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Francis W. Zwiers

Abstract

The climate literature contains a considerable amount of indirect evidence that there is a connection betweenthe size of the spring Tibetan snowpack and the strength of the subsequent Asian summer monsoon. This paperreports on a study that was conducted to search for evidence of a direct snow-monsoon interaction in a simulatedclimatology derived from two long integrations of the Canadian Climate Centre's GCM version 1. Statisticalmethods based on a combination of empirical orthogonal function analysis and canonical correlation analysiswere the primary investigative tools. Only a weak signal was found. It is therefore concluded that either thesimulated variability ofthe snow on Tibet is too small, the model does not react appropriately to the simulatedvariability, or the true natural snow-monsoon mechanism is weak and any snow-monsoon connection reliesupon a third factor. The first possibility is considered to be remote: the model simulates substantial interannualvariability of Tibetan snow. The second and third possibilities are more likely. In particular, the physical mechanism that is thought to connect Tibetan snow with the Asian monsoon may not be properly simulated inthe model.

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Francis W. Zwiers

Abstract

Resampling procedures include hypothesis testing methods based on Permutation Procedures and interval estimation methods based on bootstrap procedures. The former are widely used in the analysis of climate experiments conducted with general circulation models (GCMs) and in the comparison of the simulated and observed climates. The latter are used less frequently than their flexibility and utility warrants. Both resampling techniques are powerful tools, which provide elegant means of overcoming fundamental statistical difficulties encountered in the analysis of observed and simulated climate data. Unfortunately, inference based on both resampling schemes are as sensitive to the effects of serial correlation as classical statistical methods. These tools must therefore be used with the same amount of caution as other statistical methods when it is suspected that the data might be serially correlated.

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Francis Zwiers and Hans Von Storch

Abstract

The class of “regime dependent autoregressive” time series models (RAMs) is introduced. These nonlinear models describe variations of the moments of nonstationary time series by allowing parameter values to change with the state of an ancillary controlling time series and possibly an index series. The index series is used to indicate deterministic seasonal and regimal changes with time. Fitting and diagnostic procedures are described in the paper.

RAMs are fitted to a 102-year seasonal mean tropical Pacific sea surface temperature index time series. The models are controlled by a seasonal index series and one of two ancillary time series: seasonal mean Adelaide sea level pressure and Indian monsoon rainfall, which have previously been identified as possible precursors of the extremes of the Southern Oscillation (SO).

Analysis of the fitted models gives clear evidence for the seasonal variation of the statistical characteristics of the SO. There is strong evidence that the annual cycle of the SO index depends upon the state of the SO as represented by the ancillary time series. There is weaker evidence which suggests that its autocorrelation structure is also state dependent.

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Francis W. Zwiers and Xuebin Zhang

Abstract

Using an optimal detection technique, the extent to which the combined effect of changes in greenhouse gases and sulfate aerosols (GS) may be detected in observed surface temperatures is assessed in six spatial domains decreasing in size from the globe to Eurasia and North America, separately. The GS signal is detected in the annual mean near-surface temperatures of the past 50 yr in all domains. It is also detected in some seasonal mean temperatures of the past 50 yr, with detection in more seasons in larger domains.

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Habs von Storch and Francis W. Zwiers

Abstract

A difficulty with the statistical techniques which are ordinarily used in the analysis of climate sensitivity experiments is that they do not identify the stable, or recurrent, aspects of the experimental response. Therefore, a new concept called “recurrence” is proposed. With this concept it is possible to identify the parts of the response which are likely to recur with an a priori likelihood each time a new experimental realization is obtained. A variety of statistical tests which may be used to assess an a priori level of recurrence by means of limited samples is suggested.

A recurrence analysis is performed with data simulated by the Canadian Climate Centre general circulation model forced with climatological sea surface temperatures (SSTs) and with several El Ninño SST anomalies. All considered SST anomalies, a positive and a negative doubled standard Rasmusson and Carpenter anomaly and the winter 1982/83 anomaly excite a globally significantly response in terms of height and temperature. However, only part of the significant response is also recurrent. In the cold SST anomaly experiment, recurrence is confined to a minor part of the tropics. In the warm SST anomaly runs, recurrence is found in most of the tropics and partly over the northeastern Pacific. These results indicate that equatorial Pacific SST anomalies are associated with a rather limited predictive value, even if the anomalies are very strong.

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Xiaolan L. Wang and Francis W. Zwiers

Abstract

In this paper log–linear analysis and analysis of variance methods were used to analyze the interannual variability and potential predictability of precipitation as simulated in an ensemble of six 10-yr Atmospheric Model Intercomparison Project climate simulations conducted with CCC GCM2, the second-generation general circulation model of the Canadian Centre for Climate Modelling and Analysis. Since observed 1979–88 sea surface temperatures (SSTs) and sea ice extent were prescribed as lower boundary conditions in all six simulations, it is possible to diagnose the extent to which the variability of the seasonal frequency, seasonal mean intensity, and seasonal total of precipitation is affected by the prescribed boundary conditions. The specified SST–sea ice forcing was found to significantly affect both the frequency and intensity of precipitation, particularly in the Tropics, but also in the temperate latitudes. Precipitation frequency appears to be more sensitive to the external forcing than precipitation intensity, especially over land areas. Potential predictability from internal sources such as land surface variations is generally small.

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Viatcheslav V. Kharin and Francis W. Zwiers

Abstract

A simple statistical model of seasonal variability is used to explore the properties of probability forecasts and their accuracy measures. Two methods of estimating probabilistic information from an ensemble of deterministic forecasts are discussed. The estimators considered are the straightforward nonparametric estimator defined as the relative number of the ensemble members in an event category, and a parametric Gaussian estimator derived from a fitted Gaussian distribution. The parametric Gaussian estimator is superior to the standard nonparametric estimator on seasonal timescales. A statistical skill improvement technique is proposed and applied to a collection of 24-member ensemble seasonal hindcasts of northern winter 700-hPa temperature (T 700) and 500-hPa height (Z 500). The improvement technique is moderately successful for T 700 but fails to improve Brier skill scores of the already relatively reliable raw Z 500 probability forecasts.

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Viatcheslav V. Kharin and Francis W. Zwiers

Abstract

Changes in temperature and precipitation extremes are examined in transient climate change simulations performed with the second-generation coupled global climate model of the Canadian Centre for Climate Modelling and Analysis. Three-member ensembles were produced for the time period 1990–2100 using the IS92a, A2, and B2 emission scenarios of the Intergovernmental Panel on Climate Change. The return values of annual extremes are estimated from a fitted generalized extreme value distribution with time-dependent location and scale parameters by the method of maximum likelihood. The L-moment return value estimates are revisited and found to be somewhat biased in the context of transient climate change simulations.

The climate response is of similar magnitude in the integrations with the IS92a and A2 emission scenarios but more modest for the B2 scenario. Changes in temperature extremes are largely associated with changes in the location of the distribution of annual extremes without substantial changes in its shape over most of the globe. Exceptions are regions where land and ocean surface properties change drastically, such as the regions that experience sea ice and snow cover retreat. Globally averaged changes in warm extremes are comparable to the corresponding changes in annual mean daily maximum temperature, while globally averaged cold extremes warm up faster than annual mean daily minimum temperature. There are considerable regional differences between the magnitudes of changes in temperature extremes and the corresponding annual means. Changes in precipitation extremes are due to changes in both the location and scale of the extreme value distribution and exceed substantially the corresponding changes in the annual mean precipitation. Generally speaking, the warmer model climate becomes wetter and hydrologically more variable. The probability of precipitation events that are considered extreme at the beginning of the simulations is increased by a factor of about 2 by the end of the twenty-first century.

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Xuebin Zhang, Francis W. Zwiers, and Guilong Li

Abstract

Using Monte Carlo simulations, several methods for detecting a trend in the magnitude of extreme values are compared. Ordinary least squares regression is found to be the least reliable method. A Kendall's tau–based method provides some improvement. The advantage of this method over that of least squares diminishes when the sample size is moderate to small. Explicit consideration of the extreme value distribution when computing trend always outperforms the above two methods. The use of the r largest values as extremes enhances the power of detection for moderate values of r; the use of larger values of r may lead to bias in the magnitude of the estimated trend.

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Francis W. Zwiers, Xuebin Zhang, and Yang Feng

Abstract

Observed 1961–2000 annual extreme temperatures, namely annual maximum daily maximum (TXx) and minimum (TNx) temperatures and annual minimum daily maximum (TXn) and minimum (TNn) temperatures, are compared with those from climate simulations of multiple model ensembles with historical anthropogenic (ANT) forcing and with combined anthropogenic and natural external forcings (ALL) at both global and regional scales using a technique that allows changes in long return period extreme temperatures to be inferred. Generalized extreme value (GEV) distributions are fitted to the observed extreme temperatures using a time-evolving pattern of location parameters obtained from model-simulated extreme temperatures under ANT or ALL forcing. Evaluation of the parameters of the fitted GEV distributions shows that both ANT and ALL influence can be detected in TNx, TNn, TXn, and TXx at the global scale over the land areas for which there are observations, and also regionally over many large land areas, with detection in more regions in TNx. Therefore, it is concluded that the influence of anthropogenic forcing has had a detectable influence on extreme temperatures that have impacts on human society and natural systems at global and regional scales. External influence is estimated to have resulted in large changes in the likelihood of extreme annual maximum and minimum daily temperatures. Globally, waiting times for extreme annual minimum daily minimum and daily maximum temperature events that were expected to recur once every 20 yr in the 1960s are now estimated to exceed 35 and 30 yr, respectively. In contrast, waiting times for circa 1960s 20-yr extremes of annual maximum daily minimum and daily maximum temperatures are estimated to have decreased to fewer than 10 and 15 yr, respectively.

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