Pub Date : 2023-12-08DOI: 10.5194/gmd-16-7123-2023
J. Guerrette, Zhiquan Liu, C. Snyder, Byoung-Joo Jung, C. Schwartz, J. Ban, Steven Vahl, Yali Wu, I. Banos, Yonggang G. Yu, Soyoung Ha, Yannick Trémolet, T. Auligne, Clementine Gas, B. Ménétrier, A. Shlyaeva, M. Miesch, Stephen R. Herbener, E. Liu, D. Holdaway, B. T. Johnson
Abstract. An ensemble of 3D ensemble-variational (En-3DEnVar) data assimilations is demonstrated with the Joint Effort for Data assimilation Integration (JEDI) with the Model for Prediction Across Scales – Atmosphere (MPAS-A) (i.e., JEDI-MPAS). Basic software building blocks are reused from previously presented deterministic 3DEnVar functionality and combined with a formal experimental workflow manager in MPAS-Workflow. En-3DEnVar is used to produce an 80-member ensemble of analyses, which are cycled with ensemble forecasts in a 1-month experiment. The ensemble forecasts approximate a purely flow-dependent background error covariance (BEC) at each analysis time. The En-3DEnVar BECs and prior ensemble-mean forecast errors are compared to those produced by a similar experiment that uses the Data Assimilation Research Testbed (DART) ensemble adjustment Kalman filter (EAKF). The experiment using En-3DEnVar produces a similar ensemble spread to and slightly smaller errors than the EAKF. The ensemble forecasts initialized from En-3DEnVar and EAKF analyses are used as BECs in deterministic cycling 3DEnVar experiments, which are compared to a control experiment that uses 20-member MPAS-A forecasts initialized from Global Ensemble Forecast System (GEFS) initial conditions. The experimental ensembles achieve mostly equivalent or better performance than the off-the-shelf ensemble system in this deterministic cycling setting, although there are many obvious differences in configuration between GEFS and the two MPAS ensemble systems. An additional experiment that uses hybrid 3DEnVar, which combines the En-3DEnVar ensemble BEC with a climatological BEC, increases tropospheric forecast quality compared to the corresponding pure 3DEnVar experiment. The JEDI-MPAS En-3DEnVar is technically working and useful for future research studies. Tuning of observation errors and spread is needed to improve performance, and several algorithmic advancements are needed to improve computational efficiency for larger-scale applications.
{"title":"Data assimilation for the Model for Prediction Across Scales – Atmosphere with the Joint Effort for Data assimilation Integration (JEDI-MPAS 2.0.0-beta): ensemble of 3D ensemble-variational (En-3DEnVar) assimilations","authors":"J. Guerrette, Zhiquan Liu, C. Snyder, Byoung-Joo Jung, C. Schwartz, J. Ban, Steven Vahl, Yali Wu, I. Banos, Yonggang G. Yu, Soyoung Ha, Yannick Trémolet, T. Auligne, Clementine Gas, B. Ménétrier, A. Shlyaeva, M. Miesch, Stephen R. Herbener, E. Liu, D. Holdaway, B. T. Johnson","doi":"10.5194/gmd-16-7123-2023","DOIUrl":"https://doi.org/10.5194/gmd-16-7123-2023","url":null,"abstract":"Abstract. An ensemble of 3D ensemble-variational (En-3DEnVar) data assimilations is demonstrated with the Joint Effort for Data assimilation Integration (JEDI) with the Model for Prediction Across Scales – Atmosphere (MPAS-A) (i.e., JEDI-MPAS). Basic software building blocks are reused from previously presented deterministic 3DEnVar functionality and combined with a formal experimental workflow manager in MPAS-Workflow. En-3DEnVar is used to produce an 80-member ensemble of analyses, which are cycled with ensemble forecasts in a 1-month experiment. The ensemble forecasts approximate a purely flow-dependent background error covariance (BEC) at each analysis time. The En-3DEnVar BECs and prior ensemble-mean forecast errors are compared to those produced by a similar experiment that uses the Data Assimilation Research Testbed (DART) ensemble adjustment Kalman filter (EAKF). The experiment using En-3DEnVar produces a similar ensemble spread to and slightly smaller errors than the EAKF. The ensemble forecasts initialized from En-3DEnVar and EAKF analyses are used as BECs in deterministic cycling 3DEnVar experiments, which are compared to a control experiment that uses 20-member MPAS-A forecasts initialized from Global Ensemble Forecast System (GEFS) initial conditions. The experimental ensembles achieve mostly equivalent or better performance than the off-the-shelf ensemble system in this deterministic cycling setting, although there are many obvious differences in configuration between GEFS and the two MPAS ensemble systems. An additional experiment that uses hybrid 3DEnVar, which combines the En-3DEnVar ensemble BEC with a climatological BEC, increases tropospheric forecast quality compared to the corresponding pure 3DEnVar experiment. The JEDI-MPAS En-3DEnVar is technically working and useful for future research studies. Tuning of observation errors and spread is needed to improve performance, and several algorithmic advancements are needed to improve computational efficiency for larger-scale applications.\u0000","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":"72 4","pages":""},"PeriodicalIF":5.1,"publicationDate":"2023-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138586857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-07DOI: 10.5194/gmd-16-7107-2023
J. Rooze, Heewon Jung, Hagen Radtke
Abstract. In geoscientific models, simulating the properties associated with particles in a continuum can serve many scientific purposes, and this has commonly been addressed using Lagrangian models. As an alternative approach, we present an Eulerian method here: diffusion–advection–reaction type partial differential equations are derived for centralized moments, which can describe the distribution of properties associated with chemicals in reaction–transport models. When the property is age, the equations for centralized moments (unlike non-central moments) do not require terms to account for aging, making this method suitable for modeling age tracers. The properties described by the distributions may also represent kinetic variables affecting reaction rates. In practical applications, continuous distributions of ages and reactivities are resolved to simulate organic matter mineralization in surficial sediments, where macrofaunal and physical mixing processes typically dominate transport. In test simulations, mixing emerged as the predominant factor shaping reactivity and age distributions. Furthermore, the applications showcase the method's aptitude for modeling continua in mixed environments while also highlighting practical considerations and challenges.
{"title":"A novel Eulerian model based on central moments to simulate age and reactivity continua interacting with mixing processes","authors":"J. Rooze, Heewon Jung, Hagen Radtke","doi":"10.5194/gmd-16-7107-2023","DOIUrl":"https://doi.org/10.5194/gmd-16-7107-2023","url":null,"abstract":"Abstract. In geoscientific models, simulating the properties associated with particles in a continuum can serve many scientific purposes, and this has commonly been addressed using Lagrangian models. As an alternative approach, we present an Eulerian method here: diffusion–advection–reaction type partial differential equations are derived for centralized moments, which can describe the distribution of properties associated with chemicals in reaction–transport models. When the property is age, the equations for centralized moments (unlike non-central moments) do not require terms to account for aging, making this method suitable for modeling age tracers. The properties described by the distributions may also represent kinetic variables affecting reaction rates. In practical applications, continuous distributions of ages and reactivities are resolved to simulate organic matter mineralization in surficial sediments, where macrofaunal and physical mixing processes typically dominate transport. In test simulations, mixing emerged as the predominant factor shaping reactivity and age distributions. Furthermore, the applications showcase the method's aptitude for modeling continua in mixed environments while also highlighting practical considerations and challenges.\u0000","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":"24 1","pages":""},"PeriodicalIF":5.1,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138590099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-06DOI: 10.5194/gmd-16-7075-2023
J. Brondex, Kévin Fourteau, M. Dumont, P. Hagenmuller, N. Calonne, F. Tuzet, H. Löwe
Abstract. The poor treatment (or complete omission) of water vapor transport has been identified as a major limitation suffered by currently available snowpack models. As vapor and heat fluxes are closely intertwined, their mathematical representation amounts to a system of nonlinear and tightly coupled partial differential equations that are particularly challenging to solve numerically. The choice of the numerical scheme and the representation of couplings between processes are crucial to ensure an accurate and robust solution that guarantees mass and energy conservation while also allowing time steps in the order of 15 min. To explore the numerical treatments fulfilling these requirements, we have developed a highly modular finite-element program. The code is written in Python. Every step of the numerical formulation and solution is coded internally, except for the inversion of the linearized system of equations. We illustrate the capabilities of our approach to tackle the coupled problem of heat conduction, vapor diffusion, and settlement within a dry snowpack by running our model on several test cases proposed in recently published literature. We underline specific improvements regarding energy and mass conservation as well as time step requirements. In particular, we show that a fully coupled and fully implicit time-stepping approach enables accurate and stable solutions with little restriction on the time step.
{"title":"A finite-element framework to explore the numerical solution of the coupled problem of heat conduction, water vapor diffusion, and settlement in dry snow (IvoriFEM v0.1.0)","authors":"J. Brondex, Kévin Fourteau, M. Dumont, P. Hagenmuller, N. Calonne, F. Tuzet, H. Löwe","doi":"10.5194/gmd-16-7075-2023","DOIUrl":"https://doi.org/10.5194/gmd-16-7075-2023","url":null,"abstract":"Abstract. The poor treatment (or complete omission) of water vapor transport has been identified as a major limitation suffered by currently available snowpack models. As vapor and heat fluxes are closely intertwined, their mathematical representation amounts to a system of nonlinear and tightly coupled partial differential equations that are particularly challenging to solve numerically. The choice of the numerical scheme and the representation of couplings between processes are crucial to ensure an accurate and robust solution that guarantees mass and energy conservation while also allowing time steps in the order of 15 min. To explore the numerical treatments fulfilling these requirements, we have developed a highly modular finite-element program. The code is written in Python. Every step of the numerical formulation and solution is coded internally, except for the inversion of the linearized system of equations. We illustrate the capabilities of our approach to tackle the coupled problem of heat conduction, vapor diffusion, and settlement within a dry snowpack by running our model on several test cases proposed in recently published literature. We underline specific improvements regarding energy and mass conservation as well as time step requirements. In particular, we show that a fully coupled and fully implicit time-stepping approach enables accurate and stable solutions with little restriction on the time step.\u0000","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":"64 46","pages":""},"PeriodicalIF":5.1,"publicationDate":"2023-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138594893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-05DOI: 10.5194/gmd-16-7059-2023
A. Fotherby, H. Bradbury, J. Druhan, A. Turchyn
Abstract. We present an emulation-based approach to understand the interactions among different chemical and biological processes modelled in environmental reactive transport models (RTMs) and explore how the parameterisation of these processes influences the results of multi-component RTMs. We utilise a previously published RTM consisting of 20 primary species, 20 secondary complexes, 17 mineral reactions, and 2 biologically mediated reactions; this RTM describes bio-stimulation using sediment from a contaminated aquifer. We choose a subset of the input parameters to vary over a range of values. The result is the construction of a new dataset that describes the model behaviour over a range of environmental conditions. Using this dataset to train a statistical model creates an emulator of the underlying RTM. This is a condensed representation of the original RTM that facilitates rapid exploration of a broad range of environmental conditions and sensitivities. As an illustration of this approach, we use the emulator to explore how varying the boundary conditions in the RTM describing the aquifer impacts the rates and volumes of mineral precipitation. A key result of this work is the recognition of an unanticipated dependency of pyrite precipitation on pCO2 in the injection fluid due to the stoichiometry of the microbially mediated sulfate reduction reaction. This complex relationship was made apparent by the emulator, while the underlying RTM was not specifically constructed to create such a feedback. We argue that this emulation approach to sensitivity analysis for RTMs may be useful in discovering such new coupled sensitives in geochemical systems and for designing experiments to optimise environmental remediation. Finally, we demonstrate that this approach can maximise specific mineral precipitation or dissolution reactions by using the emulator to find local maxima, which can be widely applied in environmental systems.
{"title":"An emulation-based approach for interrogating reactive transport models","authors":"A. Fotherby, H. Bradbury, J. Druhan, A. Turchyn","doi":"10.5194/gmd-16-7059-2023","DOIUrl":"https://doi.org/10.5194/gmd-16-7059-2023","url":null,"abstract":"Abstract. We present an emulation-based approach to understand the interactions among different chemical and biological processes modelled in environmental reactive transport models (RTMs) and explore how the parameterisation of these processes influences the results of multi-component RTMs. We utilise a previously published RTM consisting of 20 primary species, 20 secondary complexes, 17 mineral reactions, and 2 biologically mediated reactions; this RTM describes bio-stimulation using sediment from a contaminated aquifer. We choose a subset of the input parameters to vary over a range of values. The result is the construction of a new dataset that describes the model behaviour over a range of environmental conditions. Using this dataset to train a statistical model creates an emulator of the underlying RTM. This is a condensed representation of the original RTM that facilitates rapid exploration of a broad range of environmental conditions and sensitivities. As an illustration of this approach, we use the emulator to explore how varying the boundary conditions in the RTM describing the aquifer impacts the rates and volumes of mineral precipitation. A key result of this work is the recognition of an unanticipated dependency of pyrite precipitation on pCO2 in the injection fluid due to the stoichiometry of the microbially mediated sulfate reduction reaction. This complex relationship was made apparent by the emulator, while the underlying RTM was not specifically constructed to create such a feedback. We argue that this emulation approach to sensitivity analysis for RTMs may be useful in discovering such new coupled sensitives in geochemical systems and for designing experiments to optimise environmental remediation. Finally, we demonstrate that this approach can maximise specific mineral precipitation or dissolution reactions by using the emulator to find local maxima, which can be widely applied in environmental systems.\u0000","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":"55 11","pages":""},"PeriodicalIF":5.1,"publicationDate":"2023-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138598325","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-04DOI: 10.5194/gmd-16-7037-2023
M. Zheng, Hongyu Liu, F. Adolphi, R. Muscheler, Zhengyao Lu, Mousong Wu, N. Prisle
Abstract. The cosmogenic radionuclides 7Be and 10Be are useful tracers for atmospheric transport studies. Combining 7Be and 10Be measurements with an atmospheric transport model can not only improve our understanding of the radionuclide transport and deposition processes but also provide an evaluation of the transport process in the model. To simulate these aerosol tracers, it is critical to evaluate the influence of radionuclide production uncertainties on simulations. Here we use the GEOS-Chem chemical transport model driven by the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) reanalysis to simulate 7Be and 10Be with the state-of-the-art production rate from the CRAC:Be (Cosmic Ray Atmospheric Cascade: Beryllium) model considering realistic spatial geomagnetic cutoff rigidities (denoted as P16spa). We also perform two sensitivity simulations: one with the default production rate in GEOS-Chem based on an empirical approach (denoted as LP67) and the other with the production rate from the CRAC:Be but considering only geomagnetic cutoff rigidities for a geocentric axial dipole (denoted as P16). The model results are comprehensively evaluated with a large number of measurements including surface air concentrations and deposition fluxes. The simulation with the P16spa production can reproduce the absolute values and temporal variability of 7Be and 10Be surface concentrations and deposition fluxes on annual and sub-annual scales, as well as the vertical profiles of air concentrations. The simulation with the LP67 production tends to overestimate the absolute values of 7Be and 10Be concentrations. The P16 simulation suggests less than 10 % differences compared to P16spa but a significant positive bias (∼18 %) in the 7Be deposition fluxes over East Asia. We find that the deposition fluxes are more sensitive to the production in the troposphere and downward transport from the stratosphere. Independent of the production models, surface air concentrations and deposition fluxes from all simulations show similar seasonal variations, suggesting a dominant meteorological influence. The model can also reasonably simulate the stratosphere–troposphere exchange process of 7Be and 10Be by producing stratospheric contribution and 10Be/7Be ratio values that agree with measurements. Finally, we illustrate the importance of including the time-varying solar modulations in the production calculation, which significantly improve the agreement between model results and measurements, especially at mid-latitudes and high latitudes. Reduced uncertainties in the production rates, as demonstrated in this study, improve the utility of 7Be and 10Be as aerosol tracers for evaluating and testing transport and scavenging processes in global models. For future GEOS-Chem simulations of 7Be and 10Be, we recommend using the P16spa (versus default LP67) production rate.
{"title":"Simulations of 7Be and 10Be with the GEOS-Chem global model v14.0.2 using state-of-the-art production rates","authors":"M. Zheng, Hongyu Liu, F. Adolphi, R. Muscheler, Zhengyao Lu, Mousong Wu, N. Prisle","doi":"10.5194/gmd-16-7037-2023","DOIUrl":"https://doi.org/10.5194/gmd-16-7037-2023","url":null,"abstract":"Abstract. The cosmogenic radionuclides 7Be and 10Be are useful tracers for atmospheric transport studies. Combining 7Be and 10Be measurements with an atmospheric transport model can not only improve our understanding of the radionuclide transport and deposition processes but also provide an evaluation of the transport process in the model. To simulate these aerosol tracers, it is critical to evaluate the influence of radionuclide production uncertainties on simulations. Here we use the GEOS-Chem chemical transport model driven by the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) reanalysis to simulate 7Be and 10Be with the state-of-the-art production rate from the CRAC:Be (Cosmic Ray Atmospheric Cascade: Beryllium) model considering realistic spatial geomagnetic cutoff rigidities (denoted as P16spa). We also perform two sensitivity simulations: one with the default production rate in GEOS-Chem based on an empirical approach (denoted as LP67) and the other with the production rate from the CRAC:Be but considering only geomagnetic cutoff rigidities for a geocentric axial dipole (denoted as P16). The model results are comprehensively evaluated with a large number of measurements including surface air concentrations and deposition fluxes. The simulation with the P16spa production can reproduce the absolute values and temporal variability of 7Be and 10Be surface concentrations and deposition fluxes on annual and sub-annual scales, as well as the vertical profiles of air concentrations. The simulation with the LP67 production tends to overestimate the absolute values of 7Be and 10Be concentrations. The P16 simulation suggests less than 10 % differences compared to P16spa but a significant positive bias (∼18 %) in the 7Be deposition fluxes over East Asia. We find that the deposition fluxes are more sensitive to the production in the troposphere and downward transport from the stratosphere. Independent of the production models, surface air concentrations and deposition fluxes from all simulations show similar seasonal variations, suggesting a dominant meteorological influence. The model can also reasonably simulate the stratosphere–troposphere exchange process of 7Be and 10Be by producing stratospheric contribution and 10Be/7Be ratio values that agree with measurements. Finally, we illustrate the importance of including the time-varying solar modulations in the production calculation, which significantly improve the agreement between model results and measurements, especially at mid-latitudes and high latitudes. Reduced uncertainties in the production rates, as demonstrated in this study, improve the utility of 7Be and 10Be as aerosol tracers for evaluating and testing transport and scavenging processes in global models. For future GEOS-Chem simulations of 7Be and 10Be, we recommend using the P16spa (versus default LP67) production rate.\u0000","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":"3 9","pages":""},"PeriodicalIF":5.1,"publicationDate":"2023-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138604465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-14DOI: 10.5194/gmd-16-6479-2023
Daniel Boateng, Sebastian G. Mutz
Abstract. The nature and severity of climate change impacts vary significantly from region to region. Consequently, high-resolution climate information is needed for meaningful impact assessments and the design of mitigation strategies. This demand has led to an increase in the application of empirical-statistical downscaling (ESD) models to general circulation model (GCM) simulations of future climate. In contrast to dynamical downscaling, the perfect prognosis ESD (PP-ESD) approach has several benefits, including low computation costs, the prevention of the propagation of GCM-specific errors, and high compatibility with different GCMs. Despite their advantages, the use of ESD models and the resulting data products is hampered by (1) the lack of accessible and user-friendly downscaling software packages that implement the entire downscaling cycle, (2) difficulties reproducing existing data products and assessing their credibility, and (3) difficulties reconciling different ESD-based predictions for the same region. We address these issues with a new open-source Python PP-ESD modeling framework called pyESD. pyESD implements the entire downscaling cycle, i.e., routines for data preparation, predictor selection and construction, model selection and training, evaluation, utility tools for relevant statistical tests, visualization, and more. The package includes a collection of well-established machine learning algorithms and allows the user to choose a variety of estimators, cross-validation schemes, objective function measures, and hyperparameter optimization in relatively few lines of code. The package is well-documented, highly modular, and flexible. It allows quick and reproducible downscaling of any climate information, such as precipitation, temperature, wind speed, or even short-term glacier length and mass changes. We demonstrate the use and effectiveness of the new PP-ESD framework by generating weather-station-based downscaling products for precipitation and temperature in complex mountainous terrain in southwestern Germany. The application example covers all important steps of the downscaling cycle and different levels of experimental complexity. All scripts and datasets used in the case study are publicly available to (1) ensure the reproducibility and replicability of the modeled results and (2) simplify learning to use the software package.
{"title":"pyESDv1.0.1: an open-source Python framework for empirical-statistical downscaling of climate information","authors":"Daniel Boateng, Sebastian G. Mutz","doi":"10.5194/gmd-16-6479-2023","DOIUrl":"https://doi.org/10.5194/gmd-16-6479-2023","url":null,"abstract":"Abstract. The nature and severity of climate change impacts vary significantly from region to region. Consequently, high-resolution climate information is needed for meaningful impact assessments and the design of mitigation strategies. This demand has led to an increase in the application of empirical-statistical downscaling (ESD) models to general circulation model (GCM) simulations of future climate. In contrast to dynamical downscaling, the perfect prognosis ESD (PP-ESD) approach has several benefits, including low computation costs, the prevention of the propagation of GCM-specific errors, and high compatibility with different GCMs. Despite their advantages, the use of ESD models and the resulting data products is hampered by (1) the lack of accessible and user-friendly downscaling software packages that implement the entire downscaling cycle, (2) difficulties reproducing existing data products and assessing their credibility, and (3) difficulties reconciling different ESD-based predictions for the same region. We address these issues with a new open-source Python PP-ESD modeling framework called pyESD. pyESD implements the entire downscaling cycle, i.e., routines for data preparation, predictor selection and construction, model selection and training, evaluation, utility tools for relevant statistical tests, visualization, and more. The package includes a collection of well-established machine learning algorithms and allows the user to choose a variety of estimators, cross-validation schemes, objective function measures, and hyperparameter optimization in relatively few lines of code. The package is well-documented, highly modular, and flexible. It allows quick and reproducible downscaling of any climate information, such as precipitation, temperature, wind speed, or even short-term glacier length and mass changes. We demonstrate the use and effectiveness of the new PP-ESD framework by generating weather-station-based downscaling products for precipitation and temperature in complex mountainous terrain in southwestern Germany. The application example covers all important steps of the downscaling cycle and different levels of experimental complexity. All scripts and datasets used in the case study are publicly available to (1) ensure the reproducibility and replicability of the modeled results and (2) simplify learning to use the software package.","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":"17 21","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134954160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-14DOI: 10.5194/gmd-16-6515-2023
Trygve Halsne, Kai Håkon Christensen, Gaute Hope, Øyvind Breivik
Abstract. Lateral changes in the group velocity of waves propagating in oceanic or coastal waters cause a deflection in their propagation path. Such refractive effects can be computed given knowledge of the ambient current field and/or the bathymetry. We present an open-source module for solving the wave ray equations by means of numerical integration in Python v3. The solver is implemented for waves on variable currents and arbitrary depths following the Wentzel–Kramers–Brillouin (WKB) approximation. The ray tracing module is implemented in a class structure, and the output is verified against analytical solutions and tested for numerical convergence. The solver is accompanied by a set of ancillary functions such as retrieval of ambient conditions using OPeNDAP, transformation of geographical coordinates, and structuring of data using community standards. A number of use examples are also provided.
{"title":"Ocean wave tracing v.1: a numerical solver of the wave ray equations for ocean waves on variable currents at arbitrary depths","authors":"Trygve Halsne, Kai Håkon Christensen, Gaute Hope, Øyvind Breivik","doi":"10.5194/gmd-16-6515-2023","DOIUrl":"https://doi.org/10.5194/gmd-16-6515-2023","url":null,"abstract":"Abstract. Lateral changes in the group velocity of waves propagating in oceanic or coastal waters cause a deflection in their propagation path. Such refractive effects can be computed given knowledge of the ambient current field and/or the bathymetry. We present an open-source module for solving the wave ray equations by means of numerical integration in Python v3. The solver is implemented for waves on variable currents and arbitrary depths following the Wentzel–Kramers–Brillouin (WKB) approximation. The ray tracing module is implemented in a class structure, and the output is verified against analytical solutions and tested for numerical convergence. The solver is accompanied by a set of ancillary functions such as retrieval of ambient conditions using OPeNDAP, transformation of geographical coordinates, and structuring of data using community standards. A number of use examples are also provided.","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":"40 40","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134954228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-14DOI: 10.5194/gmd-16-6433-2023
Catherine O. de Burgh-Day, Tennessee Leeuwenburg
Abstract. Machine learning (ML) is increasing in popularity in the field of weather and climate modelling. Applications range from improved solvers and preconditioners, to parameterization scheme emulation and replacement, and more recently even to full ML-based weather and climate prediction models. While ML has been used in this space for more than 25 years, it is only in the last 10 or so years that progress has accelerated to the point that ML applications are becoming competitive with numerical knowledge-based alternatives. In this review, we provide a roughly chronological summary of the application of ML to aspects of weather and climate modelling from early publications through to the latest progress at the time of writing. We also provide an overview of key ML terms, methodologies, and ethical considerations. Finally, we discuss some potentially beneficial future research directions. Our aim is to provide a primer for researchers and model developers to rapidly familiarize and update themselves with the world of ML in the context of weather and climate models.
{"title":"Machine learning for numerical weather and climate modelling: a review","authors":"Catherine O. de Burgh-Day, Tennessee Leeuwenburg","doi":"10.5194/gmd-16-6433-2023","DOIUrl":"https://doi.org/10.5194/gmd-16-6433-2023","url":null,"abstract":"Abstract. Machine learning (ML) is increasing in popularity in the field of weather and climate modelling. Applications range from improved solvers and preconditioners, to parameterization scheme emulation and replacement, and more recently even to full ML-based weather and climate prediction models. While ML has been used in this space for more than 25 years, it is only in the last 10 or so years that progress has accelerated to the point that ML applications are becoming competitive with numerical knowledge-based alternatives. In this review, we provide a roughly chronological summary of the application of ML to aspects of weather and climate modelling from early publications through to the latest progress at the time of writing. We also provide an overview of key ML terms, methodologies, and ethical considerations. Finally, we discuss some potentially beneficial future research directions. Our aim is to provide a primer for researchers and model developers to rapidly familiarize and update themselves with the world of ML in the context of weather and climate models.","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":"5 12","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134953869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract. Methane is the second-most important greenhouse gas after carbon dioxide and accounts for around 10 % of total European Union greenhouse gas emissions. Given that the atmospheric methane budget over a region depends on its terrestrial and aquatic methane sources, inverse modelling techniques appear as powerful tools for identifying critical areas that can later be submitted to emission mitigation strategies. In this regard, an inverse modelling system of methane emissions for Europe is being implemented based on the Weather Research and Forecasting (WRF) model: the Aarhus University Methane Inversion Algorithm (AUMIA) v1.0. The forward modelling component of AUMIA consists of the WRF model coupled to a multipurpose global database of methane anthropogenic emissions. To assure transport consistency during the inversion process, the backward modelling component will be based on the WRF model coupled to a Lagrangian particle dispersion module. A description of the modelling tools, input data sets, and 1-year forward modelling evaluation from 1 April 2018 to 31 March 2019 is provided in this paper. The a posteriori methane emission estimates, including a more focused inverse modelling for Denmark, will be provided in a second paper. A good general agreement is found between the modelling results and observations based on the TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor satellite. Model–observation discrepancies for the summer peak season are in line with previous studies conducted over urban areas in central Europe, with relative differences between simulated concentrations and observational data in this study ranging from 1 % to 2 %. Domain-wide correlation coefficients and root-mean-square errors for summer months ranged from 0.4 to 0.5 and from 27 to 30 ppb, respectively. On the other hand, model–observation discrepancies for winter months show a significant overestimation of anthropogenic emissions over the study region, with relative differences ranging from 2 % to 3 %. Domain-wide correlation coefficients and root-mean-square errors in this case ranged from 0.1 to 0.4 and from 33 to 50 ppb, respectively, indicating that a more refined inverse analysis assessment will be required for this season. According to modelling results, the methane enhancement above the background concentrations came almost entirely from anthropogenic sources; however, these sources contributed with only up to 2 % to the methane total-column concentration. Contributions from natural sources (wetlands and termites) and biomass burning were not relevant during the study period. The results found in this study contribute with a new model evaluation of methane concentrations over Europe and demonstrate a huge potential for methane inverse modelling using improved TROPOMI products in large-scale applications.
{"title":"Implementation of a satellite-based tool for the quantification of CH<sub>4</sub> emissions over Europe (AUMIA v1.0) – Part 1: forward modelling evaluation against near-surface and satellite data","authors":"Angel Liduvino Vara-Vela, Christoffer Karoff, Noelia Rojas Benavente, Janaina P. Nascimento","doi":"10.5194/gmd-16-6413-2023","DOIUrl":"https://doi.org/10.5194/gmd-16-6413-2023","url":null,"abstract":"Abstract. Methane is the second-most important greenhouse gas after carbon dioxide and accounts for around 10 % of total European Union greenhouse gas emissions. Given that the atmospheric methane budget over a region depends on its terrestrial and aquatic methane sources, inverse modelling techniques appear as powerful tools for identifying critical areas that can later be submitted to emission mitigation strategies. In this regard, an inverse modelling system of methane emissions for Europe is being implemented based on the Weather Research and Forecasting (WRF) model: the Aarhus University Methane Inversion Algorithm (AUMIA) v1.0. The forward modelling component of AUMIA consists of the WRF model coupled to a multipurpose global database of methane anthropogenic emissions. To assure transport consistency during the inversion process, the backward modelling component will be based on the WRF model coupled to a Lagrangian particle dispersion module. A description of the modelling tools, input data sets, and 1-year forward modelling evaluation from 1 April 2018 to 31 March 2019 is provided in this paper. The a posteriori methane emission estimates, including a more focused inverse modelling for Denmark, will be provided in a second paper. A good general agreement is found between the modelling results and observations based on the TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor satellite. Model–observation discrepancies for the summer peak season are in line with previous studies conducted over urban areas in central Europe, with relative differences between simulated concentrations and observational data in this study ranging from 1 % to 2 %. Domain-wide correlation coefficients and root-mean-square errors for summer months ranged from 0.4 to 0.5 and from 27 to 30 ppb, respectively. On the other hand, model–observation discrepancies for winter months show a significant overestimation of anthropogenic emissions over the study region, with relative differences ranging from 2 % to 3 %. Domain-wide correlation coefficients and root-mean-square errors in this case ranged from 0.1 to 0.4 and from 33 to 50 ppb, respectively, indicating that a more refined inverse analysis assessment will be required for this season. According to modelling results, the methane enhancement above the background concentrations came almost entirely from anthropogenic sources; however, these sources contributed with only up to 2 % to the methane total-column concentration. Contributions from natural sources (wetlands and termites) and biomass burning were not relevant during the study period. The results found in this study contribute with a new model evaluation of methane concentrations over Europe and demonstrate a huge potential for methane inverse modelling using improved TROPOMI products in large-scale applications.","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":" 560","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135186872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract. Ocean surface waves induced by wind forcing and topographic effects are a crucial physical process at the air–sea interface, which significantly affect typhoon development, ocean mixing, etc. Higher-resolution wave modeling can simulate more accurate wave states but requires a huge number of computational resources, making it difficult for Earth system models to include ocean waves as a fast-response physical process. Given that high-resolution Earth system models are in demand, efficient high-precision wave simulation is necessary and urgent. Based on the wave dispersion relation, we design a new wave modeling framework using a multiscale grid system. It has the fewest number of fine grids and reasonable grid spacing in deep-water areas. We compare the performance of wave simulation using different spatial propagation schemes, reveal the different reasons for wave simulation differences in the westerly zone and the active tropical cyclone region, and quantify the matching of spatial resolutions between wave models and wind forcing. A series of numerical experiments show that this new modeling framework can more precisely simulate wave states in shallow-water areas without losing accuracy in the deep ocean while costing a fraction of the price of traditional simulations with uniform fine-gridding space. With affordable computational expenses, the new ocean surface wave modeling can be implemented into high-resolution Earth system models, which may significantly improve the simulation of the atmospheric planetary boundary layer and upper-ocean mixing.
{"title":"Design and evaluation of an efficient high-precision ocean surface wave model with a multiscale grid system (MSG_Wav1.0)","authors":"Jiangyu Li, Shaoqing Zhang, Qingxiang Liu, Xiaolin Yu, Zhiwei Zhang","doi":"10.5194/gmd-16-6393-2023","DOIUrl":"https://doi.org/10.5194/gmd-16-6393-2023","url":null,"abstract":"Abstract. Ocean surface waves induced by wind forcing and topographic effects are a crucial physical process at the air–sea interface, which significantly affect typhoon development, ocean mixing, etc. Higher-resolution wave modeling can simulate more accurate wave states but requires a huge number of computational resources, making it difficult for Earth system models to include ocean waves as a fast-response physical process. Given that high-resolution Earth system models are in demand, efficient high-precision wave simulation is necessary and urgent. Based on the wave dispersion relation, we design a new wave modeling framework using a multiscale grid system. It has the fewest number of fine grids and reasonable grid spacing in deep-water areas. We compare the performance of wave simulation using different spatial propagation schemes, reveal the different reasons for wave simulation differences in the westerly zone and the active tropical cyclone region, and quantify the matching of spatial resolutions between wave models and wind forcing. A series of numerical experiments show that this new modeling framework can more precisely simulate wave states in shallow-water areas without losing accuracy in the deep ocean while costing a fraction of the price of traditional simulations with uniform fine-gridding space. With affordable computational expenses, the new ocean surface wave modeling can be implemented into high-resolution Earth system models, which may significantly improve the simulation of the atmospheric planetary boundary layer and upper-ocean mixing.","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":" 14","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135242872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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