.
Fisher, R. A., Wieder, W. R., Sanderson, B. M., Koven, C. D., Oleson, K. W.,
Xu, C., and Lawrence, D. M.: Parametric controls on vegetation responses to
biogeochemical forcing in the CLM5, J. Adv. Model. Earth Sy., 11, 2879–2895,
https://doi.org/10.1029/2019MS001609, 2019.
Flato, G., Marotzke, J., Abiodun, B., Braconnot, P., Chou, S. C., Collins,
W., Cox, P., Driouech, F., Emori, S., Eyring, V., Forest, C., Gleckler, P.,
Guilyardi, E., Jakob, C., Kattsov, V., Reason, C., and Rummukainen, M.:
Evaluation of climate models, in: Climate Change 2013: The Physical Science
Basis. Contribution of Working Group I to the Fifth Assessment Report of the
Intergovernmental Panel on Climate Change, edited by: Stocker, T. F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S. K., Boschung, J., Nauels, A., Xia, Y., Bex, V., and
Midgley, P. M., Cambridge University Press, Cambridge, United Kingdom
and New York, NY, USA, 2013.
Fleischer, K., Rammig, A., De Kauwe, M. G., Walker, A. P., Domingues, T. F.,
Fuchslueger, L., and Lapola, D. M.: Amazon forest response to CO2
fertilization dependent on plant phosphorus acquisition, Nat. Geosci., 12,
736–741, https://doi.org/10.1038/s41561-019-0404-9, 2019.
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. R. Soc. B, 368, 20130164–20130164, https://doi.org/10.1098/rstb.2013.0164, 2013.
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 Vo, C. J.: Nitrogen cycles: past, present, and
future, Biogeochemistry, 70, 153–226, https://doi.org/10.1007/s10533-004-0370-0, 2004.
Galloway, J. N., Townsend, A. R., Erisman, J. W., Bekunda, M., Cai, Z.,
Freney, J. R., Martinelli, L. A., Seitzinger, S. P., and Sutton, M. A.:
Transformation of the nitrogen cycle: recent trends, questions, and
potential solutions, Science, 320, 889–892, https://doi.org/10.1126/science.1136674, 2008.
Galloway, J. N., Leach, A. M., Bleeker, A., and Erisman, J. W.: A chronology
of human understanding of the nitrogen cycle, Philos. T. R. Soc. B, 368,
20130120, https://doi.org/10.1098/rstb.2013.0120, 2013.
Gibbs, H. K.: Olson's Major World Ecosystem Complexes Ranked by Carbon in
Live Vegetation: An Updated Database Using the GLC2000 Land Cover Product
NDP-017b, Oak Ridge National Laboratory, Oak Ridge, TN, https://doi.org/10.3334/CDIAC/lue.ndp017.2006, 2006.
Giglio, L., Randerson, J. T., van der Werf, G. R., Kasibhatla, P. S., Collatz, G. J., Morton, D. C., and DeFries, R. S.: Assessing variability and long-term trends in burned area by merging multiple satellite fire products, Biogeosciences, 7, 1171–1186, https://doi.org/10.5194/bg-7-1171-2010, 2010.
Gleckler, P. J., Doutriaux, C., Durack, P. J., Taylor, K. E., Zhang, Y.,
Williams, D. N., and Servonnat, J.: A more powerful reality test for climate
models, EOS, 97, 20–24, available at: https://eos.org/science-updates/a-more-powerful-reality-test-for-climate-models (last access: 1 April 2021),
2016.
Global Monitoring Laboratory.: Global monitoring Laboratory – carbon cycle
greenhouse gases, available at: https://www.esrl.noaa.gov/gmd/ccgg/trends/ (last access: 1 April 2021), 2005.
Global Soil Data Task Group.: Global Gridded Surfaces of Selected Soil
Characteristics (IGBP-DIS), Tech. Rep., available at: https://doi.org/10.3334/ORNLDAAC/569, 2002.
GLOBAL VIEW-CO2: Cooperative Global Atmospheric Data Integration
Project, updated annually, Multi-laboratory compilation of synchronized and
gap-filled atmospheric carbon dioxide records for the period 1979–2012,
NOAA, Boulder, CO, https://doi.org/10.3334/OBSPACK/1002, 2013.
Goll, D. S., Brovkin, V., Liski, J., Raddatz, T., Thum, T., and Todd-Brown,
K. E.: Strong dependence of CO2 emissions from anthropogenic land cover
change on initial land cover and soil carbon parametrization, Global
Biogeochem. Cy., 29, 1511–1523, https://doi.org/10.1002/2014GB004988, 2015.
Goll, D. S., Winkler, A. J., Raddatz, T., Dong, N., Prentice, I. C., Ciais, P., and Brovkin, V.: Carbon–nitrogen interactions in idealized simulations with JSBACH (version 3.10), Geosci. Model Dev., 10, 2009–2030, https://doi.org/10.5194/gmd-10-2009-2017, 2017.
Green, J. K., Seneviratne, S. I., Berg, A. M., Findell, K. L., Hagemann, S.,
Lawrence, D. M., and Gentine, P.: Large influence of soil moisture on
long-term terrestrial carbon uptake, Nature, 565, 476–479, https://doi.org/10.1038/s41586-018-0848-x, 2019.
Gruber, N. and Galloway, J. N.: An Earth-system perspective of the global
nitrogen cycle, Nature, 451, 293–296, https://doi.org/10.1038/nature06592, 2008.
Gulden, L. E., Rosero, E., Yang, Z. L., Wagener, T., and Niu, G. Y.: Model
performance, model robustness, and model fitness scores: A new method for
identifying good land-surface models, Geophys. Res. Lett., 35, L11404, https://doi.org/10.1029/2008GL033721, 2008.
Hajima, T., Watanabe, M., Yamamoto, A., Tatebe, H., Noguchi, M. A., Abe, M., Ohgaito, R., Ito, A., Yamazaki, D., Okajima, H., Ito, A., Takata, K., Ogochi, K., Watanabe, S., and Kawamiya, M.: Development of the MIROC-ES2L Earth system model and the evaluation of biogeochemical processes and feedbacks, Geosci. Model Dev., 13, 2197–2244, https://doi.org/10.5194/gmd-13-2197-2020, 2020.
Harper, A. B., Wiltshire, A. J., Cox, P. M., Friedlingstein, P., Jones, C. D., Mercado, L. M., Sitch, S., Williams, K., and Duran-Rojas, C.: Vegetation distribution and terrestrial carbon cycle in a carbon cycle configuration of JULES4.6 with new plant functional types, Geosci. Model Dev., 11, 2857–2873, https://doi.org/10.5194/gmd-11-2857-2018, 2018.
Hashimoto, S., Carvalhais, N., Ito, A., Migliavacca, M., Nishina, K., and Reichstein, M.: Global spatiotemporal distribution of soil respiration modeled using a global database, Biogeosciences, 12, 4121–4132, https://doi.org/10.5194/bg-12-4121-2015, 2015.
He, Y., Piao, S. L., Li, X. Y., Chen, A. P., and Qin, D. H.: Global patterns
of vegetation carbon use efficiency and their climate drivers deduced from
MODIS satellite data and process-based models, Agr. Forest
Meteorol., 256–257, 150– 158, https://doi.org/10.1016/j.agrformet.2018.03.009, 2018.
Heimann, M. and Reichstein, M.: Terrestrial ecosystem carbon dynamics and
climate feedbacks, Nature, 451, 289–292, https://doi.org/10.1038/nature06591, 2008.
Herridge, D. F., Peoples, M. B., and Boddey, R. M.: Global inputs of
biological nitrogen fixation in agricultural systems, Plant Soil, 311, 1–18,
https://doi.org/10.1007/s11104-008-9668-3, 2008.
Hoffman, F. M., Randerson, J. T., Arora, V. K., Bao, Q., Cadule, P., Ji, D.,
Jones, C. D., Kawamiya, M., Khatiwala, S., Lindsay, K., and Wu, T.: Causes
and implications of persistent atmospheric carbon dioxide biases in Earth
System Models, J. Geophys. Res.-Biogeo., 119, 141–162, https://doi.org/10.1002/2013JG002381, 2014.
Holland, E. A., Post, W. M., Matthews, E., Sulzman, J. M., Staufer, R., and
Krankina, O. N.: A global database of litterfall mass and litter pool carbon
and nutrients, ORNL DAAC, available at: https://daac.ornl.gov/VEGETATION/guides/Global_Litter_Carbon_Nutrients.html (last access: 1 April 2021), 2015.
Houlton, B. Z., Marklein, A. R., and Bai, E.: Representation of nitrogen in
climate change forecasts, Nat. Clim. Change, 5, 398–401, https://doi.org/10.1038/nclimate2538, 2015.
Hourdin, F., Rio, C., Grandpeix, J.-Y., Madeleine, J.-B., Cheruy, F.,
Rochetin, N., Jam, A., Musat, I., Idelkadi, A., Fairhead, L., Foujols,
M.-A., Mellul, L., Traore, A.-K., Ghattas, J., Gastineau, G., Dufresne,
J.-L., Boucher, O., Lefebvre, M.-P., Millour, E., Vignon, E., Jouaud, J.,
Bint Diallo, F., Bonazzola, M. and Lott, F.: LMDZ6: Improved atmospheric
component of the IPSL coupled model, J. Adv. Model. Earth Sy., 12,
e2019MS001892, https://doi.org/10.1029/2019MS001892, 2020.
Hovenden, M. and Newton, P.: Plant responses to CO2 are a question of
time, Science, 360, 263–264, https://doi.org/10.1126/science.aat2481, 2018.
Hugelius, G., Bockheim, J. G., Camill, P., Elberling, B., Grosse, G., Harden, J. W., Johnson, K., Jorgenson, T., Koven, C. D., Kuhry, P., Michaelson, G., Mishra, U., Palmtag, J., Ping, C.-L., O'Donnell, J., Schirrmeister, L., Schuur, E. A. G., Sheng, Y., Smith, L. C., Strauss, J., and Yu, Z.: A new data set for estimating organic carbon storage to 3 m depth in soils of the northern circumpolar permafrost region, Earth Syst. Sci. Data, 5, 393–402, https://doi.org/10.5194/essd-5-393-2013, 2013.
Huntzinger, D. N., Schwalm, C., Michalak, A. M., Schaefer, K., King, A. W., Wei, Y., Jacobson, A., Liu, S., Cook, R. B., Post, W. M., Berthier, G., Hayes, D., Huang, M., Ito, A., Lei, H., Lu, C., Mao, J., Peng, C. H., Peng, S., Poulter, B., Riccuito, D., Shi, X., Tian, H., Wang, W., Zeng, N., Zhao, F., and Zhu, Q.: The North American Carbon Program Multi-Scale Synthesis and Terrestrial Model Intercomparison Project – Part 1: Overview and experimental design, Geosci. Model Dev., 6, 2121–2133, https://doi.org/10.5194/gmd-6-2121-2013, 2013.
Ito, A.: A historical meta-analysis of global terrestrial net primary
productivity: are estimates converging?, Glob. Change Biol., 17, 3161–3175,
https://doi.org/10.1111/j.1365-2486.2011.02450.x, 2011.
Ito, A. and Inatomi, M.: Use of a process-based model for assessing the methane budgets of global terrestrial ecosystems and evaluation of uncertainty, Biogeosciences, 9, 759–773, https://doi.org/10.5194/bg-9-759-2012, 2012.
Ito, A., Hajima, T., Lawrence, D. M., Brovkin, V., Delire, C., Guenet, B.,
Jones, C., Malyshev, S., Materia, S., McDermid, S., Peano, D., Pongratz, J.,
Robertson, E., Shevliakova, E., Vuichard, N., Warlind, D., Wiltshire, A.,
and Ziehn, T.: Soil carbon sequestration simulated in CMIP6-LUMIP models:
implications for climatic mitigation, Environ. Res. Lett., 15, 124061,
https://doi.org/10.1088/1748-9326/abc912, 2020.
Joetzjer, E., Delire, C., Douville, H., Ciais, P., Decharme, B., Carrer, D., Verbeeck, H., De Weirdt, M., and Bonal, D.: Improving the ISBACC land surface model simulation of water and carbon fluxes and stocks over the Amazon forest, Geosci. Model Dev., 8, 1709–1727, https://doi.org/10.5194/gmd-8-1709-2015, 2015.
Jones, C. D., Arora, V., Friedlingstein, P., Bopp, L., Brovkin, V., Dunne, J., Graven, H., Hoffman, F., Ilyina, T., John, J. G., Jung, M., Kawamiya, M., Koven, C., Pongratz, J., Raddatz, T., Randerson, J. T., and Zaehle, S.: C4MIP – The Coupled Climate–Carbon Cycle Model Intercomparison Project: experimental protocol for CMIP6, Geosci. Model Dev., 9, 2853–2880, https://doi.org/10.5194/gmd-9-2853-2016, 2016.
Jonsson, A., Åberg, J., Lindroth, A., and Jansson, M.: Gas transfer rate
and CO2 flux between an unproductive lake and the atmosphere in
northern Sweden, J. Geophys. Res.-Biogeo., 113, G04006,
https://doi.org/10.1029/2008JG000688, 2008.
Jung, M., Reichstein, M., and Bondeau, A.: Towards global empirical upscaling of FLUXNET eddy covariance observations: validation of a model tree ensemble approach using a biosphere model, Biogeosciences, 6, 2001–2013, https://doi.org/10.5194/bg-6-2001-2009, 2009.
Jung, M., Reichstein, M., Ciais, P., Seneviratne, S. I., Sheffield, J.,
Goulden, M. L., Bonan, G., Cescatti, A., Chen, J., De Jeu, R., and Zhang,
K.: Recent decline in the global land evapotranspiration trend due to
limited moisture supply, Nature, 467, 951–954, https://doi.org/10.1038/nature09396, 2010.
Jung, M., Reichstein, M., Margolis, H. A., Cescatti, A., Richardson, A. D.,
Arain, M. A., Arneth, A., Bernhofer, C., Bonal, D., Chen, J., Gianelle, D.,
Gobron, N., Kiely, G., Kutsch, W., Lasslop, G., Law, B. E., Lindroth, A.,
Merbold, L., Montagnani, L., Moors, E. J., Papale, D., Sottocornola, M.,
Vaccari, F., and Williams, C.: Global patterns of land-atmosphere fluxes of
carbon dioxide, latent heat, and sensible heat derived from eddy covariance,
satellite, and meteorological observations, J. Geophys. Res.-Biogeo., 116, G00J07,
https://doi.org/10.1029/2010JG001566, 2011.
Jung, M., Reichstein, M., Schwalm, C. R., Huntingford, C., Sitch, S.,
Ahlström, A., Arneth, A., Camps-Valls, G., Ciais, P., Friedlingstein,
P., Gans, F., Ichii, K., Jain, A. K., Kato, E., Papale, D., Poulter, B.,
Raduly, B., Rödenbeck, C., Tramontana, G., Viovy, N., Wang, Y.-P.,
Weber, U., Zaehle, S., and Zeng, N.: FLUXCOM (RS+METEO) Global Land Carbon
Fluxes using CRUNCEP climate data, FLUXCOM Data Portal,
https://www.doi.org/10.17871/FLUXCOM_RS_METEO_CRUNCEPv6_1980_2013_v1, 2016.
Jung, M., Reichstein, M., Schwalm, C. R., Huntingford, C., Sitch,
S., Ahlström, A., Arneth, A., Camps-Valls, G., Ciais,
P., Friedlingstein, P., Gans, F., Ichii, K., Jain, A. K., Kato, E., Papale,
D., Poulter, B., Raduly, B., Rödenbeck, C., Tramontana, G., Viovy,
N., Wang, Y., Weber, U., Zaehle, S., and Zeng, N.: Compensatory water
effects link yearly global land CO2 sink changes to temperature,
Nature, 541, 516– 520, https://doi.org/10.1038/nature20780,
2017.
Jung, M., Koirala, S., Weber, U., Ichii, K., Gans, F., Camps-Valls, G., Papale, D., Schwalm, C., Tramontana, G., and Reichstein, M.: The FLUXCOM ensemble of global land-atmosphere energy fluxes, Sci. Data, 6, 74, https://doi.org/10.1038/s41597-019-0076-8, 2019.
Jung, M., Schwalm, C., Migliavacca, M., Walther, S., Camps-Valls, G., Koirala, S., Anthoni, P., Besnard, S., Bodesheim, P., Carvalhais, N., Chevallier, F., Gans, F., Goll, D. S., Haverd, V., Köhler, P., Ichii, K., Jain, A. K., Liu, J., Lombardozzi, D., Nabel, J. E. M. S., Nelson, J. A., O'Sullivan, M., Pallandt, M., Papale, D., Peters, W., Pongratz, J., Rödenbeck, C., Sitch, S., Tramontana, G., Walker, A., Weber, U., and Reichstein, M.: Scaling carbon fluxes from eddy covariance sites to globe: synthesis and evaluation of the FLUXCOM approach, Biogeosciences, 17, 1343–1365, https://doi.org/10.5194/bg-17-1343-2020, 2020.
Kattge, J., Knorr, W., Raddatz, T., and Wirth, C.: Quantifying
photosynthetic capacity and its relationship to leaf nitrogen content for
global-scale terrestrial biosphere models, Glob. Change Biol., 15, 976–991,
https://doi.org/10.1111/j.1365-2486.2008.01744.x, 2009.
Kellndorfer, J., Walker, W., Kirsch, K., Fiske, G., Bishop, J., Lapoint,
L., Hoppus, M., and Westfall, J.: NACP aboveground biomass and carbon baseline
data, V.2 (NBCD 2000), U.S.A., 2000, https://doi.org/10.3334/ORNLDAAC/1161, 2013.
Kindermann, G., McCallum, I., Fritz, S., and Obersteiner, M.: A global
forest growing stock, biomass and carbon map based on FAO statistics, Silva
Fenn, 42, 387–396, https://doi.org/10.14214/sf.244, 2008.
Kobayashi, K. and Salam, M. U.: Comparing simulated and measured values
using mean squared deviation and its components, Agron. J., 92, 345–352,
https://doi.org/10.2134/agronj2000.922345x, 2000.
Koch, J., Siemann, A., Stisen, S., and Sheffield, J.: Spatial validation of
large-scale land surface models against monthly land surface temperature
patterns using innovative performance metrics, J. Geophys. Res.-Atmos., 121,
5430–5452, https://doi.org/10.1002/2015JD024482, 2016.
Koven, C. D., Hugelius, G., Lawrence, D. M., and Wieder, W. R.: Higher
climatological temperature sensitivity of soil carbon in cold than warm
climates, Nat. Clim. Change, 7, 817–822, https://doi.org/10.1038/nclimate3421, 2017.
Kowalczyk, E. A., Wang, Y. P., Law, R. M., Davies, H. L., McGregor, J. L.,
and Abramowitz, G.: The CSIRO Atmosphere Biosphere Land Exchange (CABLE)
model for use in climate models and as an offline model, CSIRO Marine and
Atmospheric Research Paper, 13, 1–43, http://www.cmar.csiro.au/e-print/open/kowalczykea_2006a.pdf (last access: 1 April 2021), 2006.
Kowalczyk, E. A., Stevens, L., Law, R. M., Dix, M., Wang, Y. P., Harman, I.
N., Haynes, K., Srbinovsky, J., Pak, B., and Ziehn, T.: The land surface
model component of ACCESS: description and impact on the simulated surface
climatology, Aust. Meteorol. Oceanogr. J, 63, 65–82, http://www.bom.gov.au/jshess/docs/2013/kowalczyk_hres.pdf (last access: 1 April 2021),
2013.
Kumar, S. V., Peters-Lidard, C. D., Santanello, J., Harrison, K., Liu, Y., and Shaw, M.: Land surface Verification Toolkit (LVT) – a generalized framework for land surface model evaluation, Geosci. Model Dev., 5, 869–886, https://doi.org/10.5194/gmd-5-869-2012, 2012.
Lamarque, J.-F., Bond, T. C., Eyring, V., Granier, C., Heil, A., Klimont, Z., Lee, D., Liousse, C., Mieville, A., Owen, B., Schultz, M. G., Shindell, D., Smith, S. J., Stehfest, E., Van Aardenne, J., Cooper, O. R., Kainuma, M., Mahowald, N., McConnell, J. R., Naik, V., Riahi, K., and van Vuuren, D. P.: Historical (1850–2000) gridded anthropogenic and biomass burning emissions of reactive gases and aerosols: methodology and application, Atmos. Chem. Phys., 10, 7017–7039, https://doi.org/10.5194/acp-10-7017-2010, 2010.
Lasslop, G., Reichstein, M., Kattge, J., and Papale, D.: Influences of observation errors in eddy flux data on inverse model parameter estimation, Biogeosciences, 5, 1311–1324, https://doi.org/10.5194/bg-5-1311-2008, 2008.
Lasslop, G., Reichstein, M., Papale, D., Richardson, A. D., Arneth,
A., Barr, A., Stoy, P., and Wohlfahrt, G.: Separation of net ecosystem exchange
into assimilation and respiration using a light response curve approach:
Critical issues and global evaluation, Glob. Change Biol., 16, 187–208,
https://doi.org/10.1111/j.1365-2486.2009.02041.x, 2010.
Law, K., Stuart, A., and Zygalakis, K.: Data assimilation: A Mathematical
Introduction, Texts in Applied Mathematics, Cham, Switzerland:
Springer, 141, 1–242, https://doi.org/10.1007/978-3-319-20325-6,
2015.
Lawrence, D. M., Fisher, R. A., Koven, C. D., Oleson, K. W., Swenson, S. C.,
Bonan, G., Collier, N., Ghimire, B., van Kampenhout, L., Kennedy, D., and
Zeng, X.: The Community Land Model version 5: Description of new features,
benchmarking, and impact of forcing uncertainty, J. Adv. Model. Earth Sy., 11,
4245–4287, https://doi.org/10.1029/2018MS001583, 2019.
Le Quéré, C., Peters, G. P., Andres, R. J., Andrew, R. M., Boden, T. A., Ciais, P., Friedlingstein, P., Houghton, R. A., Marland, G., Moriarty, R., Sitch, S., Tans, P., Arneth, A., Arvanitis, A., Bakker, D. C. E., Bopp, L., Canadell, J. G., Chini, L. P., Doney, S. C., Harper, A., Harris, I., House, J. I., Jain, A. K., Jones, S. D., Kato, E., Keeling, R. F., Klein Goldewijk, K., Körtzinger, A., Koven, C., Lefèvre, N., Maignan, F., Omar, A., Ono, T., Park, G.-H., Pfeil, B., Poulter, B., Raupach, M. R., Regnier, P., Rödenbeck, C., Saito, S., Schwinger, J., Segschneider, J., Stocker, B. D., Takahashi, T., Tilbrook, B., van Heuven, S., Viovy, N., Wanninkhof, R., Wiltshire, A., and Zaehle, S.: Global carbon budget 2013, Earth Syst. Sci. Data, 6, 235–263, https://doi.org/10.5194/essd-6-235-2014, 2014.
Le Quéré, C., Andrew, R. M., Canadell, J. G., Sitch, S., Korsbakken, J. I., Peters, G. P., Manning, A. C., Boden, T. A., Tans, P. P., Houghton, R. A., Keeling, R. F., Alin, S., Andrews, O. D., Anthoni, P., Barbero, L., Bopp, L., Chevallier, F., Chini, L. P., Ciais, P., Currie, K., Delire, C., Doney, S. C., Friedlingstein, P., Gkritzalis, T., Harris, I., Hauck, J., Haverd, V., Hoppema, M., Klein Goldewijk, K., Jain, A. K., Kato, E., Körtzinger, A., Landschützer, P., Lefèvre, N., Lenton, A., Lienert, S., Lombardozzi, D., Melton, J. R., Metzl, N., Millero, F., Monteiro, P. M. S., Munro, D. R., Nabel, J. E. M. S., Nakaoka, S., O'Brien, K., Olsen, A., Omar, A. M., Ono, T., Pierrot, D., Poulter, B., Rödenbeck, C., Salisbury, J., Schuster, U., Schwinger, J., Séférian, R., Skjelvan, I., Stocker, B. D., Sutton, A. J., Takahashi, T., Tian, H., Tilbrook, B., van der Laan-Luijkx, I. T., van der Werf, G. R., Viovy, N., Walker, A. P., Wiltshire, A. J., and Zaehle, S.: Global Carbon Budget 2016, Earth Syst. Sci. Data, 8, 605–649, https://doi.org/10.5194/essd-8-605-2016, 2016.
Li, W., Zhang, Y., Shi, X., Zhou, W., Huang, A., Mu, M., Qiu, B., and Ji,
J.: Development of land surface model BCC_AVIM2.0 and its
preliminary performance in LS3MIP/CMIP6, J. Meteorol. Res.-Prc., 33, 851–869,
https://doi.org/10.1007/s13351-019-9016-y, 2019.
Liang, J., Qi, X., Souza, L., and Luo, Y.: Processes regulating progressive nitrogen limitation under elevated carbon dioxide: a meta-analysis, Biogeosciences, 13, 2689–2699, https://doi.org/10.5194/bg-13-2689-2016, 2016.
Liu, Y., Xiao, J., Ju, W., Zhu, G., Wu, X., Fan, W., and Zhou, Y.:
Satellite-derived LAI products exhibit large discrepancies and can lead to
substantial uncertainty in simulated carbon and water fluxes, Remote Sens.
Environ., 206, 174–188, https://doi.org/10.1016/j.rse.2017.12.024, 2018.
Liu, Y. Y., Van Dijk, A. I., De Jeu, R. A., Canadell, J. G., McCabe, M. F.,
Evans, J. P., and Wang, G.: Recent reversal in loss of global terrestrial
biomass, Nat. Clim. Change, 5, 470–474, https://doi.org/10.1038/nclimate2581, 2015.
Loveland, T. R., Reed, B. C., Brown, J. F., Ohlen, D. O., Zhu, Z., Yang, L.
W. M. J., and Merchant, J. W.: Development of a global land cover
characteristics database and IGBP DISCover from 1 km AVHRR data, Int. J.
Remote Sens., 21, 1303–1330,
https://doi.org/10.1080/014311600210191, 2000.
Lovenduski, N. S. and Bonan, G. B.: Reducing uncertainty in projections of
terrestrial carbon uptake, Environ. Res. Lett., 12, 044020, https://doi.org/10.1088/1748-9326/aa66b8, 2017.
Lovett, G. M., Cole, J. J., and Pace, M. L.: Is net ecosystem production
equal to ecosystem carbon accumulation?, Ecosystems, 9, 152–155, https://doi.org/10.1007/s10021-005-0036-3, 2006.
Mack, P. E.: Viewing the Earth: The social construction of the Landsat
satellite system, MIT Press, Cambridge, Massachusetts, United States, available at: https://books.google.ca/books?id=Pk7WtI2MJPgC (last access: 1 May 2021), 1990.
Maki, T., Ikegami, M., Fujita, T., Hirahara, T., Yamada, K., Mori, K.,
Takeuchi, A., Tsutsumi, Y., Suda, K., and Conway, T. J.: New technique to
analyse global distributions of CO2 concentrations and fluxes from
non-processed observational data, Tellus B, 62, 797–809, https://doi.org/10.1111/j.1600-0889.2010.00488.x, 2010.
Malhi, Y., Aragao, L. E. O., Metcalfe, D. B., Paiva, R., Quesada, C.
A., Almeida, S., Anderson, L., Brando, P., Chamber, J. Q., da Costa, A. C.
L., Hutyra, L. R., Oliveira, P., Patino, S., Pyle, E., Robertson, A., and
Teixeira, L.: Comprehensive assessment of carbon productivity, allocation
and storage in three Amazonian forests, Glob. Change Biol., 15, 1255–1274,
https://doi.org/10.1111/j.1365-2486.2008.01780.x, 2009.
Mauritsen, T., Bader, J., Becker, T., Behrens, J., Bittner, M., Brokopf, R.,
Brovkin, V., Claussen, M., Crueger, T., Esch, M., Fast, I., Fiedler, S.,
Fläschner, D., Gayler, V., Giorgetta, M., Goll, D. S., Haak, H.,
Hagemann, S., Hedemann, C., Hohenegger, C., Ilyina, T., Jahns, T.,
Jimenéz-de-la-Cuesta, D., Jungclaus, J., Kleinen, T., Kloster, S.,
Kracher, D., Kinne, S., Kleberg, D., Lasslop, G., Kornblueh, L., Marotzke,
J., Matei, D., Meraner, K., Mikolajewicz, U., Modali, K., Möbis, B.,
Müller, W. A., Nabel, J. E. M. S., Nam, C. C. W., Notz, D., Nyawira,
S.-S., Paulsen, H., Peters, K., Pincus, R., Pohlmann, H., Pongratz, J.,
Popp, M., Raddatz, T. J., Rast, S., Redler, R., Reick, C. H., Rohrschneider,
T., Schemann, V., Schmidt, H., Schnur, R., Schulzweida, U., Six, K. D.,
Stein, L., Stemmler, I., Stevens, B., von Storch, J.- S., Tian, F., Voigt,
A., Vrese, P., Wieners, K.-H., Wilkenskjeld, S., Winkler, A., and Roeckner,
E.: Developments in the MPI-M Earth System Model version 1.2 (MPI-ESM1. 2)
and its response to increasing CO2, J. Adv. Model. Earth Sy., 11, 998–1038,
https://doi.org/10.1029/2018MS001400, 2019.
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. Modell. Softw., 25, 837–853,
https://doi.org/10.1016/j.envsoft.2010.01.007, 2010.
McGroddy, M. E., Daufresne, T., and Hedin, L. O.: Scaling of
Mitchell, T. D. and Jones, P. D.: An improved method of constructing a
database of monthly climate observations and associated high-resolution
grids, Int. J. Climatol., 25, 693–712, https://doi.org/10.1002/joc.1181, 2005.
Monfreda, C., Ramankutty, N., and Foley, J. A.: Farming the planet. Part 2:
Geographic distribution of crop areas, yields, physiological types, and net
primary production in the year 2000, Global Biogeochem. Cy., 22, GB1022,
https://doi.org/10.1029/2007GB002947, 2008.
Mouillot, F., and Field, C. B.: Fire history and the global carbon budget: A
1∘ × 1∘ fire history reconstruction for the
20th century, Glob. Change Biol., 11, 398–420, https://doi.org/10.1111/j.1365-2486.2005.00920.x, 2005.
Myneni, R. B., Ramakrishna, R., Nemani, R., and Running, S. W.: Estimation
of global leaf area index and absorbed PAR using radiative transfer
models, IEEE T. Geosci. Remote, 35, 1380–1393,
https://doi.org/10.1109/36.649788, 1997.
NASA LP DAAC.: MOD17A3 Terra/MODIS net primary production yearly L4 global
1 km, NASA EOSDIS Land Processes DAAC, USGS Earth Resources Observation and
Science (EROS) Center, Sioux Falls, South Dakota,
https://doi.org/10.5067/ASTER/AST_L1T.003, 2017.
Norby, R. J., DeLucia, E. H., Gielen, B., Calfapietra, C., Giardina, C. P.,
King, J. S., and Oren, R.: Forest response to elevated CO2 is conserved
across a broad range of productivity, P. Natl. Acad. Sci. USA, 102, 18052–18056,
https://doi.org/10.1073/pnas.0509478102, 2005.
Nowak, R. S., Ellsworth, D. S., and Smith, S. D.: Functional responses of
plants to elevated atmospheric CO2 – do photosynthetic and productivity
data from FACE experiments support early predictions?, New Phytol., 162,
253–280, https://doi.org/10.1111/j.1469-8137.2004.01033.x,
2004.
Olson, D. M., Dinerstein, E., Wikramanayake, E. D., Burgess, N. D., Powell,
G. V. N., Underwood, E. C., D'amico, J. A., Itoua, I., Strand, H.
E., Morrison, J. C., Loucks, C. J., Allnutt, T. F., Ricketts, T. H., Kura,
Y., Lamoreux, J. F., Wettengel, W. W., Hedao, P., and Kassem, K. R.:
Terrestrial Ecoregions of the World: A New Map of Life on
Earth, BioScience, 51, 933–938, https://doi.org/10.1641/0006-3568(2001)051[0933:TEOTWA]2.0.CO;2, 2006.
Orth, R., Dutra, E., Trigo, I. F., and Balsamo, G.: Advancing land surface model development with satellite-based Earth observations, Hydrol. Earth Syst. Sci., 21, 2483–2495, https://doi.org/10.5194/hess-21-2483-2017, 2017.
Pastorello, G., Trotta, C., Canfora, E., Chu, H., Christianson, D., Cheah,
Y. W., and Li, Y.: The Fluxnet2015 dataset and the ONEFlux processing pipeline
for eddy covariance data, Sci. Data, 7, 1–27, https://doi.org/10.1038/s41597-020-0534-3, 2020.
Phillips, L. B., Hansen, A. J., and Flather, C. H.: Evaluating the species
energy relationship with the newest measures of ecosystem energy: NDVI
versus MODIS primary production, Remote Sens. Environ., 112, 4381–4392,
https://doi.org/10.1016/j.rse.2008.04.012, 2008.
Piao, S., Liu, Q., Chen, A., Janssens, I. A., Fu, Y., Dai, J., and Zhu, X.:
Plant phenology and global climate change: Current progresses and
challenges, Glob. Change Biol., 25, 1922–1940, https://doi.org/10.1111/gcb.14619, 2019.
Poulter, B., MacBean, N., Hartley, A., Khlystova, I., Arino, O., Betts, R., Bontemps, S., Boettcher, M., Brockmann, C., Defourny, P., Hagemann, S., Herold, M., Kirches, G., Lamarche, C., Lederer, D., Ottlé, C., Peters, M., and Peylin, P.: Plant functional type classification for earth system models: results from the European Space Agency's Land Cover Climate Change Initiative, Geosci. Model Dev., 8, 2315–2328, https://doi.org/10.5194/gmd-8-2315-2015, 2015.
Randerson, J. T., van der Werf, G. R., Giglio, L., Collatz, G. J., and Kasibhatla, P. S.: Global fire emissions database, version 4.1 (GFEDv4), ORNL
DAAC, Oak Ridge, Tennessee, USA, https://doi.org/10.3334/ORNLDAAC/1293, 2017.
Reichler, T. and Kim, J.: How well do coupled models simulate today's
climate?, B. Am. Meteorol. Soc., 89, 303–312, https://doi.org/10.1175/BAMS-89-3-303, 2008.
Richardson, A. D., Keenan, T. F., Migliavacca, M., Ryu, Y., Sonnentag, O.,
and Toomey, M.: Climate change, phenology, and phenological control of
vegetation feedbacks to the climate system, Agr. Forest Meteorol., 169,
156–173, https://doi.org/10.1016/j.agrformet.2012.09.012, 2013.
Richardson, A. D., Hufkens, K., Milliman, T., Aubrecht, D. M., Furze, M. E.,
Seyednasrollah, B., and Hanson, P. J.: Ecosystem warming extends vegetation
activity but heightens vulnerability to cold temperatures, Nature, 560,
368–371, https://doi.org/10.1038/s41586-018-0399-1, 2018.
Righi, M., Andela, B., Eyring, V., Lauer, A., Predoi, V., Schlund, M., Vegas-Regidor, J., Bock, L., Brötz, B., de Mora, L., Diblen, F., Dreyer, L., Drost, N., Earnshaw, P., Hassler, B., Koldunov, N., Little, B., Loosveldt Tomas, S., and Zimmermann, K.: Earth System Model Evaluation Tool (ESMValTool) v2.0 – technical overview, Geosci. Model Dev., 13, 1179–1199, https://doi.org/10.5194/gmd-13-1179-2020, 2020.
Rodda, S. R., Thumaty, K. C., Praveen, M. S. S., Jha, C. S., and Dadhwal, V.
K.: Multi-year eddy covariance measurements of net ecosystem exchange in
tropical dry deciduous forest of India, Agr. Forest Meteorol., 301, 108351,
https://doi.org/10.1016/j.agrformet.2021.108351, 2021.
Saatchi, S. S., Harris, N. L., Brown, S., Lefsky, M., Mitchard, E. T.,
Salas, W., Zutta, B. R., Buermann, W., Lewis, S. L., Hagen, S., and Morel, A.:
Benchmark map of forest carbon stocks in tropical regions across three
continents, P. Natl. Acad. Sci. USA, 108, 9899–9904, https://doi.org/10.1073/pnas.1019576108, 2011.
Santoro, M., Beaudoin, A., Beer, C., Cartus, O., Fransson, J. B. S., Hall,
R. J., Pathe, C., Schmullius, C., Schepaschenko, D., Shvidenko, A., Thurner,
M., and Wegmüller, U.: Forest growing stock volume of the northern
hemisphere: Spatially explicit estimates for 2010 derived from Envisat
ASAR, Remote Sens. Environ., 168, 316–334, https://doi.org/10.1016/j.rse.2015.07.005, 2015.
Saugier, B., Roy, J., and Mooney, H. A.: 23 – Estimations of Global Terrestrial Productivity: Converging toward a Single Number?, in: Physiological Ecology, Global Terrestrial Productivity, Academic Press,
San Diego, USA, 543–557, https://doi.org/10.1016/B978-012505290-0/50024-7, 2001.
Schlesinger, W. H.: Biogeochemistry: An analysis of global change, 2nd edn.,
Academic Press, Oxford, United Kingdom, https://doi.org/10.1017/S0016756898231505,
1997.
Séférian, R., Nabat, P., Michou, M., Saint-Martin, D., Voldoire, A.,
Colin, J., and Madec, G.: Evaluation of CNRM Earth System Model, CNRM-ESM2-1:
Role of Earth System Processes in Present-Day and Future Climate, J. Adv.
Model. Earth Sy., 11, 4182–4227, https://doi.org/10.1029/2019MS001791, 2019.
Seland, Ø., Bentsen, M., Olivié, D., Toniazzo, T., Gjermundsen, A., Graff, L. S., Debernard, J. B., Gupta, A. K., He, Y.-C., Kirkevåg, A., Schwinger, J., Tjiputra, J., Aas, K. S., Bethke, I., Fan, Y., Griesfeller, J., Grini, A., Guo, C., Ilicak, M., Karset, I. H. H., Landgren, O., Liakka, J., Moseid, K. O., Nummelin, A., Spensberger, C., Tang, H., Zhang, Z., Heinze, C., Iversen, T., and Schulz, M.: Overview of the Norwegian Earth System Model (NorESM2) and key climate response of CMIP6 DECK, historical, and scenario simulations, Geosci. Model Dev., 13, 6165–6200, https://doi.org/10.5194/gmd-13-6165-2020, 2020.
Sellar, A. A., Jones, C. G., Mulcahy, J. P., Tang, Y., Yool, A., Wiltshire,
A., and Zerroukat, M.: UKESM1: Description and evaluation of the UK Earth System
Model, J. Adv. Model. Earth Sy., 11, 4513–4558, https://doi.org/10.1029/2019MS001739, 2019.
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, https://doi.org/10.1175/JCLI3790.1, 2006.
Shevliakova, E., Malyshev, S., Martinez-Cano, I., Milly, P. C. D., Pacala,
S. W., Ginoux, P., Dunne, K. A., Dunne, J. P., Dupius, C., Findell, K.,
Ghannam, K., Horowitz, L. W., John, J. G., Knutson, T. R., Krasting, J. P.,
Naik, V., Zadeh, N., Zeng, F., and Zeng, Y.: The land component LM4. 1 of
the GFDL Earth System Model ESM4. 1: biophysical and biogeochemical
processes and interactions with climate, J. Adv. Model. Earth Sy.,
2019MS002040, in review, 2021.
Simard, M., Pinto, N., Fisher, J. B., and Baccini, A.: Mapping forest canopy
height globally with spaceborne lidar, J. Geophys. Res.-Biogeo., 116, G04021,
https://doi.org/10.1029/2011JG001708, 2011.
Swart, N. C., Cole, J. N. S., Kharin, V. V., Lazare, M., Scinocca, J. F., Gillett, N. P., Anstey, J., Arora, V., Christian, J. R., Hanna, S., Jiao, Y., Lee, W. G., Majaess, F., Saenko, O. A., Seiler, C., Seinen, C., Shao, A., Sigmond, M., Solheim, L., von Salzen, K., Yang, D., and Winter, B.: The Canadian Earth System Model version 5 (CanESM5.0.3), Geosci. Model Dev., 12, 4823–4873, https://doi.org/10.5194/gmd-12-4823-2019, 2019.
Taylor, K. E.: Summarizing multiple aspects of model performance in a single
diagram, J. Geophys. Res.-Atmos., 106, 7183–7192, https://doi.org/10.1029/2000JD900719, 2001.
Thurner, M., Beer, C., Santoro, M., Carvalhais, N., Wutzler,
T., Schepaschenko, D., Shvidenko, A., Kompter, E., Ahrens, B., Levick, S.
R., and Schmullius, C.: Carbon stock and density of northern boreal and
temperate forests, Global Ecol. Biogeogr., 23, 297–310, https://doi.org/10.1111/geb.12125, 2014.
Tian, H., Yang, J., Lu, C., Xu, R., Canadell, J. G., Jackson, R. B., Arneth,
A., Chang, J., Chen, G., Ciais, P., Gerber, S., Ito, A., Huang, Y., Joos,
F., Lienert, S., Messina, P., Olin, S., Pan, S., Peng, C., Saikawa, E.,
Thompson, R. L., Vuichard, N., Winiwarter, W., Zaehle, S., Zhang, B., Zhang,
K., and Zhu, Q.: The global N2O model intercomparison project, B. Am.
Meteorol. Soc., 99, 1231–1251, https://doi.org/10.1175/BAMS-D-17-0212.1, 2018.
Todd-Brown, K. E. O., Randerson, J. T., Post, W. M., Hoffman, F. M., Tarnocai, C., Schuur, E. A. G., and Allison, S. D.: Causes of variation in soil carbon simulations from CMIP5 Earth system models and comparison with observations, Biogeosciences, 10, 1717–1736, https://doi.org/10.5194/bg-10-1717-2013, 2013.
Tramontana, G., Jung, M., Schwalm, C. R., Ichii, K., Camps-Valls, G., Ráduly, B., Reichstein, M., Arain, M. A., Cescatti, A., Kiely, G., Merbold, L., Serrano-Ortiz, P., Sickert, S., Wolf, S., and Papale, D.: Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms, Biogeosciences, 13, 4291–4313, https://doi.org/10.5194/bg-13-4291-2016, 2016.
Tucker, C. J., Fung, I. Y., Keeling, C. D., and Gammon, R. H.: Relationship
between atmospheric CO2 variations and a satellite-derived vegetation
index, Nature, 319, 195–199, https://doi.org/10.1038/319195a0,
1986.
Twine, T. E., Kustas, W. P., Norman, J. M., Cook, D. R., Houser, P., Meyers,
T. P., and Wesely, M. L.: Correcting eddy-covariance flux underestimates
over a grassland, Agr. Forest Meteorol., 103, 279–300, https://doi.org/10.1016/S0168-1923(00)00123-4, 2000.
Umair, M., Kim, D., Ray, R. L., and Choi, M.: Estimating land surface
variables and sensitivity analysis for CLM and VIC simulations using remote
sensing products, Sci. Total Environ., 633, 470–483, https://doi.org/10.1016/j.scitotenv.2018.03.138, 2018.
Vafaei, S., Soosani, J., Adeli, K., Fadaei, H., Naghavi, H., Pham, T. D.,
and Tien Bui, D.: Improving accuracy estimation of Forest Aboveground
Biomass based on incorporation of ALOS-2 PALSAR-2 and Sentinel-2A imagery
and machine learning: A case study of the Hyrcanian forest area
(Iran), Remote Sens., 10, 172, https://doi.org/10.3390/rs10020172, 2018.
van den Hurk, B., Kim, H., Krinner, G., Seneviratne, S. I., Derksen, C., Oki, T., Douville, H., Colin, J., Ducharne, A., Cheruy, F., Viovy, N., Puma, M. J., Wada, Y., Li, W., Jia, B., Alessandri, A., Lawrence, D. M., Weedon, G. P., Ellis, R., Hagemann, S., Mao, J., Flanner, M. G., Zampieri, M., Materia, S., Law, R. M., and Sheffield, J.: LS3MIP (v1.0) contribution to CMIP6: the Land Surface, Snow and Soil moisture Model Intercomparison Project – aims, setup and expected outcome, Geosci. Model Dev., 9, 2809–2832, https://doi.org/10.5194/gmd-9-2809-2016, 2016.
van der Werf, G. R., Randerson, J. T., Giglio, L., van Leeuwen, T. T., Chen, Y., Rogers, B. M., Mu, M., van Marle, M. J. E., Morton, D. C., Collatz, G. J., Yokelson, R. J., and Kasibhatla, P. S.: Global fire emissions estimates during 1997–2016, Earth Syst. Sci. Data, 9, 697–720, https://doi.org/10.5194/essd-9-697-2017, 2017.
Verger, A., Filella, I., Baret, F., and Peñuelas, J.: Vegetation
baseline phenology from kilometric global LAI satellite products, Remote
Sens. Environ., 178, 1–14, https://doi.org/10.1016/j.rse.2016.02.057, 2016.
Vitousek, P. M., Menge, D. N., Reed, S. C., and Cleveland, C. C.: Biological
nitrogen fixation: rates, patterns and ecological controls in terrestrial
ecosystems, Philos. T. R. Soc. B, 368, 20130119, https://doi.org/10.1098/rstb.2013.0119, 2013.
Vuichard, N. and Papale, D.: Filling the gaps in meteorological continuous data measured at FLUXNET sites with ERA-Interim reanalysis, Earth Syst. Sci. Data, 7, 157–171, https://doi.org/10.5194/essd-7-157-2015, 2015.
Vuichard, N., Messina, P., Luyssaert, S., Guenet, B., Zaehle, S., Ghattas, J., Bastrikov, V., and Peylin, P.: Accounting for carbon and nitrogen interactions in the global terrestrial ecosystem model ORCHIDEE (trunk version, rev 4999): multi-scale evaluation of gross primary production, Geosci. Model Dev., 12, 4751–4779, https://doi.org/10.5194/gmd-12-4751-2019, 2019.
Waliser, D., Gleckler, P. J., Ferraro, R., Taylor, K. E., Ames, S., Biard, J., Bosilovich, M. G., Brown, O., Chepfer, H., Cinquini, L., Durack, P. J., Eyring, V., Mathieu, P.-P., Lee, T., Pinnock, S., Potter, G. L., Rixen, M., Saunders, R., Schulz, J., Thépaut, J.-N., and Tuma, M.: Observations for Model Intercomparison Project (Obs4MIPs): status for CMIP6, Geosci. Model Dev., 13, 2945–2958, https://doi.org/10.5194/gmd-13-2945-2020, 2020.
WCRP: CMIP Phase 6 (CMIP6), available at: https://www.wcrp-climate.org/wgcm-cmip/wgcm-cmip6 (last access: 23 January 2021), 2020.
Wei, J., Dirmeyer, P. A., Yang, Z. L., and Chen, H.: Effect of land model
ensemble versus coupled model ensemble on the simulation of precipitation
climatology and variability, Theor. Appl. Climatol., 134, 793–800, https://doi.org/10.1007/s00704-017-2310-7, 2018.
Wieder, W.: Regridded Harmonized World Soil Database v1.2, ORNL DAAC, Oak
Ridge, Tennessee, USA, https://doi.org/10.3334/ORNLDAAC/1247,
2014.
Wieder, W. R., Cleveland, C. C., Smith, W. K., and Todd-Brown, K.: Future
productivity and carbon storage limited by terrestrial nutrient
availability, Nat. Geosci., 8, 441–444, https://doi.org/10.1038/ngeo2413, 2015.
Williams, K. E., Harper, A. B., Huntingford, C., Mercado, L. M., Mathison, C. T., Falloon, P. D., Cox, P. M., and Kim, J.: How can the First ISLSCP Field Experiment contribute to present-day efforts to evaluate water stress in JULESv5.0?, Geosci. Model Dev., 12, 3207–3240, https://doi.org/10.5194/gmd-12-3207-2019, 2019.
Wu, T., Lu, Y., Fang, Y., Xin, X., Li, L., Li, W., Jie, W., Zhang, J., Liu, Y., Zhang, L., Zhang, F., Zhang, Y., Wu, F., Li, J., Chu, M., Wang, Z., Shi, X., Liu, X., Wei, M., Huang, A., Zhang, Y., and Liu, X.: The Beijing Climate Center Climate System Model (BCC-CSM): the main progress from CMIP5 to CMIP6 , Geosci. Model Dev., 12, 1573–1600, https://doi.org/10.5194/gmd-12-1573-2019, 2019.
Xiao, J., Chevallier, F., Gomez, C., Guanter, L., Hicke, J. A., Huete, A.
R., and Zhang, X.: Remote sensing of the terrestrial carbon cycle: A review
of advances over 50 years, Remote Sens. Environ., 233, 111383, https://doi.org/10.1016/j.rse.2019.111383, 2019.
Xie, X., Li, A., Tan, J., Lei, G., Jin, H., and Zhang, Z.: Uncertainty
analysis of multiple global GPP datasets in characterizing the lagged effect
of drought on photosynthesis, Ecol. Indic., 113, 106224, https://doi.org/10.1016/j.ecolind.2020.106224, 2020.
Xu, Z., Jiang, Y., Jia, B., and Zhou, G.: Elevated-CO2 response of
stomata and its dependence on environmental factors, Front. Plant Sci., 7,
657, https://doi.org/10.3389/fpls.2016.00657, 2016.
Yan, Y., Zhou, X., Jiang, L., and Luo, Y.: Effects of carbon turnover time on terrestrial ecosystem carbon storage, Biogeosciences, 14, 5441–5454, https://doi.org/10.5194/bg-14-5441-2017, 2017.
Yoshikawa, C., Kawamiya, M., Kato, T., Yamanaka, Y., and Matsuno, T.:
Geographical distribution of the feedback between future climate change and
the carbon cycle, J. Geophys. Res.-Biogeo., 113, G03002,
https://doi.org/10.1029/2007JG000570, 2008.
Zaehle, S. and Dalmonech, D.: Carbon–nitrogen interactions on land at
global scales: current understanding in modelling climate biosphere
feedbacks, Curr. Opin. Env. Sust., 3, 311–320, https://doi.org/10.1016/j.cosust.2011.08.008, 2011.
Zhang, Y. J., Yu, G. R., Yang, J., Wimberly, M. C., Zhang, X. Z., Tao,
J., Jiang, Y. B., and Zhu, J. T.: Climate-driven global changes in carbon
use efficiency, Global Ecol. Biogeogr., 23, 144–155, https://doi.org/10.1111/geb.12086, 2014.
Zhang, Z., Zhang, Y., Zhang, Y., Gobron, N., Frankenberg, C., Wang, S., and
Li, Z.: The potential of satellite FPAR product for GPP estimation: An
indirect evaluation using solar-induced chlorophyll fluorescence, Remote
Sens. Environ., 240, 111686, https://doi.org/10.1016/j.rse.2020.111686, 2020.
Zhao, M., Heinsch, F. A., Nemani, R. R., and Running, S. W.: Improvements of
the MODIS terrestrial gross and net primary production global data
set, Remote Sens. Environ., 95, 164–176, https://doi.org/10.1016/j.rse.2004.12.011, 2005.
Zhu, Q., Castellano, M. J., and Yang, G.: Coupling soil water processes and
the nitrogen cycle across spatial scales: Potentials, bottlenecks and
solutions, Earth-Sci. Rev., 187, 248–258, https://doi.org/10.1016/j.earscirev.2018.10.005, 2018.
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.
Ziehn, T., Kattge, J., Knorr, W., and Scholze, M.: Improving the
predictability of global CO2 assimilation rates under climate
change, Geophys. Res. Lett., 38, L10404,
https://doi.org/10.1029/2011GL047182, 2011.
Ziehn, T., Chamberlain, M. A., Law, R. M., Lenton, A., Bodman, R. W., Dix,
M., Stevens, L., Wang, Y. P., and Srbinovsky, J.: The Australian Earth System
Model: ACCESS-ESM1.5, Journal of Southern Hemisphere Earth Systems
Science, 70, 193–214, https://doi.org/10.1071/ES19035, 2020.
Short summary
Land biogeochemical cycles influence global climate change. Their influence is examined through complex computer models that account for the interaction of the land, ocean, and atmosphere. Improved models used in the recent round of model intercomparison used inconsistent validation methods to compare simulated land biogeochemistry to datasets. For the next round of model intercomparisons we recommend a validation protocol with explicit reference datasets and informative performance metrics.
Land biogeochemical cycles influence global climate change. Their influence is examined through...
