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Future sediment dynamics may be affected by changing climates or hydrological regimes because of the close link between hydrology and sediment erosion, deposition, and transport. Previously, investigations of these potential changes have been constrained by a combination of limited observational data, hydrological drivers, and appropriate mechanistic models. Additionally, there is often ambiguity regarding how to disentangle the impacts of climate and hydrology from direct human factors such as reservoirs and land‐use change, which often exert more control over sediment dynamics. In this study, we utilize a recently developed, large‐scale, distributed, mechanistic sediment transport model to project future sediment erosion, deposition, and transportation within the Fraser River Basin in British Columbia, Canada—a basin with historical water flux and sediment load observations and limited anthropogenic influences upstream of its delta. The sediment model is driven by synthetic land‐surface hydrology derived from Scenarios A1B, A2, and B1 of the Special Report on Emissions Scenarios, which were provided by the Pacific Climate Impacts Consortium. Resulting simulations of water flux and sediment load from 1965 to 1994 are first validated against observational data then compared with future projections. Future projections show an overall increase in annual hillslope erosion and in‐channel transportation, a shift towards earlier spring peak erosion and transportation, and longer persistence of the sediment signal through the year. These shifts in timing and annual yield may have deleterious effects on spawning sockeye salmon and are insufficient to counteract future coastal retreat caused by sea‐level rise.

The paper presents results from a climate change detection and attribution study on the decline of Arctic sea ice extent in September for the 1953–2012 period. For this period three independently derived observational datasets and simulations from multiple climate models are available to attribute observed changes in the sea ice extent to known climate forcings. Here we direct our attention to the combined cooling effect from other anthropogenic forcing agents (mainly aerosols), which has potentially masked a fraction of greenhouse gas–induced Arctic sea ice decline. The presented detection and attribution framework consists of a regression model, namely, regularized optimal fingerprinting, where observations are regressed onto model-simulated climate response patterns (i.e., fingerprints). We show that fingerprints from greenhouse gas, natural, and other anthropogenic forcings are detected in the three observed records of Arctic sea ice extent. Beyond that, our findings indicate that for the 1953–2012 period roughly 23% of the greenhouse gas–induced negative sea ice trend has been offset by a weak positive sea ice trend attributable to other anthropogenic forcing. We show that our detection and attribution results remain robust in the presence of emerging nonstationary internal climate variability acting upon sea ice using a perfect model experiment and data from two large ensembles of climate simulations.

Significant flood damage occurred near Montreal in May 2017, as flow from the upstream Ottawa River basin (ORB) reached its highest levels in over 50 years. Analysis of observations and experiments performed with the fifth generation Canadian Regional Climate Model (CRCM5) show that much above average April precipitation over the ORB, a large fraction of which fell as rain on an existing snowpack, increased streamflow to near record-high levels. Subsequently, two heavy rainfall events affected the ORB in the first week of May, ultimately resulting in flooding. This heavy precipitation during April and May was linked to large-scale atmospheric features. Results from sensitivity experiments with CRCM5 suggest that the mass and distribution of the snowpack have a major influence on spring streamflow in the ORB. Furthermore, the importance of using an appropriate frozen soil parameterization when modelling spring streamflows in cold regions was confirmed. Event attribution using CRCM5 showed that events such as the heavy April 2017 precipitation accumulation over the ORB are between two and three times as likely to occur in the present-day climate as in the pre-industrial climate. This increase in the risk of heavy precipitation is linked to increased atmospheric moisture due to warmer temperatures in the present-day climate, a direct consequence of anthropogenic emissions, rather than changes in rain-generating mechanisms or circulation patterns. Warmer temperatures in the present-day climate also reduce early-spring snowpack in the ORB, offsetting the increase in rainfall and resulting in no discernible change to the likelihood of extreme surface runoff.

We examine extreme temperature and precipitation under two potential geoengineering methods forming part of the Geoengineering Model Intercomparison Project (GeoMIP). The solar dimming experiment G1 is designed to completely offset the global mean radiative forcing due to a CO2-quadrupling experiment (abrupt4 × CO2), while in GeoMIP experiment G4, the radiative forcing due to the representative concentration pathway 4.5 (RCP4.5) scenario is partly offset by a simulated layer of aerosols in the stratosphere. Both G1 and G4 geoengineering simulations lead to lower minimum temperatures (TNn) at higher latitudes and on land, primarily through feedback effects involving high-latitude processes such as snow cover, sea ice and soil moisture. There is larger cooling of TNn and maximum temperatures (TXx) over land compared with oceans, and the land–sea cooling contrast is larger for TXx than TNn. Maximum 5-day precipitation (Rx5day) increases over subtropical oceans, whereas warm spells (WSDI) decrease markedly in the tropics, and the number of consecutive dry days (CDDs) decreases in most deserts. The precipitation during the tropical cyclone (hurricane) seasons becomes less intense, whilst the remainder of the year becomes wetter. Stratospheric aerosol injection is more effective than solar dimming in moderating extreme precipitation (and flooding). Despite the magnitude of the radiative forcing applied in G1 being ∼ 7.7 times larger than in G4 and despite differences in the aerosol chemistry and transport schemes amongst the models, the two types of geoengineering show similar spatial patterns in normalized differences in extreme temperatures changes. Large differences mainly occur at northern high latitudes, where stratospheric aerosol injection more effectively reduces TNn and TXx. While the pattern of normalized differences in extreme precipitation is more complex than that of extreme temperatures, generally stratospheric aerosol injection is more effective in reducing tropical Rx5day, while solar dimming is more effective over extra-tropical regions.

Canada has experienced some of the most rapid warming on Earth over the past few decades with a warming rate about twice that of the global mean temperature since 1948. Long-term warming is observed in Canada’s annual, winter and summer mean temperatures, and in the annual coldest and hottest daytime and nighttime temperatures. The causes of these changes are assessed by comparing observed changes with climate model simulated responses to anthropogenic and natural (solar and volcanic) external forcings. Most of the observed warming of 1.7 °C increase in annual mean temperature during 1948–2012 [90% confidence interval (1.1°, 2.2 °C)] can only be explained by external forcing on the climate system, with anthropogenic influence being the dominant factor. It is estimated that anthropogenic forcing has contributed 1.0 °C (0.6°, 1.5 °C) and natural external forcing has contributed 0.2 °C (0.1°, 0.3 °C) to the observed warming. Up to 0.5 °C of the observed warming trend may be associated with low frequency variability of the climate such as that represented by the Pacific decadal oscillation (PDO) and North Atlantic oscillation (NAO). Overall, the influence of both anthropogenic and natural external forcing is clearly evident in Canada-wide mean and extreme temperatures, and can also be detected regionally over much of the country.

This study evaluates regional-scale projections of climate indices that are relevant to climate change impacts in Canada. We consider indices of relevance to different sectors including those that describe heat conditions for different crop types, temperature threshold exceedances relevant for human beings and ecological ecosystems such as the number of days temperatures are above certain thresholds, utility relevant indices that indicate levels of energy demand for cooling or heating, and indices that represent precipitation conditions. Results are based on an ensemble of high-resolution statistically downscaled climate change projections from 24 global climate models (GCMs) under the RCP2.6, RCP4.5, and RCP8.5 emissions scenarios. The statistical downscaling approach includes a bias-correction procedure, resulting in more realistic indices than those computed from the original GCM data. We find that the level of projected changes in the indices scales well with the projected increase in the global mean temperature and is insensitive to the emission scenarios. At the global warming level about 2.1 °C above pre-industrial (corresponding to the multi-model ensemble mean for 2031–2050 under the RCP8.5 scenario), there is almost complete model agreement on the sign of projected changes in temperature indices for every region in Canada. This includes projected increases in extreme high temperatures and cooling demand, growing season length, and decrease in heating demand. Models project much larger changes in temperature indices at the higher 4.5 °C global warming level (corresponding to 2081–2100 under the RCP8.5 scenario). Models also project an increase in total precipitation, in the frequency and intensity of precipitation, and in extreme precipitation. Uncertainty is high in precipitation projections, with the result that models do not fully agree on the sign of changes in most regions even at the 4.5 °C global warming level.

Since local weather and climate greatly affect the construction and performance of buildings, reliable meteorological
data is essential when simulating building performance. It is well understood that climate change will affect future
weather and there is a growing interest in generating future weather files to support climate resilient building design.
Weather files that account for climate change have not been widely used for the lower mainland region of British
Columbia. In this study, hourly weather files for future climate conditions in Vancouver are created for three time periods
using a “morphing” methodology. Morphing uses results from global climate models to adjust observed weather data
at a specific location. In this study, daily data from climate simulations for the RCP8.5 emission scenario have been
used. The weather variables that have been adjusted are dry-bulb temperature, relative humidity, solar radiation, cloud
cover, wind speed and atmospheric pressure. The impact of climate change on the energy performance of a multi-unit
residential building located on the University of BC campus is analyzed using the energy modelling software
EnergyPlus. The simulation results indicate that the changing climate in Vancouver, following RCP8.5, would have a
considerable effect on building energy performance and energy demand due to decrease in space heating and increase
in cooling requirements.

Parties to the United Nations Framework Convention on Climate Change have agreed to hold the “increase in global average temperature to well below 2°C above preindustrial levels and to pursue efforts to limit the temperature increase to 1.5°C.” Comparison of the costs and benefits for different warming limits requires an understanding of how risks vary between warming limits. As changes in risk are often associated with changes in exposure due to projected changes in local or regional climate extremes, we analyze differences in the risks of extreme daily temperatures and extreme daily precipitation amounts under different warming limits. We show that global warming of 2°C would result in substantially larger changes in the probabilities of the extreme events than global warming of 1.5°C. For example, over the global land area, the probability of a warm extreme that occurs once every 20 years on average in the current climate is projected to increase 130% and 340% at the 1.5°C and 2.0°C warming levels, respectively (median values). Moreover, the relative changes in probability are larger for rarer, more extreme events, implying that risk assessments need to carefully consider the extreme event thresholds at which vulnerabilities occur.

The ClimDown R package publishes the routines and techniques of the Pacific Climate Impacts Consortium (PCIC) for downscaling coarse scale Global Climate Models (GCMs) to fine scale spatial resolution. PCIC’s overall downscaling algorithm is named Bias-corrected constructed analogues with quantile mapping reordering (BCCAQ) (Cannon, Sobie, and Murdock 2015; Werner and Cannon 2016). BCCAQ is a hybrid downscaling method that combines outputs from Constructed Analogues (CA) (Maurer et al. 2010) and quantile mapping at the fine-scale resolution. First, the CA and Climate Imprint (CI) (Hunter and Meentemeyer 2005) plus quantile delta mapping (QDM) (Cannon, Sobie, and Murdock 2015) algorithms are run independently. BCCAQ then combines outputs from the two by taking the daily QDM outputs at each fine-scale grid point and reordering them within a given month according to the daily CA ranks, i.e., using a form of Empirical Copula Coupling (Schefzik, Thorarinsdottir, and Gneiting 2013). The package exports high-level wrapper functions that perform each of three downscaling steps: CI, CA, and QDM, as well as one wrapper that runs the entire BCCAQ pipeline.