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  • Source Publication: Journal of Applied Meteorology and Climatology, 61, 1, 77-95, doi: 10.1175/JAMC-D-20-0260.1 Authors: Sobie, S.R. and T.Q. Murdock Publication Date: Jan 2022

    Information about snow water equivalent in southwestern British Columbia, Canada, is used for flood management, agriculture, fisheries, and water resource planning. This study evaluates whether a process-based, energy balance snow model supplied with high-resolution statistically downscaled temperature and precipitation data can effectively simulate snow water equivalent (SWE) in the mountainous terrain of this region. Daily values of SWE from 1951 to 2018 are simulated at 1-km resolution and evaluated using a reanalysis SWE product [Snow Data Assimilation System (SNODAS)], manual snow-survey measurements at 41 sites, and automated snow pillows at six locations in the study region. Simulated SWE matches observed interannual variability well (R2 > 0.8 for annual maximum SWE), but peak SWE biases of 20%–40% occur at some sites in the study domain, and higher biases occur where observed SWE is very low. Modeled SWE displays lower bias relative to SNODAS reanalysis at most manual survey locations. Future projections for the study area are produced using 12 downscaled climate model simulations and are used to illustrate the impacts of climate change on SWE at 1°, 2°, and 3°C of warming. Model results are used to quantify spring SWE changes at different elevations of the Whistler mountain ski resort and the sensitivity of annual peak SWE in the Metropolitan Vancouver municipal watersheds to moderate temperature increases. The results both illustrate the potential utility of a process-based snow model and identify areas where the input meteorological variables could be improved.

  • Source Publication: Journal of Applied Meteorology and Climatology, 61, 1, 77-95, doi:10.1175/JAMC-D-20-0260.1 Authors: Sobie, S.R. and T.Q. Murdock Publication Date: Jan 2022

    Information about snow water equivalent in southwestern British Columbia, Canada, is used for flood management, agriculture, fisheries, and water resource planning. This study evaluates whether a process-based, energy balance snow model supplied with high-resolution statistically downscaled temperature and precipitation data can effectively simulate snow water equivalent (SWE) in the mountainous terrain of this region. Daily values of SWE from 1951 to 2018 are simulated at 1-km resolution and evaluated using a reanalysis SWE product [Snow Data Assimilation System (SNODAS)], manual snow-survey measurements at 41 sites, and automated snow pillows at six locations in the study region. Simulated SWE matches observed interannual variability well (R2 > 0.8 for annual maximum SWE), but peak SWE biases of 20%–40% occur at some sites in the study domain, and higher biases occur where observed SWE is very low. Modeled SWE displays lower bias relative to SNODAS reanalysis at most manual survey locations. Future projections for the study area are produced using 12 downscaled climate model simulations and are used to illustrate the impacts of climate change on SWE at 1°, 2°, and 3°C of warming. Model results are used to quantify spring SWE changes at different elevations of the Whistler mountain ski resort and the sensitivity of annual peak SWE in the Metropolitan Vancouver municipal watersheds to moderate temperature increases. The results both illustrate the potential utility of a process-based snow model and identify areas where the input meteorological variables could be improved.

  • Source Publication: Atmosphere-Ocean, 59, 4-5, 269-284, doi:10.1080/07055900.2021.2011103 Authors: Sobie, S.R., Zwiers, F.W., and C.L. Curry Publication Date: Dec 2021

    Recent studies have identified stronger warming in the latest generation of climate model simulations globally, and the same is true for projected changes in Canada. This study examines differences for Canada and six sub-regions between simulations from the latest Sixth Coupled Model Intercomparison Project (CMIP6) and its predecessor CMIP5. Ensembles from both experiments are assessed using a set of derived indices calculated from daily precipitation and temperature, with projections compared at fixed future time intervals and fixed levels of global temperature change. For changes calculated at fixed time intervals most temperature indices display higher projected changes in CMIP6 than CMIP5 for most sub-regions, while greater precipitation changes in CMIP6 occur mainly in extreme precipitation indices. When future projections are calculated at fixed levels of global average temperature increase, the size and spread of differences for future projected changes between CMIP6 and CMIP5 are substantially reduced for most indices. Temperature scaling behaviour, or the regional response to increasing global temperatures, is similar in both ensembles, with annual temperature anomalies for Canada and its sub-regions increasing at between 1.5 and 2.5 times the rate of increase globally, depending on the region. The CMIP6 ensemble projections exhibit modestly stronger scaling behaviour for temperature anomalies in northern Canada, as well as for certain indices of moderate and extreme events. Such temperature scaling differences persist even if anomalously warm CMIP6 global climate models are omitted. Comparing the mean and variance of future projections for Canada in CMIP5 and CMIP6 simulations from the same modelling centre suggests CMIP6 models are significantly warmer in Canada than CMIP5 models at the same level of forcing, with some evidence that internal temperature variability in CMIP6 is reduced compared with CMIP5.

  • Source Publication: Geophysical Research Letters, 48, doi:10.1029/2021GL095500 Authors: Li, C., Z. Wang, F. Zwiers and X. Zhang Publication Date: Dec 2021

    The regression-based optimal fingerprinting is a key tool for quantifying human climate influence. Most studies over the past decade used Coupled Model Intercomparison Project Phase 5 (CMIP5) simulations, limiting fingerprinting regression configuration options. The CMIP6 Detection and Attribution Model Intercomparison Project (DAMIP) provides several types of individual forcing simulations and thus greater configuration flexibility. To avoid overfitting the limited observational data, we suggest that a DAMIP-based perfect model study is first used to best configure the fingerprinting regression prior to its application to observations. We find that a regression using all-forcing, aerosol-only, and natural-only simulations is an overall best option for constraining human-induced global terrestrial warming, which differs from choices commonly made previously. Applying this configuration to observations, we estimate that of the observed terrestrial warming of ∼1.5°C between 1850–1900 and 2011–2020, anthropogenic greenhouse gases contributed 1.4 to 2.3°C, offset by aerosol cooling of 0.2 to 1.2°C.

  • Source Publication: Geophysical Research Letters, doi:10.1029/2021GL095500 Authors: Li, C., Z. Wang, F. Zwiers and X. Zhang Publication Date: Nov 2021

    The regression-based optimal fingerprinting is a key tool for quantifying human climate influence. Most studies over the past decade used Coupled Model Intercomparison Project Phase 5 (CMIP5) simulations, limiting fingerprinting regression configuration options. The CMIP6 Detection and Attribution Model Intercomparison Project (DAMIP) provides several types of individual forcing simulations and thus greater configuration flexibility. To avoid overfitting the limited observational data, we suggest that a DAMIP-based perfect model study is first used to best configure the fingerprinting regression prior to its application to observations. We find that a regression using all-forcing, aerosol-only, and natural-only simulations is an overall best option for constraining human-induced global terrestrial warming, which differs from choices commonly made previously. Applying this configuration to observations, we estimate that of the observed terrestrial warming of ∼1.5°C between 1850–1900 and 2011–2020, anthropogenic greenhouse gases contributed 1.4 to 2.3°C, offset by aerosol cooling of 0.2 to 1.2°C.

  • Authors: The Pacific Climate Impacts Consortium Publication Date: Nov 2021

    This issue of the PCIC Update covers the following stories: Atmospheric River Brings Heavy Rains and Flooding to BC, PCIC Responds to Media Requests Regarding Recent Flooding, Comparing Climate Model Projections Across Canada, Modelling the Temperature of the Nechako, and Transportation Module on ClimateData.ca. The Pacific Climate Seminar Series Talks discussed in this issue are the past talk by Dr. Qiaohong Sun and the upcoming talk by Dr. Ted Shepherd. The staff profile in this issue is on James Hiebert.

  • Source Publication: Progress in Oceanography, 198, 102659. doi:10.1016/j.pocean.2021.102659 Authors: Heneghan, R. F. et al. (T.C. Tai is 21st coauthor) Publication Date: Oct 2021

    Climate change is warming the ocean and impacting lower trophic level (LTL) organisms. Marine ecosystem models can provide estimates of how these changes will propagate to larger animals and impact societal services such as fisheries, but at present these estimates vary widely. A better understanding of what drives this inter-model variation will improve our ability to project fisheries and other ecosystem services into the future, while also helping to identify uncertainties in process understanding. Here, we explore the mechanisms that underlie the diversity of responses to changes in temperature and LTLs in eight global marine ecosystem models from the Fisheries and Marine Ecosystem Model Intercomparison Project (FishMIP). Temperature and LTL impacts on total consumer biomass and ecosystem structure (defined as the relative change of small and large organism biomass) were isolated using a comparative experimental protocol. Total model biomass varied between −35% to +3% in response to warming, and -17% to +15% in response to LTL changes. There was little consensus about the spatial redistribution of biomass or changes in the balance between small and large organisms (ecosystem structure) in response to warming, an LTL impacts on total consumer biomass varied depending on the choice of LTL forcing terms. Overall, climate change impacts on consumer biomass and ecosystem structure are well approximated by the sum of temperature and LTL impacts, indicating an absence of nonlinear interaction between the models’ drivers. Our results highlight a lack of theoretical clarity about how to represent fundamental ecological mechanisms, most importantly how temperature impacts scale from individual to ecosystem level, and the need to better understand the two-way coupling between LTL organisms and consumers. We finish by identifying future research needs to strengthen global marine ecosystem modelling and improve projections of climate change impacts.

  • Source Publication: Science Advances, 7, eabh0895 Authors: Cheung, W. W. L., T.L. Frölicher, V.W.Y. Lam, M. Oyinlola, G. Reygondeau, U.R. Sumaila, T.C. Tai, L.C.L Teh and C.C.C. Wabnitz Publication Date: Oct 2021

    Extreme temperature events have occurred in all ocean basins in the past two decades with detrimental impacts on marine biodiversity, ecosystem functions, and services. However, global impacts of temperature extremes on fish stocks, fisheries, and dependent people have not been quantified. Using an integrated climate-biodiversity-fisheries-economic impact model, we project that, on average, when an annual high temperature extreme occurs in an exclusive economic zone, 77% of exploited fishes and invertebrates therein will decrease in biomass while maximum catch potential will drop by 6%, adding to the decadal-scale mean impacts under climate change. The net negative impacts of high temperature extremes on fish stocks are projected to cause losses in fisheries revenues and livelihoods in most maritime countries, creating shocks to fisheries social-ecological systems particularly in climate-vulnerable areas. Our study highlights the need for rapid adaptation responses to extreme temperatures in addition to carbon mitigation to support sustainable ocean development.

  • Source Publication: Weather and Climate Extremes, 34, 100388, doi:10.1016/j.wace.2021.100388. Authors: Ben Alaya, M.A., F.W. Zwiers and X. Zhang Publication Date: Oct 2021

    The uniform risk engineering practices that are increasingly being adopted for structural design require estimates of the extreme wind loads with very low annual probabilities of exceedance, corresponding to return periods of up to 3000-years in some cases. These estimates are necessarily based on observational wind data that typically spans only a few decades. The estimates are therefore affected by both large sampling uncertainty and, potentially, non-negligible biases. Design practices that aim to meet mandated structural reliability criteria take the sampling uncertainty of long period wind speed or wind pressure estimates into account, but reliability could be compromised if estimates are also biased. In many circumstances, estimates are obtained by fitting an extreme value distribution to annual maximum wind speed observed over a few decades. A key assumption implicit in doing so is that wind speed annual maxima are max-stable. Departures from max-stability can exacerbate the uncertainty of long-period return level estimates by inducing systematic estimation bias as well. Observational records, however, are generally too short to assess max-stability. We therefore use wind speed data from a large (50-member) ensemble of CanRCM4 historical simulations over North America to assess whether wind speed annual maxima are max-stable. While results are generally reassuring at the continental scale, disquieting evidence of a lack of max-stability is often found in the central and southern parts of the continent. Results show that when annual maximum wind speeds are not max-stable, long period return level extreme wind speeds tend to be underestimated, which would compromise reliability if used to design infrastructure such as tall buildings and towers.

  • Authors: The Pacific Climate Impacts Consortium Publication Date: Oct 2021

    This is the Pacific Climate Impacts Consortium's 2020-2021 Corporate Report.

  • Authors: The Pacific Climate Impacts Consortium Publication Date: Oct 2021

    This issue of the PCIC Update containts the following stories: Assessing Changing Flood Risk, Congratulations to the Recipients of the 2021 Nobel Prize in Physics, 2020-2021 Corporate Report Released, Supporting the Management of BC Salmon Habitats, Talks at the Pacific Climate Seminar Series. The staff profile is on Dr. Dhouha Ouali.

  • Source Publication: Ecology Letters, doi:10.1111/ele.13866 Authors: Florko, K. R. N., T.C. Tai, W.W.L. Cheung, S.H. Ferguson, U.R. Sumaila, D.J. Yurkowski and M. Auger-Méthé Publication Date: Oct 2021

    Arctic sea ice loss has direct consequences for predators. Climate-driven distribution shifts of native and invasive prey species may exacerbate these consequences. We assessed potential changes by modelling the prey base of a widely distributed Arctic predator (ringed seal; Pusa hispida) in a sentinel area for change (Hudson Bay) under high- and low-greenhouse gas emission scenarios from 1950 to 2100. All changes were relatively negligible under the low-emission scenario, but under the high-emission scenario, we projected a 50% decline in the abundance of the well-distributed, ice-adapted and energy-rich Arctic cod (Boreogadus saida) and an increase in the abundance of smaller temperate-associated fish in southern and coastal areas. Furthermore, our model predicted that all fish species declined in mean body size, but a 29% increase in total prey biomass. Declines in energy-rich prey and restrictions in their spatial range are likely to have cascading effects on Arctic predators.

  • Source Publication: Journal of Climate, doi:10.1175/JCLI-D-21-0028.1 Authors: Sun, Q., F. W. Zwiers, S. X. Zhang and J. Yan Publication Date: Oct 2021

    This study provides a comprehensive analysis of the human contribution to the observed intensification of precipitation extremes at different spatial scales. We consider the annual maxima of the logarithm of 1-day (Rx1day) and 5-day (Rx5day) precipitation amounts for 1950–2014 over the global land area, four continents, and several regions, and compare observed changes with expected responses to external forcings as simulated by CanESM2 in a large-ensemble experiment and by multiple models from phase 6 of the Coupled Model Intercomparison Project (CMIP6). We use a novel detection and attribution analysis method that is applied directly to station data in the areas considered without prior processing such as gridding, spatial or temporal dimension reduction or transformation to unitless indices and uses climate models only to obtain estimates of the space-time pattern of extreme precipitation response to external forcing. The influence of anthropogenic forcings on extreme precipitation is detected over the global land area, three continental regions (western Northern Hemisphere, western Eurasia and eastern Eurasia), and many smaller IPCC regions, including C. North-America, E. Asia, E.C. Asia, E. Europe, E. North-America, N. Europe, and W. Siberia for Rx1day, and C. North-America, E. Europe, E. North-America, N. Europe, Russian-Arctic, and W. Siberia for Rx5day. Consistent results are obtained using forcing response estimates from either CanESM2 or CMIP6. Anthropogenic influence is estimated to have substantially decreased the approximate waiting time between extreme annual maximum events in regions where anthropogenic influence has been detected, which has important implications for infrastructure design and climate change adaptation policy.

  • Source Publication: Weather and Climate Extremes, 33, 100332, doi:10.1016/j.wace.2021.100332 Authors: Huang, W.K., A.H. Monahan and F.W. Zwiers Publication Date: Aug 2021

    Simultaneous concurrence of extreme values across multiple climate variables can result in large societal and environmental impacts. Therefore, there is growing interest in understanding these concurrent extremes. In many applications, not only the frequency but also the magnitude of concurrent extremes are of interest. One way to approach this problem is to study the distribution of one climate variable given that another is extreme. In this work we develop a statistical framework for estimating bivariate concurrent extremes via a conditional approach, where univariate extreme value modeling is combined with dependence modeling of the conditional tail distribution using techniques from quantile regression and extreme value analysis to quantify concurrent extremes. We focus on the distribution of daily wind speed conditioned on daily precipitation taking its seasonal maximum. The Canadian Regional Climate Model large ensemble is used to assess the performance of the proposed framework both via a simulation study with specified dependence structure and via an analysis of the climate model-simulated dependence structure.

  • Source Publication: Hydrological Processes, 35, 7, e14253, doi:10.1002/hyp.14253 Authors: Tsuruta, K. and M.A. Schnorbus Publication Date: Aug 2021

    The mountainous watersheds of western Canada are generally thought to be in a state of transition from snow-dominated to hybrid regimes. In stream networks that are regulated, the effects of this transition on streamflow can have compelling operational consequences. Seasonal magnitude changes may impact spill-risk management, while changes in the composition of summer runoff may increase its variability and reduce the forecasting capabilities of state variables like peak snow water equivalent. Though glacier loss can have a considerable impact on summer runoff, few studies explicitly model the ongoing glacier recession in conjunction with other primary hydrological processes. In this study, we incorporate glacier dynamics from a previous run of the Regional Glaciation Model into the University of British Columbia Watershed Model via the Raven modelling framework. We use this modelling system to explore potential changes under Representative Concentration Pathways 4.5 and 8.5 to the hydrology of the ∼20000km2 Mica Basin, a regulated watershed containing the headwaters of the Columbia River. Our results project statistically significant increases in spring flow in future eras, which may force lower reservoir drafting in late winter, creating potential for energy shortfalls in early spring. We project the coefficient of variation of summer runoff generally goes unchanged in future eras as does the summer runoff forecasting capability of April 1st SWE. Hence, despite modelled glacier loss and reduced snowmelt contribution, our study does not reject the null hypothesis that the predictability of the Mica Basin's summer runoff is unchanged in future eras. We explore these results in detail because they superficially appear to contrast the conventional conceptualization that reduced snowmelt negatively affects the predictive powers of snowpack and glacier loss increases the variability of runoff. We argue that our results' apparent discordance from convention displays the complexities inherent in isolating the effects of changes to a single water balance component when other components are also non-stationary and highlights the benefits of using modelling to more explicitly explore such implications.

  • Source Publication: Scientific Reports, 11, 13574, doi:10.1038/s41598-021-92920-7 Authors: Meshesha, T.W., J. Wang and N.D. Melaku Publication Date: Aug 2021

    Groundwater is a vital resource for human welfare. However, due to various factors, groundwater pollution is one of the main environmental concerns. Yet, it is challenging to simulate groundwater quality dynamics due to the insufficient representation of nutrient percolation processes in the soil and Water Assessment Tool model. The objectives of this study were extending the SWAT module to predict groundwater quality. The results proved a linear relationship between observed and calculated groundwater quality with coefficient of determination (R2), Nash-Sutcliffe efficiency (NSE), percent bias (PBIAS) values in the satisfied ranges. While the values of R2, NSE and PBIAS were 0.69, 0.65, and 2.68 during nitrate calibration, they were 0.85, 0.85 and 5.44, respectively during nitrate validation. Whereas the values of R2, NSE and PBIAS were 0.59, 0.37, and - 2.21 during total dissolved solid (TDS) calibration and they were 0.81, 0.80, 7.5 during the validation. The results showed that the nitrate and TDS concentrations in groundwater might change with varying surface water quality. This indicated the requirement for designing adaptive management scenarios. Hence, the extended SWAT model could be a powerful tool for future regional to global scale modelling of nutrient loads and effective surface and groundwater management.

  • Source Publication: Frontiers in Marine Science, 8, 596644, doi: doi:10.3389/fmars.2021.596644 Authors: Tai T.C., U.R. Sumaila and W.W.L. Cheung, 2021 Publication Date: Aug 2021

    Elevated atmospheric carbon dioxide (CO2) is causing global ocean changes and drives changes in organism physiology, life-history traits, and population dynamics of natural marine resources. However, our knowledge of the mechanisms and consequences of ocean acidification (OA) – in combination with other climatic drivers (i.e., warming, deoxygenation) – on organisms and downstream effects on marine fisheries is limited. Here, we explored how the direct effects of multiple changes in ocean conditions on organism aerobic performance scales up to spatial impacts on fisheries catch of 210 commercially exploited marine invertebrates, known to be susceptible to OA. Under the highest CO2 trajectory, we show that global fisheries catch potential declines by as much as 12% by the year 2100 relative to present, of which 3.4% was attributed to OA. Moreover, OA effects are exacerbated in regions with greater changes in pH (e.g., West Arctic basin), but are reduced in tropical areas where the effects of ocean warming and deoxygenation are more pronounced (e.g., Indo-Pacific). Our results enhance our knowledge on multi-stressor effects on marine resources and how they can be scaled from physiology to population dynamics. Furthermore, it underscores variability of responses to OA and identifies vulnerable regions and species.

  • Source Publication: Geophysical Research Letters, 48, 9, e2021GL092831, doi:10.1029/2021GL092831 Authors: Wang, J., C. Li, F. Zwiers, X. Zhang, G. Li, Z. Jiang, P. Zhai, Y. Sun, Z. Li and Q. Yue Publication Date: Aug 2021

    Field significance tests have been widely used to detect climate change. In most cases, a local test is used to identify significant changes at individual locations, which is then followed by a field significance test that considers the number of locations in a region with locally significant changes. The choice of local test can affect the result, potentially leading to conflicting assessments of the impact of climate change on a region. We demonstrate that when considering changes in the annual extremes of daily precipitation, the simple Mann-Kendall trend test is preferred as the local test over more complex likelihood ratio tests that compare the fits of stationary and nonstationary generalized extreme value distributions. This lesson allows us to report, with enhanced confidence, that the intensification of annual extremes of daily precipitation in China since 1961 became field significant much earlier than previously reported.

  • Authors: The Pacific Climate Impacts Consortium Publication Date: Aug 2021

    This issue of the PCIC Update containts the following stories: PCIC Responds to the Extreme Heat Wave, New IPCC Assessment Report Released, Evaluating the Latest Climate Model Results, Collaborating with Forest Ecolobists to Improve BC Climate Mapping, New PCIC Science Brief: Should the RCP 8.5 Emissions Scenario Represent "Business as Usual"? The staff profile is on Dr. Qiaohong Sun.

  • Source Publication: Frontiers in Marine Science, 8, 1–12. doi:10.3389/fmars.2021.596644 Authors: Tai, T. C., U.R. Sumaila, and W.W.L. Cheung Publication Date: Jul 2021

    Elevated atmospheric carbon dioxide (CO2) is causing global ocean changes and drives changes in organism physiology, life-history traits, and population dynamics of natural marine resources. However, our knowledge of the mechanisms and consequences of ocean acidification (OA) – in combination with other climatic drivers (i.e., warming, deoxygenation) – on organisms and downstream effects on marine fisheries is limited. Here, we explored how the direct effects of multiple changes in ocean conditions on organism aerobic performance scales up to spatial impacts on fisheries catch of 210 commercially exploited marine invertebrates, known to be susceptible to OA. Under the highest CO2 trajectory, we show that global fisheries catch potential declines by as much as 12% by the year 2100 relative to present, of which 3.4% was attributed to OA. Moreover, OA effects are exacerbated in regions with greater changes in pH (e.g., West Arctic basin), but are reduced in tropical areas where the effects of ocean warming and deoxygenation are more pronounced (e.g., Indo-Pacific). Our results enhance our knowledge on multi-stressor effects on marine resources and how they can be scaled from physiology to population dynamics. Furthermore, it underscores variability of responses to OA and identifies vulnerable regions and species.

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