Articles | Volume 8, issue 7
https://doi.org/10.5194/gmd-8-1991-2015
© 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-8-1991-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Model description paper
| 
07 Jul 2015
Model description paper |
| 07 Jul 2015
System for Automated Geoscientific Analyses (SAGA) v. 2.1.4
O. Conrad
Institute of Geography, University of Hamburg, Bundesstr. 55, 20146 Hamburg, Germany
B. Bechtel
CORRESPONDING AUTHOR
Institute of Geography, University of Hamburg, Bundesstr. 55, 20146 Hamburg, Germany
M. Bock
Institute of Geography, University of Hamburg, Bundesstr. 55, 20146 Hamburg, Germany
scilands GmbH, Goetheallee 11, 37073 Göttingen, Germany
H. Dietrich
Institute of Geography, University of Hamburg, Bundesstr. 55, 20146 Hamburg, Germany
E. Fischer
Institute of Geography, University of Hamburg, Bundesstr. 55, 20146 Hamburg, Germany
L. Gerlitz
Institute of Geography, University of Hamburg, Bundesstr. 55, 20146 Hamburg, Germany
J. Wehberg
Institute of Geography, University of Hamburg, Bundesstr. 55, 20146 Hamburg, Germany
V. Wichmann
LASERDATA GmbH, Technikerstr. 21a, 6020 Innsbruck, Austria
alpS – Center for Climate Change Adaptation, Grabenweg 68, 6020 Innsbruck, Austria
J. Böhner
Institute of Geography, University of Hamburg, Bundesstr. 55, 20146 Hamburg, Germany
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This study describes the modeling system and the evaluation results for the first prototype version of a global aerosol reanalysis product at NOAA, prototype NOAA Aerosol ReAnalysis version 1.0 (pNARA v1.0). We evaluated pNARA v1.0 against independent datasets and compared it with other reanalyses. We identified deficiencies in the system (both in the forecast model and in the data assimilation system) and the uncertainties that exist in our reanalysis.
Emma Howard, Chun-Hsu Su, Christian Stassen, Rajashree Naha, Harvey Ye, Acacia Pepler, Samuel S. Bell, Andrew J. Dowdy, Simon O. Tucker, and Charmaine Franklin
Geosci. Model Dev., 17, 731–757, https://doi.org/10.5194/gmd-17-731-2024, https://doi.org/10.5194/gmd-17-731-2024, 2024
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The BARPA-R modelling configuration has been developed to produce high-resolution climate hazard projections within the Australian region. When using boundary driving data from quasi-observed historical conditions, BARPA-R shows good performance with errors generally on par with reanalysis products. BARPA-R also captures trends, known modes of climate variability, large-scale weather processes, and multivariate relationships.
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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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.
Mario C. Acosta, Sergi Palomas, Stella V. Paronuzzi Ticco, Jean-Claude André, Joachim Biercamp, Pierre-Antoine Bretonniere, Reinhard Budich, Miguel Castrillo, Arnaud Caubel, Francisco Doblas-Reyes, Italo Epicoco, Uwe Fladrich, Alok Kumar Gupta, Bryan Lawrence, Philippe Le Sager, Grenville Lister, Marie-Pierre Moine, Jean-Christophe Rioual, Joussaume Sylvie, Sophie Valcke, Niki Zadeh, and Venkatramani Balaji
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-188, https://doi.org/10.5194/gmd-2023-188, 2023
Revised manuscript accepted for GMD
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We present a collection of performance metrics gathered during the Coupled Model Intercomparison Project Phase 6 (CMIP6), a worlwide initiative to study the climate change. We analyze the metrics, which resulted from collaboration efforts among many partners and models, and describe our findigs to demonstrate the utility of our study for the scientific community. The research contributes to understand climate modelling performance on the current High-performance Computing (HPC) architectures.
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
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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
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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
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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
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We outline a request for sub-daily data to accurately capture the process-level connections between land states, surface fluxes, and the boundary layer response. This high-frequency model output will allow for more direct comparison with observational field campaigns on process-relevant 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
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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
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The CH2O-CHOO TRAIN model can simulate how climate and the long-term carbon cycle interact across millions of years on a standard PC. While efficient, the model accounts for many factors including the location of land masses, the spatial pattern of the water cycle, and fundamental climate feedbacks. The model is a powerful tool for investigating how short-term climate processes can affect long-term changes in the Earth system.
Jason Neil Steven Cole, Knut von Salzen, Jiangnan Li, John Scinocca, David Plummer, Vivek Arora, Norman McFarlane, Michael Lazare, Murray MacKay, and Diana Verseghy
Geosci. Model Dev., 16, 5427–5448, https://doi.org/10.5194/gmd-16-5427-2023, https://doi.org/10.5194/gmd-16-5427-2023, 2023
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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
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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
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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
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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
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We developed a new climate model with isotopic capabilities and simulated the pre-industrial and mid-Holocene periods. Despite certain regional model biases, the modeled isotope composition is in good agreement with observations and reconstructions. Based on our analyses, the observed isotope–temperature relationship in polar regions may have a summertime bias. Using daily model outputs, we developed a novel isotope-based approach to determine the onset date of the West African summer monsoon.
Andrew Gettelman
Geosci. Model Dev., 16, 4937–4956, https://doi.org/10.5194/gmd-16-4937-2023, https://doi.org/10.5194/gmd-16-4937-2023, 2023
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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.
Cited articles
Aichner, B., Herzschuh, U., Wilkes, H., Vieth, A., and Böhner, J.: δD values of n-alkanes in Tibetan lake sediments and aquatic macrophytes – A surface sediment study and application to a 16 ka record from Lake Koucha, Org. Geochem., 41, 779–790, https://doi.org/10.1016/j.orggeochem.2010.05.010, 2010.
Asmussen, P., Conrad, O., Günther, A., Kirsch, M., and Riller, U.: Semi-automatic segmentation of petrographic thin section images using a "seeded-region growing algorithm" with an application to characterize wheathered subarkose sandstone, Comput. Geosci., https://doi.org/10.1016/j.cageo.2015.05.001, in press, 2015.
Bechtel, B.: Multitemporal Landsat data for urban heat island assessment and classification of local climate zones, in: Urban Remote Sensing Event (JURSE), 2011 Joint, Presented at the Urban Remote Sensing Event (JURSE), 2011 Joint, IEEE, 129–132, https://doi.org/10.1109/JURSE.2011.5764736, 2011a.
Bechtel, B.: Multisensorale Fernerkundungsdaten zur mikroklimatischen Beschreibung und Klassifikation urbaner Strukturen, Photogramm.-Fernerkund.-Geoinformation, 2011, 325–338, 2011b.
Bechtel, B.: Robustness of Annual Cycle Parameters to Characterize the Urban Thermal Landscapes, IEEE Geosci. Remote Sens. Lett., 9, 876–880, https://doi.org/10.1109/LGRS.2012.2185034, 2012.
Bechtel, B.: A New Global Climatology of Annual Land Surface Temperature, Remote Sens., 7, 2850–2870, https://doi.org/10.3390/rs70302850, 2015.
Bechtel, B. and Daneke, C.: Classification of Local Climate Zones Based on Multiple Earth Observation Data, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 5, 1191–1202, https://doi.org/10.1109/JSTARS.2012.2189873, 2012.
Bechtel, B. and Schmidt, K. J.: Floristic mapping data as a proxy for the mean urban heat island, Clim. Res., 49, 45–58, https://doi.org/10.3354/cr01009, 2011.
Bechtel, B., Ringeler, A., and Böhner, J.: Segmentation for Object Extraction of Trees using MATLAB and SAGA, in: SAGA – Seconds Out, Hamburger Beiträge Zur Physischen Geographie Und Landschaftsökologie. Univ. Hamburg, Inst. für Geographie, 1–12, 2008.
Bechtel, B., Langkamp, T., Ament, F., Böhner, J., Daneke, C., Günzkofer, R., Leitl, B., Ossenbrügge, J., and Ringeler, A.: Towards an urban roughness parameterisation using interferometric SAR data taking the Metropolitan Region of Hamburg as an example, Meteorol. Z., 20, 29–37, https://doi.org/10.1127/0941-2948/2011/0496, 2011.
Bechtel, B., Daneke, C., Langkamp, T., Oßenbrügge, J., and Böhner, J.: Classification of Local Climate Zones from multitemporal remote sensing data, in: Proceedings ICUC8 – 8th International Conference on Urban Climates. Presented at the 8th International Conference on Urban Climates, 06–10 August 2012, UCD, Dublin Ireland, 2012a.
Bechtel, B., Langkamp, T., Böhner, J., Daneke, C., Oßenbrügge, J., and Schempp, S.: Classification and modelling of urban micro-climates using multitemporal remote sensing data, ISPRS – Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. XXXIX-B8, 463–468, https://doi.org/10.5194/isprsarchives-XXXIX-B8-463-2012, 2012b.
Bechtel, B., Zakšek, K., and Hoshyaripour, G.: Downscaling Land Surface Temperature in an Urban Area: A Case Study for Hamburg, Germany, Remote Sens., 4, 3184–3200, https://doi.org/10.3390/rs4103184, 2012c.
Bechtel, B., Böhner, J., Zakšek, K., and Wiesner, S.: Downscaling of diurnal land surface temperature cycles for urban heat island monitoring, in: Urban Remote Sensing Event (JURSE), 2013 Joint, Presented at the Urban Remote Sensing Event (JURSE), 2013 Joint, IEEE, 2013.
Bechtel, B., Wiesner, S., and Zaksek, K.: Estimation of Dense Time Series of Urban Air Temperatures from Multitemporal Geostationary Satellite Data, J. Sel. Top. Appl. Earth Obs. Remote Sens., 7, 4129–4137, https://doi.org/10.1109/JSTARS.2014.2322449, 2014.
Bechtel, B., Alexander, P. J., Böhner, J., Ching, J., Conrad, O., Feddema, J., Mills, G., See, L., and Stewart, I.: Mapping Local Climate Zones for a Worldwide Database of the Form and Function of Cities, ISPRS Int. J. Geo-Inf., 4, 199–219, https://doi.org/10.3390/ijgi4010199, 2015.
Behrens, T. and Scholten, T.: Digital soil mapping in Germany – a review, J. Plant Nutr. Soil Sci., 169, 434–443, 2006.
Bernardini, F., Sgambati, A., Montagnari Kokelj, M., Zaccaria, C., Micheli, R., Fragiacomo, A., Tiussi, C., Dreossi, D., Tuniz, C., and De Min, A.: Airborne LiDAR application to karstic areas: the example of Trieste province (north-eastern Italy) from prehistoric sites to Roman forts, J. Archaeol. Sci., 40, 2152–2160, https://doi.org/10.1016/j.jas.2012.12.029, 2013.
Bivand, R. S.: 14 GeoComputation and Open-Source Software, in: GeoComputation, CRC Press, 329 pp., 2014.
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Bock, M. and Köthe, R.: Predicting the Depth of Hydromorphic Soil Characteristics Influenced by Ground Water, 2008.
Bock, M., Böhner, J., Conrad, O., Köthe, R., and Ringeler, A.: Methods for creating Functional Soil Databases and applying Digital Soil Mapping with SAGA GIS, JRC Sci. Tech. Rep. EUR 22646 EN, 2007a.
Bock, M., Conrad, O., Köthe, R., and Ringeler, A.: Methods for creating functional soil databases and applying digital soil mapping with SAGA GIS, in: Status and Prospect of Soil Information in South-Eastern Europe: Soil Databases, Projects and Applications, European Communities, Luxembourg, 149–162, 2007b.
Bock, M., Böhner, J., Conrad, O., Köthe, R., and Ringeler, A.: Methods for creating Functional Soil Databases and applying Digital Soil Mapping with SAGA GIS, 2007c.
Bock, M., Günther, A., Ringeler, A., Baritz, R., and Böhner, J.: Assessment of soil parent material formation in periglacial environments through medium scale landscape evolution modelling, Geophys. Res. Abstr., p. 8796, EGU2012-8796, EGU General Assembly 2012, Vienna, Austria, 2012.
Böhner, J.: Regionalisierung bodenrelevanter Klimaparameter für das Niedersächsische Landes-amt für Bodenforschung (NLfB) und die Bundesanstalt für Geowissenschaften und Rohstoffe (BGR), Arbeitshefte Boden, 4, 17–66, 2004.
Böhner, J.: Advancements and new approaches in climate spatial prediction and environmental modelling, Arbeitsberichte Geogr. Inst. HU Zu Berl., 109, 49–90, 2005.
Böhner, J.: General climatic controls and topoclimatic variations in Central and High Asia, Boreas, 35, 279–295, https://doi.org/10.1080/03009480500456073, 2006.
Böhner, J. and Antonic, O.: Land surface parameters specific to topo-climatology, Geomorphometry-Concepts Softw. Appl., 195–226, 2009.
Böhner, J. and Kickner, S.: Woher der Wind weht, GeoBit, 5, 22–25, 2006.
Böhner, J. and Köthe, R.: Bodenregionalisierung und Prozeßmodellierung: Instrumente für den Bodenschutz, Petermann. Geogr. Mitt., 147, 72–82, 2003.
Böhner, J. and Langkamp, T.: Klimawandel und Landschaft – Regionalisierung, Rekonstruktion und Projektion des Klima- und Landschaftswandels Zentral- und Hochasiens, Hambg. Symp. Geogr., 2, 27–49, 2010.
Böhner, J. and Lehmkuhl, F.: Climate and Environmental Change Modelling in Central and High Asia, Boreas, 34, 220–231, 2005.
Böhner, J. and Selige, T.: Spatial prediction of soil attributes using terrain analysis and climate regionalisation, in: SAGA – Analysis and Modelling Applications, Göttinger Geographische Abhandlungen, Göttingen, 13–28, 2006.
Böhner, J., Köthe, R., Conrad, O., Gross, J., Ringeler, A., and Selige, T.: Soil regionalisation by means of terrain analysis and process parameterisation, Soil Classif., European Soil Bureau, Research Report 7, 213–222, 2002.
Böhner, J., Schäfer, W., Conrad, O., Gross, J., and Ringeler, A.: The WEELS model: methods, results and limitations, Catena, 52, 289–308, 2003.
Böhner, J., Dietrich, H., Fraedrich, K., Kawohl, T., Kilian, M., Lucarini, V., and Lunkeit, F.: Development and Implementation of a Hierarchical Model Chain for Modelling Regional Climate Variability and Climate Change over Southern Amazonia, in: Interdisciplinary Analysis and Modeling of Carbon-Optimized Land Management Strategies for Southern Amazonia, edite by: Gerold, G., Jungkunst, H. F., Wantzen, K. M., Schönenberg, R., Amorim, R. S. S., Couto, E. G., Madari, B., and Hohnwald, S., Universitätsdrucke Göttingen, Göttingen, 174 pp., 2014.
Bolch, T.: GIS- und fernerkundungsgestützte Analyse und Visualisierung von Klima- und Gletscheränderungen im nördlichen Tien Shan (Kasachstan/Kyrgyzstan): mit einem Vergleich zur Bernina-Gruppe, Alpen, Dissertation, Faculty of Science of the Friedrich-Alexander-Universität Erlangen-Nuernberg, Germany, 210 pp., 2006.
Bolch, T.: Climate change and glacier retreat in northern Tien Shan (Kazakhstan/Kyrgyzstan) using remote sensing data, Glob. Planet. Change, 56, 1–12, https://doi.org/10.1016/j.gloplacha.2006.07.009, 2007.
Bolch, T. and Kamp, U.: Glacier Mapping in High Mountains Using DEMs, Landsat and ASTER Data, Grazer Schriften Geogr. Raumforsch., Grazer Schriften der Geographie und Raumforschung 41, 37–48, 2006.
Boettinger, J. L.: Digital Soil Mapping: Bridging Research, Environmental Application, and Operation, Springer Science & Business Media, 2010.
Brenning, A.: Statistical geocomputing combining R and SAGA: The example of landslide susceptibility analysis with generalized additive models, SAGA–Seconds Hambg, Beitr. Zur Phys. Geogr. Landschaftsökologie 19, 23–32, 2008.
Brenning, A.: Benchmarking classifiers to optimally integrate terrain analysis and multispectral remote sensing in automatic rock glacier detection, Remote Sens. Environ., 113, 239–247, 2009.
Brenning, A., Long, S., and Fieguth, P.: Detecting rock glacier flow structures using Gabor filters and IKONOS imagery, Remote Sens. Environ., 125, 227–237, 2012.
Chang, C.-C. and Lin, C.-J.: LIBSVM: a library for support vector machines, ACM Transa. Int. Sys. Technol. (ACM TIST), 2, 1–27, 2011.
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Conrad, O., Jens-Peter, K., Michael, B., Gerhard, G., and Bohner, J.: Soil degradation risk assessment integrating terrain analysis and soil spatial prediction methods, GEOOKO-Bensh., 27, 165–174, 2006.
Czech, A.: GIS-gestützte morphometrische Analyse von Okklusalflächen mit SAGA GIS, Unpublished BSc thesis, University of Hamburg, Faculty of Earth Sciences, Institute of Geographie, Sect. Physical Geography, Hamburg, 2010.
Czegka, W. and Junge, F. W.: The use of SAGA as a mobile Field-Tool in the environmental Geochemistry, in: SAGA – Seconds Out, Hamburger Beiträge Zur Physischen Geographie Und Landschaftsökologie, Univ. Hamburg, Inst. für Geographie, Hamburg, 33–36, 2008.
Dietrich, H. and Böhner, J.: Cold Air Production and Flow in a Low Mountain Range Landscape in Hessia (Germany), in: SAGA – Seconds Out, Hamburger Beiträge Zur Physischen Geographie Und Landschaftsökologie, Univ. Hamburg, Inst. für Geographie, Hamburg, 37–48, 2008.
Enea, A., Romanescu, G., and Stoleriu, C.: Quantitative considerations concerning the source-areas for the silting of the red lake (Romania) lacustrine basin, in: Water Resources and Wetlands, Tulcea, Romania, 14–16, 2012.
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Fader, M., Böhner, J., and Gerold, G.: Precipitation Variability and Landscape Degradation in Rio Negro (Argentina), Geo-Öko, 33, 5–33, 2012.
Fenoy, G., Bozon, N., and Raghavan, V.: ZOO-Project: the open WPS platform, Appl. Geomat., 5, 19–24, 2013.
Fey, C., Zangerl, C., Wichmann, V., and Prager, C.: Back-Calculation of Medium-Scale Rockfalls Using an Empirical GIS Model, Int. Symp. Rock Slope Stab. Open Pit Min. Civ. Eng. Vancover Can, 2011.
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Gerlitz, L.: Using fuzzified regression trees for statistical downscaling and regionalization of near surface temperatures in complex terrain, Theor. Appl. Climatol., 118, 1–16, https://doi.org/10.1007/s00704-014-1285-x, 2014.
Gerlitz, L., Conrad, O., Thomas, A., and Böhner, J.: Assessment of Warming Patterns for the Tibetan Plateau and its adjacent Lowlands based on an elevation- and bias corrected ERA-Interim Data Set, Clim. Res., 58, 235–246, https://doi.org/10.3354/cr01193, 2014.
Gerlitz, L., Conrad, O., and Böhner, J.: Large-scale atmospheric forcing and topographic modification of precipitation rates over High Asia – a neural-network-based approach, Earth Syst. Dynam., 6, 61–81, https://doi.org/10.5194/esd-6-61-2015, 2015.
Goetz, J. N., Guthrie, R. H., and Brenning, A.: Integrating physical and empirical landslide susceptibility models using generalized additive models, Geomorphology, 129, 376–386, 2011.
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Haas, F., Heckmann, T., Wichmann, V., and Becht, M.: Quantification and Modeling of Fluvial Bedload Discharge from Hillslope Channels in two Alpine Catchments (Bavarian Alps, Germany), Z. Geomorphol. Suppl., 55, 147–168, https://doi.org/10.1127/0372-8854/2011/0055S3-0056, 2011.
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Heckmann, T., Bimböse, M., Krautblatter, M., Haas, F., Becht, M., and Morche, D.: From geotechnical analysis to quantification and modelling using LiDAR data: a study on rockfall in the Reintal catchment, Bavarian Alps, Germany, Earth Surf. Process. Landf., 37, 119–133, 2012.
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Short summary
The System for Automated Geoscientific Analyses (SAGA) is a comprehensive and globally established open source geographic information system (GIS) for scientific analysis and modeling. The current version 2.1.4 offers more than 700 tools that represent the broad scopes of SAGA in numerous fields of geoscientific endeavor. In this paper, we inform about the system’s architecture and functionality and highlight the wide spectrum of scientific applications of SAGA in a review of published studies.
The System for Automated Geoscientific Analyses (SAGA) is a comprehensive and globally...
