Pub Date : 2023-10-12DOI: 10.5194/gmd-16-5685-2023
Hao Chen, Tiejun Wang, Yonggen Zhang, Yun Bai, Xi Chen
Abstract. Despite recent developments in geoscientific (e.g., physics- or data-driven) models, effectively assembling multiple models for approaching a benchmark solution remains challenging in many sub-disciplines of geoscientific fields. Here, we proposed an automated machine-learning-assisted ensemble framework (AutoML-Ens) that attempts to resolve this challenge. Details of the methodology and workflow of AutoML-Ens were provided, and a prototype model was realized with the key strategy of mapping between the probabilities derived from the machine learning classifier and the dynamic weights assigned to the candidate ensemble members. Based on the newly proposed framework, its applications for two real-world examples (i.e., mapping global soil water retention parameters and estimating remotely sensed cropland evapotranspiration) were investigated and discussed. Results showed that compared to conventional ensemble approaches, AutoML-Ens was superior across the datasets (the training, testing, and overall datasets) and environmental gradients with improved performance metrics (e.g., coefficient of determination, Kling–Gupta efficiency, and root-mean-squared error). The better performance suggested the great potential of AutoML-Ens for improving quantification and reducing uncertainty in estimates due to its two unique features, i.e., assigning dynamic weights for candidate models and taking full advantage of AutoML-assisted workflow. In addition to the representative results, we also discussed the interpretational aspects of the used framework and its possible extensions. More importantly, we emphasized the benefits of combining data-driven approaches with physics constraints for geoscientific model ensemble problems with high dimensionality in space and nonlinear behaviors in nature.
{"title":"Dynamically weighted ensemble of geoscientific models via automated machine-learning-based classification","authors":"Hao Chen, Tiejun Wang, Yonggen Zhang, Yun Bai, Xi Chen","doi":"10.5194/gmd-16-5685-2023","DOIUrl":"https://doi.org/10.5194/gmd-16-5685-2023","url":null,"abstract":"Abstract. Despite recent developments in geoscientific (e.g., physics- or data-driven) models, effectively assembling multiple models for approaching a benchmark solution remains challenging in many sub-disciplines of geoscientific fields. Here, we proposed an automated machine-learning-assisted ensemble framework (AutoML-Ens) that attempts to resolve this challenge. Details of the methodology and workflow of AutoML-Ens were provided, and a prototype model was realized with the key strategy of mapping between the probabilities derived from the machine learning classifier and the dynamic weights assigned to the candidate ensemble members. Based on the newly proposed framework, its applications for two real-world examples (i.e., mapping global soil water retention parameters and estimating remotely sensed cropland evapotranspiration) were investigated and discussed. Results showed that compared to conventional ensemble approaches, AutoML-Ens was superior across the datasets (the training, testing, and overall datasets) and environmental gradients with improved performance metrics (e.g., coefficient of determination, Kling–Gupta efficiency, and root-mean-squared error). The better performance suggested the great potential of AutoML-Ens for improving quantification and reducing uncertainty in estimates due to its two unique features, i.e., assigning dynamic weights for candidate models and taking full advantage of AutoML-assisted workflow. In addition to the representative results, we also discussed the interpretational aspects of the used framework and its possible extensions. More importantly, we emphasized the benefits of combining data-driven approaches with physics constraints for geoscientific model ensemble problems with high dimensionality in space and nonlinear behaviors in nature.","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135968724","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 : 2023-10-12DOI: 10.5194/gmd-16-5653-2023
Jonathan King, Jessica Tierney, Matthew Osman, Emily J. Judd, Kevin J. Anchukaitis
Abstract. Paleoclimate data assimilation (DA) is a tool for reconstructing past climates that directly integrates proxy records with climate model output. Despite the potential for DA to expand the scope of quantitative paleoclimatology, these methods remain difficult to implement in practice due to the multi-faceted requirements and data handling necessary for DA reconstructions, the diversity of DA methods, and the need for computationally efficient algorithms. Here, we present DASH, a MATLAB toolbox designed to facilitate paleoclimate DA analyses. DASH provides command line and scripting tools that implement common tasks in DA workflows. The toolbox is highly modular and is not built around any specific analysis, and thus DASH supports paleoclimate DA for a wide variety of time periods, spatial regions, proxy networks, and algorithms. DASH includes tools for integrating and cataloguing data stored in disparate formats, building state vector ensembles, and running proxy (system) forward models. The toolbox also provides optimized algorithms for implementing ensemble Kalman filters, particle filters, and optimal sensor analyses with variable and modular parameters. This paper reviews the key components of the DASH toolbox and presents examples illustrating DASH's use for paleoclimate DA applications.
{"title":"DASH: a MATLAB toolbox for paleoclimate data assimilation","authors":"Jonathan King, Jessica Tierney, Matthew Osman, Emily J. Judd, Kevin J. Anchukaitis","doi":"10.5194/gmd-16-5653-2023","DOIUrl":"https://doi.org/10.5194/gmd-16-5653-2023","url":null,"abstract":"Abstract. Paleoclimate data assimilation (DA) is a tool for reconstructing past climates that directly integrates proxy records with climate model output. Despite the potential for DA to expand the scope of quantitative paleoclimatology, these methods remain difficult to implement in practice due to the multi-faceted requirements and data handling necessary for DA reconstructions, the diversity of DA methods, and the need for computationally efficient algorithms. Here, we present DASH, a MATLAB toolbox designed to facilitate paleoclimate DA analyses. DASH provides command line and scripting tools that implement common tasks in DA workflows. The toolbox is highly modular and is not built around any specific analysis, and thus DASH supports paleoclimate DA for a wide variety of time periods, spatial regions, proxy networks, and algorithms. DASH includes tools for integrating and cataloguing data stored in disparate formats, building state vector ensembles, and running proxy (system) forward models. The toolbox also provides optimized algorithms for implementing ensemble Kalman filters, particle filters, and optimal sensor analyses with variable and modular parameters. This paper reviews the key components of the DASH toolbox and presents examples illustrating DASH's use for paleoclimate DA applications.","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136014126","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 : 2023-10-10DOI: 10.5194/gmd-16-5601-2023
Denis E. Sergeev, Nathan J. Mayne, Thomas Bendall, Ian A. Boutle, Alex Brown, Iva Kavčič, James Kent, Krisztian Kohary, James Manners, Thomas Melvin, Enrico Olivier, Lokesh K. Ragta, Ben Shipway, Jon Wakelin, Nigel Wood, Mohamed Zerroukat
Abstract. We demonstrate that LFRic-Atmosphere, a model built using the Met Office's GungHo dynamical core, is able to reproduce idealised large-scale atmospheric circulation patterns specified by several widely used benchmark recipes. This is motivated by the rapid rate of exoplanet discovery and the ever-growing need for numerical modelling and characterisation of their atmospheres. Here we present LFRic-Atmosphere's results for the idealised tests imitating circulation regimes commonly used in the exoplanet modelling community. The benchmarks include three analytic forcing cases: the standard Held–Suarez test, the Menou–Rauscher Earth-like test, and the Merlis–Schneider tidally locked Earth test. Qualitatively, LFRic-Atmosphere agrees well with other numerical models and shows excellent conservation properties in terms of total mass, angular momentum, and kinetic energy. We then use LFRic-Atmosphere with a more realistic representation of physical processes (radiation, subgrid-scale mixing, convection, clouds) by configuring it for the four TRAPPIST-1 Habitable Atmosphere Intercomparison (THAI) scenarios. This is the first application of LFRic-Atmosphere to a possible climate of a confirmed terrestrial exoplanet. LFRic-Atmosphere reproduces the THAI scenarios within the spread of the existing models across a range of key climatic variables. Our work shows that LFRic-Atmosphere performs well in the seven benchmark tests for terrestrial atmospheres, justifying its use in future exoplanet climate studies.
{"title":"Simulations of idealised 3D atmospheric flows on terrestrial planets using LFRic-Atmosphere","authors":"Denis E. Sergeev, Nathan J. Mayne, Thomas Bendall, Ian A. Boutle, Alex Brown, Iva Kavčič, James Kent, Krisztian Kohary, James Manners, Thomas Melvin, Enrico Olivier, Lokesh K. Ragta, Ben Shipway, Jon Wakelin, Nigel Wood, Mohamed Zerroukat","doi":"10.5194/gmd-16-5601-2023","DOIUrl":"https://doi.org/10.5194/gmd-16-5601-2023","url":null,"abstract":"Abstract. We demonstrate that LFRic-Atmosphere, a model built using the Met Office's GungHo dynamical core, is able to reproduce idealised large-scale atmospheric circulation patterns specified by several widely used benchmark recipes. This is motivated by the rapid rate of exoplanet discovery and the ever-growing need for numerical modelling and characterisation of their atmospheres. Here we present LFRic-Atmosphere's results for the idealised tests imitating circulation regimes commonly used in the exoplanet modelling community. The benchmarks include three analytic forcing cases: the standard Held–Suarez test, the Menou–Rauscher Earth-like test, and the Merlis–Schneider tidally locked Earth test. Qualitatively, LFRic-Atmosphere agrees well with other numerical models and shows excellent conservation properties in terms of total mass, angular momentum, and kinetic energy. We then use LFRic-Atmosphere with a more realistic representation of physical processes (radiation, subgrid-scale mixing, convection, clouds) by configuring it for the four TRAPPIST-1 Habitable Atmosphere Intercomparison (THAI) scenarios. This is the first application of LFRic-Atmosphere to a possible climate of a confirmed terrestrial exoplanet. LFRic-Atmosphere reproduces the THAI scenarios within the spread of the existing models across a range of key climatic variables. Our work shows that LFRic-Atmosphere performs well in the seven benchmark tests for terrestrial atmospheres, justifying its use in future exoplanet climate studies.","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136357466","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 : 2023-10-10DOI: 10.5194/gmd-16-5585-2023
Tao Wang, Hang Liu, Jie Li, Shuai Wang, Youngseob Kim, Yele Sun, Wenyi Yang, Huiyun Du, Zhe Wang, Zifa Wang
Abstract. Owing to the substantial traffic emissions in urban areas, especially near road areas, the concentrations of pollutants, such as ozone (O3) and its precursors, have a large difference compared to regional averages, and their distributions cannot be captured accurately by traditional single-scale air quality models. In this study, a new version of a regional urban–street network model (an Integrated Air Quality Modeling System coupling regional urban–street: IAQMS-street v2.0) is presented. An upscaling module is implemented in IAQMS-street v2.0 to calculate the impact of mass transfer to regional scale from street network. The influence of pollutants in the street network is considered in the concentration calculation on the regional scale, which is not considered in a previous version (IAQMS-street v1.0). In this study, the simulated results in Beijing during August 2021, using IAQMS-street v2.0, IAQMS-street v1.0, and the regional model (Nested Air Quality Prediction Modeling System, NAQPMS), are compared. On-road traffic emissions in Beijing, as the key model input data, were established using intelligent image-recognition technology and real-time traffic big data from navigation applications. The simulated results showed that the O3 and nitrogen oxide (NOx) concentrations in Beijing were reproduced by using IAQMS-street v2.0 on both the regional scale and street scale. The prediction fractions within a factor of 2 (FAC2s) between simulations and observations of NO and NO2 increased from 0.11 and 0.34 in NAQPMS to 0.78 and 1.00 in IAQMS-street v2.0, respectively. The normalized mean biases (NMBs) of NO and NO2 decreased from 2.67 and 1.33 to −0.25 and 0.08. In the coupled model, the concentration of NOx at the street scale is higher than that at the regional scale, and the simulated distribution of pollutants on a regional scale was improved in IAQMS-street v2.0 when compared with that in IAQMS-street v1.0. We further used IAQMS-street v2.0 to quantify the contribution of local on-road traffic emissions to the O3 and NOx emissions and analyze the effect of traffic regulation policies in Beijing. Results showed that heavy-duty trucks are the major source of on-road traffic emissions of NOx. The relative contributions of local traffic emissions to NO2, NO, and O3 concentrations were 53.41 %, 57.45 %, and 8.49 %, respectively. We found that traffic regulation policies in Beijing largely decreased the concentrations of NOx and hydrocarbons (HC); however, the O3 concentration near the road increased due to the decrease consumption of O3 by NO. To decrease the O3 concentration in urban areas, controlling the local emissions of HC and NOx from other sources requires consideration.
{"title":"A two-way coupled regional urban–street network air quality model system for Beijing, China","authors":"Tao Wang, Hang Liu, Jie Li, Shuai Wang, Youngseob Kim, Yele Sun, Wenyi Yang, Huiyun Du, Zhe Wang, Zifa Wang","doi":"10.5194/gmd-16-5585-2023","DOIUrl":"https://doi.org/10.5194/gmd-16-5585-2023","url":null,"abstract":"Abstract. Owing to the substantial traffic emissions in urban areas, especially near road areas, the concentrations of pollutants, such as ozone (O3) and its precursors, have a large difference compared to regional averages, and their distributions cannot be captured accurately by traditional single-scale air quality models. In this study, a new version of a regional urban–street network model (an Integrated Air Quality Modeling System coupling regional urban–street: IAQMS-street v2.0) is presented. An upscaling module is implemented in IAQMS-street v2.0 to calculate the impact of mass transfer to regional scale from street network. The influence of pollutants in the street network is considered in the concentration calculation on the regional scale, which is not considered in a previous version (IAQMS-street v1.0). In this study, the simulated results in Beijing during August 2021, using IAQMS-street v2.0, IAQMS-street v1.0, and the regional model (Nested Air Quality Prediction Modeling System, NAQPMS), are compared. On-road traffic emissions in Beijing, as the key model input data, were established using intelligent image-recognition technology and real-time traffic big data from navigation applications. The simulated results showed that the O3 and nitrogen oxide (NOx) concentrations in Beijing were reproduced by using IAQMS-street v2.0 on both the regional scale and street scale. The prediction fractions within a factor of 2 (FAC2s) between simulations and observations of NO and NO2 increased from 0.11 and 0.34 in NAQPMS to 0.78 and 1.00 in IAQMS-street v2.0, respectively. The normalized mean biases (NMBs) of NO and NO2 decreased from 2.67 and 1.33 to −0.25 and 0.08. In the coupled model, the concentration of NOx at the street scale is higher than that at the regional scale, and the simulated distribution of pollutants on a regional scale was improved in IAQMS-street v2.0 when compared with that in IAQMS-street v1.0. We further used IAQMS-street v2.0 to quantify the contribution of local on-road traffic emissions to the O3 and NOx emissions and analyze the effect of traffic regulation policies in Beijing. Results showed that heavy-duty trucks are the major source of on-road traffic emissions of NOx. The relative contributions of local traffic emissions to NO2, NO, and O3 concentrations were 53.41 %, 57.45 %, and 8.49 %, respectively. We found that traffic regulation policies in Beijing largely decreased the concentrations of NOx and hydrocarbons (HC); however, the O3 concentration near the road increased due to the decrease consumption of O3 by NO. To decrease the O3 concentration in urban areas, controlling the local emissions of HC and NOx from other sources requires consideration.","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136357224","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 : 2023-10-10DOI: 10.5194/gmd-16-5627-2023
Kevin Hank, Lev Tarasov, Elisa Mantelli
Abstract. Modeling ice sheet instabilities is a numerical challenge of potentially high real-world relevance. Yet, differentiating between the impacts of model physics, numerical implementation choices, and numerical errors is not straightforward. Here, we use an idealized North American geometry and climate representation (similarly to the HEINO (Heinrich Event INtercOmparison) experiments – Calov et al., 2010) to examine the process and numerical sensitivity of ice stream surge cycling in ice flow models. Through sensitivity tests, we identify some numerical requirements for a more robust model configuration for such contexts. To partly address model-specific dependencies, we use both the Glacial Systems Model (GSM) and the Parallel Ice Sheet Model (PISM). We show that modeled surge characteristics are resolution dependent, though they converge (decreased differences between resolutions) at finer horizontal grid resolutions. Discrepancies between fine and coarse horizontal grid resolutions can be reduced by incorporating sliding at sub-freezing temperatures. The inclusion of basal hydrology increases the ice volume lost during surges, whereas the dampening of basal-temperature changes due to a bed thermal model leads to a decrease.
{"title":"Modeling sensitivities of thermally and hydraulically driven ice stream surge cycling","authors":"Kevin Hank, Lev Tarasov, Elisa Mantelli","doi":"10.5194/gmd-16-5627-2023","DOIUrl":"https://doi.org/10.5194/gmd-16-5627-2023","url":null,"abstract":"Abstract. Modeling ice sheet instabilities is a numerical challenge of potentially high real-world relevance. Yet, differentiating between the impacts of model physics, numerical implementation choices, and numerical errors is not straightforward. Here, we use an idealized North American geometry and climate representation (similarly to the HEINO (Heinrich Event INtercOmparison) experiments – Calov et al., 2010) to examine the process and numerical sensitivity of ice stream surge cycling in ice flow models. Through sensitivity tests, we identify some numerical requirements for a more robust model configuration for such contexts. To partly address model-specific dependencies, we use both the Glacial Systems Model (GSM) and the Parallel Ice Sheet Model (PISM). We show that modeled surge characteristics are resolution dependent, though they converge (decreased differences between resolutions) at finer horizontal grid resolutions. Discrepancies between fine and coarse horizontal grid resolutions can be reduced by incorporating sliding at sub-freezing temperatures. The inclusion of basal hydrology increases the ice volume lost during surges, whereas the dampening of basal-temperature changes due to a bed thermal model leads to a decrease.","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136356857","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 : 2023-10-06DOI: 10.5194/gmd-16-5561-2023
Roland Eichinger, Sebastian Rhode, Hella Garny, Peter Preusse, Petr Pisoft, Aleš Kuchař, Patrick Jöckel, Astrid Kerkweg, Bastian Kern
Abstract. The columnar approach of gravity wave (GW) parameterisations in weather and climate models has been identified as a potential reason for dynamical biases in middle-atmospheric dynamics. For example, GW momentum flux (GWMF) discrepancies between models and observations at 60∘ S arising through the lack of horizontal orographic GW propagation are suspected to cause deficiencies in representing the Antarctic polar vortex. However, due to the decomposition of the model domains onto different computing tasks for parallelisation, communication between horizontal grid boxes is computationally extremely expensive, making horizontal propagation of GWs unfeasible for global chemistry–climate simulations. To overcome this issue, we present a simplified solution to approximate horizontal GW propagation through redistribution of the GWMF at one single altitude by means of tailor-made redistribution maps. To generate the global redistribution maps averaged for each grid box, we use a parameterisation describing orography as a set of mountain ridges with specified location, orientation and height combined with a ray-tracing model describing lateral propagation of so-generated mountain waves. In the global chemistry–climate model (CCM) EMAC (ECHAM MESSy Atmospheric Chemistry), these maps then allow us to redistribute the GW momentum flux horizontally at one level, obtaining an affordable overhead of computing resources. The results of our simulations show GWMF and drag patterns that are horizontally more spread out than with the purely columnar approach; GWs are now also present above the ocean and regions without mountains. In this paper, we provide a detailed description of how the redistribution maps are computed and how the GWMF redistribution is implemented in the CCM. Moreover, an analysis shows why 15 km is the ideal altitude for the redistribution. First results with the redistributed orographic GWMF provide clear evidence that the redistributed GW drag in the Southern Hemisphere has the potential to modify and improve Antarctic polar vortex dynamics, thereby paving the way for enhanced credibility of CCM simulations and projections of polar stratospheric ozone.
{"title":"Emulating lateral gravity wave propagation in a global chemistry–climate model (EMAC v2.55.2) through horizontal flux redistribution","authors":"Roland Eichinger, Sebastian Rhode, Hella Garny, Peter Preusse, Petr Pisoft, Aleš Kuchař, Patrick Jöckel, Astrid Kerkweg, Bastian Kern","doi":"10.5194/gmd-16-5561-2023","DOIUrl":"https://doi.org/10.5194/gmd-16-5561-2023","url":null,"abstract":"Abstract. The columnar approach of gravity wave (GW) parameterisations in weather and climate models has been identified as a potential reason for dynamical biases in middle-atmospheric dynamics. For example, GW momentum flux (GWMF) discrepancies between models and observations at 60∘ S arising through the lack of horizontal orographic GW propagation are suspected to cause deficiencies in representing the Antarctic polar vortex. However, due to the decomposition of the model domains onto different computing tasks for parallelisation, communication between horizontal grid boxes is computationally extremely expensive, making horizontal propagation of GWs unfeasible for global chemistry–climate simulations. To overcome this issue, we present a simplified solution to approximate horizontal GW propagation through redistribution of the GWMF at one single altitude by means of tailor-made redistribution maps. To generate the global redistribution maps averaged for each grid box, we use a parameterisation describing orography as a set of mountain ridges with specified location, orientation and height combined with a ray-tracing model describing lateral propagation of so-generated mountain waves. In the global chemistry–climate model (CCM) EMAC (ECHAM MESSy Atmospheric Chemistry), these maps then allow us to redistribute the GW momentum flux horizontally at one level, obtaining an affordable overhead of computing resources. The results of our simulations show GWMF and drag patterns that are horizontally more spread out than with the purely columnar approach; GWs are now also present above the ocean and regions without mountains. In this paper, we provide a detailed description of how the redistribution maps are computed and how the GWMF redistribution is implemented in the CCM. Moreover, an analysis shows why 15 km is the ideal altitude for the redistribution. First results with the redistributed orographic GWMF provide clear evidence that the redistributed GW drag in the Southern Hemisphere has the potential to modify and improve Antarctic polar vortex dynamics, thereby paving the way for enhanced credibility of CCM simulations and projections of polar stratospheric ozone.","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134944295","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 : 2023-10-05DOI: 10.5194/gmd-16-5539-2023
Siddhartha Bishnu, Robert R. Strauss, Mark R. Petersen
Abstract. Some programming languages are easy to develop at the cost of slow execution, while others are fast at runtime but much more difficult to write. Julia is a programming language that aims to be the best of both worlds – a development and production language at the same time. To test Julia's utility in scientific high-performance computing (HPC), we built an unstructured-mesh shallow water model in Julia and compared it against an established Fortran-MPI ocean model, the Model for Prediction Across Scales–Ocean (MPAS-Ocean), as well as a Python shallow water code. Three versions of the Julia shallow water code were created: for single-core CPU, graphics processing unit (GPU), and Message Passing Interface (MPI) CPU clusters. Comparing identical simulations revealed that our first version of the Julia model was 13 times faster than Python using NumPy, where both used an unthreaded single-core CPU. Further Julia optimizations, including static typing and removing implicit memory allocations, provided an additional 10–20× speed-up of the single-core CPU Julia model. The GPU-accelerated Julia code was almost identical in terms of performance to the MPI parallelized code on 64 processes, an unexpected result for such different architectures. Parallelized Julia-MPI performance was identical to Fortran-MPI MPAS-Ocean for low processor counts and ranges from 2× faster to 2× slower for higher processor counts. Our experience is that Julia development is fast and convenient for prototyping but that Julia requires further investment and expertise to be competitive with compiled codes. We provide advice on Julia code optimization for HPC systems.
{"title":"Comparing the Performance of Julia on CPUs versus GPUs and Julia-MPI versus Fortran-MPI: a case study with MPAS-Ocean (Version 7.1)","authors":"Siddhartha Bishnu, Robert R. Strauss, Mark R. Petersen","doi":"10.5194/gmd-16-5539-2023","DOIUrl":"https://doi.org/10.5194/gmd-16-5539-2023","url":null,"abstract":"Abstract. Some programming languages are easy to develop at the cost of slow execution, while others are fast at runtime but much more difficult to write. Julia is a programming language that aims to be the best of both worlds – a development and production language at the same time. To test Julia's utility in scientific high-performance computing (HPC), we built an unstructured-mesh shallow water model in Julia and compared it against an established Fortran-MPI ocean model, the Model for Prediction Across Scales–Ocean (MPAS-Ocean), as well as a Python shallow water code. Three versions of the Julia shallow water code were created: for single-core CPU, graphics processing unit (GPU), and Message Passing Interface (MPI) CPU clusters. Comparing identical simulations revealed that our first version of the Julia model was 13 times faster than Python using NumPy, where both used an unthreaded single-core CPU. Further Julia optimizations, including static typing and removing implicit memory allocations, provided an additional 10–20× speed-up of the single-core CPU Julia model. The GPU-accelerated Julia code was almost identical in terms of performance to the MPI parallelized code on 64 processes, an unexpected result for such different architectures. Parallelized Julia-MPI performance was identical to Fortran-MPI MPAS-Ocean for low processor counts and ranges from 2× faster to 2× slower for higher processor counts. Our experience is that Julia development is fast and convenient for prototyping but that Julia requires further investment and expertise to be competitive with compiled codes. We provide advice on Julia code optimization for HPC systems.","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134975561","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 : 2023-10-04DOI: 10.5194/gmd-16-5515-2023
Tyler Kukla, Daniel E. Ibarra, Kimberly V. Lau, Jeremy K. C. Rugenstein
Abstract. Models of the carbon cycle and climate on geologic (>104-year) timescales have improved tremendously in the last 50 years due to parallel advances in our understanding of the Earth system and the increase in computing power to simulate its key processes. Still, balancing the Earth system's complexity with a model's computational expense is a primary challenge in model development. Simulations spanning hundreds of thousands of years or more generally require a reduction in the complexity of the climate system, omitting features such as radiative feedbacks, shifts in atmospheric circulation, and the expansion and decay of ice sheets, which can have profound effects on the long-term carbon cycle. Here, we present a model for climate and the long-term carbon cycle that captures many fundamental features of global climate while retaining the computational efficiency needed to simulate millions of years of time. The Carbon–H2O Coupled HydrOlOgical model with Terrestrial Runoff And INsolation, or CH2O-CHOO TRAIN, couples a one-dimensional (latitudinal) moist static energy balance model of climate with a model for rock weathering and the long-term carbon cycle. The CH2O-CHOO TRAIN is capable of running million-year-long simulations in about 30 min on a laptop PC. The key advantages of this framework are (1) it simulates fundamental climate forcings and feedbacks; (2) it accounts for geographic configuration; and (3) it is flexible, equipped to easily add features, change the strength of feedbacks, and prescribe conditions that are often hard-coded or emergent properties of more complex models, such as climate sensitivity and the strength of meridional heat transport. We show how climate variables governing temperature and the water cycle can impact long-term carbon cycling and climate, and we discuss how the magnitude and direction of this impact can depend on boundary conditions like continental geography. This paper outlines the model equations, presents a sensitivity analysis of the climate responses to varied climatic and carbon cycle perturbations, and discusses potential applications and next stops for the CH2O-CHOO TRAIN.
{"title":"All aboard! Earth system investigations with the CH2O-CHOO TRAIN v1.0","authors":"Tyler Kukla, Daniel E. Ibarra, Kimberly V. Lau, Jeremy K. C. Rugenstein","doi":"10.5194/gmd-16-5515-2023","DOIUrl":"https://doi.org/10.5194/gmd-16-5515-2023","url":null,"abstract":"Abstract. Models of the carbon cycle and climate on geologic (>104-year) timescales have improved tremendously in the last 50 years due to parallel advances in our understanding of the Earth system and the increase in computing power to simulate its key processes. Still, balancing the Earth system's complexity with a model's computational expense is a primary challenge in model development. Simulations spanning hundreds of thousands of years or more generally require a reduction in the complexity of the climate system, omitting features such as radiative feedbacks, shifts in atmospheric circulation, and the expansion and decay of ice sheets, which can have profound effects on the long-term carbon cycle. Here, we present a model for climate and the long-term carbon cycle that captures many fundamental features of global climate while retaining the computational efficiency needed to simulate millions of years of time. The Carbon–H2O Coupled HydrOlOgical model with Terrestrial Runoff And INsolation, or CH2O-CHOO TRAIN, couples a one-dimensional (latitudinal) moist static energy balance model of climate with a model for rock weathering and the long-term carbon cycle. The CH2O-CHOO TRAIN is capable of running million-year-long simulations in about 30 min on a laptop PC. The key advantages of this framework are (1) it simulates fundamental climate forcings and feedbacks; (2) it accounts for geographic configuration; and (3) it is flexible, equipped to easily add features, change the strength of feedbacks, and prescribe conditions that are often hard-coded or emergent properties of more complex models, such as climate sensitivity and the strength of meridional heat transport. We show how climate variables governing temperature and the water cycle can impact long-term carbon cycling and climate, and we discuss how the magnitude and direction of this impact can depend on boundary conditions like continental geography. This paper outlines the model equations, presents a sensitivity analysis of the climate responses to varied climatic and carbon cycle perturbations, and discusses potential applications and next stops for the CH2O-CHOO TRAIN.","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":"181 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135591685","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 : 2023-09-29DOI: 10.5194/gmd-16-5493-2023
Xueying Liu, Yuxuan Wang, Shailaja Wasti, Wei Li, Ehsan Soleimanian, James Flynn, Travis Griggs, Sergio Alvarez, John T. Sullivan, Maurice Roots, Laurence Twigg, Guillaume Gronoff, Timothy Berkoff, Paul Walter, Mark Estes, Johnathan W. Hair, Taylor Shingler, Amy Jo Scarino, Marta Fenn, Laura Judd
Abstract. The TRacking Aerosol Convection ExpeRiment – Air Quality (TRACER-AQ) campaign probed Houston air quality with a comprehensive suite of ground-based and airborne remote sensing measurements during the intensive operating period in September 2021. Two post-frontal high-ozone episodes (6–11 and 23–26 September) were recorded during the aforementioned period. In this study, we evaluated the simulation of the planetary boundary layer (PBL) height and the vertical ozone profile by a high-resolution (1.33 km) 3-D photochemical model, the Weather Research and Forecasting (WRF)-driven GEOS-Chem (WRF-GC). We evaluated the PBL heights with a ceilometer at the coastal site La Porte and the airborne High Spectral Resolution Lidar 2 (HSRL-2) flying over urban Houston and adjacent waters. Compared with the ceilometer at La Porte, the model captures the diurnal variations in the PBL heights with a very strong temporal correlation (R>0.7) and ±20 % biases. Compared with the airborne HSRL-2, the model exhibits a moderate to strong spatial correlation (R=0.26–0.68), with ±20 % biases during the noon and afternoon hours during ozone episodes. For land–water differences in PBL heights, the water has shallower PBL heights compared to land. The model predicts larger land–water differences than the observations because the model consistently underestimates the PBL heights over land compared to water. We evaluated vertical ozone distributions by comparing the model against vertical measurements from the TROPospheric OZone lidar (TROPOZ), the HSRL-2, and ozonesondes, as well as surface measurements at La Porte from a model 49i ozone analyzer and one Continuous Ambient Monitoring Station (CAMS). The model underestimates free-tropospheric ozone (2–3 km aloft) by 9 %–22 % but overestimates near-ground ozone (<50 m aloft) by 6 %-39 % during the two ozone episodes. Boundary layer ozone (0.5–1 km aloft) is underestimated by 1 %–11 % during 8–11 September but overestimated by 0 %–7 % during 23–26 September. Based on these evaluations, we identified two model limitations, namely the single-layer PBL representation and the free-tropospheric ozone underestimation. These limitations have implications for the predictivity of ozone's vertical mixing and distribution in other models.
{"title":"Evaluating WRF-GC v2.0 predictions of boundary layer height and vertical ozone profile during the 2021 TRACER-AQ campaign in Houston, Texas","authors":"Xueying Liu, Yuxuan Wang, Shailaja Wasti, Wei Li, Ehsan Soleimanian, James Flynn, Travis Griggs, Sergio Alvarez, John T. Sullivan, Maurice Roots, Laurence Twigg, Guillaume Gronoff, Timothy Berkoff, Paul Walter, Mark Estes, Johnathan W. Hair, Taylor Shingler, Amy Jo Scarino, Marta Fenn, Laura Judd","doi":"10.5194/gmd-16-5493-2023","DOIUrl":"https://doi.org/10.5194/gmd-16-5493-2023","url":null,"abstract":"Abstract. The TRacking Aerosol Convection ExpeRiment – Air Quality (TRACER-AQ) campaign probed Houston air quality with a comprehensive suite of ground-based and airborne remote sensing measurements during the intensive operating period in September 2021. Two post-frontal high-ozone episodes (6–11 and 23–26 September) were recorded during the aforementioned period. In this study, we evaluated the simulation of the planetary boundary layer (PBL) height and the vertical ozone profile by a high-resolution (1.33 km) 3-D photochemical model, the Weather Research and Forecasting (WRF)-driven GEOS-Chem (WRF-GC). We evaluated the PBL heights with a ceilometer at the coastal site La Porte and the airborne High Spectral Resolution Lidar 2 (HSRL-2) flying over urban Houston and adjacent waters. Compared with the ceilometer at La Porte, the model captures the diurnal variations in the PBL heights with a very strong temporal correlation (R>0.7) and ±20 % biases. Compared with the airborne HSRL-2, the model exhibits a moderate to strong spatial correlation (R=0.26–0.68), with ±20 % biases during the noon and afternoon hours during ozone episodes. For land–water differences in PBL heights, the water has shallower PBL heights compared to land. The model predicts larger land–water differences than the observations because the model consistently underestimates the PBL heights over land compared to water. We evaluated vertical ozone distributions by comparing the model against vertical measurements from the TROPospheric OZone lidar (TROPOZ), the HSRL-2, and ozonesondes, as well as surface measurements at La Porte from a model 49i ozone analyzer and one Continuous Ambient Monitoring Station (CAMS). The model underestimates free-tropospheric ozone (2–3 km aloft) by 9 %–22 % but overestimates near-ground ozone (<50 m aloft) by 6 %-39 % during the two ozone episodes. Boundary layer ozone (0.5–1 km aloft) is underestimated by 1 %–11 % during 8–11 September but overestimated by 0 %–7 % during 23–26 September. Based on these evaluations, we identified two model limitations, namely the single-layer PBL representation and the free-tropospheric ozone underestimation. These limitations have implications for the predictivity of ozone's vertical mixing and distribution in other models.","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135200794","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 : 2023-09-27DOI: 10.5194/gmd-16-5473-2023
Caroline J. van Calcar, Roderik S. W. van de Wal, Bas Blank, Bas de Boer, Wouter van der Wal
Abstract. Glacial isostatic adjustment (GIA) has a stabilizing effect on the evolution of the Antarctic ice sheet by reducing the grounding line migration following ice melt. The timescale and strength of this feedback depends on the spatially varying viscosity of the Earth's mantle. Most studies assume a relatively long and laterally homogenous response time of the bedrock. However, the mantle viscosity is spatially variable, with a high mantle viscosity beneath East Antarctica and a low mantle viscosity beneath West Antarctica. For this study, we have developed a new method to couple a 3D GIA model and an ice sheet model to study the interaction between the solid Earth and the Antarctic ice sheet during the last glacial cycle. With this method, the ice sheet model and GIA model exchange ice thickness and bedrock elevation during a fully coupled transient experiment. The feedback effect is taken into account with a high temporal resolution, where the coupling time steps between the ice sheet and GIA model are 5000 years over the glaciation phase and vary between 500 and 1000 years over the deglaciation phase of the last glacial cycle. During each coupling time step, the bedrock elevation is adjusted at every ice sheet model time step, and the deformation is computed for a linearly changing ice load. We applied the method using the ice sheet model ANICE and a 3D GIA finite element model. We used results from a regional seismic model for Antarctica embedded in the global seismic model SMEAN2 to determine the patterns in the mantle viscosity. The results of simulations over the last glacial cycle show that differences in mantle viscosity of an order of magnitude can lead to differences in the grounding line position up to 700 km and to differences in ice thickness of the order of 2 km for the present day near the Ross Embayment. These results underline and quantify the importance of including local GIA feedback effects in ice sheet models when simulating the Antarctic ice sheet evolution over the last glacial cycle.
{"title":"Simulation of a fully coupled 3D glacial isostatic adjustment – ice sheet model for the Antarctic ice sheet over a glacial cycle","authors":"Caroline J. van Calcar, Roderik S. W. van de Wal, Bas Blank, Bas de Boer, Wouter van der Wal","doi":"10.5194/gmd-16-5473-2023","DOIUrl":"https://doi.org/10.5194/gmd-16-5473-2023","url":null,"abstract":"Abstract. Glacial isostatic adjustment (GIA) has a stabilizing effect on the evolution of the Antarctic ice sheet by reducing the grounding line migration following ice melt. The timescale and strength of this feedback depends on the spatially varying viscosity of the Earth's mantle. Most studies assume a relatively long and laterally homogenous response time of the bedrock. However, the mantle viscosity is spatially variable, with a high mantle viscosity beneath East Antarctica and a low mantle viscosity beneath West Antarctica. For this study, we have developed a new method to couple a 3D GIA model and an ice sheet model to study the interaction between the solid Earth and the Antarctic ice sheet during the last glacial cycle. With this method, the ice sheet model and GIA model exchange ice thickness and bedrock elevation during a fully coupled transient experiment. The feedback effect is taken into account with a high temporal resolution, where the coupling time steps between the ice sheet and GIA model are 5000 years over the glaciation phase and vary between 500 and 1000 years over the deglaciation phase of the last glacial cycle. During each coupling time step, the bedrock elevation is adjusted at every ice sheet model time step, and the deformation is computed for a linearly changing ice load. We applied the method using the ice sheet model ANICE and a 3D GIA finite element model. We used results from a regional seismic model for Antarctica embedded in the global seismic model SMEAN2 to determine the patterns in the mantle viscosity. The results of simulations over the last glacial cycle show that differences in mantle viscosity of an order of magnitude can lead to differences in the grounding line position up to 700 km and to differences in ice thickness of the order of 2 km for the present day near the Ross Embayment. These results underline and quantify the importance of including local GIA feedback effects in ice sheet models when simulating the Antarctic ice sheet evolution over the last glacial cycle.","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135537316","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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