Regional climate modelling

Laprise, René

doi:10.1016/j.jcp.2006.10.024

Cited by 247 publications

(169 citation statements)

References 94 publications

(132 reference statements)

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“…That is, the GCM provides input for the boundaries of the LAM, but the LAM does not return any information to the GCM. LAMs that are used for climate-scale time periods (i.e., a season or longer) are commonly referred to as regional climate models (RCMs; Wang and others, 2004;Laprise, 2008). Because these models cover only a portion of the Earth, they require input at the area boundaries from a GCM for variables such as surface pressure, wind circulation, air temperature and humidity, and sea-surface temperature.…”

Section: Dynamic Downscalingmentioning

confidence: 99%

Downscaled climate projections for the Southeast United States: evaluation and use for ecological applications

Wootten¹,

Smith²,

Boyles³

et al. 2014

Open-File Report

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Section: Dynamic Downscalingmentioning

confidence: 99%

Downscaled climate projections for the Southeast United States: evaluation and use for ecological applications

Wootten¹,

Smith²,

Boyles³

et al. 2014

Open-File Report

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“…The multimodel ensemble approach has been developed to combine simulations from different models using various initial conditions (ensemble members) and therefore take into 1152 A. MAILHOT et al consideration structural as well as uncertainties associated to the natural variability in the climate system Palmer et al, 2005;Kharin et al, 2007;Meehl et al, 2007;Tebaldi and Knutti, 2007;Laprise, 2008). It has been argued that the combination of results from various models leads to more consistent and reliable forecasts by reducing the characteristic biases and uncertainties of any individual model .…”

Section: Introductionmentioning

confidence: 99%

Future changes in intense precipitation over Canada assessed from multi‐model NARCCAP ensemble simulations

Mailhot

Beauregard

Talbot

et al. 2011

Intl Journal of Climatology

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Annual maxima (AM) series of precipitation from 15 simulations of the North American Regional Climate Change Assessment Program (NARCCAP) have been analysed for gridpoints covering Canada and the northern part of United States. The NARCCAP Regional Climate Models' simulations have been classified into the following three groups based on the driving data used at the RCMs boundaries: (1) NCEP (6 simulations); (2) GCM-historical (5 simulations); and (3) GCM-future (4 simulations). Historical simulations are representative of the 1968-2000 period while future simulations cover the 2041-2070 period. A reference common grid has been defined to ease the comparison. Multi-model average intensities of AM precipitation of 6-, 12-, 24-, 72-, and 120-h for 2-, 5-, 10-, and 20-year return periods have been estimated for each simulation group. Comparison of results from NCEP and GCM-historical groups shows good overall agreement in terms of spatial distribution of AM intensities. Comparison of GCM-future and GCM-historical groups clearly shows widespread increases with median relative changes across all gridpoints ranging from 12 to 18% depending on durations and return periods. Fourteen Canadian climatic regions have been used to define regional projections and average regional changes in intense precipitation have been estimated for each duration and return period. Uncertainties on these regional values, resulting from inter-model variability, were also estimated. Results suggest that inland regions (e.g. Ontario and more specifically Southern Ontario, the Prairies, Southern Quebec) will experience the largest relative increases in AM intensities while coastal regions (e.g. Atlantic Provinces and the West Coast) will experience the smallest ones. These projections are most valuable inputs for the assessment of future impact of climate change on water infrastructures and the development of more efficient adaptation strategies.

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“…Lim et al 2011;Amengual et al 2007;Chan et al 2012). This approach is less computationally intensive than the previous one, as the RCMs use as lateral boundary conditions information from a coarser resolution GCM model (Mearns et al 2003;Laprise 2008), but it also has high-computational requirements that makes it impractical to practitioners with access to single workstations; on the other hand, the third technique, known as statistical downscaling, is based on finding statistical relationships between the atmospheric variables from coarse resolution model outputs and the finer-scale variables (e.g. Jarosch et al 2012;Nicholas and Battisti 2012;Cannon and Whitfield 2002).…”

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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Regional climate modelling

Cited by 247 publications

References 94 publications

Downscaled climate projections for the Southeast United States: evaluation and use for ecological applications

Downscaled climate projections for the Southeast United States: evaluation and use for ecological applications

Future changes in intense precipitation over Canada assessed from multi‐model NARCCAP ensemble simulations

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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