Publications Library

Weather and climate extremes are identified as major areas necessitating further progress in climate research and have thus been selected as one of the World Climate Research Programme (WCRP) Grand Challenges. Here, we provide an overview of current challenges and opportunities for scientific progress and cross-community collaboration on the topic of understanding, modeling and predicting extreme events based on an expert workshop organized as part of the implementation of the WCRP Grand Challenge on Weather and Climate Extremes. In general, the development of an extreme event depends on a favorable initial state, the presence of large-scale drivers, and positive local feedbacks, as well as stochastic processes. We, therefore, elaborate on the scientific challenges related to large-scale drivers and local-to-regional feedback processes leading to extreme events. A better understanding of the drivers and processes will improve the prediction of extremes and will support process-based evaluation of the representation of weather and climate extremes in climate model simulations. Further, we discuss how to address these challenges by focusing on short-duration (less than three days) and long-duration (weeks to months) extreme events, their underlying mechanisms and approaches for their evaluation and prediction.

Two articles recently published in the peer reviewed literature examine how the rate of snowmelt may change as the Earth's climate changes, and how droughts can evolve and move over time.

Publishing in Nature Climate Change, Musselman et al. (2017) examine the effect that global warming may have on snowmelt. They find that the portion of snow melt occurring at moderate and high melt rates in Western North America is projected to decrease, while the portion occurring at low melt rates is projected to increase. Total meltwater volume is projected to decrease.

In recent research published in Geophysical Research Letters, Herrera-Estrada et al. (2017) explore how droughts evolve in space and time across six continents. They find that clusters of droughts can travel hundreds to thousands of kilometers across each continent. In addition, the authors find that longer-lasting droughts tend to travel farther, as well as be more severe.

Wet bulb globe temperature (WBGT) accounts for the effect of environmental temperature and humidity on thermal comfort, and can be directly related to the ability of the human body to dissipate excess metabolic heat and thus avoid heat stress. Using WBGT as a measure of environmental conditions conducive to heat stress, we show that anthropogenic influence has very substantially increased the likelihood of extreme high summer mean WBGT in northern hemispheric land areas relative to the climate that would have prevailed in the absence of anthropogenic forcing. We estimate that the likelihood of summer mean WGBT exceeding the observed historical record value has increased by a factor of at least 70 at regional scales due to anthropogenic influence on the climate. We further estimate that, in most northern hemispheric regions, these changes in the likelihood of extreme summer mean WBGT are roughly an order of magnitude larger than the corresponding changes in the likelihood of extreme hot summers as simply measured by surface air temperature. Projections of future summer mean WBGT under the RCP8.5 emissions scenario that are constrained by observations indicate that by 2030s at least 50% of the summers will have mean WBGT higher than the observed historical record value in all the analyzed regions, and that this frequency of occurrence will increase to 95% by mid‐century.

Talk delivered by Dr. Dhouha Ouali, PCIC Research Associate with the Marine Environmental Observation Prediction and Response Network on October 25th, 2017.

Regional frequency analysis (RFA) of hydro-meteorological variables is a commonly used tool to provide quantile estimates of extreme events at ungauged sites. Given the high complexity of hydro-meteorological processes, it is worthwhile to account for the possible nonlinear connections between hydro-meteorological variables and catchments characteristics in all RFA steps. Moreover, to provide relatively reliable quantiles estimates, it is often recommended to only consider sites with sufficiently long data series which lead to ignoring a considerable part of the available information. A number of regression-based RFA methods are proposed to remedy the limitations of the classical approaches, mainly dealing with the non-linear aspect and the exploitation of the hydro-meteorological data. Comprehensive comparisons are carried out between the classical and the proposed methodologies using 151 hydrometric stations from the province of Quebec. The performances of the proposed methods are assessed using new and classical evaluation criteria in a cross-validation procedure.

This study assesses the ability of the Canadian Seasonal to Interannual Prediction System (CanSIPS) and the Canadian Earth-system Model 2 (CanESM2) to predict and simulate snow and sea ice from seasonal to multi-decadal timescales, with a focus on the Canadian sector. To account for observational uncertainty, model structural uncertainty, and internal climate variability, the analysis uses multi-source observations, multiple Earth-System Models (ESMs) in Phase 5 of the Coupled Model Intercomparison Project (CMIP5) archive, and initial condition ensembles of CanESM2 and other models. It is found that the ability of the CanESM2 simulation to capture snow-related climate parameters, such as cold-region temperature and precipitation, lies within the range of currently available international models. Accounting for the considerable disagreement among satellite-era observational datasets on the distribution of snow water equivalent, CanESM2 has too much springtime snow cover over the Canadian land mass, reflecting a broader Northern Hemisphere positive bias. It also exhibits retreat of springtime snow generally greater than observational estimates, after accounting for observational uncertainty and internal variability. Sea ice is biased low in the Canadian Arctic, which makes it difficult to assess the realism of long-term sea-ice trends there. The strengths and weaknesses of the modeling system need to be understood as a practical tradeoff: the Canadian models are relatively inexpensive computationally because of their moderate resolution, thus enabling their use in operational seasonal prediction and for generating large ensembles of multidecadal simulations. Improvements in climate prediction systems like CanSIPS rely not just on simulation quality but also on using novel observational constraints and the ready transfer of research to an operational setting. Improvements in seasonal forecasting practice arising from recent research include accurate initialization of snow and frozen soil, accounting for observational uncertainty in forecast verification, and sea-ice thickness initialization using statistical predictors available in real time.

The Fraser River basin (FRB) of British Columbia is one of the largest and most important watersheds in Western North America, and is home to a rich diversity of biological species and economic assets that depend implicitly upon its extensive riverine habitats. The hydrology of the FRB is dominated by snow accumulation and melt processes, leading to a prominent annual peak streamflow invariably occurring in June–July. However, while annual peak daily streamflow (APF) during the spring freshet in the FRB is historically well correlated with basin-averaged, April 1 snow water equivalent (SWE), there are numerous occurrences of anomalously large APF in below- or near-normal SWE years, some of which have resulted in damaging floods in the region. An imperfect understanding of which other climatic factors contribute to these anomalously large APFs hinders robust projections of their magnitude and frequency.

We employ the Variable Infiltration Capacity (VIC) process-based hydrological model driven by gridded observations to investigate the key controlling factors of anomalous APF events in the FRB and four of its subbasins that contribute more than 70 % of the annual flow at Fraser-Hope. The relative influence of a set of predictors characterizing the interannual variability of rainfall, snowfall, snowpack (characterized by the annual maximum value, SWEmax), soil moisture and temperature on simulated APF at Hope (the main outlet of the FRB) and at the subbasin outlets is examined within a regression framework. The influence of large-scale climate modes of variability (the Pacific Decadal Oscillation (PDO) and the El Niño-Southern Oscillation (ENSO)) on APF magnitude is also assessed, and placed in context with these more localized controls. The results indicate that next to SWEmax (which strongly controls the annual maximum of soil moisture), the snowmelt rate, the ENSO and PDO indices, and rate of warming subsequent to the date of SWEmax are the most influential predictors of APF magnitude in the FRB and its subbasins. The identification of these controls on annual peak flows in the region may be of use in the context of seasonal prediction or future projected streamflow behaviour.

Canada is expected to see an increase in fire risk under future climate projections. Large fires, such as that near Fort McMurray, Alberta in 2016, can be devastating to the communities affected. Understanding the role of human emissions in the occurrence of such extreme fire events can lend insight into how these events might change in the future. An event attribution framework is used to quantify the influence of anthropogenic forcings on extreme fire risk in the current climate of a western Canada region. Fourteen metrics from the Canadian Forest Fire Danger Rating System are used to define the extreme fire seasons. For the majority of these metrics and during the current decade, the combined effect of anthropogenic and natural forcing is estimated to have made extreme fire risk events in the region 1.5 to 6 times as likely compared to a climate that would have been with natural forcings alone.

Public talk delivered by Francis Zwiers at the 51^st Annual CMOS Congress, June 6^th, 2017