Informing BC Stakeholders

You are here

Publications Library

  • Authors: Zwiers, F., C. Li, X. Zhang and G. Li Publication Date: Dec 2018

    Poster presented at 2018 AGU Fall Meeting, Dec. 10th-14th in Washington, DC.

  • Source Publication: Journal of Climate, 31, 19, 7771-7787, doi:10.1175/JCLI-D-17-0552.1 Authors: Mueller, B.L., N.P. Gillett, A. Monahan and F.W. Zwiers Publication Date: Sep 2018

    The paper presents results from a climate change detection and attribution study on the decline of Arctic sea ice extent in September for the 1953–2012 period. For this period three independently derived observational datasets and simulations from multiple climate models are available to attribute observed changes in the sea ice extent to known climate forcings. Here we direct our attention to the combined cooling effect from other anthropogenic forcing agents (mainly aerosols), which has potentially masked a fraction of greenhouse gas–induced Arctic sea ice decline. The presented detection and attribution framework consists of a regression model, namely, regularized optimal fingerprinting, where observations are regressed onto model-simulated climate response patterns (i.e., fingerprints). We show that fingerprints from greenhouse gas, natural, and other anthropogenic forcings are detected in the three observed records of Arctic sea ice extent. Beyond that, our findings indicate that for the 1953–2012 period roughly 23% of the greenhouse gas–induced negative sea ice trend has been offset by a weak positive sea ice trend attributable to other anthropogenic forcing. We show that our detection and attribution results remain robust in the presence of emerging nonstationary internal climate variability acting upon sea ice using a perfect model experiment and data from two large ensembles of climate simulations.

  • Source Publication: Journal of Geophysical Research: Earth Surface, doi: 10.1029/2017JF004578. Authors: Tsuruta, K., M.A. Hassan, S.D. Donner and Y. Alila Publication Date: Sep 2018

    Modeling sediment transport through large basins presents a challenging problem. The relation between water flux and sediment load is complex, and substantial erosion and transport can occur over small spatial and temporal scales. Analysis of large‐scale basins often relies on lumped empirical models that do not consider spatial or subannual variability. In this study, we adapt a small‐scale, mechanistic, distributed suspended sediment transport model for application to large basins. The model is integrated into the Terrestrial Hydrology Model with Biochemistry to make use of the Terrestrial Hydrology Model with Biochemistry's dynamic water routing. The coupled model is applied to the 230,000‐km2 Fraser River Basin in British Columbia, Canada, using climatic and hydrological inputs provided by a historical run of the Variable Infiltration Capacity model. Hourly simulations are aggregated into monthly and long‐term averages which are compared against observations. Simulated long‐term lake sedimentation values are within an order of magnitude of observations, and monthly load simulations have an average R2 of 0.70 across the five study stations with available data. Model results indicate that sediment loads from tributaries do not heavily influence dynamics along the main stem and suggest the importance of network connectivity. Sensitivity analysis indicates that models may benefit from characterizing bed load irrespective of its contribution to total sediment load. Historical simulations over the 1965–2004 period reveal important changes in sediment dynamics that could not be captured with a lumped model, including a decrease in basin sediment load interannual variability driven by changes in runoff and load variability within a key subbasin.

  • Source Publication: Climate Dynamics, doi:10.1007/s00382-018-4375-0. Authors: Teufel, B., L. Sushama, O. Huzly, G.T. Diro, D.I. Jeong, K. Winger, C. Garnaud, R. de Elia, F.W. Zwiers, J.R. Gyakum, D. Matthews and V.-T.-V. Nguyen Publication Date: Sep 2018

    Significant flood damage occurred near Montreal in May 2017, as flow from the upstream Ottawa River basin (ORB) reached its highest levels in over 50 years. Analysis of observations and experiments performed with the fifth generation Canadian Regional Climate Model (CRCM5) show that much above average April precipitation over the ORB, a large fraction of which fell as rain on an existing snowpack, increased streamflow to near record-high levels. Subsequently, two heavy rainfall events affected the ORB in the first week of May, ultimately resulting in flooding. This heavy precipitation during April and May was linked to large-scale atmospheric features. Results from sensitivity experiments with CRCM5 suggest that the mass and distribution of the snowpack have a major influence on spring streamflow in the ORB. Furthermore, the importance of using an appropriate frozen soil parameterization when modelling spring streamflows in cold regions was confirmed. Event attribution using CRCM5 showed that events such as the heavy April 2017 precipitation accumulation over the ORB are between two and three times as likely to occur in the present-day climate as in the pre-industrial climate. This increase in the risk of heavy precipitation is linked to increased atmospheric moisture due to warmer temperatures in the present-day climate, a direct consequence of anthropogenic emissions, rather than changes in rain-generating mechanisms or circulation patterns. Warmer temperatures in the present-day climate also reduce early-spring snowpack in the ORB, offsetting the increase in rainfall and resulting in no discernible change to the likelihood of extreme surface runoff.

  • Source Publication: Earth's Future, 6, 5, 704-715, doi:10.1002/2018EF000813. Authors: Kharin, V.V., G.M. Flato, X. Zhang, N.P. Gillett, F.W. Zwiers and K. Anderson Publication Date: Sep 2018

    Parties to the United Nations Framework Convention on Climate Change have agreed to hold the “increase in global average temperature to well below 2°C above preindustrial levels and to pursue efforts to limit the temperature increase to 1.5°C.” Comparison of the costs and benefits for different warming limits requires an understanding of how risks vary between warming limits. As changes in risk are often associated with changes in exposure due to projected changes in local or regional climate extremes, we analyze differences in the risks of extreme daily temperatures and extreme daily precipitation amounts under different warming limits. We show that global warming of 2°C would result in substantially larger changes in the probabilities of the extreme events than global warming of 1.5°C. For example, over the global land area, the probability of a warm extreme that occurs once every 20 years on average in the current climate is projected to increase 130% and 340% at the 1.5°C and 2.0°C warming levels, respectively (median values). Moreover, the relative changes in probability are larger for rarer, more extreme events, implying that risk assessments need to carefully consider the extreme event thresholds at which vulnerabilities occur.

  • Source Publication: Atmospheric Chemistry and Physics, 18, 10133-10156, doi:10.5194/acp-18-10133-2018. Authors: Ji, D., S. Fang, C.L. Curry, H. Kashimura, S. Watanabe, J.N.S. Cole, A. Lenton, H. Muri, B. Kravitz and J.C. Moore. Publication Date: Jul 2018

    We examine extreme temperature and precipitation under two potential geoengineering methods forming part of the Geoengineering Model Intercomparison Project (GeoMIP). The solar dimming experiment G1 is designed to completely offset the global mean radiative forcing due to a CO2-quadrupling experiment (abrupt4 × CO2), while in GeoMIP experiment G4, the radiative forcing due to the representative concentration pathway 4.5 (RCP4.5) scenario is partly offset by a simulated layer of aerosols in the stratosphere. Both G1 and G4 geoengineering simulations lead to lower minimum temperatures (TNn) at higher latitudes and on land, primarily through feedback effects involving high-latitude processes such as snow cover, sea ice and soil moisture. There is larger cooling of TNn and maximum temperatures (TXx) over land compared with oceans, and the land–sea cooling contrast is larger for TXx than TNn. Maximum 5-day precipitation (Rx5day) increases over subtropical oceans, whereas warm spells (WSDI) decrease markedly in the tropics, and the number of consecutive dry days (CDDs) decreases in most deserts. The precipitation during the tropical cyclone (hurricane) seasons becomes less intense, whilst the remainder of the year becomes wetter. Stratospheric aerosol injection is more effective than solar dimming in moderating extreme precipitation (and flooding). Despite the magnitude of the radiative forcing applied in G1 being ∼ 7.7 times larger than in G4 and despite differences in the aerosol chemistry and transport schemes amongst the models, the two types of geoengineering show similar spatial patterns in normalized differences in extreme temperatures changes. Large differences mainly occur at northern high latitudes, where stratospheric aerosol injection more effectively reduces TNn and TXx. While the pattern of normalized differences in extreme precipitation is more complex than that of extreme temperatures, generally stratospheric aerosol injection is more effective in reducing tropical Rx5day, while solar dimming is more effective over extra-tropical regions.

  • Source Publication: Stochastic Environmental Research and Risk Assessment</em>, <b>32</b>, 10, 2821–2836, doi:/10.1007/s00477-018-1564-7. Authors: Ouali, D. and A.J. Cannon Publication Date: May 2018

    Intensity–duration–frequency (IDF) curves of extreme rainfall are used extensively in infrastructure design and water resources management. In this study, a novel regional framework based on quantile regression (QR) is used to estimate rainfall IDF curves at ungauged locations. Unlike standard regional approaches, such as index-storm and at-site ordinary least-squares regression, which are dependent on parametric distributional assumptions, the non-parametric QR approach directly estimates rainfall quantiles as a function of physiographic characteristics. Linear and nonlinear methods are evaluated for both the regional delineation and IDF curve estimation steps. Specifically, delineation by canonical correlation analysis (CCA) and nonlinear CCA (NLCCA) is combined, in turn, with linear QR and nonlinear QR estimation in a regional modelling framework. An exhaustive comparative study is conducted between standard regional methods and the proposed QR framework at sites across Canada. Overall, the fully nonlinear QR framework, which uses NLCCA for delineation and nonlinear QR for estimation of IDF curves at ungauged sites, leads to the best results.

  • Source Publication: Climate Dynamics, doi:10.1007/s00382-018-4145-z. Authors: Wan, H., X. Zhang and F. Zwiers Publication Date: May 2018

    Canada has experienced some of the most rapid warming on Earth over the past few decades with a warming rate about twice that of the global mean temperature since 1948. Long-term warming is observed in Canada’s annual, winter and summer mean temperatures, and in the annual coldest and hottest daytime and nighttime temperatures. The causes of these changes are assessed by comparing observed changes with climate model simulated responses to anthropogenic and natural (solar and volcanic) external forcings. Most of the observed warming of 1.7 °C increase in annual mean temperature during 1948–2012 [90% confidence interval (1.1°, 2.2 °C)] can only be explained by external forcing on the climate system, with anthropogenic influence being the dominant factor. It is estimated that anthropogenic forcing has contributed 1.0 °C (0.6°, 1.5 °C) and natural external forcing has contributed 0.2 °C (0.1°, 0.3 °C) to the observed warming. Up to 0.5 °C of the observed warming trend may be associated with low frequency variability of the climate such as that represented by the Pacific decadal oscillation (PDO) and North Atlantic oscillation (NAO). Overall, the influence of both anthropogenic and natural external forcing is clearly evident in Canada-wide mean and extreme temperatures, and can also be detected regionally over much of the country.

  • Source Publication: Comptes Rendus Geoscience, 350, 4, 41-153, https://doi.org/10.1016/j.crte.2018.03.001 Authors: Dayon G., J. Boé, É. Martin and J. Gailhard Publication Date: May 2018

    This study deals with the evolution of the hydrological cycle over France during the 21st century. A large multi-member, multi-scenario, and multi-model ensemble of climate projections is downscaled with a new statistical method to drive a physically-based hydrological model with recent improvements. For a business-as-usual scenario, annual precipitation changes generally remain small, except over southern France, where decreases close to 20% are projected. Annual streamflows roughly decrease by 10% (±20%) on the Seine, by 20% (±20%) on the Loire, by 20% (±15%) on the Rhone and by 40% (±15%) on the Garonne. Attenuation measures, as implied by the other scenarios analyzed, lead to less severe changes. However, even with a scenario generally compatible with a limitation of global warming to two degrees, some notable impacts may still occur, with for example a decrease in summer river flows close to 25% for the Garonne.

  • Source Publication: Climatic Change, 148, 1-2, 249-263, doi: 10.1007/s10584-018-2199-x Authors: Zhang, X., G. Li, A. Cannon, T. Murdock, S. Sobie, F.W. Zwiers, K. Anderson and B. Qian Publication Date: May 2018

    This study evaluates regional-scale projections of climate indices that are relevant to climate change impacts in Canada. We consider indices of relevance to different sectors including those that describe heat conditions for different crop types, temperature threshold exceedances relevant for human beings and ecological ecosystems such as the number of days temperatures are above certain thresholds, utility relevant indices that indicate levels of energy demand for cooling or heating, and indices that represent precipitation conditions. Results are based on an ensemble of high-resolution statistically downscaled climate change projections from 24 global climate models (GCMs) under the RCP2.6, RCP4.5, and RCP8.5 emissions scenarios. The statistical downscaling approach includes a bias-correction procedure, resulting in more realistic indices than those computed from the original GCM data. We find that the level of projected changes in the indices scales well with the projected increase in the global mean temperature and is insensitive to the emission scenarios. At the global warming level about 2.1 °C above pre-industrial (corresponding to the multi-model ensemble mean for 2031–2050 under the RCP8.5 scenario), there is almost complete model agreement on the sign of projected changes in temperature indices for every region in Canada. This includes projected increases in extreme high temperatures and cooling demand, growing season length, and decrease in heating demand. Models project much larger changes in temperature indices at the higher 4.5 °C global warming level (corresponding to 2081–2100 under the RCP8.5 scenario). Models also project an increase in total precipitation, in the frequency and intensity of precipitation, and in extreme precipitation. Uncertainty is high in precipitation projections, with the result that models do not fully agree on the sign of changes in most regions even at the 4.5 °C global warming level.

  • Source Publication: Hydrology and Earth System Sciences, doi:10.5194/hess-2017-531 Authors: Curry, C.L. and F.W. Zwiers Publication Date: Apr 2018

    The Fraser River Basin (FRB) of British Columbia is one of the largest and most important watersheds in western North America, and 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 May–July. Nevertheless, while annual peak daily streamflow (APF) during the spring freshet in the FRB is historically well correlated with basin-averaged, 1 April 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 nearly 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 (univariate Spearman correlation with APF of ρˆ = 0.64; 0.70 (observations; VIC simulation)), the snowmelt rate (ρˆ = 0.43 in VIC), the ENSO and PDO indices (ρˆ = −0.40; −0.41) and (ρˆ = −0.35; −0.38), respectively, and rate of warming subsequent to the date of SWEmax (ρˆ = 0.26; 0.38), 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 understanding seas

  • Source Publication: Journal of Hydrometeorology, doi: 10.1175/JHM-D-17-0110.1 Authors: Ben Alaya, M.A., F.W. Zwiers and X. Zhang Publication Date: Apr 2018

    Probable maximum precipitation (PMP) is the key parameter used to estimate the probable maximum flood (PMF), both of which are important for dam safety and civil engineering purposes. The usual operational procedure for obtaining PMP values, which is based on a moisture maximization approach, produces a single PMP value without an estimate of its uncertainty. We therefore propose a probabilistic framework based on a bivariate extreme value distribution to aid in the interpretation of these PMP values. This 1) allows us to evaluate estimates from the operational procedure relative to an estimate of a plausible distribution of PMP values, 2) enables an evaluation of the uncertainty of these values, and 3) provides clarification of the impact of the assumption that a PMP event occurs under conditions of maximum moisture availability. Results based on a 50-yr Canadian Centre for Climate Modelling and Analysis Regional Climate Model (CanRCM4) simulation over North America reveal that operational PMP estimates are highly uncertain and suggest that the assumption that PMP events have maximum moisture availability may not be valid. Specifically, in the climate simulated by CanRCM4, the operational approach applied to 50-yr data records produces a value that is similar to the value that is obtained in our approach when assuming complete dependence between extreme precipitation efficiency and extreme precipitable water. In contrast, our results suggest weaker than complete dependence. Estimates from the operational approach are 15% larger on average over North America than those obtained when accounting for the dependence between precipitation efficiency and precipitable water extremes realistically. A difference of this magnitude may have serious implications in engineering design.

  • Source Publication: Earth's Future, 6, doi: 10.1002/2018EF000813 Authors: Kharin, V.V., G.M. Flato, X. Zhang, N.P. Gillett, F.W. Zwiers and K. Anderson Publication Date: Apr 2018

    Parties to the United Nations Framework Convention on Climate Change have agreed to hold the “increase in global average temperature to well below 2°C above preindustrial levels and to pursue efforts to limit the temperature increase to 1.5°C.” Comparison of the costs and benefits for different warming limits requires an understanding of how risks vary between warming limits. As changes in risk are often associated with changes in exposure due to projected changes in local or regional climate extremes, we analyze differences in the risks of extreme daily temperatures and extreme daily precipitation amounts under different warming limits. We show that global warming of 2°C would result in substantially larger changes in the probabilities of the extreme events than global warming of 1.5°C. For example, over the global land area, the probability of a warm extreme that occurs once every 20 years on average in the current climate is projected to increase 130% and 340% at the 1.5°C and 2.0°C warming levels, respectively (median values). Moreover, the relative changes in probability are larger for rarer, more extreme events, implying that risk assessments need to carefully consider the extreme event thresholds at which vulnerabilities occur.

  • Source Publication: Agricultural and Forest Meteorology, 250–251, 226-242 doi:10.1016/j.agrformet.2017.12.253 Authors: Sgubin, G., D. Swingedouw, G. Dayon, I.G. de Cortázar-Atauri, N. Ollat, C. Pagé and , C. van Leeuwen Publication Date: Mar 2018

    Tardive frosts, i.e. frost events occurring after grapevine budburst, are a significant risk for viticultural practices, which have recently caused substantial yield losses over different winegrowing regions of France, e.g. in 2016 and 2017. So far, it is unclear whether the frequency of late frosts events is destined to increase or decrease under future climatic conditions. Here, we assess the risk of tardive frosts for the French vineyards throughout the 21st century by analyzing temperature projections from eight climate models and their statistical regional downscaling. Our approach consists in comparing the statistical occurrences of the last frost (day of the year) and the characteristic budburst date for nine grapevine varieties as simulated by three different phenological models. Climate models qualitatively agree in projecting a gradual increase in temperature all over the France, which generally produces both an earlier characteristic last frost day and an earlier characteristic budburst date. However, the latter notably depends on the specific phenological model, implying a large uncertainty in assessing the risk exposure. Overall, we identified Alsace, Burgundy and Champagne as the most vulnerable regions, where the probability of tardive frost is projected to significantly increase throughout the 21st century for two out of three phenological models. The third phenological model produces opposite results, but the comparison between simulated budburst dates and observed records over the last 60 years suggests its lower reliability. Nevertheless, for a more trustworthy risk assessment, the validity of the budburst models should be accurately tested also for warmer climate conditions, in order to narrow down the associated large uncertainty.

  • Source Publication: The Journal of Open Source Software. 3, 22, 360, doi:10.21105/joss.00360 Authors: Hiebert, J., A. Cannon, A. Schoeneberg, S. Sobie and T. Murdock Publication Date: Feb 2018

    The ClimDown R package publishes the routines and techniques of the Pacific Climate Impacts Consortium (PCIC) for downscaling coarse scale Global Climate Models (GCMs) to fine scale spatial resolution. PCIC’s overall downscaling algorithm is named Bias-corrected constructed analogues with quantile mapping reordering (BCCAQ) (Cannon, Sobie, and Murdock 2015; Werner and Cannon 2016). BCCAQ is a hybrid downscaling method that combines outputs from Constructed Analogues (CA) (Maurer et al. 2010) and quantile mapping at the fine-scale resolution. First, the CA and Climate Imprint (CI) (Hunter and Meentemeyer 2005) plus quantile delta mapping (QDM) (Cannon, Sobie, and Murdock 2015) algorithms are run independently. BCCAQ then combines outputs from the two by taking the daily QDM outputs at each fine-scale grid point and reordering them within a given month according to the daily CA ranks, i.e., using a form of Empirical Copula Coupling (Schefzik, Thorarinsdottir, and Gneiting 2013). The package exports high-level wrapper functions that perform each of three downscaling steps: CI, CA, and QDM, as well as one wrapper that runs the entire BCCAQ pipeline.

  • Source Publication: International Journal of Climatology, 38, 2, 1041-1059, doi:10.1002/joc.5235 Authors: Pingree-Shippee, K., F.W. Zwiers and D. Atkinson Publication Date: Feb 2018

    Extratropical cyclones often produce extreme and hazardous weather conditions, such as high winds, heavy precipitation, blizzard conditions, and flooding, all of which have detrimental environmental/physical and socio‐economic impacts. Furthermore, storm interaction with the ocean produces additional hazards, with major local impacts, including inundation and coastal erosion. The North American west coast is influenced by the North Pacific storm track and by ‘atmospheric river’ events while the east coast is particularly influenced by winter storms that track along two favoured routes: the St. Lawrence Valley and the Eastern Seaboard. Reanalysis provides an invaluable tool for studying the characteristics of storm events that are identified as causing the most severe impacts. However, reanalysis products differ substantially in spatial resolution, model physics, assimilation approach, and the data that are assimilated. This study evaluates the representation of storm activity along the mid‐latitude North American coastlines by six global reanalyses: NCEP‐1, NCEP‐2, ERA‐Interim, Modern‐Era Retrospective analysis for Research and Applications (MERRA), Climate Forecast System Reanalysis (CFSR), and Twentieth Century Reanalysis Version 2 (20CR). Storm activity representation is evaluated at annual and seasonal timescales (JFM, AMJ, JAS, OND, and ‘extended winter’ ONDFM) during the 1979–2010 time period through comparison with selected meteorological stations using single‐point surface pressure‐based proxies of extratropical storm activity. Stations are selected on the basis of record length, reporting frequency, coastal proximity, and relatively uniform spatial distribution. Comparisons are made using data extracted from the reanalysis grid box centre that is closest to each selected station. All reanalyses are found to successfully represent most aspects of mid‐latitude North American coastal strong storm activity, annually and seasonally, along both coasts. Nevertheless, ERA‐Interim, MERRA, and CFSR provide the better representations of mid‐latitude North American coastal strong storm activity, with ERA‐Interim performing best overall.

  • Source Publication: Journal of Climate, doi:10.1175/JCLI-D-16-0752.1 Authors: Naveau, P., A. Ribes, F.W. Zwiers, A. Hannart, A. Tuel and P. Yiou Publication Date: Jan 2018

    Both climate and statistical models play an essential role in the process of demonstrating that the distribution of some atmospheric variable has changed over time and in establishing the most likely causes for the detected change. One statistical difficulty in the research field of Detection and Attribution resides in defining events that can be easily compared and accurately inferred from reasonable sample sizes. As many impacts studies focus on extreme events, the inference of small probabilities and the computation of their associated uncertainties quickly become challenging. In the particular context of event attribution, we address the question of how to compare records between the so-called world as “it might have been been without anthropogenic forcings” and the “world that is”. Records are often the most important events in terms of impact and get much media attention. We will show how to efficiently estimate the ratio of two small probability of records. The inferential gain is particularly substantial when a simple hypothesis testing procedure is implemented. The theoretical justification of such a proposed scheme can be found in Extreme Value Theory. To illustrate our approach, classical indicators in event attribution studies like the Risk Ratio or the Fraction of Attributable Risk, are modified and tailored to handle records. We illustrate the advantages of our method through theoretical results, simulation studies, temperature records in Paris and outputs from a numerical climate model.

  • Source Publication: Nature Scientific Reports, 8, 1007, doi:10.1038/s41598-018-19288- Authors: Li, C., Y. Fang, K. Calderia, X. Zhang, N.S. Diffenbaugh, and A.M. Michalak Publication Date: Jan 2018

    A critical question for climate mitigation and adaptation is to understand when and where the signal of changes to climate extremes have persistently emerged or will emerge from the background noise of climate variability. Here we show observational evidence that such persistent changes to temperature extremes have already occurred over large parts of the Earth. We further show that climate models forced with natural and anthropogenic historical forcings underestimate these changes. In particular, persistent changes have emerged in observations earlier and over a larger spatial extent than predicted by models. The delayed emergence in the models is linked to a combination of simulated change (‘signal’) that is weaker than observed, and simulated variability (‘noise’) that is greater than observed. Over regions where persistent changes had not occurred by the year 2000, we find that most of the observed signal-to-noise ratios lie within the 16–84% range of those simulated. Examination of simulations with and without anthropogenic forcings provides evidence that the observed changes are more likely to be anthropogenic than nature in origin. Our findings suggest that further changes to temperature extremes over parts of the Earth are likely to occur earlier than projected by the current climate models.

  • Source Publication: eather and Climate Extremes, 18, 65-74,doi:10.1016/j.wace.2017.10.003 Authors: Sillmann, J., T.L. Thoranisdottir, N. Schaller, L. Alexander, G.C. Hegerl, S.I. Seneviratne, R. Vautard, X. Zhang and F.W. Zwiers Publication Date: Dec 2017

    Weather and climate extremes are identified as major areas necessitating further progress in climate research and have thus been selected as one of the World Climate Research Programme (WCRP) Grand Challenges. Here, we provide an overview of current challenges and opportunities for scientific progress and cross-community collaboration on the topic of understanding, modeling and predicting extreme events based on an expert workshop organized as part of the implementation of the WCRP Grand Challenge on Weather and Climate Extremes. In general, the development of an extreme event depends on a favorable initial state, the presence of large-scale drivers, and positive local feedbacks, as well as stochastic processes. We, therefore, elaborate on the scientific challenges related to large-scale drivers and local-to-regional feedback processes leading to extreme events. A better understanding of the drivers and processes will improve the prediction of extremes and will support process-based evaluation of the representation of weather and climate extremes in climate model simulations. Further, we discuss how to address these challenges by focusing on short-duration (less than three days) and long-duration (weeks to months) extreme events, their underlying mechanisms and approaches for their evaluation and prediction.

  • Source Publication: Geophysical Research Letters, 44, 21, 11012-11020, doi:10.1002/2017GL075016 Authors: Najafi, M.R., F.W. Zwiers and N.P. Gillett Publication Date: Oct 2017

    We study the observed decline in summer streamflow in four key river basins in British Columbia (BC), Canada, using a formal detection and attribution (D&A) analysis procedure. Reconstructed and simulated streamflow is generated using the semidistributed variable infiltration capacity hydrologic model, which is driven by 1/16° gridded observations and downscaled climate model data from the Coupled Model Intercomparison Project phase 5 (CMIP5), respectively. The internal variability of the regional hydrologic components using ~5100 years of streamflow was simulated using CMIP5 preindustrial control runs. Results show that the observed changes in summer streamflow are inconsistent with simulations representing the responses to natural forcing factors alone, while the response to anthropogenic and natural forcing factors combined is detected in these changes. A two‐signal D&A analysis indicates that the effects of anthropogenic (ANT) forcing factors are discernable from natural forcing in BC, albeit with large uncertainties.

Pages