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  • Source Publication: Climatic Change, 137, 1, 201–216, doi:10.1007/s10584-016-1669-2 Authors: Schar, C., N. Ban, E.M. Fischer, J. Rajczak, J. Schmidli, C. Frei, F. Giorgi, T.R. Karl, E.J. Kendon, A.M.G. Klein Tank, P.A. O'Gorman, J. Sillmann, X. Zhang and F.W. Zwiers Publication Date: Jul 2016

    Many climate studies assess trends and projections in heavy precipitation events using precipitation percentile (or quantile) indices. Here we investigate three different percentile indices that are commonly used. We demonstrate that these may produce very different results and thus require great care with interpretation. More specifically, consideration is given to two intensity-based indices and one frequency-based index, namely (a) all-day percentiles, (b) wet-day percentiles, and (c) frequency indices based on the exceedance of a percentile threshold.

    Wet-day percentiles are conditionally computed for the subset of wet events (with precipitation exceeding some threshold, e.g. 1 mm/d for daily precipitation). We present evidence that this commonly used methodology can lead to artifacts and misleading results if significant changes in the wet-day frequency are not accounted for. Percentile threshold indices measure the frequency of exceedance with respect to a percentile-based threshold. We show that these indices yield an assessment of changes in heavy precipitation events that is qualitatively consistent with all-day percentiles, but there are substantial differences in quantitative terms. We discuss the reasons for these effects, present a theoretical assessment, and provide a series of examples using global and regional climate models to quantify the effects in typical applications.

    Application to climate model output shows that these considerations are relevant to a wide range of typical climate-change applications. In particular, wet-day percentiles generally yield different results, and in most instances should not be used for the impact-oriented assessment of changes in heavy precipitation events.

  • Source Publication: Atmosphere-Ocean, doi:10.1080/07055900.2016.1158146. Authors: C.L. Curry, B. Tencer, K. Whan, A. J. Weaver, M. Giguère and E. Wiebe Publication Date: Jul 2016

    We evaluate the capacity of a regional climate model to represent observed extreme temperature and precipitation events and also examine the impact of increased resolution, in an effort to identify added value in this respect. Two climate simulations of western Canada (WCan) were conducted with the Canadian Regional Climate Model (version 4) at 15 (CRCM15) and 45 km (CRCM45) horizontal resolution driven at the lateral boundaries by data from the European Centre for Medium-range Weather Forecasts (ECMWF) 40-year Reanalysis (ERA-40) for the period 1973–1995. The simulations were evaluated using the spline-interpolated dataset ANUSPLIN, a daily observational gridded surface temperature and precipitation product with a nominal resolution of approximately 10 km. We examine a range of climate extremes, comprising the 10th and 90th percentiles of daily maximum (TX) and minimum (TN) temperatures, the 90th percentile of daily precipitation (PR90), and the 27 core Climate Daily Extremes (CLIMDEX) indices.

    Both simulations exhibit cold biases compared with observations over WCan, with the bias exacerbated at higher resolution, suggesting little added value for temperature overall. There are instances, however, of regional improvement in the spatial pattern of temperature extremes at the higher resolution of CRCM15 (e.g., the CLIMDEX index for the annual number of days when TX > 25°C). The high-resolution simulations also reveal similarly localized features in precipitation (e.g., rain shadows) that are not resolved at the 45 km resolution. With regard to precipitation extremes, although both simulations generally display wet biases, CRCM15 features a reduced bias in PR90 in all seasons except winter. This improvement occurs despite the fact that spatial and interannual variability of PR90 in CRCM15 is significantly overestimated relative to both CRCM45 and ANUSPLIN. We posit that these characteristics are the result of demonstrable differences between corresponding topographical datasets used in the gridded observations and CRCM, the resulting errors propagated to physical variables tied to elevation and the beneficial effect of subsequent spatial averaging. Because topographical input is often discordant between simulations and gridded observations, it is argued that a limited form of spatial averaging may contribute added value beyond that which has already been noted in previous studies with respect to small-scale climate variability.

  • Source Publication: Atmosphere-Ocean, in press. Authors: Curry, C.L., B. Tencer, K. Whan, A. J. Weaver, M. Giguère and E. Wiebe Publication Date: Jul 2016

    Currently in press.

  • Source Publication: Water Resources Research, 52, 4, 3127–3142, doi:10.1002/2016WR018607 Authors: Kumar, S., F.W. Zwiers, P.A. Dirmeyer, D.M. Lawrence., R. Shrestha and A. Werner Publication Date: Jul 2016

    This study investigates a physical basis for heterogeneity in hydrological changes, which suggests a greater detectability in wet than dry regions. Wet regions are those where atmospheric demand is less than precipitation (energy limited), and dry regions are those where atmospheric demand is greater than precipitation (water limited). Long-term streamflow trends in western North America and an analysis of Coupled Model Intercomparison Project Phase 5 (CMIP5) climate models at global scales show geographically heterogeneous detectability of hydrological changes. We apply the Budyko framework and state-of-the-art climate model data from CMIP5 to quantify the sensitivity and detectability of terrestrial hydrological changes. The Budyko framework quantifies the partitioning of precipitation into evapotranspiration and runoff components. We find that the terrestrial hydrological sensitivity is 3 times greater in regions where the hydrological cycle is energy limited rather than water limited. This additional source (the terrestrial part) contributes to 30–40% greater detectability in energy-limited regions. We also quantified the contribution of changes in the catchment efficiency parameter that oppose the effects of increasing evaporative demand in global warming scenarios. Incorporating changes to the catchment efficiency parameter in the Budyko framework reduces dry biases in global runoff change projections by 88% in the 21st century.

  • Source Publication: Climate Dynamics, doi:10.1007/s00382-016-3148-x Authors: Whan, K. and F.W. Zwiers Publication Date: Jul 2016

    The relationship between winter precipitation in North America and indices of the North Atlantic Oscillation (NAO) and El Niño–Southern Oscillation (ENSO) is evaluated using non-stationary generalized extreme value distributions with the indices as covariates. Both covariates have a statistically significant influence on precipitation that is well simulated by two regional climate models (RCMs), CanRCM4 and CRCM5. The observed influence of the NAO on extreme precipitation is largest in eastern North America, with the likelihood of a negative phase extreme rainfall event decreased in the north and increased in the south under the positive phase of the NAO. This pattern is generally well simulated by the RCMs although there are some differences in the extent of influence, particularly south of the Great Lakes. A La Niña-magnitude extreme event is more likely to occur under El Niño conditions in California and the southern United States, and less likely in most of Canada and a region south of the Great Lakes. This broad pattern is also simulated well by the RCMs but they do not capture the increased likelihood in California. In some places the extreme precipitation response in the RCMs to external forcing from a covariate is of the opposite sign, despite use of the same lateral boundary conditions and dynamical core. This demonstrates the importance of model physics for teleconnections to extreme precipitation.

  • Authors: The Pacific Climate Impacts Consortium Publication Date: Jul 2016

    This PCIC Science Brief covers a recent paper by Sigmond and Fyfe (2016) that was published in Nature Climate Change. The authors investigate the causes of cooler winters over the early 2000s in North America and find that they vary by region. In the northwest, these cooler winters were largely due to a pattern of western cooling and central warming in the tropical Pacific Ocean. In central North America, the cooler winters were primarily due to changes in the northerly winds driven by increased sea level pressure on the west coast of North America.

  • Authors: Francis Zwiers Publication Date: Jun 2016

    This presentation covers the detection and attribution of long-term changes in climate and event attribution. Specific examples include Arctic sea ice extent, the 2013 flooding in Calgary and the summer of 2013 in China.

  • Authors: Christian Seiler, Francis W. Zwiers and Kevin I. Hodges Publication Date: Jun 2016
  • Authors: T. Q. MURDOCK, A. J. CANNON, AND S.R. SOBIE Publication Date: Jun 2016

    The need for future projections of extremes is growing, particularly as users planning to adapt to climate change continue to experience record-breaking events (Figure 1). Decision-making demands that such projections possess high spatial resolution. Downscaling has been carried out for Canada by the Pacic Climate Impacts Consortium for the newest Global Climate Model (GCM) and Regional Climate Model (RCM) projections.

  • Authors: G. Bürger, T. Q. Murdock, A. T. Werner, S. R. Sobie Publication Date: Jun 2016

    Empirical downscaling is based in a statistical analysis of present climatic conditions for an area, usually recorded by a number of variables from weather stations. The result of this analysis is a set of recipes (algorithms) and parameters from which the present climate, or at least some of its crucial aspects, such as extreme temperature values, can be recovered.

  • Authors: S.R. Sobie, A.J. Cannon, T.Q. Murdock Publication Date: Jun 2016

    For terrestrial British Columbia, precipitation averages and extremes can be simulated more accurately within individual regions by using gridded downscaling to increase the resolution of regional climate models. In locations where the difference between observations and RCMs is large, bias correction tends to inflate the magnitude of projected extremes. Differences in projections between the RCMs and downscaled simulations can be large enough to affect the PIEVC Risk Assessment process, leading to higher risk values. Future work will focus on correcting the inflation of extremes in the downscaling method and extending the analysis to additional regions.

  • Authors: S.R. Sobie, A.J. Cannon, T.Q. Murdock Publication Date: Jun 2016

    For terrestrial British Columbia, precipitation averages and extremes can be simulated more accurately within individual regions by using gridded downscaling to increase the resolution of both global and regional climate models. In locations where the difference between observations and model simulations is large, bias correction tends to inflate the magnitude of high resolution projected extremes. The effect is minimal for average indices but is statistically significant in a subset of models for projected changes of 10 and 20-year return periods. Future work will focus on correcting the inflation of extremes in the downscaling method and extending the analysis to additional regions.

  • Authors: T. Q. MURDOCK, S.R. SOBIE, A. J. CANNON, AND F.W. ZWIERS Publication Date: Jun 2016

    The main effect of statistical downscaling on projected change in extremes is due to correction of historical bias. is does not necessarily mean downscaling is adding value. e coarse scale projected change in annual precipitation is retained but heavy precipitation (R95ptot) is somewhat amplied and extreme precipitation change (RP10) is considerably altered. Since it is these extremes that are needed for planning, further work is needed. In next steps we will compare results at a coarser scale (e.g. 5°) to reduce the inuence of bias correction on results and also separate into small and large scale explicitly as in Di Luca et al. (2013). Finally, we plan to compare projected changes from RCMs to that of their driving models in the same ways. Statistical downscaling methods that are explicitly designed to preserve coarse scale projected changes including extremes would be a welcome development for regional decision-making.

  • Source Publication: Climate Dynamics, doi:0.1007/s00382-016-3239-8 Authors: Teufel, B., G.T. Diro, K. Whan, S.M. Milrad, D.I. Jeong, A. Ganji, O. Huziy, K. Winger, E. Montero, J.R. Gyakum, R. de Elia, F.W. Zwiers and L. Sushama Publication Date: Jun 2016

    During 19–21 June 2013 a heavy precipitation event affected southern Alberta and adjoining regions, leading to severe flood damage in numerous communities and resulting in the costliest natural disaster in Canadian history. This flood was caused by a combination of meteorological and hydrological factors, which are investigated from weather and climate perspectives with the fifth generation Canadian Regional Climate Model. Results show that the contribution of orographic ascent to precipitation was important, exceeding 30 % over the foothills of the Rocky Mountains. Another contributing factor was evapotranspiration from the land surface, which is found to have acted as an important moisture source and was likely enhanced by antecedent rainfall that increased soil moisture over the northern Great Plains. Event attribution analysis suggests that human induced greenhouse gas increases may also have contributed by causing evapotranspiration rates to be higher than they would have been under pre-industrial conditions. Frozen and snow-covered soils at high elevations are likely to have played an important role in generating record streamflows. Results point to a doubling of surface runoff due to the frozen conditions, while 25 % of the modelled runoff originated from snowmelt. The estimated return time of the 3-day precipitation event exceeds 50 years over a large region, and an increase in the occurrence of similar extreme precipitation events is projected by the end of the 21st century. Event attribution analysis suggests that greenhouse gas increases may have increased 1-day and 3-day return levels of May–June precipitation with respect to pre-industrial climate conditions. However, no anthropogenic influence can be detected for 1-day and 3-day surface runoff, as increases in extreme precipitation in the present-day climate are offset by decreased snow cover and lower frozen water content in soils during the May–June transition months, compared to pre-industrial climate.

  • Authors: Rajesh R. Shrestha, Alex J. Cannon, Markus A. Schnorbus and Francis W. Zwiers Publication Date: Jun 2016

    Historically high extreme streamflow on the lower Fraser River has the potential to cause significant damage due the high concentration of infrastructure and human activity in the region. Using a combination of process-based and statistical modelling, we project that small (e.g. 2-20 year return period) extreme streamflow events will decrease in intensity, that the intensity of intermediate events (e.g. 40-60 year return period) will remain essentially unchanged, and that events of historic intensity (e.g. 100-200 year return period) will intensify modestly. [Extreme streamflow on the Fraser typically occurs in late spring/ early summer and is dependent on snow storage in the basin. Projected increases in winter precipitation would, all else being equal, increase the snow storage. Warming, however, tends to moderate this impact by reducing the fraction of winter precipitation stored as snow and shortening the period of snow storage]. The analysis in this paper is performed using an extreme value analysis technique that allows for nonstationarity in annual extreme streamflow by relating extreme streamflow with antecedent winter and spring precipitation and temperature. The study uses an extensive suite of existing simulations with the Variable Infiltration Capacity (VIC) hydrologic model driven by Coupled Model Intercomparison Project Phase 3 (CMIP3) climate simulations to train and evaluate the nonlinear and nonstationary Generalized Extreme Value conditional density network (GEVcdn) model of Fraser River streamflow extremes, and subsequently applies the model to project changes in Fraser River extremes under CMIP5 based climate change scenarios.

  • Authors: Krosby, M., Michalak, J., Robbins, T.O., Morgan, H., Norheim, R., Mauger, G., and T. Murdock Publication Date: Jun 2016

    Plant and animal species have historically used movement to adapt to changes in the Earth’s climate, shifting their ranges across landscapes to stay within climatically suitable habitat. Species are using this strategy to adapt to present day climate change, but the current rate of change is so rapid that many species will have difficulty keeping pace. In addition, human land use (e.g., highways, cities, farms) presents significant barriers to wildlife movement across today’s landscapes. For this reason, enhancing habitat connectivity – the ability of species to move across the landscape – is a leading strategy for helping wildlife respond to climate change. And yet, significant challenges remain in translating this high-level strategy into specific, on-the-ground actions. The Washington-British Columbia Transboundary Climate-Connectivity Project was initiated to help address these challenges. The region spanning the border of Washington state, USA, and British Columbia, Canada, faces increasing development pressure and limited transboundary coordination of land and wildlife management, both of which may threaten habitat connectivity and limit the potential for wildlife movement in response to change. In addition, the effects of climate change may further reduce habitat connectivity, and species may need novel types of habitat connectivity to complete adaptive range shifts. This project paired scientists and practitioners from both sides of the border to collaboratively identify potential climate impacts and adaptation actions for transboundary habitat connectivity, using a diverse suite of case study species, a vegetation system, and a region. Case study assessments revealed that climate change is likely to have significant implications for transboundary habitat connectivity. The adaptation actions identified to address potential impacts varied by case study, but fell into two general categories: those addressing potential climate impacts on existing habitat connectivity and those addressing novel habitat connectivity needs for climate-induced shifts in species ranges. In addition, project partners identified priority spatial locations for implementing these actions, as well as additional research needed to improve assessment of climate impacts and adaptation actions for habitat connectivity. The project resulted in a suite of products designed in collaboration with project partners to ensure their relevance and ease of application to decision-making. These products include this project overview report, which describes the project’s rationale, partnerships, approach, key findings, lessons learned, and remaining needs; detailed, stand-alone appendices for each case study, which describe the assessment process and key findings for each, and include all materials used in the assessment; and an interactive project gallery on the online mapping platform, Data Basin, which includes project reports and associated assessment materials, including interactive and downloadable connectivity and climate datasets. In addition, project participants emerged with enhanced capacity and a transboundary community of practice for addressing climate change and habitat connectivity in their decisionmaking. However, ongoing support for transboundary capacity building, collaboration, and research will be needed to promote the future resilience of our shared species and ecosystems.

  • Authors: T. Q. MURDOCK, D. NYLAND, S. SOBIE, AND J. WOLF Publication Date: Jun 2016

    Large scale infrastructure projects have long lifespans so planning them considers the long term. Highways in British Columbia are already experiencing extreme events beyond design capacity (see: photos at left). In 2008, the BC MOTI identied a pair of adaptation case studies, the results of which informed four streams of subsequent work (see timeline and other text boxes). A best practices summary of four years of adaptation work recommended MOTI produce a policy document. Following stakeholder and expert review, a ”Technical Circular” was published in 2015, requiring all projects for MOTI to consider climate. To assist with implementation, the Association of Professional Engineers and Geoscientists of BC is developing comprehensive guidelines for engineers to mainstream adaptation into their practice.

  • Authors: M. Kirchmeier-Young, F. Zwiers and N. Gillett Publication Date: May 2016

    An anthropogenic signal is detected in Arctic Sea Ice Extent (SIE) with all ensembles for the annual time series and also for September and March separately. All forcings (anthropogenic and natural, ALL) are necessary to explain the occurrence of SIE events more extreme than the current record minima (2012 for Sep., 2015 for Mar.), but not yet sufficient. If the current trends continue, ALL forcing will become sufficient for the occurrence of such events. Arctic SIE presents a counterexample to the statement that individual extreme events cannot be attributed to human influence.

  • Authors: Francis Zwiers Publication Date: May 2016

    Material includes: detection and attribution of long term changes and event attribution.

  • Source Publication: Climate Dynamics, doi:10.1007/s00382-016-3079-6 Authors: Ribes, A, F.W. Zwiers, Jean-Marc Azaïs and P. Naveau Publication Date: Apr 2016

    We propose here a new statistical approach to climate change detection and attribution that is based on additive decomposition and simple hypothesis testing. Most current statistical methods for detection and attribution rely on linear regression models where the observations are regressed onto expected response patterns to different external forcings. These methods do not use physical information provided by climate models regarding the expected response magnitudes to constrain the estimated responses to the forcings. Climate modelling uncertainty is difficult to take into account with regression based methods and is almost never treated explicitly. As an alternative to this approach, our statistical model is only based on the additivity assumption; the proposed method does not regress observations onto expected response patterns. We introduce estimation and testing procedures based on likelihood maximization, and show that climate modelling uncertainty can easily be accounted for. Some discussion is provided on how to practically estimate the climate modelling uncertainty based on an ensemble of opportunity. Our approach is based on the “models are statistically indistinguishable from the truth” paradigm, where the difference between any given model and the truth has the same distribution as the difference between any pair of models, but other choices might also be considered. The properties of this approach are illustrated and discussed based on synthetic data. Lastly, the method is applied to the linear trend in global mean temperature over the period 1951–2010. Consistent with the last IPCC assessment report, we find that most of the observed warming over this period (+0.65 K) is attributable to anthropogenic forcings (+0.67 ±± 0.12 K, 90 % confidence range), with a very limited contribution from natural forcings (−0.01±0.02−0.01±0.02 K).