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Viatcheslav V. Kharin
and
Francis W. Zwiers

Abstract

Several methods of combining individual forecasts from a group of climate models to produce an ensemble forecast are considered. These methods are applied to an ensemble of 500-hPa geopotential height forecasts derived from the Atmospheric Model Intercomparison Project (AMIP) integrations performed by 10 different modeling groups. Forecasts are verified against reanalyses from the European Centre for Medium-Range Weather Forecasts. Forecast skill is measured by means of error variance. In the Tropics, the simple ensemble mean produces the most skillful forecasts. In the extratropics, the regression-improved ensemble mean performs best. The “superensemble” forecast that is obtained by optimally weighting the individual ensemble members does not perform as well as either the simple ensemble mean or the regression-improved ensemble mean. The sample size evidently is too small to estimate reliably the relatively large number of optimal weights required for the superensemble approach.

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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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Francis W. Zwiers
and
Viatcheslav V. Kharin

Abstract

Changes due to CO2 doubling in the extremes of the surface climate as simulated by the second-generation circulation model of the Canadian Centre for Climate Modelling and Analysis are studied in two 20-yr equilibrium simulations. Extreme values of screen temperature, precipitation, and near-surface wind in the control climate are compared to those estimated from 17 yr of the NCEP–NCAR reanalysis data and from some Canadian station data.

The extremes of screen temperature are reasonably well reproduced in the control climate. Their changes under CO2 doubling can be connected with other physical changes such as surface albedo changes due to the reduction of snow and sea ice cover as well as a decrease of soil moisture in the warmer world.

The signal in the extremes of daily precipitation and near-surface wind speed due to CO2 doubling is less obvious. The precipitation extremes increase almost everywhere over the globe. The strongest change, over northwest India, is related to the intensification of the summer monsoon in this region in the warmer world. The modest reduction of wind extremes in the Tropics and middle latitudes is consistent with the reduction of the meridional temperature gradient in the 2×CO2 climate. The larger wind extremes occur in the areas where sea ice has retreated.

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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 (T700) and 500-hPa height (Z500). The improvement technique is moderately successful for T700 but fails to improve Brier skill scores of the already relatively reliable raw Z500 probability forecasts.

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Viatcheslav V. Kharin
and
Francis W. Zwiers

Abstract

The relative operating characteristic (ROC) is a measure of the quality of probability forecasts that relates the hit rate to the corresponding false-alarm rate. This paper examines some aspects of the ROC curve for probability forecasts of three equiprobable categories (below normal, near normal, and above normal) in the framework of a simple analog of a climate forecasting system. The insensitivity of the ROC score to some types of forecast biases is discussed and the link to deterministic potential predictability is established in the context of the simple forecasting system. The findings are illustrated with a collection of 24-member ensemble hindcasts of seasonal mean 700-hPa temperature produced with the second-generation general circulation model of the Canadian Centre for Climate Modelling and Analysis.

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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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Qiaohong Sun
,
Francis Zwiers
,
Xuebin Zhang
, and
Jun Yan

Abstract

This study provides a comprehensive analysis of the human contribution to the observed intensification of precipitation extremes at different spatial scales. We consider the annual maxima of the logarithm of 1-day (Rx1day) and 5-day (Rx5day) precipitation amounts for 1950–2014 over the global land area, four continents, and several regions, and compare observed changes with expected responses to external forcings as simulated by CanESM2 in a large-ensemble experiment and by multiple models from phase 6 of the Coupled Model Intercomparison Project (CMIP6). We use a novel detection and attribution analysis method that is applied directly to station data in the areas considered without prior processing such as gridding, spatial or temporal dimension reduction, or transformation to unitless indices and uses climate models only to obtain estimates of the space–time pattern of extreme precipitation response to external forcing. The influence of anthropogenic forcings on extreme precipitation is detected over the global land area, three continental regions (the western Northern Hemisphere, western Eurasia, and eastern Eurasia), and many smaller IPCC regions, including central North America, East Asia, east-central Asia, eastern Europe, eastern North America, northern Europe, and western Siberia for Rx1day, and central North America, eastern Europe, eastern North America, northern Europe, the Russian Arctic region, and western Siberia for Rx5day. Consistent results are obtained using forcing response estimates from either CanESM2 or CMIP6. Anthropogenic influence is estimated to have substantially decreased the approximate waiting time between extreme annual maximum events in regions where anthropogenic influence has been detected, which has important implications for infrastructure design and climate change adaptation policy.

Significance Statement

All previous detection and attribution studies of observed changes in extreme precipitation (i) use station data that has been heavily processed via gridding, transformation, and spatial and temporal averaging or other dimension reduction approaches, as well as using climate models to estimate the responses to external forcing, and (ii) also use models to estimate the unforced natural variability of extreme precipitation. Both aspects reduce user confidence in detection and attribution results. This study uses station data directly and avoids difficult to verify model-based estimates of the unforced variability of precipitation extremes. Results confirm findings from previous studies, and extend them to a number of subcontinental regions, thus substantially increasing confidence in detection and attribution findings precipitation.

Open access
Qiaohong Sun
,
Francis Zwiers
,
Xuebin Zhang
, and
Yaheng Tan

Abstract

El Niño–Southern Oscillation (ENSO) has a profound influence on the occurrence of extreme precipitation events at local and regional scales in the present-day climate, and thus it is important to understand how that influence may change under future global warming. We consider this question using the large-ensemble simulations of CESM2, which simulates ENSO well historically. CESM2 projects that the influence of ENSO on extreme precipitation will strengthen further under the SSP3–7.0 scenario in most regions whose extreme precipitation regimes are strongly affected by ENSO in the boreal cold season. Extreme precipitation in the boreal cold season that exceeds historical thresholds is projected to become more common throughout the ENSO cycle. The difference in the intensity of extreme precipitation events that occur under El Niño and La Niña conditions will increase, resulting in “more extreme and more variable hydroclimate extremes.” We also consider the processes that affect the future intensity of extreme precipitation and how it varies with the ENSO cycle by partitioning changes into thermodynamic and dynamic components. The thermodynamic component, which reflects increases in atmospheric moisture content, results in a relatively uniform intensification of ENSO-driven extreme precipitation variation. In contrast, the dynamic component, which reflects changes in vertical motion, produces a strong regional difference in the response to forcing. In some regions, this component amplifies the thermodynamic-induced changes, while in others, it offsets them or even results in reduction in extreme precipitation variation.

Open access
Qiaohong Sun
,
Francis Zwiers
,
Xuebin Zhang
, and
Guilong Li

Abstract

Long-term changes in extreme daily and subdaily precipitation simulated by climate models are often compared with corresponding temperature changes to estimate the sensitivity of extreme precipitation to warming. Such “trend scaling” rates are difficult to estimate from observations, however, because of limited data availability and high background variability. Intra-annual temperature scaling (here called binning scaling), which relates extreme precipitation to temperature at or near the time of occurrence, has been suggested as a possible substitute for trend scaling. We use a large ensemble simulation of the Canadian regional climate model (CanRCM4) to assess this possibility, considering both daily near-surface air temperature and daily dewpoint temperature as scaling variables. We find that binning curves that are based on precipitation data for the whole year generally look like the composite of binning curves for winter and summer, with the lower temperature portion similar to winter and the higher temperature portion similar to summer, indicating that binning curves reflect seasonal changes in the relationship between temperature and extreme precipitation. The magnitude and spatial pattern of binning and trend scaling rates are also quantitatively different, with little spatial correlation between them, regardless of precipitation duration or choice of temperature variable. The evidence therefore suggests that binning scaling with temperature is not a reliable predictor for future changes in precipitation extremes in the climate simulated by CanRCM4. Nevertheless, external forcing does have a discernable influence on binning curves, which are seen to shift upward and to the right in some regions, consistent with a general increase in extreme precipitation.

Open access
Mohammad Reza Najafi
,
Francis Zwiers
, and
Nathan Gillett

Abstract

A detection and attribution analysis on the multidecadal trend in snow water equivalent (SWE) has been conducted in four river basins located in British Columbia (BC). Monthly output from a suite of 10 general circulation models (GCMs) that participated in phase 5 of the Coupled Model Intercomparison Project (CMIP5) is used, including 40 climate simulations with anthropogenic and natural forcing combined (ALL), 40 simulations with natural forcing alone (NAT), and approximately 4200 yr of preindustrial control simulations (CTL). This output was downscaled to ° spatial resolution and daily temporal resolution to drive the Variable Infiltration Capacity hydrologic model (VIC). Observed (manual snow survey) and VIC-reconstructed SWE, which exhibit declines across BC, are projected onto the multimodel ensemble means of the VIC-simulated SWE based on the responses to different forcings using an optimal fingerprinting approach. Results of the detection and attribution analysis shows that these declines are attributable to the anthropogenic forcing, which is dominated by the effect of increases in greenhouse gas concentration, and that they are not caused by natural forcing due to volcanic activity and solar variability combined. Anthropogenic influence is detected in three of the four basins (Fraser, Columbia, and Campbell Rivers) based on the VIC-reconstructed SWE, and in all basins based on the manual snow survey records. The simulations underestimate the observed snowpack trends in the Columbia River basin, which has the highest mean elevation. Attribution is supported by the detection of human influence on the cold-season temperatures that drive the snowpack reductions. These results are robust to the use of different observed datasets and to the treatment of low-frequency variability effects.

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