Articles | Volume 16, issue 14
https://doi.org/10.5194/gmd-16-4083-2023
© 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-16-4083-2023
© 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
| 
20 Jul 2023
Model evaluation paper |
| 20 Jul 2023
| 20 Jul 2023
Modeling river water temperature with limiting forcing data: Air2stream v1.0.0, machine learning and multiple regression
MARE – Marine and Environmental Sciences Centre, ARNET – Aquatic
Research Network Associate Laboratory, NOVA School of Science and
Technology, NOVA University Lisbon, Caparica, Portugal
Pedro S. Coelho
MARE – Marine and Environmental Sciences Centre, ARNET – Aquatic
Research Network Associate Laboratory, NOVA School of Science and
Technology, NOVA University Lisbon, Caparica, Portugal
Related authors
Manuel Almeida and Pedro Coelho
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-202, https://doi.org/10.5194/gmd-2024-202, 2025
Preprint under review for GMD
Short summary
Short summary
This study aims to assess the capabilities of the advanced CE-QUAL-W2 v4.5 sediment diagenesis model, focusing on its application to a reservoir in Portugal over a six-year period (2016–2021). Our findings indicate that the model performs very well in simulating dissolved oxygen profiles, nutrient concentrations, and organic matter levels.
Manuel C. Almeida, Yurii Shevchuk, Georgiy Kirillin, Pedro M. M. Soares, Rita M. Cardoso, José P. Matos, Ricardo M. Rebelo, António C. Rodrigues, and Pedro S. Coelho
Geosci. Model Dev., 15, 173–197, https://doi.org/10.5194/gmd-15-173-2022, https://doi.org/10.5194/gmd-15-173-2022, 2022
Short summary
Short summary
In this study, we have evaluated the importance of the input of energy conveyed by river inflows into lakes and reservoirs when modeling surface water energy fluxes. Our results suggest that there is a strong correlation between water residence time and the surface water temperature prediction error and that the combined use of process-based physical models and machine-learning models will considerably improve the modeling of air–lake heat and moisture fluxes.
Manuel Almeida and Pedro Coelho
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-202, https://doi.org/10.5194/gmd-2024-202, 2025
Preprint under review for GMD
Short summary
Short summary
This study aims to assess the capabilities of the advanced CE-QUAL-W2 v4.5 sediment diagenesis model, focusing on its application to a reservoir in Portugal over a six-year period (2016–2021). Our findings indicate that the model performs very well in simulating dissolved oxygen profiles, nutrient concentrations, and organic matter levels.
Manuel C. Almeida, Yurii Shevchuk, Georgiy Kirillin, Pedro M. M. Soares, Rita M. Cardoso, José P. Matos, Ricardo M. Rebelo, António C. Rodrigues, and Pedro S. Coelho
Geosci. Model Dev., 15, 173–197, https://doi.org/10.5194/gmd-15-173-2022, https://doi.org/10.5194/gmd-15-173-2022, 2022
Short summary
Short summary
In this study, we have evaluated the importance of the input of energy conveyed by river inflows into lakes and reservoirs when modeling surface water energy fluxes. Our results suggest that there is a strong correlation between water residence time and the surface water temperature prediction error and that the combined use of process-based physical models and machine-learning models will considerably improve the modeling of air–lake heat and moisture fluxes.
Related subject area
Climate and Earth system modeling
GOSI9: UK Global Ocean and Sea Ice configurations
Decomposition of skill scores for conditional verification: impact of Atlantic Multidecadal Oscillation phases on the predictability of decadal temperature forecasts
Virtual Integration of Satellite and In-situ Observation Networks (VISION) v1.0: In-Situ Observations Simulator (ISO_simulator)
Climate model downscaling in central Asia: a dynamical and a neural network approach
Multi-year simulations at kilometre scale with the Integrated Forecasting System coupled to FESOM2.5 and NEMOv3.4
Subsurface hydrological controls on the short-term effects of hurricanes on nitrate–nitrogen runoff loading: a case study of Hurricane Ida using the Energy Exascale Earth System Model (E3SM) Land Model (v2.1)
CARIB12: a regional Community Earth System Model/Modular Ocean Model 6 configuration of the Caribbean Sea
Architectural insights into and training methodology optimization of Pangu-Weather
Evaluation of global fire simulations in CMIP6 Earth system models
Evaluating downscaled products with expected hydroclimatic co-variances
Software sustainability of global impact models
fair-calibrate v1.4.1: calibration, constraining, and validation of the FaIR simple climate model for reliable future climate projections
ISOM 1.0: a fully mesoscale-resolving idealized Southern Ocean model and the diversity of multiscale eddy interactions
A computationally lightweight model for ensemble forecasting of environmental hazards: General TAMSAT-ALERT v1.2.1
Introducing the MESMER-M-TPv0.1.0 module: spatially explicit Earth system model emulation for monthly precipitation and temperature
The need for carbon-emissions-driven climate projections in CMIP7
Robust handling of extremes in quantile mapping – “Murder your darlings”
A protocol for model intercomparison of impacts of marine cloud brightening climate intervention
An extensible perturbed parameter ensemble for the Community Atmosphere Model version 6
Coupling the regional climate model ICON-CLM v2.6.6 to the Earth system model GCOAST-AHOI v2.0 using OASIS3-MCT v4.0
A fully coupled solid-particle microphysics scheme for stratospheric aerosol injections within the aerosol–chemistry–climate model SOCOL-AERv2
An improved representation of aerosol in the ECMWF IFS-COMPO 49R1 through the integration of EQSAM4Climv12 – a first attempt at simulating aerosol acidity
At-scale Model Output Statistics in mountain environments (AtsMOS v1.0)
Impact of ocean vertical-mixing parameterization on Arctic sea ice and upper-ocean properties using the NEMO-SI3 model
Bridging the gap: a new module for human water use in the Community Earth System Model version 2.2.1
Modeling Commercial-Scale CO2 Storage in the Gas Hydrate Stability Zone with PFLOTRAN v6.0
A new lightning scheme in the Canadian Atmospheric Model (CanAM5.1): implementation, evaluation, and projections of lightning and fire in future climates
Methane dynamics in the Baltic Sea: investigating concentration, flux, and isotopic composition patterns using the coupled physical–biogeochemical model BALTSEM-CH4 v1.0
Using feature importance as exploratory data analysis tool on earth system models
CropSuite – A comprehensive open-source crop suitability model considering climate variability for climate impact assessment
ICON ComIn – The ICON Community Interface (ComIn version 0.1.0, with ICON version 2024.01-01)
Split-explicit external mode solver in the finite volume sea ice–ocean model FESOM2
Applying double cropping and interactive irrigation in the North China Plain using WRF4.5
The sea ice component of GC5: coupling SI3 to HadGEM3 using conductive fluxes
CICE on a C-grid: new momentum, stress, and transport schemes for CICEv6.5
HyPhAICC v1.0: a hybrid physics–AI approach for probability fields advection shown through an application to cloud cover nowcasting
CICERO Simple Climate Model (CICERO-SCM v1.1.1) – an improved simple climate model with a parameter calibration tool
A non-intrusive, multi-scale, and flexible coupling interface in WRF
T&C-CROP: Representing mechanistic crop growth with a terrestrial biosphere model (T&C, v1.5): Model formulation and validation
Development of a plant carbon–nitrogen interface coupling framework in a coupled biophysical-ecosystem–biogeochemical model (SSiB5/TRIFFID/DayCent-SOM v1.0)
The Earth Science Box Modeling Toolkit (ESBMTK)
High Resolution Model Intercomparison Project phase 2 (HighResMIP2) towards CMIP7
Dynamical Madden–Julian Oscillation forecasts using an ensemble subseasonal-to-seasonal forecast system of the IAP-CAS model
Presentation, Calibration and Testing of the DCESS II Earth System Model of Intermediate Complexity (version 1.0)
Baseline Climate Variables for Earth System Modelling
The DOE E3SM Version 2.1: Overview and Assessment of the Impacts of Parameterized Ocean Submesoscales
Evaluation of atmospheric rivers in reanalyses and climate models in a new metrics framework
Implementation of a brittle sea ice rheology in an Eulerian, finite-difference, C-grid modeling framework: impact on the simulated deformation of sea ice in the Arctic
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
Catherine Guiavarc'h, David Storkey, Adam T. Blaker, Ed Blockley, Alex Megann, Helene Hewitt, Michael J. Bell, Daley Calvert, Dan Copsey, Bablu Sinha, Sophia Moreton, Pierre Mathiot, and Bo An
Geosci. Model Dev., 18, 377–403, https://doi.org/10.5194/gmd-18-377-2025, https://doi.org/10.5194/gmd-18-377-2025, 2025
Short summary
Short summary
The Global Ocean and Sea Ice configuration version 9 (GOSI9) is the new UK hierarchy of model configurations based on the Nucleus for European Modelling of the Ocean (NEMO) and available at three resolutions. It will be used for various applications, e.g. weather forecasting and climate prediction. It improves upon the previous version by reducing global temperature and salinity biases and enhancing the representation of Arctic sea ice and the Antarctic Circumpolar Current.
Andy Richling, Jens Grieger, and Henning W. Rust
Geosci. Model Dev., 18, 361–375, https://doi.org/10.5194/gmd-18-361-2025, https://doi.org/10.5194/gmd-18-361-2025, 2025
Short summary
Short summary
The performance of weather and climate prediction systems is variable in time and space. It is of interest how this performance varies in different situations. We provide a decomposition of a skill score (a measure of forecast performance) as a tool for detailed assessment of performance variability to support model development or forecast improvement. The framework is exemplified with decadal forecasts to assess the impact of different ocean states in the North Atlantic on temperature forecast.
Maria R. Russo, Sadie L. Bartholomew, David Hassell, Alex M. Mason, Erica Neininger, A. James Perman, David A. J. Sproson, Duncan Watson-Parris, and Nathan Luke Abraham
Geosci. Model Dev., 18, 181–191, https://doi.org/10.5194/gmd-18-181-2025, https://doi.org/10.5194/gmd-18-181-2025, 2025
Short summary
Short summary
Observational data and modelling capabilities have expanded in recent years, but there are still barriers preventing these two data sources from being used in synergy. Proper comparison requires generating, storing, and handling a large amount of data. This work describes the first step in the development of a new set of software tools, the VISION toolkit, which can enable the easy and efficient integration of observational and model data required for model evaluation.
Bijan Fallah, Masoud Rostami, Emmanuele Russo, Paula Harder, Christoph Menz, Peter Hoffmann, Iulii Didovets, and Fred F. Hattermann
Geosci. Model Dev., 18, 161–180, https://doi.org/10.5194/gmd-18-161-2025, https://doi.org/10.5194/gmd-18-161-2025, 2025
Short summary
Short summary
We tried to contribute to a local climate change impact study in central Asia, a region that is water-scarce and vulnerable to global climate change. We use regional models and machine learning to produce reliable local data from global climate models. We find that regional models show more realistic and detailed changes in heavy precipitation than global climate models. Our work can help assess the future risks of extreme events and plan adaptation strategies in central Asia.
Thomas Rackow, Xabier Pedruzo-Bagazgoitia, Tobias Becker, Sebastian Milinski, Irina Sandu, Razvan Aguridan, Peter Bechtold, Sebastian Beyer, Jean Bidlot, Souhail Boussetta, Willem Deconinck, Michail Diamantakis, Peter Dueben, Emanuel Dutra, Richard Forbes, Rohit Ghosh, Helge F. Goessling, Ioan Hadade, Jan Hegewald, Thomas Jung, Sarah Keeley, Lukas Kluft, Nikolay Koldunov, Aleksei Koldunov, Tobias Kölling, Josh Kousal, Christian Kühnlein, Pedro Maciel, Kristian Mogensen, Tiago Quintino, Inna Polichtchouk, Balthasar Reuter, Domokos Sármány, Patrick Scholz, Dmitry Sidorenko, Jan Streffing, Birgit Sützl, Daisuke Takasuka, Steffen Tietsche, Mirco Valentini, Benoît Vannière, Nils Wedi, Lorenzo Zampieri, and Florian Ziemen
Geosci. Model Dev., 18, 33–69, https://doi.org/10.5194/gmd-18-33-2025, https://doi.org/10.5194/gmd-18-33-2025, 2025
Short summary
Short summary
Detailed global climate model simulations have been created based on a numerical weather prediction model, offering more accurate spatial detail down to the scale of individual cities ("kilometre-scale") and a better understanding of climate phenomena such as atmospheric storms, whirls in the ocean, and cracks in sea ice. The new model aims to provide globally consistent information on local climate change with greater precision, benefiting environmental planning and local impact modelling.
Yilin Fang, Hoang Viet Tran, and L. Ruby Leung
Geosci. Model Dev., 18, 19–32, https://doi.org/10.5194/gmd-18-19-2025, https://doi.org/10.5194/gmd-18-19-2025, 2025
Short summary
Short summary
Hurricanes may worsen water quality in the lower Mississippi River basin (LMRB) by increasing nutrient runoff. We found that runoff parameterizations greatly affect nitrate–nitrogen runoff simulated using an Earth system land model. Our simulations predicted increased nitrogen runoff in the LMRB during Hurricane Ida in 2021, albeit less pronounced than the observations, indicating areas for model improvement to better understand and manage nutrient runoff loss during hurricanes in the region.
Giovanni Seijo-Ellis, Donata Giglio, Gustavo Marques, and Frank Bryan
Geosci. Model Dev., 17, 8989–9021, https://doi.org/10.5194/gmd-17-8989-2024, https://doi.org/10.5194/gmd-17-8989-2024, 2024
Short summary
Short summary
A CESM–MOM6 regional configuration of the Caribbean Sea was developed in response to the rising need for high-resolution models for climate impact studies. The configuration is validated for the period 2000–2020 and improves significant errors in a low-resolution model. Oceanic properties are well represented. Patterns of freshwater associated with the Amazon River are well captured, and the mean flows of ocean waters across multiple passages in the Caribbean Sea agree with observations.
Deifilia To, Julian Quinting, Gholam Ali Hoshyaripour, Markus Götz, Achim Streit, and Charlotte Debus
Geosci. Model Dev., 17, 8873–8884, https://doi.org/10.5194/gmd-17-8873-2024, https://doi.org/10.5194/gmd-17-8873-2024, 2024
Short summary
Short summary
Pangu-Weather is a breakthrough machine learning model in medium-range weather forecasting that considers 3D atmospheric information. We show that using a simpler 2D framework improves robustness, speeds up training, and reduces computational needs by 20 %–30 %. We introduce a training procedure that varies the importance of atmospheric variables over time to speed up training convergence. Decreasing computational demand increases the accessibility of training and working with the model.
Fang Li, Xiang Song, Sandy P. Harrison, Jennifer R. Marlon, Zhongda Lin, L. Ruby Leung, Jörg Schwinger, Virginie Marécal, Shiyu Wang, Daniel S. Ward, Xiao Dong, Hanna Lee, Lars Nieradzik, Sam S. Rabin, and Roland Séférian
Geosci. Model Dev., 17, 8751–8771, https://doi.org/10.5194/gmd-17-8751-2024, https://doi.org/10.5194/gmd-17-8751-2024, 2024
Short summary
Short summary
This study provides the first comprehensive assessment of historical fire simulations from 19 Earth system models in phase 6 of the Coupled Model Intercomparison Project (CMIP6). Most models reproduce global totals, spatial patterns, seasonality, and regional historical changes well but fail to simulate the recent decline in global burned area and underestimate the fire response to climate variability. CMIP6 simulations address three critical issues of phase-5 models.
Seung H. Baek, Paul A. Ullrich, Bo Dong, and Jiwoo Lee
Geosci. Model Dev., 17, 8665–8681, https://doi.org/10.5194/gmd-17-8665-2024, https://doi.org/10.5194/gmd-17-8665-2024, 2024
Short summary
Short summary
We evaluate downscaled products by examining locally relevant co-variances during precipitation events. Common statistical downscaling techniques preserve expected co-variances during convective precipitation (a stationary phenomenon). However, they dampen future intensification of frontal precipitation (a non-stationary phenomenon) captured in global climate models and dynamical downscaling. Our study quantifies a ramification of the stationarity assumption underlying statistical downscaling.
Emmanuel Nyenah, Petra Döll, Daniel S. Katz, and Robert Reinecke
Geosci. Model Dev., 17, 8593–8611, https://doi.org/10.5194/gmd-17-8593-2024, https://doi.org/10.5194/gmd-17-8593-2024, 2024
Short summary
Short summary
Research software is vital for scientific progress but is often developed by scientists with limited skills, time, and funding, leading to challenges in usability and maintenance. Our study across 10 sectors shows strengths in version control, open-source licensing, and documentation while emphasizing the need for containerization and code quality. We recommend workshops; code quality metrics; funding; and following the findable, accessible, interoperable, and reusable (FAIR) standards.
Chris Smith, Donald P. Cummins, Hege-Beate Fredriksen, Zebedee Nicholls, Malte Meinshausen, Myles Allen, Stuart Jenkins, Nicholas Leach, Camilla Mathison, and Antti-Ilari Partanen
Geosci. Model Dev., 17, 8569–8592, https://doi.org/10.5194/gmd-17-8569-2024, https://doi.org/10.5194/gmd-17-8569-2024, 2024
Short summary
Short summary
Climate projections are only useful if the underlying models that produce them are well calibrated and can reproduce observed climate change. We formalise a software package that calibrates the open-source FaIR simple climate model to full-complexity Earth system models. Observations, including historical warming, and assessments of key climate variables such as that of climate sensitivity are used to constrain the model output.
Jingwei Xie, Xi Wang, Hailong Liu, Pengfei Lin, Jiangfeng Yu, Zipeng Yu, Junlin Wei, and Xiang Han
Geosci. Model Dev., 17, 8469–8493, https://doi.org/10.5194/gmd-17-8469-2024, https://doi.org/10.5194/gmd-17-8469-2024, 2024
Short summary
Short summary
We propose the concept of mesoscale ocean direct numerical simulation (MODNS), which should resolve the first baroclinic deformation radius and ensure the numerical dissipative effects do not directly contaminate the mesoscale motions. It can be a benchmark for testing mesoscale ocean large eddy simulation (MOLES) methods in ocean models. We build an idealized Southern Ocean model using MITgcm to generate a type of MODNS. We also illustrate the diversity of multiscale eddy interactions.
Emily Black, John Ellis, and Ross I. Maidment
Geosci. Model Dev., 17, 8353–8372, https://doi.org/10.5194/gmd-17-8353-2024, https://doi.org/10.5194/gmd-17-8353-2024, 2024
Short summary
Short summary
We present General TAMSAT-ALERT, a computationally lightweight and versatile tool for generating ensemble forecasts from time series data. General TAMSAT-ALERT is capable of combining multiple streams of monitoring and meteorological forecasting data into probabilistic hazard assessments. In this way, it complements existing systems and enhances their utility for actionable hazard assessment.
Sarah Schöngart, Lukas Gudmundsson, Mathias Hauser, Peter Pfleiderer, Quentin Lejeune, Shruti Nath, Sonia Isabelle Seneviratne, and Carl-Friedrich Schleussner
Geosci. Model Dev., 17, 8283–8320, https://doi.org/10.5194/gmd-17-8283-2024, https://doi.org/10.5194/gmd-17-8283-2024, 2024
Short summary
Short summary
Precipitation and temperature are two of the most impact-relevant climatic variables. Yet, projecting future precipitation and temperature data under different emission scenarios relies on complex models that are computationally expensive. In this study, we propose a method that allows us to generate monthly means of local precipitation and temperature at low computational costs. Our modelling framework is particularly useful for all downstream applications of climate model data.
Benjamin M. Sanderson, Ben B. B. Booth, John Dunne, Veronika Eyring, Rosie A. Fisher, Pierre Friedlingstein, Matthew J. Gidden, Tomohiro Hajima, Chris D. Jones, Colin G. Jones, Andrew King, Charles D. Koven, David M. Lawrence, Jason Lowe, Nadine Mengis, Glen P. Peters, Joeri Rogelj, Chris Smith, Abigail C. Snyder, Isla R. Simpson, Abigail L. S. Swann, Claudia Tebaldi, Tatiana Ilyina, Carl-Friedrich Schleussner, Roland Séférian, Bjørn H. Samset, Detlef van Vuuren, and Sönke Zaehle
Geosci. Model Dev., 17, 8141–8172, https://doi.org/10.5194/gmd-17-8141-2024, https://doi.org/10.5194/gmd-17-8141-2024, 2024
Short summary
Short summary
We discuss how, in order to provide more relevant guidance for climate policy, coordinated climate experiments should adopt a greater focus on simulations where Earth system models are provided with carbon emissions from fossil fuels together with land use change instructions, rather than past approaches that have largely focused on experiments with prescribed atmospheric carbon dioxide concentrations. We discuss how these goals might be achieved in coordinated climate modeling experiments.
Peter Berg, Thomas Bosshard, Denica Bozhinova, Lars Bärring, Joakim Löw, Carolina Nilsson, Gustav Strandberg, Johan Södling, Johan Thuresson, Renate Wilcke, and Wei Yang
Geosci. Model Dev., 17, 8173–8179, https://doi.org/10.5194/gmd-17-8173-2024, https://doi.org/10.5194/gmd-17-8173-2024, 2024
Short summary
Short summary
When bias adjusting climate model data using quantile mapping, one needs to prescribe what to do at the tails of the distribution, where a larger data range is likely encountered outside of the calibration period. The end result is highly dependent on the method used. We show that, to avoid discontinuities in the time series, one needs to exclude data in the calibration range to also activate the extrapolation functionality in that time period.
Philip J. Rasch, Haruki Hirasawa, Mingxuan Wu, Sarah J. Doherty, Robert Wood, Hailong Wang, Andy Jones, James Haywood, and Hansi Singh
Geosci. Model Dev., 17, 7963–7994, https://doi.org/10.5194/gmd-17-7963-2024, https://doi.org/10.5194/gmd-17-7963-2024, 2024
Short summary
Short summary
We introduce a protocol to compare computer climate simulations to better understand a proposed strategy intended to counter warming and climate impacts from greenhouse gas increases. This slightly changes clouds in six ocean regions to reflect more sunlight and cool the Earth. Example changes in clouds and climate are shown for three climate models. Cloud changes differ between the models, but precipitation and surface temperature changes are similar when their cooling effects are made similar.
Trude Eidhammer, Andrew Gettelman, Katherine Thayer-Calder, Duncan Watson-Parris, Gregory Elsaesser, Hugh Morrison, Marcus van Lier-Walqui, Ci Song, and Daniel McCoy
Geosci. Model Dev., 17, 7835–7853, https://doi.org/10.5194/gmd-17-7835-2024, https://doi.org/10.5194/gmd-17-7835-2024, 2024
Short summary
Short summary
We describe a dataset where 45 parameters related to cloud processes in the Community Earth System Model version 2 (CESM2) Community Atmosphere Model version 6 (CAM6) are perturbed. Three sets of perturbed parameter ensembles (263 members) were created: current climate, preindustrial aerosol loading and future climate with sea surface temperature increased by 4 K.
Ha Thi Minh Ho-Hagemann, Vera Maurer, Stefan Poll, and Irina Fast
Geosci. Model Dev., 17, 7815–7834, https://doi.org/10.5194/gmd-17-7815-2024, https://doi.org/10.5194/gmd-17-7815-2024, 2024
Short summary
Short summary
The regional Earth system model GCOAST-AHOI v2.0 that includes the regional climate model ICON-CLM coupled to the ocean model NEMO and the hydrological discharge model HD via the OASIS3-MCT coupler can be a useful tool for conducting long-term regional climate simulations over the EURO-CORDEX domain. The new OASIS3-MCT coupling interface implemented in ICON-CLM makes it more flexible for coupling to an external ocean model and an external hydrological discharge model.
Sandro Vattioni, Rahel Weber, Aryeh Feinberg, Andrea Stenke, John A. Dykema, Beiping Luo, Georgios A. Kelesidis, Christian A. Bruun, Timofei Sukhodolov, Frank N. Keutsch, Thomas Peter, and Gabriel Chiodo
Geosci. Model Dev., 17, 7767–7793, https://doi.org/10.5194/gmd-17-7767-2024, https://doi.org/10.5194/gmd-17-7767-2024, 2024
Short summary
Short summary
We quantified impacts and efficiency of stratospheric solar climate intervention via solid particle injection. Microphysical interactions of solid particles with the sulfur cycle were interactively coupled to the heterogeneous chemistry scheme and the radiative transfer code of an aerosol–chemistry–climate model. Compared to injection of SO2 we only find a stronger cooling efficiency for solid particles when normalizing to the aerosol load but not when normalizing to the injection rate.
Samuel Rémy, Swen Metzger, Vincent Huijnen, Jason E. Williams, and Johannes Flemming
Geosci. Model Dev., 17, 7539–7567, https://doi.org/10.5194/gmd-17-7539-2024, https://doi.org/10.5194/gmd-17-7539-2024, 2024
Short summary
Short summary
In this paper we describe the development of the future operational cycle 49R1 of the IFS-COMPO system, used for operational forecasts of atmospheric composition in the CAMS project, and focus on the implementation of the thermodynamical model EQSAM4Clim version 12. The implementation of EQSAM4Clim significantly improves the simulated secondary inorganic aerosol surface concentration. The new aerosol and precipitation acidity diagnostics showed good agreement against observational datasets.
Maximillian Van Wyk de Vries, Tom Matthews, L. Baker Perry, Nirakar Thapa, and Rob Wilby
Geosci. Model Dev., 17, 7629–7643, https://doi.org/10.5194/gmd-17-7629-2024, https://doi.org/10.5194/gmd-17-7629-2024, 2024
Short summary
Short summary
This paper introduces the AtsMOS workflow, a new tool for improving weather forecasts in mountainous areas. By combining advanced statistical techniques with local weather data, AtsMOS can provide more accurate predictions of weather conditions. Using data from Mount Everest as an example, AtsMOS has shown promise in better forecasting hazardous weather conditions, making it a valuable tool for communities in mountainous regions and beyond.
Sofia Allende, Anne Marie Treguier, Camille Lique, Clément de Boyer Montégut, François Massonnet, Thierry Fichefet, and Antoine Barthélemy
Geosci. Model Dev., 17, 7445–7466, https://doi.org/10.5194/gmd-17-7445-2024, https://doi.org/10.5194/gmd-17-7445-2024, 2024
Short summary
Short summary
We study the parameters of the turbulent-kinetic-energy mixed-layer-penetration scheme in the NEMO model with regard to sea-ice-covered regions of the Arctic Ocean. This evaluation reveals the impact of these parameters on mixed-layer depth, sea surface temperature and salinity, and ocean stratification. Our findings demonstrate significant impacts on sea ice thickness and sea ice concentration, emphasizing the need for accurately representing ocean mixing to understand Arctic climate dynamics.
Sabin I. Taranu, David M. Lawrence, Yoshihide Wada, Ting Tang, Erik Kluzek, Sam Rabin, Yi Yao, Steven J. De Hertog, Inne Vanderkelen, and Wim Thiery
Geosci. Model Dev., 17, 7365–7399, https://doi.org/10.5194/gmd-17-7365-2024, https://doi.org/10.5194/gmd-17-7365-2024, 2024
Short summary
Short summary
In this study, we improved a climate model by adding the representation of water use sectors such as domestic, industry, and agriculture. This new feature helps us understand how water is used and supplied in various areas. We tested our model from 1971 to 2010 and found that it accurately identifies areas with water scarcity. By modelling the competition between sectors when water availability is limited, the model helps estimate the intensity and extent of individual sectors' water shortages.
Michael Nole, Jonah Bartrand, Fawz Naim, and Glenn Hammond
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-162, https://doi.org/10.5194/gmd-2024-162, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
Safe carbon dioxide (CO2) storage is likely to be critical for mitigating some of the most dangerous effects of climate change. We present a simulation framework for modeling CO2 storage beneath the seafloor where CO2 can form a solid. This can aid in permanent CO2 storage for long periods of time. Our models show what a commercial-scale CO2 injection would look like in a marine environment. We discuss what would need to be considered when designing a sub-sea CO2 injection.
Cynthia Whaley, Montana Etten-Bohm, Courtney Schumacher, Ayodeji Akingunola, Vivek Arora, Jason Cole, Michael Lazare, David Plummer, Knut von Salzen, and Barbara Winter
Geosci. Model Dev., 17, 7141–7155, https://doi.org/10.5194/gmd-17-7141-2024, https://doi.org/10.5194/gmd-17-7141-2024, 2024
Short summary
Short summary
This paper describes how lightning was added as a process in the Canadian Earth System Model in order to interactively respond to climate changes. As lightning is an important cause of global wildfires, this new model development allows for more realistic projections of how wildfires may change in the future, responding to a changing climate.
Erik Gustafsson, Bo G. Gustafsson, Martijn Hermans, Christoph Humborg, and Christian Stranne
Geosci. Model Dev., 17, 7157–7179, https://doi.org/10.5194/gmd-17-7157-2024, https://doi.org/10.5194/gmd-17-7157-2024, 2024
Short summary
Short summary
Methane (CH4) cycling in the Baltic Proper is studied through model simulations, enabling a first estimate of key CH4 fluxes. A preliminary budget identifies benthic CH4 release as the dominant source and two main sinks: CH4 oxidation in the water (92 % of sinks) and outgassing to the atmosphere (8 % of sinks). This study addresses CH4 emissions from coastal seas and is a first step toward understanding the relative importance of open-water outgassing compared with local coastal hotspots.
Daniel Ries, Katherine Goode, Kellie McClernon, and Benjamin Hillman
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-133, https://doi.org/10.5194/gmd-2024-133, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
Machine learning has advanced research in the climate science domain, but its models are difficult to understand. In order to understand the impacts and consequences of climate interventions such as stratospheric aerosol injection, complex models are often necessary. We use a case study to illustrate how we can understand the inner workings of a complex model. We present this technique as an exploratory tool that can be used to quickly discover and assess relationships in complex climate data.
Florian Zabel, Matthias Knüttel, and Benjamin Poschlod
EGUsphere, https://doi.org/10.5194/egusphere-2024-2526, https://doi.org/10.5194/egusphere-2024-2526, 2024
Short summary
Short summary
CropSuite is a fuzzy-logic based high resolution open-source crop suitability model considering the impact of climate variability. We apply CropSuite for 48 important staple and cash crops at 1 km spatial resolution for Africa. We find that climate variability significantly impacts on suitable areas, but also affects optimal sowing dates, and multiple cropping potentials. The results provide information that can be used for climate impact assessments, adaptation and land-use planning.
Kerstin Hartung, Bastian Kern, Nils-Arne Dreier, Jörn Geisbüsch, Mahnoosh Haghighatnasab, Patrick Jöckel, Astrid Kerkweg, Wilton Jaciel Loch, Florian Prill, and Daniel Rieger
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-135, https://doi.org/10.5194/gmd-2024-135, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
The Icosahedral Nonhydrostatic (ICON) Model Community Interface (ComIn) library supports connecting third-party modules to the ICON model. Third-party modules can range from simple diagnostic Python scripts to full chemistry models. ComIn offers a low barrier for code extensions to ICON, provides multi-language support (Fortran, C/C++ and Python) and reduces the migration effort in response to new ICON releases. This paper presents the ComIn design principles and a range of use cases.
Tridib Banerjee, Patrick Scholz, Sergey Danilov, Knut Klingbeil, and Dmitry Sidorenko
Geosci. Model Dev., 17, 7051–7065, https://doi.org/10.5194/gmd-17-7051-2024, https://doi.org/10.5194/gmd-17-7051-2024, 2024
Short summary
Short summary
In this paper we propose a new alternative to one of the functionalities of the sea ice model FESOM2. The alternative we propose allows the model to capture and simulate fast changes in quantities like sea surface elevation more accurately. We also demonstrate that the new alternative is faster and more adept at taking advantages of highly parallelized computing infrastructure. We therefore show that this new alternative is a great addition to the sea ice model FESOM2.
Yuwen Fan, Zhao Yang, Min-Hui Lo, Jina Hur, and Eun-Soon Im
Geosci. Model Dev., 17, 6929–6947, https://doi.org/10.5194/gmd-17-6929-2024, https://doi.org/10.5194/gmd-17-6929-2024, 2024
Short summary
Short summary
Irrigated agriculture in the North China Plain (NCP) has a significant impact on the local climate. To better understand this impact, we developed a specialized model specifically for the NCP region. This model allows us to simulate the double-cropping vegetation and the dynamic irrigation practices that are commonly employed in the NCP. This model shows improved performance in capturing the general crop growth, such as crop stages, biomass, crop yield, and vegetation greenness.
Ed Blockley, Emma Fiedler, Jeff Ridley, Luke Roberts, Alex West, Dan Copsey, Daniel Feltham, Tim Graham, David Livings, Clement Rousset, David Schroeder, and Martin Vancoppenolle
Geosci. Model Dev., 17, 6799–6817, https://doi.org/10.5194/gmd-17-6799-2024, https://doi.org/10.5194/gmd-17-6799-2024, 2024
Short summary
Short summary
This paper documents the sea ice model component of the latest Met Office coupled model configuration, which will be used as the physical basis for UK contributions to CMIP7. Documentation of science options used in the configuration are given along with a brief model evaluation. This is the first UK configuration to use NEMO’s new SI3 sea ice model. We provide details on how SI3 was adapted to work with Met Office coupling methodology and documentation of coupling processes in the model.
Jean-François Lemieux, William H. Lipscomb, Anthony Craig, David A. Bailey, Elizabeth C. Hunke, Philippe Blain, Till A. S. Rasmussen, Mats Bentsen, Frédéric Dupont, David Hebert, and Richard Allard
Geosci. Model Dev., 17, 6703–6724, https://doi.org/10.5194/gmd-17-6703-2024, https://doi.org/10.5194/gmd-17-6703-2024, 2024
Short summary
Short summary
We present the latest version of the CICE model. It solves equations that describe the dynamics and the growth and melt of sea ice. To do so, the domain is divided into grid cells and variables are positioned at specific locations in the cells. A new implementation (C-grid) is presented, with the velocity located on cell edges. Compared to the previous B-grid, the C-grid allows for a natural coupling with some oceanic and atmospheric models. It also allows for ice transport in narrow channels.
Rachid El Montassir, Olivier Pannekoucke, and Corentin Lapeyre
Geosci. Model Dev., 17, 6657–6681, https://doi.org/10.5194/gmd-17-6657-2024, https://doi.org/10.5194/gmd-17-6657-2024, 2024
Short summary
Short summary
This study introduces a novel approach that combines physics and artificial intelligence (AI) for improved cloud cover forecasting. This approach outperforms traditional deep learning (DL) methods in producing realistic and physically consistent results while requiring less training data. This architecture provides a promising solution to overcome the limitations of classical AI methods and contributes to open up new possibilities for combining physical knowledge with deep learning models.
Marit Sandstad, Borgar Aamaas, Ane Nordlie Johansen, Marianne Tronstad Lund, Glen Philip Peters, Bjørn Hallvard Samset, Benjamin Mark Sanderson, and Ragnhild Bieltvedt Skeie
Geosci. Model Dev., 17, 6589–6625, https://doi.org/10.5194/gmd-17-6589-2024, https://doi.org/10.5194/gmd-17-6589-2024, 2024
Short summary
Short summary
The CICERO-SCM has existed as a Fortran model since 1999 that calculates the radiative forcing and concentrations from emissions and is an upwelling diffusion energy balance model of the ocean that calculates temperature change. In this paper, we describe an updated version ported to Python and publicly available at https://github.com/ciceroOslo/ciceroscm (https://doi.org/10.5281/zenodo.10548720). This version contains functionality for parallel runs and automatic calibration.
Sébastien Masson, Swen Jullien, Eric Maisonnave, David Gill, Guillaume Samson, Mathieu Le Corre, and Lionel Renault
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-140, https://doi.org/10.5194/gmd-2024-140, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
This article details a new feature we implemented in the most popular regional atmospheric model (WRF). This feature allows data to be exchanged between WRF and any other model (e.g. an ocean model) using the coupling library Ocean-Atmosphere-Sea-Ice-Soil – Model Coupling Toolkit (OASIS3-MCT). This coupling interface is designed to be non-intrusive, flexible and modular. It also offers the possibility of taking into account the nested zooms used in WRF or in the models with which it is coupled.
Jordi Buckley Paules, Simone Fatichi, Bonnie Warring, and Athanasios Paschalis
EGUsphere, https://doi.org/10.5194/egusphere-2024-2072, https://doi.org/10.5194/egusphere-2024-2072, 2024
Short summary
Short summary
We outline and validate developments to the pre-existing process-based model T&C to better represent cropland processes. Foreseen applications of T&C-CROP include hydrological and carbon storage implications of land-use transitions involving crop, forest, and pasture conversion, as well as studies on optimal irrigation and fertilization under a changing climate.
Zheng Xiang, Yongkang Xue, Weidong Guo, Melannie D. Hartman, Ye Liu, and William J. Parton
Geosci. Model Dev., 17, 6437–6464, https://doi.org/10.5194/gmd-17-6437-2024, https://doi.org/10.5194/gmd-17-6437-2024, 2024
Short summary
Short summary
A process-based plant carbon (C)–nitrogen (N) interface coupling framework has been developed which mainly focuses on plant resistance and N-limitation effects on photosynthesis, plant respiration, and plant phenology. A dynamic C / N ratio is introduced to represent plant resistance and self-adjustment. The framework has been implemented in a coupled biophysical-ecosystem–biogeochemical model, and testing results show a general improvement in simulating plant properties with this framework.
Ulrich Georg Wortmann, Tina Tsan, Mahrukh Niazi, Ruben Navasardyan, Magnus-Roland Marun, Bernardo S. Chede, Jingwen Zhong, and Morgan Wolfe
EGUsphere, https://doi.org/10.5194/egusphere-2024-1864, https://doi.org/10.5194/egusphere-2024-1864, 2024
Short summary
Short summary
The Earth Science Box Modeling Toolkit (ESBMTK) is a Python library designed to separate model description from numerical implementation. This approach results in well-documented, easily readable, and maintainable model code, allowing students and researchers to concentrate on conceptual challenges rather than mathematical intricacies.
Malcolm John Roberts, Kevin A. Reed, Qing Bao, Joseph J. Barsugli, Suzana J. Camargo, Louis-Philippe Caron, Ping Chang, Cheng-Ta Chen, Hannah M. Christensen, Gokhan Danabasoglu, Ivy Frenger, Neven S. Fučkar, Shabeh ul Hasson, Helene T. Hewitt, Huanping Huang, Daehyun Kim, Chihiro Kodama, Michael Lai, Lai-Yung Ruby Leung, Ryo Mizuta, Paulo Nobre, Pablo Ortega, Dominique Paquin, Christopher D. Roberts, Enrico Scoccimarro, Jon Seddon, Anne Marie Treguier, Chia-Ying Tu, Paul A. Ullrich, Pier Luigi Vidale, Michael F. Wehner, Colin M. Zarzycki, Bosong Zhang, Wei Zhang, and Ming Zhao
EGUsphere, https://doi.org/10.5194/egusphere-2024-2582, https://doi.org/10.5194/egusphere-2024-2582, 2024
Short summary
Short summary
HighResMIP2 is a model intercomparison project focussing on high resolution global climate models, that is those with grid spacings of 25 km or less in atmosphere and ocean, using simulations of decades to a century or so in length. We are proposing an update of our simulation protocol to make the models more applicable to key questions for climate variability and hazard in present day and future projections, and to build links with other communities to provide more robust climate information.
Yangke Liu, Qing Bao, Bian He, Xiaofei Wu, Jing Yang, Yimin Liu, Guoxiong Wu, Tao Zhu, Siyuan Zhou, Yao Tang, Ankang Qu, Yalan Fan, Anling Liu, Dandan Chen, Zhaoming Luo, Xing Hu, and Tongwen Wu
Geosci. Model Dev., 17, 6249–6275, https://doi.org/10.5194/gmd-17-6249-2024, https://doi.org/10.5194/gmd-17-6249-2024, 2024
Short summary
Short summary
We give an overview of the Institute of Atmospheric Physics–Chinese Academy of Sciences subseasonal-to-seasonal ensemble forecasting system and Madden–Julian Oscillation forecast evaluation of the system. Compared to other S2S models, the IAP-CAS model has its benefits but also biases, i.e., underdispersive ensemble, overestimated amplitude, and faster propagation speed when forecasting MJO. We provide a reason for these biases and prospects for further improvement of this system in the future.
Esteban Fernández and Gary Shaffer
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-122, https://doi.org/10.5194/gmd-2024-122, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
Here we describe, calibrate and test DCESS II, a new, broad, adaptable and fast Earth System Model. DCESS II has been designed for global simulations over time scales of years to millions of years using limited computer resources like a personal computer. With its flexibility and comprehensive treatment of the global carbon cycle, DCESS II should prove to be a useful, computational-friendly tool for simulations of past climates as well as for future Earth System projections.
Martin Juckes, Karl E. Taylor, Fabrizio Antonio, David Brayshaw, Carlo Buontempo, Jian Cao, Paul J. Durack, Michio Kawamiya, Hyungjun Kim, Tomas Lovato, Chloe Mackallah, Matthew Mizielinski, Alessandra Nuzzo, Martina Stockhause, Daniele Visioni, Jeremy Walton, Briony Turner, Eleanor O’Rourke, and Beth Dingley
EGUsphere, https://doi.org/10.5194/egusphere-2024-2363, https://doi.org/10.5194/egusphere-2024-2363, 2024
Short summary
Short summary
The Baseline Climate Variables for Earth System Modelling (ESM-BCVs) are defined as a list of 132 variables which have high utility for the evaluation and exploitation of climate simulations. The list reflects the most heavily used variables from Earth System Models, based on an assessment of data publication and download records from the largest archive of global climate projects.
Katherine Smith, Alice M. Barthel, LeAnn M. Conlon, Luke P. Van Roekel, Anthony Bartoletti, Jean-Christophe Golez, Chengzhu Zhang, Carolyn Branecky Begeman, James J. Benedict, Gautum Bisht, Yan Feng, Walter Hannah, Bryce E. Harrop, Nicole Jeffery, Wuyin Lin, Po-Lun Ma, Mathew E. Maltrud, Mark R. Petersen, Balwinder Singh, Qi Tang, Teklu Tesfa, Jonathan D. Wolfe, Shaocheng Xie, Xue Zheng, Karthik Balaguru, Oluwayemi Garuba, Peter Gleckler, Aixue Hu, Jiwoo Lee, Ben Moore-Maley, and Ana C. Ordonez
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-149, https://doi.org/10.5194/gmd-2024-149, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
Version 2.1 of the U.S. Department of Energy's Energy Exascale Earth System Model (E3SM) adds the Fox-Kemper et al. (2011) mixed layer eddy parameterization, which restratifies the ocean surface layer through an overturning streamfunction. Results include surface layer biases reduction in temperature, salinity, and sea-ice extent in the North Atlantic, a small strengthening of the Atlantic Meridional Overturning Circulation, and improvements in many atmospheric climatological variables.
Bo Dong, Paul Ullrich, Jiwoo Lee, Peter Gleckler, Kristin Chang, and Travis O'Brien
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-142, https://doi.org/10.5194/gmd-2024-142, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
1. A metrics package designed for easy analysis of AR characteristics and statistics is presented. 2. The tool is efficient for diagnosing systematic AR bias in climate models, and useful for evaluating new AR characteristics in model simulations. 3. In climate models, landfalling AR precipitation shows dry biases globally, and AR tracks are farther poleward (equatorward) in the north and south Atlantic (south Pacific and Indian Ocean).
Laurent Brodeau, Pierre Rampal, Einar Ólason, and Véronique Dansereau
Geosci. Model Dev., 17, 6051–6082, https://doi.org/10.5194/gmd-17-6051-2024, https://doi.org/10.5194/gmd-17-6051-2024, 2024
Short summary
Short summary
A new brittle sea ice rheology, BBM, has been implemented into the sea ice component of NEMO. We describe how a new spatial discretization framework was introduced to achieve this. A set of idealized and realistic ocean and sea ice simulations of the Arctic have been performed using BBM and the standard viscous–plastic rheology of NEMO. When compared to satellite data, our simulations show that our implementation of BBM leads to a fairly good representation of sea ice deformations.
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
Short summary
Short summary
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
Short summary
Short summary
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.
Cited articles
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C.,
Corrado, G. S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I.
J., Harp, A., Irving, G., Isard, M., Jia, Y., Józefowicz, R., Kaiser,
L., Kudlur, M., Levenberg, J., Mane, D., Monga, R., Moore, S., Murray, D.
G., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar,
K., 620 Tucker, P. A., Vanhoucke, V., Vasudevan, V., Viégas, F. B.,
Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., and Zheng, X.:
TensorFlow: Large-scale machine learning on heterogeneous distributed
systems, in: Proceedings of the 12th USENIX conference on Operating Systems
Design and Implementation, Savannah, GA, USA, 2–4 November 2016, 265–283,
https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/45166.pdf (last access: 17 July 2023), 2016.
Agrawal, A. and Petersen, M. R.: Detecting Arsenic Contamination Using
Satellite Imagery and Machine Learning, Toxics, 9, 333,
https://doi.org/10.3390/toxics9120333, 2021.
Ahmadi-Nedushan, B., St-Hilaire, A., Ouarda, T. B. M. J., Bilodeau, L.,
Robichaud, É., Thiémonge, N., and Bobée, B.: Predicting river
water temperatures using stochastic models: case study of the Moisie River
(Québec, Canada), Hydrol. Process., 21, 21–34,
https://doi.org/10.1002/hyp.6353, 2007.
Almeida, M. C. and Coelho, P. S.: mcvta/WaterPythonTemp: Release 0.2.0, Zenodo
[code and data set], https://doi.org/10.5281/zenodo.7870379, 2023.
Araújo, C. S. P., Silva, I. A. C., Ippolito, M., and Almeida, C. D.:
Evaluation of air temperature estimated by ERA5-Land reanalysis using
surface data in Pernambuco, Brazil. Environ. Monit. Assess., 194, 381,
https://doi.org/10.1007/s10661-022-10047-2, 2022.
Awad, M. and Khanna, R. (Eds.): Support vector regression, in: Efficient
learning machines, Springer, 67–80, https://doi.org/10.1007/978-1-4302-5990-9, 2015.
Bergstra, J., Yamins, D., and Cox, D. D.: Making a Science of Model Search:
Hyperparameter Optimization in Hundreds of Dimensions for Vision
Architectures, in: TProc. of the 30th International Conference on Machine
Learning (ICML 2013), 11523, https://doi.org/10.5555/3042817.3042832 (last access: 17 July 2023), 2013.
Branco, P., Ribeiro, R. P., and Torgo, L.: UBL: an R package for
utility-based learning. arXiv preprint, arXiv:1604.08079,
https://doi.org/10.48550/arXiv.1604.08079, 2016.
Branco, P., Ribeiro, R. P., Torgo, L., Krawczyk, B., and Moniz, N.: Smogn: a
pre-processing approach for imbalanced regression, Proc. Mach.
Learn. Res., 74, 36–50, 2017.
Breiman, L.: Random Forests, Mach. Learn., 45, 5–32,
https://doi.org/10.1023/A:1010933404324, 2001.
Caissie, D.: The thermal regime of rivers: a review, Freshwater Biol., 51,
1389–1406, https://doi.org/10.1111/j.1365-2427.2006.01597.x, 2006.
Cardoso, R. M., Soares, P. M. M., Miranda, P. M. A., and Belo-Pereira, M.:
WRF High resolution simulation of Iberian mean and extreme precipitation
climate, Int. J. Climatol., 33, 2591–2608,
https://doi.org/10.1002/joc.3616, 2013.
Chenard, J.-F. and Caissie, D.: Stream temperature modelling using
artificial neural networks: application on Catamaran Brook, New Brunswick,
Canada, Hydrol. Process., 22, 3361–3372,
https://doi.org/10.1002/hyp.6928, 2008.
Crisp, D. T. and Howson, G.: Effect of air temperature upon mean water
temperature in streams in the north Pennines and English Lake District,
Freshwater Biol., 12, 359–367,
https://doi.org/10.1111/j.1365-2427.1982.tb00629.x, 1982.
DeWeber, J. T. and Wagner, T.: A regional neural network ensemble for
predicting mean daily river water temperature, J. Hydrol., 517, 187–200,
https://doi.org/10.1016/j.jhydrol.2014.05.035, 2014.
Du, X., Shrestha, N. K., Ficklin, D. L., and Wang, J.: Incorporation of the equilibrium temperature approach in a Soil and Water Assessment Tool hydroclimatological stream temperature model, Hydrol. Earth Syst. Sci., 22, 2343–2357, https://doi.org/10.5194/hess-22-2343-2018, 2018.
Ducharne, A.: Importance of stream temperature to climate change impact on water quality, Hydrol. Earth Syst. Sci., 12, 797–810, https://doi.org/10.5194/hess-12-797-2008, 2008.
Fahrer, C. and Harris, D., 2004. LAMPPOST A Mnemonic device for teaching
climate variables, J. Geogr., 103, 86–90,
https://doi.org/10.1080/00221340408978579, 2004.
Farr, T. G., Rosen, P. A., Caro, E., Crippen, R., Duren, R., Hensley, S.,
Kobrick, M., Paller, M., Rodriguez, E., Roth, L., Seal, D., Shaffer, S.,
Shimada, J., Umland, J., Werner, M., Oskin, M., Burbank, D., and Alsdorf,
D.: The Shuttle Radar Topography Mission, Rev. Geophys., 45, RG2004,
https://doi.org/10.1029/2005RG000183, 2007.
Feigl, M., Lebiedzinski, K., Herrnegger, M., and Schulz, K.: Machine-learning methods for stream water temperature prediction, Hydrol. Earth Syst. Sci., 25, 2951–2977, https://doi.org/10.5194/hess-25-2951-2021, 2021.
Gallice, A., Schaefli, B., Lehning, M., Parlange, M. B., and Huwald, H.: Stream temperature prediction in ungauged basins: review of recent approaches and description of a new physics-derived statistical model, Hydrol. Earth Syst. Sci., 19, 3727–3753, https://doi.org/10.5194/hess-19-3727-2015, 2015.
Jeppesen, E. and Iversen, T. M.: Two Simple Models for Estimating
Daily Mean Water Temperatures and Diel Variations in a Danish Low Gradient
Stream, Oikos, 49, 149–155, https://doi.org/10.2307/3566020, 1987.
Jourdonnais, J. H., Walsh, R. P., Pickett, F., and Goodman D.: Structure and
calibration strategy for a water temperature model of the lower Madison
River, Montana, Rivers, 3, 153–169, 1992.
Kling, H., Fuchs, M., and Paulin, M.: Runoff conditions in the upper Danube
basin under an ensemble of climate change scenarios, J. Hydrol., 424–425,
264–277, https://doi.org/10.1016/j.jhydrol.2012.01.011, 2012.
Louppe, G.: Understanding Random Forests: From Theory to Practice, arXiv
2014, arXiv:1407.7502, 28 July 2014.
Lu, H. and Ma, X.: Hybrid decision tree-based machine learning models for
short-term water quality prediction, Chemosphere, 249, 126169,
https://doi.org/10.1016/j.chemosphere.2020.126169, 2020.
Macedo, M. N., Coe, M. T., DeFries, R., Uriarte, M., Brando, P. M., Neill,
C., and Walker, W. S.: Land-use-driven stream warming in southeastern
Amazonia, Philos. T. R. Soc. B, 368, 1619,
https://doi.org/10.1098/rstb.2012.0153, 2013.
Mackey, A. P. and Berrie, A. D.: The prediction of water temperatures in
chalk streams from air temperatures, Hydrobiologia, 210, 183–189,
https://doi.org/10.1007/BF00034676, 1991.
Mohseni, O., Stefan, H. G., and Erickson, T. R.: A nonlinear regression
model for weekly stream temperatures, Water Resour. Res., 10,
2685–2692, https://doi.org/10.1029/98WR01877, 1998.
Mohseni, O., Erickson T. R., and Stefan, H. G.: Upper bounds for stream
temperatures in the contiguous United States, Journal of Environmental
Engineering, Am. Soc. Civil Eng., 128, 4–11,
https://doi.org/10.1029/98WR01877, 2002.
Moore, R. D., Spittlehouse, D. L., and Story, A.: Riparian microclimate and
stream temperature response to forest harvesting: a review, J. Am. Water
Resour. As., 41, 813–834,
https://doi.org/10.1111/j.1752-1688.2005.tb03772.x, 2005.
Moore, R. D., Nelitz, M., and Parkinson, E.: Empirical modelling of maximum
weekly average stream temperature in British Columbia, Canada, to support
assessment of fish habitat suitability, Can. Water Resour. J., 38, 135–147,
https://doi.org/10.1080/07011784.2013.794992, 2013.
Moriasi, D. N., Arnold, J. G., Van Liew, M. W., Bingner, R. L., Harmel, R.
D., and Veith, T. L.: Model evaluation guidelines for systematic
quantification of accuracy in watershed simulations, T.
ASABE, 50, 885–900, https://doi.org/10.13031/2013.23153, 2007.
Muñoz-Sabater, J.: ERA5-Land hourly data from 1981 to present,
Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set],
https://doi.org/10.24381/cds.e2161bac, 2019.
Muñoz-Sabater, J.: ERA5-Land hourly data from 1950 to 1980, Copernicus
Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.e2161bac, 2021.
Muñoz-Sabater, J., Dutra, E., Agustí-Panareda, A., Albergel, C., Arduini, G., Balsamo, G., Boussetta, S., Choulga, M., Harrigan, S., Hersbach, H., Martens, B., Miralles, D. G., Piles, M., Rodríguez-Fernández, N. J., Zsoter, E., Buontempo, C., and Thépaut, J.-N.: ERA5-Land: a state-of-the-art global reanalysis dataset for land applications, Earth Syst. Sci. Data, 13, 4349–4383, https://doi.org/10.5194/essd-13-4349-2021, 2021.
Nash, J. E. and Sutcliffe, J. V.: River flow forecasting through conceptual
models: Part 1. A discussion of principles, J. Hydrol., 10, 282–290,
https://doi.org/10.1016/0022-1694(70)90255-6, 1970.
Neumann, D., W., Balaji, R., and Zagona E., A.: 2011, Regression model for
daily maximum stream temperature, J. Environ. Eng., 129,
667–674, https://doi.org/10.1061/(ASCE)0733-9372(2003)129:7(667), 2003.
Pedregosa, F., Varoquaux, G., Gramfort, A, Michel, V., Thirion, B., Grisel,
O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J.,
Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E.:
Scikit-learn: Machine Learning in Python, J. Mach. Learn. Res., 12, 2825–2830,
https://doi.org/10.48550/arXiv.1201.0490, 2011.
Piccolroaz, S.: Prediction of lake surface temperature using air 2stream
model: guidelines, challenges, and future perspectives, Adv.
Oceanogr. Limnol., 7, 36–50,
https://doi.org/10.4081/aiol.2016.5791, 2016.
Piotrowski, A. P., Napiorkowski, M. J., Napiorkowski, J. J., and Osuch, M.:
Comparing various artificial neural types for water temperature prediction
in rivers, J. Hydrol., 529, 302–315,
https://doi.org/10.1016/j.jhydrol.2015.07.044, 2015.
Platt, J.: Sequential Minimal Optimization: A Fast Algorithm for Training
Support Vector Machines, Microsoft, Open File Rep., MSR-TR-98-14, 98–14, https://www.microsoft.com/en-us/research/publication/sequential-minimal-optimization-a-fast-algorithm-for-training-support-vector-machines/ (last access: 19 July 2023), 1998.
Qian, N.: On the momentum term in gradient descent learning algorithms,
Neural Networks, 12, 145–151, https://doi.org/10.1016/S0893-6080(98)00116-6,
1999.
Rabi, A., Hadzima-Nyarko, M., and Šperac, M.: Modelling river
temperature from air temperature: case of the River Drava (Croatia),
Hydrol. Sci. J., 60, 1490–1507,
https://doi.org/10.1080/02626667.2014.914215, 2015.
Rajesh, M. and Rehana, S.: Prediction of river water temperature using
machine learning algorithms: a tropical river system of India, J.
Hydroinform., 23, 605–626, https://doi.org/10.2166/hydro.2021.121,
2021.
Rehana, S.: River water temperature modelling under climate change using
support vector regression, in: Hydrology in a Changing World: Challenges in
Modeling, edited by: Singh, S. K. and Dhanya, C. T., World Springer,
171–183, https://doi.org/10.1007/978-3-030-02197-9_8, 2019.
Rehana, S. and Mujumdar, P. P.: River water quality response under
hypothetical climate change scenarios in Tunga-Bhadra river, India,
Hydrol. Process., 25, 3373–3386, https://doi.org/10.1002/hyp.8057,
2011.
Segura, C., Caldwell, P., Sun, G., McNulty, S., and Zhang, Y.: A model to
predict stream water temperature across the conterminous USA, Hydrol.
Process., 29, 2178–2195, https://doi.org/10.1002/hyp.10357, 2014.
Shevchuk, Y.: Python library, https://neupy.com/pages/home.html, last
access: 7 July 2022.
Shi, Y. and Eberhart, R. C.: A modified particle swarm optimizer, in:
International Conference on Evolutionary Computation Proceedings, IEEE World
Congress on Computational Intelligence (Cat. No.98TH8360), 69–73,
https://doi.org/10.1109/ICEC.1998.699146, 1998.
Sinokrot, B. A. and Stefan, H. G.: Stream temperature dynamics:
measurements and modeling, Water Resour. Res., 29, 2299–2312, https://doi.org/10.1029/93WR00540, 1993.
Smith, K.: River water temperatures - an environmental review, Scottish
Geographical Magazine, 88, 211–220,
https://doi.org/10.1080/00369227208736229, 1972.
Smith K.: The prediction of river water temperatures, Hydrol. Sci.
Bull., 26, 19–32, https://doi.org/10.1080/02626668109490859, 1981.
Smith, K. and Lavis, M. E.: Environmental influences on the temperature of
a small upland stream, Oikos, 26, 228–236, 1975.
SMOGN: Synthetic Minority Oversampling Technique for Regression with
Gaussian Noise, GitHub repository [code], https://github.com/nickkunz/smogn (last access: 17 July 2023), 2022.
Smola, A. J. and Schölkopf, B. A.: tutorial on support vector
regression, Stat. Comput., 14, 199–222,
https://doi.org/10.1023/B:STCO.0000035301.49549.88, 2004.
Soares, P. M. M., Cardoso, R. M., Medeiros, J., Miranda, P. M. A.,
Belo-Pereira, M., and Espirito-Santo, F.: WRF High resolution dynamical
downscaling of ERA-Interim for Portugal, Clim. Dynam., 39, 2497–2522,
https://doi.org/10.1007/s00382-012-1315-2#Bib1, 2012.
Soares, P. M. M., Cardoso, R. M., Ferreira, J. J., and Miranda, P. M. A.:
Climate change and the Portuguese precipitation: ENSEMBLES regional climate
models results, Clim. Dynam., 45, 1771–1787,
https://doi.org/10.1007/s00382-014-2432-x, 2015.
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and
Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from
overfitting, J. Mach. Learn. Res., 15, 1929–1958, 2014.
Temez, J. R.: Calculo hidrometeorologico de caudales maximos em pequeñas
cuencas naturales, Madrid: Ministério de Obras 790 Publicas y Urbanismo
(MOPU), Direccion General de Carreteras, 12, 1978.
Temizyurek, M. and Dadaser-Celik, F.: Modelling the effects of
meteorological parameters on water temperature using artificial neural
networks, Water Sci. Technol., 77, 1724–1733,
https://doi.org/10.2166/wst.2018.058, 2018.
Toffolon, M. and Piccolroaz, S.: A hybrid model for river water temperature
as a function of air temperature and discharge, Environ. Res.
Lett., 10, 114011, https://doi.org/10.1088/1748-9326/10/11/114011, 2015.
Torgo, L., Ribeiro, R. P., Pfahringer, B., and Branco, P.: Smote for regression,
in: Progress in Artificial Intelligence, Springer, 378–389, https://doi.org/10.1007/978-3-642-40669-0_33, 2013.
Torgo, L., Branco, P., Ribeiro, R. P., and Pfahringer, B.: Resampling
strategies for regression, Expert Systems, 32, 465–476,
https://doi.org/10.1111/exsy.12081, 2015.
Vanella D., Longo-Minnolo, G., Belfiore, O.R. Ramírez-Cuesta, J. M.,
Pappalardo, S., Consoli, S., D'Urso, G.,Chirico, G.B.,Coppola, A., Comegna,
A. Toscano, T., Quarta, R., Provenzano, G., Ippolito, M., Castagna, A., and
Gandolfi, C.: Comparing the use of ERA5 reanalysis dataset and ground-based
agrometeorological data under different climates and topography in Italy, J.
Hydrol., 42, 101182, https://doi.org/10.1016/j.ejrh.2022.101182, 2022.
Walling, D. E. and Webb, B. W.: Water quality. I. Physical characteristics,
in: The rivers Handbook. I. Hydrological and ecological principles, edited
by: Calow, P. and Petts, G. E., Blackwell Scientific Publs., Oxford, 48–72,
ISBN 978-1-444-31386-4, 1993.
Wang, B., Spessa, A., Feng, P., Hou, X., Yue, C., Luo, J.-J., Ciais, P.,
Waters, C., Cowie, A., Nolan, R. H., Nikonovas, T., Jin, H., Walshaw, H.,
Wei, J., Guo, X., Liu, D. L., and Yu, Q.: Extreme Fire Weather Is The Major
Driver Of Severe Bushfires In Southeast Australia, Sci. Bull., 67,
655–664, https://doi.org/10.1016/j.scib.2021.10.001, 2021.
Webb, B. W. and Nobilis, F.: A long-term perspective on the nature of the
air-water temperature relationship: a case study, Hydrol. Process.,
11, 137–147, https://doi.org/10.1002/(SICI)1099-1085(199702)11:2<137::AID-HYP405>3.0.CO;2-2, 1997.
Webb, B. W., Clark, P. D., and Walling, D. E.: Water-air temperature
relationships in a Devon river system and the role of flow, Hydrol.
Process., 17, 3069–3084, https://doi.org/10.1002/hyp.1280, 2003.
Wetzel, R. G. (Ed.): Limnology. Lake and River Ecosystems, Third Edition, Academic Press, ISBN 9780127447605, 2001.
Younus, M., Hondzo, M., and Engel, B. A.: Stream Temperature Dynamics in
Upland Agricultural Watershed, J. Environ. Eng., 126,
518–526, https://doi.org/10.1061/(ASCE)0733-9372(2000)126:6(518), 2000.
Zhao, P. and He, Z.: A First Evaluation of ERA5-Land Reanalysis Temperature
Product Over the Chinese Qilian Mountains, Front. Earth Sci., 10, 907730,
https://doi.org/10.3389/feart.2022.907730, 2022.
Zhu, S., Nyarko, E. K., and Hadzima-Nyarko, M.: Modelling daily water
temperature from air temperature for the Missouri River, PeerJ, 6, e4894,
https://doi.org/10.7717/peerj.4894, 2018.
Zhu, S., Hadzima-Nyarko, M., Gao, A., Wang, F., Wu, J., and Wu, S.: Two
hybrid data-driven models for modeling water-air temperature relationship in
rivers, Environ. Sci. Pollut. R., 26, 12622–12630,
https://doi.org/10.1007/s11356-019-04716-y, 2019a.
Zhu, S., Heddam, S., Nyarko, E. K., Hadzima-Nyarko, M., Piccolroaz, S., and
Wu, S.: Modeling daily water temperature for rivers: comparison between
adaptive neuro-fuzzy inference systems and artificial neural networks
models, Environ. Sci. Pollut. Res., 26, 402–420,
https://doi.org/10.1007/s11356-018-3650-2, 2019b.
Zhu, S., Heddam, S., Wu, S., Dai, J., and Jia, B.: Extreme learning
machine-based prediction of daily water temperature for rivers,
Environ. Earth Sci., 78, 202,
https://doi.org/10.1007/s12665-019-8202-7, 2019c.
Zhu, S., Nyarko, E. K., Hadzima-Nyarko, M., Heddam, S., and Wu, S.:
Assessing the performance of a suite of machine learning models for daily
river water temperature prediction, PeerJ, 7, e7065,
https://doi.org/10.7717/peerj.7065, 2019d.
Short summary
Water temperature (WT) datasets of low-order rivers are scarce. In this study, five different models are used to predict the WT of 83 rivers. Generally, the results show that the models' hyperparameter optimization is essential and that to minimize the prediction error it is relevant to apply all the models considered in this study. Results also show that there is a logarithmic correlation among the error of the predicted river WT and the watershed time of concentration.
Water temperature (WT) datasets of low-order rivers are scarce. In this study, five different...
