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Sillmann J.,University of Victoria | Kharin V.V.,University of Victoria | Zhang X.,Environment Canada | Zwiers F.W.,Pacific Climate Impacts Consortium | Bronaugh D.,Pacific Climate Impacts Consortium
Journal of Geophysical Research: Atmospheres | Year: 2013

This paper provides a first overview of the performance of state-of-the-art global climate models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) in simulating climate extremes indices defined by the Expert Team on Climate Change Detection and Indices (ETCCDI), and compares it to that in the previous model generation (CMIP3). For the first time, the indices based on daily temperature and precipitation are calculated with a consistent methodology across multimodel simulations and four reanalysis data sets (ERA40, ERA-Interim, NCEP/NCAR, and NCEP-DOE) and are made available at the ETCCDI indices archive website. Our analyses show that the CMIP5 models are generally able to simulate climate extremes and their trend patterns as represented by the indices in comparison to a gridded observational indices data set (HadEX2). The spread amongst CMIP5 models for several temperature indices is reduced compared to CMIP3 models, despite the larger number of models participating in CMIP5. Some improvements in the CMIP5 ensemble relative to CMIP3 are also found in the representation of the magnitude of precipitation indices. We find substantial discrepancies between the reanalyses, indicating considerable uncertainties regarding their simulation of extremes. The overall performance of individual models is summarized by a "portrait" diagram based on root-mean-square errors of model climatologies for each index and model relative to four reanalyses. This metric analysis shows that the median model climatology outperforms individual models for all indices, but the uncertainties related to the underlying reference data sets are reflected in the individual model performance metrics. Key PointsWe calculate indices in a consistent manner across models and reanalysesMulti-model ensembles compare reasonably well with observation-based indicesThere are large uncertainties in the representation of extremes in reanalyses © 2013. American Geophysical Union. All Rights Reserved.

Sillmann J.,University of Victoria | Kharin V.V.,University of Victoria | Zwiers F.W.,Pacific Climate Impacts Consortium | Zhang X.,Environment Canada | Bronaugh D.,Pacific Climate Impacts Consortium
Journal of Geophysical Research: Atmospheres | Year: 2013

[1] This study provides an overview of projected changes in climate extremes indices defined by the Expert Team on Climate Change Detection and Indices (ETCCDI). The temperature- and precipitation-based indices are computed with a consistent methodology for climate change simulations using different emission scenarios in the Coupled Model Intercomparison Project Phase 3 (CMIP3) and Phase 5 (CMIP5) multimodel ensembles. We analyze changes in the indices on global and regional scales over the 21st century relative to the reference period 1981-2000. In general, changes in indices based on daily minimum temperatures are found to be more pronounced than in indices based on daily maximum temperatures. Extreme precipitation generally increases faster than total wet-day precipitation. In regions, such as Australia, Central America, South Africa, and the Mediterranean, increases in consecutive dry days coincide with decreases in heavy precipitation days and maximum consecutive 5 day precipitation, which indicates future intensification of dry conditions. Particularly for the precipitation-based indices, there can be a wide disagreement about the sign of change between the models in some regions. Changes in temperature and precipitation indices are most pronounced under RCP8.5, with projected changes exceeding those discussed in previous studies based on SRES scenarios. The complete set of indices is made available via the ETCCDI indices archive to encourage further studies on the various aspects of changes in extremes. © 2013. American Geophysical Union. All Rights Reserved.

Donat M.G.,University of New South Wales | Sillmann J.,University of Victoria | Wild S.,University of Birmingham | Alexander L.V.,University of New South Wales | And 2 more authors.
Journal of Climate | Year: 2014

Changes in climate extremes are often monitored using global gridded datasets of climate extremes based on in situ observations or reanalysis data. This study assesses the consistency of temperature and precipitation extremes between these datasets. Both the temporal evolution and spatial patterns of annual extremes of daily values are compared across multiple global gridded datasets of in situ observations and reanalyses to make inferences on the robustness of the obtained results. While normalized time series generally compare well, the actual values of annual extremes of daily data differ systematically across the different datasets. This is partly related to different computational approaches when calculating the gridded fields of climate extremes. There is strong agreement between extreme temperatures in the different in situ-based datasets. Larger differences are found for temperature extremes from the reanalyses, particularly during the presatellite era, indicating that reanalyses are most consistent with purely observational-based analyses of changes in climate extremes for the three most recent decades. In terms of both temporal and spatial correlations, theECMWFreanalyses tend to show greater agreement with the gridded in situ-based datasets than the NCEP reanalyses and Japanese 25-year Reanalysis Project (JRA- 25). Extreme precipitation is characterized by higher temporal and spatial variability than extreme temperatures, and there is less agreement between different datasets than for temperature. However, reasonable agreement between the gridded observational precipitation datasets remains. Extreme precipitation patterns and time series from reanalyses show lower agreement but generally still correlate significantly. © 2014 American Meteorological Society.

Sillmann J.,University of Oslo | Kharin V.V.,Canadian Center for Climate Modelling and Analysis | Zwiers F.W.,Pacific Climate Impacts Consortium | Zhang X.,Environment Canada | And 2 more authors.
International Journal of Climatology | Year: 2014

Percentile indices monitoring the frequency of moderate temperature extremes are widely used to assess changes in present and future temperature extremes because of their straightforward interpretation. While observed trends in such indices can be, and have been, compared with model-simulated trends, their definition relative to each model's own climatology inhibits their use for the evaluation of model-simulated temperature variability. This is unfortunate, as in many parts of the world, indices from observations remain the only source of publicly available information about extreme temperature variability. We approach this problem by introducing a novel adjustment to the standard method for deriving indices from climate models. This involves the removal of the bias in the mean annual cycle of the models and the use of percentile thresholds from a reference data set. We illustrate the technique by comparing daily minimum (TN) and maximum (TX) temperatures from the fifth phase of Coupled Model Intercomparison Project (CMIP5) historical simulations with those from an observation-based data set and from the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) and the European Centre for Medium-Range Weather Forecasts (ERA-40) reanalyses. Biases in the annual cycle also translate into biases in the representation of the percentile indices in the models and reanalyses. Generally, percentile indices based on daily TX are well represented by the models and reanalyses compared to the observations. For percentile indices based on daily minimum temperature, however, large discrepancies occur particularly between the reanalyses. © 2013 Royal Meteorological Society.

Curry C.L.,University of Victoria | Sillmann J.,University of Victoria | Sillmann J.,University of Oslo | Bronaugh D.,Pacific Climate Impacts Consortium | And 11 more authors.
Journal of Geophysical Research G: Biogeosciences | Year: 2014

Temperature and precipitation extremes are examined in the Geoengineering Model Intercomparison Project experiment G1, wherein an instantaneous quadrupling of CO2 from its preindustrial control value is offset by a commensurate reduction in solar irradiance. Compared to the preindustrial climate, changes in climate extremes under G1 are generally much smaller than under 4 × CO2 alone. However, it is also the case that extremes of temperature and precipitation in G1 differ significantly from those under preindustrial conditions. Probability density functions of standardized anomalies of monthly surface temperature Τ and precipitation Ρ in G1 exhibit an extension of the high-Τ tail over land, of the low-Τ tail over ocean, and a shift of Ρ to drier conditions. Using daily model output, we analyzed the frequency of extreme events, such as the coldest night (TNn), warmest day (TXx), and maximum 5 day precipitation amount, and also duration indicators such as cold and warm spells and consecutive dry days. The strong heating at northern high latitudes simulated under 4 × CO2 is much alleviated in G1, but significant warming remains, particularly for TNn compared to TXx. Internal feedbacks lead to regional increases in absorbed solar radiation at the surface, increasing temperatures over Northern Hemisphere land in summer. Conversely, significant cooling occurs over the tropical oceans, increasing cold spell duration there. Globally, G1 is more effective in reducing changes in temperature extremes compared to precipitation extremes and for reducing changes in precipitation extremes versus means but somewhat less effective at reducing changes in temperature extremes compared to means.

Li G.,Environment Canada | Zhang X.,Environment Canada | Zwiers F.,Pacific Climate Impacts Consortium | Wen Q.H.,CAS Institute of Atmospheric Physics
Journal of Climate | Year: 2012

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 drivingGCM 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 twentyfirst 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. © 2012 American Meteorological Society.

Kim Y.-H.,Pohang University of Science and Technology | Min S.-K.,Pohang University of Science and Technology | Zhang X.,Environment Canada | Zwiers F.,Pacific Climate Impacts Consortium | And 3 more authors.
Climate Dynamics | Year: 2015

An attribution analysis of extreme temperature changes is conducted using updated observations (HadEX2) and multi-model climate simulation (CMIP5) datasets for an extended period of 1951–2010. Compared to previous HadEX/CMIP3-based results, which identified human contributions to the observed warming of extreme temperatures on global and regional scales, the current results provide better agreement with observations, particularly for the intensification of warm extremes. Removing the influence of two major modes of natural internal variability (the Arctic Oscillation and Pacific Decadal Oscillation) from observations further improves attribution results, reducing the model-observation discrepancy in cold extremes. An optimal fingerprinting technique is used to compare observed changes in annual extreme temperature indices of coldest night and day (TNn, TXn) and warmest night and day (TNx, TXx) with multi-model simulated changes that were simulated under natural-plus-anthropogenic and natural-only (NAT) forcings. Extreme indices are standardized for better intercomparisons between datasets and locations prior to analysis and averaged over spatial domains from global to continental regions following a previous study. Results confirm previous HadEX/CMIP3-based results in which anthropogenic (ANT) signals are robustly detected in the increase in global mean and northern continental regional means of the four indices of extreme temperatures. The detected ANT signals are also clearly separable from the response to NAT forcing, and results are generally insensitive to the use of different model samples as well as different data availability. © 2015 Springer-Verlag Berlin Heidelberg

Werner A.T.,Pacific Climate Impacts Consortium | Cannon A.J.,Environment Canada
Hydrology and Earth System Sciences | Year: 2016

Gridded statistical downscaling methods are the main means of preparing climate model data to drive distributed hydrological models. Past work on the validation of climate downscaling methods has focused on temperature and precipitation, with less attention paid to the ultimate outputs from hydrological models. Also, as attention shifts towards projections of extreme events, downscaling comparisons now commonly assess methods in terms of climate extremes, but hydrologic extremes are less well explored. Here, we test the ability of gridded downscaling models to replicate historical properties of climate and hydrologic extremes, as measured in terms of temporal sequencing (i.e. correlation tests) and distributional properties (i.e. tests for equality of probability distributions). Outputs from seven downscaling methods - bias correction constructed analogues (BCCA), double BCCA (DBCCA), BCCA with quantile mapping reordering (BCCAQ), bias correction spatial disaggregation (BCSD), BCSD using minimum/maximum temperature (BCSDX), the climate imprint delta method (CI), and bias corrected CI (BCCI) - are used to drive the Variable Infiltration Capacity (VIC) model over the snow-dominated Peace River basin, British Columbia. Outputs are tested using split-sample validation on 26 climate extremes indices (ClimDEX) and two hydrologic extremes indices (3-day peak flow and 7-day peak flow). To characterize observational uncertainty, four atmospheric reanalyses are used as climate model surrogates and two gridded observational data sets are used as downscaling target data. The skill of the downscaling methods generally depended on reanalysis and gridded observational data set. However, CI failed to reproduce the distribution and BCSD and BCSDX the timing of winter 7-day low-flow events, regardless of reanalysis or observational data set. Overall, DBCCA passed the greatest number of tests for the ClimDEX indices, while BCCAQ, which is designed to more accurately resolve event-scale spatial gradients, passed the greatest number of tests for hydrologic extremes. Non-stationarity in the observational/reanalysis data sets complicated the evaluation of downscaling performance. Comparing temporal homogeneity and trends in climate indices and hydrological model outputs calculated from downscaled reanalyses and gridded observations was useful for diagnosing the reliability of the various historical data sets. We recommend that such analyses be conducted before such data are used to construct future hydro-climatic change scenarios. © Author(s) 2016.

Burger G.,Pacific Climate Impacts Consortium
Climate of the Past | Year: 2010

A systematic coherence analysis is presented for the set of the most prominent millennial reconstructions of northern hemispheric temperature. The large number of mutual coherences underwent a clustering analysis that revealed five significant, mutually incoherent ("inconsistent") clusters. The use of multiple proxies seems to be causing the clustering, at least in part, but not in an easily definable, physical way. Alternatively, a multidimensional scaling is performed on the same set of coherences. This results in a graphic, two-dimensional rendering of the reconstructions whose geometry (location and distance) is given by the coherences. Both approaches offer complementary ways in dealing with the inconsistencies. © Author(s) 2010.

Sillmann J.,CICERO Center for International Climate and Environmental Research | Donat M.G.,University of New South Wales | Fyfe J.C.,Canadian Center for Climate Modelling and Analysis | Zwiers F.W.,Pacific Climate Impacts Consortium
Environmental Research Letters | Year: 2014

The discrepancy between recent observed and simulated trends in global mean surface temperature has provoked a debate about possible causes and implications for future climate change projections. However, little has been said in this discussion about observed and simulated trends in global temperature extremes. Here we assess trend patterns in temperature extremes and evaluate the consistency between observed and simulated temperature extremes over the past four decades (1971-2010) in comparison to the recent 15 years (1996-2010). We consider the coldest night and warmest day in a year in the observational dataset HadEX2 and in the current generation of global climate models (CMIP5). In general, the observed trends fall within the simulated range of trends, with better consistency for the longer period. Spatial trend patterns differ for the warm and cold extremes, with the warm extremes showing continuous positive trends across the globe and the cold extremes exhibiting a coherent cooling pattern across the Northern Hemisphere mid-latitudes that has emerged in the recent 15 years and is not reproduced by the models. This regional inconsistency between models and observations might be a key to understanding the recent hiatus in global mean temperature warming. © 2014 IOP Publishing Ltd.

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