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

Gaitán, Carlos F.; Hsieh, William W.; Cannon, Alex J.; Gachon, Philippe

doi:10.1080/07055900.2013.857639

Cited by 20 publications

(23 citation statements)

References 61 publications

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“…The techniques used allow the determination of statistical relationships between observed local conditions (i.e., predictand) and atmospheric predictors obtained from climate data for the recent past. Many studies in the literature describe statistical downscaling methods (Boé et al 2007;Charles et al 2004;Chen et al 2011;Cannon 2012;Denault et al 2006;Déqué 2007;Farajzadeh et al 2014;Gaitan et al 2013;Hessami et al 2008;Huth and Kyselý 2000;Jeong et al 2012;Liu et al 2013;Maurer and Hidalgo 2008;Maraun et al 2010;Pagé et al 2009;Piani et al 2010;Samadi et al 2013;Wilby and Wigley 1997;Wilby et al 2004a, b;Willems 2011;Wong et al 2014;Zorita and Von Storch 1999, etc.). Figure 1 shows the different families of statistical downscaling approaches developed in these studies.…”

Section: Statistical Downscalingmentioning

confidence: 97%

Which downscaled rainfall data for climate change impact studies in urban areas? Review of current approaches and trends

Gachon

Vrac

et al. 2015

Theor Appl Climatol

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Changes in extreme precipitation should be one of the primary impacts of climate change (CC) in urban areas. To assess these impacts, rainfall data from climate models are commonly used. The main goal of this paper is to report on the state of knowledge and recent works on the study of CC impacts with a focus on urban areas, in order to produce an integrated review of various approaches to which future studies can then be compared or constructed. Model output statistics (MOS) methods are increasingly used in the literature to study the impacts of CC in urban settings. A review of previous works highlights the non-stationarity nature of future climate data, underscoring the need to revise urban drainage system design criteria. A comparison of these studies is made difficult, however, by the numerous sources of uncertainty arising from a plethora of assumptions, scenarios, and modeling options. All the methods used do, however, predict increased extreme precipitation in the future, suggesting potential risks of combined sewer overflow frequencies, flooding, and back-up in existing sewer systems in urban areas. Future studies must quantify more accurately the different sources of uncertainty by improving downscaling and correction methods. New research is necessary to improve the data validation process, an aspect that is seldom reported in the literature. Finally, the potential application of non-stationarity conditions into generalized extreme value (GEV) distribution should be assessed more closely, which will require close collaboration between engineers, hydrologists, statisticians, and climatologists, thus contributing to the ongoing reflection on this issue of social concern.

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Section: Statistical Downscalingmentioning

confidence: 97%

Which downscaled rainfall data for climate change impact studies in urban areas? Review of current approaches and trends

Gachon

Vrac

et al. 2015

Theor Appl Climatol

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“…However, it is unsuitable for estimating the long-term changes in smaller systems requiring information on a much finer scale. Global simulations cannot provide such information because solving equations in fluid thermodynamics, used for 3D simulation of the atmosphere over time, entails a colossal amount of computing resources (Gaitan et al, 2013). Downscaling methods have been developed to provide local-scale information for a clearly specified region of interest.…”

Section: Introductionmentioning

confidence: 99%

Geomatic downscaling of temperatures in the Mont Blanc massif

Joly

Berger

Buoncristiani

et al. 2017

Intl Journal of Climatology

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This article presents a novel downscaling method based on a geomatic spatial model. This model is constructed on the basis of regressions between topographical (explanatory) variables (digitized here at 200 m resolution) and climatic factors observed at meteorological stations. These regressions are used to calibrate the model in the form of two parameters: the frequency with which explanatory variables are significant at the 95% level, and the mean of the regression coefficient associated with each variable. One of the objectives of the article is to test the relevance of the method and the application made of it in this study bears on daily observed minimum (tn) and maximum (tx) temperatures in the Mont Blanc massif between 1979 and 2014. Estimations show weak statistical biases and the temperature variation from day to day is well represented. The root mean square errors of daily temperatures are of the order of 2 °C for tn and tx alike. The validation shows (1) the value of applying spatial models to two atmospheric configurations (weather regimes and local rainfall conditions) and (2) the limitations of the method. Eventually, this method shall be applied to the downscaling of large‐scale atmospheric parameters provided by earth system models for projecting the temperature of the lower layers of the atmosphere over future decades.

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“…Nevertheless, the reader must be aware of several limitations when downscaling to gridded data (e.g. RCM output and gridded observation-based datasets), including: (1) the gridded predictand represents an area average not point measurements; (2) the variance of a variable averaged over a large area is expected to be smaller than the variance of the same variable at a particular weather station/point, and (3) in the case of precipitation analyses, the wet spells calculated from the gridded data likely last longer than the observed ones (Gaitan Ospina 2013). Furthermore, Chen and Knutson (2008) cautioned the practitioners about using gridded observations as point estimates; similarly when using RCM output as pseudo-observations, one should be aware that since RCMs simulate climate over a specified area of interest, they require nesting information which describes the evolution of the atmospheric circulation at their lateral boundaries (Music and Sykes 2011), and thus are affected by the driving GCM uncertainties.…”

Section: Statistical Downscalingmentioning

confidence: 99%

Effects of variance adjustment techniques and time-invariant transfer functions on heat wave duration indices and other metrics derived from downscaled time-series. Study case: Montreal, Canada

Gaitán

2016

Nat Hazards

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Statistical downscaling techniques are often used to generate finer-scale projections of climate variables affected by local-scale processes not resolved by coarse resolution numerical models like global climate models (GCMs). Statistical downscaling models rely on several assumptions in order to produce finer-/local-scale projections of the variable of interest; one of these assumptions is the time-invariance of the relationships between predictors (e.g. coarse resolution GCM output) and the local-scale predictands (e.g. gridded observation-based time-series or weather station observations). However, in the absence of future observations, statistical downscaling studies use historical data to evaluate their models and assume that these historical simulation skills will be retained in the future. In addition, regression-based downscaling models fail to reproduce the observed variance, and hence their projections need to be adjusted accordingly. Two approaches are usually employed to perform this adjustment: randomization and variance inflation. Here, we study the effect of the stationarity assumption when downscaling daily maximum temperatures and using the downscaled information to estimate historical and future metrics like return periods and heat waves durations over Montreal, Canada; and the effect of the two variance adjustment techniques on the historical and future time-series. To do so, we used regional climate model (RCM) output from the Canadian RCM 4.2, as proxies of historical and future local climates, and daily maximum temperatures obtained from the Canadian GCM 3.1. The results show that the root-mean-squared errors between the pseudo-observations and the statistically downscaled time-series (historical and future) varied over time, with higher errors in the future period; and the effects of randomization and variance inflation on the tails of the statistically downscaled time-series.

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

Cited by 20 publications

References 61 publications

Which downscaled rainfall data for climate change impact studies in urban areas? Review of current approaches and trends

Which downscaled rainfall data for climate change impact studies in urban areas? Review of current approaches and trends

Geomatic downscaling of temperatures in the Mont Blanc massif

Effects of variance adjustment techniques and time-invariant transfer functions on heat wave duration indices and other metrics derived from downscaled time-series. Study case: Montreal, Canada

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