Publications Library

On 6–8 January 2021, a cold air outbreak swept across eastern China, peaking over the North China Plain the night of 6 January, when 219 weather stations recorded the lowest nighttime temperature since 1961. In total, 498 stations recorded the lowest daytime or nighttime temperature since 1961 during the 3-day event. This event, together with two other cold outbreaks that affected the region on 13–15 December 2020 and 29 December 2020–1 January 2021, led to historic peak electricity demand and resumption of the operation of the only remaining coalfired generating plant in Beijing. This analysis puts the cold outbreak into historical perspective by considering changes in the likelihood of such events over 1961–2020 in the context of a climate that is being warmed by anthropogenic forcing.

Understanding the dynamics of water-energy-food (WEF) nexus interactions with climate change and human intervention helps inform policymaking. This study demonstrates the WEF nexus behavior under ensembles of climate change, transboundary inflows, and policy options, and evaluates the overall nexus performance using a previously developed system dynamics-based WEF nexus model—WEF-Sask. The climate scenarios include a baseline (1986–2014) and near-future climate projections (2021–2050). The approach is demonstrated through the case study of Saskatchewan, Canada. Results show that rising temperature with increased rainfall likely maintains reliable food and feed production. The climate scenarios characterized by a combination of moderate temperature increase and slightly less rainfall or higher temperature increase with slightly higher rainfall are easier to adapt to by irrigation expansion. However, such expansion uses a large amount of water resulting in reduced hydropower production. In contrast, higher temperature, combined with less rainfall, such as SSP370 (+2.4 °C, −6 mm), is difficult to adapt to by irrigation expansion. Renewable energy expansion, the most effective climate change mitigation option in Saskatchewan, leads to the best nexus performance during 2021–2050, reducing total water demand, groundwater demand, greenhouse gas (GHG) emissions, and potentially increasing water available for food&feed production. In this study, we recommend and use food&feed and power production targets and provide an approach to assessing the impacts of hydroclimate and policy options on the WEF nexus, along with suggestions for adapting the agriculture and energy sectors to climate change.

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.

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.

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.

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.

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.

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.

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.