The North Atlantic Oscillation (NAO) is one of the most prominent and recurrent patterns of atmospheric circulation variability. It dictates climate variability from the eastern seaboard of the United States to Siberia and from the Arctic to the subtropical Atlantic, especially during boreal winter, so variations in the NAO are important to society and for the environment. Understanding the processes that govern this variability is, therefore, of high priority, especially in the context of global climate change. This review, aimed at a scientifically diverse audience, provides general background material for the other chapters in the monograph, and it synthesizes some of their central points. It begins with a description of the spatial structure of climate and climate variability, including how the NAO relates to other prominent patterns of atmospheric circulation variability. There is no unique way to define the spatial structure of the NAO, or thus its temporal evolution, but several common approaches are illustrated. The relationship between the NAO and variations in surface temperature, storms and precipitation, and thus the economy, as well as the ocean and ecosystem responses to NAO variability, are described. Although the NAO is a mode of variability internal to the atmosphere, indices of it exhibit decadal variability and trends. That not all of its variability can be attributed to intraseasonal stochastic atmospheric processes points to a role for external forcings and, perhaps, a small but useful amount of predictability. The surface, stratospheric and anthropogenic processes that may influence the phase and amplitude of the NAO are reviewed.

/pdf/an-overview-of-the-north-atlantic-oscillation-3uug0y0py4.pdf

An overview of the North Atlantic Oscillation

The authors systematically investigate two easily computed measures of the effective number of spatial degrees of freedom (ESDOF), or number of independently varying spatial patterns, of a time-varying field of data. The first measure is based on matching the mean and variance of the time series of the spatially integrated squared anomaly of the field to a chi-squared distribution. The second measure, which is equivalent to the first for a long time sample of normally distributed field values, is based on the partitioning of variance between the EOFs. Although these measures were proposed almost 30 years ago, this paper aims to provide a comprehensive discussion of them that may help promote their more widespread use. The authors summarize the theoretical basis of the two measures and considerations when estimating them with a limited time sample or from nonnormally distributed data. It is shown that standard statistical significance tests for the difference or correlation between two realizations of a field (e.g., a forecast and an observation) are approximately valid if the number of degrees of freedom is chosen using an appropriate combination of the two ESDOF measures. Also described is a method involving ESDOF for deciding whether two time-varying fields are significantly correlated to each other. A discussion of the parallels between ESDOF and the effective sample size of an autocorrelated time series is given, and the authors review how an appropriate measure of effective sample size can be computed for assessing the significance of correlations between two time series.

https://journals.ametsoc.org/doi/pdf/10.1175/1520-0442%281999%29012%3C1990%3ATENOSD%3E2.0.CO%3B2

The Effective Number of Spatial Degrees of Freedom of a Time-Varying Field

Trends in Canadian temperature and precipitation during the 20th century are analyzed using recently updated and adjusted station data. Six elements, maximum, minimum and mean temperatures along with diurnal temperature range (DTR), precipitation totals and ratio of snowfall to total precipitation are investigated. Anomalies from the 1961–1990 reference period were first obtained at individual stations, and were then used to generate gridded datasets for subsequent trend analyses. Trends were computed for 1900–1998 for southern Canada (south of 60°N), and separately for 1950–1998 for the entire country, due to insufficient data in the high arctic prior to the 1950s. From 1900–1998, the annual mean temperature has increased between 0.5 and 1.5°C in the south. The warming is greater in minimum temperature than in maximum temperature in the first half of the century, resulting in a decrease of DTR. The greatest warming occurred in the west, with statistically significant increases mostly seen during...

Temperature and precipitation trends in Canada during the 20th century

This study investigates the response of a global model of the climate to the quadrupling of the CO2 concentration in the atmosphere. The model consists of (1) a general circulation model of the atmosphere, (2) a heat and water balance model of the continents, and (3) a simple mixed layer model of the oceans. It has a global computational domain and realistic geography. For the computation of radiative transfer, the seasonal variation of insolation is imposed at the top of the model atmosphere, and the fixed distribution of cloud cover is prescribed as a function of latitude and of height. It is found that with some exceptions, the model succeeds in reproducing the large-scale characteristics of seasonal and geographical variation of the observed atmospheric temperature. The climatic effect of a CO2 increase is determined by comparing statistical equilibrium states of the model atmosphere with a normal concentration and with a 4 times the normal concentration of CO2 in the air. It is found that the warming of the model atmosphere resulting from the CO2 increase has significant seasonal and latitudinal variation. Because of the absence of an albedo feedback mechanism, the warming over the Antarctic continent is somewhat less than the warming in high latitudes of the northern hemisphere. Over the Arctic Ocean and its surroundings, the warming is much larger in winter than summer, thereby reducing the amplitude of seasonal temperature variation. It is concluded that this seasonal asymmetry in the warming results from the reduction in the coverage and thickness of the sea ice. The warming of the model atmosphere results in an enrichment of the moisture content in the air and an increase in the poleward moisture transport. The additional moisture is picked up from the tropical ocean and is brought to high latitudes where both precipitation and runoff increase throughout the year. Further, the time of rapid snowmelt and maximum runoff becomes earlier.

/pdf/sensitivity-of-a-global-climate-model-to-an-increase-of-co2-4yo0yybjjw.pdf

Sensitivity of a global climate model to an increase of CO2 concentration in the atmosphere

Data collected by the International Arctic Buoy Programme from 1979 to 1998 are analyzed to obtain statistics of sea level pressure (SLP) and sea ice motion (SIM). The annual and seasonal mean fields agree with those obtained in previous studies of Arctic climatology. The data show a 3-hPa decrease in decadal mean SLP over the central Arctic Ocean between 1979–88 and 1989–98. This decrease in SLP drives a cyclonic trend in SIM, which resembles the structure of the Arctic Oscillation (AO). Regression maps of SIM during the wintertime (January–March) AO index show 1) an increase in ice advection away from the coast of the East Siberian and Laptev Seas, which should have the effect of producing more new thin ice in the coastal flaw leads; 2) a decrease in ice advection from the western Arctic into the eastern Arctic; and 3) a slight increase in ice advection out of the Arctic through Fram Strait. Taken together, these changes suggest that at least part of the thinning of sea ice recently observed ov...

Response of Sea Ice to the Arctic Oscillation

Climate change studies with general circulation models require the estimation of climatic means by calculation of finite-time averages. A simple stochastic model is used to estimate the standard error of such an estimation method as a function of averaging time. Consequences for long-range forecasting are also discussed.

The Standard Error of Time-Average Estimates of Climatic Means

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The Standard Error of Time-Average Estimates of Climatic Means