Philippe Gachon

A Nonstationary Extreme Value Analysis for the Assessment of Changes in Extreme Annual Wind Speed over the Gulf of St. Lawrence, Canada

Abstract Changes in the extreme annual wind speed in and around the Gulf of St. Lawrence (Canada) were investigated through a nonstationary extreme value analysis of the annual maximum 10-m wind speed obtained from the North American Regional Reanalysis (NARR) dataset as well as observed data from selected stations of Environment Canada. A generalized extreme value distribution with time-dependent location and scale parameters was used to estimate quantiles of interest as functions of time at locations where significant trend was detected. A Bayesian method, the generalized maximum likelihood approach, is implemented to estimate the parameters. The analysis yielded shape parameters very close to 0, suggesting that the distribution can be modeled using the Gumbel distribution. A similar analysis using a nonstationary Gumbel model yielded similar quantiles with narrower credibility intervals. Overall, little change was detected over the period 1979–2004. Only 7% of the investigated grids exhibited trends at...

An assessment of Canadian prairie drought: past, present, and future

Within Canada, the Canadian Prairies are particularly drought-prone mainly due to their location in the lee of the western cordillera and distance from large moisture sources. Although previous studies examined the occurrence of Canadian Prairie droughts during instrumental, pre-instrumental and to a lesser extent, future periods, none have specifically focused on all time three scales. Using two different drought indicators, namely the Palmer Drought Severity Index (PDSI) and Standardized Precipitation Index (SPI), this investigation assesses the variability of summer drought duration and intensity over a core region of the Prairies during (a) the pre-instrumental record extending back several centuries (inferred from tree rings), (b) the instrumental record (1901–2005), and (c) the twenty-first century using statistically downscaled climate variables from several Atmosphere–Ocean Global climate models with multiple emission scenarios. Results reveal that observed twentieth century droughts were relatively mild when compared to pre-settlement on the Prairies, but these periods are likely to return (and even worsen) in the future due to the anticipated warming during the course of the twenty-first century. However, future drought projections are distinctly different between the two indices. All PDSI-related model runs show greater drought frequency and severity mainly due to increasing temperatures. Conversely, the precipitation-based SPI indicates no significant changes to future summer drought frequency although there tends to be a higher persistence of multi-year droughts in central and southern portions of Canadian Prairies. These findings therefore stress the importance of considering anticipated warming trends when assessing future regional-scale drought, especially given the uncertainties and lack of consistency in future precipitation signals among climate models. This study can be considered an initial step toward quantifying and understanding Canadian Prairie drought occurrence and severity over several centuries as determined from paleo, instrumental, and climate model data sources.

Comparison of transfer functions in statistical downscaling models for daily temperature and precipitation over Canada

This study compares three linear models and one non-linear model, specifically multiple linear regression (MLR) with ordinary least squares (OLS) estimates, robust regression, ridge regression, and artificial neural networks (ANNs), to identify an appropriate transfer function in statistical downscaling (SD) models for the daily maximum and minimum temperatures (Tmax and Tmin) and daily precipitation occurrence and amounts (Pocc and Pamount). This comparison was made over twenty-five observation sites located in five different Canadian provinces (British Columbia, Saskatchewan, Manitoba, Ontario, and Québec). Reanalysis data were employed as atmospheric predictor variables of SD models. Predictors of linear transfer functions and ANN were selected by linear correlations coefficient and mutual information, respectively. For each downscaled case, annual and monthly models were developed and analysed. The monthly MLR, annual ANN, annual ANN, and annual MLR yielded the best performance for Tmax, Tmin, Pocc and Pamont according to the modified Akaike information criterion (AICu). A monthly MLR is recommended for the transfer functions of the four predictands because it can provide a better performance for the Tmax and as good performance as the annual MLR for the Tmin, Pocc, and Pamount. Furthermore, a monthly MLR can provide a slightly better performance than an annual MLR for extreme events. An annual MLR approach is also equivalently recommended for the transfer functions of the four predictands because it showed as good a performance as monthly MLR in spite of its mathematical simplicity. Robust and ridge regressions are not recommended because the data used in this study are not greatly affected by outlier data and multicollinearity problems. An annual ANN is recommended only for the Tmin, based on the best performance among the models in terms of both the RMSE and AICu.

Evaluation of daily precipitation statistics and monsoon onset/retreat over western Sahel in multiple data sets

Abstract The West Africa rainfall regime constitutes a considerable challenge for Regional Climate Models (RCMs) due to the complexity of dynamical and physical processes that characterise the West African Monsoon. In this paper, daily precipitation statistics are evaluated from the contributions to the AFRICA-CORDEX experiment from two ERA-Interim driven Canadian RCMs: CanRCM4, developed at the Canadian Centre for Climate Modelling and Analysis (CCCma) and CRCM5, developed at the University of Québec at Montréal. These modelled precipitation statistics are evaluated against three gridded observed datasets—the Global Precipitation Climatology Project (GPCP), the Tropical Rainfall Measuring Mission (TRMM), and the Africa Rainfall Climatology (ARC2)—and four reanalysis products (ECMWF ERA-Interim, NCEP/DOE Reanalysis II, NASA MERRA and NOAA-CIRES Twentieth Century Reanalysis). The two RCMs share the same dynamics from the Environment Canada GEM forecast model, but have two different physics’ packages: CanRCM4 obtains its physics from CCCma’s global atmospheric model (CanAM4), while CRCM5 shares a number of its physics modules with the limited-area version of GEM forecast model. The evaluation is focused on various daily precipitation statistics (maximum number of consecutive wet days, number of moderate and very heavy precipitation events, precipitation frequency distribution) and on the monsoon onset and retreat over the Sahel region. We find that the CRCM5 has a good representation of daily precipitation statistics over the southern Sahel, with spatial distributions close to GPCP dataset. Some differences are observed in the northern part of the Sahel, where the model is characterised by a dry bias. CanRCM4 and the ERA-Interim and MERRA reanalysis products overestimate the number of wet days over Sahel with a shift in the frequency distribution toward smaller daily precipitation amounts than in observations. Both RCMs and reanalyses have difficulties in reproducing the local onset date over the Sahel region. Nevertheless, the large-scale features of the monsoon precipitation evolution over West Africa are well reproduced by the RCMs, whereas the northern limit of the rainy bands is less accurately reproduced. Both RCMs exhibit an overall good representation of the local retreat index over the Sahel region.

A nested multivariate copula approach to hydrometeorological simulations of spring floods: the case of the Richelieu River (Québec, Canada) record flood

Floods have potentially devastating consequences on populations, industries and environmental systems. They often result from a combination of effects from meteorological, physiographic and anthropogenic natures. The analysis of flood hazards under a multivariate perspective is primordial to evaluate several of the combined factors. This study analyzes spring flood-causing mechanisms in terms of the occurrence, frequency, duration and intensity of precipitation as well as temperature events and their combinations previous to and during floods using frequency analysis as well as a proposed multivariate copula approach along with hydrometeorological indices. This research was initiated over the Richelieu River watershed (Quebec, Canada), with a particular emphasis on the 2011 spring flood, constituting one of the most damaging events over the last century for this region. Although some work has already been conducted to determine certain causes of this record flood, the use of multivariate statistical analysis of hydrologic and meteorological events has not yet been explored. This study proposes a multivariate flood risk model based on fully nested Archimedean Frank and Clayton copulas in a hydrometeorological context. Several combinations of the 2011 Richelieu River flood-causing meteorological factors are determined by estimating joint and conditional return periods with the application of the proposed model in a trivariate case. The effects of the frequency of daily frost/thaw episodes in winter, the cumulative total precipitation fallen between the months of November and March and the 90th percentile of rainfall in spring on peak flow and flood duration are quantified, as these combined factors represent relevant drivers of this 2011 Richelieu River record flood. Multiple plausible and physically founded flood-causing scenarios are also analyzed to quantify various risks of inundation.

The effects of interactions between surface forcings in the development of a model-simulated polar low in Hudson Bay

A 30-km version of the Canadian Regional Climate Model is used to simulate a polar low developmentin early December 1988 over the Hudson Bay. This polar low is quantitatively analyzed in detail, in theinitial and mature stages of its development, in order to understand physically howsea surface conditionsinfluence this mesocyclone. This analysis is realized via the description of the effects of differentatmospheric forcings (i.e. thermal and vorticity advection, and turbulent and convective fluxes) on thepolar low development (called the direct effects) using the diagnostic equations of omega and vorticitytendency. Also, the effects of forcing interactions on subsequent cyclone development (called theindirect effects) is analyzed via the diagnostic equations of vorticity and thermal advection tendencies.In the early stage of development, a low-level cyclogenesis appears over the northwestern Hudson Bayessentially due to diabatic forcings in the context of low-level cold air advection. Progressively, thesynergetic effect of time rate of changes in advection terms, resulting from surface diabatic and stressforcings, favours low-level cyclogenesis and baroclinicity over open water near the sea-ice margin, whose shape is determinant for the deepening and tracking of the polar low. In the mature stage, thegrowth in advection terms becomes the main factor of cyclone intensification with the increase inlow-level convection. Forcings are maximum near the surface and differ substantially from the verticalstructure found in classical extratropical cyclones. In the upper troposphere they appear to play asecondary role in this polar low development. Finally, the polar low studied here is primarily the resultof combined forcing interactions near the sea-ice edge, which are responsible for vorticity and thermaladvection changes at low levels. It is also found that the indented sea-ice shape is a favourable factorfor the local surface cyclogenesis due to the formation of local Laplacians of diabatic and thermalforcings.

Evaluation of Linear and Non-Linear Downscaling Methods in Terms of Daily Variability and Climate Indices: Surface Temperature in Southern Ontario and Quebec, Canada

Abstract We downscaled atmospheric reanalysis data using linear regression and Bayesian neural network (BNN) ensembles to obtain daily maximum and minimum temperatures at ten weather stations in southern Quebec and Ontario, Canada. Performance of the linear and non-linear downscaling models was evaluated using four different sets of predictors, not only in terms of their ability to reproduce the magnitude of day-to-day variability (i.e., “weather,” mean absolute error between the daily values of the predictand(s) and the downscaled data) but also in terms of their ability to reproduce longer time scale variability (i.e., “climate,” indices of agreement between the predictands observed annual climate indices and the corresponding downscaled values). The climate indices used were the 90th percentile of the daily maximum temperature, 10th percentile of the daily minimum temperature, number of frost days, heat wave duration, growing season length, and intra-annual temperature range. Our results show that the non-linear models usually outperform their linear counterparts in the magnitude of daily variability and, to a greater extent, in annual climate variability. In particular, the best model simulating weather and climate was a BNN ensemble using stepwise selection from 20 reanalysis predictors, followed by a BNN ensemble using the three leading principal components from the aforementioned predictors. Finally, we showed that, on average, the first three indices presented higher skills than the growing season length, number of frost days, and the heat wave duration.

Pacific Decadal Oscillation Climate Variability and Temporal Pattern of Winter Flows in Northwestern North America

Abstract There is growing concern about the effects of large-scale oceanic atmospheric climate variability, such as the Pacific decadal oscillation (PDO), on regional hydrology and water resources. In this paper, the effects of PDO on temporal patterns of winter (January–March) flow in northwestern North America (NWNA), which is believed to be a PDO-sensitive region, is studied for the period 1943–2007 using daily streamflow data from a much larger set of 179 stations, compared to previous studies in which only smaller subsets of these stations were analyzed. Time series of winter flows were divided into two nonoverlapping blocks corresponding to change points detected in time series of December–March mean monthly PDO indices. Both parametric and nonparametric measures of correlation and average percentage differences and average standardized differences from the period-of-record mean were explored. Like some of the previous studies, it is found that, on average, winter flows tend to be higher (lower) dur...

On the Remapping Procedure of Daily Precipitation Statistics and Indices Used in Regional Climate Model Evaluation

Gridded estimates of precipitation using both satellite and observational station data are regularly used as reference products in the evaluation of basic climate fields and derived indices as simulated by regional climate models (RCMs) over the current period. One of the issues encountered in RCM evaluation is the fact that RCMs and reference fields are usually on different grids and often at different horizontal resolutions. A proper RCM evaluation requires remapping on a common grid. For the climate indices or other derived fields, the remapping can be done in two ways: either as a first-step operation on the original field with the derived index computed on the final/common grid in a second step, or to compute first the climate index on the original grid before remapping or regridding it as a last-step operation on the final/common grid. The purpose of this paper is to illustrate how the two approaches affect the final field, thus contributing to one of the Coordinated Regional Climate Downscaling Experiment (CORDEX) in Africa (CORDEX-Africa) goals of providing a benchmark framework for RCM evaluation over the West Africa monsoon area, using several daily precipitation indices. The results indicate the advantage of using the last-step remapping procedure, regardless of the mathematical method chosen for the remapping, in order to minimize errors in the indices under evaluation.

The 2011 flood event in the Richelieu River basin: Causes, assessment and damages

The 2011 Richelieu River (Québec, Canada) spring flood was unprecedented in terms of destruction of property and negative impacts on agricultural as well as fish habitat within its watershed, costing an estimated CAD