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  • Source Publication: The Cryosphere, doi:10.5194/tc-2017-157 Authors: Kushner, P.J., et al. (F.W. Zwiers 24th co-author) Publication Date: Sep 2017

    This study assesses the ability of the Canadian Seasonal to Interannual Prediction System (CanSIPS) and the Canadian Earth-system Model 2 (CanESM2) to predict and simulate snow and sea ice from seasonal to multi-decadal timescales, with a focus on the Canadian sector. To account for observational uncertainty, model structural uncertainty, and internal climate variability, the analysis uses multi-source observations, multiple Earth-System Models (ESMs) in Phase 5 of the Coupled Model Intercomparison Project (CMIP5) archive, and initial condition ensembles of CanESM2 and other models. It is found that the ability of the CanESM2 simulation to capture snow-related climate parameters, such as cold-region temperature and precipitation, lies within the range of currently available international models. Accounting for the considerable disagreement among satellite-era observational datasets on the distribution of snow water equivalent, CanESM2 has too much springtime snow cover over the Canadian land mass, reflecting a broader Northern Hemisphere positive bias. It also exhibits retreat of springtime snow generally greater than observational estimates, after accounting for observational uncertainty and internal variability. Sea ice is biased low in the Canadian Arctic, which makes it difficult to assess the realism of long-term sea-ice trends there. The strengths and weaknesses of the modeling system need to be understood as a practical tradeoff: the Canadian models are relatively inexpensive computationally because of their moderate resolution, thus enabling their use in operational seasonal prediction and for generating large ensembles of multidecadal simulations. Improvements in climate prediction systems like CanSIPS rely not just on simulation quality but also on using novel observational constraints and the ready transfer of research to an operational setting. Improvements in seasonal forecasting practice arising from recent research include accurate initialization of snow and frozen soil, accounting for observational uncertainty in forecast verification, and sea-ice thickness initialization using statistical predictors available in real time.

  • Authors: The Cowichan Valley Regional District and the Pacific Climate Impacts Consortium Publication Date: Sep 2017

    Temperatures in the Cowichan Valley are warming. Global climate models project an increase in annual average temperature of almost 3°C in our region by the 2050s. While that may seem like a small change, it is comparable to the difference between the warmest and coldest years of the past. The purpose of this report is to quantify, with the most robust projections possible, the related climate impacts (including changes to climate extremes) associated with warming. This climate information will then inform regional risk assessment, decision making, and planning in the Cowichan Valley region, with a goal of improving resilience to
    climate change. For this reason, this report focusses on the business-as-usual emissions scenario and the 2050s timeframe. By the end of the 21st century, projected warming and associated impacts are even larger. In addition, the amount of warming by that time depends more highly on the quantity of greenhouse gases emitted in the meantime.

  • Source Publication: Hydrology and Earth System Sciences, doi:10.5194/hess-2017-531 Authors: Curry, C.L. and F.W. Zwiers Publication Date: Sep 2017

    The Fraser River basin (FRB) of British Columbia is one of the largest and most important watersheds in Western North America, and is home to a rich diversity of biological species and economic assets that depend implicitly upon its extensive riverine habitats. The hydrology of the FRB is dominated by snow accumulation and melt processes, leading to a prominent annual peak streamflow invariably occurring in June–July. However, while annual peak daily streamflow (APF) during the spring freshet in the FRB is historically well correlated with basin-averaged, April 1 snow water equivalent (SWE), there are numerous occurrences of anomalously large APF in below- or near-normal SWE years, some of which have resulted in damaging floods in the region. An imperfect understanding of which other climatic factors contribute to these anomalously large APFs hinders robust projections of their magnitude and frequency.

    We employ the Variable Infiltration Capacity (VIC) process-based hydrological model driven by gridded observations to investigate the key controlling factors of anomalous APF events in the FRB and four of its subbasins that contribute more than 70 % of the annual flow at Fraser-Hope. The relative influence of a set of predictors characterizing the interannual variability of rainfall, snowfall, snowpack (characterized by the annual maximum value, SWEmax), soil moisture and temperature on simulated APF at Hope (the main outlet of the FRB) and at the subbasin outlets is examined within a regression framework. The influence of large-scale climate modes of variability (the Pacific Decadal Oscillation (PDO) and the El Niño-Southern Oscillation (ENSO)) on APF magnitude is also assessed, and placed in context with these more localized controls. The results indicate that next to SWEmax (which strongly controls the annual maximum of soil moisture), the snowmelt rate, the ENSO and PDO indices, and rate of warming subsequent to the date of SWEmax are the most influential predictors of APF magnitude in the FRB and its subbasins. The identification of these controls on annual peak flows in the region may be of use in the context of seasonal prediction or future projected streamflow behaviour.

  • Source Publication: Climatic Change, 144, 143-150, doi:10.1007/s10584-017-2049-2 Authors: Stott, P.A., D.J. Karoly and F.W. Zwiers Publication Date: Aug 2017

    The science of event attribution meets a mounting demand for reliable and timely information about the links between climate change and individual extreme events. Studies have estimated the contribution of human-induced climate change to the magnitude of an event as well as its likelihood, and many types of event have been investigated including heatwaves, floods, and droughts. Despite this progress, such approaches have been criticised for being unreliable and for being overly conservative. We argue that such criticisms are misplaced. Rather, a false dichotomy has arisen between “conventional” approaches and new alternative framings. We have three points to make about the choice of statistical paradigm for event attribution studies. First, different approaches to event attribution may choose to occupy different places on the conditioning spectrum. Providing this choice of conditioning is communicated clearly, the value of such choices depends ultimately on their utility to the user concerned. Second, event attribution is an estimation problem for which either frequentist or Bayesian paradigms can be used. Third, for hypothesis testing, the choice of null hypothesis is context specific. Thus, the null hypothesis of human influence is not inherently a preferable alternative to the usual null hypothesis of no human influence.

  • Authors: Megan C. Kirchmeier-Young, Francis W. Zwiers, Nathan P. Gillett and Alex J. Cannon Publication Date: Jul 2017

    Canada is expected to see an increase in fire risk under future climate projections. Large fires, such as that near Fort McMurray, Alberta in 2016, can be devastating to the communities affected. Understanding the role of human emissions in the occurrence of such extreme fire events can lend insight into how these events might change in the future. An event attribution framework is used to quantify the influence of anthropogenic forcings on extreme fire risk in the current climate of a western Canada region. Fourteen metrics from the Canadian Forest Fire Danger Rating System are used to define the extreme fire seasons. For the majority of these metrics and during the current decade, the combined effect of anthropogenic and natural forcing is estimated to have made extreme fire risk events in the region 1.5 to 6 times as likely compared to a climate that would have been with natural forcings alone.

  • Authors: The Pacific Climate Impacts Consortium Publication Date: Jul 2017

    To plan for and adapt to the potential impacts of climate change, there is a need among communities in British Columbia for projections of future climate and climate extremes at a suitable, locally-relevant scale. This report summarizes work completed in 2012 by the Pacific Climate Impacts Consortium (PCIC) to this end. Commissioned by a group of municipalities and regional districts in the Georgia Basin (Figure 1), PCIC developed and analyzed a set of projections of future climate and climate extremes for the area. The full report, Georgia Basin, Projected Climate Change, Extremes and Historical Analysis, is available from PCIC’s online publications library.

  • Authors: Zwiers, F. Publication Date: Jun 2017

    Public talk delivered by Francis Zwiers at the 51st Annual CMOS Congress, June 6th, 2017

  • Authors: The Capital Regional District, the Pacific Climate Impacts Consortium, Pinna Sustainability Publication Date: Jun 2017

    Temperatures in the Capital Regional District (CRD) are warming. Global climate models project an average annual warming of about 3°C in our region by the 2050s. While that may seem like a small change, it is comparable to the difference between the warmest and coldest years of the past. The purpose of this report is to quantify, with the most robust projections possible, the related climate impacts (including changes to climate extremes) associated with warming. This climate information will then inform regional vulnerability and risk assessments, decision-making, and planning in the capital region, with a goal of improving resilience to climate change.

  • Source Publication: 56, 6, 1625–1641, doi:10.1175/JAMC-D-16-0287.1 Authors: Sobie, S.R. and T.Q. Murdock Publication Date: Jun 2017

    Knowledge from high-resolution daily climatological parameters is frequently sought after for increasingly local climate change assessments. This research investigates whether applying a simple postprocessing methodology to existing statistically downscaled temperature and precipitation fields can result in improved downscaled simulations useful at the local scale. Initial downscaled daily simulations of temperature and precipitation at 10-km resolution are produced using bias correction constructed analogs with quantile mapping (BCCAQ). Higher-resolution (800 m) values are then generated using the simpler climate imprint technique in conjunction with temperature and precipitation climatologies from the Parameter-Elevation Regression on Independent Slopes Model (PRISM). The potential benefit of additional downscaling to 800 m is evaluated using the “Climdex” set of 27 indices of extremes established by the Expert Team on Climate Change Detection and Indices (ETCCDI). These indices are also calculated from weather station observations recorded at 22 locations within southwestern British Columbia, Canada, to evaluate the performance of both the 10-km and 800-m datasets in replicating the observed quantities. In a 30-yr historical evaluation period, Climdex indices computed from 800-m simulated values display reduced error relative to local station observations than those from the 10-km dataset, with the greatest reduction in error occurring at high-elevation sites for precipitation-based indices.

  • Source Publication: The Cryosphere, doi:10.5194/tc-2017-56 Authors: Snauffer, A., W. Hsieh, A. Cannon, and M. Schnorbus Publication Date: Jun 2017

    Estimates of surface snow water equivalent (SWE) in alpine regions with seasonal melts are particularly difficult in areas of high vegetation density, topographic relief and snow accumulations. These three confounding factors dominate much of the province of British Columbia (BC), Canada. An artificial neural network (ANN) was created using as predictors six gridded SWE products previously evaluated for BC: ERA-Interim/Land, GLDAS-2, MERRA, MERRA-Land, GlobSnow and ERA-Interim. Relevant spatiotemporal covariates including survey date, year, latitude, longitude, elevation and grid cell elevation differences were also included as predictors, and observations from manual snow surveys at stations located throughout BC were used as target data. Mean absolute errors (MAEs) and correlations for April surveys were found using cross validation. The ANN using the three best performing SWE products (ANN3) had the lowest mean station MAE across the entire province, improving on the performance of individual products by an average of 53 %. Mean station MAEs and April survey correlations were also found for each of BC’s five physiographic regions. ANN3 outperformed each product as well as product means and multiple linear regression (MLR) models in all regions except for the BC Plains, which has relatively few stations and much lower accumulations than other regions. Subsequent comparisons of the ANN results with predictions generated by the Variable Infiltration Capacity (VIC) hydrologic model found ANN3 to be superior over the entire VIC domain and within most physiographic regions. The superior performance of the ANN over individual products, product means, MLR and VIC was found to be statistically significant across the province.

  • Authors: The Pacific Climate Impacts Consortium Publication Date: Jun 2017

    The June 2017 PCIC Update covers the following stories: The VIC-GL Model Now Operational, Climate Variability: the Hot Cold Winter of '16-'17, Nature Geoscience Paper on Short-Duration Extreme Rainfall Events, Staff Profile: Mohamed Ali Ben Alaya, as well as PCIC in the news, an invited lecture by Francis Zwiers on Extreme Weather at CMOS, recent talks, a new Science Brief, staff changes and recent papers authored by PCIC Staff.

  • Authors: Katherine A. Pingree-Shippee, Francis W. Zwiers and David E. Atkinson Publication Date: Jun 2017

    Talk delivered by Katherine Pingree-Shippee at the 51st Annual Congress of the Canadian Meteorological and Oceanographic Society, in June of 2017.

  • Authors: Bechtet, N. and T. Murdock Publication Date: Jun 2017

    Presentation by PCIC Intern Noémie Bechtet on an analysis of a survey done on the users of PCIC's online tools.

  • Source Publication: Journal of Climate, 30, 4113-4130, doi:10.1175/JCLI-D-16-0189.1 Authors: Naja , M.R., F.W. Zwiers and N.P. Gillett Publication Date: May 2017

    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 1/16° 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.

  • Authors: Markus Schnorbus, Brian Menounos, Arelia Schoeneberg, Faron Anslow, Georg Jost and Dan Moore Publication Date: May 2017

    Presentation delivered by Markus Schnorbus at the Canadian Geophysical Annual meeting in May of 2017.

  • Authors: The Pacific Climate Impacts Consortium Publication Date: May 2017

    Two recently published articles explore how projected changes to climate and carbon dioxide in the atmosphere may affect grasslands in temperate regions and three crops in the United States. Addressing the first question in Nature Climate Change, Obermeier et al. (2017) find that the carbon dioxide fertilization effect in C3 grasslands is reduced when conditions are wetter, dryer or hotter than the conditions to which the grasses are adapted.

    Publishing in Nature Communications, Schauberger et al. (2017) examine the second question. They find that yields for wheat, soy and corn decline at projected temperatures greater than 30°C, with reductions in yield of 22% for wheat, 40% for soy and 49% for corn. While carbon fertilization does reduce the loss in yields, the effect is much smaller than that of irrigation, suggesting that water stress at higher temperatures may be largely responsible for losses.

  • Source Publication: Journal of Advances in Modeling Earth Systems, 9, 2, 1292-1306, doi:10.1002/2016MS000830 Authors: Ouali, D., F. Chebana and T.B.M.J. Ouarda Publication Date: Apr 2017

    The high complexity of hydrological systems has long been recognized. Despite the increasing number of statistical techniques that aim to estimate hydrological quantiles at ungauged sites, few approaches were designed to account for the possible nonlinear connections between hydrological variables and catchments characteristics. Recently, a number of nonlinear machine‐learning tools have received attention in regional frequency analysis (RFA) applications especially for estimation purposes. In this paper, the aim is to study nonlinearity‐related aspects in the RFA of hydrological variables using statistical and machine‐learning approaches. To this end, a variety of combinations of linear and nonlinear approaches are considered in the main RFA steps (delineation and estimation). Artificial neural networks (ANNs) and generalized additive models (GAMs) are combined to a nonlinear ANN‐based canonical correlation analysis (NLCCA) procedure to ensure an appropriate nonlinear modeling of the complex processes involved. A comparison is carried out between classical linear combinations (CCAs combined with linear regression (LR) model), semilinear combinations (e.g., NLCCA with LR) and fully nonlinear combinations (e.g., NLCCA with GAM). The considered models are applied to three different data sets located in North America. Results indicate that fully nonlinear models (in both RFA steps) are the most appropriate since they provide best performances and a more realistic description of the physical processes involved, even though they are relatively more complex than linear ones. On the other hand, semilinear models which consider nonlinearity either in the delineation or estimation steps showed little improvement over linear models. The linear approaches provided the lowest performances.

  • Source Publication: Nature Geoscience 10, 255–259, doi:10.1038/ngeo2911. Authors: Zhang, X., F.W. Zwiers, G.L. Hui Wan and A.J. Cannon Publication Date: Mar 2017

    Warming of the climate is now unequivocal. The water holding capacity of the atmosphere increases by about 7% per °C of warming, which in turn raises the expectation of more intense extreme rainfall events. Meeting the demand for robust projections for extreme short-duration rainfall is challenging, however, because of our poor understanding of its past and future behaviour. The characterization of past changes is severely limited by the availability of observational data. Climate models, including typical regional climate models, do not directly simulate all extreme rainfall producing processes, such as convection. Recently developed convection-permitting models better simulate extreme precipitation, but simulations are not yet widely available due to their computational cost, and they have their own uncertainties. Attention has thus been focused on precipitation–temperature relationships in the hope of obtaining more robust extreme precipitation projections that exploit higher confidence temperature projections. However, the observed precipitation–temperature scaling relationships have been established almost exclusively by linking precipitation extremes with day-to-day temperature variations. These scaling relationships do not appear to provide a reliable basis for projecting future precipitation extremes. Until better methods are available, the relationship of the atmosphere's water holding capacity with temperature provides better guidance for planners in the mid-latitudes, albeit with large uncertainties.

  • Source Publication: doi:10.1007/s00382-017-3634-9 Authors: C. Seiler, F. W. Zwiers, K. I. Hodges and J. F. Scinocca Publication Date: Mar 2017

    Explosive extratropical cyclones (EETCs) are rapidly intensifying low pressure systems that generate severe weather along North America’s Atlantic coast. Global climate models (GCMs) tend to simulate too few EETCs, perhaps partly due to their coarse horizontal resolution and poorly resolved moist diabatic processes. This study explores whether dynamical downscaling can reduce EETC frequency biases, and whether this affects future projections of storms along North America’s Atlantic coast. A regional climate model (CanRCM4) is forced with the CanESM2 GCM for the periods 1981 to 2000 and 2081 to 2100. EETCs are tracked from relative vorticity using an objective feature tracking algorithm. CanESM2 simulates 38% fewer EETC tracks compared to reanalysis data, which is consistent with a negative Eady growth rate bias (−0.1 day−1). Downscaling CanESM2 with CanRCM4 increases EETC frequency by one third, which reduces the frequency bias to −22%, and increases maximum EETC precipitation by 22%. Anthropogenic greenhouse gas forcing is projected to decrease EETC frequency (−15%, −18%) and Eady growth rate (−0.2 day−1, −0.2 day−1), and increase maximum EETC precipitation (46%, 52%) in CanESM2 and CanRCM4, respectively. The limited effect of dynamical downscaling on EETC frequency projections is consistent with the lack of impact on the maximum Eady growth rate. The coarse spatial resolution of GCMs presents an important limitation for simulating extreme ETCs, but Eady growth rate biases are likely just as relevant. Further bias reductions could be achieved by addressing processes that lead to an underestimation of lower tropospheric meridional temperature gradients.

  • Authors: Zwiers, F. Publication Date: Jan 2017