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  • Source Publication: Journal of Climate, 33, 16, 6957–6970, doi:10.1175/JCLI-D-19-0011.1 Authors: Ben Alaya, M.A., F.W. Zwiers and X. Zhang Publication Date: Aug 2020

    The recurring devastation caused by extreme events underscores the need for reliable estimates of their intensity and frequency. Operational frequency and intensity estimates are very often obtained from generalized extreme value (GEV) distributions fitted to samples of annual maxima. GEV distributed random variables are “max-stable,” meaning that the maximum of a sample of several values drawn from a given GEV distribution is again GEV distributed with the same shape parameter. Long-period return value estimation relies on this property of the distribution. The data to which the models are fitted may not, however, be max-stable. Observational records are generally too short to assess whether max-stability holds in the upper tail of the observations. Large ensemble climate simulations, from which we can obtain very large samples of annual extremes, provide an opportunity to assess whether max-stability holds in a model-simulated climate and to quantify the impact of the lack of max-stability on very long period return-level estimates. We use a recent large ensemble simulation of the North American climate for this purpose. We find that the annual maxima of short-duration precipitation extremes tend not to be max-stable in the simulated climate, as indicated by systematic variation in the estimated shape parameter as block length is increased from 1 to 20 years. We explore how the lack of max-stability affects the estimation of very long period return levels and discuss reasons why short-duration precipitation extremes may not be max-stable.

  • Source Publication: Science of The Total Environment, 728, 138808, doi:0.1016/j.scitotenv.2020.138808 Authors: Brubacher, J., D.M. Allen, S.J. Déry, M.W. Parkes, B. Chhetri, S. Mak, S. Sobie and T.K. Takaro Publication Date: Aug 2020

    Food- and water-borne pathogens exhibit spatial heterogeneity, but attribution to specific environmental processes is lacking while anthropogenic climate change alters these processes. The goal of this study was to investigate ecology, land-use and health associations of these pathogens and to make future disease projections. 

    The rates of five acute gastrointestinal illnesses (AGIs) (campylobacteriosis, Verotoxin- producing Escherichia coli, salmonellosis, giardiasis and cryptosporidiosis) from 2000 to 2013 in British Columbia, Canada, were calculated across three environmental variables: ecological zone, land use, and aquifer type. A correlation analysis investigated relationships between 19 climatic factors and AGI. Mean annual temperature at the ecological zone scale was used in a univariate regression model to calculate annual relative AGI risk per 1 °C increase. Future cases attributable to climate change were estimated into the 2080s.

    Each of the bacterial AGI rates was correlated with several annual temperature-related factors while the protozoan AGIs were not. In the regression model, combined relative risk for the three bacterial AGIs was 1.1 [95% CI: 1.02–1.21] for every 1 °C in mean annual temperature. Campylobacteriosis, salmonellosis and giardiasis rates were significantly higher (p < 0.05) in the urban land use class than in the rural one. In rural areas, bacteria and protozoan AGIs had significantly higher rates in the unconsolidated aquifers. Verotoxin-producing Escherichia coli rates were significantly higher in watersheds with more agricultural land, while rates of campylobacteriosis, salmonellosis and giardiasis were significantly lower in agricultural watersheds. Ecological zones with higher bacterial AGI rates were generally projected to expand in range by the 2080s.

    These findings suggest that risk of AGI can vary across ecosystem, land use and aquifer type, and that warming temperatures may be associated with an increased risk of food-borne AGI. In addition, spatial patterns of these diseases are projected to shift under climate change.

  • Source Publication: Journal of Hydrology, 587, 124939, doi:10.1016/j.jhydrol.2020.124939 Authors: Melaku, N.D., J. Wang and T.W. Meshesha Publication Date: Aug 2020

    Peatlands cover only about 3% of the Earth’s surface and store 15–30% of the Global soil carbon as a peat. However, human intervention and climate change threatens the stability of peatlands, owing to deforest, wildfire, mining, drainage, glacial retreat, and permafrost. In our study, we modified the SWAT model to couple snow, soil temperature and carbon dioxide emission. Then the modified SWAT was used for predicting snow depth, soil temperature at different depths and carbon dioxide emission from peatlands and other land uses at Athabasca river basin, Canada. The results of the study indicated that SWAT model estimated the daily snow depth with R2, NSE, RMSE and PBIAS values of 0.83, 0.76, 0.52 and −2.3 in the calibration period (2006–2007) and 0.79, 0.71, 0.97 and −3.6 for the validation period (2008–2009), respectively. The SWAT model also predicted soil temperature very well at three depths (5 cm, 10 cm and 30 cm). The simulation model results also confirmed that the modified SWAT model estimates the CO2 emission at Athabasca river basin with good model fit during calibration (R2 = 0.71, NSE = 0.67, RMSE = 2.6 and PBIAS = 3.2) and during validation (R2 = 0.63, NSE = 0.58, RMSE = 3.1 and PBIAS = 9.3). Overall, our result confirmed that SWAT model performed well in representing the dynamics of snow depth, soil temperature and CO2 emissions in the peatlands at the Athabasca river basin.

  • Source Publication: Journal of Hydrology, 587, 124952, doi:10.1016/j.jhydrol.2020.124952 Authors: Meshesha, T.W., J. Wang and N.D. Melaku Publication Date: Aug 2020

    Cold climate regions offer various ecosystem services. The water quality parameters such as dissolved oxygen (DO), water temperature (Tw), and dissolved organic carbon (DOC) have considerable impacts on the aquatic ecosystem species. Any impairments in water quality such as elevated water temperature, and low DO concentrations can limit the survival of aquatic ecosystems and its species, such as walleye, northern pike and salmon. Therefore, a good understanding of the aquatic ecosystem of rivers is essential for effective and sustainable river basin and watershed management of fisheries and aquatic resources. The objectives of this study is to improve a watershed scale module of water quality (DO, DOC and Fecal coliforms (FC) in the SWAT model to examine the spatiotemporal patterns and their impacts on aquatic ecosystem and water quality processes in the Athabasca River Basin (ARB), Alberta, Canada. The calibration and validation results of DO, DOC and FC show that the improved Soil and Water Assessment Tool (SWAT) model achieved successfully with a varied range (satisfactory to vey-good) of accuracy at the daily temporal scales. The results showed that concentrations of DO for the selected stations (spring and summer) reduced far below the thresholds for ecosystems survival. In concurrent reduction with DO, the FC concentration considerably varied in the different monitoring stations of ARB. These results highlight that DO, DOC and FC variability in the ARB may drive changes in water quality and ecosystem services that have to be understood on the specific research scale for designing adaptive management scenarios. This study reveals that the new SWAT model can be applied to other similar regions of the worlds.

  • Authors: RDH Building Science Publication Date: Jul 2020

    The primary objective of this study is to assess the implications of increasing outdoor air
    temperatures due to climate change on the thermal comfort of multifamily residential
    buildings in the Lower Mainland, and to identify cost-effective design measures that will
    maintain thermal comfort under future climate conditions.

    A variety of climate adaptation and mitigation measures (CAMMs) suitable for both new
    and existing, high and low rise multifamily residential buildings are explored using future
    climate projections. Ideally, solutions are identified that improve thermal comfort without
    sacrificing parallel societal objectives to reduce energy consumption and greenhouse gas
    emissions. It is also desirable that identified solutions improve the resiliency of buildings
    to maintain comfort during increasingly common extreme weather events such as
    unusually high temperatures, wildfire-induced poor air quality, or power outages.

    The results of this study will support development of design guidelines, policies and
    standards that ensure new building provide residents with thermally comfortable
    environments, as well as programs that improve the thermal comfort of existing
    residential buildings. This study will also guide best practises for incorporating
    projections of warmer future climate conditions into building energy modelling and

  • Source Publication: Earth System Science Data, 12, 1561–1623, doi:10.5194/essd-12-1561-2020 Authors: Saunois, M. et al. Publication Date: Jul 2020

    Understanding and quantifying the global methane (CH4) budget is important for assessing realistic pathways to mitigate climate change. Atmospheric emissions and concentrations of CH4 continue to increase, making CH4 the second most important human-influenced greenhouse gas in terms of climate forcing, after carbon dioxide (CO2). The relative importance of CH4 compared to CO2 depends on its shorter atmospheric lifetime, stronger warming potential, and variations in atmospheric growth rate over the past decade, the causes of which are still debated. Two major challenges in reducing uncertainties in the atmospheric growth rate arise from the variety of geographically overlapping CH4 sources and from the destruction of CH4 by short-lived hydroxyl radicals (OH). To address these challenges, we have established a consortium of multidisciplinary scientists under the umbrella of the Global Carbon Project to synthesize and stimulate new research aimed at improving and regularly updating the global methane budget. Following Saunois et al. (2016), we present here the second version of the living review paper dedicated to the decadal methane budget, integrating results of top-down studies (atmospheric observations within an atmospheric inverse-modelling framework) and bottom-up estimates (including process-based models for estimating land surface emissions and atmospheric chemistry, inventories of anthropogenic emissions, and data-driven extrapolations).

    For the 2008–2017 decade, global methane emissions are estimated by atmospheric inversions (a top-down approach) to be 576 Tg CH4 yr−1 (range 550–594, corresponding to the minimum and maximum estimates of the model ensemble). Of this total, 359 Tg CH4 yr−1 or ∼ 60 % is attributed to anthropogenic sources, that is emissions caused by direct human activity (i.e. anthropogenic emissions; range 336–376 Tg CH4 yr−1 or 50 %–65 %). The mean annual total emission for the new decade (2008–2017) is 29 Tg CH4 yr−1 larger than our estimate for the previous decade (2000–2009), and 24 Tg CH4 yr−1 larger than the one reported in the previous budget for 2003–2012 (Saunois et al., 2016). Since 2012, global CH4 emissions have been tracking the warmest scenarios assessed by the Intergovernmental Panel on Climate Change. Bottom-up methods suggest almost 30 % larger global emissions (737 Tg CH4 yr−1, range 594–881) than top-down inversion methods. Indeed, bottom-up estimates for natural sources such as natural wetlands, other inland water systems, and geological sources are higher than top-down estimates. The atmospheric constraints on the top-down budget suggest that at least some of these bottom-up emissions are overestimated. The latitudinal distribution of atmospheric observation-based emissions indicates a predominance of tropical emissions (∼ 65 % of the global budget,

    Some of our global source estimates are smaller than those in previously published budgets (Saunois et al., 2016; Kirschke et al., 2013). In particular wetland emissions are about 35 Tg CH4 yr−1 lower due to improved partition wetlands and other inland waters. Emissions from geological sources and wild animals are also found to be smaller by 7 Tg CH4 yr−1 by 8 Tg CH4 yr−1, respectively. However, the overall discrepancy between bottom-up and top-down estimates has been reduced by only 5 % compared to Saunois et al. (2016), due to a higher estimate of emissions from inland waters, highlighting the need for more detailed research on emissions factors. Priorities for improving the methane budget include (i) a global, high-resolution map of water-saturated soils and inundated areas emitting methane based on a robust classification of different types of emitting habitats; (ii) further development of process-based models for inland-water emissions; (iii) intensification of methane observations at local scales (e.g., FLUXNET-CH4 measurements) and urban-scale monitoring to constrain bottom-up land surface models, and at regional scales (surface networks and satellites) to constrain atmospheric inversions; (iv) improvements of transport models and the representation of photochemical sinks in top-down inversions; and (v) development of a 3D variational inversion system using isotopic and/or co-emitted species such as ethane to improve source partitioning.

    The data presented here can be downloaded from (Saunois et al., 2020) and from the Global Carbon Project.

  • Source Publication: Mine Water Environ., doi:10.1007/s10230-020-00695-6. Authors: Alam, M.S., et al. Publication Date: Jun 2020

    The oil sands industry in Canada uses soil–vegetation–atmosphere-transfer (SVAT) water balance models, calibrated against short-term (less than 10 years) field monitoring data, to evaluate long-term (≈60 years) reclamation cover design performance. These evaluations use long-term historical climate data; however, the effects of climate change should also be incorporated in these analyses. Although statistical downscaling of global climate change projections is commonly used to obtain local, site-specific climate, high resolution dynamical downscaling can also be used. The value of this latter approach to obtain local site-specific projections for mine reclamation covers has not been evaluated previously. This study explored the differences in key water balance components of three reclamation covers and three natural sites in northern Alberta, Canada, under future, site-specific, statistical, and dynamical climate change projections. Historical meteorological records were used to establish baseline periods. Temperature datasets were used to calculate potential evapotranspiration (PET) using the Hargreaves–Samani method. Statistical downscaling uses the Long Ashton Research Station Weather Generator (LARS-WG) and global circulation model (GCM) projections of temperature and precipitation. Dynamical climate change projections were generated on a 4 km grid using the weather research and forecasting (WRF) model. These climate projections were applied to a physically-based water balance model (i.e. Hydrus-1D) to simulate actual evapotranspiration (AET) and net percolation (NP) for the baseline and future periods. The key findings were: (a) LARS-WG outperformed WRF in simulating baseline temperatures and precipitation; (b) both downscaling methods showed similar directional shifts in the future temperatures and precipitation; (c) this, in turn, created similar directional shifts in future growing season median AET and NP, although the increase in future NP for LARS-WG was higher than that for WRF. The relative increases in future NP were much higher than the relative increases in future AET, particularly for the reclamation covers.

  • Source Publication: Journal of Hydrology, 582, 124513, doi: 10.1016/j.jhydrol.2019.124513. Authors: Meshesha, T.W., J. Wang and N. Demelash Melaku Publication Date: Mar 2020

    Quantifying bacteria fluxes and contaminants from the point and nonpoint sources in a watershed are important for the management of water quality and safeguard public health. Therefore, the appropriate characterization of bacteria from different sources is necessary for understanding of fate and transport of bacteria in watersheds. However, it is challenging to simulate the effects of pH on bacteria, such as Escherichia coli (E. coli) in the original version of Soil and Water Assessment Tool (SWAT). This study aimed to augment SWAT-bacteria module to evaluate the potential effect of pH on E. coli concentrations. We modified SWAT-bacteria module to incorporate pH factor and to check E. coli observations from four sites of Athabasca River Basin. The modified SWAT-bacteria model demonstrated a linear relationship between observed and simulated daily E. coli data with R2 values found between 0.70 and 0.80; NSE: 0.59 and 0.68; PBIAS: 7.94% and 17.85% during calibration for all monitoring sites (2010–2012). While during the validation (2013–2014) the performance statistics found to be: R2: 0.59–0.72; NSE: 0.55–0.66; PBIAS: 10–22%. The results of the sensitivity analysis confirmed that pH is one of the most significant fate factors of E. coli. The modified SWAT-bacteria module provides an improved estimate of E. coli concentration from the river basin. This study contributes new insight to E. coli modelling. Therefore, the modified SWAT-bacteria model could be a powerful tool for the future regional to global scale model of E. coli concentrations thus significantly contribute for the application of effective river basin management.

  • Source Publication: Advances in Water Resources, 137, 103522, doi:10.1016/j.advwatres.2020.103522. Authors: Ben Alaya, M.A., C. Ternynck, S. Dabo-Niang, F. Chebana and T.B.M.J. Ouarda Publication Date: Mar 2020

    Change point detection methods have an important role in many hydrological and hydraulic studies of river basins. These methods are very useful to characterize changes in hydrological regimes and can, therefore, lead to better understanding changes in extreme flows behavior. Flood events are generally characterized by a finite number of characteristics that may not include the entire information available in a discharge time series. The aim of the current work is to present a new approach to detect changes in flood events based on a functional data analysis framework. The use of the functional approach allows taking into account the whole information contained in the discharge time series of flood events. The presented methodology is illustrated on a flood analysis case study, from the province of Quebec, Canada. Obtained results using the proposed approach are consistent with those obtained using a traditional change point method, and demonstrate the capability of the functional framework to simultaneously consider several flood features and, therefore, presenting a comprehensive way for a better exploitation of the information contained in a discharge time series.

  • Source Publication: Journal of Climate, 33, 8, 3253–3269, doi:10.1175/JCLI-D-19-0405.1. Authors: Williamson, S.N., C. Zdanowicz, F.S. Anslow, G.K.C. Clarke, L. Copland, R.K. Danby, G.E. Flowers, G. Holdsworth, A.H. Jarosch, and D.S. Hik Publication Date: Mar 2020

    The climate of high midlatitude mountains appears to be warming faster than the global average, but evidence for such elevation-dependent warming (EDW) at higher latitudes is presently scarce. Here, we use a comprehensive network of remote meteorological stations, proximal radiosonde measurements, downscaled temperature reanalysis, ice cores, and climate indices to investigate the manifestation and possible drivers of EDW in the St. Elias Mountains in subarctic Yukon, Canada. Linear trend analysis of comprehensively validated annual downscaled North American Regional Reanalysis (NARR) gridded surface air temperatures for the years 1979–2016 indicates a warming rate of 0.028°C a−1 between 5500 and 6000 m above mean sea level (MSL), which is ~1.6 times larger than the global-average warming rate between 1970 and 2015. The warming rate between 5500 and 6000 m MSL was ~1.5 times greater than the rate at the 2000–2500 m MSL bin (0.019°C a−1), which is similar to the majority of warming rates estimated worldwide over similar elevation gradients. Accelerated warming since 1979, measured by radiosondes, indicates a maximum rate at 400 hPa (~7010 m MSL). EDW in the St. Elias region therefore appears to be driven by recent warming of the free troposphere. MODIS satellite data show no evidence for an enhanced snow albedo feedback above 2500 m MSL, and declining trends in sulfate aerosols deposited in high-elevation ice cores suggest a modest increase in radiative forcing at these elevations. In contrast, increasing trends in water vapor mixing ratio at the 500-hPa level measured by radiosonde suggest that a longwave radiation vapor feedback is contributing to EDW.

  • Source Publication: Hydrol. Earth Syst. Sci., 24, 735–759, doi:10.5194/hess-24-735-2020 Authors: Alam, M.S., L. Barbour and M. Huang Publication Date: Feb 2020

    One technique to evaluate the performance of oil sands reclamation covers is through the simulation of long-term water balance using calibrated soil–vegetation–atmosphere transfer models. Conventional practice has been to derive a single set of optimized hydraulic parameters through inverse modelling (IM) based on short-term (less than 5–10 years) monitoring datasets. This approach is unable to characterize the impact of variability in the cover properties. This study utilizes IM to optimize the hydraulic properties for 12 soil cover designs, replicated in triplicate, at Syncrude's Aurora North mine site. The hydraulic parameters for three soil types (peat cover soil, coarse-textured subsoil, and lean oil sand substrate) were optimized at each monitoring site from 2013 to 2016. The resulting 155 optimized parameter values were used to define distributions for each parameter/soil type, while the progressive Latin hypercube sampling (PLHS) method was used to sample parameter values randomly from the optimized parameter distributions. Water balance models with the sampled parameter sets were used to evaluate variations in the maximum sustainable leaf area index (LAI) for five illustrative covers and quantify uncertainty associated with long-term water balance components and LAI values. Overall, the PLHS method was able to better capture broader variability in the water balance components than a discrete interval sampling method. The results also highlight that climate variability dominates the simulated variability in actual evapotranspiration and that climate and parameter uncertainty have a similar influence on the variability in net percolation.

  • Source Publication: Hydrology and Earth System Sciences, 24, 735–759, doi: 10.5194/hess-24-735-2020. Authors: Alam, M. S., S. L. Barbour and M. Huang Publication Date: Feb 2020

    One technique to evaluate the performance of oil sands reclamation covers is through the simulation of long-term water balance using calibrated soil–vegetation–atmosphere transfer models. Conventional practice has been to derive a single set of optimized hydraulic parameters through inverse modelling (IM) based on short-term monitoring datasets. This approach is unable to characterize the impact of variability in the cover properties. This study utilizes IM to optimize the hydraulic properties for 12 soil cover designs, replicated in triplicate, at Syncrude's Aurora North mine site. The hydraulic parameters for three soil types (peat cover soil, coarse-textured subsoil, and lean oil sand substrate) were optimized at each monitoring site from 2013 to 2016. The resulting 155 optimized parameter values were used to define distributions for each parameter/soil type, while the progressive Latin hypercube sampling (PLHS) method was used to sample parameter values randomly from the optimized parameter distributions. Water balance models with the sampled parameter sets were used to evaluate variations in the maximum sustainable leaf area index (LAI) for five illustrative covers and quantify uncertainty associated with long-term water balance components and LAI values. Overall, the PLHS method was able to better capture broader variability in the water balance components than a discrete interval sampling method. The results also highlight that climate variability dominates the simulated variability in actual evapotranspiration and that climate and parameter uncertainty have a similar influence on the variability in net percolation.

  • Source Publication: Journal of Climate, Early Online Release, doi:10.1175/JCLI-D-19-0492.1. Authors: Li, C., Y.Sun, F. Zwiers, D. Wang, X. Zhang, G. Chen, and H. Wu Publication Date: Feb 2020

    Based on a newly developed observational dataset and a suite of climate model simulations, we evaluate changes in summer mean wet bulb globe temperature (WBGT) in China from 1961 through 2080. We show that summer mean WBGT has increased almost everywhere across China since 1961 due to human-induced climate change. Consequently, hot summers as measured by summer mean WBGT are becoming more frequent and more conducive to heat stress. Hot summers like the hottest on record during 1961-2015 in Western or Eastern China are now expected occur once every 3-4 years. These hot WBGT summers have become more than 140 times as likely in Eastern China in the present decade 2010s compared to a 1961-1990 baseline period, and more than 1000 times as likely in Western China. The substantially larger influence in Western China is associated with its stronger warming signal, which is likely due to the high Bowen ratio of sensible to latent heat fluxes of dry soils and increases in absorbed solar radiation from the decline in mountain snow cover extent. Observation-constrained projections of future summer mean WBGT under the RCP8.5 emissions scenario indicate that, by the 2040s, almost every summer in China will be at least as hot as the hottest summer in the historical record, and by the 2060s, common summers (that occur once every 2 years) will be even 3.0 °C hotter than the historical record, pointing to potentially large increases in the likelihood of human heat stress and to a massive adaption challenge.

  • Source Publication: Journal of Climate, 33, 4 1261-1281, doi:10.1175/JCLI-D-19-0134.1 Authors: Tan, Y., F.W. Zwiers, S. Yang, C. Li and K. Deng Publication Date: Feb 2020

    Performance in simulating atmospheric rivers (ARs) over western North America based on AR frequency and landfall latitude is evaluated for 10 models from phase 5 of the Coupled Model Intercomparison Project among which the CanESM2 model performs well. ARs are classified into southern, northern, and middle types using self-organizing maps in the ERA-Interim reanalysis and CanESM2. The southern type is associated with the development and eastward movement of anomalous lower pressure over the subtropical eastern Pacific, while the northern type is linked with the eastward movement of anomalous cyclonic circulation stimulated by warm sea surface temperatures over the subtropical western Pacific. The middle type is connected with the negative phase of North Pacific Oscillation–west Pacific teleconnection pattern. CanESM2 is further used to investigate projected AR changes at the end of the twenty-first century under the representative concentration pathway 8.5 scenario. AR definitions usually reference fixed integrated water vapor or integrated water vapor transport thresholds. AR changes under such definitions reflect both thermodynamic and dynamic influences. We therefore also use a modified AR definition that isolates change from dynamic influences only. The total AR frequency doubles compared to the historical period, with the middle AR type contributing the largest increases along the coasts of Vancouver Island and California. Atmospheric circulation (dynamic) changes decrease northern AR type frequency while increasing middle AR type frequency, indicating that future changes of circulation patterns modify the direct effect of warming on AR frequency, which would increase ARs (relative to fixed thresholds) almost everywhere along the North American coastline.

  • Authors: Sun, Q., F. Zwiers, X. Zhang and G. Li Publication Date: Jan 2020

    Trend scaling relationships between extreme precipitation and temperature are often used to represent the influence of long-term warming on the intensity of extreme precipitation. Indeed, such scaling relationships are often regarded as providing more reliable precipitation projections than direct projection, owing to higher confidence for temperature projections in model simulations. Due to limited data availability, especially for the sub-daily rainfall, so-called binning scaling relationships, which relate extreme precipitation to temperature at the time of occurrence and are estimated empirically either through a binning technique or quantile regression, have been considered as a substitute for trend scaling to project the long-term response of local extreme precipitation to temperature change (Lenderink and Van Meijgaard, 2008; Wasko and Sharma, 2014). Estimates of binning scaling rates are generally based on seasonal subdaily precipitation observations, and thus they are influenced by factors other than temperature that change systematically within a season, synchronously with the seasonal cycle (Zhang et al., 2017). In contrast to trend scaling, binning scaling often suggests faster than Clausius-Clapeyron intensification of sub-daily precipitation extremes with temperature.

    We explore this apparent contradiction between binning and trend scaling using a large ensemble of moderate resolution regional climate simulations for North America. The large amount of data that is available from this ensemble allows us to confidently estimate both trend and binning scaling rates for the climate that is simulated by that model. Specifically, we use a 35-member initial conditions ensemble of regional climate simulations produced with the Canadian CanRCM4 regional climate model for the period 1950-2100, with historical forcings for the period ending 2005 and RCP8.5 forcing subsequently. Each CanRCM4 ensemble member was driven by a corresponding member of a similar large ensemble of global simulations produced with the Canadian global Earth system model CanESM2 (Scinocca et al., 2016).

    We compare binning and trend scaling of precipitation extremes across different durations (1-hour, 3-hour, and 24-hour), considering annual and seasonal values, and both local and regional spatial scales. We provide strong evidence to clarify that binning scaling cannot project the long-term change in precipitation extreme, with substantial disagreement in the spatial pattern and magnitude of scaling rates between binning and trend scaling regardless of the duration, season, and spatial scale. Using the daily dew point temperature as scaling variable rather than dry air temperature does not eliminate the differences between binning and trend scaling rates. While shorter-duration extreme precipitation does appear to intensify faster with warming in CanRCM4, we only find super-adiabatic intensification of annual precipitation extremes in isolated regions regardless of accumulation durations. Compared with annual maximum results, winter extremes intensify more strongly over the western and southeastern North America across all timescales. A decreasing tendency of summer extremes is projected over the north and central Great Plains. The seasonal timing of the occurrences of precipitation extremes are expected to shift towards the cold season, reflecting the different changing tendencies in summer and winter extremes.


    Lenderink, G., and Van Meijgaard, E. 2008: Increase in hourly precipitation extremes beyond expectations from temperature changes. Nat. Geosci., 1,

    Scinocca, J. F., Kharin, V. V., Jiao, Y., Qian, M. W., Lazare, M., Solheim, L., and Flato. G. M., 2016: Coordinated Global and Regional Climate Modeling. J. Climate, 29, 17-35,

    Wasko, C., and Sharma, A. 2014: Quantile regression for investigating scaling of extreme precipitation with temperature. Water Resour. Res., 50, 3608-3614,

    Zhang, X. B., Zwiers F. W., Li, G. L., Wan, H., Cannon, A. J., 2017: Complexity in estimating past and future extreme short-duration rainfall. Nat. Geosci., 10, 255-239,

  • Source Publication: Bulletin of the American Meteorological Society, doi:10.1175/BAMS-D-18-0258.1. Authors: Sun, Q., C. Miao, A. Agha Kouchak, I. Mallakpour, D. Ji, and Q Duan Publication Date: Dec 2019

    The projected occurrence of anomalous precipitation under different ENSO conditions may be changed under future climate warming, with an asymmetric response to La Niña and El Niño and an increasing frequency of ENSO-related severe dry and wet events.

    Predicting the changes in teleconnection patterns and related hydroclimate extremes can provide vital information necessary to adapt to the effects of the El Niño Southern Oscillation (ENSO). This study uses the outputs of global climate models to assess the changes in ENSO-related dry/wet patterns and the frequency of severe dry/wet events. The results show anomalous precipitation responding asymmetrically to La Niña and El Niño, indicating the teleconnections may not simply be strengthened. A “dry to drier, wet to wetter” annual anomalous precipitation pattern was projected during La Niña phases in some regions, with drier conditions over southern North America, southern South America, and southern Central Asia, and wetter conditions in Southeast Asia and Australia. These results are robust, with agreement from the 26 models and from a subset of 8 models selected for their good performance in capturing observed patterns. However, we did not observe a similar strengthening of anomalous precipitation during future El Niño phases, for which the uncertainties in the projected influences are large. Under the RCP 4.5 emissions scenario, 45 river basins under El Niño conditions and 39 river basins under La Niña conditions were predicted to experience an increase in the frequency of severe dry events; similarly, 59 river basins under El Niño conditions and 61 river basins under La Niña conditions were predicted to have an increase in the frequency of severe wet events, suggesting a likely increase in the risk of floods. Our results highlight the implications of changes in ENSO patterns for natural hazards, disaster management, and engineering infrastructure.

  • Source Publication: Climatic Change, doi:10.1007/s10584-019-02591-7. Authors: Ben Alaya, M.A., F.W. Zwiers and X. Zhang Publication Date: Dec 2019

    In the context of climate change and projected increase in global temperature, the atmosphere’s water holding capacity is expected to increase at the Clausius-Clapeyron (C-C) rate by about 7% per 1 °C warming. Such an increase may lead to more intense extreme precipitation events and thus directly affect the probable maximum precipitation (PMP), a parameter that is often used for dam safety and civil engineering purposes. We therefore use a statistically motivated approach that quantifies uncertainty and accounts for nonstationarity, which allows us to determine the rate of change of PMP per 1 °C warming. This approach, which is based on a bivariate extreme value model of precipitable water (PW) and precipitation efficiency (PE), provides interpretation of how PW and PE may evolve in a warming climate. Nonstationarity is accounted for in this approach by including temperature as a covariate in the bivariate extreme value model. The approach is demonstrated by evaluating and comparing projected changes to 6-hourly PMP from two Canadian regional climate models (RCMs), CanRCM4 and CRCM5, over North America. The main results suggest that, on the continental scale, PMP increases in these models at a rate of approximately 4% per 1 °C warming, which is somewhat lower than the C-C rate. At the continental scale, PW extremes increase on average at the rate of 5% per 1 °C near surface warming for both RCMs. Most of the PMP increase is caused by the increase in PW extremes with only a minor contribution from changes in PE extremes. Nevertheless, substantial deviations from the average rate of change in PMP rates occur in some areas, and these are mostly caused by sensitivity of PE extremes to near surface warming in these regions.

  • Source Publication: Environmental Health, 18, 116, doi:10.1186/s12940-019-0550-y Authors: Chhetri, B.K, Galanis, E., Sobie, S., Brubacher, J., Balshaw, R., Otterstatter, M., Mak, S., Lem, M., Lysyshyn, M., Murdock, T., Fleury, M., Zickfeld, K., Zubel, M., Clarkson, L. and T.K. Takaro Publication Date: Dec 2019

    Climate change is increasing the number and intensity of extreme weather events in many parts of the world. Precipitation extremes have been linked to both outbreaks and sporadic cases of waterborne illness. We have previously shown a link between heavy rain and turbidity to population-level risk of sporadic cryptosporidiosis and giardiasis in a major Canadian urban population. The risk increased with 30 or more dry days in the 60 days preceding the week of extreme rain. The goal of this study was to investigate the change in cryptosporidiosis and giardiasis risk due to climate change, primarily change in extreme precipitation.

    Cases of cryptosporidiosis and giardiasis were extracted from a reportable disease system (1997–2009). We used distributed lag non-linear Poisson regression models and projections of the exposure-outcome relationship to estimate future illness (2020–2099). The climate projections are derived from twelve statistically downscaled regional climate models. Relative Concentration Pathway 8.5 was used to project precipitation derived from daily gridded weather observation data (~ 6 × 10 km resolution) covering the central of three adjacent watersheds serving metropolitan Vancouver for the 2020s, 2040s, 2060s and 2080s.

    Precipitation is predicted to steadily increase in these watersheds during the wet season (Oct. -Mar.) and decrease in other parts of the year up through the 2080s. More weeks with extreme rain (>90th percentile) are expected. These weeks are predicted to increase the annual rates of cryptosporidiosis and giardiasis by approximately 16% by the 2080s corresponding to an increase of 55–136 additional cases per year depending upon the climate model used. The predicted increase in the number of waterborne illness cases are during the wet months. The range in future projections compared to historical monthly case counts typically differed by 10–20% across climate models but the direction of change was consistent for all models.

    If new water filtration measures had not been implemented in our study area in 2010–2015, the risk of cryptosporidiosis and giardiasis would have been expected to increase with climate change, particularly precipitation changes. In addition to the predicted increase in the frequency and intensity of extreme precipitation events, the frequency and length of wet and dry spells could also affect the risk of waterborne diseases as we observed in the historical period. These findings add to the growing evidence regarding the need to prepare water systems to manage and become resilient to climate change-related health risks.

  • Source Publication: Environment International, 128, 125-136, doi:10.1016/j.envint.2019.04.025 Authors: Sun, Q., C. Miao, M. Hanel, A.G.L. Borthwick, Q. Duan, D. Jid and H. Li Publication Date: Dec 2019

    The effects of heat stress are spatially heterogeneous owing to local variations in climate response, population density, and social conditions. Using global climate and impact models from the Inter-Sectoral Impact Model Intercomparison Project, our analysis shows that the frequency and intensity of heat events increase, especially in tropical regions (geographic perspective) and developing countries (national perspective), even with global warming held to the 1.5 °C target. An additional 0.5 °C increase to the 2 °C warming target leads to >15% of global land area becoming exposed to levels of heat stress that affect human health; almost all countries in Europe will be subject to increased fire danger, with the duration of the fire season lasting 3.3 days longer; 106 countries are projected to experience an increase in the wheat production-damage index. Globally, about 38%, 50%, 46%, 36%, and 48% of the increases in exposure to health threats, wildfire, crop heat stress for soybeans, wheat, and maize could be avoided by constraining global warming to 1.5 °C rather than 2 °C. With high emissions, these impacts will continue to intensify over time, extending to almost all countries by the end of the 21st century: >95% of countries will face exposure to health-related heat stress, with India and Brazil ranked highest for integrated heat-stress exposure. The magnitude of the changes in fire season length and wildfire frequency are projected to increase substantially over 74% global land, with particularly strong effects in the United States, Canada, Brazil, China, Australia, and Russia. Our study should help facilitate climate policies that account for international variations in the heat-related threats posed by climate change.

  • Source Publication: Journal of Hydrometeorology, 20, 10, 2069-2089, doi:10.1175/JHM-D-18-0233.1 Authors: Ben Alaya, M.A.., F. Zwiers, and X. Zhang Publication Date: Oct 2019

    Recently dam managers have begun to use data produced by regional climate models to estimate how probable maximum precipitation (PMP) might evolve in the future. Before accomplishing such a task, it is essential to assess PMP estimates derived from regional climate models (RCMs). In the current study PMP over North America estimated from two Canadian RCMs, CanRCM4 and CRCM5, is compared with estimates derived from three reanalysis products: ERA-Interim, NARR, and CFSR. An additional hybrid dataset (MSWEP-ERA) produced by combining precipitation from the Multi-Source Weighted-Ensemble Precipitation (MSWEP) dataset and precipitable water (PW) from ERA-Interim is also considered to derive PMP estimates that can serve as a reference. A recently developed approach using a statistical bivariate extreme values distribution is used to provide a probabilistic description of the PMP estimates using the moisture maximization method. Such a probabilistic description naturally allows an assessment of PMP estimates that includes quantification of their uncertainty. While PMP estimates based on the two RCMs exhibit spatial patterns similar to those of MSWEP-ERA and the three sets of reanalyses on the continental scale over North America, CanRCM4 has a tendency for overestimation while CRCM5 has a tendency for modest underestimation. Generally, CRCM5 shows good agreement with ERA-Interim, while CanRCM4 is more comparable to CFSR. Overall, the good ability of the two RCMs to reproduce the major characteristics of the different components involved in the estimation of PMP suggests that they may be useful tools for PMP estimation that could serve as a basis for flood studies at the basin scale.