Abstract. The control simulation experiment (CSE) is a recently developed approach to investigate the controllability of dynamical systems,extending the well-known observing system simulation experiment (OSSE) in meteorology.For effective control of chaotic dynamical systems,it is essential to exploit the high sensitivity to initial conditions for dragging a system away from an undesired regime by applying minimal perturbations.In this study, we design a CSE for reducing the number of extreme events in the Lorenz-96 model. The 40 variables of this model represent idealized meteorological quantities evenly distributed on a latitude circle.The reduction of occurrence of extreme events over 100-year runs of the model is discussed as a function of the parameters of the CSE:the ensemble forecast length for detecting extreme events in advance,the magnitude and localization of the perturbations,and the quality and coverage of the observations.The design of the CSE is aimed at reducing weather extremes when applied to more realistic weather prediction models.
{"title":"Control simulation experiments of extreme events with the Lorenz-96 model","authors":"Q. Sun, T. Miyoshi, S. Richard","doi":"10.5194/npg-30-117-2023","DOIUrl":"https://doi.org/10.5194/npg-30-117-2023","url":null,"abstract":"Abstract. The control simulation experiment (CSE) is a recently developed approach to investigate the controllability of dynamical systems,\u0000extending the well-known observing system simulation experiment (OSSE) in meteorology.\u0000For effective control of chaotic dynamical systems,\u0000it is essential to exploit the high sensitivity to initial conditions for dragging a system away from an undesired regime by applying minimal perturbations.\u0000In this study, we design a CSE for reducing the number of extreme events in the Lorenz-96 model. The 40 variables of this model represent idealized meteorological quantities evenly distributed on a latitude circle.\u0000The reduction of occurrence of extreme events over 100-year runs of the model is discussed as a function of the parameters of the CSE:\u0000the ensemble forecast length for detecting extreme events in advance,\u0000the magnitude and localization of the perturbations,\u0000and the quality and coverage of the observations.\u0000The design of the CSE is aimed at reducing weather extremes when applied to more realistic weather prediction models.\u0000","PeriodicalId":54714,"journal":{"name":"Nonlinear Processes in Geophysics","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2023-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47078467","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract. In this paper we investigate the spatial patterns and features of meteorological droughts in Europe using concepts and methods derived from complexnetwork theory. Using event synchronization analysis, we uncover robust meteorological drought continental networks based on the co-occurrence ofthese events at different locations within a season from 1981 to 2020 and compare the results for four accumulation periods of rainfall. Eachcontinental network is then further examined to unveil regional clusters which are characterized in terms of droughts' geographical propagation andsource–sink systems. While introducing new methodologies in general climate network reconstruction from raw data, our approach brings out keyaspects concerning drought spatial dynamics, which could potentially support droughts' forecast.
{"title":"Exploring meteorological droughts' spatial patterns across Europe through complex network theory","authors":"Domenico Giaquinto, W. Marzocchi, Jürgen Kurths","doi":"10.5194/npg-30-167-2023","DOIUrl":"https://doi.org/10.5194/npg-30-167-2023","url":null,"abstract":"Abstract. In this paper we investigate the spatial patterns and features of meteorological droughts in Europe using concepts and methods derived from complex\u0000network theory. Using event synchronization analysis, we uncover robust meteorological drought continental networks based on the co-occurrence of\u0000these events at different locations within a season from 1981 to 2020 and compare the results for four accumulation periods of rainfall. Each\u0000continental network is then further examined to unveil regional clusters which are characterized in terms of droughts' geographical propagation and\u0000source–sink systems. While introducing new methodologies in general climate network reconstruction from raw data, our approach brings out key\u0000aspects concerning drought spatial dynamics, which could potentially support droughts' forecast.\u0000","PeriodicalId":54714,"journal":{"name":"Nonlinear Processes in Geophysics","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2023-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46169725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract. This contribution explores a new approach to forecasting multivariate covariances for atmospheric chemistry through the use of the parametric Kalman filter (PKF). In the PKF formalism, the error covariance matrix is modellized by a covariance model relying on parameters, for which the dynamics are then computed. The PKF has been previously formulated in univariate cases, and a multivariate extension for chemical transport models is explored here. This contribution focuses on the situation where the uncertainty is due to the chemistry but not due to the uncertainty of the weather. To do so, a simplified two-species chemical transport model over a 1D domain is introduced, based on the non-linear Lotka–Volterra equations, which allows us to propose a multivariate pseudo covariance model. Then, the multivariate PKF dynamics are formulated and their results are compared with a large ensemble Kalman filter (EnKF) in several numerical experiments. In these experiments, the PKF accurately reproduces the EnKF. Eventually, the PKF is formulated for a more complex chemical model composed of six chemical species (generic reaction set). Again, the PKF succeeds at reproducing the multivariate covariances diagnosed on the large ensemble.
{"title":"Toward a multivariate formulation of the parametric Kalman filter assimilation: application to a simplified chemical transport model","authors":"Antoine Perrot, O. Pannekoucke, V. Guidard","doi":"10.5194/npg-30-139-2023","DOIUrl":"https://doi.org/10.5194/npg-30-139-2023","url":null,"abstract":"Abstract. This contribution explores a new approach to forecasting multivariate covariances for atmospheric chemistry through the use of the parametric Kalman filter (PKF). In the PKF formalism, the error covariance matrix is modellized by a covariance model relying on parameters, for which the dynamics are then computed. The PKF has been previously formulated in univariate cases, and a multivariate extension for chemical transport models is explored here. This contribution focuses on the situation where the uncertainty is due to the chemistry but not due to the uncertainty of the weather. To do so, a simplified two-species chemical transport model over a 1D domain is introduced, based on the non-linear Lotka–Volterra equations, which allows us to propose a multivariate pseudo covariance model. Then, the multivariate PKF dynamics are formulated and their results are compared with a large ensemble Kalman filter (EnKF) in several numerical experiments. In these experiments, the PKF accurately reproduces the EnKF. Eventually, the PKF is formulated for a more complex chemical model composed of six chemical species (generic reaction set). Again, the PKF succeeds at reproducing the multivariate covariances diagnosed on the large ensemble.","PeriodicalId":54714,"journal":{"name":"Nonlinear Processes in Geophysics","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2023-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43763184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract. The state of the atmosphere, or of the ocean, cannot be exhaustively observed. Crucial parts might remain out of reach of proper monitoring. Also, defining the exact set of equations driving the atmosphere and ocean is virtually impossible because of their complexity. The goal of this paper is to obtain predictions of a partially observed dynamical system without knowing the model equations. In this data-driven context, the article focuses on the Lorenz-63 system, where only the second and third components are observed and access to the equations is not allowed. To account for those strong constraints, a combination of machine learning and data assimilation techniques is proposed. The key aspects are the following: the introduction of latent variables, a linear approximation of the dynamics and a database that is updated iteratively, maximizing the likelihood. We find that the latent variables inferred by the procedure are related to the successive derivatives of the observed components of the dynamical system. The method is also able to reconstruct accurately the local dynamics of the partially observed system. Overall, the proposed methodology is simple, is easy to code and gives promising results, even in the case of small numbers of observations.
{"title":"Data-driven reconstruction of partially observed dynamical systems","authors":"P. Tandeo, P. Ailliot, F. Sévellec","doi":"10.5194/npg-30-129-2023","DOIUrl":"https://doi.org/10.5194/npg-30-129-2023","url":null,"abstract":"Abstract. The state of the atmosphere, or of the ocean, cannot be exhaustively observed. Crucial parts might remain out of reach of proper monitoring. Also, defining the exact set of equations driving the atmosphere and ocean is virtually impossible because of their complexity. The goal of this paper is to obtain predictions of a partially observed dynamical system without knowing the model equations. In this data-driven context, the article focuses on the Lorenz-63 system, where only the second and third components are observed and access to the equations is not allowed. To account for those strong constraints, a combination of machine learning and data assimilation techniques is proposed. The key aspects are the following: the introduction of latent variables, a linear approximation of the dynamics and a database that is updated iteratively, maximizing the likelihood. We find that the latent variables inferred by the procedure are related to the successive derivatives of the observed components of the dynamical system. The method is also able to reconstruct accurately the local dynamics of the partially observed system. Overall, the proposed methodology is simple, is easy to code and gives promising results, even in the case of small numbers of observations.\u0000","PeriodicalId":54714,"journal":{"name":"Nonlinear Processes in Geophysics","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42882181","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract. The feasibility of physics-based forecasting of earthquakes depends on how well models can be calibrated to represent earthquake scenarios givenuncertainties in both models and data. We investigate whether data assimilation can estimate current and future fault states, i.e., slip rate andshear stress, in the presence of a bias in the friction parameter. We perform state estimation as well as combined state-parameter estimation usinga sequential-importance resampling particle filter in a zero-dimensional (0D) generalization of the Burridge–Knopoff spring–block model with rate-and-statefriction. Minor changes in the friction parameter ϵ can lead to different state trajectories and earthquake characteristics. Theperformance of data assimilation with respect to estimating the fault state in the presence of a parameter bias in ϵ depends on the magnitude of thebias. A small parameter bias in ϵ (+3 %) can be compensated for very well using state estimation (R2 = 0.99), whereas anintermediate bias (−14 %) can only be partly compensated for using state estimation (R2 = 0.47). When increasing particle spread by accounting for model error andan additional resampling step, R2 increases to 0.61. However, when there is a large bias (−43 %) in ϵ, only state-parameterestimation can fully account for the parameter bias (R2 = 0.97). Thus, simultaneous state and parameter estimation effectively separates theerror contributions from friction and shear stress to correctly estimate the current and future shear stress and slip rate. This illustrates thepotential of data assimilation for the estimation of earthquake sequences and provides insight into its application in other nonlinear processes withuncertain parameters.
{"title":"On parameter bias in earthquake sequence models using data assimilation","authors":"A. Banerjee, Ylona van Dinther, F. Vossepoel","doi":"10.5194/npg-30-101-2023","DOIUrl":"https://doi.org/10.5194/npg-30-101-2023","url":null,"abstract":"Abstract. The feasibility of physics-based forecasting of earthquakes depends on how well models can be calibrated to represent earthquake scenarios given\u0000uncertainties in both models and data. We investigate whether data assimilation can estimate current and future fault states, i.e., slip rate and\u0000shear stress, in the presence of a bias in the friction parameter. We perform state estimation as well as combined state-parameter estimation using\u0000a sequential-importance resampling particle filter in a zero-dimensional (0D) generalization of the Burridge–Knopoff spring–block model with rate-and-state\u0000friction. Minor changes in the friction parameter ϵ can lead to different state trajectories and earthquake characteristics. The\u0000performance of data assimilation with respect to estimating the fault state in the presence of a parameter bias in ϵ depends on the magnitude of the\u0000bias. A small parameter bias in ϵ (+3 %) can be compensated for very well using state estimation (R2 = 0.99), whereas an\u0000intermediate bias (−14 %) can only be partly compensated for using state estimation (R2 = 0.47). When increasing particle spread by accounting for model error and\u0000an additional resampling step, R2 increases to 0.61. However, when there is a large bias (−43 %) in ϵ, only state-parameter\u0000estimation can fully account for the parameter bias (R2 = 0.97). Thus, simultaneous state and parameter estimation effectively separates the\u0000error contributions from friction and shear stress to correctly estimate the current and future shear stress and slip rate. This illustrates the\u0000potential of data assimilation for the estimation of earthquake sequences and provides insight into its application in other nonlinear processes with\u0000uncertain parameters.\u0000","PeriodicalId":54714,"journal":{"name":"Nonlinear Processes in Geophysics","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2023-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43067992","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 : 2023-03-09DOI: 10.13140/rg.2.2.18433.53603
Yicun Zhen, Valentin Resseguier, Bertrand Chapron
Abstract. Motivated by the concept of "location uncertainty", initially introduced in Mémin (2014), a scheme is sought to perturb the "location" of a state variable at every forecast time step. Further considering Brenier's theorem Brenier (1991), asserting that the difference of two positive density fields on the same domain can be represented by a transportation map, perturbations are demonstrated to consistently define a SPDE from the original PDE. It ensues that certain quantities, up to the user, are conserved at every time step. Remarkably, derivations following both the SALT Holm (2015) and LU Mémin (2014); 5 Resseguier et al. (2016) settings, can be recovered from this perturbation scheme. Still, it opens broader applicability since it does not explicitly rely on Lagrangian mechanics or Newton's laws of force. For illustration, a stochastic version of the thermal shallow water equation is presented.
摘要。在“位置不确定性”概念的激励下，最初在msammin(2014)中引入，寻求一种方案来扰动状态变量在每个预测时间步长的“位置”。进一步考虑Brenier定理，Brenier(1991)断言同一域上两个正密度场的差可以用运输图表示，证明了扰动可以一致地从原始PDE定义SPDE。结果是，在每个时间步上，一定数量的量(由用户决定)是守恒的。值得注意的是，SALT Holm(2015)和LU m闵(2014)的衍生;5 Resseguier et al.(2016)设置，可以从这个摄动方案中恢复。尽管如此，由于它不明确地依赖于拉格朗日力学或牛顿的力定律，它具有更广泛的适用性。为了说明，给出了一个随机版本的浅水热方程。
{"title":"Physically Constrained Covariance Inflation from Location Uncertainty","authors":"Yicun Zhen, Valentin Resseguier, Bertrand Chapron","doi":"10.13140/rg.2.2.18433.53603","DOIUrl":"https://doi.org/10.13140/rg.2.2.18433.53603","url":null,"abstract":"<strong>Abstract.</strong> Motivated by the concept of \"location uncertainty\", initially introduced in Mémin (2014), a scheme is sought to perturb the \"location\" of a state variable at every forecast time step. Further considering Brenier's theorem Brenier (1991), asserting that the difference of two positive density fields on the same domain can be represented by a transportation map, perturbations are demonstrated to consistently define a SPDE from the original PDE. It ensues that certain quantities, up to the user, are conserved at every time step. Remarkably, derivations following both the SALT Holm (2015) and LU Mémin (2014); 5 Resseguier et al. (2016) settings, can be recovered from this perturbation scheme. Still, it opens broader applicability since it does not explicitly rely on Lagrangian mechanics or Newton's laws of force. For illustration, a stochastic version of the thermal shallow water equation is presented.","PeriodicalId":54714,"journal":{"name":"Nonlinear Processes in Geophysics","volume":"136 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2023-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138531970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract. Disentangling the effects of internal variability and anthropogenic forcing on regional climate trends remains a key challenge with far-reaching implications. Due to its largely unpredictable nature on timescales longer than a decade, internal climate variability limits the accuracy of climate model projections, introduces challenges in attributing past climate changes, and complicates climate model evaluation. Here, we highlight recent advances in climate modeling and physical understanding that have led to novel insights about these key issues. In particular, we synthesize new findings from large-ensemble simulations with Earth system models, observational large ensembles, and dynamical adjustment methodologies, with a focus on European climate.
{"title":"A range of outcomes: the combined effects of internal variability and anthropogenic forcing on regional climate trends over Europe","authors":"C. Deser, A. Phillips","doi":"10.5194/npg-30-63-2023","DOIUrl":"https://doi.org/10.5194/npg-30-63-2023","url":null,"abstract":"Abstract. Disentangling the effects of internal variability and anthropogenic forcing on regional climate trends remains a key challenge with far-reaching implications. Due to its largely unpredictable nature on timescales longer than a decade, internal climate variability limits the accuracy of climate model projections, introduces challenges in attributing past climate changes, and complicates climate model evaluation. Here, we highlight recent advances in climate modeling and physical understanding that have led to novel insights about these key issues. In particular, we synthesize new findings from large-ensemble simulations with Earth system models, observational large ensembles, and dynamical adjustment methodologies, with a focus on European climate.\u0000","PeriodicalId":54714,"journal":{"name":"Nonlinear Processes in Geophysics","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2023-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42756382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract. Data assimilation (DA), the statistical combination ofcomputer models with measurements, is applied in a variety of scientificfields involving forecasting of dynamical systems, most prominently inatmospheric and ocean sciences. The existence of misreported or unknownobservation times (time error) poses a unique and interesting problem forDA. Mapping observations to incorrect times causes bias in the prior stateand affects assimilation. Algorithms that can improve the performance ofensemble Kalman filter DA in the presence of observing time error aredescribed. Algorithms that can estimate the distribution of time error arealso developed. These algorithms are then combined to produce extensions toensemble Kalman filters that can both estimate and correct for observationtime errors. A low-order dynamical system is used to evaluate theperformance of these methods for a range of magnitudes of observation timeerror. The most successful algorithms must explicitly account for thenonlinearity in the evolution of the prediction model.
{"title":"Extending ensemble Kalman filter algorithms to assimilate observations with an unknown time offset","authors":"Elia Gorokhovsky, Jeffrey L. Anderson","doi":"10.5194/npg-30-37-2023","DOIUrl":"https://doi.org/10.5194/npg-30-37-2023","url":null,"abstract":"Abstract. Data assimilation (DA), the statistical combination of\u0000computer models with measurements, is applied in a variety of scientific\u0000fields involving forecasting of dynamical systems, most prominently in\u0000atmospheric and ocean sciences. The existence of misreported or unknown\u0000observation times (time error) poses a unique and interesting problem for\u0000DA. Mapping observations to incorrect times causes bias in the prior state\u0000and affects assimilation. Algorithms that can improve the performance of\u0000ensemble Kalman filter DA in the presence of observing time error are\u0000described. Algorithms that can estimate the distribution of time error are\u0000also developed. These algorithms are then combined to produce extensions to\u0000ensemble Kalman filters that can both estimate and correct for observation\u0000time errors. A low-order dynamical system is used to evaluate the\u0000performance of these methods for a range of magnitudes of observation time\u0000error. The most successful algorithms must explicitly account for the\u0000nonlinearity in the evolution of the prediction model.\u0000","PeriodicalId":54714,"journal":{"name":"Nonlinear Processes in Geophysics","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2023-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44682966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract. Stochastic forcing can, sometimes, stabilise atmospheric regime dynamics, increasing their persistence. This counter-intuitive effect has been observed in geophysical models of varying complexity, and here we investigate the mechanisms underlying stochastic regime dynamics in a conceptual model. We use a six-mode truncation of a barotropic β-plane model, featuring transitions between analogues of zonal and blocked flow conditions, and identify mechanisms similar to those seen previously in work on low-dimensional random maps. Namely, we show that a geometric mechanism, here relating to monotonic instability growth, allows for asymmetric action of symmetric perturbations on regime lifetime and that random scattering can “trap” the flow in more stable regions of phase space. We comment on the implications for understanding more complex atmospheric systems.
{"title":"On the interaction of stochastic forcing and regime dynamics","authors":"J. Dorrington, T. Palmer","doi":"10.5194/npg-30-49-2023","DOIUrl":"https://doi.org/10.5194/npg-30-49-2023","url":null,"abstract":"Abstract. Stochastic forcing can, sometimes, stabilise atmospheric regime dynamics, increasing their persistence. This counter-intuitive effect has been observed in geophysical models of varying complexity, and here we investigate the mechanisms underlying stochastic regime dynamics in a conceptual model. We use a six-mode truncation of a barotropic β-plane model, featuring transitions between analogues of zonal and blocked flow conditions, and identify mechanisms similar to those seen previously in work on low-dimensional random maps. Namely, we show that a geometric mechanism, here relating to monotonic instability growth, allows for asymmetric action of symmetric perturbations on regime lifetime and that random scattering can “trap” the flow in more stable regions of phase space. We comment on the implications for understanding more complex atmospheric systems.\u0000","PeriodicalId":54714,"journal":{"name":"Nonlinear Processes in Geophysics","volume":"1 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2023-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41520577","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract. For ages, the topic of climate – in the sense of “usual weather” – has in the western tradition attracted attention as a possible explanatory factor for differences in societies and in human behavior. Climate, and its purported impact on society, is an integrated element in western thinking and perception. In this essay, the history of ideas about the climatic impact on humans and society and the emergence of the ideology of climatic determinism are sketched from the viewpoint of a natural scientist. This ideology favored the perception of westerners being superior to the people in the rest of the world, giving legitimacy to colonialism. In modern times, when natural sciences instituted self-critical processes (repeatability, falsification) and norms (such as the Mertonian norms named CUDOS), the traditional host for climate issues, namely, geography, lost its grip, and physics took over. This “scientification” of climate science led to a more systematic, critical and rigorous approach of building and testing hypotheses and concepts. This gain in methodical rigor, however, went along with the loss of understanding that climate is hardly a key explanatory factor for societal differences and developments. Consequently, large segments of the field tacitly and unknowingly began reviving the abandoned concept of climatic determinism. Climate science finds itself in a “post-normal” condition, which leads to a frequent dominance of political utility over methodical rigor.
{"title":"Brief communication: Climate science as a social process – history, climatic determinism, Mertonian norms and post-normality","authors":"H. von Storch","doi":"10.5194/npg-30-31-2023","DOIUrl":"https://doi.org/10.5194/npg-30-31-2023","url":null,"abstract":"Abstract. For ages, the topic of climate – in the sense of “usual weather” – has in the western tradition attracted attention as a possible explanatory factor for differences in societies and in human behavior. Climate, and its purported impact on society, is an integrated element in western thinking and perception. In this essay, the history of ideas about the climatic impact on humans and society and the emergence of the ideology of climatic determinism are sketched from the viewpoint of a natural scientist. This ideology favored the perception of westerners being superior to the people in the rest of the world, giving legitimacy to colonialism. In modern times, when natural sciences instituted self-critical processes (repeatability, falsification) and norms (such as the Mertonian norms named CUDOS), the traditional host for climate issues, namely, geography, lost its grip, and physics took over. This “scientification” of climate science led to a more systematic, critical and rigorous approach of building and testing hypotheses and concepts. This gain in methodical rigor, however, went along with the loss of understanding that climate is hardly a key explanatory factor for societal differences and developments. Consequently, large segments of the field tacitly and unknowingly began reviving the abandoned concept of climatic determinism. Climate science finds itself in a “post-normal” condition, which leads to a frequent dominance of political utility over methodical rigor.\u0000","PeriodicalId":54714,"journal":{"name":"Nonlinear Processes in Geophysics","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2023-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43535325","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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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