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.
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.
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.
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.
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.