Abstract Climate change is rapidly altering composition, structure, and functioning of the boreal biome, across North America often broadly categorized into ecoregions. The resulting complex changes in different ecoregions present a challenge for efforts to accurately simulate carbon dioxide (CO 2 ) and energy exchanges between boreal forests and the atmosphere with terrestrial ecosystem models (TEMs). Eddy covariance measurements provide valuable information for evaluating the performance of TEMs and guiding their development. Here, we compiled a boreal forest model benchmarking dataset for North America by harmonizing eddy covariance and supporting measurements from eight black spruce ( Picea mariana )-dominated, mature forest stands. The eight forest stands, located in six boreal ecoregions of North America, differ in stand characteristics, disturbance history, climate, permafrost conditions and soil properties. By compiling various data streams, the benchmarking dataset comprises data to parameterize, force, and evaluate TEMs. Specifically, it includes half-hourly, gap-filled meteorological forcing data, ancillary data essential for model parameterization, and half-hourly, gap-filled or partitioned component flux data on CO 2 (net ecosystem production, gross primary production [GPP], and ecosystem respiration [ER]) and energy (latent [LE] and sensible heat [H]) and their daily aggregates screened based on half-hourly gap-filling quality criteria. We present a case study with the Canadian Land Surface Scheme Including Biogeochemical Cycles (CLASSIC) to: (1) demonstrate the utility of our dataset to benchmark TEMs and (2) provide guidance for model development and refinement. Model skill was evaluated using several statistical metrics and further examined through the flux responses to their environmental controls. Our results suggest that CLASSIC tended to overestimate GPP and ER among all stands. Model performance regarding the energy fluxes (i.e., LE and H) varied greatly among the stands and exhibited a moderate correlation with latitude. We identified strong relationships between simulated fluxes and their environmental controls except for H, thus highlighting current strengths and limitations of CLASSIC.

A strong atmospheric river made landfall in southwestern British Columbia, Canada on 14th November 2021, bringing two days of intense precipitation to the region. The resulting floods and landslides led to the loss of at least five lives, cut Vancouver off entirely from the rest of Canada by road and rail, and made this the costliest natural disaster in the province's history. Here we show that westerly atmospheric river events of this magnitude are approximately one in ten year events in the current climate of this region, and that such events have been made at least 60% more likely by the effects of human-induced climate change. Characterized in terms of the associated two-day precipitation, the event is approximately a one in 50-100 year event, and its probability has been increased by a best estimate of 50% by human-induced climate change. The effects of this precipitation on streamflow were exacerbated by already wet conditions preceding the event, and by rising temperatures during the event that led to significant snowmelt, which led to streamflow maxima exceeding estimated one in a hundred year events in several basins in the region. Based on a large ensemble of simulations with a hydrological model which integrates the effects of multiple climatic drivers, we find that the probability of such extreme streamflow events has been increased by human-induced climate change by a best estimate of 2 to 4. Together these results demonstrate the substantial human influence on this compound extreme event, and help motivate efforts to increase resiliency in the face of more frequent events of this kind in the future.

Extreme temperature is a major threat to urban populations; thus, it is crucial to understand future changes to plan adaptation and mitigation strategies. We assess historical and CMIP6 projected trends of minimum and maximum temperatures for the 18 most populated Canadian cities. Temperatures increase (on average 0.3°C/decade) in all cities during the historical period (1979–2014), with Prairie cities exhibiting lower rates (0.06°C/decade). Toronto (0.5°C/decade) and Montreal (0.7°C/decade) show high increasing trends in the observation period. Higher-elevation cities, among those with the same population, show slower increasing temperature rates compared to the coastal ones. Projections for cities in the Prairies show 12% more summer days compared to the other regions. The number of heat waves (HWs) increases for all cities, in both the historical and future periods; yet alarming increases are projected for Vancouver, Victoria, and Halifax from no HWs in the historical period to approximately 4 HWs/year on average, towards the end of 2100 for the SSP5–8.5. The cold waves reduce considerably for all cities in the historical period at a rate of 2 CWs/decade on average and are projected to further reduce by 50% compared to the observed period. • CMIP6 simulations for extreme temperature estimation of the largest Canadian cities. • Prairies' cities exhibit a lower rate of temperature increase compared to the cities in Great lakes in observation period. • Cities in Prairies are projected to have 12% more summer days than the rest of the cities. • The number of heat waves increases significantly, especially for Vancouver, Victoria, and Halifax. • Cold waves are expected to decrease by 50% in future.

A strong atmospheric river made landfall in southwestern British Columbia, Canada on November 14th, 2021, bringing two days of intense precipitation to the region. The resulting floods and landslides led to the loss of at least five lives, cut Vancouver off entirely from the rest of Canada by road and rail, and made this the costliest natural disaster in the province's history. Here we show that when characterised in terms of storm-averaged water vapour transport, the variable typically used to characterise the intensity of atmospheric rivers, westerly atmospheric river events of this magnitude are approximately one in ten year events in the current climate of this region, and that such events have been made at least 60% more likely by the effects of human-induced climate change. Characterised in terms of the associated two-day precipitation, the event is substantially more extreme, approximately a one in fifty to one in a hundred year event, and the probability of events at least this large has been increased by a best estimate of 45% by human-induced climate change. The effects of this precipitation on streamflow were exacerbated by already wet conditions preceding the event, and by rising temperatures during the event that led to significant snowmelt, which led to streamflow maxima exceeding estimated one in a hundred year events in several basins in the region. Based on a large ensemble of simulations with a hydrological model which integrates the effects of multiple climatic drivers, we find that the probability of such extreme streamflow events in October to December has been increased by human-induced climate change by a best estimate of 120–330%. Together these results demonstrate the substantial human influence on this compound extreme event, and help motivate efforts to increase resiliency in the face of more frequent events of this kind in the future.

Abstract. Cold region hydrology is very sensitive to the impacts of climate warming. Impacts of warming over recent decades in western Canada include glacier retreat, permafrost thaw, and changing patterns of precipitation, with an increased proportion of winter precipitation falling as rainfall and shorter durations of snow cover, as well as consequent changes in flow regimes. Future warming is expected to continue along these lines. Physically realistic and sophisticated hydrological models driven by reliable climate forcing can provide the capability to assess hydrological responses to climate change. However, the provision of reliable forcing data remains problematic, particularly in data-sparse regions. Hydrological processes in cold regions involve complex phase changes and so are very sensitive to small biases in the driving meteorology, particularly in temperature and precipitation, including precipitation phase. Cold regions often have sparse surface observations, particularly at high elevations that generate a large amount of runoff. This paper aims to provide an improved set of forcing data for large-scale hydrological models for climate change impact assessment. The best available gridded data in Canada are from the high-resolution forecasts of the Global Environmental Multiscale (GEM) atmospheric model and outputs of the Canadian Precipitation Analysis (CaPA), but these datasets have a short historical record. The EU WATCH ERA-Interim reanalysis (WFDEI) has a longer historical record but has often been found to be biased relative to observations over Canada. The aim of this study, therefore, is to blend the strengths of both datasets (GEM-CaPA and WFDEI) to produce a less-biased long-record product (WFDEI-GEM-CaPA) for hydrological modelling and climate change impact assessment over the Mackenzie River Basin. First, a multivariate generalization of the quantile mapping technique was implemented to bias-correct WFDEI against GEM-CaPA at 3 h ×0.125∘ resolution during the 2005–2016 overlap period, followed by a hindcast of WFDEI-GEM-CaPA from 1979. The derived WFDEI-GEM-CaPA data are validated against station observations as a preliminary step to assess their added value. This product is then used to bias-correct climate projections from the Canadian Centre for Climate Modelling and Analysis Canadian Regional Climate Model (CanRCM4) between 1950 and 2100 under RCP8.5, and an analysis of the datasets shows that the biases in the original WFDEI product have been removed and the climate change signals in CanRCM4 are preserved. The resulting bias-corrected datasets are a consistent set of historical and climate projection data suitable for large-scale modelling and future climate scenario analysis. The final historical product (WFDEI-GEM-CaPA, 1979–2016) is freely available at the Federated Research Data Repository at https://doi.org/10.20383/101.0111 (Asong et al., 2018), while the original and corrected CanRCM4 data are available at https://doi.org/10.20383/101.0162 (Asong et al., 2019).

We describe a spatially contiguous, temporally consistent high-resolution gridded daily meteorological dataset for northwestern North America. This >4 million km2 region has high topographic relief, seasonal snowpack, permafrost and glaciers, crosses multiple jurisdictional boundaries and contains the entire Yukon, Mackenzie, Saskatchewan, Fraser and Columbia drainages. We interpolate daily station data to 1/16° spatial resolution using a high-resolution monthly 1971-2000 climatology as a predictor in a thin-plate spline interpolating algorithm. Only temporally consistent climate stations with at least 40 years of record are included. Our approach is designed to produce a dataset well suited for driving hydrological models and training statistical downscaling schemes. We compare our results to two commonly used datasets and show improved performance for climate means, extremes and variability. When used to drive a hydrologic model, our dataset also outperforms these datasets for runoff ratios and streamflow trends in several, high elevation, sub-basins of the Fraser River.

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.