Articles | Volume 14, issue 10
https://doi.org/10.5194/gmd-14-5927-2021
© Author(s) 2021. 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-14-5927-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model evaluation paper
| 
30 Sep 2021
Model evaluation paper |
| 30 Sep 2021
| 30 Sep 2021
Performance of the Adriatic Sea and Coast (AdriSC) climate component – a COAWST V3.3-based one-way coupled atmosphere–ocean modelling suite: ocean results
Physical Oceanography Laboratory, Institute of Oceanography and Fisheries, Šetalište I.
Meštrovića 63, 21000 Split, Croatia
Cléa Denamiel
Physical Oceanography Laboratory, Institute of Oceanography and Fisheries, Šetalište I.
Meštrovića 63, 21000 Split, Croatia
Division for Marine and Environmental Research, Ruđer Bošković Institute, Bijenička cesta 54, 10000 Zagreb, Croatia
Ivica Vilibić
Physical Oceanography Laboratory, Institute of Oceanography and Fisheries, Šetalište I.
Meštrovića 63, 21000 Split, Croatia
Division for Marine and Environmental Research, Ruđer Bošković Institute, Bijenička cesta 54, 10000 Zagreb, Croatia
Related authors
Davide Bonaldo, Sandro Carniel, Renato R. Colucci, Cléa Denamiel, Petra Pranic, Fabio Raicich, Antonio Ricchi, Lorenzo Sangelantoni, Ivica Vilibic, and Maria Letizia Vitelletti
EGUsphere, https://doi.org/10.5194/egusphere-2024-1468, https://doi.org/10.5194/egusphere-2024-1468, 2024
Short summary
Short summary
We present a high-resolution modelling effort to investigate the possible end-of-century evolution of the main physical processes in the Adriatic Sea in a severe climate change scenario, with an ensemble approach (viz., use a of multiple simulations) allowing to control the uncertainty of the predictions. Our model exhibits a satisfactory capability to reproduce the recent past and provides a ground for a set of multidisciplinary studies in this area over a multi-decadal horizon.
Petra Pranić, Cléa Denamiel, Ivica Janeković, and Ivica Vilibić
Ocean Sci., 19, 649–670, https://doi.org/10.5194/os-19-649-2023, https://doi.org/10.5194/os-19-649-2023, 2023
Short summary
Short summary
In this study, we analyse and compare the results of four different approaches in modelling bora-driven dense-water dynamics in the Adriatic. The study investigated the likely requirements for modelling the ocean circulation in the Adriatic and found that a 31-year run of a fine-resolution Adriatic climate model is able to outperform most aspects of the newest reanalysis product, a short-term hindcast and data-assimilated simulation, in reproducing the dense-water dynamics in the Adriatic Sea.
Cléa Denamiel, Petra Pranić, Damir Ivanković, Iva Tojčić, and Ivica Vilibić
Geosci. Model Dev., 14, 3995–4017, https://doi.org/10.5194/gmd-14-3995-2021, https://doi.org/10.5194/gmd-14-3995-2021, 2021
Short summary
Short summary
The atmospheric results of the Adriatic Sea and Coast (AdriSC) climate simulation (1987–2017) are evaluated against available observational datasets in the Adriatic region. Generally, the AdriSC model performs better than regional climate models that have resolutions that are 4 times more coarse, except concerning summer temperatures, which are systematically underestimated. High-resolution climate models may thus provide new insights about the local impacts of global warming in the Adriatic.
Davide Bonaldo, Sandro Carniel, Renato R. Colucci, Cléa Denamiel, Petra Pranic, Fabio Raicich, Antonio Ricchi, Lorenzo Sangelantoni, Ivica Vilibic, and Maria Letizia Vitelletti
EGUsphere, https://doi.org/10.5194/egusphere-2024-1468, https://doi.org/10.5194/egusphere-2024-1468, 2024
Short summary
Short summary
We present a high-resolution modelling effort to investigate the possible end-of-century evolution of the main physical processes in the Adriatic Sea in a severe climate change scenario, with an ensemble approach (viz., use a of multiple simulations) allowing to control the uncertainty of the predictions. Our model exhibits a satisfactory capability to reproduce the recent past and provides a ground for a set of multidisciplinary studies in this area over a multi-decadal horizon.
Petra Pranić, Cléa Denamiel, Ivica Janeković, and Ivica Vilibić
Ocean Sci., 19, 649–670, https://doi.org/10.5194/os-19-649-2023, https://doi.org/10.5194/os-19-649-2023, 2023
Short summary
Short summary
In this study, we analyse and compare the results of four different approaches in modelling bora-driven dense-water dynamics in the Adriatic. The study investigated the likely requirements for modelling the ocean circulation in the Adriatic and found that a 31-year run of a fine-resolution Adriatic climate model is able to outperform most aspects of the newest reanalysis product, a short-term hindcast and data-assimilated simulation, in reproducing the dense-water dynamics in the Adriatic Sea.
Cléa Denamiel and Ivica Vilibić
EGUsphere, https://doi.org/10.5194/egusphere-2023-913, https://doi.org/10.5194/egusphere-2023-913, 2023
Preprint archived
Short summary
Short summary
We present a new methodology using coupled atmosphere-ocean-wave models and demonstrate the feasibility to provide meter scale assessments of the impact of climate change on storm surge hazards. We show that sea level variations and distributions can be derived at the climate scale in the Adriatic Sea small lagoons and bays. We expect that the newly developed methodology could lead to more targeted adaptation strategies in regions of the world vulnerable to atmospherically driven extreme events.
Begoña Pérez Gómez, Ivica Vilibić, Jadranka Šepić, Iva Međugorac, Matjaž Ličer, Laurent Testut, Claire Fraboul, Marta Marcos, Hassen Abdellaoui, Enrique Álvarez Fanjul, Darko Barbalić, Benjamín Casas, Antonio Castaño-Tierno, Srđan Čupić, Aldo Drago, María Angeles Fraile, Daniele A. Galliano, Adam Gauci, Branislav Gloginja, Víctor Martín Guijarro, Maja Jeromel, Marcos Larrad Revuelto, Ayah Lazar, Ibrahim Haktan Keskin, Igor Medvedev, Abdelkader Menassri, Mohamed Aïssa Meslem, Hrvoje Mihanović, Sara Morucci, Dragos Niculescu, José Manuel Quijano de Benito, Josep Pascual, Atanas Palazov, Marco Picone, Fabio Raicich, Mohamed Said, Jordi Salat, Erdinc Sezen, Mehmet Simav, Georgios Sylaios, Elena Tel, Joaquín Tintoré, Klodian Zaimi, and George Zodiatis
Ocean Sci., 18, 997–1053, https://doi.org/10.5194/os-18-997-2022, https://doi.org/10.5194/os-18-997-2022, 2022
Short summary
Short summary
This description and mapping of coastal sea level monitoring networks in the Mediterranean and Black seas reveals the existence of 240 presently operational tide gauges. Information is provided about the type of sensor, time sampling, data availability, and ancillary measurements. An assessment of the fit-for-purpose status of the network is also included, along with recommendations to mitigate existing bottlenecks and improve the network, in a context of sea level rise and increasing extremes.
Emma Reyes, Eva Aguiar, Michele Bendoni, Maristella Berta, Carlo Brandini, Alejandro Cáceres-Euse, Fulvio Capodici, Vanessa Cardin, Daniela Cianelli, Giuseppe Ciraolo, Lorenzo Corgnati, Vlado Dadić, Bartolomeo Doronzo, Aldo Drago, Dylan Dumas, Pierpaolo Falco, Maria Fattorini, Maria J. Fernandes, Adam Gauci, Roberto Gómez, Annalisa Griffa, Charles-Antoine Guérin, Ismael Hernández-Carrasco, Jaime Hernández-Lasheras, Matjaž Ličer, Pablo Lorente, Marcello G. Magaldi, Carlo Mantovani, Hrvoje Mihanović, Anne Molcard, Baptiste Mourre, Adèle Révelard, Catalina Reyes-Suárez, Simona Saviano, Roberta Sciascia, Stefano Taddei, Joaquín Tintoré, Yaron Toledo, Marco Uttieri, Ivica Vilibić, Enrico Zambianchi, and Alejandro Orfila
Ocean Sci., 18, 797–837, https://doi.org/10.5194/os-18-797-2022, https://doi.org/10.5194/os-18-797-2022, 2022
Short summary
Short summary
This work reviews the existing advanced and emerging scientific and societal applications using HFR data, developed to address the major challenges identified in Mediterranean coastal waters organized around three main topics: maritime safety, extreme hazards and environmental transport processes. It also includes a discussion and preliminary assessment of the capabilities of existing HFR applications, finally providing a set of recommendations towards setting out future prospects.
Pablo Lorente, Eva Aguiar, Michele Bendoni, Maristella Berta, Carlo Brandini, Alejandro Cáceres-Euse, Fulvio Capodici, Daniela Cianelli, Giuseppe Ciraolo, Lorenzo Corgnati, Vlado Dadić, Bartolomeo Doronzo, Aldo Drago, Dylan Dumas, Pierpaolo Falco, Maria Fattorini, Adam Gauci, Roberto Gómez, Annalisa Griffa, Charles-Antoine Guérin, Ismael Hernández-Carrasco, Jaime Hernández-Lasheras, Matjaž Ličer, Marcello G. Magaldi, Carlo Mantovani, Hrvoje Mihanović, Anne Molcard, Baptiste Mourre, Alejandro Orfila, Adèle Révelard, Emma Reyes, Jorge Sánchez, Simona Saviano, Roberta Sciascia, Stefano Taddei, Joaquín Tintoré, Yaron Toledo, Laura Ursella, Marco Uttieri, Ivica Vilibić, Enrico Zambianchi, and Vanessa Cardin
Ocean Sci., 18, 761–795, https://doi.org/10.5194/os-18-761-2022, https://doi.org/10.5194/os-18-761-2022, 2022
Short summary
Short summary
High-frequency radar (HFR) is a land-based remote sensing technology that can provide maps of the surface circulation over broad coastal areas, along with wave and wind information. The main goal of this work is to showcase the current status of the Mediterranean HFR network as well as present and future applications of this sensor for societal benefit such as search and rescue operations, safe vessel navigation, tracking of marine pollutants, and the monitoring of extreme events.
Petra Zemunik, Jadranka Šepić, Havu Pellikka, Leon Ćatipović, and Ivica Vilibić
Earth Syst. Sci. Data, 13, 4121–4132, https://doi.org/10.5194/essd-13-4121-2021, https://doi.org/10.5194/essd-13-4121-2021, 2021
Short summary
Short summary
A new global dataset – MISELA (Minute Sea-Level Analysis) – has been developed and contains quality-checked sea-level records from 331 tide gauges worldwide for a period from 2004 to 2019. The dataset is appropriate for research on atmospherically induced high-frequency sea-level oscillations. Research on these oscillations is important, as they can, like all sea-level extremes, seriously threaten coastal zone infrastructure and populations.
Iva Tojčić, Cléa Denamiel, and Ivica Vilibić
Nat. Hazards Earth Syst. Sci., 21, 2427–2446, https://doi.org/10.5194/nhess-21-2427-2021, https://doi.org/10.5194/nhess-21-2427-2021, 2021
Short summary
Short summary
This study quantifies the performance of the Croatian meteotsunami early warning system (CMeEWS) composed of a network of air pressure and sea level observations developed in order to help coastal communities prepare for extreme events. The system would have triggered the warnings for most of the observed events but also set off some false alarms if it was operational during the multi-meteotsunami event of 11–19 May 2020 in the eastern Adriatic. Further development of the system is planned.
Cléa Denamiel, Petra Pranić, Damir Ivanković, Iva Tojčić, and Ivica Vilibić
Geosci. Model Dev., 14, 3995–4017, https://doi.org/10.5194/gmd-14-3995-2021, https://doi.org/10.5194/gmd-14-3995-2021, 2021
Short summary
Short summary
The atmospheric results of the Adriatic Sea and Coast (AdriSC) climate simulation (1987–2017) are evaluated against available observational datasets in the Adriatic region. Generally, the AdriSC model performs better than regional climate models that have resolutions that are 4 times more coarse, except concerning summer temperatures, which are systematically underestimated. High-resolution climate models may thus provide new insights about the local impacts of global warming in the Adriatic.
Ivica Vilibić, Petra Zemunik, Jadranka Šepić, Natalija Dunić, Oussama Marzouk, Hrvoje Mihanović, Clea Denamiel, Robert Precali, and Tamara Djakovac
Ocean Sci., 15, 1351–1362, https://doi.org/10.5194/os-15-1351-2019, https://doi.org/10.5194/os-15-1351-2019, 2019
Ivica Vilibić, Hrvoje Mihanović, Ivica Janeković, Cléa Denamiel, Pierre-Marie Poulain, Mirko Orlić, Natalija Dunić, Vlado Dadić, Mira Pasarić, Stipe Muslim, Riccardo Gerin, Frano Matić, Jadranka Šepić, Elena Mauri, Zoi Kokkini, Martina Tudor, Žarko Kovač, and Tomislav Džoić
Ocean Sci., 14, 237–258, https://doi.org/10.5194/os-14-237-2018, https://doi.org/10.5194/os-14-237-2018, 2018
H. Mihanović, I. Vilibić, S. Carniel, M. Tudor, A. Russo, A. Bergamasco, N. Bubić, Z. Ljubešić, D. Viličić, A. Boldrin, V. Malačič, M. Celio, C. Comici, and F. Raicich
Ocean Sci., 9, 561–572, https://doi.org/10.5194/os-9-561-2013, https://doi.org/10.5194/os-9-561-2013, 2013
S. Pasquet, I. Vilibić, and J. Šepić
Nat. Hazards Earth Syst. Sci., 13, 473–482, https://doi.org/10.5194/nhess-13-473-2013, https://doi.org/10.5194/nhess-13-473-2013, 2013
Related subject area
Climate and Earth system modeling
A perspective on the next generation of Earth system model scenarios: towards representative emission pathways (REPs)
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
CSDMS Data Components: data–model integration tools for Earth surface processes modeling
A generic algorithm to automatically classify urban fabric according to the local climate zone system: implementation in GeoClimate 0.0.1 and application to French cities
Modelling water isotopologues (1H2H16O, 1H217O) in the coupled numerical climate model iLOVECLIM (version 1.1.5)
Accurate assessment of land–atmosphere coupling in climate models requires high-frequency data output
Towards variance-conserving reconstructions of climate indices with Gaussian process regression in an embedding space
A diatom extension to the cGEnIE Earth system model – EcoGEnIE 1.1
Carbon isotopes in the marine biogeochemistry model FESOM2.1-REcoM3
Flux coupling approach on an exchange grid for the IOW Earth System Model (version 1.04.00) of the Baltic Sea region
Using EUREC4A/ATOMIC field campaign data to improve trade wind regimes in the Community Atmosphere Model
Multivariate adjustment of drizzle bias using machine learning in European climate projections
New model ensemble reveals how forcing uncertainty and model structure alter climate simulated across CMIP generations of the Community Earth System Model
Quantifying wildfire drivers and predictability in boreal peatlands using a two-step error-correcting machine learning framework in TeFire v1.0
Benchmarking GOCART-2G in the Goddard Earth Observing System (GEOS)
Energy-conserving physics for nonhydrostatic dynamics in mass coordinate models
Evaluation and optimisation of the soil carbon turnover routine in the MONICA model (version 3.3.1)
Assessing the sensitivity of aerosol mass budget and effective radiative forcing to horizontal grid spacing in E3SMv1 using a regional refinement approach
Towards the definition of a solar forcing dataset for CMIP7
ibicus: a new open-source Python package and comprehensive interface for statistical bias adjustment and evaluation in climate modelling (v1.0.1)
Disentangling the hydrological and hydraulic controls on streamflow variability in Energy Exascale Earth System Model (E3SM) V2 – a case study in the Pantanal region
Constraining the carbon cycle in JULES-ES-1.0
The utility of simulated ocean chlorophyll observations: a case study with the Chlorophyll Observation Simulator Package (version 1) in CESMv2.2
GeoPDNN 1.0: a semi-supervised deep learning neural network using pseudo-labels for three-dimensional shallow strata modelling and uncertainty analysis in urban areas from borehole data
The prototype NOAA Aerosol Reanalysis version 1.0: description of the modeling system and its evaluation
Malte Meinshausen, Carl-Friedrich Schleussner, Kathleen Beyer, Greg Bodeker, Olivier Boucher, Josep G. Canadell, John S. Daniel, Aïda Diongue-Niang, Fatima Driouech, Erich Fischer, Piers Forster, Michael Grose, Gerrit Hansen, Zeke Hausfather, Tatiana Ilyina, Jarmo S. Kikstra, Joyce Kimutai, Andrew D. King, June-Yi Lee, Chris Lennard, Tabea Lissner, Alexander Nauels, Glen P. Peters, Anna Pirani, Gian-Kasper Plattner, Hans Pörtner, Joeri Rogelj, Maisa Rojas, Joyashree Roy, Bjørn H. Samset, Benjamin M. Sanderson, Roland Séférian, Sonia Seneviratne, Christopher J. Smith, Sophie Szopa, Adelle Thomas, Diana Urge-Vorsatz, Guus J. M. Velders, Tokuta Yokohata, Tilo Ziehn, and Zebedee Nicholls
Geosci. Model Dev., 17, 4533–4559, https://doi.org/10.5194/gmd-17-4533-2024, https://doi.org/10.5194/gmd-17-4533-2024, 2024
Short summary
Short summary
The scientific community is considering new scenarios to succeed RCPs and SSPs for the next generation of Earth system model runs to project future climate change. To contribute to that effort, we reflect on relevant policy and scientific research questions and suggest categories for representative emission pathways. These categories are tailored to the Paris Agreement long-term temperature goal, high-risk outcomes in the absence of further climate policy and worlds “that could have been”.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Cited articles
Akhtar, N., Brauch, J., and Ahrens, B.: Climate modeling over the
Mediterranean Sea: impact of resolution and ocean coupling, Clim. Dynam., 51,
933–948, https://doi.org/10.1007/s00382-017-3570-8, 2018.
Amante, C. and Eakins, B. W.: ETOPO1 1 arc-minute global relief model:
procedures, data sources and analysis, in: NOAA Technical Memorandum NESDIS,
NGDC-24, NOAA, Boulder, Colorado, 2009.
Artegiani, A., Bregant, D., Paschini, E., Pinardi, N., Raicich, F., and
Russo, A.: The Adriatic Sea general circulation, part I: air-sea
interactions and water mass structure, J. Phys. Oceanogr., 27, 1492–1514,
https://doi.org/10.1175/1520-0485(1997)027<1492:TASGCP>2.0.CO;2,
1997.
Batistić, M., Garić, R., and Molinero, J. C.: Interannual variations
in Adriatic Sea zooplankton mirror shifts in circulation regimes in the
Ionian Sea, Clim. Res., 61, 231–240, https://doi.org/10.3354/cr01248, 2014.
Beg Paklar, G., Isakov, V., Koračin, D., Kourafalou, V., and Orlić, M.: A case study of bora-driven flow and density changes on the Adriatic shelf (January 1987), Cont. Shelf Res., 21, 1751–1783,
https://doi.org/10.1016/S0278-4343(01)00029-2, 2001.
Benetazzo, A., Bergamasco, A., Bonaldo, D., Falcieri, F. M., Sclavo, M.,
Langone, L., and Carniel, S.: Response of the Adriatic Sea to an intense
cold air outbreak: Dense water dynamics and wave-induced transport, Prog.
Oceanogr., 128, 115–138, https://doi.org/10.1016/j.pocean.2014.08.015, 2014.
Bergamasco, A., Oguz, T., and Malanotte-Rizzoli, P.: Modeling dense water
mass formation and winter circulation in the northern and central Adriatic
Sea, J. Marine Syst., 20, 279–300,
https://doi.org/10.1016/S0924-7963(98)00087-6, 1999.
Boldrin, A., Carniel, S., Giani, M., Marini, M., Bernardi Aubry, F., Campanelli,
A., Grilli, F., and Russo, A.: Effects of bora wind on physical and
biogeochemical properties of stratified waters in the northern Adriatic, J.
Geophys. Res.-Oceans, 114, C08S92, https://doi.org/10.1029/2008JC004837, 2009.
Burrage, D. M., Book, J. W., and Martin, P. J.: Eddies and filaments of the
Western Adriatic Current near Cape Gargano: Analysis and prediction, J. Marine Syst., 78, S205–S226, https://doi.org/10.1016/j.jmarsys.2009.01.024, 2009.
Carniel, S., Benetazzo, A., Bonaldo, D., Falcieri, F. M., Miglietta, M. M.,
Ricchi, A., and Sclavo, M.: Scratching beneath the surface while coupling
atmosphere, ocean and waves: Analysis of a dense water formation event,
Ocean Model., 101, 101–112, https://doi.org/10.1016/j.ocemod.2016.03.007, 2016.
Cavaleri, L. and Bertotti, L.: In search of the correct wind and wave fields
in a minor basin, Mon. Weather Rev., 125, 1964–1975,
https://doi.org/10.1175/1520-0493(1997)125<1964:ISOTCW>2.0.CO;2,
1997.
Cavaleri, L., Bertotti, L., Buizza, R., Buzzi, A., Masato, V., Umgiesser,
G., and Zampieri, M.: Predictability of extreme meteo-oceanographic events
in the Adriatic Sea, Q. J. R. Meteorol. Soc., 136, 400–413,
https://doi.org/10.1002/qj.567, 2010.
Cavaleri, L., Abdalla, S., Benetazzo, A., Bertotti, L., Bidlot, J-R,
Breivik, Ø., Carniel, S., Jensen, R. E., Portilla-Yandun, Rogers, W. E.,
Roland, A., Sanchez-Arcilla, A., Smith, J. M., Staneva, J., Toledo, Y., van
Vledder, G. P., and van der Westhuysen, A. J.: Wave modelling in coastal and
inner seas, Prog. Oceanogr., 167, 164–233,
https://doi.org/10.1016/j.pocean.2018.03.010, 2018.
Chapman, D. C.: Numerical treatment of cross-shelf open boundaries in a
barotropic coastal ocean model, J. Phys. Oceanogr., 15, 1060–1075,
https://doi.org/10.1175/1520-0485(1985)015<1060:NTOCSO>2.0.CO;2,
1985.
Cushman-Roisin, B. and Naimie, C. E.: A 3d finite-element model of the
Adriatic tides, J. Mar. Syst., 37, 279–297, https://doi.org/10.1016/S0924-7963(02)00204-X, 2002.
Darmaraki, S., Somot, S., Sevault, F., Nabat, P., Cabos Narvaez, W. D.,
Cavicchia, L., Djurdjevic, V., Li, L., Sannino, G., and Sein, D. V.: Future
evolution of Marine Heatwaves in the Mediterranean Sea, Clim. Dynam., 53,
1371–1392, https://doi.org/10.1007/s00382-019-04661-z, 2019.
Denamiel, C. L.: AdriSC Climate Model: Evaluation Run, OSF [code], https://doi.org/10.17605/OSF.IO/ZB3CM, 2021.
Denamiel, C., Šepić, J., Ivanković, D., and Vilibić, I.: The
Adriatic Sea and Coast modelling suite: Evaluation of the meteotsunami
forecast component, Ocean Model., 135, 71–93,
https://doi.org/10.1016/j.ocemod.2019.02.003, 2019.
Denamiel, C., Pranić, P., Quentin, F., Mihanović, H., and
Vilibić, I.: Pseudo-global warming projections of extreme wave storms in
complex coastal regions: the case of the Adriatic Sea, Clim. Dynam.,
55, 2483–2509, https://doi.org/10.1007/s00382-020-05397-x, 2020a.
Denamiel, C., Tojčić, I., and Vilibić, I.: Far future climate
(2060–2100) of the northern Adriatic air–sea heat transfers associated
with extreme bora events, Clim. Dynam., 55, 3043–3066,
https://doi.org/10.1007/s00382-020-05435-8, 2020b.
Denamiel, C., Tojčić, I., and Vilibić, I.: Balancing accuracy
and efficiency of atmospheric models in the northern Adriatic during severe
bora events, J. Geophys. Res.-Atmos., 126, e2020JD033516,
https://doi.org/10.1029/2020JD033516, 2021a.
Denamiel, C., Pranić, P., Ivanković, D., Tojčić, I., and Vilibić, I.: Performance of the Adriatic Sea and Coast (AdriSC) climate component – a COAWST V3.3-based coupled atmosphere–ocean modelling suite: atmospheric dataset, Geosci. Model Dev., 14, 3995–4017, https://doi.org/10.5194/gmd-14-3995-2021, 2021b.
Di Luca, A., Flaounas, E., Drobinski, P., and Lebeaupin-Brossier, C.: The
atmospheric component of the Mediterranean Sea water budget in a WRF
multi-physics ensemble and observations, Clim. Dynam., 43,
2349–2375, https://doi.org/10.1007/s00382-014-2058-z, 2014.
Dunić, N., Vilibić, I., Šepić, J., Mihanović, H.,
Sevault, F., Somot, S., Waldman, R., Nabat, P., Arsouze, T., Pennel, R.,
Jordà, G., and Precali, R.: Performance of multi-decadal ocean
simulations in the Adriatic Sea, Ocean Model., 134, 84–109,
https://doi.org/10.1016/j.ocemod.2019.01.006, 2019.
Dutour Sikirić, M., Janeković, I., and Kuzmić, M.: A new
approach to bathymetry smoothing in sigma-coordinate ocean models, Ocean
Model., 29, 128–136, https://doi.org/10.1016/j.ocemod.2009.03.009, 2009.
Egbert, G. D. and Erofeeva, S. Y.: Efficient inverse modeling of barotropic
ocean tides, J. Atmos. Ocean. Technol., 19, 183–204,
https://doi.org/10.1175/1520-0426(2002)019<0183:EIMOBO>2.0.CO;2,
2002.
Egbert, G. D., Bennett, A. F., and Foreman, M. G. G.: Topex/Poseidon tides
estimated using a global inverse model, J. Geophys. Res., 99, 24821–24852,
https://doi.org/10.1029/94JC01894, 1994.
Escudier, R., Clementi, E., Omar, M., Cipollone, A., Pistoia, J., Aydogdu,
A., Drudi, M., Grandi, A., Lyubartsev, V., Lecci, R., Cretí, S.,
Masina, S., Coppini, G., and Pinardi, N.: Mediterranean Sea Physical
Reanalysis (CMEMS MED-Currents) (Version 1), Copernicus
Monitoring Environment Marine Service (CMEMS) [data set],
https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_PHY_006_004_E3R1, 2020.
Flather, R. A.: A tidal model of the north-west European continental shelf,
Mem. Soc. R. Sci Liege, 6, 141–164, 1976.
Gačić, M., Civitarese, G., Miserocchi, S., Cardin, V., Crise, A.,
and Mauri, E.: The open-ocean convection in the Southern Adriatic: A
controlling mechanism of the spring phytoplankton bloom, Cont. Shelf Res.,
22, 1897–1908, https://doi.org/10.1016/S0278-4343(02)00050-X, 2002.
Gačić, M., Borzelli, G. E., Civitarese, G., Cardin, V., and Yari,
S.: Can internal processes sustain reversals of the ocean upper circulation?
The Ionian Sea example, Geophys. Res. Lett., 37, L09608,
https://doi.org/10.1029/2010GL043216, 2010.
Gačić, M., Civitarese, G., Eusebi Borzelli, G. L.,
Kovačević, V., Poulain, P.-M., Theocharis, A., Menna, M., Catucci,
A., and Zarokanellos, N.: On the relationship between the decadal
oscillations of the northern Ionian Sea and the salinity distributions in
the eastern Mediterranean, J. Geophys. Res., 116, C12002,
https://doi.org/10.1029/2011JC007280, 2011.
Gačić, M., Civitarese, G., Kovačević, V., Ursella, L., Bensi, M., Menna, M., Cardin, V., Poulain, P.-M., Cosoli, S., Notarstefano, G., and Pizzi, C.: Extreme winter 2012 in the Adriatic: an example of climatic effect on the BiOS rhythm, Ocean Sci., 10, 513–522, https://doi.org/10.5194/os-10-513-2014, 2014.
Hersbach, H., de Rosnay, P., Bell, B., Schepers, D., Simmons, A., Soci, C.,
Abdalla, S., Alonso-Balmaseda, M., Balsamo, G., Bechtold, P., Berrisford,
P., Bidlot, J.-R., de Boisséson, E., Bonavita, M., Browne, P., Buizza,
R., Dahlgren, P., Dee, D., Dragani, R., Diamantakis, M., Flemming, J.,
Forbes, R., Geer, A.J., Haiden, T., Hólm, E., Haimberger, L., Hogan, R.,
Horányi, A., Janiskova, M., Laloyaux, P., Lopez, P., Munoz-Sabater, J.,
Peubey, C., Radu, R., Richardson, D., Thépaut, J.-N., Vitart, F., Yang,
X., Zsótér, E., and Zuo, H.: Operational global reanalysis:
Progress, future directions and synergies with NWP, ECMWF ERA Report Series No. 27, https://doi.org/10.21957/tkic6g3wm, 2018.
Horak, J., Hofer, M., Gutmann, E., Gohm, A., and Rotach, M. W.: A process-based evaluation of the Intermediate Complexity Atmospheric Research Model (ICAR) 1.0.1, Geosci. Model Dev., 14, 1657–1680, https://doi.org/10.5194/gmd-14-1657-2021, 2021.
Ivanković, D., Denamiel, C., and Jelavić, D.: Web visualization of
data from numerical models and real-time stations network in frame of
Adriatic Sea and Coast (AdriSC) Meteotsunami Forecast, OCEANS 2019 –
Marseille, France, 17-20 June 2019, 1–5, https://doi.org/10.1109/OCEANSE.2019.8867225, 2019.
Janeković, I. and Kuzmić, M.: Numerical simulation of the Adriatic Sea principal tidal constituents, Ann. Geophys., 23, 3207–3218, https://doi.org/10.5194/angeo-23-3207-2005, 2005.
Janeković, I., Mihanović, H., Vilibić, I., and Tudor, M.:
Extreme cooling and dense water formation estimates in open and coastal
regions of the Adriatic Sea during the winter of 2012, J. Geophys. Res.-Oceans, 119, 3200–3218, https://doi.org/10.1002/2014JC009865, 2014.
Janeković, I., Mihanović, H., Vilibić, I., Grčić, B.,
Ivatek-Šahdan, S., Tudor, M., and Djakovac, T.: Multi-platform 4D-Var
data assimilation for improving the Adriatic Sea dynamics, Ocean Model.,
146, 101538, https://doi.org/10.1016/j.ocemod.2019.101538, 2020.
Johnson, N. C., Krishnamurthy, L., Wittenberg, A. T., Xiang, B., Vecchi, G.
A., Kapnick, S. B., and Pascale, S.: The Impact of Sea Surface Temperature
Biases on North American Precipitation in a High-Resolution Climate
Model, J. Climate, 33, 2427–2447, https://doi.org/10.1175/JCLI-D-19-0417.1,
2020.
JPL MUR MEaSUREs Project: GHRSST Level 4 MUR Global Foundation Sea Surface
Temperature Analysis (v4.1), Ver. 4.1. PO.DAAC, CA, USA,
https://doi.org/10.5067/GHGMR-4FJ04, 2015.
Krasakopoulou, E., Souvermezoglou, E., Minas, H.J., and Scoullos, M: Organic
matter stoichiometry based on oxygen consumption—nutrients regeneration
during a stagnation period in Jabuka Pit (middle Adriatic Sea), Cont. Shelf
Res., 25, 127–142, https://doi.org/10.1016/j.csr.2004.07.026, 2005.
Larson, J., Jacob, R., and Ong, E.: The model coupling toolkit: a new
fortran90 toolkit for building multiphysics parallel coupled models,
Int. J. High Perform. Comput. Appl., 19, 277–292, https://doi.org/10.1177/1094342005056115, 2005.
L'Hévéder, B., Li, L., Sevault, F., and Somot, S.: Interannual
variability of deep convection in the Northwestern Mediterranean simulated
with a coupled AORCM, Clim. Dynam., 41,
937–960, https://doi.org/10.1007/s00382-012-1527-5, 2013.
Ličer, M., Smerkol, P., Fettich, A., Ravdas, M., Papapostolou, A., Mantziafou, A., Strajnar, B., Cedilnik, J., Jeromel, M., Jerman, J., Petan, S., Malačič, V., and Sofianos, S.: Modeling the ocean and atmosphere during an extreme bora event in northern Adriatic using one-way and two-way atmosphere–ocean coupling, Ocean Sci., 12, 71–86, https://doi.org/10.5194/os-12-71-2016, 2016.
Lipizer, M., Partescano, E., Rabitti, A., Giorgetti, A., and Crise, A.: Qualified temperature, salinity and dissolved oxygen climatologies in a changing Adriatic Sea, Ocean Sci., 10, 771–797, https://doi.org/10.5194/os-10-771-2014, 2014.
Liu, F., Mikolajewicz, U., and Six, K. D. : Drivers of the decadal variability of the North Ionian Gyre upper layer circulation during 1910–2010: a regional modelling study, Clim. Dynam., https://doi.org/10.1007/s00382-021-05714-y, 2021.
Ljubenkov, I.: Hydrodynamic modeling of stratified estuary: case study of
the Jadro River (Croatia), J. Hydrol. Hydromech., 63, 29–37,
https://doi.org/10.1515/johh-2015-0001, 2015.
Ludwig, W., Dumont, E., Meybeck, M., and Heussner, S.: River discharges of
water and nutrients to the Mediterranean Sea: major drivers for ecosystem
changes during past and future decades?, Prog. Oceanogr., 80, 199–217,
https://doi.org/10.1016/j.pocean.2009.02.001, 2009.
Malačič, V. and Petelin, B.: Climatic circulation in the Gulf of Trieste (northern Adriatic), J. Geophys. Res., 114, C07002, https://doi.org/10.1029/2008JC004904, 2009.
Manca, B. B., Kovačević, V., Gačić, M., and Viezzoli, D.:
Dense water formation in the Southern Adriatic Sea and spreading into the
Ionian Sea in the period 1997–1999, J. Mar. Syst., 33–34, 133–154,
https://doi.org/10.1016/S0924-7963(02)00056-8, 2002.
Mantziafou, A. and Lascaratos, A.: An eddy resolving numerical study of the
general circulation and deep-water formation in the Adriatic Sea, Deep-Sea
Res. I, 51, 251–292, https://doi.org/10.1016/j.dsr.2004.03.006, 2004.
Mantziafou, A. and Lascaratos, A.: Deep-water formation in the Adriatic Sea:
interannual simulations for the years 1979–1999, Deep-Sea Res. I, 55,
1403–1427, https://doi.org/10.1016/j.dsr.2008.06.005, 2008.
Marchesiello, P., McWilliams, J. C., and Shchepetkin, A.: Open boundary
conditions for long-term integration of regional oceanic models, Ocean
Model., 3, 1–20, https://doi.org/10.1016/S1463-5003(00)00013-5, 2001.
Martin, P. J., Book, J. W., Burrage, D. M., Rowley, C. D., and Tudor, M.:
Comparison of model-simulated and observed currents in the central Adriatic
during DART, J. Geophys. Res., 114, C01S05, https://doi.org/10.1029/2008JC004842, 2009.
May, P. W.: Climatological flux estimates in the Mediterranean Sea: Part 1.
Winds and wind stresses, NORDA Report 54, NSTL Station, Mississippi 39529, USA, 1982.
McKiver, W. J., Sannino, G., Braga, F., and Bellafiore, D.: Investigation of model capability in capturing vertical hydrodynamic coastal processes: a case study in the north Adriatic Sea, Ocean Sci., 12, 51–69, https://doi.org/10.5194/os-12-51-2016, 2016.
Mejia, J. F., Koračin, D., and Wilcox, E. M.: Effect of coupled global climate
models sea surface temperature biases on simulated climate of the western
United States, Int. J. Climatol., 38, 5386–5404, https://doi.org/10.1002/joc.5817,
2018.
Mihanović, H., Vilibić, I., Carniel, S., Tudor, M., Russo, A., Bergamasco, A., Bubić, N., Ljubešić, Z., Viličić, D., Boldrin, A., Malačič, V., Celio, M., Comici, C., and Raicich, F.: Exceptional dense water formation on the Adriatic shelf in the winter of 2012, Ocean Sci., 9, 561–572, https://doi.org/10.5194/os-9-561-2013, 2013.
Mihanović, H., Janeković, I., Vilibić, I., Bensi, M., and
Kovačević, V.: Modelling Interannual Changes in Dense Water
Formation on the Northern Adriatic Shelf, Pure Appl.
Geophys., 175, 4065–4081, https://doi.org/10.1007/s00024-018-1935-5, 2018.
National Centers for Environmental Information: Daily L4 Optimally
Interpolated SST (OISST) In situ and AVHRR Analysis, Ver. 2.0. PO.DAAC, CA,
USA, https://doi.org/10.5067/GHAAO-4BC02, 2016.
Oddo, P. and Guarnieri, A.: A study of the hydrographic conditions in the Adriatic Sea from numerical modelling and direct observations (2000–2008), Ocean Sci., 7, 549–567, https://doi.org/10.5194/os-7-549-2011, 2011.
Oddo, P., Pinardi, N., and Zavatarelli, M.: A numerical study of the
interannual variability of the Adriatic Sea (2000–2002), Sci. Total
Environ., 353, 39–56, https://doi.org/10.1016/j.scitotenv.2005.09.061, 2005.
Orlanski, I.: A simple boundary condition for unbounded hyperbolic flows, J.
Comput. Phys., 21, 251–269, https://doi.org/10.1016/0021-9991(76)90023-1, 1976.
Orlić, M., Dadić, V., Grbec, B., Leder, N., Marki, A., Matić,
F., Mihanović, H., Beg Paklar, G., Pasarić, M., Pasarić, Z., and
Vilibić, I.: Wintertime buoyancy forcing, changing seawater properties
and two different circulation systems produced in the Adriatic, J. Geophys.
Res., 112, C03S07, https://doi.org/10.1029/2005JC003271, 2006.
Pano, N. and Abdyli, B.: Maximum floods and their regionalization on the
Albanian hydrographic river network, in: International Conference on Flood
Estimation, 6–8 March 2002, CHR. Report II,17, Bern, Switzerland, 379–388, 2002.
Pano, N., Frasheri, A., and Avdyli, B.: The climatic change impact in water
potential processe on the Albanian hydrographic river network, in:
International Congress on Environmental Modelling and Software, 5–8 July 2010, Ottawa, Ontario, Canada, available at:
https://scholarsarchive.byu.edu/iemssconference/2010/all/266 (last access: 20 September 2021), 2010.
Parras-Berrocal, I. M., Vazquez, R., Cabos, W., Sein, D., Mañanes, R., Perez-Sanz, J., and Izquierdo, A.: The climate change signal in the Mediterranean Sea in a regionally coupled atmosphere–ocean model, Ocean Sci., 16, 743–765, https://doi.org/10.5194/os-16-743-2020, 2020.
Pinardi, N., Allen, I., Demirov, E., De Mey, P., Korres, G., Lascaratos, A., Le Traon, P.-Y., Maillard, C., Manzella, G., and Tziavos, C.: The Mediterranean ocean forecasting system: first phase of implementation (1998–2001), Ann. Geophys., 21, 3–20, https://doi.org/10.5194/angeo-21-3-2003, 2003.
Poli, P., Hersbach, H., Dee, D. P., Berrisford, P., Simmons, A. J., Vitart,
F., Laloyaux, P., Tan, D. G., Peubey, C., Thépaut, J. N., Trémolet,
Y., Hólm, E.V., Bonavita, M., Isaksen, L., and Fisher, M.: ERA-20C: An
atmospheric reanalysis of the twentieth century, J. Climate, 29,
4083–4097, https://doi.org/10.1175/JCLI-D-15-0556.1, 2016.
Pranić, P.: Evaluation of the AdriSC Climate Model: Ocean Part, OSF [data set], https://doi.org/10.17605/OSF.IO/W8F4J, 2021.
Prein, A. F., Langhans, W., Fosser, G., Ferrone, A., Ban, N., Goergen,
K., Keller, M., Tölle, M., Gutjahr, O., Feser, F., Brisson, E., Kollet,
S., Schmidli, J., van Lipzig, N. P. M., and Leung, R.: A review on regional
convection-permitting climate modeling: Demonstrations, prospects and
challenges, Rev. Geophys., 53, 323–361, https://doi.org/10.1002/2014RG000475, 2015.
Pullen, J., Doyle, J., and Signell, R.: Two-way air–sea coupling: a study
of the Adriatic, Mon. Weather Rev., 134, 1465–1483, https://doi.org/10.1175/MWR3137.1,
2006.
Pullen, J., Doyle, J. D., Haack, T., Dorman, C., Signell, R. P., and Lee,
C. M.: Bora event variability and the role of air-sea feedback, J. Geophys.
Res., 112, C03S18, https://doi.org/10.1029/2006JC003726, 2007.
Raicich, F.: Notes on the flow rates of the Adriatic rivers, Technical
Report RF 02/94, CNR, Istituto sperimentale talassografico, Trieste,
Italy, 8 pp., 1994.
Raicich, F.: On the fresh water balance of the Adriatic Sea, J. Mar. Syst.,
9, 305–319, https://doi.org/10.1016/S0924-7963(96)00042-5, 1996.
Ricchi, A., Miglietta, M. M., Falco, P. P., Benetazzo, A., Bonaldo, D.,
Bergamasco, A., Sclavo, M., and Carniel, S.: On the use of a coupled
ocean–atmosphere–wave model during an extreme cold air outbreak over the
Adriatic Sea, Atmos. Res., 172–173, 48–65,
https://doi.org/10.1016/j.atmosres.2015.12.023, 2016.
Schär, C., Frei, C., Luthi, D., and Davies, H. C.: Surrogate
climate-change scenarios for regional climate models, Geophys. Res. Lett.,
23, 669–672, https://doi.org/10.1029/96GL00265, 1996.
Schär, C., Fuhrer, O., Arteaga, A., Ban, N., Charpilloz, C., Di
Girolamo, S., Hentgen, L., Hoefler, T., Lapillonne, X., Leutwyler, D.,
Osterried, K., Panosetti, D., Rüdisühli, S., Schlemmer, L.,
Schulthess, T. C., Sprenger, M., Ubbiali, S., and Wernli, H.:
Kilometer-Scale Climate Models: Prospects and Challenges, B. Am. Meteorol. Soc., 101, E567–E587, https://doi.org/10.1175/BAMS-D-18-0167.1, 2020.
Sevault, F., Somot, S., Alias, A., Dubois, C., Lebeaupin-Brossier, C.,
Nabat, P., Adloff, F., Déqué, M., and Decharme, B.: A fully coupled
Mediterranean regional climate system model: design and evaluation of the
ocean component for the 1980–2012 period, Tellus A, 66, 23967,
https://doi.org/10.3402/tellusa.v66.23967, 2014.
Shchepetkin, A. F. and McWilliams, J. C.: Correction and commentary for “ocean forecasting in terrain-following coordinates: formulation and skill assessment of the regional ocean modeling system” by Haidvogel et al., J. Comput. Phys., 227, pp. 3595–3624, J. Comput. Phys., 228, 8985–9000, https://doi.org/10.1016/j.jcp.2009.09.002, 2009.
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, D. M., Wang, W., and Powers, J. G.: A description of the advanced research WRF version 2, NCAR Technical Note, NCAR/TN-468+STR, https://doi.org/10.5065/D6DZ069T, 2005.
Smolarkiewicz, P. K. and Grabowski, W. W.: The multidimensional positive
definite advection transport algorithm: nonoscillatory option, J. Comput.
Phys., 86, 355–375, https://doi.org/10.1016/0021-9991(90)90105-A, 1990.
Somot, S., Sevault, F., and Déqué, M.: Transient climate change
scenario simulation of the Mediterranean Sea for the twenty-first century
using a high-resolution ocean circulation model, Clim. Dynam. 27, 851–879,
https://doi.org/10.1007/s00382-006-0167-z, 2006.
Somot, S., Ruti, P., Ahrens, B., Coppola, E., Jordà, G., Sannino, G.,
and Solmon, F.: Editorial for the Med-CORDEX special issue, Clim. Dynam., 51,
771–777, https://doi.org/10.1007/s00382-018-4325-x, 2018.
Supić, N. and Orlić, M.: Seasonal and interannual variability of the
northern Adriatic surface fluxes, J. Marine Syst. 20, 205–229,
https://doi.org/10.1016/S0924-7963(98)00083-9, 1999.
Taylor, K.: Summarizing multiple aspects of model performance in a single
diagram, J. Geophys. Res, 106, 7183–7192, https://doi.org/10.1029/2000JD900719,
2001.
Theocharis, A., Krokos, G., Velaoras, D., and Korres, G.: An internal
mechanism driving the alternation of the Eastern Mediterranean dense/deep
water sources, in: The Mediterranean Sea: Temporal Variability
and Spatial Patterns, Geophysical Monograph Series, edited by: Eusebi Borzelli, G. L., Gačić M., Lionello, P., and Malanotte-Rizzoli, P., AGU, 113–137, https://doi.org/10.1002/9781118847572.ch8, 2014.
Tudor, M., Ivatek-Sahdan, S., Stanešć, A., Horvath, K., and
Bajić, A.: Forecasting weather in Croatia using ALADIN numerical weather
prediction model, in: Climate Change and Regional/Local Responses, edited by:
Zhang, Y. and Ray, P., InTech, Rijeka, Croatia, 59–88, 2013.
Umgiesser, G., Bajo, M., Ferrarin, C., Cucco, A., Lionello, P., Zanchettin, D., Papa, A., Tosoni, A., Ferla, M., Coraci, E., Morucci, S., Crosato, F., Bonometto, A., Valentini, A., Orlić, M., Haigh, I. D., Nielsen, J. W., Bertin, X., Fortunato, A. B., Pérez Gómez, B., Alvarez Fanjul, E., Paradis, D., Jourdan, D., Pasquet, A., Mourre, B., Tintoré, J., and Nicholls, R. J.: The prediction of floods in Venice: methods, models and uncertainty (review article), Nat. Hazards Earth Syst. Sci., 21, 2679–2704, https://doi.org/10.5194/nhess-21-2679-2021, 2021.
Umlauf, L. and Burchard, H.: A generic length-scale equation for geophysical
turbulence models, J. Mar. Res., 61, 235–265,
https://doi.org/10.1357/002224003322005087, 2003.
Vested, H. J., Berg, P., and Uhrenholdt, T.: Dense water formation in the
northern Adriatic, J. Mar. Syst., 18, 135–160,
https://doi.org/10.1016/S0924-7963(98)00009-8, 1998.
Vilibić, I. and Orlić, M.: Least squares tracer analysis of water
masses in the South Adriatic (1967–1990), Deep-Sea Res. I, 48, 2297–2330,
https://doi.org/10.1016/S0967-0637(01)00014-0, 2001.
Vilibić, I. and Orlić, M.: Adriatic water masses, their rates of
formation and transport through the Otranto Strait, Deep-Sea Res. I,
49, 1321–1340, https://doi.org/10.1016/S0967-0637(02)00028-6, 2002.
Vilibić, I. and Supić, N.: Dense water generation on a shelf: the
case of the Adriatic Sea, Ocean Dyn., 55, 403–415,
https://doi.org/10.1007/s10236-005-0030-5, 2005.
Vilibić, I., Šepić, and Proust, N.: Observational evidence of a
weakening of thermohaline circulation in the Adriatic Sea, Clim. Res., 55,
217–225, https://doi.org/10.3354/cr01128, 2013.
Vilibić, I., Mihanović, H., Janeković, I., and Šepić,
J.: Modelling the formation of dense water in the northern Adriatic:
sensitivity studies, Ocean Model., 101, 17–29,
https://doi.org/10.1016/j.ocemod.2016.03.001, 2016.
Vilibić, I., Mihanović, H., Janeković, I., Denamiel, C., Poulain, P.-M., Orlić, M., Dunić, N., Dadić, V., Pasarić, M., Muslim, S., Gerin, R., Matić, F., Šepić, J., Mauri, E., Kokkini, Z., Tudor, M., Kovač, Ž., and Džoić, T.: Wintertime dynamics in the coastal northeastern Adriatic Sea: the NAdEx 2015 experiment, Ocean Sci., 14, 237–258, https://doi.org/10.5194/os-14-237-2018, 2018.
Vilibić, I., Zemunik, P., Šepić, J., Dunić, N., Marzouk, O., Mihanović, H., Denamiel, C., Precali, R., and Djakovac, T.: Present climate trends and variability in thermohaline properties of the northern Adriatic shelf, Ocean Sci., 15, 1351–1362, https://doi.org/10.5194/os-15-1351-2019, 2019.
Vörösmarty, C., Fakers, B., and Tucker, B.: River discharge database, version 1.0 (RivDIS vLO), volumes 0 through 6, in: A contribution to IHP-V Theme 1, Technical Documents Series, Technical report, UNESCO, Paris, France, 1996.
Warner, J. C., Armstrong, B., He, R., and Zambon, J. B.: Development of a coupled ocean atmosphere-wave-sediment transport (COAWST) modeling system, Ocean Model, 35, 230–244, https://doi.org/10.1016/j.ocemod.2010.07.010, 2010.
Yang, B., Zhang, Y., Qian, Y., Song, F., Leung, L. R., Wu, P., Guo, Z., Lu, Y., and Huang, A.: Better monsoon precipitation in
coupled climate models due to bias compensation, npj Clim. Atmos.
Sci., 2, 43, https://doi.org/10.1038/s41612-019-0100-x, 2019.
Zavatarelli, M. and Pinardi, N.: The Adriatic Sea modelling system: a nested approach, Ann. Geophys., 21, 345–364, https://doi.org/10.5194/angeo-21-345-2003, 2003.
Zavatarelli, M., Pinardi, N., Kourafalou, V. H., and Maggiore, A.: Diagnostic
and prognostic model studies of the Adriatic Sea general circulation:
Seasonal variability, J. Geophys. Res., 107, 3004, https://doi.org/10.1029/2000JC000210,
2002.
Zlotnicki, V., Qu, Z., and Willis, J.: SEA_SURFACE_HEIGHT_ALT_GRIDS_L4_2SATS_5DAY_6THDEG_V_JPL1609, Ver. 1812, PO.DAAC [data set], CA, USA, https://doi.org/10.5067/SLREF-CDRV2, 2019.
Zore-Armanda, M.: Les masses d'eau de la mer Adriatique, Acta Adriat., 10,
5–88, 1963.
Download
The requested paper has a corresponding corrigendum published. Please read the corrigendum first before downloading the article.
- Article
(25410 KB) - Full-text XML
- Corrigendum
-
Supplement
(1931 KB) - BibTeX
- EndNote
Short summary
The Adriatic Sea and Coast model was developed due to the need for higher-resolution climate models and longer-term simulations to capture coastal atmospheric and ocean processes at climate scales in the Adriatic Sea. The ocean results of a 31-year-long simulation were compared to the observational data. The evaluation revealed that the model is capable of reproducing the observed physical properties with good accuracy and can be further used to study the dynamics of the Adriatic–Ionian basin.
The Adriatic Sea and Coast model was developed due to the need for higher-resolution climate...
