Articles | Volume 8, issue 7
Geosci. Model Dev., 8, 1991–2007, 2015
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.
Geosci. Model Dev., 8, 1991–2007, 2015
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 et al.
Related authors
Dirk Nikolaus Karger, Stefan Lange, Chantal Hari, Christopher P. O. Reyer, Olaf Conrad, Niklaus E. Zimmermann, and Katja Frieler
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2022-367, https://doi.org/10.5194/essd-2022-367, 2022
Preprint under review for ESSD
Short summary
Short summary
We present the first 1km, daily, global climate dataset for climate impact studies. We show that the high resolution data has a decreased bias, and higher correlation with measurements from meteorological stations than coarser data. The dataset will be of value for a wide range of climate change impact studies both at global and regional level that benefit from using a consistent global dataset.
Michael Bock, Olaf Conrad, Andreas Günther, Ernst Gehrt, Rainer Baritz, and Jürgen Böhner
Geosci. Model Dev., 11, 1641–1652, https://doi.org/10.5194/gmd-11-1641-2018, https://doi.org/10.5194/gmd-11-1641-2018, 2018
Short summary
Short summary
We introduce the Soil and
Landscape Evolution Model (SaLEM) for the prediction of soil parent material evolution following a lithologically differentiated approach. The GIS tool is working within the software framework SAGA GIS. Weathering, erosion and transport functions are calibrated using extrinsic and intrinsic parameter data. First results indicate that our approach shows evidence for the spatiotemporal prediction of soil parental material properties.
Peter Hoffmann, Vanessa Reinhart, Diana Rechid, Nathalie de Noblet-Ducoudré, Edouard L. Davin, Christina Asmus, Benjamin Bechtel, Jürgen Böhner, Eleni Katragkou, and Sebastiaan Luyssaert
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2022-431, https://doi.org/10.5194/essd-2022-431, 2022
Preprint under review for ESSD
Short summary
Short summary
This paper introduces the new high-resolution land-use land-cover change dataset LUCAS LUC historical and future land use and land cover change dataset for Europe (Version 1.1), tailored for use in regional climate models. Historical and projected future land use change information from the Land-Use Harmonization 2 (LUH2) dataset is translated into annual plant functional type changes from 1950 to 2015 and 2016 to 2100, respectively, by employing a newly developed land use translator.
Dirk Nikolaus Karger, Stefan Lange, Chantal Hari, Christopher P. O. Reyer, Olaf Conrad, Niklaus E. Zimmermann, and Katja Frieler
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2022-367, https://doi.org/10.5194/essd-2022-367, 2022
Preprint under review for ESSD
Short summary
Short summary
We present the first 1km, daily, global climate dataset for climate impact studies. We show that the high resolution data has a decreased bias, and higher correlation with measurements from meteorological stations than coarser data. The dataset will be of value for a wide range of climate change impact studies both at global and regional level that benefit from using a consistent global dataset.
Matthias Demuzere, Jonas Kittner, Alberto Martilli, Gerald Mills, Christian Moede, Iain D. Stewart, Jasper van Vliet, and Benjamin Bechtel
Earth Syst. Sci. Data, 14, 3835–3873, https://doi.org/10.5194/essd-14-3835-2022, https://doi.org/10.5194/essd-14-3835-2022, 2022
Short summary
Short summary
Because urban areas are key contributors to climate change but are also susceptible to multiple hazards, one needs spatially detailed information on urban landscapes to support environmental services. This global local climate zone map describes this much-needed intra-urban heterogeneity across the whole surface of the earth in a universal language and can serve as a basic infrastructure to study e.g. environmental hazards, energy demand, and climate adaptation and mitigation solutions.
Vanessa Reinhart, Peter Hoffmann, Diana Rechid, Jürgen Böhner, and Benjamin Bechtel
Earth Syst. Sci. Data, 14, 1735–1794, https://doi.org/10.5194/essd-14-1735-2022, https://doi.org/10.5194/essd-14-1735-2022, 2022
Short summary
Short summary
The LANDMATE plant functional type (PFT) land cover dataset for Europe 2015 (Version 1.0) is a gridded, high-resolution dataset for use in regional climate models. LANDMATE PFT is prepared using the expertise of regional climate modellers all over Europe and is easily adjustable to fit into different climate model families. We provide comprehensive spatial quality information for LANDMATE PFT, which can be used to reduce uncertainty in regional climate model simulations.
Peter Hoffmann, Vanessa Reinhart, Diana Rechid, Nathalie de Noblet-Ducoudré, Edouard L. Davin, Christina Asmus, Benjamin Bechtel, Jürgen Böhner, Eleni Katragkou, and Sebastiaan Luyssaert
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2021-252, https://doi.org/10.5194/essd-2021-252, 2021
Manuscript not accepted for further review
Short summary
Short summary
This paper introduces the new high-resolution land-use land-cover change dataset LUCAS LUC historical and future land use and land cover change dataset (Version 1.0), tailored for use in regional climate models. Historical and projected future land use change information from the Land-Use Harmonization 2 (LUH2) dataset is translated into annual plant functional type changes from 1950 to 2015 and 2016 to 2100, respectively, by employing a newly developed land use translator.
M. Bremer, V. Wichmann, M. Rutzinger, T. Zieher, and J. Pfeiffer
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 943–950, https://doi.org/10.5194/isprs-archives-XLII-2-W13-943-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W13-943-2019, 2019
J. Pfeiffer, T. Zieher, M. Rutzinger, M. Bremer, and V. Wichmann
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W5, 421–428, https://doi.org/10.5194/isprs-annals-IV-2-W5-421-2019, https://doi.org/10.5194/isprs-annals-IV-2-W5-421-2019, 2019
T. Zieher, M. Bremer, M. Rutzinger, J. Pfeiffer, P. Fritzmann, and V. Wichmann
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W5, 461–467, https://doi.org/10.5194/isprs-annals-IV-2-W5-461-2019, https://doi.org/10.5194/isprs-annals-IV-2-W5-461-2019, 2019
Eva Steirou, Lars Gerlitz, Heiko Apel, Xun Sun, and Bruno Merz
Hydrol. Earth Syst. Sci., 23, 1305–1322, https://doi.org/10.5194/hess-23-1305-2019, https://doi.org/10.5194/hess-23-1305-2019, 2019
Short summary
Short summary
We investigate whether flood probabilities in Europe vary for different large-scale atmospheric circulation conditions. Maximum seasonal river flows from 600 gauges in Europe and five synchronous atmospheric circulation indices are analyzed. We find that a high percentage of stations is influenced by at least one of the climate indices, especially during winter. These results can be useful for preparedness and damage planning by (re-)insurance companies.
Michael Bock, Olaf Conrad, Andreas Günther, Ernst Gehrt, Rainer Baritz, and Jürgen Böhner
Geosci. Model Dev., 11, 1641–1652, https://doi.org/10.5194/gmd-11-1641-2018, https://doi.org/10.5194/gmd-11-1641-2018, 2018
Short summary
Short summary
We introduce the Soil and
Landscape Evolution Model (SaLEM) for the prediction of soil parent material evolution following a lithologically differentiated approach. The GIS tool is working within the software framework SAGA GIS. Weathering, erosion and transport functions are calibrated using extrinsic and intrinsic parameter data. First results indicate that our approach shows evidence for the spatiotemporal prediction of soil parental material properties.
Heiko Apel, Zharkinay Abdykerimova, Marina Agalhanova, Azamat Baimaganbetov, Nadejda Gavrilenko, Lars Gerlitz, Olga Kalashnikova, Katy Unger-Shayesteh, Sergiy Vorogushyn, and Abror Gafurov
Hydrol. Earth Syst. Sci., 22, 2225–2254, https://doi.org/10.5194/hess-22-2225-2018, https://doi.org/10.5194/hess-22-2225-2018, 2018
Short summary
Short summary
Central Asia crucially depends on water resources supplied by snow melt in the mountains during summer. To support water resources management we propose a generic tool for statistical forecasts of seasonal discharge based on multiple linear regressions. The predictors are observed precipitation and temperature, snow coverage, and discharge. The automatically derived models for 13 different catchments provided very skilful forecasts in April, and acceptable forecasts in January.
Volker Wichmann
Geosci. Model Dev., 10, 3309–3327, https://doi.org/10.5194/gmd-10-3309-2017, https://doi.org/10.5194/gmd-10-3309-2017, 2017
Short summary
Short summary
The GPP model can be used to simulate the process path and run-out area of gravitational processes based on a digital terrain model. By providing several modelling approaches, the tool can be configured for different processes such as rockfall, debris flows or snow avalanches. The tool can be applied to regional-scale studies such as natural hazard susceptibility mapping. It is implemented as tool for SAGA GIS and has been released as open source.
Ramchandra Karki, Shabeh ul Hasson, Lars Gerlitz, Udo Schickhoff, Thomas Scholten, and Jürgen Böhner
Earth Syst. Dynam., 8, 507–528, https://doi.org/10.5194/esd-8-507-2017, https://doi.org/10.5194/esd-8-507-2017, 2017
Short summary
Short summary
Dynamical downscaling of climate fields at very high resolutions (convection- and topography-resolving scales) over the complex Himalayan terrain of the Nepalese Himalayas shows promising results. It clearly demonstrates the potential of mesoscale models to accurately simulate present and future climate information at very high resolutions over remote, data-scarce mountainous regions for the development of adaptation strategies and impact assessments in the context of changing climate.
Shabeh Hasson, Jürgen Böhner, and Valerio Lucarini
Earth Syst. Dynam., 8, 337–355, https://doi.org/10.5194/esd-8-337-2017, https://doi.org/10.5194/esd-8-337-2017, 2017
Short summary
Short summary
A first comprehensive and systematic hydroclimatic trend analysis for the upper Indus Basin suggests warming and drying of spring and rising early melt-season discharge over 1995–2012 period. In contrast, cooling and falling or weakly rising discharge is found within summer monsoon period that coincides well with main glacier melt season. Such seasonally distinct changes, indicating dominance of snow but suppression of glacial melt regime, address hydroclimatic explanation of
Karakoram Anomaly.
Lars Gerlitz, Sergiy Vorogushyn, Heiko Apel, Abror Gafurov, Katy Unger-Shayesteh, and Bruno Merz
Hydrol. Earth Syst. Sci., 20, 4605–4623, https://doi.org/10.5194/hess-20-4605-2016, https://doi.org/10.5194/hess-20-4605-2016, 2016
Short summary
Short summary
Most statistically based seasonal precipitation forecast models utilize a small set of well-known climate indices as potential predictor variables. However, for many target regions, these indices do not lead to sufficient results and customized predictors are required for an accurate prediction.
This study presents a statistically based routine, which automatically identifies suitable predictors from globally gridded SST and climate variables by means of an extensive data mining procedure.
B. Bechtel, M. Pesaresi, L. See, G. Mills, J. Ching, P. J. Alexander, J. J. Feddema, A. J. Florczyk, and I. Stewart
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B8, 1371–1378, https://doi.org/10.5194/isprs-archives-XLI-B8-1371-2016, https://doi.org/10.5194/isprs-archives-XLI-B8-1371-2016, 2016
B. Bechtel
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B8, 243–250, https://doi.org/10.5194/isprs-archives-XLI-B8-243-2016, https://doi.org/10.5194/isprs-archives-XLI-B8-243-2016, 2016
M. Klinge, J. Böhner, and S. Erasmi
Biogeosciences, 12, 2893–2905, https://doi.org/10.5194/bg-12-2893-2015, https://doi.org/10.5194/bg-12-2893-2015, 2015
U. Schickhoff, M. Bobrowski, J. Böhner, B. Bürzle, R. P. Chaudhary, L. Gerlitz, H. Heyken, J. Lange, M. Müller, T. Scholten, N. Schwab, and R. Wedegärtner
Earth Syst. Dynam., 6, 245–265, https://doi.org/10.5194/esd-6-245-2015, https://doi.org/10.5194/esd-6-245-2015, 2015
Short summary
Short summary
Near-natural Himalayan treelines are usually krummholz treelines, which are relatively unresponsive to climate change. Intense recruitment of treeline trees suggests a great potential for future treeline advance. Competitive abilities of tree seedlings within krummholz thickets and dwarf scrub heaths will be a major source of variation in treeline dynamics. Tree growth-climate relationships show mature treeline trees to be responsive in particular to high pre-monsoon temperature trends.
L. Gerlitz, O. Conrad, and J. Böhner
Earth Syst. Dynam., 6, 61–81, https://doi.org/10.5194/esd-6-61-2015, https://doi.org/10.5194/esd-6-61-2015, 2015
Short summary
Short summary
In order to assess high-resolution precipitation fields for the Tibetan Plateau and the Himalayan Arc, a novel downscaling approach is presented which integrates traditional statistical downscaling and GIS-based terrain parameterization techniques. The approach enables a detailed analysis of the precipitation heterogeinity over the complex target area.
S. Hasson, V. Lucarini, S. Pascale, and J. Böhner
Earth Syst. Dynam., 5, 67–87, https://doi.org/10.5194/esd-5-67-2014, https://doi.org/10.5194/esd-5-67-2014, 2014
Y. Wang, U. Herzschuh, L. S. Shumilovskikh, S. Mischke, H. J. B. Birks, J. Wischnewski, J. Böhner, F. Schlütz, F. Lehmkuhl, B. Diekmann, B. Wünnemann, and C. Zhang
Clim. Past, 10, 21–39, https://doi.org/10.5194/cp-10-21-2014, https://doi.org/10.5194/cp-10-21-2014, 2014
Related subject area
Climate and Earth system modeling
stoPET v1.0: a stochastic potential evapotranspiration generator for simulation of climate change impacts
URANOS v1.0 – the Ultra Rapid Adaptable Neutron-Only Simulation for Environmental Research
Combining regional mesh refinement with vertically enhanced physics to target marine stratocumulus biases as demonstrated in the Energy Exascale Earth System Model version 1
Evaluation of native Earth system model output with ESMValTool v2.6.0
WRF–ML v1.0: a bridge between WRF v4.3 and machine learning parameterizations and its application to atmospheric radiative transfer
The Euro-Mediterranean Center on Climate Change (CMCC) decadal prediction system
Climate impacts of parameterizing subgrid variation and partitioning of land surface heat fluxes to the atmosphere with the NCAR CESM1.2
Accelerated photosynthesis routine in LPJmL4
Improving scalability of Earth system models through coarse-grained component concurrency – a case study with the ICON v2.6.5 modelling system
Temperature forecasting by deep learning methods
Pathfinder v1.0.1: a Bayesian-inferred simple carbon–climate model to explore climate change scenarios
Inclusion of a cold hardening scheme to represent frost tolerance is essential to model realistic plant hydraulics in the Arctic–boreal zone in CLM5.0-FATES-Hydro
Implementation and evaluation of the GEOS-Chem chemistry module version 13.1.2 within the Community Earth System Model v2.1
Assessment of JSBACHv4.30 as a land component of ICON-ESM-V1 in comparison to its predecessor JSBACHv3.2 of MPI-ESM1.2
Global biomass burning fuel consumption and emissions at 500 m spatial resolution based on the Global Fire Emissions Database (GFED)
Impact of increased resolution on the representation of the Canary upwelling system in climate models
Assessing Responses and Impacts of Solar climate intervention on the Earth system with stratospheric aerosol injection (ARISE-SAI): protocol and initial results from the first simulations
Introducing the VIIRS-based Fire Emission Inventory version 0 (VFEIv0)
Impact of physical parameterizations on wind simulation with WRF V3.9.1.1 under stable conditions at planetary boundary layer gray-zone resolution: a case study over the coastal regions of North China
Advancing precipitation prediction using a new-generation storm-resolving model framework – SIMA-MPAS (V1.0): a case study over the western United States
SURFER v2.0: a flexible and simple model linking anthropogenic CO2 emissions and solar radiation modification to ocean acidification and sea level rise
Analysis of Systematic Biases in Tropospheric Hydrostatic Delay Models and Construction of Correction Model
A new bootstrap technique to quantify uncertainty in estimates of ground surface temperature and ground heat flux histories from geothermal data
Modeling the topographic influence on aboveground biomass using a coupled model of hillslope hydrology and ecosystem dynamics
Impacts of the ice-particle size distribution shape parameter on climate simulations with the Community Atmosphere Model Version 6 (CAM6)
A modeling framework to understand historical and projected ocean climate change in large coupled ensembles
TriCCo v1.1.0 – a cubulation-based method for computing connected components on triangular grids
Estimation of missing building height in OpenStreetMap data: a French case study using GeoClimate 0.0.1
The Moist Quasi-Geostrophic Coupled Model: MQ-GCM 2.0
Cell tracking of convective rainfall: sensitivity of climate-change signal to tracking algorithm and cell definition (Cell-TAO v1.0)
Transport parameterization of the Polar SWIFT model (version 2)
Analog data assimilation for the selection of suitable general circulation models
Uncertainty and sensitivity analysis for probabilistic weather and climate-risk modelling: an implementation in CLIMADA v.3.1.0
Grid refinement in ICON v2.6.4
Simulating marine neodymium isotope distributions using ND v1.0 coupled to the ocean component of the FAMOUS-MOSES1 climate model: sensitivities to reversible scavenging efficiency and benthic source distributions
Classification of tropical cyclone containing images using a convolutional neural network: performance and sensitivity to the learning dataset
The ICON-A model for direct QBO simulations on GPUs (version icon-cscs:baf28a514)
Further improvement and evaluation of nudging in the E3SM Atmosphere Model version 1 (EAMv1): simulations of the mean climate, weather events, and anthropogenic aerosol effects
HORAYZON v1.2: an efficient and flexible ray-tracing algorithm to compute horizon and sky view factor
LPJ-GUESS/LSMv1.0: a next-generation land surface model with high ecological realism
Downscaling multi-model climate projection ensembles with deep learning (DeepESD): contribution to CORDEX EUR-44
Intercomparison of four algorithms for detecting tropical cyclones using ERA5
Inland lake temperature initialization via coupled cycling with atmospheric data assimilation
wavetrisk-2.1: an adaptive dynamical core for ocean modelling
Representing surface heterogeneity in land–atmosphere coupling in E3SMv1 single-column model over ARM SGP during summertime
AWI-CM3 coupled climate model: description and evaluation experiments for a prototype post-CMIP6 model
The Seasonal-to-Multiyear Large Ensemble (SMYLE) prediction system using the Community Earth System Model version 2
Comparison and evaluation of updates to WRF-Chem (v3.9) biogenic emissions using MEGAN
Checkerboard patterns in E3SMv2 and E3SM-MMFv2
AttentionFire_v1.0: interpretable machine learning fire model for burned area predictions over tropics
Dagmawi Teklu Asfaw, Michael Bliss Singer, Rafael Rosolem, David MacLeod, Mark Cuthbert, Edisson Quichimbo Miguitama, Manuel F. Rios Gaona, and Katerina Michaelides
Geosci. Model Dev., 16, 557–571, https://doi.org/10.5194/gmd-16-557-2023, https://doi.org/10.5194/gmd-16-557-2023, 2023
Short summary
Short summary
stoPET is a new stochastic potential evapotranspiration (PET) generator for the globe at hourly resolution. Many stochastic weather generators are used to generate stochastic rainfall time series; however, no such model exists for stochastically generating plausible PET time series. As such, stoPET represents a significant methodological advance. stoPET generate many realizations of PET to conduct climate studies related to the water balance, agriculture, water resources, and ecology.
Markus Köhli, Martin Schrön, Steffen Zacharias, and Ulrich Schmidt
Geosci. Model Dev., 16, 449–477, https://doi.org/10.5194/gmd-16-449-2023, https://doi.org/10.5194/gmd-16-449-2023, 2023
Short summary
Short summary
In the last decades, Monte Carlo codes were often consulted to study neutrons near the surface. As an alternative for the growing community of CRNS, we developed URANOS. The main model features are tracking of particle histories from creation to detection, detector representations as layers or geometric shapes, a voxel-based geometry model, and material setup based on color codes in ASCII matrices or bitmap images. The entire software is developed in C++ and features a graphical user interface.
Peter A. Bogenschutz, Hsiang-He Lee, Qi Tang, and Takanobu Yamaguchi
Geosci. Model Dev., 16, 335–352, https://doi.org/10.5194/gmd-16-335-2023, https://doi.org/10.5194/gmd-16-335-2023, 2023
Short summary
Short summary
Models that are used to simulate and predict climate often have trouble representing specific cloud types, such as stratocumulus, that are particularly thin in the vertical direction. It has been found that increasing the model resolution can help improve this problem. In this paper, we develop a novel framework that increases the horizontal and vertical resolutions only for areas of the globe that contain stratocumulus, hence reducing the model runtime while providing better results.
Manuel Schlund, Birgit Hassler, Axel Lauer, Bouwe Andela, Patrick Jöckel, Rémi Kazeroni, Saskia Loosveldt Tomas, Brian Medeiros, Valeriu Predoi, Stéphane Sénési, Jérôme Servonnat, Tobias Stacke, Javier Vegas-Regidor, Klaus Zimmermann, and Veronika Eyring
Geosci. Model Dev., 16, 315–333, https://doi.org/10.5194/gmd-16-315-2023, https://doi.org/10.5194/gmd-16-315-2023, 2023
Short summary
Short summary
The Earth System Model Evaluation Tool (ESMValTool) is a community diagnostics and performance metrics tool for routine evaluation of Earth system models. Originally, ESMValTool was designed to process reformatted output provided by large model intercomparison projects like the Coupled Model Intercomparison Project (CMIP). Here, we describe a new extension of ESMValTool that allows for reading and processing native climate model output, i.e., data that have not been reformatted before.
Xiaohui Zhong, Zhijian Ma, Yichen Yao, Lifei Xu, Yuan Wu, and Zhibin Wang
Geosci. Model Dev., 16, 199–209, https://doi.org/10.5194/gmd-16-199-2023, https://doi.org/10.5194/gmd-16-199-2023, 2023
Short summary
Short summary
More and more researchers use deep learning models to replace physics-based parameterizations to accelerate weather simulations. However, embedding the ML models within the weather models is difficult as they are implemented in different languages. This work proposes a coupling framework to allow ML-based parameterizations to be coupled with the Weather Research and Forecasting (WRF) model. We also demonstrate using the coupler to couple the ML-based radiation schemes with the WRF model.
Dario Nicolì, Alessio Bellucci, Paolo Ruggieri, Panos J. Athanasiadis, Stefano Materia, Daniele Peano, Giusy Fedele, Riccardo Hénin, and Silvio Gualdi
Geosci. Model Dev., 16, 179–197, https://doi.org/10.5194/gmd-16-179-2023, https://doi.org/10.5194/gmd-16-179-2023, 2023
Short summary
Short summary
Decadal climate predictions, obtained by constraining the initial condition of a dynamical model through a truthful estimate of the observed climate state, provide an accurate assessment of the near-term climate and are useful for informing decision-makers on future climate-related risks. The predictive skill for key variables is assessed from the operational decadal prediction system compared with non-initialized historical simulations so as to quantify the added value of initialization.
Ming Yin, Yilun Han, Yong Wang, Wenqi Sun, Jianbo Deng, Daoming Wei, Ying Kong, and Bin Wang
Geosci. Model Dev., 16, 135–156, https://doi.org/10.5194/gmd-16-135-2023, https://doi.org/10.5194/gmd-16-135-2023, 2023
Short summary
Short summary
All global climate models (GCMs) use the grid-averaged surface heat fluxes to drive the atmosphere, and thus their horizontal variations within the grid cell are averaged out. In this regard, a novel scheme considering the variation and partitioning of the surface heat fluxes within the grid cell is developed. The scheme reduces the long-standing rainfall biases on the southern and eastern margins of the Tibetan Plateau. The performance of key variables at the global scale is also evaluated.
Jenny Niebsch, Werner von Bloh, Kirsten Thonicke, and Ronny Ramlau
Geosci. Model Dev., 16, 17–33, https://doi.org/10.5194/gmd-16-17-2023, https://doi.org/10.5194/gmd-16-17-2023, 2023
Short summary
Short summary
The impacts of climate change require strategies for climate adaptation. Dynamic global vegetation models (DGVMs) are used to study the effects of multiple processes in the biosphere under climate change. There is a demand for a better computational performance of the models. In this paper, the photosynthesis model in the Lund–Potsdam–Jena managed Land DGVM (4.0.002) was examined. We found a better numerical solution of a nonlinear equation. A significant run time reduction was possible.
Leonidas Linardakis, Irene Stemmler, Moritz Hanke, Lennart Ramme, Fatemeh Chegini, Tatiana Ilyina, and Peter Korn
Geosci. Model Dev., 15, 9157–9176, https://doi.org/10.5194/gmd-15-9157-2022, https://doi.org/10.5194/gmd-15-9157-2022, 2022
Short summary
Short summary
In Earth system modelling, we are facing the challenge of making efficient use of very large machines, with millions of cores. To meet this challenge we will need to employ multi-level and multi-dimensional parallelism. Component concurrency, being a function parallel technique, offers an additional dimension to the traditional data-parallel approaches. In this paper we examine the behaviour of component concurrency and identify the conditions for its optimal application.
Bing Gong, Michael Langguth, Yan Ji, Amirpasha Mozaffari, Scarlet Stadtler, Karim Mache, and Martin G. Schultz
Geosci. Model Dev., 15, 8931–8956, https://doi.org/10.5194/gmd-15-8931-2022, https://doi.org/10.5194/gmd-15-8931-2022, 2022
Short summary
Short summary
Inspired by the success of deep learning in various domains, we test the applicability of video prediction methods by generative adversarial network (GAN)-based deep learning to predict the 2 m temperature over Europe. Our video prediction models have skill in predicting the diurnal cycle of 2 m temperature up to 12 h ahead. Complemented by probing the relevance of several model parameters, this study confirms the potential of deep learning in meteorological forecasting applications.
Thomas Bossy, Thomas Gasser, and Philippe Ciais
Geosci. Model Dev., 15, 8831–8868, https://doi.org/10.5194/gmd-15-8831-2022, https://doi.org/10.5194/gmd-15-8831-2022, 2022
Short summary
Short summary
We developed a new simple climate model designed to fill a perceived gap within the existing simple climate models by fulfilling three key requirements: calibration using Bayesian inference, the possibility of coupling with integrated assessment models, and the capacity to explore climate scenarios compatible with limiting climate impacts. Here, we describe the model and its calibration using the latest data from complex CMIP6 models and the IPCC AR6, and we assess its performance.
Marius S. A. Lambert, Hui Tang, Kjetil S. Aas, Frode Stordal, Rosie A. Fisher, Yilin Fang, Junyan Ding, and Frans-Jan W. Parmentier
Geosci. Model Dev., 15, 8809–8829, https://doi.org/10.5194/gmd-15-8809-2022, https://doi.org/10.5194/gmd-15-8809-2022, 2022
Short summary
Short summary
In this study, we implement a hardening mortality scheme into CTSM5.0-FATES-Hydro and evaluate how it impacts plant hydraulics and vegetation growth. Our work shows that the hydraulic modifications prescribed by the hardening scheme are necessary to model realistic vegetation growth in cold climates, in contrast to the default model that simulates almost nonexistent and declining vegetation due to abnormally large water loss through the roots.
Thibaud M. Fritz, Sebastian D. Eastham, Louisa K. Emmons, Haipeng Lin, Elizabeth W. Lundgren, Steve Goldhaber, Steven R. H. Barrett, and Daniel J. Jacob
Geosci. Model Dev., 15, 8669–8704, https://doi.org/10.5194/gmd-15-8669-2022, https://doi.org/10.5194/gmd-15-8669-2022, 2022
Short summary
Short summary
We bring the state-of-the-science chemistry module GEOS-Chem into the Community Earth System Model (CESM). We show that some known differences between results from GEOS-Chem and CESM's CAM-chem chemistry module may be due to the configuration of model meteorology rather than inherent differences in the model chemistry. This is a significant step towards a truly modular Earth system model and allows two strong but currently separate research communities to benefit from each other's advances.
Rainer Schneck, Veronika Gayler, Julia E. M. S. Nabel, Thomas Raddatz, Christian H. Reick, and Reiner Schnur
Geosci. Model Dev., 15, 8581–8611, https://doi.org/10.5194/gmd-15-8581-2022, https://doi.org/10.5194/gmd-15-8581-2022, 2022
Short summary
Short summary
The versions of ICON-A and ICON-Land/JSBACHv4 used for this study constitute the first milestone in the development of the new ICON Earth System Model ICON-ESM. JSBACHv4 is the successor of JSBACHv3, and most of the parameterizations of JSBACHv4 are re-implementations from JSBACHv3. We assess and compare the performance of JSBACHv4 and JSBACHv3. Overall, the JSBACHv4 results are as good as JSBACHv3, but both models reveal the same main shortcomings, e.g. the depiction of the leaf area index.
Dave van Wees, Guido R. van der Werf, James T. Randerson, Brendan M. Rogers, Yang Chen, Sander Veraverbeke, Louis Giglio, and Douglas C. Morton
Geosci. Model Dev., 15, 8411–8437, https://doi.org/10.5194/gmd-15-8411-2022, https://doi.org/10.5194/gmd-15-8411-2022, 2022
Short summary
Short summary
We present a global fire emission model based on the GFED model framework with a spatial resolution of 500 m. The higher resolution allowed for a more detailed representation of spatial heterogeneity in fuels and emissions. Specific modules were developed to model, for example, emissions from fire-related forest loss and belowground burning. Results from the 500 m model were compared to GFED4s, showing that global emissions were relatively similar but that spatial differences were substantial.
Adama Sylla, Emilia Sanchez Gomez, Juliette Mignot, and Jorge López-Parages
Geosci. Model Dev., 15, 8245–8267, https://doi.org/10.5194/gmd-15-8245-2022, https://doi.org/10.5194/gmd-15-8245-2022, 2022
Short summary
Short summary
Increasing model resolution depends on the subdomain of the Canary upwelling considered. In the Iberian Peninsula, the high-resolution (HR) models do not seem to better simulate the upwelling indices, while in Morocco to the Senegalese coast, the HR models show a clear improvement. Thus increasing the resolution of a global climate model does not necessarily have to be the only way to better represent the climate system. There is still much work to be done in terms of physical parameterizations.
Jadwiga H. Richter, Daniele Visioni, Douglas G. MacMartin, David A. Bailey, Nan Rosenbloom, Brian Dobbins, Walker R. Lee, Mari Tye, and Jean-Francois Lamarque
Geosci. Model Dev., 15, 8221–8243, https://doi.org/10.5194/gmd-15-8221-2022, https://doi.org/10.5194/gmd-15-8221-2022, 2022
Short summary
Short summary
Solar climate intervention using stratospheric aerosol injection is a proposed method of reducing global mean temperatures to reduce the worst consequences of climate change. We present a new modeling protocol aimed at simulating a plausible deployment of stratospheric aerosol injection and reproducibility of simulations using other Earth system models: Assessing Responses and Impacts of Solar climate intervention on the Earth system with stratospheric aerosol injection (ARISE-SAI).
Gonzalo A. Ferrada, Meng Zhou, Jun Wang, Alexei Lyapustin, Yujie Wang, Saulo R. Freitas, and Gregory R. Carmichael
Geosci. Model Dev., 15, 8085–8109, https://doi.org/10.5194/gmd-15-8085-2022, https://doi.org/10.5194/gmd-15-8085-2022, 2022
Short summary
Short summary
The smoke from fires is composed of different compounds that interact with the atmosphere and can create poor air-quality episodes. Here, we present a new fire inventory based on satellite observations from the Visible Infrared Imaging Radiometer Suite (VIIRS). We named this inventory the VIIRS-based Fire Emission Inventory (VFEI). Advantages of VFEI are its high resolution (~500 m) and that it provides information for many species. VFEI is publicly available and has provided data since 2012.
Entao Yu, Rui Bai, Xia Chen, and Lifang Shao
Geosci. Model Dev., 15, 8111–8134, https://doi.org/10.5194/gmd-15-8111-2022, https://doi.org/10.5194/gmd-15-8111-2022, 2022
Short summary
Short summary
A large number of simulations are conducted to investigate how different physical parameterization schemes impact surface wind simulations under stable weather conditions over the coastal regions of North China using the Weather Research and Forecasting model with a horizontal grid spacing of 0.5 km. Results indicate that the simulated wind speed is most sensitive to the planetary boundary layer schemes, followed by short-wave/long-wave radiation schemes and microphysics schemes.
Xingying Huang, Andrew Gettelman, William C. Skamarock, Peter Hjort Lauritzen, Miles Curry, Adam Herrington, John T. Truesdale, and Michael Duda
Geosci. Model Dev., 15, 8135–8151, https://doi.org/10.5194/gmd-15-8135-2022, https://doi.org/10.5194/gmd-15-8135-2022, 2022
Short summary
Short summary
We focus on the recent development of a state-of-the-art storm-resolving global climate model and investigate how this next-generation model performs for precipitation prediction over the western USA. Results show realistic representations of precipitation with significantly enhanced snowpack over complex terrains. The model evaluation advances the unified modeling of large-scale forcing constraints and realistic fine-scale features to advance multi-scale climate predictions and changes.
Marina Martínez Montero, Michel Crucifix, Victor Couplet, Nuria Brede, and Nicola Botta
Geosci. Model Dev., 15, 8059–8084, https://doi.org/10.5194/gmd-15-8059-2022, https://doi.org/10.5194/gmd-15-8059-2022, 2022
Short summary
Short summary
We present SURFER, a lightweight model that links CO2 emissions and geoengineering to ocean acidification and sea level rise from glaciers, ocean thermal expansion and Greenland and Antarctic ice sheets. The ice sheet module adequately describes the tipping points of both Greenland and Antarctica. SURFER is understandable, fast, accurate up to several thousands of years, capable of emulating results obtained by state of the art models and well suited for policy analyses.
Haopeng Fan, Siran Li, Zhongmiao Sun, Guorui Xiao, Xinxing Li, and Xiaogang Liu
EGUsphere, https://doi.org/10.5194/egusphere-2022-898, https://doi.org/10.5194/egusphere-2022-898, 2022
Short summary
Short summary
The bias of traditional tropospheric zenith hydrostatic delay (ZHD) model is usually thought negligible, yet it still reaches 10 mm sometimes and would lead to mm-level position errors for space geodetic observations. Therefore, We analyzed the bias’ characteristics and present a grid model to correct the traditional ZHD formula. When we verified the efficiency based on data from ECMWF (European Centre for Medium-Range Weather Forecasts), it turned out that ZHD biases were rectified by ~50 %.
Francisco José Cuesta-Valero, Hugo Beltrami, Stephan Gruber, Almudena García-García, and J. Fidel González-Rouco
Geosci. Model Dev., 15, 7913–7932, https://doi.org/10.5194/gmd-15-7913-2022, https://doi.org/10.5194/gmd-15-7913-2022, 2022
Short summary
Short summary
Inversions of subsurface temperature profiles provide past long-term estimates of ground surface temperature histories and ground heat flux histories at timescales of decades to millennia. Theses estimates complement high-frequency proxy temperature reconstructions and are the basis for studying continental heat storage. We develop and release a new bootstrap method to derive meaningful confidence intervals for the average surface temperature and heat flux histories from any number of profiles.
Yilin Fang, L. Ruby Leung, Charles D. Koven, Gautam Bisht, Matteo Detto, Yanyan Cheng, Nate McDowell, Helene Muller-Landau, S. Joseph Wright, and Jeffrey Q. Chambers
Geosci. Model Dev., 15, 7879–7901, https://doi.org/10.5194/gmd-15-7879-2022, https://doi.org/10.5194/gmd-15-7879-2022, 2022
Short summary
Short summary
We develop a model that integrates an Earth system model with a three-dimensional hydrology model to explicitly resolve hillslope topography and water flow underneath the land surface to understand how local-scale hydrologic processes modulate vegetation along water availability gradients. Our coupled model can be used to improve the understanding of the diverse impact of local heterogeneity and water flux on nutrient availability and plant communities.
Wentao Zhang, Xiangjun Shi, and Chunsong Lu
Geosci. Model Dev., 15, 7751–7766, https://doi.org/10.5194/gmd-15-7751-2022, https://doi.org/10.5194/gmd-15-7751-2022, 2022
Short summary
Short summary
The two-moment bulk cloud microphysics scheme used in CAM6 was modified to consider the impacts of the ice-crystal size distribution shape parameter (μi). After that, how the μi impacts cloud microphysical processes and then climate simulations is clearly illustrated by offline tests and CAM6 model experiments. Our results and findings are useful for the further development of μi-related parameterizations.
Yona Silvy, Clément Rousset, Eric Guilyardi, Jean-Baptiste Sallée, Juliette Mignot, Christian Ethé, and Gurvan Madec
Geosci. Model Dev., 15, 7683–7713, https://doi.org/10.5194/gmd-15-7683-2022, https://doi.org/10.5194/gmd-15-7683-2022, 2022
Short summary
Short summary
A modeling framework is introduced to understand and decompose the mechanisms causing the ocean temperature, salinity and circulation to change since the pre-industrial period and into 21st century scenarios of global warming. This framework aims to look at the response to changes in the winds and in heat and freshwater exchanges at the ocean interface in global climate models, throughout the 1850–2100 period, to unravel their individual effects on the changing physical structure of the ocean.
Aiko Voigt, Petra Schwer, Noam von Rotberg, and Nicole Knopf
Geosci. Model Dev., 15, 7489–7504, https://doi.org/10.5194/gmd-15-7489-2022, https://doi.org/10.5194/gmd-15-7489-2022, 2022
Short summary
Short summary
In climate science, it is helpful to identify coherent objects, for example, those formed by clouds. However, many models now use unstructured grids, which makes it harder to identify coherent objects. We present a new method that solves this problem by moving model data from an unstructured triangular grid to a structured cubical grid. We implement the method in an open-source Python package and show that the method is ready to be applied to climate model data.
Jérémy Bernard, Erwan Bocher, Elisabeth Le Saux Wiederhold, François Leconte, and Valéry Masson
Geosci. Model Dev., 15, 7505–7532, https://doi.org/10.5194/gmd-15-7505-2022, https://doi.org/10.5194/gmd-15-7505-2022, 2022
Short summary
Short summary
OpenStreetMap is a collaborative project aimed at creaing a free dataset containing topographical information. Since these data are available worldwide, they can be used as standard data for geoscience studies. However, most buildings miss the height information that constitutes key data for numerous fields (urban climate, noise propagation, air pollution). In this work, the building height is estimated using statistical modeling using indicators that characterize the building's environment.
Sergey Kravtsov, Ilijana Mastilovic, Andrew McC. Hogg, William K. Dewar, and Jeffrey R. Blundell
Geosci. Model Dev., 15, 7449–7469, https://doi.org/10.5194/gmd-15-7449-2022, https://doi.org/10.5194/gmd-15-7449-2022, 2022
Short summary
Short summary
Climate is a complex system whose behavior is shaped by multitudes of processes operating on widely different spatial scales and timescales. In hierarchical modeling, one goes back and forth between highly idealized process models and state-of-the-art models coupling the entire range of climate subsystems to identify specific phenomena and understand their dynamics. The present contribution highlights an intermediate climate model focussing on midlatitude ocean–atmosphere interactions.
Edmund P. Meredith, Uwe Ulbrich, and Henning W. Rust
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-202, https://doi.org/10.5194/gmd-2022-202, 2022
Revised manuscript accepted for GMD
Short summary
Short summary
Cell tracking algorithms allow the properties of a convective cell to be studied across its lifetime and, in particular, how these respond to climate change. We investigated whether the design of the algorithm can affect the magnitude of the climate-change signal. The algorithm’s criteria for identifying a cell were found to have a strong impact on the warming response. The sensitivity of the warming response to different algorithm settings and cell types should thus be fully explored.
Ingo Wohltmann, Daniel Kreyling, and Ralph Lehmann
Geosci. Model Dev., 15, 7243–7255, https://doi.org/10.5194/gmd-15-7243-2022, https://doi.org/10.5194/gmd-15-7243-2022, 2022
Short summary
Short summary
The study evaluates the performance of the Data Assimilation Research Testbed (DART), equipped with the recently added forward operator Radiative Transfer for TOVS (RTTOV), in assimilating FY-4A visible images into the Weather Research and Forecasting (WRF) model. The ability of the WRF-DART/RTTOV system to improve the forecasting skills for a tropical storm over East Asia and the Western Pacific is demonstrated in an Observing System Simulation Experiment framework.
Juan Ruiz, Pierre Ailliot, Thi Tuyet Trang Chau, Pierre Le Bras, Valérie Monbet, Florian Sévellec, and Pierre Tandeo
Geosci. Model Dev., 15, 7203–7220, https://doi.org/10.5194/gmd-15-7203-2022, https://doi.org/10.5194/gmd-15-7203-2022, 2022
Short summary
Short summary
We present a new approach to validate numerical simulations of the current climate. The method can take advantage of existing climate simulations produced by different centers combining an analog forecasting approach with data assimilation to quantify how well a particular model reproduces a sequence of observed values. The method can be applied with different observations types and is implemented locally in space and time significantly reducing the associated computational cost.
Chahan M. Kropf, Alessio Ciullo, Laura Otth, Simona Meiler, Arun Rana, Emanuel Schmid, Jamie W. McCaughey, and David N. Bresch
Geosci. Model Dev., 15, 7177–7201, https://doi.org/10.5194/gmd-15-7177-2022, https://doi.org/10.5194/gmd-15-7177-2022, 2022
Short summary
Short summary
Mathematical models are approximations, and modellers need to understand and ideally quantify the arising uncertainties. Here, we describe and showcase the first, simple-to-use, uncertainty and sensitivity analysis module of the open-source and open-access climate-risk modelling platform CLIMADA. This may help to enhance transparency and intercomparison of studies among climate-risk modellers, help focus future research, and lead to better-informed decisions on climate adaptation.
Günther Zängl, Daniel Reinert, and Florian Prill
Geosci. Model Dev., 15, 7153–7176, https://doi.org/10.5194/gmd-15-7153-2022, https://doi.org/10.5194/gmd-15-7153-2022, 2022
Short summary
Short summary
This article describes the implementation of grid refinement in the ICOsahedral Nonhydrostatic (ICON) model, which has been jointly developed at several German institutions and constitutes a unified modeling system for global and regional numerical weather prediction and climate applications. The grid refinement allows using a higher resolution in regional domains and transferring the information back to the global domain by means of a feedback mechanism.
Suzanne Robinson, Ruza Ivanovic, Lauren Gregoire, Julia Tindall, Tina van de Flierdt, Yves Plancherel, Frerk Pöppelmeier, Kazuyo Tachikawa, and Paul Valdes
EGUsphere, https://doi.org/10.5194/egusphere-2022-606, https://doi.org/10.5194/egusphere-2022-606, 2022
Short summary
Short summary
We present the implementation of neodymium (Nd) isotopes into the ocean model of FAMOUS (ND v1.0). Nd fluxes from seafloor sediment alongside incorporation of Nd onto sinking particles represent the major global sources and sinks. However, model-data mismatch in the North Pacific and northern North Atlantic suggest that certain reactive components of the sediment interact the most with seawater. Our results are important for interpreting Nd isotopes in terms of ocean circulation.
Sébastien Gardoll and Olivier Boucher
Geosci. Model Dev., 15, 7051–7073, https://doi.org/10.5194/gmd-15-7051-2022, https://doi.org/10.5194/gmd-15-7051-2022, 2022
Short summary
Short summary
Tropical cyclones (TCs) are one of the most devastating natural disasters, which justifies monitoring and prediction in the context of a changing climate. In this study, we have adapted and tested a convolutional neural network (CNN) for the classification of reanalysis outputs (ERA5 and MERRA-2 labeled by HURDAT2) according to the presence or absence of TCs. We tested the impact of interpolation and of "mixing and matching" the training and test sets on the performance of the CNN.
Marco A. Giorgetta, William Sawyer, Xavier Lapillonne, Panagiotis Adamidis, Dmitry Alexeev, Valentin Clément, Remo Dietlicher, Jan Frederik Engels, Monika Esch, Henning Franke, Claudia Frauen, Walter M. Hannah, Benjamin R. Hillman, Luis Kornblueh, Philippe Marti, Matthew R. Norman, Robert Pincus, Sebastian Rast, Daniel Reinert, Reiner Schnur, Uwe Schulzweida, and Bjorn Stevens
Geosci. Model Dev., 15, 6985–7016, https://doi.org/10.5194/gmd-15-6985-2022, https://doi.org/10.5194/gmd-15-6985-2022, 2022
Short summary
Short summary
This work presents a first version of the ICON atmosphere model that works not only on CPUs, but also on GPUs. This GPU-enabled ICON version is benchmarked on two GPU machines and a CPU machine. While the weak scaling is very good on CPUs and GPUs, the strong scaling is poor on GPUs. But the high performance of GPU machines allowed for first simulations of a short period of the quasi-biennial oscillation at very high resolution with explicit convection and gravity wave forcing.
Shixuan Zhang, Kai Zhang, Hui Wan, and Jian Sun
Geosci. Model Dev., 15, 6787–6816, https://doi.org/10.5194/gmd-15-6787-2022, https://doi.org/10.5194/gmd-15-6787-2022, 2022
Short summary
Short summary
This study investigates the nudging implementation in the EAMv1 model. We find that (1) revising the sequence of calculations and using higher-frequency constraining data to improve the performance of a simulation nudged to EAMv1’s own meteorology, (2) using the relocated nudging tendency and 3-hourly ERA5 reanalysis to obtain a better agreement between nudged simulations and observations, and (3) using wind-only nudging are recommended for the estimates of global mean aerosol effects.
Christian R. Steger, Benjamin Steger, and Christoph Schär
Geosci. Model Dev., 15, 6817–6840, https://doi.org/10.5194/gmd-15-6817-2022, https://doi.org/10.5194/gmd-15-6817-2022, 2022
Short summary
Short summary
Terrain horizon and sky view factor are crucial quantities for many geoscientific applications; e.g. they are used to account for effects of terrain on surface radiation in climate and land surface models. Because typical terrain horizon algorithms are inefficient for high-resolution (< 30 m) elevation data, we developed a new algorithm based on a ray-tracing library. A comparison with two conventional methods revealed both its high performance and its accuracy for complex terrain.
David Martín Belda, Peter Anthoni, David Wårlind, Stefan Olin, Guy Schurgers, Jing Tang, Benjamin Smith, and Almut Arneth
Geosci. Model Dev., 15, 6709–6745, https://doi.org/10.5194/gmd-15-6709-2022, https://doi.org/10.5194/gmd-15-6709-2022, 2022
Short summary
Short summary
We present a number of augmentations to the ecosystem model LPJ-GUESS, which will allow us to use it in studies of the interactions between the land biosphere and the climate. The new module enables calculation of fluxes of energy and water into the atmosphere that are consistent with the modelled vegetation processes. The modelled fluxes are in fair agreement with observations across 21 sites from the FLUXNET network.
Jorge Baño-Medina, Rodrigo Manzanas, Ezequiel Cimadevilla, Jesús Fernández, Jose González-Abad, Antonio S. Cofiño, and José Manuel Gutiérrez
Geosci. Model Dev., 15, 6747–6758, https://doi.org/10.5194/gmd-15-6747-2022, https://doi.org/10.5194/gmd-15-6747-2022, 2022
Short summary
Short summary
Deep neural networks are used to produce downscaled regional climate change projections over Europe for temperature and precipitation for the first time. The resulting dataset, DeepESD, is analyzed against state-of-the-art downscaling methodologies, reproducing more accurately the observed climate in the historical period and showing plausible future climate change signals with low computational requirements.
Stella Bourdin, Sébastien Fromang, William Dulac, Julien Cattiaux, and Fabrice Chauvin
Geosci. Model Dev., 15, 6759–6786, https://doi.org/10.5194/gmd-15-6759-2022, https://doi.org/10.5194/gmd-15-6759-2022, 2022
Short summary
Short summary
When studying tropical cyclones in a large dataset, one needs objective and automatic procedures to detect their specific pattern. Applying four different such algorithms to a reconstruction of the climate, we show that the choice of the algorithm is crucial to the climatology obtained. Mainly, the algorithms differ in their sensitivity to weak storms so that they provide different frequencies and durations. We review the different options to consider for the choice of the tracking methodology.
Stanley G. Benjamin, Tatiana G. Smirnova, Eric P. James, Eric J. Anderson, Ayumi Fujisaki-Manome, John G. W. Kelley, Greg E. Mann, Andrew D. Gronewold, Philip Chu, and Sean G. T. Kelley
Geosci. Model Dev., 15, 6659–6676, https://doi.org/10.5194/gmd-15-6659-2022, https://doi.org/10.5194/gmd-15-6659-2022, 2022
Short summary
Short summary
Application of 1-D lake models coupled within earth-system prediction models will improve accuracy but requires accurate initialization of lake temperatures. Here, we describe a lake initialization method by cycling within a weather prediction model to constrain lake temperature evolution. We compared these lake temperature values with other estimates and found much reduced errors (down to 1-2 K). The lake cycling initialization is now applied to two operational US NOAA weather models.
Nicholas K.-R. Kevlahan and Florian Lemarié
Geosci. Model Dev., 15, 6521–6539, https://doi.org/10.5194/gmd-15-6521-2022, https://doi.org/10.5194/gmd-15-6521-2022, 2022
Short summary
Short summary
WAVETRISK-2.1 is an innovative climate model for the world's oceans. It uses state-of-the-art techniques to change the model's resolution locally, from O(100 km) to O(5 km), as the ocean changes. This dynamic adaptivity makes optimal use of available supercomputer resources, and allows two-dimensional global scales and three-dimensional submesoscales to be captured in the same simulation. WAVETRISK-2.1 is designed to be coupled its companion global atmosphere model, WAVETRISK-1.x.
Meng Huang, Po-Lun Ma, Nathaniel W. Chaney, Dalei Hao, Gautam Bisht, Megan D. Fowler, Vincent E. Larson, and L. Ruby Leung
Geosci. Model Dev., 15, 6371–6384, https://doi.org/10.5194/gmd-15-6371-2022, https://doi.org/10.5194/gmd-15-6371-2022, 2022
Short summary
Short summary
The land surface in one grid cell may be diverse in character. This study uses an explicit way to account for that subgrid diversity in a state-of-the-art Earth system model (ESM) and explores its implications for the overlying atmosphere. We find that the shallow clouds are increased significantly with the land surface diversity. Our work highlights the importance of accurately representing the land surface and its interaction with the atmosphere in next-generation ESMs.
Jan Streffing, Dmitry Sidorenko, Tido Semmler, Lorenzo Zampieri, Patrick Scholz, Miguel Andrés-Martínez, Nikolay Koldunov, Thomas Rackow, Joakim Kjellsson, Helge Goessling, Marylou Athanase, Qiang Wang, Jan Hegewald, Dmitry V. Sein, Longjiang Mu, Uwe Fladrich, Dirk Barbi, Paul Gierz, Sergey Danilov, Stephan Juricke, Gerrit Lohmann, and Thomas Jung
Geosci. Model Dev., 15, 6399–6427, https://doi.org/10.5194/gmd-15-6399-2022, https://doi.org/10.5194/gmd-15-6399-2022, 2022
Short summary
Short summary
We developed a new atmosphere–ocean coupled climate model, AWI-CM3. Our model is significantly more computationally efficient than its predecessors AWI-CM1 and AWI-CM2. We show that the model, although cheaper to run, provides results of similar quality when modeling the historic period from 1850 to 2014. We identify the remaining weaknesses to outline future work. Finally we preview an improved simulation where the reduction in computational cost has to be invested in higher model resolution.
Stephen G. Yeager, Nan Rosenbloom, Anne A. Glanville, Xian Wu, Isla Simpson, Hui Li, Maria J. Molina, Kristen Krumhardt, Samuel Mogen, Keith Lindsay, Danica Lombardozzi, Will Wieder, Who M. Kim, Jadwiga H. Richter, Matthew Long, Gokhan Danabasoglu, David Bailey, Marika Holland, Nicole Lovenduski, Warren G. Strand, and Teagan King
Geosci. Model Dev., 15, 6451–6493, https://doi.org/10.5194/gmd-15-6451-2022, https://doi.org/10.5194/gmd-15-6451-2022, 2022
Short summary
Short summary
The Earth system changes over a range of time and space scales, and some of these changes are predictable in advance. Short-term weather forecasts are most familiar, but recent work has shown that it is possible to generate useful predictions several seasons or even a decade in advance. This study focuses on predictions over intermediate timescales (up to 24 months in advance) and shows that there is promising potential to forecast a variety of changes in the natural environment.
Mauro Morichetti, Sasha Madronich, Giorgio Passerini, Umberto Rizza, Enrico Mancinelli, Simone Virgili, and Mary Barth
Geosci. Model Dev., 15, 6311–6339, https://doi.org/10.5194/gmd-15-6311-2022, https://doi.org/10.5194/gmd-15-6311-2022, 2022
Short summary
Short summary
In the present study, we explore the effect of making simple changes to the existing WRF-Chem MEGAN v2.04 emissions to provide MEGAN updates that can be used independently of the land surface model chosen. The changes made to the MEGAN algorithm implemented in WRF-Chem were the following: (i) update of the emission activity factors, (ii) update of emission factor values for each plant functional type (PFT), and (iii) the assignment of the emission factor by PFT to isoprene.
Walter Hannah, Kyle Pressel, Mikhail Ovchinnikov, and Gregory Elsaesser
Geosci. Model Dev., 15, 6243–6257, https://doi.org/10.5194/gmd-15-6243-2022, https://doi.org/10.5194/gmd-15-6243-2022, 2022
Short summary
Short summary
An unphysical checkerboard signal is identified in two configurations of the atmospheric component of E3SM. The signal is very persistent and visible after averaging years of data. The signal is very difficult to study because it is often mixed with realistic weather. A method is presented to detect checkerboard patterns and compare the model with satellite observations. The causes of the signal are identified, and a solution for one configuration is discussed.
Fa Li, Qing Zhu, William Riley, Lei Zhao, Li Xu, Kunxiaojia Yuan, Min Chen, Huayi Wu, Zhipeng Gui, Jianya Gong, and James Randerson
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-195, https://doi.org/10.5194/gmd-2022-195, 2022
Revised manuscript accepted for GMD
Short summary
Short summary
In this work, we developed an interpretable machine learning model to predict sub-seasonal and near future wildfire burned area over African and South American regions. We found strong time-lagged controls (up to 6–8 month) from local climate wetness on burned areas. A skillful use of such time-lagged controls in machine learning model result in high accurate predictions of wildfire burned area, also will help develop relevant early warming and management system for tropical wildfire.
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.
Blaschke, T.: Object based image analysis for remote sensing, ISPRS J. Photogramm. Remote Sens., 65, 2–16, 2010.
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.
Conrad, O.: SAGA – Entwurf, Funktionsumfang und Anwendung eines Systems für Automatisierte Geowissenschaftliche Analysen, Dissertation, Faculties Natural Sciences, Mathematics and Informatics, Faculty of Geosciences and Geography, 221 pp., 2007.
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.
ESRI: The ArcGIS Platform, available at: http://www.esri.com/software/arcgis, last access: 05 June 2015.
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.
Free Software Foundation: Licenses, available at: http://www.gnu.org/licenses/licenses.html, last access: 04 June 2015.
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.
Grabs, T. J., Jencso, K. G., McGlynn, B. L., and Seibert, J.: Calculating terrain indices along streams: A new method for separating stream sides, Water Resour. Res., 46, W12536, https://doi.org/10.1029/2010WR009296, 2010.
Günther, A.: SLOPEMAP: programs for automated mapping of geometrical and kinematical properties of hard rock hill slopes, Comput. Geosci., 29, 865–875, 2003.
Günther, A., Carstensen, A., and Pohl, W.: Automated sliding susceptibility mapping of rock slopes, Nat. Hazards Earth Syst. Sci., 4, 95–102, https://doi.org/10.5194/nhess-4-95-2004, 2004.
Haas, F.: Fluviale Hangprozesse in alpinen Einzugsgebieten der nördlichen Kalkalpen: Quantifizierung und Modellierungsansätze, Eichstätter Geographische Arbeiten, Profil-Verlag, München, Wien, 2008.
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.
Haas, F., Heckmann, T., Wichmann, V., and Becht, M.: Runout analysis of a large rockfall in the Dolomites/Italian Alps using LIDAR derived particle sizes and shapes, Earth Surf. Process. Landf., 37, 1444–1455, https://doi.org/10.1002/esp.3295, 2012.
Hartemink, A. E., McBratney, A. B., and de Lourdes Mendoncca-Santos, M.: Digital soil mapping with limited data, Springer, 2008.
Heckmann, T.: Untersuchungen zum Sedimenttransport durch Grundlawinen in zwei Einzugsgebieten der Nördlichen Kalkalpen: Quantifizierung, Analyse und Ansätze zur Modellierung der geomorphologischen Aktivität, Eichstätter Geographische Arbeiten, Profil-Verlag, München, 2006.
Heckmann, T. and Becht, M.: Statistical disposition modelling of mass movements, in: SAGA – Analysis and Modelling Applications, Göttinger Geographische Abhandlungen, Göttingen, 61–74, 2006.
Heckmann, T., Wichmann, V., and Becht, M.: Sediment transport by avalanches in the Bavarian Alps revisited – a perspective on modelling, Z. Geomorphol. Suppl. Issues, 138, 11–25, 2005.
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.
Heinrich, R. and Conrad, O.: Diffusion, Flow and Concentration Gradient Simulation with SAGA GIS using Cellular Automata Methods, in: SAGA – Seconds Out, Hamburger Beiträge Zur Physischen Geographie Und Landschaftsökologie, Univ. Hamburg, Inst. für Geographie, Hamburg, 59–70, 2008.
Hengl, T. and Reuter, H. I.: Geomorphometry: Concepts, Software, Applications, Newnes, 2009.
Herzschuh, U., Birks, H. J. B., Mischke, S., Zhang, C., and Böhner, J.: A modern pollen–climate calibration set based on lake sediments from the Tibetan Plateau and its application to a Late Quaternary pollen record from the Qilian Mountains, J. Biogeogr., 37, 752–766, 2010.
Herzschuh, U., Ni, J., Birks, H. J. B., and Böhner, J.: Driving forces of mid-Holocene vegetation shifts on the upper Tibetan Plateau, with emphasis on changes in atmospheric CO2 concentrations, Quaternary Sci. Rev., 30, 1907–1917, 2011.
Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G., and Jarvis, A.: Very high resolution interpolated climate surfaces for global land areas, Int. J. Climatol., 25, 1965–1978, 2005.
Jansen, P.: Flächenhafte Berechnung der Hangkinematik und Stabilitätsmodellierung im Bereich des Göttinger Waldes Unpublished MSc thesis, University of Hamburg, Faculty of Earth Sciences, Institute for Geology, Hamburg, 2014.
Jürgens, N., Oldeland, J., Hachfeld, B., Erb, E., and Schultz, C.: Ecology and spatial patterns of large-scale vegetation units within the central Namib Desert, J. Arid Environ., 93, 59–79, https://doi.org/10.1016/j.jaridenv.2012.09.009, 2013.
Kamlun, K. U., Goh, M. H., Teo, S. P., and Phua, M. H.: Monitoring of Deforestation and Fragmentation in Sarawak, Malaysia between 1990 and 2009 using Landsat and SPOT Images, J. Forest Sci., 28, 152–157, 2012.
Kaye, S.: Finding the site of Boudica's last battle: Roman logistics empowered the sword, bandaarcgeophysics, available at: http://www.bandaarcgeophysics.co.uk/arch/boudica logistics.pdf (last access: 03 December 2014), 2013.
Kessler, M., Böhner, J., and Kluge, J.: Modelling tree height to assess climatic conditions at tree lines in the Bolivian Andes, Ecol. Model., 207, 223–233, https://doi.org/10.1016/j.ecolmodel.2007.05.001, 2007.
Kidd, D. and Viscarra Rossel, R.: ACLEP-Tasmanian Digital Soil Mapping Project – a component of the Wealth from Water Land Suitability Project, available at: http://www.clw.csiro.au/aclep/documents/TAS_ACLEP_DSM_Project_Report.pdf (last access: 28 June 2015), 2011.
Klinge, M., Böhner, J., and Lehmkuhl, F.: Climate Patterns, Snow- and Timberline in the Altai Mountains, Central Asia, Erdkunde, 57, 296–308, 2003.
Klinge, M., Böhner, J., and Erasmi, S.: Modeling forest lines and forest distribution patterns with remote-sensing data in a mountainous region of semiarid central Asia, Biogeosciences, 12, 2893–2905, https://doi.org/10.5194/bg-12-2893-2015, 2015.
Köthe, R. and Bock, M.: Preprocessing of Digital Elevation Models–derived from Laser Scanning and Radar Interferometry–for Terrain Analysis in Geosciences, Proc. Geomorph., 155–161, 2009.
Köthe, R., Gehrt, E., and Böhner, J.: Automatische Reliefanalyse für geowissenschaftliche Kartierungen, Arbeitshefte Boden, 1, 31–37, 1996.
Köthe, U.: Generische Programmierung für die Bildverarbeitung, BoD–Books on Demand, 2000.
Kühn, J., Brenning, A., Wehrhan, M., Koszinski, S., and Sommer, M.: Interpretation of electrical conductivity patterns by soil properties and geological maps for precision agriculture, Precis. Agric., 10, 490–507, 2009.
Lado, L. R., Hengl, T., and Reuter, H. I.: Heavy metals in European soils: a geostatistical analysis of the FOREGS Geochemical database, Geoderma, 148, 189–199, 2008.
Lal, R. and Stewart, B. A.: Soil Management of Smallholder Agriculture, CRC Press, 2014.
Lehmkuhl, F., Böhner, J., and Stauch, G.: Geomorphologische Formungs-und Prozessregionen in Zentralasien, Petermanns Geogr. Mitteilungen, 147, 6–13, 2003.
Lehrling, M.: Klimaentwicklung in Alaska: Eine GIS-gestützte Erfassung und Analyse der raumzeitlichen Entwicklung von Temperatur und Niederschlag, ibidem-Verlag, 2006.
Leopold, M., Gannaway, E., Völkel, J., Haas, F., Becht, M., Heckmann, T., Westphal, M., and Zimmer, G.: Geophysical prospection of a bronze foundry on the southern slope of the acropolis at athens, Greece, Archaeol. Prospect., 18, 27–41, https://doi.org/10.1002/arp.402, 2011.
Liersch, S. and Volk, M.: A rainfall-runoff database to support flood risk assessment, in: Proceedings of the iEMSs Fourth Biennial Meeting, Presented at the International Congress on Environmental Modelling and Software, Barcelona, Catalonia, 494–502, 2008.
Mantovani, F., Gracia, F. J., de Cosmo, P. D., and Suma, A.: A new approach to landslide geomorphological mapping using the Open Source software in the Olvera area (Cadiz, Spain), Landslides, 7, 69–74, https://doi.org/10.1007/s10346-009-0181-4, 2010.
Marini, L., Scotton, M., Klimek, S., Isselstein, J., and Pecile, A.: Effects of local factors on plant species richness and composition of Alpine meadows, Agric. Ecosyst. Environ., 119, 281–288, 2007.
Marini, L., Fontana, P., Klimek, S., Battisti, A., and Gaston, K. J.: Impact of farm size and topography on plant and insect diversity of managed grasslands in the Alps, Biol. Conserv., 142, 394–403, https://doi.org/10.1016/j.biocon.2008.10.034, 2009.
McBratney, A. B., Mendoncca Santos, M. L., and Minasny, B.: On digital soil mapping, Geoderma, 117, 3–52, 2003.
Miehe, G., Miehe, S., Böhner, J., Kaiser, K., Hensen, I., Madsen, D., Liu, J. Q., and Opgenoorth, L.: How old is the human footprint in the world's largest alpine ecosystem? A review of multiproxy records from the Tibetan Plateau from the ecologists' viewpoint, Quaternary Sci. Rev., 86, 190–209, 2014.
Milevski, I.: Estimation of soil erosion risk in the upper part of bregalnica watershed-republic of macedonia, based on digital elevation model and satellite imagery, Proceedings from the 5th international conference on geographic information systems, Fatih University, Istanbul, 2008.
Milevski, I., Dragicevic, S., and Kostadinov, S.: Digital elevation model and satellite images an assessment of soil erosion potential in the Pcinja catchment, Glas. Srp. Geogr. Drustva, 87, 11–20, https://doi.org/10.2298/GSGD0702011M, 2007.
Morche, D., Haas, F., Baewert, H., Heckmann, T., Schmidt, K.-H., and Becht, M.: Sediment transport in the proglacial Fagge River (Kaunertal/Austria), Eros. Sediment Yields Chang. Environ, IAHS Press Wallingford IAHS Publ., 356, 72–81, 2012
Muenchow, J., Brenning, A., and Richter, M.: Geomorphic process rates of landslides along a humidity gradient in the tropical Andes, Geomorphology, 139–140, 271–284, 2012.
National Research Council (Ed.): Basic Research Opportunities in Earth Science. Washington, D.C., National Academy Press, 154 pp., 2001.
Neteler, M., Bowman, M. H., Landa, M., and Metz, M.: GRASS GIS: A multi-purpose open source GIS, Environ. Model. Softw., 31, 124–130, https://doi.org/10.1016/j.envsoft.2011.11.014, 2012.
Nothdurft, A., Wolf, T., Ringeler, A., Böhner, J., and Saborowski, J.: Spatio-temporal prediction of site index based on forest inventories and climate change scenarios, Forest Ecol. Manage., 279, 97–111, 2012.
Olaya, V. and Conrad, O.: Chapter 12 Geomorphometry in SAGA, in: Developments in Soil Science, edited by: Hengl, T. and Reuter, H. I., Elsevier, 293–308, 2009.
Patriche, C. V., P\^irnuau, R. G., Rocsca, B., and Stoica, D. L.: Assessment of Soil Erosion and its Impact on Humus Spatial and Temporal Dynamics, Bull. Univ. Agric. Sci. Vet. Med. Cluj-Napoca Agric., 69, 185–194, 2012.
Peters-Walker, D., Krause, K.-H., Strellen, T., Kleindienst, H., Petrini-Monteferri, F., and Wichmann, V.: Modellierung von Abfluss und Geschiebetransport in alpinen Wildbacheinzugsgebieten mit SimAlp/HQsim, Wildbach-Lawinenverbau, 76, 154–166, 2012.
Petrini-Montferri, F., Wichmann, V., Georges, C., Mantovani, D., and Stötter, J.: Erweiterung der GIS Software SAGA zur Verarbeitung von Laserscanning-Daten der Autonomen Provinz Bozen – Südtirol, in: Angewandte Geoinformatik, edited by: Strobl., J., Blaschke, T., and Griesebner, G., Beiträge zum 21. AGIT-Symposium, Salzburg, 618–623, 2009.
Phua, M.-H., Conrad, O., Kamlun, K. U., Fischer, M., and Böhner, J.: Multitemporal Fragmentation Analysis of Peat Swamp Forest in the Klias Peninsula, Sabah, Malaysia using GIS and Remote Sensing Techniques, in: SAGA – Seconds Out, Hamburger Beiträge Zur Physischen Geographie Und Landschaftsökologie, Univ. Hamburg, Inst. für Geographie, Hamburg, 81–90, 2008.
Prouza, Z., Beckova, V., Cespirova, I., Helebrant, J., Hulka, J., Kuca, P., Michalek, V., Rulik, P., Skrkal, J., and Hovorka, J.: Field Tests Using Radioactive Matter, Radiat. Prot. Dosimetry, ncp299, https://doi.org/10.1093/rpd/ncp299, 2010.
QGIS Development Team: QGIS Geographic Information System. Open Source Geospatial Foundation Project, 2014.
Rieg, L., Wichmann, V., Rutzinger, M., Sailer, R., Geist, T., and Stötter, J.: Data infrastructure for multitemporal airborne LiDAR point cloud analysis – Examples from physical geography in high mountain environments, Comput. Environ. Urban Syst., 45, 137–146, 2014.
Roecker, S. M., Howell, D. W., Haydu-Houdeshell, C. A., and Blinn, C.: A Qualitative Comparison of Conventional Soil Survey and Digital Soil Mapping Approaches, in: Digital Soil Mapping, Progress in Soil Science, edited by: Boettinger, D. J. L., Howell, D. W., Moore, A. C., Hartemink, P. D. A. E., and Kienast-Brown, S., Springer Netherlands, 369–384, 2010.
Russ, A. and Riek, W.: Methode zur Ableitung des Grundwasserflurabstandes aus Karten der forstlichen Standortskartierung und digitalen Geländemodellen in Brandenburg, in: Waldökologie, Landschaftsforschung und Naturschutz, 12, 85–92, 2011.
Sass, O., Haas, F., Schimmer, C., Heel, M., Bremer, M., Stöger, F., and Wetzel, K.-F.: Impact of Forest Fires on Geomorphic Processes in the Tyrolean Limestone Alps, Geogr. Ann. Ser. Phys. Geogr., 94, 117–133, https://doi.org/10.1111/j.1468-0459.2012.00452.x, 2012.
Schauppenlehner, T.: Geostatistische Analysen zur Integration von Geländemodellen und Bodenschätzungsdaten für verbesserte digitale Bodenkarten am Beispiel einer alluvialen Landschaft (Dissertation), University of natural resources and life sciences, 2008.
Setiawan, M. A.: Integrated Soil Erosion Risk Management in the upper Serayu Watershed, Wonosobo District, Central Java Province, Indonesia, Faculty of Geo- and Atmospheric Sciences of the Universität Innsbruck, Innsbruck, 2012.
Smart, J., Hock, K., and Csomor, S.: Cross-platform GUI programming with wxWidgets, Prentice Hall Professional, 2005.
Soria-Auza, R. W.: Diversität und Biogeographie der Farne und Vögel Boliviens, Niche Modellierung GIS Applicationen, 2010.
Soria-Auza, R. W., Kessler, M., Bach, K., Barajas-Barbosa, P. M., Lehnert, M., Herzog, S. K., and Böhner, J.: Impact of the quality of climate models for modelling species occurrences in countries with poor climatic documentation: a case study from Bolivia, Ecol. Model., 221, 1221–1229, 2010.
Steiniger, S. and Bocher, E.: An overview on current free and open source desktop GIS developments, Int. J. Geogr. Inf. Sci., 23, 1345–1370, https://doi.org/10.1080/13658810802634956, 2009.
Stötter, J. and Sailer, R.: Permafrost in Südtirol, Innsbrucker Geographische Studien, 2012.
Stroustrup, B.: Programming: principles and practice using C++, Pearson Education, 2014.
Sun, Z.: MicroCity – A Spatial Analysis and Simulation Framework, available at: http://microcity.sourceforge.net/, 2015.
Vanselow, K. and Samimi, C.: GIS und statistische Modelle im Weidemanagement: Ein Beispiel anhand der Hochgebirgsweiden im Ostpamir (Tadschikistan), Angewandte Geoinformatik, 23, 625–634, 2011.
Varga, K., Becht, M., and Damm, B.: Ansätze der GIS-gestützten räumlichen Modellierung von Rutschgefahren in Buntsandsteingebieten Nordhessens und Südniedersachsens (BRD), in: Angewandte Geoinformatik, edited by: Strobl, J., Blaschke, T., and Griesebner, G., Beiträge zum 21. AGIT-Symposium, Salzburg, 679–684, 2006.
Wang, Y., Herzschuh, U., Shumilovskikh, L. S., Mischke, S., Birks, H. J. B., Wischnewski, J., Böhner, J., Schlütz, F., Lehmkuhl, F., Diekmann, B., Wünnemann, B., and Zhang, C.: Quantitative reconstruction of precipitation changes on the NE Tibetan Plateau since the Last Glacial Maximum – extending the concept of pollen source area to pollen-based climate reconstructions from large lakes, Clim. Past, 10, 21–39, https://doi.org/10.5194/cp-10-21-2014, 2014.
Wehberg, J., Bock, M., Weinzierl, T., Conrad, O., Böhner, J., Stellmes, M., and Landschreiber, L.: Terrain-based Landscape Structure Classification in Relation to Remote Sensing Products and Soil Data for the Okavango Catchment, Biodivers. Ecol., 5, 221–233, 2013.
Weinzierl, T., Conrad, O., Böhner, J., and Wehberg, J.: Regionalization of baseline climatologies and time series for the Okavango Catchment, Biodivers. Ecol., 5, 235–245, 2013.
Wichmann, V.: Modellierung geomorphologischer Prozesse in einem alpinen Einzugsgebiet – Abgrenzung und Klassifizierung der Wirkungsräume von Sturzprozessen und Muren mit einem GIS, Eichstätt. Geogr. Arb., 15, 231 pp., 2006.
Wichmann, V. and Becht, M.: Spatial modelling of debris flows in an alpine drainage basin, IAHS-AISH P., 288, 370–376, 2004.
Wichmann, V. and Becht, M.: Modelling Of Geomorphic Processes In An Alpine Catchment, GeoDynamics, 151–167, 2005.
Wichmann, V. and Becht, M.: Modelling Of Geomorphic Processes In An Alpine Catchment, in: Geodynamics, edited by: Atkinson, P. M., Foody, G. M., Darby, S. E., and Wu, F., CRC Press, Boca Raton, 151–167, 2005.
Wichmann, V., Rutzinger, M., and Vetter, M.: Digital Terrain Model Generation from airborne Laser Scanning Point Data and the Effect of grid-cell size on the Simulation Results of a Debris Flow Model, in: Hamburger Beiträge Zur Physischen Geographie Und Landschaftsökologie (SAGA – Seconds Out), Hamburger Beiträge Zur Physischen Geographie Und Landschaftsökologie, edited by: Böhner, J., Blaschke, T., and Montanarella, L., Univ. Hamburg, Inst. für Geographie, 103–113, 2008.
Willer, J., Baritz, R., Eberhardt, E., Milbert, G., and Jahn, R.: Projekt SIAM – Entwicklung eines Boden-Landschaftsmodells zur Datenharmonisierung und Qualitätssicherung für Bodenübersichtskarten, available at: http://eprints.dbges.de/477/ (last access: 13 February 2013), 2009.
Wilson, J. P. and Gallant, J. C.: Terrain analysis: principles and applications. John Wiley & Sons, 2000.
Wischmeier, W. H. and Smith, D. D.: Predicting rainfall erosion losses-a guide to conservation planning, Agriculture Handbook, 537 pp., 1978.
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...
