Articles | Volume 8, issue 7
https://doi.org/10.5194/gmd-8-1955-2015
© Author(s) 2015. 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-8-1955-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Methods for assessment of models
| 
07 Jul 2015
Methods for assessment of models |
| 07 Jul 2015
GASAKe: forecasting landslide activations by a genetic-algorithms-based hydrological model
O. G. Terranova
CNR-IRPI (National Research Council – Research Institute for Geo-Hydrological Protection), via Cavour 6, 87036, Rende, Cosenza, Italy
CNR-IRPI (National Research Council – Research Institute for Geo-Hydrological Protection), via Madonna Alta 126, 06128, Perugia, Italy
University of Perugia, Department of Physics and Geology, via A. Pascoli, 06123, Perugia, Italy
P. Iaquinta
CNR-IRPI (National Research Council – Research Institute for Geo-Hydrological Protection), via Cavour 6, 87036, Rende, Cosenza, Italy
G. G. R. Iovine
CNR-IRPI (National Research Council – Research Institute for Geo-Hydrological Protection), via Cavour 6, 87036, Rende, Cosenza, Italy
Related authors
O. G. Terranova and S. L. Gariano
Nat. Hazards Earth Syst. Sci., 14, 2423–2434, https://doi.org/10.5194/nhess-14-2423-2014, https://doi.org/10.5194/nhess-14-2423-2014, 2014
Silvia Peruccacci, Stefano Luigi Gariano, Massimo Melillo, Monica Solimano, Fausto Guzzetti, and Maria Teresa Brunetti
Earth Syst. Sci. Data, 15, 2863–2877, https://doi.org/10.5194/essd-15-2863-2023, https://doi.org/10.5194/essd-15-2863-2023, 2023
Short summary
Short summary
ITALICA (ITAlian rainfall-induced LandslIdes CAtalogue) is the largest catalogue of rainfall-induced landslides accurately located in space and time available in Italy. ITALICA currently lists 6312 landslides that occurred between January 1996 and December 2021. The information was collected using strict objective and homogeneous criteria. The high spatial and temporal accuracy makes the catalogue suitable for reliably defining the rainfall conditions capable of triggering future landslides.
Stefan Steger, Mateo Moreno, Alice Crespi, Peter James Zellner, Stefano Luigi Gariano, Maria Teresa Brunetti, Massimo Melillo, Silvia Peruccacci, Francesco Marra, Robin Kohrs, Jason Goetz, Volkmar Mair, and Massimiliano Pittore
Nat. Hazards Earth Syst. Sci., 23, 1483–1506, https://doi.org/10.5194/nhess-23-1483-2023, https://doi.org/10.5194/nhess-23-1483-2023, 2023
Short summary
Short summary
We present a novel data-driven modelling approach to determine season-specific critical precipitation conditions for landslide occurrence. It is shown that the amount of precipitation required to trigger a landslide in South Tyrol varies from season to season. In summer, a higher amount of preparatory precipitation is required to trigger a landslide, probably due to denser vegetation and higher temperatures. We derive dynamic thresholds that directly relate to hit rates and false-alarm rates.
Maria Teresa Brunetti, Massimo Melillo, Stefano Luigi Gariano, Luca Ciabatta, Luca Brocca, Giriraj Amarnath, and Silvia Peruccacci
Hydrol. Earth Syst. Sci., 25, 3267–3279, https://doi.org/10.5194/hess-25-3267-2021, https://doi.org/10.5194/hess-25-3267-2021, 2021
Short summary
Short summary
Satellite and rain gauge data are tested to predict landslides in India, where the annual toll of human lives and loss of property urgently demands the implementation of strategies to prevent geo-hydrological instability. For this purpose, we calculated empirical rainfall thresholds for landslide initiation. The validation of thresholds showed that satellite-based rainfall data perform better than ground-based data, and the best performance is obtained with an hourly temporal resolution.
Massimo Melillo, Stefano Luigi Gariano, Silvia Peruccacci, Roberto Sarro, Rosa Marìa Mateos, and Maria Teresa Brunetti
Nat. Hazards Earth Syst. Sci., 20, 2307–2317, https://doi.org/10.5194/nhess-20-2307-2020, https://doi.org/10.5194/nhess-20-2307-2020, 2020
Short summary
Short summary
In the Canary Islands, a link between rainfall and rockfall occurrence is found for most of the year, except for the warm season. Empirical rainfall thresholds for rockfalls are first proposed for Gran Canaria and Tenerife, and the dependence of the thresholds on the mean annual rainfall is discussed. The use of thresholds in early-warning systems might contribute to the mitigation of the rockfall hazard in the archipelago and reduce the associated risk.
Samuele Segoni, Luca Piciullo, and Stefano Luigi Gariano
Nat. Hazards Earth Syst. Sci., 18, 3179–3186, https://doi.org/10.5194/nhess-18-3179-2018, https://doi.org/10.5194/nhess-18-3179-2018, 2018
S. L. Gariano, O. Petrucci, and F. Guzzetti
Nat. Hazards Earth Syst. Sci., 15, 2313–2330, https://doi.org/10.5194/nhess-15-2313-2015, https://doi.org/10.5194/nhess-15-2313-2015, 2015
Short summary
Short summary
We study temporal and geographical variations in the occurrence of 1466 rainfall-induced landslides in Calabria, southern Italy, in the period 1921–2010. To evaluate the impact on the population, we compare the number of rainfall-induced landslides with the size of population in the 409 municipalities in Calabria. We find variations in yearly and geographical distribution of rainfall-induced landslides, variations in rainfall triggering conditions, and changes in the impact on the population.
O. G. Terranova and S. L. Gariano
Nat. Hazards Earth Syst. Sci., 14, 2423–2434, https://doi.org/10.5194/nhess-14-2423-2014, https://doi.org/10.5194/nhess-14-2423-2014, 2014
C. Vennari, S. L. Gariano, L. Antronico, M. T. Brunetti, G. Iovine, S. Peruccacci, O. Terranova, and F. Guzzetti
Nat. Hazards Earth Syst. Sci., 14, 317–330, https://doi.org/10.5194/nhess-14-317-2014, https://doi.org/10.5194/nhess-14-317-2014, 2014
Related subject area
Climate and Earth system modeling
Monsoon Mission Coupled Forecast System version 2.0: model description and Indian monsoon simulations
Exploring the ocean mesoscale at reduced computational cost with FESOM 2.5: efficient modeling strategies applied to the Southern Ocean
Truly conserving with conservative remapping methods
High-resolution downscaling of CMIP6 Earth system and global climate models using deep learning for Iberia
Earth system modeling on modular supercomputing architecture: coupled atmosphere–ocean simulations with ICON 2.6.6-rc
Global Downscaled Projections for Climate Impacts Research (GDPCIR): preserving quantile trends for modeling future climate impacts
Understanding changes in cloud simulations from E3SM version 1 to version 2
WRF (v4.0)–SUEWS (v2018c) coupled system: development, evaluation and application
Scenario setup and forcing data for impact model evaluation and impact attribution within the third round of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3a)
Deep learning model based on multi-scale feature fusion for precipitation nowcasting
The Framework for Assessing Changes To Sea-level (FACTS) v1.0: a platform for characterizing parametric and structural uncertainty in future global, relative, and extreme sea-level change
Getting the leaves right matters for estimating temperature extremes
The Southern Ocean Freshwater Input from Antarctica (SOFIA) Initiative: scientific objectives and experimental design
Modeling and evaluating the effects of irrigation on land–atmosphere interaction in southwestern Europe with the regional climate model REMO2020–iMOVE using a newly developed parameterization
Process-oriented models of autumn leaf phenology: ways to sound calibration and implications of uncertain projections
An evaluation of the LLC4320 global-ocean simulation based on the submesoscale structure of modeled sea surface temperature fields
An emulation-based approach for interrogating reactive transport models
A sub-grid parameterization scheme for topographic vertical motion in CAM5-SE
Technology to aid the analysis of large-volume multi-institute climate model output at a central analysis facility (PRIMAVERA Data Management Tool V2.10)
A diffusion-based kernel density estimator (diffKDE, version 1) with optimal bandwidth approximation for the analysis of data in geoscience and ecological research
Monte Carlo drift correction – quantifying the drift uncertainty of global climate models
Improvements in the Canadian Earth System Model (CanESM) through systematic model analysis: CanESM5.0 and CanESM5.1
Earth System Model Aerosol–Cloud Diagnostics (ESMAC Diags) package, version 2: assessing aerosols, clouds, and aerosol–cloud interactions via field campaign and long-term observations
CIOFC1.0: a common parallel input/output framework based on C-Coupler2.0
Overcoming computational challenges to realize meter- to submeter-scale resolution in cloud simulations using the super-droplet method
Introducing a new floodplain scheme in ORCHIDEE (version 7885): validation and evaluation over the Pantanal wetlands
URock 2023a: an open-source GIS-based wind model for complex urban settings
DASH: a MATLAB toolbox for paleoclimate data assimilation
Accurate Assessment of Land-Atmosphere Coupling in Climate Models Requires High Frequency Data Output
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
Energy conserving physics for nonhydrostatic dynamics in mass coordinate models
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
Performance and process-based evaluation of the BARPA-R Australasian regional climate model version 1
Assessing the Sensitivity of Aerosol Mass Budget and Effective Radiative Forcing to Horizontal Grid Spacing in E3SMv1 Using A Regional Refinement Approach
Rainbows and climate change: a tutorial on climate model diagnostics and parameterization
Exchange-grid coupling approach for the IOW Earth System Model (version 1.04.00) of the Baltic Sea region
Disentangling the Hydrological and Hydraulic Controls on Streamflow Variability in E3SM V2 – A Case Study in the Pantanal Region
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
ibicus: a new open-source Python package and comprehensive interface for statistical bias adjustment and evaluation in climate modelling (v1.0.1)
Carbon isotopes in the marine biogeochemistry model FESOM2.1-REcoM3
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
Deepeshkumar Jain, Suryachandra A. Rao, Ramu A. Dandi, Prasanth A. Pillai, Ankur Srivastava, Maheswar Pradhan, and Kiran V. Gangadharan
Geosci. Model Dev., 17, 709–729, https://doi.org/10.5194/gmd-17-709-2024, https://doi.org/10.5194/gmd-17-709-2024, 2024
Short summary
Short summary
The present paper discusses and evaluates the new Monsoon Mission Coupled Forecast System model (MMCFS) version 2.0 which upgrades the currently operational MMCFS v1.0 at the Indian Meteorological Department, India. The individual model components have been substantially upgraded independently by their respective scientific groups. MMCFS v2.0 includes these upgrades in the operational coupled model. The new model shows significant skill improvement in simulating the Indian monsoon.
Nathan Beech, Thomas Rackow, Tido Semmler, and Thomas Jung
Geosci. Model Dev., 17, 529–543, https://doi.org/10.5194/gmd-17-529-2024, https://doi.org/10.5194/gmd-17-529-2024, 2024
Short summary
Short summary
Cost-reducing modeling strategies are applied to high-resolution simulations of the Southern Ocean in a changing climate. They are evaluated with respect to observations and traditional, lower-resolution modeling methods. The simulations effectively reproduce small-scale ocean flows seen in satellite data and are largely consistent with traditional model simulations after 4 °C of warming. Small-scale flows are found to intensify near bathymetric features and to become more variable.
Karl E. Taylor
Geosci. Model Dev., 17, 415–430, https://doi.org/10.5194/gmd-17-415-2024, https://doi.org/10.5194/gmd-17-415-2024, 2024
Short summary
Short summary
Remapping gridded data in a way that preserves the conservative properties of the climate system can be essential in coupling model components and for accurate assessment of the system’s energy and mass constituents. Remapping packages capable of handling a wide variety of grids can, for some common grids, calculate remapping weights that are somewhat inaccurate. Correcting for these errors, guidelines are provided to ensure conservation when the weights are used in practice.
Pedro M. M. Soares, Frederico Johannsen, Daniela C. A. Lima, Gil Lemos, Virgílio A. Bento, and Angelina Bushenkova
Geosci. Model Dev., 17, 229–259, https://doi.org/10.5194/gmd-17-229-2024, https://doi.org/10.5194/gmd-17-229-2024, 2024
Short summary
Short summary
This study uses deep learning (DL) to downscale global climate models for the Iberian Peninsula. Four DL architectures were evaluated and trained using historical climate data and then used to downscale future projections from the global models. These show agreement with the original models and reveal a warming of 2 ºC to 6 ºC, along with decreasing precipitation in western Iberia after 2040. This approach offers key regional climate change information for adaptation strategies in the region.
Abhiraj Bishnoi, Olaf Stein, Catrin I. Meyer, René Redler, Norbert Eicker, Helmuth Haak, Lars Hoffmann, Daniel Klocke, Luis Kornblueh, and Estela Suarez
Geosci. Model Dev., 17, 261–273, https://doi.org/10.5194/gmd-17-261-2024, https://doi.org/10.5194/gmd-17-261-2024, 2024
Short summary
Short summary
We enabled the weather and climate model ICON to run in a high-resolution coupled atmosphere–ocean setup on the JUWELS supercomputer, where the ocean and the model I/O runs on the CPU Cluster, while the atmosphere is running simultaneously on GPUs. Compared to a simulation performed on CPUs only, our approach reduces energy consumption by 45 % with comparable runtimes. The experiments serve as preparation for efficient computing of kilometer-scale climate models on future supercomputing systems.
Diana R. Gergel, Steven B. Malevich, Kelly E. McCusker, Emile Tenezakis, Michael T. Delgado, Meredith A. Fish, and Robert E. Kopp
Geosci. Model Dev., 17, 191–227, https://doi.org/10.5194/gmd-17-191-2024, https://doi.org/10.5194/gmd-17-191-2024, 2024
Short summary
Short summary
The freely available Global Downscaled Projections for Climate Impacts Research (GDPCIR) dataset gives researchers a new tool for studying how future climate will evolve at a local or regional level, corresponding to the latest global climate model simulations prepared as part of the UN Intergovernmental Panel on Climate Change’s Sixth Assessment Report. Those simulations represent an enormous advance in quality, detail, and scope that GDPCIR translates to the local level.
Yuying Zhang, Shaocheng Xie, Yi Qin, Wuyin Lin, Jean-Christophe Golaz, Xue Zheng, Po-Lun Ma, Yun Qian, Qi Tang, Christopher R. Terai, and Meng Zhang
Geosci. Model Dev., 17, 169–189, https://doi.org/10.5194/gmd-17-169-2024, https://doi.org/10.5194/gmd-17-169-2024, 2024
Short summary
Short summary
We performed systematic evaluation of clouds simulated in the Energy
Exascale Earth System Model (E3SMv2) to document model performance and understand what updates in E3SMv2 have caused changes in clouds from E3SMv1 to E3SMv2. We find that stratocumulus clouds along the subtropical west coast of continents are dramatically improved, primarily due to the retuning done in CLUBB. This study offers additional insights into clouds simulated in E3SMv2 and will benefit future E3SM developments.
Exascale Earth System Model (E3SMv2) to document model performance and understand what updates in E3SMv2 have caused changes in clouds from E3SMv1 to E3SMv2. We find that stratocumulus clouds along the subtropical west coast of continents are dramatically improved, primarily due to the retuning done in CLUBB. This study offers additional insights into clouds simulated in E3SMv2 and will benefit future E3SM developments.
Ting Sun, Hamidreza Omidvar, Zhenkun Li, Ning Zhang, Wenjuan Huang, Simone Kotthaus, Helen C. Ward, Zhiwen Luo, and Sue Grimmond
Geosci. Model Dev., 17, 91–116, https://doi.org/10.5194/gmd-17-91-2024, https://doi.org/10.5194/gmd-17-91-2024, 2024
Short summary
Short summary
For the first time, we coupled a state-of-the-art urban land surface model – Surface Urban Energy and Water Scheme (SUEWS) – with the widely-used Weather Research and Forecasting (WRF) model, creating an open-source tool that may benefit multiple applications. We tested our new system at two UK sites and demonstrated its potential by examining how human activities in various areas of Greater London influence local weather conditions.
Katja Frieler, Jan Volkholz, Stefan Lange, Jacob Schewe, Matthias Mengel, María del Rocío Rivas López, Christian Otto, Christopher P. O. Reyer, Dirk Nikolaus Karger, Johanna T. Malle, Simon Treu, Christoph Menz, Julia L. Blanchard, Cheryl S. Harrison, Colleen M. Petrik, Tyler D. Eddy, Kelly Ortega-Cisneros, Camilla Novaglio, Yannick Rousseau, Reg A. Watson, Charles Stock, Xiao Liu, Ryan Heneghan, Derek Tittensor, Olivier Maury, Matthias Büchner, Thomas Vogt, Tingting Wang, Fubao Sun, Inga J. Sauer, Johannes Koch, Inne Vanderkelen, Jonas Jägermeyr, Christoph Müller, Sam Rabin, Jochen Klar, Iliusi D. Vega del Valle, Gitta Lasslop, Sarah Chadburn, Eleanor Burke, Angela Gallego-Sala, Noah Smith, Jinfeng Chang, Stijn Hantson, Chantelle Burton, Anne Gädeke, Fang Li, Simon N. Gosling, Hannes Müller Schmied, Fred Hattermann, Jida Wang, Fangfang Yao, Thomas Hickler, Rafael Marcé, Don Pierson, Wim Thiery, Daniel Mercado-Bettín, Robert Ladwig, Ana Isabel Ayala-Zamora, Matthew Forrest, and Michel Bechtold
Geosci. Model Dev., 17, 1–51, https://doi.org/10.5194/gmd-17-1-2024, https://doi.org/10.5194/gmd-17-1-2024, 2024
Short summary
Short summary
Our paper provides an overview of all observational climate-related and socioeconomic forcing data used as input for the impact model evaluation and impact attribution experiments within the third round of the Inter-Sectoral Impact Model Intercomparison Project. The experiments are designed to test our understanding of observed changes in natural and human systems and to quantify to what degree these changes have already been induced by climate change.
Jinkai Tan, Qiqiao Huang, and Sheng Chen
Geosci. Model Dev., 17, 53–69, https://doi.org/10.5194/gmd-17-53-2024, https://doi.org/10.5194/gmd-17-53-2024, 2024
Short summary
Short summary
This study presents a deep learning architecture, multi-scale feature fusion (MFF), to improve the forecast skills of precipitations especially for heavy precipitations. MFF uses multi-scale receptive fields so that the movement features of precipitation systems are well captured. MFF uses the mechanism of discrete probability to reduce uncertainties and forecast errors so that heavy precipitations are produced.
Robert E. Kopp, Gregory G. Garner, Tim H. J. Hermans, Shantenu Jha, Praveen Kumar, Alexander Reedy, Aimée B. A. Slangen, Matteo Turilli, Tamsin L. Edwards, Jonathan M. Gregory, George Koubbe, Anders Levermann, Andre Merzky, Sophie Nowicki, Matthew D. Palmer, and Chris Smith
Geosci. Model Dev., 16, 7461–7489, https://doi.org/10.5194/gmd-16-7461-2023, https://doi.org/10.5194/gmd-16-7461-2023, 2023
Short summary
Short summary
Future sea-level rise projections exhibit multiple forms of uncertainty, all of which must be considered by scientific assessments intended to inform decision-making. The Framework for Assessing Changes To Sea-level (FACTS) is a new software package intended to support assessments of global mean, regional, and extreme sea-level rise. An early version of FACTS supported the development of the IPCC Sixth Assessment Report sea-level projections.
Gregory Duveiller, Mark Pickering, Joaquin Muñoz-Sabater, Luca Caporaso, Souhail Boussetta, Gianpaolo Balsamo, and Alessandro Cescatti
Geosci. Model Dev., 16, 7357–7373, https://doi.org/10.5194/gmd-16-7357-2023, https://doi.org/10.5194/gmd-16-7357-2023, 2023
Short summary
Short summary
Some of our best tools to describe the state of the land system, including the intensity of heat waves, have a problem. The model currently assumes that the number of leaves in ecosystems always follows the same cycle. By using satellite observations of when leaves are present, we show that capturing the yearly changes in this cycle is important to avoid errors in estimating surface temperature. We show that this has strong implications for our capacity to describe heat waves across Europe.
Neil C. Swart, Torge Martin, Rebecca Beadling, Jia-Jia Chen, Christopher Danek, Matthew H. England, Riccardo Farneti, Stephen M. Griffies, Tore Hattermann, Judith Hauck, F. Alexander Haumann, André Jüling, Qian Li, John Marshall, Morven Muilwijk, Andrew G. Pauling, Ariaan Purich, Inga J. Smith, and Max Thomas
Geosci. Model Dev., 16, 7289–7309, https://doi.org/10.5194/gmd-16-7289-2023, https://doi.org/10.5194/gmd-16-7289-2023, 2023
Short summary
Short summary
Current climate models typically do not include full representation of ice sheets. As the climate warms and the ice sheets melt, they add freshwater to the ocean. This freshwater can influence climate change, for example by causing more sea ice to form. In this paper we propose a set of experiments to test the influence of this missing meltwater from Antarctica using multiple different climate models.
Christina Asmus, Peter Hoffmann, Joni-Pekka Pietikäinen, Jürgen Böhner, and Diana Rechid
Geosci. Model Dev., 16, 7311–7337, https://doi.org/10.5194/gmd-16-7311-2023, https://doi.org/10.5194/gmd-16-7311-2023, 2023
Short summary
Short summary
Irrigation modifies the land surface and soil conditions. The effects can be quantified using numerical climate models. Our study introduces a new irrigation parameterization, which simulates the effects of irrigation on land, atmosphere, and vegetation. We applied the parameterization and evaluated the results in terms of their physical consistency. We found an improvement in the model results in the 2 m temperature representation in comparison with observational data for our study.
Michael Meier and Christof Bigler
Geosci. Model Dev., 16, 7171–7201, https://doi.org/10.5194/gmd-16-7171-2023, https://doi.org/10.5194/gmd-16-7171-2023, 2023
Short summary
Short summary
We analyzed >2.3 million calibrations and 39 million projections of leaf coloration models, considering 21 models, 5 optimization algorithms, ≥7 sampling procedures, and 26 climate scenarios. Models based on temperature, day length, and leaf unfolding performed best, especially when calibrated with generalized simulated annealing and systematically balanced or stratified samples. Projected leaf coloration shifts between −13 and +20 days by 2080–2099.
Katharina Gallmeier, J. Xavier Prochaska, Peter Cornillon, Dimitris Menemenlis, and Madolyn Kelm
Geosci. Model Dev., 16, 7143–7170, https://doi.org/10.5194/gmd-16-7143-2023, https://doi.org/10.5194/gmd-16-7143-2023, 2023
Short summary
Short summary
This paper introduces an approach to evaluate numerical models of ocean circulation. We compare the structure of satellite-derived sea surface temperature anomaly (SSTa) instances determined by a machine learning algorithm at 10–80 km scales to those output by a high-resolution MITgcm run. The simulation over much of the ocean reproduces the observed distribution of SSTa patterns well. This general agreement, alongside a few notable exceptions, highlights the potential of this approach.
Angus Fotherby, Harold J. Bradbury, Jennifer L. Druhan, and Alexandra V. Turchyn
Geosci. Model Dev., 16, 7059–7074, https://doi.org/10.5194/gmd-16-7059-2023, https://doi.org/10.5194/gmd-16-7059-2023, 2023
Short summary
Short summary
We demonstrate how, given a simulation of fluid and rock interacting, we can emulate the system using machine learning. This means that, for a given initial condition, we can predict the final state, avoiding the simulation step once the model has been trained. We present a workflow for applying this approach to any fluid–rock simulation and showcase two applications to different fluid–rock simulations. This approach has applications for improving model development and sensitivity analyses.
Yaqi Wang, Lanning Wang, Juan Feng, Zhenya Song, Qizhong Wu, and Huaqiong Cheng
Geosci. Model Dev., 16, 6857–6873, https://doi.org/10.5194/gmd-16-6857-2023, https://doi.org/10.5194/gmd-16-6857-2023, 2023
Short summary
Short summary
In this study, to noticeably improve precipitation simulation in steep mountains, we propose a sub-grid parameterization scheme for the topographic vertical motion in CAM5-SE to revise the original vertical velocity by adding the topographic vertical motion. The dynamic lifting effect of topography is extended from the lowest layer to multiple layers, thus improving the positive deviations of precipitation simulation in high-altitude regions and negative deviations in low-altitude regions.
Jon Seddon, Ag Stephens, Matthew S. Mizielinski, Pier Luigi Vidale, and Malcolm J. Roberts
Geosci. Model Dev., 16, 6689–6700, https://doi.org/10.5194/gmd-16-6689-2023, https://doi.org/10.5194/gmd-16-6689-2023, 2023
Short summary
Short summary
The PRIMAVERA project aimed to develop a new generation of advanced global climate models. The large volume of data generated was uploaded to a central analysis facility (CAF) and was analysed by 100 PRIMAVERA scientists there. We describe how the PRIMAVERA project used the CAF's facilities to enable users to analyse this large dataset. We believe that similar, multi-institute, big-data projects could also use a CAF to efficiently share, organise and analyse large volumes of data.
Maria-Theresia Pelz, Markus Schartau, Christopher J. Somes, Vanessa Lampe, and Thomas Slawig
Geosci. Model Dev., 16, 6609–6634, https://doi.org/10.5194/gmd-16-6609-2023, https://doi.org/10.5194/gmd-16-6609-2023, 2023
Short summary
Short summary
Kernel density estimators (KDE) approximate the probability density of a data set without the assumption of an underlying distribution. We used the solution of the diffusion equation, and a new approximation of the optimal smoothing parameter build on two pilot estimation steps, to construct such a KDE best suited for typical characteristics of geoscientific data. The resulting KDE is insensitive to noise and well resolves multimodal data structures as well as boundary-close data.
Benjamin S. Grandey, Zhi Yang Koh, Dhrubajyoti Samanta, Benjamin P. Horton, Justin Dauwels, and Lock Yue Chew
Geosci. Model Dev., 16, 6593–6608, https://doi.org/10.5194/gmd-16-6593-2023, https://doi.org/10.5194/gmd-16-6593-2023, 2023
Short summary
Short summary
Global climate models are susceptible to spurious trends known as drift. Fortunately, drift can be corrected when analysing data produced by models. To explore the uncertainty associated with drift correction, we develop a new method: Monte Carlo drift correction. For historical simulations of thermosteric sea level rise, drift uncertainty is relatively large. When analysing data susceptible to drift, researchers should consider drift uncertainty.
Michael Sigmond, James Anstey, Vivek Arora, Ruth Digby, Nathan Gillett, Viatcheslav Kharin, William Merryfield, Catherine Reader, John Scinocca, Neil Swart, John Virgin, Carsten Abraham, Jason Cole, Nicolas Lambert, Woo-Sung Lee, Yongxiao Liang, Elizaveta Malinina, Landon Rieger, Knut von Salzen, Christian Seiler, Clint Seinen, Andrew Shao, Reinel Sospedra-Alfonso, Libo Wang, and Duo Yang
Geosci. Model Dev., 16, 6553–6591, https://doi.org/10.5194/gmd-16-6553-2023, https://doi.org/10.5194/gmd-16-6553-2023, 2023
Short summary
Short summary
We present a new activity which aims to organize the analysis of biases in the Canadian Earth System model (CanESM) in a systematic manner. Results of this “Analysis for Development” (A4D) activity includes a new CanESM version, CanESM5.1, which features substantial improvements regarding the simulation of dust and stratospheric temperatures, a second CanESM5.1 variant with reduced climate sensitivity, and insights into potential avenues to reduce various other model biases.
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., 16, 6355–6376, https://doi.org/10.5194/gmd-16-6355-2023, https://doi.org/10.5194/gmd-16-6355-2023, 2023
Short summary
Short summary
To assess the ability of Earth system model (ESM) predictions, we developed a tool called ESMAC Diags to understand how aerosols, clouds, and aerosol–cloud interactions are represented in ESMs. This paper describes its version 2 functionality. We compared the model predictions with measurements taken by planes, ships, satellites, and ground instruments over four regions across the world. Results show that this new tool can help identify model problems and guide future development of ESMs.
Xinzhu Yu, Li Liu, Chao Sun, Qingu Jiang, Biao Zhao, Zhiyuan Zhang, Hao Yu, and Bin Wang
Geosci. Model Dev., 16, 6285–6308, https://doi.org/10.5194/gmd-16-6285-2023, https://doi.org/10.5194/gmd-16-6285-2023, 2023
Short summary
Short summary
In this paper we propose a new common, flexible, and efficient parallel I/O framework for earth system modeling based on C-Coupler2.0. CIOFC1.0 can handle data I/O in parallel and provides a configuration file format that enables users to conveniently change the I/O configurations. It can automatically make grid and time interpolation, output data with an aperiodic time series, and accelerate data I/O when the field size is large.
Toshiki Matsushima, Seiya Nishizawa, and Shin-ichiro Shima
Geosci. Model Dev., 16, 6211–6245, https://doi.org/10.5194/gmd-16-6211-2023, https://doi.org/10.5194/gmd-16-6211-2023, 2023
Short summary
Short summary
A particle-based cloud model was developed for meter- to submeter-scale resolution in cloud simulations. Our new cloud model's computational performance is superior to a bin method and comparable to a two-moment bulk method. A highlight of this study is the 2 m resolution shallow cloud simulations over an area covering ∼10 km2. This model allows for studying turbulence and cloud physics at spatial scales that overlap with those covered by direct numerical simulations and field studies.
Anthony Schrapffer, Jan Polcher, Anna Sörensson, and Lluís Fita
Geosci. Model Dev., 16, 5755–5782, https://doi.org/10.5194/gmd-16-5755-2023, https://doi.org/10.5194/gmd-16-5755-2023, 2023
Short summary
Short summary
The present paper introduces a floodplain 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.
Jérémy Bernard, Fredrik Lindberg, and Sandro Oswald
Geosci. Model Dev., 16, 5703–5727, https://doi.org/10.5194/gmd-16-5703-2023, https://doi.org/10.5194/gmd-16-5703-2023, 2023
Short summary
Short summary
The UMEP plug-in integrated in the free QGIS software can now calculate the spatial variation of the wind speed within urban settings. This paper shows that the new wind model, URock, generally fits observations well and highlights the main needed improvements. According to this work, pedestrian wind fields and outdoor thermal comfort can now simply be estimated by any QGIS user (researchers, students, and practitioners).
Jonathan King, Jessica Tierney, Matthew Osman, Emily J. Judd, and Kevin J. Anchukaitis
Geosci. Model Dev., 16, 5653–5683, https://doi.org/10.5194/gmd-16-5653-2023, https://doi.org/10.5194/gmd-16-5653-2023, 2023
Short summary
Short summary
Paleoclimate data assimilation is a useful method that allows researchers to combine climate models with natural archives of past climates. However, it can be difficult to implement in practice. To facilitate this method, we present DASH, a MATLAB toolbox. The toolbox provides routines that implement common steps of paleoclimate data assimilation, and it can be used to implement assimilations for a wide variety of time periods, spatial regions, data networks, and analytical algorithms.
Kirsten L. Findell, Zun Yin, Eunkyo Seo, Paul A. Dirmeyer, Nathan P. Arnold, Nathaniel Chaney, Megan D. Fowler, Meng Huang, David M. Lawrence, Po-Lun Ma, and Joseph A. Santanello Jr.
EGUsphere, https://doi.org/10.5194/egusphere-2023-2048, https://doi.org/10.5194/egusphere-2023-2048, 2023
Short summary
Short summary
We outline a request for sub-daily data to accurately capture the process-level connections between land states, surface fluxes, and the boundary layer response. This high-frequency model output will allow for more direct comparison with observational field campaigns on process-relevant time scales, enable demonstration of inter-model spread in land-atmosphere coupling processes, and aid in targeted identification of sources of deficiencies and opportunities for improvement of the models.
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.
Oksana Guba, Mark A. Taylor, Peter A. Bosler, Christopher Eldred, and Peter H. Lauritzen
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-184, https://doi.org/10.5194/gmd-2023-184, 2023
Revised manuscript accepted for GMD
Short summary
Short summary
We want to reduce errors in the moist energy budget in atmospheric models. We study a few common assumptions and mechanisms that are used for the moist physics. Some mechanisms are more consistent with underlying equations; other may be more practical. Separately, we study how assumptions about models' thermodynamics affect the modeled energy of precipitation. We also explain how to conserve energy in the moist physics for nonhydrostatic models.
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.
Emma Howard, Chun-Hsu Su, Christian Stassen, Rajashree Naha, Harvey Ye, Acacia Pepler, Samuel S. Bell, Andrew J. Dowdy, Simon O. Tucker, and Charmaine Franklin
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-156, https://doi.org/10.5194/gmd-2023-156, 2023
Revised manuscript accepted for GMD
Short summary
Short summary
1. The BARPA-R modelling configuration has been developed to produce high resolution climate hazard projections within the Australian Region. 2. When using boundary driving data from quasi-observed historical conditions, BARPA-R shows good performance with errors generally on par with reanalysis products. 3. BARPA-R also captures trends, known modes of climate variability, large-scale weather processes, and multivariate relationships.
Jianfeng Li, Kai Zhang, Taufiq Hassan, Shixuan Zhang, Po-Lun Ma, Balwinder Singh, Qiyang Yan, and Huilin Huang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-73, https://doi.org/10.5194/gmd-2023-73, 2023
Revised manuscript accepted for GMD
Short summary
Short summary
By comparing E3SM simulations with and without regional refinement, we find that model horizontal grid spacing considerably affects the simulated aerosol mass budget, aerosol-cloud interactions, and the effective radiative forcing of anthropogenic aerosols. The study identifies the critical physical processes strongly influenced by model resolution. It also highlights the benefit of applying regional refinement in future modeling studies at higher or even convection-permitting resolutions.
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.
Sven Karsten, Hagen Radtke, Matthias Gröger, Ha T. M. Ho-Hagemann, Hossein Mashayekh, Thomas Neumann, and H. E. Markus Meier
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-166, https://doi.org/10.5194/gmd-2023-166, 2023
Revised manuscript accepted for GMD
Short summary
Short summary
This paper describes the development of a regional Earth System Model for the Baltic Sea region. In contrast to conventional coupling approaches, the presented model includes a flux calculator operating on a common exchange grid. This approach automatically ensures a locally consistent treatment of fluxes and simplifies the exchange of model components. The presented model can be used for various scientific questions, such as studies of natural variability and ocean-atmosphere interactions.
Donghui Xu, Gautam Bisht, Zeli Tan, Chang Liao, Tian Zhou, Hong-Yi Li, and Lai-Yung Ruby Leung
EGUsphere, https://doi.org/10.5194/egusphere-2023-1879, https://doi.org/10.5194/egusphere-2023-1879, 2023
Short summary
Short summary
We aim to disentangle the hydrological and hydraulic controls on streamflow variability in a fully coupled Earth System Model. We found that calibrate only one process (i.e., traditional calibration procedure) will result in unrealistic parameter values and poor performance of water cycle, while the simulated streamflow is improved. To address this issue, we further proposed a two-step calibration procedure to reconcile the impacts from hydrological and hydraulic processes on streamflow.
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.
Fiona Raphaela Spuler, Jakob Benjamin Wessel, Edward Comyn-Platt, James Varndell, and Chiara Cagnazzo
EGUsphere, https://doi.org/10.5194/egusphere-2023-1481, https://doi.org/10.5194/egusphere-2023-1481, 2023
Short summary
Short summary
Bias adjustment is commonly applied to climate models before using them to study the impacts of climate change to ensure the correspondence of models with observations at a local scale. However, this can introduce undesirable distortions in the climate model. In this paper, we present an open-source python package called ibicus to enable the comparison and detailed evaluation of bias adjustment methods to facilitate their transparent and rigorous application.
Martin Butzin, Ying Ye, Christoph Völker, Özgür Gürses, Judith Hauck, and Peter Köhler
EGUsphere, https://doi.org/10.5194/egusphere-2023-1718, https://doi.org/10.5194/egusphere-2023-1718, 2023
Short summary
Short summary
In this paper we describe the implementation of the carbon isotopes 13C and 14C into the marine biogeochemistry model FESOM2.1-REcoM3 and present results of long-term test simulations. Our model results are largely consistent with marine carbon isotope reconstructions for the pre-anthropogenic period but also also exhibit some discrepancies.
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.
Cited articles
Aleotti, P.: A warning system for rainfall-induced shallow failures, Eng. Geol., 73, 247–265, 2004.
Alfieri, L., Salamon, P., Pappenberger, F., Wetterhall, F., and Thielen, J.: Operational early warning systems for water-related hazards in Europe, Environ. Sci. Policy, 21, 35–49, 2012.
Amodio-Morelli, L., Bonardi, G., Colonna, V., Dietrich, D., Giunta, G., Ippolito, F., Liguori, V., Lorenzoni, S., Paglionico, A., Perrone, V., Piccarreta, G., Russo, M., Scandone, P., Zanettin Lorenzoni, E., and Zuppetta, A.: L'arco calabro-peloritano nell'orogene appenninico-maghrebide, Mem. Soc. Geol. Ital., 17, 1–60, 1976 (in Italian).
AMRA: Overview of intense rainfall on volcanic soils–regional and local scale, in: SafeLand – Living with landslide risk in Europe: Assessment, effects of global change, and risk management strategies, edited by: Crosta, G. B., Agliardi, F., Frattini, P., and Sosio, R., 101–122, 2012.
Berti, M., Martina, M. L. V., Franceschini, S., Pignone, S., Simoni, A., and Pizziolo, M.: Probabilistic rainfall thresholds for landslide occurrence using a Bayesian approach, J. Geophys. Res., 117, F04006, https://doi.org/10.1029/2012JF002367, 2012.
Brand, E. W., Premchitt, J., and Phillipson, H. B.: Relationship between rainfall and landslides, in: Proceedings of the 4th International Symposium on Landslides, Toronto, vol. 1, BiTech Publishers, Vancouver, Canada, 377–384, 1984.
Brunetti, M. T., Peruccacci, S., Rossi, M., Luciani, S., Valigi, D., and Guzzetti, F.: Rainfall thresholds for the possible occurrence of landslides in Italy, Nat. Hazards Earth Syst. Sci., 10, 447–458, https://doi.org/10.5194/nhess-10-447-2010, 2010.
Brunsden, D.: Mudslides, in: Slope Instability, edited by: Brunsden, D. and Prior, D. B., John Wiley & Sons, London, 363–418, 1984.
Caine, N.: The rainfall intensity-duration control of shallow landslides and debris flows, Geograf. Ann., 62A, 23–27, 1980.
Calcaterra, D. and Santo, A.: The January 10, 1997 Pozzano landslide, Sorrento Peninsula, Italy, Eng. Geol., 75, 181–200, 2004.
Campbell, R. H.: Debris flow originating from soil slip during rainstorm in southern California, Q. J. Eng. Geol., 7, 377–384, 1975.
Cannon, S. H. and Ellen, S. D.: Rainfall conditions for abundant debris avalanches, San Francisco Bay region, California, California Geology, 38, 267–272, 1985.
Capparelli, G. and Versace, P.: FLaIR and SUSHI: two mathematical models for early warning of landslides induced by rainfall, Landslides, 8, 67–79, 2011.
Capparelli, G., Iaquinta, P., Iovine, G., Terranova, O. G., and Versace P.: Modelling the rainfall-induced mobilization of a large slope movement in northern Calabria, Nat. Hazards, 61, 247–256, 2012.
Carter, J.: The notion of Threshold: an investigation into conceptual accompaniment in Aristotle and Hegel, Conserveries mémorielles, 7, available at: http://cm.revues.org/431 (last access: 10 December 2014), 2010.
Cascini, L. and Versace, P.: Eventi pluviometrici e movimenti franosi, in: Atti del XVI Convegno Nazionale di Geotecnica, Bologna, Italy, 14–16 May 1986, 171–184, 1986 (in Italian).
Cascini, L. and Versace, P.: Relationship between rainfall and landslide in a gneissic cover, in: Proceedings of the 5th International Symposium on Landslides, Lausanne, Switzerland, 565–570, 1988.
Cascini, L., Sorbino, G., Cuomo, S., and Ferlisi, S.: Seasonal effects of rainfall on the shallow pyroclastic deposits of the Campania region (southern Italy), Landslides, 11, 779–792, 2014.
CASMEZ: Carta Geologica della Calabria, F.229IIINE "Montalto Uffugo" (in scale 1:25000), Poligrafica & CarteValori, Ercolano, Napoli, Italy, 1967 (in Italian).
Cauvin, S., Cordier, M.-O., Dousson, C., Laborie, P., Lévy, F., Montmain, J., Porcheron, M., Servet, I., and Travé-Massuyès, L.: Monitoring and alarm interpretation in industrial environments, AI Commun., 11, 139–173, 1998.
Chow, V. T., Maidment, D. R., and Mays, L. W.: Applied Hydrology, Mc Graw Hill, New York, USA, 572 pp., 1988.
Corominas, J.: Landslides and climate, Keynote lecture, in: Proceedings of the 8th International Symposium on Landslides, vol. 4, Cardiff, United Kingdom, 26–30 June 2000, 1–33, 2000.
Crosta, G. B.: Regionalization of rainfall thresholds: an aid to landslide hazard evaluation, Environ. Geol., 35, 131–145, 1998.
Crosta, G. B., Dal Negro, P., and Frattini, P.: Soil slips and debris flows on terraced slopes, Nat. Hazards Earth Syst. Sci., 3, 31–42, https://doi.org/10.5194/nhess-3-31-2003, 2003.
Crozier, M. J.: The climate-landslide couple: a southern hemisphere perspective, in: Rapid mass movement as a source of climatic evidence for the Holocene, edited by: Matthews, J. A., Brunsden, D., Frenzel, B., Gläser, B., and Weiß, M. M., Gustav Fischer, Stuttgart, 333–354, 1997.
Cuomo, S. and Della Sala, M.: Rainfall-induced infiltration, runoff and failure in steep unsaturated shallow soil deposits, Eng. Geol., 162, 118–127, 2013.
D'Ambrosio, D., Spataro, W., and Iovine, G.: Parallel genetic algorithms for optimising cellular automata models of natural complex phenomena: an application to debris-flows, Comput. Geosci., 32, 861–875, 2006.
De Jong, K. A.: An analysis of the behavior of a class of genetic adaptive systems, PhD dissertation, Department of Computer and Communication Sciences, University of Michigan, Ann Arbor, USA, 1975.
Del Prete, M., Guadagno, F. M., and Hawkins, A. B.: Preliminary report on the landslides of 5 May 1998, Campania, southern Italy, Bull. Eng. Geol. Env., 57, 113–129, 1998.
Di Crescenzo, G. and Santo, A.: Analisi morfologica delle frane da scorrimento-colata rapida in depositi piroclastici della Penisola Sorrentina (Campania), Geogr. Fis. Dinam. Quat., 22, 57–72, 1999 (in Italian).
Di Crescenzo, G. and Santo, A.: Debris slides-rapid earth flows in the carbonate massifs of the Campania region (Southern Italy): morphological and morphometric data for evaluating triggering susceptibility, Geomorphology, 66, 255–276, 2005.
Dikau, R. and Schrott, L.: The temporal stability and activity of landslides in Europe with respect to climatic change (TESLEC): main objectives and results, Geomorphology, 30, 1–12, 1999.
Ducci, D. and Tranfaglia, G.: L'impatto dei cambiamenti climatici sulle risorse idriche sotterranee in Campania, Geologi. Boll. Ordine Geologi della Campania, 1–4, 13–21, 2005 (in Italian).
Ellen, S. D.: Description and mechanics of soil slip/debris flows in the storm, in: Landslides, floods, and marine effects of the storm of January 3–5, 1982, in the San Francisco Bay region, California, edited by: Ellen, S. D. and Wieczorek, G. F., U.S. Geol. Surv. Prof. Pap., 1434, 63–112, 1988.
Fienen, M. N., Doherty, J. E., Hunt, R. J., and Reeves, H. W.: Using prediction uncertainty analysis to design hydrologic monitoring networks: example applications from the Great Lakes water availability pilot project, US Geol. Surv., Reston Virginia, Scientific Investigations Report 2010-5159, 44 pp., 2010.
Fiorillo, F. and Wilson, R.: Rainfall induced debris flows in pyroclastic deposits, Campania (southern Italy), Eng. Geol., 75, 263–289, 2004.
Gariano, S. L., Brunetti, M. T., Iovine, G., Melillo, M., Peruccacci, S., Terranova, O., Vennari, C., and Guzzetti, F.: Calibration and validation of rainfall thresholds for shallow landslide forecasting in Sicily, southern Italy, Geomorphology, 228, 653–665, 2015.
Goldberg, D. E.: Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley, Boston, USA, 1989.
Gullà, G., Aceto, L., Antronico, L., Cilento, M., Niceforo, D., Perna, E., and Terranova, O.: Failure and post failure conditions of a landslide involving weathered and degraded rocks, in Proceedings of the IX International Symposium on Landslide, Rio de Janeiro, Brazil, 28 June-02 July 2004, 1241–1245, 2004.
Guzzetti, F.: Landslide fatalities and evaluation of landslide risk in Italy, Eng. Geol., 58, 89–107, 2000.
Guzzetti, F., Peruccacci, S., Rossi, M., and Stark, C. P.: Rainfall thresholds for the initiation of landslides in central and southern Europe, Meteorol. Atmos. Phys., 98, 239–267, 2007.
Guzzetti, F., Peruccacci, S., Rossi, M., and Stark, C. P.: The rainfall intensity-duration control of shallow landslides and debris flow: an update, Landslides, 5, 3–17, 2008.
Hinton, G. E. and Nowlan, S. J.: How learning can guide evolution, Complex Systems, 1, 495–502, 1987.
Holland, J. H.: Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann Arbor, USA, 1975.
Hutchinson, J. N.: Deep-seated mass movements on slopes, Mem. Soc. Geol. It., 50, 147–164, 1995.
Iovine, G., D'Ambrosio, D., and Di Gregorio, S.: Applying genetic algorithms for calibrating a hexagonal cellular automata model for the simulation of debris flows characterised by strong inertial effects, Geomorphology, 66, 287–303, 2005.
Iovine, G., Iaquinta, P., and Terranova, O.: Emergency management of landslide risk during Autumn-Winter 2008/2009 in Calabria (Italy). The example of San Benedetto Ullano, in: Proceedings of the 18th World IMACS Congress and MODSIM09 International Congress on Modelling and Simulation , Modelling and Simulation Society of Australia and New Zealand and International Association for Mathematics and Computers in Simulation, July 2009, 2686–2693, 2009.
Iovine, G. G. R., Lollino, P., Gariano, S. L., and Terranova, O. G.: Coupling limit equilibrium analyses and real-time monitoring to refine a landslide surveillance system in Calabria (southern Italy), Nat. Hazards Earth Syst. Sci., 10, 2341–2354, https://doi.org/10.5194/nhess-10-2341-2010, 2010.
Jakob, M. and Weatherly, H.: A hydroclimatic threshold for landslide initiation on the North Shore Mountains of Vancouver, British Columbia, Geomorphology, 54, 137–156, 2003.
Keefer, D. K., Wilson, R. C., Mark, R. K., Brabb, E. E., Brown III, W. M., Ellen S. D., Harp, E. L., Wieczorek, G. F., Alger, C. S., and Zatkin, R. S.: Real-Time Landslide Warning During Heavy Rainfall, Science, 238, 921–925, 1987.
Köppen, W.: Climatologia, con un estudio de los climas de la tierra, Fondo de Cultura Economica, Mexico, 479 pp., 1948.
Krause, P., Boyle, D. P., and Bäse, F.: Comparison of different efficiency criteria for hydrological model assessment, Adv. Geosci., 5, 89–97, https://doi.org/10.5194/adgeo-5-89-2005, 2005.
Lanzafame, G. and Mercuri, T.: Interruzioni ferroviarie in Calabria conseguenti a fenomeni naturali (1950–1973), Geodata, 3, Cosenza, Italy, 46 pp., 1975 (in Italian).
Long, A. J.: RRAWFLOW: Rainfall-Response Aquifer and Watershed Flow Model (v1.15), Geosci. Model Dev., 8, 865–880, https://doi.org/10.5194/gmd-8-865-2015, 2015.
Marques R., Zêzere J., Trigo R., Gaspar J., and Trigo, I.: Rainfall patterns and critical values associated with landslides in Povoação County (São Miguel Island, Azores): relationships with the North Atlantic Oscillation, Hydrol. Process., 22, 478–494, 2008.
Mele, R. and Del Prete, S.: Lo studio della franosità storica come utile strumento per la valutazione della pericolosità da frane. Un esempio nell'area di Gragnano (Campania), Boll. Soc. Geol. Ital., 118, 91–111, 1999 (in Italian).
Mitchell, M.: An Introduction to Genetic Algorithms, MIT Press, Cambridge, United Kingdom, 1996.
Monaco, C. and Tortorici, L.: Active faulting in the Calabrian arc and eastern Sicily, J. Geodynam., 29, 407–424, 2000.
Montgomery, D. R. and Dietrich, W. E.: A physically-based model for the topographic control on shallow landsliding, Water Resour. Res., 30, 1153–1171, 1994.
Nolfi, S. and Marocco, D.: Evolving robots able to integrate sensory-motor information over time, Theor. Biosci., 120, 287–310, 2001.
Peres, D. J. and Cancelliere, A.: Derivation and evaluation of landslide-triggering thresholds by a Monte Carlo approach, Hydrol. Earth Syst. Sci., 18, 4913–4931, https://doi.org/10.5194/hess-18-4913-2014, 2014.
Petley, D. N.: The global occurrence of fatal landslides in 2007, in: International Conference on Management of Landslide Hazard in the Asia-Pacific Region, Tokyo, Japan, 590–600, 2008.
Pinault, J.-L., Plagnes, V., and Aquilina, L.: Inverse modeling of the hydrological and the hydrochemical behavior of hydrosystems: Characterization of karst system functioning, Water Resour. Res., 37, 2191–2204, 2001.
Pisani, G., Castelli, M., and Scavia, C.: Hydrogeological model and hydraulic behaviour of a large landslide in the Italian Western Alps, Nat. Hazards Earth Syst. Sci., 10, 2391–2406, https://doi.org/10.5194/nhess-10-2391-2010, 2010.
Poon, P. W. and Parks, G. T.: Optimizing PWR reload core design, Parallel Solving from Nature, 2, 371–380, 1992.
Pradhan, B. and Buchroithner, M.: Terrigenous Mass Movements: Detection, Modelling, Early Warning and Mitigation Using Geoinformation Technology, Springer, 400 pp., 2012.
Rossi, F. and Villani, P. (Eds.): Valutazione delle piene in Campania, CNR-GNDCI publications, Grafica Metellioana &C., Cava de' Tirreni, Italy, 310 pp., 1994.
Salvati, P., Bianchi, C., Rossi, M., and Guzzetti, F.: Societal landslide and flood risk in Italy, Nat. Hazards Earth Syst. Sci., 10, 465–483, https://doi.org/10.5194/nhess-10-465-2010, 2010.
Sengupta, A., Gupta, S., and Anbarasu, K.: Rainfall thresholds for the initiation of landslide at Lanta Khola in north Sikkim, India, Nat. Hazards, 52, 31–42, 2010.
Servizio Geologico, Sismico dei Suoli: I numeri delle frane, Regione Emilia-Romagna Publisher, Bologna, 94 pp., 1999 (in Italian).
Servizio Idrografico: Annali Idrologici, Parte I, Compartimento di Napoli, Istituto poligrafico e Zecca dello Stato, Rome, Italy, 1948–1999.
Sirangelo, B. and Versace, P.: A real time forecasting for landslides triggered by rainfall, Meccanica, 31, 1–13, 1996.
Sorriso-Valvo, G. M., Agnesi, V., Gulla, G., Merenda, L., Antronico, L., Di Maggio, C., Filice, E., Petrucci, O., and Tansi, C.: Temporal and spatial occurrence of landsliding and correlation with precipitation time series in Montalto Uffugo (Calabria) and Imera (Sicilia) areas, in: Temporal occurrence and forecasting of landslides in the European Community, Final Report, II, European Community, Programme EPOCH, Contract 90 0025, edited: by Casale, R., Fantechi, R., and Flageollet, J. C., 825–869, 1994.
Sorriso-Valvo, G. M., Antronico, L., Catalano, E., Gullà, G., Tansi, C., Dramis, F., Ferrucci, F., and Fantucci, R.: Final Report (June 1996), CNR-IRPI, in: The temporal stability and activity of landslides in Europe with respect to climatic change (TESLEC), Final Report, Part II, edited by: Dikau, R., Schrot, L., Dehn, M., Hennrich, K., and Rasemann, S., 87–152, 1996.
Terlien, M. T. J.: The determination of statistical and deterministic hydrological landslide-triggering thresholds, Environ. Geol., 35, 125–130, 1998.
Terranova, O.: Caratteristiche degli eventi pluviometrici a scala giornaliera in Calabria, in: XXIX Convegno di Idraulica e Costruzioni Idrauliche, Trento, Italy, 07–10 September 2004, 343–350, 2004 (in Italian).
Terranova, O., Antronico, L., and Gullà, G.: Pluviometrical events and slope stability on weathered and degraded rocks (Acri, Calabria, Italy), in Proceedings of the IX International Symposium on Landslide, Rio de Janeiro, Brazil, 28 June–02 July 2004, 335–342, 2004.
Terranova, O., Iaquinta, P., Gariano, S. L., Greco, R., and Iovine, G.: CMSAKe: A hydrological model to forecasting landslide activations, in: Landslide Science and Practice, vol. 3, edited by: Margottini, C., Canuti, P., and Sassa K., Springer, 73–79, 2013.
Terzaghi, K.: Stability of steep slopes on hard unweathered rock, Geotechnique, 12, 251–270, 1962.
Trigila, A.: Rapporto sulle frane in Italia. Il Progetto IFFI. Metodologia, risultati e rapporti regionali, APAT, Roma, Italy, 681 pp., 2007 (in Italian).
Trigo, R. M., Zêzere, J. L., Rodrigues, M. L., and Trigo, I. F.: The influence of the North Atlantic Oscillation on rainfall triggering of Landslides near Lisbon, Nat. Hazards, 36, 331–354, 2005.
UNDRO: Mitigating Natural Disasters. Phenomena, Effects and Options, United Nations, New York, USA, 164 pp., 1991.
Van Asch, Th. V. J., Buma, J., and Van Beek, L. P. H.: A view on some hydrological triggering systems in landslides, Geomorphology, 30, 25–32, 1999.
Vennari, C., Gariano, S. L., Antronico, L., Brunetti, M. T., Iovine, G., Peruccacci, S., Terranova, O., and Guzzetti, F.: Rainfall thresholds for shallow landslide occurrence in Calabria, southern Italy, Nat. Hazards Earth Syst. Sci., 14, 317–330, https://doi.org/10.5194/nhess-14-317-2014, 2014.
White, I. D., Mottershead, D. N., and Harrison, J. J.: Environmental Systems, 2nd Edn., Chapman & Hall, London, United Kongdom, 616 pp., 1996.
Wieczorek, G. F.: Effect of rainfall intensity and duration on debris flows in central Santa Cruz Mountains, California, in: Debris Flows/Avalanches: Processes, Recognition and Mitigation, edited by: Costa, J. E. and Wieczorek G. F., Geological Society of America, Reviews in Engineering Geology 7, 93–104, 1987.
Wieczorek, G. F. and Glade, T.: Climatic factors influencing occurrence of debris flows, in: Debris flow hazards and related phenomena, edited by: Jakob, M. and Hungr, O., Berlin Heidelberg, Springer, 325–362, 2005.
Wilson, R. C. and Wieczorek, G. F.: Rainfall thresholds for the initiation of debris flow at La Honda, California, Environ. Eng. Geosci., 1, 11–27, 1995.
Zêzere, J. L. and Rodrigues, M. L.: Rainfall thresholds for landsliding in Lisbon area (Portugal), in Landslides, edited by: Rybar, J., Stemberk, J., and Wagner, P., A.A. Balkema, Lisse, The Netherlands, 333–338, 2002.
Short summary
A model for predicting the timing of activation of rainfall-induced landslides is presented. Calibration against real events is based on genetic algorithms, and provides a family of optimal solutions (kernels) that maximize a fitness function. Accordingly, a set of mobility functions is obtained through convolution with rainfall. Once properly validated, the model allows one to estimate future landslide activations in the same study area, by employing either recorded or forecasted rainfall.
A model for predicting the timing of activation of rainfall-induced landslides is presented....
