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  • Authors: Curry, C.L., I. Farmer and S.R. Sobie Publication Date: Jun 2024

    The Earth’s climate system is warming, and signs of climate change are increasingly evident worldwide. The Regional District of Nanaimo (RDN), located on eastern Vancouver Island, is no exception. How these changes impact our region will depend – in part – on how well we understand and prepare for them. Using the latest generation of comprehensive global climate models, the RDN worked with the Pacific Climate Impacts Consortium (PCIC) to prepare this report describing anticipated climate change in our region. These results will be used to inform regional hazard, vulnerability and risk assessments, infrastructure design, decision-making, and planning in the region, with the goal of improving our resilience to climate change.

  • Authors: The Pacific Climate Impacts Consortium Publication Date: Apr 2024

    The Earth’s climate system is warming, and signs of climate change are becoming evident across the planet. The capital region, located on Southern Vancouver Island and Gulf Islands of British Columbia (BC), is no exception. The Capital Regional District (CRD) has partnered with the Pacific Climate Impacts Consortium (PCIC) to produce high-resolution regional projections for temperature, precipitation, and related indices of extremes. These projections use the most up-to-date global modeling data (i.e., the Sixth Coupled Model Intercomparison Project, CMIP6) to illustrate how the region’s climate may change by the middle of this century. Information provided by this report and the accompanying data is intended to support decision makers and community partners in the region with an improved understanding of projected local climate change and related impacts.

  • 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 https://www.metoffice.gov.uk/hadobs/hadex3 and https://www.climdex.org.

  • 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.

  • Authors: The Pacific Climate Impacts Consortium Publication Date: Feb 2024

    This edition of the PCIC Update contains the following stories: New PCIC Director Featured in UVic News, City of Vancouver Climate Report Released, PCIC’s Evolution Over the Past 15 Years: A Retrospective by Rachel Goldsworthy and Supporting Risk Assessments in BC, along with updates on staff changes and the Pacific Climate Seminar Series. The staff issue in this profile is on Markus Schnorbus.

  • 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.

  • Authors: The Pacific Climate Impacts Consortium Publication Date: Nov 2023

    The Earth’s climate system—atmosphere, land, ocean and the ecosystems they support—is changing in response to increasing concentrations of greenhouse gases and various pollutants in the atmosphere from ongoing industrial development. The increase in global mean temperature in recent decades has been unequivocally established and attributed to these anthropogenic drivers, and similar changes are being detected in key climate variables on con8nental scales. Recent historical temperature change in British Columbia is also emerging from the noise of climate variability, and future projections indicate this trend will contonue without significant cuts in carbon emissions. The regional and local manifestations of global climate change need to be studied and monitored, in order to design appropriate responses and adaptations.

    Like other major cities worldwide, the City of Vancouver requires up-to-date, science-based, spatially resolved information to enable effective planning and policy decisions. This short report summarizes the data produced by the Pacific Climate Impacts Consortium delivered as part of this project. We hope that it will be helpful as a stepping stone for the City as it develops plans and effective responses to these upcoming challenges.

  • 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.

  • Authors: The Pacific Climate Impacts Consortium Publication Date: Oct 2023

    This is the Pacific Climate Impacts Consortium's 2022-2023 Corporate Report.

  • 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: pp. 17-21. In: Boldt, J.L., Joyce, E., Tucker, S., and Gauthier, S. (Eds.). 2023. State of the physical, biological and selected fishery resources of Pacific Canadian marine ecosystems in 2022. Can. Tec Authors: Curry, C.L. and I. Lao Publication Date: Sep 2023

    Fisheries and Oceans Canada is responsible for the management and protection of marine
    resources on the Pacific coast of Canada. Oceanographically there is strong seasonality in
    coastal upwelling and downwelling, considerable freshwater influence, and variability from
    coupling with events and conditions in the tropical and North Pacific Ocean. The region supports
    ecologically and economically important resident and migratory populations of invertebrates,
    groundfish, pelagic fishes, marine mammals and seabirds.
    Since 1999 an annual State of the Pacific Ocean meeting has been convened by DFO to bring
    together the marine science community in the Pacific Region and present the results of the most
    recent year’s monitoring in the context of previous observations and expected future conditions.
    The workshop to review ecosystem conditions in 2022 was a hybrid meeting, convened both inperson in Victoria, B.C. and virtually, March 9-10, 2023. This technical report includes
    submissions based on presentations given at the meeting and poster summaries.
    Climate change is a dominant pressure acting on North Pacific marine ecosystems, causing, for
    example, increasing temperatures, deoxygenation, and acidification, and changes to circulation
    and vertical mixing. These pressures impact ecosystem nutrient concentrations and primary and
    secondary productivity, which then affect higher trophic levels through the food chain.

  • Source Publication: Global Water Futures, University of Saskatchewan, 2pp. Authors: Zwiers, F.W., Li, Y., and Debeer, C., 2023 Publication Date: Sep 2023

    As global temperatures rise, extreme rainfall and other precipitation events are becoming more
    common and more intense. The disastrous consequences are also becoming increasingly
    apparent. A research project within the Global Water Futures program, Short-Duration Extreme
    Precipitation in Future Climate, takes a closer look at these changes.

  • Authors: The Pacific Climate Impacts Consortium Publication Date: Sep 2023

    This issue of the PCIC Update contains the following stories: Data Portal for Canada’s Western Arctic Released, Supporting the Management of BC Salmon Habitats and New Future-Adjusted Weather Files for Canada. It also contains an update on the Pacific Climate Seminar Series, staff changes at PCIC and PCIC's most recent publications. The staff profile in this issue is on Eric Yvorchuk.

  • 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.

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