Articles | Volume 12, issue 4
https://doi.org/10.5194/gmd-12-1613-2019
© Author(s) 2019. 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-12-1613-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model evaluation paper
| 
24 Apr 2019
Model evaluation paper |
| 24 Apr 2019
| 24 Apr 2019
The Brazilian Earth System Model ocean–atmosphere (BESM-OA) version 2.5: evaluation of its CMIP5 historical simulation
Sandro F. Veiga
CORRESPONDING AUTHOR
Earth System Science Center (CCST), National Institute for Space
Research (INPE), São José dos Campos 12227-010, São Paulo,
Brazil
Paulo Nobre
Center for Weather Forecasting and Climate Studies (CPTEC), National
Institute for Space Research (INPE), Cachoeira Paulista 12630-000, São
Paulo, Brazil
Emanuel Giarolla
Center for Weather Forecasting and Climate Studies (CPTEC), National
Institute for Space Research (INPE), São José dos Campos 12227-010,
São Paulo, Brazil
Vinicius Capistrano
Amazonas State University (UEA), Manaus 69005-010, Amazonas, Brazil
Manoel Baptista Jr.
Center for Weather Forecasting and Climate Studies (CPTEC), National
Institute for Space Research (INPE), Cachoeira Paulista 12630-000, São
Paulo, Brazil
André L. Marquez
Center for Weather Forecasting and Climate Studies (CPTEC), National
Institute for Space Research (INPE), Cachoeira Paulista 12630-000, São
Paulo, Brazil
Silvio Nilo Figueroa
Center for Weather Forecasting and Climate Studies (CPTEC), National
Institute for Space Research (INPE), Cachoeira Paulista 12630-000, São
Paulo, Brazil
José Paulo Bonatti
Center for Weather Forecasting and Climate Studies (CPTEC), National
Institute for Space Research (INPE), Cachoeira Paulista 12630-000, São
Paulo, Brazil
Paulo Kubota
Center for Weather Forecasting and Climate Studies (CPTEC), National
Institute for Space Research (INPE), Cachoeira Paulista 12630-000, São
Paulo, Brazil
Carlos A. Nobre
Institute for Advanced Studies, University of São Paulo, São Paulo 05508-050, São Paulo, Brazil
Related authors
Vinicius Buscioli Capistrano, Paulo Nobre, Sandro F. Veiga, Renata Tedeschi, Josiane Silva, Marcus Bottino, Manoel Baptista da Silva Jr., Otacílio Leandro Menezes Neto, Silvio Nilo Figueroa, José Paulo Bonatti, Paulo Yoshio Kubota, Julio Pablo Reyes Fernandez, Emanuel Giarolla, Jessica Vial, and Carlos A. Nobre
Geosci. Model Dev., 13, 2277–2296, https://doi.org/10.5194/gmd-13-2277-2020, https://doi.org/10.5194/gmd-13-2277-2020, 2020
Short summary
Short summary
This work represents the product of our recent efforts to develop a Brazilian climate model and helps address some scientific issues on the frontier of knowledge (e.g., cloud feedback studies). The BESM results show climate sensitivity and thermodynamical responses similar to a CMIP5 ensemble. More than that, BESM has the objective of being an additional climate model with the ability to reproduce changes that are physically understood in order to study the global climate system.
Malcolm John Roberts, Kevin A. Reed, Qing Bao, Joseph J. Barsugli, Suzana J. Camargo, Louis-Philippe Caron, Ping Chang, Cheng-Ta Chen, Hannah M. Christensen, Gokhan Danabasoglu, Ivy Frenger, Neven S. Fučkar, Shabeh ul Hasson, Helene T. Hewitt, Huanping Huang, Daehyun Kim, Chihiro Kodama, Michael Lai, Lai-Yung Ruby Leung, Ryo Mizuta, Paulo Nobre, Pablo Ortega, Dominique Paquin, Christopher D. Roberts, Enrico Scoccimarro, Jon Seddon, Anne Marie Treguier, Chia-Ying Tu, Paul A. Ullrich, Pier Luigi Vidale, Michael F. Wehner, Colin M. Zarzycki, Bosong Zhang, Wei Zhang, and Ming Zhao
EGUsphere, https://doi.org/10.5194/egusphere-2024-2582, https://doi.org/10.5194/egusphere-2024-2582, 2024
Short summary
Short summary
HighResMIP2 is a model intercomparison project focussing on high resolution global climate models, that is those with grid spacings of 25 km or less in atmosphere and ocean, using simulations of decades to a century or so in length. We are proposing an update of our simulation protocol to make the models more applicable to key questions for climate variability and hazard in present day and future projections, and to build links with other communities to provide more robust climate information.
Bjorn Stevens, Stefan Adami, Tariq Ali, Hartwig Anzt, Zafer Aslan, Sabine Attinger, Jaana Bäck, Johanna Baehr, Peter Bauer, Natacha Bernier, Bob Bishop, Hendryk Bockelmann, Sandrine Bony, Guy Brasseur, David N. Bresch, Sean Breyer, Gilbert Brunet, Pier Luigi Buttigieg, Junji Cao, Christelle Castet, Yafang Cheng, Ayantika Dey Choudhury, Deborah Coen, Susanne Crewell, Atish Dabholkar, Qing Dai, Francisco Doblas-Reyes, Dale Durran, Ayoub El Gaidi, Charlie Ewen, Eleftheria Exarchou, Veronika Eyring, Florencia Falkinhoff, David Farrell, Piers M. Forster, Ariane Frassoni, Claudia Frauen, Oliver Fuhrer, Shahzad Gani, Edwin Gerber, Debra Goldfarb, Jens Grieger, Nicolas Gruber, Wilco Hazeleger, Rolf Herken, Chris Hewitt, Torsten Hoefler, Huang-Hsiung Hsu, Daniela Jacob, Alexandra Jahn, Christian Jakob, Thomas Jung, Christopher Kadow, In-Sik Kang, Sarah Kang, Karthik Kashinath, Katharina Kleinen-von Königslöw, Daniel Klocke, Uta Kloenne, Milan Klöwer, Chihiro Kodama, Stefan Kollet, Tobias Kölling, Jenni Kontkanen, Steve Kopp, Michal Koran, Markku Kulmala, Hanna Lappalainen, Fakhria Latifi, Bryan Lawrence, June Yi Lee, Quentin Lejeun, Christian Lessig, Chao Li, Thomas Lippert, Jürg Luterbacher, Pekka Manninen, Jochem Marotzke, Satoshi Matsouoka, Charlotte Merchant, Peter Messmer, Gero Michel, Kristel Michielsen, Tomoki Miyakawa, Jens Müller, Ramsha Munir, Sandeep Narayanasetti, Ousmane Ndiaye, Carlos Nobre, Achim Oberg, Riko Oki, Tuba Özkan-Haller, Tim Palmer, Stan Posey, Andreas Prein, Odessa Primus, Mike Pritchard, Julie Pullen, Dian Putrasahan, Johannes Quaas, Krishnan Raghavan, Venkatachalam Ramaswamy, Markus Rapp, Florian Rauser, Markus Reichstein, Aromar Revi, Sonakshi Saluja, Masaki Satoh, Vera Schemann, Sebastian Schemm, Christina Schnadt Poberaj, Thomas Schulthess, Cath Senior, Jagadish Shukla, Manmeet Singh, Julia Slingo, Adam Sobel, Silvina Solman, Jenna Spitzer, Philip Stier, Thomas Stocker, Sarah Strock, Hang Su, Petteri Taalas, John Taylor, Susann Tegtmeier, Georg Teutsch, Adrian Tompkins, Uwe Ulbrich, Pier-Luigi Vidale, Chien-Ming Wu, Hao Xu, Najibullah Zaki, Laure Zanna, Tianjun Zhou, and Florian Ziemen
Earth Syst. Sci. Data, 16, 2113–2122, https://doi.org/10.5194/essd-16-2113-2024, https://doi.org/10.5194/essd-16-2113-2024, 2024
Short summary
Short summary
To manage Earth in the Anthropocene, new tools, new institutions, and new forms of international cooperation will be required. Earth Virtualization Engines is proposed as an international federation of centers of excellence to empower all people to respond to the immense and urgent challenges posed by climate change.
Enner Alcântara, José A. Marengo, José Mantovani, Luciana R. Londe, Rachel Lau Yu San, Edward Park, Yunung Nina Lin, Jingyu Wang, Tatiana Mendes, Ana Paula Cunha, Luana Pampuch, Marcelo Seluchi, Silvio Simões, Luz Adriana Cuartas, Demerval Goncalves, Klécia Massi, Regina Alvalá, Osvaldo Moraes, Carlos Souza Filho, Rodolfo Mendes, and Carlos Nobre
Nat. Hazards Earth Syst. Sci., 23, 1157–1175, https://doi.org/10.5194/nhess-23-1157-2023, https://doi.org/10.5194/nhess-23-1157-2023, 2023
Short summary
Short summary
The municipality of Petrópolis (approximately 305 687 inhabitants) is nestled in the mountains 68 km outside the city of Rio de Janeiro. On 15 February 2022, the city of Petrópolis in Rio de Janeiro, Brazil, received an unusually high volume of rain within 3 h (258 mm). This resulted in flash floods and subsequent landslides that caused 231 fatalities, the deadliest landslide disaster recorded in Petrópolis. This work shows how the disaster was triggered.
Layrson J. M. Gonçalves, Simone M. S. C. Coelho, Paulo Y. Kubota, and Dayana C. Souza
Atmos. Chem. Phys., 22, 15509–15526, https://doi.org/10.5194/acp-22-15509-2022, https://doi.org/10.5194/acp-22-15509-2022, 2022
Short summary
Short summary
This research aims to study the environmental conditions that are favorable and not favorable to cloud formation, in this case specifically for the Amazon region. The results found in this research will be used to improve the representation of clouds in numerical models that are used in weather and climate prediction. In general, it is expected that with better knowledge regarding the cloud–radiation interaction, it is possible to make a better forecast of weather and climate.
Yongkang Xue, Tandong Yao, Aaron A. Boone, Ismaila Diallo, Ye Liu, Xubin Zeng, William K. M. Lau, Shiori Sugimoto, Qi Tang, Xiaoduo Pan, Peter J. van Oevelen, Daniel Klocke, Myung-Seo Koo, Tomonori Sato, Zhaohui Lin, Yuhei Takaya, Constantin Ardilouze, Stefano Materia, Subodh K. Saha, Retish Senan, Tetsu Nakamura, Hailan Wang, Jing Yang, Hongliang Zhang, Mei Zhao, Xin-Zhong Liang, J. David Neelin, Frederic Vitart, Xin Li, Ping Zhao, Chunxiang Shi, Weidong Guo, Jianping Tang, Miao Yu, Yun Qian, Samuel S. P. Shen, Yang Zhang, Kun Yang, Ruby Leung, Yuan Qiu, Daniele Peano, Xin Qi, Yanling Zhan, Michael A. Brunke, Sin Chan Chou, Michael Ek, Tianyi Fan, Hong Guan, Hai Lin, Shunlin Liang, Helin Wei, Shaocheng Xie, Haoran Xu, Weiping Li, Xueli Shi, Paulo Nobre, Yan Pan, Yi Qin, Jeff Dozier, Craig R. Ferguson, Gianpaolo Balsamo, Qing Bao, Jinming Feng, Jinkyu Hong, Songyou Hong, Huilin Huang, Duoying Ji, Zhenming Ji, Shichang Kang, Yanluan Lin, Weiguang Liu, Ryan Muncaster, Patricia de Rosnay, Hiroshi G. Takahashi, Guiling Wang, Shuyu Wang, Weicai Wang, Xu Zhou, and Yuejian Zhu
Geosci. Model Dev., 14, 4465–4494, https://doi.org/10.5194/gmd-14-4465-2021, https://doi.org/10.5194/gmd-14-4465-2021, 2021
Short summary
Short summary
The subseasonal prediction of extreme hydroclimate events such as droughts/floods has remained stubbornly low for years. This paper presents a new international initiative which, for the first time, introduces spring land surface temperature anomalies over high mountains to improve precipitation prediction through remote effects of land–atmosphere interactions. More than 40 institutions worldwide are participating in this effort. The experimental protocol and preliminary results are presented.
Fernanda Casagrande, Ronald Buss de Souza, Paulo Nobre, and Andre Lanfer Marquez
Ann. Geophys., 38, 1123–1138, https://doi.org/10.5194/angeo-38-1123-2020, https://doi.org/10.5194/angeo-38-1123-2020, 2020
Short summary
Short summary
Polar amplification is possibly one of the most important sensitive indicators of climate change. Our results showed that the polar regions are much more vulnerable to large warming due to an increase in atmospheric CO2 forcing than the rest of the world, particularly during the cold season. Despite the asymmetry in warming between the Arctic and Antarctic, both poles show systematic polar amplification in all climate models.
Vinicius Buscioli Capistrano, Paulo Nobre, Sandro F. Veiga, Renata Tedeschi, Josiane Silva, Marcus Bottino, Manoel Baptista da Silva Jr., Otacílio Leandro Menezes Neto, Silvio Nilo Figueroa, José Paulo Bonatti, Paulo Yoshio Kubota, Julio Pablo Reyes Fernandez, Emanuel Giarolla, Jessica Vial, and Carlos A. Nobre
Geosci. Model Dev., 13, 2277–2296, https://doi.org/10.5194/gmd-13-2277-2020, https://doi.org/10.5194/gmd-13-2277-2020, 2020
Short summary
Short summary
This work represents the product of our recent efforts to develop a Brazilian climate model and helps address some scientific issues on the frontier of knowledge (e.g., cloud feedback studies). The BESM results show climate sensitivity and thermodynamical responses similar to a CMIP5 ensemble. More than that, BESM has the objective of being an additional climate model with the ability to reproduce changes that are physically understood in order to study the global climate system.
Mabel Costa Calim, Paulo Nobre, Peter Oke, Andreas Schiller, Leo San Pedro Siqueira, and Guilherme Pimenta Castelão
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2018-5, https://doi.org/10.5194/gmd-2018-5, 2018
Revised manuscript not accepted
Short summary
Short summary
A new tool inspired on tides is introduced. The Spectral Taylor Diagram designed for evaluating and monitoring models performance in frequency domain calculates the degree of correspondence between simulated and observed fields for a given frequency (or a band of frequencies). It's a powerful tool to detect co-oscillating patterns in multi scale analysis, without using filtering techniques.
Related subject area
Climate and Earth system modeling
Subsurface hydrological controls on the short-term effects of hurricanes on nitrate–nitrogen runoff loading: a case study of Hurricane Ida using the Energy Exascale Earth System Model (E3SM) Land Model (v2.1)
CARIB12: a regional Community Earth System Model/Modular Ocean Model 6 configuration of the Caribbean Sea
Architectural insights into and training methodology optimization of Pangu-Weather
Evaluation of global fire simulations in CMIP6 Earth system models
Evaluating downscaled products with expected hydroclimatic co-variances
Software sustainability of global impact models
fair-calibrate v1.4.1: calibration, constraining, and validation of the FaIR simple climate model for reliable future climate projections
ISOM 1.0: a fully mesoscale-resolving idealized Southern Ocean model and the diversity of multiscale eddy interactions
A computationally lightweight model for ensemble forecasting of environmental hazards: General TAMSAT-ALERT v1.2.1
Introducing the MESMER-M-TPv0.1.0 module: spatially explicit Earth system model emulation for monthly precipitation and temperature
The need for carbon-emissions-driven climate projections in CMIP7
Robust handling of extremes in quantile mapping – “Murder your darlings”
A protocol for model intercomparison of impacts of marine cloud brightening climate intervention
An extensible perturbed parameter ensemble for the Community Atmosphere Model version 6
Coupling the regional climate model ICON-CLM v2.6.6 to the Earth system model GCOAST-AHOI v2.0 using OASIS3-MCT v4.0
A fully coupled solid-particle microphysics scheme for stratospheric aerosol injections within the aerosol–chemistry–climate model SOCOL-AERv2
An improved representation of aerosol in the ECMWF IFS-COMPO 49R1 through the integration of EQSAM4Climv12 – a first attempt at simulating aerosol acidity
At-scale Model Output Statistics in mountain environments (AtsMOS v1.0)
Impact of ocean vertical-mixing parameterization on Arctic sea ice and upper-ocean properties using the NEMO-SI3 model
Bridging the gap: a new module for human water use in the Community Earth System Model version 2.2.1
Modeling Commercial-Scale CO2 Storage in the Gas Hydrate Stability Zone with PFLOTRAN v6.0
A new lightning scheme in the Canadian Atmospheric Model (CanAM5.1): implementation, evaluation, and projections of lightning and fire in future climates
Methane dynamics in the Baltic Sea: investigating concentration, flux, and isotopic composition patterns using the coupled physical–biogeochemical model BALTSEM-CH4 v1.0
Using feature importance as exploratory data analysis tool on earth system models
CropSuite – A comprehensive open-source crop suitability model considering climate variability for climate impact assessment
ICON ComIn – The ICON Community Interface (ComIn version 0.1.0, with ICON version 2024.01-01)
Split-explicit external mode solver in the finite volume sea ice–ocean model FESOM2
Applying double cropping and interactive irrigation in the North China Plain using WRF4.5
The sea ice component of GC5: coupling SI3 to HadGEM3 using conductive fluxes
CICE on a C-grid: new momentum, stress, and transport schemes for CICEv6.5
HyPhAICC v1.0: a hybrid physics–AI approach for probability fields advection shown through an application to cloud cover nowcasting
CICERO Simple Climate Model (CICERO-SCM v1.1.1) – an improved simple climate model with a parameter calibration tool
A non-intrusive, multi-scale, and flexible coupling interface in WRF
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
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
Updating the radiation infrastructure in MESSy (based on MESSy version 2.55)
An urban module coupled with the Variable Infiltration Capacity model to improve hydrothermal simulations in urban systems
Bayesian hierarchical model for bias-correcting climate models
Evaluation of the coupling of EMACv2.55 to the land surface and vegetation model JSBACHv4
Reduced floating-point precision in regional climate simulations: an ensemble-based statistical verification
TorchClim v1.0: a deep-learning plugin for climate model physics
The very-high resolution configuration of the EC-Earth global model for HighResMIP
DiuSST: A conceptual model of diurnal warm layers for idealized atmospheric simulations with interactive SST
Yilin Fang, Hoang Viet Tran, and L. Ruby Leung
Geosci. Model Dev., 18, 19–32, https://doi.org/10.5194/gmd-18-19-2025, https://doi.org/10.5194/gmd-18-19-2025, 2025
Short summary
Short summary
Hurricanes may worsen water quality in the lower Mississippi River basin (LMRB) by increasing nutrient runoff. We found that runoff parameterizations greatly affect nitrate–nitrogen runoff simulated using an Earth system land model. Our simulations predicted increased nitrogen runoff in the LMRB during Hurricane Ida in 2021, albeit less pronounced than the observations, indicating areas for model improvement to better understand and manage nutrient runoff loss during hurricanes in the region.
Giovanni Seijo-Ellis, Donata Giglio, Gustavo Marques, and Frank Bryan
Geosci. Model Dev., 17, 8989–9021, https://doi.org/10.5194/gmd-17-8989-2024, https://doi.org/10.5194/gmd-17-8989-2024, 2024
Short summary
Short summary
A CESM–MOM6 regional configuration of the Caribbean Sea was developed in response to the rising need for high-resolution models for climate impact studies. The configuration is validated for the period 2000–2020 and improves significant errors in a low-resolution model. Oceanic properties are well represented. Patterns of freshwater associated with the Amazon River are well captured, and the mean flows of ocean waters across multiple passages in the Caribbean Sea agree with observations.
Deifilia To, Julian Quinting, Gholam Ali Hoshyaripour, Markus Götz, Achim Streit, and Charlotte Debus
Geosci. Model Dev., 17, 8873–8884, https://doi.org/10.5194/gmd-17-8873-2024, https://doi.org/10.5194/gmd-17-8873-2024, 2024
Short summary
Short summary
Pangu-Weather is a breakthrough machine learning model in medium-range weather forecasting that considers 3D atmospheric information. We show that using a simpler 2D framework improves robustness, speeds up training, and reduces computational needs by 20 %–30 %. We introduce a training procedure that varies the importance of atmospheric variables over time to speed up training convergence. Decreasing computational demand increases the accessibility of training and working with the model.
Fang Li, Xiang Song, Sandy P. Harrison, Jennifer R. Marlon, Zhongda Lin, L. Ruby Leung, Jörg Schwinger, Virginie Marécal, Shiyu Wang, Daniel S. Ward, Xiao Dong, Hanna Lee, Lars Nieradzik, Sam S. Rabin, and Roland Séférian
Geosci. Model Dev., 17, 8751–8771, https://doi.org/10.5194/gmd-17-8751-2024, https://doi.org/10.5194/gmd-17-8751-2024, 2024
Short summary
Short summary
This study provides the first comprehensive assessment of historical fire simulations from 19 Earth system models in phase 6 of the Coupled Model Intercomparison Project (CMIP6). Most models reproduce global totals, spatial patterns, seasonality, and regional historical changes well but fail to simulate the recent decline in global burned area and underestimate the fire response to climate variability. CMIP6 simulations address three critical issues of phase-5 models.
Seung H. Baek, Paul A. Ullrich, Bo Dong, and Jiwoo Lee
Geosci. Model Dev., 17, 8665–8681, https://doi.org/10.5194/gmd-17-8665-2024, https://doi.org/10.5194/gmd-17-8665-2024, 2024
Short summary
Short summary
We evaluate downscaled products by examining locally relevant co-variances during precipitation events. Common statistical downscaling techniques preserve expected co-variances during convective precipitation (a stationary phenomenon). However, they dampen future intensification of frontal precipitation (a non-stationary phenomenon) captured in global climate models and dynamical downscaling. Our study quantifies a ramification of the stationarity assumption underlying statistical downscaling.
Emmanuel Nyenah, Petra Döll, Daniel S. Katz, and Robert Reinecke
Geosci. Model Dev., 17, 8593–8611, https://doi.org/10.5194/gmd-17-8593-2024, https://doi.org/10.5194/gmd-17-8593-2024, 2024
Short summary
Short summary
Research software is vital for scientific progress but is often developed by scientists with limited skills, time, and funding, leading to challenges in usability and maintenance. Our study across 10 sectors shows strengths in version control, open-source licensing, and documentation while emphasizing the need for containerization and code quality. We recommend workshops; code quality metrics; funding; and following the findable, accessible, interoperable, and reusable (FAIR) standards.
Chris Smith, Donald P. Cummins, Hege-Beate Fredriksen, Zebedee Nicholls, Malte Meinshausen, Myles Allen, Stuart Jenkins, Nicholas Leach, Camilla Mathison, and Antti-Ilari Partanen
Geosci. Model Dev., 17, 8569–8592, https://doi.org/10.5194/gmd-17-8569-2024, https://doi.org/10.5194/gmd-17-8569-2024, 2024
Short summary
Short summary
Climate projections are only useful if the underlying models that produce them are well calibrated and can reproduce observed climate change. We formalise a software package that calibrates the open-source FaIR simple climate model to full-complexity Earth system models. Observations, including historical warming, and assessments of key climate variables such as that of climate sensitivity are used to constrain the model output.
Jingwei Xie, Xi Wang, Hailong Liu, Pengfei Lin, Jiangfeng Yu, Zipeng Yu, Junlin Wei, and Xiang Han
Geosci. Model Dev., 17, 8469–8493, https://doi.org/10.5194/gmd-17-8469-2024, https://doi.org/10.5194/gmd-17-8469-2024, 2024
Short summary
Short summary
We propose the concept of mesoscale ocean direct numerical simulation (MODNS), which should resolve the first baroclinic deformation radius and ensure the numerical dissipative effects do not directly contaminate the mesoscale motions. It can be a benchmark for testing mesoscale ocean large eddy simulation (MOLES) methods in ocean models. We build an idealized Southern Ocean model using MITgcm to generate a type of MODNS. We also illustrate the diversity of multiscale eddy interactions.
Emily Black, John Ellis, and Ross I. Maidment
Geosci. Model Dev., 17, 8353–8372, https://doi.org/10.5194/gmd-17-8353-2024, https://doi.org/10.5194/gmd-17-8353-2024, 2024
Short summary
Short summary
We present General TAMSAT-ALERT, a computationally lightweight and versatile tool for generating ensemble forecasts from time series data. General TAMSAT-ALERT is capable of combining multiple streams of monitoring and meteorological forecasting data into probabilistic hazard assessments. In this way, it complements existing systems and enhances their utility for actionable hazard assessment.
Sarah Schöngart, Lukas Gudmundsson, Mathias Hauser, Peter Pfleiderer, Quentin Lejeune, Shruti Nath, Sonia Isabelle Seneviratne, and Carl-Friedrich Schleussner
Geosci. Model Dev., 17, 8283–8320, https://doi.org/10.5194/gmd-17-8283-2024, https://doi.org/10.5194/gmd-17-8283-2024, 2024
Short summary
Short summary
Precipitation and temperature are two of the most impact-relevant climatic variables. Yet, projecting future precipitation and temperature data under different emission scenarios relies on complex models that are computationally expensive. In this study, we propose a method that allows us to generate monthly means of local precipitation and temperature at low computational costs. Our modelling framework is particularly useful for all downstream applications of climate model data.
Benjamin M. Sanderson, Ben B. B. Booth, John Dunne, Veronika Eyring, Rosie A. Fisher, Pierre Friedlingstein, Matthew J. Gidden, Tomohiro Hajima, Chris D. Jones, Colin G. Jones, Andrew King, Charles D. Koven, David M. Lawrence, Jason Lowe, Nadine Mengis, Glen P. Peters, Joeri Rogelj, Chris Smith, Abigail C. Snyder, Isla R. Simpson, Abigail L. S. Swann, Claudia Tebaldi, Tatiana Ilyina, Carl-Friedrich Schleussner, Roland Séférian, Bjørn H. Samset, Detlef van Vuuren, and Sönke Zaehle
Geosci. Model Dev., 17, 8141–8172, https://doi.org/10.5194/gmd-17-8141-2024, https://doi.org/10.5194/gmd-17-8141-2024, 2024
Short summary
Short summary
We discuss how, in order to provide more relevant guidance for climate policy, coordinated climate experiments should adopt a greater focus on simulations where Earth system models are provided with carbon emissions from fossil fuels together with land use change instructions, rather than past approaches that have largely focused on experiments with prescribed atmospheric carbon dioxide concentrations. We discuss how these goals might be achieved in coordinated climate modeling experiments.
Peter Berg, Thomas Bosshard, Denica Bozhinova, Lars Bärring, Joakim Löw, Carolina Nilsson, Gustav Strandberg, Johan Södling, Johan Thuresson, Renate Wilcke, and Wei Yang
Geosci. Model Dev., 17, 8173–8179, https://doi.org/10.5194/gmd-17-8173-2024, https://doi.org/10.5194/gmd-17-8173-2024, 2024
Short summary
Short summary
When bias adjusting climate model data using quantile mapping, one needs to prescribe what to do at the tails of the distribution, where a larger data range is likely encountered outside of the calibration period. The end result is highly dependent on the method used. We show that, to avoid discontinuities in the time series, one needs to exclude data in the calibration range to also activate the extrapolation functionality in that time period.
Philip J. Rasch, Haruki Hirasawa, Mingxuan Wu, Sarah J. Doherty, Robert Wood, Hailong Wang, Andy Jones, James Haywood, and Hansi Singh
Geosci. Model Dev., 17, 7963–7994, https://doi.org/10.5194/gmd-17-7963-2024, https://doi.org/10.5194/gmd-17-7963-2024, 2024
Short summary
Short summary
We introduce a protocol to compare computer climate simulations to better understand a proposed strategy intended to counter warming and climate impacts from greenhouse gas increases. This slightly changes clouds in six ocean regions to reflect more sunlight and cool the Earth. Example changes in clouds and climate are shown for three climate models. Cloud changes differ between the models, but precipitation and surface temperature changes are similar when their cooling effects are made similar.
Trude Eidhammer, Andrew Gettelman, Katherine Thayer-Calder, Duncan Watson-Parris, Gregory Elsaesser, Hugh Morrison, Marcus van Lier-Walqui, Ci Song, and Daniel McCoy
Geosci. Model Dev., 17, 7835–7853, https://doi.org/10.5194/gmd-17-7835-2024, https://doi.org/10.5194/gmd-17-7835-2024, 2024
Short summary
Short summary
We describe a dataset where 45 parameters related to cloud processes in the Community Earth System Model version 2 (CESM2) Community Atmosphere Model version 6 (CAM6) are perturbed. Three sets of perturbed parameter ensembles (263 members) were created: current climate, preindustrial aerosol loading and future climate with sea surface temperature increased by 4 K.
Ha Thi Minh Ho-Hagemann, Vera Maurer, Stefan Poll, and Irina Fast
Geosci. Model Dev., 17, 7815–7834, https://doi.org/10.5194/gmd-17-7815-2024, https://doi.org/10.5194/gmd-17-7815-2024, 2024
Short summary
Short summary
The regional Earth system model GCOAST-AHOI v2.0 that includes the regional climate model ICON-CLM coupled to the ocean model NEMO and the hydrological discharge model HD via the OASIS3-MCT coupler can be a useful tool for conducting long-term regional climate simulations over the EURO-CORDEX domain. The new OASIS3-MCT coupling interface implemented in ICON-CLM makes it more flexible for coupling to an external ocean model and an external hydrological discharge model.
Sandro Vattioni, Rahel Weber, Aryeh Feinberg, Andrea Stenke, John A. Dykema, Beiping Luo, Georgios A. Kelesidis, Christian A. Bruun, Timofei Sukhodolov, Frank N. Keutsch, Thomas Peter, and Gabriel Chiodo
Geosci. Model Dev., 17, 7767–7793, https://doi.org/10.5194/gmd-17-7767-2024, https://doi.org/10.5194/gmd-17-7767-2024, 2024
Short summary
Short summary
We quantified impacts and efficiency of stratospheric solar climate intervention via solid particle injection. Microphysical interactions of solid particles with the sulfur cycle were interactively coupled to the heterogeneous chemistry scheme and the radiative transfer code of an aerosol–chemistry–climate model. Compared to injection of SO2 we only find a stronger cooling efficiency for solid particles when normalizing to the aerosol load but not when normalizing to the injection rate.
Samuel Rémy, Swen Metzger, Vincent Huijnen, Jason E. Williams, and Johannes Flemming
Geosci. Model Dev., 17, 7539–7567, https://doi.org/10.5194/gmd-17-7539-2024, https://doi.org/10.5194/gmd-17-7539-2024, 2024
Short summary
Short summary
In this paper we describe the development of the future operational cycle 49R1 of the IFS-COMPO system, used for operational forecasts of atmospheric composition in the CAMS project, and focus on the implementation of the thermodynamical model EQSAM4Clim version 12. The implementation of EQSAM4Clim significantly improves the simulated secondary inorganic aerosol surface concentration. The new aerosol and precipitation acidity diagnostics showed good agreement against observational datasets.
Maximillian Van Wyk de Vries, Tom Matthews, L. Baker Perry, Nirakar Thapa, and Rob Wilby
Geosci. Model Dev., 17, 7629–7643, https://doi.org/10.5194/gmd-17-7629-2024, https://doi.org/10.5194/gmd-17-7629-2024, 2024
Short summary
Short summary
This paper introduces the AtsMOS workflow, a new tool for improving weather forecasts in mountainous areas. By combining advanced statistical techniques with local weather data, AtsMOS can provide more accurate predictions of weather conditions. Using data from Mount Everest as an example, AtsMOS has shown promise in better forecasting hazardous weather conditions, making it a valuable tool for communities in mountainous regions and beyond.
Sofia Allende, Anne Marie Treguier, Camille Lique, Clément de Boyer Montégut, François Massonnet, Thierry Fichefet, and Antoine Barthélemy
Geosci. Model Dev., 17, 7445–7466, https://doi.org/10.5194/gmd-17-7445-2024, https://doi.org/10.5194/gmd-17-7445-2024, 2024
Short summary
Short summary
We study the parameters of the turbulent-kinetic-energy mixed-layer-penetration scheme in the NEMO model with regard to sea-ice-covered regions of the Arctic Ocean. This evaluation reveals the impact of these parameters on mixed-layer depth, sea surface temperature and salinity, and ocean stratification. Our findings demonstrate significant impacts on sea ice thickness and sea ice concentration, emphasizing the need for accurately representing ocean mixing to understand Arctic climate dynamics.
Sabin I. Taranu, David M. Lawrence, Yoshihide Wada, Ting Tang, Erik Kluzek, Sam Rabin, Yi Yao, Steven J. De Hertog, Inne Vanderkelen, and Wim Thiery
Geosci. Model Dev., 17, 7365–7399, https://doi.org/10.5194/gmd-17-7365-2024, https://doi.org/10.5194/gmd-17-7365-2024, 2024
Short summary
Short summary
In this study, we improved a climate model by adding the representation of water use sectors such as domestic, industry, and agriculture. This new feature helps us understand how water is used and supplied in various areas. We tested our model from 1971 to 2010 and found that it accurately identifies areas with water scarcity. By modelling the competition between sectors when water availability is limited, the model helps estimate the intensity and extent of individual sectors' water shortages.
Michael Nole, Jonah Bartrand, Fawz Naim, and Glenn Hammond
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-162, https://doi.org/10.5194/gmd-2024-162, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
Safe carbon dioxide (CO2) storage is likely to be critical for mitigating some of the most dangerous effects of climate change. We present a simulation framework for modeling CO2 storage beneath the seafloor where CO2 can form a solid. This can aid in permanent CO2 storage for long periods of time. Our models show what a commercial-scale CO2 injection would look like in a marine environment. We discuss what would need to be considered when designing a sub-sea CO2 injection.
Cynthia Whaley, Montana Etten-Bohm, Courtney Schumacher, Ayodeji Akingunola, Vivek Arora, Jason Cole, Michael Lazare, David Plummer, Knut von Salzen, and Barbara Winter
Geosci. Model Dev., 17, 7141–7155, https://doi.org/10.5194/gmd-17-7141-2024, https://doi.org/10.5194/gmd-17-7141-2024, 2024
Short summary
Short summary
This paper describes how lightning was added as a process in the Canadian Earth System Model in order to interactively respond to climate changes. As lightning is an important cause of global wildfires, this new model development allows for more realistic projections of how wildfires may change in the future, responding to a changing climate.
Erik Gustafsson, Bo G. Gustafsson, Martijn Hermans, Christoph Humborg, and Christian Stranne
Geosci. Model Dev., 17, 7157–7179, https://doi.org/10.5194/gmd-17-7157-2024, https://doi.org/10.5194/gmd-17-7157-2024, 2024
Short summary
Short summary
Methane (CH4) cycling in the Baltic Proper is studied through model simulations, enabling a first estimate of key CH4 fluxes. A preliminary budget identifies benthic CH4 release as the dominant source and two main sinks: CH4 oxidation in the water (92 % of sinks) and outgassing to the atmosphere (8 % of sinks). This study addresses CH4 emissions from coastal seas and is a first step toward understanding the relative importance of open-water outgassing compared with local coastal hotspots.
Daniel Ries, Katherine Goode, Kellie McClernon, and Benjamin Hillman
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-133, https://doi.org/10.5194/gmd-2024-133, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
Machine learning has advanced research in the climate science domain, but its models are difficult to understand. In order to understand the impacts and consequences of climate interventions such as stratospheric aerosol injection, complex models are often necessary. We use a case study to illustrate how we can understand the inner workings of a complex model. We present this technique as an exploratory tool that can be used to quickly discover and assess relationships in complex climate data.
Florian Zabel, Matthias Knüttel, and Benjamin Poschlod
EGUsphere, https://doi.org/10.5194/egusphere-2024-2526, https://doi.org/10.5194/egusphere-2024-2526, 2024
Short summary
Short summary
CropSuite is a fuzzy-logic based high resolution open-source crop suitability model considering the impact of climate variability. We apply CropSuite for 48 important staple and cash crops at 1 km spatial resolution for Africa. We find that climate variability significantly impacts on suitable areas, but also affects optimal sowing dates, and multiple cropping potentials. The results provide information that can be used for climate impact assessments, adaptation and land-use planning.
Kerstin Hartung, Bastian Kern, Nils-Arne Dreier, Jörn Geisbüsch, Mahnoosh Haghighatnasab, Patrick Jöckel, Astrid Kerkweg, Wilton Jaciel Loch, Florian Prill, and Daniel Rieger
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-135, https://doi.org/10.5194/gmd-2024-135, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
The Icosahedral Nonhydrostatic (ICON) Model Community Interface (ComIn) library supports connecting third-party modules to the ICON model. Third-party modules can range from simple diagnostic Python scripts to full chemistry models. ComIn offers a low barrier for code extensions to ICON, provides multi-language support (Fortran, C/C++ and Python) and reduces the migration effort in response to new ICON releases. This paper presents the ComIn design principles and a range of use cases.
Tridib Banerjee, Patrick Scholz, Sergey Danilov, Knut Klingbeil, and Dmitry Sidorenko
Geosci. Model Dev., 17, 7051–7065, https://doi.org/10.5194/gmd-17-7051-2024, https://doi.org/10.5194/gmd-17-7051-2024, 2024
Short summary
Short summary
In this paper we propose a new alternative to one of the functionalities of the sea ice model FESOM2. The alternative we propose allows the model to capture and simulate fast changes in quantities like sea surface elevation more accurately. We also demonstrate that the new alternative is faster and more adept at taking advantages of highly parallelized computing infrastructure. We therefore show that this new alternative is a great addition to the sea ice model FESOM2.
Yuwen Fan, Zhao Yang, Min-Hui Lo, Jina Hur, and Eun-Soon Im
Geosci. Model Dev., 17, 6929–6947, https://doi.org/10.5194/gmd-17-6929-2024, https://doi.org/10.5194/gmd-17-6929-2024, 2024
Short summary
Short summary
Irrigated agriculture in the North China Plain (NCP) has a significant impact on the local climate. To better understand this impact, we developed a specialized model specifically for the NCP region. This model allows us to simulate the double-cropping vegetation and the dynamic irrigation practices that are commonly employed in the NCP. This model shows improved performance in capturing the general crop growth, such as crop stages, biomass, crop yield, and vegetation greenness.
Ed Blockley, Emma Fiedler, Jeff Ridley, Luke Roberts, Alex West, Dan Copsey, Daniel Feltham, Tim Graham, David Livings, Clement Rousset, David Schroeder, and Martin Vancoppenolle
Geosci. Model Dev., 17, 6799–6817, https://doi.org/10.5194/gmd-17-6799-2024, https://doi.org/10.5194/gmd-17-6799-2024, 2024
Short summary
Short summary
This paper documents the sea ice model component of the latest Met Office coupled model configuration, which will be used as the physical basis for UK contributions to CMIP7. Documentation of science options used in the configuration are given along with a brief model evaluation. This is the first UK configuration to use NEMO’s new SI3 sea ice model. We provide details on how SI3 was adapted to work with Met Office coupling methodology and documentation of coupling processes in the model.
Jean-François Lemieux, William H. Lipscomb, Anthony Craig, David A. Bailey, Elizabeth C. Hunke, Philippe Blain, Till A. S. Rasmussen, Mats Bentsen, Frédéric Dupont, David Hebert, and Richard Allard
Geosci. Model Dev., 17, 6703–6724, https://doi.org/10.5194/gmd-17-6703-2024, https://doi.org/10.5194/gmd-17-6703-2024, 2024
Short summary
Short summary
We present the latest version of the CICE model. It solves equations that describe the dynamics and the growth and melt of sea ice. To do so, the domain is divided into grid cells and variables are positioned at specific locations in the cells. A new implementation (C-grid) is presented, with the velocity located on cell edges. Compared to the previous B-grid, the C-grid allows for a natural coupling with some oceanic and atmospheric models. It also allows for ice transport in narrow channels.
Rachid El Montassir, Olivier Pannekoucke, and Corentin Lapeyre
Geosci. Model Dev., 17, 6657–6681, https://doi.org/10.5194/gmd-17-6657-2024, https://doi.org/10.5194/gmd-17-6657-2024, 2024
Short summary
Short summary
This study introduces a novel approach that combines physics and artificial intelligence (AI) for improved cloud cover forecasting. This approach outperforms traditional deep learning (DL) methods in producing realistic and physically consistent results while requiring less training data. This architecture provides a promising solution to overcome the limitations of classical AI methods and contributes to open up new possibilities for combining physical knowledge with deep learning models.
Marit Sandstad, Borgar Aamaas, Ane Nordlie Johansen, Marianne Tronstad Lund, Glen Philip Peters, Bjørn Hallvard Samset, Benjamin Mark Sanderson, and Ragnhild Bieltvedt Skeie
Geosci. Model Dev., 17, 6589–6625, https://doi.org/10.5194/gmd-17-6589-2024, https://doi.org/10.5194/gmd-17-6589-2024, 2024
Short summary
Short summary
The CICERO-SCM has existed as a Fortran model since 1999 that calculates the radiative forcing and concentrations from emissions and is an upwelling diffusion energy balance model of the ocean that calculates temperature change. In this paper, we describe an updated version ported to Python and publicly available at https://github.com/ciceroOslo/ciceroscm (https://doi.org/10.5281/zenodo.10548720). This version contains functionality for parallel runs and automatic calibration.
Sébastien Masson, Swen Jullien, Eric Maisonnave, David Gill, Guillaume Samson, Mathieu Le Corre, and Lionel Renault
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-140, https://doi.org/10.5194/gmd-2024-140, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
This article details a new feature we implemented in the most popular regional atmospheric model (WRF). This feature allows data to be exchanged between WRF and any other model (e.g. an ocean model) using the coupling library Ocean-Atmosphere-Sea-Ice-Soil – Model Coupling Toolkit (OASIS3-MCT). This coupling interface is designed to be non-intrusive, flexible and modular. It also offers the possibility of taking into account the nested zooms used in WRF or in the models with which it is coupled.
Zheng Xiang, Yongkang Xue, Weidong Guo, Melannie D. Hartman, Ye Liu, and William J. Parton
Geosci. Model Dev., 17, 6437–6464, https://doi.org/10.5194/gmd-17-6437-2024, https://doi.org/10.5194/gmd-17-6437-2024, 2024
Short summary
Short summary
A process-based plant carbon (C)–nitrogen (N) interface coupling framework has been developed which mainly focuses on plant resistance and N-limitation effects on photosynthesis, plant respiration, and plant phenology. A dynamic C / N ratio is introduced to represent plant resistance and self-adjustment. The framework has been implemented in a coupled biophysical-ecosystem–biogeochemical model, and testing results show a general improvement in simulating plant properties with this framework.
Ulrich Georg Wortmann, Tina Tsan, Mahrukh Niazi, Ruben Navasardyan, Magnus-Roland Marun, Bernardo S. Chede, Jingwen Zhong, and Morgan Wolfe
EGUsphere, https://doi.org/10.5194/egusphere-2024-1864, https://doi.org/10.5194/egusphere-2024-1864, 2024
Short summary
Short summary
The Earth Science Box Modeling Toolkit (ESBMTK) is a Python library designed to separate model description from numerical implementation. This approach results in well-documented, easily readable, and maintainable model code, allowing students and researchers to concentrate on conceptual challenges rather than mathematical intricacies.
Malcolm John Roberts, Kevin A. Reed, Qing Bao, Joseph J. Barsugli, Suzana J. Camargo, Louis-Philippe Caron, Ping Chang, Cheng-Ta Chen, Hannah M. Christensen, Gokhan Danabasoglu, Ivy Frenger, Neven S. Fučkar, Shabeh ul Hasson, Helene T. Hewitt, Huanping Huang, Daehyun Kim, Chihiro Kodama, Michael Lai, Lai-Yung Ruby Leung, Ryo Mizuta, Paulo Nobre, Pablo Ortega, Dominique Paquin, Christopher D. Roberts, Enrico Scoccimarro, Jon Seddon, Anne Marie Treguier, Chia-Ying Tu, Paul A. Ullrich, Pier Luigi Vidale, Michael F. Wehner, Colin M. Zarzycki, Bosong Zhang, Wei Zhang, and Ming Zhao
EGUsphere, https://doi.org/10.5194/egusphere-2024-2582, https://doi.org/10.5194/egusphere-2024-2582, 2024
Short summary
Short summary
HighResMIP2 is a model intercomparison project focussing on high resolution global climate models, that is those with grid spacings of 25 km or less in atmosphere and ocean, using simulations of decades to a century or so in length. We are proposing an update of our simulation protocol to make the models more applicable to key questions for climate variability and hazard in present day and future projections, and to build links with other communities to provide more robust climate information.
Yangke Liu, Qing Bao, Bian He, Xiaofei Wu, Jing Yang, Yimin Liu, Guoxiong Wu, Tao Zhu, Siyuan Zhou, Yao Tang, Ankang Qu, Yalan Fan, Anling Liu, Dandan Chen, Zhaoming Luo, Xing Hu, and Tongwen Wu
Geosci. Model Dev., 17, 6249–6275, https://doi.org/10.5194/gmd-17-6249-2024, https://doi.org/10.5194/gmd-17-6249-2024, 2024
Short summary
Short summary
We give an overview of the Institute of Atmospheric Physics–Chinese Academy of Sciences subseasonal-to-seasonal ensemble forecasting system and Madden–Julian Oscillation forecast evaluation of the system. Compared to other S2S models, the IAP-CAS model has its benefits but also biases, i.e., underdispersive ensemble, overestimated amplitude, and faster propagation speed when forecasting MJO. We provide a reason for these biases and prospects for further improvement of this system in the future.
Katherine Smith, Alice M. Barthel, LeAnn M. Conlon, Luke P. Van Roekel, Anthony Bartoletti, Jean-Christophe Golez, Chengzhu Zhang, Carolyn Branecky Begeman, James J. Benedict, Gautum Bisht, Yan Feng, Walter Hannah, Bryce E. Harrop, Nicole Jeffery, Wuyin Lin, Po-Lun Ma, Mathew E. Maltrud, Mark R. Petersen, Balwinder Singh, Qi Tang, Teklu Tesfa, Jonathan D. Wolfe, Shaocheng Xie, Xue Zheng, Karthik Balaguru, Oluwayemi Garuba, Peter Gleckler, Aixue Hu, Jiwoo Lee, Ben Moore-Maley, and Ana C. Ordonez
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-149, https://doi.org/10.5194/gmd-2024-149, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
Version 2.1 of the U.S. Department of Energy's Energy Exascale Earth System Model (E3SM) adds the Fox-Kemper et al. (2011) mixed layer eddy parameterization, which restratifies the ocean surface layer through an overturning streamfunction. Results include surface layer biases reduction in temperature, salinity, and sea-ice extent in the North Atlantic, a small strengthening of the Atlantic Meridional Overturning Circulation, and improvements in many atmospheric climatological variables.
Bo Dong, Paul Ullrich, Jiwoo Lee, Peter Gleckler, Kristin Chang, and Travis O'Brien
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-142, https://doi.org/10.5194/gmd-2024-142, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
1. A metrics package designed for easy analysis of AR characteristics and statistics is presented. 2. The tool is efficient for diagnosing systematic AR bias in climate models, and useful for evaluating new AR characteristics in model simulations. 3. In climate models, landfalling AR precipitation shows dry biases globally, and AR tracks are farther poleward (equatorward) in the north and south Atlantic (south Pacific and Indian Ocean).
Laurent Brodeau, Pierre Rampal, Einar Ólason, and Véronique Dansereau
Geosci. Model Dev., 17, 6051–6082, https://doi.org/10.5194/gmd-17-6051-2024, https://doi.org/10.5194/gmd-17-6051-2024, 2024
Short summary
Short summary
A new brittle sea ice rheology, BBM, has been implemented into the sea ice component of NEMO. We describe how a new spatial discretization framework was introduced to achieve this. A set of idealized and realistic ocean and sea ice simulations of the Arctic have been performed using BBM and the standard viscous–plastic rheology of NEMO. When compared to satellite data, our simulations show that our implementation of BBM leads to a fairly good representation of sea ice deformations.
Joseph P. Hollowed, Christiane Jablonowski, Hunter Y. Brown, Benjamin R. Hillman, Diana L. Bull, and Joseph L. Hart
Geosci. Model Dev., 17, 5913–5938, https://doi.org/10.5194/gmd-17-5913-2024, https://doi.org/10.5194/gmd-17-5913-2024, 2024
Short summary
Short summary
Large volcanic eruptions deposit material in the upper atmosphere, which is capable of altering temperature and wind patterns of Earth's atmosphere for subsequent years. This research describes a new method of simulating these effects in an idealized, efficient atmospheric model. A volcanic eruption of sulfur dioxide is described with a simplified set of physical rules, which eventually cools the planetary surface. This model has been designed as a test bed for climate attribution studies.
Hong Li, Yi Yang, Jian Sun, Yuan Jiang, Ruhui Gan, and Qian Xie
Geosci. Model Dev., 17, 5883–5896, https://doi.org/10.5194/gmd-17-5883-2024, https://doi.org/10.5194/gmd-17-5883-2024, 2024
Short summary
Short summary
Vertical atmospheric motions play a vital role in convective-scale precipitation forecasts by connecting atmospheric dynamics with cloud development. A three-dimensional variational vertical velocity assimilation scheme is developed within the high-resolution CMA-MESO model, utilizing the adiabatic Richardson equation as the observation operator. A 10 d continuous run and an individual case study demonstrate improved forecasts, confirming the scheme's effectiveness.
Matthias Nützel, Laura Stecher, Patrick Jöckel, Franziska Winterstein, Martin Dameris, Michael Ponater, Phoebe Graf, and Markus Kunze
Geosci. Model Dev., 17, 5821–5849, https://doi.org/10.5194/gmd-17-5821-2024, https://doi.org/10.5194/gmd-17-5821-2024, 2024
Short summary
Short summary
We extended the infrastructure of our modelling system to enable the use of an additional radiation scheme. After calibrating the model setups to the old and the new radiation scheme, we find that the simulation with the new scheme shows considerable improvements, e.g. concerning the cold-point temperature and stratospheric water vapour. Furthermore, perturbations of radiative fluxes associated with greenhouse gas changes, e.g. of methane, tend to be improved when the new scheme is employed.
Yibing Wang, Xianhong Xie, Bowen Zhu, Arken Tursun, Fuxiao Jiang, Yao Liu, Dawei Peng, and Buyun Zheng
Geosci. Model Dev., 17, 5803–5819, https://doi.org/10.5194/gmd-17-5803-2024, https://doi.org/10.5194/gmd-17-5803-2024, 2024
Short summary
Short summary
Urban expansion intensifies challenges like urban heat and urban dry islands. To address this, we developed an urban module, VIC-urban, in the Variable Infiltration Capacity (VIC) model. Tested in Beijing, VIC-urban accurately simulated turbulent heat fluxes, runoff, and land surface temperature. We provide a reliable tool for large-scale simulations considering urban environment and a systematic urban modelling framework within VIC, offering crucial insights for urban planners and designers.
Jeremy Carter, Erick A. Chacón-Montalván, and Amber Leeson
Geosci. Model Dev., 17, 5733–5757, https://doi.org/10.5194/gmd-17-5733-2024, https://doi.org/10.5194/gmd-17-5733-2024, 2024
Short summary
Short summary
Climate models are essential tools in the study of climate change and its wide-ranging impacts on life on Earth. However, the output is often afflicted with some bias. In this paper, a novel model is developed to predict and correct bias in the output of climate models. The model captures uncertainty in the correction and explicitly models underlying spatial correlation between points. These features are of key importance for climate change impact assessments and resulting decision-making.
Anna Martin, Veronika Gayler, Benedikt Steil, Klaus Klingmüller, Patrick Jöckel, Holger Tost, Jos Lelieveld, and Andrea Pozzer
Geosci. Model Dev., 17, 5705–5732, https://doi.org/10.5194/gmd-17-5705-2024, https://doi.org/10.5194/gmd-17-5705-2024, 2024
Short summary
Short summary
The study evaluates the land surface and vegetation model JSBACHv4 as a replacement for the simplified submodel SURFACE in EMAC. JSBACH mitigates earlier problems of soil dryness, which are critical for vegetation modelling. When analysed using different datasets, the coupled model shows strong correlations of key variables, such as land surface temperature, surface albedo and radiation flux. The versatility of the model increases significantly, while the overall performance does not degrade.
Hugo Banderier, Christian Zeman, David Leutwyler, Stefan Rüdisühli, and Christoph Schär
Geosci. Model Dev., 17, 5573–5586, https://doi.org/10.5194/gmd-17-5573-2024, https://doi.org/10.5194/gmd-17-5573-2024, 2024
Short summary
Short summary
We investigate the effects of reduced-precision arithmetic in a state-of-the-art regional climate model by studying the results of 10-year-long simulations. After this time, the results of the reduced precision and the standard implementation are hardly different. This should encourage the use of reduced precision in climate models to exploit the speedup and memory savings it brings. The methodology used in this work can help researchers verify reduced-precision implementations of their model.
David Fuchs, Steven C. Sherwood, Abhnil Prasad, Kirill Trapeznikov, and Jim Gimlett
Geosci. Model Dev., 17, 5459–5475, https://doi.org/10.5194/gmd-17-5459-2024, https://doi.org/10.5194/gmd-17-5459-2024, 2024
Short summary
Short summary
Machine learning (ML) of unresolved processes offers many new possibilities for improving weather and climate models, but integrating ML into the models has been an engineering challenge, and there are performance issues. We present a new software plugin for this integration, TorchClim, that is scalable and flexible and thereby allows a new level of experimentation with the ML approach. We also provide guidance on ML training and demonstrate a skillful hybrid ML atmosphere model.
Eduardo Moreno-Chamarro, Thomas Arsouze, Mario Acosta, Pierre-Antoine Bretonnière, Miguel Castrillo, Eric Ferrer, Amanda Frigola, Daria Kuznetsova, Eneko Martin-Martinez, Pablo Ortega, and Sergi Palomas
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-119, https://doi.org/10.5194/gmd-2024-119, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
We present the high-resolution model version of the EC-Earth global climate model to contribute to HighResMIP. The combined model resolution is about 10-15 km in both the ocean and atmosphere, which makes it one of the finest ever used to complete historical and scenario simulations. This model is compared with two lower-resolution versions, with a 100-km and a 25-km grid. The three models are compared with observations to study the improvements thanks to the increased in the resolution.
Reyk Börner, Jan O. Haerter, and Romain Fiévet
EGUsphere, https://doi.org/10.5194/egusphere-2024-1876, https://doi.org/10.5194/egusphere-2024-1876, 2024
Short summary
Short summary
The daily warming and cooling of sea surface temperature (SST) impacts cloud formation above the ocean and can modulate the clustering of thunderstorms, as relevant for rainfall extremes and hurricanes. However, the daily SST cycle is often poorly represented in idealized modeling studies of cloud organization. To address this, we present a simple, wind-responsive model of upper ocean temperature for use in atmospheric simulations. We evaluate the model against observations and other models.
Cited articles
Adler, R. F., Huffman, G. J., Chang, A., Ferraro, R., Xie, P.-P., Janowiak,
J., Rudolf, B., Schneider, U., Curtis, S., Bolvin, D., Gruber, A., Susskind,
J., Arkin, P., and Nelkin, E.: The Version-2 Global Precipitation Climatology
Project (GPCP) Monthly Precipitation Analysis (1979–Present), J.
Hydrometeorol., 4, 1147–1167,
https://doi.org/10.1175/1525-7541(2003)004<1147:TVGPCP>2.0.CO;2, 2003.
Ahn, M. S., Kim, D., Sperber, K. R., Kang, I. S., Maloney, E., Waliser,
D., and Hendon, H.: MJO simulation in CMIP5 climate models: MJO skill metrics
and process-oriented diagnosis, Clim. Dynam., 49, 4023–4045,
https://doi.org/10.1007/s00382-017-3558-4, 2017.
Bentsen, M., Bethke, I., Debernard, J. B., Iversen, T., Kirkevåg, A.,
Seland, Ø., Drange, H., Roelandt, C., Seierstad, I. A., Hoose, C., and
Kristjánsson, J. E.: The Norwegian Earth System Model, NorESM1-M – Part 1:
Description and basic evaluation of the physical climate, Geosci. Model Dev.,
6, 687–720, https://doi.org/10.5194/gmd-6-687-2013, 2013.
Bottino, M. J. and Nobre, P.: Impacts of cloud cover schemes on the
Atlantic climate in the Brazilian Earth System Model – BESM-OA2.3.,
Clim. Dynam., submitted, 2019.
Buckley, M. W. and Marshall, J.: Observations, inferences, and mechanisms of
the Atlantic Meridional Overturning Circulation: A review, Rev. Geophys., 54,
5–63, https://doi.org/10.1002/2015RG000493, 2016.
Cao, J., Wang, B., Yang, Y.-M., Ma, L., Li, J., Sun, B., Bao, Y., He, J.,
Zhou, X., and Wu, L.: The NUIST Earth System Model (NESM) version 3:
description and preliminary evaluation, Geosci. Model Dev., 11, 2975–2993,
https://doi.org/10.5194/gmd-11-2975-2018, 2018.
Capistrano, V. B., Nobre, P., Tedeschi, R., Silva, J., Bottino, M., da Silva
Jr., M. B., Menezes Neto, O. L., Figueroa, S. N., Bonatti, J. P., Kubota, P.
Y., Reyes Fernandez, J. P., Giarolla, E., Vial, J., and Nobre, C. A.:
Overview of climate change in the BESM-OA2.5 climate model, Geosci. Model
Dev. Discuss., https://doi.org/10.5194/gmd-2018-209, in review, 2018.
Carvalho, L. M. V, Jones, C., and Liebmann, B.: The South Atlantic
convergence zone: Intensity, form, persistence, and relationships with
intraseasonal to interannual activity and extreme rainfall, J. Climate, 17,
88–108, https://doi.org/10.1175/1520-0442(2004)017<0088:TSACZI>2.0.CO;2, 2004.
Chang, P., Ki, L., and Li, H.: A decadal climate variation in the tropical
Atlantic Ocean from thermodynamic air-sea interactions, Nature, 385,
516–518, 1997.
Charlton-Perez, A. J., Baldwin, M. P., Birner, T., Black, R. X., Butler, A.
H., Calvo, N., Davis, N. A., Gerber, E. P., Gillett, N., Hardiman, S., Kim,
J., Krüger, K., Lee, Y. Y., Manzini, E., McDaniel, B. A., Polvani, L.,
Reichler, T., Shaw, T. A., Sigmond, M., Son, S. W., Toohey, M., Wilcox, L.,
Yoden, S., Christiansen, B., Lott, F., Shindell, D., Yukimoto, S., and
Watanabe, S.: On the lack of stratospheric dynamical variability in low-top
versions of the CMIP5 models, J. Geophys. Res.-Atmos., 118, 2494–2505,
https://doi.org/10.1002/jgrd.50125, 2013.
Chaves, R. R. and Nobre, P.: Interactions between sea surface temperature
over the South Atlantic Ocean and the South Atlantic Convergence Zone,
Geophys. Res. Lett., 31, 1–4, https://doi.org/10.1029/2003GL018647, 2004.
Cheng, W., Chiang, J. C. H., and Zhang, D.: Atlantic meridional overturning
circulation (AMOC) in CMIP5 Models: RCP and historical simulations, J.
Climate, 26, 7187–7197, https://doi.org/10.1175/JCLI-D-12-00496.1, 2013.
Chiang, J. C. H. and Vimont, D. J.: Analogous Pacific and Atlantic
Meridional Modes of Tropical Atmosphere – Ocean Variability, J. Climate, 17,
4143–4158, https://doi.org/10.1175/JCLI4953.1, 2004.
Chou, M.-D. and Suarez, M. J.: A solar radiation parame- terization
(CLIRAD-SW) for atmospheric studies, NASA Tech. Memo NASA/TM-1999-104606, 40
pp., 1999.
Chou, S. C., Lyra, A., Mourão, C., Dereczynski, C., Pilotto, I., Gomes,
J., Bustamante, J., Tavares, P., Silva, A., Rodrigues, D., Campos, D.,
Chagas, D., Sueiro, G., Siqueira, G., Nobre, P., and Marengo, J.: Evaluation
of the Eta Simulations Nested in Three Global Climate Models, Am. J. Clim.
Chang., 3, 438–454, https://doi.org/10.4236/ajcc.2014.35039, 2014.
Compo, G. P., Whitaker, J. S., Sardeshmukh, P. D., Matsui, N., Allan, R. J.,
Yin, X., Gleason, B. E., Vose, R. S., Rutledge, G., Bessemoulin, P.,
Bronnimann, S., Brunet, M., Crouthamel, R. I., Grant, A. N., Groisman, P. Y.,
Jones, P. D., Kruk, M. C., Kruger, A. C., Marshall, G. J., Maugeri, M., Mok,
H. Y., Nordli, O., Ross, T. F., Trigo, R. M., Wang, X. L., Woodruff, S. D.,
and Worley, S. J.: The Twentieth Century Reanalysis Project, Q. J. Roy.
Meteor. Soc., 137, 1–28, https://doi.org/10.1002/qj.776, 2011.
Delworth, T. L., Zeng, F., Vecchi, G. A., Yang, X., Zhang, L., and Zhang, R.:
The North Atlantic Oscillation as a driver of rapid climate change in the
Northern Hemisphere, Nat. Geosci., 9, 509–512, https://doi.org/10.1038/ngeo2738, 2016.
de Oliveira Vieira, S., Satyamurty, P., and Andreoli, R. V.: On the South
Atlantic Convergence Zone affecting southern Amazonia in austral summer,
Atmos. Sci. Lett., 14, 1–6, https://doi.org/10.1002/asl2.401, 2013.
Dijkstra, H. A.: The ENSO phenomenon: theory and mechanisms, Adv. Geosci., 6,
3–15, https://doi.org/10.5194/adgeo-6-3-2006, 2006.
Ferrier, B. S., Jin, Y., Lin, Y., Black, T., Rogers, E., and DiMego, G.:
Implementation of a 527 new grid-scale cloud and precipitation scheme in the
NCEP Eta model, American Meteor Society, 19th Conf. on weather Analysis and Forecasting/15th Conf. on Numerical Weather Prediction, 280–283,
2002.
Figueroa, S. N., Bonatti, J. P., Kubota, P. Y., Grell, G. A., Morrison, H.,
Barros, S. R. M., Fernandez, J. P. R., Ramirez, E., Capistrano, V. B., Alvim,
D. S., Enoré, D. P., Diniz, F. L. R., Barbosa, H. M. J., Mendes, C. L.,
and Panetta, J.: The Brazilian Global Atmospheric Model (BAM): Performance
for Tropical Rainfall Forecasting and Sensitivity to Convective Scheme and
Horizontal Resolution, Weather Forecast., 31, 1547–1572,
https://doi.org/10.1175/WAF-D-16-0062.1, 2016.
Flato, G. M.: Earth system models: An overview, Wires
Clim. Change, 2, 783–800, https://doi.org/10.1002/wcc.148, 2011.
Flato, G., Marotzke, J., Abiodun, B., Braconnot, P., Chou, S. C., Collins, W.,
Cox, P., Driouech, F., Emori, S., Eyring, V., Forest, C., Gleckler, P.,
Guilyardi, E., Jakob, C., Kattsov, V., Reason, C., and Rummukainen, M.:
Evaluation of Climate Models. In: Climate Change 2013: The Physical Science
Basis. Contribution of Working Group I to the Fifth Assessment Report of the
Intergovernmental Panel on Climate Change, edited by: Stocker, T. F., Qin,
D., Plattner, G.-K., Tignor, M., Allen, S. K., Boschung, J., Nauels, A., Xia,
Y., Bex, V., and Midgley, P. M., Cambridge University Press, Cambridge,
United Kingdom and New York, NY, USA, 2013.
Gent, P. R., Danabasoglu, G., Donner, L. J., Holland, M. M., Hunke, E. C.,
Jayne, S. R., Lawrence, D. M., Neale, R. B., Rasch, P. J., Vertenstein, M.,
Worley, P. H., Yang, Z.-L., and Zhang, M.: The Community Climate System Model
Version 4, J. Climate, 24, 4973–4991, https://doi.org/10.1175/2011JCLI4083.1, 2011.
Giarolla, E., Siqueira, L. S. P., Bottino, M. J., Malagutti, M., Capistrano,
V. B., and Nobre, P.: Equatorial Atlantic Ocean dynamics in a coupled ocean
atmosphere model simulation, Ocean Dynam., 65, 831–843,
https://doi.org/10.1007/s10236-015-0836-8, 2015.
Gong, D. and Wang, S.: Definition of Antarctic Oscillation Index, Geophys.
Res. Lett., 26, 459–462, https://doi.org/10.1029/1999GL900003, 1999.
Grell, G. and Dévényi, D. A.: A generalized approach to
parameterizing convection combining ensemble and data assimilation
techniques, Geophys. Res. Lett., 29, 10–13, https://doi.org/10.1029/2002GL015311, 2002.
Griffies, S. M.: Elements of MOM4p1. NOAA/Geophysical Fluid Dynamics
Laboratory Ocean Group Tech. Rep. 6, 444 pp., 2009.
Grimm, A. M.: The El Niño impact on the summer monsoon in Brazil:
Regional processes versus remote influences, J. Climate, 16, 263–280,
https://doi.org/10.1175/1520-0442(2003)016<0263:TENIOT>2.0.CO;2, 2003.
Harshvardhan, Davies, R., Randall, D. A., and Corsetti, T. G.: A fast
radiation parameterization for atmospheric circulation models, J. Geophys.
Res., 92, 1009–1016, https://doi.org/10.1029/JD092iD01p01009, 1987.
Hu, Z. Z. and Huang, B.: Interferential impact of ENSO and PDO on dry and
wet conditions in the U.S. great plains, J. Climate, 22, 6047–6065,
https://doi.org/10.1175/2009JCLI2798.1, 2009.
Huang, B., Banzon, V. F., Freeman, E., Lawrimore, J., Liu, W., Peterson, T.
C., Smith, T. M., Thorne, P. W., Woodruff, S. D., and Zhang, H. M.: Extended
reconstructed sea surface temperature version 4 (ERSST.v4). Part I: Upgrades
and intercomparisons, J. Climate, 28, 911–930,
https://doi.org/10.1175/JCLI-D-14-00006.1, 2015.
Huffman, G. J., Adler, R. F., Bolvin, D. T., and Gu, G.: Improving the global
precipitation record: GPCP Version 2.1, Geophys. Res. Lett., 36, L17808,
https://doi.org/10.1029/2009GL040000, 2009.
Hurrell, J. W. and Deser, C.: North Atlantic climate variability: The role
of the North Atlantic Oscillation, J. Marine Syst., 78, 28–41,
https://doi.org/10.1016/j.jmarsys.2008.11.026, 2009.
Hurrell, J. W., Kushnir, Y., Otterson, G., and Visbeck, M.: An Overview of
the North Atlantic Oscillation, The North Atlantic Oscillation: Climatic
Significance and Environmental Impact, Geophysical Monograph Series, 134,
263, https://doi.org/10.1029/GM134, 2003.
Hwang, Y.-T. and Frierson, D. M. W.: Link between the double-Intertropical
Convergence Zone problem and cloud biases over the Southern Ocean, P. Natl.
Acad. Sci. USA, 110, 4935–4940, https://doi.org/10.1073/pnas.1213302110, 2013.
Ji, D., Wang, L., Feng, J., Wu, Q., Cheng, H., Zhang, Q., Yang, J., Dong, W.,
Dai, Y., Gong, D., Zhang, R.-H., Wang, X., Liu, J., Moore, J. C., Chen, D.,
and Zhou, M.: Description and basic evaluation of Beijing Normal University
Earth System Model (BNU-ESM) version 1, Geosci. Model Dev., 7, 2039–2064,
https://doi.org/10.5194/gmd-7-2039-2014, 2014.
Jiménez, P. A., Dudhia, J., González-Rouco, J. F., Navarro, J.,
Montávez, J. P., and García-Bustamante, E.: A Revised Scheme for the
WRF Surface Layer Formulation, Mon. Weather Rev., 140, 898–918,
https://doi.org/10.1175/MWR-D-11-00056.1, 2012.
Jones, C. and Carvalho, L. M. V: Active and break phases in the South
American monsoon system, J. Climate, 15, 905–914,
https://doi.org/10.1175/1520-0442(2002)015<0905:AABPIT>2.0.CO;2, 2002.
Karoly, D. J.: Southern Hemisphere Circulation Features Associated with
El-Nino-Southern Oscillation Events, J. Climate, 2, 1239–1252,
https://doi.org/10.1175/1520-0442(1989)002<1239:SHCFAW>2.0.CO;2, 1989.
Kidson, J. W.: Interannual Variations in the Southern Hemisphere
Circulation, J. Climate, 1, 939–953,
https://doi.org/10.1175/1520-0442(1988)001<1177:IVITSH>2.0.CO;2, 1988.
Kim, D., Sperber, K., Stern, W., Waliser, D., Kang, I. S., Maloney, E.,
Wang, W., Weickmann, K., Benedict, J., Khairoutdinov, M., Lee, M. I., Neale,
R., Suarez, M., Thayer-Calder, K., and Zhang, G.: Application of MJO
simulation diagnostics to climate models, J. Climate, 22, 6413–6436,
https://doi.org/10.1175/2009JCLI3063.1, 2009.
Krishnamurthy, L. and Krishnamurthy, V.: Indian monsoon' s relation with the
decadal part of PDO in observations and NCAR CCSM4, Int. J. Climatol., 37, 1824–1833,
https://doi.org/10.1002/joc.4815, 2016.
Large, W. G. and Yeager, S. G.: The global climatology of an interannually
varying air – Sea flux data set, Clim. Dynam., 33, 341–364,
https://doi.org/10.1007/s00382-008-0441-3, 2009.
Leathers, D. J., Yarnal, B., Palecki, M. A., Leathers, D. J., Yarnal, B., and
Palecki, M. A.: The Pacific/North American Teleconnection Pattern and United
States Climate. Part I: Regional Temperature and Precipitation Associations,
J. Climate, 4, 517–528, https://doi.org/10.1175/1520-0442(1991)004<0517:TPATPA>2.0.CO;2,
1991.
Levitus, S.: Climatological Atlas of the World Ocean, NOAA Prof. Paper 13,
173 pp. and 17 microfich, 1982.
Li, G. and Xie, S. P.: Tropical biases in CMIP5 multimodel ensemble: The
excessive equatorial pacific cold tongue and double ITCZ problems, J.
Climate, 27, 1765–1780, https://doi.org/10.1175/JCLI-D-13-00337.1, 2014.
Liebmann, B., Hendon, H. H., and Glick, J. D.: The Relationship Between
Tropical Cyclones of the Western Pacific and Indian Oceans and the
Madden-Julian Oscillation, J. Meteorol. Soc. Jpn., 72, 401–412,
https://doi.org/10.2151/jmsj1965.72.3_401, 1994.
Lin, H., Brunet, G., and Derome, J.: An observed connection between the North
Atlantic oscillation and the Madden-Julian oscillation, J. Climate, 22,
364–380, https://doi.org/10.1175/2008JCLI2515.1, 2009.
Lumpkin, R. and Speer, K.: Global Ocean Meridional Overturning, J. Phys.
Oceanogr., 37, 2550–2562, https://doi.org/10.1175/JPO3130.1, 2007.
Lutz, K., Jacobeit, J., and Rathmann, J.: Atlantic warm and cold water events
and impact on African west coast precipitation, Int. J. Climatol., 35,
128–141, https://doi.org/10.1002/joc.3969, 2015.
Madden, R. A. and Julian, P. R.: Detection of a 40–50 Day Oscillation in
the Zonal Wind in the Tropical Pacific, J. Atmos. Sci., 28, 702–708,
https://doi.org/10.1175/1520-0469(1971)028<0702:DOADOI>2.0.CO;2, 1971.
Madden, R. A. and Julian, P. R.: Description of Global-Scale Circulation
Cells in the Tropics with a 40–50 Day Period, J. Atmos. Sci., 29,
1109–1123, https://doi.org/10.1175/1520-0469(1972)029<1109:DOGSCC>2.0.CO;2, 1972.
Mantua, N. J., Hare, S. R., Zhang, Y., Wallace, J. M., and Francis, R. C.: A
Pacific Interdecadal Climate Oscillation with Impacts on Salmon Production,
B. Am. Meteorol. Soc., 78, 1069–1079,
https://doi.org/10.1175/1520-0477(1997)078<1069:APICOW>2.0.CO;2, 1997.
Marengo, J. A., Calvalcanti, I. F. A., Satyamurty, P., Trosnikov, I., Nobre,
C. A., Bonatti, J. P., Camargo, H., Sampaio, G., Sanches, M. B., Manzi, A.
O., Castro, C. A. C., D'Almeida, C., Pezzi, L. P., and Candido, L.:
Assessment of regional seasonal rainfall predictability using the CPTEC/COLA
atmospheric GCM, Clim. Dynam., 21, 459–475, https://doi.org/10.1007/s00382-003-0346-0,
2003.
McCarthy, G. D., Smeed, D. A., Johns, W. E., Frajka-Williams, E., Moat, B.
I., Rayner, D., Baringer, M. O., Meinen, C. S., Collins, J., and Bryden, H.
L.: Measuring the Atlantic Meridional Overturning Circulation at
26∘ N, Prog. Oceanogr., 130, 91–111,
https://doi.org/10.1016/j.pocean.2014.10.006, 2015.
McPhaden, M. J., Zebiak, S. E., and Glantz, M. H.: ENSO as an integrating
concept in earth science, Science, 314, 1740–1745,
https://doi.org/10.1126/science.1132588, 2006.
Meehl, G. A., Moss, R., Taylor, K. E., Eyring, V., Stouffer, R. J., Bony,
S., and Stevens, B.: Climate model intercomparisons: Preparing for the next
phase, Eos, 95, 77–78, https://doi.org/10.1002/2014EO090001, 2014.
Mellor, G. L. and Yamada, T.: Development of a turbulence closure model for
geophysical fluid problems, Rev. Geophys., 20, 851–875,
https://doi.org/10.1029/RG020i004p00851, 1982.
Menary, M. B., Kuhlbrodt, T., Ridley, J., Andrews, M. B., Dimdore-Miles, O. B., Deshayes, J., Eade, R., Gray, L., Ineson, S., Mignot, J., Roberts, C. D., Robson, J., Wood, R. A., and Xavier, P.: Preindustrial
control simulations with HadGEM3-GC3.1 for CMIP6, J. Adv. Model. Earth Sy.,
10, 3049–3075, https://doi.org/10.1029/2018MS001495, 2018.
Mo, K. C. and Peagle, J. N.: The Pacific-South American modes and their
downstream effects, Int. J. Climatol., 21, 1211–1229, https://doi.org/10.1002/joc.685,
2001.
Morice, C. P., Kennedy, J. J., Rayner, N. A., and Jones, P. D.: Quantifying
uncertainties in global and regional temperature change using an ensemble of
observational estimates: The HadCRUT4 data set, J. Geophys. Res.-Atmos., 117,
1–22, https://doi.org/10.1029/2011JD017187, 2012.
Newman, M., Alexander, M. A., Ault, T. R., Cobb, K. M., Deser, C., Di
Lorenzo, E., Mantua, N. J., Miller, A. J., Minobe, S., Nakamura, H.,
Schneider, N., Vimont, D. J., Phillips, A. S., Scott, J. D., and Smith, C.
A.: The Pacific decadal oscillation, revisited, J. Climate, 29, 4399–4427,
https://doi.org/10.1175/JCLI-D-15-0508.1, 2016.
Ning, L. and Bradley, R. S.: NAO and PNA influences on winter temperature
and precipitation over the eastern United States in CMIP5 GCMs, Clim. Dynam.,
46, 1257–1276, https://doi.org/10.1007/s00382-015-2643-9, 2016.
Nobre, P. and Shukla, J.: Variation of Sea surface Temperature, Wind Stress,
and Rainfall over the Tropical Atlantic and South America, J. Climate, 9,
2464–2479, https://doi.org/10.1175/1520-0442(1996)009<2464:VOSSTW>2.0.CO;2, 1996.
Nobre, P., Marengo, J. A., Cavalcanti, I. F. A., Obregon, G., Barros, V.,
Camilloni, I., Campos, N., and Ferreira, A. G.: Seasonal-to-decadal
predictability and prediction of South American climate, J. Climate, 19,
5988–6004, https://doi.org/10.1175/JCLI3946.1, 2006.
Nobre, P., De Almeida, R. A., Malagutti, M., and Giarolla, E.: Coupled
ocean-atmosphere variations over the South Atlantic Ocean, J. Climate, 25,
6349–6358, https://doi.org/10.1175/JCLI-D-11-00444.1, 2012.
Nobre, P., Siqueira, L. S. P., De Almeida, R. A. F., Malagutti, M.,
Giarolla, E., Castelã O, G. P., Bottino, M. J., Kubota, P., Figueroa, S.
N., Costa, M. C., Baptista, M., Irber, L., and Marcondes, G. G.: Climate
simulation and change in the brazilian climate model, J. Climate, 26,
6716–6732, https://doi.org/10.1175/JCLI-D-12-00580.1, 2013.
Nogués-Paegle, J. and Mo, K. C.: Alternating Wet and Dry Conditions over
South America during Summer, Mon. Weather Rev., 125, 279–291,
https://doi.org/10.1175/1520-0493(1997)125<0279:AWADCO>2.0.CO;2, 1997.
Obukhov, A. M.: Turbulence in an atmosphere with a non-uniform temperature,
Bound.-Lay. Meteorol., 2, 7–29, https://doi.org/10.1007/BF00718085, 1971.
Richter, I.: Climate model biases in the eastern tropical oceans: Causes,
impacts and ways forward, Wires Clim. Change, 6, 345–358,
https://doi.org/10.1002/wcc.338, 2015.
Richter, I., Xie, S. P., Behera, S. K., Doi, T., and Masumoto, Y.: Equatorial
Atlantic variability and its relation to mean state biases in CMIP5, Clim.
Dynam., 42, 171–188, https://doi.org/10.1007/s00382-012-1624-5, 2014.
Robertson, A. and Mechoso, C.: Interannual and interdecadal variability of
the South Atlantic Convergence Zone, Mon. Weather Rev., 128, 2947–2957,
https://doi.org/10.1175/1520-0493(2000)128<2947:IAIVOT>2.0.CO;2, 2000.
Rogers, J. C. and van Loon, H.: Spatial Variability of Sea Level Pressure
and 500 mb Height Anomalies over the Southern Hemisphere, Mon. Weather Rev.,
110, 1375–1392, https://doi.org/10.1175/1520-0493(1982)110<1375:SVOSLP>2.0.CO;2, 1982.
Rossow, W. B. and Schiffer, R. a: Advances in Understanding Clouds from
ISCCP, B. Am. Meteorol. Soc., 80, 2261–2287,
https://doi.org/10.1175/1520-0477(1999)080<2261:AIUCFI>2.0.CO;2, 1999.
Straus, D. M. and Shukla, J.: Does ENSO force the PNA?, J. Climate, 15,
2340–2358, https://doi.org/10.1175/1520-0442(2002)015<2340:DEFTP>2.0.CO;2, 2002.
Swapna, P., Krishnan, R., Sandeep, N., Prajeesh, A. G., Ayantika, D. C.,
Manmeet, S., and Vellore, R.: Long-Term Climate Simulations Using the IITM
Earth System Model (IITM-ESMv2) With Focus on the South Asian Monsoon, J.
Adv. Model. Earth Sy., 10, 1127–1149, https://doi.org/10.1029/2017MS001262, 2018.
Takayabu, Y. N., Iguchl, T., Kachi, M., Shibata, A., and Kanzawa, H.: Abrupt
termination of the 1997-98 El Nino in response to a Madden-Julian
oscillation, Nature, 402, 279–282, https://doi.org/10.1038/46254, 1999.
Tarasova, T. A. and Fomin, B. A.: Solar Radiation Absorption due to Water
Vapor: Advanced Broadband Parameterizations, J. Appl. Meteorol., 39,
1947–1951, https://doi.org/10.1175/1520-0450(2000)039<1947:SRADTW>2.0.CO;2, 2000.
Tian, B.: Spread of model climate sensitivity linked to double-Intertropical
Convergence Zone bias, Geophys. Res. Lett., 42, 4133–4141,
https://doi.org/10.1002/2015GL064119, 2015.
Tian, B., Fetzer, E. J., Kahn, B. H., Teixeira, J., Manning, E., and Hearty,
T.: Evaluating CMIP5 models using AIRS tropospheric air temperature and
specific humidity climatology, J. Geophys. Res.-Atmos., 118, 114–134,
https://doi.org/10.1029/2012JD018607, 2013.
Tiedtke, M.: The sensitivity of the time-mean large-scale flow to cumulus
convection in the ECMWF model, Proc. Work-shop on Convection in Large-Scale
Models, Reading, United Kingdom, ECMWF, 297–316, 1983.
von Storch, H.: Climate models and modeling: an editorial essay,
Wires Clim. Change, 1, 305–310, https://doi.org/10.1002/wcc.12, 2010.
Waliser, D., Sperber, K., Hendon, H., Kim, D., Maloney, E., Wheeler, M.,
Weickmann, K., Zhang, C., Donner, L., Gottschalck, J., Higgins, W., Kang, I.
S., Legler, D., Moncrieff, M., Schubert, S., Stern, W., Vitart, F., Wang, B.,
Wang, W., and Woolnough, S.: MJO simulation diagnostics, J. Climate, 22,
3006–3030, https://doi.org/10.1175/2008JCLI2731.1, 2009.
Wallace, J. M. and Gutzler, D. S.: Teleconnections in the Geopotential
Height Field during the Northern Hemisphere Winter, Mon. Weather Rev., 109,
784–812, https://doi.org/10.1175/1520-0493(1981)109<0784:TITGHF>2.0.CO;2, 1981.
Wang, C., Zhang, L. and Lee, S.: A global perspective on CMIP5 climate model
biases, Nat. Clim. Change, 4, 201–205, https://doi.org/10.1038/NCLIMATE2118, 2014.
Wanner, H., Brönnimann, S., Casty, C., Luterbacher, J., Schmutz, C., and
David, B.: North Atlantic Oscillation – Concepts and Studies, Surv.
Geophys., 22, 321–382, https://doi.org/10.1023/A:1014217317898, 2001.
Weaver, A. J., Sedláček, J., Eby, M., Alexander, K., Crespin, E.,
Fichefet, T., Philippon-Berthier, G., Joos, F., Kawamiy, M., Matsumoto, K.,
Steinacher, M., Tachiiri, K., Tokos, K., Yoshimori, M., and Zickfeld, K.:
Stability of the Atlantic meridional overturning circulation: A model
intercomparison, Geophys. Res. Lett., 39, 1–7, https://doi.org/10.1029/2012GL053763,
2012.
Webster, S., Brown, A. R., Cameron, D. R., and Jones, P. C.: Improvements to
the representation of orography in the Met Office Unified Model, Q. J. Roy.
Meteor. Soc., 129, 1989–2010, https://doi.org/10.1256/qj.02.133, 2003.
Winton, M.: A reformulated three-layer sea ice model, J. Atmos. Ocean.
Tech., 17, 525–531, https://doi.org/10.1175/1520-0426(2000)017<0525:ARTLSI>2.0.CO;2,
2000.
Wu, X. and Mao, J.: Interdecadal variability of early summer monsoon
rainfall over South China in association with the Pacific Decadal
Oscillation, Int. J. Climatol., https://doi.org/10.1002/joc.4734, 2016.
Wu, Z., Li, J., Jiang, Z., He, J., and Zhu, X.: Possible effects of the North
Atlantic Oscillation on the strengthening relationship between the East Asian
Summer monsoon and ENSO, Int. J. Climatol., 32, 794–800,
https://doi.org/10.1002/joc.2309, 2012.
Xie, P. and Arkin, P. A.: Global precipitation: A 17-year monthly
analysis based on gauge observations, satellite estimates, and numerical
model outputs, B. Am. Meteorol. Soc., 78, 2539–2558, 1997.
Xie, S.-P.: A Dynamic Ocean – Atmosphere Model of the Tropical Atlantic
Decadal Variability, J. Climate, 12, 64–71, 1999.
Xie, S.-P. and Philander, S. G. H.: A coupled ocean-atmosphere model of
relevance to the ITCZ in the eastern Pacific, Tellus A, 46, 340–350,
https://doi.org/10.1034/j.1600-0870.1994.t01-1-00001.x, 1994.
Xue, Y., Sellers, P., Kinter, J., and Shukla, J.: A Simplified Biosphere
Model for Global Climate Studies, J. Climate, 4, 345–364,
https://doi.org/10.1175/1520-0442(1991)004<0345:ASBMFG>2.0.CO;2, 1991.
Yu, B. and Zwiers, F. W.: The impact of combined ENSO and PDO on the PNA
climate: A 1,000-year climate modeling study, Clim. Dynam., 29, 837–851,
https://doi.org/10.1007/s00382-007-0267-4, 2007.
Yu, R. and Zhou, T.: Impacts of winter-NAO on March cooling trends over
subtropical Eurasia continent in the recent half century, Geophys. Res.
Lett., 31, 3–6, https://doi.org/10.1029/2004GL019814, 2004.
Yuan, X. and Yonekura, E.: Decadal variability in the Southern Hemisphere,
J. Geophys. Res., 116, 1–12, https://doi.org/10.1029/2011JD015673, 2011.
Zebiak, S. E.: Air–Sea Interaction in the Equatorial Atlantic Region, J.
Climate, 6, 1567–1586, https://doi.org/10.1175/1520-0442(1993)006<1567:AIITEA>2.0.CO;2,
1993.
Zhang, C.: Madden-Julian Oscillation, Rev. Geophys., 43, 1–36,
https://doi.org/10.1029/2004RG000158, 2005.
Zhang, L. and Wang, C.: Multidecadal North Atlantic sea surface temperature
and Atlantic meridional overturning circulation variability in CMIP5
historical simulations, J. Geophys. Res.-Oceans, 118, 5772–5791,
https://doi.org/10.1002/jgrc.20390, 2013.
Zhang, L., Ma, H., and Wu, L.: Dynamics and mechanisms of decadal variability
of the Pacific-South America mode over the 20th century, Clim. Dynam., 46,
3657–3667, https://doi.org/10.1007/s00382-015-2794-8, 2016.
Zheng, F., Li, J., Clark, R. T., and Nnamchi, H. C.: Simulation and
projection of the Southern Hemisphere annular mode in CMIP5 models, J.
Climate, 26, 9860–9879, https://doi.org/10.1175/JCLI-D-13-00204.1, 2013.
Download
The requested paper has a corresponding corrigendum published. Please read the corrigendum first before downloading the article.
- Article
(16628 KB) - Full-text XML
Short summary
This study evaluates the Brazilian Earth System Model with coupled ocean–atmosphere version 2.5 (BESM-OA2.5) and the effectiveness of reproducing the main characteristics of the atmospheric and oceanic variability in a real-life-based scenario of greenhouse gas increase (the CMIP5 historical protocol). The evaluation specifically focuses on how the model simulates the mean climate state, as well as the most important large-scale climate patterns.
This study evaluates the Brazilian Earth System Model with coupled ocean–atmosphere version 2.5...
