Articles | Volume 14, issue 11
Geosci. Model Dev., 14, 7133–7153, 2021
https://doi.org/10.5194/gmd-14-7133-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Geosci. Model Dev., 14, 7133–7153, 2021
https://doi.org/10.5194/gmd-14-7133-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper 24 Nov 2021
Development and technical paper | 24 Nov 2021
How biased are our models? – a case study of the alpine region
Denise Degen et al.
Related authors
Denise Degen and Mauro Cacace
Geosci. Model Dev., 14, 1699–1719, https://doi.org/10.5194/gmd-14-1699-2021, https://doi.org/10.5194/gmd-14-1699-2021, 2021
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In this work, we focus on improving the understanding of subsurface processes with respect to interactions with climate dynamics. We present advanced, open-source mathematical methods that enable us to investigate the influence of various model properties on the final outcomes. By relying on our approach, we have been able to showcase their importance in improving our understanding of the subsurface and highlighting the current shortcomings of currently adopted models.
Denise Degen and Mauro Cacace
Geosci. Model Dev., 14, 1699–1719, https://doi.org/10.5194/gmd-14-1699-2021, https://doi.org/10.5194/gmd-14-1699-2021, 2021
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In this work, we focus on improving the understanding of subsurface processes with respect to interactions with climate dynamics. We present advanced, open-source mathematical methods that enable us to investigate the influence of various model properties on the final outcomes. By relying on our approach, we have been able to showcase their importance in improving our understanding of the subsurface and highlighting the current shortcomings of currently adopted models.
Cameron Spooner, Magdalena Scheck-Wenderoth, Mauro Cacace, and Denis Anikiev
Solid Earth Discuss., https://doi.org/10.5194/se-2020-202, https://doi.org/10.5194/se-2020-202, 2020
Revised manuscript not accepted
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By comparing long term lithospheric strength to seismicity patterns across the Alpine region, we show that most seismicity occurs where strengths are highest within the crust. The lower crust appears largely aseismic due to energy being dissipated by ongoing creep from low viscosities. Lithospheric structure appears to exert a primary control on seismicity distribution, with both forelands display a different distribution patterns, likely reflecting their different tectonic settings.
Denis Anikiev, Adrian Lechel, Maria Laura Gomez Dacal, Judith Bott, Mauro Cacace, and Magdalena Scheck-Wenderoth
Adv. Geosci., 49, 225–234, https://doi.org/10.5194/adgeo-49-225-2019, https://doi.org/10.5194/adgeo-49-225-2019, 2019
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We have developed a first Germany-wide 3D data-based density and temperature model integrating geoscientific observations and physical processes. The model can serve as a reference for local detailed studies dealing with temperature, pressure, stress, subsidence and sedimentation. Our results help to improve subsurface utilization concepts, reveal current geomechanical conditions crucial for hazard assessment and gather information on viable resources such groundwater and deep geothermal energy.
Cameron Spooner, Magdalena Scheck-Wenderoth, Hans-Jürgen Götze, Jörg Ebbing, György Hetényi, and the AlpArray Working Group
Solid Earth, 10, 2073–2088, https://doi.org/10.5194/se-10-2073-2019, https://doi.org/10.5194/se-10-2073-2019, 2019
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By utilising both the observed gravity field of the Alps and their forelands and indications from deep seismic surveys, we were able to produce a 3-D structural model of the region that indicates the distribution of densities within the lithosphere. We found that the present-day Adriatic crust is both thinner and denser than the European crust and that the properties of Alpine crust are strongly linked to their provenance.
Nora Koltzer, Magdalena Scheck-Wenderoth, Mauro Cacace, Maximilian Frick, and Judith Bott
Adv. Geosci., 49, 197–206, https://doi.org/10.5194/adgeo-49-197-2019, https://doi.org/10.5194/adgeo-49-197-2019, 2019
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In this study we investigate groundwater flow in the deep subsurface of the Upper Rhine Graben. We make use of a 3-D numerical model covering the entire Upper Rhine Graben. The deep hydrodynamics are characterized by fluid flow from the graben flanks towards its center and in the southern half of the graben from south to north. Moreover, local heterogeneities in the shallow flow field arise from the interaction between regional groundwater flow and the heterogeneous sedimentary configuration.
Guido Blöcher, Christian Kluge, Harald Milsch, Mauro Cacace, Antoine B. Jacquey, and Jean Schmittbuhl
Adv. Geosci., 49, 95–104, https://doi.org/10.5194/adgeo-49-95-2019, https://doi.org/10.5194/adgeo-49-95-2019, 2019
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The focus of the paper is to evaluate the permeability change of a matrix-fracture systems under mechanical loading und to understand the processes behind. This evaluation is based on data from laboratory experiments in comparison to 3-D numerical modelling results.
Maximilian Frick, Magdalena Scheck-Wenderoth, Mauro Cacace, and Michael Schneider
Adv. Geosci., 49, 9–18, https://doi.org/10.5194/adgeo-49-9-2019, https://doi.org/10.5194/adgeo-49-9-2019, 2019
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The study presented in this paper aims at reproducing findings from chemical and isotopic groundwater sample analysis along with quantifying the influence of regional (cross-boundary) flow for the area of Berlin, Germany. For this purpose we built 3-D models of the subsurface, populating them with material parameters (e.g. porosity, permeability) and solving them for coupled fluid and heat transport. Special focus was given to the setup of boundary conditions, i.e. fixed pressure at the sides.
Elisabeth Peters, Guido Blöcher, Saeed Salimzadeh, Paul J. P. Egberts, and Mauro Cacace
Adv. Geosci., 45, 209–215, https://doi.org/10.5194/adgeo-45-209-2018, https://doi.org/10.5194/adgeo-45-209-2018, 2018
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Accuracy of well inflow modelling in different numerical simulation approaches was compared for a multi-lateral well with laterals of varying diameter. For homogeneous cases, all simulators generally were reasonably close in terms of the total well flow (deviations smaller than 4 %). The distribution of the flow over the different laterals in a well can vary significantly between simulators (> 20 %). In a heterogeneous case with a fault the deviations between the approaches were much larger.
Nasrin Haacke, Maximilian Frick, Magdalena Scheck-Wenderoth, Michael Schneider, and Mauro Cacace
Adv. Geosci., 45, 177–184, https://doi.org/10.5194/adgeo-45-177-2018, https://doi.org/10.5194/adgeo-45-177-2018, 2018
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The main goal of this study was to understand how different realizations of the impact of groundwater pumping activities in a major urban center would affect the results of 3-D numerical models. In detail we looked at two model scenarios which both rely on the same geological structural model but differ in the realization of the groundwater boundary conditions. The results show, that it is necessary to use groundwater wells as an active parameter to reproduce local movement patterns.
Mauro Cacace and Antoine B. Jacquey
Solid Earth, 8, 921–941, https://doi.org/10.5194/se-8-921-2017, https://doi.org/10.5194/se-8-921-2017, 2017
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The paper describes theory and numerical implementation for coupled thermo–hydraulic–mechanical processes focusing on reservoir (mainly related to geothermal energy) applications.
Judith Sippel, Christian Meeßen, Mauro Cacace, James Mechie, Stewart Fishwick, Christian Heine, Magdalena Scheck-Wenderoth, and Manfred R. Strecker
Solid Earth, 8, 45–81, https://doi.org/10.5194/se-8-45-2017, https://doi.org/10.5194/se-8-45-2017, 2017
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The Kenya Rift is a zone along which the African continental plate is stretched as evidenced by strong earthquake and volcanic activity. We want to understand the controlling factors of past and future tectonic deformation; hence, we assess the structural and strength configuration of the rift system at the present-day. Data-driven 3-D numerical models show how the inherited composition of the crust and a thermal anomaly in the deep mantle interact to form localised zones of tectonic weakness.
Y. Cherubini, M. Cacace, M. Scheck-Wenderoth, and V. Noack
Geoth. Energ. Sci., 2, 1–20, https://doi.org/10.5194/gtes-2-1-2014, https://doi.org/10.5194/gtes-2-1-2014, 2014
Related subject area
Numerical methods
An explicit GPU-based material point method solver for elastoplastic problems (ep2-3De v1.0)
MagIC v5.10: a two-dimensional message-passing interface (MPI) distribution for pseudo-spectral magnetohydrodynamics simulations in spherical geometry
Machine-learning models to replicate large-eddy simulations of air pollutant concentrations along boulevard-type streets
Recalculation of error growth models' parameters for the ECMWF forecast system
B-flood 1.0: an open-source Saint-Venant model for flash-flood simulation using adaptive refinement
A micro-genetic algorithm (GA v1.7.1a) for combinatorial optimization of physics parameterizations in the Weather Research and Forecasting model (v4.0.3) for quantitative precipitation forecast in Korea
SymPKF (v1.0): a symbolic and computational toolbox for the design of parametric Kalman filter dynamics
A three-dimensional variational data assimilation system for aerosol optical properties based on WRF-Chem: design, development, and application of assimilating Himawari-8 aerosol observations
A new sampling capability for uncertainty quantification in the Ice-sheet and Sea-level System Model v4.19 using Gaussian Markov random fields
NDCmitiQ v1.0.0: a tool to quantify and analyse greenhouse gas mitigation targets
Combining ensemble Kalman filter and reservoir computing to predict spatiotemporal chaotic systems from imperfect observations and models
The Coastline Evolution Model 2D (CEM2D) V1.1
An iterative process for efficient optimisation of parameters in geoscientific models: a demonstration using the Parallel Ice Sheet Model (PISM) version 0.7.3
Ocean Plastic Assimilator v0.2: assimilation of plastic concentration data into Lagrangian dispersion models
Development of a moving point source model for shipping emission dispersion modeling in EPISODE–CityChem v1.3
Efficient Bayesian inference for large chaotic dynamical systems
Constraining stochastic 3-D structural geological models with topology information using approximate Bayesian computation in GemPy 2.1
Retrieval of process rate parameters in the general dynamic equation for aerosols using Bayesian state estimation: BAYROSOL1.0
A discontinuous Galerkin finite-element model for fast channelized lava flows v1.0
A nested multi-scale system implemented in the large-eddy simulation model PALM model system 6.0
Extending legacy climate models by adaptive mesh refinement for single-component tracer transport: a case study with ECHAM6-HAMMOZ (ECHAM6.3-HAM2.3-MOZ1.0)
Using the Després and Lagoutière (1999) antidiffusive transport scheme: a promising and novel method against excessive vertical diffusion in chemistry-transport models
A method for assessment of the general circulation model quality using K-means clustering algorithm
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Effects of transient processes for thermal simulations of the Central European Basin
A note on precision-preserving compression of scientific data
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A framework to evaluate IMEX schemes for atmospheric models
Inequality-constrained free-surface evolution in a full Stokes ice flow model (evolve_glacier v1.1)
A fast and efficient MATLAB-based MPM solver: fMPMM-solver v1.1
Necessary conditions for algorithmic tuning of weather prediction models using OpenIFS as an example
Development of a submerged aquatic vegetation growth model in the Coupled Ocean–Atmosphere–Wave–Sediment Transport (COAWST v3.4) model
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Development of a semi-Lagrangian advection scheme for the NEMO ocean model (3.1)
Efficient multi-scale Gaussian process regression for massive remote sensing data with satGP v0.1.2
PDE-NetGen 1.0: from symbolic partial differential equation (PDE) representations of physical processes to trainable neural network representations
Simple algorithms to compute meridional overturning and barotropic streamfunctions on unstructured meshes
Development of a two-way-coupled ocean–wave model: assessment on a global NEMO(v3.6)–WW3(v6.02) coupled configuration
Surrogate-assisted Bayesian inversion for landscape and basin evolution models
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A one-dimensional model of turbulent flow through “urban” canopies (MLUCM v2.0): updates based on large-eddy simulation
Slate: extending Firedrake's domain-specific abstraction to hybridized solvers for geoscience and beyond
A model of Black Sea circulation with strait exchange (2008–2018)
The Land Variational Ensemble Data Assimilation Framework: LAVENDAR v1.0.0
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Emmanuel Wyser, Yury Alkhimenkov, Michel Jaboyedoff, and Yury Y. Podladchikov
Geosci. Model Dev., 14, 7749–7774, https://doi.org/10.5194/gmd-14-7749-2021, https://doi.org/10.5194/gmd-14-7749-2021, 2021
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We propose an implementation of the material point method using graphical processing units (GPUs) to solve elastoplastic problems in three-dimensional configurations, such as the granular collapse or the slumping mechanics, i.e., landslide. The computational power of GPUs promotes fast code executions, compared to a traditional implementation using central processing units (CPUs). This allows us to study complex three-dimensional problems tackling high spatial resolution.
Rafael Lago, Thomas Gastine, Tilman Dannert, Markus Rampp, and Johannes Wicht
Geosci. Model Dev., 14, 7477–7495, https://doi.org/10.5194/gmd-14-7477-2021, https://doi.org/10.5194/gmd-14-7477-2021, 2021
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In this work we discuss a two-dimensional distributed parallelization of MagIC, an open-source code for the numerical solution of the magnetohydrodynamics equations. Such a parallelization involves several challenges concerning the distribution of work and data. We detail our algorithm and compare it with the established, optimized, one-dimensional distribution in the context of the dynamo benchmark and discuss the merits of both implementations.
Moritz Lange, Henri Suominen, Mona Kurppa, Leena Järvi, Emilia Oikarinen, Rafael Savvides, and Kai Puolamäki
Geosci. Model Dev., 14, 7411–7424, https://doi.org/10.5194/gmd-14-7411-2021, https://doi.org/10.5194/gmd-14-7411-2021, 2021
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This study aims to replicate computationally expensive high-resolution large-eddy simulations (LESs) with regression models to simulate urban air quality and pollutant dispersion. The model development, including feature selection, model training and cross-validation, and detection of concept drift, has been described in detail. Of the models applied, log-linear regression shows the best performance. A regression model can replace LES unless high accuracy is needed.
Hynek Bednář, Aleš Raidl, and Jiří Mikšovský
Geosci. Model Dev., 14, 7377–7389, https://doi.org/10.5194/gmd-14-7377-2021, https://doi.org/10.5194/gmd-14-7377-2021, 2021
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Forecast errors in numerical weather prediction systems grow in time. To quantify the impacts of this growth, parametric error growth models may be employed. This study recalculates and newly defines parameters for several statistic models approximating error growth in the ECMWF forecasting system. Accurate values of parameters are important because they are used to evaluate improvements of the forecasting systems or to estimate predictability.
Geoffroy Kirstetter, Olivier Delestre, Pierre-Yves Lagrée, Stéphane Popinet, and Christophe Josserand
Geosci. Model Dev., 14, 7117–7132, https://doi.org/10.5194/gmd-14-7117-2021, https://doi.org/10.5194/gmd-14-7117-2021, 2021
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The development of forecasting tools may help to limit the impacts of flash floods. Our purpose here is to demonstrate the possibility of using b-flood, which is a 2D tool based on shallow-water equations and adaptive mesh refinement.
Sojung Park and Seon K. Park
Geosci. Model Dev., 14, 6241–6255, https://doi.org/10.5194/gmd-14-6241-2021, https://doi.org/10.5194/gmd-14-6241-2021, 2021
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One of the biggest uncertainties in numerical weather predictions (NWPs) comes from treating subgrid-scale physical processes. Physical processes, such as cumulus, microphysics, and planetary boundary layer processes, are parameterized in NWP models by empirical and theoretical backgrounds. We developed an interface between a micro-genetic algorithm and the WRF model for a combinatorial optimization of physics for heavy rainfall events in Korea. The system improved precipitation forecasts.
Olivier Pannekoucke and Philippe Arbogast
Geosci. Model Dev., 14, 5957–5976, https://doi.org/10.5194/gmd-14-5957-2021, https://doi.org/10.5194/gmd-14-5957-2021, 2021
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This contributes to research on uncertainty prediction, which is important either for determining the weather today or estimating the risk in prediction. The problem is that uncertainty prediction is numerically very expensive. An alternative has been proposed wherein uncertainty is presented in a simplified form with only the dynamics of certain parameters required. This tool allows for the determination of the symbolic equations of these parameter dynamics and their numerical computation.
Daichun Wang, Wei You, Zengliang Zang, Xiaobin Pan, Yiwen Hu, and Yanfei Liang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2021-215, https://doi.org/10.5194/gmd-2021-215, 2021
Revised manuscript accepted for GMD
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This paper presents a 3DVAR data assimilation system for aerosol optical properties, including aerosol optical thickness (AOT) retrievals and lidar-based aerosol profiles, which was developed for the Model for a size-resolved sectional model in WRF-Chem. To directly assimilate aerosol optical properties, an observation operator based on the Mie scattering theory was designed. The results show that Himawari-8 AOT assimilation can significantly improve model aerosol analyses and forecasts.
Kevin Bulthuis and Eric Y. Larour
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2021-321, https://doi.org/10.5194/gmd-2021-321, 2021
Revised manuscript accepted for GMD
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We discuss a new method to sample spatially and temporal varying uncertain input parameters in ice sheet models, like the ice thickness or the surface mass balance. We represent these source of uncertainty of Gaussian random fields with Matérn covariance function. We generate random samples of this random field using an efficient computational approach based on solving a stochastic partial differential equation.
Annika Günther, Johannes Gütschow, and Mairi Louise Jeffery
Geosci. Model Dev., 14, 5695–5730, https://doi.org/10.5194/gmd-14-5695-2021, https://doi.org/10.5194/gmd-14-5695-2021, 2021
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The mitigation components of the nationally determined contributions (NDCs) under the Paris Agreement are essential in our fight against climate change. Regular updates with increased ambition are requested to limit global warming to 1.5–2 °C. The new and easy-to-update open-source tool NDCmitiQ can be used to quantify the NDCs' mitigation targets and construct resulting emissions pathways. In use cases, we show target uncertainties from missing clarity, data, and methodological challenges.
Futo Tomizawa and Yohei Sawada
Geosci. Model Dev., 14, 5623–5635, https://doi.org/10.5194/gmd-14-5623-2021, https://doi.org/10.5194/gmd-14-5623-2021, 2021
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A new method to predict chaotic systems from observation and process-based models is proposed by combining machine learning with data assimilation. Our method is robust to the sparsity of observation networks and can predict more accurately than a process-based model when it is biased. Our method effectively works when both observations and models are imperfect, which is often the case in geoscience. Therefore, our method is useful to solve a wide variety of prediction problems in this field.
Chloe Leach, Tom Coulthard, Andrew Barkwith, Daniel R. Parsons, and Susan Manson
Geosci. Model Dev., 14, 5507–5523, https://doi.org/10.5194/gmd-14-5507-2021, https://doi.org/10.5194/gmd-14-5507-2021, 2021
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Numerical models can be used to understand how coastal systems evolve over time, including likely responses to climate change. However, many existing models are aimed at simulating 10- to 100-year time periods do not represent a vertical dimension and are thus unable to include the effect of sea-level rise. The Coastline Evolution Model 2D (CEM2D) presented in this paper is an advance in this field, with the inclusion of the vertical coastal profile against which the water level can be altered.
Steven J. Phipps, Jason L. Roberts, and Matt A. King
Geosci. Model Dev., 14, 5107–5124, https://doi.org/10.5194/gmd-14-5107-2021, https://doi.org/10.5194/gmd-14-5107-2021, 2021
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Simplified schemes, known as parameterisations, are sometimes used to describe physical processes within numerical models. However, the values of the parameters are uncertain. This introduces uncertainty into the model outputs. We develop a simple approach to identify plausible ranges for model parameters. Using a model of the Antarctic Ice Sheet, we find that the value of one parameter can depend on the values of others. We conclude that a single optimal set of parameter values does not exist.
Axel Peytavin, Bruno Sainte-Rose, Gael Forget, and Jean-Michel Campin
Geosci. Model Dev., 14, 4769–4780, https://doi.org/10.5194/gmd-14-4769-2021, https://doi.org/10.5194/gmd-14-4769-2021, 2021
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We present a new algorithm developed at The Ocean Cleanup to update ocean plastic models based on measurements from the field to improve future cleaning operations. Prepared in collaboration with MIT researchers, this initial study presents its use in several analytical and real test cases in which two observers in a flow field record regular observations to update a plastic forecast. We demonstrate this improves the prediction, even with inaccurate knowledge of the water flows driving plastic.
Kang Pan, Mei Qi Lim, Markus Kraft, and Epaminondas Mastorakos
Geosci. Model Dev., 14, 4509–4534, https://doi.org/10.5194/gmd-14-4509-2021, https://doi.org/10.5194/gmd-14-4509-2021, 2021
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A new moving point source (MPS) model was developed to simulate the dispersion of emissions generated by the moving ships. Compared to the commonly used line source (LS) or fixed point source (FPS) model, the MPS model provides more emission distribution details generated by the moving ships and matches reasonably with the measurements. Therefore, the MPS model should be a valuable alternative for the environmental society to evaluate the pollutant dispersion contributed from the moving ships.
Sebastian Springer, Heikki Haario, Jouni Susiluoto, Aleksandr Bibov, Andrew Davis, and Youssef Marzouk
Geosci. Model Dev., 14, 4319–4333, https://doi.org/10.5194/gmd-14-4319-2021, https://doi.org/10.5194/gmd-14-4319-2021, 2021
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Model predictions always contain uncertainty. But in some cases, such as weather forecasting or climate modeling, chaotic unpredictability increases the difficulty to say exactly how much uncertainty there is. We combine two recently proposed mathematical methods to show how the uncertainty can be analyzed in models that are simplifications of true weather models. The results can be extended in the future to show how forecasts from large-scale models can be improved.
Alexander Schaaf, Miguel de la Varga, Florian Wellmann, and Clare E. Bond
Geosci. Model Dev., 14, 3899–3913, https://doi.org/10.5194/gmd-14-3899-2021, https://doi.org/10.5194/gmd-14-3899-2021, 2021
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Uncertainty is an inherent property of any model of the subsurface. We show how geological topology information – how different regions of rocks in the subsurface are connected – can be used to train uncertain geological models to reduce uncertainty. More widely, the method demonstrates the use of probabilistic machine learning (Bayesian inference) to train structural geological models on auxiliary geological knowledge that can be encoded in graph structures.
Matthew Ozon, Aku Seppänen, Jari P. Kaipio, and Kari E. J. Lehtinen
Geosci. Model Dev., 14, 3715–3739, https://doi.org/10.5194/gmd-14-3715-2021, https://doi.org/10.5194/gmd-14-3715-2021, 2021
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Experimental research has provided large amounts of high-quality data on aerosol over the last 2 decades. However, inference of the process rates (e.g., the rates at which particles are generated) is still typically done by simple curve-fitting methods and does not assess the credibility of the estimation. The devised method takes advantage of the Bayesian framework to not only retrieve the state of the observed aerosol system but also to estimate the process rates (e.g., growth rate).
Colton J. Conroy and Einat Lev
Geosci. Model Dev., 14, 3553–3575, https://doi.org/10.5194/gmd-14-3553-2021, https://doi.org/10.5194/gmd-14-3553-2021, 2021
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Lava flows present a natural hazard to communities around volcanoes and are usually slow-moving (< 1-5 cm/s). Lava flows during the 2018 eruption of Kilauea volcano, Hawai’i, however, reached speeds as high as 11 m/s. To investigate these dynamics we develop a new lava flow computer model that incorporates a nonlinear expression for the fluid viscosity. Model results indicate that the lava flows at Site 8 of the eruption displayed shear thickening behavior due to the flow's high bubble content.
Antti Hellsten, Klaus Ketelsen, Matthias Sühring, Mikko Auvinen, Björn Maronga, Christoph Knigge, Fotios Barmpas, Georgios Tsegas, Nicolas Moussiopoulos, and Siegfried Raasch
Geosci. Model Dev., 14, 3185–3214, https://doi.org/10.5194/gmd-14-3185-2021, https://doi.org/10.5194/gmd-14-3185-2021, 2021
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Large-eddy simulation (LES) of the urban atmospheric boundary layer involves a large separation of turbulent scales, leading to prohibitive computational costs. An online LES–LES nesting scheme is implemented into the PALM model system 6.0 to overcome this problem. Test results show that the accuracy within the high-resolution nest domains approach the non-nested high-resolution reference results. The nesting can reduce the CPU by time up to 80 % compared to the fine-resolution reference runs.
Yumeng Chen, Konrad Simon, and Jörn Behrens
Geosci. Model Dev., 14, 2289–2316, https://doi.org/10.5194/gmd-14-2289-2021, https://doi.org/10.5194/gmd-14-2289-2021, 2021
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Mesh adaptivity can reduce overall model error by only refining meshes in specific areas where it us necessary in the runtime. Here we suggest a way to integrate mesh adaptivity into an existing Earth system model, ECHAM6, without having to redesign the implementation from scratch. We show that while the additional computational effort is manageable, the error can be reduced compared to a low-resolution standard model using an idealized test and relatively realistic dust transport tests.
Sylvain Mailler, Romain Pennel, Laurent Menut, and Mathieu Lachâtre
Geosci. Model Dev., 14, 2221–2233, https://doi.org/10.5194/gmd-14-2221-2021, https://doi.org/10.5194/gmd-14-2221-2021, 2021
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Representing the advection of thin polluted plumes in numerical models is a challenging task since these models usually tend to excessively diffuse these plumes in the vertical direction. This numerical diffusion process is the cause of major difficulties in representing such dense and thin polluted plumes in numerical models. We propose here, and test in an academic framework, a novel method to solve this problem through the use of an antidiffusive advection scheme in the vertical direction.
Urmas Raudsepp and Ilja Maljutenko
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2021-68, https://doi.org/10.5194/gmd-2021-68, 2021
Revised manuscript accepted for GMD
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The model's ability to reproduce the state of the simulated object is always a subject of discussion. A new method for the multivariate assessment of numerical model skills uses K-means algorithm for clustering of model errors. All available data that fall into model domain and simulation period is incorporated into the skill assessment. The clustered errors are used for the spatial and temporal analysis of the model accuracy. The method can be applied to different types of geoscientific models.
Daniel Otoo and David Hodgetts
Geosci. Model Dev., 14, 2075–2095, https://doi.org/10.5194/gmd-14-2075-2021, https://doi.org/10.5194/gmd-14-2075-2021, 2021
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The forward stratigraphic simulation method is used to predict lithofacies, porosity, and permeability in a reservoir model. The objective of using this approach is to enhance subsurface property modelling through geologic realistic 3-D stratigraphic patterns.
Results show realistic stratigraphic sequences. Given this, we can derive spatial and geometric data as secondary data to constrain property simulation in a reservoir model. The approach can reduce the uncertainty of property modelling.
Denise Degen and Mauro Cacace
Geosci. Model Dev., 14, 1699–1719, https://doi.org/10.5194/gmd-14-1699-2021, https://doi.org/10.5194/gmd-14-1699-2021, 2021
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In this work, we focus on improving the understanding of subsurface processes with respect to interactions with climate dynamics. We present advanced, open-source mathematical methods that enable us to investigate the influence of various model properties on the final outcomes. By relying on our approach, we have been able to showcase their importance in improving our understanding of the subsurface and highlighting the current shortcomings of currently adopted models.
Rostislav Kouznetsov
Geosci. Model Dev., 14, 377–389, https://doi.org/10.5194/gmd-14-377-2021, https://doi.org/10.5194/gmd-14-377-2021, 2021
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Resetting of non-significant figures (precision trimming) enables efficient data compression and helps to avoid excessive use of storage space and network bandwidth while having well-constrained distortion to the data. The paper analyses accuracy losses and artifacts caused by trimming methods and by the widely used linear packing method. The paper presents several methods with implementation, evaluation, and illustrations and includes subroutines directly usable in geoscientific models.
Bertrand Bessagnet, Laurent Menut, and Maxime Beauchamp
Geosci. Model Dev., 14, 91–106, https://doi.org/10.5194/gmd-14-91-2021, https://doi.org/10.5194/gmd-14-91-2021, 2021
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This paper presents a new interpolator useful for geophysics applications. It can explore N-dimensional meshes, grids or look-up tables. The code accepts irregular but structured grids. Written in Fortran, it is easy to implement in existing codes and very fast and portable. We have compared it with a Python library. Python is convenient but suffers from portability and is sometimes not optimized enough. As an application case, this method is applied to atmospheric sciences.
Oksana Guba, Mark A. Taylor, Andrew M. Bradley, Peter A. Bosler, and Andrew Steyer
Geosci. Model Dev., 13, 6467–6480, https://doi.org/10.5194/gmd-13-6467-2020, https://doi.org/10.5194/gmd-13-6467-2020, 2020
Anna Wirbel and Alexander Helmut Jarosch
Geosci. Model Dev., 13, 6425–6445, https://doi.org/10.5194/gmd-13-6425-2020, https://doi.org/10.5194/gmd-13-6425-2020, 2020
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We present an open-source numerical tool to simulate the free-surface evolution of gravity-driven flows (e.g. glaciers) constrained by bed topography. No ad hoc post-processing is required to enforce positive ice thickness and mass conservation. We utilise finite elements, define benchmark tests, and showcase glaciological examples. In addition, we provide a thorough analysis of the applicability and robustness of different spatial stabilisation and time discretisation methods.
Emmanuel Wyser, Yury Alkhimenkov, Michel Jaboyedoff, and Yury Y. Podladchikov
Geosci. Model Dev., 13, 6265–6284, https://doi.org/10.5194/gmd-13-6265-2020, https://doi.org/10.5194/gmd-13-6265-2020, 2020
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In this work, we present an efficient and fast material point method (MPM) implementation in MATLAB. We first discuss the vectorization strategies to adapt this numerical method to a MATLAB implementation. We report excellent agreement of the solver compared with classical analysis among the MPM community, such as the cantilever beam problem. The solver achieves a performance gain of 28 compared with a classical iterative implementation.
Lauri Tuppi, Pirkka Ollinaho, Madeleine Ekblom, Vladimir Shemyakin, and Heikki Järvinen
Geosci. Model Dev., 13, 5799–5812, https://doi.org/10.5194/gmd-13-5799-2020, https://doi.org/10.5194/gmd-13-5799-2020, 2020
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This paper presents general guidelines on how to utilise computer algorithms efficiently in order to tune weather models so that they would produce better forecasts. The main conclusions are that the computer algorithms work most efficiently with a suitable cost function, certain forecast length and ensemble size. We expect that our results will facilitate the use of algorithmic methods in the tuning of weather models.
Tarandeep S. Kalra, Neil K. Ganju, and Jeremy M. Testa
Geosci. Model Dev., 13, 5211–5228, https://doi.org/10.5194/gmd-13-5211-2020, https://doi.org/10.5194/gmd-13-5211-2020, 2020
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The paper covers the description of a 3-D open-source model that dynamically couples the biophysical interactions between submerged aquatic vegetation (SAV), hydrodynamics (currents, waves), sediment dynamics, and nutrient loading. Based on SAV growth model, SAV can use growth or dieback while contributing and sequestering nutrients from the water column (modifying the biological environment) and subsequently affect the hydrodynamics and sediment transport (modifying the physical environment).
Xiaoshuang Li, Richard Garth James Bellerby, Jianzhong Ge, Philip Wallhead, Jing Liu, and Anqiang Yang
Geosci. Model Dev., 13, 5103–5117, https://doi.org/10.5194/gmd-13-5103-2020, https://doi.org/10.5194/gmd-13-5103-2020, 2020
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We have developed an ANN model to predict pH using 11 cruise datasets from 2013 to 2017,
demonstrated its reliability using three cruise datasets during 2018 and applied it to
retrieve monthly pH for the period 2000 to 2016 on the East China Sea shelf using the
ANN model in combination with input variables from the Changjiang biology Finite-Volume
Coastal Ocean Model. This approach may be a valuable tool for understanding the seasonal
variation of pH in poorly observed regions.
Christopher Subich, Pierre Pellerin, Gregory Smith, and Frederic Dupont
Geosci. Model Dev., 13, 4379–4398, https://doi.org/10.5194/gmd-13-4379-2020, https://doi.org/10.5194/gmd-13-4379-2020, 2020
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This work presents a semi-Lagrangian advection module for the NEMO (OPA) ocean model. Semi-Lagrangian advection transports fluid properties (temperature, salinity, velocity) between time steps by following fluid motion and interpolating from upstream locations of fluid parcels.
This method is commonly used in atmospheric models to extend time step size, but it has not previously been applied to operational ocean models. Overcoming this required a new approach for solid boundaries (coastlines).
Jouni Susiluoto, Alessio Spantini, Heikki Haario, Teemu Härkönen, and Youssef Marzouk
Geosci. Model Dev., 13, 3439–3463, https://doi.org/10.5194/gmd-13-3439-2020, https://doi.org/10.5194/gmd-13-3439-2020, 2020
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We describe a new computer program that is able produce maps of carbon dioxide or other quantities based on data collected by satellites that orbit the Earth. When working with such data there is often too much data in one area and none in another. The program is able to describe the fields even when data is not available. To be able to do so, new computational methods were developed. The program is also able to describe how uncertain the estimated carbon dioxide or other fields are.
Olivier Pannekoucke and Ronan Fablet
Geosci. Model Dev., 13, 3373–3382, https://doi.org/10.5194/gmd-13-3373-2020, https://doi.org/10.5194/gmd-13-3373-2020, 2020
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Learning physics from data using a deep neural network is a challenge that requires an appropriate but unknown network architecture. The package introduced here helps to design an architecture by translating known physical equations into a network, which the experimenter completes to capture unknown physical processes. A test bed is introduced to illustrate how this learning allows us to focus on truly unknown physical processes in the hope of making better use of data and digital resources.
Dmitry Sidorenko, Sergey Danilov, Nikolay Koldunov, Patrick Scholz, and Qiang Wang
Geosci. Model Dev., 13, 3337–3345, https://doi.org/10.5194/gmd-13-3337-2020, https://doi.org/10.5194/gmd-13-3337-2020, 2020
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Computation of barotropic and meridional overturning streamfunctions for models formulated on unstructured meshes is commonly preceded by interpolation to a regular mesh. This operation destroys the original conservation, which can be then be artificially imposed to make the computation possible. An elementary method is proposed that avoids interpolation and preserves conservation in a strict model sense.
Xavier Couvelard, Florian Lemarié, Guillaume Samson, Jean-Luc Redelsperger, Fabrice Ardhuin, Rachid Benshila, and Gurvan Madec
Geosci. Model Dev., 13, 3067–3090, https://doi.org/10.5194/gmd-13-3067-2020, https://doi.org/10.5194/gmd-13-3067-2020, 2020
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Within the framework of the Copernicus Marine Environment Monitoring Service (CMEMS), an objective is to demonstrate the contribution of coupling the high-resolution analysis and forecasting system with a wave model. This study describes the necessary steps and discusses the various choices made for coupling a wave model and an oceanic model for global-scale applications.
Rohitash Chandra, Danial Azam, Arpit Kapoor, and R. Dietmar Müller
Geosci. Model Dev., 13, 2959–2979, https://doi.org/10.5194/gmd-13-2959-2020, https://doi.org/10.5194/gmd-13-2959-2020, 2020
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Forward landscape and sedimentary basin evolution models pose a major challenge in the development of efficient inference and optimization methods. Bayesian inference provides a methodology for estimation and uncertainty quantification of free model parameters. In this paper, we present an application of a surrogate-assisted Bayesian parallel tempering method where that surrogate mimics a landscape evolution model. We use the method for parameter estimation and uncertainty quantification.
Juliette Mignot, Carlos Mejia, Charles Sorror, Adama Sylla, Michel Crépon, and Sylvie Thiria
Geosci. Model Dev., 13, 2723–2742, https://doi.org/10.5194/gmd-13-2723-2020, https://doi.org/10.5194/gmd-13-2723-2020, 2020
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The most robust representation of climate is usually obtained by averaging a large number of simulations, thereby cancelling individual model errors. Here, we work towards an objective way of selecting the least biased models over a certain region, based on physical parameters. This statistical method based on a neural classifier and multi-correspondence analysis is illustrated here for the Senegalo-Mauritanian region, but it could potentially be developed for any other region or process.
Mathieu Gravey and Grégoire Mariethoz
Geosci. Model Dev., 13, 2611–2630, https://doi.org/10.5194/gmd-13-2611-2020, https://doi.org/10.5194/gmd-13-2611-2020, 2020
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Stochastic simulations are key tools to generate complex spatial structures uses as input in geoscientific models. In this paper, we present a new open-source tool that enables to simulate complex structures in a straightforward and efficient manner, based on analogues. The method is tested on a variety of use cases to demonstrate the generality of the framework.
Gong Cheng, Per Lötstedt, and Lina von Sydow
Geosci. Model Dev., 13, 2245–2258, https://doi.org/10.5194/gmd-13-2245-2020, https://doi.org/10.5194/gmd-13-2245-2020, 2020
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A full Stokes subgrid scheme in two dimensions for the grounding line migration problem is presented in the open-source finite-element framework Elmer/ICE. This method can achieve comparable results to previous research using a more than 20 times larger mesh size, which can be used to improve the efficiency in marine ice sheet simulations.
Colin Grudzien, Marc Bocquet, and Alberto Carrassi
Geosci. Model Dev., 13, 1903–1924, https://doi.org/10.5194/gmd-13-1903-2020, https://doi.org/10.5194/gmd-13-1903-2020, 2020
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All scales of a dynamical physical process cannot be resolved accurately in a multiscale, geophysical model. The behavior of unresolved scales of motion are often parametrized by a random process to emulate their effects on the dynamically resolved variables, and this results in a random–dynamical model. We study how the choice of a numerical discretization of such a system affects the model forecast and estimation statistics, when the random–dynamical model is unbiased in its parametrization.
Theo Baracchini, Philip Y. Chu, Jonas Šukys, Gian Lieberherr, Stefan Wunderle, Alfred Wüest, and Damien Bouffard
Geosci. Model Dev., 13, 1267–1284, https://doi.org/10.5194/gmd-13-1267-2020, https://doi.org/10.5194/gmd-13-1267-2020, 2020
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Lake physical processes occur at a wide range of spatiotemporal scales. 3D hydrodynamic lake models are the only information source capable of solving those scales; however, they still need observations to be calibrated and to constrain their uncertainties. The optimal combination of a 3D hydrodynamic model, in situ measurements, and remote sensing observations is achieved through data assimilation. Here we present a complete data assimilation experiment for lakes using open-source tools.
Negin Nazarian, E. Scott Krayenhoff, and Alberto Martilli
Geosci. Model Dev., 13, 937–953, https://doi.org/10.5194/gmd-13-937-2020, https://doi.org/10.5194/gmd-13-937-2020, 2020
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We present an update to the Multi-Layer Urban Canopy Model by revisiting the parameterization of length scales based on high-resolution and validated large-eddy simulations. Additionally, the inclusion of dispersive fluxes in the parameterization schemes are also discussed. The results demonstrate that updated parameterizations improve the accuracy of the vertical exchange of momentum in the street canyon.
Thomas H. Gibson, Lawrence Mitchell, David A. Ham, and Colin J. Cotter
Geosci. Model Dev., 13, 735–761, https://doi.org/10.5194/gmd-13-735-2020, https://doi.org/10.5194/gmd-13-735-2020, 2020
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Galerkin finite element discretizations for atmospheric modeling often require the solution of ill-conditioned, saddle point equations which can be efficiently solved using a hybridized method. By extending Firedrake's domain-specific abstraction, we provide a mechanism for the rapid implementation of hybridization methods for a wide class of methods. In this paper, we show that hybridization is an effective alternative to traditional block solvers for simulating geophysical flows.
Murat Gunduz, Emin Özsoy, and Robinson Hordoir
Geosci. Model Dev., 13, 121–138, https://doi.org/10.5194/gmd-13-121-2020, https://doi.org/10.5194/gmd-13-121-2020, 2020
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The Bosphorus exchange is of critical importance for hydrodynamics and hydroclimatology of the Black Sea. In this study, we report on the development of a medium-resolution circulation model of the Black Sea, making use of surface atmospheric forcing with high space and time resolution, climatic river fluxes and strait exchange, enabled by adding elementary details of strait and coastal topography and seasonal hydrology specified in an artificial box on the Marmara Sea side.
Ewan Pinnington, Tristan Quaife, Amos Lawless, Karina Williams, Tim Arkebauer, and Dave Scoby
Geosci. Model Dev., 13, 55–69, https://doi.org/10.5194/gmd-13-55-2020, https://doi.org/10.5194/gmd-13-55-2020, 2020
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We present LAVENDAR, a mathematical method for combining observations with models of the terrestrial environment. Here we use it to improve estimates of crop growth in the UK Met Office land surface model. However, the method is model agnostic, requires no modification to the underlying code and can be applied to any part of the model. In the example application we improve estimates of maize yield by 74 % by assimilating observations of leaf area, crop height and photosynthesis.
Xavier Delaunay, Aurélie Courtois, and Flavien Gouillon
Geosci. Model Dev., 12, 4099–4113, https://doi.org/10.5194/gmd-12-4099-2019, https://doi.org/10.5194/gmd-12-4099-2019, 2019
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This research aimed at finding a compression method suitable for the ground processing of CFOSAT and SWOT satellite datasets. Lossless algorithms did not allow enough compression. That is why we began studying lossy alternatives. This work introduces the digit rounding algorithm which reduces the volume of scientific datasets keeping only the significant digits in each sample value. The number of digits kept is relative to each sample so that both small and high values are similarly preserved.
Richard Scalzo, David Kohn, Hugo Olierook, Gregory Houseman, Rohitash Chandra, Mark Girolami, and Sally Cripps
Geosci. Model Dev., 12, 2941–2960, https://doi.org/10.5194/gmd-12-2941-2019, https://doi.org/10.5194/gmd-12-2941-2019, 2019
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Producing 3-D models of structures under the Earth's surface based on sensor data is a key problem in geophysics (for example, in mining exploration). There may be multiple models that explain the data well. We use the open-source Obsidian software to look at the efficiency of different methods for exploring the model space and attaching probabilities to models, leading to less biased results and a better idea of how sensor data interact with geological assumptions.
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Short summary
In times of worldwide energy transitions, an understanding of the subsurface is increasingly important to provide renewable energy sources such as geothermal energy. To validate our understanding of the subsurface we require data. However, the data are usually not distributed equally and introduce a potential misinterpretation of the subsurface. Therefore, in this study we investigate the influence of measurements on temperature distribution in the European Alps.
In times of worldwide energy transitions, an understanding of the subsurface is increasingly...
