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Yanjun Jiao and Daniel Caya

Abstract

In the present paper, a 5-yr baseline integration for the period 1987–91 was carried out over a Pan-Canadian domain to validate the performance of the third-generation Canadian Regional Climate Model (CRCM). The CRCM simulated the large-scale circulation over North America well; it also correctly captured the seasonal variability of surface temperature and reproduced the winter precipitation over North America realistically. However, the CRCM systematically overestimated the summer precipitation over the continent when compared with the observed values.

Extensive experiments have been conducted to trace down the sources of error of summer precipitation. Particular attention has been given to the water-vapor-related physical parameterization processes such as the mass flux convection scheme in the CRCM. Experiments involving spectral nudging of the specific humidity toward the values of large-scale driving data enabled the authors to link overestimation with abundant water vapor accumulated in the lower boundary layer resulting from an excessive amount of moisture stored in the soil. A strong boundary layer mixing process from the third generation of the Canadian Atmospheric General Circulation Model was then implemented into the CRCM along with an adjustment to the soil water holding capacity. A final analysis of a 14-month experiment showed that these modifications significantly improved the simulation of summer precipitation over North America without adversely affecting the simulation of winter precipitation.

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Daniel Caya and René Laprise

Abstract

A new regional climate model (RCM) is presented in this paper and its performance is investigated through a pair of 60-day simulations. This new model is based on the dynamical formulation of the Cooperative Centre for Research in Mesometeorology (CCRM) mesoscale nonhydrostatic community model and on the complete subgrid-scale physical parameterization package of the second-generation Canadian Centre for Climate modeling and analysis General Circulation Model (CCCma GCMII). The main feature of the Canadian RCM (CRCM) comes from the very efficient semi-implicit and semi-Lagrangian (SISL) numerical scheme used for the integration of the fully elastic nonhydrostatic Euler equations. The efficiency of the SISL scheme allows the use of longer time steps (at least by a factor of 5) for the integration of this model (e.g., the 45-km resolution version of the model uses a 15-min time step). A complete description of the numerical formulation of the model is presented with a review of the principal characteristics of the physical package. A pair of two-month-long winter simulations is also analyzed to investigate the behavior of the model and to evaluate the potential of the SISL integration scheme in the context of regional climate simulation. The two integrations, produced with a 45-km resolution version of the model, developed realistic small-scale details from the low-resolution GCMII fields used to initialize and drive the RCM.

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Biljana Music and Daniel Caya

Abstract

This study investigates the sensitivity of components of the hydrological cycle simulated by the Canadian Regional Climate Model (CRCM) to lateral boundary forcing, the complexity of the land surface scheme (LSS), and the internal variability arising from different models’ initial conditions. This evaluation is a contribution to the estimation of the uncertainty associated to regional climate model (RCM) simulations. The analysis was carried out over the period 1961–99 for three North American watersheds, and it looked at climatological seasonal means, mean (climatological) annual cycles, and interanual variability. The three watersheds—the Mississippi, the St. Lawrence, and the Mackenzie River basins—were selected to cover a large range of climate conditions. An evaluation of simulated water budget components with available observations was also included in the analysis.

Results indicated that the response of climatological means and annual cycles of water budget components to land surface parameterizations and lateral boundary conditions varied from basin to basin. Sensitivity to lateral boundary conditions is, in general, smaller than sensitivity to LSS and tends to be stronger for the northern basins (Mackenzie and St. Lawrence). Interannual variability was unaffected by changes in LSS and in driving data. Internal variability triggered by different initial conditions and the nonlinear nature of the climate model did not significantly affect either the 39-yr climatology, the climatological annual cycles, or the interannual variability. A comparison with observations suggests that although the simple Manabe-based LSS may be adequate for simulations of climatological means, skillful simulation of annual cycles require the use of a state-of-the-art LSS.

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Biljana Music and Daniel Caya

Abstract

The water cycle over a given region is governed by many complex multiscale interactions and feedbacks, and their representation in climate models can vary in complexity. To understand which of the key processes require better representation, evaluation and validation of all components of the simulated water cycle are required. Adequate assessing of the simulated hydrological cycle over a given region is not trivial because observations for various water cycle components are seldom available at the regional scale.

In this paper, a comprehensive validation method of the water budget components over a river basin is presented. In addition, the sensitivity of the hydrological cycle in the Canadian Regional Climate Model (CRCM) to a more realistic representation of the land surface processes, as well as radiation, cloud cover, and atmospheric boundary layer mixing is investigated. The changes to the physical parameterizations are assessed by evaluating the CRCM hydrological cycle over the Mississippi River basin. The first part of the evaluation looks at the basin annual means. The second part consists of the analysis and validation of the annual cycle of all water budget components. Finally, the third part is directed toward the spatial distribution of the annual mean precipitation, evapotranspiration, and runoff.

Results indicate a strong response of the CRCM evapotranspiration and precipitation biases to the physical parameterization changes. Noticeable improvement was obtained in the simulated annual cycles of precipitation, evapotranspiration, moisture flux convergence, and terrestrial water storage tendency when more sophisticated physical parameterizations were used. Some improvements are also observed for the simulated spatial distribution of precipitation and evapotranspiration. The simulated runoff is less sensitive to changes in the CRCM physical parameterizations.

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Philippe Lucas-Picher, Daniel Caya, Sébastien Biner, and René Laprise

Abstract

The present work introduces a new and useful tool to quantify the lateral boundary forcing of a regional climate model (RCM). This tool, an aging tracer, computes the time the air parcels spend inside the limited-area domain of an RCM. The aging tracers are initialized to zero when the air parcels enter the domain and grow older during their migrations through the domain with each time step in the integration of the model. This technique was employed in a 10-member ensemble of 10-yr (1980–89) simulations with the Canadian RCM on a large domain covering North America. The residency time is treated and archived as the other simulated meteorological variables, therefore allowing computation of its climate diagnostics. These diagnostics show that the domain-averaged residency time is shorter in winter than in summer as a result of the faster winter atmospheric circulation. The residency time decreases with increasing height above the surface because of the faster atmospheric circulation at high levels dominated by the jet stream. Within the domain, the residency time increases from west to east according to the transportation of the aging tracer with the westerly general atmospheric circulation. A linear relation is found between the spatial distribution of the internal variability—computed with the variance between the ensemble members—and residency time. This relation indicates that the residency time can be used as a quantitative indicator to estimate the level of control exerted by the lateral boundary conditions on the RCM simulations.

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Jean-Luc Martel, Alain Mailhot, François Brissette, and Daniel Caya

Abstract

Climate change will impact both mean and extreme precipitation, having potentially significant consequences on water resources. The implementation of efficient adaptation measures must rely on the development of reliable projections of future precipitation and on the assessment of their related uncertainty. Natural climate variability is a key uncertainty component, which can result in apparent decadal trends that may be greater or lower than the long-term underlying anthropogenic climate change trend. The goal of the present study is to assess how natural climate variability affects the ability to detect the climate change signal for mean and extreme precipitation. Annual and seasonal total precipitation are used as indicators of the mean, whereas annual and seasonal maximum daily precipitation are used as indicators of extremes. This is done using the CanESM2 50-member and CESM1 40-member large ensembles of simulations over the 1950–2100 period. At the local scale, results indicate that natural climate variability will dominate the uncertainty for annual and seasonal extreme precipitation going up to the end of the century in many parts of the world. The climate change signal can, however, be reliably detected much earlier at the regional scale for extreme precipitation. In the case of annual and seasonal total precipitation, the climate change signal can be reliably detected at the local scale without resorting to a regional analysis. Nonetheless, natural climate variability can impede the detection of the anthropogenic climate change signal until the middle to late century in many parts of the world for mean and extreme precipitation.

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Marco Braun, Daniel Caya, Anne Frigon, and Michel Slivitzky

Abstract

The effect of a regional climate model’s (RCM’s) internal variability (IV) on climate statistics of annual series of hydrological variables is investigated at the scale of 21 eastern Canada watersheds in Quebec and Labrador. The analysis is carried out on 30-yr pairs of simulations (twins), performed with the Canadian Regional Climate Model (CRCM) for present (reanalysis and global climate model driven) and future (global climate model driven) climates. The twins differ only by the starting date of the regional simulation—a standard procedure used to trigger internal variability in RCMs. Two different domain sizes are considered: one comparable to domains used for RCM simulations over Europe and the other comparable to domains used for North America. Results for the larger North American domain indicate that mean relative differences between twin pairs of 30-yr climates reach ±5% when spectral nudging is used. Larger differences are found for extreme annual events, reaching about ±10% for 10% and 90% quantiles (Q10 and Q90). IV is smaller by about one order of magnitude in the smaller domain. Internal variability is unaffected by the period (past versus future climate) and by the type of driving data (reanalysis versus global climate model simulation) but shows a dependence on watershed size. When spectral nudging is deactivated in the large domain, the relative difference between pairs of 30-yr climate means almost doubles and approaches the magnitude of a global climate model’s internal variability. This IV at the level of the natural climate variability has a profound impact on the interpretation, analysis, and validation of RCM simulations over large domains.

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Juan Alberto Velázquez, Magali Troin, Daniel Caya, and François Brissette

Abstract

The bias correction of climate model outputs is based on the main assumption of the time invariance of the bias, in which the statistical relationship between observations and climate model outputs in the historical period stays constant in the future period. The present study aims to assess statistical bias correction under nonstationary bias conditions and its implications on the simulated streamflow over two snowmelt-driven Canadian catchments. A pseudoreality approach is employed in order to derive a proxy of future observations. In this approach, CRCM–ECHAM5 ensemble simulations are used as pseudoreality observations to correct for bias in the CRCM–CGCM3 ensemble simulations in the reference (1971–2000) period. The climate model simulations are then bias corrected in the future (2041–70) period and compared with the future pseudoreality observations. This process demonstrates that biases (precipitation and temperature) remain after the bias correction. In a second step, the uncorrected and bias-corrected CRCM–CGCM3 simulations are used to drive the Soil and Water Assessment Tool (SWAT) hydrological model in both periods. The bias correction decreases the error on mean monthly streamflow over the reference period; such findings are more mixed over the future period. The results of various hydrological indicators show that the climate change signal on streamflow obtained with uncorrected and bias-corrected simulations is similar in both its magnitude and its direction for the mean monthly streamflow only. Regarding the indicators of extreme hydrological events, more mixed results are found with site dependence. All in all, bias correction under nonstationary bias is an additional source of uncertainty that cannot be neglected in hydrological climate change impact studies.

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René Laprise, Mundakkara Ravi Varma, Bertrand Denis, Daniel Caya, and Isztar Zawadzki

Abstract

This note investigates the nature of the extended predictability commonly attributed to high-resolution limited-area models (LAM) nested with low-resolution data at their lateral boundaries. LAM simulations are performed with two different sets of initial, nesting, and verification data: one is a set of regional objective analyses and the other is a synthetic high-resolution model-generated dataset. The simulation differences (equivalent to forecast errors in an operational framework) are studied in terms of their horizontal scale distribution normalized by the natural variability in each scale, as a measure of predictability, which constitutes an original contribution of this note. The results suggest that the extended predictability in LAM is confined to those scales that are present both in the initial condition and lateral boundary conditions (LBCs). No evidence is found for extended predictability of scales that are not forced through the LBCs. Instead, these smaller scales exhibit predictive timescales in direct relation to their spatial scales, similar to the behavior in autonomous global models.

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Linda O. Mearns, Melissa S. Bukovsky, Ruby Leung, Yun Qian, Ray Arritt, William Gutowski, Eugene S. Takle, Sébastien Biner, Daniel Caya, James Correia Jr., Richard Jones, Lisa Sloan, and Mark Snyder
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