Pub Date : 2019-04-01DOI: 10.5194/ascmo-5-115-2019
M. Lang, G. Mayr, R. Stauffer, A. Zeileis
Abstract. A new probabilistic post-processing method for wind vectors is presented in a distributional regression framework employing the bivariate Gaussian distribution. In contrast to previous studies, all parameters of the distribution are simultaneously modeled, namely the location and scale parameters for both wind components and also the correlation coefficient between them employing flexible regression splines. To capture a possible mismatch between the predicted and observed wind direction, ensemble forecasts of both wind components are included using flexible two-dimensional smooth functions. This encompasses a smooth rotation of the wind direction conditional on the season and the forecasted ensemble wind direction. The performance of the new method is tested for stations located in plains, in mountain foreland, and within an alpine valley, employing ECMWF ensemble forecasts as explanatory variables for all distribution parameters. The rotation-allowing model shows distinct improvements in terms of predictive skill for all sites compared to a baseline model that post-processes each wind component separately. Moreover, different correlation specifications are tested, and small improvements compared to the model setup with no estimated correlation could be found for stations located in alpine valleys.
{"title":"Bivariate Gaussian models for wind vectors in a distributional regression framework","authors":"M. Lang, G. Mayr, R. Stauffer, A. Zeileis","doi":"10.5194/ascmo-5-115-2019","DOIUrl":"https://doi.org/10.5194/ascmo-5-115-2019","url":null,"abstract":"Abstract. A new probabilistic post-processing method for wind vectors is presented in a distributional regression framework employing the bivariate Gaussian distribution. In contrast to previous studies, all parameters of the distribution are simultaneously modeled, namely the location and scale parameters for both wind components and also the correlation coefficient between them employing flexible regression splines. To capture a possible mismatch between the predicted and observed wind direction, ensemble forecasts of both wind components are included using flexible two-dimensional smooth functions. This encompasses a smooth rotation of the wind direction conditional on the season and the forecasted ensemble wind direction. The performance of the new method is tested for stations located in plains, in mountain foreland, and within an alpine valley, employing ECMWF ensemble forecasts as explanatory variables for all distribution parameters. The rotation-allowing model shows distinct improvements in terms of predictive skill for all sites compared to a baseline model that post-processes each wind component separately. Moreover, different correlation specifications are tested, and small improvements compared to the model setup with no estimated correlation could be found for stations located in alpine valleys.","PeriodicalId":36792,"journal":{"name":"Advances in Statistical Climatology, Meteorology and Oceanography","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48761250","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 describes results of an experiment that perturbed the initial conditions for the ocean's temperature field of the Community Earth System Model (CESM) with a well defined design. The resulting 30-member ensemble of CESM simulations, each of 10 years in length, is used to create an emulator (a nonlinear regression relating the initial conditions to various outcomes) from the simulators. Through the use of the emulator to expand the output distribution space, we estimate the spatial uncertainties at 10 years for surface air temperature, 25 m ocean temperature, precipitation, and rain. Outside the tropics, basin averages for the uncertainty in the ocean temperature field range between 0.48 ∘C (Indian Ocean) and 0.87 ∘C (North Pacific) (2 standard deviation). The tropical Pacific uncertainty is the largest due to different phasings of the ENSO signal. Over land areas, the regional temperature uncertainty varies from 1.03 ∘C (South America) to 10.82 ∘C (Europe) (2 standard deviation). Similarly, the regional average uncertainty in precipitation varies from 0.001 cm day−1 over Antarctica to 0.163 cm day−1 over Australia with a global average of 0.075 cm day−1. In general, both temperature and precipitation uncertainties are larger over land than over the ocean. A maximum covariance analysis is used to examine how ocean temperatures affect both surface air temperatures and precipitation over land. The analysis shows that the tropical Pacific influences the temperature over North America, but the North America surface temperature is also moderated by the state of the North Pacific outside the tropics. It also indicates which regions show a high degree of variance between the simulations in the ensemble and are, therefore, less predictable. The calculated uncertainties are also compared to an estimate of internal variability within CESM. Finally, the importance of feedback processes on the solution of the simulation over the 10 years of the experiment is quantified. These estimates of uncertainty do not take into consideration the anthropogenic effect on warming of the atmosphere and ocean.
摘要本文描述了一项实验的结果，该实验扰动了共同体地球系统模型(CESM)的海洋温度场初始条件，并具有良好的设计。由此产生的30个成员的CESM模拟集合，每个长度为10年，用于从模拟器创建模拟器(将初始条件与各种结果联系起来的非线性回归)。利用仿真器扩展输出分布空间，估算了10年的地表气温、25 m海温、降水和降雨的空间不确定性。在热带以外，海洋温度场的不确定性的盆地平均值在0.48°C(印度洋)和0.87°C(北太平洋)之间(2个标准差)。由于ENSO信号的不同相位，热带太平洋的不确定性最大。在陆地上，区域温度的不确定度从1.03°C(南美)到10.82°C(欧洲)不等(2个标准差)。同样，区域平均降水的不确定性从南极洲的0.001 cm day - 1到澳大利亚的0.163 cm day - 1，全球平均值为0.075 cm day - 1。一般来说，陆地上的温度和降水的不确定性都比海洋上的大。最大协方差分析用于检验海洋温度如何影响地表气温和陆地降水。分析表明，热带太平洋影响北美上空的温度，但北美地表温度也受到热带以外的北太平洋状态的缓和。它还指出，在整体模拟中，哪些区域表现出高度差异，因此难以预测。计算的不确定性也与CESM内部变率的估计进行了比较。最后，量化了反馈过程在10年实验中对模拟解的重要性。这些对不确定性的估计没有考虑到人为对大气和海洋变暖的影响。
{"title":"Influence of initial ocean conditions on temperature and precipitation in a coupled climate model's solution","authors":"R. Tokmakian, P. Challenor","doi":"10.5194/ASCMO-5-17-2019","DOIUrl":"https://doi.org/10.5194/ASCMO-5-17-2019","url":null,"abstract":"Abstract. This paper describes results of an experiment that perturbed the\u0000initial conditions for the ocean's temperature field of the Community Earth\u0000System Model (CESM) with a well defined design. The resulting 30-member\u0000ensemble of CESM simulations, each of 10 years in length, is used to create\u0000an emulator (a nonlinear regression relating the initial conditions to\u0000various outcomes) from the simulators. Through the use of the emulator to\u0000expand the output distribution space, we estimate the spatial uncertainties\u0000at 10 years for surface air temperature, 25 m ocean temperature,\u0000precipitation, and rain. Outside the tropics, basin averages for the\u0000uncertainty in the ocean temperature field range between 0.48 ∘C\u0000(Indian Ocean) and 0.87 ∘C (North Pacific) (2 standard\u0000deviation). The tropical Pacific uncertainty is the largest due to different\u0000phasings of the ENSO signal. Over land areas, the regional temperature\u0000uncertainty varies from 1.03 ∘C (South America) to 10.82 ∘C\u0000(Europe) (2 standard deviation). Similarly, the regional average\u0000uncertainty in precipitation varies from\u00000.001 cm day−1 over Antarctica to\u00000.163 cm day−1 over Australia with a global average of\u00000.075 cm day−1. In general, both temperature and precipitation\u0000uncertainties are larger over land than over the ocean. A maximum covariance\u0000analysis is used to examine how ocean temperatures affect both surface air\u0000temperatures and precipitation over land. The analysis shows that the\u0000tropical Pacific influences the temperature over North America, but the North\u0000America surface temperature is also moderated by the state of the North\u0000Pacific outside the tropics. It also indicates which regions show a high\u0000degree of variance between the simulations in the ensemble and are,\u0000therefore, less predictable. The calculated uncertainties are also compared\u0000to an estimate of internal variability within CESM. Finally, the importance\u0000of feedback processes on the solution of the simulation over the 10 years of\u0000the experiment is quantified. These estimates of uncertainty do not take into\u0000consideration the anthropogenic effect on warming of the atmosphere and ocean.\u0000","PeriodicalId":36792,"journal":{"name":"Advances in Statistical Climatology, Meteorology and Oceanography","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47423655","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 method to predict lightning by postprocessing numerical weather prediction (NWP) output is developed for the region of the European Eastern Alps. Cloud-to-ground (CG) flashes – detected by the ground-based Austrian Lightning Detection & Information System (ALDIS) network – are counted on the 18×18 km2 grid of the 51-member NWP ensemble of the European Centre for Medium-Range Weather Forecasts (ECMWF). These counts serve as the target quantity in count data regression models for the occurrence of lightning events and flash counts of CG. The probability of lightning occurrence is modelled by a Bernoulli distribution. The flash counts are modelled with a hurdle approach where the Bernoulli distribution is combined with a zero-truncated negative binomial. In the statistical models the parameters of the distributions are described by additive predictors, which are assembled using potentially nonlinear functions of NWP covariates. Measures of location and spread of 100 direct and derived NWP covariates provide a pool of candidates for the nonlinear terms. A combination of stability selection and gradient boosting identifies the nine (three) most influential terms for the parameters of the Bernoulli (zero-truncated negative binomial) distribution, most of which turn out to be associated with either convective available potential energy (CAPE) or convective precipitation. Markov chain Monte Carlo (MCMC) sampling estimates the final model to provide credible inference of effects, scores, and predictions. The selection of terms and MCMC sampling are applied for data of the year 2016, and out-of-sample performance is evaluated for 2017. The occurrence model outperforms a reference climatology – based on 7 years of data – up to a forecast horizon of 5 days. The flash count model is calibrated and also outperforms climatology for exceedance probabilities, quantiles, and full predictive distributions.
{"title":"NWP-based lightning prediction using flexible count data regression","authors":"T. Simon, G. Mayr, Nikolaus Umlauf, A. Zeileis","doi":"10.5194/ASCMO-5-1-2019","DOIUrl":"https://doi.org/10.5194/ASCMO-5-1-2019","url":null,"abstract":"Abstract. A method to predict lightning by postprocessing numerical weather prediction\u0000(NWP) output is developed for the region of the European Eastern Alps.\u0000Cloud-to-ground (CG) flashes – detected by the ground-based Austrian\u0000Lightning Detection & Information System (ALDIS) network – are counted on\u0000the 18×18 km2 grid of the 51-member NWP ensemble of the European\u0000Centre for Medium-Range Weather Forecasts (ECMWF). These counts serve as the\u0000target quantity in count data regression models for the occurrence of\u0000lightning events and flash counts of CG. The probability of lightning\u0000occurrence is modelled by a Bernoulli distribution. The flash counts are\u0000modelled with a hurdle approach where the Bernoulli distribution is combined\u0000with a zero-truncated negative binomial. In the statistical models the\u0000parameters of the distributions are described by additive predictors, which\u0000are assembled using potentially nonlinear functions of NWP covariates.\u0000Measures of location and spread of 100 direct and derived NWP covariates\u0000provide a pool of candidates for the nonlinear terms. A combination of\u0000stability selection and gradient boosting identifies the nine (three) most\u0000influential terms for the parameters of the Bernoulli (zero-truncated\u0000negative binomial) distribution, most of which turn out to be associated with\u0000either convective available potential energy (CAPE) or convective\u0000precipitation. Markov chain Monte Carlo (MCMC) sampling estimates the final\u0000model to provide credible inference of effects, scores, and\u0000predictions. The selection of terms and MCMC sampling are applied for data of\u0000the year 2016, and out-of-sample performance is evaluated for 2017. The\u0000occurrence model outperforms a reference climatology – based on 7 years of\u0000data – up to a forecast horizon of 5 days. The flash count model is\u0000calibrated and also outperforms climatology for exceedance probabilities,\u0000quantiles, and full predictive distributions.\u0000","PeriodicalId":36792,"journal":{"name":"Advances in Statistical Climatology, Meteorology and Oceanography","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49657489","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}
O. Haug, T. Thorarinsdottir, S. Sørbye, C. Franzke
Abstract. Classical assessments of trends in gridded temperature data perform independent evaluations across the grid, thus, ignoring spatial correlations in the trend estimates. In particular, this affects assessments of trend significance as evaluation of the collective significance of individual tests is commonly neglected. In this article we build a space–time hierarchical Bayesian model for temperature anomalies where the trend coefficient is modelled by a latent Gaussian random field. This enables us to calculate simultaneous credible regions for joint significance assessments. In a case study, we assess summer season trends in 65 years of gridded temperature data over Europe. We find that while spatial smoothing generally results in larger regions where the null hypothesis of no trend is rejected, this is not the case for all subregions.
{"title":"Spatial trend analysis of gridded temperature data at varying spatial scales","authors":"O. Haug, T. Thorarinsdottir, S. Sørbye, C. Franzke","doi":"10.5194/ascmo-6-1-2020","DOIUrl":"https://doi.org/10.5194/ascmo-6-1-2020","url":null,"abstract":"Abstract. Classical assessments of trends in gridded temperature data perform\u0000independent evaluations across the grid, thus, ignoring spatial correlations\u0000in the trend estimates. In particular, this affects assessments of trend\u0000significance as evaluation of the collective significance of individual tests\u0000is commonly neglected. In this article we build a space–time hierarchical\u0000Bayesian model for temperature anomalies where the trend coefficient is\u0000modelled by a latent Gaussian random field. This enables us to calculate\u0000simultaneous credible regions for joint significance assessments. In a case\u0000study, we assess summer season trends in 65 years of gridded temperature data\u0000over Europe. We find that while spatial smoothing generally results in larger\u0000regions where the null hypothesis of no trend is rejected, this is not the\u0000case for all subregions.\u0000","PeriodicalId":36792,"journal":{"name":"Advances in Statistical Climatology, Meteorology and Oceanography","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45903458","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}
R. Stauffer, G. Mayr, Jakob W. Messner, A. Zeileis
Abstract. Accurate and high-resolution snowfall and fresh snow forecasts are important for a range of economic sectors as well as for the safety of people and infrastructure, especially in mountainous regions. In this article a new hybrid statistical postprocessing method is proposed, which combines standardized anomaly model output statistics (SAMOS) with ensemble copula coupling (ECC) and a novel re-weighting scheme to produce spatially and temporally high-resolution probabilistic snow forecasts. Ensemble forecasts and hindcasts of the European Centre for Medium-Range Weather Forecasts (ECMWF) serve as input for the statistical postprocessing method, while measurements from two different networks provide the required observations.This new approach is applied to a region with very complex topography in the eastern European Alps. The results demonstrate that the new hybrid method allows one not only to provide reliable high-resolution forecasts, but also to combine different data sources with different temporal resolutions to create hourly probabilistic and physically consistent predictions.
{"title":"Hourly probabilistic snow forecasts over complex terrain: a hybrid ensemble postprocessing approach","authors":"R. Stauffer, G. Mayr, Jakob W. Messner, A. Zeileis","doi":"10.5194/ASCMO-4-65-2018","DOIUrl":"https://doi.org/10.5194/ASCMO-4-65-2018","url":null,"abstract":"Abstract. Accurate and high-resolution snowfall and fresh snow forecasts are important for a range of economic sectors as well as for the safety of people and infrastructure, especially in mountainous regions. In this article a new hybrid statistical postprocessing method is proposed, which combines standardized anomaly model output statistics (SAMOS) with ensemble copula coupling (ECC) and a novel re-weighting scheme to produce spatially and temporally high-resolution probabilistic snow forecasts. Ensemble forecasts and hindcasts of the European Centre for Medium-Range Weather Forecasts (ECMWF) serve as input for the statistical postprocessing method, while measurements from two different networks provide the required observations.This new approach is applied to a region with very complex topography in the eastern European Alps. The results demonstrate that the new hybrid method allows one not only to provide reliable high-resolution forecasts, but also to combine different data sources with different temporal resolutions to create hourly probabilistic and physically consistent predictions.\u0000","PeriodicalId":36792,"journal":{"name":"Advances in Statistical Climatology, Meteorology and Oceanography","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44855294","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. Projections of coastal storm surge hazard are a basic requirement for effective management of coastal risks. A common approach for estimating hazards posed by extreme sea levels is to use a statistical model, which may use a time series of a climate variable as a covariate to modulate the statistical model and account for potentially nonstationary storm surge behavior (e.g., North Atlantic Oscillation index). Previous works using nonstationary statistical approaches to assess coastal flood hazard have demonstrated the importance of accounting for many key modeling uncertainties. However, many assessments have typically relied on a single climate covariate, which may leave out important processes and lead to potential biases in the projected flood hazards. Here, I employ a recently developed approach to integrate stationary and nonstationary statistical models, and characterize the effects of choice of covariate time series on projected flood hazard. Furthermore, I expand upon this approach by developing a nonstationary storm surge statistical model that makes use of multiple covariate time series, namely, global mean temperature, sea level, the North Atlantic Oscillation index and time. Using Norfolk, Virginia, as a case study, I show that a storm surge model that accounts for additional processes raises the projected 100-year storm surge return level by up to 23 cm relative to a stationary model or one that employs a single covariate time series. I find that the total model posterior probability associated with each candidate covariate, as well as a stationary model, is about 20 %. These results shed light on how including a wider range of physical process information and considering nonstationary behavior can better enable modeling efforts to inform coastal risk management.
{"title":"An integration and assessment of multiple covariates of nonstationary storm surge statistical behavior by Bayesian model averaging","authors":"T. Wong","doi":"10.5194/ASCMO-4-53-2018","DOIUrl":"https://doi.org/10.5194/ASCMO-4-53-2018","url":null,"abstract":"Abstract. Projections of coastal storm surge hazard are a basic requirement for\u0000effective management of coastal risks. A common approach for estimating\u0000hazards posed by extreme sea levels is to use a statistical model, which may\u0000use a time series of a climate variable\u0000as a covariate to modulate the statistical model and account for potentially\u0000nonstationary storm surge behavior (e.g., North Atlantic Oscillation index).\u0000Previous works using nonstationary statistical approaches to assess coastal\u0000flood hazard have demonstrated the importance of accounting for many key\u0000modeling uncertainties. However, many assessments have typically relied on a\u0000single climate covariate, which may leave out important processes and lead to\u0000potential biases in the projected flood hazards. Here, I employ a recently\u0000developed approach to integrate stationary and nonstationary statistical\u0000models, and characterize the effects of choice of covariate time series on\u0000projected flood hazard. Furthermore, I expand upon this approach by\u0000developing a nonstationary storm surge statistical model that makes use of\u0000multiple covariate time series, namely, global mean temperature, sea level,\u0000the North Atlantic Oscillation index and time. Using Norfolk, Virginia, as a\u0000case study, I show that a storm surge model that accounts for additional\u0000processes raises the projected 100-year storm surge return level by up to\u000023 cm relative to a stationary model or one that employs a single covariate\u0000time series. I find that the total model posterior probability associated\u0000with each candidate covariate, as well as a stationary model, is about\u000020 %. These results shed light on how including a wider range of physical\u0000process information and considering nonstationary behavior can better enable\u0000modeling efforts to inform coastal risk management.\u0000","PeriodicalId":36792,"journal":{"name":"Advances in Statistical Climatology, Meteorology and Oceanography","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46898207","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}
R. Benestad, B. V. van Oort, F. Justino, F. Stordal, Kajsa M. Parding, A. Mezghani, H. Erlandsen, J. Sillmann, Milton E. Pereira-Flores
Abstract. A methodology for estimating and downscaling the probability associated with the duration of heatwaves is presented and applied as a case study for Indian wheat crops. These probability estimates make use of empirical-statistical downscaling and statistical modelling of probability of occurrence and streak length statistics, and we present projections based on large multi-model ensembles of global climate models from the Coupled Model Intercomparison Project Phase 5 and three different emissions scenarios: Representative Concentration Pathways (RCPs) 2.6, 4.5, and 8.5. Our objective was to estimate the probabilities for heatwaves with more than 5 consecutive days with daily maximum temperature above 35 ∘C, which represent a condition that limits wheat yields. Such heatwaves are already quite frequent under current climate conditions, and downscaled estimates of the probability of occurrence in 2010 is in the range of 20 %–84 % depending on the location. For the year 2100, the high-emission scenario RCP8.5 suggests more frequent occurrences, with a probability in the range of 36 %–88 %. Our results also point to increased probabilities for a hot day to turn into a heatwave lasting more than 5 days, from roughly 8 %–20 % at present to 9 %–23 % in 2100 assuming future emissions according to the RCP8.5 scenario; however, these estimates were to a greater extent subject to systematic biases. We also demonstrate a downscaling methodology based on principal component analysis that can produce reasonable results even when the data are sparse with variable quality.
{"title":"Downscaling probability of long heatwaves based on seasonal mean daily maximum temperatures","authors":"R. Benestad, B. V. van Oort, F. Justino, F. Stordal, Kajsa M. Parding, A. Mezghani, H. Erlandsen, J. Sillmann, Milton E. Pereira-Flores","doi":"10.5194/ASCMO-4-37-2018","DOIUrl":"https://doi.org/10.5194/ASCMO-4-37-2018","url":null,"abstract":"Abstract. A methodology for estimating and downscaling the probability associated with the duration of heatwaves is presented and applied as a case study for Indian wheat crops. These probability estimates make use of empirical-statistical downscaling and statistical modelling of probability of occurrence and streak length statistics, and we present projections based on large multi-model ensembles of global climate models from the Coupled Model Intercomparison Project Phase 5 and three different emissions scenarios: Representative Concentration Pathways (RCPs) 2.6, 4.5, and 8.5. Our objective was to estimate the probabilities for heatwaves with more than 5 consecutive days with daily maximum temperature above 35 ∘C, which represent a condition that limits wheat yields. Such heatwaves are already quite frequent under current climate conditions, and downscaled estimates of the probability of occurrence in 2010 is in the range of 20 %–84 % depending on the location. For the year 2100, the high-emission scenario RCP8.5 suggests more frequent occurrences, with a probability in the range of 36 %–88 %. Our results also point to increased probabilities for a hot day to turn into a heatwave lasting more than 5 days, from roughly 8 %–20 % at present to 9 %–23 % in 2100 assuming future emissions according to the RCP8.5 scenario; however, these estimates were to a greater extent subject to systematic biases. We also demonstrate a downscaling methodology based on principal component analysis that can produce reasonable results even when the data are sparse with variable quality.","PeriodicalId":36792,"journal":{"name":"Advances in Statistical Climatology, Meteorology and Oceanography","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47292124","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. Historical time series of surface temperature and ocean heat content changes are commonly used metrics to diagnose climate change and estimate properties of the climate system. We show that recent trends, namely the slowing of surface temperature rise at the beginning of the 21st century and the acceleration of heat stored in the deep ocean, have a substantial impact on these estimates. Using the Massachusetts Institute of Technology Earth System Model (MESM), we vary three model parameters that influence the behavior of the climate system: effective climate sensitivity (ECS), the effective ocean diffusivity of heat anomalies by all mixing processes (Kv), and the net anthropogenic aerosol forcing scaling factor. Each model run is compared to observed changes in decadal mean surface temperature anomalies and the trend in global mean ocean heat content change to derive a joint probability distribution function for the model parameters. Marginal distributions for individual parameters are found by integrating over the other two parameters. To investigate how the inclusion of recent temperature changes affects our estimates, we systematically include additional data by choosing periods that end in 1990, 2000, and 2010. We find that estimates of ECS increase in response to rising global surface temperatures when data beyond 1990 are included, but due to the slowdown of surface temperature rise in the early 21st century, estimates when using data up to 2000 are greater than when data up to 2010 are used. We also show that estimates of Kv increase in response to the acceleration of heat stored in the ocean as data beyond 1990 are included. Further, we highlight how including spatial patterns of surface temperature change modifies the estimates. We show that including latitudinal structure in the climate change signal impacts properties with spatial dependence, namely the aerosol forcing pattern, more than properties defined for the global mean, climate sensitivity, and ocean diffusivity.
{"title":"Estimates of climate system properties incorporating recent climate change","authors":"A. Libardoni, C. Forest, A. Sokolov, E. Monier","doi":"10.5194/ASCMO-4-19-2018","DOIUrl":"https://doi.org/10.5194/ASCMO-4-19-2018","url":null,"abstract":"Abstract. Historical time series of surface temperature and ocean heat content changes\u0000are commonly used metrics to diagnose climate change and estimate properties\u0000of the climate system. We show that recent trends, namely the slowing of\u0000surface temperature rise at the beginning of the 21st century and the\u0000acceleration of heat stored in the deep ocean, have a substantial impact on\u0000these estimates. Using the Massachusetts Institute of Technology Earth System\u0000Model (MESM), we vary three model parameters that influence the behavior of\u0000the climate system: effective climate sensitivity (ECS), the effective ocean\u0000diffusivity of heat anomalies by all mixing processes (Kv), and the net\u0000anthropogenic aerosol forcing scaling factor. Each model run is compared to\u0000observed changes in decadal mean surface temperature anomalies and the trend\u0000in global mean ocean heat content change to derive a joint probability\u0000distribution function for the model parameters. Marginal distributions for\u0000individual parameters are found by integrating over the other two parameters.\u0000To investigate how the inclusion of recent temperature changes affects our\u0000estimates, we systematically include additional data by choosing periods that\u0000end in 1990, 2000, and 2010. We find that estimates of ECS increase in\u0000response to rising global surface temperatures when data beyond 1990 are\u0000included, but due to the slowdown of surface temperature rise in the early\u000021st century, estimates when using data up to 2000 are greater than when data\u0000up to 2010 are used. We also show that estimates of Kv increase in\u0000response to the acceleration of heat stored in the ocean as data beyond 1990\u0000are included. Further, we highlight how including spatial patterns of surface\u0000temperature change modifies the estimates. We show that including latitudinal\u0000structure in the climate change signal impacts properties with spatial\u0000dependence, namely the aerosol forcing pattern, more than properties defined\u0000for the global mean, climate sensitivity, and ocean diffusivity.\u0000","PeriodicalId":36792,"journal":{"name":"Advances in Statistical Climatology, Meteorology and Oceanography","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49665457","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. The field of statistics has become one of the mathematical foundations in forecast evaluation studies, especially with regard to computing scoring rules. The classical paradigm of scoring rules is to discriminate between two different forecasts by comparing them with observations. The probability distribution of the observed record is assumed to be perfect as a verification benchmark. In practice, however, observations are almost always tainted by errors and uncertainties. These may be due to homogenization problems, instrumental deficiencies, the need for indirect reconstructions from other sources (e.g., radar data), model errors in gridded products like reanalysis, or any other data-recording issues. If the yardstick used to compare forecasts is imprecise, one can wonder whether such types of errors may or may not have a strong influence on decisions based on classical scoring rules. We propose a new scoring rule scheme in the context of models that incorporate errors of the verification data. We rely on existing scoring rules and incorporate uncertainty and error of the verification data through a hidden variable and the conditional expectation of scores when they are viewed as a random variable. The proposed scoring framework is applied to standard setups, mainly an additive Gaussian noise model and a multiplicative Gamma noise model. These classical examples provide known and tractable conditional distributions and, consequently, allow us to interpret explicit expressions of our score. By considering scores to be random variables, one can access the entire range of their distribution. In particular, we illustrate that the commonly used mean score can be a misleading representative of the distribution when the latter is highly skewed or has heavy tails. In a simulation study, through the power of a statistical test, we demonstrate the ability of the newly proposed score to better discriminate between forecasts when verification data are subject to uncertainty compared with the scores used in practice. We apply the benefit of accounting for the uncertainty of the verification data in the scoring procedure on a dataset of surface wind speed from measurements and numerical model outputs. Finally, we open some discussions on the use of this proposed scoring framework for non-explicit conditional distributions.
{"title":"Forecast score distributions with imperfect observations","authors":"J. Bessac, P. Naveau","doi":"10.5194/ascmo-7-53-2021","DOIUrl":"https://doi.org/10.5194/ascmo-7-53-2021","url":null,"abstract":"Abstract. The field of statistics has become one of the mathematical foundations in forecast evaluation studies, especially with regard to computing scoring rules. The classical paradigm of scoring rules is to discriminate between two different forecasts by comparing them with observations.\u0000The probability distribution of the observed record is assumed to be perfect as a verification benchmark.\u0000In practice, however, observations are almost always tainted by errors and uncertainties.\u0000These may be due to homogenization problems, instrumental deficiencies, the need for indirect reconstructions from other sources (e.g., radar data), model errors in gridded products like reanalysis, or any other data-recording issues.\u0000If the yardstick used to compare forecasts is imprecise, one can wonder whether such types of errors may or may not have a strong influence on decisions based on classical scoring rules.\u0000We propose a new scoring rule scheme in the context of models that incorporate errors of the verification data.\u0000We rely on existing scoring rules and incorporate uncertainty and error of the verification data through a hidden variable and the conditional expectation of scores when they are viewed as a random variable.\u0000The proposed scoring framework is applied to standard setups, mainly an additive Gaussian noise model and a multiplicative Gamma noise model.\u0000These classical examples provide known and tractable conditional distributions and, consequently, allow us to interpret explicit expressions of our score.\u0000By considering scores to be random variables, one can access the entire range of their distribution. In particular, we illustrate that the commonly used mean score can be a misleading representative of the distribution when the latter is highly skewed or has heavy tails. In a simulation study, through the power of a statistical test, we demonstrate the ability of the newly proposed score to better discriminate between forecasts when verification data are subject to uncertainty compared with the scores used in practice.\u0000We apply the benefit of accounting for the uncertainty of the verification data in the scoring procedure on a dataset of surface wind speed from measurements and numerical model outputs. Finally, we open some discussions on the use of this proposed scoring framework for non-explicit conditional distributions.\u0000","PeriodicalId":36792,"journal":{"name":"Advances in Statistical Climatology, Meteorology and Oceanography","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47828299","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 : 2017-01-01Epub Date: 2017-07-14DOI: 10.5194/ascmo-3-67-2017
Joshua P French, Seth McGinnis, Armin Schwartzman
We assess similarities and differences between model effects for the North American Regional Climate Change Assessment Program (NARCCAP) climate models using varying classes of linear regression models. Specifically, we consider how the average temperature effect differs for the various global and regional climate model combinations, including assessment of possible interaction between the effects of global and regional climate models. We use both pointwise and simultaneous inference procedures to identify regions where global and regional climate model effects differ. We also show conclusively that results from pointwise inference are misleading, and that accounting for multiple comparisons is important for making proper inference.
{"title":"Assessing NARCCAP climate model effects using spatial confidence regions.","authors":"Joshua P French, Seth McGinnis, Armin Schwartzman","doi":"10.5194/ascmo-3-67-2017","DOIUrl":"10.5194/ascmo-3-67-2017","url":null,"abstract":"<p><p>We assess similarities and differences between model effects for the North American Regional Climate Change Assessment Program (NARCCAP) climate models using varying classes of linear regression models. Specifically, we consider how the average temperature effect differs for the various global and regional climate model combinations, including assessment of possible interaction between the effects of global and regional climate models. We use both pointwise and simultaneous inference procedures to identify regions where global and regional climate model effects differ. We also show conclusively that results from pointwise inference are misleading, and that accounting for multiple comparisons is important for making proper inference.</p>","PeriodicalId":36792,"journal":{"name":"Advances in Statistical Climatology, Meteorology and Oceanography","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5604436/pdf/nihms898559.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35428166","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","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：