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

    This Science Brief covers recent research by Mao et al. (2016) published in Nature Climate Change. The authors find that the observed greening of the land surface between 30-75° north over the 1982-2011 period is largely due to anthropogenic greenhouse gas emissions.

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

    This newsletter contains articles on the following: 2016 as a record-warm year for the province, recent PCIC research on Fraser River Basin climate impacts, recent Data Portal upgrades, Director Francis Zwiers's keynote at the Wildland Fire Canada Meeting and recognition as a highly-cited researcher, a staff profile on Megan Kirchmeier-Young, our Pacific Climate Seminar Series, PCIC's contributions to the AGU Fall Meeting and Northwest Climate Conference, the most recent Science Brief, staff changes and recent papers by PCIC staff and affiliates.

  • Authors: Norman J. Shippee, Christian Seiler and Francis Zwiers Publication Date: Jan 2017

    Presented by Norman Shippee at the American Meteorological Society's 97th Annual Meeting in January, 2017:  CanSIPS reproduces the overall spatial pattern of cyclone track density found in ERA-Interim. Individual models of CanCM3 and CanCM4 over and under-represent the observed storm track, respectively. CanSIPS mean storm track and spread of track density is well represented, though an overall overprediction of storm track density is evident across the North Pacific, particularly from 45 - 60 deg N. This overprediction of storm track density is present in all components of the CanSIPS system. Moderate to strong correlation between CanSIPS mean storm track density and ERA-Interim track density are found across the primary storm track in the North Pacific and in the primary formation regions for Atlantic cyclones affecting North America (Gulf of Mexico and Cape Hatteras) Most CanSIPS bias in the North Pacific is centralized in the exit regions of the Pacific storm track, localized to Gulf of Alaska and coastal BC.

  • Source Publication: Geoscientific Model Development, 9, 3751-3777 doi:10.5194/gmd-2016-78 Authors: Boer, G.J., D.M. Smith, C. Cassou, F. Doblas-Reyes, G. Danabasoglu, B. Kirtman, Y. Kushnir, M. Kimoto, G.A. Meehl, R. Msadek, W.A. Mueller, K. Taylor and F.W. Zwiers Publication Date: Jan 2017

    The Decadal Climate Prediction Project (DCPP) is a coordinated multi-model investigation into decadal climate prediction, predictability, and variability. The DCPP makes use of past experience in simulating and predicting decadal variability and forced climate change gained from the fifth Coupled Model Intercomparison Project (CMIP5) and elsewhere. It builds on recent improvements in models, in the reanalysis of climate data, in methods of initialization and ensemble generation, and in data treatment and analysis to propose an extended comprehensive decadal prediction investigation as a contribution to CMIP6 (Eyring et al., 2016) and to the WCRP Grand Challenge on Near Term Climate Prediction (Kushnir et al., 2016). The DCPP consists of three components. Component A comprises the production and analysis of an extensive archive of retrospective forecasts to be used to assess and understand historical decadal prediction skill, as a basis for improvements in all aspects of end-toend decadal prediction, and as a basis for forecasting on annual to decadal timescales. Component B undertakes ongoing production, analysis and dissemination of experimental quasi-real-time multi-model forecasts as a basis for potential operational forecast production. Component C involves the organization and coordination of case studies of particular climate shifts and variations, both natural and naturally forced (e.g. the “hiatus”, volcanoes), including the study of the mechanisms that determine these behaviours.

    Groups are invited to participate in as many or as few of the components of the DCPP, each of which are separately prioritized, as are of interest to them. The Decadal Climate Prediction Project addresses a range of scientific issues involving the ability of the climate system to be predicted on annual to decadal timescales, the skill that is currently and potentially available, the mechanisms involved in long timescale variability, and the production of forecasts of benefit to both science and society

  • Authors: Schoeneberg (née Werner), A.T., M.A. Schnorbus and M.R. Najafi Publication Date: Dec 2016

    Understanding future climate change impacts on hydro-climatic extremes in British Columbia, Canada requires hydrologic models that can accurately represent the cryospheric processes specific to mountainous regions in higher latitudes. Consequently, hydrologic simulations are conducted using a newly modified version of the Variable Infiltration Capacity (VIC) hydrologic model that couples to a dynamic glacier model. Using this coupled model, we project changes to streamflow extremes in the Columbia and Peace River basins based on a selection of CMIP5 models, run under two representative concentration pathways, statistically downscaled with multiple methods. The modified version of VIC is calibrated against daily streamflow and monthly evaporation using recently developed gridded climate observations, and the dynamic glacier model is evaluated with observed glacier mass balance and coverage data. We analyze changes in the frequency and intensity of peak and low flow events and compare these to previous simulations, which used a simpler version of VIC driven by statistically downscaled CMIP3 outputs.

  • Source Publication: Journal of Climate, 30, 553-571, doi:10.1175/JCLI-D-16-0412.1 Authors: Kirchmeier-Young, M.C., F.W. Zwiers and N.P. Gillett Publication Date: Dec 2016

    Arctic sea ice extent (SIE) has decreased over recent decades, with record-setting minimum events in 2007 and again in 2012. A question of interest across many disciplines concerns the extent to which such extreme events can be attributed to anthropogenic influences. First, a detection and attribution analysis is performed for trends in SIE anomalies over the observed period. The main objective of this study is an event attribution analysis for extreme minimum events in Arctic SIE. Although focus is placed on the 2012 event, the results are generalized to extreme events of other magnitudes, including both past and potential future extremes. Several ensembles of model responses are used, including two single-model large ensembles. Using several different metrics to define the events in question, it is shown that an extreme SIE minimum of the magnitude seen in 2012 is consistent with a scenario including anthropogenic influence and is extremely unlikely in a scenario excluding anthropogenic influence. Hence, the 2012 Arctic sea ice minimum provides a counterexample to the often-quoted idea that individual extreme events cannot be attributed to human influence.

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