Downscaling the hydrological cycle in the Mackenzie basin with the Canadian regional climate model

Abstract

The Canadian Regional Climate Model (CRCM) has been nested within the Canadian Centre for Climate Modelling and Analysis ‘ second generation General Circulation Model (GCM), for a single month simulation over the Mackenzie River Basin and environs. The purpose of the study is to assess the ability of the higher resolution CRCM to downscale the hydrological cycle of the nesting GCM. A second 1‐month experiment, in which the CRCM was nested within analyzed fields of a global data assimilation system, was also performed to examine the sensitivity of the basin moisture budget to atmospheric lateral boundary forcing.

We have found that the CRCM can produce realistic lee cyclogenesis, preferentially in the Liard sub‐basin, along with associated circulation and precipitation patterns, as well as an improved rainshadow in the lee of the Rocky Mountains compared to the GCM. While these features do quantitatively affect the monthly average climate statistics, the basin scale moisture budgets of the models were remarkably similar, though some of this agreement is due to compensating errors in the GCM. Both models produced excessive precipitation compared to a recent climatology for the region, the cause of which is traced to lateral boundary forcing. A second experiment, identical to the first except that the CRCM was forced with analyzed fields at the lateral boundaries, produced a qualitatively different basin moisture budget, including a much more realistic precipitation field. Errors in the moisture budget of the first experiment appear to be associated with the poor representation of the Aleutian Low in the GCM, and do not appear to be strongly connected to (local) surface processes within the models. This suggests that an effective strategy for modelling the hydrological cycle of the Mackenzie Basin on the fast climate timescale ‐ a major requirement of the Mackenzie GEWEX Study ‐ will involve nesting the CRCM within analyzed (or re‐analyzed) atmospheric fields.     

Present climate and climate change over North America as simulated by the fifth-generation Canadian regional climate model

The fifth-generation Canadian Regional Climate Model (CRCM5) was used to dynamically downscale two Coupled Global Climate Model (CGCM) simulations of the transient climate change for the period 1950–2100, over North America, following the CORDEX protocol. The CRCM5 was driven by data from the CanESM2 and MPI-ESM-LR CGCM simulations, based on the historical (1850–2005) and future (2006–2100) RCP4.5 radiative forcing scenario. The results show that the CRCM5 simulations reproduce relatively well the current-climate North American regional climatic features, such as the temperature and precipitation multiannual means, annual cycles and temporal variability at daily scale. A cold bias was noted during the winter season over western and southern portions of the continent. CRCM5-simulated precipitation accumulations at daily temporal scale are much more realistic when compared with its driving CGCM simulations, especially in summer when small-scale driven convective precipitation has a large contribution over land. The CRCM5 climate projections imply a general warming over the continent in the 21st century, especially over the northern regions in winter. The winter warming is mostly contributed by the lower percentiles of daily temperatures, implying a reduction in the frequency and intensity of cold waves. A precipitation decrease is projected over Central America and an increase over the rest of the continent. For the average precipitation change in summer however there is little consensus between the simulations. Some of these differences can be attributed to the uncertainties in CGCM-projected changes in the position and strength of the Pacific Ocean subtropical high pressure.     

Climate projections over CORDEX Africa domain using the fifth-generation Canadian Regional Climate Model (CRCM5)

Following the CORDEX experimental protocol, climate simulations and climate-change projections for Africa were made with the new fifth-generation Canadian Regional Climate Model (CRCM5). The model was driven by two Global Climate Models (GCMs), one developed by the Max-Planck-Institut für Meteorologie and the other by the Canadian Centre for Climate Modelling and Analysis, for the period 1950–2100 under the RCP4.5 emission scenario. The performance of the CRCM5 simulations for current climate is discussed first and compared also with a reanalysis-driven CRCM5 simulation. It is shown that errors in lateral boundary conditions and sea-surface temperature from the GCMs have deleterious consequences on the skill of the CRCM5 at reproducing specific regional climate features such as the West African Monsoon and the annual cycle of precipitation. For other aspects of the African climate however the regional model is able to add value compared to the simulations of the driving GCMs. Climate-change projections for periods until the end of this century are also analysed. All models project a warming throughout the twenty-first century, although the details of the climate changes differ notably between model projections, especially for precipitation changes. It is shown that the climate changes projected by CRCM5 often differ noticeably from those of the driving GCMs.     

Evaluation of Total Precipitable Water from CRCM4 using the NVAP-MEaSUREs Dataset and ERA-Interim Reanalysis Data

The fourth-generation Canadian Regional Climate Model’s (CRCM4) precipitable water is evaluated and compared with observational data and ERA-Interim reanalysis data over five Canadian basins with simulations driven by ERA-Interim (two) and global climate models (two). Considering the 22 years of data available in the observations, we analyze precipitable water’s behaviour through its annual cycle, its daily distribution, and its annual daily maxima. For the simulations driven by reanalyses, differences in annual daily maximum values and their correlations with observations are examined. In general, the values for precipitable water simulated by CRCM4 are similar to those observed, and the model reproduces both the interannual and inter-basin variabilities. The simulation at 15 km resolution produces higher extreme values than simulations performed at 45 km resolution and higher than the observations taken at coarser resolution (1°), without much influence on the mean behaviour. Some underestimation is found with the simulation driven by the Canadian Centre for Climate Modelling and Analysis Model, version 3, a sign of a cold and dry bias, whereas the run driven by the European Centre Hamburg Model, version 5, is much closer to the observations, pointing to the importance of closely considering the regional–global model combination. Overall, CRCM4's ability to reproduce the major characteristics of observed precipitable water makes it a possible tool for providing precipitable water data that could serve as a basis for probable maximum precipitation and probable maximum flood studies at the basin scale.     

Application of the Canadian regional climate model to the Laurentian great lakes region: Implementation of a lake model

Abstract

This study reports on the implementation of an interactive mixed‐layer/thermodynamic‐ice lake model coupled with the Canadian Regional Climate Model (CRCM). For this application the CRCM, which uses a grid mesh of 45 km on a polar stereographic projection, 10 vertical levels, and a timestep of 15 min, is nested with the second generation Canadian General Circulation Model (GCM) simulated output. A numerical simulation of the climate of eastern North America, including the Laurentian Great Lakes, is then performed in order to evaluate the coupled model. The lakes are represented by a “mixed layer” model to simulate the evolution of the surface water temperature, and a thermodynamic ice model to simulate evolution of the ice cover. The mixed‐layer depth is allowed to vary spatially. Lake‐ice leads are parametrized as a function of ice thickness based on observations. Results from a 5‐year integration show that the coupled CRCM/lake model is capable of simulating the seasonal evolution of surface temperature and ice cover in the Great Lakes. When compared with lake climatology, the simulated mean surface water temperature agrees within 0.12°C on average. The seasonal evolution of the lake‐ice cover is realistic but the model tends to underestimate the monthly mean ice concentration on average. The simulated winter lake‐induced precipitation is also shown, and snow accumulation patterns on downwind shores of the lakes are found to be realistic when compared with observations.     

Evaluation of uncertainties in the CRCM-simulated North American climate

This work is a first step in the analysis of uncertainty sources in the RCM-simulated climate over North America. Three main sets of sensitivity studies were carried out: the first estimates the magnitude of internal variability, which is needed to evaluate the significance of changes in the simulated climate induced by any model modification. The second is devoted to the role of CRCM configuration as a source of uncertainty, in particular the sensitivity to nesting technique, domain size, and driving reanalysis. The third study aims to assess the relative importance of the previously estimated sensitivities by performing two additional sensitivity experiments: one, in which the reanalysis driving data is replaced by data generated by the second generation Coupled Global Climate Model (CGCM2), and another, in which a different CRCM version is used. Results show that the internal variability, triggered by differences in initial conditions, is much smaller than the sensitivity to any other source. Results also show that levels of uncertainty originating from liberty of choices in the definition of configuration parameters are comparable among themselves and are smaller than those due to the choice of CGCM or CRCM version used. These results suggest that uncertainty originated by the CRCM configuration latitude (freedom of choice among domain sizes, nesting techniques and reanalysis dataset), although important, does not seem to be a major obstacle to climate downscaling. Finally, with the aim of evaluating the combined effect of the different uncertainties, the ensemble spread is estimated for a subset of the analysed simulations. Results show that downscaled surface temperature is in general more uncertain in the northern regions, while precipitation is more uncertain in the central and eastern US.     

Projected Changes in Surface Air Temperature and Surface Wind in the Gulf of St. Lawrence

Abstract

The impacts of climate change on surface air temperature (SAT) and winds in the Gulf of St. Lawrence (GSL) are investigated by performing simulations from 1970 to 2099 with the Canadian Regional Climate Model (CRCM), driven by a five-member ensemble. Three members are from Canadian Global Climate Model (CGCM3) simulations following scenario A1B from the Intergovernmental Panel on Climate Change (IPCC); one member is from the Community Climate System Model, version 3 (CCSM3) simulation, also following the A1B scenario; and one member is from the CCSM4 (version 4) simulation following the Representative Concentration Pathway (RCP8.5) scenario. Compared with North America Regional Reanalysis (NARR) data, it is shown that CRCM can reproduce the observed SAT spatial patterns; for example, both CRCM simulations and NARR data show a warm SAT tongue along the eastern Gulf; CRCM simulations also capture the dominant northwesterly winds in January and the southwesterly winds in July. In terms of future climate scenarios, the spatial patterns of SAT show plausible seasonal variations. In January, the warming is 3°–3.5°C in the northern Gulf and 2.5°–3°C near Cabot Strait during 2040–2069, whereas the warming is more uniform during 2070–2099, with SAT increases of 4°–5°C. In summer, the warming gradually decreases from the western side of the GSL to the eastern side because of the different heat capacities between land and water. Moreover, the January winds increase by 0.2–0.4?m?s^?1 during 2040–2069, related to weakening stability in the atmospheric planetary boundary layer. However, during 2070–2099, the winds decrease by 0.2–0.4?m?s^?1 over the western Gulf, reflecting the northeastward shift in northwest Atlantic storm tracks. In July, enhanced baroclinicity along the east coast of North America dominates the wind changes, with increases of 0.2–0.4?m?s^?1. On average, the variance for the SAT changes is about 10% of the SAT increase, and the variance for projected wind changes is the same magnitude as the projected changes, suggesting uncertainty in the latter.     

Searching for Added Value in Simulating Climate Extremes with a High-Resolution Regional Climate Model over Western Canada. II: Basin-Scale Results

We evaluate the capacity of a regional climate model to simulate the statistics of extreme events, and also examine the effect of differing horizontal resolution, at the scale of individual hydrological basins in the topographically complex province of British Columbia, Canada. Two climate simulations of western Canada (WCan) were conducted with the Canadian Regional Climate Model (version 4) at 15 (CRCM15) and 45?km (CRCM45) horizontal resolution driven at the lateral boundaries by global reanalysis over the period 1973–1995. The simulations were evaluated with ANUSPLIN, a daily observational gridded surface temperature and precipitation product and with meteorological data recorded at 28 stations within the upper Peace, Nechako, and upper Columbia River basins. In this work, we focus largely on a comparison of the skill of each model configuration in simulating the 90th percentile of daily precipitation (PR90). The companion paper describes the results for a wider range of temperature and precipitation extremes over the entire WCan domain.

Over all three watersheds, both simulations exhibit cold biases compared with observations, with the bias exacerbated at higher resolution. Although both simulations generally display wet biases in median precipitation, CRCM15 features a reduced bias in PR90 in all three basins in summer and throughout the year in the upper Columbia River basin. However, the higher resolution model is inferior to CRCM45 with respect to rarer heavy precipitation events and also displays high spatial variability and lower spatial correlations with ANUSPLIN compared with the coarser resolution model. A reduction in the range of PR90 biases over the upper Columbia basin is noted when the 15?km results are averaged to the 45?km grid. This improvement is partly attributable to the averaging of errors between different elevation data used in the gridded observations and CRCM, but the sensitivity of CRCM15 to resolved topography is also clear from spatial maps of seasonal extremes. At the station scale, modest but systematic reductions in the bias of PR90 relative to ANUSPLIN are again found when the CRCM15 results are averaged to the 45?km grid. Furthermore, the annual cycle of inter-station spatial variance in the upper Columbia River basin is well reproduced by CRCM15 but not by ANUSPLIN or CRCM45. The former result highlights the beneficial effect of spatial averaging of small-scale climate variability, whereas the latter is evidently a demonstration of the added value at high resolution vis-à-vis the improved simulation of precipitation at the resolution limit of the model.     

Searching for Added Value in Simulating Climate Extremes with a High-Resolution Regional Climate Model over Western Canada

We evaluate the capacity of a regional climate model to represent observed extreme temperature and precipitation events and also examine the impact of increased resolution, in an effort to identify added value in this respect. Two climate simulations of western Canada (WCan) were conducted with the Canadian Regional Climate Model (version 4) at 15 (CRCM15) and 45?km (CRCM45) horizontal resolution driven at the lateral boundaries by data from the European Centre for Medium-range Weather Forecasts (ECMWF) 40-year Reanalysis (ERA-40) for the period 1973–1995. The simulations were evaluated using the spline-interpolated dataset ANUSPLIN, a daily observational gridded surface temperature and precipitation product with a nominal resolution of approximately 10?km. We examine a range of climate extremes, comprising the 10th and 90th percentiles of daily maximum (TX) and minimum (TN) temperatures, the 90th percentile of daily precipitation (PR90), and the 27 core Climate Daily Extremes (CLIMDEX) indices.

Both simulations exhibit cold biases compared with observations over WCan, with the bias exacerbated at higher resolution, suggesting little added value for temperature overall. There are instances, however, of regional improvement in the spatial pattern of temperature extremes at the higher resolution of CRCM15 (e.g., the CLIMDEX index for the annual number of days when TX?>?25°C). The high-resolution simulations also reveal similarly localized features in precipitation (e.g., rain shadows) that are not resolved at the 45?km resolution. With regard to precipitation extremes, although both simulations generally display wet biases, CRCM15 features a reduced bias in PR90 in all seasons except winter. This improvement occurs despite the fact that spatial and interannual variability of PR90 in CRCM15 is significantly overestimated relative to both CRCM45 and ANUSPLIN. We posit that these characteristics are the result of demonstrable differences between corresponding topographical datasets used in the gridded observations and CRCM, the resulting errors propagated to physical variables tied to elevation and the beneficial effect of subsequent spatial averaging. Because topographical input is often discordant between simulations and gridded observations, it is argued that a limited form of spatial averaging may contribute added value beyond that which has already been noted in previous studies with respect to small-scale climate variability.     

Reanalysis-driven climate simulation over CORDEX North America domain using the Canadian Regional Climate Model, version 5: model performance evaluation

The performance of reanalysis-driven Canadian Regional Climate Model, version 5 (CRCM5) in reproducing the present climate over the North American COordinated Regional climate Downscaling EXperiment domain for the 1989–2008 period has been assessed in comparison with several observation-based datasets. The model reproduces satisfactorily the near-surface temperature and precipitation characteristics over most part of North America. Coastal and mountainous zones remain problematic: a cold bias (2–6 °C) prevails over Rocky Mountains in summertime and all year-round over Mexico; winter precipitation in mountainous coastal regions is overestimated. The precipitation patterns related to the North American Monsoon are well reproduced, except on its northern limit. The spatial and temporal structure of the Great Plains Low-Level Jet is well reproduced by the model; however, the night-time precipitation maximum in the jet area is underestimated. The performance of CRCM5 was assessed against earlier CRCM versions and other RCMs. CRCM5 is shown to have been substantially improved compared to CRCM3 and CRCM4 in terms of seasonal mean statistics, and to be comparable to other modern RCMs.     

Surface‐absorbed and top‐of‐atmosphere radiation fluxes for the Mackenzie River basin from satellite observations and a regional climate model and an evaluation of the model

Abstract

Both the earth‐reflected shortwave and outgoing longwave radiation (OLR) fluxes at the top of the atmosphere (TOA) as well as surface‐absorbed solar fluxes from Canadian Regional Climate Model (CRCM) simulations of the Mackenzie River Basin for the period March 2000 to September 2003 are compared with the radiation fluxes deduced from satellite observations. The differences between the model and satellite solar fluxes at the TOA and at the surface, which are used in this paper to evaluate the CRCM performance, have opposite biases under clear skies and overcast conditions, suggesting that the surface albedo is underestimated while cloud albedo is overestimated. The slightly larger differences between the model and satellite fluxes at the surface compared to those at the TOA indicate the existence of a small positive atmospheric absorption bias in the model. The persistent overestimation of TOA reflected solar fluxes and underestimation of the surface‐absorbed solar fluxes by the CRCM under all sky conditions are consistent with the overestimation of cloud fraction by the CRCM. This results in a larger shortwave cloud radiative forcing (CRF) both at the TOA and at the surface in the CRCM simulation. The OLR from the CRCM agrees well with the satellite observations except for persistent negative biases during the winter months under all sky conditions. Under clear skies, the OLR is slightly underestimated by the CRCM during the winter months and overestimated in the other months. Under overcast conditions the OLR is underestimated by the CRCM, suggesting an underestimation of cloud‐top temperature by the CRCM. There is an improvement in differences between model and satellite fluxes compared to previously reported results largely because of changes to the treatment of the surface in the model.     

Sensitivity of the CRCM atmospheric and the Gulf of St. Lawrence Ocean‐Ice models to each other

Abstract

The sensitivity of the Canadian Regional Climate Model (CRCM), developed at the Université du Québec à Montréal, and the Gulf of St. Lawrence Ocean Model (GOM), developed at the Institut Maurice‐ Lamontagne, to each other is tested with an ensemble of simulations over eastern Canada from 1 November 1989 to 31 March 1990. The goal of this study is to investigate the interaction of the CRCM and GOM with respect to each other's forcing fields. In the first part of the experiment, a series of simulations were performed using an iterative strategy, where both models run separately and alternately, using variables from the other model to supply the needed forcing fields for the computation of surface fluxes. The runs are iterated several times over the same period from the output of the previous run to allow the atmosphere and the ocean to interact several times with each other and to study the evolution of the solutions from one iteration to the next. In the second part of the experiment, a two‐way coupled simulation is performed over the same period. The results indicate that on a monthly or longer timescale, the CRCM is not very sensitive to the details of the oceanic fields from GOM, except locally over the Gulf of St. Lawrence (GSL). However, GOM is quite sensitive to the differences in atmospheric fields from the CRCM. The results of several iterations converge to a unique solution, suggesting that the CRCM and GOM reach equilibrium with respect to each other's forcing fields. Furthermore, the results of the coupled run also converge to this same solution.     

Comparison of statistically downscaled precipitation in terms of future climate indices and daily variability for southern Ontario and Quebec,Canada

Given the coarse resolution of global climate models, downscaling techniques are often needed to generate finer scale projections of variables affected by local-scale processes such as precipitation. However, classical statistical downscaling experiments for future climate rely on the time-invariance assumption as one cannot know the true change in the variable of interest, nor validate the models with data not yet observed. Our experimental setup involves using the Canadian regional climate model (CRCM) outputs as pseudo-observations to estimate model performance in the context of future climate projections by replacing historical and future observations with model simulations from the CRCM, nested within the domain of the Canadian global climate model (CGCM). In particular, we evaluated statistically downscaled daily precipitation time series in terms of the Peirce skill score, mean absolute errors, and climate indices. Specifically, we used a variety of linear and nonlinear methods such as artificial neural networks (ANN), decision trees and ensembles, multiple linear regression, and k-nearest neighbors to generate present and future daily precipitation occurrences and amounts. We obtained the predictors from the CGCM 3.1 20C3M (1971–2000) and A2 (2041–2070) simulations, and precipitation outputs from the CRCM 4.2 (forced with the CGCM 3.1 boundary conditions) as predictands. Overall, ANN models and tree ensembles outscored the linear models and simple nonlinear models in terms of precipitation occurrences, without performance deteriorating in future climate. In contrast, for the precipitation amounts and related climate indices, the performance of downscaling models deteriorated in future climate.     

Temperature Extremes from Canadian Regional Climate Model (CRCM) Climate Change Projections

Future climate projections of extreme events can help forewarn society of high-impact events and allow the development of better adaptation strategies. In this study a non-stationary model for Generalized Extreme Value (GEV) distributions is used to analyze the trend in extreme temperatures in the context of a changing climate and compare it with the trend in average temperatures.

The analysis is performed using the climate projections of the Canadian Regional Climate Model (CRCM), under an IPCC SRES A2 greenhouse gas emissions scenario, over North America. Annual extremes in daily minimum and maximum temperatures are analyzed. Significant positive trends for the location parameter of the GEV distribution are found, indicating an expected increase in extreme temperature values. The scale parameter of the GEV distribution, on the other hand, reveals a decrease in the variability of temperature extremes in some continental regions. Trends in the annual minimum and maximum temperatures are compared with trends in average winter and summer temperatures, respectively. In some regions, extreme temperatures exhibit a significantly larger increase than the seasonal average temperatures.

The CRCM projections are compared with those of its driving model and framed in the context of the Coupled Model Intercomparison Project, phase 3 (CMIP3) Global Climate Model projections. This enables us to establish the CRCM position within the CMIP3 climate projection uncertainty range. The CRCM is validated against the HadEX2 dataset in order to assess the CRCM representation of temperature extremes in the present climate. The validation is also framed in the context of CMIP3 validation results. The CRCM cold extremes validate better and are closer to the driving model and CMIP3 projections than the hot extremes.     

A copula-based multivariate analysis of Canadian RCM projected changes to flood characteristics for northeastern Canada

In the present work, climate change impacts on three spring (March–June) flood characteristics, i.e. peak, volume and duration, for 21 northeast Canadian basins are evaluated, based on Canadian regional climate model (CRCM) simulations. Conventional univariate frequency analysis for each flood characteristic and copula based bivariate frequency analysis for mutually correlated pairs of flood characteristics (i.e. peak–volume, peak–duration and volume–duration) are carried out. While univariate analysis is focused on return levels of selected return periods (5-, 20- and 50-year), the bivariate analysis is focused on the joint occurrence probabilities P1 and P2 of the three pairs of flood characteristics, where P1 is the probability of any one characteristic in a pair exceeding its threshold and P2 is the probability of both characteristics in a pair exceeding their respective thresholds at the same time. The performance of CRCM is assessed by comparing ERA40 (the European Centre for Medium-Range Weather Forecasts 40-year reanalysis) driven CRCM simulated flood statistics and univariate and bivariate frequency analysis results for the current 1970–1999 period with those observed at selected 16 gauging stations for the same time period. The Generalized Extreme Value distribution is selected as the marginal distribution for flood characteristics and the Clayton copula for developing bivariate distribution functions. The CRCM performs well in simulating mean, standard deviation, and 5-, 20- and 50-year return levels of flood characteristics. The joint occurrence probabilities are also simulated well by the CRCM. A five-member ensemble of the CRCM simulated streamflow for the current (1970–1999) and future (2041–2070) periods, driven by five different members of a Canadian Global Climate Model ensemble, are used in the assessment of projected changes, where future simulations correspond to A2 scenario. The results of projected changes, in general, indicate increases in the marginal values, i.e. return levels of flood characteristics, and the joint occurrence probabilities P1 and P2. It is found that the future marginal values of flood characteristics and P1 and P2 values corresponding to longer return periods will be affected more by anthropogenic climate change than those corresponding to shorter return periods but the former ones are subjected to higher uncertainties.     

A regional climate model downscaling projection of China future climate change

Climate changes over China from the present (1990–1999) to future (2046–2055) under the A1FI (fossil fuel intensive) and A1B (balanced) emission scenarios are projected using the Regional Climate Model version 3 (RegCM3) nests with the National Center for Atmospheric Research (NCAR) Community Climate System Model (CCSM). For the present climate, RegCM3 downscaling corrects several major deficiencies in the driving CCSM, especially the wet and cold biases over the Sichuan Basin. As compared with CCSM, RegCM3 produces systematic higher spatial pattern correlation coefficients with observations for precipitation and surface air temperature except during winter. The projected future precipitation changes differ largely between CCSM and RegCM3, with strong regional and seasonal dependence. The RegCM3 downscaling produces larger regional precipitation trends (both decreases and increases) than the driving CCSM. Contrast to substantial trend differences projected by CCSM, RegCM3 produces similar precipitation spatial patterns under different scenarios except autumn. Surface air temperature is projected to consistently increase by both CCSM and RegCM3, with greater warming under A1FI than A1B. The result demonstrates that different scenarios can induce large uncertainties even with the same RCM-GCM nesting system. Largest temperature increases are projected in the Tibetan Plateau during winter and high-latitude areas in the northern China during summer under both scenarios. This indicates that high elevation and northern regions are more vulnerable to climate change. Notable discrepancies for precipitation and surface air temperature simulated by RegCM3 with the driving conditions of CCSM versus the model for interdisciplinary research on climate under the same A1B scenario further complicated the uncertainty issue. The geographic distributions for precipitation difference among various simulations are very similar between the present and future climate with very high spatial pattern correlation coefficients. The result suggests that the model present climate biases are systematically propagate into the future climate projections. The impacts of the model present biases on projected future trends are, however, highly nonlinear and regional specific, and thus cannot be simply removed by a linear method. A model with more realistic present climate simulations is anticipated to yield future climate projections with higher credibility.     

Analysis of streamflow characteristics over Northeastern Canada in a changing climate

An analysis of streamflow characteristics (i.e. mean annual and seasonal flows and extreme high and low flows) in current and future climates for 21 watersheds of north-east Canada covering mainly the province of Quebec is presented in this article. For the analysis, streamflows are derived from a 10-member ensemble of Canadian Regional Climate Model (CRCM) simulations, driven by the Canadian Global Climate Model simulations, of which five correspond to current 1970–1999 period, while the other five correspond to future 2041–2070 period. For developing projected changes of streamflow characteristics from current to future periods, two different approaches are used: one based on the concept of ensemble averaging while the other approach is based on merged samples of current and similarly future simulations following multiple comparison tests. Verification of the CRCM simulated streamflow characteristics for the 1970–1999 period suggests that the model simulated mean hydrographs and high flow characteristics compare well with those observed, while the model tends to underestimate low flow extremes. Results of projected changes to mean annual streamflow suggest statistically significant increases nearly all over the study domain, while those for seasonal streamflow show increases/decreases depending on the season. Two- and 5-year return levels of 15-day low flows are projected to increase significantly over most part of the study domain, though the changes are small in absolute terms. Based on the ensemble averaging approach, changes to 10- and 30-year return levels of high flows are not generally found significant. However, when a similar analysis is performed using longer samples, significant increases to high flow return levels are found mainly for northernmost watersheds. This study highlights the need for longer samples, particularly for extreme events in the development of robust projections.     

The Canadian boreal snow water equivalent band

Abstract

Satellite and conventional snow water equivalent (SWE) dataseis reveal a well‐defined zone of high winter season SWE (>100 mm) that extends across the northern boreal forest of Canada. SWE coefficient of variation (CV) patterns derived from a monthly averaged (1978–2002) passive microwave derived time series show a high degree of interannual variability across open prairie, southern boreal, and open tundra regions of North America while SWE across the northern boreal forest was highly invariant. The potential existence of a consistent SWE zone resistant to interannual climatic variability over the past 25 years is intriguing in the context of the sensitivity of snow cover to climate variability and change. A ground sampling campaign conceived specifically to evaluate SWE distribution across the northern boreal forest was conducted in northern Manitoba during the 2003–04 winter season. Data from this survey confirmed the SWE gradient across the boreal forest, although satellite‐derived retrievals for the tundra were consistently low.

A series of Canadian Regional Climate Model (CRCM) simulations were conducted to identify feedbacks between the atmosphere and land surface for a domain focused on the northern boreal forest. A control simulation produced monthly patterns of SWE distribution that closely matched the passive microwave retrievals. Water budget computations showed the SWE accumulation pattern to be a function of the modelled regional precipitation pattern, and not the result of surface processes such as melt or evaporation/sublimation. Mean monthly patterns of 850‐hPa fronto genesis forcing corresponded closely to the patterns of accumulated SWE suggesting that lower tropospheric frontal activity was responsible for the snowfall events that led directly to the deposition of the northern boreal SWE band. CRCM sensitivity experiments were conducted with perturbed land cover and terrain. Only subtle differences in SWE accumulation and frontogenesis patterns relative to the control run were found when complete grassland cover was prescribed, though removing orography greatly enhanced the magnitude and zonal extent of the SWE band.     

Internal variability of RCM simulations over an annual cycle

Three one-year simulations generated with the Canadian RCM (CRCM) are compared to each other in order to study internal variability in nested regional climate models and to evaluate the influence exerted by the lateral boundaries information supplied by the nesting procedure. All simulations are generated over a large domain and cover an annual cycle. The simulations use different combinations of surface and atmospheric initial conditions but all of them share the same set of time-dependent lateral boundary conditions taken from a simulation by the Canadian GCM. A first simulation is used as control, the second simulation is launched with different atmospheric and surface initial conditions (IC) and the third simulation is launched taking its surface IC from the control simulation. Comparison of the root-mean-square differences (RMSD) between each pair of simulations shows two distinct seasonal behaviours in the time series of the RMSD. In winter all simulations are almost identical to each other resulting in very low RMSD values while in summer large discrepancies develop between pairs of simulations. For water vapour related fields such as precipitation or specific humidity, these discrepancies are sometimes as large as the monthly averaged variability. However, analysis of the climate statistics shows that, although the evolution of the various summer weather systems is different, the climates of each simulation are similar.     

Current and perturbed climate as simulated by the second-generation Canadian Regional Climate Model (CRCM-II) over northwestern North America

An updated version of the Canadian Regional Climate Model (CRCM-II) has been used to perform time-slice simulations over northwestern North America, nested in the coupled Canadian General Circulation Model (CGCM2). Both driving and driven models are integrated in a scenario of transient greenhouse gases (GHG) and aerosols. The time slices span three decades that were chosen to correspond roughly to single, double and triple current GHG concentration levels. Several enhancements have been implemented in CRCM-II since the CRCM-I climate-change simulations reported upon earlier. The larger computational domain, extending further to the west, north and south, allows for a better spin-up of weather systems as they enter the regional domain. The increased length of the simulations, from 5 to 10 years, strengthens the statistical robustness of the results. The improvements to the physical parameterisation, notably the moist convection scheme and the diagnostic cloud formulation, cure the excessive cloud cover problem present in CRCM-I, reduce the warm surface bias and prevent the occurrence of grid-point precipitation storms that occurred with CRCM-I in summer. The dynamical ocean and sea-ice components of CGCM2 that is used to provide atmospheric lateral and surface boundary conditions to CRCM-II, as well as the use of transient rather than equilibrium conditions of GHG and the inclusion of direct aerosols forcing, in both CGCM2 and CRCM-II, increase the realism of the CRCM-II climate-change simulation.