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Guilong Li
,
Xuebin Zhang
,
Francis Zwiers
, and
Qiuzi H. Wen

Abstract

A framework for the construction of probabilistic projections of high-resolution monthly temperature over North America using available outputs of opportunity from ensembles of multiple general circulation models (GCMs) and multiple regional climate models (RCMs) is proposed. In this approach, a statistical relationship is first established between RCM output and that from the respective driving GCM and then this relationship is applied to downscale outputs from a larger number of GCM simulations. Those statistically downscaled projections were used to estimate empirical quantiles at high resolution. Uncertainty in the projected temperature was partitioned into four sources including differences in GCMs, internal variability simulated by GCMs, differences in RCMs, and statistical downscaling including internal variability at finer spatial scale. Large spatial variability in projected future temperature changes is found, with increasingly larger changes toward the north in winter temperature and larger changes in the central United States in summer temperature. Under a given emission scenario, downscaling from large scale to small scale is the most important source of uncertainty, though structural errors in GCMs become equally important by the end of the twenty-first century. Different emission scenarios yield different projections of temperature change. This difference increases with time. The difference between the IPCC’s Special Report on Emissions Scenarios (SRES) A2 and B1 in the median values of projected changes in 30-yr mean temperature is small for the coming 30 yr, but can become almost as large as the total variance due to internal variability and modeling errors in both GCM and RCM later in the twenty-first century.

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Aurélien Ribes
,
Nathan P. Gillett
, and
Francis W. Zwiers

Abstract

Climate change detection and attribution studies rely on historical simulations using specified combinations of forcings to quantify the contributions from greenhouse gases and other forcings to observed climate change. In the last CMIP5 exercise, in addition to the so-called all-forcings simulations, which are driven with a combination of anthropogenic and natural forcings, natural forcings–only and greenhouse gas–only simulations were prioritized among other possible experiments. This study addresses the question of optimally designing this set of experiments to estimate the recent greenhouse gas–induced warming, which is highly relevant to the problem of constraining estimates of the transient climate response. Based on Monte Carlo simulations and considering experimental designs with a fixed budget for the number of simulations that modeling centers can perform, the most accurate estimate of historical greenhouse gas–induced warming is obtained with a design using a combination of all-forcings, natural forcings–only, and aerosol forcing–only simulations. An investigation of optimal ensemble sizes, given the constraint on the total number of simulations, indicates that allocating larger ensemble sizes to weaker forcings, such as natural-only, is optimal.

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Xiaolan L. Wang
,
Francis W. Zwiers
, and
Val R. Swail

Abstract

Using the observed relationships between sea level pressure (SLP) and significant wave height (SWH) as represented by regression models, climate change scenarios of SWH in the North Atlantic were constructed by means of redundancy analysis (for seasonal means and 90th percentiles of SWH) and nonstationary generalized extreme value analysis (for seasonal extreme SWH). SWH scenarios are constructed using output from a coupled climate model under three different forcing scenarios. Scenarios of future anomaly seasonal statistics of SWH are constructed using climate model projections of anomaly seasonal mean SLP while projections of seasonal extreme SWH are made using projections of seasonal mean SLP and seasonal SLP gradient index. The projected changes in SWH are assessed by means of a trend analysis.

The northeast Atlantic is projected to have increases in both winter and fall seasonal means and extremes of SWH in the twenty-first century under all three forcing scenarios. These changes are generally accompanied by decreases in the midlatitudes of the North Atlantic and increases in the southwest North Atlantic. The rate and sign of the projected SWH change is not constant throughout the twenty-first century. In the Norwegian and North Seas, the projected SWH changes are characterized either by faster increases in the late decades than in the early decades, or by decreases in the early decades followed by increases, depending on the forcing scenario and the specific location. Using lower or higher rates of increase in greenhouse gases forcing generally leads to reduced or increased rates of change, respectively, in ocean wave heights. The sign and rate of future wave height changes in the North Sea in particular appear to be quite dependent on the forcing conditions. In general, global warming is associated with more frequent occurrence of the positive phase of the North Atlantic Oscillation (NAO) and strong cyclones, which leads to increases of wave heights in the northeast Atlantic.

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Aiming Wu
,
William W. Hsieh
, and
Francis W. Zwiers

Abstract

Nonlinear principal component analysis (NLPCA), via a neural network (NN) approach, was applied to an ensemble of six 47-yr simulations conducted by the Canadian Centre for Climate Modelling and Analysis (CCCma) second-generation atmospheric general circulation model (AGCM2). Each simulation was forced with the observed sea surface temperature [from the Global Sea Ice and Sea Surface Temperature dataset (GISST)] from January 1948 to November 1994. The NLPCA modes reveal nonlinear structures in both the winter 500-mb geopotential height (Z500) anomalies and surface air temperature (SAT) anomalies over North America, with asymmetric spatial anomaly patterns during the opposite phases of an NLPCA mode. Only during its negative phase is the first NLPCA mode related to the El Niño–Southern Oscillation (ENSO); the positive phase is related to a weakened jet stream. Spatial patterns of the NLPCA mode for the Z500 anomalies generally agree with those for the SAT anomalies.

Nonlinear canonical correlation analysis (NLCCA), also via an NN approach, was then applied to the midlatitude winter GCM data and the observed SST of the tropical Pacific. Nonlinearity was detected in both the forcing field (SST) and the response field (Z500 or SAT) at zero time lag. The leading NLCCA mode for the SST anomalies is a nonlinear ENSO mode, with a 30°–40° eastward shift of the positive SST anomalies during El Niño relative to the negative SST anomalies during La Niña. The leading NLCCA mode for the Z500 anomaly field is a nonlinear Pacific–North American (PNA) teleconnection pattern. The ray path of the Rossby waves induced during El Niño is 10°–15° east of that induced during La Niña. The nonlinear atmospheric response to ENSO is also found in the leading NLCCA mode for the SAT anomalies.

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Xuebin Zhang
,
Francis W. Zwiers
, and
P. A. Stott

Abstract

Using an optimal detection technique and climate change simulations produced with two versions of two GCMs, we have assessed the causes of twentieth-century temperature changes from global to regional scales. Our analysis is conducted in nine spatial domains: 1) the globe; 2) the Northern Hemisphere; four large regions in the Northern Hemispheric midlatitudes covering 30°–70°N including 3) Eurasia, 4) North America, 5) Northern Hemispheric land only, 6) the entire 30°–70°N belt; and three smaller regions over 7) southern Canada, 8) southern Europe, and 9) China. We find that the effect of anthropogenic forcing on climate is clearly detectable at global through regional scales.

The effect of combined greenhouse gases and sulfate aerosol forcing is detectable in all nine domains in annual and seasonal mean temperatures observed during the second half of the twentieth century. The effect of greenhouse gases can also be separated from that of sulfate aerosols over this period at continental and regional scales. Uncertainty in these results is larger in the smaller spatial domains. Detection is improved when an ensemble of models is used to estimate the response to anthropogenic forcing and the underlying internal variability of the climate system. Our detection results hold after removal of North Atlantic Oscillation (NAO)-related variability in temperature observations—variability that may or may not be associated with anthropogenic forcing. They also continue to hold when our estimates of natural internal climate variability are doubled.

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Xiaolan L. Wang
,
Val R. Swail
, and
Francis W. Zwiers

Abstract

In this study, a cyclone detection/tracking algorithm was used to identify cyclones from two gridded 6-hourly mean sea level pressure datasets: the 40-yr ECMWF Re-Analysis (ERA-40) and the NCEP–NCAR reanalysis (NNR) for 1958–2001. The cyclone activity climatology and changes inferred from the two reanalyses are intercompared. The cyclone climatologies and trends are found to be in reasonably good agreement with each other over northern Europe and eastern North America, while ERA-40 shows systematically stronger cyclone activity over the boreal extratropical oceans than does NNR. However, significant differences between ERA-40 and NNR are seen over the austral extratropics. In particular, ERA-40 shows significantly greater strong-cyclone activity and less weak-cyclone activity over all oceanic areas south of 40°S in all seasons, while it shows significantly stronger cyclone activity over most areas of the austral subtropics in the warm seasons.

The most notable historical trends in cyclone activity are found to be associated with strong-cyclone activity. Over the boreal extratropics, both ERA-40 and NNR show a significant increasing trend in January–March (JFM) strong-cyclone activity over the high-latitude North Atlantic and over the midlatitude North Pacific, with a significant decreasing trend over the midlatitude North Atlantic and a small increasing trend over northern Europe. The JFM changes over the North Atlantic are associated with the mean position of the storm track shifting about 181 km northward. Importantly, there is no evidence of abrupt changes identified for the boreal extratropics, although previous studies have suggested that the upward trend found in the NNR data could be biased high. However, there exist a few abrupt changes over the austral extratropics, which appear to be attributable to the increasing availability of observations assimilated in the reanalyses. After diminishing the effects of these abrupt changes, strong-cyclone activity over the austral circumpolar oceanic region is identified to have an increasing trend in October–December (OND) and July–September (JAS), with a decreasing trend over the 40°–60°S zone in JAS.

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Viatcheslav V. Kharin
,
Francis W. Zwiers
,
Xuebin Zhang
, and
Gabriele C. Hegerl

Abstract

Temperature and precipitation extremes and their potential future changes are evaluated in an ensemble of global coupled climate models participating in the Intergovernmental Panel on Climate Change (IPCC) diagnostic exercise for the Fourth Assessment Report (AR4). Climate extremes are expressed in terms of 20-yr return values of annual extremes of near-surface temperature and 24-h precipitation amounts. The simulated changes in extremes are documented for years 2046–65 and 2081–2100 relative to 1981–2000 in experiments with the Special Report on Emissions Scenarios (SRES) B1, A1B, and A2 emission scenarios.

Overall, the climate models simulate present-day warm extremes reasonably well on the global scale, as compared to estimates from reanalyses. The model discrepancies in simulating cold extremes are generally larger than those for warm extremes, especially in sea ice–covered areas. Simulated present-day precipitation extremes are plausible in the extratropics, but uncertainties in extreme precipitation in the Tropics are very large, both in the models and the available observationally based datasets.

Changes in warm extremes generally follow changes in the mean summertime temperature. Cold extremes warm faster than warm extremes by about 30%–40%, globally averaged. The excessive warming of cold extremes is generally confined to regions where snow and sea ice retreat with global warming. With the exception of northern polar latitudes, relative changes in the intensity of precipitation extremes generally exceed relative changes in annual mean precipitation, particularly in tropical and subtropical regions. Consistent with the increased intensity of precipitation extremes, waiting times for late-twentieth-century extreme precipitation events are reduced almost everywhere, with the exception of a few subtropical regions. The multimodel multiscenario consensus on the projected change in the globally averaged 20-yr return values of annual extremes of 24-h precipitation amounts is that there will be an increase of about 6% with each kelvin of global warming, with the bulk of models simulating values in the range of 4%–10% K−1. The very large intermodel disagreements in the Tropics suggest that some physical processes associated with extreme precipitation are not well represented in models. This reduces confidence in the projected changes in extreme precipitation.

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Terry C. K. Lee
,
Francis W. Zwiers
,
Gabriele C. Hegerl
,
Xuebin Zhang
, and
Min Tsao

Abstract

A Bayesian analysis of the evidence for human-induced climate change in global surface temperature observations is described. The analysis uses the standard optimal detection approach and explicitly incorporates prior knowledge about uncertainty and the influence of humans on the climate. This knowledge is expressed through prior distributions that are noncommittal on the climate change question. Evidence for detection and attribution is assessed probabilistically using clearly defined criteria. Detection requires that there is high likelihood that a given climate-model-simulated response to historical changes in greenhouse gas concentration and sulphate aerosol loading has been identified in observations. Attribution entails a more complex process that involves both the elimination of other plausible explanations of change and an assessment of the likelihood that the climate-model-simulated response to historical forcing changes is correct. The Bayesian formalism used in this study deals with this latter aspect of attribution in a more satisfactory way than the standard attribution consistency test. Very strong evidence is found to support the detection of an anthropogenic influence on the climate of the twentieth century. However, the evidence from the Bayesian attribution assessment is not as strong, possibly due to the limited length of the available observational record or sources of external forcing on the climate system that have not been accounted for in this study. It is estimated that strong evidence from a Bayesian attribution assessment using a relatively stringent attribution criterion may be available by 2020.

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Terry C. K. Lee
,
Francis W. Zwiers
,
Xuebin Zhang
, and
Min Tsao

Abstract

It is argued that simulations of the twentieth century performed with coupled global climate models with specified historical changes in external radiative forcing can be interpreted as climate hindcasts. A simple Bayesian method for postprocessing such simulations is described, which produces probabilistic hindcasts of interdecadal temperature changes on large spatial scales. Hindcasts produced for the last two decades of the twentieth century are shown to be skillful. The suggestion that skillful decadal forecasts can be produced on large regional scales by exploiting the response to anthropogenic forcing provides additional evidence that anthropogenic change in the composition of the atmosphere has influenced the climate. In the absence of large negative volcanic forcing on the climate system (which cannot presently be forecast), it is predicted that the global mean temperature for the decade 2000–09 will lie above the 1970–99 normal with a probability of 0.94. The global mean temperature anomaly for this decade relative to 1970–99 is predicted to be 0.35°C with a 5%–95% confidence range of 0.21°–0.48°C.

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Chao Li
,
Francis Zwiers
,
Xuebin Zhang
,
Guilong Li
,
Ying Sun
, and
Michael Wehner

Abstract

This study presents an analysis of daily temperature and precipitation extremes with return periods ranging from 2 to 50 years in phase 6 of the Coupled Model Intercomparison Project (CMIP6) multimodel ensemble of simulations. Judged by similarity with reanalyses, the new-generation models simulate the present-day temperature and precipitation extremes reasonably well. In line with previous CMIP simulations, the new simulations continue to project a large-scale picture of more frequent and more intense hot temperature extremes and precipitation extremes and vanishing cold extremes under continued global warming. Changes in temperature extremes outpace changes in global annual mean surface air temperature (GSAT) over most landmasses, while changes in precipitation extremes follow changes in GSAT globally at roughly the Clausius–Clapeyron rate of ~7% °C−1. Changes in temperature and precipitation extremes normalized with respect to GSAT do not depend strongly on the choice of forcing scenario or model climate sensitivity, and do not vary strongly over time, but with notable regional variations. Over the majority of land regions, the projected intensity increases and relative frequency increases tend to be larger for more extreme hot temperature and precipitation events than for weaker events. To obtain robust estimates of these changes at local scales, large initial-condition ensemble simulations are needed. Appropriate spatial pooling of data from neighboring grid cells within individual simulations can, to some extent, reduce the needed ensemble size.

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