Pub Date : 2023-09-01DOI: 10.5194/gmd-16-5049-2023
D. Reynolds, E. Gutmann, B. Kruyt, Michael Haugeneder, T. Jonas, F. Gerber, M. Lehning, R. Mott
Abstract. High-resolution (< 1 km) atmospheric modeling is increasingly used to study precipitation distributions in complex terrain and cryosphere–atmospheric processes. While this approach has yielded insightful results, studies over annual timescales or at the spatial extents of watersheds remain unrealistic due to the computational costs of running most atmospheric models. In this paper we introduce a high-resolution variant of the Intermediate Complexity Atmospheric Research (ICAR) model, HICAR. We detail the model development that enabled HICAR simulations at the hectometer scale, including changes to the advection scheme and the wind solver. The latter uses near-surface terrain parameters which allow HICAR to simulate complex topographic flow features. These model improvements clearly influence precipitation distributions at the ridge scale (50 m), suggesting that HICAR can approximate processes dependent on particle–flow interactions such as preferential deposition. A 250 m HICAR simulation over most of the Swiss Alps also shows monthly precipitation patterns similar to two different gridded precipitation products which assimilate available observations. Benchmarking runs show that HICAR uses 594 times fewer computational resources than the Weather Research and Forecasting (WRF) atmospheric model. This gain in efficiency makes dynamic downscaling accessible to ecohydrological research, where downscaled data are often required at hectometer resolution for whole basins at seasonal timescales. These results motivate further development of HICAR, including refinement of parameterizations used in the wind solver and coupling of the model with an intermediate-complexity snow model.
{"title":"The High-resolution Intermediate Complexity Atmospheric Research (HICAR v1.1) model enables fast dynamic downscaling to the hectometer scale","authors":"D. Reynolds, E. Gutmann, B. Kruyt, Michael Haugeneder, T. Jonas, F. Gerber, M. Lehning, R. Mott","doi":"10.5194/gmd-16-5049-2023","DOIUrl":"https://doi.org/10.5194/gmd-16-5049-2023","url":null,"abstract":"Abstract. High-resolution (< 1 km) atmospheric modeling is increasingly used to study precipitation distributions in complex terrain and cryosphere–atmospheric processes. While this approach has yielded insightful results, studies over annual timescales or at the spatial extents of watersheds remain unrealistic due to the computational costs of running most atmospheric models. In this paper we introduce a high-resolution variant of the Intermediate Complexity Atmospheric Research (ICAR) model, HICAR. We detail the model development that enabled HICAR simulations at the hectometer scale, including changes to the advection scheme and the wind solver. The latter uses near-surface terrain parameters which allow HICAR to simulate complex topographic flow features. These model improvements clearly influence precipitation distributions at the ridge scale (50 m), suggesting that HICAR can approximate processes dependent on particle–flow interactions such as preferential deposition. A 250 m HICAR simulation over most of the Swiss Alps also shows monthly precipitation patterns similar to two different gridded precipitation products which assimilate available observations. Benchmarking runs show that HICAR uses 594 times fewer computational resources than the Weather Research and Forecasting (WRF) atmospheric model. This gain in efficiency makes dynamic downscaling accessible to ecohydrological research, where downscaled data are often required at hectometer resolution for whole basins at seasonal timescales. These results motivate further development of HICAR, including refinement of parameterizations used in the wind solver and coupling of the model with an intermediate-complexity snow model.\u0000","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":" ","pages":""},"PeriodicalIF":5.1,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48269400","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-01DOI: 10.5194/gmd-16-5035-2023
J. Díez-Sierra, Salvador Navas, Manuel del Jesus
Abstract. Long time series of rainfall at different levels of aggregation (daily or hourly in most cases) constitute the basic input for hydrological, hydraulic and climate studies. However, oftentimes the length, completeness, time resolution or spatial coverage of the available records falls short of the minimum requirements to build robust estimations. Here, we introduce NEOPRENE, a Python library to generate synthetic time series of rainfall. NEOPRENE simulates multi-site synthetic rainfall that reproduces observed statistics at different time aggregations. Three case studies exemplify the use of the library, focusing on extreme rainfall, as well as on disaggregating daily rainfall observations into hourly rainfall records. NEOPRENE is distributed from GitHub with an open license (GPLv3), free for research and commercial purposes alike. We also provide Jupyter notebooks with the example use cases to promote its adoption by researchers and practitioners involved in vulnerability, impact and adaptation studies.
{"title":"NEOPRENE v1.0.1: a Python library for generating spatial rainfall based on the Neyman–Scott process","authors":"J. Díez-Sierra, Salvador Navas, Manuel del Jesus","doi":"10.5194/gmd-16-5035-2023","DOIUrl":"https://doi.org/10.5194/gmd-16-5035-2023","url":null,"abstract":"Abstract. Long time series of rainfall at different levels of aggregation (daily or hourly in most cases) constitute the basic input for hydrological, hydraulic and climate studies. However, oftentimes the length, completeness, time resolution or spatial coverage of the available records falls short of the minimum requirements to build robust estimations. Here, we introduce NEOPRENE, a Python library to generate synthetic time series of rainfall. NEOPRENE simulates multi-site synthetic rainfall that reproduces observed statistics at different time aggregations. Three case studies exemplify the use of the library, focusing on extreme rainfall, as well as on disaggregating daily rainfall observations into hourly rainfall records. NEOPRENE is distributed from GitHub with an open license (GPLv3), free for research and commercial purposes alike. We also provide Jupyter notebooks with the example use cases to promote its adoption by researchers and practitioners involved in vulnerability, impact and adaptation studies.\u0000","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":" ","pages":""},"PeriodicalIF":5.1,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44719958","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 : 2023-08-31DOI: 10.5194/gmd-16-4957-2023
A. Pasik, A. Gruber, Wolfgang Preimesberger, D. De Santis, W. Dorigo
Abstract. Soil moisture is a key variable in monitoring climate and an important component of the hydrological, carbon, and energy cycles. Satellite products ameliorate the sparsity of field measurements but are inherently limited to observing the near-surface layer, while water available in the unobserved root-zone controls critical processes like plant water uptake and evapotranspiration. A variety of approaches exist for modelling root-zone soil moisture (RZSM), including approximating it from surface layer observations. While the number of available RZSM datasets is growing, they usually do not contain estimates of their uncertainty. In this paper we derive a long-term RZSM dataset (2002–2020) from the Copernicus Climate Change Service (C3S) surface soil moisture (SSM) COMBINED product via the exponential filter (EF) method. We identify the optimal value of the method's model parameter T, which controls the level of smoothing and delaying applied to the surface observations, by maximizing the correlation of RZSM estimates with field measurements from the International Soil Moisture Network (ISMN). Optimized T-parameter values were calculated for four soil depth layers (0–10, 10–40, 40–100, and 100–200 cm) and used to calculate a global RZSM dataset. The quality of this dataset is then globally evaluated against RZSM estimates of the ERA5-Land reanalysis. Results of the product comparison show satisfactory skill in all four layers, with the median Pearson correlation ranging from 0.54 in the topmost to 0.28 in the deepest soil layer. Temporally dynamic product uncertainties for each of the RZSM product layers are estimated by applying standard uncertainty propagation to SSM input data and by estimating structural uncertainties in the EF method from ISMN ground reference measurements taken at the surface and at varying depths. Uncertainty estimates were found to exhibit both realistic absolute magnitudes and temporal variations. The product described here is, to the best of our knowledge, the first global, long-term, uncertainty-characterized, and purely observation-based product for RZSM estimates up to 2 m depth.
{"title":"Uncertainty estimation for a new exponential-filter-based long-term root-zone soil moisture dataset from Copernicus Climate Change Service (C3S) surface observations","authors":"A. Pasik, A. Gruber, Wolfgang Preimesberger, D. De Santis, W. Dorigo","doi":"10.5194/gmd-16-4957-2023","DOIUrl":"https://doi.org/10.5194/gmd-16-4957-2023","url":null,"abstract":"Abstract. Soil moisture is a key variable in monitoring climate and an important component of the hydrological, carbon, and energy cycles. Satellite products ameliorate the sparsity of field measurements but are inherently limited to observing the near-surface layer, while water available in the unobserved root-zone controls critical processes like plant water uptake and evapotranspiration. A variety of approaches exist for modelling root-zone soil moisture (RZSM), including approximating it from surface layer observations. While the number of available RZSM datasets is growing, they usually do not contain estimates of their uncertainty. In this paper we derive a long-term RZSM dataset (2002–2020) from the Copernicus Climate Change Service (C3S) surface soil moisture (SSM) COMBINED product via the exponential filter (EF) method. We identify the optimal value of the method's model parameter T, which controls the level of smoothing and delaying applied to the surface observations, by maximizing the correlation of RZSM estimates with field measurements from the International Soil Moisture Network (ISMN). Optimized T-parameter values were calculated for four soil depth layers (0–10, 10–40, 40–100, and 100–200 cm) and used to calculate a global RZSM dataset. The quality of this dataset is then globally evaluated against RZSM estimates of the ERA5-Land reanalysis. Results of the product comparison show satisfactory skill in all four layers, with the median Pearson correlation ranging from 0.54 in the topmost to 0.28 in the deepest soil layer. Temporally dynamic product uncertainties for each of the RZSM product layers are estimated by applying standard uncertainty propagation to SSM input data and by estimating structural uncertainties in the EF method from ISMN ground reference measurements taken at the surface and at varying depths. Uncertainty estimates were found to exhibit both realistic absolute magnitudes and temporal variations. The product described here is, to the best of our knowledge, the first global, long-term, uncertainty-characterized, and purely observation-based product for RZSM estimates up to 2 m depth.\u0000","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":" ","pages":""},"PeriodicalIF":5.1,"publicationDate":"2023-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47193450","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 : 2023-08-31DOI: 10.5194/gmd-16-4977-2023
Chengzhu Gong, F. Ueckerdt, R. Pietzcker, Adrian Odenweller, W. Schill, M. Kittel, Gunnar Luderer
Abstract. Integrated assessment models (IAMs) are a central tool for the quantitative analysis of climate change mitigation strategies. However, due to their global, cross-sectoral and centennial scope, IAMs cannot explicitly represent the temporal and spatial details required to properly analyze the key role of variable renewable energy (VRE) in decarbonizing the power sector and enabling emission reductions through end-use electrification. In contrast, power sector models (PSMs) can incorporate high spatiotemporal resolutions but tend to have narrower sectoral and geographic scopes and shorter time horizons. To overcome these limitations, here we present a novel methodology: an iterative and fully automated soft-coupling framework that combines the strengths of a long-term IAM and a detailed PSM. The key innovation is that the framework uses the market values of power generations and the capture prices of demand flexibilities in the PSM as price signals that change the capacity and power mix of the IAM. Hence, both models make endogenous investment decisions, leading to a joint solution. We apply the method to Germany in a proof-of-concept study using the IAM REgional Model of INvestments and Development (REMIND) v3.0.0 and the PSM Dispatch and Investment Evaluation Tool with Endogenous Renewables (DIETER) v1.0.2 and confirm the theoretical prediction of almost-full convergence in terms of both decision variables and (shadow) prices. At the end of the iterative process, the absolute model difference between the generation shares of any generator type for any year is < 5 % for a simple configuration (no storage, no flexible demand) under a “proof-of-concept” baseline scenario and 6 %–7 % for a more realistic and detailed configuration (with storage and flexible demand). For the simple configuration, we mathematically show that this coupling scheme corresponds uniquely to an iterative mapping of the Lagrangians of two power sector optimization problems of different time resolutions, which can lead to a comprehensive model convergence of both decision variables and (shadow) prices. The remaining differences in the two models can be explained by a slight mismatch between the standing capacities in the real world and optimal modeling solutions based purely on cost competition. Since our approach is based on fundamental economic principles, it is also applicable to other IAM–PSM pairs.
{"title":"Bidirectional coupling of the long-term integrated assessment model REgional Model of INvestments and Development (REMIND) v3.0.0 with the hourly power sector model Dispatch and Investment Evaluation Tool with Endogenous Renewables (DIETER) v1.0.2","authors":"Chengzhu Gong, F. Ueckerdt, R. Pietzcker, Adrian Odenweller, W. Schill, M. Kittel, Gunnar Luderer","doi":"10.5194/gmd-16-4977-2023","DOIUrl":"https://doi.org/10.5194/gmd-16-4977-2023","url":null,"abstract":"Abstract. Integrated assessment models (IAMs) are a central tool for the\u0000quantitative analysis of climate change mitigation strategies. However, due\u0000to their global, cross-sectoral and centennial scope, IAMs cannot explicitly\u0000represent the temporal and spatial details required to properly analyze the\u0000key role of variable renewable energy (VRE) in decarbonizing the power\u0000sector and enabling emission reductions through end-use electrification. In\u0000contrast, power sector models (PSMs) can incorporate high spatiotemporal\u0000resolutions but tend to have narrower sectoral and geographic scopes and\u0000shorter time horizons. To overcome these limitations, here we present a\u0000novel methodology: an iterative and fully automated soft-coupling framework\u0000that combines the strengths of a long-term IAM and a detailed PSM. The key\u0000innovation is that the framework uses the market values of power generations\u0000and the capture prices of demand flexibilities in the PSM as price signals that change the capacity and power mix of the IAM. Hence, both\u0000models make endogenous investment decisions, leading to a joint solution. We\u0000apply the method to Germany in a proof-of-concept study using the IAM REgional Model of INvestments and Development (REMIND) v3.0.0 and the PSM Dispatch and Investment Evaluation Tool with Endogenous Renewables (DIETER) v1.0.2 and confirm the theoretical prediction of\u0000almost-full convergence in terms of both decision variables and (shadow)\u0000prices. At the end of the iterative process, the absolute model difference\u0000between the generation shares of any generator type for any year is\u0000< 5 % for a simple configuration (no storage, no flexible demand)\u0000under a “proof-of-concept” baseline scenario and 6 %–7 % for a more\u0000realistic and detailed configuration (with storage and flexible demand). For\u0000the simple configuration, we mathematically show that this coupling scheme\u0000corresponds uniquely to an iterative mapping of the Lagrangians of two power\u0000sector optimization problems of different time resolutions, which can lead\u0000to a comprehensive model convergence of both decision variables and (shadow)\u0000prices. The remaining differences in the two models can be explained by a\u0000slight mismatch between the standing capacities in the real world and\u0000optimal modeling solutions based purely on cost competition. Since our\u0000approach is based on fundamental economic principles, it is also applicable\u0000to other IAM–PSM pairs.\u0000","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":" ","pages":""},"PeriodicalIF":5.1,"publicationDate":"2023-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47105486","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 : 2023-08-30DOI: 10.5194/gmd-16-4883-2023
Özgür Gürses, L. Oziel, O. Karakuş, D. Sidorenko, C. Völker, Y. Ye, Moritz Zeising, M. Butzin, J. Hauck
Abstract. The cycling of carbon in the oceans is affected by feedbacks driven by changes in climate and atmospheric CO2. Understanding these feedbacks is therefore an important prerequisite for projecting future climate. Marine biogeochemistry models are a useful tool but, as with any model, are a simplification and need to be continually improved. In this study, we coupled the Finite-volumE Sea ice–Ocean Model (FESOM2.1) to the Regulated Ecosystem Model version 3 (REcoM3). FESOM2.1 is an update of the Finite-Element Sea ice–Ocean Model (FESOM1.4) and operates on unstructured meshes. Unlike standard structured-mesh ocean models, the mesh flexibility allows for a realistic representation of small-scale dynamics in key regions at an affordable computational cost. Compared to the previous coupled model version of FESOM1.4–REcoM2, the model FESOM2.1–REcoM3 utilizes a new dynamical core, based on a finite-volume discretization instead of finite elements, and retains central parts of the biogeochemistry model. As a new feature, carbonate chemistry, including water vapour correction, is computed by mocsy 2.0. Moreover, REcoM3 has an extended food web that includes macrozooplankton and fast-sinking detritus. Dissolved oxygen is also added as a new tracer. In this study, we assess the ocean and biogeochemical state simulated with FESOM2.1–REcoM3 in a global set-up at relatively low spatial resolution forced with JRA55-do (Tsujino et al., 2018) atmospheric reanalysis. The focus is on the recent period (1958–2021) to assess how well the model can be used for present-day and future climate change scenarios on decadal to centennial timescales. A bias in the global ocean–atmosphere preindustrial CO2 flux present in the previous model version (FESOM1.4–REcoM2) could be significantly reduced. In addition, the computational efficiency is 2–3 times higher than that of FESOM1.4–REcoM2. Overall, it is found that FESOM2.1–REcoM3 is a skilful tool for ocean biogeochemical modelling applications.
摘要海洋中的碳循环受到气候和大气中二氧化碳变化所驱动的反馈影响。因此，了解这些反馈是预测未来气候的重要先决条件。海洋生物地球化学模型是一个有用的工具，但与任何模型一样，都是一种简化，需要不断改进。在这项研究中，我们将有限体积海冰-海洋模型(FESOM2.1)与调节生态系统模型第3版(REcoM3)进行了耦合。FESOM2.1是对有限元海冰-海洋模型(FESOM1.4)的更新，并在非结构化网格上运行。与标准的结构网格海洋模型不同，网格灵活性允许以可承受的计算成本在关键区域真实地表示小规模动态。与之前的FESOM1.4-REcoM2耦合模型版本相比，FESOM2.1-REcoM3模型采用了新的动力学核心，基于有限体积离散而不是有限元，并保留了生物地球化学模型的核心部分。作为一个新的特征，碳酸盐岩化学，包括水蒸气校正，是由mocsy2.0计算的。此外，REcoM3有一个扩展的食物网，包括大型浮游动物和快速下沉的碎屑。还添加了溶解氧作为新的示踪剂。在本研究中，我们评估了在JRA55-do (Tsujino et al.， 2018)大气再分析强迫下，在相对低空间分辨率的全球设置下，FESOM2.1-REcoM3模拟的海洋和生物地球化学状态。重点关注最近时期(1958-2021)，以评估该模式在十年至百年时间尺度上用于当前和未来气候变化情景的效果。在以前的模式版本(FESOM1.4-REcoM2)中存在的全球海洋-大气工业化前CO2通量的偏差可以大大降低。计算效率是FESOM1.4-REcoM2的2-3倍。总体而言，FESOM2.1-REcoM3是一个熟练的海洋生物地球化学模拟工具。
{"title":"Ocean biogeochemistry in the coupled ocean–sea ice–biogeochemistry model FESOM2.1–REcoM3","authors":"Özgür Gürses, L. Oziel, O. Karakuş, D. Sidorenko, C. Völker, Y. Ye, Moritz Zeising, M. Butzin, J. Hauck","doi":"10.5194/gmd-16-4883-2023","DOIUrl":"https://doi.org/10.5194/gmd-16-4883-2023","url":null,"abstract":"Abstract. The cycling of carbon in the oceans is affected by feedbacks driven by changes in climate and atmospheric CO2. Understanding these feedbacks is therefore an important prerequisite for projecting future climate. Marine biogeochemistry models are a useful tool but, as with any model, are a simplification and need to be continually improved. In this study, we coupled the Finite-volumE Sea ice–Ocean Model (FESOM2.1) to the Regulated Ecosystem Model version 3 (REcoM3). FESOM2.1 is an update of the Finite-Element Sea ice–Ocean Model (FESOM1.4) and operates on unstructured meshes. Unlike standard structured-mesh ocean models, the mesh flexibility allows for a realistic representation of small-scale dynamics in key regions at an affordable computational cost. Compared to the previous coupled model version of FESOM1.4–REcoM2, the model FESOM2.1–REcoM3 utilizes a new dynamical core, based on a finite-volume discretization instead of finite elements, and retains central parts of the biogeochemistry model. As a new feature, carbonate chemistry, including water vapour correction, is computed by mocsy 2.0. Moreover, REcoM3 has an extended food web that includes macrozooplankton and fast-sinking detritus. Dissolved oxygen is also added as a new tracer. In this study, we assess the ocean and biogeochemical state simulated with FESOM2.1–REcoM3 in a global set-up at relatively low spatial resolution forced with JRA55-do (Tsujino et al., 2018) atmospheric reanalysis. The focus is on the recent period (1958–2021) to assess how well the model can be used for present-day and future climate change scenarios on decadal to centennial timescales. A bias in the global ocean–atmosphere preindustrial CO2 flux present in the previous model version (FESOM1.4–REcoM2) could be significantly reduced. In addition, the computational efficiency is 2–3 times higher than that of FESOM1.4–REcoM2. Overall, it is found that FESOM2.1–REcoM3 is a skilful tool for ocean biogeochemical modelling applications.\u0000","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":" ","pages":""},"PeriodicalIF":5.1,"publicationDate":"2023-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45928529","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 : 2023-08-29DOI: 10.5194/gmd-16-4867-2023
Li Fang, Jianbing Jin, A. Segers, H. Liao, Ke Li, Bufan Xu, Wei Han, Mijie Pang, H. Lin
Abstract. Statistical methods, particularly machine learning models, have gained significant popularity in air quality predictions. These prediction models are commonly trained using the historical measurement datasets independently collected at the environmental monitoring stations and their operational forecasts in advance using inputs of the real-time ambient pollutant observations. Therefore, these high-quality machine learning models only provide site-available predictions and cannot solely be used as the operational forecast. In contrast, deterministic chemical transport models (CTMs), which simulate the full life cycles of air pollutants, provide predictions that are continuous in the 3D field. Despite their benefits, CTM predictions are typically biased, particularly on a fine scale, owing to the complex error sources due to the emission, transport, and removal of pollutants. In this study, we proposed a fusion of site-available machine learning prediction, which is from our regional feature selection-based machine learning model (RFSML v1.0), and a CTM prediction. Compared to the normal pure machine learning model, the fusion system provides a gridded prediction with relatively high accuracy. The prediction fusion was conducted using the Bayesian-theory-based ensemble Kalman filter (EnKF). Background error covariance was an essential part in the assimilation process. Ensemble CTM predictions driven by the perturbed emission inventories were initially used for representing their spatial covariance statistics, which could resolve the main part of the CTM error. In addition, a covariance inflation algorithm was designed to amplify the ensemble perturbations to account for other model errors next to the uncertainty in emission inputs. Model evaluation tests were conducted based on independent measurements. Our EnKF-based prediction fusion presented superior performance compared to the pure CTM. Moreover, covariance inflation further enhanced the fused prediction, particularly in cases of severe underestimation.
{"title":"A gridded air quality forecast through fusing site-available machine learning predictions from RFSML v1.0 and chemical transport model results from GEOS-Chem v13.1.0 using the ensemble Kalman filter","authors":"Li Fang, Jianbing Jin, A. Segers, H. Liao, Ke Li, Bufan Xu, Wei Han, Mijie Pang, H. Lin","doi":"10.5194/gmd-16-4867-2023","DOIUrl":"https://doi.org/10.5194/gmd-16-4867-2023","url":null,"abstract":"Abstract. Statistical methods, particularly machine learning models, have gained significant popularity in air quality predictions. These prediction models are commonly trained using the historical measurement datasets independently collected at the environmental monitoring stations and their operational forecasts in advance using inputs of the real-time ambient pollutant observations. Therefore, these high-quality machine learning models only provide site-available predictions and cannot solely be used as the operational forecast. In contrast, deterministic chemical transport models (CTMs), which simulate the full life cycles of air pollutants, provide predictions that are continuous in the 3D field. Despite their benefits, CTM predictions are typically biased, particularly on a fine scale, owing to the complex error sources due to the emission, transport, and removal of pollutants. In this study, we proposed a fusion of site-available machine learning prediction, which is from our regional feature selection-based machine learning model (RFSML v1.0), and a CTM prediction. Compared to the normal pure machine learning model, the fusion system provides a gridded prediction with relatively high accuracy. The prediction fusion was conducted using the Bayesian-theory-based ensemble Kalman filter (EnKF). Background error covariance was an essential part in the assimilation process. Ensemble CTM predictions driven by the perturbed emission inventories were initially used for representing their spatial covariance statistics, which could resolve the main part of the CTM error. In addition, a covariance inflation algorithm was designed to amplify the ensemble perturbations to account for other model errors next to the uncertainty in emission inputs. Model evaluation tests were conducted based on independent measurements. Our EnKF-based prediction fusion presented superior performance compared to the pure CTM. Moreover, covariance inflation further enhanced the fused prediction, particularly in cases of severe underestimation.\u0000","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":" ","pages":""},"PeriodicalIF":5.1,"publicationDate":"2023-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47373622","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 : 2023-08-28DOI: 10.5194/gmd-16-4853-2023
R. Hand, Eric Samakinwa, Laura Lipfert, S. Brönnimann
Abstract. We introduce ModE-Sim (Modern Era SIMulations), a medium-sized ensemble of simulations with the atmospheric general circulation model ECHAM6 in its LR (low-resolution) version (T63; approx. 1.8∘ horizontal grid width with 47 vertical levels). At the lower boundary we use prescribed sea surface temperatures and sea ice that reflect observed values while accounting for uncertainties in these. Furthermore we use radiative forcings that also reflect observed values while accounting for uncertainties in the timing and strength of volcanic eruptions. The simulations cover the period from 1420 to 2009. With 60 ensemble members between 1420 and 1850 and 36 ensemble members from 1850 to 2009, ModE-Sim consists of 31 620 simulated years in total. ModE-Sim is suitable for many applications as its various subsets can be used as initial-condition and boundary-condition ensembles to study climate variability. The main intention of this paper is to give a comprehensive description of the experimental setup of ModE-Sim and to provide an evaluation, mainly focusing on the two key variables, 2 m temperature and precipitation. We demonstrate ModE-Sim's ability to represent their mean state, to produce a reasonable response to external forcings, and to sample internal variability. Through the example of heat waves, we show that the ensemble is even capable of capturing certain types of extreme events.
{"title":"ModE-Sim – a medium-sized atmospheric general circulation model (AGCM) ensemble to study climate variability during the modern era (1420 to 2009)","authors":"R. Hand, Eric Samakinwa, Laura Lipfert, S. Brönnimann","doi":"10.5194/gmd-16-4853-2023","DOIUrl":"https://doi.org/10.5194/gmd-16-4853-2023","url":null,"abstract":"Abstract. We introduce ModE-Sim (Modern Era SIMulations), a medium-sized ensemble of simulations with the atmospheric general circulation model ECHAM6 in its LR (low-resolution) version (T63; approx. 1.8∘ horizontal grid width with 47 vertical levels). At the lower boundary we use prescribed sea surface temperatures and sea ice that reflect observed values while accounting for uncertainties in these. Furthermore we use radiative forcings that also reflect observed values while accounting for uncertainties in the timing and strength of volcanic eruptions. The simulations cover the period from 1420 to 2009. With 60 ensemble members between 1420 and 1850 and 36 ensemble members from 1850 to 2009, ModE-Sim consists of 31 620 simulated years in total.\u0000ModE-Sim is suitable for many applications as its various subsets can be used as initial-condition and boundary-condition ensembles to study climate variability. The main intention of this paper is to give a comprehensive description of the experimental setup of ModE-Sim and to provide an evaluation, mainly focusing on the two key variables, 2 m temperature and precipitation. We demonstrate ModE-Sim's ability to represent their mean state, to produce a reasonable response to external forcings, and to sample internal variability. Through the example of heat waves, we show that the ensemble is even capable of capturing certain types of extreme events.\u0000","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":" ","pages":""},"PeriodicalIF":5.1,"publicationDate":"2023-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43270419","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 : 2023-08-25DOI: 10.5194/gmd-16-4835-2023
M. Goudar, Juliëtte C. S. Anema, Rajesh Kumar, T. Borsdorff, J. Landgraf
Abstract. This paper presents the automated plume detection and emission estimation algorithm (APE), developed to detect CO plumes from isolated biomass burning events and to quantify the corresponding CO emission rate. APE uses the CO product of the Tropospheric Monitoring Instrument (TROPOMI) on board the Copernicus Sentinel-5 Precursor (S5P) satellite, launched in 2017, and collocated active fire data from the Visible Infrared Imaging Radiometer Suite (VIIRS), the latter flying 3 min ahead of S5P. After identifying appropriate fire events using VIIRS data, an automated plume detection algorithm based on traditional image processing algorithms selects plumes for further data interpretation. The approach is based on thresholds optimized for data over the United States in September 2020. Subsequently, the CO emission rate is estimated using the cross-sectional flux method, which requires horizontal wind fields at the plume height. Three different plume heights were considered, and the ECMWF Reanalysis v5 (ERA5) data were used to compute emissions. A varying plume height in the downwind direction based on three-dimensional Lagrangian simulation was considered appropriate. APE is verified for observations over Australia and Siberia. For all fire sources identified by VIIRS, only 16 % of the data corresponded to clear-sky TROPOMI CO data with plume signature. Furthermore, the quality filters of APE resulted in emission estimations for 26 % of the TROPOMI CO data with plume signatures. Visual filtering of the APE's output showed a true-positive confidence level of 97.7 %. Finally, we provide an estimate of the emission uncertainties. The greatest contribution of error comes from the uncertainty in Global Fire Assimilation System (GFAS) injection height that leads to emission errors <100 %, followed by systematic errors in the ERA5 wind data. The assumption of constant emission during plume formation and spatial under-sampling of CO column concentration by TROPOMI yields an error of <20 %. The randomized errors from the ensemble ERA5 wind data are found to be less than 20 % for 97 % of the cases.
{"title":"Plume detection and emission estimate for biomass burning plumes from TROPOMI carbon monoxide observations using APE v1.1","authors":"M. Goudar, Juliëtte C. S. Anema, Rajesh Kumar, T. Borsdorff, J. Landgraf","doi":"10.5194/gmd-16-4835-2023","DOIUrl":"https://doi.org/10.5194/gmd-16-4835-2023","url":null,"abstract":"Abstract. This paper presents the automated plume detection and emission estimation algorithm (APE), developed to detect CO plumes from isolated biomass burning events and to quantify the corresponding CO emission rate. APE uses the CO product of the Tropospheric Monitoring Instrument (TROPOMI) on board the Copernicus Sentinel-5 Precursor (S5P) satellite, launched in 2017, and collocated active fire data from the Visible Infrared Imaging Radiometer Suite (VIIRS), the latter flying 3 min ahead of S5P. After identifying appropriate fire events using VIIRS data, an automated plume detection algorithm based on traditional image processing algorithms selects plumes for further data interpretation. The approach is based on thresholds optimized for data over the United States in September 2020. Subsequently, the CO emission rate is estimated using the cross-sectional flux method, which requires horizontal wind fields at the plume height. Three different plume heights were considered, and the ECMWF Reanalysis v5 (ERA5) data were used to compute emissions. A varying plume height in the downwind direction based on three-dimensional Lagrangian simulation was considered appropriate. APE is verified for observations over Australia and Siberia. For all fire sources identified by VIIRS, only 16 % of the data corresponded to clear-sky TROPOMI CO data with plume signature. Furthermore, the quality filters of APE resulted in emission estimations for 26 % of the TROPOMI CO data with plume signatures. Visual filtering of the APE's output showed a true-positive confidence level of 97.7 %. Finally, we provide an estimate of the emission uncertainties. The greatest contribution of error comes from the uncertainty in Global Fire Assimilation System (GFAS) injection height that leads to emission errors <100 %, followed by systematic errors in the ERA5 wind data. The assumption of constant emission during plume formation and spatial under-sampling of CO column concentration by TROPOMI yields an error of <20 %. The randomized errors from the ensemble ERA5 wind data are found to be less than 20 % for 97 % of the cases.\u0000","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":" ","pages":""},"PeriodicalIF":5.1,"publicationDate":"2023-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48730540","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 : 2023-08-24DOI: 10.5194/gmd-16-4767-2023
Po-Wei Huang, B. Flemisch, C. Qin, M. Saar, A. Ebigbo
Abstract. Reactive transport processes in natural environments often involve many ionic species. The diffusivities of ionic species vary. Since assigning different diffusivities in the advection–diffusion equation leads to charge imbalance, a single diffusivity is usually used for all species. In this work, we apply the Nernst–Planck equation, which resolves unequal diffusivities of the species in an electroneutral manner, to model reactive transport. To demonstrate the advantages of the Nernst–Planck model, we compare the simulation results of transport under reaction-driven flow conditions using the Nernst–Planck model with those of the commonly used single-diffusivity model. All simulations are also compared to well-defined experiments on the scale of centimeters. Our results show that the Nernst–Planck model is valid and particularly relevant for modeling reactive transport processes with an intricate interplay among diffusion, reaction, electromigration, and density-driven convection.
{"title":"Validating the Nernst–Planck transport model under reaction-driven flow conditions using RetroPy v1.0","authors":"Po-Wei Huang, B. Flemisch, C. Qin, M. Saar, A. Ebigbo","doi":"10.5194/gmd-16-4767-2023","DOIUrl":"https://doi.org/10.5194/gmd-16-4767-2023","url":null,"abstract":"Abstract. Reactive transport processes in natural environments often involve many ionic species. The diffusivities of ionic species vary. Since assigning different diffusivities in the advection–diffusion equation leads to charge imbalance, a single diffusivity is usually used for all species. In this work, we apply the Nernst–Planck equation, which resolves unequal diffusivities of the species in an electroneutral manner, to model reactive transport. To demonstrate the advantages of the Nernst–Planck model, we compare the simulation results of transport under reaction-driven flow conditions using the Nernst–Planck model with those of the commonly used single-diffusivity model. All simulations are also compared to well-defined experiments on the scale of centimeters. Our results show that the Nernst–Planck model is valid and particularly relevant for modeling reactive transport processes with an intricate interplay among diffusion, reaction, electromigration, and density-driven convection.\u0000","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":" ","pages":""},"PeriodicalIF":5.1,"publicationDate":"2023-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46785110","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 : 2023-08-24DOI: 10.5194/gmd-16-4793-2023
D. Pendergrass, D. Jacob, H. Nesser, D. Varon, M. Sulprizio, K. Miyazaki, K. Bowman
Abstract. We present a versatile, powerful, and user-friendly chemical data assimilation toolkit for simultaneously optimizing emissions and concentrations of chemical species based on atmospheric observations from satellites or suborbital platforms. The CHemistry and Emissions REanalysis Interface with Observations (CHEEREIO) exploits the GEOS-Chem chemical transport model and a localized ensemble transform Kalman filter algorithm (LETKF) to determine the Bayesian optimal (posterior) emissions and/or concentrations of a set of species based on observations and prior information using an easy-to-modify configuration file with minimal changes to the GEOS-Chem or LETKF code base. The LETKF algorithm readily allows for nonlinear chemistry and produces flow-dependent posterior error covariances from the ensemble simulation spread. The object-oriented Python-based design of CHEEREIO allows users to easily add new observation operators such as for satellites. CHEEREIO takes advantage of the Harmonized Emissions Component (HEMCO) modular structure of input data management in GEOS-Chem to update emissions from the assimilation process independently from the GEOS-Chem code. It can seamlessly support GEOS-Chem version updates and is adaptable to other chemical transport models with similar modular input data structure. A post-processing suite combines ensemble output into consolidated NetCDF files and supports a wide variety of diagnostic data and visualizations. We demonstrate CHEEREIO's capabilities with an out-of-the-box application, assimilating global methane emissions and concentrations at weekly temporal resolution and 2∘ × 2.5∘ spatial resolution for 2019 using TROPOspheric Monitoring Instrument (TROPOMI) satellite observations. CHEEREIO achieves a 50-fold improvement in computational performance compared to the equivalent analytical inversion of TROPOMI observations.
{"title":"CHEEREIO 1.0: a versatile and user-friendly ensemble-based chemical data assimilation and emissions inversion platform for the GEOS-Chem chemical transport model","authors":"D. Pendergrass, D. Jacob, H. Nesser, D. Varon, M. Sulprizio, K. Miyazaki, K. Bowman","doi":"10.5194/gmd-16-4793-2023","DOIUrl":"https://doi.org/10.5194/gmd-16-4793-2023","url":null,"abstract":"Abstract. We present a versatile, powerful, and user-friendly chemical data assimilation toolkit for simultaneously optimizing emissions and concentrations of\u0000chemical species based on atmospheric observations from satellites or suborbital platforms. The CHemistry and Emissions REanalysis Interface with\u0000Observations (CHEEREIO) exploits the GEOS-Chem chemical transport model and a localized ensemble transform Kalman filter algorithm (LETKF) to\u0000determine the Bayesian optimal (posterior) emissions and/or concentrations of a set of species based on observations and prior information using an\u0000easy-to-modify configuration file with minimal changes to the GEOS-Chem or LETKF code base. The LETKF algorithm readily allows for nonlinear\u0000chemistry and produces flow-dependent posterior error covariances from the ensemble simulation spread. The object-oriented Python-based design of\u0000CHEEREIO allows users to easily add new observation operators such as for satellites. CHEEREIO takes advantage of the Harmonized Emissions Component (HEMCO) modular structure of\u0000input data management in GEOS-Chem to update emissions from the assimilation process independently from the GEOS-Chem code. It can seamlessly\u0000support GEOS-Chem version updates and is adaptable to other chemical transport models with similar modular input data structure. A post-processing\u0000suite combines ensemble output into consolidated NetCDF files and supports a wide variety of diagnostic data and visualizations. We demonstrate\u0000CHEEREIO's capabilities with an out-of-the-box application, assimilating global methane emissions and concentrations at weekly temporal resolution\u0000and 2∘ × 2.5∘ spatial resolution for 2019 using TROPOspheric Monitoring Instrument (TROPOMI) satellite observations. CHEEREIO achieves a 50-fold improvement in\u0000computational performance compared to the equivalent analytical inversion of TROPOMI observations.\u0000","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":" ","pages":""},"PeriodicalIF":5.1,"publicationDate":"2023-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41861349","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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