Pub Date : 2023-11-01DOI: 10.5194/gmd-16-6127-2023
Doroteaciro Iovino, Pier Giuseppe Fogli, Simona Masina
Abstract. This paper describes the global eddying ocean–sea ice simulation produced at the Euro-Mediterranean Center on Climate Change (CMCC) obtained following the experimental design of the Ocean Model Intercomparison Project phase 2 (OMIP2). The eddy-rich model (GLOB16) is based on the NEMOv3.6 framework, with a global horizontal resolution of 1/16∘ and 98 vertical levels and was originally designed for an operational short-term ocean forecasting system. Here, it is driven by one multi-decadal cycle of the prescribed JRA55-do atmospheric reanalysis and runoff dataset in order to perform a long-term benchmarking experiment. To assess the accuracy of simulated 3D ocean fields and highlight the relative benefits of resolving mesoscale processes, the GLOB16 performances are evaluated via a selection of key climate metrics against observational datasets and two other NEMO configurations at lower resolutions: an eddy-permitting resolution (ORCA025) and a non-eddying resolution (ORCA1) designed to form the ocean–sea ice component of the fully coupled CMCC climate model. The well-known biases in the low-resolution simulations are significantly improved in the high-resolution model. The evolution and spatial pattern of large-scale features (such as sea surface temperature biases and winter mixed-layer structure) in GLOB16 are generally better reproduced, and the large-scale circulation is remarkably improved compared to the low-resolution oceans. We find that eddying resolution is an advantage in resolving the structure of western boundary currents, the overturning cells, and flow through key passages. GLOB16 might be an appropriate tool for ocean climate modeling efforts, even though the benefit of eddying resolution does not provide unambiguous advances for all ocean variables in all regions.
{"title":"Evaluation of the CMCC global eddying ocean model for the Ocean Model Intercomparison Project (OMIP2)","authors":"Doroteaciro Iovino, Pier Giuseppe Fogli, Simona Masina","doi":"10.5194/gmd-16-6127-2023","DOIUrl":"https://doi.org/10.5194/gmd-16-6127-2023","url":null,"abstract":"Abstract. This paper describes the global eddying ocean–sea ice simulation produced at the Euro-Mediterranean Center on Climate Change (CMCC) obtained following the experimental design of the Ocean Model Intercomparison Project phase 2 (OMIP2). The eddy-rich model (GLOB16) is based on the NEMOv3.6 framework, with a global horizontal resolution of 1/16∘ and 98 vertical levels and was originally designed for an operational short-term ocean forecasting system. Here, it is driven by one multi-decadal cycle of the prescribed JRA55-do atmospheric reanalysis and runoff dataset in order to perform a long-term benchmarking experiment. To assess the accuracy of simulated 3D ocean fields and highlight the relative benefits of resolving mesoscale processes, the GLOB16 performances are evaluated via a selection of key climate metrics against observational datasets and two other NEMO configurations at lower resolutions: an eddy-permitting resolution (ORCA025) and a non-eddying resolution (ORCA1) designed to form the ocean–sea ice component of the fully coupled CMCC climate model. The well-known biases in the low-resolution simulations are significantly improved in the high-resolution model. The evolution and spatial pattern of large-scale features (such as sea surface temperature biases and winter mixed-layer structure) in GLOB16 are generally better reproduced, and the large-scale circulation is remarkably improved compared to the low-resolution oceans. We find that eddying resolution is an advantage in resolving the structure of western boundary currents, the overturning cells, and flow through key passages. GLOB16 might be an appropriate tool for ocean climate modeling efforts, even though the benefit of eddying resolution does not provide unambiguous advances for all ocean variables in all regions.","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":"126 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135326079","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 : 2023-11-01DOI: 10.5194/gmd-16-6161-2023
Dien Wu, Joshua L. Laughner, Junjie Liu, Paul I. Palmer, John C. Lin, Paul O. Wennberg
Abstract. Satellites monitoring air pollutants (e.g., nitrogen oxides; NOx = NO + NO2) or greenhouse gases (GHGs) are widely utilized to understand the spatiotemporal variability in and evolution of emission characteristics, chemical transformations, and atmospheric transport over anthropogenic hotspots. Recently, the joint use of space-based long-lived GHGs (e.g., carbon dioxide; CO2) and short-lived pollutants has made it possible to improve our understanding of emission characteristics. Some previous studies, however, lack consideration of the non-linear NOx chemistry or complex atmospheric transport. Considering the increase in satellite data volume and the demand for emission monitoring at higher spatiotemporal scales, it is crucial to construct a local-scale emission optimization system that can handle both long-lived GHGs and short-lived pollutants in a coupled and effective manner. This need motivates us to develop a Lagrangian chemical transport model that accounts for NOx chemistry and fine-scale atmospheric transport (STILT–NOx) and to investigate how physical and chemical processes, anthropogenic emissions, and background may affect the interpretation of tropospheric NO2 columns (tNO2). Interpreting emission signals from tNO2 commonly involves either an efficient statistical model or a sophisticated chemical transport model. To balance computational expenses and chemical complexity, we describe a simplified representation of the NOx chemistry that bypasses an explicit solution of individual chemical reactions while preserving the essential non-linearity that links NOx emissions to its concentrations. This NOx chemical parameterization is then incorporated into an existing Lagrangian modeling framework that is widely applied in the GHG community. We further quantify uncertainties associated with the wind field and chemical parameterization and evaluate modeled columns against retrieved columns from the TROPOspheric Monitoring Instrument (TROPOMI v2.1). Specifically, simulations with alternative model configurations of emissions, meteorology, chemistry, and inter-parcel mixing are carried out over three United States (US) power plants and two urban areas across seasons. Using the U.S. Environmental Protection Agency (EPA)-reported emissions for power plants with non-linear NOx chemistry improves the model–data alignment in tNO2 (a high bias of ≤ 10 % on an annual basis), compared to simulations using either the Emissions Database for Global Atmospheric Research (EDGAR) model or without chemistry (bias approaching 100 %). The largest model–data mismatches are associated with substantial biases in wind directions or conditions of slower atmospheric mixing and photochemistry. More importantly, our model development illustrates (1) how NOx chemistry affects the relationship between NOx and CO2 in terms of the spatial and seasonal variability and (2) how assimilating tNO2 can quantify systematic biases in modeled wind directions and emission
摘要监测空气污染物(如氮氧化物)的卫星;NOx = NO + NO2)或温室气体(ghg)被广泛用于了解人为热点地区排放特征、化学转化和大气输送的时空变化及其演化。最近，联合使用天基长寿命温室气体(如二氧化碳;二氧化碳)和短寿命污染物使我们有可能提高对排放特性的理解。然而，以往的一些研究缺乏对氮氧化物非线性化学或复杂大气输送的考虑。考虑到卫星数据量的增加和对更高时空尺度排放监测的需求，构建一个能够耦合有效处理长寿命温室气体和短寿命污染物的局地尺度排放优化系统至关重要。这一需求促使我们开发一个拉格朗日化学输运模型来解释氮氧化物化学和精细尺度大气输运(STILT-NOx)，并研究物理和化学过程、人为排放和背景如何影响对流层NO2柱(tNO2)的解释。解释二氧化氮的排放信号通常涉及有效的统计模型或复杂的化学输运模型。为了平衡计算费用和化学复杂性，我们描述了氮氧化物化学的简化表示，该表示绕过单个化学反应的显式解决方案，同时保留了将氮氧化物排放与其浓度联系起来的基本非线性。然后将这种氮氧化物化学参数化纳入到现有的拉格朗日建模框架中，该框架已广泛应用于温室气体领域。我们进一步量化了与风场和化学参数化相关的不确定性，并将模拟柱与对流层监测仪器(TROPOMI v2.1)检索的柱进行了比较。具体来说，使用排放、气象学、化学和包裹间混合的替代模型配置进行了模拟，在美国的三个发电厂和两个城市地区进行了跨季节的模拟。与使用全球大气研究排放数据库(EDGAR)模型或不使用化学模型(偏差接近100%)的模拟相比，使用美国环境保护署(EPA)报告的具有非线性氮氧化物化学的发电厂的排放可以改善tNO2模型数据的一致性(每年的高偏差≤10%)。最大的模式数据不匹配与风向或较慢的大气混合和光化学条件的实质性偏差有关。更重要的是，我们的模型开发说明了(1)氮氧化物化学如何在空间和季节变化方面影响氮氧化物和二氧化碳之间的关系;(2)同化tNO2如何量化模拟风向和排放分布在氮氧化物和二氧化碳先前清单中的系统偏差，这为局域尺度的多示踪剂排放优化系统奠定了基础。
{"title":"A simplified non-linear chemistry transport model for analyzing NO<sub>2</sub> column observations: STILT–NO<sub><i>x</i></sub>","authors":"Dien Wu, Joshua L. Laughner, Junjie Liu, Paul I. Palmer, John C. Lin, Paul O. Wennberg","doi":"10.5194/gmd-16-6161-2023","DOIUrl":"https://doi.org/10.5194/gmd-16-6161-2023","url":null,"abstract":"Abstract. Satellites monitoring air pollutants (e.g., nitrogen oxides; NOx = NO + NO2) or greenhouse gases (GHGs) are widely utilized to understand the spatiotemporal variability in and evolution of emission characteristics, chemical transformations, and atmospheric transport over anthropogenic hotspots. Recently, the joint use of space-based long-lived GHGs (e.g., carbon dioxide; CO2) and short-lived pollutants has made it possible to improve our understanding of emission characteristics. Some previous studies, however, lack consideration of the non-linear NOx chemistry or complex atmospheric transport. Considering the increase in satellite data volume and the demand for emission monitoring at higher spatiotemporal scales, it is crucial to construct a local-scale emission optimization system that can handle both long-lived GHGs and short-lived pollutants in a coupled and effective manner. This need motivates us to develop a Lagrangian chemical transport model that accounts for NOx chemistry and fine-scale atmospheric transport (STILT–NOx) and to investigate how physical and chemical processes, anthropogenic emissions, and background may affect the interpretation of tropospheric NO2 columns (tNO2). Interpreting emission signals from tNO2 commonly involves either an efficient statistical model or a sophisticated chemical transport model. To balance computational expenses and chemical complexity, we describe a simplified representation of the NOx chemistry that bypasses an explicit solution of individual chemical reactions while preserving the essential non-linearity that links NOx emissions to its concentrations. This NOx chemical parameterization is then incorporated into an existing Lagrangian modeling framework that is widely applied in the GHG community. We further quantify uncertainties associated with the wind field and chemical parameterization and evaluate modeled columns against retrieved columns from the TROPOspheric Monitoring Instrument (TROPOMI v2.1). Specifically, simulations with alternative model configurations of emissions, meteorology, chemistry, and inter-parcel mixing are carried out over three United States (US) power plants and two urban areas across seasons. Using the U.S. Environmental Protection Agency (EPA)-reported emissions for power plants with non-linear NOx chemistry improves the model–data alignment in tNO2 (a high bias of ≤ 10 % on an annual basis), compared to simulations using either the Emissions Database for Global Atmospheric Research (EDGAR) model or without chemistry (bias approaching 100 %). The largest model–data mismatches are associated with substantial biases in wind directions or conditions of slower atmospheric mixing and photochemistry. More importantly, our model development illustrates (1) how NOx chemistry affects the relationship between NOx and CO2 in terms of the spatial and seasonal variability and (2) how assimilating tNO2 can quantify systematic biases in modeled wind directions and emission","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":"62 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135326360","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 : 2023-10-31DOI: 10.5194/gmd-16-6067-2023
Jiangshan Zhu, Ross Noel Bannister
Abstract. The prediction of convection (in terms of position, timing, and strength) is important to achieve for high-resolution weather forecasting. This problem requires not only good convective-scale models, but also data assimilation systems that give initial conditions which neither improperly hinder nor improperly hasten convection in the ensuing forecasts. Solving this problem is difficult and expensive using operational-scale numerical weather prediction systems, and so a simplified model of convective-scale flow is under development (called the “ABC model”). This paper extends the existing ABC model of dry convective-scale flow to include mixing ratios of vapour and condensate phases of water. The revised model is called “Hydro-ABC”. Hydro-ABC includes transport of the vapour and condensate mixing ratios within a dynamical core, and it transitions between these two phases via a micro-physics scheme. A saturated mixing ratio is derived from model quantities, which helps determine whether evaporation or condensation happens. Latent heat is exchanged with the buoyancy variable (ABC's potential-temperature-like variable) in such a way to conserve total energy, where total energy is the sum of dry energy and latent heat. The model equations are designed to conserve the domain-total mass, water, and energy. An example numerical model integration is performed and analysed, which shows the development of a realistic looking anvil cloud and excitation of inertio-gravity and acoustic modes over a wide range of frequencies. This behaviour means that Hydro-ABC is a sufficiently challenging model which will allow experimentation with innovative data assimilation strategies in future work. An ensemble of Hydro-ABC integrations is performed in order to study the possible forecast error covariance statistics (knowledge of which is necessary for data assimilation). These show patterns that are dependent on the presence of convective activity (at any model's vertical column), thus giving a taste of flow-dependent error statistics. Candidate indicators/harbingers of convection are also evaluated (namely relative humidity, hydrostatic imbalance, horizontal divergence, convective available potential energy, convective inhibition, vertical wind, and the condensate mixing ratio), some of which appear to be reliable diagnostics concerning the presence of convection. These diagnostics will be useful in the selection of the relevant forecast error covariance statistics when data assimilation for Hydro-ABC is developed.
{"title":"The Hydro-ABC model (Version 2.0): a simplified convective-scale model with moist dynamics","authors":"Jiangshan Zhu, Ross Noel Bannister","doi":"10.5194/gmd-16-6067-2023","DOIUrl":"https://doi.org/10.5194/gmd-16-6067-2023","url":null,"abstract":"Abstract. The prediction of convection (in terms of position, timing, and strength) is important to achieve for high-resolution weather forecasting. This problem requires not only good convective-scale models, but also data assimilation systems that give initial conditions which neither improperly hinder nor improperly hasten convection in the ensuing forecasts. Solving this problem is difficult and expensive using operational-scale numerical weather prediction systems, and so a simplified model of convective-scale flow is under development (called the “ABC model”). This paper extends the existing ABC model of dry convective-scale flow to include mixing ratios of vapour and condensate phases of water. The revised model is called “Hydro-ABC”. Hydro-ABC includes transport of the vapour and condensate mixing ratios within a dynamical core, and it transitions between these two phases via a micro-physics scheme. A saturated mixing ratio is derived from model quantities, which helps determine whether evaporation or condensation happens. Latent heat is exchanged with the buoyancy variable (ABC's potential-temperature-like variable) in such a way to conserve total energy, where total energy is the sum of dry energy and latent heat. The model equations are designed to conserve the domain-total mass, water, and energy. An example numerical model integration is performed and analysed, which shows the development of a realistic looking anvil cloud and excitation of inertio-gravity and acoustic modes over a wide range of frequencies. This behaviour means that Hydro-ABC is a sufficiently challenging model which will allow experimentation with innovative data assimilation strategies in future work. An ensemble of Hydro-ABC integrations is performed in order to study the possible forecast error covariance statistics (knowledge of which is necessary for data assimilation). These show patterns that are dependent on the presence of convective activity (at any model's vertical column), thus giving a taste of flow-dependent error statistics. Candidate indicators/harbingers of convection are also evaluated (namely relative humidity, hydrostatic imbalance, horizontal divergence, convective available potential energy, convective inhibition, vertical wind, and the condensate mixing ratio), some of which appear to be reliable diagnostics concerning the presence of convection. These diagnostics will be useful in the selection of the relevant forecast error covariance statistics when data assimilation for Hydro-ABC is developed.","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":"59 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135813620","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. The Rapid Adaptive Optimization Model for Atmospheric Chemistry (ROMAC) is a flexible and computationally efficient photochemical box model. Its unique adaptive dynamic optimization module allows for the dynamic and rapid estimation of the impact of chemical and physical processes on pollutant concentration. ROMAC outperforms traditional box models in evaluating the influence of physical processes on pollutant concentrations. Its ability to quantify the effects of chemical and physical processes on pollutant concentrations has been confirmed through chamber and field observation cases. Since the development of a variable-step and variable-order numerical solver that eliminates the need for Jacobian matrix processing, the computational efficiency of ROMAC has seen a marked improvement with only a marginal increase in error. Specifically, the computational efficiency has improved by 96 % when compared to several established box models, such as F0AM and AtChem. Moreover, the solver maintains a discrepancy of less than 0.1 % when its results are compared with those obtained from a high-precision solver in AtChem.
{"title":"Rapid Adaptive Optimization Model for Atmospheric Chemistry (ROMAC) v1.0","authors":"Jiangyong Li, Chunlin Zhang, Wenlong Zhao, Shijie Han, Yu Wang, Hao Wang, Boguang Wang","doi":"10.5194/gmd-16-6049-2023","DOIUrl":"https://doi.org/10.5194/gmd-16-6049-2023","url":null,"abstract":"Abstract. The Rapid Adaptive Optimization Model for Atmospheric Chemistry (ROMAC) is a flexible and computationally efficient photochemical box model. Its unique adaptive dynamic optimization module allows for the dynamic and rapid estimation of the impact of chemical and physical processes on pollutant concentration. ROMAC outperforms traditional box models in evaluating the influence of physical processes on pollutant concentrations. Its ability to quantify the effects of chemical and physical processes on pollutant concentrations has been confirmed through chamber and field observation cases. Since the development of a variable-step and variable-order numerical solver that eliminates the need for Jacobian matrix processing, the computational efficiency of ROMAC has seen a marked improvement with only a marginal increase in error. Specifically, the computational efficiency has improved by 96 % when compared to several established box models, such as F0AM and AtChem. Moreover, the solver maintains a discrepancy of less than 0.1 % when its results are compared with those obtained from a high-precision solver in AtChem.","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":"80 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136102329","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 : 2023-10-27DOI: 10.5194/gmd-16-6029-2023
Lina Vitali, Kees Cuvelier, Antonio Piersanti, Alexandra Monteiro, Mario Adani, Roberta Amorati, Agnieszka Bartocha, Alessandro D'Ausilio, Paweł Durka, Carla Gama, Giulia Giovannini, Stijn Janssen, Tomasz Przybyła, Michele Stortini, Stijn Vranckx, Philippe Thunis
Abstract. A standardized methodology for the validation of short-term air quality forecast applications was developed in the framework of the Forum for Air quality Modeling (FAIRMODE) activities. The proposed approach, focusing on specific features to be checked when evaluating a forecasting application, investigates the model's capability to detect sudden changes in pollutant concentration levels, predict threshold exceedances and reproduce air quality indices. The proposed formulation relies on the definition of specific forecast modelling quality objectives and performance criteria, defining the minimum level of quality to be achieved by a forecasting application when it is used for policy purposes. The persistence model, which uses the most recent observed value as the predicted value, is used as a benchmark for the forecast evaluation. The validation protocol has been applied to several forecasting applications across Europe, using different modelling paradigms and covering a range of geographical contexts and spatial scales. The method is successful, with room for improvement, in highlighting shortcomings and strengths of forecasting applications. This provides a useful basis for using short-term air quality forecasts as a supporting tool for providing correct information to citizens and regulators.
{"title":"A standardized methodology for the validation of air quality forecast applications (F-MQO): lessons learnt from its application across Europe","authors":"Lina Vitali, Kees Cuvelier, Antonio Piersanti, Alexandra Monteiro, Mario Adani, Roberta Amorati, Agnieszka Bartocha, Alessandro D'Ausilio, Paweł Durka, Carla Gama, Giulia Giovannini, Stijn Janssen, Tomasz Przybyła, Michele Stortini, Stijn Vranckx, Philippe Thunis","doi":"10.5194/gmd-16-6029-2023","DOIUrl":"https://doi.org/10.5194/gmd-16-6029-2023","url":null,"abstract":"Abstract. A standardized methodology for the validation of short-term air quality forecast applications was developed in the framework of the Forum for Air quality Modeling (FAIRMODE) activities. The proposed approach, focusing on specific features to be checked when evaluating a forecasting application, investigates the model's capability to detect sudden changes in pollutant concentration levels, predict threshold exceedances and reproduce air quality indices. The proposed formulation relies on the definition of specific forecast modelling quality objectives and performance criteria, defining the minimum level of quality to be achieved by a forecasting application when it is used for policy purposes. The persistence model, which uses the most recent observed value as the predicted value, is used as a benchmark for the forecast evaluation. The validation protocol has been applied to several forecasting applications across Europe, using different modelling paradigms and covering a range of geographical contexts and spatial scales. The method is successful, with room for improvement, in highlighting shortcomings and strengths of forecasting applications. This provides a useful basis for using short-term air quality forecasts as a supporting tool for providing correct information to citizens and regulators.","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":"269 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136262286","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 : 2023-10-26DOI: 10.5194/gmd-16-6001-2023
Wenfu Tang, Louisa K. Emmons, Helen M. Worden, Rajesh Kumar, Cenlin He, Benjamin Gaubert, Zhonghua Zheng, Simone Tilmes, Rebecca R. Buchholz, Sara-Eva Martinez-Alonso, Claire Granier, Antonin Soulie, Kathryn McKain, Bruce C. Daube, Jeff Peischl, Chelsea Thompson, Pieternel Levelt
Abstract. The Multi-Scale Infrastructure for Chemistry and Aerosols Version 0 (MUSICAv0) is a new community modeling infrastructure that enables the study of atmospheric composition and chemistry across all relevant scales. We develop a MUSICAv0 grid with Africa refinement (∼ 28 km × 28 km over Africa). We evaluate the MUSICAv0 simulation for 2017 with in situ observations and compare the model results to satellite products over Africa. A simulation from the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), a regional model that is widely used in Africa studies, is also included in the analyses as a reference. Overall, the performance of MUSICAv0 is comparable to WRF-Chem. Both models underestimate carbon monoxide (CO) compared to in situ observations and satellite CO column retrievals from the Measurements of Pollution in the Troposphere (MOPITT) satellite instrument. MUSICAv0 tends to overestimate ozone (O3), likely due to overestimated stratosphere-to-troposphere flux of ozone. Both models significantly underestimate fine particulate matter (PM2.5) at two surface sites in East Africa. The MUSICAv0 simulation agrees better with aerosol optical depth (AOD) retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS) and tropospheric nitrogen dioxide (NO2) column retrievals from the Ozone Monitoring Instrument (OMI) than WRF-Chem. MUSICAv0 has a consistently lower tropospheric formaldehyde (HCHO) column than OMI retrievals. Based on model–satellite discrepancies between MUSICAv0 and WRF-Chem and MOPITT CO, MODIS AOD, and OMI tropospheric NO2, we find that future field campaign(s) and more in situ observations in the East African region (5∘ S–5∘ N, 30–45∘ E) could substantially improve the predictive skill of atmospheric chemistry model(s). This suggested focus region exhibits the largest model–in situ observation discrepancies, as well as targets for high population density, land cover variability, and anthropogenic pollution sources.
{"title":"Application of the Multi-Scale Infrastructure for Chemistry and Aerosols version 0 (MUSICAv0) for air quality research in Africa","authors":"Wenfu Tang, Louisa K. Emmons, Helen M. Worden, Rajesh Kumar, Cenlin He, Benjamin Gaubert, Zhonghua Zheng, Simone Tilmes, Rebecca R. Buchholz, Sara-Eva Martinez-Alonso, Claire Granier, Antonin Soulie, Kathryn McKain, Bruce C. Daube, Jeff Peischl, Chelsea Thompson, Pieternel Levelt","doi":"10.5194/gmd-16-6001-2023","DOIUrl":"https://doi.org/10.5194/gmd-16-6001-2023","url":null,"abstract":"Abstract. The Multi-Scale Infrastructure for Chemistry and Aerosols Version 0 (MUSICAv0) is a new community modeling infrastructure that enables the study of atmospheric composition and chemistry across all relevant scales. We develop a MUSICAv0 grid with Africa refinement (∼ 28 km × 28 km over Africa). We evaluate the MUSICAv0 simulation for 2017 with in situ observations and compare the model results to satellite products over Africa. A simulation from the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), a regional model that is widely used in Africa studies, is also included in the analyses as a reference. Overall, the performance of MUSICAv0 is comparable to WRF-Chem. Both models underestimate carbon monoxide (CO) compared to in situ observations and satellite CO column retrievals from the Measurements of Pollution in the Troposphere (MOPITT) satellite instrument. MUSICAv0 tends to overestimate ozone (O3), likely due to overestimated stratosphere-to-troposphere flux of ozone. Both models significantly underestimate fine particulate matter (PM2.5) at two surface sites in East Africa. The MUSICAv0 simulation agrees better with aerosol optical depth (AOD) retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS) and tropospheric nitrogen dioxide (NO2) column retrievals from the Ozone Monitoring Instrument (OMI) than WRF-Chem. MUSICAv0 has a consistently lower tropospheric formaldehyde (HCHO) column than OMI retrievals. Based on model–satellite discrepancies between MUSICAv0 and WRF-Chem and MOPITT CO, MODIS AOD, and OMI tropospheric NO2, we find that future field campaign(s) and more in situ observations in the East African region (5∘ S–5∘ N, 30–45∘ E) could substantially improve the predictive skill of atmospheric chemistry model(s). This suggested focus region exhibits the largest model–in situ observation discrepancies, as well as targets for high population density, land cover variability, and anthropogenic pollution sources.","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134908205","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 : 2023-10-26DOI: 10.5194/gmd-16-5979-2023
Danica L. Lombardozzi, William R. Wieder, Negin Sobhani, Gordon B. Bonan, David Durden, Dawn Lenz, Michael SanClements, Samantha Weintraub-Leff, Edward Ayres, Christopher R. Florian, Kyla Dahlin, Sanjiv Kumar, Abigail L. S. Swann, Claire M. Zarakas, Charles Vardeman, Valerio Pascucci
Abstract. Global change research demands a convergence among academic disciplines to understand complex changes in Earth system function. Limitations related to data usability and computing infrastructure, however, present barriers to effective use of the research tools needed for this cross-disciplinary collaboration. To address these barriers, we created a computational platform that pairs meteorological data and site-level ecosystem characterizations from the National Ecological Observatory Network (NEON) with the Community Terrestrial System Model (CTSM) that is developed with university partners at the National Center for Atmospheric Research (NCAR). This NCAR–NEON system features a simplified user interface that facilitates access to and use of NEON observations and NCAR models. We present preliminary results that compare observed NEON fluxes with CTSM simulations and describe how the collaboration between NCAR and NEON that can be used by the global change research community improves both the data and model. Beyond datasets and computing, the NCAR–NEON system includes tutorials and visualization tools that facilitate interaction with observational and model datasets and further enable opportunities for teaching and research. By expanding access to data, models, and computing, cyberinfrastructure tools like the NCAR–NEON system will accelerate integration across ecology and climate science disciplines to advance understanding in Earth system science and global change.
{"title":"Overcoming barriers to enable convergence research by integrating ecological and climate sciences: the NCAR–NEON system Version 1","authors":"Danica L. Lombardozzi, William R. Wieder, Negin Sobhani, Gordon B. Bonan, David Durden, Dawn Lenz, Michael SanClements, Samantha Weintraub-Leff, Edward Ayres, Christopher R. Florian, Kyla Dahlin, Sanjiv Kumar, Abigail L. S. Swann, Claire M. Zarakas, Charles Vardeman, Valerio Pascucci","doi":"10.5194/gmd-16-5979-2023","DOIUrl":"https://doi.org/10.5194/gmd-16-5979-2023","url":null,"abstract":"Abstract. Global change research demands a convergence among academic disciplines to understand complex changes in Earth system function. Limitations related to data usability and computing infrastructure, however, present barriers to effective use of the research tools needed for this cross-disciplinary collaboration. To address these barriers, we created a computational platform that pairs meteorological data and site-level ecosystem characterizations from the National Ecological Observatory Network (NEON) with the Community Terrestrial System Model (CTSM) that is developed with university partners at the National Center for Atmospheric Research (NCAR). This NCAR–NEON system features a simplified user interface that facilitates access to and use of NEON observations and NCAR models. We present preliminary results that compare observed NEON fluxes with CTSM simulations and describe how the collaboration between NCAR and NEON that can be used by the global change research community improves both the data and model. Beyond datasets and computing, the NCAR–NEON system includes tutorials and visualization tools that facilitate interaction with observational and model datasets and further enable opportunities for teaching and research. By expanding access to data, models, and computing, cyberinfrastructure tools like the NCAR–NEON system will accelerate integration across ecology and climate science disciplines to advance understanding in Earth system science and global change.","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":"168 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136381510","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 : 2023-10-20DOI: 10.5194/gmd-16-5931-2023
Megan A. Stretton, William Morrison, Robin J. Hogan, Sue Grimmond
Abstract. Cities' materials and urban form impact radiative exchanges and surface and air temperatures. Here, the SPARTACUS (Speedy Algorithm for Radiative Transfer through Cloud Sides) multi-layer approach to modelling longwave radiation in urban areas (SPARTACUS-Urban) is evaluated using the explicit DART (Discrete Anisotropic Radiative Transfer) model. SPARTACUS-Urban describes realistic 3D urban geometry statistically rather than assuming an infinite street canyon. Longwave flux profiles are compared across an August day for a 2 km × 2 km domain in central London. Simulations are conducted with multiple temperature configurations, including realistic temperature profiles derived from thermal camera observations. The SPARTACUS-Urban model performs well (cf. DART, 2022) when all facets are prescribed a single temperature, with normalised bias errors (nBEs) <2.5 % for downwelling fluxes, and <0.5 % for top-of-canopy upwelling fluxes. Errors are larger (nBE <8 %) for net longwave fluxes from walls and roofs. Using more realistic surface temperatures, varying depending on surface shading, the nBE in upwelling longwave increases to ∼2 %. Errors in roof and wall net longwave fluxes increase through the day, but nBEs are still 8 %–11 %. This increase in nBE occurs because SPARTACUS-Urban represents vertical but not horizontal surface temperature variation within a domain. Additionally, SPARTACUS-Urban outperforms the Harman single-layer canyon approach, particularly in the longwave interception by roofs. We conclude that SPARTACUS-Urban accurately predicts longwave fluxes, requiring less computational time (cf. DART, 2022) but with larger errors when surface temperatures vary due to shading. SPARTACUS-Urban could enhance multi-layer urban energy balance scheme prediction of within-canopy temperatures and fluxes.
{"title":"Evaluation of vertically resolved longwave radiation in SPARTACUS-Urban 0.7.3 and the sensitivity to urban surface temperatures","authors":"Megan A. Stretton, William Morrison, Robin J. Hogan, Sue Grimmond","doi":"10.5194/gmd-16-5931-2023","DOIUrl":"https://doi.org/10.5194/gmd-16-5931-2023","url":null,"abstract":"Abstract. Cities' materials and urban form impact radiative exchanges and surface and air temperatures. Here, the SPARTACUS (Speedy Algorithm for Radiative Transfer through Cloud Sides) multi-layer approach to modelling longwave radiation in urban areas (SPARTACUS-Urban) is evaluated using the explicit DART (Discrete Anisotropic Radiative Transfer) model. SPARTACUS-Urban describes realistic 3D urban geometry statistically rather than assuming an infinite street canyon. Longwave flux profiles are compared across an August day for a 2 km × 2 km domain in central London. Simulations are conducted with multiple temperature configurations, including realistic temperature profiles derived from thermal camera observations. The SPARTACUS-Urban model performs well (cf. DART, 2022) when all facets are prescribed a single temperature, with normalised bias errors (nBEs) <2.5 % for downwelling fluxes, and <0.5 % for top-of-canopy upwelling fluxes. Errors are larger (nBE <8 %) for net longwave fluxes from walls and roofs. Using more realistic surface temperatures, varying depending on surface shading, the nBE in upwelling longwave increases to ∼2 %. Errors in roof and wall net longwave fluxes increase through the day, but nBEs are still 8 %–11 %. This increase in nBE occurs because SPARTACUS-Urban represents vertical but not horizontal surface temperature variation within a domain. Additionally, SPARTACUS-Urban outperforms the Harman single-layer canyon approach, particularly in the longwave interception by roofs. We conclude that SPARTACUS-Urban accurately predicts longwave fluxes, requiring less computational time (cf. DART, 2022) but with larger errors when surface temperatures vary due to shading. SPARTACUS-Urban could enhance multi-layer urban energy balance scheme prediction of within-canopy temperatures and fluxes.","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135571061","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. Top-down atmospheric inversion infers surface–atmosphere fluxes from spatially distributed observations of atmospheric composition in order to quantify anthropogenic and natural emissions. In this study, we developed a Regional multi-Air Pollutant Assimilation System (RAPAS v1.0) based on the Weather Research and Forecasting–Community Multiscale Air Quality (WRF–CMAQ) modeling system model, the three-dimensional variational (3D-Var) algorithm, and the ensemble square root filter (EnSRF) algorithm. This system can simultaneously assimilate hourly in situ CO, SO2, NO2, PM2.5, and PM10 observations to infer gridded emissions of CO, SO2, NOx, primary PM2.5 (PPM2.5), and coarse PM10 (PMC) on a regional scale. In each data assimilation window, we use a “two-step” scheme, in which the emissions are inferred first and then input into the CMAQ model to simulate initial conditions (ICs) of the next window. The posterior emissions are then transferred to the next window as prior emissions, and the original emission inventory is only used in the first window. Additionally, a “super-observation” approach is implemented to decrease the computational costs, observation error correlations, and influence of representative errors. Using this system, we estimated the emissions of CO, SO2, NOx, PPM2.5, and PMC in December and July 2016 over China using nationwide surface observations. The results show that compared to the prior emissions (2016 Multi-resolution Emission Inventory for China – MEIC 2016)), the posterior emissions of CO, SO2, NOx, PPM2.5, and PMC in December 2016 increased by 129 %, 20 %, 5 %, 95 %, and 1045 %, respectively, and the emission uncertainties decreased by 44 %, 45 %, 34 %, 52 %, and 56 %, respectively. With the inverted emissions, the RMSE of simulated concentrations decreased by 40 %–56 %. Sensitivity tests were conducted with different prior emissions, prior uncertainties, and observation errors. The results showed that the two-step scheme employed in RAPAS is robust in estimating emissions using nationwide surface observations over China. This study offers a useful tool for accurately quantifying multi-species anthropogenic emissions at large scales and in near-real time.
{"title":"A Regional multi-Air Pollutant Assimilation System (RAPAS v1.0) for emission estimates: system development and application","authors":"Shuzhuang Feng, Fei Jiang, Zheng Wu, Hengmao Wang, Wei He, Yang Shen, Lingyu Zhang, Yanhua Zheng, Chenxi Lou, Ziqiang Jiang, Weimin Ju","doi":"10.5194/gmd-16-5949-2023","DOIUrl":"https://doi.org/10.5194/gmd-16-5949-2023","url":null,"abstract":"Abstract. Top-down atmospheric inversion infers surface–atmosphere fluxes from spatially distributed observations of atmospheric composition in order to quantify anthropogenic and natural emissions. In this study, we developed a Regional multi-Air Pollutant Assimilation System (RAPAS v1.0) based on the Weather Research and Forecasting–Community Multiscale Air Quality (WRF–CMAQ) modeling system model, the three-dimensional variational (3D-Var) algorithm, and the ensemble square root filter (EnSRF) algorithm. This system can simultaneously assimilate hourly in situ CO, SO2, NO2, PM2.5, and PM10 observations to infer gridded emissions of CO, SO2, NOx, primary PM2.5 (PPM2.5), and coarse PM10 (PMC) on a regional scale. In each data assimilation window, we use a “two-step” scheme, in which the emissions are inferred first and then input into the CMAQ model to simulate initial conditions (ICs) of the next window. The posterior emissions are then transferred to the next window as prior emissions, and the original emission inventory is only used in the first window. Additionally, a “super-observation” approach is implemented to decrease the computational costs, observation error correlations, and influence of representative errors. Using this system, we estimated the emissions of CO, SO2, NOx, PPM2.5, and PMC in December and July 2016 over China using nationwide surface observations. The results show that compared to the prior emissions (2016 Multi-resolution Emission Inventory for China – MEIC 2016)), the posterior emissions of CO, SO2, NOx, PPM2.5, and PMC in December 2016 increased by 129 %, 20 %, 5 %, 95 %, and 1045 %, respectively, and the emission uncertainties decreased by 44 %, 45 %, 34 %, 52 %, and 56 %, respectively. With the inverted emissions, the RMSE of simulated concentrations decreased by 40 %–56 %. Sensitivity tests were conducted with different prior emissions, prior uncertainties, and observation errors. The results showed that the two-step scheme employed in RAPAS is robust in estimating emissions using nationwide surface observations over China. This study offers a useful tool for accurately quantifying multi-species anthropogenic emissions at large scales and in near-real time.","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135617074","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 : 2023-10-20DOI: 10.5194/gmd-16-5915-2023
Siyu Zhu, Peipei Wu, Siyi Zhang, Oliver Jahn, Shu Li, Yanxu Zhang
Abstract. Mercury (Hg) is a global persistent contaminant. Modeling studies are useful means of synthesizing a current understanding of the Hg cycle. Previous studies mainly use coarse-resolution models, which makes it impossible to analyze the role of turbulence in the Hg cycle and inaccurately describes the transport of kinetic energy. Furthermore, all of them are coupled with offline biogeochemistry, and therefore they cannot respond to short-term variability in oceanic Hg concentration. In our approach, we utilize a high-resolution ocean model (MITgcm-ECCO2, referred to as “high-resolution-MITgcm”) coupled with the concurrent simulation of biogeochemistry processes from the Darwin Project (referred to as “online”). This integration enables us to comprehensively simulate the global biogeochemical cycle of Hg with a horizontal resolution of 1/5∘. The finer portrayal of surface Hg concentrations in estuarine and coastal areas, strong western boundary flow and upwelling areas, and concentration diffusion as vortex shapes demonstrate the effects of turbulence that are neglected in previous models. Ecological events such as algal blooms can cause a sudden enhancement of phytoplankton biomass and chlorophyll concentrations, which can also result in a dramatic change in particle-bound Hg (HgaqP) sinking flux simultaneously in our simulation. In the global estuary region, including riverine Hg input in the high-resolution model allows us to reveal the outward spread of Hg in an eddy shape driven by fine-scale ocean currents. With faster current velocities and diffusion rates, our model captures the transport and mixing of Hg from river discharge in a more accurate and detailed way and improves our understanding of Hg cycle in the ocean.
{"title":"A high-resolution marine mercury model MITgcm-ECCO2-Hg with online biogeochemistry","authors":"Siyu Zhu, Peipei Wu, Siyi Zhang, Oliver Jahn, Shu Li, Yanxu Zhang","doi":"10.5194/gmd-16-5915-2023","DOIUrl":"https://doi.org/10.5194/gmd-16-5915-2023","url":null,"abstract":"Abstract. Mercury (Hg) is a global persistent contaminant. Modeling studies are useful means of synthesizing a current understanding of the Hg cycle. Previous studies mainly use coarse-resolution models, which makes it impossible to analyze the role of turbulence in the Hg cycle and inaccurately describes the transport of kinetic energy. Furthermore, all of them are coupled with offline biogeochemistry, and therefore they cannot respond to short-term variability in oceanic Hg concentration. In our approach, we utilize a high-resolution ocean model (MITgcm-ECCO2, referred to as “high-resolution-MITgcm”) coupled with the concurrent simulation of biogeochemistry processes from the Darwin Project (referred to as “online”). This integration enables us to comprehensively simulate the global biogeochemical cycle of Hg with a horizontal resolution of 1/5∘. The finer portrayal of surface Hg concentrations in estuarine and coastal areas, strong western boundary flow and upwelling areas, and concentration diffusion as vortex shapes demonstrate the effects of turbulence that are neglected in previous models. Ecological events such as algal blooms can cause a sudden enhancement of phytoplankton biomass and chlorophyll concentrations, which can also result in a dramatic change in particle-bound Hg (HgaqP) sinking flux simultaneously in our simulation. In the global estuary region, including riverine Hg input in the high-resolution model allows us to reveal the outward spread of Hg in an eddy shape driven by fine-scale ocean currents. With faster current velocities and diffusion rates, our model captures the transport and mixing of Hg from river discharge in a more accurate and detailed way and improves our understanding of Hg cycle in the ocean.","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135618822","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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