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
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.
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
Comparing the Performance of Julia on CPUs versus GPUs and Julia-MPI versus Fortran-MPI: a case study with MPAS-Ocean (Version 7.1)
All aboard! Earth system investigations with the CH2O-CHOO TRAIN v1.0
The Canadian Atmospheric Model version 5 (CanAM5.0.3)
The Teddy tool v1.1: temporal disaggregation of daily climate model data for climate impact analysis
Assimilation of the AMSU-A radiances using the CESM (v2.1.0) and the DART (v9.11.13)–RTTOV (v12.3)
Modernizing the open-source community Noah with multi-parameterization options (Noah-MP) land surface model (version 5.0) with enhanced modularity, interoperability, and applicability
Simulated stable water isotopes during the mid-Holocene and pre-industrial periods using AWI-ESM-2.1-wiso
Rainbows and climate change: a tutorial on climate model diagnostics and parameterization
ModE-Sim – a medium-sized atmospheric general circulation model (AGCM) ensemble to study climate variability during the modern era (1420 to 2009)
MESMAR v1: a new regional coupled climate model for downscaling, predictability, and data assimilation studies in the Mediterranean region
Climate model Selection by Independence, Performance, and Spread (ClimSIPS v1.0.1) for regional applications
IceTFT v1.0.0: interpretable long-term prediction of Arctic sea ice extent with deep learning
The KNMI Large Ensemble Time Slice (KNMI–LENTIS)
ENSO statistics, teleconnections, and atmosphere–ocean coupling in the Taiwan Earth System Model version 1
Using probabilistic machine learning to better model temporal patterns in parameterizations: a case study with the Lorenz 96 model
The Regional Aerosol Model Intercomparison Project (RAMIP)
DSCIM-Coastal v1.1: an open-source modeling platform for global impacts of sea level rise
TIMBER v0.1: a conceptual framework for emulating temperature responses to tree cover change
Recalibration of a three-dimensional water quality model with a newly developed autocalibration toolkit (EFDC-ACT v1.0.0): how much improvement will be achieved with a wider hydrological variability?
Description and evaluation of the JULES-ES set-up for ISIMIP2b
Simplified Kalman smoother and ensemble Kalman smoother for improving reanalyses
Modelling the terrestrial nitrogen and phosphorus cycle in the UVic ESCM
Modeling river water temperature with limiting forcing data: Air2stream v1.0.0, machine learning and multiple regression
A machine learning approach targeting parameter estimation for plant functional type coexistence modeling using ELM-FATES (v2.0)
The fully coupled regionally refined model of E3SM version 2: overview of the atmosphere, land, and river results
The mixed-layer depth in the Ocean Model Intercomparison Project (OMIP): impact of resolving mesoscale eddies
A new simplified parameterization of secondary organic aerosol in the Community Earth System Model Version 2 (CESM2; CAM6.3)
Deep learning for stochastic precipitation generation – deep SPG v1.0
Developing spring wheat in the Noah-MP land surface model (v4.4) for growing season dynamics and responses to temperature stress
Robust 4D climate-optimal flight planning in structured airspace using parallelized simulation on GPUs: ROOST V1.0
The Earth system model CLIMBER-X v1.0 – Part 2: The global carbon cycle
SMLFire1.0: a stochastic machine learning (SML) model for wildfire activity in the western United States
LandInG 1.0: a toolbox to derive input datasets for terrestrial ecosystem modelling at variable resolutions from heterogeneous sources
Conservation of heat and mass in P-SKRIPS version 1: the coupled atmosphere–ice–ocean model of the Ross Sea
Predicting the climate impact of aviation for en-route emissions: the algorithmic climate change function submodel ACCF 1.0 of EMAC 2.53
Implementation of a machine-learned gas optics parameterization in the ECMWF Integrated Forecasting System: RRTMGP-NN 2.0
Differentiable programming for Earth system modeling
Evaluation of CMIP6 model performances in simulating fire weather spatiotemporal variability on global and regional scales
Data-driven aeolian dust emission scheme for climate modelling evaluated with EMAC 2.55.2
Testing the reconstruction of modelled particulate organic carbon from surface ecosystem components using PlankTOM12 and machine learning
An improved method of the Globally Resolved Energy Balance model by the Bayesian networks
Assessing predicted cirrus ice properties between two deterministic ice formation parameterizations
Various ways of using empirical orthogonal functions for climate model evaluation
C-Coupler3.0: an integrated coupler infrastructure for Earth system modelling
FEOTS v0.0.0: a new offline code for the fast equilibration of tracers in the ocean
Pace v0.2: a Python-based performance-portable atmospheric model
Introducing a new floodplain scheme in ORCHIDEE (version 7885): validation and evaluation over the Pantanal wetlands
Earth System Model Aerosol-Cloud Diagnostics Package (ESMAC Diags) Version 2: Assessments of Aerosols, Clouds and Aerosol-Cloud Interactions Through Field Campaign and Long-Term Observations
Hydrological modelling on atmospheric grids: using graphs of sub-grid elements to transport energy and water
The sea level simulator v1.0: a model for integration of mean sea level change and sea level extremes into a joint probabilistic framework
Siddhartha Bishnu, Robert R. Strauss, and Mark R. Petersen
Geosci. Model Dev., 16, 5539–5559, https://doi.org/10.5194/gmd-16-5539-2023, https://doi.org/10.5194/gmd-16-5539-2023, 2023
Short summary
Short summary
Here we test Julia, a relatively new programming language, which is designed to be simple to write, but also fast on advanced computer architectures. We found that Julia is both convenient and fast, but there is no free lunch. Our first attempt to develop an ocean model in Julia was relatively easy, but the code was slow. After several months of further development, we created a Julia code that is as fast on supercomputers as a Fortran ocean model.
Tyler Kukla, Daniel E. Ibarra, Kimberly V. Lau, and Jeremy K. C. Rugenstein
Geosci. Model Dev., 16, 5515–5538, https://doi.org/10.5194/gmd-16-5515-2023, https://doi.org/10.5194/gmd-16-5515-2023, 2023
Short summary
Short summary
The CH2O-CHOO TRAIN model can simulate how climate and the long-term carbon cycle interact across millions of years on a standard PC. While efficient, the model accounts for many factors including the location of land masses, the spatial pattern of the water cycle, and fundamental climate feedbacks. The model is a powerful tool for investigating how short-term climate processes can affect long-term changes in the Earth system.
Jason Neil Steven Cole, Knut von Salzen, Jiangnan Li, John Scinocca, David Plummer, Vivek Arora, Norman McFarlane, Michael Lazare, Murray MacKay, and Diana Verseghy
Geosci. Model Dev., 16, 5427–5448, https://doi.org/10.5194/gmd-16-5427-2023, https://doi.org/10.5194/gmd-16-5427-2023, 2023
Short summary
Short summary
The Canadian Atmospheric Model version 5 (CanAM5) is used to simulate on a global scale the climate of Earth's atmosphere, land, and lakes. We document changes to the physics in CanAM5 since the last major version of the model (CanAM4) and evaluate the climate simulated relative to observations and CanAM4. The climate simulated by CanAM5 is similar to CanAM4, but there are improvements, including better simulation of temperature and precipitation over the Amazon and better simulation of cloud.
Florian Zabel and Benjamin Poschlod
Geosci. Model Dev., 16, 5383–5399, https://doi.org/10.5194/gmd-16-5383-2023, https://doi.org/10.5194/gmd-16-5383-2023, 2023
Short summary
Short summary
Today, most climate model data are provided at daily time steps. However, more and more models from different sectors, such as energy, water, agriculture, and health, require climate information at a sub-daily temporal resolution for a more robust and reliable climate impact assessment. Here we describe and validate the Teddy tool, a new model for the temporal disaggregation of daily climate model data for climate impact analysis.
Young-Chan Noh, Yonghan Choi, Hyo-Jong Song, Kevin Raeder, Joo-Hong Kim, and Youngchae Kwon
Geosci. Model Dev., 16, 5365–5382, https://doi.org/10.5194/gmd-16-5365-2023, https://doi.org/10.5194/gmd-16-5365-2023, 2023
Short summary
Short summary
This is the first attempt to assimilate the observations of microwave temperature sounders into the global climate forecast model in which the satellite observations have not been assimilated in the past. To do this, preprocessing schemes are developed to make the satellite observations suitable to be assimilated. In the assimilation experiments, the model analysis is significantly improved by assimilating the observations of microwave temperature sounders.
Cenlin He, Prasanth Valayamkunnath, Michael Barlage, Fei Chen, David Gochis, Ryan Cabell, Tim Schneider, Roy Rasmussen, Guo-Yue Niu, Zong-Liang Yang, Dev Niyogi, and Michael Ek
Geosci. Model Dev., 16, 5131–5151, https://doi.org/10.5194/gmd-16-5131-2023, https://doi.org/10.5194/gmd-16-5131-2023, 2023
Short summary
Short summary
Noah-MP is one of the most widely used open-source community land surface models in the world, designed for applications ranging from uncoupled land surface and ecohydrological process studies to coupled numerical weather prediction and decadal climate simulations. To facilitate model developments and applications, we modernize Noah-MP by adopting modern Fortran code and data structures and standards, which substantially enhance model modularity, interoperability, and applicability.
Xiaoxu Shi, Alexandre Cauquoin, Gerrit Lohmann, Lukas Jonkers, Qiang Wang, Hu Yang, Yuchen Sun, and Martin Werner
Geosci. Model Dev., 16, 5153–5178, https://doi.org/10.5194/gmd-16-5153-2023, https://doi.org/10.5194/gmd-16-5153-2023, 2023
Short summary
Short summary
We developed a new climate model with isotopic capabilities and simulated the pre-industrial and mid-Holocene periods. Despite certain regional model biases, the modeled isotope composition is in good agreement with observations and reconstructions. Based on our analyses, the observed isotope–temperature relationship in polar regions may have a summertime bias. Using daily model outputs, we developed a novel isotope-based approach to determine the onset date of the West African summer monsoon.
Andrew Gettelman
Geosci. Model Dev., 16, 4937–4956, https://doi.org/10.5194/gmd-16-4937-2023, https://doi.org/10.5194/gmd-16-4937-2023, 2023
Short summary
Short summary
A representation of rainbows is developed for a climate model. The diagnostic raises many common issues. Simulated rainbows are evaluated against limited observations. The pattern of rainbows in the model matches observations and theory about when and where rainbows are most common. The diagnostic is used to assess the past and future state of rainbows. Changes to clouds from climate change are expected to increase rainbows as cloud cover decreases in a warmer world.
Ralf Hand, Eric Samakinwa, Laura Lipfert, and Stefan Brönnimann
Geosci. Model Dev., 16, 4853–4866, https://doi.org/10.5194/gmd-16-4853-2023, https://doi.org/10.5194/gmd-16-4853-2023, 2023
Short summary
Short summary
ModE-Sim is an ensemble of simulations with an atmosphere model. It uses observed sea surface temperatures, sea ice conditions, and volcanic aerosols for 1420 to 2009 as model input while accounting for uncertainties in these conditions. This generates several representations of the possible climate given these preconditions. Such a setup can be useful to understand the mechanisms that contribute to climate variability. This paper describes the setup of ModE-Sim and evaluates its performance.
Andrea Storto, Yassmin Hesham Essa, Vincenzo de Toma, Alessandro Anav, Gianmaria Sannino, Rosalia Santoleri, and Chunxue Yang
Geosci. Model Dev., 16, 4811–4833, https://doi.org/10.5194/gmd-16-4811-2023, https://doi.org/10.5194/gmd-16-4811-2023, 2023
Short summary
Short summary
Regional climate models are a fundamental tool for a very large number of applications and are being increasingly used within climate services, together with other complementary approaches. Here, we introduce a new regional coupled model, intended to be later extended to a full Earth system model, for climate investigations within the Mediterranean region, coupled data assimilation experiments, and several downscaling exercises (reanalyses and long-range predictions).
Anna L. Merrifield, Lukas Brunner, Ruth Lorenz, Vincent Humphrey, and Reto Knutti
Geosci. Model Dev., 16, 4715–4747, https://doi.org/10.5194/gmd-16-4715-2023, https://doi.org/10.5194/gmd-16-4715-2023, 2023
Short summary
Short summary
Using all Coupled Model Intercomparison Project (CMIP) models is unfeasible for many applications. We provide a subselection protocol that balances user needs for model independence, performance, and spread capturing CMIP’s projection uncertainty simultaneously. We show how sets of three to five models selected for European applications map to user priorities. An audit of model independence and its influence on equilibrium climate sensitivity uncertainty in CMIP is also presented.
Bin Mu, Xiaodan Luo, Shijin Yuan, and Xi Liang
Geosci. Model Dev., 16, 4677–4697, https://doi.org/10.5194/gmd-16-4677-2023, https://doi.org/10.5194/gmd-16-4677-2023, 2023
Short summary
Short summary
To improve the long-term forecast skill for sea ice extent (SIE), we introduce IceTFT, which directly predicts 12 months of averaged Arctic SIE. The results show that IceTFT has higher forecasting skill. We conducted a sensitivity analysis of the variables in the IceTFT model. These sensitivities can help researchers study the mechanisms of sea ice development, and they also provide useful references for the selection of variables in data assimilation or the input of deep learning models.
Laura Muntjewerf, Richard Bintanja, Thomas Reerink, and Karin van der Wiel
Geosci. Model Dev., 16, 4581–4597, https://doi.org/10.5194/gmd-16-4581-2023, https://doi.org/10.5194/gmd-16-4581-2023, 2023
Short summary
Short summary
The KNMI Large Ensemble Time Slice (KNMI–LENTIS) is a large ensemble of global climate model simulations with EC-Earth3. It covers two climate scenarios by focusing on two time slices: the present day (2000–2009) and a future +2 K climate (2075–2084 in the SSP2-4.5 scenario). We have 1600 simulated years for the two climates with (sub-)daily output frequency. The sampled climate variability allows for robust and in-depth research into (compound) extreme events such as heat waves and droughts.
Yi-Chi Wang, Wan-Ling Tseng, Yu-Luen Chen, Shih-Yu Lee, Huang-Hsiung Hsu, and Hsin-Chien Liang
Geosci. Model Dev., 16, 4599–4616, https://doi.org/10.5194/gmd-16-4599-2023, https://doi.org/10.5194/gmd-16-4599-2023, 2023
Short summary
Short summary
This study focuses on evaluating the performance of the Taiwan Earth System Model version 1 (TaiESM1) in simulating the El Niño–Southern Oscillation (ENSO), a significant tropical climate pattern with global impacts. Our findings reveal that TaiESM1 effectively captures several characteristics of ENSO, such as its seasonal variation and remote teleconnections. Its pronounced ENSO strength bias is also thoroughly investigated, aiming to gain insights to improve climate model performance.
Raghul Parthipan, Hannah M. Christensen, J. Scott Hosking, and Damon J. Wischik
Geosci. Model Dev., 16, 4501–4519, https://doi.org/10.5194/gmd-16-4501-2023, https://doi.org/10.5194/gmd-16-4501-2023, 2023
Short summary
Short summary
How can we create better climate models? We tackle this by proposing a data-driven successor to the existing approach for capturing key temporal trends in climate models. We combine probability, allowing us to represent uncertainty, with machine learning, a technique to learn relationships from data which are undiscoverable to humans. Our model is often superior to existing baselines when tested in a simple atmospheric simulation.
Laura J. Wilcox, Robert J. Allen, Bjørn H. Samset, Massimo A. Bollasina, Paul T. Griffiths, James Keeble, Marianne T. Lund, Risto Makkonen, Joonas Merikanto, Declan O'Donnell, David J. Paynter, Geeta G. Persad, Steven T. Rumbold, Toshihiko Takemura, Kostas Tsigaridis, Sabine Undorf, and Daniel M. Westervelt
Geosci. Model Dev., 16, 4451–4479, https://doi.org/10.5194/gmd-16-4451-2023, https://doi.org/10.5194/gmd-16-4451-2023, 2023
Short summary
Short summary
Changes in anthropogenic aerosol emissions have strongly contributed to global and regional climate change. However, the size of these regional impacts and the way they arise are still uncertain. With large changes in aerosol emissions a possibility over the next few decades, it is important to better quantify the potential role of aerosol in future regional climate change. The Regional Aerosol Model Intercomparison Project will deliver experiments designed to facilitate this.
Nicholas Depsky, Ian Bolliger, Daniel Allen, Jun Ho Choi, Michael Delgado, Michael Greenstone, Ali Hamidi, Trevor Houser, Robert E. Kopp, and Solomon Hsiang
Geosci. Model Dev., 16, 4331–4366, https://doi.org/10.5194/gmd-16-4331-2023, https://doi.org/10.5194/gmd-16-4331-2023, 2023
Short summary
Short summary
This work presents a novel open-source modeling platform for evaluating future sea level rise (SLR) impacts. Using nearly 10 000 discrete coastline segments around the world, we estimate 21st-century costs for 230 SLR and socioeconomic scenarios. We find that annual end-of-century costs range from USD 100 billion under a 2 °C warming scenario with proactive adaptation to 7 trillion under a 4 °C warming scenario with minimal adaptation, illustrating the cost-effectiveness of coastal adaptation.
Shruti Nath, Lukas Gudmundsson, Jonas Schwaab, Gregory Duveiller, Steven J. De Hertog, Suqi Guo, Felix Havermann, Fei Luo, Iris Manola, Julia Pongratz, Sonia I. Seneviratne, Carl F. Schleussner, Wim Thiery, and Quentin Lejeune
Geosci. Model Dev., 16, 4283–4313, https://doi.org/10.5194/gmd-16-4283-2023, https://doi.org/10.5194/gmd-16-4283-2023, 2023
Short summary
Short summary
Tree cover changes play a significant role in climate mitigation and adaptation. Their regional impacts are key in informing national-level decisions and prioritising areas for conservation efforts. We present a first step towards exploring these regional impacts using a simple statistical device, i.e. emulator. The emulator only needs to train on climate model outputs representing the maximal impacts of aff-, re-, and deforestation, from which it explores plausible in-between outcomes itself.
Chen Zhang and Tianyu Fu
Geosci. Model Dev., 16, 4315–4329, https://doi.org/10.5194/gmd-16-4315-2023, https://doi.org/10.5194/gmd-16-4315-2023, 2023
Short summary
Short summary
A new automatic calibration toolkit was developed and implemented into the recalibration of a 3-D water quality model, with observations in a wider range of hydrological variability. Compared to the model calibrated with the original strategy, the recalibrated model performed significantly better in modeled total phosphorus, chlorophyll a, and dissolved oxygen. Our work indicates that hydrological variability in the calibration periods has a non-negligible impact on the water quality models.
Camilla Mathison, Eleanor Burke, Andrew J. Hartley, Douglas I. Kelley, Chantelle Burton, Eddy Robertson, Nicola Gedney, Karina Williams, Andy Wiltshire, Richard J. Ellis, Alistair A. Sellar, and Chris D. Jones
Geosci. Model Dev., 16, 4249–4264, https://doi.org/10.5194/gmd-16-4249-2023, https://doi.org/10.5194/gmd-16-4249-2023, 2023
Short summary
Short summary
This paper describes and evaluates a new modelling methodology to quantify the impacts of climate change on water, biomes and the carbon cycle. We have created a new configuration and set-up for the JULES-ES land surface model, driven by bias-corrected historical and future climate model output provided by the Inter-Sectoral Impacts Model Intercomparison Project (ISIMIP). This allows us to compare projections of the impacts of climate change across multiple impact models and multiple sectors.
Bo Dong, Ross Bannister, Yumeng Chen, Alison Fowler, and Keith Haines
Geosci. Model Dev., 16, 4233–4247, https://doi.org/10.5194/gmd-16-4233-2023, https://doi.org/10.5194/gmd-16-4233-2023, 2023
Short summary
Short summary
Traditional Kalman smoothers are expensive to apply in large global ocean operational forecast and reanalysis systems. We develop a cost-efficient method to overcome the technical constraints and to improve the performance of existing reanalysis products.
Makcim L. De Sisto, Andrew H. MacDougall, Nadine Mengis, and Sophia Antoniello
Geosci. Model Dev., 16, 4113–4136, https://doi.org/10.5194/gmd-16-4113-2023, https://doi.org/10.5194/gmd-16-4113-2023, 2023
Short summary
Short summary
In this study, we developed a nitrogen and phosphorus cycle in an intermediate-complexity Earth system climate model. We found that the implementation of nutrient limitation in simulations has reduced the capacity of land to take up atmospheric carbon and has decreased the vegetation biomass, hence, improving the fidelity of the response of land to simulated atmospheric CO2 rise.
Manuel C. Almeida and Pedro S. Coelho
Geosci. Model Dev., 16, 4083–4112, https://doi.org/10.5194/gmd-16-4083-2023, https://doi.org/10.5194/gmd-16-4083-2023, 2023
Short summary
Short summary
Water temperature (WT) datasets of low-order rivers are scarce. In this study, five different models are used to predict the WT of 83 rivers. Generally, the results show that the models' hyperparameter optimization is essential and that to minimize the prediction error it is relevant to apply all the models considered in this study. Results also show that there is a logarithmic correlation among the error of the predicted river WT and the watershed time of concentration.
Lingcheng Li, Yilin Fang, Zhonghua Zheng, Mingjie Shi, Marcos Longo, Charles D. Koven, Jennifer A. Holm, Rosie A. Fisher, Nate G. McDowell, Jeffrey Chambers, and L. Ruby Leung
Geosci. Model Dev., 16, 4017–4040, https://doi.org/10.5194/gmd-16-4017-2023, https://doi.org/10.5194/gmd-16-4017-2023, 2023
Short summary
Short summary
Accurately modeling plant coexistence in vegetation demographic models like ELM-FATES is challenging. This study proposes a repeatable method that uses machine-learning-based surrogate models to optimize plant trait parameters in ELM-FATES. Our approach significantly improves plant coexistence modeling, thus reducing errors. It has important implications for modeling ecosystem dynamics in response to climate change.
Qi Tang, Jean-Christophe Golaz, Luke P. Van Roekel, Mark A. Taylor, Wuyin Lin, Benjamin R. Hillman, Paul A. Ullrich, Andrew M. Bradley, Oksana Guba, Jonathan D. Wolfe, Tian Zhou, Kai Zhang, Xue Zheng, Yunyan Zhang, Meng Zhang, Mingxuan Wu, Hailong Wang, Cheng Tao, Balwinder Singh, Alan M. Rhoades, Yi Qin, Hong-Yi Li, Yan Feng, Yuying Zhang, Chengzhu Zhang, Charles S. Zender, Shaocheng Xie, Erika L. Roesler, Andrew F. Roberts, Azamat Mametjanov, Mathew E. Maltrud, Noel D. Keen, Robert L. Jacob, Christiane Jablonowski, Owen K. Hughes, Ryan M. Forsyth, Alan V. Di Vittorio, Peter M. Caldwell, Gautam Bisht, Renata B. McCoy, L. Ruby Leung, and David C. Bader
Geosci. Model Dev., 16, 3953–3995, https://doi.org/10.5194/gmd-16-3953-2023, https://doi.org/10.5194/gmd-16-3953-2023, 2023
Short summary
Short summary
High-resolution simulations are superior to low-resolution ones in capturing regional climate changes and climate extremes. However, uniformly reducing the grid size of a global Earth system model is too computationally expensive. We provide an overview of the fully coupled regionally refined model (RRM) of E3SMv2 and document a first-of-its-kind set of climate production simulations using RRM at an economic cost. The key to this success is our innovative hybrid time step method.
Anne Marie Treguier, Clement de Boyer Montégut, Alexandra Bozec, Eric P. Chassignet, Baylor Fox-Kemper, Andy McC. Hogg, Doroteaciro Iovino, Andrew E. Kiss, Julien Le Sommer, Yiwen Li, Pengfei Lin, Camille Lique, Hailong Liu, Guillaume Serazin, Dmitry Sidorenko, Qiang Wang, Xiaobio Xu, and Steve Yeager
Geosci. Model Dev., 16, 3849–3872, https://doi.org/10.5194/gmd-16-3849-2023, https://doi.org/10.5194/gmd-16-3849-2023, 2023
Short summary
Short summary
The ocean mixed layer is the interface between the ocean interior and the atmosphere and plays a key role in climate variability. We evaluate the performance of the new generation of ocean models for climate studies, designed to resolve
ocean eddies, which are the largest source of ocean variability and modulate the mixed-layer properties. We find that the mixed-layer depth is better represented in eddy-rich models but, unfortunately, not uniformly across the globe and not in all models.
Duseong S. Jo, Simone Tilmes, Louisa K. Emmons, Siyuan Wang, and Francis Vitt
Geosci. Model Dev., 16, 3893–3906, https://doi.org/10.5194/gmd-16-3893-2023, https://doi.org/10.5194/gmd-16-3893-2023, 2023
Short summary
Short summary
A new simple secondary organic aerosol (SOA) scheme has been developed for the Community Atmosphere Model (CAM) based on the complex SOA scheme in CAM with detailed chemistry (CAM-chem). The CAM with the new SOA scheme shows better agreements with CAM-chem in terms of aerosol concentrations and radiative fluxes, which ensures more consistent results between different compsets in the Community Earth System Model. The new SOA scheme also has technical advantages for future developments.
Leroy J. Bird, Matthew G. W. Walker, Greg E. Bodeker, Isaac H. Campbell, Guangzhong Liu, Swapna Josmi Sam, Jared Lewis, and Suzanne M. Rosier
Geosci. Model Dev., 16, 3785–3808, https://doi.org/10.5194/gmd-16-3785-2023, https://doi.org/10.5194/gmd-16-3785-2023, 2023
Short summary
Short summary
Deriving the statistics of expected future changes in extreme precipitation is challenging due to these events being rare. Regional climate models (RCMs) are computationally prohibitive for generating ensembles capable of capturing large numbers of extreme precipitation events with statistical robustness. Stochastic precipitation generators (SPGs) provide an alternative to RCMs. We describe a novel single-site SPG that learns the statistics of precipitation using a machine-learning approach.
Zhe Zhang, Yanping Li, Fei Chen, Phillip Harder, Warren Helgason, James Famiglietti, Prasanth Valayamkunnath, Cenlin He, and Zhenhua Li
Geosci. Model Dev., 16, 3809–3825, https://doi.org/10.5194/gmd-16-3809-2023, https://doi.org/10.5194/gmd-16-3809-2023, 2023
Short summary
Short summary
Crop models incorporated in Earth system models are essential to accurately simulate crop growth processes on Earth's surface and agricultural production. In this study, we aim to model the spring wheat in the Northern Great Plains, focusing on three aspects: (1) develop the wheat model at a point scale, (2) apply dynamic planting and harvest schedules, and (3) adopt a revised heat stress function. The results show substantial improvements and have great importance for agricultural production.
Abolfazl Simorgh, Manuel Soler, Daniel González-Arribas, Florian Linke, Benjamin Lührs, Maximilian M. Meuser, Simone Dietmüller, Sigrun Matthes, Hiroshi Yamashita, Feijia Yin, Federica Castino, Volker Grewe, and Sabine Baumann
Geosci. Model Dev., 16, 3723–3748, https://doi.org/10.5194/gmd-16-3723-2023, https://doi.org/10.5194/gmd-16-3723-2023, 2023
Short summary
Short summary
This paper addresses the robust climate optimal trajectory planning problem under uncertain meteorological conditions within the structured airspace. Based on the optimization methodology, a Python library has been developed, which can be accessed using the following DOI: https://doi.org/10.5281/zenodo.7121862. The developed tool is capable of providing robust trajectories taking into account all probable realizations of meteorological conditions provided by an EPS computationally very fast.
Matteo Willeit, Tatiana Ilyina, Bo Liu, Christoph Heinze, Mahé Perrette, Malte Heinemann, Daniela Dalmonech, Victor Brovkin, Guy Munhoven, Janine Börker, Jens Hartmann, Gibran Romero-Mujalli, and Andrey Ganopolski
Geosci. Model Dev., 16, 3501–3534, https://doi.org/10.5194/gmd-16-3501-2023, https://doi.org/10.5194/gmd-16-3501-2023, 2023
Short summary
Short summary
In this paper we present the carbon cycle component of the newly developed fast Earth system model CLIMBER-X. The model can be run with interactive atmospheric CO2 to investigate the feedbacks between climate and the carbon cycle on temporal scales ranging from decades to > 100 000 years. CLIMBER-X is expected to be a useful tool for studying past climate–carbon cycle changes and for the investigation of the long-term future evolution of the Earth system.
Jatan Buch, A. Park Williams, Caroline S. Juang, Winslow D. Hansen, and Pierre Gentine
Geosci. Model Dev., 16, 3407–3433, https://doi.org/10.5194/gmd-16-3407-2023, https://doi.org/10.5194/gmd-16-3407-2023, 2023
Short summary
Short summary
We leverage machine learning techniques to construct a statistical model of grid-scale fire frequencies and sizes using climate, vegetation, and human predictors. Our model reproduces the observed trends in fire activity across multiple regions and timescales. We provide uncertainty estimates to inform resource allocation plans for fuel treatment and fire management. Altogether the accuracy and efficiency of our model make it ideal for coupled use with large-scale dynamical vegetation models.
Sebastian Ostberg, Christoph Müller, Jens Heinke, and Sibyll Schaphoff
Geosci. Model Dev., 16, 3375–3406, https://doi.org/10.5194/gmd-16-3375-2023, https://doi.org/10.5194/gmd-16-3375-2023, 2023
Short summary
Short summary
We present a new toolbox for generating input datasets for terrestrial ecosystem models from diverse and partially conflicting data sources. The toolbox documents the sources and processing of data and is designed to make inconsistencies between source datasets transparent so that users can make their own decisions on how to resolve these should they not be content with our default assumptions. As an example, we use the toolbox to create input datasets at two different spatial resolutions.
Alena Malyarenko, Alexandra Gossart, Rui Sun, and Mario Krapp
Geosci. Model Dev., 16, 3355–3373, https://doi.org/10.5194/gmd-16-3355-2023, https://doi.org/10.5194/gmd-16-3355-2023, 2023
Short summary
Short summary
Simultaneous modelling of ocean, sea ice, and atmosphere in coupled models is critical for understanding all of the processes that happen in the Antarctic. Here we have developed a coupled model for the Ross Sea, P-SKRIPS, that conserves heat and mass between the ocean and sea ice model (MITgcm) and the atmosphere model (PWRF). We have shown that our developments reduce the model drift, which is important for long-term simulations. P-SKRIPS shows good results in modelling coastal polynyas.
Feijia Yin, Volker Grewe, Federica Castino, Pratik Rao, Sigrun Matthes, Katrin Dahlmann, Simone Dietmüller, Christine Frömming, Hiroshi Yamashita, Patrick Peter, Emma Klingaman, Keith P. Shine, Benjamin Lührs, and Florian Linke
Geosci. Model Dev., 16, 3313–3334, https://doi.org/10.5194/gmd-16-3313-2023, https://doi.org/10.5194/gmd-16-3313-2023, 2023
Short summary
Short summary
This paper describes a newly developed submodel ACCF V1.0 based on the MESSy 2.53.0 infrastructure. The ACCF V1.0 is based on the prototype algorithmic climate change functions (aCCFs) v1.0 to enable climate-optimized flight trajectories. One highlight of this paper is that we describe a consistent full set of aCCFs formulas with respect to fuel scenario and metrics. We demonstrate the usage of the ACCF submodel using AirTraf V2.0 to optimize trajectories for cost and climate impact.
Peter Ukkonen and Robin J. Hogan
Geosci. Model Dev., 16, 3241–3261, https://doi.org/10.5194/gmd-16-3241-2023, https://doi.org/10.5194/gmd-16-3241-2023, 2023
Short summary
Short summary
Climate and weather models suffer from uncertainties resulting from approximated processes. Solar and thermal radiation is one example, as it is computationally too costly to simulate precisely. This has led to attempts to replace radiation codes based on physical equations with neural networks (NNs) that are faster but uncertain. In this paper we use global weather simulations to demonstrate that a middle-ground approach of using NNs only to predict optical properties is accurate and reliable.
Maximilian Gelbrecht, Alistair White, Sebastian Bathiany, and Niklas Boers
Geosci. Model Dev., 16, 3123–3135, https://doi.org/10.5194/gmd-16-3123-2023, https://doi.org/10.5194/gmd-16-3123-2023, 2023
Short summary
Short summary
Differential programming is a technique that enables the automatic computation of derivatives of the output of models with respect to model parameters. Applying these techniques to Earth system modeling leverages the increasing availability of high-quality data to improve the models themselves. This can be done by either using calibration techniques that use gradient-based optimization or incorporating machine learning methods that can learn previously unresolved influences directly from data.
Carolina Gallo, Jonathan M. Eden, Bastien Dieppois, Igor Drobyshev, Peter Z. Fulé, Jesús San-Miguel-Ayanz, and Matthew Blackett
Geosci. Model Dev., 16, 3103–3122, https://doi.org/10.5194/gmd-16-3103-2023, https://doi.org/10.5194/gmd-16-3103-2023, 2023
Short summary
Short summary
This study conducts the first global evaluation of the latest generation of global climate models to simulate a set of fire weather indicators from the Canadian Fire Weather Index System. Models are shown to perform relatively strongly at the global scale, but they show substantial regional and seasonal differences. The results demonstrate the value of model evaluation and selection in producing reliable fire danger projections, ultimately to support decision-making and forest management.
Klaus Klingmüller and Jos Lelieveld
Geosci. Model Dev., 16, 3013–3028, https://doi.org/10.5194/gmd-16-3013-2023, https://doi.org/10.5194/gmd-16-3013-2023, 2023
Short summary
Short summary
Desert dust has significant impacts on climate, public health, infrastructure and ecosystems. An impact assessment requires numerical predictions, which are challenging because the dust emissions are not well known. We present a novel approach using satellite observations and machine learning to more accurately estimate the emissions and to improve the model simulations.
Anna Denvil-Sommer, Erik T. Buitenhuis, Rainer Kiko, Fabien Lombard, Lionel Guidi, and Corinne Le Quéré
Geosci. Model Dev., 16, 2995–3012, https://doi.org/10.5194/gmd-16-2995-2023, https://doi.org/10.5194/gmd-16-2995-2023, 2023
Short summary
Short summary
Using outputs of global biogeochemical ocean model and machine learning methods, we demonstrate that it will be possible to identify linkages between surface environmental and ecosystem structure and the export of carbon to depth by sinking organic particles using real observations. It will be possible to use this knowledge to improve both our understanding of ecosystem dynamics and of their functional representation within models.
Zhenxia Liu, Zengjie Wang, Jian Wang, Zhengfang Zhang, Dongshuang Li, Zhaoyuan Yu, Linwang Yuan, and Wen Luo
Geosci. Model Dev., 16, 2939–2955, https://doi.org/10.5194/gmd-16-2939-2023, https://doi.org/10.5194/gmd-16-2939-2023, 2023
Short summary
Short summary
This study introduces an improved method of the Globally Resolved Energy Balance (GREB) model by the Bayesian network. The improved method constructs a coarse–fine structure that combines a dynamical model with a statistical model based on employing the GREB model as the global framework and utilizing Bayesian networks as the local optimization. The results show that the improved model has better applicability and stability on a global scale and maintains good robustness on the timescale.
Colin Tully, David Neubauer, and Ulrike Lohmann
Geosci. Model Dev., 16, 2957–2973, https://doi.org/10.5194/gmd-16-2957-2023, https://doi.org/10.5194/gmd-16-2957-2023, 2023
Short summary
Short summary
A new method to simulate deterministic ice nucleation processes based on the differential activated fraction was evaluated against a cumulative approach. Box model simulations of heterogeneous-only ice nucleation within cirrus suggest that the latter approach likely underpredicts the ice crystal number concentration. Longer simulations with a GCM show that choosing between these two approaches impacts ice nucleation competition within cirrus but leads to small and insignificant climate effects.
Rasmus E. Benestad, Abdelkader Mezghani, Julia Lutz, Andreas Dobler, Kajsa M. Parding, and Oskar A. Landgren
Geosci. Model Dev., 16, 2899–2913, https://doi.org/10.5194/gmd-16-2899-2023, https://doi.org/10.5194/gmd-16-2899-2023, 2023
Short summary
Short summary
A mathematical method known as common EOFs is not widely used within the climate research community, but it offers innovative ways of evaluating climate models. We show how common EOFs can be used to evaluate large ensembles of global climate model simulations and distill information about their ability to reproduce salient features of the regional climate. We can say that they represent a kind of machine learning (ML) for dealing with big data.
Li Liu, Chao Sun, Xinzhu Yu, Hao Yu, Qingu Jiang, Xingliang Li, Ruizhe Li, Bin Wang, Xueshun Shen, and Guangwen Yang
Geosci. Model Dev., 16, 2833–2850, https://doi.org/10.5194/gmd-16-2833-2023, https://doi.org/10.5194/gmd-16-2833-2023, 2023
Short summary
Short summary
C-Coupler3.0 is an integrated coupler infrastructure with new features, i.e. a series of parallel-optimization technologies, a common halo-exchange library, a common module-integration framework, a common framework for conveniently developing a weakly coupled ensemble data assimilation system, and a common framework for flexibly inputting and outputting fields in parallel. It is able to handle coupling under much finer resolutions (e.g. more than 100 million horizontal grid cells).
Joseph Schoonover, Wilbert Weijer, and Jiaxu Zhang
Geosci. Model Dev., 16, 2795–2809, https://doi.org/10.5194/gmd-16-2795-2023, https://doi.org/10.5194/gmd-16-2795-2023, 2023
Short summary
Short summary
FEOTS aims to enhance the value of data produced by state-of-the-art climate models by providing a framework to diagnose and use ocean transport operators for offline passive tracer simulations. We show that we can capture ocean transport operators from a validated climate model and employ these operators to estimate water mass budgets in an offline regional simulation, using a small fraction of the compute resources required to run a full climate simulation.
Johann Dahm, Eddie Davis, Florian Deconinck, Oliver Elbert, Rhea George, Jeremy McGibbon, Tobias Wicky, Elynn Wu, Christopher Kung, Tal Ben-Nun, Lucas Harris, Linus Groner, and Oliver Fuhrer
Geosci. Model Dev., 16, 2719–2736, https://doi.org/10.5194/gmd-16-2719-2023, https://doi.org/10.5194/gmd-16-2719-2023, 2023
Short summary
Short summary
It is hard for scientists to write code which is efficient on different kinds of supercomputers. Python is popular for its user-friendliness. We converted a Fortran code, simulating Earth's atmosphere, into Python. This new code auto-converts to a faster language for processors or graphic cards. Our code runs 3.5–4 times faster on graphic cards than the original on processors in a specific supercomputer system.
Anthony Schrapffer, Jan Polcher, Anna Sörensson, and Lluís Fita
EGUsphere, https://doi.org/10.5194/egusphere-2023-549, https://doi.org/10.5194/egusphere-2023-549, 2023
Short summary
Short summary
The present paper introduces a floodplains scheme for a high resolution Land Surface Model river routing. It was developed and evaluated over one of the world’s largest floodplains: the Pantanal in South America. This shows the impact of tropical floodplains on land surface conditions (soil moisture, temperature) and on land atmosphere fluxes and highlights the potential impact of floodplains on land-atmosphere interactions and the importance of integrating this module in coupled simulations.
Shuaiqi Tang, Adam C. Varble, Jerome D. Fast, Kai Zhang, Peng Wu, Xiquan Dong, Fan Mei, Mikhail Pekour, Joseph C. Hardin, and Po-Lun Ma
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-51, https://doi.org/10.5194/gmd-2023-51, 2023
Revised manuscript accepted for GMD
Short summary
Short summary
To assess the ability of Earth System Model (ESM) predictions, we developed a tool called ESMAC Diags to understand the details of how aerosols, clouds, and ACI are represented in ESMs, and this paper describes its version 2 functionality. We compared the model predictions with measurements taken by airplanes, ships, satellites, and ground instruments over four regions over the world. Results show that this new tool can help identify model problems and guide future development of ESMs.
Jan Polcher, Anthony Schrapffer, Eliott Dupont, Lucia Rinchiuso, Xudong Zhou, Olivier Boucher, Emmanuel Mouche, Catherine Ottlé, and Jérôme Servonnat
Geosci. Model Dev., 16, 2583–2606, https://doi.org/10.5194/gmd-16-2583-2023, https://doi.org/10.5194/gmd-16-2583-2023, 2023
Short summary
Short summary
The proposed graphs of hydrological sub-grid elements for atmospheric models allow us to integrate the topographical elements needed in land surface models for a realistic representation of horizontal water and energy transport. The study demonstrates the numerical properties of the automatically built graphs and the simulated water flows.
Magnus Hieronymus
Geosci. Model Dev., 16, 2343–2354, https://doi.org/10.5194/gmd-16-2343-2023, https://doi.org/10.5194/gmd-16-2343-2023, 2023
Short summary
Short summary
A statistical model called the sea level simulator is presented and made freely available. The sea level simulator integrates mean sea level rise and sea level extremes into a joint probabilistic framework that is useful for flood risk estimation. These flood risk estimates are contingent on probabilities given to different emission scenarios and the length of the planning period. The model is also useful for uncertainty quantification and in decision and adaptation problems.
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...
