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F. W. Zwiers

Abstract

This paper describes a potential predictability study on the results of a 20.5 year simulation conducted with the Canadian Climate Centre (CCC) General Circulation Model (GCM). The CCC GCM is an atmosphere GCM with surface hydrology, soil moisture and snow cover representations. Except for these terms, the only source of interannual variability in the CCC GCM-simulated climate is “internal” dynamics. The study addresses the question of whether an atmospheric GCM of this sort can simulate interannual variability which is potentially predictable in a statistical sense.

Strong evidence is found for potential predictability of 500 mb height and surface pressure in the December, January, February (DJF) season of the simulated climate, and somewhat weaker evidence in the March, April, May (MAM) season. There is little evidence of potential predictability during the other seasons. The evidence does not indicate that potential predictability is related to the presence of surface hydrology, soil moisture and snow cover terms in the CCC GCM. The results have the same general characteristics as those which have been obtained in atmospheric studies: F-ratios measuring potential predictability are generally smallest in midlatitudes and larger in the tropics and towards the poles. Also, consistent with atmospheric studies, F-ratios are larger in the Southern Hemisphere than in the Northern Hemisphere, and are largest in the DJF season. However, unlike the atmosphere, it is impossible to link potential predictability in the simulated climate with any sort of quasi-periodic phenomenon such as the Quasi-biennial Oscillation or the El Niño–Southern Oscillation. Potential predictability in the simulated climate appears to arise from the occurrence of a single large anomaly extending over a period of about a season, during which atmospheric mass is systematically shifted from the tropics to the high latitudes of the Southern Hemisphere. Such large extended anomalies have previously been observed in other climate simulations and in the atmosphere.

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F. W. Zwiers

Abstract

Several multivariate tests for differences of the mean which are based upon resampling schemes are examined in a series of Monte Carlo experiments. We examine the power of these tests under two sets of experimental situations: one in which the resolution of the simulated observing network increases, and one in which the simulated observing network expands geographically with a fixed resolution The behavior of these essentially nonparametric tests is compared with classical multivariate tests and it is argued that the sensitivity of one with respect to the other depends upon the spatial correlation structure of the observed fields. The question of whether or not to reduce the dimensionality of the observed fields prior to conducting a statistical test is studied, as is the effect of temporal correlation upon tests based on resampling schemes.

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F. W. Zwiers and H. J. Thiébaux

Abstract

Statistical tests used in model intercomparisons or model/climate comparisons may be either “scalar” or “multivariate” tests. The former are employed when testing a hypothesis about a single variable observed at a single location, or through a single derived coefficient. The latter are employed when testing a hypothesis about an entire field, or a set of derived coefficients. In this paper we examine several scalar tests for differences of mean and variance. The tests can be broadly classed as “standard” tests which operate on samples of time averages, and “time-series”-based tests which operate on samples of time series. The latter have the potential to be more powerful than standard tests because they use more of the information available in the sample, but they have the disadvantage that they are “asymptotic” tests, meaning that the properties of these tests are only well known in the case of very large samples. The properties of these tests in the case of relatively small samples are examined by means of a series of Monte Carlo experiments which are meant to mimic a broad range of stochastic behavior. It is shown that the actual significance level of time-series-based tests, especially those comparing means, ran be considerably different from the nominal significance level. Models are developed which relate the true significance level of these tests to sample size and the stochastic properties of the data, and them models are used to make recommendations for the design of experiments using time-series-based tests.

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B. Yu, A. Shabbar, and F. W. Zwiers

Abstract

This study provides further evidence of the impacts of tropical Pacific interannual [El Niño–Southern Oscillation (ENSO)] and Northern Pacific decadal–interdecadal [North Pacific index (NPI)] variability on the Pacific–North American (PNA) sector. Both the tropospheric circulation and the North American temperature suggest an enhanced PNA-like climate response and impacts on North America when ENSO and NPI variability are out of phase. In association with this variability, large stationary wave activity fluxes appear in the mid- to high latitudes originating from the North Pacific and flowing downstream toward North America. Atmospheric heating anomalies associated with ENSO variability are confined to the Tropics, and generally have the same sign throughout the troposphere with maximum anomalies at 400 hPa. The heating anomalies that correspond to the NPI variability exhibit a center over the midlatitude North Pacific in which the heating changes sign with height, along with tropical anomalies of comparable magnitudes. Atmospheric heating anomalies of the same sign appear in both the tropical Pacific and the North Pacific with the out-of-phase combination of ENSO and NPI. Both sources of variability provide energy transports toward North America and tend to favor the occurrence of stationary wave anomalies.

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H. J. Thiébaux and F. W. Zwiers

Abstract

Statistical and dynamical relationships between observed values of a geophysical system or model effectively reduce the number of independent data. This reduction is expressible in terms of the covariance structure of the process and, in some instances, it is reasonable to devise a measure of the “effective sample size” in terms of sample statistics. Here we discuss the concept of “effective sample size,” and, having settled upon one of several possible definitions, examine various methods of estimating this quantity. It is found that “effective sample size” is quite difficult to estimate reliably. However, a procedure is described which we feel could be used successfully; it is noted that the concept could be extended to spatial arrays of data, in some circumstances.

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F. W. Zwiers and G. J. Boer

Abstract

A comparison is made between the climate simulated by the Canadian Climate Centre (CCC) General Circulation Model when run its usual “annual cycle”mode, in which the solar declination angle varies annually, and the climate which is simulated when the model is run in “perpetual mode”, in which the solar declination angle is fixed. In particular, the annual cycle and perpetual mode January climates are compared as are the corresponding July climates. The comparison includes a global assessment of the significance of differences observed in mean sea level pressure, 500 mb height, lowest model level temperature, surface heat and evaporation fluxes, precipitation amounts, and zonal cross sections of temperature, zonal wind, and standing and transient eddy momentum transports. Clear and significant differences in climate between the annually forced and perpetually forced cases are observed. These changes are linked to differences in radiative heating and surface balances of energy and moisture.

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B. L. Mueller, N. P. Gillett, A. H. Monahan, and F. W. Zwiers

Abstract

The paper presents results from a climate change detection and attribution study on the decline of Arctic sea ice extent in September for the 1953–2012 period. For this period three independently derived observational datasets and simulations from multiple climate models are available to attribute observed changes in the sea ice extent to known climate forcings. Here we direct our attention to the combined cooling effect from other anthropogenic forcing agents (mainly aerosols), which has potentially masked a fraction of greenhouse gas–induced Arctic sea ice decline. The presented detection and attribution framework consists of a regression model, namely, regularized optimal fingerprinting, where observations are regressed onto model-simulated climate response patterns (i.e., fingerprints). We show that fingerprints from greenhouse gas, natural, and other anthropogenic forcings are detected in the three observed records of Arctic sea ice extent. Beyond that, our findings indicate that for the 1953–2012 period roughly 23% of the greenhouse gas–induced negative sea ice trend has been offset by a weak positive sea ice trend attributable to other anthropogenic forcing. We show that our detection and attribution results remain robust in the presence of emerging nonstationary internal climate variability acting upon sea ice using a perfect model experiment and data from two large ensembles of climate simulations.

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Sarah J. Doherty, Stephan Bojinski, Ann Henderson-Sellers, Kevin Noone, David Goodrich, Nathaniel L. Bindoff, John A. Church, Kathy A. Hibbard, Thomas R. Karl, Lucka Kajfez-Bogataj, Amanda H. Lynch, David E. Parker, I. Colin Prentice, Venkatachalam Ramaswamy, Roger W. Saunders, Mark Stafford Smith, Konrad Steffen, Thomas F. Stocker, Peter W. Thorne, Kevin E. Trenberth, Michel M. Verstraete, and Francis W. Zwiers

The Fourth Assessment Report (AR4) of the Intergovernmental Panel on Climate Change (IPCC) concluded that global warming is “unequivocal” and that most of the observed increase since the mid-twentieth century is very likely due to the increase in anthropogenic greenhouse gas concentrations, with discernible human influences on ocean warming, continental-average temperatures, temperature extremes, wind patterns, and other physical and biological indicators, impacting both socioeconomic and ecological systems. It is now clear that we are committed to some level of global climate change, and it is imperative that this be considered when planning future climate research and observational strategies. The Global Climate Observing System program (GCOS), the World Climate Research Programme (WCRP), and the International Geosphere-Biosphere Programme (IGBP) therefore initiated a process to summarize the lessons learned through AR4 Working Groups I and II and to identify a set of high-priority modeling and observational needs. Two classes of recommendations emerged. First is the need to improve climate models, observational and climate monitoring systems, and our understanding of key processes. Second, the framework for climate research and observations must be extended to document impacts and to guide adaptation and mitigation efforts. Research and observational strategies specifically aimed at improving our ability to predict and understand impacts, adaptive capacity, and societal and ecosystem vulnerabilities will serve both purposes and are the subject of the specific recommendations made in this paper.

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G. Myhre, P. M. Forster, B. H. Samset, Ø. Hodnebrog, J. Sillmann, S. G. Aalbergsjø, T. Andrews, O. Boucher, G. Faluvegi, D. Fläschner, T. Iversen, M. Kasoar, V. Kharin, A. Kirkevåg, J.-F. Lamarque, D. Olivié, T. B. Richardson, D. Shindell, K. P. Shine, C. W. Stjern, T. Takemura, A. Voulgarakis, and F. Zwiers

Abstract

As the global temperature increases with changing climate, precipitation rates and patterns are affected through a wide range of physical mechanisms. The globally averaged intensity of extreme precipitation also changes more rapidly than the globally averaged precipitation rate. While some aspects of the regional variation in precipitation predicted by climate models appear robust, there is still a large degree of intermodel differences unaccounted for. Individual drivers of climate change initially alter the energy budget of the atmosphere, leading to distinct rapid adjustments involving changes in precipitation. Differences in how these rapid adjustment processes manifest themselves within models are likely to explain a large fraction of the present model spread and better quantifications are needed to improve precipitation predictions. Here, the authors introduce the Precipitation Driver and Response Model Intercomparison Project (PDRMIP), where a set of idealized experiments designed to understand the role of different climate forcing mechanisms were performed by a large set of climate models. PDRMIP focuses on understanding how precipitation changes relating to rapid adjustments and slower responses to climate forcings are represented across models. Initial results show that rapid adjustments account for large regional differences in hydrological sensitivity across multiple drivers. The PDRMIP results are expected to dramatically improve understanding of the causes of the present diversity in future climate projections.

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