Alex Cannon

Environment and Climate Change Canada · Climate Research Division

The qrnn package for R implements the quantile regression neural network, which is an artificial neural network extension of linear quantile regression. The model formulation follows from previous work on the estimation of censored regression quantiles. The result is a nonparametric, nonlinear model suitable for making probabilistic predictions of...

Quantile mapping bias correction algorithms are commonly used to correct systematic distributional biases in precipitation outputs from climate models. Although they are effective at removing historical biases relative to observations, it has been found that quantile mapping can artificially corrupt future model-projected trends. Previous studies o...

Most bias correction algorithms used in climatology, for example quantile mapping, are applied to univariate time series. They neglect the dependence between different variables. Those that are multivariate often correct only limited measures of joint dependence, such as Pearson or Spearman rank correlation. Here, an image processing technique desi...

Climate models are the major tools to study the climate system and its evolutions in the future. However, climate simulations often present statistical biases and have to be corrected against observations before being used in impact assessments. Several bias correction (BC) methods have therefore been developed in the literature over the last 2 dec...

The goal of quantile regression is to estimate conditional quantiles for specified values of quantile probability using linear or nonlinear regression equations. These estimates are prone to "quantile crossing", where regression predictions for different quantile probabilities do not increase as probability increases. In the context of the environm...

Spring barley ( Hordeum vulgare L.), being a cold‐tolerant crop may not benefit as much from a warmer climate and a lengthening of the growing season due to climate change though its suitable production area could expand further north. The objectives of this study were to assess the impact of climate change on barley yields across Canada for both c...

This paper explores the application of emerging machine learning methods from image super-resolution (SR) to the task of statistical downscaling. We specifically focus on convolutional neural network-based Generative Adversarial Networks (GANs). Our GANs are conditioned on low-resolution (LR) inputs to generate high-resolution (HR) surface winds em...

Given the growing number of global climate models (GCMs) with simulations available for impacts and adaptation studies, methods have been introduced to select models that are ‘fit-for-purpose’. This study applies a GCM selection process to historical and future climate projections from 38 and 43 GCMs contributing to the fifth and sixth phases of th...

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 (CO2) and energy exchanges between boreal forests and the atmosphe...

Candidate probability distributions for the Standardized Precipitation Evapotranspiration Index (SPEI) for Canada were examined using the Canadian Gridded (CANGRD) temperature and precipitation dataset and CMIP5 projections. The probability distribution is a core component to the calculation of standardized values. For SPEI, a continuous probabilit...

Canada's boreal forests and tundra ecosystems are responding to unprecedented climate change with implications for the global carbon (C) cycle and global climate. However, our ability to model the response of Canada's terrestrial ecosystems to climate change is limited and there has been no comprehensive, process‐based assessment of Canada's terres...

Providing small-scale information about weather and climate is challenging, especially for variables strongly controlled by processes that are unresolved by low-resolution (LR) models. This paper explores emerging machine learning methods from the fields of image super-resolution (SR) and deep learning for statistical downscaling of near-surface wi...

The projected increase in the frequency and intensity of extreme heat events due to climate change means an associated increase in risk of heat‐related illnesses and mortality. Public health systems need to be prepared to identify and reduce the susceptibility of vulnerable populations to increased occurrence of heat‐related illness and stress. To...

Individual and joint variations of extreme temperature and precipitation are assessed across Canada using the large ensemble of Canadian Regional Climate Model simulations (CanRCM4-LE) and two corresponding multivariate bias-corrected datasets (Canadian Large Ensembles Adjusted Datasets, CanLEAD-E & S). The overall performance of the three 50-membe...

This study investigates changes in linkages between atmospheric blocking and winter (December–February) cold spells over the Pacific-North America region in two large-ensembles of Canadian Earth System Models (CanESM2 and CanESM5 under high-emission scenarios). The two ensembles show decreases in winter blocking frequency over the North Pacific fro...

As summer heatwaves have severe adverse impacts on human society and ecosystems, there is need to better understand their meteorological drivers and future projections under climate change. This study investigates the linkage between atmospheric blocking and summer (June–August) heatwaves over North America using two reanalysis datasets (ERA-Interi...

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 disast...

A multivariate bias correction based on N‐dimensional probability density function transform (MBCn) technique is applied to four different high‐resolution regional climate change simulations and key meteorological variables, namely precipitation, mean near‐surface air temperature, near‐surface maximum air temperature, near‐surface minimum air tempe...

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...

Use of downscaled global climate model projections is expanding rapidly as climate change vulnerability assessments and adaptation planning become mainstream in many sectors. Many climate change impact analysesuse climate model projections downscaled at very high spatial resolution (~1 km) but very low temporal resolution (20‐ to 30‐year normals)....

The Canadian Large Ensembles Adjusted Dataset version 1 (CanLEADv1) contains 50‐member ensembles of bias‐adjusted near‐surface global and regional climate model variables on a 0.5° grid over North America for historical and future scenarios (1950–2100). Canadian Earth System Model Large Ensembles (CanESM2 LE) and Canadian Regional Climate Model Lar...

Leveraging advances in artificial intelligence could revolutionize the Earth and environmental sciences. We must ensure that our research funding and training choices give the next generation of geoscientists the capacity to realize this potential.

In the present study, an updating of the probabilistic models used for calibrating the wind load, snow load and companion load factors for the National Building Code of Canada (NBCC) are presented. The update of probabilistic models for the extreme wind speed and ground snow depth is based on historical climatological data over Canadian sites. For...

Representative subsets of global climate models (GCMs) are often used in climate change impact studies to account for uncertainty in ensemble climate projections. However, the effectiveness of such subsets has seldom been assessed for the estimations of either the mean or the spread of the full ensembles. We assessed two different approaches that w...

Many studies of climate change impacts and adaptation use climate model projections downscaled at very high spatial resolution (~1km) but very low temporal resolution (20- to 30-year normals). These applications have model selection priorities that are distinct from analyses at high temporal resolution. Here, we select a 13-model CMIP6 ensemble des...

Climate change can affect different drivers of flooding in low-lying coastal areas of the world, challenging the design and planning of communities and infrastructure. The concurrent occurrence of multiple flood drivers such as high river flows and extreme sea levels can aggravate such impacts and result in catastrophic damages. In this study, the...

Climate change in the Arctic is leading to shifts in vegetation communities, permafrost degradation and alteration of tundra surface–atmosphere energy and carbon (C) fluxes, among other changes. However, year-round C and energy flux measurements at high-latitude sites remain rare. This poses a challenge for evaluating the impacts of climate change...

Due to the significant negative consequences of winter cold extremes, there is need to better understand and simulate the mechanisms driving their occurrence. The impact of atmospheric blocking on winter cold spells over North America is investigated using ERA-Interim and NCEP-DOE-R2 reanalyses for 1981–2010. Initial-condition large-ensembles of tw...

Using climate scenarios from only one or a small number of global climate models (GCMs) in climate change impact studies may lead to biased assessment due to large uncertainty in climate projections. Ensemble means in impact projections derived from a multi-GCM ensemble are often used as best estimates to reduce bias. However, it is often time-cons...

The dependence structure of temperature-precipitation compound events is analyzed across Canada using three datasets derived from Canadian Regional Climate Model Large Ensemble simulations, including raw model outputs (CanRCM4-LE) and two sets of multivariate bias-corrected model outputs (Canadian Large Ensembles Adjusted Datasets, CanLEAD-EWEMBI/S...

Extracted data for individual river basins are available at https://open.canada.ca/data/en/dataset/f8996696-4354- 471d-808e-f681fa0091b2.

Anthropogenic climate change is affecting the snowpack freshwater storage, with socioeconomic and ecological impacts. We present an assessment of maximum snow water equivalent (SWE max) change in large river basins of the northwestern North America region using the Canadian Regional Climate Model 50-member ensemble under 1.0°C to 4.0°C global warmi...

The Arctic is warming more rapidly than other regions of the world leading to ecosystem change including shifts in vegetation communities, permafrost degradation and alteration of tundra surface-atmosphere energy and carbon (C) fluxes, among others. However, year-round C and energy flux measurements at high-latitude sites remain rare. This poses a...

The report provides an assessment of how climatic design data relevant to the National Building Code of Canada (NBCC 2015, Table C-2) and the Canadian Highway Bridge Design Code (CHBDC/CSA S6 2014, Annex A3.1) might change as the climate continues to warm. The approach in this report is based on an assessment of the current understanding of climate...

Strong wind coinciding with rainfall is an important weather phenomenon in many science and engineering fields. This study investigates changes in hourly extreme driving rain wind pressure (DRWP)—a climatic variable used in building design in Canada—for future periods of specified global mean temperature change using an ensemble of a Canadian regio...

Internal climate variability (ICV) is one of the major sources of uncertainty in climate projections, yet it is seldom quantified for projections of crop production. Our study focuses on quantifying the uncertainty due to ICV in projections of crop productions in Canada. We utilize climate scenarios from two large ensembles (LEs, CanESM2-LE and Can...

This study evaluates and compares historical simulations of daily sea-level pressure circulation types over 6 continental-scale regions (North America, South America, Europe, Africa, East Asia, and Australasia) by 15 pairs of global climate models from modeling centers that contributed to both Coupled Model Intercomparison Project Phase 5 (CMIP5) a...

Climate models are the major tools to estimate climate variables evolutions in the future. However, climate simulations often present statistical biases and have to be corrected against observations before being used in impact assessments. Several bias correction (BC) methods have therefore been developed in the literature over the last two decades...

The EU WATCH ERA-Interim reanalysis WFDEI (Weedon et al., 2014) 3h*0.5ᵒ dataset (1979-2016) was used to bias correct an initial condition ensemble of 15 members of CanRCM4 simulations (Scinocca et al., 2016) that were obtained by downscaling CanESM2 global simulations over the North American CORDEX domain. CanESM2 was forced with historical emissio...

Powdery mildew (Erysiphe necator) is a fungal disease causing significant loss of grape yield in commercial vineyards. The rate of development of this disease varies annually and is driven by complex interactions between the pathogen, its host, and environmental conditions. The long term impacts of weather and climate variability on disease develop...

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 chan...

Bias correction methods remove systematic differences in the distributional properties of climate model outputs with respect to observations, often as a means of pre‐processing model outputs for use in hydrological impact studies. Traditionally, bias correction is applied at each weather station individually, neglecting the dependence that exists b...

Abstract. Climate models are the major tools to estimate climate variables evolutions in the future. However, climate simulations often present statistical biases and have to be corrected against observations before being used in impact assessments. Several bias correction (BC) methods have therefore been developed in the literature over the last t...

Wind-driven rain (WDR) on building façades reduces hygrothermal performance and durability of wall assemblies. This paper evaluates projected changes to WDR exposure of building façades in Canada for future periods corresponding to specified levels (0.5 ~ 3.5 °C) of global warming above the 1986–2016 baseline. Projections are based on a large ensem...

Atmospheric moisture loading affects the performance and durability of building exteriors. In a changing climate, designing for historical moisture loads may no longer be adequate. This study investigates potential climate change impacts on the moisture index used in the design and management of buildings over Canada. Projections are obtained for f...

Global warming is expected to produce modifications in the intensity, as well as in the seasonality and spatiotemporal structure of extreme precipitation. In the present study, the temporal evolution of simulated daily and subdaily precipitation extremes was analyzed to assess how they respond to climate warming over different time horizons. Poolin...

The characterization of extreme precipitation at fine spatiotemporal scale represents a paramount challenge in hydroclimate sciences due to large uncertainties affecting the precipitation estimation from existing datasets. Comparing the spatiotemporal structure of precipitation extremes estimated from different datasets thus represents an essential...

Cold regions 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 increased proportion of winter precipitation falling as rainfall and shorter durations of snowcover, and consequent changes in flow...

Cold regions 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 increased proportion of winter precipitation falling as rainfall and shorter durations of snowcover, and consequent changes in flow...

This analysis documents projected changes in daily precipitation and temperature characteristics over Canada based on a 15-member ensemble which had been downscaled using the Canadian Regional Climate Model–CanRCM4 at 50 km resolution by the Canadian Centre for Climate Modelling and Analysis (CCCma) under Representative Concentration Pathway (RCP)...

Science-based assessments of climate change impacts on cropping systems under different levels of global warming are essential for informing stakeholders which global climate targets and potential adaptation strategies may be effective. A comprehensive evaluation of climate change impacts on Canada's crop production under different levels of global...

We describe a state-of-the-art framework for projecting hydrologic impacts due to enhanced warming and amplified moisture fluxes in the subarctic environment under anthropogenic climate change. We projected future hydrologic changes based on Coupled Model Intercomparison Project Phase 5 global climate model simulations using the Variable Infiltrati...

This dataset provides an improved set of forcing data for large scale hydrological models for climate change impacts assessment in Mackenzie River Basin (MRB). The best available gridded data in the MRB is from the high resolution forecasts of the Global Environmental Multiscale (GEM) atmospheric model and outputs of the Canadian Precipitation Anal...

Atmospheric ice accretion caused by freezing precipitation (FP) can lead to severe damage and the failure of buildings and infrastructure. This study investigates projected changes to extreme ice loads – those used to design infrastructure over North America (NA) – for future periods of specified global mean temperature change (GMTC), relative to t...

One of the main challenges in climate change impact assessment studies is selecting climate change scenarios. By focusing on selecting projected extremes in a high dimensional space, one is confronted with the shrinkage of ensemble size while preserving the projection spread. This study proposes a novel integrated computational geometry algorithm t...

Alpine catchments show a high sensitivity to climate variation as they include the elevation range of the snow line. Therefore, the correct representation of climate variables and their interdependence is crucial when describing or predicting hydrological processes. When using climate model simulations in hydrological impact studies, forcing meteor...

Convection-permitting climate models have been recommended for use in projecting future changes in local-scale, short-duration rainfall extremes that are of the greatest relevance to engineering and infrastructure design, e.g., as commonly summarized in intensity–duration–frequency (IDF) curves. Based on thermodynamic arguments, it is expected that...