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
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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
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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 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
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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.
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Climate and Earth system modeling
A machine learning approach targeting parameter estimation for plant functional type coexistence modeling using ELM-FATES (v2.0)
The fully coupled regionally refined model of E3SM version 2: overview of the atmosphere, land, and river results
The mixed-layer depth in the Ocean Model Intercomparison Project (OMIP): impact of resolving mesoscale eddies
A new simplified parameterization of secondary organic aerosol in the Community Earth System Model Version 2 (CESM2; CAM6.3)
Deep learning for stochastic precipitation generation – deep SPG v1.0
Developing spring wheat in the Noah-MP land surface model (v4.4) for growing season dynamics and responses to temperature stress
Robust 4D climate-optimal flight planning in structured airspace using parallelized simulation on GPUs: ROOST V1.0
The Earth system model CLIMBER-X v1.0 – Part 2: The global carbon cycle
SMLFire1.0: a stochastic machine learning (SML) model for wildfire activity in the western United States
LandInG 1.0: a toolbox to derive input datasets for terrestrial ecosystem modelling at variable resolutions from heterogeneous sources
Conservation of heat and mass in P-SKRIPS version 1: the coupled atmosphere–ice–ocean model of the Ross Sea
Predicting the climate impact of aviation for en-route emissions: the algorithmic climate change function submodel ACCF 1.0 of EMAC 2.53
Implementation of a machine-learned gas optics parameterization in the ECMWF Integrated Forecasting System: RRTMGP-NN 2.0
Differentiable programming for Earth system modeling
Evaluation of CMIP6 model performances in simulating fire weather spatiotemporal variability on global and regional scales
Data-driven aeolian dust emission scheme for climate modelling evaluated with EMAC 2.55.2
Testing the reconstruction of modelled particulate organic carbon from surface ecosystem components using PlankTOM12 and machine learning
An improved method of the Globally Resolved Energy Balance model by the Bayesian networks
Assessing predicted cirrus ice properties between two deterministic ice formation parameterizations
Various ways of using empirical orthogonal functions for climate model evaluation
C-Coupler3.0: an integrated coupler infrastructure for Earth system modelling
FEOTS v0.0.0: a new offline code for the fast equilibration of tracers in the ocean
Pace v0.2: a Python-based performance-portable atmospheric model
Hydrological modelling on atmospheric grids: using graphs of sub-grid elements to transport energy and water
MESMAR v1: A new regional coupled climate model for downscaling, predictability, and data assimilation studies in the Mediterranean region
The sea level simulator v1.0: a model for integration of mean sea level change and sea level extremes into a joint probabilistic framework
Structural k-means (S k-means) and clustering uncertainty evaluation framework (CUEF) for mining climate data
The emergence of the Gulf Stream and interior western boundary as key regions to constrain the future North Atlantic carbon uptake
Evaluating wind profiles in a numerical weather prediction model with Doppler lidar
Evaluation of bias correction methods for a multivariate drought index: case study of the Upper Jhelum Basin
The impact of lateral boundary forcing in the CORDEX-Africa ensemble over southern Africa
Effects of complex terrain on the shortwave radiative balance: a sub-grid-scale parameterization for the GFDL Earth System Model version 4.1
Understanding AMOC stability: the North Atlantic Hosing Model Intercomparison Project
Assessing methods for representing soil heterogeneity through a flexible approach within the Joint UK Land Environment Simulator (JULES) at version 3.4.1
Nudging allows direct evaluation of coupled climate models with in situ observations: a case study from the MOSAiC expedition
Importance of ice nucleation and precipitation on climate with the Parameterization of Unified Microphysics Across Scales version 1 (PUMASv1)
ENSO statistics, teleconnections, and atmosphere-ocean coupling in the Taiwan Earth System Model version 1
UKESM1.1: development and evaluation of an updated configuration of the UK Earth System Model
Simplified Kalman smoother and ensemble Kalman smoother for improving reanalyses
Rainbows and Climate Change: A tutorial on climate model diagnostics and parameterization
Recalibration of a three-dimensional water quality model with a newly developed autocalibration toolkit (EFDC-ACT v1.0.0): does the model calibrated with a wider hydrological variability become more robust?
Porting the WAVEWATCH III (v6.07) wave action source terms to GPU
Yeti 1.0: a generalized framework for constructing bottom-up emission inventories from traffic sources at road-link resolutions
Analysis of systematic biases in tropospheric hydrostatic delay models and construction of a correction model
Climate model Selection by Independence, Performance, and Spread (ClimSIPS) for regional applications
A new precipitation emulator (PREMU v1.0) for lower-complexity models
Simulating marine neodymium isotope distributions using Nd v1.0 coupled to the ocean component of the FAMOUS–MOSES1 climate model: sensitivities to reversible scavenging efficiency and benthic source distributions
CMIP6 simulations with the compact Earth system model OSCAR v3.1
Application of a satellite-retrieved sheltering parameterization (v1.0) for dust event simulation with WRF-Chem v4.1
The pseudo-global-warming (PGW) approach: methodology, software package PGW4ERA5 v1.1, validation, and sensitivity analyses
Lingcheng Li, Yilin Fang, Zhonghua Zheng, Mingjie Shi, Marcos Longo, Charles D. Koven, Jennifer A. Holm, Rosie A. Fisher, Nate G. McDowell, Jeffrey Chambers, and L. Ruby Leung
Geosci. Model Dev., 16, 4017–4040, https://doi.org/10.5194/gmd-16-4017-2023, https://doi.org/10.5194/gmd-16-4017-2023, 2023
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Accurately modeling plant coexistence in vegetation demographic models like ELM-FATES is challenging. This study proposes a repeatable method that uses machine-learning-based surrogate models to optimize plant trait parameters in ELM-FATES. Our approach significantly improves plant coexistence modeling, thus reducing errors. It has important implications for modeling ecosystem dynamics in response to climate change.
Qi Tang, Jean-Christophe Golaz, Luke P. Van Roekel, Mark A. Taylor, Wuyin Lin, Benjamin R. Hillman, Paul A. Ullrich, Andrew M. Bradley, Oksana Guba, Jonathan D. Wolfe, Tian Zhou, Kai Zhang, Xue Zheng, Yunyan Zhang, Meng Zhang, Mingxuan Wu, Hailong Wang, Cheng Tao, Balwinder Singh, Alan M. Rhoades, Yi Qin, Hong-Yi Li, Yan Feng, Yuying Zhang, Chengzhu Zhang, Charles S. Zender, Shaocheng Xie, Erika L. Roesler, Andrew F. Roberts, Azamat Mametjanov, Mathew E. Maltrud, Noel D. Keen, Robert L. Jacob, Christiane Jablonowski, Owen K. Hughes, Ryan M. Forsyth, Alan V. Di Vittorio, Peter M. Caldwell, Gautam Bisht, Renata B. McCoy, L. Ruby Leung, and David C. Bader
Geosci. Model Dev., 16, 3953–3995, https://doi.org/10.5194/gmd-16-3953-2023, https://doi.org/10.5194/gmd-16-3953-2023, 2023
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High-resolution simulations are superior to low-resolution ones in capturing regional climate changes and climate extremes. However, uniformly reducing the grid size of a global Earth system model is too computationally expensive. We provide an overview of the fully coupled regionally refined model (RRM) of E3SMv2 and document a first-of-its-kind set of climate production simulations using RRM at an economic cost. The key to this success is our innovative hybrid time step method.
Anne Marie Treguier, Clement de Boyer Montégut, Alexandra Bozec, Eric P. Chassignet, Baylor Fox-Kemper, Andy McC. Hogg, Doroteaciro Iovino, Andrew E. Kiss, Julien Le Sommer, Yiwen Li, Pengfei Lin, Camille Lique, Hailong Liu, Guillaume Serazin, Dmitry Sidorenko, Qiang Wang, Xiaobio Xu, and Steve Yeager
Geosci. Model Dev., 16, 3849–3872, https://doi.org/10.5194/gmd-16-3849-2023, https://doi.org/10.5194/gmd-16-3849-2023, 2023
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The ocean mixed layer is the interface between the ocean interior and the atmosphere and plays a key role in climate variability. We evaluate the performance of the new generation of ocean models for climate studies, designed to resolve
ocean eddies, which are the largest source of ocean variability and modulate the mixed-layer properties. We find that the mixed-layer depth is better represented in eddy-rich models but, unfortunately, not uniformly across the globe and not in all models.
Duseong S. Jo, Simone Tilmes, Louisa K. Emmons, Siyuan Wang, and Francis Vitt
Geosci. Model Dev., 16, 3893–3906, https://doi.org/10.5194/gmd-16-3893-2023, https://doi.org/10.5194/gmd-16-3893-2023, 2023
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A new simple secondary organic aerosol (SOA) scheme has been developed for the Community Atmosphere Model (CAM) based on the complex SOA scheme in CAM with detailed chemistry (CAM-chem). The CAM with the new SOA scheme shows better agreements with CAM-chem in terms of aerosol concentrations and radiative fluxes, which ensures more consistent results between different compsets in the Community Earth System Model. The new SOA scheme also has technical advantages for future developments.
Leroy J. Bird, Matthew G. W. Walker, Greg E. Bodeker, Isaac H. Campbell, Guangzhong Liu, Swapna Josmi Sam, Jared Lewis, and Suzanne M. Rosier
Geosci. Model Dev., 16, 3785–3808, https://doi.org/10.5194/gmd-16-3785-2023, https://doi.org/10.5194/gmd-16-3785-2023, 2023
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Deriving the statistics of expected future changes in extreme precipitation is challenging due to these events being rare. Regional climate models (RCMs) are computationally prohibitive for generating ensembles capable of capturing large numbers of extreme precipitation events with statistical robustness. Stochastic precipitation generators (SPGs) provide an alternative to RCMs. We describe a novel single-site SPG that learns the statistics of precipitation using a machine-learning approach.
Zhe Zhang, Yanping Li, Fei Chen, Phillip Harder, Warren Helgason, James Famiglietti, Prasanth Valayamkunnath, Cenlin He, and Zhenhua Li
Geosci. Model Dev., 16, 3809–3825, https://doi.org/10.5194/gmd-16-3809-2023, https://doi.org/10.5194/gmd-16-3809-2023, 2023
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Crop models incorporated in Earth system models are essential to accurately simulate crop growth processes on Earth's surface and agricultural production. In this study, we aim to model the spring wheat in the Northern Great Plains, focusing on three aspects: (1) develop the wheat model at a point scale, (2) apply dynamic planting and harvest schedules, and (3) adopt a revised heat stress function. The results show substantial improvements and have great importance for agricultural production.
Abolfazl Simorgh, Manuel Soler, Daniel González-Arribas, Florian Linke, Benjamin Lührs, Maximilian M. Meuser, Simone Dietmüller, Sigrun Matthes, Hiroshi Yamashita, Feijia Yin, Federica Castino, Volker Grewe, and Sabine Baumann
Geosci. Model Dev., 16, 3723–3748, https://doi.org/10.5194/gmd-16-3723-2023, https://doi.org/10.5194/gmd-16-3723-2023, 2023
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This paper addresses the robust climate optimal trajectory planning problem under uncertain meteorological conditions within the structured airspace. Based on the optimization methodology, a Python library has been developed, which can be accessed using the following DOI: https://doi.org/10.5281/zenodo.7121862. The developed tool is capable of providing robust trajectories taking into account all probable realizations of meteorological conditions provided by an EPS computationally very fast.
Matteo Willeit, Tatiana Ilyina, Bo Liu, Christoph Heinze, Mahé Perrette, Malte Heinemann, Daniela Dalmonech, Victor Brovkin, Guy Munhoven, Janine Börker, Jens Hartmann, Gibran Romero-Mujalli, and Andrey Ganopolski
Geosci. Model Dev., 16, 3501–3534, https://doi.org/10.5194/gmd-16-3501-2023, https://doi.org/10.5194/gmd-16-3501-2023, 2023
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In this paper we present the carbon cycle component of the newly developed fast Earth system model CLIMBER-X. The model can be run with interactive atmospheric CO2 to investigate the feedbacks between climate and the carbon cycle on temporal scales ranging from decades to > 100 000 years. CLIMBER-X is expected to be a useful tool for studying past climate–carbon cycle changes and for the investigation of the long-term future evolution of the Earth system.
Jatan Buch, A. Park Williams, Caroline S. Juang, Winslow D. Hansen, and Pierre Gentine
Geosci. Model Dev., 16, 3407–3433, https://doi.org/10.5194/gmd-16-3407-2023, https://doi.org/10.5194/gmd-16-3407-2023, 2023
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We leverage machine learning techniques to construct a statistical model of grid-scale fire frequencies and sizes using climate, vegetation, and human predictors. Our model reproduces the observed trends in fire activity across multiple regions and timescales. We provide uncertainty estimates to inform resource allocation plans for fuel treatment and fire management. Altogether the accuracy and efficiency of our model make it ideal for coupled use with large-scale dynamical vegetation models.
Sebastian Ostberg, Christoph Müller, Jens Heinke, and Sibyll Schaphoff
Geosci. Model Dev., 16, 3375–3406, https://doi.org/10.5194/gmd-16-3375-2023, https://doi.org/10.5194/gmd-16-3375-2023, 2023
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We present a new toolbox for generating input datasets for terrestrial ecosystem models from diverse and partially conflicting data sources. The toolbox documents the sources and processing of data and is designed to make inconsistencies between source datasets transparent so that users can make their own decisions on how to resolve these should they not be content with our default assumptions. As an example, we use the toolbox to create input datasets at two different spatial resolutions.
Alena Malyarenko, Alexandra Gossart, Rui Sun, and Mario Krapp
Geosci. Model Dev., 16, 3355–3373, https://doi.org/10.5194/gmd-16-3355-2023, https://doi.org/10.5194/gmd-16-3355-2023, 2023
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Simultaneous modelling of ocean, sea ice, and atmosphere in coupled models is critical for understanding all of the processes that happen in the Antarctic. Here we have developed a coupled model for the Ross Sea, P-SKRIPS, that conserves heat and mass between the ocean and sea ice model (MITgcm) and the atmosphere model (PWRF). We have shown that our developments reduce the model drift, which is important for long-term simulations. P-SKRIPS shows good results in modelling coastal polynyas.
Feijia Yin, Volker Grewe, Federica Castino, Pratik Rao, Sigrun Matthes, Katrin Dahlmann, Simone Dietmüller, Christine Frömming, Hiroshi Yamashita, Patrick Peter, Emma Klingaman, Keith P. Shine, Benjamin Lührs, and Florian Linke
Geosci. Model Dev., 16, 3313–3334, https://doi.org/10.5194/gmd-16-3313-2023, https://doi.org/10.5194/gmd-16-3313-2023, 2023
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This paper describes a newly developed submodel ACCF V1.0 based on the MESSy 2.53.0 infrastructure. The ACCF V1.0 is based on the prototype algorithmic climate change functions (aCCFs) v1.0 to enable climate-optimized flight trajectories. One highlight of this paper is that we describe a consistent full set of aCCFs formulas with respect to fuel scenario and metrics. We demonstrate the usage of the ACCF submodel using AirTraf V2.0 to optimize trajectories for cost and climate impact.
Peter Ukkonen and Robin J. Hogan
Geosci. Model Dev., 16, 3241–3261, https://doi.org/10.5194/gmd-16-3241-2023, https://doi.org/10.5194/gmd-16-3241-2023, 2023
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Climate and weather models suffer from uncertainties resulting from approximated processes. Solar and thermal radiation is one example, as it is computationally too costly to simulate precisely. This has led to attempts to replace radiation codes based on physical equations with neural networks (NNs) that are faster but uncertain. In this paper we use global weather simulations to demonstrate that a middle-ground approach of using NNs only to predict optical properties is accurate and reliable.
Maximilian Gelbrecht, Alistair White, Sebastian Bathiany, and Niklas Boers
Geosci. Model Dev., 16, 3123–3135, https://doi.org/10.5194/gmd-16-3123-2023, https://doi.org/10.5194/gmd-16-3123-2023, 2023
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Differential programming is a technique that enables the automatic computation of derivatives of the output of models with respect to model parameters. Applying these techniques to Earth system modeling leverages the increasing availability of high-quality data to improve the models themselves. This can be done by either using calibration techniques that use gradient-based optimization or incorporating machine learning methods that can learn previously unresolved influences directly from data.
Carolina Gallo, Jonathan M. Eden, Bastien Dieppois, Igor Drobyshev, Peter Z. Fulé, Jesús San-Miguel-Ayanz, and Matthew Blackett
Geosci. Model Dev., 16, 3103–3122, https://doi.org/10.5194/gmd-16-3103-2023, https://doi.org/10.5194/gmd-16-3103-2023, 2023
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This study conducts the first global evaluation of the latest generation of global climate models to simulate a set of fire weather indicators from the Canadian Fire Weather Index System. Models are shown to perform relatively strongly at the global scale, but they show substantial regional and seasonal differences. The results demonstrate the value of model evaluation and selection in producing reliable fire danger projections, ultimately to support decision-making and forest management.
Klaus Klingmüller and Jos Lelieveld
Geosci. Model Dev., 16, 3013–3028, https://doi.org/10.5194/gmd-16-3013-2023, https://doi.org/10.5194/gmd-16-3013-2023, 2023
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Desert dust has significant impacts on climate, public health, infrastructure and ecosystems. An impact assessment requires numerical predictions, which are challenging because the dust emissions are not well known. We present a novel approach using satellite observations and machine learning to more accurately estimate the emissions and to improve the model simulations.
Anna Denvil-Sommer, Erik T. Buitenhuis, Rainer Kiko, Fabien Lombard, Lionel Guidi, and Corinne Le Quéré
Geosci. Model Dev., 16, 2995–3012, https://doi.org/10.5194/gmd-16-2995-2023, https://doi.org/10.5194/gmd-16-2995-2023, 2023
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Using outputs of global biogeochemical ocean model and machine learning methods, we demonstrate that it will be possible to identify linkages between surface environmental and ecosystem structure and the export of carbon to depth by sinking organic particles using real observations. It will be possible to use this knowledge to improve both our understanding of ecosystem dynamics and of their functional representation within models.
Zhenxia Liu, Zengjie Wang, Jian Wang, Zhengfang Zhang, Dongshuang Li, Zhaoyuan Yu, Linwang Yuan, and Wen Luo
Geosci. Model Dev., 16, 2939–2955, https://doi.org/10.5194/gmd-16-2939-2023, https://doi.org/10.5194/gmd-16-2939-2023, 2023
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This study introduces an improved method of the Globally Resolved Energy Balance (GREB) model by the Bayesian network. The improved method constructs a coarse–fine structure that combines a dynamical model with a statistical model based on employing the GREB model as the global framework and utilizing Bayesian networks as the local optimization. The results show that the improved model has better applicability and stability on a global scale and maintains good robustness on the timescale.
Colin Tully, David Neubauer, and Ulrike Lohmann
Geosci. Model Dev., 16, 2957–2973, https://doi.org/10.5194/gmd-16-2957-2023, https://doi.org/10.5194/gmd-16-2957-2023, 2023
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A new method to simulate deterministic ice nucleation processes based on the differential activated fraction was evaluated against a cumulative approach. Box model simulations of heterogeneous-only ice nucleation within cirrus suggest that the latter approach likely underpredicts the ice crystal number concentration. Longer simulations with a GCM show that choosing between these two approaches impacts ice nucleation competition within cirrus but leads to small and insignificant climate effects.
Rasmus E. Benestad, Abdelkader Mezghani, Julia Lutz, Andreas Dobler, Kajsa M. Parding, and Oskar A. Landgren
Geosci. Model Dev., 16, 2899–2913, https://doi.org/10.5194/gmd-16-2899-2023, https://doi.org/10.5194/gmd-16-2899-2023, 2023
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A mathematical method known as common EOFs is not widely used within the climate research community, but it offers innovative ways of evaluating climate models. We show how common EOFs can be used to evaluate large ensembles of global climate model simulations and distill information about their ability to reproduce salient features of the regional climate. We can say that they represent a kind of machine learning (ML) for dealing with big data.
Li Liu, Chao Sun, Xinzhu Yu, Hao Yu, Qingu Jiang, Xingliang Li, Ruizhe Li, Bin Wang, Xueshun Shen, and Guangwen Yang
Geosci. Model Dev., 16, 2833–2850, https://doi.org/10.5194/gmd-16-2833-2023, https://doi.org/10.5194/gmd-16-2833-2023, 2023
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C-Coupler3.0 is an integrated coupler infrastructure with new features, i.e. a series of parallel-optimization technologies, a common halo-exchange library, a common module-integration framework, a common framework for conveniently developing a weakly coupled ensemble data assimilation system, and a common framework for flexibly inputting and outputting fields in parallel. It is able to handle coupling under much finer resolutions (e.g. more than 100 million horizontal grid cells).
Joseph Schoonover, Wilbert Weijer, and Jiaxu Zhang
Geosci. Model Dev., 16, 2795–2809, https://doi.org/10.5194/gmd-16-2795-2023, https://doi.org/10.5194/gmd-16-2795-2023, 2023
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FEOTS aims to enhance the value of data produced by state-of-the-art climate models by providing a framework to diagnose and use ocean transport operators for offline passive tracer simulations. We show that we can capture ocean transport operators from a validated climate model and employ these operators to estimate water mass budgets in an offline regional simulation, using a small fraction of the compute resources required to run a full climate simulation.
Johann Dahm, Eddie Davis, Florian Deconinck, Oliver Elbert, Rhea George, Jeremy McGibbon, Tobias Wicky, Elynn Wu, Christopher Kung, Tal Ben-Nun, Lucas Harris, Linus Groner, and Oliver Fuhrer
Geosci. Model Dev., 16, 2719–2736, https://doi.org/10.5194/gmd-16-2719-2023, https://doi.org/10.5194/gmd-16-2719-2023, 2023
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It is hard for scientists to write code which is efficient on different kinds of supercomputers. Python is popular for its user-friendliness. We converted a Fortran code, simulating Earth's atmosphere, into Python. This new code auto-converts to a faster language for processors or graphic cards. Our code runs 3.5–4 times faster on graphic cards than the original on processors in a specific supercomputer system.
Jan Polcher, Anthony Schrapffer, Eliott Dupont, Lucia Rinchiuso, Xudong Zhou, Olivier Boucher, Emmanuel Mouche, Catherine Ottlé, and Jérôme Servonnat
Geosci. Model Dev., 16, 2583–2606, https://doi.org/10.5194/gmd-16-2583-2023, https://doi.org/10.5194/gmd-16-2583-2023, 2023
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The proposed graphs of hydrological sub-grid elements for atmospheric models allow us to integrate the topographical elements needed in land surface models for a realistic representation of horizontal water and energy transport. The study demonstrates the numerical properties of the automatically built graphs and the simulated water flows.
Andrea Storto, Yassmin Hesham Essa, Vincenzo de Toma, Alessandro Anav, Gianmaria Sannino, Rosalia Santoleri, and Chunxue Yang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-77, https://doi.org/10.5194/gmd-2023-77, 2023
Revised manuscript accepted for GMD
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Regional climate models are a fundamental tool for a very large number of applications and are being increasingly used within climate services, together with other complementary approaches. Here, we introduce a new regional coupled model, intended to be later extended to a full Earth System model, for climate investigations within the Mediterranean region, coupled data assimilation experiments, and several downscaling exercises (reanalyses, and long-range predictions).
Magnus Hieronymus
Geosci. Model Dev., 16, 2343–2354, https://doi.org/10.5194/gmd-16-2343-2023, https://doi.org/10.5194/gmd-16-2343-2023, 2023
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A statistical model called the sea level simulator is presented and made freely available. The sea level simulator integrates mean sea level rise and sea level extremes into a joint probabilistic framework that is useful for flood risk estimation. These flood risk estimates are contingent on probabilities given to different emission scenarios and the length of the planning period. The model is also useful for uncertainty quantification and in decision and adaptation problems.
Quang-Van Doan, Toshiyuki Amagasa, Thanh-Ha Pham, Takuto Sato, Fei Chen, and Hiroyuki Kusaka
Geosci. Model Dev., 16, 2215–2233, https://doi.org/10.5194/gmd-16-2215-2023, https://doi.org/10.5194/gmd-16-2215-2023, 2023
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This study proposes (i) the structural k-means (S k-means) algorithm for clustering spatiotemporally structured climate data and (ii) the clustering uncertainty evaluation framework (CUEF) based on the mutual-information concept.
Nadine Goris, Klaus Johannsen, and Jerry Tjiputra
Geosci. Model Dev., 16, 2095–2117, https://doi.org/10.5194/gmd-16-2095-2023, https://doi.org/10.5194/gmd-16-2095-2023, 2023
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Climate projections of a high-CO2 future are highly uncertain. A new study provides a novel approach to identifying key regions that dynamically explain the model uncertainty. To yield an accurate estimate of the future North Atlantic carbon uptake, we find that a correct simulation of the upper- and interior-ocean volume transport at 25–30° N is key. However, results indicate that models rarely perform well for both indicators and point towards inconsistencies within the model ensemble.
Pyry Pentikäinen, Ewan J. O'Connor, and Pablo Ortiz-Amezcua
Geosci. Model Dev., 16, 2077–2094, https://doi.org/10.5194/gmd-16-2077-2023, https://doi.org/10.5194/gmd-16-2077-2023, 2023
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We used Doppler lidar to evaluate the wind profiles generated by a weather forecast model. We first compared the Doppler lidar observations with co-located radiosonde profiles, and they agree well. The model performs best over marine and coastal locations. Larger errors were seen in locations where the surface was more complex, especially in the wind direction. Our results show that Doppler lidar is a suitable instrument for evaluating the boundary layer wind profiles in atmospheric models.
Rubina Ansari, Ana Casanueva, Muhammad Usman Liaqat, and Giovanna Grossi
Geosci. Model Dev., 16, 2055–2076, https://doi.org/10.5194/gmd-16-2055-2023, https://doi.org/10.5194/gmd-16-2055-2023, 2023
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Bias correction (BC) has become indispensable to climate model output as a post-processing step to render output more useful for impact assessment studies. The current work presents a comparison of different state-of-the-art BC methods (univariate and multivariate) and BC approaches (direct and component-wise) for climate model simulations from three initiatives (CMIP6, CORDEX, and CORDEX-CORE) for a multivariate drought index (i.e., standardized precipitation evapotranspiration index).
Maria Chara Karypidou, Stefan Pieter Sobolowski, Lorenzo Sangelantoni, Grigory Nikulin, and Eleni Katragkou
Geosci. Model Dev., 16, 1887–1908, https://doi.org/10.5194/gmd-16-1887-2023, https://doi.org/10.5194/gmd-16-1887-2023, 2023
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Southern Africa is listed among the climate change hotspots; hence, accurate climate change information is vital for the optimal preparedness of local communities. In this work we assess the degree to which regional climate models (RCMs) are influenced by the global climate models (GCMs) from which they receive their lateral boundary forcing. We find that although GCMs exert a strong impact on RCMs, RCMs are still able to display substantial improvement relative to the driving GCMs.
Enrico Zorzetto, Sergey Malyshev, Nathaniel Chaney, David Paynter, Raymond Menzel, and Elena Shevliakova
Geosci. Model Dev., 16, 1937–1960, https://doi.org/10.5194/gmd-16-1937-2023, https://doi.org/10.5194/gmd-16-1937-2023, 2023
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In this paper we develop a methodology to model the spatial distribution of solar radiation received by land over mountainous terrain. The approach is designed to be used in Earth system models, where coarse grid cells hinder the description of fine-scale land–atmosphere interactions. We adopt a clustering algorithm to partition the land domain into a set of homogeneous sub-grid
tiles, and for each tile we evaluate solar radiation received by land based on terrain properties.
Laura C. Jackson, Eduardo Alastrué de Asenjo, Katinka Bellomo, Gokhan Danabasoglu, Helmuth Haak, Aixue Hu, Johann Jungclaus, Warren Lee, Virna L. Meccia, Oleg Saenko, Andrew Shao, and Didier Swingedouw
Geosci. Model Dev., 16, 1975–1995, https://doi.org/10.5194/gmd-16-1975-2023, https://doi.org/10.5194/gmd-16-1975-2023, 2023
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The Atlantic meridional overturning circulation (AMOC) has an important impact on the climate. There are theories that freshening of the ocean might cause the AMOC to cross a tipping point (TP) beyond which recovery is difficult; however, it is unclear whether TPs exist in global climate models. Here, we outline a set of experiments designed to explore AMOC tipping points and sensitivity to additional freshwater input as part of the North Atlantic Hosing Model Intercomparison Project (NAHosMIP).
Heather S. Rumbold, Richard J. J. Gilham, and Martin J. Best
Geosci. Model Dev., 16, 1875–1886, https://doi.org/10.5194/gmd-16-1875-2023, https://doi.org/10.5194/gmd-16-1875-2023, 2023
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The Joint UK Land Environment Simulator (JULES) uses a tiled representation of land cover but can only model a single dominant soil type within a grid box; hence there is no representation of sub-grid soil heterogeneity. This paper evaluates a new surface–soil tiling scheme in JULES and demonstrates the impacts of the scheme using several soil tiling approaches. Results show that soil tiling has an impact on the water and energy exchanges due to the way vegetation accesses the soil moisture.
Felix Pithan, Marylou Athanase, Sandro Dahlke, Antonio Sánchez-Benítez, Matthew D. Shupe, Anne Sledd, Jan Streffing, Gunilla Svensson, and Thomas Jung
Geosci. Model Dev., 16, 1857–1873, https://doi.org/10.5194/gmd-16-1857-2023, https://doi.org/10.5194/gmd-16-1857-2023, 2023
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Evaluating climate models usually requires long observational time series, but we present a method that also works for short field campaigns. We compare climate model output to observations from the MOSAiC expedition in the central Arctic Ocean. All models show how the arrival of a warm air mass warms the Arctic in April 2020, but two models do not show the response of snow temperature to the diurnal cycle. One model has too little liquid water and too much ice in clouds during cold days.
Andrew Gettelman, Hugh Morrison, Trude Eidhammer, Katherine Thayer-Calder, Jian Sun, Richard Forbes, Zachary McGraw, Jiang Zhu, Trude Storelvmo, and John Dennis
Geosci. Model Dev., 16, 1735–1754, https://doi.org/10.5194/gmd-16-1735-2023, https://doi.org/10.5194/gmd-16-1735-2023, 2023
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Clouds are a critical part of weather and climate prediction. In this work, we document updates and corrections to the description of clouds used in several Earth system models. These updates include the ability to run the scheme on graphics processing units (GPUs), changes to the numerical description of precipitation, and a correction to the ice number. There are big improvements in the computational performance that can be achieved with GPU acceleration.
Yi-Chi Wang, Wan-Ling Tseng, Yu-Luen Chen, Shi-Yu Lee, Huang-Hsiung Hsu, and Hsin-Chien Liang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-41, https://doi.org/10.5194/gmd-2023-41, 2023
Revised manuscript accepted for GMD
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This study examines how well the Taiwan Earth System Model version 1 (TaiESM1) simulates the El Niño Southern Oscillation (ENSO), which is an important tropical climate pattern that affects climate around the world. We found TaiESM1 can replicate the key features of ENSO, including its seasonal changes and how it affects the remote regions, but has a much stronger ENSO than observations. This bias is further examined to provide insights into how to improve ENSO in future climate models.
Jane P. Mulcahy, Colin G. Jones, Steven T. Rumbold, Till Kuhlbrodt, Andrea J. Dittus, Edward W. Blockley, Andrew Yool, Jeremy Walton, Catherine Hardacre, Timothy Andrews, Alejandro Bodas-Salcedo, Marc Stringer, Lee de Mora, Phil Harris, Richard Hill, Doug Kelley, Eddy Robertson, and Yongming Tang
Geosci. Model Dev., 16, 1569–1600, https://doi.org/10.5194/gmd-16-1569-2023, https://doi.org/10.5194/gmd-16-1569-2023, 2023
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Recent global climate models simulate historical global mean surface temperatures which are too cold, possibly to due to excessive aerosol cooling. This raises questions about the models' ability to simulate important climate processes and reduces confidence in future climate predictions. We present a new version of the UK Earth System Model, which has an improved aerosols simulation and a historical temperature record. Interestingly, the long-term response to CO2 remains largely unchanged.
Bo Dong, Ross Bannister, Yumeng Chen, Alison Fowler, and Keith Haines
EGUsphere, https://doi.org/10.5194/egusphere-2023-337, https://doi.org/10.5194/egusphere-2023-337, 2023
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Traditional Kalman smoothers are expensive to be applied in large global ocean operational forecast and reanalysis system. We develop a cost-efficient method to overcome the technical constraints and to improve the performance of existing reanalysis products.
Andrew Gettelman
EGUsphere, https://doi.org/10.5194/egusphere-2023-227, https://doi.org/10.5194/egusphere-2023-227, 2023
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A representation of rainbows is developed for a climate model. The diagnostic raises many common issues. Simulated rainbows are evaluated against limited observations. The pattern of rainbows in the model matches observations and theory about when and where rainbows are most common. The diagnostic is used to assess the past and future state of rainbows. Changes to clouds from climate change are expected to increase rainbows as cloud cover decreases in a warmer world.
Chen Zhang and Tianyu Fu
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-303, https://doi.org/10.5194/gmd-2022-303, 2023
Revised manuscript accepted for GMD
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A new automatic calibration toolkit was developed and implemented into the recalibration of a three-dimensional water quality model with observations in a wider range of hydrological variability. Compared to the original model, the recalibrated model performed significantly better in modeled TP, Chla, and DO. Our work indicates that hydrological variability in the calibration periods has a non-negligible impact on the water quality models.
Olawale James Ikuyajolu, Luke Van Roekel, Steven R. Brus, Erin E. Thomas, Yi Deng, and Sarat Sreepathi
Geosci. Model Dev., 16, 1445–1458, https://doi.org/10.5194/gmd-16-1445-2023, https://doi.org/10.5194/gmd-16-1445-2023, 2023
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Wind-generated waves play an important role in modifying physical processes at the air–sea interface, but they have been traditionally excluded from climate models due to the high computational cost of running spectral wave models for climate simulations. To address this, our work identified and accelerated the computationally intensive section of WAVEWATCH III on GPU using OpenACC. This allows for high-resolution modeling of atmosphere–wave–ocean feedbacks in century-scale climate integrations.
Edward C. Chan, Joana Leitão, Andreas Kerschbaumer, and Timothy M. Butler
Geosci. Model Dev., 16, 1427–1444, https://doi.org/10.5194/gmd-16-1427-2023, https://doi.org/10.5194/gmd-16-1427-2023, 2023
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Yeti is a Handbook Emission Factors for Road Transport-based traffic emission inventory written in the Python 3 scripting language, which adopts a generalized treatment for activity data using traffic information of varying levels of detail introduced in a systematic and consistent manner, with the ability to maximize reusability. Thus, Yeti has been conceived and implemented with a high degree of data and process symmetry, allowing scalable and flexible execution while affording ease of use.
Haopeng Fan, Siran Li, Zhongmiao Sun, Guorui Xiao, Xinxing Li, and Xiaogang Liu
Geosci. Model Dev., 16, 1345–1358, https://doi.org/10.5194/gmd-16-1345-2023, https://doi.org/10.5194/gmd-16-1345-2023, 2023
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The traditional tropospheric zenith hydrostatic delay (ZHD) model's bias is usually thought negligible, yet it still reaches 10 mm sometimes and would lead to millimeter-level position errors for space geodetic observations. Therefore, we analyzed the bias’ characteristics and present a grid model to correct the traditional ZHD formula. When verifying the efficiency based on data from the ECMWF (European Centre for Medium-Range Weather Forecasts), ZHD biases were rectified by ~50 %.
Anna Louise Merrifield, Lukas Brunner, Ruth Lorenz, Vincent Humphrey, and Reto Knutti
EGUsphere, https://doi.org/10.5194/egusphere-2022-1520, https://doi.org/10.5194/egusphere-2022-1520, 2023
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Using all Coupled Model Intercomparison Project (CMIP) models is unfeasible for many applications. We provide a subselection protocol that balances user needs for model independence, performance, and spread capturing CMIP’s projection uncertainty simultaneously for the first time. We show how sets of 3–5 models selected for European applications map to user priorities. An audit of model independence and its influence on equilibrium climate sensitivity uncertainty in CMIP is also presented.
Gang Liu, Shushi Peng, Chris Huntingford, and Yi Xi
Geosci. Model Dev., 16, 1277–1296, https://doi.org/10.5194/gmd-16-1277-2023, https://doi.org/10.5194/gmd-16-1277-2023, 2023
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Due to computational limits, lower-complexity models (LCMs) were developed as a complementary tool for accelerating comprehensive Earth system models (ESMs) but still lack a good precipitation emulator for LCMs. Here, we developed a data-calibrated precipitation emulator (PREMU), a computationally effective way to better estimate historical and simulated precipitation by current ESMs. PREMU has potential applications related to land surface processes and their interactions with climate change.
Suzanne Robinson, Ruza F. Ivanovic, Lauren J. Gregoire, Julia Tindall, Tina van de Flierdt, Yves Plancherel, Frerk Pöppelmeier, Kazuyo Tachikawa, and Paul J. Valdes
Geosci. Model Dev., 16, 1231–1264, https://doi.org/10.5194/gmd-16-1231-2023, https://doi.org/10.5194/gmd-16-1231-2023, 2023
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We present the implementation of neodymium (Nd) isotopes into the ocean model of FAMOUS (Nd v1.0). Nd fluxes from seafloor sediment and incorporation of Nd onto sinking particles represent the major global sources and sinks, respectively. However, model–data mismatch in the North Pacific and northern North Atlantic suggest that certain reactive components of the sediment interact the most with seawater. Our results are important for interpreting Nd isotopes in terms of ocean circulation.
Yann Quilcaille, Thomas Gasser, Philippe Ciais, and Olivier Boucher
Geosci. Model Dev., 16, 1129–1161, https://doi.org/10.5194/gmd-16-1129-2023, https://doi.org/10.5194/gmd-16-1129-2023, 2023
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The model OSCAR is a simple climate model, meaning its representation of the Earth system is simplified but calibrated on models of higher complexity. Here, we diagnose its latest version using a total of 99 experiments in a probabilistic framework and under observational constraints. OSCAR v3.1 shows good agreement with observations, complex Earth system models and emerging properties. Some points for improvements are identified, such as the ocean carbon cycle.
Sandra L. LeGrand, Theodore W. Letcher, Gregory S. Okin, Nicholas P. Webb, Alex R. Gallagher, Saroj Dhital, Taylor S. Hodgdon, Nancy P. Ziegler, and Michelle L. Michaels
Geosci. Model Dev., 16, 1009–1038, https://doi.org/10.5194/gmd-16-1009-2023, https://doi.org/10.5194/gmd-16-1009-2023, 2023
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Ground cover affects dust emissions by reducing wind flow over the immediate soil surface. This study reviews a method for estimating ground cover effects on wind erosion from satellite-detected terrain shadows. We conducted a case study for a US dust event using the Weather Research and Forecasting with Chemistry (WRF-Chem) model. Adding the shadow-based method for ground cover effects markedly improved simulated results and may lead to better dust modeling outcomes in vegetated drylands.
Roman Brogli, Christoph Heim, Jonas Mensch, Silje Lund Sørland, and Christoph Schär
Geosci. Model Dev., 16, 907–926, https://doi.org/10.5194/gmd-16-907-2023, https://doi.org/10.5194/gmd-16-907-2023, 2023
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The pseudo-global-warming (PGW) approach is a downscaling methodology that imposes the large-scale GCM-based climate change signal on the boundary conditions of a regional climate simulation. It offers several benefits in comparison to conventional downscaling. We present a detailed description of the methodology, provide companion software to facilitate the preparation of PGW simulations, and present validation and sensitivity studies.
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...
