Pub Date : 2022-12-14DOI: 10.5194/ascmo-8-249-2022
Katarina Lashgari, A. Moberg, G. Brattström
Abstract. The performance of a new statistical framework, developed for the evaluation of simulated temperature responses to climate forcings against temperature reconstructions derived from climate proxy data for the last millennium, is evaluated in a so-called pseudo-proxy experiment, where the true unobservable temperature is replaced with output data from a selected simulation with a climate model. Being an extension of the statistical model used in many detection and attribution (D&A) studies, the framework under study involves two main 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. Within the present pseudo-proxy experiment, each statistical model was fitted to seven continental-scale regional data sets. In addition, their performance for each defined region was compared to the performance of the corresponding statistical model used in D&A studies. The results of this experiment indicated that the SEM specification is the most appropriate one for describing the underlying latent structure of the simulated temperature data in question. The conclusions of the experiment have been confirmed in a cross-validation study, presuming the availability of several simulation data sets within each studied region. Since the experiment is performed only for zero noise level in the pseudo-proxy data, all statistical models, chosen as final regional models, await further investigation to thoroughly test their performance for realistic levels of added noise, similar to what is found in real proxy data for past temperature variations.
{"title":"Evaluation of simulated responses to climate forcings: a flexible statistical framework using confirmatory factor analysis and structural equation modelling – Part 2: Numerical experiment","authors":"Katarina Lashgari, A. Moberg, G. Brattström","doi":"10.5194/ascmo-8-249-2022","DOIUrl":"https://doi.org/10.5194/ascmo-8-249-2022","url":null,"abstract":"Abstract. The performance of a new statistical framework, developed for\u0000the evaluation of simulated temperature responses to climate forcings against\u0000temperature reconstructions derived from climate proxy data for the last millennium, is evaluated\u0000in a so-called pseudo-proxy experiment, where the true unobservable temperature is replaced\u0000with output data from a selected simulation with a climate model. Being an extension of the statistical\u0000model used in many detection and attribution (D&A) studies,\u0000the framework under study involves two main types of statistical models, each of which is based\u0000on the concept of latent (unobservable) variables: confirmatory factor analysis (CFA) models\u0000and structural equation modelling (SEM) models.\u0000Within the present pseudo-proxy experiment, each statistical model was fitted\u0000to seven continental-scale regional data sets. In addition, their performance for each defined\u0000region was compared to the performance of the corresponding statistical model used in D&A studies. The results of\u0000this experiment indicated that the SEM specification is the most appropriate one for describing\u0000the underlying latent structure of the simulated temperature data in question.\u0000The conclusions of the experiment have been confirmed in a cross-validation study, presuming\u0000the availability of several simulation data sets within each studied region. Since the experiment is\u0000performed only for zero noise level in the pseudo-proxy data, all statistical models, chosen as final\u0000regional models, await further investigation to thoroughly test their performance for realistic levels of\u0000added noise, similar to what is found in real proxy data for past temperature variations.\u0000","PeriodicalId":36792,"journal":{"name":"Advances in Statistical Climatology, Meteorology and Oceanography","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44715003","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 : 2022-12-02DOI: 10.5194/ascmo-8-205-2022
Qiuyi Wu, J. Bessac, Whitney K. Huang, Jiali Wang, R. Kotamarthi
Abstract. This study develops a statistical conditional approach to evaluate climate model performance in wind speed and direction and to project their future changes under the Representative Concentration Pathway (RCP) 8.5 scenario over inland and offshore locations across the continental United States (CONUS). The proposed conditional approach extends the scope of existing studies by a combined characterization of the wind direction distribution and conditional distribution of wind on the direction, hence enabling an assessment of the joint wind speed and direction distribution and their changes. A von Mises mixture distribution is used to model wind directions across models and climate conditions. Wind speed distributions conditioned on wind direction are estimated using two statistical methods, i.e., a Weibull distributional regression model and a quantile regression model, both of which enforce the circular constraint to their resultant estimated distributions. Projected uncertainties associated with different climate models and model internal variability are investigated and compared with the climate change signal to quantify the robustness of the future projections. In particular, this work extends the concept of internal variability in the climate mean to the standard deviation and high quantiles to assess the relative magnitudes to their projected changes. The evaluation results show that the studied climate model captures both historical wind speed and wind direction and their dependencies reasonably well over both inland and offshore locations. Under the RCP8.5 scenario, most of the studied locations show no significant changes in the mean wind speeds in both winter and summer, while the changes in the standard deviation and 95th quantile show some robust changes over certain locations in winter. Specifically, high wind speeds (95th quantile) conditioned on direction in winter are projected to decrease in the northwestern, Colorado, and northern Great Plains locations in our study. In summer, high wind speeds conditioned on direction over the southern Great Plains increase slightly, while high wind speeds conditioned on direction over offshore locations do not change much. The proposed conditional approach enables a combined characterization of the wind speed distributions conditioned on direction and wind direction distributions, which offers a flexible alternative that can provide additional insights for the joint assessment of speed and direction.
{"title":"A conditional approach for joint estimation of wind speed and direction under future climates","authors":"Qiuyi Wu, J. Bessac, Whitney K. Huang, Jiali Wang, R. Kotamarthi","doi":"10.5194/ascmo-8-205-2022","DOIUrl":"https://doi.org/10.5194/ascmo-8-205-2022","url":null,"abstract":"Abstract. This study develops a statistical conditional approach to evaluate climate model performance in wind speed and direction and to project their future changes under the Representative Concentration Pathway (RCP) 8.5 scenario over inland and offshore locations across the continental United States (CONUS). The proposed conditional approach extends the scope of existing studies by a combined characterization of the wind direction distribution and conditional distribution of wind on the direction, hence enabling an assessment of the joint wind speed and direction distribution and their changes. A von Mises mixture distribution is used to model wind directions across models and climate conditions. Wind speed distributions conditioned on wind direction are estimated using two statistical methods, i.e., a Weibull distributional regression model and a quantile regression model, both of which enforce the circular constraint to their resultant estimated distributions. Projected uncertainties associated with different climate models and model internal variability are investigated and compared with the climate change signal to quantify the robustness of the future projections. In particular, this work extends the concept of internal variability in the climate mean to the standard deviation and high quantiles to assess the relative magnitudes to their projected changes. The evaluation results show that the studied climate model captures both historical wind speed and wind direction and their dependencies reasonably well over both inland and offshore locations. Under the RCP8.5 scenario, most of the studied locations show no significant changes in the mean wind speeds in both winter and summer, while the changes in the standard deviation and 95th quantile show some robust changes over certain locations in winter. Specifically, high wind speeds (95th quantile) conditioned on direction in winter are projected to decrease in the northwestern, Colorado, and northern Great Plains locations in our study. In summer, high wind speeds conditioned on direction over the southern Great Plains increase slightly, while high wind speeds conditioned on direction over offshore locations do not change much. The proposed conditional approach enables a combined characterization of the wind speed distributions conditioned on direction and wind direction distributions, which offers a flexible alternative that can provide additional insights for the joint assessment of speed and direction.\u0000","PeriodicalId":36792,"journal":{"name":"Advances in Statistical Climatology, Meteorology and Oceanography","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70959314","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 : 2022-09-30DOI: 10.5194/ascmo-8-187-2022
T. DelSole, M. Tippett
Abstract. This paper derives a test for deciding whether two time series come from the same stochastic model, where the time series contains periodic and serially correlated components. This test is useful for comparing dynamical model simulations to observations. The framework for deriving this test is the same as in the previous three parts: the time series are first fit to separate autoregressive models, and then the hypothesis that their parameters are equal is tested. This paper generalizes the previous tests to a limited class of nonstationary processes, namely, those represented by an autoregressive model with deterministic forcing terms. The statistic for testing differences in parameters can be decomposed into independent terms that quantify differences in noise variance, differences in autoregression parameters, and differences in forcing parameters (e.g., differences in annual cycle forcing). A hierarchical procedure for testing individual terms and quantifying the overall significance level is derived from standard methods. The test is applied to compare observations of the meridional overturning circulation from the RAPID array to Coupled Model Intercomparison Project Phase 5 (CMIP5) models. Most CMIP5 models are inconsistent with observations, with the strongest differences arising from having too little noise variance, though differences in annual cycle forcing also contribute significantly to discrepancies from observations. This appears to be the first use of a rigorous criterion to decide “equality of annual cycles” in regards to all their attributes (e.g., phases, amplitudes, frequencies) while accounting for serial correlations.
{"title":"Comparing climate time series – Part 4: Annual cycles","authors":"T. DelSole, M. Tippett","doi":"10.5194/ascmo-8-187-2022","DOIUrl":"https://doi.org/10.5194/ascmo-8-187-2022","url":null,"abstract":"Abstract. This paper derives a test for deciding whether two time series come from the same stochastic model, where the time series contains periodic and serially correlated components. This test is useful for comparing dynamical model simulations to observations. The framework for deriving this test is the same as in the previous three parts: the time series are first fit to separate autoregressive models, and then the hypothesis that their parameters are equal is tested. This paper generalizes the previous tests to a limited class of nonstationary processes, namely, those represented by an autoregressive model with deterministic forcing terms. The statistic for testing differences in parameters can be decomposed into independent terms that quantify differences in noise variance, differences in autoregression parameters, and differences in forcing parameters (e.g., differences in annual cycle forcing). A hierarchical procedure for testing individual terms and quantifying the overall significance level is derived from standard methods. The test is applied to compare observations of the meridional overturning circulation from the RAPID array to Coupled Model Intercomparison Project Phase 5 (CMIP5) models. Most CMIP5 models are inconsistent with observations, with the strongest differences arising from having too little noise variance, though differences in annual cycle forcing also contribute significantly to discrepancies from observations. This appears to be the first use of a rigorous criterion to decide “equality of annual cycles” in regards to all their attributes (e.g., phases, amplitudes, frequencies) while accounting for serial correlations.\u0000","PeriodicalId":36792,"journal":{"name":"Advances in Statistical Climatology, Meteorology and Oceanography","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49342767","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 : 2022-09-01DOI: 10.5194/ascmo-8-155-2022
F. Pons, D. Faranda
Abstract. The description and analysis of compound extremes affecting mid- and high latitudes in the winter requires an accurate estimation of snowfall. This variable is often missing from in situ observations and biased in climate model outputs, both in the magnitude and number of events. While climate models can be adjusted using bias correction (BC), snowfall presents additional challenges compared to other variables, preventing one from applying traditional univariate BC methods. We extend the existing literature on the estimation of the snowfall fraction from near-surface temperature, which usually involves binary thresholds or nonlinear least square fitting of sigmoidal functions. We show that, considering methods such as segmented and spline regressions and nonlinear least squares fitting, it is possible to obtain accurate out-of-sample estimates of snowfall over Europe in ERA5 reanalysis and to perform effective BC on the IPSL_WRF high-resolution EURO-CORDEX climate model when only relying on bias-adjusted temperature and precipitation. In particular, we find that cubic spline regression offers the best tradeoff as a feasible and accurate way to reconstruct or adjust snowfall observations, without requiring multivariate or conditional bias correction and stochastic generation of unobserved events.
{"title":"Statistical reconstruction of European winter snowfall in reanalysis and climate models based on air temperature and total precipitation","authors":"F. Pons, D. Faranda","doi":"10.5194/ascmo-8-155-2022","DOIUrl":"https://doi.org/10.5194/ascmo-8-155-2022","url":null,"abstract":"Abstract. The description and analysis of compound extremes affecting mid- and high latitudes in the winter requires an accurate estimation of snowfall. This variable is often missing from in situ observations and biased in climate model outputs, both in the magnitude and number of events. While climate models can be adjusted using bias correction (BC), snowfall presents additional challenges compared to other variables, preventing one from applying traditional univariate BC methods. We extend the existing literature on the estimation of the snowfall fraction from near-surface temperature, which usually involves binary thresholds or nonlinear least square fitting of sigmoidal functions. We show that, considering methods such as segmented and spline regressions and nonlinear least squares fitting, it is possible to obtain accurate out-of-sample estimates of snowfall over Europe in ERA5 reanalysis and to perform effective BC on the IPSL_WRF high-resolution EURO-CORDEX climate model when only relying on bias-adjusted temperature and precipitation. In particular, we find that cubic spline regression offers the best tradeoff as a feasible and accurate way to reconstruct or adjust snowfall observations, without requiring multivariate or conditional bias correction and stochastic generation of unobserved events.\u0000","PeriodicalId":36792,"journal":{"name":"Advances in Statistical Climatology, Meteorology and Oceanography","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48808173","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 : 2022-06-13DOI: 10.5194/ascmo-8-135-2022
D. Gilford, A. Pershing, B. Strauss, K. Haustein, F. Otto
Abstract. Human-driven climate change has caused a wide range of extreme weather events to become more frequent in recent decades. Although increased and intense periods of extreme weather are expected consequences of anthropogenic climate warming, it remains challenging to rapidly and continuously assess the degree to which human activity alters the probability of specific events. This study introduces a new framework to enable the production and communication of global real-time estimates of how human-driven climate change has changed the likelihood of daily weather events. The framework's multi-method approach implements one model-based and two observation-based methods to provide ensemble attribution estimates with accompanying confidence levels. The framework is designed to be computationally lightweight to allow attributable probability changes to be rapidly calculated using forecasts or the latest observations. The framework is particularly suited for highlighting ordinary weather events that have been altered by human-caused climate change. An example application using daily maximum temperature in Phoenix, AZ, USA, highlights the framework's effectiveness in estimating the attributable human influence on observed daily temperatures (and deriving associated confidence levels). Global analyses show that the framework is capable of producing worldwide complementary observational- and model-based assessments of how human-caused climate change changes the likelihood of daily maximum temperatures. For instance, over 56 % of the Earth's total land area, all three framework methods agree that maximum temperatures greater than the preindustrial 99th percentile have become at least twice as likely in today's human-influenced climate. Additionally, over 52 % of land in the tropics, human-caused climate change is responsible for at least five-fold increases in the likelihood of preindustrial 99th percentile maximum temperatures. By systematically applying this framework to near-term forecasts or daily observations, local attribution analyses can be provided in real time worldwide. These new analyses create opportunities to enhance communication and provide input and/or context for policy, adaptation, human health, and other ecosystem/human system impact studies.
{"title":"A multi-method framework for global real-time climate attribution","authors":"D. Gilford, A. Pershing, B. Strauss, K. Haustein, F. Otto","doi":"10.5194/ascmo-8-135-2022","DOIUrl":"https://doi.org/10.5194/ascmo-8-135-2022","url":null,"abstract":"Abstract. Human-driven climate change has caused a wide range of extreme weather events to become more frequent in recent decades. Although increased and intense periods of extreme weather are expected consequences of anthropogenic climate warming, it remains challenging to rapidly and continuously assess the degree to which human activity alters the probability of specific events. This study introduces a new framework to enable the production and communication of global real-time estimates of how human-driven climate change has changed the likelihood of daily weather events. The framework's multi-method approach implements one model-based and two observation-based methods to provide ensemble attribution estimates with accompanying confidence levels. The framework is designed to be computationally lightweight to allow attributable probability changes to be rapidly calculated using forecasts or the latest observations. The framework is particularly suited for highlighting ordinary weather events that have been altered by human-caused climate change. An example application using daily maximum temperature in Phoenix, AZ, USA, highlights the framework's effectiveness in estimating the attributable human influence on observed daily temperatures (and deriving associated confidence levels). Global analyses show that the framework is capable of producing worldwide complementary observational- and model-based assessments of how human-caused climate change changes the likelihood of daily maximum temperatures. For instance, over 56 % of the Earth's total land area, all three framework methods agree that maximum temperatures greater than the preindustrial 99th percentile have become at least twice as likely in today's human-influenced climate. Additionally, over 52 % of land in the tropics, human-caused climate change is responsible for at least five-fold increases in the likelihood of preindustrial 99th percentile maximum temperatures. By systematically applying this framework to near-term forecasts or daily observations, local attribution analyses can be provided in real time worldwide. These new analyses create opportunities to enhance communication and provide input and/or context for policy, adaptation, human health, and other ecosystem/human system impact studies.\u0000","PeriodicalId":36792,"journal":{"name":"Advances in Statistical Climatology, Meteorology and Oceanography","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48077321","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 parts I and II of this paper series, rigorous tests for equality of stochastic processes were proposed. These tests provide objective criteria for deciding whether two processes differ, but they provide no information about the nature of those differences. This paper develops a systematic and optimal approach to diagnosing differences between multivariate stochastic processes. Like the tests, the diagnostics are framed in terms of vector autoregressive (VAR) models, which can be viewed as a dynamical system forced by random noise. The tests depend on two statistics, one that measures dissimilarity in dynamical operators and another that measures dissimilarity in noise covariances. Under suitable assumptions, these statistics are independent and can be tested separately for significance. If a term is significant, then the linear combination of variables that maximizes that term is obtained. The resulting indices contain all relevant information about differences between data sets. These techniques are applied to diagnose how the variability of annual-mean North Atlantic sea surface temperature differs between climate models and observations. For most models, differences in both noise processes and dynamics are important. Over 40 % of the differences in noise statistics can be explained by one or two discriminant components, though these components can be model dependent. Maximizing dissimilarity in dynamical operators identifies situations in which some climate models predict large-scale anomalies with the wrong sign.
{"title":"Comparing climate time series – Part 3: Discriminant analysis","authors":"T. DelSole, M. Tippett","doi":"10.5194/ascmo-8-97-2022","DOIUrl":"https://doi.org/10.5194/ascmo-8-97-2022","url":null,"abstract":"Abstract. In parts I and II of this paper series, rigorous tests for equality of stochastic processes were proposed. These tests provide objective criteria for deciding whether two processes differ, but they provide no information about the nature of those differences. This paper develops a systematic and optimal approach to diagnosing differences between multivariate stochastic processes. Like the tests, the diagnostics are framed in terms of vector autoregressive (VAR) models, which can be viewed as a dynamical system forced by random noise. The tests depend on two statistics, one that measures dissimilarity in dynamical operators and another that measures dissimilarity in noise covariances. Under suitable assumptions, these statistics are independent and can be tested separately for significance. If a term is significant, then the linear combination of variables that maximizes that term is obtained. The resulting indices contain all relevant information about differences between data sets. These techniques are applied to diagnose how the variability of annual-mean North Atlantic sea surface temperature differs between climate models and observations. For most models, differences in both noise processes and dynamics are important. Over 40 % of the differences in noise statistics can be explained by one or two discriminant components, though these components can be model dependent. Maximizing dissimilarity in dynamical operators identifies situations in which some climate models predict large-scale anomalies with the wrong sign.\u0000","PeriodicalId":36792,"journal":{"name":"Advances in Statistical Climatology, Meteorology and Oceanography","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43278493","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}
Marceau Michel, Said Obakrim, N. Raillard, P. Ailliot, V. Monbet
Abstract. Numerous marine applications require the prediction of medium- and long-term sea states. Climate models are mainly focused on the description of the atmosphere and global ocean variables, most often on a synoptic scale. Downscaling models exist to move from these atmospheric variables to the integral descriptors of the surface state; however, they are most often complex numerical models based on physics equations that entail significant computational costs. Statistical downscaling models provide an alternative to these models by constructing an empirical relationship between large-scale atmospheric variables and local variables, using historical data. Among the existing methods, deep learning methods are attracting increasing interest because of their ability to build hierarchical representations of features. To our knowledge, these models have not yet been tested in the case of sea state downscaling. In this study, a convolutional neural network (CNN)-type model for the prediction of significant wave height from wind fields in the Bay of Biscay is presented. The performance of this model is evaluated at several points and compared to other statistical downscaling methods and to WAVEWATCH III hindcast databases. The results obtained from these different stations show that the proposed method is suitable for predicting sea states. The observed performances are superior to those of the other statistical downscaling methods studied but remain inferior to those of the physical models. The low computational cost and the ease of implementation are, however, important assets for this method.
{"title":"Deep learning for statistical downscaling of sea states","authors":"Marceau Michel, Said Obakrim, N. Raillard, P. Ailliot, V. Monbet","doi":"10.5194/ascmo-8-83-2022","DOIUrl":"https://doi.org/10.5194/ascmo-8-83-2022","url":null,"abstract":"Abstract. Numerous marine applications require the prediction of\u0000medium- and long-term sea states. Climate models are mainly focused on the description of the atmosphere and global ocean variables, most often on a synoptic scale. Downscaling models exist to move from these atmospheric variables to the integral descriptors of the surface state; however, they are most often complex numerical models based on physics equations that entail significant computational costs. Statistical downscaling models\u0000provide an alternative to these models by constructing an empirical relationship between large-scale atmospheric variables and local variables, using historical data. Among the existing methods, deep learning methods are attracting increasing interest because of their ability to build hierarchical representations of features. To our knowledge, these models have not yet been tested in the case of sea state downscaling. In this study, a convolutional neural network (CNN)-type model for the prediction of significant wave height from wind fields in the Bay of Biscay is presented. The performance of this model is evaluated at several points and compared to other statistical downscaling methods and to WAVEWATCH III hindcast databases. The results obtained from\u0000these different stations show that the proposed method is suitable for predicting sea states. The observed performances are superior to those of the other statistical downscaling methods studied but remain inferior to those of the physical models. The low computational cost and the ease of implementation are, however, important assets for this method.\u0000","PeriodicalId":36792,"journal":{"name":"Advances in Statistical Climatology, Meteorology and Oceanography","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49153338","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}
Willem Stefaan Conradie, P. Wolski, B. Hewitson
Abstract. The 2014–2018 drought over South Africa's winter rainfall zone (WRZ) created a critical water crisis which highlighted the region's drought and climate change vulnerability. Consequently, it is imperative to better understand the climatic characteristics of the drought in order to inform regional adaptation to projected climate change. In this paper we investigate the spatio-temporal patterns of drought intensity and the recent rainfall trends, focusing on assessing the consistency of the prevailing conceptual model of drought drivers with observed patterns. For this we use the new spatial subdivision for the region encompassing the WRZ introduced in our companion paper (Conradie et al., 2022). Compared to previous droughts since 1979, the 2014–2018 drought in the WRZ core was characterised by a markedly lower frequency of very wet days (exceeding the climatological 99.5th percentile daily rainfall – including dry days) and of wet months (SPI1>0.5), a sub-seasonal attribute not previously reported. There was considerable variability in the spatial footprint of the drought. Short-term drought began in the south-western core WRZ in spring 2014. The peak intensity gradually spread north-eastward, although a spatially near-uniform peak is seen during mid-2017. The overall drought intensity for the 2015–2017 period transitions radially from most severe in the WRZ core to least severe in the surroundings. During 2014 and 2015, the drought was most severe at those stations receiving the largest proportion of their rainfall from westerly and north-westerly winds; by 2018, those stations receiving the most rain from the south and south-east were most severely impacted. This indicates an evolving set of dynamic drivers associated with distinct rain-bearing synoptic flows. No evidence is found to support the suggestion that the drought was more severe in the mountain catchments of Cape Town's major supply reservoirs than elsewhere in the core nor that rain day frequency trends since 1979 are more negative in this subdomain. Rainfall and rain day trend rates also exhibit some connections to the spatial seasonality structure of the WRZ, although this is weaker than for drought intensity. Caution should be applied in assessing South African rain day trends given their high sensitivity to observed data shortcomings. Our findings suggest an important role for zonally asymmetric dynamics in the region's drought evolution. This analysis demonstrates the utility of the spatial subdivisions proposed in the companion paper by highlighting spatial structure in drought intensity evolution linked to rainfall dynamics.
摘要2014-2018年南非冬季降雨区的干旱造成了严重的水危机，凸显了该地区的干旱和气候变化脆弱性。因此，必须更好地了解干旱的气候特征，以便为区域适应预计的气候变化提供信息。在本文中，我们研究了干旱强度的时空模式和最近的降雨趋势，重点评估了干旱驱动因素的主流概念模型与观测模式的一致性。为此，我们使用了我们的配套论文（Conradie et al.，2022）中引入的WRZ区域的新空间细分。与1979年以来的历次干旱相比，WRZ核心区2014-2018年干旱的特点是非常潮湿的日子（超过气候99.5%的日降雨量，包括干燥的日子）和潮湿的月份（SPI1>0.5）的频率明显较低，这是以前没有报道过的亚季节性属性。干旱的空间足迹变化很大。2014年春季，WRZ西南核心区开始出现短期干旱。峰值强度逐渐向东北方向扩散，尽管在2017年年中出现了空间上接近均匀的峰值。2015-2017年期间的总体干旱强度从WRZ核心的最严重向周围的最不严重呈放射状转变。2014年和2015年期间，干旱最为严重的是那些降雨量中来自西风和西北风的站点；到2018年，南部和东南部降雨量最大的气象站受到的影响最为严重。这表明了与不同的含雨天气流相关的一组不断发展的动态驱动因素。没有证据支持开普敦主要供水水库的山区集水区的干旱比核心区其他地方更严重的说法，也没有证据支持自1979年以来该子域的降雨日频率趋势更为负面的说法。降雨和降雨日趋势率也与WRZ的空间季节性结构有一些联系，尽管这比干旱强度弱。鉴于南非对观测数据缺陷的高度敏感性，在评估其降雨日趋势时应谨慎行事。我们的研究结果表明，地带不对称动力学在该地区干旱演变中发挥着重要作用。该分析通过强调与降雨动力学相关的干旱强度演变中的空间结构，证明了配套论文中提出的空间细分的效用。
{"title":"Spatial heterogeneity of 2015–2017 drought intensity in South Africa's winter rainfall zone","authors":"Willem Stefaan Conradie, P. Wolski, B. Hewitson","doi":"10.5194/ascmo-8-63-2022","DOIUrl":"https://doi.org/10.5194/ascmo-8-63-2022","url":null,"abstract":"Abstract. The 2014–2018 drought over South Africa's winter rainfall zone (WRZ) created a critical water crisis which highlighted the region's drought and climate change vulnerability. Consequently, it is imperative to better understand the climatic characteristics of the drought in order to inform regional adaptation to projected climate change. In this paper we investigate the spatio-temporal patterns of drought intensity and the recent rainfall trends, focusing on assessing the consistency of the prevailing conceptual model of drought drivers with observed patterns. For this we use the new spatial subdivision for the region encompassing the WRZ introduced in our companion paper (Conradie et al., 2022). Compared to previous droughts since 1979, the 2014–2018 drought in the WRZ core was characterised by a markedly lower frequency of very wet days (exceeding the climatological 99.5th percentile daily rainfall – including dry days) and of wet months (SPI1>0.5), a sub-seasonal attribute not previously reported. There was considerable variability in the spatial footprint of the drought. Short-term drought began in the south-western core WRZ in spring 2014. The peak intensity gradually spread north-eastward, although a spatially near-uniform peak is seen during mid-2017. The overall drought intensity for the 2015–2017 period transitions radially from most severe in the WRZ core to least severe in the surroundings. During 2014 and 2015, the drought was most severe at those stations receiving the largest proportion of their rainfall from westerly and north-westerly winds; by 2018, those stations receiving the most rain from the south and south-east were most severely impacted. This indicates an evolving set of dynamic drivers associated with distinct rain-bearing synoptic flows. No evidence is found to support the suggestion that the drought was more severe in the mountain catchments of Cape Town's major supply reservoirs than elsewhere in the core nor that rain day frequency trends since 1979 are more negative in this subdomain. Rainfall and rain day trend rates also exhibit some connections to the spatial seasonality structure of the WRZ, although this is weaker than for drought intensity. Caution should be applied in assessing South African rain day trends given their high sensitivity to observed data shortcomings. Our findings suggest an important role for zonally asymmetric dynamics in the region's drought evolution. This analysis demonstrates the utility of the spatial subdivisions proposed in the companion paper by highlighting spatial structure in drought intensity evolution linked to rainfall dynamics.\u0000","PeriodicalId":36792,"journal":{"name":"Advances in Statistical Climatology, Meteorology and Oceanography","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44034707","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. A renewed focus on southern Africa's winter rainfall zone (WRZ) following the Day Zero drought and water crisis has not shed much light on the spatial patterns of its rainfall variability and climatological seasonality. However, such understanding remains essential in studying past and potential future climate changes. Using a dense station network covering the region encompassing the WRZ, we study spatial heterogeneity in rainfall seasonality and temporal variability. These spatial patterns are compared to those of rainfall occurring under each ERA5 synoptic-scale wind direction sector. A well-defined “true” WRZ is identified with strong spatial coherence between temporal variability and seasonality not previously reported. The true WRZ is composed of a core and periphery beyond which lies a transition zone to the surrounding year-round rainfall zone (YRZ) and late summer rainfall zone. In places, this transition is highly complex, including where the YRZ extends much further westward along the southern mountains than has previously been reported. The core receives around 80 % of its rainfall with westerly or north-westerly flow compared to only 30 % in the south-western YRZ incursion, where below-average rainfall occurs on days with (usually pre-frontal) north-westerly winds. This spatial pattern corresponds closely to those of rainfall seasonality and temporal variability. Rainfall time series of the core and surroundings are very weakly correlated (R2<0.1), also in the winter half-year, implying that the YRZ is not simply the superposition of summer and winter rainfall zones. In addition to rain-bearing winds, latitude and annual rain day climatology appear to influence the spatial structure of rainfall variability but have little effect on seasonality. Mean annual rainfall in the true WRZ exhibits little association with the identified patterns of seasonality and rainfall variability despite the driest core WRZ stations being an order of magnitude drier than the wettest stations. This is consistent with the general pattern of near homogeneity within the true WRZ, in contrast to steep and complex spatial change outside it.
{"title":"Spatial heterogeneity in rain-bearing winds, seasonality and rainfall variability in southern Africa's winter rainfall zone","authors":"W. S. Conradie, P. Wolski, B. Hewitson","doi":"10.5194/ascmo-8-31-2022","DOIUrl":"https://doi.org/10.5194/ascmo-8-31-2022","url":null,"abstract":"Abstract. A renewed focus on southern Africa's winter rainfall zone (WRZ) following the Day Zero drought and water crisis has not shed much light on the spatial patterns of its rainfall variability and climatological seasonality. However, such understanding remains essential in studying past and potential future climate changes. Using a dense station network covering the region encompassing the WRZ, we study spatial heterogeneity in rainfall seasonality and temporal variability. These spatial patterns are compared to those of rainfall occurring under each ERA5 synoptic-scale wind direction sector. A well-defined “true” WRZ is identified with strong spatial coherence between temporal variability and seasonality not previously reported. The true WRZ is composed of a core and periphery beyond which lies a transition zone to the surrounding year-round rainfall zone (YRZ) and late summer rainfall zone. In places, this transition is highly complex, including where the YRZ extends much further westward along the southern mountains than has previously been reported. The core receives around 80 % of its rainfall with westerly or north-westerly flow compared to only 30 % in the south-western YRZ incursion, where below-average rainfall occurs on days with (usually pre-frontal) north-westerly winds. This spatial pattern corresponds closely to those of rainfall seasonality and temporal variability. Rainfall time series of the core and surroundings are very weakly correlated (R2<0.1), also in the winter half-year, implying that the YRZ is not simply the superposition of summer and winter rainfall zones. In addition to rain-bearing winds, latitude and annual rain day climatology appear to influence the spatial structure of rainfall variability but have little effect on seasonality. Mean annual rainfall in the true WRZ exhibits little association with the identified patterns of seasonality and rainfall variability despite the driest core WRZ stations being an order of magnitude drier than the wettest stations. This is consistent with the general pattern of near homogeneity within the true WRZ, in contrast to steep and complex spatial change outside it.\u0000","PeriodicalId":36792,"journal":{"name":"Advances in Statistical Climatology, Meteorology and Oceanography","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48733819","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 : 2022-01-14DOI: 10.1002/essoar.10510147.2
Said Obakrim, P. Ailliot, V. Monbet, N. 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, P. Ailliot, V. Monbet, N. Raillard","doi":"10.1002/essoar.10510147.2","DOIUrl":"https://doi.org/10.1002/essoar.10510147.2","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\u0000coast (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.\u0000","PeriodicalId":36792,"journal":{"name":"Advances in Statistical Climatology, Meteorology and Oceanography","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42955868","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}
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