Pub Date : 2024-02-12DOI: 10.5194/gmd-17-1111-2024
Alexandros Milousis, A. Tsimpidi, Holger Tost, S. Pandis, A. Nenes, A. Kiendler-Scharr, V. Karydis
Abstract. This study explores the differences in performance and results by various versions of the ISORROPIA thermodynamic module implemented within the ECHAM/MESSy Atmospheric Chemistry (EMAC) model. Three different versions of the module were used, ISORROPIA II v1, ISORROPIA II v2.3, and ISORROPIA-lite. First, ISORROPIA II v2.3 replaced ISORROPIA II v1 in EMAC to improve pH predictions close to neutral conditions. The newly developed ISORROPIA-lite has been added to EMAC alongside ISORROPIA II v2.3. ISORROPIA-lite is more computationally efficient and assumes that atmospheric aerosols exist always as supersaturated aqueous (metastable) solutions, while ISORROPIA II includes the option to allow for the formation of solid salts at low RH conditions (stable state). The predictions of EMAC by employing all three aerosol thermodynamic models were compared to each other and evaluated against surface measurements from three regional observational networks in the polluted Northern Hemisphere (Interagency Monitoring of Protected Visual Environments (IMPROVE), European Monitoring and Evaluation Programme (EMEP), and Acid Deposition Monitoring Network of East Asia (EANET)). The differences between ISORROPIA II v2.3 and ISORROPIA-lite were minimal in all comparisons with the normalized mean absolute difference for the concentrations of all major aerosol components being less than 11 % even when different phase state assumptions were used. The most notable differences were lower aerosol concentrations predicted by ISORROPIA-lite in regions with relative humidity in the range of 20 % to 60 % compared to the predictions of ISORROPIA II v2.3 in stable mode. The comparison against observations yielded satisfactory agreement especially over the USA and Europe but higher deviations over East Asia, where the overprediction of EMAC for nitrate was as high as 4 µg m−3 (∼20 %). The mean annual aerosol pH predicted by ISORROPIA-lite was on average less than a unit lower than ISORROPIA II v2.3 in stable mode, mainly for coarse-mode aerosols over the Middle East. The use of ISORROPIA-lite accelerated EMAC by nearly 5 % compared to the use of ISORROPIA II v2.3 even if the aerosol thermodynamic calculations consume a relatively small fraction of the EMAC computational time. ISORROPIA-lite can therefore be a reliable and computationally efficient alternative to the previous thermodynamic module in EMAC.
摘要本研究探讨了在 ECHAM/MESSy Atmospheric Chemistry (EMAC) 模型中实施的不同版本的 ISORROPIA 热力学模块在性能和结果上的差异。使用了三个不同版本的模块：ISORROPIA II v1、ISORROPIA II v2.3 和 ISORROPIA-lite。首先，ISORROPIA II v2.3 取代了 EMAC 中的 ISORROPIA II v1，以改进接近中性条件下的 pH 预测。新开发的 ISORROPIA-lite 与 ISORROPIA II v2.3 一起添加到了 EMAC 中。ISORROPIA-lite 计算效率更高，它假定大气气溶胶始终以过饱和水溶液（稳定状态）的形式存在，而 ISORROPIA II 包括允许在低相对湿度条件下形成固态盐（稳定状态）的选项。通过采用所有三种气溶胶热力学模型，对 EMAC 的预测结果进行了相互比较，并与北半球污染地区的三个区域观测网络（机构间视觉环境监测网络（IMPROVE）、欧洲监测与评估计划（EMEP）和东亚酸沉积监测网络（EANET））的地表测量结果进行了评估。在所有比较中，ISORROPIA II v2.3 和 ISORROPIA-lite 的差异都很小，即使使用不同的相态假设，所有主要气溶胶成分浓度的归一化平均绝对差异都小于 11%。最显著的差异是，在相对湿度为 20% 至 60% 的地区，ISORROPIA-lite 预测的气溶胶浓度低于 ISORROPIA II v2.3 在稳定模式下的预测值。与观测结果的比较结果令人满意，尤其是在美国和欧洲，但在东亚偏差较大，EMAC 对硝酸盐的预测偏高达 4 µg m-3 （20%）。ISORROPIA-lite 预测的年平均气溶胶 pH 值比稳定模式下的 ISORROPIA II v2.3 平均低不到一个单位，主要是中东地区上空的粗模式气溶胶。与 ISORROPIA II v2.3 相比，ISORROPIA-lite 的使用将 EMAC 的计算速度提高了近 5%，即使气溶胶热力学计算只消耗了 EMAC 计算时间的一小部分。因此，ISORROPIA-lite 可以作为 EMAC 先前热力学模块的可靠且计算效率高的替代方案。
{"title":"Implementation of the ISORROPIA-lite aerosol thermodynamics model into the EMAC chemistry climate model (based on MESSy v2.55): implications for aerosol composition and acidity","authors":"Alexandros Milousis, A. Tsimpidi, Holger Tost, S. Pandis, A. Nenes, A. Kiendler-Scharr, V. Karydis","doi":"10.5194/gmd-17-1111-2024","DOIUrl":"https://doi.org/10.5194/gmd-17-1111-2024","url":null,"abstract":"Abstract. This study explores the differences in performance and results by various versions of the ISORROPIA thermodynamic module implemented within the ECHAM/MESSy Atmospheric Chemistry (EMAC) model. Three different versions of the module were used, ISORROPIA II v1, ISORROPIA II v2.3, and ISORROPIA-lite. First, ISORROPIA II v2.3 replaced ISORROPIA II v1 in EMAC to improve pH predictions close to neutral conditions. The newly developed ISORROPIA-lite has been added to EMAC alongside ISORROPIA II v2.3. ISORROPIA-lite is more computationally efficient and assumes that atmospheric aerosols exist always as supersaturated aqueous (metastable) solutions, while ISORROPIA II includes the option to allow for the formation of solid salts at low RH conditions (stable state). The predictions of EMAC by employing all three aerosol thermodynamic models were compared to each other and evaluated against surface measurements from three regional observational networks in the polluted Northern Hemisphere (Interagency Monitoring of Protected Visual Environments (IMPROVE), European Monitoring and Evaluation Programme (EMEP), and Acid Deposition Monitoring Network of East Asia (EANET)). The differences between ISORROPIA II v2.3 and ISORROPIA-lite were minimal in all comparisons with the normalized mean absolute difference for the concentrations of all major aerosol components being less than 11 % even when different phase state assumptions were used. The most notable differences were lower aerosol concentrations predicted by ISORROPIA-lite in regions with relative humidity in the range of 20 % to 60 % compared to the predictions of ISORROPIA II v2.3 in stable mode. The comparison against observations yielded satisfactory agreement especially over the USA and Europe but higher deviations over East Asia, where the overprediction of EMAC for nitrate was as high as 4 µg m−3 (∼20 %). The mean annual aerosol pH predicted by ISORROPIA-lite was on average less than a unit lower than ISORROPIA II v2.3 in stable mode, mainly for coarse-mode aerosols over the Middle East. The use of ISORROPIA-lite accelerated EMAC by nearly 5 % compared to the use of ISORROPIA II v2.3 even if the aerosol thermodynamic calculations consume a relatively small fraction of the EMAC computational time. ISORROPIA-lite can therefore be a reliable and computationally efficient alternative to the previous thermodynamic module in EMAC.\u0000","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":null,"pages":null},"PeriodicalIF":5.1,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139785119","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 : 2024-02-12DOI: 10.5194/gmd-17-1153-2024
Guoqiang Tang, Andrew W. Wood, A. J. Newman, M. P. Clark, S. Papalexiou
Abstract. Ensemble geophysical datasets are foundational for research to understand the Earth system in an uncertainty-aware context and to drive applications that require quantification of uncertainties, such as probabilistic hydro-meteorological estimation or prediction. Yet ensemble estimation is more challenging than single-value spatial interpolation, and open-access routines and tools are limited in this area, hindering the generation and application of ensemble geophysical datasets. A notable exception in the last decade has been the Gridded Meteorological Ensemble Tool (GMET), which is implemented in FORTRAN and has typically been configured for ensemble estimation of precipitation, mean air temperature, and daily temperature range, based on station observations. GMET has been used to generate a variety of local, regional, national, and global meteorological datasets, which in turn have driven multiple retrospective and real-time hydrological applications. Motivated by an interest in expanding GMET flexibility, application scope, and range of methods, we have developed the Python-based Geospatial Probabilistic Estimation Package (GPEP) that offers GMET functionality along with additional methodological and usability improvements, including variable independence and flexibility, an efficient alternative cross-validation strategy, internal parallelization, and the availability of the scikit-learn machine learning library for both local and global regression. This paper describes GPEP and illustrates some of its capabilities using several demonstration experiments, including the estimation of precipitation, temperature, and snow water equivalent ensemble analyses on various scales.
{"title":"GPEP v1.0: the Geospatial Probabilistic Estimation Package to support Earth science applications","authors":"Guoqiang Tang, Andrew W. Wood, A. J. Newman, M. P. Clark, S. Papalexiou","doi":"10.5194/gmd-17-1153-2024","DOIUrl":"https://doi.org/10.5194/gmd-17-1153-2024","url":null,"abstract":"Abstract. Ensemble geophysical datasets are foundational for research to understand the Earth system in an uncertainty-aware context and to drive applications that require quantification of uncertainties, such as probabilistic hydro-meteorological estimation or prediction. Yet ensemble estimation is more challenging than single-value spatial interpolation, and open-access routines and tools are limited in this area, hindering the generation and application of ensemble geophysical datasets. A notable exception in the last decade has been the Gridded Meteorological Ensemble Tool (GMET), which is implemented in FORTRAN and has typically been configured for ensemble estimation of precipitation, mean air temperature, and daily temperature range, based on station observations. GMET has been used to generate a variety of local, regional, national, and global meteorological datasets, which in turn have driven multiple retrospective and real-time hydrological applications. Motivated by an interest in expanding GMET flexibility, application scope, and range of methods, we have developed the Python-based Geospatial Probabilistic Estimation Package (GPEP) that offers GMET functionality along with additional methodological and usability improvements, including variable independence and flexibility, an efficient alternative cross-validation strategy, internal parallelization, and the availability of the scikit-learn machine learning library for both local and global regression. This paper describes GPEP and illustrates some of its capabilities using several demonstration experiments, including the estimation of precipitation, temperature, and snow water equivalent ensemble analyses on various scales.\u0000","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":null,"pages":null},"PeriodicalIF":5.1,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139842084","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 : 2024-02-12DOI: 10.5194/gmd-17-1111-2024
Alexandros Milousis, A. Tsimpidi, Holger Tost, S. Pandis, A. Nenes, A. Kiendler-Scharr, V. Karydis
Abstract. This study explores the differences in performance and results by various versions of the ISORROPIA thermodynamic module implemented within the ECHAM/MESSy Atmospheric Chemistry (EMAC) model. Three different versions of the module were used, ISORROPIA II v1, ISORROPIA II v2.3, and ISORROPIA-lite. First, ISORROPIA II v2.3 replaced ISORROPIA II v1 in EMAC to improve pH predictions close to neutral conditions. The newly developed ISORROPIA-lite has been added to EMAC alongside ISORROPIA II v2.3. ISORROPIA-lite is more computationally efficient and assumes that atmospheric aerosols exist always as supersaturated aqueous (metastable) solutions, while ISORROPIA II includes the option to allow for the formation of solid salts at low RH conditions (stable state). The predictions of EMAC by employing all three aerosol thermodynamic models were compared to each other and evaluated against surface measurements from three regional observational networks in the polluted Northern Hemisphere (Interagency Monitoring of Protected Visual Environments (IMPROVE), European Monitoring and Evaluation Programme (EMEP), and Acid Deposition Monitoring Network of East Asia (EANET)). The differences between ISORROPIA II v2.3 and ISORROPIA-lite were minimal in all comparisons with the normalized mean absolute difference for the concentrations of all major aerosol components being less than 11 % even when different phase state assumptions were used. The most notable differences were lower aerosol concentrations predicted by ISORROPIA-lite in regions with relative humidity in the range of 20 % to 60 % compared to the predictions of ISORROPIA II v2.3 in stable mode. The comparison against observations yielded satisfactory agreement especially over the USA and Europe but higher deviations over East Asia, where the overprediction of EMAC for nitrate was as high as 4 µg m−3 (∼20 %). The mean annual aerosol pH predicted by ISORROPIA-lite was on average less than a unit lower than ISORROPIA II v2.3 in stable mode, mainly for coarse-mode aerosols over the Middle East. The use of ISORROPIA-lite accelerated EMAC by nearly 5 % compared to the use of ISORROPIA II v2.3 even if the aerosol thermodynamic calculations consume a relatively small fraction of the EMAC computational time. ISORROPIA-lite can therefore be a reliable and computationally efficient alternative to the previous thermodynamic module in EMAC.
摘要本研究探讨了在 ECHAM/MESSy Atmospheric Chemistry (EMAC) 模型中实施的不同版本的 ISORROPIA 热力学模块在性能和结果上的差异。使用了三个不同版本的模块：ISORROPIA II v1、ISORROPIA II v2.3 和 ISORROPIA-lite。首先，ISORROPIA II v2.3 取代了 EMAC 中的 ISORROPIA II v1，以改进接近中性条件下的 pH 预测。新开发的 ISORROPIA-lite 与 ISORROPIA II v2.3 一起添加到了 EMAC 中。ISORROPIA-lite 计算效率更高，它假定大气气溶胶始终以过饱和水溶液（稳定状态）的形式存在，而 ISORROPIA II 包括允许在低相对湿度条件下形成固态盐（稳定状态）的选项。通过采用所有三种气溶胶热力学模型，对 EMAC 的预测结果进行了相互比较，并与北半球污染地区的三个区域观测网络（机构间视觉环境监测网络（IMPROVE）、欧洲监测与评估计划（EMEP）和东亚酸沉积监测网络（EANET））的地表测量结果进行了评估。在所有比较中，ISORROPIA II v2.3 和 ISORROPIA-lite 的差异都很小，即使使用不同的相态假设，所有主要气溶胶成分浓度的归一化平均绝对差异都小于 11%。最显著的差异是，在相对湿度为 20% 至 60% 的地区，ISORROPIA-lite 预测的气溶胶浓度低于 ISORROPIA II v2.3 在稳定模式下的预测值。与观测结果的比较结果令人满意，尤其是在美国和欧洲，但在东亚偏差较大，EMAC 对硝酸盐的预测偏高达 4 µg m-3 （20%）。ISORROPIA-lite 预测的年平均气溶胶 pH 值比稳定模式下的 ISORROPIA II v2.3 平均低不到一个单位，主要是中东地区上空的粗模式气溶胶。与 ISORROPIA II v2.3 相比，ISORROPIA-lite 的使用将 EMAC 的计算速度提高了近 5%，即使气溶胶热力学计算只消耗了 EMAC 计算时间的一小部分。因此，ISORROPIA-lite 可以作为 EMAC 先前热力学模块的可靠且计算效率高的替代方案。
{"title":"Implementation of the ISORROPIA-lite aerosol thermodynamics model into the EMAC chemistry climate model (based on MESSy v2.55): implications for aerosol composition and acidity","authors":"Alexandros Milousis, A. Tsimpidi, Holger Tost, S. Pandis, A. Nenes, A. Kiendler-Scharr, V. Karydis","doi":"10.5194/gmd-17-1111-2024","DOIUrl":"https://doi.org/10.5194/gmd-17-1111-2024","url":null,"abstract":"Abstract. This study explores the differences in performance and results by various versions of the ISORROPIA thermodynamic module implemented within the ECHAM/MESSy Atmospheric Chemistry (EMAC) model. Three different versions of the module were used, ISORROPIA II v1, ISORROPIA II v2.3, and ISORROPIA-lite. First, ISORROPIA II v2.3 replaced ISORROPIA II v1 in EMAC to improve pH predictions close to neutral conditions. The newly developed ISORROPIA-lite has been added to EMAC alongside ISORROPIA II v2.3. ISORROPIA-lite is more computationally efficient and assumes that atmospheric aerosols exist always as supersaturated aqueous (metastable) solutions, while ISORROPIA II includes the option to allow for the formation of solid salts at low RH conditions (stable state). The predictions of EMAC by employing all three aerosol thermodynamic models were compared to each other and evaluated against surface measurements from three regional observational networks in the polluted Northern Hemisphere (Interagency Monitoring of Protected Visual Environments (IMPROVE), European Monitoring and Evaluation Programme (EMEP), and Acid Deposition Monitoring Network of East Asia (EANET)). The differences between ISORROPIA II v2.3 and ISORROPIA-lite were minimal in all comparisons with the normalized mean absolute difference for the concentrations of all major aerosol components being less than 11 % even when different phase state assumptions were used. The most notable differences were lower aerosol concentrations predicted by ISORROPIA-lite in regions with relative humidity in the range of 20 % to 60 % compared to the predictions of ISORROPIA II v2.3 in stable mode. The comparison against observations yielded satisfactory agreement especially over the USA and Europe but higher deviations over East Asia, where the overprediction of EMAC for nitrate was as high as 4 µg m−3 (∼20 %). The mean annual aerosol pH predicted by ISORROPIA-lite was on average less than a unit lower than ISORROPIA II v2.3 in stable mode, mainly for coarse-mode aerosols over the Middle East. The use of ISORROPIA-lite accelerated EMAC by nearly 5 % compared to the use of ISORROPIA II v2.3 even if the aerosol thermodynamic calculations consume a relatively small fraction of the EMAC computational time. ISORROPIA-lite can therefore be a reliable and computationally efficient alternative to the previous thermodynamic module in EMAC.\u0000","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":null,"pages":null},"PeriodicalIF":5.1,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139844961","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 : 2024-02-12DOI: 10.5194/gmd-17-1153-2024
Guoqiang Tang, Andrew W. Wood, A. J. Newman, M. P. Clark, S. Papalexiou
Abstract. Ensemble geophysical datasets are foundational for research to understand the Earth system in an uncertainty-aware context and to drive applications that require quantification of uncertainties, such as probabilistic hydro-meteorological estimation or prediction. Yet ensemble estimation is more challenging than single-value spatial interpolation, and open-access routines and tools are limited in this area, hindering the generation and application of ensemble geophysical datasets. A notable exception in the last decade has been the Gridded Meteorological Ensemble Tool (GMET), which is implemented in FORTRAN and has typically been configured for ensemble estimation of precipitation, mean air temperature, and daily temperature range, based on station observations. GMET has been used to generate a variety of local, regional, national, and global meteorological datasets, which in turn have driven multiple retrospective and real-time hydrological applications. Motivated by an interest in expanding GMET flexibility, application scope, and range of methods, we have developed the Python-based Geospatial Probabilistic Estimation Package (GPEP) that offers GMET functionality along with additional methodological and usability improvements, including variable independence and flexibility, an efficient alternative cross-validation strategy, internal parallelization, and the availability of the scikit-learn machine learning library for both local and global regression. This paper describes GPEP and illustrates some of its capabilities using several demonstration experiments, including the estimation of precipitation, temperature, and snow water equivalent ensemble analyses on various scales.
{"title":"GPEP v1.0: the Geospatial Probabilistic Estimation Package to support Earth science applications","authors":"Guoqiang Tang, Andrew W. Wood, A. J. Newman, M. P. Clark, S. Papalexiou","doi":"10.5194/gmd-17-1153-2024","DOIUrl":"https://doi.org/10.5194/gmd-17-1153-2024","url":null,"abstract":"Abstract. Ensemble geophysical datasets are foundational for research to understand the Earth system in an uncertainty-aware context and to drive applications that require quantification of uncertainties, such as probabilistic hydro-meteorological estimation or prediction. Yet ensemble estimation is more challenging than single-value spatial interpolation, and open-access routines and tools are limited in this area, hindering the generation and application of ensemble geophysical datasets. A notable exception in the last decade has been the Gridded Meteorological Ensemble Tool (GMET), which is implemented in FORTRAN and has typically been configured for ensemble estimation of precipitation, mean air temperature, and daily temperature range, based on station observations. GMET has been used to generate a variety of local, regional, national, and global meteorological datasets, which in turn have driven multiple retrospective and real-time hydrological applications. Motivated by an interest in expanding GMET flexibility, application scope, and range of methods, we have developed the Python-based Geospatial Probabilistic Estimation Package (GPEP) that offers GMET functionality along with additional methodological and usability improvements, including variable independence and flexibility, an efficient alternative cross-validation strategy, internal parallelization, and the availability of the scikit-learn machine learning library for both local and global regression. This paper describes GPEP and illustrates some of its capabilities using several demonstration experiments, including the estimation of precipitation, temperature, and snow water equivalent ensemble analyses on various scales.\u0000","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":null,"pages":null},"PeriodicalIF":5.1,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139782246","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 : 2024-02-12DOI: 10.5194/gmd-17-1133-2024
Jonathan Hobbs, M. Katzfuss, Hai Nguyen, Vineet Yadav, Junjie Liu
Abstract. The constellation of Earth-observing satellites has now produced atmospheric greenhouse gas concentration estimates covering a period of several years. Their global coverage is providing additional information on the global carbon cycle. These products can be combined with complex inversion systems to infer the magnitude of carbon sources and sinks around the globe. Multiple factors, including the atmospheric transport model and satellite product aggregation method, can impact such flux estimates. Analysis of variance (ANOVA) is a well-established statistical framework for estimating common signals while partitioning variability across factors in the analysis of experiments. Functional ANOVA extends this approach with a statistical model that incorporates spatiotemporal correlation for each ANOVA component. The approach is illustrated on inversion experiments with different satellite retrieval aggregation methods and identifies consistent significant patterns in flux increments that span large spatial scales. Functional ANOVA identifies these patterns while accounting for the uncertainty at small spatial scales that is attributed to differences in the aggregation method. Functional ANOVA is also applied to a recent flux model intercomparison project (MIP), and the relative magnitudes of inversion system effects and data source (satellite versus in situ) are similar but exhibit slightly different importance for fluxes over different continents. In all examples, the unexplained residual variability is locally sizable in magnitude but with limited spatial and temporal correlation. These common behaviors across flux inversion experiments demonstrate the diagnostic capability for functional ANOVA to simultaneously distinguish the spatiotemporal coherence of carbon cycle processes and algorithmic factors.
{"title":"Functional analysis of variance (ANOVA) for carbon flux estimates from remote sensing data","authors":"Jonathan Hobbs, M. Katzfuss, Hai Nguyen, Vineet Yadav, Junjie Liu","doi":"10.5194/gmd-17-1133-2024","DOIUrl":"https://doi.org/10.5194/gmd-17-1133-2024","url":null,"abstract":"Abstract. The constellation of Earth-observing satellites has now produced atmospheric greenhouse gas concentration estimates covering a period of several years. Their global coverage is providing additional information on the global carbon cycle. These products can be combined with complex inversion systems to infer the magnitude of carbon sources and sinks around the globe. Multiple factors, including the atmospheric transport model and satellite product aggregation method, can impact such flux estimates. Analysis of variance (ANOVA) is a well-established statistical framework for estimating common signals while partitioning variability across factors in the analysis of experiments. Functional ANOVA extends this approach with a statistical model that incorporates spatiotemporal correlation for each ANOVA component. The approach is illustrated on inversion experiments with different satellite retrieval aggregation methods and identifies consistent significant patterns in flux increments that span large spatial scales. Functional ANOVA identifies these patterns while accounting for the uncertainty at small spatial scales that is attributed to differences in the aggregation method. Functional ANOVA is also applied to a recent flux model intercomparison project (MIP), and the relative magnitudes of inversion system effects and data source (satellite versus in situ) are similar but exhibit slightly different importance for fluxes over different continents. In all examples, the unexplained residual variability is locally sizable in magnitude but with limited spatial and temporal correlation. These common behaviors across flux inversion experiments demonstrate the diagnostic capability for functional ANOVA to simultaneously distinguish the spatiotemporal coherence of carbon cycle processes and algorithmic factors.\u0000","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":null,"pages":null},"PeriodicalIF":5.1,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139843708","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 : 2024-02-09DOI: 10.5194/gmd-17-1091-2024
Marie-Adèle Magnaldo, Q. Libois, S. Riette, Christine Lac
Abstract. With the worldwide development of the solar energy sector, the need for reliable surface shortwave downward radiation (SWD) forecasts has significantly increased in recent years. SWD forecasts of a few hours to a few days based on numerical weather prediction (NWP) models are essential to facilitate the incorporation of solar energy into the electric grid and ensure network stability. However, SWD errors in NWP models can be substantial. In order to characterize the performances of AROME in detail, the operational NWP model of the French weather service Météo-France, a full year of hourly AROME forecasts is compared to corresponding in situ SWD measurements from 168 high-quality pyranometers covering France. In addition, to classify cloud scenes at high temporal frequency and over the whole territory, cloud products derived from the Satellite Application Facility for Nowcasting and Very Short Range Forecasting (SAF NWC) from geostationary satellites are also used. The 2020 mean bias is positive, with a value of 18 W m−2, meaning that AROME on average overestimates the SWD. The root-mean-square error is 98 W m−2. The situations that contribute the most to the bias correspond to cloudy skies in the model and in the observations, situations that are very frequent (66 %) and characterized by an annual bias of 24 W m−2. Part of this positive bias probably comes from an underestimation of cloud fraction in AROME, although this is not fully addressed in this study due to the lack of consistent observations at kilometer resolution. The other situations have less impact on SWD errors. Missed cloudy situations and erroneously predicted clouds, which generally correspond to clouds with a low impact on the SWD, also have low occurrence (4 % and 11 %). Likewise, well-predicted clear-sky conditions are characterized by a low bias (3 W m−2). When limited to overcast situations in the model, the bias in cloudy skies is small (1 W m−2) but results from large compensating errors. Indeed, further investigation shows that high clouds are systematically associated with a SWD positive bias, while low clouds are associated with a negative bias. This detailed analysis shows that the errors result from a combination of incorrect cloud optical properties and cloud fraction errors, highlighting the need for a more detailed evaluation of cloud properties. This study also provides valuable insights into the potential improvement of AROME physical parametrizations.
摘要近年来，随着全球太阳能产业的发展，对可靠的地表短波向下辐射（SWD）预报的需求显著增加。基于数值天气预报（NWP）模型的几小时到几天的 SWD 预报对于促进太阳能并入电网和确保电网稳定至关重要。然而，NWP 模型的 SWD 误差可能很大。为了详细描述 AROME 的性能，法国气象服务机构 Météo-France 的实用 NWP 模型将全年的每小时 AROME 预测与来自法国 168 个高质量高温计的相应现场 SWD 测量结果进行了比较。此外，为了对高时间频率和全境的云景进行分类，还使用了来自地球静止卫星的正预报和甚短程预报卫星应用设施（SAF NWC）的云产品。2020 年的平均偏差为正值，数值为 18 W m-2，这意味着阿罗美平均高估了西南气旋。均方根误差为 98 W m-2。造成偏差最大的情况是模型和观测数据中的多云天气，这种情况非常频繁（66%），年偏差为 24 W m-2。这种正偏差的部分原因可能是低估了 AROME 中的云量，但由于缺乏千米分辨率的一致观测数据，本研究并未完全解决这一问题。其他情况对 SWD 误差的影响较小。漏报的多云情况和错误预测的多云情况通常与对西南分量影响较小的多云情况相对应，其发生率也较低（4% 和 11%）。同样，预测良好的晴空条件的偏差也很低（3 W m-2）。如果仅限于模型中的阴天情况，阴天的偏差较小（1 W m-2），但这是由于补偿误差较大造成的。事实上，进一步的研究表明，高云系统性地与西南气压正偏差相关，而低云则与负偏差相关。详细的分析表明，误差是由不正确的云光学特性和云分数误差共同造成的，这凸显了对云特性进行更详细评估的必要性。这项研究还为 AROME 物理参数的潜在改进提供了有价值的见解。
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Pub Date : 2024-02-09DOI: 10.5194/gmd-17-1091-2024
Marie-Adèle Magnaldo, Q. Libois, S. Riette, Christine Lac
Abstract. With the worldwide development of the solar energy sector, the need for reliable surface shortwave downward radiation (SWD) forecasts has significantly increased in recent years. SWD forecasts of a few hours to a few days based on numerical weather prediction (NWP) models are essential to facilitate the incorporation of solar energy into the electric grid and ensure network stability. However, SWD errors in NWP models can be substantial. In order to characterize the performances of AROME in detail, the operational NWP model of the French weather service Météo-France, a full year of hourly AROME forecasts is compared to corresponding in situ SWD measurements from 168 high-quality pyranometers covering France. In addition, to classify cloud scenes at high temporal frequency and over the whole territory, cloud products derived from the Satellite Application Facility for Nowcasting and Very Short Range Forecasting (SAF NWC) from geostationary satellites are also used. The 2020 mean bias is positive, with a value of 18 W m−2, meaning that AROME on average overestimates the SWD. The root-mean-square error is 98 W m−2. The situations that contribute the most to the bias correspond to cloudy skies in the model and in the observations, situations that are very frequent (66 %) and characterized by an annual bias of 24 W m−2. Part of this positive bias probably comes from an underestimation of cloud fraction in AROME, although this is not fully addressed in this study due to the lack of consistent observations at kilometer resolution. The other situations have less impact on SWD errors. Missed cloudy situations and erroneously predicted clouds, which generally correspond to clouds with a low impact on the SWD, also have low occurrence (4 % and 11 %). Likewise, well-predicted clear-sky conditions are characterized by a low bias (3 W m−2). When limited to overcast situations in the model, the bias in cloudy skies is small (1 W m−2) but results from large compensating errors. Indeed, further investigation shows that high clouds are systematically associated with a SWD positive bias, while low clouds are associated with a negative bias. This detailed analysis shows that the errors result from a combination of incorrect cloud optical properties and cloud fraction errors, highlighting the need for a more detailed evaluation of cloud properties. This study also provides valuable insights into the potential improvement of AROME physical parametrizations.
摘要近年来，随着全球太阳能产业的发展，对可靠的地表短波向下辐射（SWD）预报的需求显著增加。基于数值天气预报（NWP）模型的几小时到几天的 SWD 预报对于促进太阳能并入电网和确保电网稳定至关重要。然而，NWP 模型的 SWD 误差可能很大。为了详细描述 AROME 的性能，法国气象服务机构 Météo-France 的实用 NWP 模型将全年的每小时 AROME 预测与来自法国 168 个高质量高温计的相应现场 SWD 测量结果进行了比较。此外，为了对高时间频率和全境的云景进行分类，还使用了来自地球静止卫星的正预报和甚短程预报卫星应用设施（SAF NWC）的云产品。2020 年的平均偏差为正值，数值为 18 W m-2，这意味着阿罗美平均高估了西南气旋。均方根误差为 98 W m-2。造成偏差最大的情况是模型和观测数据中的多云天气，这种情况非常频繁（66%），年偏差为 24 W m-2。这种正偏差的部分原因可能是低估了 AROME 中的云量，但由于缺乏千米分辨率的一致观测数据，本研究并未完全解决这一问题。其他情况对 SWD 误差的影响较小。漏报的多云情况和错误预测的多云情况通常与对西南分量影响较小的多云情况相对应，其发生率也较低（4% 和 11%）。同样，预测良好的晴空条件的偏差也很低（3 W m-2）。如果仅限于模型中的阴天情况，阴天的偏差较小（1 W m-2），但这是由于补偿误差较大造成的。事实上，进一步的研究表明，高云系统性地与西南气压正偏差相关，而低云则与负偏差相关。详细的分析表明，误差是由不正确的云光学特性和云分数误差共同造成的，这凸显了对云特性进行更详细评估的必要性。这项研究还为 AROME 物理参数的潜在改进提供了有价值的见解。
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Pub Date : 2024-02-08DOI: 10.5194/gmd-17-1041-2024
Lizz Ultee, A. Robel, Stefano Castruccio
Abstract. Many scientific and societal questions that draw on ice sheet modeling necessitate sampling a wide range of potential climatic changes and realizations of internal climate variability. For example, coastal planning literature demonstrates a demand for probabilistic sea level projections with quantified uncertainty. Further, robust attribution of past and future ice sheet change to specific processes or forcings requires a full understanding of the space of possible ice sheet behaviors. The wide sampling required to address such questions is computationally infeasible with sophisticated numerical climate models at the resolution required to accurately force ice sheet models. Stochastic generation of climate forcing of ice sheets offers a complementary alternative. Here, we describe a method to construct a stochastic generator for ice sheet surface mass balance varying in time and space. We demonstrate the method with an application to Greenland Ice Sheet surface mass balance for 1980–2012. We account for spatial correlations among glacier catchments using sparse covariance techniques, and we apply an elevation-dependent downscaling to recover gridded surface mass balance fields suitable for forcing an ice sheet model while including feedback from changing ice sheet surface elevation. The efficiency gained in the stochastic method supports large-ensemble simulations of ice sheet change in a new stochastic ice sheet model. We provide open source Python workflows to support use of our stochastic approach for a broad range of applications.
{"title":"A stochastic parameterization of ice sheet surface mass balance for the Stochastic Ice-Sheet and Sea-Level System Model (StISSM v1.0)","authors":"Lizz Ultee, A. Robel, Stefano Castruccio","doi":"10.5194/gmd-17-1041-2024","DOIUrl":"https://doi.org/10.5194/gmd-17-1041-2024","url":null,"abstract":"Abstract. Many scientific and societal questions that draw on ice sheet modeling necessitate sampling a wide range of potential climatic changes and realizations of internal climate variability. For example, coastal planning literature demonstrates a demand for probabilistic sea level projections with quantified uncertainty. Further, robust attribution of past and future ice sheet change to specific processes or forcings requires a full understanding of the space of possible ice sheet behaviors. The wide sampling required to address such questions is computationally infeasible with sophisticated numerical climate models at the resolution required to accurately force ice sheet models. Stochastic generation of climate forcing of ice sheets offers a complementary alternative. Here, we describe a method to construct a stochastic generator for ice sheet surface mass balance varying in time and space. We demonstrate the method with an application to Greenland Ice Sheet surface mass balance for 1980–2012. We account for spatial correlations among glacier catchments using sparse covariance techniques, and we apply an elevation-dependent downscaling to recover gridded surface mass balance fields suitable for forcing an ice sheet model while including feedback from changing ice sheet surface elevation. The efficiency gained in the stochastic method supports large-ensemble simulations of ice sheet change in a new stochastic ice sheet model. We provide open source Python workflows to support use of our stochastic approach for a broad range of applications.\u0000","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":null,"pages":null},"PeriodicalIF":5.1,"publicationDate":"2024-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139851943","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 : 2024-02-08DOI: 10.5194/gmd-17-1041-2024
Lizz Ultee, A. Robel, Stefano Castruccio
Abstract. Many scientific and societal questions that draw on ice sheet modeling necessitate sampling a wide range of potential climatic changes and realizations of internal climate variability. For example, coastal planning literature demonstrates a demand for probabilistic sea level projections with quantified uncertainty. Further, robust attribution of past and future ice sheet change to specific processes or forcings requires a full understanding of the space of possible ice sheet behaviors. The wide sampling required to address such questions is computationally infeasible with sophisticated numerical climate models at the resolution required to accurately force ice sheet models. Stochastic generation of climate forcing of ice sheets offers a complementary alternative. Here, we describe a method to construct a stochastic generator for ice sheet surface mass balance varying in time and space. We demonstrate the method with an application to Greenland Ice Sheet surface mass balance for 1980–2012. We account for spatial correlations among glacier catchments using sparse covariance techniques, and we apply an elevation-dependent downscaling to recover gridded surface mass balance fields suitable for forcing an ice sheet model while including feedback from changing ice sheet surface elevation. The efficiency gained in the stochastic method supports large-ensemble simulations of ice sheet change in a new stochastic ice sheet model. We provide open source Python workflows to support use of our stochastic approach for a broad range of applications.
{"title":"A stochastic parameterization of ice sheet surface mass balance for the Stochastic Ice-Sheet and Sea-Level System Model (StISSM v1.0)","authors":"Lizz Ultee, A. Robel, Stefano Castruccio","doi":"10.5194/gmd-17-1041-2024","DOIUrl":"https://doi.org/10.5194/gmd-17-1041-2024","url":null,"abstract":"Abstract. Many scientific and societal questions that draw on ice sheet modeling necessitate sampling a wide range of potential climatic changes and realizations of internal climate variability. For example, coastal planning literature demonstrates a demand for probabilistic sea level projections with quantified uncertainty. Further, robust attribution of past and future ice sheet change to specific processes or forcings requires a full understanding of the space of possible ice sheet behaviors. The wide sampling required to address such questions is computationally infeasible with sophisticated numerical climate models at the resolution required to accurately force ice sheet models. Stochastic generation of climate forcing of ice sheets offers a complementary alternative. Here, we describe a method to construct a stochastic generator for ice sheet surface mass balance varying in time and space. We demonstrate the method with an application to Greenland Ice Sheet surface mass balance for 1980–2012. We account for spatial correlations among glacier catchments using sparse covariance techniques, and we apply an elevation-dependent downscaling to recover gridded surface mass balance fields suitable for forcing an ice sheet model while including feedback from changing ice sheet surface elevation. The efficiency gained in the stochastic method supports large-ensemble simulations of ice sheet change in a new stochastic ice sheet model. We provide open source Python workflows to support use of our stochastic approach for a broad range of applications.\u0000","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":null,"pages":null},"PeriodicalIF":5.1,"publicationDate":"2024-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139791858","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 : 2024-02-08DOI: 10.5194/gmd-17-1059-2024
Douglas McNeall, Eddy Robertson, Andy Wiltshire
Abstract. Land surface models are an important tool in the study of climate change and its impacts, but their use can be hampered by uncertainties in input parameter settings and by errors in the models. We apply uncertainty quantification (UQ) techniques to constrain the input parameter space and corresponding historical simulations of JULES-ES-1.0 (Joint UK Land Environment Simulator Earth System), the land surface component of the UK Earth System Model, UKESM1.0. We use an ensemble of historical simulations of the land surface model to rule out ensemble members and corresponding input parameter settings that do not match modern observations of the land surface and carbon cycle. As JULES-ES-1.0 is computationally expensive, we use a cheap statistical proxy termed an emulator, trained on the ensemble of model runs, to rule out parts of the parameter space where the simulator has not yet been run. We use history matching, an iterated approach to constraining JULES-ES-1.0, running an initial ensemble and training the emulator, before choosing a second wave of ensemble members consistent with historical land surface observations. We successfully rule out 88 % of the initial input parameter space as being statistically inconsistent with observed land surface behaviour. The result is a set of historical simulations and a constrained input space that are statistically consistent with observations. Furthermore, we use sensitivity analysis to identify the most (and least) important input parameters for controlling the global output of JULES-ES-1.0 and provide information on how parameters might be varied to improve the performance of the model and eliminate model biases.
{"title":"Constraining the carbon cycle in JULES-ES-1.0","authors":"Douglas McNeall, Eddy Robertson, Andy Wiltshire","doi":"10.5194/gmd-17-1059-2024","DOIUrl":"https://doi.org/10.5194/gmd-17-1059-2024","url":null,"abstract":"Abstract. Land surface models are an important tool in the study of climate change and its impacts, but their use can be hampered by uncertainties in input parameter settings and by errors in the models. We apply uncertainty quantification (UQ) techniques to constrain the input parameter space and corresponding historical simulations of JULES-ES-1.0 (Joint UK Land Environment Simulator Earth System), the land surface component of the UK Earth System Model, UKESM1.0. We use an ensemble of historical simulations of the land surface model to rule out ensemble members and corresponding input parameter settings that do not match modern observations of the land surface and carbon cycle. As JULES-ES-1.0 is computationally expensive, we use a cheap statistical proxy termed an emulator, trained on the ensemble of model runs, to rule out parts of the parameter space where the simulator has not yet been run. We use history matching, an iterated approach to constraining JULES-ES-1.0, running an initial ensemble and training the emulator, before choosing a second wave of ensemble members consistent with historical land surface observations. We successfully rule out 88 % of the initial input parameter space as being statistically inconsistent with observed land surface behaviour. The result is a set of historical simulations and a constrained input space that are statistically consistent with observations. Furthermore, we use sensitivity analysis to identify the most (and least) important input parameters for controlling the global output of JULES-ES-1.0 and provide information on how parameters might be varied to improve the performance of the model and eliminate model biases.\u0000","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":null,"pages":null},"PeriodicalIF":5.1,"publicationDate":"2024-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139853683","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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