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.
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
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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
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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.
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
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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
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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
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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
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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
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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
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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
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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
Parallel SnowModel (v1.0): a parallel implementation of a distributed snow-evolution modeling system (SnowModel)
LB-SCAM: a learning-based method for efficient large-scale sensitivity analysis and tuning of the Single Column Atmosphere Model (SCAM)
Quantifying the impact of SST feedback frequency on Madden–Julian oscillation simulations
Systematic and objective evaluation of Earth system models: PCMDI Metrics Package (PMP) version 3
A revised model of global silicate weathering considering the influence of vegetation cover on erosion rate
A radiative–convective model computing precipitation with the maximum entropy production hypothesis
Leveraging regional mesh refinement to simulate future climate projections for California using the Simplified Convection-Permitting E3SM Atmosphere Model Version 0
Machine learning parameterization of the multi-scale Kain–Fritsch (MSKF) convection scheme and stable simulation coupled in the Weather Research and Forecasting (WRF) model using WRF–ML v1.0
Impacts of spatial heterogeneity of anthropogenic aerosol emissions in a regionally refined global aerosol–climate model
cfr (v2024.1.26): a Python package for climate field reconstruction
NEWTS1.0: Numerical model of coastal Erosion by Waves and Transgressive Scarps
Evaluation of isoprene emissions from the coupled model SURFEX–MEGANv2.1
A comprehensive Earth system model (AWI-ESM2.1) with interactive icebergs: effects on surface and deep-ocean characteristics
The regional climate–chemistry–ecology coupling model RegCM-Chem (v4.6)–YIBs (v1.0): development and application
An overview of cloud–radiation denial experiments for the Energy Exascale Earth System Model version 1
The computational and energy cost of simulation and storage for climate science: lessons from CMIP6
Subgrid-scale variability of cloud ice in the ICON-AES 1.3.00
INFERNO-peat v1.0.0: a representation of northern high-latitude peat fires in the JULES-INFERNO global fire model
The 4DEnVar-based weakly coupled land data assimilation system for E3SM version 2
Continental-scale bias-corrected climate and hydrological projections for Australia
G6-1.5K-SAI: a new Geoengineering Model Intercomparison Project (GeoMIP) experiment integrating recent advances in solar radiation modification studies
Modeling the effects of tropospheric ozone on the growth and yield of global staple crops with DSSAT v4.8.0
A one-dimensional urban flow model with an eddy-diffusivity mass-flux (EDMF) scheme and refined turbulent transport (MLUCM v3.0)
DCMIP2016: the tropical cyclone test case
Interactions between atmospheric composition and climate change – progress in understanding and future opportunities from AerChemMIP, PDRMIP, and RFMIP
CD-type discretization for sea ice dynamics in FESOM version 2
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Accurate assessment of land–atmosphere coupling in climate models requires high-frequency data output
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New model ensemble reveals how forcing uncertainty and model structure alter climate simulated across CMIP generations of the Community Earth System Model
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Ross Mower, Ethan D. Gutmann, Glen E. Liston, Jessica Lundquist, and Soren Rasmussen
Geosci. Model Dev., 17, 4135–4154, https://doi.org/10.5194/gmd-17-4135-2024, https://doi.org/10.5194/gmd-17-4135-2024, 2024
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Higher-resolution model simulations are better at capturing winter snowpack changes across space and time. However, increasing resolution also increases the computational requirements. This work provides an overview of changes made to a distributed snow-evolution modeling system (SnowModel) to allow it to leverage high-performance computing resources. Continental simulations that were previously estimated to take 120 d can now be performed in 5 h.
Jiaxu Guo, Juepeng Zheng, Yidan Xu, Haohuan Fu, Wei Xue, Lanning Wang, Lin Gan, Ping Gao, Wubing Wan, Xianwei Wu, Zhitao Zhang, Liang Hu, Gaochao Xu, and Xilong Che
Geosci. Model Dev., 17, 3975–3992, https://doi.org/10.5194/gmd-17-3975-2024, https://doi.org/10.5194/gmd-17-3975-2024, 2024
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To enhance the efficiency of experiments using SCAM, we train a learning-based surrogate model to facilitate large-scale sensitivity analysis and tuning of combinations of multiple parameters. Employing a hybrid method, we investigate the joint sensitivity of multi-parameter combinations across typical cases, identifying the most sensitive three-parameter combination out of 11. Subsequently, we conduct a tuning process aimed at reducing output errors in these cases.
Yung-Yao Lan, Huang-Hsiung Hsu, and Wan-Ling Tseng
Geosci. Model Dev., 17, 3897–3918, https://doi.org/10.5194/gmd-17-3897-2024, https://doi.org/10.5194/gmd-17-3897-2024, 2024
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This study uses the CAM5–SIT coupled model to investigate the effects of SST feedback frequency on the MJO simulations with intervals at 30 min, 1, 3, 6, 12, 18, 24, and 30 d. The simulations become increasingly unrealistic as the frequency of the SST feedback decreases. Our results suggest that more spontaneous air--sea interaction (e.g., ocean response within 3 d in this study) with high vertical resolution in the ocean model is key to the realistic simulation of the MJO.
Jiwoo Lee, Peter J. Gleckler, Min-Seop Ahn, Ana Ordonez, Paul A. Ullrich, Kenneth R. Sperber, Karl E. Taylor, Yann Y. Planton, Eric Guilyardi, Paul Durack, Celine Bonfils, Mark D. Zelinka, Li-Wei Chao, Bo Dong, Charles Doutriaux, Chengzhu Zhang, Tom Vo, Jason Boutte, Michael F. Wehner, Angeline G. Pendergrass, Daehyun Kim, Zeyu Xue, Andrew T. Wittenberg, and John Krasting
Geosci. Model Dev., 17, 3919–3948, https://doi.org/10.5194/gmd-17-3919-2024, https://doi.org/10.5194/gmd-17-3919-2024, 2024
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We introduce an open-source software, the PCMDI Metrics Package (PMP), developed for a comprehensive comparison of Earth system models (ESMs) with real-world observations. Using diverse metrics evaluating climatology, variability, and extremes simulated in thousands of simulations from the Coupled Model Intercomparison Project (CMIP), PMP aids in benchmarking model improvements across generations. PMP also enables efficient tracking of performance evolutions during ESM developments.
Haoyue Zuo, Yonggang Liu, Gaojun Li, Zhifang Xu, Liang Zhao, Zhengtang Guo, and Yongyun Hu
Geosci. Model Dev., 17, 3949–3974, https://doi.org/10.5194/gmd-17-3949-2024, https://doi.org/10.5194/gmd-17-3949-2024, 2024
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Compared to the silicate weathering fluxes measured at large river basins, the current models tend to systematically overestimate the fluxes over the tropical region, which leads to an overestimation of the global total weathering flux. The most possible cause of such bias is found to be the overestimation of tropical surface erosion, which indicates that the tropical vegetation likely slows down physical erosion significantly. We propose a way of taking this effect into account in models.
Quentin Pikeroen, Didier Paillard, and Karine Watrin
Geosci. Model Dev., 17, 3801–3814, https://doi.org/10.5194/gmd-17-3801-2024, https://doi.org/10.5194/gmd-17-3801-2024, 2024
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All accurate climate models use equations with poorly defined parameters, where knobs for the parameters are turned to fit the observations. This process is called tuning. In this article, we use another paradigm. We use a thermodynamic hypothesis, the maximum entropy production, to compute temperatures, energy fluxes, and precipitation, where tuning is impossible. For now, the 1D vertical model is used for a tropical atmosphere. The correct order of magnitude of precipitation is computed.
Jishi Zhang, Peter Bogenschutz, Qi Tang, Philip Cameron-smith, and Chengzhu Zhang
Geosci. Model Dev., 17, 3687–3731, https://doi.org/10.5194/gmd-17-3687-2024, https://doi.org/10.5194/gmd-17-3687-2024, 2024
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We developed a regionally refined climate model that allows resolved convection and performed a 20-year projection to the end of the century. The model has a resolution of 3.25 km in California, which allows us to predict climate with unprecedented accuracy, and a resolution of 100 km for the rest of the globe to achieve efficient, self-consistent simulations. The model produces superior results in reproducing climate patterns over California that typical modern climate models cannot resolve.
Xiaohui Zhong, Xing Yu, and Hao Li
Geosci. Model Dev., 17, 3667–3685, https://doi.org/10.5194/gmd-17-3667-2024, https://doi.org/10.5194/gmd-17-3667-2024, 2024
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In order to forecast localized warm-sector rainfall in the south China region, numerical weather prediction models are being run with finer grid spacing. The conventional convection parameterization (CP) performs poorly in the gray zone, necessitating the development of a scale-aware scheme. We propose a machine learning (ML) model to replace the scale-aware CP scheme. Evaluation against the original CP scheme has shown that the ML-based CP scheme can provide accurate and reliable predictions.
Taufiq Hassan, Kai Zhang, Jianfeng Li, Balwinder Singh, Shixuan Zhang, Hailong Wang, and Po-Lun Ma
Geosci. Model Dev., 17, 3507–3532, https://doi.org/10.5194/gmd-17-3507-2024, https://doi.org/10.5194/gmd-17-3507-2024, 2024
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Anthropogenic aerosol emissions are an essential part of global aerosol models. Significant errors can exist from the loss of emission heterogeneity. We introduced an emission treatment that significantly improved aerosol emission heterogeneity in high-resolution model simulations, with improvements in simulated aerosol surface concentrations. The emission treatment will provide a more accurate representation of aerosol emissions and their effects on climate.
Feng Zhu, Julien Emile-Geay, Gregory J. Hakim, Dominique Guillot, Deborah Khider, Robert Tardif, and Walter A. Perkins
Geosci. Model Dev., 17, 3409–3431, https://doi.org/10.5194/gmd-17-3409-2024, https://doi.org/10.5194/gmd-17-3409-2024, 2024
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Climate field reconstruction encompasses methods that estimate the evolution of climate in space and time based on natural archives. It is useful to investigate climate variations and validate climate models, but its implementation and use can be difficult for non-experts. This paper introduces a user-friendly Python package called cfr to make these methods more accessible, thanks to the computational and visualization tools that facilitate efficient and reproducible research on past climates.
Rose V. Palermo, J. Taylor Perron, Jason M. Soderblom, Samuel P. D. Birch, Alexander G. Hayes, and Andrew D. Ashton
Geosci. Model Dev., 17, 3433–3445, https://doi.org/10.5194/gmd-17-3433-2024, https://doi.org/10.5194/gmd-17-3433-2024, 2024
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Models of rocky coastal erosion help us understand the controls on coastal morphology and evolution. In this paper, we present a simplified model of coastline erosion driven by either uniform erosion where coastline erosion is constant or wave-driven erosion where coastline erosion is a function of the wave power. This model can be used to evaluate how coastline changes reflect climate, sea-level history, material properties, and the relative influence of different erosional processes.
Safae Oumami, Joaquim Arteta, Vincent Guidard, Pierre Tulet, and Paul David Hamer
Geosci. Model Dev., 17, 3385–3408, https://doi.org/10.5194/gmd-17-3385-2024, https://doi.org/10.5194/gmd-17-3385-2024, 2024
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In this paper, we coupled the SURFEX and MEGAN models. The aim of this coupling is to improve the estimation of biogenic fluxes by using the SURFEX canopy environment model. The coupled model results were validated and several sensitivity tests were performed. The coupled-model total annual isoprene flux is 442 Tg; this value is within the range of other isoprene estimates reported. The ultimate aim of this coupling is to predict the impact of climate change on biogenic emissions.
Lars Ackermann, Thomas Rackow, Kai Himstedt, Paul Gierz, Gregor Knorr, and Gerrit Lohmann
Geosci. Model Dev., 17, 3279–3301, https://doi.org/10.5194/gmd-17-3279-2024, https://doi.org/10.5194/gmd-17-3279-2024, 2024
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We present long-term simulations with interactive icebergs in the Southern Ocean. By melting, icebergs reduce the temperature and salinity of the surrounding ocean. In our simulations, we find that this cooling effect of iceberg melting is not limited to the surface ocean but also reaches the deep ocean and propagates northward into all ocean basins. Additionally, the formation of deep-water masses in the Southern Ocean is enhanced.
Nanhong Xie, Tijian Wang, Xiaodong Xie, Xu Yue, Filippo Giorgi, Qian Zhang, Danyang Ma, Rong Song, Beiyao Xu, Shu Li, Bingliang Zhuang, Mengmeng Li, Min Xie, Natalya Andreeva Kilifarska, Georgi Gadzhev, and Reneta Dimitrova
Geosci. Model Dev., 17, 3259–3277, https://doi.org/10.5194/gmd-17-3259-2024, https://doi.org/10.5194/gmd-17-3259-2024, 2024
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For the first time, we coupled a regional climate chemistry model, RegCM-Chem, with a dynamic vegetation model, YIBs, to create a regional climate–chemistry–ecology model, RegCM-Chem–YIBs. We applied it to simulate climatic, chemical, and ecological parameters in East Asia and fully validated it on a variety of observational data. Results show that RegCM-Chem–YIBs model is a valuable tool for studying the terrestrial carbon cycle, atmospheric chemistry, and climate change on a regional scale.
Bryce E. Harrop, Jian Lu, L. Ruby Leung, William K. M. Lau, Kyu-Myong Kim, Brian Medeiros, Brian J. Soden, Gabriel A. Vecchi, Bosong Zhang, and Balwinder Singh
Geosci. Model Dev., 17, 3111–3135, https://doi.org/10.5194/gmd-17-3111-2024, https://doi.org/10.5194/gmd-17-3111-2024, 2024
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Seven new experimental setups designed to interfere with cloud radiative heating have been added to the Energy Exascale Earth System Model (E3SM). These experiments include both those that test the mean impact of cloud radiative heating and those examining its covariance with circulations. This paper documents the code changes and steps needed to run these experiments. Results corroborate prior findings for how cloud radiative heating impacts circulations and rainfall patterns.
Mario C. Acosta, Sergi Palomas, Stella V. Paronuzzi Ticco, Gladys Utrera, Joachim Biercamp, Pierre-Antoine Bretonniere, Reinhard Budich, Miguel Castrillo, Arnaud Caubel, Francisco Doblas-Reyes, Italo Epicoco, Uwe Fladrich, Sylvie Joussaume, Alok Kumar Gupta, Bryan Lawrence, Philippe Le Sager, Grenville Lister, Marie-Pierre Moine, Jean-Christophe Rioual, Sophie Valcke, Niki Zadeh, and Venkatramani Balaji
Geosci. Model Dev., 17, 3081–3098, https://doi.org/10.5194/gmd-17-3081-2024, https://doi.org/10.5194/gmd-17-3081-2024, 2024
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We present a collection of performance metrics gathered during the Coupled Model Intercomparison Project Phase 6 (CMIP6), a worldwide initiative to study climate change. We analyse the metrics that resulted from collaboration efforts among many partners and models and describe our findings to demonstrate the utility of our study for the scientific community. The research contributes to understanding climate modelling performance on the current high-performance computing (HPC) architectures.
Sabine Doktorowski, Jan Kretzschmar, Johannes Quaas, Marc Salzmann, and Odran Sourdeval
Geosci. Model Dev., 17, 3099–3110, https://doi.org/10.5194/gmd-17-3099-2024, https://doi.org/10.5194/gmd-17-3099-2024, 2024
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Especially over the midlatitudes, precipitation is mainly formed via the ice phase. In this study we focus on the initial snow formation process in the ICON-AES, the aggregation process. We use a stochastical approach for the aggregation parameterization and investigate the influence in the ICON-AES. Therefore, a distribution function of cloud ice is created, which is evaluated with satellite data. The new approach leads to cloud ice loss and an improvement in the process rate bias.
Katie R. Blackford, Matthew Kasoar, Chantelle Burton, Eleanor Burke, Iain Colin Prentice, and Apostolos Voulgarakis
Geosci. Model Dev., 17, 3063–3079, https://doi.org/10.5194/gmd-17-3063-2024, https://doi.org/10.5194/gmd-17-3063-2024, 2024
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Peatlands are globally important stores of carbon which are being increasingly threatened by wildfires with knock-on effects on the climate system. Here we introduce a novel peat fire parameterization in the northern high latitudes to the INFERNO global fire model. Representing peat fires increases annual burnt area across the high latitudes, alongside improvements in how we capture year-to-year variation in burning and emissions.
Pengfei Shi, L. Ruby Leung, Bin Wang, Kai Zhang, Samson M. Hagos, and Shixuan Zhang
Geosci. Model Dev., 17, 3025–3040, https://doi.org/10.5194/gmd-17-3025-2024, https://doi.org/10.5194/gmd-17-3025-2024, 2024
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Improving climate predictions have profound socio-economic impacts. This study introduces a new weakly coupled land data assimilation (WCLDA) system for a coupled climate model. We demonstrate improved simulation of soil moisture and temperature in many global regions and throughout the soil layers. Furthermore, significant improvements are also found in reproducing the time evolution of the 2012 US Midwest drought. The WCLDA system provides the groundwork for future predictability studies.
Justin Peter, Elisabeth Vogel, Wendy Sharples, Ulrike Bende-Michl, Louise Wilson, Pandora Hope, Andrew Dowdy, Greg Kociuba, Sri Srikanthan, Vi Co Duong, Jake Roussis, Vjekoslav Matic, Zaved Khan, Alison Oke, Margot Turner, Stuart Baron-Hay, Fiona Johnson, Raj Mehrotra, Ashish Sharma, Marcus Thatcher, Ali Azarvinand, Steven Thomas, Ghyslaine Boschat, Chantal Donnelly, and Robert Argent
Geosci. Model Dev., 17, 2755–2781, https://doi.org/10.5194/gmd-17-2755-2024, https://doi.org/10.5194/gmd-17-2755-2024, 2024
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We detail the production of datasets and communication to end users of high-resolution projections of rainfall, runoff, and soil moisture for the entire Australian continent. This is important as previous projections for Australia were for small regions and used differing techniques for their projections, making comparisons difficult across Australia's varied climate zones. The data will be beneficial for research purposes and to aid adaptation to climate change.
Daniele Visioni, Alan Robock, Jim Haywood, Matthew Henry, Simone Tilmes, Douglas G. MacMartin, Ben Kravitz, Sarah J. Doherty, John Moore, Chris Lennard, Shingo Watanabe, Helene Muri, Ulrike Niemeier, Olivier Boucher, Abu Syed, Temitope S. Egbebiyi, Roland Séférian, and Ilaria Quaglia
Geosci. Model Dev., 17, 2583–2596, https://doi.org/10.5194/gmd-17-2583-2024, https://doi.org/10.5194/gmd-17-2583-2024, 2024
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This paper describes a new experimental protocol for the Geoengineering Model Intercomparison Project (GeoMIP). In it, we describe the details of a new simulation of sunlight reflection using the stratospheric aerosols that climate models are supposed to run, and we explain the reasons behind each choice we made when defining the protocol.
Jose Rafael Guarin, Jonas Jägermeyr, Elizabeth A. Ainsworth, Fabio A. A. Oliveira, Senthold Asseng, Kenneth Boote, Joshua Elliott, Lisa Emberson, Ian Foster, Gerrit Hoogenboom, David Kelly, Alex C. Ruane, and Katrina Sharps
Geosci. Model Dev., 17, 2547–2567, https://doi.org/10.5194/gmd-17-2547-2024, https://doi.org/10.5194/gmd-17-2547-2024, 2024
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The effects of ozone (O3) stress on crop photosynthesis and leaf senescence were added to maize, rice, soybean, and wheat crop models. The modified models reproduced growth and yields under different O3 levels measured in field experiments and reported in the literature. The combined interactions between O3 and additional stresses were reproduced with the new models. These updated crop models can be used to simulate impacts of O3 stress under future climate change and air pollution scenarios.
Jiachen Lu, Negin Nazarian, Melissa Anne Hart, E. Scott Krayenhoff, and Alberto Martilli
Geosci. Model Dev., 17, 2525–2545, https://doi.org/10.5194/gmd-17-2525-2024, https://doi.org/10.5194/gmd-17-2525-2024, 2024
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This study enhances urban canopy models by refining key assumptions. Simulations for various urban scenarios indicate discrepancies in turbulent transport efficiency for flow properties. We propose two modifications that involve characterizing diffusion coefficients for momentum and turbulent kinetic energy separately and introducing a physics-based
mass-fluxterm. These adjustments enhance the model's performance, offering more reliable temperature and surface flux estimates.
Justin L. Willson, Kevin A. Reed, Christiane Jablonowski, James Kent, Peter H. Lauritzen, Ramachandran Nair, Mark A. Taylor, Paul A. Ullrich, Colin M. Zarzycki, David M. Hall, Don Dazlich, Ross Heikes, Celal Konor, David Randall, Thomas Dubos, Yann Meurdesoif, Xi Chen, Lucas Harris, Christian Kühnlein, Vivian Lee, Abdessamad Qaddouri, Claude Girard, Marco Giorgetta, Daniel Reinert, Hiroaki Miura, Tomoki Ohno, and Ryuji Yoshida
Geosci. Model Dev., 17, 2493–2507, https://doi.org/10.5194/gmd-17-2493-2024, https://doi.org/10.5194/gmd-17-2493-2024, 2024
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Accurate simulation of tropical cyclones (TCs) is essential to understanding their behavior in a changing climate. One way this is accomplished is through model intercomparison projects, where results from multiple climate models are analyzed to provide benchmark solutions for the wider climate modeling community. This study describes and analyzes the previously developed TC test case for nine climate models in an intercomparison project, providing solutions that aid in model development.
Stephanie Fiedler, Vaishali Naik, Fiona M. O'Connor, Christopher J. Smith, Paul Griffiths, Ryan J. Kramer, Toshihiko Takemura, Robert J. Allen, Ulas Im, Matthew Kasoar, Angshuman Modak, Steven Turnock, Apostolos Voulgarakis, Duncan Watson-Parris, Daniel M. Westervelt, Laura J. Wilcox, Alcide Zhao, William J. Collins, Michael Schulz, Gunnar Myhre, and Piers M. Forster
Geosci. Model Dev., 17, 2387–2417, https://doi.org/10.5194/gmd-17-2387-2024, https://doi.org/10.5194/gmd-17-2387-2024, 2024
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Climate scientists want to better understand modern climate change. Thus, climate model experiments are performed and compared. The results of climate model experiments differ, as assessed in the latest Intergovernmental Panel on Climate Change (IPCC) assessment report. This article gives insights into the challenges and outlines opportunities for further improving the understanding of climate change. It is based on views of a group of experts in atmospheric composition–climate interactions.
Sergey Danilov, Carolin Mehlmann, Dmitry Sidorenko, and Qiang Wang
Geosci. Model Dev., 17, 2287–2297, https://doi.org/10.5194/gmd-17-2287-2024, https://doi.org/10.5194/gmd-17-2287-2024, 2024
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Sea ice models are a necessary component of climate models. At very high resolution they are capable of simulating linear kinematic features, such as leads, which are important for better prediction of heat exchanges between the ocean and atmosphere. Two new discretizations are described which improve the sea ice component of the Finite volumE Sea ice–Ocean Model (FESOM version 2) by allowing simulations of finer scales.
Tian Gan, Gregory E. Tucker, Eric W. H. Hutton, Mark D. Piper, Irina Overeem, Albert J. Kettner, Benjamin Campforts, Julia M. Moriarty, Brianna Undzis, Ethan Pierce, and Lynn McCready
Geosci. Model Dev., 17, 2165–2185, https://doi.org/10.5194/gmd-17-2165-2024, https://doi.org/10.5194/gmd-17-2165-2024, 2024
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This study presents the design, implementation, and application of the CSDMS Data Components. The case studies demonstrate that the Data Components provide a consistent way to access heterogeneous datasets from multiple sources, and to seamlessly integrate them with various models for Earth surface process modeling. The Data Components support the creation of open data–model integration workflows to improve the research transparency and reproducibility.
Jérémy Bernard, Erwan Bocher, Matthieu Gousseff, François Leconte, and Elisabeth Le Saux Wiederhold
Geosci. Model Dev., 17, 2077–2116, https://doi.org/10.5194/gmd-17-2077-2024, https://doi.org/10.5194/gmd-17-2077-2024, 2024
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Geographical features may have a considerable effect on local climate. The local climate zone (LCZ) system proposed by Stewart and Oke (2012) is seen as a standard approach for classifying any zone according to a set of geographic indicators. While many methods already exist to map the LCZ, only a few tools are openly and freely available. We present the algorithm implemented in GeoClimate software to identify the LCZ of any place in the world using OpenStreetMap data.
Thomas Extier, Thibaut Caley, and Didier M. Roche
Geosci. Model Dev., 17, 2117–2139, https://doi.org/10.5194/gmd-17-2117-2024, https://doi.org/10.5194/gmd-17-2117-2024, 2024
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Stable water isotopes are used to infer changes in the hydrological cycle for different time periods in climatic archive and climate models. We present the implementation of the δ2H and δ17O water isotopes in the coupled climate model iLOVECLIM and calculate the d- and 17O-excess. Results of a simulation under preindustrial conditions show that the model correctly reproduces the water isotope distribution in the atmosphere and ocean in comparison to data and other global circulation models.
Kirsten L. Findell, Zun Yin, Eunkyo Seo, Paul A. Dirmeyer, Nathan P. Arnold, Nathaniel Chaney, Megan D. Fowler, Meng Huang, David M. Lawrence, Po-Lun Ma, and Joseph A. Santanello Jr.
Geosci. Model Dev., 17, 1869–1883, https://doi.org/10.5194/gmd-17-1869-2024, https://doi.org/10.5194/gmd-17-1869-2024, 2024
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We outline a request for sub-daily data to accurately capture the process-level connections between land states, surface fluxes, and the boundary layer response. This high-frequency model output will allow for more direct comparison with observational field campaigns on process-relevant timescales, enable demonstration of inter-model spread in land–atmosphere coupling processes, and aid in targeted identification of sources of deficiencies and opportunities for improvement of the models.
Marlene Klockmann, Udo von Toussaint, and Eduardo Zorita
Geosci. Model Dev., 17, 1765–1787, https://doi.org/10.5194/gmd-17-1765-2024, https://doi.org/10.5194/gmd-17-1765-2024, 2024
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Reconstructions of climate variability before the observational period rely on climate proxies and sophisticated statistical models to link the proxy information and climate variability. Existing models tend to underestimate the true magnitude of variability, especially if the proxies contain non-climatic noise. We present and test a promising new framework for climate-index reconstructions, based on Gaussian processes, which reconstructs robust variability estimates from noisy and sparse data.
Aaron A. Naidoo-Bagwell, Fanny M. Monteiro, Katharine R. Hendry, Scott Burgan, Jamie D. Wilson, Ben A. Ward, Andy Ridgwell, and Daniel J. Conley
Geosci. Model Dev., 17, 1729–1748, https://doi.org/10.5194/gmd-17-1729-2024, https://doi.org/10.5194/gmd-17-1729-2024, 2024
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As an extension to the EcoGEnIE 1.0 Earth system model that features a diverse plankton community, EcoGEnIE 1.1 includes siliceous plankton diatoms and also considers their impact on biogeochemical cycles. With updates to existing nutrient cycles and the introduction of the silicon cycle, we see improved model performance relative to observational data. Through a more functionally diverse plankton community, the new model enables more comprehensive future study of ocean ecology.
Martin Butzin, Ying Ye, Christoph Völker, Özgür Gürses, Judith Hauck, and Peter Köhler
Geosci. Model Dev., 17, 1709–1727, https://doi.org/10.5194/gmd-17-1709-2024, https://doi.org/10.5194/gmd-17-1709-2024, 2024
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In this paper we describe the implementation of the carbon isotopes 13C and 14C into the marine biogeochemistry model FESOM2.1-REcoM3 and present results of long-term test simulations. Our model results are largely consistent with marine carbon isotope reconstructions for the pre-anthropogenic period, but also exhibit some discrepancies.
Sven Karsten, Hagen Radtke, Matthias Gröger, Ha T. M. Ho-Hagemann, Hossein Mashayekh, Thomas Neumann, and H. E. Markus Meier
Geosci. Model Dev., 17, 1689–1708, https://doi.org/10.5194/gmd-17-1689-2024, https://doi.org/10.5194/gmd-17-1689-2024, 2024
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This paper describes the development of a regional Earth System Model for the Baltic Sea region. In contrast to conventional coupling approaches, the presented model includes a flux calculator operating on a common exchange grid. This approach automatically ensures a locally consistent treatment of fluxes and simplifies the exchange of model components. The presented model can be used for various scientific questions, such as studies of natural variability and ocean–atmosphere interactions.
Skyler Graap and Colin M. Zarzycki
Geosci. Model Dev., 17, 1627–1650, https://doi.org/10.5194/gmd-17-1627-2024, https://doi.org/10.5194/gmd-17-1627-2024, 2024
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A key target for improving climate models is how low, bright clouds are predicted over tropical oceans, since they have important consequences for the Earth's energy budget. A climate model has been updated to improve the physical realism of the treatment of how momentum is moved up and down in the atmosphere. By comparing this updated model to real-world observations from balloon launches, it can be shown to more accurately depict atmospheric structure in trade-wind areas close to the Equator.
Georgia Lazoglou, Theo Economou, Christina Anagnostopoulou, George Zittis, Anna Tzyrkalli, Pantelis Georgiades, and Jos Lelieveld
EGUsphere, https://doi.org/10.5194/egusphere-2024-45, https://doi.org/10.5194/egusphere-2024-45, 2024
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This study focused on the important issue of the drizzle bias effect in regional climate models, described by an over-prediction of the number of rainy days while underestimating associated precipitation amounts. For this purpose, two distinct methodologies were applied and rigorously evaluated. These results are encouraging for using the multivariate machine learning method of Random Forest for increasing the accuracy of climate models, concerning the projection of the number of wet days.
Marika M. Holland, Cecile Hannay, John Fasullo, Alexandra Jahn, Jennifer E. Kay, Michael Mills, Isla R. Simpson, William Wieder, Peter Lawrence, Erik Kluzek, and David Bailey
Geosci. Model Dev., 17, 1585–1602, https://doi.org/10.5194/gmd-17-1585-2024, https://doi.org/10.5194/gmd-17-1585-2024, 2024
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Climate evolves in response to changing forcings, as prescribed in simulations. Models and forcings are updated over time to reflect new understanding. This makes it difficult to attribute simulation differences to either model or forcing changes. Here we present new simulations which enable the separation of model structure and forcing influence between two widely used simulation sets. Results indicate a strong influence of aerosol emission uncertainty on historical climate.
Rongyun Tang, Mingzhou Jin, Jiafu Mao, Daniel M. Ricciuto, Anping Chen, and Yulong Zhang
Geosci. Model Dev., 17, 1525–1542, https://doi.org/10.5194/gmd-17-1525-2024, https://doi.org/10.5194/gmd-17-1525-2024, 2024
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Carbon-rich boreal peatlands are at risk of burning. The reproducibility and predictability of rare peatland fire events are investigated by constructing a two-step error-correcting machine learning framework to tackle such complex systems. Fire occurrence and impacts are highly predictable with our approach. Factor-controlling simulations revealed that temperature, moisture, and freeze–thaw cycles control boreal peatland fires, indicating thermal impacts on causing peat fires.
Allison B. Collow, Peter R. Colarco, Arlindo M. da Silva, Virginie Buchard, Huisheng Bian, Mian Chin, Sampa Das, Ravi Govindaraju, Dongchul Kim, and Valentina Aquila
Geosci. Model Dev., 17, 1443–1468, https://doi.org/10.5194/gmd-17-1443-2024, https://doi.org/10.5194/gmd-17-1443-2024, 2024
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The GOCART aerosol module within the Goddard Earth Observing System recently underwent a major refactoring and update to the representation of physical processes. Code changes that were included in GOCART Second Generation (GOCART-2G) are documented, and we establish a benchmark simulation that is to be used for future development of the system. The 4-year benchmark simulation was evaluated using in situ and spaceborne measurements to develop a baseline and prioritize future development.
Oksana Guba, Mark A. Taylor, Peter A. Bosler, Christopher Eldred, and Peter H. Lauritzen
Geosci. Model Dev., 17, 1429–1442, https://doi.org/10.5194/gmd-17-1429-2024, https://doi.org/10.5194/gmd-17-1429-2024, 2024
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We want to reduce errors in the moist energy budget in numerical atmospheric models. We study a few common assumptions and mechanisms that are used for the moist physics. Some mechanisms are more consistent with the underlying equations. Separately, we study how assumptions about models' thermodynamics affect the modeled energy of precipitation. We also explain how to conserve energy in the moist physics for nonhydrostatic models.
Konstantin Aiteew, Jarno Rouhiainen, Claas Nendel, and René Dechow
Geosci. Model Dev., 17, 1349–1385, https://doi.org/10.5194/gmd-17-1349-2024, https://doi.org/10.5194/gmd-17-1349-2024, 2024
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This study evaluated the biogeochemical model MONICA and its performance in simulating soil organic carbon changes. MONICA can reproduce plant growth, carbon and nitrogen dynamics, soil water and temperature. The model results were compared with five established carbon turnover models. With the exception of certain sites, adequate reproduction of soil organic carbon stock change rates was achieved. The MONICA model was capable of performing similar to or even better than the other models.
Jianfeng Li, Kai Zhang, Taufiq Hassan, Shixuan Zhang, Po-Lun Ma, Balwinder Singh, Qiyang Yan, and Huilin Huang
Geosci. Model Dev., 17, 1327–1347, https://doi.org/10.5194/gmd-17-1327-2024, https://doi.org/10.5194/gmd-17-1327-2024, 2024
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By comparing E3SM simulations with and without regional refinement, we find that model horizontal grid spacing considerably affects the simulated aerosol mass budget, aerosol–cloud interactions, and the effective radiative forcing of anthropogenic aerosols. The study identifies the critical physical processes strongly influenced by model resolution. It also highlights the benefit of applying regional refinement in future modeling studies at higher or even convection-permitting resolutions.
Bernd Funke, Thierry Dudok de Wit, Ilaria Ermolli, Margit Haberreiter, Doug Kinnison, Daniel Marsh, Hilde Nesse, Annika Seppälä, Miriam Sinnhuber, and Ilya Usoskin
Geosci. Model Dev., 17, 1217–1227, https://doi.org/10.5194/gmd-17-1217-2024, https://doi.org/10.5194/gmd-17-1217-2024, 2024
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We outline a road map for the preparation of a solar forcing dataset for the upcoming Phase 7 of the Coupled Model Intercomparison Project (CMIP7), considering the latest scientific advances made in the reconstruction of solar forcing and in the understanding of climate response while also addressing the issues that were raised during CMIP6.
Fiona Raphaela Spuler, Jakob Benjamin Wessel, Edward Comyn-Platt, James Varndell, and Chiara Cagnazzo
Geosci. Model Dev., 17, 1249–1269, https://doi.org/10.5194/gmd-17-1249-2024, https://doi.org/10.5194/gmd-17-1249-2024, 2024
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Before using climate models to study the impacts of climate change, bias adjustment is commonly applied to the models to ensure that they correspond with observations at a local scale. However, this can introduce undesirable distortions into the climate model. In this paper, we present an open-source python package called ibicus to enable the comparison and detailed evaluation of bias adjustment methods, facilitating their transparent and rigorous application.
Donghui Xu, Gautam Bisht, Zeli Tan, Chang Liao, Tian Zhou, Hong-Yi Li, and L. Ruby Leung
Geosci. Model Dev., 17, 1197–1215, https://doi.org/10.5194/gmd-17-1197-2024, https://doi.org/10.5194/gmd-17-1197-2024, 2024
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We aim to disentangle the hydrological and hydraulic controls on streamflow variability in a fully coupled earth system model. We found that calibrating only one process (i.e., traditional calibration procedure) will result in unrealistic parameter values and poor performance of the water cycle, while the simulated streamflow is improved. To address this issue, we further proposed a two-step calibration procedure to reconcile the impacts from hydrological and hydraulic processes on streamflow.
Douglas McNeall, Eddy Robertson, and Andy Wiltshire
Geosci. Model Dev., 17, 1059–1089, https://doi.org/10.5194/gmd-17-1059-2024, https://doi.org/10.5194/gmd-17-1059-2024, 2024
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We can run simulations of the land surface and carbon cycle, using computer models to help us understand and predict climate change and its impacts. These simulations are not perfect reproductions of the real land surface, and that can make them less effective tools. We use new statistical and computational techniques to help us understand how different our models are from the real land surface, how to make them more realistic, and how well we can simulate past and future climate.
Genevieve L. Clow, Nicole S. Lovenduski, Michael N. Levy, Keith Lindsay, and Jennifer E. Kay
Geosci. Model Dev., 17, 975–995, https://doi.org/10.5194/gmd-17-975-2024, https://doi.org/10.5194/gmd-17-975-2024, 2024
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Satellite observations of chlorophyll allow us to study marine phytoplankton on a global scale; yet some of these observations are missing due to clouds and other issues. To investigate the impact of missing data, we developed a satellite simulator for chlorophyll in an Earth system model. We found that missing data can impact the global mean chlorophyll by nearly 20 %. The simulated observations provide a more direct comparison to real-world data and can be used to improve model validation.
Jiateng Guo, Xuechuang Xu, Luyuan Wang, Xulei Wang, Lixin Wu, Mark Jessell, Vitaliy Ogarko, Zhibin Liu, and Yufei Zheng
Geosci. Model Dev., 17, 957–973, https://doi.org/10.5194/gmd-17-957-2024, https://doi.org/10.5194/gmd-17-957-2024, 2024
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This study proposes a semi-supervised learning algorithm using pseudo-labels for 3D geological modelling. We establish a 3D geological model using borehole data from a complex real urban local survey area in Shenyang and make an uncertainty analysis of this model. The method effectively expands the sample space, which is suitable for geomodelling and uncertainty analysis from boreholes. The modelling results perform well in terms of spatial morphology and geological semantics.
Shih-Wei Wei, Mariusz Pagowski, Arlindo da Silva, Cheng-Hsuan Lu, and Bo Huang
Geosci. Model Dev., 17, 795–813, https://doi.org/10.5194/gmd-17-795-2024, https://doi.org/10.5194/gmd-17-795-2024, 2024
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This study describes the modeling system and the evaluation results for the first prototype version of a global aerosol reanalysis product at NOAA, prototype NOAA Aerosol ReAnalysis version 1.0 (pNARA v1.0). We evaluated pNARA v1.0 against independent datasets and compared it with other reanalyses. We identified deficiencies in the system (both in the forecast model and in the data assimilation system) and the uncertainties that exist in our reanalysis.
Emma Howard, Chun-Hsu Su, Christian Stassen, Rajashree Naha, Harvey Ye, Acacia Pepler, Samuel S. Bell, Andrew J. Dowdy, Simon O. Tucker, and Charmaine Franklin
Geosci. Model Dev., 17, 731–757, https://doi.org/10.5194/gmd-17-731-2024, https://doi.org/10.5194/gmd-17-731-2024, 2024
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The BARPA-R modelling configuration has been developed to produce high-resolution climate hazard projections within the Australian region. When using boundary driving data from quasi-observed historical conditions, BARPA-R shows good performance with errors generally on par with reanalysis products. BARPA-R also captures trends, known modes of climate variability, large-scale weather processes, and multivariate relationships.
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.
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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...