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D. Caya and I. Zawadzki

Abstract

A critical review of the velocity-azimuth display (VAD) analysis for the retrieval of wind, divergence, and deformation from single-Doppler observations is presented. It is shown that in situations when the linear wind assumption is not valid the VAD analysis leads to incorrect conclusions. The range and height dependence of single-Doppler data contains information on the nonlinearity of the wind field and allows a generalized analysis by which vertical profiles of wind, divergence, and deformation at the radar site can in principle be obtained. These ideas are illustrated by two case analyses of single-Doppler observations in clear air.

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P. Gagnon, A. N. Rousseau, A. Mailhot, and D. Caya

Abstract

Precipitation has a high spatial variability, and thus some modeling applications require high-resolution data (<10 km). Unfortunately, in some cases, such as meteorological forecasts and future regional climate projections, only spatial averages over large areas are available. While some attention has been given to the disaggregation of mean areal precipitation estimates, the computation of a disaggregated field with a realistic spatial structure remains a difficult task. This paper describes the development of a statistical disaggregation model based on Gibbs sampling. The model disaggregates 45.6-km-resolution rainfall fields to grids with pixel sizes ranging from 3.8 to 22.8 km. The model is conceptually simple, as the algorithm is straightforward to compute with only a few parameters to estimate. The rainfall depth at each grid pixel is related to the depths of the neighboring pixels, while the spatial variability is related to the convective available potential energy (CAPE) field. The model is developed using daily rainfall data over a 40 000-km2 area located in the southeastern United States. Four-kilometer-resolution rainfall estimates obtained from NCEP’s stage IV analysis were used to estimate the model parameters (2002–04) and as a reference to validate the disaggregated fields (2005/06). Results show that the model accurately simulates rainfall depths and the spatial structure of the observed field. Because the model has low computational requirements, an ensemble of disaggregated data series can be generated.

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Z. Long, W. Perrie, J. Gyakum, D. Caya, and R. Laprise

Abstract

It is well known that large lakes can perturb local weather and climate through mesoscale circulations, for example, lake effects on storms and lake breezes, and the impacts on fluxes of heat, moisture, and momentum. However, for both large and small lakes, the importance of atmosphere–lake interactions in northern Canada is largely unknown. Here, the Canadian Regional Climate Model (CRCM) is used to simulate seasonal time scales for the Mackenzie River basin and northwest region of Canada, coupled to simulations of Great Bear and Great Slave Lakes using the Princeton Ocean Model (POM) to examine the interactions between large northern lakes and the atmosphere. The authors consider the lake impacts on the local water and energy cycles and on regional seasonal climate. Verification of model results is achieved with atmospheric sounding and surface flux data collected during the Canadian Global Energy and Water Cycle Experiment (GEWEX) program. The coupled atmosphere–lake model is shown to be able to successfully simulate the variation of surface heat fluxes and surface water temperatures and to give a good representation of the vertical profiles of water temperatures, the warming and cooling processes, and the lake responses to the seasonal and interannual variation of surface heat fluxes. These northern lakes can significantly influence the local water and energy cycles.

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D. A. Plummer, D. Caya, A. Frigon, H. Côté, M. Giguère, D. Paquin, S. Biner, R. Harvey, and R. de Elia

Abstract

An analysis of several multidecadal simulations of the present (1971–90) and future (2041–60) climate from the Canadian Regional Climate Model (CRCM) is presented. The effects on the CRCM climate of model domain size, internal variability of the general circulation model (GCM) used to provide boundary conditions, and modifications to the physical parameterizations used in the CRCM are investigated. The influence of boundary conditions is further investigated by comparing the GCM-driven simulations of the current climate with simulations performed using boundary conditions from meteorological reanalyses. The present climate of the model in these different configurations is assessed by comparing the seasonal averages and interannual variability of precipitation and surface air temperature with an observed climatology. Generally, small differences are found between the two simulations on different domains, though both domains are quite large as compared with previously reported results. Simulations driven by GCM output show a significant warm bias for wintertime surface air temperatures over northern regions. This warm bias is much reduced in the GCM-driven simulation when an updated set of physical parameterizations is used in the CRCM. The warm bias is also reduced for simulations with the standard set of physical parameterizations when the CRCM is driven with reanalysis data. However, use of the modified physics package for reanalysis-driven simulations results in surface air temperatures that are colder than the observations. Summertime precipitation in the model is much larger than observed, a bias that is present in both the GCM-driven and reanalysis-driven simulations. The bias in summertime precipitation is reduced for both types of driving data when the updated set of physical parameterizations is used. Model projections of climate change between the present and future periods are also presented and the sensitivity of these projections to many of the above-mentioned modifications is assessed. Changes in surface air temperature are predicted to be largest over northern regions in winter, with smaller changes over more southerly regions and in the summer season. Changes in seasonal average precipitation are projected to be in the range of ±10% of present-day amounts for most regions and seasons. The CRCM projections of surface air temperature changes are strongly affected by the internal variability of the driving GCM over high northern latitudes and to changes in the physical parameterizations over many regions for the summer season.

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B. Mladjic, L. Sushama, M. N. Khaliq, R. Laprise, D. Caya, and R. Roy

Abstract

Changes to the intensity and frequency of hydroclimatic extremes can have significant impacts on sectors associated with water resources, and therefore it is relevant to assess their vulnerabilities in a changing climate. This study focuses on the assessment of projected changes to selected return levels of 1-, 2-, 3-, 5-, 7- and 10-day annual (April–September) maximum precipitation amounts, over Canada, using an ensemble of five 30-yr integrations each for current reference (1961–90) and future (2040–71) periods performed with the Canadian Regional Climate Model (CRCM); the future simulations correspond to the A2 Special Report on Emissions Scenarios (SRES) scenario. Two methods, the regional frequency analysis (RFA), which operates at the scale of statistically homogenous units of predefined climatic regions, with the possibility of downscaling to gridcell level, and the individual gridbox analysis (GBA), are used in this study, with the time-slice stationarity assumption. Validation of model simulated 20-, 50- and 100-yr return levels of single- and multiday precipitation extremes against those observed for the 1961–90 period using both the RFA and GBA methods suggest an underestimation of extreme events by the CRCM over most of Canada. The CRCM projected changes, realized with the RFA method at regional scale, to selected return levels for the future (2041–70) period, in comparison to the reference (1961–90) period, suggest statistically significant increases in event magnitudes for 7 out of 10 studied climatic regions. Though the results of the RFA and GBA methods at gridcell level suggest positive changes to studied return levels for most parts of Canada, the results corresponding to the 20-yr return period for the two methods agree better, while the agreement abates with increasing return periods, that is, 50 and 100 yr. It is expected that the increase in return levels of short and longer duration precipitation extremes will have severe implications for various water resource–related development and management activities.

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