Uncertainty analysis of statistically downscaled temperature and precipitation regimes in Northern Canada

Dibike, Yonas; Gachon, Philippe; St‐Hilaire, André; Ouarda, Taha B. M. J.; Nguyen, Van Kien

doi:10.1007/s00704-007-0299-z

Cited by 101 publications

(73 citation statements)

References 23 publications

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“…To improve models, process studies must occur in study basins, but then techniques learned in these efforts must propagate into the models. Local downscaling is an essential component of hydrologic projections, but (3) a better quantitative evaluation of the uncertainty of these downscaled products is needed, based on emerging statistical techniques and ensemble simulations (Barsugli et al, 2013;Chen et al, 2012Chen et al, , 2014Dibike et al, 2008).…”

Section: Estimating and Reducing Uncertaintymentioning

confidence: 99%

Planning for climate change impacts on hydropower in the Far North

Cherry

Knapp

Trainor

et al. 2017

Hydrol. Earth Syst. Sci.

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Abstract. Unlike much of the contiguous United States, new hydropower development continues in the Far North, where climate models project precipitation will likely increase over the next century. Regional complexities in the Arctic and subArctic, such as glacier recession and permafrost thaw, however, introduce uncertainties about the hydrologic responses to climate change that impact water resource management. This work reviews hydroclimate changes in the Far North and their impacts on hydropower; it provides a template for application of current techniques for prediction and estimating uncertainty, and it describes best practices for integrating science into management and decision-making. The growing number of studies on hydrologic impacts suggests that information resulting from climate change science has matured enough that it can and should be integrated into hydropower scoping, design, and management. Continuing to ignore the best available information in lieu of status quo planning is likely to prove costly to society in the long term.

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Section: Estimating and Reducing Uncertaintymentioning

confidence: 99%

Planning for climate change impacts on hydropower in the Far North

Cherry

Knapp

Trainor

et al. 2017

Hydrol. Earth Syst. Sci.

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“…Table II shows an example of the selection of the five most significant predictors identified by the backward stepwise regression for the computation of T max predictand at St Alban station. The use of only five most significant predictors derives from need to describe adequately the local climate while achieving the parsimony of the downscaling model as well as to prevent the possible excessive colinearity between predictors when too many variables are selected (Gachon et al, 2005;Dibike et al, 2008;Hessami et al, 2008). Notice that the two predictors, near-surface specific humidity, and mean temperature at 2 m, have been omitted in this study to avoid the issue that the observed temperature data were assimilated within these two NCEP/NCAR predictors.…”

Section: Predictors Selectionmentioning

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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“…Cheng 2012), policy-makers and government agencies interested in providing local-scale, relevant information to their stakeholders (e.g. Barrow et al 2004;Fuhrer et al 2006;Eum et al 2011;Dibike et al 2007;Khalili et al 2013).…”

Section: Introductionmentioning

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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Uncertainty analysis of statistically downscaled temperature and precipitation regimes in Northern Canada

Cited by 101 publications

References 23 publications

Planning for climate change impacts on hydropower in the Far North

Planning for climate change impacts on hydropower in the Far North

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

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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