Temperature change signals in northern Canada: convergence of statistical downscaling results using two driving GCMs

Gachon, Philippe; Dibike, Yonas

doi:10.1002/joc.1582

Cited by 45 publications

(36 citation statements)

References 36 publications

(60 reference statements)

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“…Comparisons between the SDSM and other downscaling methods have shown that the SDSM performed well in reproducing observed climate variability (Dibike and Coulibaly, 2005;Diaz-Nieto and Wilby, 2005;Khan et al, 2006;Wetterhall et al, 2006;Gachon and Dibike, 2007;Prudhomme and Davies, 2009). For example, Khan et al (2006) compared three downscaling methods, SDSM, Long Ashton Research Station Weather Generator (LARS-WG) model and an artificial neural network (ANN) for downscaling daily precipitation and maximum and minimum temperatures in a watershed of Canada and found SDSM performed the best.…”

Section: Introductionmentioning

confidence: 95%

“…SDSM has been widely applied in SD studies for both climate variables and air quality variables (Diaz-Nieto and Wilby, 2005;Dibike and Coulibaly, 2005;Khan et al, 2006;Wetterhall et al, 2006;Gachon and Dibike, 2007;Prudhomme and Davies, 2009;Wise, 2009;and others), and has been recommended by the Canadian Climate Impacts and Scenarios (CCIS) project (http://www.cics.uvic.ca).…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of two statistical downscaling models for daily precipitation over an arid basin in China

Liu

Charles

et al. 2011

Intl Journal of Climatology

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Two statistical downscaling (SD) models, the nonhomogeneous hidden Markov model (NHMM) and the statistical down-scaling model (SDSM), which have been widely applied and proved skillful in terms of downscaling precipitation, were evaluated based on observed daily precipitation over the Tarim River basin, an arid basin located in China. The evaluated metrics included residual functions, correlation analyses, probability density functions (PDFs) and distributions. Overall, both models exhibited stability with little model performance difference between the calibration and validation periods. There was little difference for model performance on dry-spell length (dsl) and wet-spell length (wsl) between NHMM and SDSM. NHMM showed skill in simulating wet-day precipitation amount (wpa), whilst SDSM performed relatively poorly on extreme values of wpa, especially for dry stations with annual precipitation lower than 200 mm. Both NHMM and SDSM captured the spatial distribution characteristics of precipitation for most of the stations, as 11 months had an at-station correlation coefficient being greater than 0.9 and 0.8 for NHMM and SDSM in calibration period. NHMM showed slightly better model performance than SDSM on simulating monthly precipitation, as the former was able to model precipitation well for all months, whereas the later was well only for certain months. SDSM was able to capture the inter-site correlation characteristics of observed series, whilst the NHMM multi-site simulation over estimated inter-site correlation. Both NHMM and SDSM had less skill downscaling annual series because of stochastic components in precipitation amounts modelling.

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Section: Introductionmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

Evaluation of two statistical downscaling models for daily precipitation over an arid basin in China

Liu

Charles

et al. 2011

Intl Journal of Climatology

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“…This behaviour could be attributed to the only use of the coarse-scale NCEP/NCAR predictors in this procedure, without taking explicitly into account the regional-scale variables such as surface conditions or diabatic fluxes from the surface needed to capture all range of variability related to the occurrence of temperature extremes. These can come from nonlinear processes and feedbacks linked with frost and thaw conditions of the soil, and/or presence or absence of snow on the ground as indicated by Gachon and Dibike (2007) for northern Canada. As also noted in Hessami et al (2008), for the simulation of T max90p over eastern Quebec in Canada, outliers appear more often for the case of NCEP/NCARdriven conditions than for the GCM ones.…”

Section: Resultsmentioning

confidence: 99%

“…The main limitation of the SD techniques is related to the stationarity assumption of the SD model parameters (Wilby, 1997), which means that the statistical relationships developed for the current climate also hold under the different climatic conditions of future climate. Despite this constraint, SD methods have been commonly used in many different climate change impact studies (Wilby et al, 2002;Gachon and Dibike, 2007;Dibike et al, 2008;Nguyen and Nguyen, 2008).…”

Section: Introductionmentioning

confidence: 99%

A statistical approach to multi‐site multivariate downscaling of daily extreme temperature series

Khalili

Gachon

2011

Intl Journal of Climatology

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Downscaling methods for describing the linkage between global-scale climate variables and local climatic conditions have been frequently used in climate-related impact assessment studies. Previous works, however, have been mainly dealing with downscaling of climatic processes for a single site, but very few studies are concerned with the downscaling of these processes for multi-sites because of the complexity in accurately describing both observed at-site temporal persistence and spatial dependence between different locations. In the present study, a multi-site multivariate statistical downscaling (SD) approach was developed for simulating daily maximum (T max) and minimum (T min) temperature series at many sites concurrently. The proposed approach consists of a combination of a linear regression component to describe the linkage between global climate predictors and local temperature extremes, and a stochastic component based on a spatial moving average process to reproduce the observed spatial dependence between temperature extremes at different sites. The feasibility of the suggested SD method was assessed using observed daily extreme temperature data available at 10 weather stations located in the southwest region of Quebec and the southeast region of Ontario in Canada, as well as climate predictors from the NCEP/NCAR (National Centers for Environmental Prediction/National Centre for Atmospheric Research) reanalysis dataset for the 1961-1990 period. It was found that the proposed SD approach was able to accurately describe various T max and T min characteristics, including their spatial and temporal variation as well as their interannual anomalies. In addition, comparison of the results from the proposed multi-site multivariate SD method and one simulation series from the Canadian Regional Climate Model (CRCM) at a 45-km resolution (a dynamic downscaling procedure) has indicated that the suggested SD approach was able to more accurately describe the observed spatial and temporal characteristics of extreme temperature series at the regional scale than the CRCM-based dynamic downscaling method.

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“…An investigation using various SD methods to predict temperatures in central Europe indicated that Multiple Linear Regression (MLR) using one circulation-and one temperature-related variable produced the best predictions (Huth, 2002). Other studies have shown that SD is capable of capturing past low frequency climate variability (Dibike et al, 2008;Gachon & Dibike, 2007). The predictions of SD are, however, sensitive to the choice of reanalysis dataset used to train the regression.…”

Section: Introductionmentioning

confidence: 99%

Improving Statistical Downscaling of General Circulation Models

Titus¹,

Jiang

Greatbatch

et al. 2013

Atmosphere-Ocean

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We present a new method for the statistical downscaling of coarse-resolution General Circulation Model (GCM) fields to predict local climate change. Most atmospheric variables have strong seasonal cycles. We show that the prediction of the non-seasonal variability of maximum and minimum daily surface temperature is improved if the seasonal cycle is removed prior to the statistical analysis. The new method consists of three major steps. First, the average seasonal cycles of both predictands and predictors are removed. Second, a principal component-based multiple linear regression model between the deseasonalized predictands and predictors is developed and validated. Finally, the regression is used to make projections of future changes in maximum and minimum daily surface temperature at Shearwater, Nova Scotia. This projection is made using the local gridscale variables of the Canadian General Circulation Model Version 3 (CGCM3) climate model as predictors. Our statistical downscaling method indicates significant skill in predicting the observed distribution of temperature using GCM predictors. Projections suggest minimum and maximum temperatures at Shearwater will be up to about five degrees warmer by 2100 under the current "business-as-usual" scenario.RÉSUMÉ [Traduit par la rédaction] Nous présentons une nouvelle méthode pour la réduction d'échelle statistique des champs des modèles de circulation générale (MCG) à faible résolution pour prévoir les changements du climat local. La plupart des variables atmosphériques ont des cycles saisonniers bien marqués. Nous démontrons que la prédiction de la variabilité non saisonnière de la température de surface quotidienne minimum et maximum est meilleure si on retranche le cycle saisonnier avant de procéder à l'analyse statistique. Voici les trois grandes étapes de cette nouvelle méthode. D'abord, nous retirons les cycles saisonniers moyens des prédictants et des prédicteurs. Ensuite, nous concevons et validons un modèle de régression linéaire multiple sur composantes principales entre les prédictants et les prédicteurs désaisonnalisés. Enfin, nous nous servons de la régression afin d'établir des projections pour les changements à venir dans la température de surface quotidienne minimum et maximum à Shearwater en Nouvelle-Écosse. Cette projection est établie au moyen des variables locales à l'échelle du maillage de la troisième version du modèle canadien de circulation générale (MCCG3). Notre méthode de réduction d'échelle statistique se révèle très efficace pour prédire la répartition observée de la température au moyen des prédicteurs du MCG. D'après les projections, les températures minimum et maximum à Shearwater connaîtront une augmentation d'environ cinq degrés d'ici 2100 dans le scénario actuel de type « statu quo ».

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Temperature change signals in northern Canada: convergence of statistical downscaling results using two driving GCMs

Cited by 45 publications

References 36 publications

Evaluation of two statistical downscaling models for daily precipitation over an arid basin in China

Evaluation of two statistical downscaling models for daily precipitation over an arid basin in China

A statistical approach to multi‐site multivariate downscaling of daily extreme temperature series

Improving Statistical Downscaling of General Circulation Models

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