Articles | Volume 9, issue 10
https://doi.org/10.5194/gmd-9-3533-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-9-3533-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Model description paper
| 
04 Oct 2016
Model description paper |
| 04 Oct 2016
LAND-SE: a software for statistically based landslide susceptibility zonation, version 1.0
CNR IRPI, via Madonna Alta 126, 06128 Perugia, Italy
Paola Reichenbach
CNR IRPI, via Madonna Alta 126, 06128 Perugia, Italy
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The paper focuses on classifying continuous landslide conditioning factors for susceptibility modelling, which resulted in 54 landslide susceptibility models that tested 11 classification criteria in combination with 5 statistical methods. The novelty of the research is that using stretched landslide conditioning factor values results in models with higher accuracy and that certain statistical methods are more sensitive to the landslide conditioning factor classification criteria than others.
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Short summary
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This study proposes a novel workflow to precisely model rockfalls. It compares three methods for defining source areas to enhance model accuracy. Identified areas are inputted into a runout model to identify vulnerable zones. A new approach generates probabilistic susceptibility maps using ECDFs. Validation strategies employing various inventory types are included. Comparing six susceptibility maps highlights the impact of source area definition on model precision.
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The estimation of the temporal frequency of the involved rockfall processes is an important part in hazard and risk assessments. Different methods can be used to collect and analyse rockfall data. From a statistical point of view, rockfall datasets are nearly always incomplete. Accurate data collection approaches and the application of statistical methods on existing rockfall data series as reported in this study should be better considered in rockfall hazard and risk assessments in the future.
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We present a database of the main scientific articles published on earthquake-triggered landslides in the last 4 decades. To enhance data viewing, the articles were catalogued into a web-based GIS, which was specifically designed to show different types of information, such as bibliometric information, the relevant topic and sub-topic category (or categories), and earthquake(s) addressed. Such information can be useful to obtain a general overview of the topic, especially for a broad readership.
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LAND-SUITE is a software package designed to support landslide susceptibility zonation. The software integrates, extends, and completes LAND-SE (Rossi et al., 2010; Rossi and Reichenbach, 2016). The software is implemented in R, a free software environment for statistical computing and graphics, and gives expert users the possibility to perform easier, more flexible, and more informed statistically based landslide susceptibility applications and zonations.
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In this article, we present an automatic processing chain aimed to support the detection of landslides that induce sharp land cover changes. The chain exploits free software and spaceborne SAR data, allowing the systematic monitoring of wide mountainous regions exposed to mass movements. In the test site, we verified a general accordance between the spatial distribution of seismically induced landslides and the detected land cover changes, demonstrating its potential use in emergency management.
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While producing a landslide susceptibility map using a fieldwork-based landslide inventory and a logistic regression model, two crucial questions came to our minds. (i) Shall we consider unsurveyed regions of the study area, for which landslide absence is typically assumed? (ii) Which reference mapping unit should be used in our model? So we compared four maps and found that rejecting unsurveyed regions together with slope units as reference mapping unit should be the best option.
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This paper describes the criteria for the optimal selection of remote sensing images to map event landslides, discussing the ability of monoscopic and stereoscopic VHR satellite images and ultra-high-resolution UAV images to resolve the landslide photographical and morphological signatures. The findings can be useful to decide on the optimal imagery and technique to be used when planning the production of a landslide inventory map.
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While country-specific, continental and global susceptibility maps are increasingly available, local and regional susceptibility studies remain rare in remote and data-poor settings. Here, we provide a landslide susceptibility assessment for the inhabited region of the Rwenzori Mountains. We find that higher spatial resolutions do not necessarily lead to better models and that models built for local case studies perform better than aggregated susceptibility assessments on the regional scale.
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Slope units are morphological mapping units bounded by drainage and divide lines that maximize within-unit homogeneity and between-unit heterogeneity. We use r.slopeunits, a software for the automatic delination of slope units. We outline an objective procedure to optimize the software input parameters for landslide susceptibility (LS) zonation. Optimization is achieved by maximizing an objective function that simultaneously evaluates terrain aspect segmentation quality and LS model performance.
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Short summary
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The article deals with strategies to (i) reduce computation time and to (ii) appropriately account for uncertain input parameters when applying an open source GIS sliding surface model to estimate landslide susceptibility for a 90km² study area in central Italy. For (i), the area is split into a large number of tiles, enabling the exploitation of multi-processor computing environments. For (ii), the model is run with various parameter combinations to compute the slope failure probability.
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Short summary
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The paper focuses on classifying continuous landslide conditioning factors for susceptibility modelling, which resulted in 54 landslide susceptibility models that tested 11 classification criteria in combination with 5 statistical methods. The novelty of the research is that using stretched landslide conditioning factor values results in models with higher accuracy and that certain statistical methods are more sensitive to the landslide conditioning factor classification criteria than others.
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Short summary
Short summary
This study proposes a novel workflow to precisely model rockfalls. It compares three methods for defining source areas to enhance model accuracy. Identified areas are inputted into a runout model to identify vulnerable zones. A new approach generates probabilistic susceptibility maps using ECDFs. Validation strategies employing various inventory types are included. Comparing six susceptibility maps highlights the impact of source area definition on model precision.
Sandra Melzner, Marco Conedera, Johannes Hübl, and Mauro Rossi
Nat. Hazards Earth Syst. Sci., 23, 3079–3093, https://doi.org/10.5194/nhess-23-3079-2023, https://doi.org/10.5194/nhess-23-3079-2023, 2023
Short summary
Short summary
The estimation of the temporal frequency of the involved rockfall processes is an important part in hazard and risk assessments. Different methods can be used to collect and analyse rockfall data. From a statistical point of view, rockfall datasets are nearly always incomplete. Accurate data collection approaches and the application of statistical methods on existing rockfall data series as reported in this study should be better considered in rockfall hazard and risk assessments in the future.
Luca Schilirò, Mauro Rossi, Federica Polpetta, Federica Fiorucci, Carolina Fortunato, and Paola Reichenbach
Nat. Hazards Earth Syst. Sci., 23, 1789–1804, https://doi.org/10.5194/nhess-23-1789-2023, https://doi.org/10.5194/nhess-23-1789-2023, 2023
Short summary
Short summary
We present a database of the main scientific articles published on earthquake-triggered landslides in the last 4 decades. To enhance data viewing, the articles were catalogued into a web-based GIS, which was specifically designed to show different types of information, such as bibliometric information, the relevant topic and sub-topic category (or categories), and earthquake(s) addressed. Such information can be useful to obtain a general overview of the topic, especially for a broad readership.
Mauro Rossi, Txomin Bornaetxea, and Paola Reichenbach
Geosci. Model Dev., 15, 5651–5666, https://doi.org/10.5194/gmd-15-5651-2022, https://doi.org/10.5194/gmd-15-5651-2022, 2022
Short summary
Short summary
LAND-SUITE is a software package designed to support landslide susceptibility zonation. The software integrates, extends, and completes LAND-SE (Rossi et al., 2010; Rossi and Reichenbach, 2016). The software is implemented in R, a free software environment for statistical computing and graphics, and gives expert users the possibility to perform easier, more flexible, and more informed statistically based landslide susceptibility applications and zonations.
Giuseppe Esposito, Ivan Marchesini, Alessandro Cesare Mondini, Paola Reichenbach, Mauro Rossi, and Simone Sterlacchini
Nat. Hazards Earth Syst. Sci., 20, 2379–2395, https://doi.org/10.5194/nhess-20-2379-2020, https://doi.org/10.5194/nhess-20-2379-2020, 2020
Short summary
Short summary
In this article, we present an automatic processing chain aimed to support the detection of landslides that induce sharp land cover changes. The chain exploits free software and spaceborne SAR data, allowing the systematic monitoring of wide mountainous regions exposed to mass movements. In the test site, we verified a general accordance between the spatial distribution of seismically induced landslides and the detected land cover changes, demonstrating its potential use in emergency management.
Michele Santangelo, Massimiliano Alvioli, Marco Baldo, Mauro Cardinali, Daniele Giordan, Fausto Guzzetti, Ivan Marchesini, and Paola Reichenbach
Nat. Hazards Earth Syst. Sci., 19, 325–335, https://doi.org/10.5194/nhess-19-325-2019, https://doi.org/10.5194/nhess-19-325-2019, 2019
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The paper discusses the use of rockfall modelling software and photogrammetry applied to images acquired by RPAS to provide support to civil protection agencies during emergency response. The paper focuses on a procedure that was applied to define the residual rockfall risk for a road that was hit by an earthquake-triggered rockfall that occurred during the seismic sequence that hit central Italy on 24 August 2016. Road reopening conditions were decided based on the results of this study.
Txomin Bornaetxea, Mauro Rossi, Ivan Marchesini, and Massimiliano Alvioli
Nat. Hazards Earth Syst. Sci., 18, 2455–2469, https://doi.org/10.5194/nhess-18-2455-2018, https://doi.org/10.5194/nhess-18-2455-2018, 2018
Short summary
Short summary
While producing a landslide susceptibility map using a fieldwork-based landslide inventory and a logistic regression model, two crucial questions came to our minds. (i) Shall we consider unsurveyed regions of the study area, for which landslide absence is typically assumed? (ii) Which reference mapping unit should be used in our model? So we compared four maps and found that rejecting unsurveyed regions together with slope units as reference mapping unit should be the best option.
O. Monserrat, A. Barra, G. Herrera, S. Bianchini, C. Lopez, R. Onori, P. Reichenbach, R. Sarro, R. M. Mateos, L. Solari, S. Ligüérzana, and I. P. Carralero
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3-W4, 351–355, https://doi.org/10.5194/isprs-archives-XLII-3-W4-351-2018, https://doi.org/10.5194/isprs-archives-XLII-3-W4-351-2018, 2018
Federica Fiorucci, Daniele Giordan, Michele Santangelo, Furio Dutto, Mauro Rossi, and Fausto Guzzetti
Nat. Hazards Earth Syst. Sci., 18, 405–417, https://doi.org/10.5194/nhess-18-405-2018, https://doi.org/10.5194/nhess-18-405-2018, 2018
Short summary
Short summary
This paper describes the criteria for the optimal selection of remote sensing images to map event landslides, discussing the ability of monoscopic and stereoscopic VHR satellite images and ultra-high-resolution UAV images to resolve the landslide photographical and morphological signatures. The findings can be useful to decide on the optimal imagery and technique to be used when planning the production of a landslide inventory map.
Liesbet Jacobs, Olivier Dewitte, Jean Poesen, John Sekajugo, Adriano Nobile, Mauro Rossi, Wim Thiery, and Matthieu Kervyn
Nat. Hazards Earth Syst. Sci., 18, 105–124, https://doi.org/10.5194/nhess-18-105-2018, https://doi.org/10.5194/nhess-18-105-2018, 2018
Short summary
Short summary
While country-specific, continental and global susceptibility maps are increasingly available, local and regional susceptibility studies remain rare in remote and data-poor settings. Here, we provide a landslide susceptibility assessment for the inhabited region of the Rwenzori Mountains. We find that higher spatial resolutions do not necessarily lead to better models and that models built for local case studies perform better than aggregated susceptibility assessments on the regional scale.
Maria Elena Martinotti, Luca Pisano, Ivan Marchesini, Mauro Rossi, Silvia Peruccacci, Maria Teresa Brunetti, Massimo Melillo, Giuseppe Amoruso, Pierluigi Loiacono, Carmela Vennari, Giovanna Vessia, Maria Trabace, Mario Parise, and Fausto Guzzetti
Nat. Hazards Earth Syst. Sci., 17, 467–480, https://doi.org/10.5194/nhess-17-467-2017, https://doi.org/10.5194/nhess-17-467-2017, 2017
Short summary
Short summary
We studied a period of torrential rain between 1 and 6 September 2014 in the Gargano Promontory, Puglia, southern Italy, which caused a variety of geohydrological hazards, including landslides, flash floods, inundations and sinkholes. We used the rainfall and the landslide information available to us to design and test the new ensemble – non-exceedance probability (E-NEP) algorithm for the quantitative evaluation of the probability of the occurrence of rainfall-induced landslides.
Massimiliano Alvioli, Ivan Marchesini, Paola Reichenbach, Mauro Rossi, Francesca Ardizzone, Federica Fiorucci, and Fausto Guzzetti
Geosci. Model Dev., 9, 3975–3991, https://doi.org/10.5194/gmd-9-3975-2016, https://doi.org/10.5194/gmd-9-3975-2016, 2016
Short summary
Short summary
Slope units are morphological mapping units bounded by drainage and divide lines that maximize within-unit homogeneity and between-unit heterogeneity. We use r.slopeunits, a software for the automatic delination of slope units. We outline an objective procedure to optimize the software input parameters for landslide susceptibility (LS) zonation. Optimization is achieved by maximizing an objective function that simultaneously evaluates terrain aspect segmentation quality and LS model performance.
R. Schlögel, J.-P. Malet, P. Reichenbach, A. Remaître, and C. Doubre
Nat. Hazards Earth Syst. Sci., 15, 2369–2389, https://doi.org/10.5194/nhess-15-2369-2015, https://doi.org/10.5194/nhess-15-2369-2015, 2015
Short summary
Short summary
The paper proposes an approach to prepare a multi-date landslide inventory for the Ubaye valley (French Alps), a complex mountainous area affected by several landslide types with different degrees of activity. The inventory covering the period 1956-2010 have been analysed in order to quantify the uncertainties associated to the mapping, to measure the evolution of morphological indicators and to estimate temporal occurrence. Evolution of landslide activity is compared to other inventory sources.
M. Mergili, I. Marchesini, M. Alvioli, M. Metz, B. Schneider-Muntau, M. Rossi, and F. Guzzetti
Geosci. Model Dev., 7, 2969–2982, https://doi.org/10.5194/gmd-7-2969-2014, https://doi.org/10.5194/gmd-7-2969-2014, 2014
Short summary
Short summary
The article deals with strategies to (i) reduce computation time and to (ii) appropriately account for uncertain input parameters when applying an open source GIS sliding surface model to estimate landslide susceptibility for a 90km² study area in central Italy. For (i), the area is split into a large number of tiles, enabling the exploitation of multi-processor computing environments. For (ii), the model is run with various parameter combinations to compute the slope failure probability.
G. Vessia, M. Parise, M. T. Brunetti, S. Peruccacci, M. Rossi, C. Vennari, and F. Guzzetti
Nat. Hazards Earth Syst. Sci., 14, 2399–2408, https://doi.org/10.5194/nhess-14-2399-2014, https://doi.org/10.5194/nhess-14-2399-2014, 2014
I. Marchesini, F. Ardizzone, M. Alvioli, M. Rossi, and F. Guzzetti
Nat. Hazards Earth Syst. Sci., 14, 2215–2231, https://doi.org/10.5194/nhess-14-2215-2014, https://doi.org/10.5194/nhess-14-2215-2014, 2014
S. Raia, M. Alvioli, M. Rossi, R. L. Baum, J. W. Godt, and F. Guzzetti
Geosci. Model Dev., 7, 495–514, https://doi.org/10.5194/gmd-7-495-2014, https://doi.org/10.5194/gmd-7-495-2014, 2014
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We introduce new climate models that simulate Australia’s future climate at regional scales, including at an unprecedented resolution of 4 km for 1950–2100. We describe the model design process used to create these new climate models. We show how the new models perform relative to previous-generation models and compare their climate projections. This work is of national and international relevance as it can help guide climate model design and the use and interpretation of climate projections.
Jiawang Feng, Chun Zhao, Qiuyan Du, Zining Yang, and Chen Jin
Geosci. Model Dev., 18, 585–603, https://doi.org/10.5194/gmd-18-585-2025, https://doi.org/10.5194/gmd-18-585-2025, 2025
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In this study, we improved the calculation of how aerosols in the air interact with radiation in WRF-Chem. The original model used a simplified method, but we developed a more accurate approach. We found that this method significantly changes the properties of the estimated aerosols and their effects on radiation, especially for dust aerosols. It also impacts the simulated weather conditions. Our work highlights the importance of correctly representing aerosol–radiation interactions in models.
Eduardo Moreno-Chamarro, Thomas Arsouze, Mario Acosta, Pierre-Antoine Bretonnière, Miguel Castrillo, Eric Ferrer, Amanda Frigola, Daria Kuznetsova, Eneko Martin-Martinez, Pablo Ortega, and Sergi Palomas
Geosci. Model Dev., 18, 461–482, https://doi.org/10.5194/gmd-18-461-2025, https://doi.org/10.5194/gmd-18-461-2025, 2025
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We present the high-resolution model version of the EC-Earth global climate model to contribute to HighResMIP. The combined model resolution is about 10–15 km in both the ocean and atmosphere, which makes it one of the finest ever used to complete historical and scenario simulations. This model is compared with two lower-resolution versions, with a 100 km and a 25 km grid. The three models are compared with observations to study the improvements thanks to the increased resolution.
Catherine Guiavarc'h, David Storkey, Adam T. Blaker, Ed Blockley, Alex Megann, Helene Hewitt, Michael J. Bell, Daley Calvert, Dan Copsey, Bablu Sinha, Sophia Moreton, Pierre Mathiot, and Bo An
Geosci. Model Dev., 18, 377–403, https://doi.org/10.5194/gmd-18-377-2025, https://doi.org/10.5194/gmd-18-377-2025, 2025
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The Global Ocean and Sea Ice configuration version 9 (GOSI9) is the new UK hierarchy of model configurations based on the Nucleus for European Modelling of the Ocean (NEMO) and available at three resolutions. It will be used for various applications, e.g. weather forecasting and climate prediction. It improves upon the previous version by reducing global temperature and salinity biases and enhancing the representation of Arctic sea ice and the Antarctic Circumpolar Current.
Andy Richling, Jens Grieger, and Henning W. Rust
Geosci. Model Dev., 18, 361–375, https://doi.org/10.5194/gmd-18-361-2025, https://doi.org/10.5194/gmd-18-361-2025, 2025
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The performance of weather and climate prediction systems is variable in time and space. It is of interest how this performance varies in different situations. We provide a decomposition of a skill score (a measure of forecast performance) as a tool for detailed assessment of performance variability to support model development or forecast improvement. The framework is exemplified with decadal forecasts to assess the impact of different ocean states in the North Atlantic on temperature forecast.
Maria R. Russo, Sadie L. Bartholomew, David Hassell, Alex M. Mason, Erica Neininger, A. James Perman, David A. J. Sproson, Duncan Watson-Parris, and Nathan Luke Abraham
Geosci. Model Dev., 18, 181–191, https://doi.org/10.5194/gmd-18-181-2025, https://doi.org/10.5194/gmd-18-181-2025, 2025
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Observational data and modelling capabilities have expanded in recent years, but there are still barriers preventing these two data sources from being used in synergy. Proper comparison requires generating, storing, and handling a large amount of data. This work describes the first step in the development of a new set of software tools, the VISION toolkit, which can enable the easy and efficient integration of observational and model data required for model evaluation.
Bijan Fallah, Masoud Rostami, Emmanuele Russo, Paula Harder, Christoph Menz, Peter Hoffmann, Iulii Didovets, and Fred F. Hattermann
Geosci. Model Dev., 18, 161–180, https://doi.org/10.5194/gmd-18-161-2025, https://doi.org/10.5194/gmd-18-161-2025, 2025
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We tried to contribute to a local climate change impact study in central Asia, a region that is water-scarce and vulnerable to global climate change. We use regional models and machine learning to produce reliable local data from global climate models. We find that regional models show more realistic and detailed changes in heavy precipitation than global climate models. Our work can help assess the future risks of extreme events and plan adaptation strategies in central Asia.
Thomas Rackow, Xabier Pedruzo-Bagazgoitia, Tobias Becker, Sebastian Milinski, Irina Sandu, Razvan Aguridan, Peter Bechtold, Sebastian Beyer, Jean Bidlot, Souhail Boussetta, Willem Deconinck, Michail Diamantakis, Peter Dueben, Emanuel Dutra, Richard Forbes, Rohit Ghosh, Helge F. Goessling, Ioan Hadade, Jan Hegewald, Thomas Jung, Sarah Keeley, Lukas Kluft, Nikolay Koldunov, Aleksei Koldunov, Tobias Kölling, Josh Kousal, Christian Kühnlein, Pedro Maciel, Kristian Mogensen, Tiago Quintino, Inna Polichtchouk, Balthasar Reuter, Domokos Sármány, Patrick Scholz, Dmitry Sidorenko, Jan Streffing, Birgit Sützl, Daisuke Takasuka, Steffen Tietsche, Mirco Valentini, Benoît Vannière, Nils Wedi, Lorenzo Zampieri, and Florian Ziemen
Geosci. Model Dev., 18, 33–69, https://doi.org/10.5194/gmd-18-33-2025, https://doi.org/10.5194/gmd-18-33-2025, 2025
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Detailed global climate model simulations have been created based on a numerical weather prediction model, offering more accurate spatial detail down to the scale of individual cities ("kilometre-scale") and a better understanding of climate phenomena such as atmospheric storms, whirls in the ocean, and cracks in sea ice. The new model aims to provide globally consistent information on local climate change with greater precision, benefiting environmental planning and local impact modelling.
Yilin Fang, Hoang Viet Tran, and L. Ruby Leung
Geosci. Model Dev., 18, 19–32, https://doi.org/10.5194/gmd-18-19-2025, https://doi.org/10.5194/gmd-18-19-2025, 2025
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Hurricanes may worsen water quality in the lower Mississippi River basin (LMRB) by increasing nutrient runoff. We found that runoff parameterizations greatly affect nitrate–nitrogen runoff simulated using an Earth system land model. Our simulations predicted increased nitrogen runoff in the LMRB during Hurricane Ida in 2021, albeit less pronounced than the observations, indicating areas for model improvement to better understand and manage nutrient runoff loss during hurricanes in the region.
Giovanni Seijo-Ellis, Donata Giglio, Gustavo Marques, and Frank Bryan
Geosci. Model Dev., 17, 8989–9021, https://doi.org/10.5194/gmd-17-8989-2024, https://doi.org/10.5194/gmd-17-8989-2024, 2024
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A CESM–MOM6 regional configuration of the Caribbean Sea was developed in response to the rising need for high-resolution models for climate impact studies. The configuration is validated for the period 2000–2020 and improves significant errors in a low-resolution model. Oceanic properties are well represented. Patterns of freshwater associated with the Amazon River are well captured, and the mean flows of ocean waters across multiple passages in the Caribbean Sea agree with observations.
Deifilia To, Julian Quinting, Gholam Ali Hoshyaripour, Markus Götz, Achim Streit, and Charlotte Debus
Geosci. Model Dev., 17, 8873–8884, https://doi.org/10.5194/gmd-17-8873-2024, https://doi.org/10.5194/gmd-17-8873-2024, 2024
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Pangu-Weather is a breakthrough machine learning model in medium-range weather forecasting that considers 3D atmospheric information. We show that using a simpler 2D framework improves robustness, speeds up training, and reduces computational needs by 20 %–30 %. We introduce a training procedure that varies the importance of atmospheric variables over time to speed up training convergence. Decreasing computational demand increases the accessibility of training and working with the model.
Fang Li, Xiang Song, Sandy P. Harrison, Jennifer R. Marlon, Zhongda Lin, L. Ruby Leung, Jörg Schwinger, Virginie Marécal, Shiyu Wang, Daniel S. Ward, Xiao Dong, Hanna Lee, Lars Nieradzik, Sam S. Rabin, and Roland Séférian
Geosci. Model Dev., 17, 8751–8771, https://doi.org/10.5194/gmd-17-8751-2024, https://doi.org/10.5194/gmd-17-8751-2024, 2024
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This study provides the first comprehensive assessment of historical fire simulations from 19 Earth system models in phase 6 of the Coupled Model Intercomparison Project (CMIP6). Most models reproduce global totals, spatial patterns, seasonality, and regional historical changes well but fail to simulate the recent decline in global burned area and underestimate the fire response to climate variability. CMIP6 simulations address three critical issues of phase-5 models.
Seung H. Baek, Paul A. Ullrich, Bo Dong, and Jiwoo Lee
Geosci. Model Dev., 17, 8665–8681, https://doi.org/10.5194/gmd-17-8665-2024, https://doi.org/10.5194/gmd-17-8665-2024, 2024
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We evaluate downscaled products by examining locally relevant co-variances during precipitation events. Common statistical downscaling techniques preserve expected co-variances during convective precipitation (a stationary phenomenon). However, they dampen future intensification of frontal precipitation (a non-stationary phenomenon) captured in global climate models and dynamical downscaling. Our study quantifies a ramification of the stationarity assumption underlying statistical downscaling.
Emmanuel Nyenah, Petra Döll, Daniel S. Katz, and Robert Reinecke
Geosci. Model Dev., 17, 8593–8611, https://doi.org/10.5194/gmd-17-8593-2024, https://doi.org/10.5194/gmd-17-8593-2024, 2024
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Research software is vital for scientific progress but is often developed by scientists with limited skills, time, and funding, leading to challenges in usability and maintenance. Our study across 10 sectors shows strengths in version control, open-source licensing, and documentation while emphasizing the need for containerization and code quality. We recommend workshops; code quality metrics; funding; and following the findable, accessible, interoperable, and reusable (FAIR) standards.
Chris Smith, Donald P. Cummins, Hege-Beate Fredriksen, Zebedee Nicholls, Malte Meinshausen, Myles Allen, Stuart Jenkins, Nicholas Leach, Camilla Mathison, and Antti-Ilari Partanen
Geosci. Model Dev., 17, 8569–8592, https://doi.org/10.5194/gmd-17-8569-2024, https://doi.org/10.5194/gmd-17-8569-2024, 2024
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Climate projections are only useful if the underlying models that produce them are well calibrated and can reproduce observed climate change. We formalise a software package that calibrates the open-source FaIR simple climate model to full-complexity Earth system models. Observations, including historical warming, and assessments of key climate variables such as that of climate sensitivity are used to constrain the model output.
Jingwei Xie, Xi Wang, Hailong Liu, Pengfei Lin, Jiangfeng Yu, Zipeng Yu, Junlin Wei, and Xiang Han
Geosci. Model Dev., 17, 8469–8493, https://doi.org/10.5194/gmd-17-8469-2024, https://doi.org/10.5194/gmd-17-8469-2024, 2024
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We propose the concept of mesoscale ocean direct numerical simulation (MODNS), which should resolve the first baroclinic deformation radius and ensure the numerical dissipative effects do not directly contaminate the mesoscale motions. It can be a benchmark for testing mesoscale ocean large eddy simulation (MOLES) methods in ocean models. We build an idealized Southern Ocean model using MITgcm to generate a type of MODNS. We also illustrate the diversity of multiscale eddy interactions.
Emily Black, John Ellis, and Ross I. Maidment
Geosci. Model Dev., 17, 8353–8372, https://doi.org/10.5194/gmd-17-8353-2024, https://doi.org/10.5194/gmd-17-8353-2024, 2024
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We present General TAMSAT-ALERT, a computationally lightweight and versatile tool for generating ensemble forecasts from time series data. General TAMSAT-ALERT is capable of combining multiple streams of monitoring and meteorological forecasting data into probabilistic hazard assessments. In this way, it complements existing systems and enhances their utility for actionable hazard assessment.
Sarah Schöngart, Lukas Gudmundsson, Mathias Hauser, Peter Pfleiderer, Quentin Lejeune, Shruti Nath, Sonia Isabelle Seneviratne, and Carl-Friedrich Schleussner
Geosci. Model Dev., 17, 8283–8320, https://doi.org/10.5194/gmd-17-8283-2024, https://doi.org/10.5194/gmd-17-8283-2024, 2024
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Precipitation and temperature are two of the most impact-relevant climatic variables. Yet, projecting future precipitation and temperature data under different emission scenarios relies on complex models that are computationally expensive. In this study, we propose a method that allows us to generate monthly means of local precipitation and temperature at low computational costs. Our modelling framework is particularly useful for all downstream applications of climate model data.
Gang Tang, Zebedee Nicholls, Chris Jones, Thomas Gasser, Alexander Norton, Tilo Ziehn, Alejandro Romero-Prieto, and Malte Meinshausen
EGUsphere, https://doi.org/10.5194/egusphere-2024-3522, https://doi.org/10.5194/egusphere-2024-3522, 2024
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We analyzed carbon and nitrogen mass conservation in data from CMIP6 Earth System Models. Our findings reveal significant discrepancies between flux and pool size data, particularly in nitrogen, where cumulative imbalances can reach hundreds of gigatons. These imbalances appear primarily due to missing or inconsistently reported fluxes – especially for land use and fire emissions. To enhance data quality, we recommend that future climate data protocols address this issue at the reporting stage.
Benjamin M. Sanderson, Ben B. B. Booth, John Dunne, Veronika Eyring, Rosie A. Fisher, Pierre Friedlingstein, Matthew J. Gidden, Tomohiro Hajima, Chris D. Jones, Colin G. Jones, Andrew King, Charles D. Koven, David M. Lawrence, Jason Lowe, Nadine Mengis, Glen P. Peters, Joeri Rogelj, Chris Smith, Abigail C. Snyder, Isla R. Simpson, Abigail L. S. Swann, Claudia Tebaldi, Tatiana Ilyina, Carl-Friedrich Schleussner, Roland Séférian, Bjørn H. Samset, Detlef van Vuuren, and Sönke Zaehle
Geosci. Model Dev., 17, 8141–8172, https://doi.org/10.5194/gmd-17-8141-2024, https://doi.org/10.5194/gmd-17-8141-2024, 2024
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We discuss how, in order to provide more relevant guidance for climate policy, coordinated climate experiments should adopt a greater focus on simulations where Earth system models are provided with carbon emissions from fossil fuels together with land use change instructions, rather than past approaches that have largely focused on experiments with prescribed atmospheric carbon dioxide concentrations. We discuss how these goals might be achieved in coordinated climate modeling experiments.
Peter Berg, Thomas Bosshard, Denica Bozhinova, Lars Bärring, Joakim Löw, Carolina Nilsson, Gustav Strandberg, Johan Södling, Johan Thuresson, Renate Wilcke, and Wei Yang
Geosci. Model Dev., 17, 8173–8179, https://doi.org/10.5194/gmd-17-8173-2024, https://doi.org/10.5194/gmd-17-8173-2024, 2024
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When bias adjusting climate model data using quantile mapping, one needs to prescribe what to do at the tails of the distribution, where a larger data range is likely encountered outside of the calibration period. The end result is highly dependent on the method used. We show that, to avoid discontinuities in the time series, one needs to exclude data in the calibration range to also activate the extrapolation functionality in that time period.
Philip J. Rasch, Haruki Hirasawa, Mingxuan Wu, Sarah J. Doherty, Robert Wood, Hailong Wang, Andy Jones, James Haywood, and Hansi Singh
Geosci. Model Dev., 17, 7963–7994, https://doi.org/10.5194/gmd-17-7963-2024, https://doi.org/10.5194/gmd-17-7963-2024, 2024
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We introduce a protocol to compare computer climate simulations to better understand a proposed strategy intended to counter warming and climate impacts from greenhouse gas increases. This slightly changes clouds in six ocean regions to reflect more sunlight and cool the Earth. Example changes in clouds and climate are shown for three climate models. Cloud changes differ between the models, but precipitation and surface temperature changes are similar when their cooling effects are made similar.
Trude Eidhammer, Andrew Gettelman, Katherine Thayer-Calder, Duncan Watson-Parris, Gregory Elsaesser, Hugh Morrison, Marcus van Lier-Walqui, Ci Song, and Daniel McCoy
Geosci. Model Dev., 17, 7835–7853, https://doi.org/10.5194/gmd-17-7835-2024, https://doi.org/10.5194/gmd-17-7835-2024, 2024
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We describe a dataset where 45 parameters related to cloud processes in the Community Earth System Model version 2 (CESM2) Community Atmosphere Model version 6 (CAM6) are perturbed. Three sets of perturbed parameter ensembles (263 members) were created: current climate, preindustrial aerosol loading and future climate with sea surface temperature increased by 4 K.
Ha Thi Minh Ho-Hagemann, Vera Maurer, Stefan Poll, and Irina Fast
Geosci. Model Dev., 17, 7815–7834, https://doi.org/10.5194/gmd-17-7815-2024, https://doi.org/10.5194/gmd-17-7815-2024, 2024
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The regional Earth system model GCOAST-AHOI v2.0 that includes the regional climate model ICON-CLM coupled to the ocean model NEMO and the hydrological discharge model HD via the OASIS3-MCT coupler can be a useful tool for conducting long-term regional climate simulations over the EURO-CORDEX domain. The new OASIS3-MCT coupling interface implemented in ICON-CLM makes it more flexible for coupling to an external ocean model and an external hydrological discharge model.
Sandro Vattioni, Rahel Weber, Aryeh Feinberg, Andrea Stenke, John A. Dykema, Beiping Luo, Georgios A. Kelesidis, Christian A. Bruun, Timofei Sukhodolov, Frank N. Keutsch, Thomas Peter, and Gabriel Chiodo
Geosci. Model Dev., 17, 7767–7793, https://doi.org/10.5194/gmd-17-7767-2024, https://doi.org/10.5194/gmd-17-7767-2024, 2024
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We quantified impacts and efficiency of stratospheric solar climate intervention via solid particle injection. Microphysical interactions of solid particles with the sulfur cycle were interactively coupled to the heterogeneous chemistry scheme and the radiative transfer code of an aerosol–chemistry–climate model. Compared to injection of SO2 we only find a stronger cooling efficiency for solid particles when normalizing to the aerosol load but not when normalizing to the injection rate.
Ingo Richter, Ping Chang, Gokhan Danabasoglu, Dietmar Dommenget, Guillaume Gastineau, Aixue Hu, Takahito Kataoka, Noel Keenlyside, Fred Kucharski, Yuko Okumura, Wonsun Park, Malte Stuecker, Andrea Taschetto, Chunzai Wang, Stephen Yeager, and Sang-Wook Yeh
EGUsphere, https://doi.org/10.5194/egusphere-2024-3110, https://doi.org/10.5194/egusphere-2024-3110, 2024
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The tropical ocean basins influence each other through multiple pathways and mechanisms, here referred to as tropical basin interaction (TBI). Many researchers have examined TBI using comprehensive climate models, but have obtained conflicting results. This may be partly due to differences in experiment protocols, and partly due to systematic model errors. TBIMIP aims to address this problem by designing a set of TBI experiments that will be performed by multiple models.
Samuel Rémy, Swen Metzger, Vincent Huijnen, Jason E. Williams, and Johannes Flemming
Geosci. Model Dev., 17, 7539–7567, https://doi.org/10.5194/gmd-17-7539-2024, https://doi.org/10.5194/gmd-17-7539-2024, 2024
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In this paper we describe the development of the future operational cycle 49R1 of the IFS-COMPO system, used for operational forecasts of atmospheric composition in the CAMS project, and focus on the implementation of the thermodynamical model EQSAM4Clim version 12. The implementation of EQSAM4Clim significantly improves the simulated secondary inorganic aerosol surface concentration. The new aerosol and precipitation acidity diagnostics showed good agreement against observational datasets.
Maximillian Van Wyk de Vries, Tom Matthews, L. Baker Perry, Nirakar Thapa, and Rob Wilby
Geosci. Model Dev., 17, 7629–7643, https://doi.org/10.5194/gmd-17-7629-2024, https://doi.org/10.5194/gmd-17-7629-2024, 2024
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This paper introduces the AtsMOS workflow, a new tool for improving weather forecasts in mountainous areas. By combining advanced statistical techniques with local weather data, AtsMOS can provide more accurate predictions of weather conditions. Using data from Mount Everest as an example, AtsMOS has shown promise in better forecasting hazardous weather conditions, making it a valuable tool for communities in mountainous regions and beyond.
Sofia Allende, Anne Marie Treguier, Camille Lique, Clément de Boyer Montégut, François Massonnet, Thierry Fichefet, and Antoine Barthélemy
Geosci. Model Dev., 17, 7445–7466, https://doi.org/10.5194/gmd-17-7445-2024, https://doi.org/10.5194/gmd-17-7445-2024, 2024
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We study the parameters of the turbulent-kinetic-energy mixed-layer-penetration scheme in the NEMO model with regard to sea-ice-covered regions of the Arctic Ocean. This evaluation reveals the impact of these parameters on mixed-layer depth, sea surface temperature and salinity, and ocean stratification. Our findings demonstrate significant impacts on sea ice thickness and sea ice concentration, emphasizing the need for accurately representing ocean mixing to understand Arctic climate dynamics.
Sabin I. Taranu, David M. Lawrence, Yoshihide Wada, Ting Tang, Erik Kluzek, Sam Rabin, Yi Yao, Steven J. De Hertog, Inne Vanderkelen, and Wim Thiery
Geosci. Model Dev., 17, 7365–7399, https://doi.org/10.5194/gmd-17-7365-2024, https://doi.org/10.5194/gmd-17-7365-2024, 2024
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In this study, we improved a climate model by adding the representation of water use sectors such as domestic, industry, and agriculture. This new feature helps us understand how water is used and supplied in various areas. We tested our model from 1971 to 2010 and found that it accurately identifies areas with water scarcity. By modelling the competition between sectors when water availability is limited, the model helps estimate the intensity and extent of individual sectors' water shortages.
Florian Börgel, Sven Karsten, Karoline Rummel, and Ulf Gräwe
EGUsphere, https://doi.org/10.5194/egusphere-2024-2685, https://doi.org/10.5194/egusphere-2024-2685, 2024
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Forecasting river runoff, crucial for managing water resources and understanding climate impacts, can be challenging. This study introduces a new method using Convolutional Long Short-Term Memory (ConvLSTM) networks, a machine learning model that processes spatial and temporal data. Focusing on the Baltic Sea region, our model uses weather data as input to predict daily river runoff for 97 rivers.
Thi Nhu Ngoc Do, Kengo Sudo, Akihiko Ito, Louisa Emmons, Vaishali Naik, Kostas Tsigaridis, Øyvind Seland, Gerd A. Folberth, and Douglas I. Kelley
EGUsphere, https://doi.org/10.5194/egusphere-2024-2313, https://doi.org/10.5194/egusphere-2024-2313, 2024
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Understanding historical isoprene emission changes is important for predicting future climate, but trends and their controlling factors remain uncertain. This study shows that long-term isoprene trends vary among Earth System Models mainly due to partially incorporating CO2 effects and land cover changes rather than climate. Future models that refine these factors’ effects on isoprene emissions, along with long-term observations, are essential for better understanding plant-climate interactions.
Michael Nole, Jonah Bartrand, Fawz Naim, and Glenn Hammond
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-162, https://doi.org/10.5194/gmd-2024-162, 2024
Revised manuscript accepted for GMD
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Safe carbon dioxide (CO2) storage is likely to be critical for mitigating some of the most dangerous effects of climate change. We present a simulation framework for modeling CO2 storage beneath the seafloor where CO2 can form a solid. This can aid in permanent CO2 storage for long periods of time. Our models show what a commercial-scale CO2 injection would look like in a marine environment. We discuss what would need to be considered when designing a sub-sea CO2 injection.
Cynthia Whaley, Montana Etten-Bohm, Courtney Schumacher, Ayodeji Akingunola, Vivek Arora, Jason Cole, Michael Lazare, David Plummer, Knut von Salzen, and Barbara Winter
Geosci. Model Dev., 17, 7141–7155, https://doi.org/10.5194/gmd-17-7141-2024, https://doi.org/10.5194/gmd-17-7141-2024, 2024
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This paper describes how lightning was added as a process in the Canadian Earth System Model in order to interactively respond to climate changes. As lightning is an important cause of global wildfires, this new model development allows for more realistic projections of how wildfires may change in the future, responding to a changing climate.
Erik Gustafsson, Bo G. Gustafsson, Martijn Hermans, Christoph Humborg, and Christian Stranne
Geosci. Model Dev., 17, 7157–7179, https://doi.org/10.5194/gmd-17-7157-2024, https://doi.org/10.5194/gmd-17-7157-2024, 2024
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Methane (CH4) cycling in the Baltic Proper is studied through model simulations, enabling a first estimate of key CH4 fluxes. A preliminary budget identifies benthic CH4 release as the dominant source and two main sinks: CH4 oxidation in the water (92 % of sinks) and outgassing to the atmosphere (8 % of sinks). This study addresses CH4 emissions from coastal seas and is a first step toward understanding the relative importance of open-water outgassing compared with local coastal hotspots.
Daniel Ries, Katherine Goode, Kellie McClernon, and Benjamin Hillman
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-133, https://doi.org/10.5194/gmd-2024-133, 2024
Revised manuscript accepted for GMD
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Machine learning has advanced research in the climate science domain, but its models are difficult to understand. In order to understand the impacts and consequences of climate interventions such as stratospheric aerosol injection, complex models are often necessary. We use a case study to illustrate how we can understand the inner workings of a complex model. We present this technique as an exploratory tool that can be used to quickly discover and assess relationships in complex climate data.
Florian Zabel, Matthias Knüttel, and Benjamin Poschlod
EGUsphere, https://doi.org/10.5194/egusphere-2024-2526, https://doi.org/10.5194/egusphere-2024-2526, 2024
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CropSuite is a fuzzy-logic based high resolution open-source crop suitability model considering the impact of climate variability. We apply CropSuite for 48 important staple and cash crops at 1 km spatial resolution for Africa. We find that climate variability significantly impacts on suitable areas, but also affects optimal sowing dates, and multiple cropping potentials. The results provide information that can be used for climate impact assessments, adaptation and land-use planning.
Kerstin Hartung, Bastian Kern, Nils-Arne Dreier, Jörn Geisbüsch, Mahnoosh Haghighatnasab, Patrick Jöckel, Astrid Kerkweg, Wilton Jaciel Loch, Florian Prill, and Daniel Rieger
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-135, https://doi.org/10.5194/gmd-2024-135, 2024
Revised manuscript accepted for GMD
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The Icosahedral Nonhydrostatic (ICON) Model Community Interface (ComIn) library supports connecting third-party modules to the ICON model. Third-party modules can range from simple diagnostic Python scripts to full chemistry models. ComIn offers a low barrier for code extensions to ICON, provides multi-language support (Fortran, C/C++ and Python) and reduces the migration effort in response to new ICON releases. This paper presents the ComIn design principles and a range of use cases.
Tridib Banerjee, Patrick Scholz, Sergey Danilov, Knut Klingbeil, and Dmitry Sidorenko
Geosci. Model Dev., 17, 7051–7065, https://doi.org/10.5194/gmd-17-7051-2024, https://doi.org/10.5194/gmd-17-7051-2024, 2024
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In this paper we propose a new alternative to one of the functionalities of the sea ice model FESOM2. The alternative we propose allows the model to capture and simulate fast changes in quantities like sea surface elevation more accurately. We also demonstrate that the new alternative is faster and more adept at taking advantages of highly parallelized computing infrastructure. We therefore show that this new alternative is a great addition to the sea ice model FESOM2.
Yuwen Fan, Zhao Yang, Min-Hui Lo, Jina Hur, and Eun-Soon Im
Geosci. Model Dev., 17, 6929–6947, https://doi.org/10.5194/gmd-17-6929-2024, https://doi.org/10.5194/gmd-17-6929-2024, 2024
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Irrigated agriculture in the North China Plain (NCP) has a significant impact on the local climate. To better understand this impact, we developed a specialized model specifically for the NCP region. This model allows us to simulate the double-cropping vegetation and the dynamic irrigation practices that are commonly employed in the NCP. This model shows improved performance in capturing the general crop growth, such as crop stages, biomass, crop yield, and vegetation greenness.
Gang Tang, Zebedee Nicholls, Alexander Norton, Sönke Zaehle, and Malte Meinshausen
EGUsphere, https://doi.org/10.5194/egusphere-2024-1941, https://doi.org/10.5194/egusphere-2024-1941, 2024
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We studied the coupled carbon-nitrogen cycle effect in Earth System Models by developing a carbon-nitrogen coupling in a reduced complexity model, MAGICC. Our model successfully emulated the global carbon-nitrogen cycle dynamics seen in CMIP6 complex models. Results indicate consistent nitrogen limitations on plant growth (net primary production) from 1850 to 2100. Our findings suggest that nitrogen deficiency could reduce future land carbon sequestration.
Ed Blockley, Emma Fiedler, Jeff Ridley, Luke Roberts, Alex West, Dan Copsey, Daniel Feltham, Tim Graham, David Livings, Clement Rousset, David Schroeder, and Martin Vancoppenolle
Geosci. Model Dev., 17, 6799–6817, https://doi.org/10.5194/gmd-17-6799-2024, https://doi.org/10.5194/gmd-17-6799-2024, 2024
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This paper documents the sea ice model component of the latest Met Office coupled model configuration, which will be used as the physical basis for UK contributions to CMIP7. Documentation of science options used in the configuration are given along with a brief model evaluation. This is the first UK configuration to use NEMO’s new SI3 sea ice model. We provide details on how SI3 was adapted to work with Met Office coupling methodology and documentation of coupling processes in the model.
Jean-François Lemieux, William H. Lipscomb, Anthony Craig, David A. Bailey, Elizabeth C. Hunke, Philippe Blain, Till A. S. Rasmussen, Mats Bentsen, Frédéric Dupont, David Hebert, and Richard Allard
Geosci. Model Dev., 17, 6703–6724, https://doi.org/10.5194/gmd-17-6703-2024, https://doi.org/10.5194/gmd-17-6703-2024, 2024
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We present the latest version of the CICE model. It solves equations that describe the dynamics and the growth and melt of sea ice. To do so, the domain is divided into grid cells and variables are positioned at specific locations in the cells. A new implementation (C-grid) is presented, with the velocity located on cell edges. Compared to the previous B-grid, the C-grid allows for a natural coupling with some oceanic and atmospheric models. It also allows for ice transport in narrow channels.
Rachid El Montassir, Olivier Pannekoucke, and Corentin Lapeyre
Geosci. Model Dev., 17, 6657–6681, https://doi.org/10.5194/gmd-17-6657-2024, https://doi.org/10.5194/gmd-17-6657-2024, 2024
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This study introduces a novel approach that combines physics and artificial intelligence (AI) for improved cloud cover forecasting. This approach outperforms traditional deep learning (DL) methods in producing realistic and physically consistent results while requiring less training data. This architecture provides a promising solution to overcome the limitations of classical AI methods and contributes to open up new possibilities for combining physical knowledge with deep learning models.
Marit Sandstad, Borgar Aamaas, Ane Nordlie Johansen, Marianne Tronstad Lund, Glen Philip Peters, Bjørn Hallvard Samset, Benjamin Mark Sanderson, and Ragnhild Bieltvedt Skeie
Geosci. Model Dev., 17, 6589–6625, https://doi.org/10.5194/gmd-17-6589-2024, https://doi.org/10.5194/gmd-17-6589-2024, 2024
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The CICERO-SCM has existed as a Fortran model since 1999 that calculates the radiative forcing and concentrations from emissions and is an upwelling diffusion energy balance model of the ocean that calculates temperature change. In this paper, we describe an updated version ported to Python and publicly available at https://github.com/ciceroOslo/ciceroscm (https://doi.org/10.5281/zenodo.10548720). This version contains functionality for parallel runs and automatic calibration.
Sébastien Masson, Swen Jullien, Eric Maisonnave, David Gill, Guillaume Samson, Mathieu Le Corre, and Lionel Renault
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-140, https://doi.org/10.5194/gmd-2024-140, 2024
Revised manuscript accepted for GMD
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This article details a new feature we implemented in the most popular regional atmospheric model (WRF). This feature allows data to be exchanged between WRF and any other model (e.g. an ocean model) using the coupling library Ocean-Atmosphere-Sea-Ice-Soil – Model Coupling Toolkit (OASIS3-MCT). This coupling interface is designed to be non-intrusive, flexible and modular. It also offers the possibility of taking into account the nested zooms used in WRF or in the models with which it is coupled.
Jordi Buckley Paules, Simone Fatichi, Bonnie Warring, and Athanasios Paschalis
EGUsphere, https://doi.org/10.5194/egusphere-2024-2072, https://doi.org/10.5194/egusphere-2024-2072, 2024
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We outline and validate developments to the pre-existing process-based model T&C to better represent cropland processes. Foreseen applications of T&C-CROP include hydrological and carbon storage implications of land-use transitions involving crop, forest, and pasture conversion, as well as studies on optimal irrigation and fertilization under a changing climate.
Zheng Xiang, Yongkang Xue, Weidong Guo, Melannie D. Hartman, Ye Liu, and William J. Parton
Geosci. Model Dev., 17, 6437–6464, https://doi.org/10.5194/gmd-17-6437-2024, https://doi.org/10.5194/gmd-17-6437-2024, 2024
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A process-based plant carbon (C)–nitrogen (N) interface coupling framework has been developed which mainly focuses on plant resistance and N-limitation effects on photosynthesis, plant respiration, and plant phenology. A dynamic C / N ratio is introduced to represent plant resistance and self-adjustment. The framework has been implemented in a coupled biophysical-ecosystem–biogeochemical model, and testing results show a general improvement in simulating plant properties with this framework.
Cited articles
Alvioli, M., Marchesini, I., Reichenbach, P., Rossi, M., Ardizzone, F., Fiorucci, F., and Guzzetti, F.: Automatic delineation of geomorphological slope-units and their optimization for landslide susceptibility modelling, Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2016-118, in review, 2016.
Ardizzone, F., Cardinali, M., Carrara, A., Guzzetti, F., and Reichenbach, P.: Impact of mapping errors on the reliability of landslide hazard maps, Nat. Hazards Earth Syst. Sci., 2, 3–14, https://doi.org/10.5194/nhess-2-3-2002, 2002.
Ardizzone, F., Basile, G., Cardinali, M., Casagli, N., Del Conte, S., Del Ventisette, C., Fiorucci, F., Garfagnoli, F., Gigli, G., Guzzetti, F., Iovine, G., Mondini, A. C., Moretti, S., Panebianco, M., Raspini, F., Reichenbach, P., Rossi, M., Tanteri, L., and Terranova, O.: Landslide inventory map for the Briga and the Giampilieri catchments, NE Sicily, Italy, J. Maps, 8, 176–180, 2012.
Atkinson, P., Jiskoot, H., Massari, R., and Murray, T.: Generalized linear modelling in geomorphology, Earth Surf. Proc. Land., 23, 1185–1195, 1998
Atkinson, P. M. and Massari, R.: Autologistic modelling of susceptibility to landsliding in the central Apennines, Italy, Geomorphology, 130, 55–64, https://doi.org/10.1016/j.geomorph.2011.02.001, 2011.
Belsley, D. A.: Conditioning diagnostics, collinearity and weak data in regression, John Wiley & Sons, New York, USA, 1991.
Brabb, E. E.: Innovative approaches to landslide hazard mapping, Proceedings 4th International Symposium on Landslides, 16–21 September 1984, Toronto, Canada, 1, 307–324, 1984.
Brown, C. E.: Applied Multivariate Statistics in Geohydrology and Related Sciences, Springer-Verlag, Berlin, Germany, 248 pp., 1998
Carrara, A., Cardinali, M., Detti, R., Guzzetti, F., Pasqui, V., and Reichenbach, P.: GIS techniques and statistical models in evaluating landslide hazard, Earth Surf. Proc. Land., 16, 427–445, 1991.
Carrara, A., Cardinali, M., Guzzetti, F., and Reichenbach, P.: GIS technology in mapping landslide hazard, edited by: Carrara, A. and Guzzetti, F., Geographical Information Systems in Assessing Natural Hazards, Kluwer Academic Publisher, Dordrecht, the Netherlands, 135–175, 1995.
Carrara, A., Crosta, G., and Frattini, P.: Comparing models of debris-flow susceptibility in the alpine environment, Geomorphology, 94, 353–378, 2008.
Chung, C.-J. F. and Fabbri, A. G.: Probabilistic prediction models for landslide hazard mapping, Photogramm. Eng. Rem. S., 65, 1389–1399, 1999.
Chung, C.-J. F. and Fabbri, A. G.: Validation of spatial prediction models for landslide hazard mapping, Nat. Hazards, 30, 451–472, 2003.
Cohen, J.: A coefficient of agreement for nominal scales, Educ. Psychol. Meas., 20, 37–46, 1960.
Cox, D. R.: The regression analysis of binary sequences, J. Roy. Stat. Soc. B Met., 20, 215–242, 1958.
Davison, A. C. and Hinkley, D.: Bootstrap methods and their applications, 8th ed., Cambridge Series in Statistical and Probabilistic Mathematics, Cambridge University Press, Cambridge, UK, 2006.
Efron, B.: Bootstrap methods: another look at the jack knife, Annals Statistics, 7, 1–26, 1979.
Fawcett, T.: An introduction to ROC analysis, Pattern Recogn. Lett., 27, 861–874, 2006.
Felicísimo, Á., Cuartero, A., Remondo, J., and Quirós, E.: Mapping landslide susceptibility with logistic regression, multiple adaptive regression splines, classification and regression trees, and maximum entropy methods: a comparative study, Landslides, 10, 175–189, 2013
Fisher, R. A.: The use of multiple measurements in taxonomic problems, Annales Eugenics, 7, 179–188, 1936.
Goetz, J. N., Brenning, A., Petschko, H., and Leopold, P.: Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling, Comput. Geosci., 81, 1–11, https://doi.org/10.1016/j.cageo.2015.04.007, 2015.
Green, D. M. and Swets, J. M.: Signal detection theory and psychophysics, John Wiley and Sons, New York, USA, 1966.
Guzzetti, F., Carrara, A., Cardinali, M., and Reichenbach, P.: Landslide hazard evaluation: a review of current techniques and their application in a multi–scale study, Central Italy, Geomorphology, 31, 181–216, 1999.
Guzzetti, F., Reichenbach, P., Ardizzone, F., Cardinali, M., and Galli, M.: Estimating the quality of landslide susceptibility models, Geomorphology, 81, 166–184, 2006.
Guzzetti, F., Mondini, A. C., Cardinali, M., Fiorucci, F., Santangelo, M., and Chang, K. T.: Landslide inventory maps: New tools for an old problem. Earth-Sci. Rev., 112, 42–66, 2012.
Heckmann, T., Gegg, K., Gegg, A., and Becht, M.: Sample size matters: investigating the effect of sample size on a logistic regression susceptibility model for debris flows, Nat. Hazards Earth Syst. Sci., 14, 259–278, https://doi.org/10.5194/nhess-14-259-2014, 2014.
Hendrickx, J.: perturb: Tools for evaluating collinearity, R package version 2.05, available at: http://CRAN.R-project.org/package=perturb (last access: 20 September 2016), 2012.
Hussin Y. H., Zumpano, V., Reichenbach, P., Sterlacchini, S., Micu, M., van Westen, C., and Bălteanu, D.: Different landslide sampling strategies in a grid-based bi-variate statistical susceptibility model, Geomorphology, 253, 508–523, https://doi.org/10.1016/j.geomorph.2015.10.030, 2016.
Jollifee, I. T. and Stephenson, D. B.: Forecast verification, A Practitioner's Guide in Atmospheric Science, John Wiley, Chichester, UK, 240 pp., 2003.
Kuhn, M. and Kjell, J.: Applied predictive modeling, Springer, New York, USA, 2013.
Malamud, B., Mihir, M., Reichenbach, P., and Rossi, M.: D6.3-Report on standards for landslide susceptibility modelling and terrain zonations, LAMPRE FP7 Project deliverables, available at: http://www.lampre-project.eu (last access: 20 September 2016), 2014.
Marchesini, I., Alvioli, M., Rossi, M., Santangelo, M., Cardinali, M., Reichenbach, P., Ardizzone, F., Fiorucci, F., Balducci, V., Mondini, A. C., and Guzzetti, F.: WPS tools to support geological and geomorphological mapping, OGRS 2012, Open Source Geospatial Research & Education Symposium, 24–26 October 2012, Yverdon-les-Bains, Switzerland, available at: http://ogrs2012.heig-vd.ch/public/ogrs2012/abstracts/WPS tools to support geological and geomorphological mapping.pdf (last access: 20 September 2016), 2012.
Mason, S. J. and Graham, N. E.: Areas beneath the relative operating characteristics (ROC) and relative operating levels (ROL) curves: statistical significance and interpretation, Q. J. Roy. Meteor. Soc., 128, 2145–2166, 2002.
Maugeri, M. and Motta, E.: Effects of heavy rainfalls on slope behavior: the October 1, 2009 disaster of Messina (Italy), edited by: Iai, S., Geotechnics and Earthquake Geotechnics Towards Global Sustainability, Geotechnical, Geological and Earthquake Engineering 15, Springer Science + Business Media, Springer Netherlands, 2011.
Metz, M., Mitasova, H., and Harmon, R. S.: Efficient extraction of drainage networks from massive, radar-based elevation models with least cost path search, Hydrol. Earth Syst. Sci., 15, 667–678, https://doi.org/10.5194/hess-15-667-2011, 2011.
Meyer, D., Zeileis, A., and Hornik, K.: vcd: Visualizing Categorical Data, R package version 1.4-0, available at: https://cran.r-project.org/web/packages/vcd/ (last access: 20 September 2016), 2015.
Mondini, A. C., Guzzetti, F., Reichenbach, P., Rossi, M., Cardinali, M., and Ardizzone, F.: Semi-automatic recognition and mapping of rainfall induced shallow landslides using optical satellite images, Remote Sens. Environ., 115, 1743–1757, 2011.
NCAR – Research Applications Laboratory: verification: Weather Forecast Verification Utilities, R package version 1.41, available at: https://cran.r-project.org/web/packages/verification/index.html (last access: 20 September 2016), 2014.
Owen, M., Imai, K., King, G., and Lau, O.: Zelig everyone's statistical software, R package version 4.2-1, available at: https://cran.r-project.org/web/packages/Zelig/index.html (last access: 20 September 2016), 2013.
Petschko, H., Brenning, A., Bell, R., Goetz, J., and Glade, T.: Assessing the quality of landslide susceptibility maps – case study Lower Austria, Nat. Hazards Earth Syst. Sci., 14, 95–118, https://doi.org/10.5194/nhess-14-95-2014, 2014.
R Core Team: R: A language and environment for statistical computing, R Foundation for Statistical Computing, Vienna, Austria, available at: https://www.R-project.org/ (last access: 20 September 2016), 2015.
Regmi, N. R., Giardino, J. R., McDonald, E. V., and Vitek, J. D.: A comparison of logistic regression-based models of susceptibility to landslides in western Colorado, USA, Landslides, 11, 247–262, https://doi.org/10.1007/s10346-012-0380-2, 2014.
Reichenbach, P., Busca, C., Mondini, A. C., and Rossi, M.: The influence of land use change on landslide susceptibility zonation: the Briga catchment test site (Messina, Italy), Environ. Manage., 54, 1372–1384, 2014.
Reichenbach, P., Busca, C., Mondini, A. C., and Rossi, M.: Land use change scenarios and landslide susceptibility zonation: the Briga catchment test area (Messina, Italy), Engineering Geology for Society and Territory, 1, 557–561, Springer International Publishing, Switzerland, 2015.
Ripley, B. D.: Pattern Recognition and Neural Networks, Cambridge University Press, Cambridge, UK, 416 pp., 1996.
Rossi, M.: maurorossi/LAND-SE v1r0b30 [Data set], Zenodo, https://doi.org/10.5281/zenodo.154723, 2016.
Rossi M., Guzzetti F., Reichenbach P., Mondini A. C., and Peruccacci S.: Optimal landslide susceptibility zonation based on multiple forecasts, Geomorphology, 114, 129–142, 2010.
Van Den Eeckhaut, M., Marre, A., and Poesen, J.: Comparison of two landslide susceptibility assessments in the Champagne–Ardenne region (France), Geomorphology, 115, 141–155, 2010.
Varnes D. J.: IAEG Commission on Landslides: Landslide hazard zonation: a review of principles and practice, 3, United Nations Educational, Paris, France, 63 pp., 1984.
Venables, W. N. and Ripley, B. D.: Modern Applied Statistics with S, Fourth edition, Springer, Berlin, Germany, 495 pp., 2002.
von Ruette, J., Papritz, A., Lehmann, P., Rickli, C., and Or, D.: Spatial statistical modeling of shallow landslides – Validating predictions for different landslide inventories and rainfall events, Geomorphology, 133, 11–22, 2011.
Short summary
Landslide susceptibility maps show places where landslides may occur in the future. These maps are prepared using different approaches, information on past landslides distribution and a variety of geo-environmental data. The paper describes LAND-SE (LANDslide Susceptibility Evaluation), an open-source software coded in R for statistically based susceptibility zonation that provides estimates of model performances and uncertainty. A user guide and example data are distributed with the software.
Landslide susceptibility maps show places where landslides may occur in the future. These maps...
