Articles | Volume 16, issue 23
https://doi.org/10.5194/gmd-16-7171-2023
© Author(s) 2023. 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-16-7171-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model evaluation paper
| 
11 Dec 2023
Model evaluation paper |
| 11 Dec 2023
| 11 Dec 2023
Process-oriented models of autumn leaf phenology: ways to sound calibration and implications of uncertain projections
Forest Ecology, Department of Environmental Systems Science, ETH Zurich, Zurich, Switzerland
CEFE, Univ. Montpellier, CNRS, EPHE, IRD, Montpellier, France
Christof Bigler
Forest Ecology, Department of Environmental Systems Science, ETH Zurich, Zurich, Switzerland
Related subject area
Climate and Earth system modeling
Development of a plant carbon–nitrogen interface coupling framework in a coupled biophysical-ecosystem–biogeochemical model (SSiB5/TRIFFID/DayCent-SOM v1.0)
Dynamical Madden–Julian Oscillation forecasts using an ensemble subseasonal-to-seasonal forecast system of the IAP-CAS model
Implementation of a brittle sea ice rheology in an Eulerian, finite-difference, C-grid modeling framework: impact on the simulated deformation of sea ice in the Arctic
HSW-V v1.0: localized injections of interactive volcanic aerosols and their climate impacts in a simple general circulation model
A 3D-Var assimilation scheme for vertical velocity with CMA-MESO v5.0
Updating the radiation infrastructure in MESSy (based on MESSy version 2.55)
An urban module coupled with the Variable Infiltration Capacity model to improve hydrothermal simulations in urban systems
Bayesian hierarchical model for bias-correcting climate models
Evaluation of the coupling of EMACv2.55 to the land surface and vegetation model JSBACHv4
Reduced floating-point precision in regional climate simulations: an ensemble-based statistical verification
TorchClim v1.0: a deep-learning plugin for climate model physics
Linking global terrestrial and ocean biogeochemistry with process-based, coupled freshwater algae–nutrient–solid dynamics in LM3-FANSY v1.0
Validating a microphysical prognostic stratospheric aerosol implementation in E3SMv2 using observations after the Mount Pinatubo eruption
Implementing detailed nucleation predictions in the Earth system model EC-Earth3.3.4: sulfuric acid–ammonia nucleation
Modeling biochar effects on soil organic carbon on croplands in a microbial decomposition model (MIMICS-BC_v1.0)
Hector V3.2.0: functionality and performance of a reduced-complexity climate model
Evaluation of CMIP6 model simulations of PM2.5 and its components over China
Assessment of a tiling energy budget approach in a land surface model, ORCHIDEE-MICT (r8205)
Multivariate adjustment of drizzle bias using machine learning in European climate projections
Development and evaluation of the interactive Model for Air Pollution and Land Ecosystems (iMAPLE) version 1.0
A perspective on the next generation of Earth system model scenarios: towards representative emission pathways (REPs)
Parallel SnowModel (v1.0): a parallel implementation of a distributed snow-evolution modeling system (SnowModel)
LB-SCAM: a learning-based method for efficient large-scale sensitivity analysis and tuning of the Single Column Atmosphere Model (SCAM)
Quantifying the impact of SST feedback frequency on Madden–Julian oscillation simulations
Systematic and objective evaluation of Earth system models: PCMDI Metrics Package (PMP) version 3
A revised model of global silicate weathering considering the influence of vegetation cover on erosion rate
A radiative–convective model computing precipitation with the maximum entropy production hypothesis
Leveraging regional mesh refinement to simulate future climate projections for California using the Simplified Convection-Permitting E3SM Atmosphere Model Version 0
Machine learning parameterization of the multi-scale Kain–Fritsch (MSKF) convection scheme and stable simulation coupled in the Weather Research and Forecasting (WRF) model using WRF–ML v1.0
Impacts of spatial heterogeneity of anthropogenic aerosol emissions in a regionally refined global aerosol–climate model
cfr (v2024.1.26): a Python package for climate field reconstruction
NEWTS1.0: Numerical model of coastal Erosion by Waves and Transgressive Scarps
Evaluation of isoprene emissions from the coupled model SURFEX–MEGANv2.1
A comprehensive Earth system model (AWI-ESM2.1) with interactive icebergs: effects on surface and deep-ocean characteristics
The regional climate–chemistry–ecology coupling model RegCM-Chem (v4.6)–YIBs (v1.0): development and application
Coupling the regional climate model ICON-CLM v2.6.6 into the Earth system model GCOAST-AHOI v2.0 using OASIS3-MCT v4.0
An overview of cloud–radiation denial experiments for the Energy Exascale Earth System Model version 1
The computational and energy cost of simulation and storage for climate science: lessons from CMIP6
Subgrid-scale variability of cloud ice in the ICON-AES 1.3.00
INFERNO-peat v1.0.0: a representation of northern high-latitude peat fires in the JULES-INFERNO global fire model
The 4DEnVar-based weakly coupled land data assimilation system for E3SM version 2
Continental-scale bias-corrected climate and hydrological projections for Australia
G6-1.5K-SAI: a new Geoengineering Model Intercomparison Project (GeoMIP) experiment integrating recent advances in solar radiation modification studies
Bridging the gap: a new module for human water use in the Community Earth System Model version 2.2.1
Modeling the effects of tropospheric ozone on the growth and yield of global staple crops with DSSAT v4.8.0
A one-dimensional urban flow model with an eddy-diffusivity mass-flux (EDMF) scheme and refined turbulent transport (MLUCM v3.0)
DCMIP2016: the tropical cyclone test case
At-scale Model Output Statistics in mountain environments (AtsMOS v1.0)
Impact of ocean vertical mixing parameterization on Arctic sea ice and upper ocean properties using the NEMO-SI3 model
Methane dynamics in the Baltic Sea: investigating concentration, flux and isotopic composition patterns using the coupled physical-biogeochemical model BALTSEM-CH4 v1.0
Zheng Xiang, Yongkang Xue, Weidong Guo, Melannie D. Hartman, Ye Liu, and William J. Parton
Geosci. Model Dev., 17, 6437–6464, https://doi.org/10.5194/gmd-17-6437-2024, https://doi.org/10.5194/gmd-17-6437-2024, 2024
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A process-based plant carbon (C)–nitrogen (N) interface coupling framework has been developed which mainly focuses on plant resistance and N-limitation effects on photosynthesis, plant respiration, and plant phenology. A dynamic C / N ratio is introduced to represent plant resistance and self-adjustment. The framework has been implemented in a coupled biophysical-ecosystem–biogeochemical model, and testing results show a general improvement in simulating plant properties with this framework.
Yangke Liu, Qing Bao, Bian He, Xiaofei Wu, Jing Yang, Yimin Liu, Guoxiong Wu, Tao Zhu, Siyuan Zhou, Yao Tang, Ankang Qu, Yalan Fan, Anling Liu, Dandan Chen, Zhaoming Luo, Xing Hu, and Tongwen Wu
Geosci. Model Dev., 17, 6249–6275, https://doi.org/10.5194/gmd-17-6249-2024, https://doi.org/10.5194/gmd-17-6249-2024, 2024
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We give an overview of the Institute of Atmospheric Physics–Chinese Academy of Sciences subseasonal-to-seasonal ensemble forecasting system and Madden–Julian Oscillation forecast evaluation of the system. Compared to other S2S models, the IAP-CAS model has its benefits but also biases, i.e., underdispersive ensemble, overestimated amplitude, and faster propagation speed when forecasting MJO. We provide a reason for these biases and prospects for further improvement of this system in the future.
Laurent Brodeau, Pierre Rampal, Einar Ólason, and Véronique Dansereau
Geosci. Model Dev., 17, 6051–6082, https://doi.org/10.5194/gmd-17-6051-2024, https://doi.org/10.5194/gmd-17-6051-2024, 2024
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A new brittle sea ice rheology, BBM, has been implemented into the sea ice component of NEMO. We describe how a new spatial discretization framework was introduced to achieve this. A set of idealized and realistic ocean and sea ice simulations of the Arctic have been performed using BBM and the standard viscous–plastic rheology of NEMO. When compared to satellite data, our simulations show that our implementation of BBM leads to a fairly good representation of sea ice deformations.
Joseph P. Hollowed, Christiane Jablonowski, Hunter Y. Brown, Benjamin R. Hillman, Diana L. Bull, and Joseph L. Hart
Geosci. Model Dev., 17, 5913–5938, https://doi.org/10.5194/gmd-17-5913-2024, https://doi.org/10.5194/gmd-17-5913-2024, 2024
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Large volcanic eruptions deposit material in the upper atmosphere, which is capable of altering temperature and wind patterns of Earth's atmosphere for subsequent years. This research describes a new method of simulating these effects in an idealized, efficient atmospheric model. A volcanic eruption of sulfur dioxide is described with a simplified set of physical rules, which eventually cools the planetary surface. This model has been designed as a test bed for climate attribution studies.
Hong Li, Yi Yang, Jian Sun, Yuan Jiang, Ruhui Gan, and Qian Xie
Geosci. Model Dev., 17, 5883–5896, https://doi.org/10.5194/gmd-17-5883-2024, https://doi.org/10.5194/gmd-17-5883-2024, 2024
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Vertical atmospheric motions play a vital role in convective-scale precipitation forecasts by connecting atmospheric dynamics with cloud development. A three-dimensional variational vertical velocity assimilation scheme is developed within the high-resolution CMA-MESO model, utilizing the adiabatic Richardson equation as the observation operator. A 10 d continuous run and an individual case study demonstrate improved forecasts, confirming the scheme's effectiveness.
Matthias Nützel, Laura Stecher, Patrick Jöckel, Franziska Winterstein, Martin Dameris, Michael Ponater, Phoebe Graf, and Markus Kunze
Geosci. Model Dev., 17, 5821–5849, https://doi.org/10.5194/gmd-17-5821-2024, https://doi.org/10.5194/gmd-17-5821-2024, 2024
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We extended the infrastructure of our modelling system to enable the use of an additional radiation scheme. After calibrating the model setups to the old and the new radiation scheme, we find that the simulation with the new scheme shows considerable improvements, e.g. concerning the cold-point temperature and stratospheric water vapour. Furthermore, perturbations of radiative fluxes associated with greenhouse gas changes, e.g. of methane, tend to be improved when the new scheme is employed.
Yibing Wang, Xianhong Xie, Bowen Zhu, Arken Tursun, Fuxiao Jiang, Yao Liu, Dawei Peng, and Buyun Zheng
Geosci. Model Dev., 17, 5803–5819, https://doi.org/10.5194/gmd-17-5803-2024, https://doi.org/10.5194/gmd-17-5803-2024, 2024
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Urban expansion intensifies challenges like urban heat and urban dry islands. To address this, we developed an urban module, VIC-urban, in the Variable Infiltration Capacity (VIC) model. Tested in Beijing, VIC-urban accurately simulated turbulent heat fluxes, runoff, and land surface temperature. We provide a reliable tool for large-scale simulations considering urban environment and a systematic urban modelling framework within VIC, offering crucial insights for urban planners and designers.
Jeremy Carter, Erick A. Chacón-Montalván, and Amber Leeson
Geosci. Model Dev., 17, 5733–5757, https://doi.org/10.5194/gmd-17-5733-2024, https://doi.org/10.5194/gmd-17-5733-2024, 2024
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Climate models are essential tools in the study of climate change and its wide-ranging impacts on life on Earth. However, the output is often afflicted with some bias. In this paper, a novel model is developed to predict and correct bias in the output of climate models. The model captures uncertainty in the correction and explicitly models underlying spatial correlation between points. These features are of key importance for climate change impact assessments and resulting decision-making.
Anna Martin, Veronika Gayler, Benedikt Steil, Klaus Klingmüller, Patrick Jöckel, Holger Tost, Jos Lelieveld, and Andrea Pozzer
Geosci. Model Dev., 17, 5705–5732, https://doi.org/10.5194/gmd-17-5705-2024, https://doi.org/10.5194/gmd-17-5705-2024, 2024
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The study evaluates the land surface and vegetation model JSBACHv4 as a replacement for the simplified submodel SURFACE in EMAC. JSBACH mitigates earlier problems of soil dryness, which are critical for vegetation modelling. When analysed using different datasets, the coupled model shows strong correlations of key variables, such as land surface temperature, surface albedo and radiation flux. The versatility of the model increases significantly, while the overall performance does not degrade.
Hugo Banderier, Christian Zeman, David Leutwyler, Stefan Rüdisühli, and Christoph Schär
Geosci. Model Dev., 17, 5573–5586, https://doi.org/10.5194/gmd-17-5573-2024, https://doi.org/10.5194/gmd-17-5573-2024, 2024
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We investigate the effects of reduced-precision arithmetic in a state-of-the-art regional climate model by studying the results of 10-year-long simulations. After this time, the results of the reduced precision and the standard implementation are hardly different. This should encourage the use of reduced precision in climate models to exploit the speedup and memory savings it brings. The methodology used in this work can help researchers verify reduced-precision implementations of their model.
David Fuchs, Steven C. Sherwood, Abhnil Prasad, Kirill Trapeznikov, and Jim Gimlett
Geosci. Model Dev., 17, 5459–5475, https://doi.org/10.5194/gmd-17-5459-2024, https://doi.org/10.5194/gmd-17-5459-2024, 2024
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Machine learning (ML) of unresolved processes offers many new possibilities for improving weather and climate models, but integrating ML into the models has been an engineering challenge, and there are performance issues. We present a new software plugin for this integration, TorchClim, that is scalable and flexible and thereby allows a new level of experimentation with the ML approach. We also provide guidance on ML training and demonstrate a skillful hybrid ML atmosphere model.
Minjin Lee, Charles A. Stock, John P. Dunne, and Elena Shevliakova
Geosci. Model Dev., 17, 5191–5224, https://doi.org/10.5194/gmd-17-5191-2024, https://doi.org/10.5194/gmd-17-5191-2024, 2024
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Modeling global freshwater solid and nutrient loads, in both magnitude and form, is imperative for understanding emerging eutrophication problems. Such efforts, however, have been challenged by the difficulty of balancing details of freshwater biogeochemical processes with limited knowledge, input, and validation datasets. Here we develop a global freshwater model that resolves intertwined algae, solid, and nutrient dynamics and provide performance assessment against measurement-based estimates.
Hunter York Brown, Benjamin Wagman, Diana Bull, Kara Peterson, Benjamin Hillman, Xiaohong Liu, Ziming Ke, and Lin Lin
Geosci. Model Dev., 17, 5087–5121, https://doi.org/10.5194/gmd-17-5087-2024, https://doi.org/10.5194/gmd-17-5087-2024, 2024
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Explosive volcanic eruptions lead to long-lived, microscopic particles in the upper atmosphere which act to cool the Earth's surface by reflecting the Sun's light back to space. We include and test this process in a global climate model, E3SM. E3SM is tested against satellite and balloon observations of the 1991 eruption of Mt. Pinatubo, showing that with these particles in the model we reasonably recreate Pinatubo and its global effects. We also explore how particle size leads to these effects.
Carl Svenhag, Moa K. Sporre, Tinja Olenius, Daniel Yazgi, Sara M. Blichner, Lars P. Nieradzik, and Pontus Roldin
Geosci. Model Dev., 17, 4923–4942, https://doi.org/10.5194/gmd-17-4923-2024, https://doi.org/10.5194/gmd-17-4923-2024, 2024
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Our research shows the importance of modeling new particle formation (NPF) and growth of particles in the atmosphere on a global scale, as they influence the outcomes of clouds and our climate. With the global model EC-Earth3 we show that using a new method for NPF modeling, which includes new detailed processes with NH3 and H2SO4, significantly impacts the number of particles in the air and clouds and changes the radiation balance of the same magnitude as anthropogenic greenhouse emissions.
Mengjie Han, Qing Zhao, Xili Wang, Ying-Ping Wang, Philippe Ciais, Haicheng Zhang, Daniel S. Goll, Lei Zhu, Zhe Zhao, Zhixuan Guo, Chen Wang, Wei Zhuang, Fengchang Wu, and Wei Li
Geosci. Model Dev., 17, 4871–4890, https://doi.org/10.5194/gmd-17-4871-2024, https://doi.org/10.5194/gmd-17-4871-2024, 2024
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The impact of biochar (BC) on soil organic carbon (SOC) dynamics is not represented in most land carbon models used for assessing land-based climate change mitigation. Our study develops a BC model that incorporates our current understanding of BC effects on SOC based on a soil carbon model (MIMICS). The BC model can reproduce the SOC changes after adding BC, providing a useful tool to couple dynamic land models to evaluate the effectiveness of BC application for CO2 removal from the atmosphere.
Kalyn Dorheim, Skylar Gering, Robert Gieseke, Corinne Hartin, Leeya Pressburger, Alexey N. Shiklomanov, Steven J. Smith, Claudia Tebaldi, Dawn L. Woodard, and Ben Bond-Lamberty
Geosci. Model Dev., 17, 4855–4869, https://doi.org/10.5194/gmd-17-4855-2024, https://doi.org/10.5194/gmd-17-4855-2024, 2024
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Hector is an easy-to-use, global climate–carbon cycle model. With its quick run time, Hector can provide climate information from a run in a fraction of a second. Hector models on a global and annual basis. Here, we present an updated version of the model, Hector V3. In this paper, we document Hector’s new features. Hector V3 is capable of reproducing historical observations, and its future temperature projections are consistent with those of more complex models.
Fangxuan Ren, Jintai Lin, Chenghao Xu, Jamiu A. Adeniran, Jingxu Wang, Randall V. Martin, Aaron van Donkelaar, Melanie S. Hammer, Larry W. Horowitz, Steven T. Turnock, Naga Oshima, Jie Zhang, Susanne Bauer, Kostas Tsigaridis, Øyvind Seland, Pierre Nabat, David Neubauer, Gary Strand, Twan van Noije, Philippe Le Sager, and Toshihiko Takemura
Geosci. Model Dev., 17, 4821–4836, https://doi.org/10.5194/gmd-17-4821-2024, https://doi.org/10.5194/gmd-17-4821-2024, 2024
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We evaluate the performance of 14 CMIP6 ESMs in simulating total PM2.5 and its 5 components over China during 2000–2014. PM2.5 and its components are underestimated in almost all models, except that black carbon (BC) and sulfate are overestimated in two models, respectively. The underestimation is the largest for organic carbon (OC) and the smallest for BC. Models reproduce the observed spatial pattern for OC, sulfate, nitrate and ammonium well, yet the agreement is poorer for BC.
Yi Xi, Chunjing Qiu, Yuan Zhang, Dan Zhu, Shushi Peng, Gustaf Hugelius, Jinfeng Chang, Elodie Salmon, and Philippe Ciais
Geosci. Model Dev., 17, 4727–4754, https://doi.org/10.5194/gmd-17-4727-2024, https://doi.org/10.5194/gmd-17-4727-2024, 2024
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The ORCHIDEE-MICT model can simulate the carbon cycle and hydrology at a sub-grid scale but energy budgets only at a grid scale. This paper assessed the implementation of a multi-tiling energy budget approach in ORCHIDEE-MICT and found warmer surface and soil temperatures, higher soil moisture, and more soil organic carbon across the Northern Hemisphere compared with the original version.
Georgia Lazoglou, Theo Economou, Christina Anagnostopoulou, George Zittis, Anna Tzyrkalli, Pantelis Georgiades, and Jos Lelieveld
Geosci. Model Dev., 17, 4689–4703, https://doi.org/10.5194/gmd-17-4689-2024, https://doi.org/10.5194/gmd-17-4689-2024, 2024
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This study focuses on the important issue of the drizzle bias effect in regional climate models, described by an over-prediction of the number of rainy days while underestimating associated precipitation amounts. For this purpose, two distinct methodologies are applied and rigorously evaluated. These results are encouraging for using the multivariate machine learning method random forest to increase the accuracy of climate models concerning the projection of the number of wet days.
Xu Yue, Hao Zhou, Chenguang Tian, Yimian Ma, Yihan Hu, Cheng Gong, Hui Zheng, and Hong Liao
Geosci. Model Dev., 17, 4621–4642, https://doi.org/10.5194/gmd-17-4621-2024, https://doi.org/10.5194/gmd-17-4621-2024, 2024
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We develop the interactive Model for Air Pollution and Land Ecosystems (iMAPLE). The model considers the full coupling between carbon and water cycles, dynamic fire emissions, wetland methane emissions, biogenic volatile organic compound emissions, and trait-based ozone vegetation damage. Evaluations show that iMAPLE is a useful tool for the study of the interactions among climate, chemistry, and ecosystems.
Malte Meinshausen, Carl-Friedrich Schleussner, Kathleen Beyer, Greg Bodeker, Olivier Boucher, Josep G. Canadell, John S. Daniel, Aïda Diongue-Niang, Fatima Driouech, Erich Fischer, Piers Forster, Michael Grose, Gerrit Hansen, Zeke Hausfather, Tatiana Ilyina, Jarmo S. Kikstra, Joyce Kimutai, Andrew D. King, June-Yi Lee, Chris Lennard, Tabea Lissner, Alexander Nauels, Glen P. Peters, Anna Pirani, Gian-Kasper Plattner, Hans Pörtner, Joeri Rogelj, Maisa Rojas, Joyashree Roy, Bjørn H. Samset, Benjamin M. Sanderson, Roland Séférian, Sonia Seneviratne, Christopher J. Smith, Sophie Szopa, Adelle Thomas, Diana Urge-Vorsatz, Guus J. M. Velders, Tokuta Yokohata, Tilo Ziehn, and Zebedee Nicholls
Geosci. Model Dev., 17, 4533–4559, https://doi.org/10.5194/gmd-17-4533-2024, https://doi.org/10.5194/gmd-17-4533-2024, 2024
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The scientific community is considering new scenarios to succeed RCPs and SSPs for the next generation of Earth system model runs to project future climate change. To contribute to that effort, we reflect on relevant policy and scientific research questions and suggest categories for representative emission pathways. These categories are tailored to the Paris Agreement long-term temperature goal, high-risk outcomes in the absence of further climate policy and worlds “that could have been”.
Ross Mower, Ethan D. Gutmann, Glen E. Liston, Jessica Lundquist, and Soren Rasmussen
Geosci. Model Dev., 17, 4135–4154, https://doi.org/10.5194/gmd-17-4135-2024, https://doi.org/10.5194/gmd-17-4135-2024, 2024
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Higher-resolution model simulations are better at capturing winter snowpack changes across space and time. However, increasing resolution also increases the computational requirements. This work provides an overview of changes made to a distributed snow-evolution modeling system (SnowModel) to allow it to leverage high-performance computing resources. Continental simulations that were previously estimated to take 120 d can now be performed in 5 h.
Jiaxu Guo, Juepeng Zheng, Yidan Xu, Haohuan Fu, Wei Xue, Lanning Wang, Lin Gan, Ping Gao, Wubing Wan, Xianwei Wu, Zhitao Zhang, Liang Hu, Gaochao Xu, and Xilong Che
Geosci. Model Dev., 17, 3975–3992, https://doi.org/10.5194/gmd-17-3975-2024, https://doi.org/10.5194/gmd-17-3975-2024, 2024
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To enhance the efficiency of experiments using SCAM, we train a learning-based surrogate model to facilitate large-scale sensitivity analysis and tuning of combinations of multiple parameters. Employing a hybrid method, we investigate the joint sensitivity of multi-parameter combinations across typical cases, identifying the most sensitive three-parameter combination out of 11. Subsequently, we conduct a tuning process aimed at reducing output errors in these cases.
Yung-Yao Lan, Huang-Hsiung Hsu, and Wan-Ling Tseng
Geosci. Model Dev., 17, 3897–3918, https://doi.org/10.5194/gmd-17-3897-2024, https://doi.org/10.5194/gmd-17-3897-2024, 2024
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This study uses the CAM5–SIT coupled model to investigate the effects of SST feedback frequency on the MJO simulations with intervals at 30 min, 1, 3, 6, 12, 18, 24, and 30 d. The simulations become increasingly unrealistic as the frequency of the SST feedback decreases. Our results suggest that more spontaneous air--sea interaction (e.g., ocean response within 3 d in this study) with high vertical resolution in the ocean model is key to the realistic simulation of the MJO.
Jiwoo Lee, Peter J. Gleckler, Min-Seop Ahn, Ana Ordonez, Paul A. Ullrich, Kenneth R. Sperber, Karl E. Taylor, Yann Y. Planton, Eric Guilyardi, Paul Durack, Celine Bonfils, Mark D. Zelinka, Li-Wei Chao, Bo Dong, Charles Doutriaux, Chengzhu Zhang, Tom Vo, Jason Boutte, Michael F. Wehner, Angeline G. Pendergrass, Daehyun Kim, Zeyu Xue, Andrew T. Wittenberg, and John Krasting
Geosci. Model Dev., 17, 3919–3948, https://doi.org/10.5194/gmd-17-3919-2024, https://doi.org/10.5194/gmd-17-3919-2024, 2024
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We introduce an open-source software, the PCMDI Metrics Package (PMP), developed for a comprehensive comparison of Earth system models (ESMs) with real-world observations. Using diverse metrics evaluating climatology, variability, and extremes simulated in thousands of simulations from the Coupled Model Intercomparison Project (CMIP), PMP aids in benchmarking model improvements across generations. PMP also enables efficient tracking of performance evolutions during ESM developments.
Haoyue Zuo, Yonggang Liu, Gaojun Li, Zhifang Xu, Liang Zhao, Zhengtang Guo, and Yongyun Hu
Geosci. Model Dev., 17, 3949–3974, https://doi.org/10.5194/gmd-17-3949-2024, https://doi.org/10.5194/gmd-17-3949-2024, 2024
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Compared to the silicate weathering fluxes measured at large river basins, the current models tend to systematically overestimate the fluxes over the tropical region, which leads to an overestimation of the global total weathering flux. The most possible cause of such bias is found to be the overestimation of tropical surface erosion, which indicates that the tropical vegetation likely slows down physical erosion significantly. We propose a way of taking this effect into account in models.
Quentin Pikeroen, Didier Paillard, and Karine Watrin
Geosci. Model Dev., 17, 3801–3814, https://doi.org/10.5194/gmd-17-3801-2024, https://doi.org/10.5194/gmd-17-3801-2024, 2024
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All accurate climate models use equations with poorly defined parameters, where knobs for the parameters are turned to fit the observations. This process is called tuning. In this article, we use another paradigm. We use a thermodynamic hypothesis, the maximum entropy production, to compute temperatures, energy fluxes, and precipitation, where tuning is impossible. For now, the 1D vertical model is used for a tropical atmosphere. The correct order of magnitude of precipitation is computed.
Jishi Zhang, Peter Bogenschutz, Qi Tang, Philip Cameron-smith, and Chengzhu Zhang
Geosci. Model Dev., 17, 3687–3731, https://doi.org/10.5194/gmd-17-3687-2024, https://doi.org/10.5194/gmd-17-3687-2024, 2024
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We developed a regionally refined climate model that allows resolved convection and performed a 20-year projection to the end of the century. The model has a resolution of 3.25 km in California, which allows us to predict climate with unprecedented accuracy, and a resolution of 100 km for the rest of the globe to achieve efficient, self-consistent simulations. The model produces superior results in reproducing climate patterns over California that typical modern climate models cannot resolve.
Xiaohui Zhong, Xing Yu, and Hao Li
Geosci. Model Dev., 17, 3667–3685, https://doi.org/10.5194/gmd-17-3667-2024, https://doi.org/10.5194/gmd-17-3667-2024, 2024
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In order to forecast localized warm-sector rainfall in the south China region, numerical weather prediction models are being run with finer grid spacing. The conventional convection parameterization (CP) performs poorly in the gray zone, necessitating the development of a scale-aware scheme. We propose a machine learning (ML) model to replace the scale-aware CP scheme. Evaluation against the original CP scheme has shown that the ML-based CP scheme can provide accurate and reliable predictions.
Taufiq Hassan, Kai Zhang, Jianfeng Li, Balwinder Singh, Shixuan Zhang, Hailong Wang, and Po-Lun Ma
Geosci. Model Dev., 17, 3507–3532, https://doi.org/10.5194/gmd-17-3507-2024, https://doi.org/10.5194/gmd-17-3507-2024, 2024
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Anthropogenic aerosol emissions are an essential part of global aerosol models. Significant errors can exist from the loss of emission heterogeneity. We introduced an emission treatment that significantly improved aerosol emission heterogeneity in high-resolution model simulations, with improvements in simulated aerosol surface concentrations. The emission treatment will provide a more accurate representation of aerosol emissions and their effects on climate.
Feng Zhu, Julien Emile-Geay, Gregory J. Hakim, Dominique Guillot, Deborah Khider, Robert Tardif, and Walter A. Perkins
Geosci. Model Dev., 17, 3409–3431, https://doi.org/10.5194/gmd-17-3409-2024, https://doi.org/10.5194/gmd-17-3409-2024, 2024
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Climate field reconstruction encompasses methods that estimate the evolution of climate in space and time based on natural archives. It is useful to investigate climate variations and validate climate models, but its implementation and use can be difficult for non-experts. This paper introduces a user-friendly Python package called cfr to make these methods more accessible, thanks to the computational and visualization tools that facilitate efficient and reproducible research on past climates.
Rose V. Palermo, J. Taylor Perron, Jason M. Soderblom, Samuel P. D. Birch, Alexander G. Hayes, and Andrew D. Ashton
Geosci. Model Dev., 17, 3433–3445, https://doi.org/10.5194/gmd-17-3433-2024, https://doi.org/10.5194/gmd-17-3433-2024, 2024
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Models of rocky coastal erosion help us understand the controls on coastal morphology and evolution. In this paper, we present a simplified model of coastline erosion driven by either uniform erosion where coastline erosion is constant or wave-driven erosion where coastline erosion is a function of the wave power. This model can be used to evaluate how coastline changes reflect climate, sea-level history, material properties, and the relative influence of different erosional processes.
Safae Oumami, Joaquim Arteta, Vincent Guidard, Pierre Tulet, and Paul David Hamer
Geosci. Model Dev., 17, 3385–3408, https://doi.org/10.5194/gmd-17-3385-2024, https://doi.org/10.5194/gmd-17-3385-2024, 2024
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In this paper, we coupled the SURFEX and MEGAN models. The aim of this coupling is to improve the estimation of biogenic fluxes by using the SURFEX canopy environment model. The coupled model results were validated and several sensitivity tests were performed. The coupled-model total annual isoprene flux is 442 Tg; this value is within the range of other isoprene estimates reported. The ultimate aim of this coupling is to predict the impact of climate change on biogenic emissions.
Lars Ackermann, Thomas Rackow, Kai Himstedt, Paul Gierz, Gregor Knorr, and Gerrit Lohmann
Geosci. Model Dev., 17, 3279–3301, https://doi.org/10.5194/gmd-17-3279-2024, https://doi.org/10.5194/gmd-17-3279-2024, 2024
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We present long-term simulations with interactive icebergs in the Southern Ocean. By melting, icebergs reduce the temperature and salinity of the surrounding ocean. In our simulations, we find that this cooling effect of iceberg melting is not limited to the surface ocean but also reaches the deep ocean and propagates northward into all ocean basins. Additionally, the formation of deep-water masses in the Southern Ocean is enhanced.
Nanhong Xie, Tijian Wang, Xiaodong Xie, Xu Yue, Filippo Giorgi, Qian Zhang, Danyang Ma, Rong Song, Beiyao Xu, Shu Li, Bingliang Zhuang, Mengmeng Li, Min Xie, Natalya Andreeva Kilifarska, Georgi Gadzhev, and Reneta Dimitrova
Geosci. Model Dev., 17, 3259–3277, https://doi.org/10.5194/gmd-17-3259-2024, https://doi.org/10.5194/gmd-17-3259-2024, 2024
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For the first time, we coupled a regional climate chemistry model, RegCM-Chem, with a dynamic vegetation model, YIBs, to create a regional climate–chemistry–ecology model, RegCM-Chem–YIBs. We applied it to simulate climatic, chemical, and ecological parameters in East Asia and fully validated it on a variety of observational data. Results show that RegCM-Chem–YIBs model is a valuable tool for studying the terrestrial carbon cycle, atmospheric chemistry, and climate change on a regional scale.
Ha Thi Minh Ho-Hagemann, Vera Maurer, Stefan Poll, and Irina Fast
EGUsphere, https://doi.org/10.5194/egusphere-2024-923, https://doi.org/10.5194/egusphere-2024-923, 2024
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The regional Earth system model GCOAST-AHOI version 2.0 including the regional climate model ICON-CLM coupled with the ocean model NEMO and the hydrological discharge model HD via the OASIS3-MCT coupler can be a useful tool for conducting long-term regional climate simulations over the EURO-CORDEX domain. The new OASIS3-MCT coupling interface implemented in the ICON-CLM model makes it more flexible to couple with an external ocean model and an external hydrological discharge model.
Bryce E. Harrop, Jian Lu, L. Ruby Leung, William K. M. Lau, Kyu-Myong Kim, Brian Medeiros, Brian J. Soden, Gabriel A. Vecchi, Bosong Zhang, and Balwinder Singh
Geosci. Model Dev., 17, 3111–3135, https://doi.org/10.5194/gmd-17-3111-2024, https://doi.org/10.5194/gmd-17-3111-2024, 2024
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Seven new experimental setups designed to interfere with cloud radiative heating have been added to the Energy Exascale Earth System Model (E3SM). These experiments include both those that test the mean impact of cloud radiative heating and those examining its covariance with circulations. This paper documents the code changes and steps needed to run these experiments. Results corroborate prior findings for how cloud radiative heating impacts circulations and rainfall patterns.
Mario C. Acosta, Sergi Palomas, Stella V. Paronuzzi Ticco, Gladys Utrera, Joachim Biercamp, Pierre-Antoine Bretonniere, Reinhard Budich, Miguel Castrillo, Arnaud Caubel, Francisco Doblas-Reyes, Italo Epicoco, Uwe Fladrich, Sylvie Joussaume, Alok Kumar Gupta, Bryan Lawrence, Philippe Le Sager, Grenville Lister, Marie-Pierre Moine, Jean-Christophe Rioual, Sophie Valcke, Niki Zadeh, and Venkatramani Balaji
Geosci. Model Dev., 17, 3081–3098, https://doi.org/10.5194/gmd-17-3081-2024, https://doi.org/10.5194/gmd-17-3081-2024, 2024
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We present a collection of performance metrics gathered during the Coupled Model Intercomparison Project Phase 6 (CMIP6), a worldwide initiative to study climate change. We analyse the metrics that resulted from collaboration efforts among many partners and models and describe our findings to demonstrate the utility of our study for the scientific community. The research contributes to understanding climate modelling performance on the current high-performance computing (HPC) architectures.
Sabine Doktorowski, Jan Kretzschmar, Johannes Quaas, Marc Salzmann, and Odran Sourdeval
Geosci. Model Dev., 17, 3099–3110, https://doi.org/10.5194/gmd-17-3099-2024, https://doi.org/10.5194/gmd-17-3099-2024, 2024
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Especially over the midlatitudes, precipitation is mainly formed via the ice phase. In this study we focus on the initial snow formation process in the ICON-AES, the aggregation process. We use a stochastical approach for the aggregation parameterization and investigate the influence in the ICON-AES. Therefore, a distribution function of cloud ice is created, which is evaluated with satellite data. The new approach leads to cloud ice loss and an improvement in the process rate bias.
Katie R. Blackford, Matthew Kasoar, Chantelle Burton, Eleanor Burke, Iain Colin Prentice, and Apostolos Voulgarakis
Geosci. Model Dev., 17, 3063–3079, https://doi.org/10.5194/gmd-17-3063-2024, https://doi.org/10.5194/gmd-17-3063-2024, 2024
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Peatlands are globally important stores of carbon which are being increasingly threatened by wildfires with knock-on effects on the climate system. Here we introduce a novel peat fire parameterization in the northern high latitudes to the INFERNO global fire model. Representing peat fires increases annual burnt area across the high latitudes, alongside improvements in how we capture year-to-year variation in burning and emissions.
Pengfei Shi, L. Ruby Leung, Bin Wang, Kai Zhang, Samson M. Hagos, and Shixuan Zhang
Geosci. Model Dev., 17, 3025–3040, https://doi.org/10.5194/gmd-17-3025-2024, https://doi.org/10.5194/gmd-17-3025-2024, 2024
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Improving climate predictions have profound socio-economic impacts. This study introduces a new weakly coupled land data assimilation (WCLDA) system for a coupled climate model. We demonstrate improved simulation of soil moisture and temperature in many global regions and throughout the soil layers. Furthermore, significant improvements are also found in reproducing the time evolution of the 2012 US Midwest drought. The WCLDA system provides the groundwork for future predictability studies.
Justin Peter, Elisabeth Vogel, Wendy Sharples, Ulrike Bende-Michl, Louise Wilson, Pandora Hope, Andrew Dowdy, Greg Kociuba, Sri Srikanthan, Vi Co Duong, Jake Roussis, Vjekoslav Matic, Zaved Khan, Alison Oke, Margot Turner, Stuart Baron-Hay, Fiona Johnson, Raj Mehrotra, Ashish Sharma, Marcus Thatcher, Ali Azarvinand, Steven Thomas, Ghyslaine Boschat, Chantal Donnelly, and Robert Argent
Geosci. Model Dev., 17, 2755–2781, https://doi.org/10.5194/gmd-17-2755-2024, https://doi.org/10.5194/gmd-17-2755-2024, 2024
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We detail the production of datasets and communication to end users of high-resolution projections of rainfall, runoff, and soil moisture for the entire Australian continent. This is important as previous projections for Australia were for small regions and used differing techniques for their projections, making comparisons difficult across Australia's varied climate zones. The data will be beneficial for research purposes and to aid adaptation to climate change.
Daniele Visioni, Alan Robock, Jim Haywood, Matthew Henry, Simone Tilmes, Douglas G. MacMartin, Ben Kravitz, Sarah J. Doherty, John Moore, Chris Lennard, Shingo Watanabe, Helene Muri, Ulrike Niemeier, Olivier Boucher, Abu Syed, Temitope S. Egbebiyi, Roland Séférian, and Ilaria Quaglia
Geosci. Model Dev., 17, 2583–2596, https://doi.org/10.5194/gmd-17-2583-2024, https://doi.org/10.5194/gmd-17-2583-2024, 2024
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This paper describes a new experimental protocol for the Geoengineering Model Intercomparison Project (GeoMIP). In it, we describe the details of a new simulation of sunlight reflection using the stratospheric aerosols that climate models are supposed to run, and we explain the reasons behind each choice we made when defining the protocol.
Sabin I. Taranu, David M. Lawrence, Yoshihide Wada, Ting Tang, Erik Kluzek, Sam Rabin, Yi Yao, Steven J. De Hertog, Inne Vanderkelen, and Wim Thiery
EGUsphere, https://doi.org/10.5194/egusphere-2024-362, https://doi.org/10.5194/egusphere-2024-362, 2024
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In this study, we improve an existing climate model to account for human water usage across domestic, industrial, and agriculture purposes. With the new capabilities, the model is now better equipped for studying questions related to water scarcity in both present and future conditions under climate change. Despite the advancements, there remains important limitations in our modelling framework which requires further work.
Jose Rafael Guarin, Jonas Jägermeyr, Elizabeth A. Ainsworth, Fabio A. A. Oliveira, Senthold Asseng, Kenneth Boote, Joshua Elliott, Lisa Emberson, Ian Foster, Gerrit Hoogenboom, David Kelly, Alex C. Ruane, and Katrina Sharps
Geosci. Model Dev., 17, 2547–2567, https://doi.org/10.5194/gmd-17-2547-2024, https://doi.org/10.5194/gmd-17-2547-2024, 2024
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The effects of ozone (O3) stress on crop photosynthesis and leaf senescence were added to maize, rice, soybean, and wheat crop models. The modified models reproduced growth and yields under different O3 levels measured in field experiments and reported in the literature. The combined interactions between O3 and additional stresses were reproduced with the new models. These updated crop models can be used to simulate impacts of O3 stress under future climate change and air pollution scenarios.
Jiachen Lu, Negin Nazarian, Melissa Anne Hart, E. Scott Krayenhoff, and Alberto Martilli
Geosci. Model Dev., 17, 2525–2545, https://doi.org/10.5194/gmd-17-2525-2024, https://doi.org/10.5194/gmd-17-2525-2024, 2024
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This study enhances urban canopy models by refining key assumptions. Simulations for various urban scenarios indicate discrepancies in turbulent transport efficiency for flow properties. We propose two modifications that involve characterizing diffusion coefficients for momentum and turbulent kinetic energy separately and introducing a physics-based
mass-fluxterm. These adjustments enhance the model's performance, offering more reliable temperature and surface flux estimates.
Justin L. Willson, Kevin A. Reed, Christiane Jablonowski, James Kent, Peter H. Lauritzen, Ramachandran Nair, Mark A. Taylor, Paul A. Ullrich, Colin M. Zarzycki, David M. Hall, Don Dazlich, Ross Heikes, Celal Konor, David Randall, Thomas Dubos, Yann Meurdesoif, Xi Chen, Lucas Harris, Christian Kühnlein, Vivian Lee, Abdessamad Qaddouri, Claude Girard, Marco Giorgetta, Daniel Reinert, Hiroaki Miura, Tomoki Ohno, and Ryuji Yoshida
Geosci. Model Dev., 17, 2493–2507, https://doi.org/10.5194/gmd-17-2493-2024, https://doi.org/10.5194/gmd-17-2493-2024, 2024
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Accurate simulation of tropical cyclones (TCs) is essential to understanding their behavior in a changing climate. One way this is accomplished is through model intercomparison projects, where results from multiple climate models are analyzed to provide benchmark solutions for the wider climate modeling community. This study describes and analyzes the previously developed TC test case for nine climate models in an intercomparison project, providing solutions that aid in model development.
Maximillian Van Wyk de Vries, Tom Matthews, L. Baker Perry, Nirakar Thapa, and Rob Wilby
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-36, https://doi.org/10.5194/gmd-2024-36, 2024
Revised manuscript accepted for GMD
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This paper introduces the AtsMOS workflow, a new tool for improving weather forecasts in mountainous areas. By combining advanced statistical techniques with local weather data, AtsMOS can provide more accurate predictions of weather conditions. Using data from Mount Everest as an example, AtsMOS has shown promise in better forecasting hazardous weather conditions, making it a valuable tool for communities in mountainous regions and beyond.
Sofia Allende, Anne Marie Treguier, Camille Lique, Clément de Boyer Montégut, François Massonnet, Thierry Fichefet, and Antoine Barthélemy
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-49, https://doi.org/10.5194/gmd-2024-49, 2024
Revised manuscript accepted for GMD
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We study the parameters involved in the turbulent kinetic energy mixed layer penetration scheme of the NEMO model in Arctic sea ice-covered regions. This evaluation reveals the impact of these parameters on mixed layer depth, sea surface temperature and salinity, and ocean stratification. Our findings also demonstrate considerable impacts on sea ice thickness and sea ice concentration, emphasizing the importance of accurate ocean mixing representation in understanding Arctic climate dynamics.
Erik Gustafsson, Bo G. Gustafsson, Martijn Hermans, Christoph Humborg, and Christian Stranne
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-211, https://doi.org/10.5194/gmd-2023-211, 2024
Revised manuscript accepted for GMD
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Methane (CH4) cycling in the Baltic Sea is studied through model simulations, allowing a first estimate of key CH4 fluxes. A preliminary budget identifies benthic CH4 release as the dominant source, and two main sinks: CH4 oxidation in the water (87 % of the sinks) and outgassing to the atmosphere (13 % of the sinks). This study addresses CH4 emissions from coastal seas and is a first step towards understanding the relative importance of open water outgassing compared to local coastal hotspots.
Cited articles
Alberto, F., Bouffier, L., Louvet, J. M., Lamy, J. B., Delzon, S., and Kremer, A.: Adaptive responses for seed and leaf phenology in natural populations of sessile oak along an altitudinal gradient, J. Evol. Biol., 24, 1442–1454, https://doi.org/10.1111/j.1420-9101.2011.02277.x, 2011.
Anderson, E., Bai, Z., Bischof, C., Blackford, L. S., Demmel, J., Dongarra, J., Du Croz, J., Greenbaum, A., Hammarling, S., McKenney, A., and Sorensen, D.: LAPACK users' guide, Third, Society for Industrial and Applied Mathematics, SIAM, Philadelphia, PA, ISBN 0-89871-447-8, 1999.
Arend, M., Gessler, A., and Schaub, M.: The influence of the soil on spring and autumn phenology in European beech, Tree Physiol., 36, 78–85, https://doi.org/10.1093/treephys/tpv087, 2016.
Asse, D., Chuine, I., Vitasse, Y., Yoccoz, N. G., Delpierre, N., Badeau, V., Delestrade, A., and Randin, C. F.: Warmer winters reduce the advance of tree spring phenology induced by warmer springs in the Alps, Agr. Forest Meteorol., 252, 220–230, https://doi.org/10.1016/j.agrformet.2018.01.030, 2018.
Basler, D.: Evaluating phenological models for the prediction of leaf-out dates in six temperate tree species across central Europe, Agr. Forest Meteorol., 217, 10–21, https://doi.org/10.1016/j.agrformet.2015.11.007, 2016.
Bates, D., Machler, M., Bolker, B. M., and Walker, S. C.: Fitting linear mixed-effects models using lme4, J. Stat. Softw., 67, 1–48, 2015.
Beaudoing, H. and Rodell, M.: GLDAS Noah land surface model L4 3 hourly 0.25 x 0.25 degree V2.0, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/342OHQM9AK6Q, 2019.
Beaudoing, H. and Rodell, M.: GLDAS Noah land surface model L4 3 hourly 0.25 x 0.25 degree V2.1, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/E7TYRXPJKWOQ, 2020.
Bendtsen, C.: pso: Particle Swarm Optimization, R package version 1.0.4, 2012.
Benjamin, D. J. and Berger, J. O.: Three recommendations for improving the use of p-values, Am. Stat., 73, 186–191, https://doi.org/10.1080/00031305.2018.1543135, 2019.
Bigler, C. and Vitasse, Y.: Premature leaf discoloration of European deciduous trees is caused by drought and heat in late spring and cold spells in early fall, Agr. Forest Meteorol., 307, 108492, https://doi.org/10.1016/j.agrformet.2021.108492, 2021.
Bourdeau, P. F.: A Test of random versus systematic ecological samplig, Ecology, 34, 499–512, https://doi.org/10.2307/1929722, 1953.
Braconnot, P., Harrison, S. P., Kageyama, M., Bartlein, P. J., Masson-Delmotte, V., Abe-Ouchi, A., Otto-Bliesner, B., and Zhao, Y.: Evaluation of climate models using palaeoclimatic data, Nat. Clim. Change, 2, 417–424, https://doi.org/10.1038/nclimate1456, 2012.
Brock, T. D.: Calculating solar radiation for ecological studies, Ecol. Model., 14, 1–19, https://doi.org/10.1016/0304-3800(81)90011-9, 1981.
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, https://doi.org/10.3354/cr00983, 2011.
Candelieri, A.: A gentle introduction to Bayesian Optimization, Winter Simulation Conference (WSC), Phoenix, AZ, virtual, 12–15 December 2021, 1–16, https://doi.org/10.1109/WSC52266.2021.9715413, 2021.
Cawley, G. C. and Talbot, N. L. C.: On over-fitting in model selection and subsequent selection bias in performance evaluation, J. Mach. Learn. Res., 11, 2079–2107, 2010.
Chandler, R. E. and Scott, E. M.: Statistical methods for trend detection and analysis in the environmental sciences, Statistics in practice, Wiley, Chichester, 368 pp., ISBN 978-0-470-01543-8, 2011.
Charlet de Sauvage, J., Vitasse, Y., Meier, M., Delzon, S., and Bigler, C.: Temperature rather than individual growing period length determines radial growth of sessile oak in the Pyrenees, Agr. Forest Meteorol., 317, 108885, https://doi.org/10.1016/j.agrformet.2022.108885, 2022.
Chen, L., Huang, J. G., Ma, Q. Q., Hanninen, H., Rossi, S., Piao, S. L., and Bergeron, Y.: Spring phenology at different altitudes is becoming more uniform under global warming in Europe, Glob. Change Biol., 24, 3969–3975, https://doi.org/10.1111/gcb.14288, 2018.
Chuine, I.: Why does phenology drive species distribution?, Philos. T. R. Soc. B, 365, 3149–3160, https://doi.org/10.1098/rstb.2010.0142, 2010.
Chuine, I. and Beaubien, E. G.: Phenology is a major determinant of tree species range, Ecol. Lett., 4, 500–510, https://doi.org/10.1046/j.1461-0248.2001.00261.x, 2001.
Chuine, I. and Régnière, J.: Process-based models of phenology for plants and animals, Annu. Rev. Ecol. Evol. S., 48, 159–182, https://doi.org/10.1146/annurev-ecolsys-110316-022706, 2017.
Chuine, I., Cour, P., and Rousseau, D. D.: Fitting models predicting dates of flowering of temperate-zone trees using simulated annealing, Plant Cell Environ., 21, 455–466, https://doi.org/10.1046/j.1365-3040.1998.00299.x, 1998.
Chuine, I., Cour, P., and Rousseau, D. D.: Selecting models to predict the timing of flowering of temperate trees: implications for tree phenology modelling, Plant Cell Environ., 22, 1–13, https://doi.org/10.1046/j.1365-3040.1999.00395.x, 1999.
Chuine, I., Belmonte, J., and Mignot, A.: A modelling analysis of the genetic variation of phenology between tree populations, J. Ecol., 88, 561–570, https://doi.org/10.1046/j.1365-2745.2000.00468.x, 2000.
Chuine, I., de Cortazar-Atauri, I. G., Kramer, K., and Hänninen, H.: Plant development models, in: Phenology: An integrative environmental science, edited by: Schwartz, M. D., Springer Netherlands, Dordrecht, 275–293, https://doi.org/10.1007/978-94-007-6925-0_15, 2013.
Clark, M.: mixedup: Miscellaneous functions for mixed models, R package version 0.3.9 [code], https://m-clark.github.io/mixedup (last access: 4 January 2022), 2022.
Clarke, L., Edmonds, J., Jacoby, H., Pitcher, H., Reilly, J., and Richels, R.: Scenarios of greenhouse gas emissions and atmospheric concentrations, Sub-report 2.1a of synthesis and assessment product 2.1 by the U.S. climate change science program and the subcommittee on global change research, Department of Energy, Office of Biological & Environmental Research, Washington DC, 2007.
Clerc, M.: From theory to practice in Particle Swarm Optimization, in: Handbook of swarm intelligence: Concepts, principles and applications, edited by: Panigrahi, B. K., Shi, Y., and Lim, M.-H., Springer Berlin Heidelberg, Berlin, Heidelberg, 3–36, https://doi.org/10.1007/978-3-642-17390-5_1, 2011.
Clerc, M.: Standard Particle Swarm Optimisation, hal-00764996, https://hal.archives-ouvertes.fr/hal-00764996 (last access: 23 September 2012), 2012.
Cochran, W. G.: Relative accuracy of systematic and stratified random samples for a certain class of populations, Ann. Math. Stat., 17, 164–177, 1946.
Colin, J., Déqué, M., Radu, R., and Somot, S.: Sensitivity study of heavy precipitation in Limited Area Model climate simulations: influence of the size of the domain and the use of the spectral nudging technique, Tellus A, 62, 591–604, https://doi.org/10.1111/j.1600-0870.2010.00467.x, 2010.
Coppola, E., Nogherotto, R., Ciarlo', J. M., Giorgi, F., van Meijgaard, E., Kadygrov, N., Iles, C., Corre, L., Sandstad, M., Somot, S., Nabat, P., Vautard, R., Levavasseur, G., Schwingshackl, C., Sillmann, J., Kjellström, E., Nikulin, G., Aalbers, E., Lenderink, G., Christensen, O. B., Boberg, F., Sørland, S. L., Demory, M.-E., Bülow, K., Teichmann, C., Warrach-Sagi, K., and Wulfmeyer, V.: Assessment of the European climate projections as simulated by the large EURO-CORDEX regional and global climate model ensemble, J. Geophys. Res.-Atmos., 126, e2019JD032356, https://doi.org/10.1029/2019JD032356, 2021.
Delpierre, N., Dufrene, E., Soudani, K., Ulrich, E., Cecchini, S., Boe, J., and Francois, C.: Modelling interannual and spatial variability of leaf senescence for three deciduous tree species in France, Agr. Forest Meteorol., 149, 938–948, https://doi.org/10.1016/j.agrformet.2008.11.014, 2009.
de Réaumur, R. A. F.: Observations du thermomètre, faites à Paris pendant l'année 1735, comparées avec celles qui ont été faites sous la ligne, à l'isle de France, a Alger et quelques-unes de nos isles de l'Amérique, Académie royale des sciences, des lettres et des beaux-arts de Belgique, 33, 1735.
Dowle, M. and Srinivasan, A.: data.table: Extension of “data.frame”, R package version 1.14.2 [code], https://CRAN.R-project.org/package=data.table (last access: 4 January 2021), 2021.
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.
Dufrêne, E., Davi, H., Francois, C., le Maire, G., Le Dantec, V., and Granier, A.: Modelling carbon and water cycles in a beech forest Part I: Model description and uncertainty analysis on modelled NEE, Ecol. Model., 185, 407–436, https://doi.org/10.1016/j.ecolmodel.2005.01.004, 2005.
Dupuy, D., Helbert, C., and Franco, J.: DiceDesign and DiceEval: Two R packages for design and analysis of computer experiments, J. Stat. Softw., 65, 1–38, https://doi.org/10.18637/jss.v065.i11, 2015.
Foley, A. M.: Uncertainty in regional climate modelling: A review, Progress in Physical Geography: Earth and Environment, 34, 647–670, https://doi.org/10.1177/0309133310375654, 2010.
Fu, Y., Li, X., Zhou, X., Geng, X., Guo, Y., and Zhang, Y.: Progress in plant phenology modeling under global climate change, Sci. China Earth Sci., 63, 1237–1247, https://doi.org/10.1007/s11430-019-9622-2, 2020.
Fu, Y. H., Piao, S. L., Op de Beeck, M., Cong, N., Zhao, H. F., Zhang, Y., Menzel, A., and Janssens, I. A.: Recent spring phenology shifts in western Central Europe based on multiscale observations, Glob. Ecol. Biogeogr., 23, 1255–1263, https://doi.org/10.1111/geb.12210, 2014.
Fu, Y. H., Piao, S. L., Delpierre, N., Hao, F. H., Hanninen, H., Geng, X. J., Penuelas, J., Zhang, X., Janssens, I. A., and Campioli, M.: Nutrient availability alters the correlation between spring leaf-out and autumn leaf senescence dates, Tree Physiol., 39, 1277–1284, https://doi.org/10.1093/treephys/tpz041, 2019.
Fu, Y. S. H., Campioli, M., Vitasse, Y., De Boeck, H. J., Van den Berge, J., AbdElgawad, H., Asard, H., Piao, S. L., Deckmyn, G., and Janssens, I. A.: Variation in leaf flushing date influences autumnal senescence and next year's flushing date in two temperate tree species, P. Natl. Acad. Sci. USA, 111, 7355–7360, https://doi.org/10.1073/pnas.1321727111, 2014.
Gill, A. L., Gallinat, A. S., Sanders-DeMott, R., Rigden, A. J., Gianotti, D. J. S., Mantooth, J. A., and Templer, P. H.: Changes in autumn senescence in northern hemisphere deciduous trees: a meta-analysis of autumn phenology studies, Ann. Bot., 116, 875–888, https://doi.org/10.1093/aob/mcv055, 2015.
Goodman, S.: A Dirty Dozen: Twelve P-Value Misconceptions, Semin. Hematol., 45, 135–140, https://doi.org/10.1053/j.seminhematol.2008.04.003, 2008.
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., and Moore, R.: Google Earth Engine: Planetary-scale geospatial analysis for everyone, Remote Sens. Environ., 202, 18–27, https://doi.org/10.1016/j.rse.2017.06.031, 2017.
Hack, H., Bleiholder, H., Buhr, L., Meier, U., Schnock-Fricke, U., Weber, E., and Witzenberger, A.: Einheitliche Codierung der phänologischen Entwicklungsstadien mono-und dikotyler Pflanzen – Erweiterte BBCH-Skala, Allgemein, Nachrichtenbl. Deut. Pflanzenschutzd, 44, 265–270, 1992.
Hansen, N.: The CMA evolution strategy: A comparing review, in: Towards a new evolutionary computation: Advances in the estimation of distribution algorithms, edited by: Lozano, J. A., Larrañaga, P., Inza, I., and Bengoetxea, E., Springer Berlin Heidelberg, Berlin, Heidelberg, 75–102, https://doi.org/10.1007/3-540-32494-1_4, 2006.
Hansen, N.: The CMA evolution strategy: A tutorial, arXiv [preprint], https://doi.org/10.48550/arXiv.1604.00772, 2016.
Held, L. and Ott, M.: How the maximal evidence of P-values against point null hypotheses depends on sample size, Am. Stat., 70, 335–341, https://doi.org/10.1080/00031305.2016.1209128, 2016.
Held, L. and Ott, M.: On p-Values and Bayes Factors, Annu. Rev. Stat. Appl., 5, 393–419, https://doi.org/10.1146/annurev-statistics-031017-100307, 2018.
Hengl, T., Mendes de Jesus, J., Heuvelink, G. B. M., Ruiperez Gonzalez, M., Kilibarda, M., Blagotić, A., Shangguan, W., Wright, M. N., Geng, X., Bauer-Marschallinger, B., Guevara, M. A., Vargas, R., MacMillan, R. A., Batjes, N. H., Leenaars, J. G. B., Ribeiro, E., Wheeler, I., Mantel, S., and Kempen, B.: SoilGrids250m: Global gridded soil information based on machine learning, PLOS ONE, 12, e0169748, https://doi.org/10.1371/journal.pone.0169748, 2017.
Herr, D. G.: On the history of ANOVA in unbalanced, factorial designs: The first 30 years, Am. Stat., 40, 265–270, https://doi.org/10.2307/2684597, 1986.
Holtmeier, F. K. and Broll, G.: Treeline research – From the roots of the past to present time. A review, Forests, 11, 38, https://doi.org/10.3390/f11010038, 2020.
Hufkens, K., Basler, D., Milliman, T., Melaas, E. K., and Richardson, A. D.: An integrated phenology modelling framework in R, Methods Ecol. Evol., 9, 1276–1285, https://doi.org/10.1111/2041-210x.12970, 2018.
Ibáñez, I., Primack, R. B., Miller-Rushing, A. J., Ellwood, E., Higuchi, H., Lee, S. D., Kobori, H., and Silander, J. A.: Forecasting phenology under global warming, Philos. T. R. Soc. B, 365, 3247–3260, https://doi.org/10.1098/rstb.2010.0120, 2010.
Ioannidis, J. P. A.: Why most published research findings are false, PLOS Med., 2, e124, https://doi.org/10.1371/journal.pmed.0020124, 2005.
Ioannidis, J. P. A.: What have we (not) learnt from millions of scientific papers with P values?, Am. Stat., 73, 20–25, https://doi.org/10.1080/00031305.2018.1447512, 2019.
Jacob, D., Petersen, J., Eggert, B., Alias, A., Christensen, O. B., Bouwer, L. M., Braun, A., Colette, A., Deque, M., Georgievski, G., Georgopoulou, E., Gobiet, A., Menut, L., Nikulin, G., Haensler, A., Hempelmann, N., Jones, C., Keuler, K., Kovats, S., Kroner, N., Kotlarski, S., Kriegsmann, A., Martin, E., van Meijgaard, E., Moseley, C., Pfeifer, S., Preuschmann, S., Radermacher, C., Radtke, K., Rechid, D., Rounsevell, M., Samuelsson, P., Somot, S., Soussana, J. F., Teichmann, C., Valentini, R., Vautard, R., Weber, B., and Yiou, P.: EURO-CORDEX: New high-resolution climate change projections for European impact research, Reg. Environ. Change, 14, 563–578, https://doi.org/10.1007/s10113-013-0499-2, 2014.
James, G., Witten, D., Hastie, T., and Tibishirani, R.: An introduction to statistical learning: with applications in R, Springer, New York, https://doi.org/10.1007/978-1-4614-7138-7, 2017.
Jenkins, D. G. and Quintana-Ascencio, P. F.: A solution to minimum sample size for regressions, PLOS ONE, 15, e0229345, https://doi.org/10.1371/journal.pone.0229345, 2020.
Jibran, R., Hunter, D. A., and Dijkwel, P. P.: Hormonal regulation of leaf senescence through integration of developmental and stress signals, Plant Mol. Biol., 82, 547–561, https://doi.org/10.1007/s11103-013-0043-2, 2013.
Jochner, S., Caffarra, A., and Menzel, A.: Can spatial data substitute temporal data in phenological modelling? A survey using birch flowering, Tree Physiol., 33, 1256–1268, https://doi.org/10.1093/treephys/tpt079, 2013.
Johnson, V. E.: Bayes Factors based on test statistics, J. Roy. Stat. Soc. B, 67, 689–701, 2005.
Jourdan, M., François, C., Delpierre, N., St-Paul, N. M., and Dufrêne, E.: Reliable predictions of forest ecosystem functioning require flawless climate forcings, Agr. Forest Meteorol., 311, 108703, https://doi.org/10.1016/j.agrformet.2021.108703, 2021.
Kassambara, A.: ggpubr: “ggplot2” Based Publication Ready Plots. R package version 0.4.0 [code], https://CRAN.R-project.org/package=ggpubr (last access: 4 January 2020), 2020.
Keenan, R. J.: Climate change impacts and adaptation in forest management: a review, Ann. Forest Sci., 72, 145–167, https://doi.org/10.1007/s13595-014-0446-5, 2015.
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, https://doi.org/10.1111/gcb.12890, 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., Wing, I. S., 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.
Kendall, M. G.: A new measure of rank correlation, Biometrika, 30, 81–93, https://doi.org/10.1093/biomet/30.1-2.81, 1938.
Krige, D.: A statistical approach to some mine valuations and allied problems at the Witwatersrand, Unpublished Master's Thesis, University of the Witwatersrand, Witwatersrand, South Africa, 1951.
Lang, G. A., Early, J. D., Martin, G. C., and Darnell, R. L.: Endo-, para-, and ecodormancy: Physiological teriminology and classification for dormancy research, Hortscience, 22, 371–377, 1987.
Lang, W., Chen, X., Qian, S., Liu, G., and Piao, S.: A new process-based model for predicting autumn phenology: How is leaf senescence controlled by photoperiod and temperature coupling?, Agr. Forest Meteorol., 268, 124–135, https://doi.org/10.1016/j.agrformet.2019.01.006, 2019.
Lazić, I., Tošić, M., and Djurdjević, V.: Verification of the EURO-CORDEX RCM historical run results over the Pannonian basin for the summer season, Atmosphere, 12, 714, https://doi.org/10.3390/atmos12060714, 2021.
Liang, L. and Wu, J. X.: An empirical method to account for climatic adaptation in plant phenology models, Int. J. Biometeorol., 65, 1953–1966, https://doi.org/10.1007/s00484-021-02152-7, 2021.
Lieth, H.: Purposes of a Phenology Book, in: Phenology and Seasonality Modeling, edited by: Lieth, H., Springer, Berlin, Heidelberg, 3–19, https://doi.org/10.1007/978-3-642-51863-8_1, 1974.
Lim, P. O., Kim, H. J., and Gil Nam, H.: Leaf senescence, Annu. Rev. Plant Biol., 58, 115–136, https://doi.org/10.1146/annurev.arplant.57.032905.105316, 2007.
Liu, G., Chen, X. Q., Fu, Y. S., and Delpierre, N.: Modelling leaf coloration dates over temperate China by considering effects of leafy season climate, Ecol. Model., 394, 34–43, https://doi.org/10.1016/j.ecolmodel.2018.12.020, 2019.
Liu, G., Chuine, I., Denechere, R., Jean, F., Dufrene, E., Vincent, G., Berveiller, D., and Delpierre, N.: Higher sample sizes and observer inter-calibration are needed for reliable scoring of leaf phenology in trees, J. Ecol., 14, 2461–2474, https://doi.org/10.1111/1365-2745.13656, 2021.
Liu, Q., Piao, S. L., Campioli, M., Gao, M. D., Fu, Y. S. H., Wang, K., He, Y., Li, X. Y., and Janssens, I. A.: Modeling leaf senescence of deciduous tree species in Europe, Glob. Change Biol., 15, 34–43, https://doi.org/10.1111/gcb.15132, 2020.
Lu, X. and Keenan, T. F.: No evidence for a negative effect of growing season photosynthesis on leaf senescence timing, Glob. Change Biol., 28, 3083–3093, https://doi.org/10.1111/gcb.16104, 2022.
Maes, F., Wehenkel, L., and Ernst, D.: Meta-learning of exploration/exploitation strategies: The Multi-armed bandit case, Agents and Artificial Intelligence, Berlin, Heidelberg, ISBN 978-3-642-36907-0, 2013.
Mao, J. and Yan, B.: Global monthly mean leaf area index climatology, 1981–2015, ORNL DAAC, Oak Ridge, Tennessee, USA, [data set], https://doi.org/10.3334/ORNLDAAC/1653, 2019.
Mariën, B., Dox, I., De Boeck, H. J., Willems, P., Leys, S., Papadimitriou, D., and Campioli, M.: Does drought advance the onset of autumn leaf senescence in temperate deciduous forest trees?, Biogeosciences, 18, 3309–3330, https://doi.org/10.5194/bg-18-3309-2021, 2021.
Marini, F. and Walczak, B.: Particle Swarm Optimization (PSO). A tutorial, Chemometr. Intell. Lab., 149, 153–165, https://doi.org/10.1016/j.chemolab.2015.08.020, 2015.
Marqués, L., Hufkens, K., Bigler, C., Crowther, T. W., Zohner, C. M., and Stocker, B. D.: Acclimation of phenology relieves leaf longevity constraints in deciduous forests, Nat. Ecol. Evol., 7, 198–204, https://doi.org/10.1038/s41559-022-01946-1, 2023.
Maurya, J. P. and Bhalerao, R. P.: Photoperiod- and temperature-mediated control of growth cessation and dormancy in trees: a molecular perspective, Ann. Bot., 120, 351–360, https://doi.org/10.1093/aob/mcx061, 2017.
Meier, M.: Process-oriented models of autumn leaf phenology (1.0), Zenodo [code], https://doi.org/10.5281/zenodo.7188160, 2022.
Meier, M. and Bigler, C.: Modelled past autumn leaf phenology of deciduous trees, Dryad [data set], https://doi.org/10.5061/dryad.dv41ns22k, 2023a.
Meier, M. and Bigler, C.: Projected future autumn leaf phenology of deciduous trees, Dryad [data set], https://doi.org/10.5061/dryad.mw6m90613, 2023b.
Meier, M., Fuhrer, J., and Holzkamper, A.: Changing risk of spring frost damage in grapevines due to climate change? A case study in the Swiss Rhone Valley, Int. J. Biometeorol., 62, 991–1002, https://doi.org/10.1007/s00484-018-1501-y, 2018.
Meier, M., Vitasse, Y., Bugmann, H., and Bigler, C.: Phenological shifts induced by climate change amplify drought for broad-leaved trees at low elevations in Switzerland, Agr. Forest Meteorol., 307, 108485, https://doi.org/10.1016/j.agrformet.2021.108485, 2021.
Meier, U.: Growth stages of mono-and dicotyledonous plants, 2. Edition, Blackwell Wissenschafts-Verlag, 157 pp., ISBN 3-8263-3152-4, 2001.
Meinshausen, M., Smith, S. J., Calvin, K., Daniel, J. S., Kainuma, M. L. T., Lamarque, J. F., Matsumoto, K., Montzka, S. A., Raper, S. C. B., Riahi, K., Thomson, A., Velders, G. J. M., and van Vuuren, D. P. P.: The RCP greenhouse gas concentrations and their extensions from 1765 to 2300, Climatic Change, 109, 213–241, https://doi.org/10.1007/s10584-011-0156-z, 2011.
Meinshausen, M., Vogel, E., Nauels, A., Lorbacher, K., Meinshausen, N., Etheridge, D. M., Fraser, P. J., Montzka, S. A., Rayner, P. J., Trudinger, C. M., Krummel, P. B., Beyerle, U., Canadell, J. G., Daniel, J. S., Enting, I. G., Law, R. M., Lunder, C. R., O'Doherty, S., Prinn, R. G., Reimann, S., Rubino, M., Velders, G. J. M., Vollmer, M. K., Wang, R. H. J., and Weiss, R.: Historical greenhouse gas concentrations for climate modelling (CMIP6), Geosci. Model Dev., 10, 2057–2116, https://doi.org/10.5194/gmd-10-2057-2017, 2017.
Menzel, A., Yuan, Y., Matiu, M., Sparks, T., Scheifinger, H., Gehrig, R., and Estrella, N.: Climate change fingerprints in recent European plant phenology, Glob. Change Biol., 26, 14, https://doi.org/10.1111/gcb.15000, 2020.
Morin, X., Lechowicz, M. J., Augspurger, C., O' Keefe, J., Viner, D., and Chuine, I.: Leaf phenology in 22 North American tree species during the 21st century, Glob. Change Biol., 15, 961–975, https://doi.org/10.1111/j.1365-2486.2008.01735.x, 2009.
Nabat, P., Somot, S., Cassou, C., Mallet, M., Michou, M., Bouniol, D., Decharme, B., Drugé, T., Roehrig, R., and Saint-Martin, D.: Modulation of radiative aerosols effects by atmospheric circulation over the Euro-Mediterranean region, Atmos. Chem. Phys., 20, 8315–8349, https://doi.org/10.5194/acp-20-8315-2020, 2020.
Norby, R. J.: Comment on “Increased growing-season productivity drives earlier autumn leaf senescence in temperate trees”, Science, 371, eabg1438, https://doi.org/10.1126/science.abg1438, 2021.
Nuzzo, R. L.: The Inverse Fallacy and Interpreting P Values, PM&R, 7, 311–314, https://doi.org/10.1016/j.pmrj.2015.02.011, 2015.
Oxman, A. D. and Guyatt, G. H.: A consumer's guide to subgroup analyses, Ann. Intern. Med., 116, 78–84, https://doi.org/10.7326/0003-4819-116-1-78, 1992.
Palmer, T. N., Shutts, G. J., Hagedorn, R., Doblas-Reyes, F. J., Jung, T., and Leutbecher, M.: Representing model uncertainty in weather and climate prediction, Annu. Rev. Earth Planet. Sci., 33, 163–193, https://doi.org/10.1146/annurev.earth.33.092203.122552, 2005.
Peaucelle, M., Janssens, I., Stocker, B., Ferrando, A., Fu, Y., Molowny-Horas, R., Ciais, P., and Peñuelas, J.: Spatial variance of spring phenology in temperate deciduous forests is constrained by background climatic conditions, Nat. Commun., 10, 5388, https://doi.org/10.1038/s41467-019-13365-1, 2019.
Peres, D. J., Senatore, A., Nanni, P., Cancelliere, A., Mendicino, G., and Bonaccorso, B.: Evaluation of EURO-CORDEX (Coordinated Regional Climate Downscaling Experiment for the Euro-Mediterranean area) historical simulations by high-quality observational datasets in southern Italy: insights on drought assessment, Nat. Hazards Earth Syst. Sci., 20, 3057–3082, https://doi.org/10.5194/nhess-20-3057-2020, 2020.
Piao, S. L., Liu, Q., Chen, A. P., Janssens, I. A., Fu, Y. S., Dai, J. H., Liu, L. L., Lian, X., Shen, M. G., and Zhu, X. L.: Plant phenology and global climate change: Current progresses and challenges, Glob. Change Biol., 25, 1922–1940, https://doi.org/10.1111/gcb.14619, 2019.
Picheny, V. and Ginsbourger, D.: Noisy kriging-based optimization methods: A unified implementation within the DiceOptim package, Comput. Stat. Data An., 71, 1035–1053, https://doi.org/10.1016/j.csda.2013.03.018, 2014.
Picheny, V., Ginsbourger Green, D., and Roustant, O.: DiceOptim: Kriging-based optimization for computer experiments, R package version 2.1.1 [code], https://CRAN.R-project.org/package=DiceOptim (last access: 4 January 2021), 2021.
Pinheiro, J. C. and Bates, D. M.: Mixed-effects models in S and S-PLUS, Statistics and computing, Springer, New York, 528 pp., ISBN 978-1-4419-0317-4, 2000.
Quan, X. K. and Wang, C. K.: Acclimation and adaptation of leaf photosynthesis, respiration and phenology to climate change: A 30-year Larix gmelinii common-garden experiment, Forest Ecol. Manag., 411, 166–175, https://doi.org/10.1016/j.foreco.2018.01.024, 2018.
R Core Team: R: A language and environment for statistical computing. R Foundation for Statistical Computing, 2022.
Riahi, K., Grübler, A., and Nakicenovic, N.: Scenarios of long-term socio-economic and environmental development under climate stabilization, Technol. Forecast. Soc., 74, 887–935, https://doi.org/10.1016/j.techfore.2006.05.026, 2007.
Riahi, K., Rao, S., Krey, V., Cho, C., Chirkov, V., Fischer, G., Kindermann, G., Nakicenovic, N., and Rafaj, P.: RCP 8.5–A scenario of comparatively high greenhouse gas emissions, Climatic Change, 109, 33–57, https://doi.org/10.1007/s10584-011-0149-y, 2011.
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.
Rodell, M., Houser, P. R., Jambor, U., Gottschalck, J., Mitchell, K., Meng, C. J., Arsenault, K., Cosgrove, B., Radakovich, J., Bosilovich, M., Entin, J. K., Walker, J. P., Lohmann, D., and Toll, D.: The global land data assimilation system, B. Am. Meteorol. Soc., 85, 381–394, https://doi.org/10.1175/bams-85-3-381, 2004.
Smith, S. J. and Wigley, T. M. L.: Multi-gas forcing stabilization with Minicam, Energ. J., 27, 373–391, 2006.
Taherdoost, H.: Sampling Methods in Research Methodology; How to Choose a Sampling Technique for Research (April 10, 2016), International Journal of Academic Research in Management, 5, 18–27, 2016.
Templ, B., Koch, E., Bolmgren, K., Ungersbock, M., Paul, A., Scheifinger, H., Rutishauser, T., Busto, M., Chmielewski, F. M., Hajkova, L., Hodzic, S., Kaspar, F., Pietragalla, B., Romero-Fresneda, R., Tolvanen, A., Vucetic, V., Zimmermann, K., and Zust, A.: Pan European Phenological database (PEP725): A single point of access for European data, Int. J. Biometeorol., 62, 1109–1113, https://doi.org/10.1007/s00484-018-1512-8, 2018.
Thomson, A. M., Calvin, K. V., Smith, S. J., Kyle, G. P., Volke, A., Patel, P., Delgado-Arias, S., Bond-Lamberty, B., Wise, M. A., Clarke, L. E., and Edmonds, J. A.: RCP4.5: A pathway for stabilization of radiative forcing by 2100, Clim. Change, 109, 77–94, https://doi.org/10.1007/s10584-011-0151-4, 2011.
Thoning, K. W., Crotwell, A. M., and Mund, J. W.: Atmospheric carbon dioxide dry air mole fractions from continuous measurements at Mauna Loa, Hawaii, Barrow, Alaska, American Samoa and South Pole, 1973–2020, Version 2021-08-09, NOAA GML, Colorado, USA [data set], https://doi.org/10.15138/yaf1-bk21, 2021.
Trautmann, H., Mersmann, O., and Arnu, D.: cmaes: Covariance Matrix Adapting Evolutionary Strategy, R package version 1.0-12, 2011.
Vautard, R., Kadygrov, N., Iles, C., Boberg, F., Buonomo, E., Bülow, K., Coppola, E., Corre, L., van Meijgaard, E., Nogherotto, R., Sandstad, M., Schwingshackl, C., Somot, S., Aalbers, E., Christensen, O. B., Ciarlo, J. M., Demory, M.-E., Giorgi, F., Jacob, D., Jones, R. G., Keuler, K., Kjellström, E., Lenderink, G., Levavasseur, G., Nikulin, G., Sillmann, J., Solidoro, C., Sørland, S. L., Steger, C., Teichmann, C., Warrach-Sagi, K., and Wulfmeyer, V.: Evaluation of the large EURO-CORDEX regional climate model ensemble, J. Geophys. Res.-Atmos., 126, e2019JD032344, https://doi.org/10.1029/2019JD032344, 2021.
Vitasse, Y., Baumgarten, F., Zohner, C. M., Kaewthongrach, R., Fu, Y. H., Walde, M., and Moser, B.: Impact of microclimatic conditions and resource availability on spring and autumn phenology of temperate tree seedlings, New Phytol., 232, 537–550, https://doi.org/10.1111/nph.17606, 2021.
Voeten, C. C.: buildmer: Stepwise elimination and term reordering for mixed-effects regression, R package version 2.4, 2022.
Voldoire, A., Sanchez-Gomez, E., Salas y Mélia, D., Decharme, B., Cassou, C., Sénési, S., Valcke, S., Beau, I., Alias, A., Chevallier, M., Déqué, M., Deshayes, J., Douville, H., Fernandez, E., Madec, G., Maisonnave, E., Moine, M. P., Planton, S., Saint-Martin, D., Szopa, S., Tyteca, S., Alkama, R., Belamari, S., Braun, A., Coquart, L., and Chauvin, F.: The CNRM-CM5.1 global climate model: description and basic evaluation, Clim. Dynam., 40, 2091–2121, https://doi.org/10.1007/s00382-011-1259-y, 2013.
Wasserstein, R. L. and Lazar, N. A.: The ASA statement on p-values: Context, process, and purpose, Am. Stat., 70, 129–133, https://doi.org/10.1080/00031305.2016.1154108, 2016.
Wasserstein, R. L., Schirm, A. L., and Lazar, N. A.: Moving to a world beyond “p <0.05”, Am. Stat., 73, 1–19, https://doi.org/10.1080/00031305.2019.1583913, 2019.
Wickham, H.: ggplot2: Elegant graphics for data analysis, Springer-Verlag, New York, ISBN 978-3-319-24277-4, 2016.
Wickham, H. and Pedersen, T. L.: gtable: Arrange “grobs” in tables. R package version 0.3.0 [code], https://CRAN.R-project.org/package=gtable (last access: 4 January 2019), 2019.
Wise, M., Calvin, K., Thomson, A., Clarke, L., Bond-Lamberty, B., Sands, R., Smith, S. J., Janetos, A., and Edmonds, J.: Implications of limiting CO2 concentrations for land use and energy, Science, 324, 1183–1186, https://doi.org/10.1126/science.1168475, 2009.
Wood, S. N.: Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models, J. Roy. Stat. Soc. B, 73, 3–36, https://doi.org/10.1111/j.1467-9868.2010.00749.x, 2011.
Wood, S. N.: Generalized additive models: An introduction with R, 2nd edition, Chapman and Hall/CRC, New York, https://doi.org/10.1201/9781315370279, 2017.
Xiang, Y., Sun, D. Y., Fan, W., and Gong, X. G.: Generalized Simulated Annealing algorithm and its application to the Thomson model, Phys. Lett. A, 233, 216–220, https://doi.org/10.1016/s0375-9601(97)00474-x, 1997.
Xiang, Y., Gubian, S., Martin, F., Suomela, B., and Hoeng, J.: Generalized Simulated Annealing for Global Optimization: The GenSA Package, R J., 5, 13–28, https://doi.org/10.32614/RJ-2013-002, 2013.
Xie, Y., Wang, X. J., and Silander, J. A.: Deciduous forest responses to temperature, precipitation, and drought imply complex climate change impacts, P. Natl. Acad. Sci. USA, 112, 13585–13590, https://doi.org/10.1073/pnas.1509991112, 2015.
Xie, Y., Wang, X. J., Wilson, A. M., and Silander, J. A.: Predicting autumn phenology: How deciduous tree species respond to weather stressors, Agr. Forest Meteorol., 250, 127–137, https://doi.org/10.1016/j.agrformet.2017.12.259, 2018.
Xie, Z., Zhu, W. Q., Qiao, K., Li, P. X., and Liu, H.: Joint influence mechanism of phenology and climate on the dynamics of gross primary productivity: Insights from temperate deciduous broadleaf forests in North America, J. Geophys. Res.-Biogeo., 126, e2020JG006049, https://doi.org/10.1029/2020jg006049, 2021.
Yates, F.: The analysis of multiple classifications with unequal numbers in the different classes, J. Am. Stat. Assoc., 29, 51–66, https://doi.org/10.2307/2278459, 1934.
Zambrano-Bigiarini, M.: hydroGOF: Goodness-of-fit functions for comparison of simulated and observed hydrological time series, Zenodo [code], https://doi.org/10.5281/zenodo.839854, 2020.
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, https://doi.org/10.1126/science.abd8911, 2020.
Zani, D., Crowther, T. W., Mo, L., Renner, S. S., and Zohner, C. M.: Response to Comment on “Increased growing-season productivity drives earlier autumn leaf senescence in temperate trees”, Science, 371, eabg2679, https://doi.org/10.1126/science.abg2679, 2021.
zanid90: zanid90/AutumnPhenology: Autumn Phenology repository, Zenodo [code], https://doi.org/10.5281/zenodo.4058162, 2021.
Zhao, H. F., Fu, Y. H., Wang, X. H., Zhang, Y., Liu, Y. W., and Janssens, I. A.: Diverging models introduce large uncertainty in future climate warming impact on spring phenology of temperate deciduous trees, Sci. Total Environ., 757, 143903, https://doi.org/10.1016/j.scitotenv.2020.143903, 2021.
Zhu, Z. C., Bi, J., Pan, Y. Z., Ganguly, S., Anav, A., Xu, L., Samanta, A., Piao, S. L., Nemani, R. R., and Myneni, R. B.: Global data sets of vegetation Leaf Area Index (LAI)3g and fraction of photosynthetically active radiation (FPAR)3g derived from Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) for the period 1981 to 2011, Remote Sens., 5, 927–948, https://doi.org/10.3390/rs5020927, 2013.
Short summary
We analyzed >2.3 million calibrations and 39 million projections of leaf coloration models, considering 21 models, 5 optimization algorithms, ≥7 sampling procedures, and 26 climate scenarios. Models based on temperature, day length, and leaf unfolding performed best, especially when calibrated with generalized simulated annealing and systematically balanced or stratified samples. Projected leaf coloration shifts between −13 and +20 days by 2080–2099.
We analyzed >2.3 million calibrations and 39 million projections of leaf coloration models,...
