Optimal Ranking Regime Analysis of Intra- to Multidecadal U.S. Climate Variability. Part II: Precipitation and Streamflow

In Part I of this paper, the optimal ranking regime (ORR) method was used to identify intradecadal to multidecadal (IMD) regimes in U.S. climate division temperature data during 1896–2012. Here, the method is used to test for annual and seasonal precipitation regimes during that same period. Water-year mean streamflow rankings at 125 U.S. Hydro-Climatic Data Network gauge stations are also evaluated during 1939–2011. The precipitation and streamflow regimes identified are compared with ORR-derived regimes in the Pacific decadal oscillation (PDO), the Atlantic multidecadal oscillation (AMO), and indices derived from gridded SST anomaly (SSTA) analysis data. Using a graphic display approach that allows for the comparison of IMD climate regimes in multiple time series, an interdecadal cycle in western precipitation is apparent after 1980, as is a similar cycle in northwestern streamflow. Before 1980, IMD regimes in northwestern streamflow and annual precipitation are in approximate antiphase with the PDO. One of the clearest IMD climate signals found in this analysis are post-1970 wet regimes in eastern U.S streamflow and annual precipitation, as well as in fall [September–November (SON)] precipitation. Pearson correlations between time series of annual and seasonal precipitation averaged over the eastern United States and SSTA analysis data show relatively extensive positive correlations between warming tropical SSTA and increasing fall precipitation. The possible Pacific and northern Atlantic roots of the recent eastern U.S. wet regime, as well as the general characteristics of U.S. climate variability in recent decades that emerge from this analysis and that of Part I, are discussed

Optimal Ranking Regime Analysis of Intra- to Multidecadal U.S. Climate Variability. Part I: Temperature

The optimal ranking regime (ORR) method was used to identify intradecadal to multidecadal (IMD) time windows containing significant ranking sequences in U.S. climate division temperature data. The simplicity of the ORR procedure’s output—a time series’ most significant nonoverlapping periods of high or low rankings—makes it possible to graphically identify common temporal breakpoints and spatial patterns of IMD variability in the analyses of 102 climate division temperature series. This approach is also applied to annual Atlantic multidecadal oscillation (AMO) and Pacific decadal oscillation (PDO) climate indices, a Northern Hemisphere annual temperature (NHT) series, and divisional annual and seasonal temperature data during 1896–2012. In addition, Pearson correlations are calculated between PDO, AMO, and NHT series and the divisional temperature series. Although PDO phase seems to be an important influence on spring temperatures in the northwestern United States, eastern temperature regimes in annual, winter, summer, and fall temperatures are more coincident with cool and warm phase AMO regimes. Annual AMO values also correlate significantly with summer temperatures along the Eastern Seaboard and fall temperatures in the U.S. Southwest. Given evidence of the abrupt onset of cold winter temperatures in the eastern United States during 1957/58, possible climate mechanisms associated with the cause and duration of the eastern U.S. warming hole period—identified here as a cool temperature regime occurring between the late 1950s and late 1980s—are discussed

Reproduction of Twentieth Century Intradecadal to Multidecadal Surface Temperature Variability in Radiatively Forced Coupled Climate Models

[1] Coupled Model Intercomparison Project 3 simulations that included time-varying radiative forcings were ranked according to their ability to consistently reproduce twentieth century intradecadal to multidecadal (IMD) surface temperature variability at the 5° by 5° spatial scale. IMD variability was identified using the running Mann-Whitney Z method. Model rankings were given context by comparing the IMD variability in preindustrial control runs to observations and by contrasting the IMD variability among the ensemble members within each model. These experiments confirmed that the inclusion of time-varying external forcings brought simulations into closer agreement with observations. Additionally, they illustrated that the magnitude of unforced variability differed between models. This led to a supplementary metric that assessed model ability to reproduce observations while accounting for each model\u27s own degree of unforced variability. These two metrics revealed that discernable differences in skill exist between models and that none of the models reproduced observations at their theoretical optimum level. Overall, these results demonstrate a methodology for assessing coupled models relative to each other within a multimodel framework

Evaluating Modeled Intra- to Multidecadal Climate Variability Using Running Mann–Whitney \u3cem\u3eZ\u3c/em\u3e Statistics

An analysis method previously used to detect observed intra- to multidecadal (IMD) climate regimes was adapted to compare observed and modeled IMD climate variations. Pending the availability of the more appropriate phase 5 Coupled Model Intercomparison Project (CMIP-5) simulations, the method is demonstrated using CMIP-3 model simulations. Although the CMIP-3 experimental design will almost certainly prevent these model runs from reproducing features of historical IMD climate variability, these simulations allow for the demonstration of the method and illustrate how the models and observations disagree. This method samples a time series’s data rankings over moving time windows, converts those ranking sets to a Mann–Whitney U statistic, and then normalizes the U statistic into a Z statistic. By detecting optimally significant IMD ranking regimes of arbitrary onset and varying duration, this process generates time series of Z values that are an adaptively low-passed and normalized transformation of the original time series. Principal component (PC) analysis of the Z series derived from observed annual temperatures at 92 U.S. grid locations during 1919–2008 shows two dominant modes: a PC1 mode with cool temperatures before the late 1960s and warm temperatures after the mid-1980s, and a PC2 mode indicating a multidecadal temperature cycle over the Southeast. Using a graphic analysis of a Z error metric that compares modeled and observed Z series, the three CMIP-3 model simulations tested here are shown to reproduce the PC1 mode but not the PC2 mode. By providing a way to compare grid-level IMD climate response patterns in observed and modeled data, this method can play a useful diagnostic role in future model development and decadal climate forecasting

Comparing the Model-simulated Global Warming Signal to Observations Using Empirical Estimates of Unforced Noise

The comparison of observed global mean surface air temperature (GMT) change to the mean change simulated by climate models has received much public and scientific attention. For a given global warming signal produced by a climate model ensemble, there exists an envelope of GMT values representing the range of possible unforced states of the climate system (the Envelope of Unforced Noise; EUN). Typically, the EUN is derived from climate models themselves, but climate models might not accurately simulate the correct characteristics of unforced GMT variability. Here, we simulate a new, empirical, EUN that is based on instrumental and reconstructed surface temperature records. We compare the forced GMT signal produced by climate models to observations while noting the range of GMT values provided by the empirical EUN. We find that the empirical EUN is wide enough so that the interdecadal variability in the rate of global warming over the 20th century does not necessarily require corresponding variability in the rate-of-increase of the forced signal. The empirical EUN also indicates that the reduced GMT warming over the past decade or so is still consistent with a middle emission scenario’s forced signal, but is likely inconsistent with the steepest emission scenario’s forced signal

Morphogenetic analysis of the phenotypic variability of the architectural unit of Hydrangea macrophylla

Hydrangea macrophylla is a ligneous plant that has attracted the attention of many plant breeders and agronomists for the purpose of enhancing its phenotypic plasticity. However, this plasticity was always exploited empirically.Can this plasticity be assessed by a more scientific approach? In this work, the phenotypic variation is analysed via a description of the different development sequences of the plant and by exposing the plant to different contrasted environments. The architectural unit consists of two morphogenetic units: the Vegetative Unit (VU) and the Vegetative and Floral Unit (VFU). They result in four successive development sequences: an organogenetic phase accompanied by continuous growth (sequence A), floral transformation (sequence B), dormancy (sequence C) and flower bloom (sequence D). Under the effect of environmental factors, the formation of the mixed terminal bud (sequence B) provides a considerable source of spatial variability, whereas the absence or presence of dormancy (sequence C) is responsible for a source of temporal variation. The in-depth description of the architectural unit with its morphological components and the characterisation of the four development sequences provide a necessary scientific basis to identify environmental effects on plant development and for the integrated use of its plasticity

Mining association rules for the quality improvement of the production process

Academics and practitioners have a common interest in the continuing development of methods and computer applications that support or perform knowledge-intensive engineering tasks. Operations management dysfunctions and lost production time are problems of enormous magnitude that impact the performance and quality of industrial systems as well as their cost of production. Association rule mining is a data mining technique used to find out useful and invaluable information from huge databases. This work develops a better conceptual base for improving the application of association rule mining methods to extract knowledge on operations and information management. The emphasis of the paper is on the improvement of the operations processes. The application example details an industrial experiment in which association rule mining is used to analyze the manufacturing process of a fully integrated provider of drilling products. The study reports some new interesting results with data mining and knowledge discovery techniques applied to a drill production process. Experiment’s results on real-life data sets show that the proposed approach is useful in finding effective knowledge associated to dysfunctions causes

Pseudovasculitis and corticosteroid therapy

Pseudovasculitis, vasculitis-like syndromes, vasculitis look-alikes, or mimics of vasculitis represent a heterogeneous collection of disorders that are capable of simulating vasculitis. Inappropriate diagnosis leads to delay or absence of proper management and exposure to potentially deleterious treatment modalities such as corticosteroids and cytotoxic agents. We report the case of fibromuscular dysplasia suspected to be a polyarteritis nodosa. The progression of the lesions visualized by the ultrasonographic study and computed tomography (CT) scan after 10days of treatment led to an emergency laparotomy. The possible deleterious role of steroids given to treat the suspected vasculitis is discusse

Association Between Hypovitaminosis D and Late Stages of Age-Related Macular Degeneration: A Case-Control Study

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