Articles | Volume 14, issue 9
Geosci. Model Dev., 14, 5863–5889, 2021
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.
Geosci. Model Dev., 14, 5863–5889, 2021
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
Validation of terrestrial biogeochemistry in CMIP6 Earth system models: a review
Lynsay Spafford and Andrew H. MacDougall
Related authors
No articles found.
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
WETMETH 1.0: a new wetland methane model for implementation in Earth system models
Effect of horizontal resolution on the simulation of tropical cyclones in the Chinese Academy of Sciences FGOALS-f3 climate system model
Grid-stretching capability for the GEOS-Chem 13.0.0 atmospheric chemistry model
Performance of the Adriatic Sea and Coast (AdriSC) climate component – a COAWST V3.3-based one-way coupled atmosphere–ocean modelling suite: ocean results
FAMOUS version xotzt (FAMOUS-ice): a general circulation model (GCM) capable of energy- and water-conserving coupling to an ice sheet model
EC-Earth3-AerChem: a global climate model with interactive aerosols and atmospheric chemistry participating in CMIP6
Vertical grid refinement for stratocumulus clouds in the radiation scheme of the global climate model ECHAM6.3-HAM2.3-P3
Cloud Feedbacks from CanESM2 to CanESM5.0 and their influence on climate sensitivity
ATTRICI v1.1 – counterfactual climate for impact attribution
Mitigation of the double ITCZ syndrome in BCC-CSM2-MR through improving parameterizations of boundary-layer turbulence and shallow convection
COSMO-CLM regional climate simulations in the Coordinated Regional Climate Downscaling Experiment (CORDEX) framework: a review
TempestExtremes v2.1: a community framework for feature detection, tracking, and analysis in large datasets
ICONGETM v1.0 – flexible NUOPC-driven two-way coupling via ESMF exchange grids between the unstructured-grid atmosphere model ICON and the structured-grid coastal ocean model GETM
A permafrost implementation in the simple carbon–climate model Hector v.2.3pf
The SMHI Large Ensemble (SMHI-LENS) with EC-Earth3.3.1
Oil palm modelling in the global land surface model ORCHIDEE-MICT
Testing the reliability of interpretable neural networks in geoscience using the Madden–Julian oscillation
Climate-model-informed deep learning of global soil moisture distribution
fv3gfs-wrapper: a Python wrapper of the FV3GFS atmospheric model
ENSO-ASC 1.0.0: ENSO Deep Learning Forecast Model with a Multivariate Air–Sea Coupler
Recalibrating decadal climate predictions – what is an adequate model for the drift?
Multi-variate factorisation of numerical simulations
Inclusion of a suite of weathering tracers in the cGENIE Earth system model – muffin release v.0.9.23
The ENEA-REG system (v1.0), a multi-component regional Earth system model: sensitivity to different atmospheric components over the Med-CORDEX (Coordinated Regional Climate Downscaling Experiment) region
CM2Mc-LPJmL v1.0: biophysical coupling of a process-based dynamic vegetation model with managed land to a general circulation model
ESM-Tools version 5.0: a modular infrastructure for stand-alone and coupled Earth system modelling (ESM)
Performance of the Adriatic Sea and Coast (AdriSC) climate component – a COAWST V3.3-based coupled atmosphere–ocean modelling suite: atmospheric dataset
Model of Early Diagenesis in the Upper Sediment with Adaptable complexity – MEDUSA (v. 2): a time-dependent biogeochemical sediment module for Earth system models, process analysis and teaching
A Markov chain method for weighting climate model ensembles
Iodine chemistry in the chemistry-climate model SOCOL-AERv2-iodine
Building indoor model in PALM-4U: indoor climate, energy demand, and the interaction between buildings and the urban microclimate
Improvement of modeling plant responses to low soil moisture in JULESvn4.9 and evaluation against flux tower measurements
Earth System Model Evaluation Tool (ESMValTool) v2.0 – diagnostics for extreme events, regional and impact evaluation, and analysis of Earth system models in CMIP
Reproducing complex simulations of economic impacts of climate change with lower-cost emulators
A Simplified Chemistry-Dynamical Model
An improved multivariable integrated evaluation method and tool (MVIETool) v1.0 for multimodel intercomparison
Physically regularized machine learning emulators of aerosol activation
Decadal climate predictions with the Canadian Earth System Model version 5 (CanESM5)
FaIRv2.0.0: a generalized impulse response model for climate uncertainty and future scenario exploration
BCC-CSM2-HR: a high-resolution version of the Beijing Climate Center Climate System Model
A Schwarz iterative method to evaluate ocean–atmosphere coupling schemes: implementation and diagnostics in IPSL-CM6-SW-VLR
Unstructured global to coastal wave modeling for the Energy Exascale Earth System Model using WAVEWATCH III version 6.07
TransEBM v. 1.0: description, tuning, and validation of a transient model of the Earth's energy balance in two dimensions
SimCloud version 1.0: a simple diagnostic cloud scheme for idealized climate models
Sensitivity of precipitation and temperature over the Mount Kenya area to physics parameterization options in a high-resolution model simulation performed with WRFV3.8.1
The GPU version of LASG/IAP Climate System Ocean Model version 3 (LICOM3) under the heterogeneous-compute interface for portability (HIP) framework and its large-scale application
Developing a common, flexible and efficient framework for weakly coupled ensemble data assimilation based on C-Coupler2.0
JULES-CN: a coupled terrestrial carbon–nitrogen scheme (JULES vn5.1)
Ensemble prediction using a new dataset of ECMWF initial states – OpenEnsemble 1.0
Global evaluation of the nutrient-enabled version of the land surface model ORCHIDEE-CNP v1.2 (r5986)
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.
Jinxiao Li, Qing Bao, Yimin Liu, Lei Wang, Jing Yang, Guoxiong Wu, Xiaofei Wu, Bian He, Xiaocong Wang, Xiaoqi Zhang, Yaoxian Yang, and Zili Shen
Geosci. Model Dev., 14, 6113–6133, https://doi.org/10.5194/gmd-14-6113-2021, https://doi.org/10.5194/gmd-14-6113-2021, 2021
Short summary
Short summary
The configuration and simulated performance of tropical cyclones (TCs) in FGOALS-f3-L/H will be introduced firstly. The results indicate that the simulated performance of TC activities is improved globally with the increased horizontal resolution especially in TC counts, seasonal cycle, interannual variabilities and intensity aspects. It is worth establishing a high-resolution coupled dynamic prediction system based on FGOALS-f3-H (~ 25 km) to improve the prediction skill of TCs.
Liam Bindle, Randall V. Martin, Matthew J. Cooper, Elizabeth W. Lundgren, Sebastian D. Eastham, Benjamin M. Auer, Thomas L. Clune, Hongjian Weng, Jintai Lin, Lee T. Murray, Jun Meng, Christoph A. Keller, William M. Putman, Steven Pawson, and Daniel J. Jacob
Geosci. Model Dev., 14, 5977–5997, https://doi.org/10.5194/gmd-14-5977-2021, https://doi.org/10.5194/gmd-14-5977-2021, 2021
Short summary
Short summary
Atmospheric chemistry models like GEOS-Chem are versatile tools widely used in air pollution and climate studies. The simulations used in such studies can be very computationally demanding, and thus it is useful if the model can simulate a specific geographic region at a higher resolution than the rest of the globe. Here, we implement, test, and demonstrate a new variable-resolution capability in GEOS-Chem that is suitable for simulations conducted on supercomputers.
Petra Pranić, Cléa Denamiel, and Ivica Vilibić
Geosci. Model Dev., 14, 5927–5955, https://doi.org/10.5194/gmd-14-5927-2021, https://doi.org/10.5194/gmd-14-5927-2021, 2021
Short summary
Short summary
The Adriatic Sea and Coast model was developed due to the need for higher-resolution climate models and longer-term simulations to capture coastal atmospheric and ocean processes at climate scales in the Adriatic Sea. The ocean results of a 31-year-long simulation were compared to the observational data. The evaluation revealed that the model is capable of reproducing the observed physical properties with good accuracy and can be further used to study the dynamics of the Adriatic–Ionian basin.
Robin S. Smith, Steve George, and Jonathan M. Gregory
Geosci. Model Dev., 14, 5769–5787, https://doi.org/10.5194/gmd-14-5769-2021, https://doi.org/10.5194/gmd-14-5769-2021, 2021
Short summary
Short summary
Many of the complex computer models used to study the physics of the natural world treat ice sheets as fixed and unchanging, capable of only simple interactions with the rest of the climate. This is partly because it is technically very difficult to usefully do anything more realistic. We have adapted a climate model so it can be joined together with a dynamical model of the Greenland ice sheet. This gives us a powerful tool to help us better understand how ice sheets and the climate interact.
Twan van Noije, Tommi Bergman, Philippe Le Sager, Declan O'Donnell, Risto Makkonen, María Gonçalves-Ageitos, Ralf Döscher, Uwe Fladrich, Jost von Hardenberg, Jukka-Pekka Keskinen, Hannele Korhonen, Anton Laakso, Stelios Myriokefalitakis, Pirkka Ollinaho, Carlos Pérez García-Pando, Thomas Reerink, Roland Schrödner, Klaus Wyser, and Shuting Yang
Geosci. Model Dev., 14, 5637–5668, https://doi.org/10.5194/gmd-14-5637-2021, https://doi.org/10.5194/gmd-14-5637-2021, 2021
Short summary
Short summary
This paper documents the global climate model EC-Earth3-AerChem, one of the members of the EC-Earth3 family of models participating in CMIP6. We give an overview of the model and describe in detail how it differs from its predecessor and the other EC-Earth3 configurations. The model's performance is characterized using coupled simulations conducted for CMIP6. The model has an effective equilibrium climate sensitivity of 3.9 °C and a transient climate response of 2.1 °C.
Paolo Pelucchi, David Neubauer, and Ulrike Lohmann
Geosci. Model Dev., 14, 5413–5434, https://doi.org/10.5194/gmd-14-5413-2021, https://doi.org/10.5194/gmd-14-5413-2021, 2021
Short summary
Short summary
Stratocumulus are thin clouds whose cloud cover is underestimated in climate models partly due to overly low vertical resolution. We develop a scheme that locally refines the vertical grid based on a physical constraint for the cloud top. Global simulations show that the scheme, implemented only in the radiation routine, can increase stratocumulus cloud cover. However, this effect is poorly propagated to the simulated cloud cover. The scheme's limitations and possible ways forward are discussed.
John G. Virgin, Christopher G. Fletcher, Jason N. S. Cole, Knut von Salzen, and Toni Mitovski
Geosci. Model Dev., 14, 5355–5372, https://doi.org/10.5194/gmd-14-5355-2021, https://doi.org/10.5194/gmd-14-5355-2021, 2021
Short summary
Short summary
Equilibrium climate sensitivity, or the amount of warming the Earth would exhibit a result of a doubling of atmospheric CO2, is a common metric used in assessments of climate models. Here, we compare climate sensitivity between two versions of the Canadian Earth System Model. We find the newest iteration of the model (version 5) to have higher climate sensitivity due to reductions in low-level clouds, which reflect radiation and cool the planet, as the surface warms.
Matthias Mengel, Simon Treu, Stefan Lange, and Katja Frieler
Geosci. Model Dev., 14, 5269–5284, https://doi.org/10.5194/gmd-14-5269-2021, https://doi.org/10.5194/gmd-14-5269-2021, 2021
Short summary
Short summary
To identify the impacts of historical climate change it is necessary to separate the effect of the different impact drivers. To address this, one needs to compare historical impacts to a counterfactual world with impacts that would have been without climate change. We here present an approach that produces counterfactual climate data and can be used in climate impact models to simulate counterfactual impacts. We make these data available through the ISIMIP project.
Yixiong Lu, Tongwen Wu, Yubin Li, and Ben Yang
Geosci. Model Dev., 14, 5183–5204, https://doi.org/10.5194/gmd-14-5183-2021, https://doi.org/10.5194/gmd-14-5183-2021, 2021
Short summary
Short summary
The spurious precipitation in the tropical southeastern Pacific and southern Atlantic is one of the most prominent systematic biases in coupled atmosphere–ocean general circulation models. This study significantly promotes the marine stratus simulation and largely alleviates the excessive precipitation biases through improving parameterizations of boundary-layer turbulence and shallow convection, providing an effective solution to the long-standing bias in the tropical precipitation simulation.
Silje Lund Sørland, Roman Brogli, Praveen Kumar Pothapakula, Emmanuele Russo, Jonas Van de Walle, Bodo Ahrens, Ivonne Anders, Edoardo Bucchignani, Edouard L. Davin, Marie-Estelle Demory, Alessandro Dosio, Hendrik Feldmann, Barbara Früh, Beate Geyer, Klaus Keuler, Donghyun Lee, Delei Li, Nicole P. M. van Lipzig, Seung-Ki Min, Hans-Jürgen Panitz, Burkhardt Rockel, Christoph Schär, Christian Steger, and Wim Thiery
Geosci. Model Dev., 14, 5125–5154, https://doi.org/10.5194/gmd-14-5125-2021, https://doi.org/10.5194/gmd-14-5125-2021, 2021
Short summary
Short summary
We review the contribution from the CLM-Community to regional climate projections following the CORDEX framework over Europe, South Asia, East Asia, Australasia, and Africa. How the model configuration, horizontal and vertical resolutions, and choice of driving data influence the model results for the five domains is assessed, with the purpose of aiding the planning and design of regional climate simulations in the future.
Paul A. Ullrich, Colin M. Zarzycki, Elizabeth E. McClenny, Marielle C. Pinheiro, Alyssa M. Stansfield, and Kevin A. Reed
Geosci. Model Dev., 14, 5023–5048, https://doi.org/10.5194/gmd-14-5023-2021, https://doi.org/10.5194/gmd-14-5023-2021, 2021
Short summary
Short summary
TempestExtremes (TE) is a multifaceted framework for feature detection, tracking, and scientific analysis of regional or global Earth system datasets. Version 2.1 of TE now provides extensive support for nodal and areal features. This paper describes the algorithms that have been added to the TE framework since version 1.0 and gives several examples of how these can be combined to produce composite algorithms for evaluating and understanding atmospheric features.
Tobias Peter Bauer, Peter Holtermann, Bernd Heinold, Hagen Radtke, Oswald Knoth, and Knut Klingbeil
Geosci. Model Dev., 14, 4843–4863, https://doi.org/10.5194/gmd-14-4843-2021, https://doi.org/10.5194/gmd-14-4843-2021, 2021
Short summary
Short summary
We present the coupled atmosphere–ocean model system ICONGETM. The added value and potential of using the latest coupling technologies are discussed in detail. An exchange grid handles the different coastlines from the unstructured atmosphere and the structured ocean grids. Due to a high level of automated processing, ICONGETM requires only minimal user input. The application to a coastal upwelling scenario demonstrates significantly improved model results compared to uncoupled simulations.
Dawn L. Woodard, Alexey N. Shiklomanov, Ben Kravitz, Corinne Hartin, and Ben Bond-Lamberty
Geosci. Model Dev., 14, 4751–4767, https://doi.org/10.5194/gmd-14-4751-2021, https://doi.org/10.5194/gmd-14-4751-2021, 2021
Short summary
Short summary
We have added a representation of the permafrost carbon feedback to the simple, open-source global carbon–climate model Hector and calibrated the results to be consistent with historical data and Earth system model projections. Our results closely match previous work, estimating around 0.2 °C of warming from permafrost this century. This capability will be useful to explore uncertainties in this feedback and for coupling with integrated assessment models for policy and economic analysis.
Klaus Wyser, Torben Koenigk, Uwe Fladrich, Ramon Fuentes-Franco, Mehdi Pasha Karami, and Tim Kruschke
Geosci. Model Dev., 14, 4781–4796, https://doi.org/10.5194/gmd-14-4781-2021, https://doi.org/10.5194/gmd-14-4781-2021, 2021
Short summary
Short summary
This paper describes the large ensemble done by SMHI with the EC-Earth3 climate model. The ensemble comprises 50 realizations for each of the historical experiments after 1970 and four different future projections for CMIP6. We describe the creation of the initial states for the ensemble and the reduced set of output variables. A first look at the results illustrates the changes in the climate during this century and puts them in relation to the uncertainty from the model's internal variability.
Yidi Xu, Philippe Ciais, Le Yu, Wei Li, Xiuzhi Chen, Haicheng Zhang, Chao Yue, Kasturi Kanniah, Arthur P. Cracknell, and Peng Gong
Geosci. Model Dev., 14, 4573–4592, https://doi.org/10.5194/gmd-14-4573-2021, https://doi.org/10.5194/gmd-14-4573-2021, 2021
Short summary
Short summary
In this study, we implemented the specific morphology, phenology and harvest process of oil palm in the global land surface model ORCHIDEE-MICT. The improved model generally reproduces the same leaf area index, biomass density and life cycle fruit yield as observations. This explicit representation of oil palm in a global land surface model offers a useful tool for understanding the ecological processes of oil palm growth and assessing the environmental impacts of oil palm plantations.
Benjamin A. Toms, Karthik Kashinath, Prabhat, and Da Yang
Geosci. Model Dev., 14, 4495–4508, https://doi.org/10.5194/gmd-14-4495-2021, https://doi.org/10.5194/gmd-14-4495-2021, 2021
Short summary
Short summary
We test whether a type of machine learning called neural networks can be used trustfully within the geosciences. We do so by challenging the networks to understand the spatial patterns of a commonly studied geoscientific phenomenon. The neural networks can correctly identify the spatial patterns, which lends confidence that similar networks can be used for more uncertain problems. The results of this study may give geoscientists confidence when using neural networks in their research.
Klaus Klingmüller and Jos Lelieveld
Geosci. Model Dev., 14, 4429–4441, https://doi.org/10.5194/gmd-14-4429-2021, https://doi.org/10.5194/gmd-14-4429-2021, 2021
Short summary
Short summary
Soil moisture is of great importance for weather and climate. We present a machine learning model that produces accurate predictions of satellite-observed surface soil moisture, based on meteorological data from a climate model. It can be used as soil moisture parametrisation in climate models and to produce comprehensive global soil moisture datasets. Moreover, it may motivate similar applications of machine learning in climate science.
Jeremy McGibbon, Noah D. Brenowitz, Mark Cheeseman, Spencer K. Clark, Johann P. S. Dahm, Eddie C. Davis, Oliver D. Elbert, Rhea C. George, Lucas M. Harris, Brian Henn, Anna Kwa, W. Andre Perkins, Oliver Watt-Meyer, Tobias F. Wicky, Christopher S. Bretherton, and Oliver Fuhrer
Geosci. Model Dev., 14, 4401–4409, https://doi.org/10.5194/gmd-14-4401-2021, https://doi.org/10.5194/gmd-14-4401-2021, 2021
Short summary
Short summary
FV3GFS is a weather and climate model written in Fortran. It uses Fortran so that it can run fast, but this makes it hard to add features if you do not (or even if you do) know Fortran. We have written a Python interface to FV3GFS that lets you import the Fortran model as a Python package. We show examples of how this is used to write
modelscripts, which reproduce or build on what the Fortran model can do. You could do this same wrapping for any compiled model, not just FV3GFS.
Bin Mu, Bo Qin, and Shijin Yuan
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2021-213, https://doi.org/10.5194/gmd-2021-213, 2021
Revised manuscript accepted for GMD
Short summary
Short summary
Considering the sophisticated energy exchanges and multivariate coupling in ENSO, we subjectively incorporate the prior physical knowledge into the modeling process and build up an ENSO deep learning forecast model with a multivariate air-sea coupler, named ENSO-ASC, the performance of which outperforms the other state-of-the-art models. The extensive experiments indicate that ENSO-ASC is a powerful tool for both the ENSO prediction and for the analysis of the underlying complex mechanisms.
Alexander Pasternack, Jens Grieger, Henning W. Rust, and Uwe Ulbrich
Geosci. Model Dev., 14, 4335–4355, https://doi.org/10.5194/gmd-14-4335-2021, https://doi.org/10.5194/gmd-14-4335-2021, 2021
Short summary
Short summary
Decadal climate ensemble forecasts are increasingly being used to guide adaptation measures. To ensure the applicability of these probabilistic predictions, inherent systematic errors of the prediction system must be adjusted. Since it is not clear which statistical model is optimal for this purpose, we propose a recalibration strategy with a systematic model selection based on non-homogeneous boosting for identifying the most relevant features for both ensemble mean and ensemble spread.
Daniel J. Lunt, Deepak Chandan, Alan M. Haywood, George M. Lunt, Jonathan C. Rougier, Ulrich Salzmann, Gavin A. Schmidt, and Paul J. Valdes
Geosci. Model Dev., 14, 4307–4317, https://doi.org/10.5194/gmd-14-4307-2021, https://doi.org/10.5194/gmd-14-4307-2021, 2021
Short summary
Short summary
Often in science we carry out experiments with computers in which several factors are explored, for example, in the field of climate science, how the factors of greenhouse gases, ice, and vegetation affect temperature. We can explore the relative importance of these factors by
swapping in and outdifferent values of these factors, and can also carry out experiments with many different combinations of these factors. This paper discusses how best to analyse the results from such experiments.
Markus Adloff, Andy Ridgwell, Fanny M. Monteiro, Ian J. Parkinson, Alexander J. Dickson, Philip A. E. Pogge von Strandmann, Matthew S. Fantle, and Sarah E. Greene
Geosci. Model Dev., 14, 4187–4223, https://doi.org/10.5194/gmd-14-4187-2021, https://doi.org/10.5194/gmd-14-4187-2021, 2021
Short summary
Short summary
We present the first representation of the trace metals Sr, Os, Li and Ca in a 3D Earth system model (cGENIE). The simulation of marine metal sources (weathering, hydrothermal input) and sinks (deposition) reproduces the observed concentrations and isotopic homogeneity of these metals in the modern ocean. With these new tracers, cGENIE can be used to test hypotheses linking these metal cycles and the cycling of other elements like O and C and simulate their dynamic response to external forcing.
Alessandro Anav, Adriana Carillo, Massimiliano Palma, Maria Vittoria Struglia, Ufuk Utku Turuncoglu, and Gianmaria Sannino
Geosci. Model Dev., 14, 4159–4185, https://doi.org/10.5194/gmd-14-4159-2021, https://doi.org/10.5194/gmd-14-4159-2021, 2021
Short summary
Short summary
The Mediterranean Basin is a complex region, characterized by the presence of pronounced topography and a complex land–sea distribution including a considerable number of islands and straits; these features generate strong local atmosphere–sea interactions.
Regional Earth system models have been developed and used to study both present and future Mediterranean climate systems. The main aims of this paper are to present and evaluate the newly developed regional Earth system model ENEA-REG.
Markus Drüke, Werner von Bloh, Stefan Petri, Boris Sakschewski, Sibyll Schaphoff, Matthias Forkel, Willem Huiskamp, Georg Feulner, and Kirsten Thonicke
Geosci. Model Dev., 14, 4117–4141, https://doi.org/10.5194/gmd-14-4117-2021, https://doi.org/10.5194/gmd-14-4117-2021, 2021
Short summary
Short summary
In this study, we couple the well-established and comprehensively validated state-of-the-art dynamic LPJmL5 global vegetation model to the CM2Mc coupled climate model (CM2Mc-LPJmL v.1.0). Several improvements to LPJmL5 were implemented to allow a fully functional biophysical coupling. The new climate model is able to capture important biospheric processes, including fire, mortality, permafrost, hydrological cycling and the the impacts of managed land (crop growth and irrigation).
Dirk Barbi, Nadine Wieters, Paul Gierz, Miguel Andrés-Martínez, Deniz Ural, Fatemeh Chegini, Sara Khosravi, and Luisa Cristini
Geosci. Model Dev., 14, 4051–4067, https://doi.org/10.5194/gmd-14-4051-2021, https://doi.org/10.5194/gmd-14-4051-2021, 2021
Cléa Denamiel, Petra Pranić, Damir Ivanković, Iva Tojčić, and Ivica Vilibić
Geosci. Model Dev., 14, 3995–4017, https://doi.org/10.5194/gmd-14-3995-2021, https://doi.org/10.5194/gmd-14-3995-2021, 2021
Short summary
Short summary
The atmospheric results of the Adriatic Sea and Coast (AdriSC) climate simulation (1987–2017) are evaluated against available observational datasets in the Adriatic region. Generally, the AdriSC model performs better than regional climate models that have resolutions that are 4 times more coarse, except concerning summer temperatures, which are systematically underestimated. High-resolution climate models may thus provide new insights about the local impacts of global warming in the Adriatic.
Guy Munhoven
Geosci. Model Dev., 14, 3603–3631, https://doi.org/10.5194/gmd-14-3603-2021, https://doi.org/10.5194/gmd-14-3603-2021, 2021
Short summary
Short summary
Sea-floor sediments play an important role in biogeochemical cycling of elements (e.g. carbon, silicon, nutrients) in the ocean. Realistic sediment modules are, however, not yet commonly used in global ocean biogeochemical models. Here we present MEDUSA, a model of the processes taking place in the surface sea-floor sediments which control the interaction between the sediments and the ocean. MEDUSA can be configured to meet the exact needs of any given ocean biogeochemical model.
Max Kulinich, Yanan Fan, Spiridon Penev, Jason P. Evans, and Roman Olson
Geosci. Model Dev., 14, 3539–3551, https://doi.org/10.5194/gmd-14-3539-2021, https://doi.org/10.5194/gmd-14-3539-2021, 2021
Short summary
Short summary
We present a novel stochastic approach based on Markov chains to estimate climate model weights of multi-model ensemble means. This approach showed improved performance (better correlation with observations) over existing alternatives during cross-validation and model-as-truth tests. The results of this comparative analysis should serve to motivate further studies in applications of Markov chain and other nonlinear methods to find optimal model weights for constructing ensemble means.
Arseniy Karagodin-Doyennel, Eugene Rozanov, Timofei Sukhodolov, Tatiana Egorova, Alfonso Saiz-Lopez, Carlos A. Cuevas, Rafael P. Fernandez, Tomás Sherwen, Rainer Volkamer, Theodore K. Koenig, Tanguy Giroud, and Thomas Peter
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2021-107, https://doi.org/10.5194/gmd-2021-107, 2021
Revised manuscript accepted for GMD
Short summary
Short summary
Here, we present the iodine chemistry module in the SOCOL-AERv2 model. The obtained iodine distribution showed a good agreement when validated against other simulations and available observations. We also estimated the contribution of iodine to ozone loss in the case of present-day iodine emissions, the sensitivity of ozone to doubled iodine emissions, and when considering only organic or inorganic iodine sources. The new model can be used as a tool to further studies of iodine effect on ozone.
Jens Pfafferott, Sascha Rißmann, Matthias Sühring, Farah Kanani-Sühring, and Björn Maronga
Geosci. Model Dev., 14, 3511–3519, https://doi.org/10.5194/gmd-14-3511-2021, https://doi.org/10.5194/gmd-14-3511-2021, 2021
Short summary
Short summary
The building model is integrated via an urban surface model into the urban climate model.
There is a strong interaction between the built environment and the urban climate.
According to the building energy concept, the energy demand results in a waste heat; this is directly transferred to the urban environment.
The impact of buildings on the urban climate is defined by different physical building parameters with different technical facilities for ventilation, heating and cooling.
Anna B. Harper, Karina E. Williams, Patrick C. McGuire, Maria Carolina Duran Rojas, Debbie Hemming, Anne Verhoef, Chris Huntingford, Lucy Rowland, Toby Marthews, Cleiton Breder Eller, Camilla Mathison, Rodolfo L. B. Nobrega, Nicola Gedney, Pier Luigi Vidale, Fred Otu-Larbi, Divya Pandey, Sebastien Garrigues, Azin Wright, Darren Slevin, Martin G. De Kauwe, Eleanor Blyth, Jonas Ardö, Andrew Black, Damien Bonal, Nina Buchmann, Benoit Burban, Kathrin Fuchs, Agnès de Grandcourt, Ivan Mammarella, Lutz Merbold, Leonardo Montagnani, Yann Nouvellon, Natalia Restrepo-Coupe, and Georg Wohlfahrt
Geosci. Model Dev., 14, 3269–3294, https://doi.org/10.5194/gmd-14-3269-2021, https://doi.org/10.5194/gmd-14-3269-2021, 2021
Short summary
Short summary
We evaluated 10 representations of soil moisture stress in the JULES land surface model against site observations of GPP and latent heat flux. Increasing the soil depth and plant access to deep soil moisture improved many aspects of the simulations, and we recommend these settings in future work using JULES. In addition, using soil matric potential presents the opportunity to include parameters specific to plant functional type to further improve modeled fluxes.
Katja Weigel, Lisa Bock, Bettina K. Gier, Axel Lauer, Mattia Righi, Manuel Schlund, Kemisola Adeniyi, Bouwe Andela, Enrico Arnone, Peter Berg, Louis-Philippe Caron, Irene Cionni, Susanna Corti, Niels Drost, Alasdair Hunter, Llorenç Lledó, Christian Wilhelm Mohr, Aytaç Paçal, Núria Pérez-Zanón, Valeriu Predoi, Marit Sandstad, Jana Sillmann, Andreas Sterl, Javier Vegas-Regidor, Jost von Hardenberg, and Veronika Eyring
Geosci. Model Dev., 14, 3159–3184, https://doi.org/10.5194/gmd-14-3159-2021, https://doi.org/10.5194/gmd-14-3159-2021, 2021
Short summary
Short summary
This work presents new diagnostics for the Earth System Model Evaluation Tool (ESMValTool) v2.0 on the hydrological cycle, extreme events, impact assessment, regional evaluations, and ensemble member selection. The ESMValTool v2.0 diagnostics are developed by a large community of scientists aiming to facilitate the evaluation and comparison of Earth system models (ESMs) with a focus on the ESMs participating in the Coupled Model Intercomparison Project (CMIP).
Jun'ya Takakura, Shinichiro Fujimori, Kiyoshi Takahashi, Naota Hanasaki, Tomoko Hasegawa, Yukiko Hirabayashi, Yasushi Honda, Toshichika Iizumi, Chan Park, Makoto Tamura, and Yasuaki Hijioka
Geosci. Model Dev., 14, 3121–3140, https://doi.org/10.5194/gmd-14-3121-2021, https://doi.org/10.5194/gmd-14-3121-2021, 2021
Short summary
Short summary
To simplify calculating economic impacts of climate change, statistical methods called emulators are developed and evaluated. There are trade-offs between model complexity and emulation performance. Aggregated economic impacts can be approximated by relatively simple emulators, but complex emulators are necessary to accommodate finer-scale economic impacts.
Hao-Jhe Hong and Thomas Reichler
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2021-149, https://doi.org/10.5194/gmd-2021-149, 2021
Revised manuscript accepted for GMD
Short summary
Short summary
The Arctic wintertime circulation of the stratosphere has pronounced impacts on the troposphere and surface climate. Changes in the stratospheric circulation can lead to either increases or decreases in Arctic ozone. Understanding the interactions between ozone and the circulation will have the benefit of model prediction for the climate. This study introduces an economical and fast simplified model that represents the realistic distribution of ozone and its interaction with the circulation.
Meng-Zhuo Zhang, Zhongfeng Xu, Ying Han, and Weidong Guo
Geosci. Model Dev., 14, 3079–3094, https://doi.org/10.5194/gmd-14-3079-2021, https://doi.org/10.5194/gmd-14-3079-2021, 2021
Short summary
Short summary
The Multivariable Integrated Evaluation Tool (MVIETool) is a simple-to-use and straightforward tool designed for evaluation and intercomparison of climate models in terms of vector fields or multiple fields. The tool incorporates some new improvements in vector field evaluation (VFE) and multivariable integrated evaluation (MVIE) methods, which are introduced in this paper.
Sam J. Silva, Po-Lun Ma, Joseph C. Hardin, and Daniel Rothenberg
Geosci. Model Dev., 14, 3067–3077, https://doi.org/10.5194/gmd-14-3067-2021, https://doi.org/10.5194/gmd-14-3067-2021, 2021
Short summary
Short summary
The activation of aerosol into cloud droplets is an important but uncertain process in the Earth system. The physical and chemical interactions that govern this process are too computationally expensive to explicitly resolve in modern Earth system models. Here, we demonstrate how hybrid machine learning approaches can provide a potential path forward, enabling the representation of the more detailed physics and chemistry at a reduced computational cost while still retaining physical information.
Reinel Sospedra-Alfonso, William J. Merryfield, George J. Boer, Viatsheslav V. Kharin, Woo-Sung Lee, Christian Seiler, and James R. Christian
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2021-1, https://doi.org/10.5194/gmd-2021-1, 2021
Revised manuscript accepted for GMD
Short summary
Short summary
CanESM5 decadal predictions started from observed climate states represent the observed evolution of upper ocean temperatures, surface climate, and the carbon cycle better than ones not started from observed climate states for several years into the forecast. This is due both to better representing climate internal variability, and to corrections of the model response to external forcing including changes in GHG emissions and aerosols.
Nicholas J. Leach, Stuart Jenkins, Zebedee Nicholls, Christopher J. Smith, John Lynch, Michelle Cain, Tristram Walsh, Bill Wu, Junichi Tsutsui, and Myles R. Allen
Geosci. Model Dev., 14, 3007–3036, https://doi.org/10.5194/gmd-14-3007-2021, https://doi.org/10.5194/gmd-14-3007-2021, 2021
Short summary
Short summary
This paper presents an update of the FaIR simple climate model, which can estimate the impact of anthropogenic greenhouse gas and aerosol emissions on the global climate. This update aims to significantly increase the structural simplicity of the model, making it more understandable and transparent. This simplicity allows it to be implemented in a wide range of environments, including Excel. We suggest that it could be used widely in academia, corporate research, and education.
Tongwen Wu, Rucong Yu, Yixiong Lu, Weihua Jie, Yongjie Fang, Jie Zhang, Li Zhang, Xiaoge Xin, Laurent Li, Zaizhi Wang, Yiming Liu, Fang Zhang, Fanghua Wu, Min Chu, Jianglong Li, Weiping Li, Yanwu Zhang, Xueli Shi, Wenyan Zhou, Junchen Yao, Xiangwen Liu, He Zhao, Jinghui Yan, Min Wei, Wei Xue, Anning Huang, Yaocun Zhang, Yu Zhang, Qi Shu, and Aixue Hu
Geosci. Model Dev., 14, 2977–3006, https://doi.org/10.5194/gmd-14-2977-2021, https://doi.org/10.5194/gmd-14-2977-2021, 2021
Short summary
Short summary
This paper presents the high-resolution version of the Beijing Climate Center (BCC) Climate System Model, BCC-CSM2-HR, and describes its climate simulation performance including the atmospheric temperature and wind; precipitation; and the tropical climate phenomena such as TC, MJO, QBO, and ENSO. BCC-CSM2-HR is our model version contributing to the HighResMIP. We focused on its updates and differential characteristics from its predecessor, the medium-resolution version BCC-CSM2-MR.
Olivier Marti, Sébastien Nguyen, Pascale Braconnot, Sophie Valcke, Florian Lemarié, and Eric Blayo
Geosci. Model Dev., 14, 2959–2975, https://doi.org/10.5194/gmd-14-2959-2021, https://doi.org/10.5194/gmd-14-2959-2021, 2021
Short summary
Short summary
State-of-the-art Earth system models, like the ones used in CMIP6, suffer from temporal inconsistencies at the ocean–atmosphere interface. In this study, a mathematically consistent iterative Schwarz method is used as a reference. Its tremendous computational cost makes it unusable for production runs, but it allows us to evaluate the error made when using legacy coupling schemes. The impact on the climate at longer timescales of days to decades is not evaluated.
Steven R. Brus, Phillip J. Wolfram, Luke P. Van Roekel, and Jessica D. Meixner
Geosci. Model Dev., 14, 2917–2938, https://doi.org/10.5194/gmd-14-2917-2021, https://doi.org/10.5194/gmd-14-2917-2021, 2021
Short summary
Short summary
Wind-generated waves are an important process in the global climate system. They mediate many interactions between the ocean, atmosphere, and sea ice. Models which describe these waves are computationally expensive and have often been excluded from coupled Earth system models. To address this, we have developed a capability for the WAVEWATCH III model which allows model resolution to be varied globally across the coastal open ocean. This allows for improved accuracy at reduced computing time.
Elisa Ziegler and Kira Rehfeld
Geosci. Model Dev., 14, 2843–2866, https://doi.org/10.5194/gmd-14-2843-2021, https://doi.org/10.5194/gmd-14-2843-2021, 2021
Short summary
Short summary
Past climate changes are the only record of how the climate responds to changes in conditions on Earth, but simulations with complex climate models are challenging. We extended a simple climate model such that it simulates the development of temperatures over time. In the model, changes in carbon dioxide and ice distribution affect the simulated temperatures the most. The model is very efficient and can therefore be used to examine past climate changes happening over long periods of time.
Qun Liu, Matthew Collins, Penelope Maher, Stephen I. Thomson, and Geoffrey K. Vallis
Geosci. Model Dev., 14, 2801–2826, https://doi.org/10.5194/gmd-14-2801-2021, https://doi.org/10.5194/gmd-14-2801-2021, 2021
Short summary
Short summary
Clouds play an vital role in Earth's energy budget, and even a small change in cloud fields can have a large impact on the climate system. They also bring lots of uncertainties to climate models. Here we implement a simple diagnostic cloud scheme in order to reproduce the general radiative properties of clouds. The scheme can capture some key features of the cloud fraction and cloud radiative properties and thus provide a useful tool to explore unsolved problems relating to clouds.
Martina Messmer, Santos J. González-Rojí, Christoph C. Raible, and Thomas F. Stocker
Geosci. Model Dev., 14, 2691–2711, https://doi.org/10.5194/gmd-14-2691-2021, https://doi.org/10.5194/gmd-14-2691-2021, 2021
Short summary
Short summary
Sensitivity experiments with the WRF model are run to find an optimal parameterization setup for precipitation around Mount Kenya at a scale that resolves convection (1 km). Precipitation is compared against many weather stations and gridded observational data sets. Both the temporal correlation of precipitation sums and pattern correlations show that fewer nests lead to a more constrained simulation with higher correlation. The Grell–Freitas cumulus scheme obtains the most accurate results.
Pengfei Wang, Jinrong Jiang, Pengfei Lin, Mengrong Ding, Junlin Wei, Feng Zhang, Lian Zhao, Yiwen Li, Zipeng Yu, Weipeng Zheng, Yongqiang Yu, Xuebin Chi, and Hailong Liu
Geosci. Model Dev., 14, 2781–2799, https://doi.org/10.5194/gmd-14-2781-2021, https://doi.org/10.5194/gmd-14-2781-2021, 2021
Short summary
Short summary
Global ocean general circulation models are a fundamental tool for oceanography research, ocean forecast, and climate change research. The increasing resolution will greatly improve simulations of the models, but it also demands much more computing resources. In this study, we have ported an ocean general circulation model to a heterogeneous computing system and have developed a 3–5 km model version. A 14-year integration has been conducted and the preliminary results have been evaluated.
Chao Sun, Li Liu, Ruizhe Li, Xinzhu Yu, Hao Yu, Biao Zhao, Guansuo Wang, Juanjuan Liu, Fangli Qiao, and Bin Wang
Geosci. Model Dev., 14, 2635–2657, https://doi.org/10.5194/gmd-14-2635-2021, https://doi.org/10.5194/gmd-14-2635-2021, 2021
Short summary
Short summary
Data assimilation (DA) provides better initial states of model runs by combining observations and models. This work focuses on the technical challenges in developing a coupled ensemble-based DA system and proposes a new DA framework DAFCC1 based on C-Coupler2. DAFCC1 enables users to conveniently integrate a DA method into a model with automatic and efficient data exchanges. A sample DA system that combines GSI/EnKF and FIO-AOW demonstrates the effectiveness of DAFCC1.
Andrew J. Wiltshire, Eleanor J. Burke, Sarah E. Chadburn, Chris D. Jones, Peter M. Cox, Taraka Davies-Barnard, Pierre Friedlingstein, Anna B. Harper, Spencer Liddicoat, Stephen Sitch, and Sönke Zaehle
Geosci. Model Dev., 14, 2161–2186, https://doi.org/10.5194/gmd-14-2161-2021, https://doi.org/10.5194/gmd-14-2161-2021, 2021
Short summary
Short summary
Limited nitrogen availbility can restrict the growth of plants and their ability to assimilate carbon. It is important to include the impact of this process on the global land carbon cycle. This paper presents a model of the coupled land carbon and nitrogen cycle, which is included within the UK Earth System model to improve projections of climate change and impacts on ecosystems.
Pirkka Ollinaho, Glenn D. Carver, Simon T. K. Lang, Lauri Tuppi, Madeleine Ekblom, and Heikki Järvinen
Geosci. Model Dev., 14, 2143–2160, https://doi.org/10.5194/gmd-14-2143-2021, https://doi.org/10.5194/gmd-14-2143-2021, 2021
Short summary
Short summary
OpenEnsemble 1.0 is a novel dataset that aims to open ensemble or probabilistic weather forecasting research up to the academic community. The dataset contains atmospheric states that are required for running model forecasts of atmospheric evolution. Our capacity to observe the atmosphere is limited; thus, a single reconstruction of the atmospheric state contains some errors. Our dataset provides sets of 50 slightly different atmospheric states so that these errors can be taken into account.
Yan Sun, Daniel S. Goll, Jinfeng Chang, Philippe Ciais, Betrand Guenet, Julian Helfenstein, Yuanyuan Huang, Ronny Lauerwald, Fabienne Maignan, Victoria Naipal, Yilong Wang, Hui Yang, and Haicheng Zhang
Geosci. Model Dev., 14, 1987–2010, https://doi.org/10.5194/gmd-14-1987-2021, https://doi.org/10.5194/gmd-14-1987-2021, 2021
Short summary
Short summary
We evaluated the performance of the nutrient-enabled version of the land surface model ORCHIDEE-CNP v1.2 against remote sensing, ground-based measurement networks and ecological databases. The simulated carbon, nitrogen and phosphorus fluxes among different spatial scales are generally in good agreement with data-driven estimates. However, the recent carbon sink in the Northern Hemisphere is substantially underestimated. Potential causes and model development priorities are discussed.
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. 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.
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...
