Advances in Statistical Climatology, Meteorology and Oceanography最新文献

{"title":"Comparing climate time series – Part 2: A multivariate test","authors":"T. DelSole, M. Tippett","doi":"10.5194/ascmo-7-73-2021","DOIUrl":"https://doi.org/10.5194/ascmo-7-73-2021","url":null,"abstract":"Abstract. This paper proposes a criterion for deciding whether climate model simulations are consistent with observations. Importantly, the criterion accounts for correlations in both space and time. The basic idea is to fit each multivariate time series to a vector autoregressive (VAR) model and then test the hypothesis that the parameters of the two models are equal. In the special case of a first-order VAR model, the model is a linear inverse model (LIM) and the test constitutes a difference-in-LIM test. This test is applied to decide whether climate models generate realistic internal variability of annual mean North Atlantic sea surface temperature. Given the disputed origin of multidecadal variability in the North Atlantic (e.g., some studies argue it is forced by anthropogenic aerosols, while others argue it arises naturally from internal variability), the time series are filtered in two different ways appropriate to the two driving mechanisms. In either case, only a few climate models out of three dozen are found to generate internal variability consistent with observations. In fact, it is shown that climate models differ not only from observations, but also from each other, unless they come from the same modeling center. In addition to these discrepancies in internal variability, other studies show that models exhibit significant discrepancies with observations in terms of the response to external forcing. Taken together, these discrepancies imply that, at the present time, climate models do not provide a satisfactory explanation of observed variability in the North Atlantic.\u0000","PeriodicalId":36792,"journal":{"name":"Advances in Statistical Climatology, Meteorology and Oceanography","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43976153","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}

{"title":"A protocol for probabilistic extreme event attribution analyses","authors":"S. Philip, S. Kew, G. J. van Oldenborgh, F. Otto, R. Vautard, Karin van der Wiel, A. King, F. Lott, J. Arrighi, Roop K. Singh, M. V. van Aalst","doi":"10.5194/ascmo-6-177-2020","DOIUrl":"https://doi.org/10.5194/ascmo-6-177-2020","url":null,"abstract":"Abstract. Over the last few years, methods have been developed to answer questions on the effect of global warming on recent extreme events. Many “event attribution” studies have now been performed, a sizeable fraction even within a few weeks of the event, to increase the usefulness of the results. In doing these analyses, it has become apparent that the attribution itself is only one step of an extended process that leads from the observation of an extreme event to a successfully communicated attribution statement. In this paper we detail the protocol that was developed by the World Weather Attribution group over the course of the last 4 years and about two dozen rapid and slow attribution studies covering warm, cold, wet, dry, and stormy extremes. It starts from the choice of which events to analyse and proceeds with the event definition, observational analysis, model evaluation, multi-model multi-method attribution, hazard synthesis, vulnerability and exposure analysis and ends with the communication procedures. This article documents this protocol. It is hoped that our protocol will be useful in designing future event attribution studies and as a starting point of a protocol for an operational attribution service.\u0000","PeriodicalId":36792,"journal":{"name":"Advances in Statistical Climatology, Meteorology and Oceanography","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49556579","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}

{"title":"Robust regional clustering and modeling of nonstationary summer temperature extremes across Germany","authors":"Meagan Carney, H. Kantz","doi":"10.5194/ascmo-6-61-2020","DOIUrl":"https://doi.org/10.5194/ascmo-6-61-2020","url":null,"abstract":"Abstract. We use sophisticated machine-learning techniques on a network of summer temperature and precipitation time series taken from stations throughout Germany for the years from 1960 to 2018. In particular, we consider (normalized) maximized mutual information as the measure of similarity and expand on recent clustering methods for climate modeling by applying a weighted kernel-based k-means algorithm. We find robust regional clusters that are both time invariant and shared by networks defined separately by precipitation and temperature time series. Finally, we use the resulting clusters to create a nonstationary model of regional summer temperature extremes throughout Germany and are thereby able to quantify the increase in the probability of observing high extreme summer temperature values (>35 ∘C) compared with the last 30 years.\u0000","PeriodicalId":36792,"journal":{"name":"Advances in Statistical Climatology, Meteorology and Oceanography","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41425717","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}

{"title":"Nonlinear time series models for the North Atlantic Oscillation","authors":"Thomas Önskog, C. Franzke, A. Hannachi","doi":"10.5194/egusphere-egu2020-13481","DOIUrl":"https://doi.org/10.5194/egusphere-egu2020-13481","url":null,"abstract":"Abstract. The North Atlantic Oscillation (NAO) is the dominant mode of climate variability over the North Atlantic basin and has a significant impact on seasonal climate and surface weather\u0000conditions. This is the result of complex and nonlinear interactions\u0000between many spatio-temporal scales. Here, the authors study a number\u0000of linear and nonlinear models for a station-based time series of the\u0000daily winter NAO index. It is found that nonlinear autoregressive\u0000models, including both short and long lags, perform excellently in\u0000reproducing the characteristic statistical properties of the NAO, such\u0000as skewness and fat tails of the distribution, and the different timescales of the two phases. As a spin-off of the modelling procedure, we\u0000can deduce that the interannual dependence of the NAO mostly\u0000affects the positive phase, and that timescales of 1 to 3 weeks\u0000are more dominant for the negative phase. Furthermore, the statistical\u0000properties of the model make it useful for the generation of realistic climate noise.\u0000","PeriodicalId":36792,"journal":{"name":"Advances in Statistical Climatology, Meteorology and Oceanography","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45216383","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}

{"title":"Approaches to attribution of extreme temperature and precipitation events using multi-model and single-member ensembles of general circulation models","authors":"S. Lewis, S. Perkins‐Kirkpatrick, A. King","doi":"10.5194/ASCMO-5-133-2019","DOIUrl":"https://doi.org/10.5194/ASCMO-5-133-2019","url":null,"abstract":"Abstract. Extreme temperature and precipitation events occurring in Australia in\u0000recent decades have caused significant socio-economic and environmental\u0000impacts, and thus determining the factors contributing to these extremes is\u0000an active area of research. Many recently occurring record-breaking\u0000temperature and rainfall events have now been examined from an extreme event\u0000attribution (EEA) perspective. This paper describes a set of studies that have\u0000examined the causes of extreme climate events using various general\u0000circulation models (GCMs), presenting a comprehensive methodology for\u0000GCM-based attribution of\u0000extremes of temperature and precipitation observed on large spatial and temporal scales in Australia. First, we review how\u0000Coupled Model Intercomparison Project Phase 5 (CMIP5) models have been used\u0000to examine the changing odds of observed extremes. Second, we review how a\u0000large perturbed initial condition ensemble of a single climate model (CESM)\u0000has been used to quantitatively examine the changing characteristics of\u0000Australian heat extremes. For each approach, methodological details and\u0000applications are provided and limitations highlighted. The conclusions of\u0000this methodological review discuss the limitations and uncertainties\u0000associated with this approach and identify key unexplored applications of\u0000GCM-based attribution of extremes. Ideally, this information will be useful\u0000for the application of the described extreme event attribution\u0000approaches elsewhere.\u0000","PeriodicalId":36792,"journal":{"name":"Advances in Statistical Climatology, Meteorology and Oceanography","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44969024","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}

{"title":"Low-visibility forecasts for different flight planning horizons using tree-based boosting models","authors":"S. Dietz, P. Kneringer, G. Mayr, A. Zeileis","doi":"10.5194/ASCMO-5-101-2019","DOIUrl":"https://doi.org/10.5194/ASCMO-5-101-2019","url":null,"abstract":"Abstract. Low-visibility conditions enforce special procedures that reduce the\u0000operational flight capacity at airports. Accurate and probabilistic forecasts\u0000of these capacity-reducing low-visibility procedure (lvp) states help the\u0000air traffic management in optimizing flight planning and regulation. In this\u0000paper, we investigate nowcasts, medium-range forecasts, and the\u0000predictability limit of the lvp states at Vienna International Airport. The forecasts are\u0000generated with boosting trees, which outperform persistence, climatology,\u0000direct output of numerical weather prediction (NWP) models, and ordered\u0000logistic regression. The boosting trees consist of an ensemble of decision\u0000trees grown iteratively on information from previous trees. Their input is\u0000observations at Vienna International Airport as well as output of a high resolution and an\u0000ensemble NWP model. Observations have the highest impact for nowcasts up to a\u0000lead time of +2 h. Afterwards, a mix of observations and NWP forecast\u0000variables generates the most accurate predictions. With lead times longer\u0000than +7 h, NWP output dominates until the predictability limit is reached\u0000at +12 d. For lead times longer than +2 d, output from an ensemble of\u0000NWP models improves the forecast more than using a deterministic but finer\u0000resolved NWP model. The most important predictors for lead times up to\u0000+18 h are observations of lvp and dew point depression as well as NWP\u0000dew point depression. At longer lead times, dew point depression and\u0000evaporation from the NWP models are most important.\u0000","PeriodicalId":36792,"journal":{"name":"Advances in Statistical Climatology, Meteorology and Oceanography","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44048596","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}

{"title":"Skewed logistic distribution for statistical temperature post-processing in mountainous areas","authors":"Manuel Gebetsberger, R. Stauffer, G. Mayr, A. Zeileis","doi":"10.5194/ASCMO-5-87-2019","DOIUrl":"https://doi.org/10.5194/ASCMO-5-87-2019","url":null,"abstract":"Abstract. Nonhomogeneous post-processing is often used to improve the predictive\u0000performance of probabilistic ensemble forecasts. A common quantity used to develop,\u0000test, and demonstrate new methods is the near-surface air temperature, which is\u0000frequently assumed to follow a Gaussian response distribution. However,\u0000Gaussian regression models with only a few covariates are often not able to\u0000account for site-specific local features leading to uncalibrated forecasts and skewed residuals. This residual skewness remains even if many covariates are incorporated.\u0000Therefore, a simple refinement of the classical nonhomogeneous Gaussian\u0000regression model is proposed to overcome this problem by assuming a skewed\u0000response distribution to account for possible skewness.\u0000This study shows a comprehensive analysis of the performance of nonhomogeneous\u0000post-processing for the 2 m temperature for three different site types, comparing\u0000Gaussian, logistic, and skewed logistic response distributions.\u0000The logistic and skewed logistic distributions show satisfying results, in particular for sharpness, but also in terms of the calibration of the probabilistic\u0000predictions.\u0000","PeriodicalId":36792,"journal":{"name":"Advances in Statistical Climatology, Meteorology and Oceanography","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46531700","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}

{"title":"Comparison and assessment of large-scale surface temperature in climate model simulations","authors":"Raquel Barata, R. Prado, B. Sansó","doi":"10.5194/ASCMO-5-67-2019","DOIUrl":"https://doi.org/10.5194/ASCMO-5-67-2019","url":null,"abstract":"Abstract. We present a data-driven approach to assess and compare the behavior of large-scale spatial averages of surface temperature in climate model simulations and in observational products. We rely on univariate and multivariate dynamic linear model (DLM) techniques to estimate both long-term and seasonal changes in temperature. The residuals from the DLM analyses capture the internal variability of the climate system and exhibit complex temporal autocorrelation structure. To characterize this internal variability, we explore the structure of these residuals using univariate and multivariate autoregressive (AR) models. As a proof of concept that can easily be extended to other climate models, we apply our approach to one particular climate model (MIROC5). Our results illustrate model versus data differences in both long-term and seasonal changes in temperature. Despite differences in the underlying factors contributing to variability, the different types of simulation yield very similar spectral estimates of internal temperature variability. In general, we find that there is no evidence that the MIROC5 model systematically underestimates the amplitude of observed surface temperature variability on multi-decadal timescales – a finding that has considerable relevance regarding efforts to identify anthropogenic “fingerprints” in observational surface temperature data. Our methodology and results present a novel approach to obtaining data-driven estimates of climate variability for purposes of model evaluation.","PeriodicalId":36792,"journal":{"name":"Advances in Statistical Climatology, Meteorology and Oceanography","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49154097","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}

{"title":"Fitting a stochastic fire spread model to data","authors":"X. J. Wang, John R. J. Thompson, W. J. Braun, D. Woolford","doi":"10.5194/ASCMO-5-57-2019","DOIUrl":"https://doi.org/10.5194/ASCMO-5-57-2019","url":null,"abstract":"Abstract. As the climate changes, it is important to understand the effects on the\u0000environment. Changes in wildland fire risk are an important example. A\u0000stochastic lattice-based wildland fire spread model was proposed by Boychuk\u0000et al. (2007), followed by a more realistic variant (Braun and Woolford,\u00002013). Fitting such a model to data from remotely sensed images could be used\u0000to provide accurate fire spread risk maps, but an intermediate step on the\u0000path to that goal is to verify the model on data collected under\u0000experimentally controlled conditions. This paper presents the analysis of\u0000data from small-scale experimental fires that were digitally video-recorded.\u0000Data extraction and processing methods and issues are discussed, along with\u0000an estimation methodology that uses differential equations for the moments of\u0000certain statistics that can be derived from a sequential set of photographs\u0000from a fire. The interaction between model variability and raster resolution\u0000is discussed and an argument for partial validation of the model is provided.\u0000Visual diagnostics show that the model is doing well at capturing the\u0000distribution of key statistics recorded during observed fires.\u0000","PeriodicalId":36792,"journal":{"name":"Advances in Statistical Climatology, Meteorology and Oceanography","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43792855","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}

{"title":"Future climate emulations using quantile regressions on large ensembles","authors":"Matz A. Haugen, M. Stein, R. Sriver, E. Moyer","doi":"10.5194/ASCMO-5-37-2019","DOIUrl":"https://doi.org/10.5194/ASCMO-5-37-2019","url":null,"abstract":"Abstract. The study of climate change and its impacts depends on\u0000generating projections of future temperature and other climate variables. For\u0000detailed studies, these projections usually require some combination of\u0000numerical simulation and observations, given that simulations of even the current\u0000climate do not perfectly reproduce local conditions. We present a methodology\u0000for generating future climate projections that takes advantage of the\u0000emergence of climate model ensembles, whose large amounts of data allow for\u0000detailed modeling of the probability distribution of temperature or other\u0000climate variables. The procedure gives us estimated changes in model\u0000distributions that are then applied to observations to yield projections that\u0000preserve the spatiotemporal dependence in the observations. We use quantile\u0000regression to estimate a discrete set of quantiles of daily temperature as a\u0000function of seasonality and long-term change, with smooth spline functions of\u0000season, long-term trends, and their interactions used as basis functions for\u0000the quantile regression. A particular innovation is that more extreme\u0000quantiles are modeled as exceedances above less extreme quantiles in a nested\u0000fashion, so that the complexity of the model for exceedances decreases the\u0000further out into the tail of the distribution one goes. We apply this method\u0000to two large ensembles of model runs using the same forcing scenario, both\u0000based on versions of the Community Earth System Model (CESM), run at\u0000different resolutions. The approach generates observation-based future\u0000simulations with no processing or modeling of the observed climate needed\u0000other than a simple linear rescaling. The resulting quantile maps illuminate\u0000substantial differences between the climate model ensembles, including\u0000differences in warming in the Pacific Northwest that are particularly large\u0000in the lower quantiles during winter. We show how the availability of two\u0000ensembles allows the efficacy of the method to be tested with a “perfect model”\u0000approach, in which we estimate transformations using the lower-resolution\u0000ensemble and then apply the estimated transformations to single runs from the\u0000high-resolution ensemble. Finally, we describe and implement a simple method\u0000for adjusting a transformation estimated from a large ensemble of one climate\u0000model using only a single run of a second, but hopefully more realistic,\u0000climate model.\u0000","PeriodicalId":36792,"journal":{"name":"Advances in Statistical Climatology, Meteorology and Oceanography","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47787616","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}