Pub Date : 2024-02-03DOI: 10.5194/ascmo-10-51-2024
G. Baiamonte, C. Agnese, C. Cammalleri, Elvira Di Nardo, Stefano Ferraris, Tommaso Martini
Abstract. The modeling of the occurrence of a rainfall dry spell and wet spell (ds and ws, respectively) can be jointly conveyed using interarrival times (its). While the modeling has the advantage of requiring a single fitting for the description of all rainfall time characteristics (including wet and dry chains, an extension of the concept of spells), the assumption of the independence and identical distribution of the renewal times it implicitly imposes a memoryless property on the derived ws, which may not be true in some cases. In this study, two different methods for the modeling of rainfall time characteristics at the station scale have been applied: (i) a direct method (DM) that fits the discrete Lerch distribution to it records and that then derives ws and ds (as well as the corresponding chains) from the it distribution and (ii) an indirect method (IM) that fits the Lerch distribution to the ws and ds records separately, relaxing the assumptions of the renewal process. The results of this application over six stations in Europe, characterized by a wide range of rainfall regimes, highlight how the geometric distribution does not always reasonably reproduce the ws frequencies, even when its are modeled well by the Lerch distribution. Improved performances are obtained with the IM thanks to the relaxation of the assumption of the independence and identical distribution of the renewal times. A further improvement of the fittings is obtained when the datasets are separated into two periods, suggesting that the inferences may benefit from accounting for the local seasonality.
{"title":"Applying different methods to model dry and wet spells at daily scale in a large range of rainfall regimes across Europe","authors":"G. Baiamonte, C. Agnese, C. Cammalleri, Elvira Di Nardo, Stefano Ferraris, Tommaso Martini","doi":"10.5194/ascmo-10-51-2024","DOIUrl":"https://doi.org/10.5194/ascmo-10-51-2024","url":null,"abstract":"Abstract. The modeling of the occurrence of a rainfall dry spell and wet spell (ds and ws, respectively) can be jointly conveyed using interarrival times (its). While the modeling has the advantage of requiring a single fitting for the description of all rainfall time characteristics (including wet and dry chains, an extension of the concept of spells), the assumption of the independence and identical distribution of the renewal times it implicitly imposes a memoryless property on the derived ws, which may not be true in some cases. In this study, two different methods for the modeling of rainfall time characteristics at the station scale have been applied: (i) a direct method (DM) that fits the discrete Lerch distribution to it records and that then derives ws and ds (as well as the corresponding chains) from the it distribution and (ii) an indirect method (IM) that fits the Lerch distribution to the ws and ds records separately, relaxing the assumptions of the renewal process. The results of this application over six stations in Europe, characterized by a wide range of rainfall regimes, highlight how the geometric distribution does not always reasonably reproduce the ws frequencies, even when its are modeled well by the Lerch distribution. Improved performances are obtained with the IM thanks to the relaxation of the assumption of the independence and identical distribution of the renewal times. A further improvement of the fittings is obtained when the datasets are separated into two periods, suggesting that the inferences may benefit from accounting for the local seasonality.\u0000","PeriodicalId":36792,"journal":{"name":"Advances in Statistical Climatology, Meteorology and Oceanography","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140462099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-22DOI: 10.5194/ascmo-10-29-2024
Svenja Szemkus, Petra Friederichs
Abstract. We present a method for the analysis and compact description of large-scale multivariate weather extremes. Spatial patterns of extreme events are identified using the tail pairwise dependence matrix (TPDM) proposed by Cooley and Thibaud (2019). We also introduce the cross-TPDM to identify patterns of common extremes in two variables. An extremal pattern index (EPI) is developed to provide a pattern-based aggregation of temperature. A heat wave definition based on EPI is able to detect the most important heat waves over Europe. As an extension for considering simultaneous extremes in two variables, we propose the threshold-based EPI (TEPI) that captures the compound character of spatial extremes. We investigate daily temperature maxima and precipitation deficits at different accumulation times and find evidence that preceding precipitation deficits have a significant influence on the development of heat waves and that heat waves often co-occur with short-term drought conditions. We exemplarily show for the European heat waves of 2003 and 2010 that TEPI is suitable for describing the large-scale compound character of heat waves.
摘要我们提出了一种分析和紧凑描述大尺度多变量极端天气的方法。利用库利和蒂博（2019）提出的尾部成对依赖矩阵（TPDM）来识别极端事件的空间模式。我们还引入了交叉 TPDM，以识别两个变量中共同的极端事件模式。我们开发了极端模式指数（EPI），以提供基于模式的气温聚合。基于 EPI 的热浪定义能够检测出欧洲最重要的热浪。作为考虑两个变量中同时出现的极端事件的扩展，我们提出了基于阈值的极端模式指数（TEPI），该指数能够捕捉空间极端事件的复合特征。我们研究了不同累积时间的日最高气温和降水不足，发现有证据表明之前的降水不足对热浪的发展有重大影响，而且热浪往往与短期干旱状况同时出现。我们以 2003 年和 2010 年的欧洲热浪为例，说明 TEPI 适合描述热浪的大尺度复合特征。
{"title":"Spatial patterns and indices for heat waves and droughts over Europe using a decomposition of extremal dependency","authors":"Svenja Szemkus, Petra Friederichs","doi":"10.5194/ascmo-10-29-2024","DOIUrl":"https://doi.org/10.5194/ascmo-10-29-2024","url":null,"abstract":"Abstract. We present a method for the analysis and compact description of large-scale multivariate weather extremes. Spatial patterns of extreme events are identified using the tail pairwise dependence matrix (TPDM) proposed by Cooley and Thibaud (2019). We also introduce the cross-TPDM to identify patterns of common extremes in two variables. An extremal pattern index (EPI) is developed to provide a pattern-based aggregation of temperature. A heat wave definition based on EPI is able to detect the most important heat waves over Europe. As an extension for considering simultaneous extremes in two variables, we propose the threshold-based EPI (TEPI) that captures the compound character of spatial extremes. We investigate daily temperature maxima and precipitation deficits at different accumulation times and find evidence that preceding precipitation deficits have a significant influence on the development of heat waves and that heat waves often co-occur with short-term drought conditions. We exemplarily show for the European heat waves of 2003 and 2010 that TEPI is suitable for describing the large-scale compound character of heat waves.\u0000","PeriodicalId":36792,"journal":{"name":"Advances in Statistical Climatology, Meteorology and Oceanography","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139607266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract. This paper develops a method for determining whether two vector time series originate from a common stochastic process. The stochastic process considered incorporates both serial correlations and multivariate annual cycles. Specifically, the process is modeled as a vector autoregressive model with periodic forcing, referred to as a VARX model (where X stands for exogenous variables). The hypothesis that two VARX models share the same parameters is tested using the likelihood ratio method. The resulting test can be further decomposed into a series of tests to assess whether disparities in the VARX models stem from differences in noise parameters, autoregressive parameters, or annual cycle parameters. A comprehensive procedure for compressing discrepancies between VARX models into a minimal number of components is developed based on discriminant analysis. Using this method, the realism of climate model simulations of monthly mean North Atlantic sea surface temperatures is assessed. As expected, different simulations from the same climate model cannot be distinguished stochastically. Similarly, observations from different periods cannot be distinguished. However, every climate model differs stochastically from observations. Furthermore, each climate model differs stochastically from every other model, except when they originate from the same center. In essence, each climate model possesses a distinct fingerprint that sets it apart stochastically from both observations and models developed by other research centers. The primary factor contributing to these differences is the difference in annual cycles. The difference in annual cycles is often dominated by a single component, which can be extracted and illustrated using discriminant analysis.
{"title":"Comparison of climate time series – Part 5: Multivariate annual cycles","authors":"T. DelSole, M. Tippett","doi":"10.5194/ascmo-10-1-2024","DOIUrl":"https://doi.org/10.5194/ascmo-10-1-2024","url":null,"abstract":"Abstract. This paper develops a method for determining whether two vector time series originate from a common stochastic process. The stochastic process considered incorporates both serial correlations and multivariate annual cycles. Specifically, the process is modeled as a vector autoregressive model with periodic forcing, referred to as a VARX model (where X stands for exogenous variables). The hypothesis that two VARX models share the same parameters is tested using the likelihood ratio method. The resulting test can be further decomposed into a series of tests to assess whether disparities in the VARX models stem from differences in noise parameters, autoregressive parameters, or annual cycle parameters. A comprehensive procedure for compressing discrepancies between VARX models into a minimal number of components is developed based on discriminant analysis. Using this method, the realism of climate model simulations of monthly mean North Atlantic sea surface temperatures is assessed. As expected, different simulations from the same climate model cannot be distinguished stochastically. Similarly, observations from different periods cannot be distinguished. However, every climate model differs stochastically from observations. Furthermore, each climate model differs stochastically from every other model, except when they originate from the same center. In essence, each climate model possesses a distinct fingerprint that sets it apart stochastically from both observations and models developed by other research centers. The primary factor contributing to these differences is the difference in annual cycles. The difference in annual cycles is often dominated by a single component, which can be extracted and illustrated using discriminant analysis.\u0000","PeriodicalId":36792,"journal":{"name":"Advances in Statistical Climatology, Meteorology and Oceanography","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139620694","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-22DOI: 10.5194/ascmo-9-121-2023
Jhayron S. Pérez-Carrasquilla, Paola A. Montoya, Juan Manuel Sánchez, K. Hernández, Mauricio Ramírez
Abstract. We develop a framework to forecast 24 h averaged particulate matter (PM2.5) concentrations 4 d in advance in ground-based stations over the metropolitan area of the Aburrá Valley, Colombia. The input variables are gathered from a highly diverse set of sources, including in situ real-time PM2.5 observations, meteorological forecasts from the Global Forecasting System (GFS), aerosol optical depth (AOD) forecasts from the European Copernicus Atmosphere Monitoring Service (CAMS), and the Moderate Resolution Imaging Spectroradiometer (MODIS) active fire products. We compare the performance of two tree-based machine learning (ML) methods, random forests (RFs) and gradient boosting (GB), with linear regression as a baseline for error metrics. One of the disadvantages of tree-based models is their inability to make skillful predictions out of the domain in which the models were trained. To address that problem, we implement piecewise linear regression learners within the models. Additionally, to enhance the performance of the models, we use a customized loss function that considers the probability distribution of the target values. Tree-based models highly outperform the linear regression, with GB showing the best results in most of the 19 stations used in this study. We also test two approaches for the multi-step output problem, a direct multi-output (MO) scheme and a recursive (RC) scheme, with the GB–MO approach showing the best results. According to the performance analysis, the predictability is less for values away from the mean and decreases between 06:00 LT (local time) and the early afternoon, when the expansion of the boundary layer occurs. To contribute to understanding the sources of predictability and uncertainty of air quality in the city, we perform a feature importance analysis revealing that the relevance of the different independent variables is a function of the lead time. Particularly, apart from the past concentrations, the variables that most affect the predictability are the forecasted aerosol optical depth (AOD), the integrated fire radiative power over a forecasted back trajectory (BT-IFRP), and the predicted planetary boundary layer height (PBLH). In the testing period, the models showed the ability to forecast poor-air-quality events in the valley with more than 1 d of anticipation. This study serves as a framework for developing and evaluating the ML-based air quality forecasting models over the Andean region.
{"title":"Forecasting 24 h averaged PM2.5 concentration in the Aburrá Valley using tree-based machine learning models, global forecasts, and satellite information","authors":"Jhayron S. Pérez-Carrasquilla, Paola A. Montoya, Juan Manuel Sánchez, K. Hernández, Mauricio Ramírez","doi":"10.5194/ascmo-9-121-2023","DOIUrl":"https://doi.org/10.5194/ascmo-9-121-2023","url":null,"abstract":"Abstract. We develop a framework to forecast 24 h averaged particulate matter (PM2.5) concentrations 4 d in advance in ground-based stations over the metropolitan area of the Aburrá Valley, Colombia. The input variables are gathered from a highly diverse set of sources, including in situ real-time PM2.5 observations, meteorological forecasts from the Global Forecasting System (GFS), aerosol optical depth (AOD) forecasts from the European Copernicus Atmosphere Monitoring Service (CAMS), and the Moderate Resolution Imaging Spectroradiometer (MODIS) active fire products. We compare the performance of two tree-based machine learning (ML) methods, random forests (RFs) and gradient boosting (GB), with linear regression as a baseline for error metrics. One of the disadvantages of tree-based models is their inability to make skillful predictions out of the domain in which the models were trained. To address that problem, we implement piecewise linear regression learners within the models. Additionally, to enhance the performance of the models, we use a customized loss function that considers the probability distribution of the target values. Tree-based models highly outperform the linear regression, with GB showing the best results in most of the 19 stations used in this study. We also test two approaches for the multi-step output problem, a direct multi-output (MO) scheme and a recursive (RC) scheme, with the GB–MO approach showing the best results. According to the performance analysis, the predictability is less for values away from the mean and decreases between 06:00 LT (local time) and the early afternoon, when the expansion of the boundary layer occurs. To contribute to understanding the sources of predictability and uncertainty of air quality in the city, we perform a feature importance analysis revealing that the relevance of the different independent variables is a function of the lead time. Particularly, apart from the past concentrations, the variables that most affect the predictability are the forecasted aerosol optical depth (AOD), the integrated fire radiative power over a forecasted back trajectory (BT-IFRP), and the predicted planetary boundary layer height (PBLH). In the testing period, the models showed the ability to forecast poor-air-quality events in the valley with more than 1 d of anticipation. This study serves as a framework for developing and evaluating the ML-based air quality forecasting models over the Andean region.\u0000","PeriodicalId":36792,"journal":{"name":"Advances in Statistical Climatology, Meteorology and Oceanography","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138947266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract. Recent heatwaves such as the 2021 Pacific Northwest heatwave have shattered temperature records across the globe. The likelihood of experiencing extreme temperature events today is already strongly increased by anthropogenic climate change, but it remains challenging to determine to what degree prevalent atmospheric and land surface conditions aggravated the intensity of a specific heatwave event. Quantifying the respective contributions is therefore paramount for process understanding but also for attribution and future projection statements conditional on the state of atmospheric circulation or land surface conditions. We here propose and evaluate a statistical framework based on extreme value theory, which enables us to learn the respective statistical relationship between extreme temperature and process variables in initial-condition large ensemble climate model simulations. Elements of statistical learning theory are implemented in order to integrate the effect of the governing regional circulation pattern. The learned statistical models can be applied to reanalysis data to quantify the relevance of physical process variables in observed heatwave events. The method also allows us to make conditional attribution statements and answer “what if” questions. For instance, how much would a heatwave intensify given the same dynamic conditions but at a different warming level? How much additional warming is needed for the same heatwave intensity to occur under average circulation conditions? Changes in the exceedance probability under varying large- and regional-scale conditions can also be assessed. We show that each additional degree of global warming increases the 7 d maximum temperature for the Pacific Northwest area by almost 2 ∘C, and likewise, we quantify the direct effect of anti-cyclonic conditions on heatwave intensity. Based on this, we find that the combined global warming and circulation effect of at least 2.9 ∘C accounts for 60 %–80 % of the 2021 excess event intensity relative to average pre-industrial heatwave conditions.
{"title":"Quantifying the statistical dependence of mid-latitude heatwave intensity and likelihood on prevalent physical drivers and climate change","authors":"J. Zeder, E. Fischer","doi":"10.5194/ascmo-9-83-2023","DOIUrl":"https://doi.org/10.5194/ascmo-9-83-2023","url":null,"abstract":"Abstract. Recent heatwaves such as the 2021 Pacific Northwest heatwave have shattered temperature records across the globe. The likelihood of experiencing extreme temperature events today is already strongly increased by anthropogenic climate change, but it remains challenging to determine to what degree prevalent atmospheric and land surface conditions aggravated the intensity of a specific heatwave event. Quantifying the respective contributions is therefore paramount for process understanding but also for attribution and future projection statements conditional on the state of atmospheric circulation or land surface conditions. We here propose and evaluate a statistical framework based on extreme value theory, which enables us to learn the respective statistical relationship between extreme temperature and process variables in initial-condition large ensemble climate model simulations. Elements of statistical learning theory are implemented in order to integrate the effect of the governing regional circulation pattern. The learned statistical models can be applied to reanalysis data to quantify the relevance of physical process variables in observed heatwave events. The method also allows us to make conditional attribution statements and answer “what if” questions. For instance, how much would a heatwave intensify given the same dynamic conditions but at a different warming level? How much additional warming is needed for the same heatwave intensity to occur under average circulation conditions? Changes in the exceedance probability under varying large- and regional-scale conditions can also be assessed. We show that each additional degree of global warming increases the 7 d maximum temperature for the Pacific Northwest area by almost 2 ∘C, and likewise, we quantify the direct effect of anti-cyclonic conditions on heatwave intensity. Based on this, we find that the combined global warming and circulation effect of at least 2.9 ∘C accounts for 60 %–80 % of the 2021 excess event intensity relative to average pre-industrial heatwave conditions.\u0000","PeriodicalId":36792,"journal":{"name":"Advances in Statistical Climatology, Meteorology and Oceanography","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44346179","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Said Obakrim, Pierre Ailliot, Valérie Monbet, Nicolas Raillard
Abstract. Many marine activities, such as designing ocean structures and planning marine operations, require the characterization of sea-state climate. This study investigates the statistical relationship between wind and sea states, considering its spatiotemporal behavior. A transfer function is established between wind fields over the North Atlantic (predictors) and the significant wave height (predictand) at three locations: southwest of the French coast (Gironde), the English Channel, and the Gulf of Maine. The developed method considers both wind seas and swells by including local and global predictors. Using a fully data-driven approach, the global predictors' spatiotemporal structure is defined to account for the non-local and non-instantaneous relationship between wind and waves. Weather types are constructed using a regression-guided clustering method, and the resulting clusters correspond to different wave systems (swells and wind seas). Then, in each weather type, a penalized linear regression model is fitted between the predictor and the predictand. The validation analysis proves the models skill in predicting the significant wave height, with a root mean square error of approximately 0.3 m in the three considered locations. Additionally, the study discusses the physical insights underlying the proposed method.
{"title":"Statistical modeling of the space–time relation between wind and significant wave height","authors":"Said Obakrim, Pierre Ailliot, Valérie Monbet, Nicolas Raillard","doi":"10.5194/ascmo-9-67-2023","DOIUrl":"https://doi.org/10.5194/ascmo-9-67-2023","url":null,"abstract":"Abstract. Many marine activities, such as designing ocean structures and planning marine operations, require the characterization of sea-state climate. This study investigates the statistical relationship between wind and sea states, considering its spatiotemporal behavior. A transfer function is established between wind fields over the North Atlantic (predictors) and the significant wave height (predictand) at three locations: southwest of the French coast (Gironde), the English Channel, and the Gulf of Maine. The developed method considers both wind seas and swells by including local and global predictors. Using a fully data-driven approach, the global predictors' spatiotemporal structure is defined to account for the non-local and non-instantaneous relationship between wind and waves. Weather types are constructed using a regression-guided clustering method, and the resulting clusters correspond to different wave systems (swells and wind seas). Then, in each weather type, a penalized linear regression model is fitted between the predictor and the predictand. The validation analysis proves the models skill in predicting the significant wave height, with a root mean square error of approximately 0.3 m in the three considered locations. Additionally, the study discusses the physical insights underlying the proposed method.","PeriodicalId":36792,"journal":{"name":"Advances in Statistical Climatology, Meteorology and Oceanography","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135703324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract. In this study we detect and quantify changes in the distribution of the annual maximum daily maximum temperature (TXx) in a large observation-based gridded data set of European daily temperature during the years 1950–2018. Several statistical models are considered, each of which analyses TXx using a generalized extreme-value (GEV) distribution with the GEV parameters varying smoothly over space. In contrast to several previous studies which fit independent GEV models at the grid-box level, our models pull information from neighbouring grid boxes for more efficient parameter estimation. The GEV location and scale parameters are allowed to vary in time using the log of atmospheric CO2 as a covariate. Changes are detected most strongly in the GEV location parameter, with the TXx distributions generally shifting towards hotter temperatures. Averaged across our spatial domain, the 100-year return level of TXx based on the 2018 climate is approximately 2 ∘C (95 % confidence interval of [2.03,2.12] ∘C) hotter than that based on the 1950 climate. Moreover, averaged across our spatial domain, the 100-year return level of TXx based on the 1950 climate corresponds approximately to a 6-year return level in the 2018 climate.
{"title":"Changes in the distribution of annual maximum temperatures in Europe","authors":"G. Auld, G. Hegerl, I. Papastathopoulos","doi":"10.5194/ascmo-9-45-2023","DOIUrl":"https://doi.org/10.5194/ascmo-9-45-2023","url":null,"abstract":"Abstract. In this study we detect and quantify changes in the distribution of the annual maximum daily maximum temperature (TXx)\u0000in a large observation-based gridded data set of European daily temperature during the years 1950–2018. Several statistical models are considered, each of which analyses TXx using a generalized extreme-value (GEV) distribution with the GEV parameters varying smoothly over space.\u0000In contrast to several previous studies which fit independent GEV models at the grid-box level, our models pull information from neighbouring grid boxes for more efficient parameter estimation. The GEV location and scale parameters are allowed to\u0000vary in time using the log of atmospheric CO2 as a covariate.\u0000Changes are detected most strongly in the GEV location parameter, with the TXx distributions generally shifting towards hotter temperatures. Averaged across our spatial domain, the 100-year return level of TXx based on the 2018 climate\u0000is approximately 2 ∘C (95 % confidence interval of [2.03,2.12] ∘C) hotter than that based on the 1950 climate. Moreover, averaged across our spatial domain, the 100-year return level of TXx based on the 1950 climate corresponds approximately to a 6-year return level in the 2018 climate.\u0000","PeriodicalId":36792,"journal":{"name":"Advances in Statistical Climatology, Meteorology and Oceanography","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46961386","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract. Daily meteorological data such as temperature or precipitation from climate models are needed for many climate impact studies, e.g., in hydrology or agriculture, but direct model output can contain large systematic errors. A large variety of methods exist to adjust the bias of climate model outputs. Here we review existing statistical bias-adjustment methods and their shortcomings, and compare quantile mapping (QM), scaled distribution mapping (SDM), quantile delta mapping (QDM) and an empiric version of PresRAT (PresRATe). We then test these methods using real and artificially created daily temperature and precipitation data for Austria. We compare the performance in terms of the following demands: (1) the model data should match the climatological means of the observational data in the historical period; (2) the long-term climatological trends of means (climate change signal), either defined as difference or as ratio, should not be altered during bias adjustment; and (3) even models with too few wet days (precipitation above 0.1 mm) should be corrected accurately, so that the wet day frequency is conserved. QDM and PresRATe combined fulfill all three demands. For (2) for precipitation, PresRATe already includes an additional correction that assures that the climate change signal is conserved.
{"title":"Evaluating skills and issues of quantile-based bias adjustment for climate change scenarios","authors":"F. Lehner, I. Nadeem, H. Formayer","doi":"10.5194/ascmo-9-29-2023","DOIUrl":"https://doi.org/10.5194/ascmo-9-29-2023","url":null,"abstract":"Abstract. Daily meteorological data such as temperature or precipitation from climate models are needed for many climate impact studies, e.g., in hydrology or agriculture, but direct model output can contain large systematic errors. A large variety of methods exist to adjust the bias of climate model outputs. Here we review existing statistical bias-adjustment methods and their shortcomings, and compare quantile mapping (QM), scaled distribution mapping (SDM), quantile delta mapping (QDM) and an empiric version of PresRAT (PresRATe). We then test these methods using real and artificially created daily temperature and precipitation data for Austria. We compare the performance in terms of the following demands: (1) the model data should match the climatological means of the observational data in the historical period; (2) the long-term climatological trends of means (climate change signal), either defined as difference or as ratio, should not be altered during bias adjustment; and (3) even models with too few wet days (precipitation above 0.1 mm) should be corrected accurately, so that the wet day frequency is conserved. QDM and PresRATe combined fulfill all three demands. For (2) for precipitation, PresRATe already includes an additional correction that assures that the climate change signal is conserved.\u0000","PeriodicalId":36792,"journal":{"name":"Advances in Statistical Climatology, Meteorology and Oceanography","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46861930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract. General circulation model (GCM) outputs are a primary source of information for climate change impact assessments. However, raw GCM data rarely are used directly for regional-scale impact assessments as they frequently contain systematic error or bias. In this article, we propose a novel extension to standard quantile mapping that allows for a continuous seasonal change in bias magnitude using localized regression. Our primary goal is to examine the efficacy of this tool in the context of larger statistical downscaling efforts on the tropical island of Puerto Rico, where localized downscaling can be particularly challenging. Along the way, we utilize a multivariate infilling algorithm to estimate missing data within an incomplete climate data network spanning Puerto Rico. Next, we apply a combination of multivariate downscaling methods to generate in situ climate projections at 23 locations across Puerto Rico from three general circulation models in two carbon emission scenarios: RCP4.5 and RCP8.5. Finally, our bias-correction methods are applied to these downscaled GCM climate projections. These bias-correction methods allow GCM bias to vary as a function of a user-defined season (here, Julian day). Bias is estimated using a continuous curve rather than a moving window or monthly breaks. Results from the selected ensemble agree that Puerto Rico will continue to warm through the coming century. Under the RCP4.5 forcing scenario, our methods indicate that the dry season will have increased rainfall, while the early and late rainfall seasons will likely have a decline in total rainfall. Our methods applied to the RCP8.5 forcing scenario favor a wetter climate for Puerto Rico, driven by an increase in the frequency of high-magnitude rainfall events during Puerto Rico's early rainfall season (April to July) as well as its late rainfall season (August to November).
{"title":"Modeling general circulation model bias via a combination of localized regression and quantile mapping methods","authors":"Benjamin Washington, L. Seymour, T. Mote","doi":"10.5194/ascmo-9-1-2023","DOIUrl":"https://doi.org/10.5194/ascmo-9-1-2023","url":null,"abstract":"Abstract. General circulation model (GCM) outputs are a primary source of information for\u0000climate change impact assessments. However, raw GCM data rarely are used directly for\u0000regional-scale impact assessments as they frequently contain systematic error or bias. In this\u0000article, we propose a novel extension to standard quantile mapping that allows for a continuous\u0000seasonal change in bias magnitude using localized regression. Our primary goal is to examine the\u0000efficacy of this tool in the context of larger statistical downscaling efforts on the tropical\u0000island of Puerto Rico, where localized downscaling can be particularly challenging. Along the\u0000way, we utilize a multivariate infilling algorithm to estimate missing data within an incomplete\u0000climate data network spanning Puerto Rico. Next, we apply a combination of multivariate\u0000downscaling methods to generate in situ climate projections at 23 locations across Puerto Rico\u0000from three general circulation models in two carbon emission scenarios: RCP4.5 and RCP8.5.\u0000Finally, our bias-correction methods are applied to these downscaled GCM climate projections.\u0000These bias-correction methods allow GCM bias to vary as a function of a user-defined season\u0000(here, Julian day). Bias is estimated using a continuous curve rather than a moving window or\u0000monthly breaks. Results from the selected ensemble agree that Puerto Rico will continue to warm\u0000through the coming century. Under the RCP4.5 forcing scenario, our methods indicate that the dry\u0000season will have increased rainfall, while the early and late rainfall seasons will likely have a\u0000decline in total rainfall. Our methods applied to the RCP8.5 forcing scenario favor a wetter\u0000climate for Puerto Rico, driven by an increase in the frequency of high-magnitude rainfall events\u0000during Puerto Rico's early rainfall season (April to July) as well as its late rainfall season\u0000(August to November).\u0000","PeriodicalId":36792,"journal":{"name":"Advances in Statistical Climatology, Meteorology and Oceanography","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43690859","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-14DOI: 10.5194/ascmo-8-225-2022
Katarina Lashgari, G. Brattström, A. Moberg, R. Sundberg
Abstract. Evaluation of climate model simulations is a crucial task in climate research. Here, a new statistical framework is proposed for evaluation of simulated temperature responses to climate forcings against temperature reconstructions derived from climate proxy data for the last millennium. The framework includes two types of statistical models, each of which is based on the concept of latent (unobservable) variables: confirmatory factor analysis (CFA) models and structural equation modelling (SEM) models. Each statistical model presented is developed for use with data from a single region, which can be of any size. The ideas behind the framework arose partly from a statistical model used in many detection and attribution (D&A) studies. Focusing on climatological characteristics of five specific forcings of natural and anthropogenic origin, the present work theoretically motivates an extension of the statistical model used in D&A studies to CFA and SEM models, which allow, for example, for non-climatic noise in observational data without assuming the additivity of the forcing effects. The application of the ideas of CFA is exemplified in a small numerical study, whose aim was to check the assumptions typically placed on ensembles of climate model simulations when constructing mean sequences. The result of this study indicated that some ensembles for some regions may not satisfy the assumptions in question.
{"title":"Evaluation of simulated responses to climate forcings: a flexible statistical framework using confirmatory factor analysis and structural equation modelling – Part 1: Theory","authors":"Katarina Lashgari, G. Brattström, A. Moberg, R. Sundberg","doi":"10.5194/ascmo-8-225-2022","DOIUrl":"https://doi.org/10.5194/ascmo-8-225-2022","url":null,"abstract":"Abstract. Evaluation of climate model simulations is a crucial task in climate research. Here, a new\u0000statistical framework is proposed for evaluation of simulated temperature responses\u0000to climate forcings against temperature reconstructions derived from climate proxy data for\u0000the last millennium. The framework includes two types of statistical models, each of which is\u0000based on the concept of latent (unobservable)\u0000variables: confirmatory factor analysis (CFA) models and structural equation modelling\u0000(SEM) models. Each statistical model presented is developed for use with data from a single region,\u0000which can be of any size. The ideas behind the framework arose partly from a statistical model\u0000used in many detection and attribution (D&A) studies.\u0000Focusing on climatological characteristics of\u0000five specific forcings of natural and anthropogenic origin, the present work theoretically\u0000motivates an extension of the statistical model used in D&A studies to CFA and SEM models,\u0000which allow, for example, for non-climatic noise in observational data without assuming\u0000the additivity of the forcing effects.\u0000The application of the ideas of CFA is exemplified in a small numerical study, whose aim was\u0000to check the assumptions typically placed on ensembles\u0000of climate model simulations when constructing mean sequences. The result of this study indicated\u0000that some ensembles for some regions may not satisfy the assumptions in question.\u0000","PeriodicalId":36792,"journal":{"name":"Advances in Statistical Climatology, Meteorology and Oceanography","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45614533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：