Monthly North Atlantic Research Articles

Understanding the Signal‐to‐Noise Paradox with a Simple Markov Model

AbstractThere is a growing list of examples for the existence of the signal‐to‐noise paradox, where in the ensemble‐based climate prediction, the model ensemble mean forecast generally shows higher correlations with observations than with individual ensemble members. This seems to lead to a paradox that the model makes better predictions for the real world than predicting itself. Here we introduce a Markov model to represent the ensemble forecasts and reproduce the signal‐to‐noise paradox, which we argue is primarily dependent on the magnitude of the persistence and noise variance between the models and the corresponding observations. The monthly North Atlantic Oscillation indices based on uninitialized historical simulations of 40 CMIP5 models have been analyzed, suggesting that the signal‐to‐noise paradox is common in currently available coupled models, and the paradox is not due to problems with initialization processes used in the seasonal‐to‐decadal predictions in previous studies and is instead a general model problem.

SynthETC: A Statistical Model for Severe Winter Storm Hazard on Eastern North America.

We develop, evaluate, and apply "SynthETC," a statistical-stochastic model of winter extra-tropical cyclones (ETCs) over eastern North America. SynthETC simulates the life cycle of ETCs from formation to termination, and it can be used to estimate the probability of extreme ETC events beyond the historical record. Two modes of climate variability are used as independent covariates: El Niño/Southern Oscillation (ENSO) Niño3.4 and the monthly North Atlantic Oscillation (NAO). We use SynthETC to estimate the annual occurrence rate over sites in eastern North America of intense ETC passage in different ENSO and NAO states. Positive NAO is associated with increased rates over the North Atlantic, while negative NAO is associated with decreased rates over the North Atlantic and increased rates over northern Quebec. Positive ENSO is associated with decreased rates over the North Atlantic, Ontario, and the Canadian Maritime, while negative ENSO is associated with increased rates over those regions, as well as the Great Lakes region.

Attributing seasonal variation of daily extreme precipitation events across The Netherlands

A recent study showed a rise in total and extreme precipitation in the Netherlands over the past century. The present study attempts to characterize and attribute the seasonal variation of daily extreme precipitation events in the Netherlands. Statistical models for extreme values were used to fit daily rainfall maxima for all months during the period 1961–2014, using data from the 231 rain gauges distributed across the country. A generalized extreme value (GEV) approach was used to determine the probability distribution of extreme values and their dependency on time and the monthly North Atlantic Oscillation (NAO) index. The non-stationary models used to represent the annual cycle of the GEV parameters assumed an invariant shape parameter and harmonic functions as location and scale parameters. The best non-stationary model was selected using Akaike’s information criterion (AIC) and the log-likelihood ratio test (LRT). The results indicated that the estimates derived from the non-stationary model differed from those obtained with the aid of the stationary model, and had lower uncertainties. These non-stationary estimates were within the confidence intervals (CI) of the stationary estimates at most rain gauge stations. The non-stationary model estimated parameters with less uncertainty and with smaller CI, thus permitting more accurate representation of extreme precipitation in the Netherlands. The spatial pattern of annual mean location and scale GEV parameters was compatible with coastal, land cover (such as the wooded and heathland areas of the Veluwe region of the province of Gelderland) and orography (in the southeast of the country). The location parameter peaked over the west coast, especially on the central west coast during the summer half-year, while the centre and east of the country had the highest values during the winter half-year. The scale parameter peaked in the centre of the country during the summer, in the east in the early summer and along the west coast in the spring. The 10-year and 50-year return levels were calculated with the aid of the non-stationary model for all months. The spatial distribution of these extreme event probability clearly reflects the regional differences in the Netherlands.

A Climatology of Wintertime Barrier Winds off Southeast Greenland

A climatology of barrier winds along the southeastern coast of Greenland is presented based on 20 yr of winter months (1989–2008) from the ECMWF Interim Reanalysis (ERA-Interim). Barrier wind events occur predominantly at two locations: Denmark Strait North (DSN; 67.7°N, 25.3°W) and Denmark Strait South (DSS; 64.9°N, 35.9°W). Events stronger than 20 m s−1 occur on average once per week during winter with considerable interannual variability—from 7 to 20 events per winter. The monthly frequency of barrier wind events correlates with the monthly North Atlantic oscillation (NAO) index with a correlation coefficient of 0.57 (0.31) at DSN (DSS). The associated total turbulent heat fluxes for barrier wind events (area averaged) were typically about 200 W m−2 with peak values of 400 W m−2 common in smaller regions. Area-averaged surface stresses were typically between 0.5 and 1 N m−2. Total precipitation rates were larger at DSS than DSN, both typically less than 1 mm h−1. The total turbulent heat fluxes were shown to have a large range as a result of a large range in 2-m air temperature. Two classes of barrier winds—warm and cold—were investigated and found to develop in different synoptic-scale situations. Warm barrier winds developed when there was a blocking high pressure over the Nordic seas, while cold barrier winds owed their presence to a train of cyclones channeling through the region.

Wavelet-Based Multifractal Analysis of the El Nino/Southern Oscillation, the Indian Ocean Dipole and the North Atlantic Oscillation

Climate change can be interpreted from the perspective of fractals. This study uses the wavelet transform to analyze the multifractal behavior of the monthly El Nino/Southern Oscillation index (NINO3.4), the monthly Indian Ocean Dipole Mode Index (DMI), and the monthly North Atlantic Oscillation (NAO) index. Strong multifractality was observed when NINO3.4 and DMI had high coherency. Changes from multifractal to monofractal behavior were observed at the 1976/77 regime shift for NINO3.4 and DMI, and at the 1988/89 regime shift for NAO index. We found that a regime shift occurs when the fluctuation is large and multifractality is strong.

Predictability of the monthly North Atlantic Oscillation index based on fractal analyses and dynamic system theory

Abstract. The predictability of the monthly North Atlantic Oscillation, NAO, index is analysed from the point of view of different fractal concepts and dynamic system theory such as lacunarity, rescaled analysis (Hurst exponent) and reconstruction theorem (embedding and correlation dimensions, Kolmogorov entropy and Lyapunov exponents). The main results point out evident signs of randomness and the necessity of stochastic models to represent time evolution of the NAO index. The results also show that the monthly NAO index behaves as a white-noise Gaussian process. The high minimum number of nonlinear equations needed to describe the physical process governing the NAO index fluctuations is evidence of its complexity. A notable predictive instability is indicated by the positive Lyapunov exponents. Besides corroborating the complex time behaviour of the NAO index, present results suggest that random Cantor sets would be an interesting tool to model lacunarity and time evolution of the NAO index.

Hierarchical analysis of a remote, Arctic, artisanal longline fishery

Abstract Dennard, S. T., MacNeil, M. A., Treble, M. A., Campana, S., and Fisk, A. T. 2010. Hierarchical analysis of a remote, Arctic, artisanal longline fishery. – ICES Journal of Marine Science, 67: 41–51. This is the first paper to explore trends in catch per unit effort (cpue) through time of a Greenland halibut Reinhardtius hippoglossoides stock targeted by an artisanal, winter fishery in Cumberland Sound on southern Baffin Island, Canada. We modelled cpue data from 1987 to 2003, looking at two questions: what factors have driven cpue trends, and is cpue an accurate index of a stock's abundance? In the context of limited data availability, we used generalized linear models (GLMs) and hierarchical models to assess important predictors of cpue. Hierarchical models with multiple fixed environmental effects contained fishing location or individual fisher as random effects. A month effect showed greatest catch rates during February and March; the monthly North Atlantic Oscillation index was positively associated with catch rates; and a change from decreasing to increasing cpue after 1996 was linked to reduced fishery participation following a large storm. The best Akaike's information criterion-ranked GLM identified a negative relationship of cpue with shark bycatch. Although data limitations precluded conventional stock assessment, our models implicated the environment and fisher behaviour as drivers of cpue trends. Additionally, using multiple hierarchical models to predict cpue provided a more informative analysis for understanding trends in cpue than a GLM alone.

MINIMIZING STOCHASTICITY IN THE NAO INDEX

We show that the monthly North Atlantic Oscillation index routinely used in climatology as an indicator for global climate variability is not an optimal choice. A critical Markov analysis of the two pressure time-series for both, monthly and daily NAO index, indicates that the monthly index, due to its low sampling rate, contains higher stochastic terms than the daily index. Applying a recently developed variationally optimized Markov analysis leads to a new NAO index with minimal stochasticity.

Quasi-biennial oscillations extracted from the monthly NAO index and temperature records are phase-synchronized

Abstract. Using the extension of Monte Carlo Singular System Analysis (MC SSA), based on evaluating and testing the regularity of the dynamics of the SSA modes against the colored noise null hypothesis, we demonstrate detection of oscillatory modes with period of about 27 months in records of monthly average near-surface air temperature from several European locations, as well as in the monthly North Atlantic Oscillation index. According to their period, the detected modes can be attributed to the quasi-biennial oscillations (QBO). The QBO modes extracted from the temperature and from the NAO index underwent synchronization analysis and their phase synchronization has been confirmed with high statistical significance.

Enhanced Monte Carlo Singular System Analysis and detection of period 7.8 years oscillatory modes in the monthly NAO index and temperature records

Abstract. An extension of the Monte Carlo Singular System Analysis (MC SSA) is described, based on evaluating and testing regularity of dynamics of the SSA modes against the colored noise null hypothesis, in addition to the test based on variance (eigenvalues). The application of the regularity index, computed from a coarse-grained estimation of mutual information, enhances the test sensitivity and reliability in detection of relatively more regular dynamical modes than those obtained by decomposition of colored noises, in particular, in detection of irregular oscillations embedded in red noise. This enhanced MC SSA is successfully applied in detection of period 7.8 years oscillatory modes in records of monthly average near-surface air temperature from several European locations, as well as in the monthly North Atlantic Oscillation index.