Annual Progress Report provided by the Canadian Regional ...€¦ · Annual Progress Report...

Annual Progress Report provided by theCanadian Regional Climate Modelling Network

to CFCAS for the period of July 1 2005 – June 30 2006August 11, 2006

1. Progress..................................................................................................................................................... 1THEME 1. Diagnostic and budget studies.................................................................................................. 1THEME 2. Optimisation of dynamical downscaling ................................................................................ 12THEME 3. Development and improvements in the CRCM...................................................................... 17

2. Impact ..................................................................................................................................................... 263. Level of support ...................................................................................................................................... 274. Dissemination.......................................................................................................................................... 275. Training................................................................................................................................................... 33

1. ProgressThis section presents the activities of the Canadian Regional Climate Modelling (CRCM) Network for theperiod from 1 July 2005 to 30 June 2006, according to the definition given in the detailed proposal presentedto CFCAS on September 9 2003, entitled « The Canadian Regional Climate Modelling Network », for the 3-year period, 2003-2006, and in revised form on February 4, 2005.To summarise, the current Network includes 15 Co-Is, 17 Graduate Students, 1 Postdoctoral Fellow and 8Research Associates, and the Network programme is distributed in 13 Research Projects.The overall research goals of the Network remain under three themes: Theme 1 – Diagnostics and budgetstudies, Theme 2 – Dynamical downscaling approaches, and Theme 3 – Development of the CRCM system.The scientific progress report by subprojects is given hereinafter; it describes the progress made by each ofthe subprojects towards achieving their objectives and milestones.

THEME 1. Diagnostic and budget studies

Sub-project 4.1.1 "Scale-selective diagnostic budget studies"; Laprise, Boer and Caya;RA, Soline Bielli, UQAM; PhD student, Leticia Hernandez-Diaz, UQAM.

2005 – 2006 : • Generalize the scale-selective regional diagnostic approach to variables such as energy,momentum or enstrophy;

• Complete regional-scale Lorenz energy cycle budgets.This subproject on diagnostic and budget studies aims at a better undestanding of the added value of RCMover lower resolution objective analyses or GCM used to drive the high-resolution simulations. As a keyfactor in the energetics of earth’s climate, the atmospheric water budget is used to apply the scaledecomposition method developed by Bielli and Laprise (2006). At first, a spectral Fourier analysis isperformed individually on each term of the vertically integrated atmospheric water budget, i.e. divergence ofthe vertically integrated moisture flux, vertically integrated water vapour tendency, evapotranspiration rateand precipitation rate. In order to study the regional implication of scale interactions, each term isdecomposed spectrally into three spatial scales: the first spectral band represents the very large scales that arenot resolved by the RCM, the second includes the scales that are resolved by the RCM and its global large-

Progress Report – CRCM Network – 2005-2006 2 / 34

scale driving data, and the third band accounts for the small scales that are only resolved by the RCM. Toisolate the contribution of different spatial scales, spectral decomposition is applied on each term on pressurelevels. The vertically integrated moisture flux is then calculated during the integration of the model for eachfiltered simulated atmospheric field, at every time step of the CRCM and cumulated as time integralsbetween 6-hour archival periods.Before diagnosing long climate simulation, the scale-decomposed methodology was tested on a singlesimulated winter (Bielli and Laprise 2006). Afterwards, the method has been exploited to investigate theatmospheric water budget for different seasons through analysis of 25 years of simulation by the CanadianRegional Climate Model, for the period 1975-99 over North America. Seasonal and monthly analysis ofvariances of the vertically integrated moisture flux divergence and its large-scale and small-scale parts werecalculated and analyzed (Figs. 4.1.1.1 and 4.1.1.2).

Fig. 4.1.1.1: Intra-seasonal climatological standard deviation of the large-scale (left panel) and small-scale (right panel) part of thedivergence of the vertically integrated moisture flux for the winter season (December, January and February) from 1975 to 1999simulated by the CRCM.

Fig. 4.1.1.2: Same as Fig. 4.1.1.1 but for the summer season (June, July and August).Theses figures show the intra-seasonal climatological variability of the large-scale part and the small scale-part of the vertically integrated moisture flux divergence for winter (Fig. 4.1.1.1) and summer (Fig. 4.1.1.2)seasons over the 25 years of simulation. The small-scale part represents the added value of the CRCM. Thestructures are quite different in winter from those in summer. Moreover, the added value in winter is as largeas the variability of the large-scale part while the small scales dominate the variability over the continent insummer (Fig. 4.1.1.2). A paper focusing on winter and summer seasons will be submitted to ClimateDynamics shortly.


At the same time, Leticia Hernandez-Diaz, PhD student, is working on a paper describing the formalism ofenergy conversions at the regional scale. The formulation is cast for a domain limited horizontally andvertically, on pressure levels, and includes the presence of topography through a Boer’s mask. Three forms ofenergy emerge from the analysis: kinetic, enthalpy and potential gravitational energy corresponding to themass in the domain. Only kinetic energy is separated in zonal means and deviation from the mean. Contraryto the Lorenz approach used extensively elsewhere, the formulation does not include an arbitrary basic stateused to separate thermodynamic energy into available and unavailable portions. This is exact formally and isapplicable to the regional scale.

The figure above shows the energy cycle for a domain limited horizontally and vertically, in pressurecoordinates respecting topography. The boxes represent the different types of energy: enthalpy, potentialgravitational and kinetic. The arrows linking the boxes represent the conversion terms or energy transfers,whereas those leaving or entering the boxes represent the energy fluxes through the domain boundaries,dissipation of energy due to friction and the diabatic generation of energy. The vertical integration isrepresented by the symbol |p. The square brackets identify the zonal mean, which is in this case an averagealong the X axis of the polar-stereographic grid, and the oblique brackets identify the average throughout thedomain or global average.The computer programming of these energy balance equations is well under way and will soon be applied tothe specific case of African easterly waves.

Sub-project 4.1.2 "A decadal-scale Canadian experiment"; Caya and Laprise;PhD student, Biljana Music, UQAM; RA, Zav Kothavala, UQAM.

2005 – 2006 : • Complete the validation of CRCM water and energy budget over other Canadiancatchment areas with available observations.

Climate models require the fluxes of radiation, momentum, sensible and latent heat across the soil-vegetation-atmosphere interface. These fluxes are provided by land-surface parameterization schemes. Theappropriate level of complexity of land-surface schemes for use in climate models is still an unresolved issue.Simple Manabe-based parameterizations of land surface processes (e.g. the ‘bucket’ representation of soil),as well as very complex formulations (e.g. CLASS [1]), where exchange between the atmosphere and the land

1 Verseghy, D.L., 1991: CLASS – A Canadian land surface scheme for GCMs. Part I: Soil model. Int. J. Climatol. 11, 111–133.Verseghy, D.L., et al., 1993: CLASS – A Canadian land surface scheme for GCMs. Part II: Vegetation model and coupledruns. Int. J. Climatol. 3,347–370.


surface is controlled by plant physiology, continue to coexist. Numerous investigations performed withdifferent climate models have shown that the simulated climate is sensitive to the formulation of land-surfaceprocesses.In her doctoral project, Biljana Music investigates the sensitivity of hydrological cycle simulated by twoCRCM versions to the formulation of land-surface processes. Specifically, CRCM_3.7 and CRCM_4.0 differonly in the land surface parameterization scheme. The CRCM_3.7 uses the ‘bucket’ representation of soil,while the CRCM_4.0 is coupled to CLASS. The sensitivity study is carried out through a comparison of timeand spatial averages of the water budget components, over the period 1961-99, for six river basins, calculatedfrom two CRCM simulations. The studied river basins, Mississippi, Nelson, Churchill, Fraser, Mackenzieand Yukon, cover the major climate regions of North America and differ in size and topography.Then, in order to evaluate the CRCM ability to simulate all water cycle components at the scale of a largeriver basin, an integrative approach linking both the atmospheric and terrestrial branches of the hydrologicalcycle (Music and Caya 2006) is applied over the Mississippi River Basin (Fig. 4.1.2.1).

Fig. 4.1.2.1. Annual mean of the water budget components in mm/day. W (kg/m2) represents the storage of atmospheric water(precipitable water), (M+S) (kg/m2) is the storage of water soil moisture (M) and the accumulated snowpack (S), E (kg/m2) is theevapotranspiration, P (kg/m2) is the precipitation, C (kg/m2) is the water vapor flux convergence, and finally R (kg/m2) is the totalrunoff.

The results show that the simulated hydrological cycle at the scale of the Mississipi Basin is more realisticwhen CLASS is used. The simulated hydrological cycle is sensitive to the choice of the land-surface scheme;


size, location and topography differences of the studied river basins are responsible for their differenthydrological responses to the change of land-surface scheme. Finally, the implementation of CLASS inCRCM_4.0 causes an increase of annual mean precipitation for Mississippi, Nelson and Churchill, which isconsistent with the increase of annual-mean evapotranspiration (Mississippi) and annual-mean convergenceand runoff (Nelson and Churchill). In contrast, Mackenzie, Fraser and Yukon basins indicate a decrease ofthe annual-mean precipitation that is related to a decrease of the annual-mean evapotranspiration (Mackenzie,Fraser and Yukon) and water vapour convergence (Fraser).In the meantime, Dr. Zav Kothavala has been evaluating the overall performance of the GEM Limited-AreaModel (LAM) compared to the newest version of the CRCM as a step towards migrating to the GEM-LAMmodel as the next model for regional climate simulations at the CRCM Network. The CRCM_4.1.1 includingthe land-surface scheme CLASS 2.7, is the newest operational version of the Climate Simulations Team ofOuranos which is used to produce high-resolution climate-change simulations. This model inter-comparisonis being conducted under the auspices of the Inter-comparison Transferability Study (ICTS). Eight regionalclimate modelling groups from Europe and North America, are participating in this GEWEX sub-project. Dr.Kothavala has been performing a series of diagnostics to evaluate the performance of these RCMs in sevenregions of the globe (Fig. 4.1.2.2), with a special attention over North-America domain (Fig. 4.1.2.3).

Fig. 4.1.2.2. Illustration of the Inter-Continental Scale Experiments (ICTS) domains.

Fig. 4.1.2.3 ICTS North-America domain used by CRCM(121x121 grid points). Red crosses indicate theCoordinated Enhanced Observation Period(CEOP) stations. The two used sites are locatedin the right corner: Fort Peck (48.31N-105.1W)and Old Black Spruce forest (53.98N-105.11W) in southern Saskatchewan.

Initial results show that the newest version of the CRCM with the CLASS land-surface scheme and the GEMmodel with the ISBA land-surface parameterization produce simulations close to the observations over theNorth American continent (Fig. 4.1.2.4).

Fig. 4.1.2.4. Comparison of the frequency of observed daily precipitation to that simulated by the MRCC and GEM models. FortPeck (top panel). BERMS Old Black Spruce Forest (lower panel).

The figure above shows the comparison of daily precipitation simulated by the CRCM (i.e. MRCC) andGEM models compared to the field observations for two sites: Fort Peck and Old Black Spruce forest. Boththese points fall near the centre of the model domains (see Figure 4.1.2.3) and are not likely to be affected bythe boundary conditions in the sponge zone. The observed data were collected hourly, from July 1 toSeptember 30 2001, at both sites as part of GEWEX [Global Energy and Water Cycle Experiment] GAPP,and the MAGS Boreal Ecosystem Research and Monitoring Sites (BERMS) for Fort Peck and the Old BlackSpruce forest. The total daily precipitation amounts are placed in ten bins. At the Fort Peck site, both modelsshow the same number of days with zero precipitation. Since it is not practical to measure precipitationbelow 0.2 millimeters, the observations at both sites show no amounts in these bins. For both sites, theCRCM model shows a greater frequency of daily precipitation falling as drizzle compared to the observationsand the GEM model. The GEM model is closer to the observations at the BERMS site.This type of analysis reveals the differences in the convective schemes of the two models. Dr Kothavala ispreparing a publication to summarise this model intercomparison shortly.

Sub-project 4.1.3 " Regional-Scale Seasonal Prediction Project "; Brunet, Jones, Laprise, Caya and Zwiers;MSc student, Etienne Tourigny, UQAM.

2005 – 2006 : • Evaluate the added value of GEM-VR against lower, uniform-resolution configurationGEM.

In this project, MSc student Etienne Tourigny is assessing the feasibility of using a high-resolution RCM tosimulate seasonal climate anomalies, over central and South America, associated with ENSO (El Niño –Southern Oscillation) SST variability. Two RCMs are being used in this assessment: the Rossby Centre


Regional Atmospheric Model Version 3 (RCA3) and the limited-area version of GEM (GEM-LAM). TheRCMs have been configured to encompass Central America and the Eastern tropical Pacific (see Figure4.1.3.1) and integrated using ECMWF Reanalysis data as lateral boundary conditions and observed SeaSurface Temperatures (SSTs).

Fig. 4.1.3.1 Illustration of the domain. The topography contours are every 500 m.

The RCMs have been run for the period February to November for each of the years 1970 to 2002. In thismanner a variety of El Niño and La Niña events are prescribed through either the SST field or the analysedlateral boundary conditions. Our analysis of these results will concentrate on 3 questions:1. Given nominally the correct SSTs and external large-scale forcing, how accurately do the respectiveRCMs simulate the regional climatology over the Central and South American regions?2. How accurately do the RCM physical parameterisation packages respond to anomalous SST forcing in theEast tropical Pacific associated with ENSO variability? Furthermore, are the RCMs capable of simulatingseasonal climatic anomalies over Central and South America under the influence of ENSO SST variability?3. Does increased RCM resolution allow for improved depiction of regional climate anomalies over Centraland South America?Performing these integrations in hindcast mode, with analysed and observed SSTs is a prerequisite to doingthe same type of analysis with GCM predicted boundary conditions.To address the questions listed above, RCA3 and GEM have been integrated with 0.33° grid mesh and thesimulated seasonal climatologies evaluated against observations. Composite El Niño and La Niña seasonalmeans have been developed from observations, reanalyses and RCM simulation results and therepresentation of seasonal timescale, climatic anomalies over Central America from the respective RCMs isbeing assessed.


Fig. 4.1.3.2 Precipitation difference (%) by season.

Figure 4.1.3.2 shows the observed and RCA3-simulated composite mean precipitation anomalies (El Niño –La Niña), normalised by the respective climatological values (e.g. model or observed climatology) andexpressed as a percentage difference. Results are presented for the summer season (JAS) preceding the peakEl Nino event (defined as December with the maximum SST anomaly in the East Pacific) and 3 months after(AMJ +1) the peak El Niño occurrence. The top panel shows percent precipitation anomalies from the GPCPsatellite data set [2] and the lower panel from the land-only CRU data set [3]. The central panels show theequivalent RCA3 results. The large increase in precipitation during El Niño years over the Central and eastPacific is relatively well captured by RCA3, as are remote increases in precipitation over the tropical Atlanticand Amazon regions. Central America is observed to be drier in El Niño years, with a normalized-percentagereduction in precipitation relative to La Niña years of ~50%. This reduction is also simulated in RCA3 but isoverestimated, being closer to a 100% reduction. The reasons for this signal amplification are currently underinvestigation.A small RCA3 model domain has also been configured with 0.15° grid mesh, centred on Central America,and will use results from RCA3 at 0.33° as lateral boundary conditions to make equivalent high-resolutionintegrations over Central America. This will aid in determining the benefits of increased resolution indownscaling seasonal climatic anomalies forced by ENSO variability. These runs are presently in progress.

2 Global Precipitation Climatology Project (GPCP) http://www.gewex.org/gpcpdata.htm3 Mitchell T.D., and P.D. Jones 2005: An improved method of constructing a database of monthly climate observations and associated high-resolution grids. Int. J. Climatol. DOI: 10.1002/joc.1181.


Sub-project 4.1.4 " Atmospheric Models Intercomparison Project (AMIP-II – SGMIP)"; Zadra, Côté,Jones, Laprise and Caya;RA, Katja Winger and Dr Zav Kothavala, UQAM, MSc student, Jonathan Mainville, andInternship Student, Marc Verville, UQAM.

2005 – 2006 : • Continue experimentation with a variable-resolution version of GEM in SGMIP mode, i.e.multi-annual climate simulations made with analysed sea surface temperatures and sea ice.

The Global Environmental Multiscale (GEM) model has been used operationally by the CanadianMeteorological Centre to produce short- and medium-range weather forecasts; more recently, the GEMmodel began to be applied for seasonal forecasts. The GEM model is routinely run in climate mode as avalidation tool, differences between the model's climate and that derived from recent re-analysis experimentsprovide useful indications of GEM's strengths and deficiencies. The model physics configuration closelyresembles that of the new high-resolution operational global model to be implemented shortly.Most of the year has been spent configuring the new climate version of GEM and using it to complete thesecond phase of the international Streched-Grid Model Intercomparison Project (SGMIP2). Two sets of three26-year high-resolution climate simulations have been completed and their monthly data has been sent to theUniversity of Maryland to be compared with other SGMIP2 results. The first set of integrations used aclimatological annual cycle of sea surface temperatures and sea-ice fractions, while the second set was forcedby historically varying monthly mean sea-surface conditions as prescribed by the Atmospheric ModelIntercomparison Project 2 (AMIP2) experimental protocol. Two of these six GEM SGMIP simulations wereconfigured with a 0.5° high-resolution core area over North America and two with a 0.5° core area overEurope. The last two simulations contributed to the SGMIP2 effort are the corresponding 1° global uniform-resolution controls. All of these simulations have roughly the same large number of degrees of freedom in thehorizontal domain and the same vertical setup. The model’s set of physical parameterizations is the same inall simulations. The Limited-Area Model (LAM) version of GEM is also being used to extend locally theSGMIP2 experiment. Two 26-year GEM-LAM 0.5° horizontal grid mesh simulations are nearingcompletion, their domains reproducing the North-American and European high-resolution core areas of theGEM SGMIP2 simulations for the same time period. The lateral boundary conditions for these GEM-LAMexperiments are provided by another 1.5° global uniform-resolution GEM simulation, again retaining thesame set of physical parameterizations.We are continuing the comparison of the regional climate simulations produced with two limited-areamodels, GEM-LAM and the operational version of the CRCM of the Ouranos consortium (CRCM 4.1) and a45-year GEM-LAM simulation driven by lateral boundary conditions from the ERA40 re-analysis data willbe started shortly. The domain will include the North-American continent and the boundary conditions willbe provided by reanalyses. Several CRCM runs driven by both reanalyses and CGCM2 atmospheric datahave been generated with various versions of the CRCM. These simulations will serve as a referencedatabase for comparison with GEM-LAM simulations using similar boundary conditions. This projectrepresents one of the steps in the development of a new system for regional climate scenarios, incollaboration with the Climate Simulations Team of the Ouranos Consortium and with the UQAM CentreESCER.During the first half of 2006, Dr. Kothavala has collaborated closely with Dr. Bernard Dugas at RPN/MSC todetermine the scalability of the GEM model to different resolutions. With the assistance of Dr. Dugas andothers at RPN, Dr. Kothavala conducted a global simulation of the GEM model with AMIP II specificationsat a horizontal resolution of 1.5°. The output from this simulation was used as driving data for a multi-decadal simulation (1978-present) of GEM-LAM over Europe and North America. The results of thesesimulations are currently being compared to a similar run with the CRCM model.In the meantime, MSc student Jonathan Mainville is evaluating the representation of tropical variabilityassociated with ENSO SST forcing in the AMIP II integrations made with GEM for the period 1979-2004.


Interannual variability associated with ENSO cycles explains a large percentage of the forced atmosphericvariability over North America. This variability is a result of anomalous planetary-wave forcing in theatmosphere, associated with anomalous tropical deep-convective activity in response to varying SST forcingwithin the ENSO cycle. The AMIP II integrations utilised prescribed time-varying SSTs and included withinthe integration period a number of large-amplitude El Niño and La Niña occurrences. The aim of this projectis to evaluate the response of the GEM atmosphere to this varying SST forcing associated with the ENSOphases encompassed by the integration period. A prerequisite for GEM to successfully simulate ENSO forcedvariability over North America is that the response of tropical deep convection to varying SST forcing isaccurate. GEM has been integrated at a variety of model resolutions for the period listed above, using theAMIP II protocol. A suite of observations needed to evaluate simulated tropical variability have also beencollated and archived. Initial work has concentrated on developing suitable diagnostic techniques forcomparing the GEM simulations with observations. These diagnostic techniques are now being applied to theGEM-simulated results.

Sub-project 4.1.5 "Arctic Model Intercomparison Project (ARCMIP – GLIMPSE)"; Girard, Jones,Blanchet, McFarlane, Laprise and Caya;

PDF Dr Ping Du, UQAM.2005 – 2006 : • Resume analysis of the CRCM_3 and CRCM_4 GLIMPSE simulations. Identify strengths

and weaknesses of the CRCM. Perform CRCM_4.C simulation over the ARCMIP domainand comparison with the 2 other versions of the model. Identify the benefits (if applicable)of having prognostic cloud for the simulation of cloud radiative effects during winter.

The CRCM_3, the operational version of the CRCM, has been used for the intercomparison of clouds andradiation with other participating models in ARCMIP. In its current version, the CRCM have shown someweaknesses, particularly during winter. The boundary layer is not well reproduced with too few mixingoccurring in the lowest 1 km (too warm near the surface and too cold at the top of the boundary layer). Thisbias causes important problems for the simulation of cloud cover, particularly at the top of the boundarylayer. The radiation fluxes at the surface are reasonably well simulated during winter for the SHEBA point.In summer, shortwave radiation is overestimated while longwave radiation is underestimated. This bias islikely to be caused by the underestimation of summertime cloud cover. The CRCM results are relativelygood for the whole domain when compared to the ECMWF re-analyses. However, the cloud cover is notcaptured by the model and all ARCMIP models miss the annual cycle of cloud cover (see Figure 4.1.5.1).Results of this analysis will be submitted shortly for publication (Wyser et al. [4]).In order to resolve this problem, we have implemented a new boundary-layer scheme [5] based on thesimilarity theory, which has shown its robustness in other models such as the ECMWF. Figure 4.1.5.2 showsthe improvement of the wintertime and summertime boundary-layer profile of temperature and humidity afterthe implementation of the new scheme. These results now compare very well to the observations. With thenew boundary-layer scheme, CRCM still agrees well with the observed radiation fluxes on the surface.Compared to the original one, it increases the simulated cloud cover, especially in summer, while in winter itoverestimates the cloud cover. However, the new version now well captures the lowest clouds both insummer and in winter. To further improve the model simulations, we focus on the cloud-coverparameterization. Three methods of cloud cover parameterizations will be tested in CRCM. One is using Xu& Randall (1996) scheme [6], in which the cloud cover is based on the relative humidity and cloud watercontent. A second one is based on Slingo (1987) [7], in which the cloud cover is based on relative humiditywith a threshold RH depending on height. The last one is an offline test to adjust the threshold of relative 4 Wyser, et al. Evaluation of an Ensemble of eight Arctic Regional Climate Models: cloud and radiation, manuscript.5 Troen and Mahart, Bound. Lay. Met., 37, 129-1486 Xu,K.-M.,and D.A.Randall, 1996a A semiempirical cloudiness parameterization for use in climate models, J. Atmos. Sci., 53, 3084–3102.7 Slingo, J.M., 1987: The development and verification of a cloud prediction scheme for the ECMWF model, Q.J.R.Meteotol.Soc., 113, 899-927.


humidity and then to be used to calculate cloud cover to fit the Arctic conditions. We are currently in theprocess of testing these cloud-cover schemes. So far, only wintertime simulations have been performed.Preliminary results with the Xu and Randall (1996) [8] show no significant improvements. Other simulationshave to be performed with other parameter settings and with a prognostic cloud scheme to test this cloudcover parameterization.

Fig. 4.1.5.1 The monthly average value of shortwave (SW) down, longwave (LW) down, SFC albedo (surface albedo), cloud coverand precipitable water from all of the RCMs for ARCMIP and the observations. The red line is for CRCM.

Fig. 4.1.5.2 Wintertime and summertime averaged temperature and specific humidity vertical profile for the SHEBA observation(obs), original version of the CRCM (ori) and modified version (new boundary layer scheme) of the CRCM (ver).

8 Xu, K.-M., and D. A. Randall, 1996b :Evaluation of statistically based cloudiness Parameterizations used in climate models, J. Atmos.Sci., 53,3103–3119.


Sub-project 4.1.6 "Project to Intercompare Regional Climate Simulations (PIRCS)"; Caya, Laprise andCôté.MSc student, Raphaël Brochu, UQAM

2005 – 2006 : • Use the PIRCS experimental protocol as a standard benchmark to validate new versions ofthe CRCM.

Within the framework of PIRCS_1c, Mr Raphaël Brochu, MSc at UQAM, has evaluated the surface energyand water budgets of the operational version CRCM_3.6.1 and developmental version CRCM_4 includingCLASS with re-analyses and available observations. The conclusion of his study is that the CRCM_4constitutes an improvement over the operational version, particularly for the precipitation, evaporation anddiurnal screen temperature range. Raphaël has completed his MSc in September 2005; a paper has beensubmitted and is currently being reviewed.The newly installed North American Climate Change Assessment Program (NARCCAP) is taking overPIRCS for validation of RCMs over North America. Since December 2005, NARCCAP is the maininternational initiative for intercomparison of RCM simulations over North America. The Iowa StateUniversity (ISU) team leading PIRCS has now taken the responsibilities for all RCMs simulations driven byreanalyses in NARCCAP following the protocol developed in PIRCS. The NARCCAP domain is muchlarger than the PIRCS domain and covers almost the complete North American continent making it muchmore appropriate for validation of the Canadian RCM. The NARCCAP domain and the AMNO domain arenow the basic domains for validating the CRCM. The validation simulations on these larger domains are alsomuch longer in time and cover the 25-year period of 1979-2003.

THEME 2. Optimisation of dynamical downscaling

Sub-project 4.2.1 "The Big-Brother Experiment"; Laprise and Caya.MSc students, Emilia-Paula Diaconescu and Martin Leduc, UQAM.

2004 – 2006 : • Perform simulations with various degree of LBC imperfection and analyse the impact onthe RCM-simulated climate.

The central objective of this study is to investigate the impact of the lateral boundary condition (LBC) errorson the climate of a nested Regional Climate Model (RCM). We assess if the Canadian Regional ClimateModel (CRCM) amplifies or attenuates these errors and if these large-scale errors affect the small scalesgenerated by the CRCM.The methodology is based on a perfect-model framework nick-named the “Big-Brother Experiment”designed by Denis et al. (2002a) [9]. This method permits to evaluate the errors due to the nesting processexcluding other model errors. First, a high-resolution (45 km) RCM simulation is made over a large domain.This simulation, called the Perfect Big Brother (PBB), is driven by reanalyses from the National Centres forEnvironmental Prediction (NCEP); it serves as reference virtual-reality climate to which other RCM runs willbe compared. Errors of adjustable magnitude are introduced by performing RCM simulations withincreasingly larger domains at lower horizontal resolution (90 km); such simulations are called the ImperfectBig-Brother (IBB) simulations and they are used, after removing small scales in order to achieve low-resolution typical of today’s General Circulation Models (GCM), as LBCs for smaller domain high-resolution RCM runs. These small-domain high-resolution simulations are called Little Brother (LB)simulations. The climate statistics of the LB are compared to those of the PBB in order to estimate the errorsresulting solely from nesting with imperfect LBCs, while the difference between the climate statistics of theIBB and those of PBB simulations mimic errors of the nesting model. 9 Denis, B., R. Laprise, D. Caya and J. Côté, 2002: Downscaling ability of one-way-nested regional climate models: The Big-Brother experiment.Clim. Dyn. 18, 627-646.


MSc student Emilia Diaconescu has performed all the simulations for five February months: from 1990 to1994. The statistical analyses were performed over the common zone of LB simulations excluding thesponge zone of 10 points. A spatial decomposition was applied to separate fields into their large-scale andsmall-scale components. A temporal decomposition of fields was also performed to separate stationary andtransient components. The errors in each component are indicated by the statistical coefficients displayed intoTaylor diagram diagrams (Denis et al. 2003 [10], Taylor 2001 [11]). The results for the precipitation field aresummarized in the above figure, which presents the Taylor diagrams for the stationary (a and c) and transient(b and d) components of the large scales (a and b) and for the small scales (c and d).

a) Stationary part - larges scales b) Transient part - larges scales c) Stationary part - smalls scales d) Transient part - smalls scales

Fig. 4.2.1. Summary Taylor diagrams showing the errors induced in the IBB and LB precipitation rate fields, for the stationary andtransient parts of the large- and small-scale components of the field.

Errors are present in both stationary and transient parts for the IBB simulations, but the transient componentsof the field exhibit the largest errors due to rather weak temporal correlation. The points corresponding to theLB fields are close to those corresponding to the driving IBBs for all four components of the fields,indicating the presence of similar errors in the precipitation rate fields of LBs to those contained in thecorresponding fields of IBBs. The LB reproduces a great part of the large-scales errors of corresponding IBB.For the small scales there is a slight improvement in regions with important orographic forcing, andgenerally, a reproduction of most part of the driving-model small-scale errors, even if these small scale donot take part in the nesting process. This result suggests that, for this particular LB domain and period, thelarge scales precondition the small scales. Similar results were observed for the mean sea level pressure andtemperature at 850 hPa fields (Diaconescu et al. 2005). A paper has been submitted and is now beingreviewed. In conclusion for this study of the impact of LBC errors on the climate of a nested RCM, thequality of lateral boundary data plays a critical role in regional climate modelling, highlighting the need forgood LBCs (Diaconescu 2006). Therefore it is necessary to provide the correct large-scale circulation at thelateral boundary of RCM in order to obtain the correct small scales too.Furthermore, RCMs are increasingly used to add small-scale features that are not present in their LBCs. It iswell known that the limited area over which a model integrates must be large enough to allow the fulldevelopment of small-scale features (Jones et al., 1995[12]). On the other hand, integrations on very largedomains have shown important departures from the driving data, unless nudging of the large scales is applied(e.g., Castro and Pielke, 2005[13]). MSc student Martin Leduc is assessing the effects of domain size on thedevelopment of small scales using the "Big-Brother" approach. Similarly to the previous study, a referenceclimate is established by performing a high-resolution simulation over a large domain (the Big Brother). The 10 Denis, B., R. Laprise and D. Caya, 2003: Sensitivity of a Regional Climate Model to the spatial resolution and temporal updating frequency ofthe lateral boundary conditions. Clim. Dyn., 20, 107-126.11 Taylor K.E., 2001: Summarizing multiple aspects of model performance in a single diagram. J. Geophys. Res. 106: 7183-7192.12 Jones, R. G., J. M. Murphy, and M. Noguer, 1995: Simulation of climate change over Europe using a nested regional-climate model. I:Assessment of control climate, including sensitivity to location of lateral boundaries. Quart. J. Roy. Meteorol. Soc., 121, 1413-1449.13 Castro, C. L. and R. A. Pielke, 2004: Dynamical Downscaling: Assessment of Value Retained and Added Using the Regional AtmosphericModeling System (RAMS). J. Geophys. Res., 110, 1-21.


next step is to degrade this dataset with a low-pass filter based on discrete cosine transform (DCT; Denis etal., 2002b [14]) to emulate coarse-resolution LBCs that are usually taken from GCMs or reanalyses. A secondsimulation (the Little Brother) is driven by the coarse-resolution LBCs and generates its own small-scalefeatures inside the new smaller domain. Driven and added scales of the Little Brother can then be comparedwith the Big-Brother (unfiltered) ones by using the DCT-filter again. Three February months (1990, 1991and 1992) were integrated over a continental grid (Big Brother: 196x196 gridpoints) with grid mesh of 45 kmcovering almost the entire North-America. After filtering, this dataset is used to drive five simulations withvarying domain size (48x48, 72x72, 96x96, 120x120 and 144x144) centred on the same geographic location;all other parameters are kept constant. Monthly statistics of the five Little Brothers are compared with thevirtual reference (Big Brother) over the common domain (28x28) corresponding to the smallest Little Brotherbut without its sponge zone (Fig. 4.2.1.2). Results show that temporal correlation of large-scale eventsincreases when the domain size is reduced from 144x144 to 48x48. For the same domain change, thecorrelation improves in small-scale features, suggesting that their consistence is in some way helped by theincreased correlation of the large-scale flow. A second effect of the domain size on small scales is based onthe fact that these scales need a spin-up time to fully develop from the low-resolution lateral boundaryconditions. Variance-ratio maps show that the relative intensity of small-scales tend to grow from theirentrance inside the domain until they reach the full amplitude of the Big-Brother variance. A spin-up zone isobserved for small-scales of all examined fields (e.g. geopotential, temperature, relative humidity and relativevorticity). The spin-up area grows in size at higher levels where winds are stronger, suggesting that small-scale features are advected out of the domain area before they have time to fully develop.

Fig. 4.2.1.2 The percentage of the Big-Brother's variance in small-scales for the relative vorticity field at 700-hPa is given for thefive Little-Brother simulations which have domain sizes of a) 144x144, b) 120x120, c) 96x96, d) 72x72 and e) 48x48,all displayed through a window of 28x28 gridpoints.

14 Denis, B., J. Côté and R. Laprise, 2002: Spectral decomposition of two-dimensional atmospheric fields on limited-area domains using discretecosine transforms (DFT). Mon. Wea. Rev. 130 (7),1812-1829.


Sub-project 4.2.2 " Influence of surface forcing and large-scale nudging on RCM internal variability ";Caya, Laprise and de Elía;PhD student, Philippe Lucas-Picher, UQÀMNon-Network MSc student, Jean-Philippe Paquin, UQAM.

2005 – 2006 : • Study the forcing from the CLASS multi-layer land-surface scheme and investigate theinfluence of its long memory (order of years) on the relatively short (order of decades)regional climate simulations; Perform a scale decomposition of the variability andinvestigate the influence of the land-surface boundary on the development of fine-scaledetails in RCM simulations.

Unlike global climate models (GCM), regional climate models (RCM) simulations require to be driven at thelateral boundaries of their limited area domain by continuous atmospheric information. This nesting imposesan additional forcing on the RCM, which reduces the internal variability. The intensity of this forcing isfunction of the flow regime, domain size and season. Despite this forcing, the RCM exhibits a certain level ofinternal variability (IV). Ensemble of simulations with the Canadian RCM started with different initialconditions for various domain sizes were realized by PhD student Philippe Lucas-Picher. Statistical analysisof the simulations shows that lateral boundary forcing is less effective as the domain expanded, increasingthe internal variability. An attempt was made to investigate the cause of this dependence. With this objective,an ageing tracer was implemented in the Canadian RCM to measure the residency time of the atmosphericparcels into the limited-area domain. This tool can serve to determine the flow-regime properties and toevaluate the degree of constraint exerted by the lateral boundary information on the RCM simulation (Fig.4.2.2). A quasi-linear relation between the residency time and the RCM’s internal variability was found.Surface temperature and mean sea level pressure internal variability increase linearly with the residency time.It was also found that the internal variability increases more in winter than in summer with the residencytime. This idea is opposite to general perception.

Fig. 4.2.2 Ten-year mean atmospheric circulation for summer season (JJA) at 500 hPa. The colour scale represents the timeresidency in days while the arrows indicate the wind speed in m/s.

In the meantime, Jean-Philippe Paquin, M.Sc. student at UQAM, is performing simulations on a hemisphericdomain with the CRCM at a lower resolution (180 km instead of the usual 45 km) in order to test theimprovement of using an additional step in the nesting within the driving data with an intermediate-resolutionversion of the model. The idea is to test the approach developed at the Hadley Center in UK over NorthAmerica. Simulations carried at the Hadley Center over Europe have shown that the use of the intermediate-resolution helps in representing the large-scale circulation in the high-resolution model. Some problems in


the development of the intermediate version of the CRCM have introduced some delays in Jean-Philippe'sproject. He has now resolved most of the problems and has resumed his simulations.

Sub-project 4.2.3 " Internal variability of RCM in ensemble simulations "; De Elía, Caya and Laprise;M.Sc. students Adelina Alexandru, Leo Separovic, UQAM. Both students continued the researchstarted by PDF Ramón de Elía (hired by Ouranos Consortium in August 2004).

2005 – 2006 : •Comparison of internal variability of both the GCM and the RCM over a season.Characterize the internal variability for different weather regimes.

Regional climate models do not behave like a “magnifying glass” when used to increase fine details fromlow-resolution fields. On the contrary, they do show some freedom (known as internal variability), whichforces the user to carefully assess the validity of the obtained high-resolution features.Adelina Alexandru studied the internal variability (IV) in RCMs by means of an ensemble approach over theNorth American region. Several twenty-member ensemble simulations over different domains weregenerated, allowing for a detailed study of the spatial and temporal variation of IV with domain size. Whilepreviously it was believed that IV grows with domain size, it was shown that the increase in IV is notmonotonic with domain size. In addition, it was shown that the geographical distribution of the IV canchange substantially with domain size, and that areas of large IV in a small domain can lie within areas ofsmall IV in a larger domain.A detailed investigation of the temporal evolution of the IV in different domain sizes showed that, althoughsmall domains tend to develop in average less IV, they do have isolated episodes of strong IV. These isolatedepisodes leave a trace in the IV seasonal average, suggesting that even for small integration domains theensemble approach may be necessary. Adelina has been submitted her Master thesis at the end of June 2006;she is now working on an article focussing on internal variability in regional climate downscaling at theseasonal scale.

Fig. 4.2.3.1 Internal variability experiment, Ensemble of 20 simulations with a domain of 120 x 120: Seasonal ensemble spread forthe precipitation (mm/day) (left-side panel) and for the 850-hPa geopotential (m) (right-side panel).MSc student Leo Separovic took advantage of the large database generated by A. Alexandru and investigatedthe amount of information (in the spectral space) contained in the ensemble average and the departures fromthe average. This has been done on an instantaneous basis (at each archived timestep) as well as on theseasonal average.Results show that some of the small scales added by the dynamical downscaling (scales absent in the drivingfields) are present in the ensemble average, while others exist only in the deviations from the ensemble (Fig.4.2.3.2). The dominant term varies with weather pattern: for periods of intense IV almost no small-scaleinformation remains in the ensemble average, while periods of low IV show significant development of smallscales in the ensemble everage. These results show that in certain cases the low-resolution boundary


conditions that drive the RCM predetermine the small scales inside the integration domain, while in certaincases (high IV), the regional model’s generation of small scales is less dependent on boundary conditions.

Fig. 4.2.3.2. Spectrally decomposed spatial variance of instantaneous 925-hPa geopotential generated by the ensemble of CRCMruns. The variance is sampled every 6 hours, and averaged during June, July, and August 1993. Dashed line represents the variancebetween members within the ensemble average, the dotted line represents the variance within the deviations from the ensembleaverage, and full line is the sum of the two previous ones. The red line represents the spectral variance of regridded NCEPreanalyses used to define the lateral boundary conditions. The intersection of the dashed and the doted curve at wavenumber 20(~270 km) indicates the spatial scale at which the dynamical downscaling process generates information dominantly in stochasticform.

THEME 3. Development and improvements in the CRCM

Sub-project 4.3.1 "Regional ocean coupling"; Saucier, Caya, Laprise and Boer;RA, Simon Senneville, UQAR, Dr Minwei Qian, UQAM; MSc Student, Marko Markovic,UQAM.Non-CRCM Network PhD student, Marc Defossey.

2005 – 2006 : • Evaluate performance of coupled system with available observations;• Investigate the effect of coupling over the Hudson Bay on the simulated climate and in

particular on the water cycle, and evaluate with available observations.One of the primary atmospheric inputs controlling the quality of simulated regional ocean model surfacetemperatures and ice coverage is the incoming solar and terrestrial radiation at the ocean surface. In order toincrease confidence in the quality of the simulated surface radiation budget in Regional Climate Models usedby the CRCM Network, MSc student Marko Markovic has performed an in-depth evaluation of the surfaceradiation simulated by3 RCMs integrated over North America, using analysed lateral boundary conditions forthe recent observed past.NOAA US-Surface Radiation Network observations have been used to evaluate the models surface radiationbudget across a variety of climatic regimes over North America and throughout the seasonal cycle. Threemodels have been evaluated for the period 1999-2004 are:1. The latest version of the CRCM model, version 4.1, including the most recent physical parameterisationpackage under development collaboratively between the CRCM Network and Ouranos scientists.2. The climate version of GEM-LAM. This is the RCM targeted to be the next default Canadian RegionalClimate Model and is presently being extensively evaluated as a Regional Climate Model.


3. The Rossby Centre Regional Atmosphere Model version 3 (RCA3). In forthcoming CRCMD Network [15],it is intended to introduce the RCA3 physics package, along with the CRCM version 4.1 physics into theGEM-LAM dynamical core. From this perspective it is worthwhile to include this model in our evaluation ofthe surface radiation budget.The US-Surface radiation Network offers high temporal frequency (hourly) observations of total-skyshortwave and longwave downwelling radiation, along with fractional cloud coverage at 6 sites across NorthAmerica. For the simulated period 1999-2004, RCM grid box values of these variables have been extractedand compared to the surface observations. Total-sky and clear-sky downwelling radiation have been analysedalong with the simulated cloud cover. From this analysis the annual cycle of surface cloud-radiative forcingcan be constructed and compared to observed values. Systematic biases in aspects of the cloud and radiationinteraction in the 3 RCMs have been identified and updated models are in the process of being evaluated.

Figure 4.3.1.1Mean annual cycle of downwelling solar and terrestrial radiation at all US Surface Radiation Network sites and assimulated by the 3 respective RCMs for the period 1999-2004.Figure 4.3.1.1 shows the observed and simulated mean annual cycle of downwelling solar and terrestrialradiation averaged across the 6 surface observation stations, along with the annual cycle of RCM errors inthese two quantities. Parallel analysis has enabled a comparison of the simulated surface cloud-radiativeforcing and assisted in identifying which aspects of the cloud-radiation or clear-sky radiation are responsiblefor the total surface radiation errors.The high temporal frequency of the observations also allows an evaluation of the simulated mean diurnalcycle of surface radiation and cloud cover. Climate models frequently have problems simulating the meandiurnal cycle of convection and associated cloud fields during the summer season often resulting in large

15 Canadian Regional Climate Modelling and Diagnosis Network; CFCAS has approved the research Network initiative lead by Colin Jones,including 16 co-Is from Universities, Ouranos and Meteorological service of Canada (MSC / CCCma and RPN) for the period 2006-2010.


errors in the seasonal mean surface radiation budget and a gradual drying out of soil moisture. Figure 4.3.1.2shows the mean diurnal cycle of surface radiation for the summer season (June-July-August) averaged acrossthe 6 observation sites and the years 1999-2004. Results are shown separately for all-sky and clear-skyconditions, along with the derived diurnal cycle of cloud-radiative forcing. This type of analysis assists inunderstanding the source of errors in the seasonal mean surface radiation budget simulated by RCMs.

Fig. 4.3.1.2 Mean diurnal cycle of JJA surface solar radiation for all sky and clear-sky conditions along with the mean diurnal cycleof cloud-radiative forcing from observations and the 3 RCMs. Values are averaged over the 6 Observation sites and for the years1999-2004In the meantime, Mr Marc Defossez, Ph.D. student, has investigated the deep waters formation andcirculation in the Hudson Bay system during his first year at the University of Québec at Rimouski. He hasfocused on the deep waters renewal in Foxe Basin (Defossez et al., 2005) using Saucier's et al. (2004 [16]) seaice-ocean model and year-long time series observations retrieved from a mooring at the bottom of FoxeChannel in 2004. He now examines the dense waters formation mechanisms and their dependance toatmospheric changes by sensitivity experiments. This research is done with the collaboration of CLIVARNetwork scientists Saucier, Myers and Caya.Dr. Minwei Qian continued his work on the coupled model used to perform a new simulation from 1st Aug.1991 to 31st Jan. 1998. This simulation was used to demonstrate the effect of the NAO index on inter-annualvariation in ice freeze-up and and break-up period. NAO is the most important mode of atmosphericvariability over the North Atlantic Ocean, and plays a major role in sea-ice formation, sea surfacetemperature (SST) and surface current over the Hudson Bay. However, NAO is not the only mode. TheSouthern Oscillation (SO) is another important mode that has impact on Hudson Bay. During the period from1992 to 1996, there are neither strong El Niño nor strong La Niña events, while 1992 and 1993 are strongpositive-NAO years and 1995 and 1996 are strong negative-NAO years. By make a comparison between thesimulations in positive- and negative-NAO episodes, the variations of sea-ice cover, SST and surface currentare obtained.The results shows:1. The coupled model captures the interannual variation of sea-ice cover. In the sea-ice freeze-up period from

21st November to 10th December, the sea-ice cover in positive-NAO episode is 31% more than that innegative-NAO episode.

2. The interannual variation of SST in October is well simulated. The SST in positive-NAO episode is 1.3°Clower than that in negative-NAO episode.

3. The average cyclonic surface current in October-November-December season is 12 to 14 cm/s (Fig.4.3.1.2 in left panel). However, the surface current in positive-NAO episode is 6 to 10 cm/s faster than that innegative-NAO episode in southwestern and eastern coasts of the Hudson Bay (Fig. 4.3.1.2, right panel).

16 Saucier, F.J., Senneville, S., Prinsenberg, S., Roy, F., Smith, G., Gachon, P., Caya, D., and Laprise, R. 2004. Modelling the sea ice-oceanseasonal cycle in Hudson Bay, Foxe Basin and Hudson Strait, Canada. Clim. Dyn., 23: 303-326.


Fig. 4.3.1.2 Average sea surface current (cm/s) in Hudson Bay (left panel) and Sea surface current difference between positive-andnegative-NAO episodes (right panel).

Sub-project 4.3.2 " River-routing and surface water processes "; Caya, Slivitzky, Larocque, Laprise andSaucier;MSc, Ivana Popadic, RA, Dr Laxmi Sushama, UQAM (hired by Ouranos Consortium in January2006).

2004 – 2006 : • Continue efforts on more fundamental issues to identify potential candidates for improvedmodels of land surface, river, lake and permafrost for adaptation to the CRCM.

The sensitivity of the Canadian Regional Climate Model (CRCM) projected changes to the climatologicalmeans and extremes of selected basin-scale surface fields to model errors, for six basins covering the majorclimate regions in North America, were studied using current and future (A2 and IS92a scenarios) climatesimulations performed with two versions of CRCM (Sushama et al., 2006a). Climate change is commonlyevaluated as differences between simulated climates under future and current forcing, based on theassumption that systematic errors in the current climate simulation do not affect the climate-change signal.Assessment of errors in two CRCM versions suggests the presence of non-negligible biases in the surfacefields, due primarily to the internal dynamics and physics of the regional model and to the errors in thedriving data at the boundaries. In general, results demonstrate that, in spite of the errors in the two modelversions, the simulated climate-change signals associated with the long-term monthly climatology of varioussurface water balance components (such as precipitation, evaporation, snow water equivalent, runoff and soilmoisture) are consistent in sign, but differ in magnitude (Fig. 4.3.2.1). The same is found for projectedchanges to the low-flow characteristics such as frequency, timing and return levels. High-flowcharacteristics, particularly the seasonal distribution and return levels, appear to be more sensitive to modelversion. It should be noted that the precipitation and runoff biases in the model were partly due to its simpleland-surface scheme. Study suggests the need to have a more physically based land-surface scheme toimprove the near surface processes which could improve the high flows along with other fields.


Fig. 4.3.2.1 Relative magnitude of the 10-year 7-day low flows for the six basins, with 95% confidence intervals for currentCRCM-CGCM2-s (black), CRCM-CGCM2-u (green) climates and future CRCM-A2-s (blue), CRCM-IS92a-s (red) and CRCM-A2-u (purple) climates.In parallel with the above, an evaluation of changes in the soil thermal regime for Northeastern Canada wasperformed using a one-dimensional heat conduction model (Popadic, 2006; Sushama et al., 2006b). Projectedchanges were estimated as the difference between two simulations of the soil model corresponding to theIPCC IS92a future scenario (2041–2070), which has effective CO2 concentration increasing at 1% per year(2041–2070), and current (1961–1990) climates. The surface temperature and snow cover from time series oftransient climate simulations with the CRCM were used to drive the soil model.

ALT (1961-1990) ALT (2041-2070)

Fig.4.3.2.2 Simulated distributions of average ALT for SM_CRCM permafrost zones. The average ALT is at an interval of 2 m.


Results suggest significant warming trends in the annual mean, maximal and minimal near-surface soiltemperatures, with the mean annual near-surface soil temperature increasing by 4°C for the continuouspermafrost zone and by 2–3°C for the rest of the permafrost zones in Northeastern Canada (Fig. 4.3.2.2).Results also suggest significant deepening of the active layer for the period 2041–2070, with its thickness(ALT) increasing by 40 to 80% for the continuous permafrost region.

Sub-project 4.3.3 " Physical parameterisation including land-surface processes "; Jones, Laprise, Cayaand McFarlane;RA, Dr Yanjun Jiao, UQAM; MSc student, Cynthia Papon, UQAM.

2005 – 2006: • Perform and analyse regional climate model experiments to assess and improve therepresentation of convection and convectively forced clouds in the CRCM and GEMmodels.

•Repeat the CRCM integrations with higher model resolution. Analyse the representation ofconvection/clouds and compare to the low-resolution models. Identify areas requiringimprovement for the representation of convection at higher resolution.

The present version of the CRCM, version 4.1, has been extensively improved in order to accuratelyrepresent stratocumulus clouds, shallow cumulus and deep convection, and the phase transition betweenthese regimes. This work has been performed by RA Dr Yanjun Jiao within the GEWEX Pacific Cross-section Intercomparison project (GPCI), an international project to evaluate and improve the representationof cloud and precipitation processes in weather prediction and climate models. In the GPCI, different modelsand observations are analyzed and compared along a cross-section over the Pacific Ocean from thestratocumulus regions off the coast of California, across the shallow cumulus areas, to the deep convectionregions of the ITCZ.According to the requirement of the GPCI, the CRCM_4 was run on a 180-km grid mesh over the PacificOcean with 115x75 grid points in the polar-stereographic projection (Fig.4.3.3.1). Instantaneous modelresults are output every 3 hours for the periods of June-July-August 1998 and 2003.To get better results, based on the preliminary results produced by the original CRCM_4, a number ofmodifications have been introduced in the Bechtold-Kain-Fritsch convective scheme, vertical diffusion in theboundary layer and cloud parameterization schemes of the CRCM_4.

Fig. 4.3.3.1. Computational domain (outlined by the thickblue line) of the CRCM_4 over the Pacific Ocean. Thedotted line indicates the location of the cross-sectionproposed by the GPCI and the solid white line represents thecommon area required by the GPCI.


The modifications are briefly summarized as:1. Shallow convection is turned off once the deepconvection had been detected at a given grid point.2. A temperature perturbation based on the relativehumidity in the mixing layer has been added in the triggerfunction of shallow convection.3. The free convective vertical velocity scale has beenused in the cloud-base mass flux closure of the shallowconvection.4. Cloud radius of the deep convection, which controls themaximum possible entrainment rate, has been specified tovary as a function of vertical velocity at liftingcondensation level.5. The minimum cloud-depth threshold has beenparameterized according to the cloud-base temperaturerather than remaining constant.6. A dilute updraft ascent has been used to calculateconvective available potential energy (CAPE), whichprovides a more accurate calculation in convection rainfalland mass flux.7. The turbulent diffusion scheme of the ECMWF hasbeen used to calculate momentum, heat and moisturetransfers in boundary layer.8. A cloud scheme that considers the cloud liquid water incalculating cloud amount, has been introduced.9. The evaporation of the larger scale precipitation hasalso been considered.

Fig. 4.3.3.2. June-July-August averaged cross-sections of relative humidity (a) simulated by theoriginal CRCM4, (b) simulated by the modifiedCRCM4, and (c) the ECMWF analysis used as areference.

These modifications have a significant beneficial influence on the CRCM_4 simulation such as the verticalstructure of the boundary layer (Fig. 4.3.3.2), cloud amount and distribution, the location and amount of theconvective and large-scale precipitation. The results have been submitted to the GPCI working group.One aspect of this development has involved the introduction of a more physically based parameterisation ofcloud amounts associated with shallow and deep convection. This has been the work of MSc student CynthiaPapon.In the original version of CRCM_4.1 cloud amounts associated with shallow and deep convection wereassumed to be a constant value when and where either convective type occurred. There was no formalparameterisation of the cloud amount which linked cloud fraction to the intensity of convection, convectivewater content or the environmental conditions within which the convection occurred. In an attempt toincrease the physical realism of the cloud amounts associated with shallow and deep convection, two newparameterisations of these cloud types have been implemented into the CRCM model. These new approachesformally link the amount of convective cloud to the intensity of convection, the amount of convectivelydetrained cloud and ice water and are influenced by the thermodynamic conditions within which the

(a)

(b)

(c)


convection is embedded. The parameterisation schemes were initially evaluated in a constrained single-column setting and are now presently being evaluated in full 3D CRCM integrations following the GEWEXGPCI experiment protocol. Figure 4.3.3.3 shows some initial results from the updated CRCM (in black)along with observations (in green). Results have been extracted for a cross-section through the Pacificstratocumulus, shallow cumulus and deep convection regimes extending from the coast of California to thewest Pacific warm-pool region. Figure 4.3.3.3 shows the simulated and observed seasonal mean integratedliquid water path (LWP), total cloud cover and precipitation along this cross-section for the period June-July-August 1998. The updated CRCM has a relatively accurate simulation of the various cloud types, only a clearunderestimate of LWP in the stratocumulus region is evident. This problem is presently under investigation.

Fig. 4.3.3.3. Parameterisation of cloud amounts associated with shallow and deep convection: CRCM (in black) versusobservations (in green).This updated CRCM version is being evaluated over North America for present climate conditions to assessthe improvement in the representation of clouds and surface radiation as a result of the updated shallow anddeep convective cloud parameterisation.


Sub-project 4.3.4 " Computer code parallelisation "; Zadra, Jones, Côté, Laprise, Zwiers and Caya;RA, Dr Ron McTaggart-Cowan, UQAM.

2004 – 2006 : • Develop a distributed-memory parallelised version of CRCM, CRCM_5, based on thedynamical kernel of the Canadian operational forecast model GEM in a limited-areaversion called GEM-LAM.

One of the primary goals of this sub-project is to develop the GEM model for implementation as the primaryCanadian regional climate model. The limitations involved with using the Environment Canada IBMsupercomputer at the Canadian Meteorological Centre in Dorval are significant, given the heavy load on themachine that leads to queued wait times ranging between 6 and 24 hours for typical regional climate modelgrid sizes. We have therefore made considerable efforts to ensure that the GEM model will run effectively ona variety of platforms, including those currently in use at Ouranos.As a result of this project, the GEM model and its supporting libraries have now been ported to architecturesincluding 64-bit Linux, SUPER-UX, and Catamount. As part of a CFI [17] acquisition process, a series ofbenchmarks has been run on these platforms in order to obtain accurate measures of performance and scaling.One of the primary findings of this work is that the performance of the GEM model on a distributed memorycluster with fast interconnects exceeds the expectations of the model developers. This is important forupcoming supercomputer acquisitions since commodity-based cluster solutions tend to be more cost effectivethan their “fat node” or vector counterparts.Another contribution of this project has been the vectorization of the GEM model. Although the code wasoriginally written to take advantage of the vector machines at the Canadian Meteorological Centre in the late1990’s, it has since been optimized for use on cache-based architectures. The vector operation ratio of thecode was increased from 78% to >98% and the wall-clock run time of the benchmark simulation reduced bya factor of 3. These significant performance enhancements, to be implemented and fully supported in the nextrelease of the GEM model, will allow the regional climate version of the GEM to effectively leverage thevector-based computing platforms (NEC SX-6 running a SUPER-UX operating system) available atOuranos. This will make the conversion from the existing non-parallel regional model to the GEM climateversion possible without the need for restructured computing facilities.

Fig. 4.3.4 GEM RCM climate scaling: Almost linear to 200 PEs18 offering fast integrations speeds on parallel systems.

17 Canadian Foundation Innovation18 PE Processor Equivalent


As a part of the GEM climate model verification project (again, in preparation for its implementation as theprimary Canadian regional climate model), a regional climatology for southern North America and CentralAmerica is being produced. This region is of particular interest because it encompasses the western NorthAtlantic, Gulf of Mexico and Eastern Pacific hurricane genesis regions, as well as containing the intertropicalconvergence zone during the summer months. Accurate simulation of each of these features is known to beproblematic in global models, and it is hoped that this next-generation forecasting model, properly adaptedfor extended climate simulation, will be more successful in resolving the processes and forcings that areunique to this region. While the development of this climatology continues, a series of collaborativediagnostic studies are being performed in order to assess tropical cyclone development in this region, and itsimpacts on the global circulation.

2. ImpactThe Network includes Universities, Ouranos, IML and MSC (CCCma and RPN) scientists working todevelop and use advanced modelling tools in a complementry fashion. Together these researchers aim atdeveloping, validating and improving a complete regional-scale climate modelling and diagnostics system,coupling the atmosphere, coastal oceans, lakes, ice and land surfaces. This initiative represents the Canadiancontribution to the international efforts in regional-scale climate modelling and analysis.Governmental institutions are the logical end-users of the science results produced by the Network. Theparticipation of scientists of Ouranos, CCCma, RPN and IML to the Network ensures continuity of thecommunication channels, and these co-applicants are part of the technology transfer mechanism. The CRCMNetwork continues, as in the past, to maintain contact with the Impact and Adaptation (IA) community,through participating in and organising users' workshops. Facing the management of consequences ofextreme climate events, the policy makers show a growing interest for the climate change at regional scaleand tools such as the CRCM, in order to be prepared to climate-change consequences. The IA communityrequires the highest possible resolution projections in order to establish detailed climate-change scenarios forthe Canadian community. Scientific publications and the participation in Canadian and internationalconferences and workshops are the principal external communication strategy.As an important client for the R&D of the CRCM Network, Ouranos also relieves the Network from climate-change simulations via its Climate Simulations Team (CST) under the leadership of Dr D. Caya, Co-I ofCRCM Network. The CST has the national mandate, in collaboration with MSC, of the production ofCanada-wide regional climate-change projections. The collaboration with Ouranos is consistent with theacademic research, training and technology transfer mandate of the Network. The products of the Network’sresearch is intended to give operational sectors, governmental services and Canadian companies access to theprivileged knowledge, expertise and access to highly qualified personnel, thus offering opportunities foreconomic benefits to Canada.Another important focus of the Network is the training of HQP in the field of regional climate modelling.The expertise of this CRCM Network is unique in Canada, and it constitutes an ideal nest for developing theneeded HQP. As a consequence, HPQ trained through this Network are currently active in the Canadian andinterational regional climate regional modelling field.Despite the limited Canadian resources, the global and regional modelling researchers involved in the CRCMNetwork at UQAM and MSC are widely recognised as important players in the field. This is reflected bysubstantial contributions through publication of peer-reviewed papers and participation in internationalprojects through Model Intercomparison Projects (MIPs) and World Climate Research Programme (WCRP)(see section 4 ‘Contributions’). In particular, the involment of several members as Contributing or LeadAuthors in the forthcoming IPCC (Inter-governmental Panel on Climate Change) Fourth Assessment Report


(AR4) helps ensure to maintain the CRCM Network's research at the forefront of science, and enhance thecurrent high profile of Canadian research in this area.

3. Level of supportThe proportion of the total CRCM Network budget provided by CFCAS was around 68% during thereporting period. The Finance Department of UQÀM has issued a financial statement of the CRCM activitiesfinanced by CFCAS.The CLIVAR Network (funded by NSERC and CFCAS), the US Department of Energy (US DoE), and theQuébec Government, through the Global Environmental and Climate-Change Centre (GEC3, funded byFQRNT) and the Consortium Ouranos, « Consortium sur le climat régional et l’adaptation aux changementsclimatiques » have provided the funds required to complete the budget of the CRCM Network for the periodcovered by this report.Since September 2002, the Consortium Ouranos provides space and facilities to house a part of the CRCMNetwork at UQÀM at its research laboratories, at 550 Sherbrooke West, 19th Floor, West Tower, Montréal(Québec). The value of this in-kind contribution to the CRCM Network amounts to about 200 K$ per year.Substantial in-kind contributions are also provided by Environment Canada, in terms of significantcomputational facilities and scientists contributing to the network objectives.The blend between the research focus of the Universtities, the model development focus of MSC and theapplications focus of Ouranos provided the infrastructure necessary to the CRCM Network. Specifically thiscomputing infractructure located at Ouranos includes a CLUMEQ CFI-funded system composed of a 32-CPU Origin 3000 SGI computer, a DMF automatic archival system, RAID mass storage and a UPS unit. Thislocal equipment complements the Ouranos and MSC supercomputer for CRCM computing.

4. Dissemination

(1) Peer Rewieved Publications (published, in press or accepted for publication)Beaulne, A, H. Barker and J.P. Blanchet, 2005: Estimating Cloud Optical Depth from Surface Radiometric Observations:

Sensitivity to Instrument Noise and Aerosol Contamination, J. Atmosph. Sc, 62 (11): 4095–4104. Bielli, S., and R. Laprise 2006: A methodology for regional scale-decomposed atmospheric water budget: Application to a

simulation of the Canadian RCM nested by NCEP reanalyses over North America. Mon. Wea. Rev., 134: 854-873.de Elía, R. and R. Laprise, 2005: Probabilities, probabilities, and probabilities. Essay in Bull. Amer. Meteo. Soc. 86, 1224-1225.Desgagné, M., R. McTaggart-Cowan, W. Ohfuchi, G. Brunet, M. K. Yau, J. Gyakum, Y. Furukawa and M. Valin, 2006: Large

atmospheric computation on the Earth Simulator: the LACES project. Scientific Computing (in press).Dimitrijevic, M., and R. Laprise, 2005: Validation of the nesting technique in a RCM and sensitivity tests to the resolution of the

lateral boundary conditions during summer. Clim. Dyn. 25, 555-580.Fox-Rabinovitz, M.S., J. Côté, B. Dugas, M. Deque and J. McGregor, 2006: Variable-Resolution GCMs: Stretched-Grid Model

Intercomparison Project (SGMIP). Journal of Geophysical Research – Atmospheres (in press).Girard., É., and B. Bekcic, 2005. Sensitivity of an Artic Regional Climate Model to the horizontal resolution during winter:

implications for aerosol simulation. Internat. J. Climat. 25 (11):1455-1473.Girard, E., Blanchet, J.-P., and Y., Dubois. 2004. Effects of arctic sulphuric acid aerosols on wintertime low-level atmospheric ice

cyrstals, humidity, and temperature at Alert, Nunavut. Atmos. Res., 73 (1-2): 131-149.Hu, R.M., J.-P.Blanchet, and E.Girard, 2005a: Evaluation of the Direct and Indirect Radiative and Climate Effects of Aerosols over

the Western Arctic, J. Geophys. Res., 110 (11) (DOI 10.1029/2004JD005043). Hu, R.M., J.-P.Blanchet, and E.Girard, 2005b: Aerosol Effect on Surface Cloud Radiative Forcing in the Arctic. Atmos. Chem.

Phys., 5, 9039-9063, 2005.


Jiao, Y., and D. Caya, 2006 : An investigation of summer precipitation simulated by the Canadian Regional Climate Model. Mon.Wea. Rev. 134 (3), 919–932.

Le Fouest, V., B. Zakardjian, F.J. Saucier and M. Starr, 2005 : Seasonal versus synoptic variability in planktonic production in ahigh-latitude marginal sea: the Gulf of St. Lawrence (Canada). J. Geophys. Res. 110 (9) (DOI 10.1029/2004JC002423).

McTaggart-Cowan, R., L. F. Bosart, J. R. Gyakum and E. Atallah, 2006: Hurricane Juan (2003). Part II: forecasting and numericalsimulation. Mon. Wea. Rev. (in press).

Plummer, D., D. Caya, H. Côté, A. Frigon, S. Biner, M. Giguère, D. Paquin, R. Harvey and R. De Elía 2006: Climate and climatechange over North America as simulated by the Canadian Regional Climate Model. J. Climate (in press).

Rinke, A., K. Dethloff, J. Cassano, J. H. Christensen, J. A. Curry, P. Du, E. Girard, J.-E. Haugen, D. Jacob, C. G. Jones, M.Køltzow, R. Laprise, A. H. Lynch, S. Pfeifer, M. C. Serreze, M. J. Shaw, M. Tjernström, K. Wyser and M. Zagar, 2006.Evaluation of an ensemble of Arctic regional climate models: spatiotemporal fields during the SHEBA year. Clim. Dyn., 26:459-472.

Smith G, F. J. Saucier, and D. Straub, 2005. Circulation in the St. Lawrence Estuary in wintertime. J. Phys. Oceanogr. (accepted)Sushama, L., R. Laprise, D. Caya, A. Frigon and M. Slivitzky, 2006a: Canadian RCM projected climate change signal and its

sensitivity to model errors. Int. J. Climatol. DOI: 10.1002/joc.1362 (in press).Sushama, L., R. Laprise, I. Popadic and M. Allard, 2006b : Modeled current and future soil thermal regime for North East Canada.

J. Geophys. Res. – Atmosphere (accepted for publication).Tjernström, M., M. Zagar, G. Svensson, J.J. Cassano, S. Pfeifer, A. Rinke, K. Wyser, K. Dethloff, C. Jones, T. Semmler and M.

Shaw, 2005 : Modelling the arctic boundary layer : An evaluation of six ARCMIP regional-scale models using data from theSHEBA project. Boundary Layer Meteorology 117 (2): 337

Wyser, K., and C.G. Jones, 2005 : Aerosol and Clouds – Modeled and observed clouds during surface heat budget of Arctic Ocean(SHEBA). J. Geophys. Res. 110 (9) (DOI 10.1029/2004JD004751).

Zhao, T.L., S.L. Gong, X.Y. Zhang, J.-P. Blanchet, I.G. McKendry and Z. J. Zhou, 2005: A Simulated Climatology of Asian DustAerosol and its Trans-Pacific Transport: 1. Mean Climate and Validation. J. Clim., 19 (1): 88–103.

(2) Peer Rewieved Publications (submitted)Brochu, R. and R. Laprise: Surface water and energy budgets as simulated by two generations of the Canadian Regional Climate

Model over the Mississippi and Columbia River Basins. Accepted with corrections in Atmosphere and Ocean, 2006de Elía, R. and D. Caya: Extracting additional information from Taylor diagrams. Submitted to J. Climate, 2006.de Elía, R., D. Caya, A. Frigon, H. Côté, M. Giguère, D. Paquin, S. Biner, R. Harvey, D. Plummer: Evaluation of uncertainties in

the CRCM-simulated North American climate: nesting-related issues. Submitted to Climate Dynamics, 2006.Dethloff, K., C. Jones, Rinke, J. Cassano, J.H. Christensen, J.A. Curry, P. Du, E. Girard, J.-E. Haugen, D. Jacob, M. Køltzow, A.H.

Lynch, S. Pfeifer, M.C Serreze, M.J. Shaw, M. Tjernström, K. Wyser, M. Zagar, 2005: Clouds and radiation in ARCMIP. (enpréparation).

Diaconescu, E. P., R. Laprise and L. Sushama, 2006: The impact of lateral boundary data errors on the simulated climate of anested regional climate model. Climate Dynamics (submitted November 2005)

Hu, R., J. Blanchet, and E. Girard, 2005: Aerosol effect on surface cloud radiative forcing in the Arctic. Atmospheric Chemistryand Physic (in review process).

Long, Z., W. Perrie, J. Gyakum, R. Laprise, and D. Caya: Northern Lake Impacts on Local Seasonal Climate. Submitted to. J. ofHydrometeorology.

McTaggart-Cowan, R., L. F. Bosart, J. R. Gyakum and E. Atallah: Evolution and global impacts of a diabatically-generated warmpool: Hurricane Katrina (2005). Monthly Weather Review (in review).

Music, B., and D. Caya: Evaluation of the Water Cycle over the Mississippi River Basin as simulated by the Canadian RegionalClimate Model (CRCM). Journal of Hydrometeorology (in review).

(3) Chapter in reports or booksAlexandru, A., R. de Elía and R. Laprise, 2006 : Geographical Distribution of Internal Variability in Regional Climate

Downscaling. Research activities in Atmospheric and Oceanic Modelling, WMO/TD, edited by J. Côté, April 2006, xx.Barrette, N., and R. Laprise, 2005: A one-dimensional model for simulating the vertical transport of dissolved CO2 and CH4 in

hydroelectric reservoirs. Chap. 24, 575-595, in: Greenhouse Gas Emissions: Fluxes and Processes, Hydroelectric Reservoirsand Natural Environments. A. Tremblay, L. Varfalvy, C. Roehm and M. Garneau (Eds.). Environmental Science Series,Springer, Berlin – Heidelberg – New York, 732 pp.


Diaconescu, E. P., and René Laprise The impact of lateral boundary data errors on the simulated climate of a nested RegionalClimate Model. Research activities in Atmospheric and Oceanic Modelling, WMO/TD, edited by J. Côté, April 2006, xx.

Fox-Rabinovitz, M.S., J. Cote, B. Dugas, M. Deque and J. McGregor, 2006: Regional Modeling with Variable-Resolution GCMs:International SGMIP, WMO/WGNE, Research Activities, 2006 Edition, p. 3-07.

Frigon, A., M. Slivitzky1 and D. Caya, 2006 : Hydrology of Northern Quebec as seen by the Canadian Regional Climate Model.Research activities in Atmospheric and Oceanic Modelling, WMO/TD, edited by J. Côté, April 2006, xx.

Laprise, R., 2007 (a Lead Author to Chap. 11): « Regional Climate Projections ». IPCC Fourth Assessment Report (AR4) «ClimateChange 2007: The Physical Science Basis ».

Leduc M. and R. Laprise, 2006: CRCM sensitivity to domain size. Research activities in Atmospheric and Oceanic Modelling,WMO/TD, edited by J. Côté, April 2006, xx.

Lucas-Picher, P., D. Caya and S. Biner, 2006: Relation between RCM’s internal variability and residency time of the atmosphericparcels into the limited area domain. Research activities in Atmospheric and Oceanic Modelling, WMO/TD, edited by J. Côté,April 2006, xx.

Music, B., and D. Caya, 2006: Evaluation and validation of the hydrological cycle simulated by the Canadian Regional ClimateModel (CRCM) using an integrative approach. Research activities in Atmospheric and Oceanic Modelling, WMO/TD, editedby J. Côté, April 2006, xx.

Radojevic, M., P. Zwack and R. Laprise, 2006 a: Northern-Hemisphere extra-tropical cyclone activity in 1961-1990: Comparisonof the CGCM3 with the NCEP/NCAR reanalyses. Research activities in Atmospheric and Oceanic Modelling, WMO/TD,edited by J. Côté, April 2006 – xxxx.

Radojevic, M., P. Zwack and R. Laprise, 2006 b: Impact of enhanced greenhouse gases on Northern Hemisphere extra-tropicalcyclone activity in 2041-2070 as simulated by the CGCM3. Research activities in Atmospheric and Oceanic Modelling,WMO/TD, edited by J. Côté, April 2006 – xxxx.

Rosu, C., and R. laprise, 2006 : The Relationship between Cyclone Characteristics and Annual Hydrological Resources overQuébec. Research activities in Atmospheric and Oceanic Modelling, WMO/TD, edited by J. Côté, April 2006 – xxxx.

Separovic, L., R. de Elia and R. Laprise, 2006: Stochastic and deterministic components in limited-area model downscaling.Research activities in Atmospheric and Oceanic Modelling, WMO/TD, edited by J. Côté, April 2006 – xxxx.

(4) Papers in preparationAlexandru, A., R. de Elía and R. Laprise, 2006: internal variability in regional climate downscaling at the seasonal scale.Lucas-Picher P., D. Caya and S. Biner, 2005: Influence of domain size, domain location and spectral nudging on RCM's internal

variability.Qian, M., F. Saucier, D.Caya and R. Laprise, 2005: The study of sea ice in Hudson Bay and its effect on regional climate using

coupled regional atmospheric model with an ice-ocean model. To be submitted to Monthly Weather Review.Laprise, R., 2006 : Regional climate modelling. J. Comp. Phys. (Invited paper), Special issue on « Predicting weather, climate and

extreme events » (in preparation).Wyser, Girard, Hu, Jones et al. Evaluation of an Ensemble of eight Arctic Regional Climate Models: cloud and radiation (in

preparation)

(5) Conferences ProceedingsDiaconescu, E. P., and R. Laprise : La réponse d’un modèle régional du climat aux erreurs du pilote. Conference Proceeding of the

Ateliers de modélisation atmosphérique (AMA). 18-20 janvier 2006. Toulouse, France.Leduc, M., and R. Laprise : Effets reliés à la taille du domaine d'intégration d'une simulation climatique régionale. Conference

Proceeding of the Ateliers de modélisation atmosphérique (AMA). 18-20 janvier 2006. Toulouse, France.Lucas-Picher P., D. Caya and S. Biner : Corrélation entre la variabilité interne d’un MRC et le temps de résidence de l'écoulement

atmosphérique dans le domaine. Conference Proceeding of the Ateliers de modélisation atmosphérique (AMA). 18-20 janvier2006. Toulouse, France.

(6) SeminarsCaya, D., 2006 (inivited speaker): Regional Climate Simulations for Impact Studies. Ministry of Forestry, Victoria, BC, 3 mai

2006.

Caya, D., 2006 (inivited speaker):: La simulation climatique : un outil de planification. Alcan, Rencontre des cadres, Alma, QC, 26avril 2006.


Caya, D., 2006 (inivited speaker):: Latest Canadian Regional Climate Model (CRCM) results for all of Canada. Hydro Power andClimate Change Workshop Agenda, Winnipeg, Manitoba, 2 et 3 mars 2006.

Caya, D., 2005 (inivited speaker):: La simulation climatique régionale à Ouranos, Centre d’études nordiques, Université Laval,Québec, QC, 2 décembre 2005.

McTaggart-Cowan, R., L. Bosart, J. Gyakum and E. Atallah, 2006: Global impacts of a diabatically generated warm pool:Hurricane Katrina (2005). The 27th Conference on Hurricanes and Tropical Meteorology. April 2006, Monterey, California.

Atallah, E. H., J. R. Gyakum and R. McTaggart-Cowan, 2006: Forecast errors associated with Hurricane Rita (2005). The 27thConference on Hurricanes and Tropical Meteorology. April 2006, Monterey, California.

Bosart, L. F., R. McTaggart-Cowan, C. A. Davis and M. T. Montgomery, 2006: The tropical transition of Hurricane Alex (2004):An observational perspective. The 27th Conference on Hurricanes and Tropical Meteorology. April 2006, Monterey,California.

Davis, C. A., L. F. Bosart and R. McTaggart-Cowan, 2006: Tropical transition: possible mechanisms and observational needs. The27th Conference on Hurricanes and Tropical Meteorology. April 2006, Monterey, California.

Fox-Rabinovitz, M.S., J. Côté, B. Dugas, M. Deque and J. McGregor, 2006: Regional modeling with variable-resolution GCMs:Stretched-Grid Model Intercomparison Project (SGMIP), 2006 Workshop on the Solution of Partial Differential Equations onthe Sphere, June 26-29 2006, Monterey CA.

Fox-Rabinovitz, M.S., J. Cote, B. Dugas, M. Deque and J. McGregor, 2005: International SGMIP: Results of the phase-1 and thepreliminary results the phase-2, progress report, WMO/WCRP/WGNE Meeting, November 2005, St. Petersburg, Russia.

McTaggart-Cowan, R., L. F. Bosart, J. R. Gyakum and E. H. Atallah, 2006: Global impacts of a diabatically generated warm pool:Hurricane Katrina (2005). University at Albany Lecture Series. March 2006, Albany, New York.

McTaggart-Cowan, R., L. F. Bosart, J. R. Gyakum and E. H. Atallah, 2006: Global impacts of a diabatically generated warm pool:Hurricane Katrina (2005). Rutgers University Lecture Series. March 2006, New Brunswick, New Jersey.

McTaggart-Cowan, R., L. Bosart, J. Gyakum and E. Atallah, 2005: The impact of Hurricane Katrina (2005) on the midlatitudeflow. The 2nd International Workshop on Extratropical Transition. December 2005, Perth, Western Australia.

McTaggart-Cowan, R., L. Bosart and C. D

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