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  • Source Publication: Earth and Space Science, 11, 4, e2023EA003279 doi:10.1029/2023EA003279 Authors: Dunn, R. J. H. and 27 co-authors including X. Zhang Publication Date: Apr 2024

    Global gridded data sets of observed extremes indices underpin assessments of changes in climate extremes. However, similar efforts to enable the assessment of indices relevant to different sectors of society have been missing. Here we present a data set of sector-specific indices, based on daily station data, that extends the HadEX3 data set of climate extremes indices. These additional indices, which can be used singly or in combinations, have been recommended by the World Meteorological Organization and are intended to empower decision makers in different sectors with accurate historical information about how sector-relevant measures of the climate are changing, especially in regions where in situ daily temperature and rainfall data are hard to come by. The annual and/or monthly indices have been interpolated on to a 1.875° × 1.25° longitude-latitude grid for 1901–2018. We show changes in globally-averaged time series of these indices in comparison with reanalysis products. Changes in temperature-based indices are consistent with global scale warming, with days with Tmax > 30°C (TXge30) increasing virtually everywhere with potential impacts on crop fertility. At the other end of the scale, the number of days with Tmin and

  • Source Publication: Geophysical Research Letters, 51, 3, e2023GL105605, doi: 10.1029/2023GL105605 Authors: Li, C., Q. Sun, J. Wang, Y. Liang, F.W. Zwiers, X. Zhang and T. Li Publication Date: Feb 2024

    Rare precipitation events with return periods of multiple decades to hundreds of years are particularly damaging to natural and societal systems. Projections of such rare, damaging precipitation events in the future climate are, however, subject to large inter-model variations. We show that a substantial portion of these differences can be ascribed to the projected warming uncertainty, and can be robustly reduced by using the warming observed during recent decades as an observational constraint, implemented either by directly constraining the projections with the observed warming or by conditioning them on constrained warming projections, as verified by extensive model-based cross-validation. The temperature constraint reduces >40% of the warming-induced uncertainty in the projected intensification of future rare daily precipitation events for a climate that is 2°C warmer than preindustrial across most regions. This uncertainty reduction together with validation of the reliability of the projections should permit more confident adaptation planning at regional levels.

  • Source Publication: The Journal of Climate, 37, 5, 1567-1580, doi: 10.1175/JCLI-D-23-0312.1 Authors: Li, T., X. Zhang, and Z. Jiang Publication Date: Feb 2024

    Weighting models according to their performance has been used to produce multimodel climate change projections. But the added value of model weighting for future projection is not always examined. Here we apply an imperfect model framework to evaluate the added value of model weighting in projecting summer temperature changes over China. Members of large-ensemble simulations by three climate models of different climate sensitivities are used as pseudo-observations for the past and the future. Performance of the models participating in the phase 6 of the Coupled Model Intercomparison Project (CMIP6) are evaluated against the pseudo-observations based on simulated historical climatology and trends in global, regional, and local temperatures to determine the model weights for future projection. The weighted projections are then compared with the pseudo-observations in the future period. We find that regional trend as a metric of model performance yields generally better skill for future projection, while past climatology as performance metric does not lead to a significant improvement to projection. Trend at the grid-box scale is also not a good performance indicator as small-scale trend is highly uncertain. For the model weighting to be effective, the metric for evaluating the model’s performance must be relatable to future changes, with the response signal separable from internal variability. Projected summer warming based on model weighting is similar to that of unweighted projection but the 5th–95th-percentile uncertainty range of the weighted projection is 38% smaller with the reduction mainly in the upper bound, with the largest reduction appearing in southeast China.

  • Source Publication: Climatic Change, 176, 161, doi: 10.1007/s10584-023-03632-y Authors: Larabi, S., M.A. Schnorbus and F.W. Zwiers Publication Date: Nov 2023

    Water regulation has contributed to the decline in Pacific salmon in British Columbia (Canada) despite attempts to manage reservoir operations to achieve operational requirements while meeting environmental needs to limit fish thermal stress. The ability of reservoir managers to meet these trade-offs in a changing climate is unknown. Here, we examine the reliability and vulnerability of the Nechako Reservoir to meet hydropower production commitments and fisheries needs under two projected Shared Socioeconomic Pathway scenarios (SSP2-4.5 and SSP5-8.5). While our findings are specific to the operation of the Nechako Reservoir, the issues that emerge are likely common to many reservoirs in areas where reservoir inflow regimes are currently snow-storage dominated. We found that projected changes in the timing of water availability have little to no influence on hydropower generation commitments. However, larger water releases will be required to avoid compromising reservoir safety, possibly endangering downstream fish habitat through scouring. Furthermore, the temperature of water released from the reservoir is projected to more frequently exceed a level, 20°C, that is detrimental to migrating sockeye salmon. Water released is subject to further warming as it travels towards the lower reaches of the Nechako River used by migrating salmon. Hence, there is a need to adapt reservoir operations to ensure reservoir safety and mitigate adverse effects on salmon habitat.

  • Source Publication: Climatic Change, 176, 164, doi:10.1007/s10584-023-03634-w Authors: Khorsandi, M., A. St-Hilaire, R. Arsenault, J.-L. Martel, S. Larabi, M. Schnorbus, F.W. Zwiers Publication Date: Nov 2023

    Water temperature is a key variable affecting fish habitat in rivers. The Sockeye salmon (Oncorhynchus nerka), a keystone species in north western aquatic ecosystems of North America, is profoundly affected by thermal regime changes in rivers, and it holds a pivotal role in ecological and economic contexts due to its life history, extensive distribution, and commercial fishery. In this study, we explore the effects of climate change on the thermal regime of the Nechako River (British Columbia, Canada), a relatively large river partially controlled by the Skins Lake Spillway. The CEQUEAU hydrological-thermal model was calibrated using discharge and water temperature observations. The model was forced using the Fifth generation of ECMWF Atmospheric Reanalysis data for the past and meteorological projections (downscaled and bias-corrected) from climate models for future scenarios. Hydrological calibration was completed for the 1980–2019 period using data from two hydrometric stations, and water temperature calibration was implemented using observations for 2005–2019 from eight water temperature stations. Changes in water temperature were assessed for two future periods (2040–2069 and 2070–2099) using eight Coupled Model Intercomparison Project Phase 6 climate models and using two Shared Socioeconomic Pathway scenarios (4.5 and 8.5 W/m2 by 2100) for each period. Results show that water temperatures above 20°C (an upper threshold for adequate thermal habitat for Sockeye salmon migration in this river) at the Vanderhoof station will increase in daily frequency. While the frequency of occurrence of this phenomenon is 1% (0–9 days/summer) based on 2005–2019 observations, this number range is 3.8–36% (0–62 days/summer) according to the ensemble of climate change scenarios. These results show the decreasing habitat availability for Sockeye salmon due to climate change and the importance of water management in addressing this issue.

  • Source Publication: Journal of Climate, 36, 20, 7109-7122, doi: 10.1175/JCLI-D-22-0681.1 Authors: Ma, S., T. Wang, J. Yan, and X. Zhang Publication Date: Oct 2023

    Climate change detection and attribution have played a central role in establishing the influence of human activities on climate. Optimal fingerprinting, a linear regression with errors in variables (EIVs), has been widely used in detection and attribution analyses of climate change. The method regresses observed climate variables on the expected climate responses to the external forcings, which are measured with EIVs. The reliability of the method depends critically on proper point and interval estimations of the regression coefficients. The confidence intervals constructed from the prevailing method, total least squares (TLS), have been reported to be too narrow to match their nominal confidence levels. We propose a novel framework to estimate the regression coefficients based on an efficient, bias-corrected estimating equations approach. The confidence intervals are constructed with a pseudo residual bootstrap variance estimator that takes advantage of the available control runs. Our regression coefficient estimator is unbiased, with a smaller variance than the TLS estimator. Our estimation of the sampling variability of the estimator has a low bias compared to that from TLS, which is substantially negatively biased. The resulting confidence intervals for the regression coefficients have coverage rates close to the nominal level, which ensures valid inferences in detection and attribution analyses. In applications to the annual mean near-surface air temperature at the global, continental, and subcontinental scales during 1951–2020, the proposed method led to shorter confidence intervals than those based on TLS in most of the analyses.

  • Source Publication: Statistics and Computing, 33, 125 doi:10.1007/s11222-023-10290-8 Authors: Lau, Y.T.A., T. Wang, J. Yan and X. Zhang Publication Date: Sep 2023

    The generalized extreme value (GEV) regression provides a framework for modeling extreme events across various fields by incorporating covariates into the location parameter of GEV distributions. When the covariates are subject to errors-in-variables (EIV) or measurement error, ignoring the EIVs leads to biased estimation and degraded inferences. This problem arises in detection and attribution analyses of changes in climate extremes because the covariates are estimated with uncertainty. It has not been studied even for the case of independent EIVs, let alone the case of dependent EIVs, due to the complex structure of GEV. Here we propose a general Monte Carlo corrected score method and extend it to address temporally correlated EIVs in GEV modeling with application to the detection and attribution analyses for climate extremes. Through extensive simulation studies, the proposed method provides an unbiased estimator and valid inference. In the application to the detection and attribution analyses of temperature extremes in central regions of China, with the proposed method, the combined anthropogenic and natural signal is detected in the change in the annual minimum of daily maximum and the annual minimum of daily minimum.

  • Source Publication: Hydrology and Earth System Sciences, 27, 3241–3263, doi:10.5194/hess-27-3241-2023 Authors: Larabi, S., J. Mai, M. Schnorbus, B.A. Tolson and F. Zwiers Publication Date: Sep 2023

    Land surface models have many parameters that have a spatially variable impact on model outputs. In applying these models, sensitivity analysis (SA) is sometimes performed as an initial step to select calibration parameters. As these models are applied to large domains, performing sensitivity analysis across the domain is computationally prohibitive. Here, using a Variable Infiltration Capacity model (VIC) deployment to a large domain as an example, we show that watershed classification based on climatic attributes and vegetation land cover helps to identify the spatial pattern of parameter sensitivity within the domain at a reduced cost. We evaluate the sensitivity of 44 VIC model parameters with regard to streamflow, evapotranspiration and snow water equivalent over 25 basins with a median size of 5078 km2. Basins are clustered based on their climatic and land cover attributes. Performance in transferring parameter sensitivity between basins of the same cluster is evaluated by the F1 score. Results show that two donor basins per cluster are sufficient to correctly identify sensitive parameters in a target basin, with F1 scores ranging between 0.66 (evapotranspiration) and 1 (snow water equivalent). While climatic attributes are sufficient to identify sensitive parameters for streamflow and evapotranspiration, including the vegetation class significantly improves skill in identifying sensitive parameters for the snow water equivalent. This work reveals that there is opportunity to leverage climate and land cover attributes to greatly increase the efficiency of parameter sensitivity analysis and facilitate more rapid deployment of land surface models over large spatial domains.

  • Source Publication: Geosciences, 13, 264, doi: 10.3390/geosciences13090264 Authors: Dah, A., B. Khouider, and C. Schumacher Publication Date: Aug 2023

    Coastal convection is often organized into multiple mesoscale systems that propagate in either direction across the coastline (i.e., landward and oceanward). These systems interact non-trivially with synoptic and intraseasonal disturbances such as convectively coupled waves and the Madden–Julian oscillation. Despite numerous theoretical and observational efforts to understand coastal convection, global climate models still fail to represent it adequately, mainly because of limitations in spatial resolution and shortcomings in the underlying cumulus parameterization schemes. Here, we use a simplified climate model of intermediate complexity to simulate coastal convection under the influence of the diurnal cycle of solar heating. Convection is parameterized via a stochastic multicloud model (SMCM), which mimics the subgrid dynamics of organized convection due to interactions (through the environment) between the cloud types that characterize organized tropical convection. Numerical results demonstrate that the model is able to capture the key modes of coastal convection variability, such as the diurnal cycle of convection and the accompanying sea and land breeze reversals, the slowly propagating mesoscale convective systems that move from land to ocean and vice-versa, and numerous moisture-coupled gravity wave modes. The physical features of the simulated modes, such as their propagation speeds, the timing of rainfall peaks, the penetration of the sea and land breezes, and how they are affected by the latitudinal variation in the Coriolis force, are generally consistent with existing theoretical and observational studies.

  • Authors: C.L. Curry, D. Ouali, S.R. Sobie and F.W. Zwiers Publication Date: Jul 2023

    This report outlines a method for selecting a subset of earth system models (ESMs) from the Sixth Coupled Model Intercomparison Project (CMIP6) that is sufficiently representative of an ensemble of 26 models from CMIP6 for Canada and its subregions. The specific objective is to obtain a subset of reasonably independent ESMs that captures the overall range of projected change in a representative set of climate extremes (ETCCDI or Climdex) indices constructed from the ESM outputs. Projections are calculated for a future epoch corresponding to a global mean temperature change of 2 ℃ relative to 1971-2000, using results from two of the CMIP6 Shared Socioeconomic Pathways (SSPs), SSP2-4.5, and SSP5-8.5. The selection procedure is described below and representative subsets are provided for Canada and five of its subregions.

  • Source Publication: Atmosphere-Ocean, 62, 3, 193-205, doi:10.1080/07055900.2023.2288632 Authors: Tang, B., B. Bonsal, X. Zhang, Q. Zhang, and R. Rong Publication Date: Jun 2023

    Recently, concerns have arisen as to whether temperature-based proxy methods used to estimate potential evapotranspiration (PET) are reliable when examining future drought severity, especially in the context of a warmer climate. The objective of this study was to assess the effect of different PET approaches, focusing on proxies for radiation and humidity, on future Standardized Precipitation Evapotranspiration Index (SPEI) calculations across Canada. Using output from 22 CMIP6 global climate models (GCMs), seasonal and annual SPEI comparisons were carried out between the physically-based Penman-Monteith (PM) method and two approaches that incorporate temperature proxies to calculate radiation and/or humidity. These included the temperature-based Hargreaves (HG) approach and a PM method with derived humidity (PM-m). Results revealed that although the general patterns of SPEI projections across Canada were consistent among the methods, notable spatial and temporal differences were apparent. Specifically, both median and extreme SPEI projections based on the two temperature proxy methods revealed less annual and summer drying in much of central, eastern, and northern regions of Canada when compared to the physically based SPEI-PM. In extreme western regions (British Columbia, Yukon) these two methods, particularly HG, projected drier conditions. Differences of using temperature derived radiation and humidity were also most apparent in spring (and to a lesser degree, autumn), where the HG approach overestimated spring drying (and autumn wetting) over large regions of the country. Overall, differences tended to be more pronounced for the fully temperature-based HG approach during all periods considered. Results from this study strongly suggest that when possible, a physically-based approach be used when estimating PET to assess future drought projections. If a temperature proxy is used, the differences to a physically-based method should be understood and resultant implications be evaluated.

  • Source Publication: Science China Earth Sciences, 66, 2125–2141, doi:10.1007/s11430-022-1154-7 Authors: Zhu, H., Z. Jiang, L. Li, W. Li, S. Jiang, P. Zhou, W. Zhao and T. Li Publication Date: Jun 2023

    Climate change adaptation and relevant policy-making need reliable projections of future climate. Methods based on multi-model ensemble are generally considered as the most efficient way to achieve the goal. However, their efficiency varies and inter-comparison is a challenging task, as they use a variety of target variables, geographic regions, time periods, or model pools. Here, we construct and use a consistent framework to evaluate the performance of five ensemble-processing methods, i.e., multi-model ensemble mean (MME), rank-based weighting (RANK), reliability ensemble averaging (REA), climate model weighting by independence and performance (ClimWIP), and Bayesian model averaging (BMA). We investigate the annual mean temperature (Tav) and total precipitation (Prcptot) changes (relative to 1995–2014) over China and its seven subregions at 1.5 and 2 °C warming levels (relative to pre-industrial). All ensemble-processing methods perform better than MME, and achieve generally consistent results in terms of median values. But they show different results in terms of inter-model spread, served as a measure of uncertainty, and signal-to-noise ratio (SNR). ClimWIP is the most optimal method with its good performance in simulating current climate and in providing credible future projections. The uncertainty, measured by the range of 10th-90th percentiles, is reduced by about 30% for Tav, and 15% for Prcptot in China, with a certain variation among subregions. Based on ClimWIP, and averaged over whole China under 1.5/2 °C global warming levels, Tav increases by about 1.1/1.8 °C (relative to 1995–2014), while Prcptot increases by about 5.4%/11.2%, respectively. Reliability of projections is found dependent on investigated regions and indices. The projection for Tav is credible across all regions, as its SNR is generally larger than 2, while the SNR is lower than 1 for Prcptot over most regions under 1.5 °C warming. The largest warming is found in northeastern China, with increase of 1.3 (0.6-1.7)/2.0 (1.4-2.6) °C(ensemble’s median and range of the 10th–90th percentiles) under 1.5/2 °C warming, followed by northern and northwestern China. The smallest but the most robust warming is in southwestern China, with values exceeding 0.9 (0.6–1.1)/1.5 (1.1–1.7) °C. The most robust projection and largest increase is achieved in northwestern China for Prcptot, with increase of 9.1%(-1.6–24.7%)/17.9% (0.5–36.4%) under 1.5/2 °C warming. Followed by northern China, where the increase is 6.0%(-2.6–17.8%)/11.8% (2.4–25.1%), respectively. The precipitation projection is of large uncertainty in southwestern China, even with uncertain sign of variation. For the additional half-degree warming, Tav increases more than 0.5 °C throughout China. Almost all regions witness an increase of Prcptot, with the largest increase in northwestern China.

  • Source Publication: Environmental Modelling & Software, 163, 105682, doi:10.1016/j.envsoft.2023.105682 Authors: Souaissi, Z, T.B. Ouarda, A. St-Hilaire and D. Ouali Publication Date: Jun 2023

    Improve the estimation of water temperature extremes at ungauged sites.
    Incorporate non-linearities in the homogenous region delineation step using NLCCA.
    Consider non-linear models in the whole estimation procedure (NLCCA + GAM).
    Compare fully and partially non-linear approaches for water temperature regionalization.
    The results underline the importance of considering the non-linearity of thermal processes.

  • Source Publication: Environmental Science and Technology, 57, 19, 7401–7409, doi:10.1021/acs.est.2c08243 Authors: Lao, I.R., A. Feinberg, and N. Borduas-Dedekind Publication Date: Jun 2023

    Selenium (Se) is an essential nutrient for humans and enters our food chain through bioavailable Se in soil. Atmospheric deposition is a major source of Se to soils, driving the need to investigate the sources and sinks of atmospheric Se. Here, we used Se concentrations from PM2.5 data at 82 sites from 1988 to 2010 from the Interagency Monitoring of Protected Visual Environments (IMPROVE) network in the US to identify the sources and sinks of particulate Se. We identified 6 distinct seasonal profiles of atmospheric Se, grouped by geographical location: West, Southwest, Midwest, Southeast, Northeast, and North Northeast. Across most of the regions, coal combustion is the largest Se source, with a terrestrial source dominating in the West. We also found evidence for gas-to-particle partitioning in the wintertime in the Northeast. Wet deposition is an important sink of particulate Se, as determined by Se/PM2.5 ratios. The Se concentrations from the IMPROVE network compare well to modeled output from a global chemistry-climate model, SOCOL-AER, except in the Southeast US. Our analysis constrains the sources and sinks of atmospheric Se, thereby improving the predictions of Se distribution under climate change.

  • Source Publication: Journal of Climate, 36, 18, 6393-6407, doi:10.1175/JCLI-D-22-0713.1 Authors: Sun, Q. F.W. Zwiers, X. Zhang and Y. Tan Publication Date: Jun 2023

    El Niño–Southern Oscillation (ENSO) has a profound influence on the occurrence of extreme precipitation events at local and regional scales in the present-day climate, and thus it is important to understand how that influence may change under future global warming. We consider this question using the large-ensemble simulations of CESM2, which simulates ENSO well historically. CESM2 projects that the influence of ENSO on extreme precipitation will strengthen further under the SSP3–7.0 scenario in most regions whose extreme precipitation regimes are strongly affected by ENSO in the boreal cold season. Extreme precipitation in the boreal cold season that exceeds historical thresholds is projected to become more common throughout the ENSO cycle. The difference in the intensity of extreme precipitation events that occur under El Niño and La Niña conditions will increase, resulting in “more extreme and more variable hydroclimate extremes.” We also consider the processes that affect the future intensity of extreme precipitation and how it varies with the ENSO cycle by partitioning changes into thermodynamic and dynamic components. The thermodynamic component, which reflects increases in atmospheric moisture content, results in a relatively uniform intensification of ENSO-driven extreme precipitation variation. In contrast, the dynamic component, which reflects changes in vertical motion, produces a strong regional difference in the response to forcing. In some regions, this component amplifies the thermodynamic-induced changes, while in others, it offsets them or even results in reduction in extreme precipitation variation.

  • Authors: City of Terrace, The Pacific Climate Impacts Consortium, Pinna Sustainability Publication Date: May 2023

    The Climate Projections for the City of Terrace report provides projections and impacts analysis for the City of Terrace, BC and is intended to support decision making throughout the region and to help community partners better understand how their work may be affected by the changing climate

  • Source Publication: Journal of Hydrology: Regional Studies, 44, 101237, doi:10.1016/j.ejrh.2022.101237 Authors: Larabi, S., M. A. Schnorbus, and F. Zwiers Publication Date: Dec 2022

    Study region:
    Nechako Reservoir, British Columbia, Canada.

    Study focus:
    Hydrological regulation affect both hydrological and thermal conditions in the reservoir and downstream reach, subsequently disrupting fish habitats. This paper aims at developing an integrated model simulating physical processes that govern the quantity and quality of inflow, reservoir, and outflow water of the Nechako Reservoir. Such a model would help stakeholders understand the response of in-reservoir water temperature stratification and downstream water temperature to changes in inflow and reservoir operation under future climate change.

    New hydrological insights for the region:
    The model was calibrated against historical reservoir levels and in-reservoir and outlet water temperature field data. The integrated model simulated accurately the wide variation of reservoir levels as well as the in-reservoir water temperature at Kenney Dam and the outlet temperature. Sensitivity analysis shows that reservoir water temperature particularly the epilimnion is sensitive to changes in both meteorological and hydrological forcing. Forcing the model with different outflow scenarios shows the weak sensitivity of temperature of water released to outflow rates. Given epilimnion water releases at the spillway, the Summer Temperature Management Program could be inefficient to provide cool water in the Nechako River during the critical period of salmon migration in a warming climate. However, colder water remains available at depth at Kenney Dam to potentially mitigate and better control downstream water temperature.

  • Source Publication: International Journal of Climatology, 42, 10, doi: org/10.1002/joc.7833.8 Authors: Diaconescu, E., H. Sankare, K. Chow, T.Q. Murdock, and A.J. Cannon Publication Date: Dec 2022

    The projected increase in the frequency and intensity of extreme heat events due to climate change means an associated increase in risk of heat-related illnesses and mortality. Public health systems need to be prepared to identify and reduce the susceptibility of vulnerable populations to increased occurrence of heat-related illness and stress. To facilitate this, climate services have begun developing climate change projections for heat-stress indices based on exceedances of thresholds used operationally in meteorological heat warning systems. This task is complicated by the fact that heat-stress indices are generally computed using hourly data whereas climate model outputs are often archived at daily or longer time steps. This study focuses on Humidex, a heat-stress index used in heat alerts issued by the Meteorological Service of Canada. Several potential solutions for computing robust Humidex indices using daily data are examined, including a new approximation method. Indices obtained with the new method are compared with indices obtained using the classic method based on hourly data as well as with other two methods based on average daily values. The new approximation gives good estimations for humidex indices, while the daily-average-value methods present biases with respect to the hourly-value method.

  • Source Publication: Journal of Hydrology X, 17, 100144, doi:10.1016/j.hydroa.2022.100144 Authors: Tsuruta, K. and M. A. Schnorbus Publication Date: Dec 2022

    As glaciers across the world continue to recede, there is a concern that their loss as a fresh water reservoir within mountainous basins will have a negative impact on stream temperatures and downstream water resources. Currently, there are relatively few glacio-hydrological models (GHMs) appropriate to study such phenomena and studies that have used GHMs generally acknowledge the high uncertainty associated with their simulations. Calibration techniques present a particular issue in GHMs as available glacier observations are limited and errors in the glacierized portion of a basin can be compensated by errors in the non-glacierized portion. Using as a study site the Cheakamus Basin in British Columbia, Canada, we 1) present a new, fully-coupled GHM, 2) analyze the effects different calibration techniques have on the model’s summer streamflow projections, and 3) compare the fully-coupled GHM results to projections using a one-way GHM. The calibration techniques studied vary in terms of glacier representation (dynamic/static), and glacier constraint (mass balance/thinning rates/thinning rates and area change). We find projected future climate forcings are sufficiently strong in the Cheakamus Basin so as to generally make the sign and significance of changes to the basin’s hydrology insensitive to the calibration and projection procedures studied. However, the variation among these procedures produces significant changes in the projected magnitude of future hydrological changes and therefore should be carefully considered in studies where precision beyond the sign and significance of change is required. Based on analysis of the variation within each procedure’s set of model outputs, we conclude 1) the two-way GHM has benefits over the one-way model, 2) calibration using dynamic glaciers and a thinning rate constraint is preferable for the new GHM, and 3) there is a need for additional studies on the uncertainties associated with the calibration of glacio-hydrological models.

  • Source Publication: Canadian Journal of Statistics, 50, 4, 1355-1386, doi:10.1002/cjs.11743 Authors: Dean, C.B., A.H. El-Shaarawi, S.R. Esterby, J. Mills-Flemming, R.D. Routledge, S.W. Taylor, D.G. Woolford, J.V. Zidek and F.W. Zwiers Publication Date: Dec 2022

    This article focuses on the importance of collaboration in statistics by Canadian researchers and highlights the contributions that Canadian statisticians have made to many research areas in environmetrics. We provide a discussion about different vehicles that have been developed for collaboration by Canadians in the environmetrics context as well as specific scientific areas that are focused on environmetrics research in Canada including climate science, forestry, and fisheries, which are areas of importance for natural resources in Canada.