Pub Date : 2023-12-19DOI: 10.5194/gmd-16-7339-2023
O. Gómez-Novell, B. Pace, F. Visini, J. F. Faure Walker, Oona Scotti
Abstract. A key challenge in paleoseismology is constraining the timing and occurrence of past earthquakes to create an earthquake history along faults that can be used for testing or building fault-based seismic hazard assessments. We present a new methodological approach and accompanying code (Paleoseismic EArthquake CHronologies, PEACH) to meet this challenge. By using the integration of multi-site paleoseismic records through probabilistic modeling of the event times and an unconditioned correlation, PEACH improves the objectivity of constraining paleoearthquake chronologies along faults, including highly populated records and poorly dated events. Our approach reduces uncertainties in event times and allows increased resolution of the trench records. By extension, the approach can potentially reduce the uncertainties in the estimation of parameters for seismic hazard assessment such as earthquake recurrence times and coefficient of variation. We test and discuss this methodology in two well-studied cases: the Paganica Fault in Italy and the Wasatch Fault in the United States.
{"title":"Deciphering past earthquakes from the probabilistic modeling of paleoseismic records – the Paleoseismic EArthquake CHronologies code (PEACH, version 1)","authors":"O. Gómez-Novell, B. Pace, F. Visini, J. F. Faure Walker, Oona Scotti","doi":"10.5194/gmd-16-7339-2023","DOIUrl":"https://doi.org/10.5194/gmd-16-7339-2023","url":null,"abstract":"Abstract. A key challenge in paleoseismology is constraining the timing and occurrence of past earthquakes to create an earthquake history along faults that can be used for testing or building fault-based seismic hazard assessments. We present a new methodological approach and accompanying code (Paleoseismic EArthquake CHronologies, PEACH) to meet this challenge. By using the integration of multi-site paleoseismic records through probabilistic modeling of the event times and an unconditioned correlation, PEACH improves the objectivity of constraining paleoearthquake chronologies along faults, including highly populated records and poorly dated events. Our approach reduces uncertainties in event times and allows increased resolution of the trench records. By extension, the approach can potentially reduce the uncertainties in the estimation of parameters for seismic hazard assessment such as earthquake recurrence times and coefficient of variation. We test and discuss this methodology in two well-studied cases: the Paganica Fault in Italy and the Wasatch Fault in the United States.\u0000","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":"110 14","pages":""},"PeriodicalIF":5.1,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138959238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-19DOI: 10.5194/gmd-16-7375-2023
D. Degen, Daniel Caviedes Voullième, S. Buiter, H. Hendricks Franssen, H. Vereecken, A. González-Nicolás, F. Wellmann
Abstract. An accurate assessment of the physical states of the Earth system is an essential component of many scientific, societal, and economical considerations. These assessments are becoming an increasingly challenging computational task since we aim to resolve models with high resolutions in space and time, to consider complex coupled partial differential equations, and to estimate uncertainties, which often requires many realizations. Machine learning methods are becoming a very popular method for the construction of surrogate models to address these computational issues. However, they also face major challenges in producing explainable, scalable, interpretable, and robust models. In this paper, we evaluate the perspectives of geoscience applications of physics-based machine learning, which combines physics-based and data-driven methods to overcome the limitations of each approach taken alone. Through three designated examples (from the fields of geothermal energy, geodynamics, and hydrology), we show that the non-intrusive reduced-basis method as a physics-based machine learning approach is able to produce highly precise surrogate models that are explainable, scalable, interpretable, and robust.
{"title":"Perspectives of physics-based machine learning strategies for geoscientific applications governed by partial differential equations","authors":"D. Degen, Daniel Caviedes Voullième, S. Buiter, H. Hendricks Franssen, H. Vereecken, A. González-Nicolás, F. Wellmann","doi":"10.5194/gmd-16-7375-2023","DOIUrl":"https://doi.org/10.5194/gmd-16-7375-2023","url":null,"abstract":"Abstract. An accurate assessment of the physical states of the Earth system is an essential component of many scientific, societal, and economical considerations. These assessments are becoming an increasingly challenging computational task since we aim to resolve models with high resolutions in space and time, to consider complex coupled partial differential equations, and to estimate uncertainties, which often requires many realizations. Machine learning methods are becoming a very popular method for the construction of surrogate models to address these computational issues. However, they also face major challenges in producing explainable, scalable, interpretable, and robust models. In this paper, we evaluate the perspectives of geoscience applications of physics-based machine learning, which combines physics-based and data-driven methods to overcome the limitations of each approach taken alone. Through three designated examples (from the fields of geothermal energy, geodynamics, and hydrology), we show that the non-intrusive reduced-basis method as a physics-based machine learning approach is able to produce highly precise surrogate models that are explainable, scalable, interpretable, and robust.\u0000","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":"107 7","pages":""},"PeriodicalIF":5.1,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138959245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-19DOI: 10.5194/gmd-16-7311-2023
Christina Asmus, P. Hoffmann, J. Pietikäinen, J. Böhner, D. Rechid
Abstract. Irrigation is a crucial land use practice to adapt agriculture to unsuitable climate and soil conditions. Aiming to improve the growth of plants, irrigation modifies the soil condition, which causes atmospheric effects and feedbacks through land–atmosphere interaction. These effects can be quantified with numerical climate models, as has been done in various studies. It could be shown that irrigation effects, such as air temperature reduction and humidity increase, are well understood and should not be neglected on local and regional scales. However, there is a lack of studies including the role of vegetation in the altered land–atmosphere interaction. With the increasing resolution of numerical climate models, these detailed processes have a chance to be better resolved and studied. This study aims to analyze the effects of irrigation on land–atmosphere interaction, including the effects and feedbacks of vegetation. We developed a new parameterization for irrigation, implemented it into the REgional climate MOdel (REMO2020), and coupled it with the interactive MOsaic-based VEgetation module (iMOVE). Following this new approach of a separate irrigated fraction, the parameterization is suitable as a subgrid parameterization for high-resolution studies and resolves irrigation effects on land, atmosphere, and vegetation. Further, the parameterization is designed with three different water application schemes in order to analyze different parameterization approaches and their influence on the representation of irrigation effects. We apply the irrigation parameterization for southwestern Europe including the Mediterranean region at a 0.11∘ horizontal resolution for hot extremes. The simulation results are evaluated in terms of the consistency of physical processes. We found direct effects of irrigation, like a changed surface energy balance with increased latent and decreased sensible heat fluxes, and a surface temperature reduction of more than −4 K as a mean during the growing season. Further, vegetation reacts to irrigation with direct effects, such as reduced water stress, but also with feedbacks, such as a delayed growing season caused by the reduction of the near-surface temperature. Furthermore, the results were compared to observational data, showing a significant bias reduction in the 2 m mean temperature when using the irrigation parameterization.
摘要灌溉是使农业适应不适宜的气候和土壤条件的重要土地利用方式。为了改善植物的生长，灌溉改变了土壤条件，从而通过土地-大气相互作用对大气产生影响和反馈。这些影响可以通过数值气候模型进行量化，正如各种研究中所做的那样。研究表明，灌溉效应，如降低气温和增加湿度，已得到充分理解，在地方和区域范围内不应被忽视。然而，目前还缺乏对植被在改变的陆地-大气相互作用中的作用的研究。随着数值气候模式分辨率的提高，这些详细过程有机会得到更好的解析和研究。本研究旨在分析灌溉对陆地-大气相互作用的影响，包括植被的影响和反馈。我们开发了一种新的灌溉参数化方法，将其应用于区域气候模拟（REMO2020），并将其与基于MOsaic的交互式植被模块（iMOVE）相结合。采用这种单独灌溉部分的新方法，该参数化适合作为高分辨率研究的子网格参数化，并解决灌溉对土地、大气和植被的影响。此外，该参数化设计了三种不同的施水方案，以分析不同的参数化方法及其对灌溉效应表示的影响。我们以 0.11∘的水平分辨率对欧洲西南部（包括地中海地区）的极端炎热天气进行了灌溉参数化。根据物理过程的一致性对模拟结果进行了评估。我们发现了灌溉的直接影响，如改变地表能量平衡，增加潜热通量，减少显热通量，以及在生长季节平均降低地表温度-4 K 以上。此外，植被对灌溉的反应不仅有直接影响，如减少水分胁迫，还有反馈作用，如近地表温度降低导致生长季节推迟。此外，研究结果还与观测数据进行了比较，结果表明，在使用灌溉参数化时，2 米平均温度的偏差明显减小。
{"title":"Modeling and evaluating the effects of irrigation on land–atmosphere interaction in southwestern Europe with the regional climate model REMO2020–iMOVE using a newly developed parameterization","authors":"Christina Asmus, P. Hoffmann, J. Pietikäinen, J. Böhner, D. Rechid","doi":"10.5194/gmd-16-7311-2023","DOIUrl":"https://doi.org/10.5194/gmd-16-7311-2023","url":null,"abstract":"Abstract. Irrigation is a crucial land use practice to adapt agriculture to unsuitable climate and soil conditions. Aiming to improve the growth of plants, irrigation modifies the soil condition, which causes atmospheric effects and feedbacks through land–atmosphere interaction. These effects can be quantified with numerical climate models, as has been done in various studies. It could be shown that irrigation effects, such as air temperature reduction and humidity increase, are well understood and should not be neglected on local and regional scales. However, there is a lack of studies including the role of vegetation in the altered land–atmosphere interaction. With the increasing resolution of numerical climate models, these detailed processes have a chance to be better resolved and studied. This study aims to analyze the effects of irrigation on land–atmosphere interaction, including the effects and feedbacks of vegetation. We developed a new parameterization for irrigation, implemented it into the REgional climate MOdel (REMO2020), and coupled it with the interactive MOsaic-based VEgetation module (iMOVE). Following this new approach of a separate irrigated fraction, the parameterization is suitable as a subgrid parameterization for high-resolution studies and resolves irrigation effects on land, atmosphere, and vegetation. Further, the parameterization is designed with three different water application schemes in order to analyze different parameterization approaches and their influence on the representation of irrigation effects. We apply the irrigation parameterization for southwestern Europe including the Mediterranean region at a 0.11∘ horizontal resolution for hot extremes. The simulation results are evaluated in terms of the consistency of physical processes. We found direct effects of irrigation, like a changed surface energy balance with increased latent and decreased sensible heat fluxes, and a surface temperature reduction of more than −4 K as a mean during the growing season. Further, vegetation reacts to irrigation with direct effects, such as reduced water stress, but also with feedbacks, such as a delayed growing season caused by the reduction of the near-surface temperature. Furthermore, the results were compared to observational data, showing a significant bias reduction in the 2 m mean temperature when using the irrigation parameterization.\u0000","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":" 711","pages":""},"PeriodicalIF":5.1,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138960303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-19DOI: 10.5194/gmd-16-7289-2023
Neil C. Swart, Torge Martin, Rebecca Beadling, Jia‐Jia Chen, Christopher Danek, Matthew H. England, R. Farneti, S. Griffies, T. Hattermann, Judith Hauck, F. Haumann, A. Jüling, Qian Li, John Marshall, Morven Muilwijk, Andrew G. Pauling, A. Purich, Inga J. Smith, Max Thomas
Abstract. As the climate warms, the grounded ice sheet and floating ice shelves surrounding Antarctica are melting and releasing additional freshwater into the Southern Ocean. Nonetheless, almost all existing coupled climate models have fixed ice sheets and lack the physics required to represent the dominant sources of Antarctic melt. These missing ice dynamics represent a key uncertainty that is typically unaccounted for in current global climate change projections. Previous modelling studies that have imposed additional Antarctic meltwater have demonstrated regional impacts on Southern Ocean stratification, circulation, and sea ice, as well as remote changes in atmospheric circulation, tropical precipitation, and global temperature. However, these previous studies have used widely varying rates of freshwater forcing, have been conducted using different climate models and configurations, and have reached differing conclusions on the magnitude of meltwater–climate feedbacks. The Southern Ocean Freshwater Input from Antarctica (SOFIA) initiative brings together a team of scientists to quantify the climate system response to Antarctic meltwater input along with key aspects of the uncertainty. In this paper, we summarize the state of knowledge on meltwater discharge from the Antarctic ice sheet and ice shelves to the Southern Ocean and explain the scientific objectives of our initiative. We propose a series of coupled and ocean–sea ice model experiments, including idealized meltwater experiments, historical experiments with observationally consistent meltwater input, and future scenarios driven by meltwater inputs derived from stand-alone ice sheet models. Through coordinating a multi-model ensemble of simulations using a common experimental design, open data archiving, and facilitating scientific collaboration, SOFIA aims to move the community toward better constraining our understanding of the climate system response to Antarctic melt.
{"title":"The Southern Ocean Freshwater Input from Antarctica (SOFIA) Initiative: scientific objectives and experimental design","authors":"Neil C. Swart, Torge Martin, Rebecca Beadling, Jia‐Jia Chen, Christopher Danek, Matthew H. England, R. Farneti, S. Griffies, T. Hattermann, Judith Hauck, F. Haumann, A. Jüling, Qian Li, John Marshall, Morven Muilwijk, Andrew G. Pauling, A. Purich, Inga J. Smith, Max Thomas","doi":"10.5194/gmd-16-7289-2023","DOIUrl":"https://doi.org/10.5194/gmd-16-7289-2023","url":null,"abstract":"Abstract. As the climate warms, the grounded ice sheet and floating ice shelves surrounding Antarctica are melting and releasing additional freshwater into the Southern Ocean. Nonetheless, almost all existing coupled climate models have fixed ice sheets and lack the physics required to represent the dominant sources of Antarctic melt. These missing ice dynamics represent a key uncertainty that is typically unaccounted for in current global climate change projections. Previous modelling studies that have imposed additional Antarctic meltwater have demonstrated regional impacts on Southern Ocean stratification, circulation, and sea ice, as well as remote changes in atmospheric circulation, tropical precipitation, and global temperature. However, these previous studies have used widely varying rates of freshwater forcing, have been conducted using different climate models and configurations, and have reached differing conclusions on the magnitude of meltwater–climate feedbacks. The Southern Ocean Freshwater Input from Antarctica (SOFIA) initiative brings together a team of scientists to quantify the climate system response to Antarctic meltwater input along with key aspects of the uncertainty. In this paper, we summarize the state of knowledge on meltwater discharge from the Antarctic ice sheet and ice shelves to the Southern Ocean and explain the scientific objectives of our initiative. We propose a series of coupled and ocean–sea ice model experiments, including idealized meltwater experiments, historical experiments with observationally consistent meltwater input, and future scenarios driven by meltwater inputs derived from stand-alone ice sheet models. Through coordinating a multi-model ensemble of simulations using a common experimental design, open data archiving, and facilitating scientific collaboration, SOFIA aims to move the community toward better constraining our understanding of the climate system response to Antarctic melt.\u0000","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":"105 32","pages":""},"PeriodicalIF":5.1,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138959418","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-19DOI: 10.5194/gmd-16-7357-2023
G. Duveiller, M. Pickering, J. Muñoz‐Sabater, L. Caporaso, S. Boussetta, G. Balsamo, A. Cescatti
Abstract. Atmospheric reanalyses combine observations and models through data assimilation techniques to provide spatio-temporally continuous fields of key surface variables. They can do so for extended historical periods whilst ensuring a coherent representation of the main Earth system cycles. ERA5 and its enhanced land surface component, ERA5-Land, are widely used in Earth system science and form the flagship products of the Copernicus Climate Change Service (C3S) of the European Commission. Such land surface modelling frameworks generally rely on a state variable called leaf area index (LAI), representing the number of leaves in a grid cell at a given time, to quantify the fluxes of carbon, water and energy between the vegetation and the atmosphere. However, the LAI within the modelling framework behind ERA5 and ERA5-Land is prescribed as a climatological seasonal cycle, neglecting any interannual variability and the potential consequences that this uncoupling between vegetation and atmosphere may have on the surface energy balance and the climate. To evaluate the impact of this mismatch in LAI, we analyse the corresponding effect it has on land surface temperature (LST) by comparing what is simulated to satellite observations. We characterise a hysteretic behaviour between LST biases and LAI biases that evolves differently along the year depending on the background climate. We further analyse the repercussions for the reconstructed climate during more extreme conditions in terms of LAI deviations, with a specific focus on the 2003, 2010 and 2018 heat waves in Europe for which LST mismatches are exacerbated. We anticipate that our results will assist users of ERA5 and ERA5-Land data in understanding where and when the larger discrepancies can be expected, but also guide developers towards improving the modelling framework. Finally, this study could provide a blueprint for a wider benchmarking framework for land surface model evaluation that exploits the capacity of LST to integrate the effects of both radiative and non-radiative processes affecting the surface energy.
摘要。大气再分析通过数据同化技术将观测数据和模式结合起来，提供关键地表变量的时空连续场。大气再分析可提供较长历史时期的数据，同时确保对主要地球系统周期的连贯表述。ERA5及其增强型陆地表面组件ERA5-Land被广泛应用于地球系统科学，是欧盟委员会哥白尼气候变化服务（C3S）的旗舰产品。此类陆地表面建模框架通常依赖于一个称为叶面积指数（LAI）的状态变量，代表某一时间网格单元中的叶片数量，以量化植被与大气之间的碳、水和能量通量。然而，ERA5 和 ERA5-Land 模拟框架中的叶面积指数被规定为气候学上的季节周期，忽略了任何年际变化以及植被与大气之间的这种不耦合可能对地表能量平衡和气候造成的潜在后果。为了评估这种 LAI 不匹配的影响，我们将模拟结果与卫星观测结果进行比较，分析其对地表温度（LST）的相应影响。我们描述了地表温度偏差和 LAI 偏差之间的滞后行为，这种行为根据背景气候的不同而在一年中发生不同的变化。我们进一步分析了在更极端条件下 LAI 偏差对重建气候的影响，重点关注欧洲 2003 年、2010 年和 2018 年的热浪，这些热浪加剧了 LST 的不匹配。我们预计，我们的研究结果将有助于ERA5和ERA5-Land数据的用户了解何时何地会出现较大的差异，同时也将指导开发人员改进建模框架。最后，这项研究可以为更广泛的陆地表面模式评估基准框架提供一个蓝图，利用 LST 的能力来综合影响地表能量的辐射和非辐射过程的影响。
{"title":"Getting the leaves right matters for estimating temperature extremes","authors":"G. Duveiller, M. Pickering, J. Muñoz‐Sabater, L. Caporaso, S. Boussetta, G. Balsamo, A. Cescatti","doi":"10.5194/gmd-16-7357-2023","DOIUrl":"https://doi.org/10.5194/gmd-16-7357-2023","url":null,"abstract":"Abstract. Atmospheric reanalyses combine observations and models through data assimilation techniques to provide spatio-temporally continuous fields of key surface variables. They can do so for extended historical periods whilst ensuring a coherent representation of the main Earth system cycles. ERA5 and its enhanced land surface component, ERA5-Land, are widely used in Earth system science and form the flagship products of the Copernicus Climate Change Service (C3S) of the European Commission. Such land surface modelling frameworks generally rely on a state variable called leaf area index (LAI), representing the number of leaves in a grid cell at a given time, to quantify the fluxes of carbon, water and energy between the vegetation and the atmosphere. However, the LAI within the modelling framework behind ERA5 and ERA5-Land is prescribed as a climatological seasonal cycle, neglecting any interannual variability and the potential consequences that this uncoupling between vegetation and atmosphere may have on the surface energy balance and the climate. To evaluate the impact of this mismatch in LAI, we analyse the corresponding effect it has on land surface temperature (LST) by comparing what is simulated to satellite observations. We characterise a hysteretic behaviour between LST biases and LAI biases that evolves differently along the year depending on the background climate. We further analyse the repercussions for the reconstructed climate during more extreme conditions in terms of LAI deviations, with a specific focus on the 2003, 2010 and 2018 heat waves in Europe for which LST mismatches are exacerbated. We anticipate that our results will assist users of ERA5 and ERA5-Land data in understanding where and when the larger discrepancies can be expected, but also guide developers towards improving the modelling framework. Finally, this study could provide a blueprint for a wider benchmarking framework for land surface model evaluation that exploits the capacity of LST to integrate the effects of both radiative and non-radiative processes affecting the surface energy.\u0000","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":" 47","pages":""},"PeriodicalIF":5.1,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138962299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-18DOI: 10.5194/gmd-16-7275-2023
Jungmin M. Lee, W. Hannah, D. Bader
Abstract. In the Energy Exascale Earth System Model (E3SM) Multi-scale Modeling Framework (MMF), where parameterizations of convection and turbulence are replaced by a 2-D cloud-resolving model (CRM), there are multiple options to represent land–atmosphere interactions. Here, we propose three different coupling strategies, namely the (1) coupling of a single land surface model to the global grid (MMF), (2) coupling a single land copy directly to the embedded CRM (SFLX2CRM), and (3) coupling a single copy of land model to each column of the CRM grid (MAML). In the MAML (Multi-Atmosphere Multi-Land) framework, a land model is coupled to CRM at the CRM-grid scale by coupling an individual copy of a land model to each CRM grid. Therefore, we can represent intra-CRM heterogeneity in the land–atmosphere interaction processes. There are 5-year global simulations run using these three coupling strategies, and we find some regional differences but overall small changes with respect to whether a land model is coupled to CRM or a global atmosphere. In MAML, the spatial heterogeneity within CRM induces stronger turbulence, which leads to the changes in soil moisture, surface heat fluxes, and precipitation. However, the differences in the MAML from the other two cases are rather weak, suggesting that the impact of using MAML does not justify the increase in cost.
{"title":"Representation of atmosphere-induced heterogeneity in land–atmosphere interactions in E3SM–MMFv2","authors":"Jungmin M. Lee, W. Hannah, D. Bader","doi":"10.5194/gmd-16-7275-2023","DOIUrl":"https://doi.org/10.5194/gmd-16-7275-2023","url":null,"abstract":"Abstract. In the Energy Exascale Earth System Model (E3SM) Multi-scale Modeling Framework (MMF), where parameterizations of convection and turbulence are replaced by a 2-D cloud-resolving model (CRM), there are multiple options to represent land–atmosphere interactions. Here, we propose three different coupling strategies, namely the (1) coupling of a single land surface model to the global grid (MMF), (2) coupling a single land copy directly to the embedded CRM (SFLX2CRM), and (3) coupling a single copy of land model to each column of the CRM grid (MAML). In the MAML (Multi-Atmosphere Multi-Land) framework, a land model is coupled to CRM at the CRM-grid scale by coupling an individual copy of a land model to each CRM grid. Therefore, we can represent intra-CRM heterogeneity in the land–atmosphere interaction processes. There are 5-year global simulations run using these three coupling strategies, and we find some regional differences but overall small changes with respect to whether a land model is coupled to CRM or a global atmosphere. In MAML, the spatial heterogeneity within CRM induces stronger turbulence, which leads to the changes in soil moisture, surface heat fluxes, and precipitation. However, the differences in the MAML from the other two cases are rather weak, suggesting that the impact of using MAML does not justify the increase in cost.\u0000","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":" 8","pages":""},"PeriodicalIF":5.1,"publicationDate":"2023-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138963592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-15DOI: 10.5194/gmd-16-7237-2023
C. Spa, O. Rojas, J. de la Puente
Abstract. This paper develops a calibration methodology of the artificial absorbing techniques typically used by Fourier pseudo-spectral time-domain (PSTD) methods for geoacoustic wave simulations. Specifically, we consider the damped wave equation (DWE) that results from adding a dissipation term to the original wave equation, the sponge boundary layer (SBL) that applies an exponentially decaying factor directly to the wavefields, and finally, a classical split formulation of the perfectly matched layer (PML). These three techniques belong to the same family of absorbing boundary layers (ABLs), where outgoing waves and edge reflections are progressively damped across a grid zone of NABL consecutive layers. The ABLs used are compatible with a pure Fourier formulation of PSTD. We first characterize the three ABLs with respect to multiple sets of NABL and their respective absorption parameters for homogeneous media. Next, we validate our findings in heterogeneous media of increasing complexity, starting with a layered medium and finishing with the SEG/EAGE 3D salt model. Finally, we algorithmically compare the three PSTD–ABL methods in terms of memory demands and computational cost. An interesting result is that PML, despite outperforming the absorption of the other two ABLs for a given NABL value, requires approximately double the computational time. The parameter configurations reported in this paper can be readily used for PSTD simulations in other test cases, and the ABL calibration methodology can be applied to other wave propagation schemes.
{"title":"Calibration of absorbing boundary layers for geoacoustic wave modeling in pseudo-spectral time-domain methods","authors":"C. Spa, O. Rojas, J. de la Puente","doi":"10.5194/gmd-16-7237-2023","DOIUrl":"https://doi.org/10.5194/gmd-16-7237-2023","url":null,"abstract":"Abstract. This paper develops a calibration methodology of the artificial absorbing techniques typically used by Fourier pseudo-spectral time-domain (PSTD) methods for geoacoustic wave simulations. Specifically, we consider the damped wave equation (DWE) that results from adding a dissipation term to the original wave equation, the sponge boundary layer (SBL) that applies an exponentially decaying factor directly to the wavefields, and finally, a classical split formulation of the perfectly matched layer (PML). These three techniques belong to the same family of absorbing boundary layers (ABLs), where outgoing waves and edge reflections are progressively damped across a grid zone of NABL consecutive layers. The ABLs used are compatible with a pure Fourier formulation of PSTD. We first characterize the three ABLs with respect to multiple sets of NABL and their respective absorption parameters for homogeneous media. Next, we validate our findings in heterogeneous media of increasing complexity, starting with a layered medium and finishing with the SEG/EAGE 3D salt model. Finally, we algorithmically compare the three PSTD–ABL methods in terms of memory demands and computational cost. An interesting result is that PML, despite outperforming the absorption of the other two ABLs for a given NABL value, requires approximately double the computational time. The parameter configurations reported in this paper can be readily used for PSTD simulations in other test cases, and the ABL calibration methodology can be applied to other wave propagation schemes.\u0000","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":"22 9","pages":""},"PeriodicalIF":5.1,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138997769","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-14DOI: 10.5194/gmd-16-7223-2023
Liangke Huang, Shengwei Lan, Ge Zhu, Fade Chen, Junyu Li, Lilong Liu
Abstract. The accuracy of tropospheric delay correction heavily depends on the quality of the tropospheric model, and the zenith tropospheric delay (ZTD) is an important factor affecting the tropospheric delay. Therefore, it is essential to establish a precise ZTD empirical model. The existing ZTD models are constrained by a single fitting function, lack consideration for daily cycle variations, and rely solely on data with one resolution for modeling. To address these limitations, we proposed a global piecewise ZTD empirical grid (GGZTD-P) model. This model considers the daily cycle variation and latitude factor of ZTD, using the sliding window algorithm based on fifth-generation European Centre for Medium-Range Weather Forecasts atmospheric reanalysis data (ERA5). The ZTD data from 545 radiosonde stations and the second Modern-Era Retrospective analysis for Research and Applications (MERRA-2) atmospheric reanalysis data are used to validate the accuracy of the GGZTD-P model. The results indicate that the GGZTD-P model outperforms the global pressure and temperature 3 (GPT3) model, exhibiting 26 % and 53 % lower bias and rms, respectively, when using radiosonde stations as reference values. Furthermore, when evaluated using MERRA-2 atmospheric reanalysis data, the GGZTD-P model consistently exhibits superior performance across various latitude regions. It is expected that the application of this new model will provide improved services for high-precision global navigation satellite system (GNSS) positioning and GNSS meteorology.
{"title":"A global grid model for the estimation of zenith tropospheric delay considering the variations at different altitudes","authors":"Liangke Huang, Shengwei Lan, Ge Zhu, Fade Chen, Junyu Li, Lilong Liu","doi":"10.5194/gmd-16-7223-2023","DOIUrl":"https://doi.org/10.5194/gmd-16-7223-2023","url":null,"abstract":"Abstract. The accuracy of tropospheric delay correction heavily depends on the quality of the tropospheric model, and the zenith tropospheric delay (ZTD) is an important factor affecting the tropospheric delay. Therefore, it is essential to establish a precise ZTD empirical model. The existing ZTD models are constrained by a single fitting function, lack consideration for daily cycle variations, and rely solely on data with one resolution for modeling. To address these limitations, we proposed a global piecewise ZTD empirical grid (GGZTD-P) model. This model considers the daily cycle variation and latitude factor of ZTD, using the sliding window algorithm based on fifth-generation European Centre for Medium-Range Weather Forecasts atmospheric reanalysis data (ERA5). The ZTD data from 545 radiosonde stations and the second Modern-Era Retrospective analysis for Research and Applications (MERRA-2) atmospheric reanalysis data are used to validate the accuracy of the GGZTD-P model. The results indicate that the GGZTD-P model outperforms the global pressure and temperature 3 (GPT3) model, exhibiting 26 % and 53 % lower bias and rms, respectively, when using radiosonde stations as reference values. Furthermore, when evaluated using MERRA-2 atmospheric reanalysis data, the GGZTD-P model consistently exhibits superior performance across various latitude regions. It is expected that the application of this new model will provide improved services for high-precision global navigation satellite system (GNSS) positioning and GNSS meteorology.\u0000","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":"7 10","pages":""},"PeriodicalIF":5.1,"publicationDate":"2023-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138971017","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-12DOI: 10.5194/gmd-16-7203-2023
Weihang Liu, Tao Ye, Christoph Müller, Jonas Jägermeyr, J. Franke, Haynes Stephens, Shuo Chen
Abstract. Understanding the impact of climate change on year-to-year variation of crop yield is critical to global food stability and security. While crop model emulators are believed to be lightweight tools to replace the models, few emulators have been developed to capture such interannual variation of crop yield in response to climate variability. In this study, we developed a statistical emulator with a machine learning algorithm to reproduce the response of year-to-year variation of four crop yields to CO2 (C), temperature (T), water (W), and nitrogen (N) perturbations defined in the Global Gridded Crop Model Intercomparison Project (GGCMI) phase 2. The emulators were able to explain more than 52 % of the variance of simulated yield and performed well in capturing the year-to-year variation of global average and gridded crop yield over current croplands in the baseline. With the changes in CO2–temperature–water–nitrogen (CTWN) perturbations, the emulators could reproduce the year-to-year variation of crop yield well over most current cropland. The variation of R and the mean absolute error was small under the single CTWN perturbations and dual-factor perturbations. These emulators thus provide statistical response surfaces of yield, including both its mean and interannual variability, to climate factors. They could facilitate spatiotemporal downscaling of crop model simulation, projecting the changes in crop yield variability in the future and serving as a lightweight tool for multi-model ensemble simulation. The emulators enhanced the flexibility of crop yield estimates and expanded the application of large-ensemble simulations of crop yield under climate change.
{"title":"The statistical emulators of GGCMI phase 2: responses of year-to-year variation of crop yield to CO2, temperature, water, and nitrogen perturbations","authors":"Weihang Liu, Tao Ye, Christoph Müller, Jonas Jägermeyr, J. Franke, Haynes Stephens, Shuo Chen","doi":"10.5194/gmd-16-7203-2023","DOIUrl":"https://doi.org/10.5194/gmd-16-7203-2023","url":null,"abstract":"Abstract. Understanding the impact of climate change on year-to-year variation of crop yield is critical to global food stability and security. While crop model emulators are believed to be lightweight tools to replace the models, few emulators have been developed to capture such interannual variation of crop yield in response to climate variability. In this study, we developed a statistical emulator with a machine learning algorithm to reproduce the response of year-to-year variation of four crop yields to CO2 (C), temperature (T), water (W), and nitrogen (N) perturbations defined in the Global Gridded Crop Model Intercomparison Project (GGCMI) phase 2. The emulators were able to explain more than 52 % of the variance of simulated yield and performed well in capturing the year-to-year variation of global average and gridded crop yield over current croplands in the baseline. With the changes in CO2–temperature–water–nitrogen (CTWN) perturbations, the emulators could reproduce the year-to-year variation of crop yield well over most current cropland. The variation of R and the mean absolute error was small under the single CTWN perturbations and dual-factor perturbations. These emulators thus provide statistical response surfaces of yield, including both its mean and interannual variability, to climate factors. They could facilitate spatiotemporal downscaling of crop model simulation, projecting the changes in crop yield variability in the future and serving as a lightweight tool for multi-model ensemble simulation. The emulators enhanced the flexibility of crop yield estimates and expanded the application of large-ensemble simulations of crop yield under climate change.\u0000","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":"2 10","pages":""},"PeriodicalIF":5.1,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139008222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-11DOI: 10.5194/gmd-16-7171-2023
M. Meier, C. Bigler
Abstract. Autumn leaf phenology marks the end of the growing season, during which trees assimilate atmospheric CO2. The length of the growing season is affected by climate change because autumn phenology responds to climatic conditions. Thus, the timing of autumn phenology is often modeled to assess possible climate change effects on future CO2-mitigating capacities and species compositions of forests. Projected trends have been mainly discussed with regards to model performance and climate change scenarios. However, there has been no systematic and thorough evaluation of how performance and projections are affected by the calibration approach. Here, we analyzed >2.3 million performances and 39 million projections across 21 process-oriented models of autumn leaf phenology, 5 optimization algorithms, ≥7 sampling procedures, and 26 climate model chains from two representative concentration pathways. Calibration and validation were based on >45 000 observations for beech, oak, and larch from 500 central European sites each. Phenology models had the largest influence on model performance. The best-performing models were (1) driven by daily temperature, day length, and partly by seasonal temperature or spring leaf phenology; (2) calibrated with the generalized simulated annealing algorithm; and (3) based on systematically balanced or stratified samples. Autumn phenology was projected to shift between −13 and +20 d by 2080–2099 compared to 1980–1999. Climate scenarios and sites explained more than 80 % of the variance in these shifts and thus had an influence 8 to 22 times greater than the phenology models. Warmer climate scenarios and better-performing models predominantly projected larger backward shifts than cooler scenarios and poorer models. Our results justify inferences from comparisons of process-oriented phenology models to phenology-driving processes, and we advocate for species-specific models for such analyses and subsequent projections. For sound calibration, we recommend a combination of cross-validations and independent tests, using randomly selected sites from stratified bins based on mean annual temperature and average autumn phenology, respectively. Poor performance and little influence of phenology models on autumn phenology projections suggest that current models are overlooking relevant drivers. While the uncertain projections indicate an extension of the growing season, further studies are needed to develop models that adequately consider the relevant processes for autumn phenology.
{"title":"Process-oriented models of autumn leaf phenology: ways to sound calibration and implications of uncertain projections","authors":"M. Meier, C. Bigler","doi":"10.5194/gmd-16-7171-2023","DOIUrl":"https://doi.org/10.5194/gmd-16-7171-2023","url":null,"abstract":"Abstract. Autumn leaf phenology marks the end of the growing season, during which trees assimilate atmospheric CO2. The length of the growing season is affected by climate change because autumn phenology responds to climatic conditions. Thus, the timing of autumn phenology is often modeled to assess possible climate change effects on future CO2-mitigating capacities and species compositions of forests. Projected trends have been mainly discussed with regards to model performance and climate change scenarios. However, there has been no systematic and thorough evaluation of how performance and projections are affected by the calibration approach. Here, we analyzed >2.3 million performances and 39 million projections across 21 process-oriented models of autumn leaf phenology, 5 optimization algorithms, ≥7 sampling procedures, and 26 climate model chains from two representative concentration pathways. Calibration and validation were based on >45 000 observations for beech, oak, and larch from 500 central European sites each. Phenology models had the largest influence on model performance. The best-performing models were (1) driven by daily temperature, day length, and partly by seasonal temperature or spring leaf phenology; (2) calibrated with the generalized simulated annealing algorithm; and (3) based on systematically balanced or stratified samples. Autumn phenology was projected to shift between −13 and +20 d by 2080–2099 compared to 1980–1999. Climate scenarios and sites explained more than 80 % of the variance in these shifts and thus had an influence 8 to 22 times greater than the phenology models. Warmer climate scenarios and better-performing models predominantly projected larger backward shifts than cooler scenarios and poorer models. Our results justify inferences from comparisons of process-oriented phenology models to phenology-driving processes, and we advocate for species-specific models for such analyses and subsequent projections. For sound calibration, we recommend a combination of cross-validations and independent tests, using randomly selected sites from stratified bins based on mean annual temperature and average autumn phenology, respectively. Poor performance and little influence of phenology models on autumn phenology projections suggest that current models are overlooking relevant drivers. While the uncertain projections indicate an extension of the growing season, further studies are needed to develop models that adequately consider the relevant processes for autumn phenology.\u0000","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":"2 8","pages":""},"PeriodicalIF":5.1,"publicationDate":"2023-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138981197","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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