Search Results

You are looking at 1 - 10 of 53 items for

  • Author or Editor: Francis Zwiers x
  • Refine by Access: All Content x
Clear All Modify Search
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.

Full access
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.

Full access
Francis W. Zwiers
and
Hans von Storch

Abstract

Recurrence analysis was introduced to infer the degree of separation between a “control” and an “anomaly” ensemble of, say, seasonal means simulated in general circulation model (GCM) experiments. The concept of recurrence analysis is described as a particular application of a statistical technique called multiple discriminant analysis (MDA). Using MDA, univariate recurrence is easily generalized to multicomponent problems. Algorithms that can be used to estimate the level of recurrence and tests that can be used to assess the confidence in a priori specified levels of recurrence are presented.

Several of the techniques are used to reanalyze a series of El Niño sensitivity experiments conducted with the Canadian Climate Centre GCM. The simulated El Niño response in DJF mean 500 mb height are all estimated to be more than 94% recurrent in the tropics and are estimated to be between 90% and 959b recurrent in the Northern Hemisphere between 20° and 60°N latitude.

Discrimination rules that can be used to classify individual realizations of climate as members of the control or “experimental” ensemble are obtained as a by-product of the multiple recurrence analysis. We show that it is possible to make reasonable inferences about the state of the eastern Pacific sea surface temperature by classifying observed DJF 500 mb height fields with discrimination rules derived from the GCM experiments.

Full access
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.

Full access
Viatcheslav V. Kharin
and
Francis W. Zwiers

Abstract

The extremes of surface temperature, precipitation, and wind speed and their changes under projected changes in radiative forcing are examined in an ensemble of three transient climate change simulations for the years 1900–2100 conducted with the global coupled model of the Canadian Centre for Climate Modelling and Analysis. The evolution of the greenhouse gases and aerosols in these simulations is consistent with the Intergovernmental Panel on Climate Change 1992 scenario A. The extremes are analyzed in three 21-yr time periods centered at years 1985, 2050, and 2090.

The model simulates reasonably well the extremes of the contemporary near-surface climate. Changes in extremes of daily maximum and daily minimum temperature are distinctively different and are related to changes in the mean screen temperature, soil moisture, and snow and sea-ice cover. Extreme precipitation increases almost everywhere on the globe. Relative change in extreme precipitation is larger than change in total precipitation. Extreme wind speed in the extratropics changes only modestly. Changes in duration of extended wet and dry periods are consistent with changes in total precipitation. There are temperature-related changes in cooling and heating degree days.

Full access
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.

Full access
Francis W. Zwiers
and
Hans von Storch

Abstract

The comparison of means derived from samples of noisy data is a standard pan of climatology. When the data are not serially correlated the appropriate statistical tool for this task is usually the conventional Student's t-test. However, frequently data are serially correlated in climatological applications with the result that the t test in its standard form is not applicable. The usual solution to this problem is to scale the t statistic by a factor that depends upon the equivalent sample size ne .

It is shown, by means of simulations, that the revised t tea is often conservative (the actual significance level is smaller than the specified significance level) when the equivalent sample size is known. However, in most practical cases the equivalent sample size is not known. Then the test becomes liberal (the actual significance level is greater than the specified significance level). This systematic error becomes small when the true equivalent sample size is large (greater than approximately 30).

The difficulties inherent in difference of means tests when there is serial dependence are reexamined. Guidelines for the application of the “usual” t test are provided and two alternative tests are proposed that substantially improve upon the “usual” t test when samples are small.

Full access
Kirien Whan
,
Francis Zwiers
, and
Jana Sillmann

Abstract

Regional climate models (RCMs) are the primary source of high-resolution climate projections, and it is of crucial importance to evaluate their ability to simulate extreme events under current climate conditions. Many extreme events are influenced by circulation features that occur outside, or on the edges of, RCM domains. Thus, it is of interest to know whether such dynamically controlled aspects of extremes are well represented by RCMs. This study assesses the relationship between upstream blocking and cold temperature extremes over North America in observations, reanalysis products (ERA-Interim and NARR), and RCMs (CanRCM4, CRCM5, HIRHAM5, and RCA4). Generalized extreme value distributions were fitted to winter minimum temperature (TNn) incorporating blocking frequency (BF) as a covariate, which is shown to have a significant influence on TNn. The magnitude of blocking influence in the RCMs is consistent with observations, but the spatial extent varies. CRCM5 and HIRHAM5 reproduce the pattern of influence best compared to observations. CanRCM4 and RCA4 capture the influence of blocking in British Columbia and the northeastern United States, but the extension of influence that is seen in observations and reanalysis into the southern United States is not evident. The difference in the 20-yr return value (20RV) of TNn between high and low BF in the Pacific Ocean indicates that blocking is associated with a decrease of up to 15°C in the 20RV over the majority of the United States and in western Canada. In northern North America the difference in the 20RV is positive as blocking is associated with warmer extreme cold temperatures. The 20RVs are generally simulated well by the RCMs.

Full access
Bárbara Tencer
,
Andrew Weaver
, and
Francis Zwiers

Abstract

The occurrence of individual extremes such as temperature and precipitation extremes can have a great impact on the environment. Agriculture, energy demands, and human health, among other activities, can be affected by extremely high or low temperatures and by extremely dry or wet conditions. The simultaneous or proximate occurrence of both types of extremes could lead to even more profound consequences, however. For example, a dry period can have more negative consequences on agriculture if it is concomitant with or followed by a period of extremely high temperatures. This study analyzes the joint occurrence of very wet conditions and high/low temperature events at stations in Canada. More than one-half of the stations showed a significant positive relationship at the daily time scale between warm nights (daily minimum temperature greater than the 90th percentile) or warm days (daily maximum temperature above the 90th percentile) and heavy-precipitation events (daily precipitation exceeding the 75th percentile), with the greater frequencies found for the east and southwest coasts during autumn and winter. Cold days (daily maximum temperature below the 10th percentile) occur together with intense precipitation more frequently during spring and summer. Simulations by regional climate models show good agreement with observations in the seasonal and spatial variability of the joint distribution, especially when an ensemble of simulations was used.

Full access
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.

Full access