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 authors
Michael Meier, Christof Bigler, and Isabelle Chuine
EGUsphere, https://doi.org/10.5194/egusphere-2025-460, https://doi.org/10.5194/egusphere-2025-460, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
Formulated according to the leaf development process, the DP3 model of leaf coloring considerably contrasts previous models and allows to set up new hypotheses, e.g. on aging versus stress caused color changes. The DP3 model was as accurate as previous models and a comparison to the constant simulation of the mean date of leaf coloring indicated that noisy leaf coloring data forced the models to resort to this mean, which hinders model evaluation.
Michael Meier, Christof Bigler, and Isabelle Chuine
EGUsphere, https://doi.org/10.5194/egusphere-2025-460, https://doi.org/10.5194/egusphere-2025-460, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
Formulated according to the leaf development process, the DP3 model of leaf coloring considerably contrasts previous models and allows to set up new hypotheses, e.g. on aging versus stress caused color changes. The DP3 model was as accurate as previous models and a comparison to the constant simulation of the mean date of leaf coloring indicated that noisy leaf coloring data forced the models to resort to this mean, which hinders model evaluation.
Related subject area
Climate and Earth system modeling
Baseline Climate Variables for Earth System Modelling
PaleoSTeHM v1.0: a modern, scalable spatiotemporal hierarchical modeling framework for paleo-environmental data
The Tropical Basin Interaction Model Intercomparison Project (TBIMIP)
ZEMBA v1.0: an energy and moisture balance climate model to investigate Quaternary climate
Development and evaluation of a new 4DEnVar-based weakly coupled ocean data assimilation system in E3SMv2
TemDeep: a self-supervised framework for temporal downscaling of atmospheric fields at arbitrary time resolutions
The ensemble consistency test: from CESM to MPAS and beyond
Presentation, calibration and testing of the DCESS II Earth system model of intermediate complexity (version 1.0)
Synthesizing global carbon–nitrogen coupling effects – the MAGICC coupled carbon–nitrogen cycle model v1.0
Historical trends and controlling factors of isoprene emissions in CMIP6 Earth system models
Investigating carbon and nitrogen conservation in reported CMIP6 Earth system model data
From weather data to river runoff: using spatiotemporal convolutional networks for discharge forecasting
A Fortran–Python interface for integrating machine learning parameterization into earth system models
A rapid-application emissions-to-impacts tool for scenario assessment: Probabilistic Regional Impacts from Model patterns and Emissions (PRIME)
The DOE E3SM version 2.1: overview and assessment of the impacts of parameterized ocean submesoscales
WRF-ELM v1.0: a regional climate model to study land–atmosphere interactions over heterogeneous land use regions
Modeling commercial-scale CO2 storage in the gas hydrate stability zone with PFLOTRAN v6.0
DiuSST: a conceptual model of diurnal warm layers for idealized atmospheric simulations with interactive sea surface temperature
High-Resolution Model Intercomparison Project phase 2 (HighResMIP2) towards CMIP7
T&C-CROP: representing mechanistic crop growth with a terrestrial biosphere model (T&C, v1.5) – model formulation and validation
An updated non-intrusive, multi-scale, and flexible coupling interface in WRF 4.6.0
Monitoring and benchmarking Earth system model simulations with ESMValTool v2.12.0
The Earth Science Box Modeling Toolkit (ESBMTK 0.14.0.11): a Python library for research and teaching
CropSuite v1.0 – a comprehensive open-source crop suitability model considering climate variability for climate impact assessment
ICON ComIn – the ICON Community Interface (ComIn version 0.1.0, with ICON version 2024.01-01)
Using feature importance as an exploratory data analysis tool on Earth system models
A new metrics framework for quantifying and intercomparing atmospheric rivers in observations, reanalyses, and climate models
The real challenges for climate and weather modelling on its way to sustained exascale performance: a case study using ICON (v2.6.6)
Improving the representation of major Indian crops in the Community Land Model version 5.0 (CLM5) using site-scale crop data
Evaluation of CORDEX ERA5-forced NARCliM2.0 regional climate models over Australia using the Weather Research and Forecasting (WRF) model version 4.1.2
Design, evaluation, and future projections of the NARCliM2.0 CORDEX-CMIP6 Australasia regional climate ensemble
The Detection and Attribution Model Intercomparison Project (DAMIP v2.0) contribution to CMIP7
Amending the algorithm of aerosol–radiation interactions in WRF-Chem (v4.4)
The very-high-resolution configuration of the EC-Earth global model for HighResMIP
GOSI9: UK Global Ocean and Sea Ice configurations
Decomposition of skill scores for conditional verification: impact of Atlantic Multidecadal Oscillation phases on the predictability of decadal temperature forecasts
Virtual Integration of Satellite and In-situ Observation Networks (VISION) v1.0: In-Situ Observations Simulator (ISO_simulator)
Climate model downscaling in central Asia: a dynamical and a neural network approach
Advanced climate model evaluation with ESMValTool v2.11.0 using parallel, out-of-core, and distributed computing
Multi-year simulations at kilometre scale with the Integrated Forecasting System coupled to FESOM2.5 and NEMOv3.4
Subsurface hydrological controls on the short-term effects of hurricanes on nitrate–nitrogen runoff loading: a case study of Hurricane Ida using the Energy Exascale Earth System Model (E3SM) Land Model (v2.1)
CARIB12: a regional Community Earth System Model/Modular Ocean Model 6 configuration of the Caribbean Sea
Process-based modeling framework for sustainable irrigation management at the regional scale: Integrating rice production, water use, and greenhouse gas emissions
Architectural insights into and training methodology optimization of Pangu-Weather
Evaluation of global fire simulations in CMIP6 Earth system models
Evaluating downscaled products with expected hydroclimatic co-variances
Software sustainability of global impact models
fair-calibrate v1.4.1: calibration, constraining, and validation of the FaIR simple climate model for reliable future climate projections
ISOM 1.0: a fully mesoscale-resolving idealized Southern Ocean model and the diversity of multiscale eddy interactions
A computationally lightweight model for ensemble forecasting of environmental hazards: General TAMSAT-ALERT v1.2.1
Martin Juckes, Karl E. Taylor, Fabrizio Antonio, David Brayshaw, Carlo Buontempo, Jian Cao, Paul J. Durack, Michio Kawamiya, Hyungjun Kim, Tomas Lovato, Chloe Mackallah, Matthew Mizielinski, Alessandra Nuzzo, Martina Stockhause, Daniele Visioni, Jeremy Walton, Briony Turner, Eleanor O'Rourke, and Beth Dingley
Geosci. Model Dev., 18, 2639–2663, https://doi.org/10.5194/gmd-18-2639-2025, https://doi.org/10.5194/gmd-18-2639-2025, 2025
Short summary
Short summary
The Baseline Climate Variables for Earth System Modelling (ESM-BCVs) are defined as a list of 135 variables which have high utility for the evaluation and exploitation of climate simulations. The list reflects the most frequently used variables from Earth system models based on an assessment of data publication and download records from the largest archive of global climate projects.
Yucheng Lin, Robert E. Kopp, Alexander Reedy, Matteo Turilli, Shantenu Jha, and Erica L. Ashe
Geosci. Model Dev., 18, 2609–2637, https://doi.org/10.5194/gmd-18-2609-2025, https://doi.org/10.5194/gmd-18-2609-2025, 2025
Short summary
Short summary
PaleoSTeHM v1.0 is a state-of-the-art framework designed to reconstruct past environmental conditions using geological data. Built on modern machine learning techniques, it efficiently handles the sparse and noisy nature of paleo-records, allowing scientists to make accurate and scalable inferences about past environmental change. By using flexible statistical models, PaleoSTeHM separates different sources of uncertainty, improving the precision of historical climate reconstructions.
Ingo Richter, Ping Chang, Ping-Gin Chiu, Gokhan Danabasoglu, Takeshi Doi, Dietmar Dommenget, Guillaume Gastineau, Zoe E. Gillett, Aixue Hu, Takahito Kataoka, Noel S. Keenlyside, Fred Kucharski, Yuko M. Okumura, Wonsun Park, Malte F. Stuecker, Andréa S. Taschetto, Chunzai Wang, Stephen G. Yeager, and Sang-Wook Yeh
Geosci. Model Dev., 18, 2587–2608, https://doi.org/10.5194/gmd-18-2587-2025, https://doi.org/10.5194/gmd-18-2587-2025, 2025
Short summary
Short summary
Tropical ocean basins influence each other through multiple pathways and mechanisms, referred to here as tropical basin interaction (TBI). Many researchers have examined TBI using comprehensive climate models but have obtained conflicting results. This may be partly due to differences in experiment protocols and partly due to systematic model errors. The Tropical Basin Interaction Model Intercomparison Project (TBIMIP) aims to address this problem by designing a set of TBI experiments that will be performed by multiple models.
Daniel F. J. Gunning, Kerim H. Nisancioglu, Emilie Capron, and Roderik S. W. van de Wal
Geosci. Model Dev., 18, 2479–2508, https://doi.org/10.5194/gmd-18-2479-2025, https://doi.org/10.5194/gmd-18-2479-2025, 2025
Short summary
Short summary
This work documents the first results from ZEMBA: an energy balance model of the climate system. The model is a computationally efficient tool designed to study the response of climate to changes in the Earth's orbit. We demonstrate that ZEMBA reproduces many features of the Earth's climate for both the pre-industrial period and the Earth's most recent cold extreme – the Last Glacial Maximum. We intend to develop ZEMBA further and investigate the glacial cycles of the last 2.5 million years.
Pengfei Shi, L. Ruby Leung, and Bin Wang
Geosci. Model Dev., 18, 2443–2460, https://doi.org/10.5194/gmd-18-2443-2025, https://doi.org/10.5194/gmd-18-2443-2025, 2025
Short summary
Short summary
Improving climate predictions has significant socio-economic impacts. In this study, we develop and apply a new weakly coupled ocean data assimilation (WCODA) system to a coupled climate model. The WCODA system improves simulations of ocean temperature and salinity across many global regions. This system is meant to advance our understanding of the ocean's role in climate predictability.
Liwen Wang, Qian Li, Qi Lv, Xuan Peng, and Wei You
Geosci. Model Dev., 18, 2427–2442, https://doi.org/10.5194/gmd-18-2427-2025, https://doi.org/10.5194/gmd-18-2427-2025, 2025
Short summary
Short summary
Our research presents a novel deep learning approach called "TemDeep" for downscaling atmospheric variables at arbitrary time resolutions based on temporal coherence. Results show that our method can accurately recover evolution details superior to other methods, reaching 53.7 % in the restoration rate. Our findings are important for advancing weather forecasting models and enabling more precise and reliable predictions to support disaster preparedness, agriculture, and sustainable development.
Teo Price-Broncucia, Allison Baker, Dorit Hammerling, Michael Duda, and Rebecca Morrison
Geosci. Model Dev., 18, 2349–2372, https://doi.org/10.5194/gmd-18-2349-2025, https://doi.org/10.5194/gmd-18-2349-2025, 2025
Short summary
Short summary
The ensemble consistency test (ECT) and its ultrafast variant (UF-ECT) have become powerful tools in the development community for the identification of unwanted changes in the Community Earth System Model (CESM). We develop a generalized setup framework to enable easy adoption of the ECT approach for other model developers and communities. This framework specifies test parameters to accurately characterize model variability and balance test sensitivity and computational cost.
Esteban Fernández Villanueva and Gary Shaffer
Geosci. Model Dev., 18, 2161–2192, https://doi.org/10.5194/gmd-18-2161-2025, https://doi.org/10.5194/gmd-18-2161-2025, 2025
Short summary
Short summary
We describe, calibrate and test the Danish Center for Earth System Science (DCESS) II model, a new, broad, adaptable and fast Earth system model. DCESS II is designed for global simulations over timescales of years to millions of years using limited computer resources like a personal computer. With its flexibility and comprehensive treatment of the global carbon cycle, DCESS II is a useful, computationally friendly tool for simulations of past climates as well as for future Earth system projections.
Gang Tang, Zebedee Nicholls, Alexander Norton, Sönke Zaehle, and Malte Meinshausen
Geosci. Model Dev., 18, 2193–2230, https://doi.org/10.5194/gmd-18-2193-2025, https://doi.org/10.5194/gmd-18-2193-2025, 2025
Short summary
Short summary
We studied carbon–nitrogen coupling in Earth system models by developing a global carbon–nitrogen cycle model (CNit v1.0) within the widely used emulator MAGICC. CNit effectively reproduced the global carbon–nitrogen cycle dynamics observed in complex models. Our results show persistent nitrogen limitations on plant growth (net primary production) from 1850 to 2100, suggesting that nitrogen deficiency may constrain future land carbon sequestration.
Ngoc Thi Nhu Do, Kengo Sudo, Akihiko Ito, Louisa K. Emmons, Vaishali Naik, Kostas Tsigaridis, Øyvind Seland, Gerd A. Folberth, and Douglas I. Kelley
Geosci. Model Dev., 18, 2079–2109, https://doi.org/10.5194/gmd-18-2079-2025, https://doi.org/10.5194/gmd-18-2079-2025, 2025
Short summary
Short summary
Understanding historical isoprene emission changes is important for predicting future climate, but trends and their controlling factors remain uncertain. This study shows that long-term isoprene trends vary among Earth system models mainly due to partially incorporating CO2 effects and land cover changes rather than to climate. Future models that refine these factors’ effects on isoprene emissions, along with long-term observations, are essential for better understanding plant–climate interactions.
Gang Tang, Zebedee Nicholls, Chris Jones, Thomas Gasser, Alexander Norton, Tilo Ziehn, Alejandro Romero-Prieto, and Malte Meinshausen
Geosci. Model Dev., 18, 2111–2136, https://doi.org/10.5194/gmd-18-2111-2025, https://doi.org/10.5194/gmd-18-2111-2025, 2025
Short summary
Short summary
We analyzed carbon and nitrogen mass conservation in data from various Earth system models. Our findings reveal significant discrepancies between flux and pool size data, where cumulative imbalances can reach hundreds of gigatons of carbon or nitrogen. These imbalances appear primarily due to missing or inconsistently reported fluxes – especially for land-use and fire emissions. To enhance data quality, we recommend that future climate data protocols address this issue at the reporting stage.
Florian Börgel, Sven Karsten, Karoline Rummel, and Ulf Gräwe
Geosci. Model Dev., 18, 2005–2019, https://doi.org/10.5194/gmd-18-2005-2025, https://doi.org/10.5194/gmd-18-2005-2025, 2025
Short summary
Short summary
Forecasting river runoff, which is crucial for managing water resources and understanding climate impacts, can be challenging. This study introduces a new method using convolutional long short-term memory (ConvLSTM) networks, a machine learning model that processes spatial and temporal data. Focusing on the Baltic Sea region, our model uses weather data as input to predict daily river runoff for 97 rivers.
Tao Zhang, Cyril Morcrette, Meng Zhang, Wuyin Lin, Shaocheng Xie, Ye Liu, Kwinten Van Weverberg, and Joana Rodrigues
Geosci. Model Dev., 18, 1917–1928, https://doi.org/10.5194/gmd-18-1917-2025, https://doi.org/10.5194/gmd-18-1917-2025, 2025
Short summary
Short summary
Earth system models (ESMs) struggle with the uncertainties associated with parameterizing subgrid physics. Machine learning (ML) algorithms offer a solution by learning the important relationships and features from high-resolution models. To incorporate ML parameterizations into ESMs, we develop a Fortran–Python interface that allows for calling Python functions within Fortran-based ESMs. Through two case studies, this interface demonstrates its feasibility, modularity, and effectiveness.
Camilla Mathison, Eleanor J. Burke, Gregory Munday, Chris D. Jones, Chris J. Smith, Norman J. Steinert, Andy J. Wiltshire, Chris Huntingford, Eszter Kovacs, Laila K. Gohar, Rebecca M. Varney, and Douglas McNeall
Geosci. Model Dev., 18, 1785–1808, https://doi.org/10.5194/gmd-18-1785-2025, https://doi.org/10.5194/gmd-18-1785-2025, 2025
Short summary
Short summary
We present PRIME (Probabilistic Regional Impacts from Model patterns and Emissions), which is designed to take new emissions scenarios and rapidly provide regional impact information. PRIME allows large ensembles to be run on multi-centennial timescales, including the analysis of many important variables for impact assessments. Our evaluation shows that PRIME reproduces the climate response for known scenarios, providing confidence in using PRIME for novel scenarios.
Katherine M. Smith, Alice M. Barthel, LeAnn M. Conlon, Luke P. Van Roekel, Anthony Bartoletti, Jean-Christophe Golaz, Chengzhu Zhang, Carolyn Branecky Begeman, James J. Benedict, Gautam Bisht, Yan Feng, Walter Hannah, Bryce E. Harrop, Nicole Jeffery, Wuyin Lin, Po-Lun Ma, Mathew E. Maltrud, Mark R. Petersen, Balwinder Singh, Qi Tang, Teklu Tesfa, Jonathan D. Wolfe, Shaocheng Xie, Xue Zheng, Karthik Balaguru, Oluwayemi Garuba, Peter Gleckler, Aixue Hu, Jiwoo Lee, Ben Moore-Maley, and Ana C. Ordoñez
Geosci. Model Dev., 18, 1613–1633, https://doi.org/10.5194/gmd-18-1613-2025, https://doi.org/10.5194/gmd-18-1613-2025, 2025
Short summary
Short summary
Version 2.1 of the U.S. Department of Energy's Energy Exascale Earth System Model (E3SM) adds the Fox-Kemper et al. (2011) mixed-layer eddy parameterization, which restratifies the ocean surface layer through an overturning streamfunction. Results include surface layer bias reduction in temperature, salinity, and sea ice extent in the North Atlantic; a small strengthening of the Atlantic meridional overturning circulation; and improvements to many atmospheric climatological variables.
Huilin Huang, Yun Qian, Gautam Bisht, Jiali Wang, Tirthankar Chakraborty, Dalei Hao, Jianfeng Li, Travis Thurber, Balwinder Singh, Zhao Yang, Ye Liu, Pengfei Xue, William J. Sacks, Ethan Coon, and Robert Hetland
Geosci. Model Dev., 18, 1427–1443, https://doi.org/10.5194/gmd-18-1427-2025, https://doi.org/10.5194/gmd-18-1427-2025, 2025
Short summary
Short summary
We integrate the E3SM Land Model (ELM) with the WRF model through the Lightweight Infrastructure for Land Atmosphere Coupling (LILAC) Earth System Modeling Framework (ESMF). This framework includes a top-level driver, LILAC, for variable communication between WRF and ELM and ESMF caps for ELM initialization, execution, and finalization. The LILAC–ESMF framework maintains the integrity of the ELM's source code structure and facilitates the transfer of future ELM model developments to WRF-ELM.
Michael Nole, Jonah Bartrand, Fawz Naim, and Glenn Hammond
Geosci. Model Dev., 18, 1413–1425, https://doi.org/10.5194/gmd-18-1413-2025, https://doi.org/10.5194/gmd-18-1413-2025, 2025
Short summary
Short summary
Safe carbon dioxide (CO2) storage is likely to be critical for mitigating some of the most severe effects of climate change. We present a simulation framework for modeling CO2 storage beneath the seafloor, where CO2 can form a solid. This can aid in permanent CO2 storage for long periods of time. Our models show what a commercial-scale CO2 injection would look like in a marine environment. We discuss what would need to be considered when designing a subsea CO2 injection.
Reyk Börner, Jan O. Haerter, and Romain Fiévet
Geosci. Model Dev., 18, 1333–1356, https://doi.org/10.5194/gmd-18-1333-2025, https://doi.org/10.5194/gmd-18-1333-2025, 2025
Short summary
Short summary
The daily cycle of sea surface temperature (SST) impacts clouds above the ocean and could influence the clustering of thunderstorms linked to extreme rainfall and hurricanes. However, daily SST variability is often poorly represented in modeling studies of how clouds cluster. We present a simple, wind-responsive model of upper-ocean temperature for use in atmospheric simulations. Evaluating the model against observations, we show that it performs significantly better than common slab models.
Malcolm J. Roberts, Kevin A. Reed, Qing Bao, Joseph J. Barsugli, Suzana J. Camargo, Louis-Philippe Caron, Ping Chang, Cheng-Ta Chen, Hannah M. Christensen, Gokhan Danabasoglu, Ivy Frenger, Neven S. Fučkar, Shabeh ul Hasson, Helene T. Hewitt, Huanping Huang, Daehyun Kim, Chihiro Kodama, Michael Lai, Lai-Yung Ruby Leung, Ryo Mizuta, Paulo Nobre, Pablo Ortega, Dominique Paquin, Christopher D. Roberts, Enrico Scoccimarro, Jon Seddon, Anne Marie Treguier, Chia-Ying Tu, Paul A. Ullrich, Pier Luigi Vidale, Michael F. Wehner, Colin M. Zarzycki, Bosong Zhang, Wei Zhang, and Ming Zhao
Geosci. Model Dev., 18, 1307–1332, https://doi.org/10.5194/gmd-18-1307-2025, https://doi.org/10.5194/gmd-18-1307-2025, 2025
Short summary
Short summary
HighResMIP2 is a model intercomparison project focusing on high-resolution global climate models, that is, those with grid spacings of 25 km or less in the atmosphere and ocean, using simulations of decades to a century in length. We are proposing an update of our simulation protocol to make the models more applicable to key questions for climate variability and hazard in present-day and future projections and to build links with other communities to provide more robust climate information.
Jordi Buckley Paules, Simone Fatichi, Bonnie Warring, and Athanasios Paschalis
Geosci. Model Dev., 18, 1287–1305, https://doi.org/10.5194/gmd-18-1287-2025, https://doi.org/10.5194/gmd-18-1287-2025, 2025
Short summary
Short summary
We present and validate enhancements to the process-based T&C model aimed at improving its representation of crop growth and management practices. The updated model, T&C-CROP, enables applications such as analysing the hydrological and carbon storage impacts of land use transitions (e.g. conversions between crops, forests, and pastures) and optimizing irrigation and fertilization strategies in response to climate change.
Sébastien Masson, Swen Jullien, Eric Maisonnave, David Gill, Guillaume Samson, Mathieu Le Corre, and Lionel Renault
Geosci. Model Dev., 18, 1241–1263, https://doi.org/10.5194/gmd-18-1241-2025, https://doi.org/10.5194/gmd-18-1241-2025, 2025
Short summary
Short summary
This article details a new feature we implemented in the popular regional atmospheric model WRF. This feature allows for data exchange between WRF and any other model (e.g. an ocean model) using the coupling library Ocean–Atmosphere–Sea–Ice–Soil Model Coupling Toolkit (OASIS3-MCT). This coupling interface is designed to be non-intrusive, flexible and modular. It also offers the possibility of taking into account the nested zooms used in WRF or in the models with which it is coupled.
Axel Lauer, Lisa Bock, Birgit Hassler, Patrick Jöckel, Lukas Ruhe, and Manuel Schlund
Geosci. Model Dev., 18, 1169–1188, https://doi.org/10.5194/gmd-18-1169-2025, https://doi.org/10.5194/gmd-18-1169-2025, 2025
Short summary
Short summary
Earth system models are important tools to improve our understanding of current climate and to project climate change. Thus, it is crucial to understand possible shortcomings in the models. New features of the ESMValTool software package allow one to compare and visualize a model's performance with respect to reproducing observations in the context of other climate models in an easy and user-friendly way. We aim to help model developers assess and monitor climate simulations more efficiently.
Ulrich G. Wortmann, Tina Tsan, Mahrukh Niazi, Irene A. Ma, Ruben Navasardyan, Magnus-Roland Marun, Bernardo S. Chede, Jingwen Zhong, and Morgan Wolfe
Geosci. Model Dev., 18, 1155–1167, https://doi.org/10.5194/gmd-18-1155-2025, https://doi.org/10.5194/gmd-18-1155-2025, 2025
Short summary
Short summary
The Earth Science Box Modeling Toolkit (ESBMTK) is a user-friendly Python library that simplifies the creation of models to study earth system processes, such as the carbon cycle and ocean chemistry. It enhances learning by emphasizing concepts over programming and is accessible to students and researchers alike. By automating complex calculations and promoting code clarity, ESBMTK accelerates model development while improving reproducibility and the usability of scientific research.
Florian Zabel, Matthias Knüttel, and Benjamin Poschlod
Geosci. Model Dev., 18, 1067–1087, https://doi.org/10.5194/gmd-18-1067-2025, https://doi.org/10.5194/gmd-18-1067-2025, 2025
Short summary
Short summary
CropSuite is a new open-source crop suitability model. It provides a GUI and a wide range of options, including a spatial downscaling of climate data. We apply CropSuite to 48 staple and opportunity crops at a 1 km spatial resolution in Africa. We find that climate variability significantly impacts suitable areas but also affects optimal sowing dates and multiple cropping potential. The results provide valuable information for climate impact assessments, adaptation, and land-use planning.
Kerstin Hartung, Bastian Kern, Nils-Arne Dreier, Jörn Geisbüsch, Mahnoosh Haghighatnasab, Patrick Jöckel, Astrid Kerkweg, Wilton Jaciel Loch, Florian Prill, and Daniel Rieger
Geosci. Model Dev., 18, 1001–1015, https://doi.org/10.5194/gmd-18-1001-2025, https://doi.org/10.5194/gmd-18-1001-2025, 2025
Short summary
Short summary
The ICOsahedral Non-hydrostatic (ICON) model system Community Interface (ComIn) library supports connecting third-party modules to the ICON model. Third-party modules can range from simple diagnostic Python scripts to full chemistry models. ComIn offers a low barrier for code extensions to ICON, provides multi-language support (Fortran, C/C++, and Python), and reduces the migration effort in response to new ICON releases. This paper presents the ComIn design principles and a range of use cases.
Daniel Ries, Katherine Goode, Kellie McClernon, and Benjamin Hillman
Geosci. Model Dev., 18, 1041–1065, https://doi.org/10.5194/gmd-18-1041-2025, https://doi.org/10.5194/gmd-18-1041-2025, 2025
Short summary
Short summary
Machine learning has advanced research in the climate science domain, but its models are difficult to understand. In order to understand the impacts and consequences of climate interventions such as stratospheric aerosol injection, complex models are often necessary. We use a case study to illustrate how we can understand the inner workings of a complex model. We present this technique as an exploratory tool that can be used to quickly discover and assess relationships in complex climate data.
Bo Dong, Paul Ullrich, Jiwoo Lee, Peter Gleckler, Kristin Chang, and Travis A. O'Brien
Geosci. Model Dev., 18, 961–976, https://doi.org/10.5194/gmd-18-961-2025, https://doi.org/10.5194/gmd-18-961-2025, 2025
Short summary
Short summary
A metrics package designed for easy analysis of atmospheric river (AR) characteristics and statistics is presented. The tool is efficient for diagnosing systematic AR bias in climate models and useful for evaluating new AR characteristics in model simulations. In climate models, landfalling AR precipitation shows dry biases globally, and AR tracks are farther poleward (equatorward) in the North and South Atlantic (South Pacific and Indian Ocean).
Panagiotis Adamidis, Erik Pfister, Hendryk Bockelmann, Dominik Zobel, Jens-Olaf Beismann, and Marek Jacob
Geosci. Model Dev., 18, 905–919, https://doi.org/10.5194/gmd-18-905-2025, https://doi.org/10.5194/gmd-18-905-2025, 2025
Short summary
Short summary
In this paper, we investigated performance indicators of the climate model ICON (ICOsahedral Nonhydrostatic) on different compute architectures to answer the question of how to generate high-resolution climate simulations. Evidently, it is not enough to use more computing units of the conventionally used architectures; higher memory throughput is the most promising approach. More potential can be gained from single-node optimization rather than simply increasing the number of compute nodes.
Kangari Narender Reddy, Somnath Baidya Roy, Sam S. Rabin, Danica L. Lombardozzi, Gudimetla Venkateswara Varma, Ruchira Biswas, and Devavat Chiru Naik
Geosci. Model Dev., 18, 763–785, https://doi.org/10.5194/gmd-18-763-2025, https://doi.org/10.5194/gmd-18-763-2025, 2025
Short summary
Short summary
The study aimed to improve the representation of wheat and rice in a land model for the Indian region. The modified model performed significantly better than the default model in simulating crop phenology, yield, and carbon, water, and energy fluxes compared to observations. The study highlights the need for global land models to use region-specific crop parameters for accurately simulating vegetation processes and land surface processes.
Giovanni Di Virgilio, Fei Ji, Eugene Tam, Jason P. Evans, Jatin Kala, Julia Andrys, Christopher Thomas, Dipayan Choudhury, Carlos Rocha, Yue Li, and Matthew L. Riley
Geosci. Model Dev., 18, 703–724, https://doi.org/10.5194/gmd-18-703-2025, https://doi.org/10.5194/gmd-18-703-2025, 2025
Short summary
Short summary
We evaluate the skill in simulating the Australian climate of some of the latest generation of regional climate models. We show when and where the models simulate this climate with high skill versus model limitations. We show how new models perform relative to the previous-generation models, assessing how model design features may underlie key performance improvements. This work is of national and international relevance as it can help guide the use and interpretation of climate projections.
Giovanni Di Virgilio, Jason P. Evans, Fei Ji, Eugene Tam, Jatin Kala, Julia Andrys, Christopher Thomas, Dipayan Choudhury, Carlos Rocha, Stephen White, Yue Li, Moutassem El Rafei, Rishav Goyal, Matthew L. Riley, and Jyothi Lingala
Geosci. Model Dev., 18, 671–702, https://doi.org/10.5194/gmd-18-671-2025, https://doi.org/10.5194/gmd-18-671-2025, 2025
Short summary
Short summary
We introduce new climate models that simulate Australia’s future climate at regional scales, including at an unprecedented resolution of 4 km for 1950–2100. We describe the model design process used to create these new climate models. We show how the new models perform relative to previous-generation models and compare their climate projections. This work is of national and international relevance as it can help guide climate model design and the use and interpretation of climate projections.
Nathan P. Gillett, Isla R. Simpson, Gabi Hegerl, Reto Knutti, Dann Mitchell, Aurélien Ribes, Hideo Shiogama, Dáithí Stone, Claudia Tebaldi, Piotr Wolski, Wenxia Zhang, and Vivek K. Arora
EGUsphere, https://doi.org/10.5194/egusphere-2024-4086, https://doi.org/10.5194/egusphere-2024-4086, 2025
Short summary
Short summary
Climate model simulations of the response to human and natural influences together, natural climate influences alone, and greenhouse gases alone, among others, are key to quantifying human influence on the climate. The last set of such coordinated simulations underpinned key findings in the last Intergovernmental Panel on Climate Change (IPCC) report. Here we propose a new set of such simulations to be used in the next generation of attribution studies, and to underpin the next IPCC report.
Jiawang Feng, Chun Zhao, Qiuyan Du, Zining Yang, and Chen Jin
Geosci. Model Dev., 18, 585–603, https://doi.org/10.5194/gmd-18-585-2025, https://doi.org/10.5194/gmd-18-585-2025, 2025
Short summary
Short summary
In this study, we improved the calculation of how aerosols in the air interact with radiation in WRF-Chem. The original model used a simplified method, but we developed a more accurate approach. We found that this method significantly changes the properties of the estimated aerosols and their effects on radiation, especially for dust aerosols. It also impacts the simulated weather conditions. Our work highlights the importance of correctly representing aerosol–radiation interactions in models.
Eduardo Moreno-Chamarro, Thomas Arsouze, Mario Acosta, Pierre-Antoine Bretonnière, Miguel Castrillo, Eric Ferrer, Amanda Frigola, Daria Kuznetsova, Eneko Martin-Martinez, Pablo Ortega, and Sergi Palomas
Geosci. Model Dev., 18, 461–482, https://doi.org/10.5194/gmd-18-461-2025, https://doi.org/10.5194/gmd-18-461-2025, 2025
Short summary
Short summary
We present the high-resolution model version of the EC-Earth global climate model to contribute to HighResMIP. The combined model resolution is about 10–15 km in both the ocean and atmosphere, which makes it one of the finest ever used to complete historical and scenario simulations. This model is compared with two lower-resolution versions, with a 100 km and a 25 km grid. The three models are compared with observations to study the improvements thanks to the increased resolution.
Catherine Guiavarc'h, David Storkey, Adam T. Blaker, Ed Blockley, Alex Megann, Helene Hewitt, Michael J. Bell, Daley Calvert, Dan Copsey, Bablu Sinha, Sophia Moreton, Pierre Mathiot, and Bo An
Geosci. Model Dev., 18, 377–403, https://doi.org/10.5194/gmd-18-377-2025, https://doi.org/10.5194/gmd-18-377-2025, 2025
Short summary
Short summary
The Global Ocean and Sea Ice configuration version 9 (GOSI9) is the new UK hierarchy of model configurations based on the Nucleus for European Modelling of the Ocean (NEMO) and available at three resolutions. It will be used for various applications, e.g. weather forecasting and climate prediction. It improves upon the previous version by reducing global temperature and salinity biases and enhancing the representation of Arctic sea ice and the Antarctic Circumpolar Current.
Andy Richling, Jens Grieger, and Henning W. Rust
Geosci. Model Dev., 18, 361–375, https://doi.org/10.5194/gmd-18-361-2025, https://doi.org/10.5194/gmd-18-361-2025, 2025
Short summary
Short summary
The performance of weather and climate prediction systems is variable in time and space. It is of interest how this performance varies in different situations. We provide a decomposition of a skill score (a measure of forecast performance) as a tool for detailed assessment of performance variability to support model development or forecast improvement. The framework is exemplified with decadal forecasts to assess the impact of different ocean states in the North Atlantic on temperature forecast.
Maria R. Russo, Sadie L. Bartholomew, David Hassell, Alex M. Mason, Erica Neininger, A. James Perman, David A. J. Sproson, Duncan Watson-Parris, and Nathan Luke Abraham
Geosci. Model Dev., 18, 181–191, https://doi.org/10.5194/gmd-18-181-2025, https://doi.org/10.5194/gmd-18-181-2025, 2025
Short summary
Short summary
Observational data and modelling capabilities have expanded in recent years, but there are still barriers preventing these two data sources from being used in synergy. Proper comparison requires generating, storing, and handling a large amount of data. This work describes the first step in the development of a new set of software tools, the VISION toolkit, which can enable the easy and efficient integration of observational and model data required for model evaluation.
Bijan Fallah, Masoud Rostami, Emmanuele Russo, Paula Harder, Christoph Menz, Peter Hoffmann, Iulii Didovets, and Fred F. Hattermann
Geosci. Model Dev., 18, 161–180, https://doi.org/10.5194/gmd-18-161-2025, https://doi.org/10.5194/gmd-18-161-2025, 2025
Short summary
Short summary
We tried to contribute to a local climate change impact study in central Asia, a region that is water-scarce and vulnerable to global climate change. We use regional models and machine learning to produce reliable local data from global climate models. We find that regional models show more realistic and detailed changes in heavy precipitation than global climate models. Our work can help assess the future risks of extreme events and plan adaptation strategies in central Asia.
Manuel Schlund, Bouwe Andela, Jörg Benke, Ruth Comer, Birgit Hassler, Emma Hogan, Peter Kalverla, Axel Lauer, Bill Little, Saskia Loosveldt Tomas, Francesco Nattino, Patrick Peglar, Valeriu Predoi, Stef Smeets, Stephen Worsley, Martin Yeo, and Klaus Zimmermann
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-236, https://doi.org/10.5194/gmd-2024-236, 2025
Revised manuscript accepted for GMD
Short summary
Short summary
The Earth System Model Evaluation Tool (ESMValTool) is a community diagnostics and performance metrics tool for the evaluation of Earth system models. Here, we describe recent significant improvements of ESMValTool’s computational efficiency including parallel, out-of-core, and distributed computing. Evaluations with the enhanced version of ESMValTool are faster, use less computational resources, and can handle input data larger than the available memory.
Thomas Rackow, Xabier Pedruzo-Bagazgoitia, Tobias Becker, Sebastian Milinski, Irina Sandu, Razvan Aguridan, Peter Bechtold, Sebastian Beyer, Jean Bidlot, Souhail Boussetta, Willem Deconinck, Michail Diamantakis, Peter Dueben, Emanuel Dutra, Richard Forbes, Rohit Ghosh, Helge F. Goessling, Ioan Hadade, Jan Hegewald, Thomas Jung, Sarah Keeley, Lukas Kluft, Nikolay Koldunov, Aleksei Koldunov, Tobias Kölling, Josh Kousal, Christian Kühnlein, Pedro Maciel, Kristian Mogensen, Tiago Quintino, Inna Polichtchouk, Balthasar Reuter, Domokos Sármány, Patrick Scholz, Dmitry Sidorenko, Jan Streffing, Birgit Sützl, Daisuke Takasuka, Steffen Tietsche, Mirco Valentini, Benoît Vannière, Nils Wedi, Lorenzo Zampieri, and Florian Ziemen
Geosci. Model Dev., 18, 33–69, https://doi.org/10.5194/gmd-18-33-2025, https://doi.org/10.5194/gmd-18-33-2025, 2025
Short summary
Short summary
Detailed global climate model simulations have been created based on a numerical weather prediction model, offering more accurate spatial detail down to the scale of individual cities ("kilometre-scale") and a better understanding of climate phenomena such as atmospheric storms, whirls in the ocean, and cracks in sea ice. The new model aims to provide globally consistent information on local climate change with greater precision, benefiting environmental planning and local impact modelling.
Yilin Fang, Hoang Viet Tran, and L. Ruby Leung
Geosci. Model Dev., 18, 19–32, https://doi.org/10.5194/gmd-18-19-2025, https://doi.org/10.5194/gmd-18-19-2025, 2025
Short summary
Short summary
Hurricanes may worsen water quality in the lower Mississippi River basin (LMRB) by increasing nutrient runoff. We found that runoff parameterizations greatly affect nitrate–nitrogen runoff simulated using an Earth system land model. Our simulations predicted increased nitrogen runoff in the LMRB during Hurricane Ida in 2021, albeit less pronounced than the observations, indicating areas for model improvement to better understand and manage nutrient runoff loss during hurricanes in the region.
Giovanni Seijo-Ellis, Donata Giglio, Gustavo Marques, and Frank Bryan
Geosci. Model Dev., 17, 8989–9021, https://doi.org/10.5194/gmd-17-8989-2024, https://doi.org/10.5194/gmd-17-8989-2024, 2024
Short summary
Short summary
A CESM–MOM6 regional configuration of the Caribbean Sea was developed in response to the rising need for high-resolution models for climate impact studies. The configuration is validated for the period 2000–2020 and improves significant errors in a low-resolution model. Oceanic properties are well represented. Patterns of freshwater associated with the Amazon River are well captured, and the mean flows of ocean waters across multiple passages in the Caribbean Sea agree with observations.
Yan Bo, Hao Liang, Tao Li, and Feng Zhou
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-212, https://doi.org/10.5194/gmd-2024-212, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
This study proposed an advancing framework for modeling regional rice production, water use, and greenhouse gas emissions. The framework integrated a process-based soil-crop model with key physiological effects, a novel model upscaling method, and the NSGA-II multi-objective optimization algorithm at a parallel computing platform. The framework provides a valuable tool for irrigation optimization to deliver co-benefits of ensuring food production, reducing water use and greenhouse gas emissions.
Deifilia To, Julian Quinting, Gholam Ali Hoshyaripour, Markus Götz, Achim Streit, and Charlotte Debus
Geosci. Model Dev., 17, 8873–8884, https://doi.org/10.5194/gmd-17-8873-2024, https://doi.org/10.5194/gmd-17-8873-2024, 2024
Short summary
Short summary
Pangu-Weather is a breakthrough machine learning model in medium-range weather forecasting that considers 3D atmospheric information. We show that using a simpler 2D framework improves robustness, speeds up training, and reduces computational needs by 20 %–30 %. We introduce a training procedure that varies the importance of atmospheric variables over time to speed up training convergence. Decreasing computational demand increases the accessibility of training and working with the model.
Fang Li, Xiang Song, Sandy P. Harrison, Jennifer R. Marlon, Zhongda Lin, L. Ruby Leung, Jörg Schwinger, Virginie Marécal, Shiyu Wang, Daniel S. Ward, Xiao Dong, Hanna Lee, Lars Nieradzik, Sam S. Rabin, and Roland Séférian
Geosci. Model Dev., 17, 8751–8771, https://doi.org/10.5194/gmd-17-8751-2024, https://doi.org/10.5194/gmd-17-8751-2024, 2024
Short summary
Short summary
This study provides the first comprehensive assessment of historical fire simulations from 19 Earth system models in phase 6 of the Coupled Model Intercomparison Project (CMIP6). Most models reproduce global totals, spatial patterns, seasonality, and regional historical changes well but fail to simulate the recent decline in global burned area and underestimate the fire response to climate variability. CMIP6 simulations address three critical issues of phase-5 models.
Seung H. Baek, Paul A. Ullrich, Bo Dong, and Jiwoo Lee
Geosci. Model Dev., 17, 8665–8681, https://doi.org/10.5194/gmd-17-8665-2024, https://doi.org/10.5194/gmd-17-8665-2024, 2024
Short summary
Short summary
We evaluate downscaled products by examining locally relevant co-variances during precipitation events. Common statistical downscaling techniques preserve expected co-variances during convective precipitation (a stationary phenomenon). However, they dampen future intensification of frontal precipitation (a non-stationary phenomenon) captured in global climate models and dynamical downscaling. Our study quantifies a ramification of the stationarity assumption underlying statistical downscaling.
Emmanuel Nyenah, Petra Döll, Daniel S. Katz, and Robert Reinecke
Geosci. Model Dev., 17, 8593–8611, https://doi.org/10.5194/gmd-17-8593-2024, https://doi.org/10.5194/gmd-17-8593-2024, 2024
Short summary
Short summary
Research software is vital for scientific progress but is often developed by scientists with limited skills, time, and funding, leading to challenges in usability and maintenance. Our study across 10 sectors shows strengths in version control, open-source licensing, and documentation while emphasizing the need for containerization and code quality. We recommend workshops; code quality metrics; funding; and following the findable, accessible, interoperable, and reusable (FAIR) standards.
Chris Smith, Donald P. Cummins, Hege-Beate Fredriksen, Zebedee Nicholls, Malte Meinshausen, Myles Allen, Stuart Jenkins, Nicholas Leach, Camilla Mathison, and Antti-Ilari Partanen
Geosci. Model Dev., 17, 8569–8592, https://doi.org/10.5194/gmd-17-8569-2024, https://doi.org/10.5194/gmd-17-8569-2024, 2024
Short summary
Short summary
Climate projections are only useful if the underlying models that produce them are well calibrated and can reproduce observed climate change. We formalise a software package that calibrates the open-source FaIR simple climate model to full-complexity Earth system models. Observations, including historical warming, and assessments of key climate variables such as that of climate sensitivity are used to constrain the model output.
Jingwei Xie, Xi Wang, Hailong Liu, Pengfei Lin, Jiangfeng Yu, Zipeng Yu, Junlin Wei, and Xiang Han
Geosci. Model Dev., 17, 8469–8493, https://doi.org/10.5194/gmd-17-8469-2024, https://doi.org/10.5194/gmd-17-8469-2024, 2024
Short summary
Short summary
We propose the concept of mesoscale ocean direct numerical simulation (MODNS), which should resolve the first baroclinic deformation radius and ensure the numerical dissipative effects do not directly contaminate the mesoscale motions. It can be a benchmark for testing mesoscale ocean large eddy simulation (MOLES) methods in ocean models. We build an idealized Southern Ocean model using MITgcm to generate a type of MODNS. We also illustrate the diversity of multiscale eddy interactions.
Emily Black, John Ellis, and Ross I. Maidment
Geosci. Model Dev., 17, 8353–8372, https://doi.org/10.5194/gmd-17-8353-2024, https://doi.org/10.5194/gmd-17-8353-2024, 2024
Short summary
Short summary
We present General TAMSAT-ALERT, a computationally lightweight and versatile tool for generating ensemble forecasts from time series data. General TAMSAT-ALERT is capable of combining multiple streams of monitoring and meteorological forecasting data into probabilistic hazard assessments. In this way, it complements existing systems and enhances their utility for actionable hazard assessment.
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,...
