Articles | Volume 8, issue 2
https://doi.org/10.5194/gmd-8-151-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-8-151-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Model description paper
| 
05 Feb 2015
Model description paper |
| 05 Feb 2015
Implementation and comparison of a suite of heat stress metrics within the Community Land Model version 4.5
Department of Earth Sciences, University of New Hampshire, Durham, New Hampshire, USA
Earth Systems Research Center, Institute for the Study of Earth, Ocean, and Space, University of New Hampshire, Durham, New Hampshire, USA
K. Oleson
National Center for Atmospheric Research, Boulder, Colorado, USA
Department of Earth Sciences, University of New Hampshire, Durham, New Hampshire, USA
Earth Systems Research Center, Institute for the Study of Earth, Ocean, and Space, University of New Hampshire, Durham, New Hampshire, USA
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Gary Shaffer, Esteban Fernández Villanueva, Roberto Rondanelli, Jens Olaf Pepke Pedersen, Steffen Malskær Olsen, and Matthew Huber
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In this paper we describe the experimental design for a set of simulations which will be carried out by a range of climate models, all investigating the climate of the Eocene, about 50 million years ago. The intercomparison of model results is called 'DeepMIP', and we anticipate that we will contribute to the next IPCC report through an analysis of these simulations and the geological data to which we will compare them.
Matthew J. Carmichael, Daniel J. Lunt, Matthew Huber, Malte Heinemann, Jeffrey Kiehl, Allegra LeGrande, Claire A. Loptson, Chris D. Roberts, Navjit Sagoo, Christine Shields, Paul J. Valdes, Arne Winguth, Cornelia Winguth, and Richard D. Pancost
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In this paper, we assess how well model-simulated precipitation rates compare to those indicated by geological data for the early Eocene, a warm interval 56–49 million years ago. Our results show that a number of models struggle to produce sufficient precipitation at high latitudes, which likely relates to cool simulated temperatures in these regions. However, calculating precipitation rates from plant fossils is highly uncertain, and further data are now required.
G. B. Bonan, M. Williams, R. A. Fisher, and K. W. Oleson
Geosci. Model Dev., 7, 2193–2222, https://doi.org/10.5194/gmd-7-2193-2014, https://doi.org/10.5194/gmd-7-2193-2014, 2014
N. Herold, J. Buzan, M. Seton, A. Goldner, J. A. M. Green, R. D. Müller, P. Markwick, and M. Huber
Geosci. Model Dev., 7, 2077–2090, https://doi.org/10.5194/gmd-7-2077-2014, https://doi.org/10.5194/gmd-7-2077-2014, 2014
A. Goldner, N. Herold, and M. Huber
Clim. Past, 10, 523–536, https://doi.org/10.5194/cp-10-523-2014, https://doi.org/10.5194/cp-10-523-2014, 2014
E. Gasson, D. J. Lunt, R. DeConto, A. Goldner, M. Heinemann, M. Huber, A. N. LeGrande, D. Pollard, N. Sagoo, M. Siddall, A. Winguth, and P. J. Valdes
Clim. Past, 10, 451–466, https://doi.org/10.5194/cp-10-451-2014, https://doi.org/10.5194/cp-10-451-2014, 2014
A. Goldner, M. Huber, and R. Caballero
Clim. Past, 9, 173–189, https://doi.org/10.5194/cp-9-173-2013, https://doi.org/10.5194/cp-9-173-2013, 2013
R. L. Sriver, M. Huber, and L. Chafik
Earth Syst. Dynam., 4, 1–10, https://doi.org/10.5194/esd-4-1-2013, https://doi.org/10.5194/esd-4-1-2013, 2013
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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This paper introduces the AtsMOS workflow, a new tool for improving weather forecasts in mountainous areas. By combining advanced statistical techniques with local weather data, AtsMOS can provide more accurate predictions of weather conditions. Using data from Mount Everest as an example, AtsMOS has shown promise in better forecasting hazardous weather conditions, making it a valuable tool for communities in mountainous regions and beyond.
Sofia Allende, Anne Marie Treguier, Camille Lique, Clément de Boyer Montégut, François Massonnet, Thierry Fichefet, and Antoine Barthélemy
Geosci. Model Dev., 17, 7445–7466, https://doi.org/10.5194/gmd-17-7445-2024, https://doi.org/10.5194/gmd-17-7445-2024, 2024
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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A process-based plant carbon (C)–nitrogen (N) interface coupling framework has been developed which mainly focuses on plant resistance and N-limitation effects on photosynthesis, plant respiration, and plant phenology. A dynamic C / N ratio is introduced to represent plant resistance and self-adjustment. The framework has been implemented in a coupled biophysical-ecosystem–biogeochemical model, and testing results show a general improvement in simulating plant properties with this framework.
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
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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
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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
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We give an overview of the Institute of Atmospheric Physics–Chinese Academy of Sciences subseasonal-to-seasonal ensemble forecasting system and Madden–Julian Oscillation forecast evaluation of the system. Compared to other S2S models, the IAP-CAS model has its benefits but also biases, i.e., underdispersive ensemble, overestimated amplitude, and faster propagation speed when forecasting MJO. We provide a reason for these biases and prospects for further improvement of this system in the future.
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
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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
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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
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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
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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
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A new brittle sea ice rheology, BBM, has been implemented into the sea ice component of NEMO. We describe how a new spatial discretization framework was introduced to achieve this. A set of idealized and realistic ocean and sea ice simulations of the Arctic have been performed using BBM and the standard viscous–plastic rheology of NEMO. When compared to satellite data, our simulations show that our implementation of BBM leads to a fairly good representation of sea ice deformations.
Joseph P. Hollowed, Christiane Jablonowski, Hunter Y. Brown, Benjamin R. Hillman, Diana L. Bull, and Joseph L. Hart
Geosci. Model Dev., 17, 5913–5938, https://doi.org/10.5194/gmd-17-5913-2024, https://doi.org/10.5194/gmd-17-5913-2024, 2024
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Large volcanic eruptions deposit material in the upper atmosphere, which is capable of altering temperature and wind patterns of Earth's atmosphere for subsequent years. This research describes a new method of simulating these effects in an idealized, efficient atmospheric model. A volcanic eruption of sulfur dioxide is described with a simplified set of physical rules, which eventually cools the planetary surface. This model has been designed as a test bed for climate attribution studies.
Hong Li, Yi Yang, Jian Sun, Yuan Jiang, Ruhui Gan, and Qian Xie
Geosci. Model Dev., 17, 5883–5896, https://doi.org/10.5194/gmd-17-5883-2024, https://doi.org/10.5194/gmd-17-5883-2024, 2024
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Vertical atmospheric motions play a vital role in convective-scale precipitation forecasts by connecting atmospheric dynamics with cloud development. A three-dimensional variational vertical velocity assimilation scheme is developed within the high-resolution CMA-MESO model, utilizing the adiabatic Richardson equation as the observation operator. A 10 d continuous run and an individual case study demonstrate improved forecasts, confirming the scheme's effectiveness.
Cited articles
Adam-Poupart, A., LaBreche, F., Smargiassi, A., Duguay, P., Busque, M., Gagne, C., Rintamaki, H., Kjellstrom, T., and Zayed, J.: Climate change and occupational health and safety in a temperate climate: potential impacts and research priorities in Quebec, Canada, Indust. Health, 51, 68–78, 2013.
American College of Sports Medicine: Position stand on the prevention of thermal injuries during distance running, Medical J. Austr., 141, 876–879, 1984.
American College of Sports Medicine: Position stand on the prevention of thermal injuries during distance running, Medicine Sci. Sport. Exercise, 19, 529–533, 1987.
Alfano, F. R. D. A., Palella, B. I., and Riccio, G.: On the Problems Related to Natural Wet Bulb Temperature Indirect Evaluation for the Assessment of Hot Thermal Environments by Means of WBGT, Ann. Occup. Hyg., 56, 1063–1079, 2012.
Australian Bureau of Meteorology: About the WBGT and Apparent Temperature indices–Australian Bureau of Meteorology, available at: http://www.bom.gov.au/info/thermal_stress/ (last access: March 2014), 2014.
Barriopedro, D., Fischer, E., Luterbacher, J., Trigo, R., and García-Herrera, R.: The Hot Summer of 2010: Redrawing the Temperature Record Map of Europe, Science, 332, 220–224, 2011.
Belding, H. S. and Hatch, T. F.: Index for evaluating heat stress in terms of resulting physiological strain, Heat.-Piping-Air Cond., 27, 129 pp., 1955.
Benestad, A.: A New Global Set of Downscaled Temperature Scenarios, J. Climate, 24, 2080–2098, 2011.
Berglund, L. G. and Yokota, M.: Comparison of human responses to prototype and standard uniforms using three different human simulation models: HSDA, Scenario_J and Simulink2NM (No. USARIEM-T05-08), Army Research Inst of Environmental Medicine Natick MA Biophysics and Biomedical Modeling Div, 2005.
Betts, A. K. and Dugan, F. J.: Empirical formula for saturation pseudoadiabats and saturation equivalent potential temperature, J. Appl. Meteorol., 12, 731–732, 1973.
Bolton, D.: The computation of equivalent potential temperature, Mon. Weather Rev., 108, 1046–1053, 1980.
Brake, D. J.: Calculation of the natural (unventilated) wet bulb temperature, psychrometric dry bulb temperature and wet bulb globe temperature from standard psychrometric measurements, J. Mine Vent. Soc. S. Afr., 54, 108–112, 2001.
Breckenridge, J. R. and Goldman, R. F.: Solar heat load in man, J. Appl. Physiol., 31, 659–663, 1971.
Bröde, P., Krüger, E. L., Rossi, F. A., and Fiala, D.: Predicting urban outdoor thermal comfort by the Universal Thermal Climate Index UTCI – a case study in Southern Brazil, Int. J. Biometeorol., 56, 471–480, 2012.
Bröde, P., Blazejczyk, K., Fiala, D., Havenith, G., Holmer, I., Jendritzky, G., Kuklane, K., and Kampmann, B.: The Universal Thermal Climate Index UTCI compared to ergonomics standards for assessing the thermal environment, Industrial Health, 51, 16–24, 2013.
Buck, A. L.: New equations for computing vapor pressure and enhancement factor, J. Appl. Meteorol., 20, 1527–1532, 1981.
Budd, G. M.: Wet-bulb globe temperature (WBGT) – its history and its limitations, J. Sci. Med. Sport, 11, 20–32, 2008.
Casado, M., Ortega, P., Masson-Delmotte, V., Risi, C., Swingedouw, D., Daux, V., Genty, D., Maignan, F., Solomina, O., Vinther, B., Viovy, N., and Yiou, P.: Impact of precipitation intermittency on NAO-temperature signals in proxy records, Clim. Past, 9, 871–886, https://doi.org/10.5194/cp-9-871-2013, 2013.
Chan, A. P., Yam, M. C., Chung, J. W., and Yi, W.: Developing a heat stress model for construction workers, J. Facilities Manage., 10, 59–74, 2012.
Davies-Jones, R.: An efficient and accurate method for computing the wet-bulb temperature along pseudoadiabats, Mon. Weather Rev., 136, 2764–2785, 2008.
Davies-Jones, R.: On formulas for equivalent potential temperature, Mon. Weather Rev., 137, 3137–3148, 2009.
Diffenbaugh, N. S., Pal, J. S., Giorgi, F., and Gao, X.: Heat stress intensification in the Mediterranean climate change hotspot, Geophys. Res. Lett., 34, https://doi.org/10.1029/2007GL030000, 2007.
Dufton, A. M.: The eupatheostat, J. Sci. Instrum., 6, 249–251, 1929.
Dunne, J. P., Stouffer, R. J., and John, J. G.: Reductions in labour capacity from heat stress under climate warming, Nat. Clim. Change, 3, 563–566, https://doi.org/10.1038/nclimate1827, 2013.
Epstein, Y. and Moran, D. S.: Thermal comfort and the heat stress indices, Indust. Health, 44, 388–398, 2006.
Fiala, D., Lomas, K. J., and Stohrer, M.: A computer model of human thermoregulation for a wide range of environmental conditions: the passive system, J. Appl. Physiol., 87, 1957–1972, 1999.
Fiala, D., Lomas, K. J., and Stohrer, M.: Computer prediction of human thermoregulatory and temperature responses to a wide range of environmental conditions, Int. J. Biometeorol., 45, 143–159, 2001.
Fiala, D., Psikuta, A., Jendritzky, G., Paulke, S., Nelson, D., van Marken Lichtenbelt, W., and Frijns, W.: Physiological modeling for technical, clinical and research applications, Front Biosci. S., 2, 939–968, 2010.
Fiala, D., Havenith, G., Brode, P., Kampmann, B., and Jendritzky, G.: UTCI-Fiala multi-node model of human heat transfer and temperature regulation, Int. J. Biometeorol., 56, 429–441, https://doi.org/10.1007/s00484-011-0424-7, 2011.
Fischer, E. M. and Knutti, R.: Robust projections of combined humidity and temperature extremes, Nat. Clim. Change, 39, L03705, https://doi.org/10.1029/2011gl050576, 2012.
Fischer, E. M. and Schar, C.: Consistent geographical patterns of changes in high-impact European heatwaves, Nat. Geosci., 3, 398–403, https://doi.org/10.1038/ngeo866, 2010.
Fischer, E. M., Seneviratne, S., Luthi, D., and Schar, C.: Contribution of land-atmosphere coupling to recent European summer heat waves, Geophys. Res. Lett., 34, L06707, https://doi.org/10.1029/2006gl029068, 2007.
Fischer, E. M., Oleson, K. W., and Lawrence, D. M.: Contrasting urban and rural heat stress responses to climate change, Geophys. Res. Lett., 39, L03705, https://doi.org/10.1029/2011gl050576, 2012.
Flatau, P. J., Walko, R. L., and Cotton, W. R.: Polynomial fits to saturation vapor pressure, J. Appl. Meteorol., 31, 1507–1507, 1992.
Gagge, A. P.: An effective temperature scale based on a simple model of human physiological regulatory response, ASHRAE Trans., 77, 247–262, 1972.
García-Herrera, R., Diaz, J., Trigo, R. M., Luterbacher, J., and Fischer, E. M.: A Review of the European Summer Heat Wave of 2003, Crit. Rev. Environ. Sci. Technol., 40, 267–306, 2010.
Gates, R. S., Timmons, M. B., and Bottcher, R. W.: Numerical optimization of evaporative misting systems, Trans. ASABE, 34, 0275–0280, https://doi.org/10.13031/2013.31658, 1991a.
Gates, R. S., Usry, J. L., Nienaber, J. A., Turner, L. W., and Bridges, T. C.: An optimal misting method for cooling livestock housing, Trans. ASAE, 34, 2199–2206, 1991b.
Giles, B. D., Balafoutis, C., and Maheras, P.: Too hot for comfort: the heatwaves of Greece in 1987 and 1988, Int. J. Biometeorol., 34, 98–104, 1990.
Gonzalez, R. R.: SCENARIO revisited: comparisons of operational and rational models in predicing human responses to the environment, J. Thermal Biol., 29, 515–527, 2004.
Gonzalez, R. R., Cheuvront, S. N., Ely, B. R., Moran, D. S., Hadid, A., Endrusick, T. L., and Sawka, M. N.: Sweat rate prediction equations for outdoor exercise with transient solar radiation, J. Appl. Physiol., 112, 1300–1310, 2012.
Gribok, A. V., Buller, M. J., and Reifman, J.: Individualized Short-Term Core Temperature Prediction in Humans Using Biomathematical Models, IEEE Trans. Biomed. Eng., 55, 1477–1487, 2008.
Haldane, J. S.: The influence of high air temperatures No. 1, J. Hygiene, 5, 494–513, 1905.
Haslam, R. and Parsons, K.: Using computer-based models for predicting human thermal responses to hot and cold environments, Ergonomics, 37, 399–416, 1994.
Havenith, G., Fiala, D., B\l azejczyk, K., Richards, M., Bröde, P., Holmér, I., Rintamaki, H., Benshabat, Y., and Jendritzky, G.: The UTCI-clothing model, Int. J., Biometeorol., 56, 461–470, 2012.
US Army: Heat stress control and heat casualty management: Technical Bulletin Medical 507/Air Force Pamphlet, 48–152, 2003.
Höppe, P.: The physiological equivalent temperature – a universal index for the biometeorological assessment of the thermal environment, Int. J. Biometeorol., 43, 71–75, 1999.
Hurrell, J. W., Holland, M. M., Gent, P. R., Ghan, S., Kay, J. E., Kushner, P. J., Lamarque, J. F., Large, W. G., Lawrence, D., Lindsay, K., Lipscomb, W. H., Long, M. C., Mahowald, N., Marsh, D. R., Neale, R. B., Rasch, P., Vavrus, S., Vertenstein, M., Bader, D., Collins, W. D., Hack, J. J., Kiehl, J., and Marshall, S.: The Community Earth System Model: A Framework for Collaborative Research, B. Am. Meteorol. Soc., 94, 1339–1360, https://doi.org/10.1175/bams-d-12-00121.1, 2013.
Houghton, F. and Yaglou, C.: Determining equal comfort lines, J. Am. Soc. Heat. Vent. Eng., 29, 165–176, 1923.
Hyatt, O. M., Lemke, B., and Kjellstrom, T.: Regional maps of occupational heat exposure: past, present, and potential future, Global Health Action, 3, 5715, https://doi.org/10.3402/gha.v3i0.5715, 2010.
Ingram, D. L.: Evaporative cooling in the pig, Nature, 207, 415–416, 1965.
Jendritzky, G. and Tinz, B.: The thermal environment of the human being on the global scale, Global Health Action, 2, https://doi.org/10.3402/gha.v2i0.2005, 2009.
Jendritzky, G., Havenith, G., Weihs, P., and Batchvarova, E.: Towards a Universal Thermal Climate Index UTCI for assessing the thermal environment of the human being, Final Report COST Action, 730, 2009.
Kalnay, E., Kanamitsu, M., Kistler, R., Collins, W., Deaven, D., Gandin, L., Iredell, M., Saha, S., White, G., Woollen, J., Zhu, Y., Chelliah, M., Ebisuzaki, W., Higgins, W., Janowiak, J., Mo, K. C., Ropelewski, C., Wang, J., Leetmaa, A., Reynolds, R., Jenne, R., and Joseph, D.: The NCEP/NCAR 40-year reanalysis project, Bull. Am. Meteorol. Soc., 77, 437–471, 1996.
Khan, Z. A., Maniyan, S., Mokhtar, M., Quadir, G. A., and Seetharamu, K. N.: A Generalised Transient Thermal Model for Human Body, Jurnal Mekanikal, 18, 78–97, 2004.
Kjellstrom, T., Gabrysch, S., Lemke, B., and Dear, K.: The `Hothaps' programme for assessing climate change impacts on occupational health and productivity: an invitation to carry out field studies, Global Health Action, 2, https://doi.org/10.3402/gha.v2i0.2082, 2009a.
Kjellstrom, T., Kovats, R. S., Lloyd, S. J., Holt, T., and Tol, R. S.: The direct impact of climate change on regional labor productivity, Arch. Environ. Occup. Health, 64, 217–227, 2009b.
Kjellstrom, T., Holmer, I., and Lemke, B.: Workplace heat stress, health and productivity – an increasing challenge for low and middle-income countries during climate change, Global Health Action, 2, https://doi.org/10.3402/gha.v2i0.2047, 2009c.
Kjellstrom, T., Lemke, B., and Otto, M.: Mapping occupational heat exposure and effects in South-East Asia: ongoing time trends 1980–2011 and future estimates to 2050, Industrial Health, 51, 56–67, 2013.
Koca, R. W., Hughes, W. C., and Christianson, L. L.: Evaporative cooling pads: test procedure and evaluation, Appl. Eng. Agricult., 7, 485–490, 1991.
Kraning, K. K. and Gonzalez, R. R.: A mechanistic computer simulation of human work in heat that accounts for physical and physiological effects of clothing, aerobic fitness, and progressive dehydration, J. Therm. Biol., 22, 331–342, 1997.
Lawrence, M. G.: The relationship between relative humidity and the dewpoint temperature in moist air: A simple conversion and applications, Bull. Am. Meteorol. Soc., 86, 225–233, 2005.
Liang, C., Zheng, G., Zhu, N., Tian, Z., Lu, S., and Chen, Y.: A new environmental heat stress index for indoor hot and humid environments based on Cox regression, Build. Environ., 46, 2472–2479, 2011.
Liljegren, J. C., Carhart, R. A., Lawday, P., Tschopp, S., and Sharp, R.: Modeling the wet bulb globe temperature using standard meteorological measurements, J. Occup. Environ. Hyg., 5, 645–655, 2008.
Lucas, E. M., Randall, J. M., and Meneses, J. F.: Potential for evaporative cooling during heat stress periods in pig production in Portugal (Alentejo), J. Agr. Eng. Res., 76, 363–371, 2000.
Maloney, S. K. and Forbes, C. F.: What effect will a few degrees of climate change have on human heat balance? Implications for human activity, Int. J. Biometeorol., 55, 147–160, 2011.
Masterson, J. M. and Richardson, F. A.: Humidex, a method of quantifying human discomfort due to excessive heat and humidity, Environment Canada, Atmospheric Environment Service, Downsview, Ontario, CLI 1-79, 1979.
Meehl, G. A., and Tebaldi, C.: More intense, more frequent, and longer lasting heat waves in the 21st century, Science, 305, 994–997, https://doi.org/10.1126/science.1098704, 2004.
Minard, D., Belding, H. S., and Kingston, J. R.: Prevention of heat casualties, J. Ame. Medical Assoc., 165, 1813–1818, 1957.
Miralles, D., Teuling, A., Van Heerwaarden, C., and Arellano, J.: Mega-heatwave temperatures due to combined soil desiccation and atmospheric heat accumulation, Nat. Geosci., 7, 345–349, 2014.
Mitchell, T. D. and Jones, P. D.: An improved method of constructing a database of monthly climate observations and associated high-resolution grids, Int. J. Climatol., 25, 693–712, 2005.
Moran, D. S., Pandolf, K. B., Shapiro, Y., Heled, Y., Shani, Y., Mathew, W. T., and Gonzalez, R. R.: An environmental stress index (ESI) as a substitute for the wet bulb globe temperature (WBGT), J. Thermal Biol., 26, 427–431, 2001.
Mueller, B. and Seneviratne, S.: Hot days induced by precipitation deficits at the global scale, Proc. Natl. Aca. Sci., 109, 12398–12403, 2012.
Nag, P., Dutta, P., and Nag, A.: Critical body temperature profile as indicator of heat stress vulnerability, Industrial Health, 51, 113–122, 2013.
National Weather Service Central Region (NWSCR): Livestock hot weather stress. Regional operations manual letter C-31-76, Kansas City, Mo., National Weather Service, Central Region, 1976.
Nilsson, M. and Kjellstrom, T.: Climate change impacts on working people: how to develop prevention policies, Global Health Action, 3, 5774, https://doi.org/10.3402/gha.v3i0.5774, 2010.
NOAAWatch: available at: http://www.noaawatch.gov/themes/heat.php, last access: 12 March 2014.
Oke, T. R.: Boundary Layer Climates, 2nd Edn., London, Methuen and Co., Chapter 4, 1987.
Oleson, K.: Contrasts between urban and rural climate in CCSM4 CMIP5 climate change scenarios, J. Climate, 25, 1390–1412, 2012.
Oleson, K. W., Niu, G. Y., Yang, Z. L., Lawrence, D. M., Thornton, P. E., Lawrence, P. J., Stockli, R., Dickinson, R. E., Bonan, G. B., Levis, S., Dai, A., and Qian, T.: Improvements to the Community Land Model and their impact on the hydrological cycle, J. Geophys. Res.-Biogeosci., 113, G01021, https://doi.org/10.1029/2007jg000563, 2008a.
Oleson, K. W., Bonan, G. B., Feddema, J., Vertenstein, M., and Grimmond, C. S. B.: An urban parameterization for a global climate model. Part I: Formulation and evaluation for two cities, J. Appl. Meteorol. Climatol., 47, 1038–1060, 2008b.
Oleson, K. W., Bonan, G. B., Feddema, J., and Vertenstein, M.: An urban parameterization for a global climate model. Part II: Sensitivity to input parameters and the simulated urban heat island in offline Simulations, J. Appl. Meteorol. Climatol., 47, 1061–1076, 2008c.
Oleson, K. W., Lawrence, D. M., Bonan, G. B., Flanner, M. G., Kluzek, E., Lawrence, P. J., Levis, S., Swenson, S. C., Thornton, P. E., Dai, A., Decker, M., Dickinson, R., Feddema, J., Heald, C. L., Hoffman, F., Lamarque, J.-F., Mahowald, N., Niu, G.-Y., Qian, T., Randerson, J., Running, S., Sakaguchi, K., Slater, A., Stockli, R., Wang, A., Yang, Z.-L., Zeng, X., and Zeng, Xu.: Technical Description of version 4.0 of the Community Land Model (CLM), NCAR Technical Note NCAR/TN-478+STR, National Center for Atmospheric Research, Boulder, CO, 257 pp., 2010a.
Oleson, K. W., Bonan, G. B., Feddema, J., Vertenstein, M., and Kluzek, E.: Technical Description of an Urban Parameterization for the Community Land Model (CLMU). NCAR Technical Note NCAR/TN-480+STR, https://doi.org/10.5065/D6K35RM9, 2010b.
Oleson, K. W., Bonan, G. B., Feddema, J., and Jackson, T.: An examination of urban heat island characteristics in a global climate model, Int. J. Climatol., 31, 1848–1865, 2011.
Oleson, K. W., Lawrence, D. M., Bonan, G. B., Drewniak, B., Huang, M., Koven, C. D., Levis, S., Li, F., Riley, W. J., Subin, Z. M., Swenson, S. C., Thornton, P. E., Bozbiyik, A., Fisher, R., Kluzek, E., Lamarque, J.-F., Lawrence, P. J., Leung, L. R., Lipscomb, W., Muszala, S., Ricciuto, D. M., Sacks, W., Sun, Y., Tang, J., and Yang, Z.-L.: Technical Description of version 4.5 of the Community Land Model (CLM), Ncar Technical Note NCAR/TN-503+STR, National Center for Atmospheric Research, Boulder, CO, 422 pp., https://doi.org/10.5065/D6RR1W7M, 2013a.
Oleson, K. W., Monaghan, A., Wilhelmi, O., Barlage, M., Brunsell, N., Feddema, J., Hu, L., and Steinhoff, D. F.: Interactions between urbanization, heat stress, and climate change, Clim. Change, 1–17, https://doi.org/10.1007/s10584-013-0936-8, 2013b.
Parsons, K.: Heat stress standard ISO 7243 and its global application, Industrial Health, 44, 368–379, 2006.
Parsons, K.: Occupational health impacts of climate change: current and future ISO standards for the assessment of heat stress, Industrial Health, 51, 86–100, 2013.
Pradhan, B., Shrestha, S., Shrestha, R., Pradhanang, S., Kayastha, B., and Pradhan, P.: Assessing climate change and heat stress responses in the Tarai region of Nepal, Industrial Health, 51, 101–112, 2013.
Renaudeau, D., Collin, A., Yahav, S., De Basilio, V., Gourdine, J. L., and Collier, R. J.: Adaptation to hot climate and strategies to alleviate heat stress in livestock production, Animal, 6, 707–728, 2012.
Robine, J.-M., Cheung, S. L. K., Roy, S. L., van Oyen, H., Griffiths, C., Michel, J.-P., and Herrmann, F. R.: Death toll exceeded 70,000 in Europe during the summer of 2003, C. R. Biologies, 331, 171–178, 2008.
Rothfusz, L. P.: The heat index equation (or, more than you ever wanted to know about heat index), Fort Worth, Texas: National Oceanic and Atmospheric Administration, National Weather Service, Office of Meteorology, 90-23, 1990.
Seneviratne, S. I., Nicholls, N., Easterling, D., Goodess, C. M., Kanae, S., Kossin, J., Luo, Y., Marengo, J., McInnes, K., Rahimi, M., Reichstein, M., Sorteberg, A., Vera, C., and Zhang, X.: Changes in climate extremes and their impacts on the natural physical environment, in: Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation, edited by: Field, C. B., Barros, V., Stocker, T. F., Qin, D., Dokken, D. J., Ebi, K.L., Mastrandrea, M. D., Mach, K. J., Plattner, G.-K., Allen, S. K., Tignor, M., and Midgley, P. M.: A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change (IPCC), Cambridge University Press, Cambridge, UK, and New York, NY, USA, 109–230, 2012.
Seneviratne, S., Donat, M., Mueller, B., and Alexander, L.: No pause in the increase of hot temperature extremes, Nat. Clim. Change, 4, 161–163, 2014.
Sherwood, S. C. and Huber, M.: An adaptability limit to climate change due to heat stress, Proc. Natl. Aca. Sci., 107, 9552–9555, 2010.
Simpson, R. H.: On the computation of equivalent potential temperature, Mon. Weather Rev., 106, 124–130, 1978.
SREX IPCC: Summary for Policymakers, in: Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation, edited by: Field, C. B., Barros, V., Stocker, T. F., Qin, D., Dokken, D. J., Ebi, K. L., Mastrandrea, M. D., Mach, K. J., Plattner, G.-K., Allen, S. K., Tignor, M., and Midgley, P. M., A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, UK, and New York, NY, USA, 1–19, 2012.
Steadman, R. G.: The assessment of sultriness, Part I: A temperature-humidity index based on human physiology and clothing science, J. Appl. Meteorol., 18, 861–873, 1979a.
Steadman, R. G.: The Assessment of Sultriness. Part II: Effects of Wind, Extra Radiation and Barometric Pressure on Apparent Temperature, J. Appl. Meteorol., 18, 874–884, 1979b.
Steadman, R. G.: A universal scale of apparent temperature, J. Clim. Appl. Meteorol., 23, 1674–1687, 1984.
Steadman, R. G.: Norms of apparent temperature in Australia, Aust. Met. Mag., 43, 1–16, 1994.
Stoecklin-Marois, M., Hennessy-Burt, T., Mitchell, D., and Schenker, M.: Heat-related illness knowledge and practices among California hired farm workers in the MICASA study, Industrial Health, 51, 47–55, 2013.
Stolwijk, J.: A mathematical model of physiological temperature regulation in man, Report CR-1855 NASA, Washington, DC, 1971.
Stolwijk, J.: Mathematical models of thermal regulation, Ann. New York Aca. Sci., 335, 98–106, 1980.
Stolwijk, J. and Hardy, J.: Temperature regulation in man-A theoretical study, Ptlilgers Archiv, 129, 129–162, 1966.
Stull, R.: Wet-bulb temperature from relative humidity and air temperature, J. Appl. Meteorol. Climatol., 50, 2267–2269, 2011.
Tawatsupa, B., Yiengrugsawan, V., Kjellstrom, T., Berecki-Gisolf, J., Seubsman, S., and Sleigh, A.: Association between heat stress and occupational injury among Thai workers: Findings of the Thai Cohort Study, Industrial Health, 51, 34–46, 2013.
Taylor, K. E., Stouffer, R. J., and Meehl, G. A.: An overview of CMIP5 and the experiment design, Bull. Am. Meteorol. Soc., 93, 485–498, 2012.
Thom, E. C.: The discomfort index, Weatherwise, 12, 57–61, 1959.
Willett, K. M. and Sherwood, S.: Exceedance of heat index thresholds for 15 regions under a warming climate using the wet-bulb globe temperature, Int. J. Climatol., 32, 161–177, 2012.
Wyndham, C. and Atkins, A.: A physiological scheme and mathematical model of temperature regulation in man, Pflügers Archiv, 303, 14–30, 1968.
Yaglou, C. P. and Minard, D.: Control of heat casualties at military training centers, American Medical Association Archives of Industrial Health, 16, 302–316, 1957.
Yokota, M., Berglund, L., Cheuvront, S., Santee, W., Latzka, W., Montain, S., Kolka, M., and Moran, D.: Thermoregulatory model to predict physiological status from ambient environment and heart rate, Comput. Biol. Medicine, 38, 1187–1193, 2008.
Short summary
We implemented the HumanIndexMod, which calculates 13 diagnostic heat stress metrics, into the Community Land Model (CLM4.5). The goal of this module is to have a common predictive framework for measuring heat stress globally. These metrics are in operational use by weather forecasters, industry, and agriculture. We show metric-dependent results of regional partitioning of extreme moisture and temperature levels in a 1901-2010 simulation.
We implemented the HumanIndexMod, which calculates 13 diagnostic heat stress metrics, into the...
