Articles | Volume 17, issue 7
https://doi.org/10.5194/gmd-17-2509-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-17-2509-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
04 Apr 2024
Development and technical paper |
| 04 Apr 2024
| 04 Apr 2024
A new temperature–photoperiod coupled phenology module in LPJ-GUESS model v4.1: optimizing estimation of terrestrial carbon and water processes
Shouzhi Chen
College of Water Sciences, Beijing Normal University, Beijing 100875, China
College of Water Sciences, Beijing Normal University, Beijing 100875, China
Plants and Ecosystems, Department of Biology, University of Antwerp, Antwerp, Belgium
Mingwei Li
College of Water Sciences, Beijing Normal University, Beijing 100875, China
Zitong Jia
College of Water Sciences, Beijing Normal University, Beijing 100875, China
Yishuo Cui
College of Water Sciences, Beijing Normal University, Beijing 100875, China
Center for Volatile Interactions, Department of Biology, University of Copenhagen, Copenhagen, Denmark
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Climate-driven shifts in vegetation phenology have a significant impact on hydrological processes. In this study, we integrated a process-based phenology module into the SWAT-Carbon model, which led to a substantial improvement in the simulation of vegetation dynamics and hydrological processes in the Jinsha River watershed. Our findings highlight the critical need to incorporate vegetation phenology into hydrological models to achieve a more accurate representation of ecohydrological processes.
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Global changes have significantly altered vegetation phenology, affecting terrestrial carbon cycle. While various remote-sensing-based phenology datasets exist, they often suffer from inconsistencies and uncertainties. To address this, we developed a new phenology dataset spanning 1982 to 2022 using a reliability ensemble averaging method. Validated against ground data, our dataset demonstrates substantially improved accuracy, providing a novel and reliable source for global ecological studies.
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Climate-driven shifts in vegetation phenology have a significant impact on hydrological processes. In this study, we integrated a process-based phenology module into the SWAT-Carbon model, which led to a substantial improvement in the simulation of vegetation dynamics and hydrological processes in the Jinsha River watershed. Our findings highlight the critical need to incorporate vegetation phenology into hydrological models to achieve a more accurate representation of ecohydrological processes.
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Global changes have significantly altered vegetation phenology, affecting terrestrial carbon cycle. While various remote-sensing-based phenology datasets exist, they often suffer from inconsistencies and uncertainties. To address this, we developed a new phenology dataset spanning 1982 to 2022 using a reliability ensemble averaging method. Validated against ground data, our dataset demonstrates substantially improved accuracy, providing a novel and reliable source for global ecological studies.
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Michał Michalak, Christian Gerhards, and Peter Menzel
EGUsphere, https://doi.org/10.5194/egusphere-2024-2004, https://doi.org/10.5194/egusphere-2024-2004, 2024
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This study presents a novel method for fault detection on geological terrains. Using synthetic models, we applied machine learning to classify terrain shape and nearby features. Testing on real borehole data validated its effectiveness across various fault orientations. The supervised approach represents a significant improvement over older methods that relied on simpler clustering techniques which were capable of identifying less orientations of faults.
Sia Ghelichkhan, Angus Gibson, D. Rhodri Davies, Stephan C. Kramer, and David A. Ham
Geosci. Model Dev., 17, 5057–5086, https://doi.org/10.5194/gmd-17-5057-2024, https://doi.org/10.5194/gmd-17-5057-2024, 2024
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We introduce the Geoscientific ADjoint Optimisation PlaTform (G-ADOPT), designed for inverse modelling of Earth system processes, with an initial focus on mantle dynamics. G-ADOPT is built upon Firedrake, Dolfin-Adjoint and the Rapid Optimisation Library, which work together to optimise models using an adjoint method, aligning them with seismic and geologic datasets. We demonstrate G-ADOPT's ability to reconstruct mantle evolution and thus be a powerful tool in geosciences.
Rene Gassmöller, Juliane Dannberg, Wolfgang Bangerth, Elbridge Gerry Puckett, and Cedric Thieulot
Geosci. Model Dev., 17, 4115–4134, https://doi.org/10.5194/gmd-17-4115-2024, https://doi.org/10.5194/gmd-17-4115-2024, 2024
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Numerical models that use simulated particles are a powerful tool for investigating flow in the interior of the Earth, but the accuracy of these models is not fully understood. Here we present two new benchmarks that allow measurement of model accuracy. We then document that better accuracy matters for applications like convection beneath an oceanic plate. Our benchmarks and methods are freely available to help the community develop better models.
Malte Jörn Ziebarth and Sebastian von Specht
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Thermal energy from Earth’s active interior constantly dissipates through Earth’s surface. This heat flow is not spatially uniform, and its exact pattern is hard to predict since it depends on crustal and mantle properties, both varying across scales. Our new model REHEATFUNQ addresses this difficulty by treating the fluctuations of heat flow within a region statistically. REHEATFUNQ estimates the regional distribution of heat flow and quantifies known structural signals therein.
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Geosci. Model Dev., 17, 2039–2052, https://doi.org/10.5194/gmd-17-2039-2024, https://doi.org/10.5194/gmd-17-2039-2024, 2024
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Knowing the rate at which earthquakes happen along active faults is crucial to characterize the hazard that they pose. We present an approach (Paleoseismic EArthquake CHronologies, PEACH) to correlate and compute seismic histories using paleoseismic data, a type of data that characterizes past seismic activity from the geological record. Our approach reduces the uncertainties of the seismic histories and overall can improve the knowledge on fault rupture behavior for the seismic hazard.
Sébastien Carretier, Vincent Regard, Youssouf Abdelhafiz, and Bastien Plazolles
Geosci. Model Dev., 16, 6741–6755, https://doi.org/10.5194/gmd-16-6741-2023, https://doi.org/10.5194/gmd-16-6741-2023, 2023
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We present the development of a code to simulate simultaneously the dynamics of landscapes over geological time and the evolution of the concentration of cosmogenic isotopes in grains throughout their transport from the slopes to the river outlets. This new model makes it possible to study the relationship between the detrital signal of cosmogenic isotope concentration measured in sediment and the erosion--deposition processes in watersheds.
Mattia de' Michieli Vitturi, Tomaso Esposti Ongaro, and Samantha Engwell
Geosci. Model Dev., 16, 6309–6336, https://doi.org/10.5194/gmd-16-6309-2023, https://doi.org/10.5194/gmd-16-6309-2023, 2023
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We present version 2 of the numerical code IMEX-SfloW2D. With this version it is possible to simulate a wide range of volcanic mass flows (pyroclastic avalanches, lahars, pyroclastic surges), and here we present its application to transient dilute pyroclastic density currents (PDCs). A simulation of the 1883 Krakatau eruption demonstrates the capability of the numerical model to face a complex natural case involving the propagation of PDCs over the sea surface and across topographic obstacles.
Caroline J. van Calcar, Roderik S. W. van de Wal, Bas Blank, Bas de Boer, and Wouter van der Wal
Geosci. Model Dev., 16, 5473–5492, https://doi.org/10.5194/gmd-16-5473-2023, https://doi.org/10.5194/gmd-16-5473-2023, 2023
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The waxing and waning of the Antarctic ice sheet caused the Earth’s surface to deform, which is stabilizing the ice sheet and mainly determined by the spatially variable viscosity of the mantle. Including this feedback in model simulations led to significant differences in ice sheet extent and ice thickness over the last glacial cycle. The results underline and quantify the importance of including this local feedback effect in ice sheet models when simulating the Antarctic ice sheet evolution.
Baoyi Zhang, Linze Du, Umair Khan, Yongqiang Tong, Lifang Wang, and Hao Deng
Geosci. Model Dev., 16, 3651–3674, https://doi.org/10.5194/gmd-16-3651-2023, https://doi.org/10.5194/gmd-16-3651-2023, 2023
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We propose a Hermite–Birkhoff radial basis function (HRBF) formulation, AdaHRBF, with an adaptive gradient magnitude for continuous 3D stratigraphic potential field (SPF) modeling of multiple stratigraphic interfaces. In the linear system of HRBF interpolants constrained by the scattered on-contact attribute points and off-contact attitude points of a set of strata in 3D space, we add a novel optimization term to iteratively obtain the true gradient magnitude.
Mohammad Moulaeifard, Simon Bernard, and Florian Wellmann
Geosci. Model Dev., 16, 3565–3579, https://doi.org/10.5194/gmd-16-3565-2023, https://doi.org/10.5194/gmd-16-3565-2023, 2023
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In this work, we propose a flexible framework to generate and interact with geological models using explicit surface representations. The essence of the work lies in the determination of the flexible control mesh, topologically similar to the main geological structure, watertight and controllable with few control points, to manage the geological structures. We exploited the subdivision surface method in our work, which is commonly used in the animation and gaming industry.
Leonardo Mingari, Antonio Costa, Giovanni Macedonio, and Arnau Folch
Geosci. Model Dev., 16, 3459–3478, https://doi.org/10.5194/gmd-16-3459-2023, https://doi.org/10.5194/gmd-16-3459-2023, 2023
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Two novel techniques for ensemble-based data assimilation, suitable for semi-positive-definite variables with highly skewed uncertainty distributions such as tephra deposit mass loading, are applied to reconstruct the tephra fallout deposit resulting from the 2015 Calbuco eruption in Chile. The deposit spatial distribution and the ashfall volume according to the analyses are in good agreement with estimations based on field measurements and isopach maps reported in previous studies.
Hui Gao, Xinming Wu, Jinyu Zhang, Xiaoming Sun, and Zhengfa Bi
Geosci. Model Dev., 16, 2495–2513, https://doi.org/10.5194/gmd-16-2495-2023, https://doi.org/10.5194/gmd-16-2495-2023, 2023
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We propose a workflow to automatically generate synthetic seismic data and corresponding stratigraphic labels (e.g., clinoform facies, relative geologic time, and synchronous horizons) by geological and geophysical forward modeling. Trained with only synthetic datasets, our network works well to accurately and efficiently predict clinoform facies in 2D and 3D field seismic data. Such a workflow can be easily extended for other geological and geophysical scenarios in the future.
Ibsen Chivata Cardenas, Terje Aven, and Roger Flage
Geosci. Model Dev., 16, 1601–1615, https://doi.org/10.5194/gmd-16-1601-2023, https://doi.org/10.5194/gmd-16-1601-2023, 2023
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We discuss challenges in uncertainty quantification for geohazard assessments. The challenges arise from limited data and the one-off nature of geohazard features. The challenges include the credibility of predictions, input uncertainty, and assumptions’ impact. Considerations to increase credibility of the quantification are provided. Crucial tasks in the quantification are the exhaustive scrutiny of the background knowledge coupled with the assessment of deviations of assumptions made.
Zhengfa Bi, Xinming Wu, Zhaoliang Li, Dekuan Chang, and Xueshan Yong
Geosci. Model Dev., 15, 6841–6861, https://doi.org/10.5194/gmd-15-6841-2022, https://doi.org/10.5194/gmd-15-6841-2022, 2022
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We present an implicit modeling method based on deep learning to produce a geologically valid and structurally compatible model from unevenly sampled structural data. Trained with automatically generated synthetic data with realistic features, our network can efficiently model geological structures without the need to solve large systems of mathematical equations, opening new opportunities for further leveraging deep learning to improve modeling capacity in many Earth science applications.
D. Rhodri Davies, Stephan C. Kramer, Sia Ghelichkhan, and Angus Gibson
Geosci. Model Dev., 15, 5127–5166, https://doi.org/10.5194/gmd-15-5127-2022, https://doi.org/10.5194/gmd-15-5127-2022, 2022
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Firedrake is a state-of-the-art system that automatically generates highly optimised code for simulating finite-element (FE) problems in geophysical fluid dynamics. It creates a separation of concerns between employing the FE method and implementing it. Here, we demonstrate the applicability and benefits of Firedrake for simulating geodynamical flows, with a focus on the slow creeping motion of Earth's mantle over geological timescales, which is ultimately the engine driving our dynamic Earth.
Federico Brogi, Simone Colucci, Jacopo Matrone, Chiara Paola Montagna, Mattia De' Michieli Vitturi, and Paolo Papale
Geosci. Model Dev., 15, 3773–3796, https://doi.org/10.5194/gmd-15-3773-2022, https://doi.org/10.5194/gmd-15-3773-2022, 2022
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Computer simulations play a fundamental role in understanding volcanic phenomena. The growing complexity of these simulations requires the development of flexible computational tools that can easily switch between sub-models and solution techniques as well as optimizations. MagmaFOAM is a newly developed library that allows for maximum flexibility for solving multiphase volcanic flows and promotes collaborative work for in-house and community model development, testing, and comparison.
Grace A. Nield, Matt A. King, Rebekka Steffen, and Bas Blank
Geosci. Model Dev., 15, 2489–2503, https://doi.org/10.5194/gmd-15-2489-2022, https://doi.org/10.5194/gmd-15-2489-2022, 2022
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We present a finite-element model of post-seismic solid Earth deformation built in the software package Abaqus for the purpose of calculating post-seismic deformation in the far field of major earthquakes. The model is benchmarked against an existing open-source post-seismic model demonstrating good agreement. The advantage over existing models is the potential for simple modification to include 3-D Earth structure, non-linear rheologies and alternative or multiple sources of stress change.
Alessandro Lechmann, David Mair, Akitaka Ariga, Tomoko Ariga, Antonio Ereditato, Ryuichi Nishiyama, Ciro Pistillo, Paola Scampoli, Mykhailo Vladymyrov, and Fritz Schlunegger
Geosci. Model Dev., 15, 2441–2473, https://doi.org/10.5194/gmd-15-2441-2022, https://doi.org/10.5194/gmd-15-2441-2022, 2022
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Muon tomography is a technology that is used often in geoscientific research. The know-how of data analysis is, however, still possessed by physicists who developed this technology. This article aims at providing geoscientists with the necessary tools to perform their own analyses. We hope that a lower threshold to enter the field of muon tomography will allow more geoscientists to engage with muon tomography. SMAUG is set up in a modular way to allow for its own modules to work in between.
Zuzanna M. Swirad and Adam P. Young
Geosci. Model Dev., 15, 1499–1512, https://doi.org/10.5194/gmd-15-1499-2022, https://doi.org/10.5194/gmd-15-1499-2022, 2022
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Cliff base and top lines that delimit coastal cliff faces are usually manually digitized based on maps, aerial photographs, terrain models, etc. However, manual mapping is time consuming and depends on the mapper's decisions and skills. To increase the objectivity and efficiency of cliff mapping, we developed CliffDelineaTool, an algorithm that identifies cliff base and top positions along cross-shore transects using elevation and slope characteristics.
Holly Kyeore Han, Natalya Gomez, and Jeannette Xiu Wen Wan
Geosci. Model Dev., 15, 1355–1373, https://doi.org/10.5194/gmd-15-1355-2022, https://doi.org/10.5194/gmd-15-1355-2022, 2022
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Interactions between ice sheets, sea level and the solid Earth occur over a range of timescales from years to tens of thousands of years. This requires coupled ice-sheet–sea-level models to exchange information frequently, leading to a quadratic increase in computation time with the number of model timesteps. We present a new sea-level model algorithm that allows coupled models to improve the computational feasibility and precisely capture short-term interactions within longer simulations.
Jérémie Giraud, Vitaliy Ogarko, Roland Martin, Mark Jessell, and Mark Lindsay
Geosci. Model Dev., 14, 6681–6709, https://doi.org/10.5194/gmd-14-6681-2021, https://doi.org/10.5194/gmd-14-6681-2021, 2021
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We review different techniques to model the Earth's subsurface from geophysical data (gravity field anomaly, magnetic field anomaly) using geological models and measurements of the rocks' properties. We show examples of application using idealised examples reproducing realistic features and provide theoretical details of the open-source algorithm we use.
Eric A. de Kemp
Geosci. Model Dev., 14, 6661–6680, https://doi.org/10.5194/gmd-14-6661-2021, https://doi.org/10.5194/gmd-14-6661-2021, 2021
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This is a proof of concept and review paper of spatial agents, with initial research focusing on geomodelling. The results may be of interest to others working on complex regional geological modelling with sparse data. Structural agent-based swarming behaviour is key to advancing this field. The study provides groundwork for research in structural geology 3D modelling with spatial agents. This work was done with NetLogo, a free agent modelling platform used mostly for teaching complex systems.
José M. Bastías Espejo, Andy Wilkins, Gabriel C. Rau, and Philipp Blum
Geosci. Model Dev., 14, 6257–6272, https://doi.org/10.5194/gmd-14-6257-2021, https://doi.org/10.5194/gmd-14-6257-2021, 2021
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The hydraulic and mechanical properties of the subsurface are inherently heterogeneous. RHEA is a simulator that can perform couple hydro-geomechanical processes in heterogeneous porous media with steep gradients. RHEA is able to fully integrate spatial heterogeneity, allowing allocation of distributed hydraulic and geomechanical properties at mesh element level. RHEA is a valuable tool that can simulate problems considering realistic heterogeneity inherent to geologic formations.
Lachlan Grose, Laurent Ailleres, Gautier Laurent, Guillaume Caumon, Mark Jessell, and Robin Armit
Geosci. Model Dev., 14, 6197–6213, https://doi.org/10.5194/gmd-14-6197-2021, https://doi.org/10.5194/gmd-14-6197-2021, 2021
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Fault discontinuities in rock packages represent the plane where two blocks of rock have moved. They are challenging to incorporate into geological models because the geometry of the faulted rock units are defined by not only the location of the discontinuity but also the kinematics of the fault. In this paper, we outline a structural geology framework for incorporating faults into geological models by directly incorporating kinematics into the mathematical framework of the model.
Florence Colleoni, Laura De Santis, Enrico Pochini, Edy Forlin, Riccardo Geletti, Giuseppe Brancatelli, Magdala Tesauro, Martina Busetti, and Carla Braitenberg
Geosci. Model Dev., 14, 5285–5305, https://doi.org/10.5194/gmd-14-5285-2021, https://doi.org/10.5194/gmd-14-5285-2021, 2021
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PALEOSTRIP has been developed in the framework of past Antarctic ice sheet reconstructions for periods when bathymetry around Antarctica differed substantially from today. It has been designed for users with no knowledge of numerical modelling and allows users to switch on and off the processes involved in backtracking and backstripping. Applications are broad, and it can be used to restore any continental margin bathymetry or sediment thickness and to perform basin analysis.
Lachlan Grose, Laurent Ailleres, Gautier Laurent, and Mark Jessell
Geosci. Model Dev., 14, 3915–3937, https://doi.org/10.5194/gmd-14-3915-2021, https://doi.org/10.5194/gmd-14-3915-2021, 2021
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LoopStructural is an open-source 3D geological modelling library with a model design allowing for multiple different algorithms to be used for comparison for the same geology. Geological structures are modelled using structural geology concepts and techniques, allowing for complex structures such as overprinted folds and faults to be modelled. In the paper, we demonstrate automatically generating a 3-D model from map2loop-processed geological survey data of the Flinders Ranges, South Australia.
Zhenjiao Jiang, Dirk Mallants, Lei Gao, Tim Munday, Gregoire Mariethoz, and Luk Peeters
Geosci. Model Dev., 14, 3421–3435, https://doi.org/10.5194/gmd-14-3421-2021, https://doi.org/10.5194/gmd-14-3421-2021, 2021
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Fast and reliable tools are required to extract hidden information from big geophysical and remote sensing data. A deep-learning model in 3D image construction from 2D image(s) is here developed for paleovalley mapping from globally available digital elevation data. The outstanding performance for 3D subsurface imaging gives confidence that this generic novel tool will make better use of existing geophysical and remote sensing data for improved management of limited earth resources.
Stephan C. Kramer, D. Rhodri Davies, and Cian R. Wilson
Geosci. Model Dev., 14, 1899–1919, https://doi.org/10.5194/gmd-14-1899-2021, https://doi.org/10.5194/gmd-14-1899-2021, 2021
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Computational models of Earth's mantle require rigorous verification and validation. Analytical solutions of the underlying Stokes equations provide a method to verify that these equations are accurately solved for. However, their derivation in spherical and cylindrical shell domains with physically relevant boundary conditions is involved. This paper provides a number of solutions. They are provided in a Python package (Assess) and their use is demonstrated in a convergence study with Fluidity.
Bastian van den Bout, Theo van Asch, Wei Hu, Chenxiao X. Tang, Olga Mavrouli, Victor G. Jetten, and Cees J. van Westen
Geosci. Model Dev., 14, 1841–1864, https://doi.org/10.5194/gmd-14-1841-2021, https://doi.org/10.5194/gmd-14-1841-2021, 2021
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Landslides, debris flows and other types of dense gravity-driven flows threaten livelihoods around the globe. Understanding the mechanics of these flows can be crucial for predicting their behaviour and reducing disaster risk. Numerical models assume that the solids and fluids of the flow are unstructured. The newly presented model captures the internal structure during movement. This important step can lead to more accurate predictions of landslide movement.
Andrzej Górszczyk and Stéphane Operto
Geosci. Model Dev., 14, 1773–1799, https://doi.org/10.5194/gmd-14-1773-2021, https://doi.org/10.5194/gmd-14-1773-2021, 2021
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We present the 3D multi-parameter synthetic geomodel of the subduction zone, as well as the workflow designed to implement all of its components. The model contains different geological structures of various scales and complexities. It is intended to serve as a tool for the geophysical community to validate imaging approaches, design acquisition techniques, estimate uncertainties, benchmark computing approaches, etc.
Mattia de' Michieli Vitturi and Federica Pardini
Geosci. Model Dev., 14, 1345–1377, https://doi.org/10.5194/gmd-14-1345-2021, https://doi.org/10.5194/gmd-14-1345-2021, 2021
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Here, we present PLUME-MoM-TSM, a volcanic plume model that allows us to quantify the formation of aggregates during the rise of the plume, model the phase change of water, and include the possibility to simulate the initial spreading of the tephra umbrella cloud intruding from the volcanic column into the atmosphere. The model is first applied to the 2015 Calbuco eruption (Chile) and provides an analytical relationship between the upwind spreading and some characteristic of the volcanic column.
Zhikui Guo, Lars Rüpke, and Chunhui Tao
Geosci. Model Dev., 13, 6547–6565, https://doi.org/10.5194/gmd-13-6547-2020, https://doi.org/10.5194/gmd-13-6547-2020, 2020
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We present the 3-D hydro-thermo-transport model HydrothermalFoam v1.0, which we designed to provide the marine geosciences community with an easy-to-use and state-of-the-art tool for simulating mass and energy transport in submarine hydrothermal systems. HydrothermalFoam is based on the popular open-source platform OpenFOAM, comes with a number of tutorials, and is published under the GNU General Public License v3.0.
Marisol Monterrubio-Velasco, F. Ramón Zúñiga, Quetzalcoatl Rodríguez-Pérez, Otilio Rojas, Armando Aguilar-Meléndez, and Josep de la Puente
Geosci. Model Dev., 13, 6361–6381, https://doi.org/10.5194/gmd-13-6361-2020, https://doi.org/10.5194/gmd-13-6361-2020, 2020
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The Mexican subduction zone along the Pacific coast is one of the most active seismic zones in the world, where every year larger-magnitude earthquakes shake huge inland cities such as Mexico City. In this work, we use TREMOL (sThochastic Rupture Earthquake ModeL) to simulate the seismicity observed in this zone. Our numerical results reinforce the hypothesis that in some subduction regions single asperities are responsible for producing the observed seismicity.
Thomas Zwinger, Grace A. Nield, Juha Ruokolainen, and Matt A. King
Geosci. Model Dev., 13, 1155–1164, https://doi.org/10.5194/gmd-13-1155-2020, https://doi.org/10.5194/gmd-13-1155-2020, 2020
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We present a newly developed flat-earth model, Elmer/Earth, for viscoelastic treatment of solid earth deformation under ice loads. Unlike many previous approaches with proprietary software, this model is based on the open-source FEM code Elmer, with the advantage for scientists to apply and alter the model without license constraints. The new-generation full-stress ice-sheet model Elmer/Ice shares the same code base, enabling future coupled ice-sheet–glacial-isostatic-adjustment simulations.
Swarup Chauhan, Kathleen Sell, Wolfram Rühaak, Thorsten Wille, and Ingo Sass
Geosci. Model Dev., 13, 315–334, https://doi.org/10.5194/gmd-13-315-2020, https://doi.org/10.5194/gmd-13-315-2020, 2020
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We present CobWeb 1.0, a graphical user interface for analysing tomographic images of geomaterials. CobWeb offers different machine learning techniques for accurate multiphase image segmentation and visualizing material specific parameters such as pore size distribution, relative porosity and volume fraction. We demonstrate a novel approach of dual filtration and dual segmentation to eliminate edge enhancement artefact in synchrotron-tomographic datasets and provide the computational code.
Loïc Huder, Nicolas Gillet, and Franck Thollard
Geosci. Model Dev., 12, 3795–3803, https://doi.org/10.5194/gmd-12-3795-2019, https://doi.org/10.5194/gmd-12-3795-2019, 2019
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The pygeodyn package is a geomagnetic data assimilation tool written in Python. It gives access to the Earth's core flow dynamics, controlled by geomagnetic observations, by means of a reduced numerical model anchored to geodynamo simulation statistics. It aims to provide the community with a user-friendly and tunable data assimilation algorithm. It can be used for education, geomagnetic model production or tests in conjunction with webgeodyn, a set of visualization tools for geomagnetic models.
Mattia de' Michieli Vitturi, Tomaso Esposti Ongaro, Giacomo Lari, and Alvaro Aravena
Geosci. Model Dev., 12, 581–595, https://doi.org/10.5194/gmd-12-581-2019, https://doi.org/10.5194/gmd-12-581-2019, 2019
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Pyroclastic avalanches are a type of granular flow generated at active volcanoes by different mechanisms, including the collapse of steep pyroclastic deposits (e.g., scoria and ash cones) and fountaining during moderately explosive eruptions. We present IMEX_SfloW2D, a depth-averaged flow model describing the granular mixture as a single-phase granular fluid. Benchmark cases and preliminary application to the simulation of the 11 February pyroclastic avalanche at Mt. Etna (Italy) are shown.
Yihao Wu, Zhicai Luo, Bo Zhong, and Chuang Xu
Geosci. Model Dev., 11, 4797–4815, https://doi.org/10.5194/gmd-11-4797-2018, https://doi.org/10.5194/gmd-11-4797-2018, 2018
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A multilayer approach is parameterized for model development, and the multiple layers are located at different depths beneath the Earth’s surface. This method may be beneficial for gravity/manget field modeling, which may outperform the traditional single-layer approach.
Cited articles
Ahl, D. E., Gower, S. T., Burrows, S. N., Shabanov, N. V., Myneni, R. B., and Knyazikhin, Y.: Monitoring spring canopy phenology of a deciduous broadleaf forest using MODIS, Remote Sens. Environ., 104, 88–95, 2006.
Ahlström, A., Xia, J., Arneth, A., Luo, Y., and Smith, B.: Importance of vegetation dynamics for future terrestrial carbon cycling, Environ. Res. Lett., 10, 054019, https://doi.org/10.1088/1748-9326/10/5/054019, 2015.
Augspurger, C. K.: Spring 2007 warmth and frost: phenology, damage and refoliation in a temperate deciduous forest, Funct. Ecol., 23, 1031–1039, 2009.
Badeck, F. W., Bondeau, A., Böttcher, K., Doktor, D., Lucht, W., Schaber, J., and Sitch, S.: Responses of spring phenology to climate change, New Phytol., 162, 295–309, 2004.
Bartholome, E. and Belward, A. S.: GLC2000: a new approach to global land cover mapping from Earth observation data, Int. J. Remote Sens., 26, 1959–1977, 2005.
Bigler, C. and Bugmann, H.: Climate-induced shifts in leaf unfolding and frost risk of European trees and shrubs, Sci. Rep., 8, 9865, https://doi.org/10.1038/s41598-018-27893-1, 2018.
Caffarra, A., Donnelly, A., and Chuine, I.: Modelling the timing of Betula pubescens budburst. II. Integrating complex effects of photoperiod into process-based models, Clim. Res., 46, 159–170, 2011.
Cao, S., Li, M., Zhu, Z., Wang, Z., Zha, J., Zhao, W., Duanmu, Z., Chen, J., Zheng, Y., Chen, Y., Myneni, R. B., and Piao, S.: Spatiotemporally consistent global dataset of the GIMMS leaf area index (GIMMS LAI4g) from 1982 to 2020, Earth Syst. Sci. Data, 15, 4877–4899, https://doi.org/10.5194/essd-15-4877-2023, 2023.
Chen, S., Fu, Y. H., Hao, F., Li, X., Zhou, S., Liu, C., and Tang, J.: Vegetation phenology and its ecohydrological implications from individual to global scales, Geography and Sustainability, 3, 334–338, https://doi.org/0.1016/j.geosus.2022.10.002, 2022a.
Chen, S., Fu, Y. H., Geng, X., Hao, Z., Tang, J., Zhang, X., Xu, Z., and Hao, F.: Influences of Shifted Vegetation Phenology on Runoff Across a Hydroclimatic Gradient, Front. Plant Sci., 12, 802664, https://doi.org/10.3389/fpls.2021.802664, 2022b.
Chen, S., Fu, Y. H., Wu, Z., Hao, F., Hao, Z., Guo, Y., Geng, X., Li, X., Zhang, X., and Tang, J.: Informing the SWAT model with remote sensing detected vegetation phenology for improved modeling of ecohydrological processes, J. Hydrol., 616, 128817, https://doi.org/10.1016/j.jhydrol.2022.128817, 2023a.
Chen, S., Fu, Y., and Tang, J.: LPJ-GUESS code with a new temperature-photoperiod coupled phenology module, Zenodo [code], https://doi.org/10.5281/zenodo.10416649, 2023b.
Chen, X., Wang, D., Chen, J., Wang, C., and Shen, M.: The mixed pixel effect in land surface phenology: A simulation study, Remote Sens. Environ., 211, 338–344, 2018.
Chuine, I.: A unified model for budburst of trees, J. Theor. Biol., 207, 337–347, 2000.
Chuine, I.: Why does phenology drive species distribution?, Philosophical Transactions of the Royal Society B: Biological Sciences, 365, 3149–3160, 2010.
Cong, N., Piao, S., Chen, A., Wang, X., Lin, X., Chen, S., Han, S., Zhou, G., and Zhang, X.: Spring vegetation green-up date in China inferred from SPOT NDVI data: A multiple model analysis, Agr. Forest Meteorol., 165, 104–113, https://doi.org/10.1016/j.agrformet.2012.06.009, 2012.
Dai, W., Jin, H., Zhou, L., Liu, T., Zhang, Y., Zhou, Z., Fu, Y. H., and Jin, G.: Testing machine learning algorithms on a binary classification phenological model, Global Ecol. Biogeogr., 32, 178–190, 2023.
Delpierre, N., Dufrêne, E., Soudani, K., Ulrich, E., Cecchini, S., Boé, J., and François, C.: Modelling interannual and spatial variability of leaf senescence for three deciduous tree species in France, Agr. Forest Meteorol., 149, 938–948, 2009.
Deng, F., Chen, J. M., Plummer, S., Chen, M., and Pisek, J.: Algorithm for global leaf area index retrieval using satellite imagery, IEEE Trans. Geosci. Remote Sens., 44, 2219–2229, 2006.
Dijkstra, J. A., Westerman, E. L., and Harris, L. G.: The effects of climate change on species composition, succession and phenology: a case study, Glob. Change Biol., 17, 2360–2369, 2011.
Drepper, B., Gobin, A., and Van Orshoven, J.: Spatio-temporal assessment of frost risks during the flowering of pear trees in Belgium for 1971–2068, Agr. Forest Meteorol., 315, 108822, https://doi.org/10.1016/j.agrformet.2022.108822, 2022.
Fang, J. and Lechowicz, M. J.: Climatic limits for the present distribution of beech (Fagus L.) species in the world, J. Biogeogr., 33, 1804–1819, 2006.
Forrest, J., Inouye, D. W., and Thomson, J. D.: Flowering phenology in subalpine meadows: Does climate variation influence community co-flowering patterns?, Ecology, 91, 431–440, 2010.
Fu, Y., Li, X., Zhou, X., Geng, X., Guo, Y., and Zhang, Y.: Progress in plant phenology modeling under global climate change, Science China Earth Sciences, 63, 1237–1247, 2020.
Fu, Y. H., Piao, S., Op de Beeck, M., Cong, N., Zhao, H., Zhang, Y., Menzel, A., and Janssens, I. A.: Recent spring phenology shifts in western C entral E urope based on multiscale observations, Global Ecol. Biogeogr., 23, 1255–1263, 2014.
Fu, Y. H., Zhou, X., Li, X., Zhang, Y., Geng, X., Hao, F., Zhang, X., Hanninen, H., Guo, Y., and De Boeck, H. J.: Decreasing control of precipitation on grassland spring phenology in temperate China, Global Ecol. Biogeogr., 30, 490–499, 2021.
Fu, Y. H., Li, X., Chen, S., Wu, Z., Su, J., Li, X., Li, S., Zhang, J., Tang, J., and Xiao, J.: Soil moisture regulates warming responses of autumn photosynthetic transition dates in subtropical forests, Glob. Change Biol., 28, 4935–4946, 2022.
Fu, Y. H., Geng, X., Chen, S., Wu, H., Hao, F., Zhang, X., Wu, Z., Zhang, J., Tang, J., and Vitasse, Y.: Global warming is increasing the discrepancy between green (actual) and thermal (potential) seasons of temperate trees, Glob. Change Biol., 29, 1377–1389, 2023.
Geng, X., Zhou, X., Yin, G., Hao, F., Zhang, X., Hao, Z., Singh, V. P., and Fu, Y. H.: Extended growing season reduced river runoff in Luanhe River basin, J. Hydrol., 582, 124538, https://doi.org/10.1016/j.jhydrol.2019.124538, 2020.
Guan, K., Pan, M., Li, H., Wolf, A., Wu, J., Medvigy, D., Caylor, K. K., Sheffield, J., Wood, E. F., and Malhi, Y.: Photosynthetic seasonality of global tropical forests constrained by hydroclimate, Nat. Geosci., 8, 284–289, 2015.
Hänninen, H.: Modelling bud dormancy release in trees from cool and temperate regions, Acta Forestalia Fennica, Finnish Forest Research Institute, Helsinki, Finland, No. 213, 47 pp., 1990.
Hickler, T., Smith, B., Sykes, M. T., Davis, M. B., Sugita, S., and Walker, K.: Using a generalized vegetation model to simulate vegetation dynamics in northeastern USA, Ecology, 85, 519–530, 2004.
Hmimina, G., Dufrêne, E., Pontailler, J.-Y., Delpierre, N., Aubinet, M., Caquet, B., De Grandcourt, A., Burban, B., Flechard, C., and Granier, A.: Evaluation of the potential of MODIS satellite data to predict vegetation phenology in different biomes: An investigation using ground-based NDVI measurements, Remote Sens. Environ., 132, 145–158, 2013.
Huang, M., Piao, S., Janssens, I. A., Zhu, Z., Wang, T., Wu, D., Ciais, P., Myneni, R. B., Peaucelle, M., and Peng, S.: Velocity of change in vegetation productivity over northern high latitudes, Nat. Ecol. Evol., 1, 1649–1654, 2017.
Jain, A. K. and Yang, X.: Modeling the effects of two different land cover change data sets on the carbon stocks of plants and soils in concert with CO2 and climate change, Global Biogeochem. Cy., 19, GB2015, https://doi.org/10.1029/2004GB002349, 2005.
Kaufmann, R. K., Zhou, L., Knyazikhin, Y., Shabanov, V., Myneni, R. B., and Tucker, C. J.: Effect of orbital drift and sensor changes on the time series of AVHRR vegetation index data, IEEE T. Geosci. Remote Sens., 38, 2584–2597, 2000.
Keenan, T. F. and Richardson, A. D.: The timing of autumn senescence is affected by the timing of spring phenology: implications for predictive models, Glob. Change Biol., 21, 2634–2641, 2015.
Keenan, T. F., Gray, J., Friedl, M. A., Toomey, M., Bohrer, G., Hollinger, D. Y., Munger, J. W., O'Keefe, J., Schmid, H. P., SueWing, I., Yang, B., and Richardson, A. D.: Net carbon uptake has increased through warming-induced changes in temperate forest phenology, Nat. Clim. Change, 4, 598–604, https://doi.org/10.1038/Nclimate2253, 2014.
Kim, J. H., Hwang, T., Yang, Y., Schaaf, C. L., Boose, E., and Munger, J. W.: Warming-induced earlier greenup leads to reduced stream discharge in a temperate mixed forest catchment, J. Geophys. Res.-Biogeo., 123, 1960–1975, 2018.
Kramer, K.: Selecting a model to predict the onset of growth of Fagus sylvatica, J. Appl. Ecol., 31,, 172–181, https://doi.org/10.2307/2404609, 1994.
Krinner, G., Viovy, N., de Noblet-Ducoudré, N., Ogée, J., Polcher, J., Friedlingstein, P., Ciais, P., Sitch, S., and Prentice, I. C.: A dynamic global vegetation model for studies of the coupled atmosphere-biosphere system, Global Biogeochem. Cy., 19, GB1015, https://doi.org/10.1029/2003GB002199, 2005.
Kucharik, C. J., Barford, C. C., El Maayar, M., Wofsy, S. C., Monson, R. K., and Baldocchi, D. D.: A multiyear evaluation of a Dynamic Global Vegetation Model at three AmeriFlux forest sites: Vegetation structure, phenology, soil temperature, and CO2 and H2O vapor exchange, Ecol. Modell., 196, 1–31, https://doi.org/10.1016/j.ecolmodel.2005.11.031, 2006.
Li, X., Fu, Y. H., Chen, S., Xiao, J., Yin, G., Li, X., Zhang, X., Geng, X., Wu, Z., and Zhou, X.: Increasing importance of precipitation in spring phenology with decreasing latitudes in subtropical forest area in China, Agr. Forest Meteorol., 304, 108427, https://doi.org/10.1016/j.agrformet.2021.108427, 2021.
Liu, Q., Fu, Y. H., Liu, Y., Janssens, I. A., and Piao, S.: Simulating the onset of spring vegetation growth across the Northern Hemisphere, Glob. Change Biol., 24, 1342–1356, 2018a.
Liu, Q., Piao, S., Janssens, I. A., Fu, Y., Peng, S., Lian, X., Ciais, P., Myneni, R. B., Peñuelas, J., and Wang, T.: Extension of the growing season increases vegetation exposure to frost, Nat. Commun., 9, 426, https://doi.org/10.1038/s41467-017-02690-y, 2018b.
Lu, J., Wang, G., Chen, T., Li, S., Hagan, D. F. T., Kattel, G., Peng, J., Jiang, T., and Su, B.: A harmonized global land evaporation dataset from model-based products covering 1980–2017, Earth Syst. Sci. Data, 13, 5879–5898, https://doi.org/10.5194/essd-13-5879-2021, 2021a.
Lu, J., Wang, G., Chen, T., Li, S., Hagan, D. F. T., Kattel, G., Peng, J., Jiang, T., and Su, B.: A Harmonized Global Land Evaporation Dataset from Model-based Products Covering 1980–2017, Zenodo [data set], https://doi.org/10.5281/zenodo.4595941, 2021b.
Marini, F. and Walczak, B.: Particle swarm optimization (PSO). A tutorial, Chemometr. Intell. Lab., 149, 153–165, 2015.
Medvigy, D., Wofsy, S., Munger, J., Hollinger, D., and Moorcroft, P.: Mechanistic scaling of ecosystem function and dynamics in space and time: Ecosystem Demography model version 2, J. Geophys. Res.-Biogeo., 114, G01002, https://doi.org/10.1029/2008JG000812, 2009.
Morales, P., Sykes, M. T., Prentice, I. C., Smith, P., Smith, B., Bugmann, H., Zierl, B., Friedlingstein, P., Viovy, N., and Sabaté, S.: Comparing and evaluating process-based ecosystem model predictions of carbon and water fluxes in major European forest biomes, Glob. Change Biol., 11, 2211–2233, 2005.
Morisette, J. T., Richardson, A. D., Knapp, A. K., Fisher, J. I., Graham, E. A., Abatzoglou, J., Wilson, B. E., Breshears, D. D., Henebry, G. M., and Hanes, J. M.: Tracking the rhythm of the seasons in the face of global change: phenological research in the 21st century, Front. Ecol. Environ., 7, 253–260, 2009.
Piao, S., Fang, J., Zhou, L., Ciais, P., and Zhu, B.: Variations in satellite-derived phenology in China's temperate vegetation, Glob. Change Biol., 12, 672–685, 2006.
Piao, S., Liu, Q., Chen, A., Janssens, I. A., Fu, Y., Dai, J., Liu, L., Lian, X., Shen, M., and Zhu, X.: Plant phenology and global climate change: Current progresses and challenges, Glob. Change Biol., 25, 1922–1940, 2019.
Pinzon, J. E. and Tucker, C. J.: A non-stationary 1981–2012 AVHRR NDVI3g time series, Remote Sens., 6, 6929–6960, 2014.
Poli, R., Kennedy, J., and Blackwell, T.: Particle swarm optimization: An overview, Swarm Intell., 1, 33–57, 2007.
Prevéy, J., Vellend, M., Rüger, N., Hollister, R. D., Bjorkman, A. D., Myers-Smith, I. H., Elmendorf, S. C., Clark, K., Cooper, E. J., and Elberling, B.: Greater temperature sensitivity of plant phenology at colder sites: implications for convergence across northern latitudes, Glob. Change Biol., 23, 2660–2671, 2017.
Reed, B. C., Brown, J. F., VanderZee, D., Loveland, T. R., Merchant, J. W., and Ohlen, D. O.: Measuring phenological variability from satellite imagery, J. Veg. Sci., 5, 703–714, 1994.
Richardson, A. D., Anderson, R. S., Arain, M. A., Barr, A. G., Bohrer, G., Chen, G., Chen, J. M., Ciais, P., Davis, K. J., and Desai, A. R.: Terrestrial biosphere models need better representation of vegetation phenology: results from the North American Carbon Program Site Synthesis, Glob. Change Biol., 18, 566–584, 2012.
Rinnan, R., Iversen, L. L., Tang, J., Vedel-Petersen, I., Schollert, M., and Schurgers, G.: Separating direct and indirect effects of rising temperatures on biogenic volatile emissions in the Arctic, P. Natl. Acad. Sci. USA, 117, 32476–32483, https://doi.org/10.1073/pnas.2008901117, 2020.
Roberts, A. M., Tansey, C., Smithers, R. J., and Phillimore, A. B.: Predicting a change in the order of spring phenology in temperate forests, Glob. Change Biol., 21, 2603–2611, 2015.
Rollinson, C. R. and Kaye, M. W.: Experimental warming alters spring phenology of certain plant functional groups in an early successional forest community, Glob. Change Biol., 18, 1108–1116, 2012.
Ryu, S.-R., Chen, J., Noormets, A., Bresee, M. K., and Ollinger, S. V.: Comparisons between PnET-Day and eddy covariance based gross ecosystem production in two Northern Wisconsin forests, Agr. Forest Meteorol., 148, 247–256, 2008.
Sarvas, R.: Investigations on the annual cycle of development of forest trees. Active period, 76, Metsantutkimuslaitoksen Julkaisuja, 110 pp., 1972.
Savitzky, A. and Golay, M. J.: Smoothing and differentiation of data by simplified least squares procedures, Anal. Chem., 36, 1627–1639, 1964.
Schaefer, K., Collatz, G. J., Tans, P., Denning, A. S., Baker, I., Berry, J., Prihodko, L., Suits, N., and Philpott, A.: Combined simple biosphere/Carnegie-Ames-Stanford approach terrestrial carbon cycle model, J. Geophys. Res.-Biogeo., 113, G03034, https://doi.org/10.1029/2007JG000603, 2008.
Sellers, P., Mintz, Y., Sud, Y. E. A., and Dalcher, A.: A simple biosphere model (SiB) for use within general circulation models, J. Atmos. Sci., 43, 505–531, 1986.
Sellers, P., Randall, D., Collatz, G., Berry, J., Field, C., Dazlich, D., Zhang, C., Collelo, G., and Bounoua, L.: A revised land surface parameterization (SiB2) for atmospheric GCMs. Part I: Model formulation, J. Climate, 9, 676–705, 1996.
Sitch, S., Smith, B., Prentice, I. C., Arneth, A., Bondeau, A., Cramer, W., Kaplan, J. O., Levis, S., Lucht, W., and Sykes, M. T.: Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model, Glob. Change Biol., 9, 161–185, 2003.
Smith, B., Prentice, I. C., and Sykes, M. T.: Representation of vegetation dynamics in the modelling of terrestrial ecosystems: comparing two contrasting approaches within European climate space, Global Ecol. Biogeogr., 10, 621–637, 2001.
Sykes, M. T., Prentice, I. C., and Cramer, W.: A bioclimatic model for the potential distributions of north European tree species under present and future climates, J. Biogeogr., 23, 203–233, 1996.
Tang, J., Zhou, P., Miller, P. A., Schurgers, G., Gustafson, A., Makkonen, R., Fu, Y. H., and Rinnan, R.: High-latitude vegetation changes will determine future plant volatile impacts on atmospheric organic aerosols, npj Climate and Atmospheric Science, 6, 147, https://doi.org/10.1038/s41612-023-00463-7, 2023.
Thornton, P. E., Law, B. E., Gholz, H. L., Clark, K. L., Falge, E., Ellsworth, D. S., Goldstein, A. H., Monson, R. K., Hollinger, D., and Falk, M.: Modeling and measuring the effects of disturbance history and climate on carbon and water budgets in evergreen needleleaf forests, Agr. Forest Meteorol., 113, 185–222, 2002.
Tremblay, N. O. and Larocque, G. R.: Seasonal dynamics of understory vegetation in four eastern Canadian forest types, Int. J. Plant Sci., 162, 271–286, 2001.
Tucker, C. J., Pinzon, J. E., Brown, M. E., Slayback, D. A., Pak, E. W., Mahoney, R., Vermote, E. F., and El Saleous, N.: An extended AVHRR 8-km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data, Int. J. Remote Sens., 26, 4485–4498, 2005.
Viovy, N.: CRUNCEP Version 7 – Atmospheric Forcing Data for the Community Land Model, Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory [data set], https://doi.org/10.5065/PZ8F-F017, 2018.
White, M. A., Thornton, P. E., and Running, S. W.: A continental phenology model for monitoring vegetation responses to interannual climatic variability, Global Biogeochem. Cy., 11, 217–234, 1997.
White, M. A., de Beurs, K. M., Didan, K., Inouye, D. W., Richardson, A. D., Jensen, O. P., O'keefe, J., Zhang, G., Nemani, R. R., and van Leeuwen, W. J.: Intercomparison, interpretation, and assessment of spring phenology in North America estimated from remote sensing for 1982–2006, Glob. Change Biol., 15, 2335–2359, 2009.
Wolkovich, E. M., Cook, B. I., Allen, J. M., Crimmins, T., Betancourt, J. L., Travers, S. E., Pau, S., Regetz, J., Davies, T. J., and Kraft, N. J.: Warming experiments underpredict plant phenological responses to climate change, Nature, 485, 494–497, 2012.
Zani, D., Crowther, T. W., Mo, L., Renner, S. S., and Zohner, C. M.: Increased growing-season productivity drives earlier autumn leaf senescence in temperate trees, Science, 370, 1066–1071, 2020.
Zhang, Y., Xiao, X., Wu, X., Zhou, S., Zhang, G., Qin, Y., and Dong, J.: A global moderate resolution dataset of gross primary production of vegetation for 2000–2016, Sci. Data, 4, 170165, https://doi.org/10.1038/sdata.2017.165, 2017a.
Zhang, Y., Xiao, X., Wu, X., Zhou, S., Zhang, G., Qin, Y., et al.: A global moderate resolution dataset of gross primary production of vegetation for 2000–2016, figshare [data set], https://doi.org/10.6084/m9.figshare.c.3789814.v1, 2017b.
Zhang, Y., Commane, R., Zhou, S., Williams, A. P., and Gentine, P.: Light limitation regulates the response of autumn terrestrial carbon uptake to warming, Nat. Clim. Change, 10, 739–743, 2020.
Zheng, J., Jia, G., and Xu, X.: Earlier snowmelt predominates advanced spring vegetation greenup in Alaska, Agr. Forest Meteorol., 315, 108828, 2022.
Zhou, X., Geng, X., Yin, G., Hänninen, H., Hao, F., Zhang, X., and Fu, Y. H.: Legacy effect of spring phenology on vegetation growth in temperate China, Agr. Forest Meteorol., 281, 107845, https://doi.org/10.1016/j.agrformet.2019.107845, 2020.
Zhu, Z., Piao, S., Myneni, R. B., Huang, M., Zeng, Z., Canadell, J. G., Ciais, P., Sitch, S., Friedlingstein, P., and Arneth, A.: Greening of the Earth and its drivers, Nat. Clim. Change, 6, 791–795, 2016.
Zohner, C. M., Mirzagholi, L., Renner, S. S., Mo, L., Rebindaine, D., Bucher, R., Palouš, D., Vitasse, Y., Fu, Y. H., and Stocker, B. D.: Effect of climate warming on the timing of autumn leaf senescence reverses after the summer solstice, Science, 381, eadf5098, https://doi.org/10.1126/science.adf5098, 2023.
Short summary
It is still a challenge to achieve an accurate simulation of vegetation phenology in the dynamic global vegetation models (DGVMs). We implemented and coupled the spring and autumn phenology models into one of the DGVMs, LPJ-GUESS, and substantially improved the accuracy in capturing the start and end dates of growing seasons. Our study highlights the importance of getting accurate phenology estimations to reduce the uncertainties in plant distribution and terrestrial carbon and water cycling.
It is still a challenge to achieve an accurate simulation of vegetation phenology in the dynamic...
