Sensitivity of an Arctic regional climate model to the horizontal resolution during winter: implications for aerosol simulation

INTERNATIONAL JOURNAL OF CLIMATOLOGY

Int. J. Climatol. 25: 1455–1471 (2005)

Published online in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/joc.1205

SENSITIVITY OF AN ARCTIC REGIONAL CLIMATE MODEL TO THEHORIZONTAL RESOLUTION DURING WINTER: IMPLICATIONS FOR

AEROSOL SIMULATION

ERIC GIRARD* and BILJANA BEKCICRegional Climate Modelling Laboratory, Department of Earth and Atmospheric Sciences, University of Quebec at Montreal, Montreal

(Quebec), Canada

Received 5 October 2004Revised 1 April 2005

Accepted 4 April 2005

ABSTRACT

Our ability to properly simulate current climate and its future change depends upon the exactitude of the physical processesthat are parameterized on the one hand, and on model configuration on the other hand. In this paper, we focus on the latterand investigate the effect of the horizontal grid resolution on the simulation of a month of January over the Arctic. Alimited-area numerical climate model is used to simulate the month of January 1990 over a grid that includes the Arctic andsub-Arctic regions. Two grid resolutions are used: 50 km and 100 km. Results show that finer details appear for regionalcirculation, temperature, and humidity when increasing horizontal resolution. This is particularly true for continental andsea ice boundaries, which are much better resolved by high-resolution model simulations. The Canadian Archipelagoand rivers in northern Russia appear to benefit the most from higher horizontal resolution. High-resolution simulationscapture some frozen rivers and narrow straits between islands. Therefore, much colder surface air temperature is simulatedover these areas. Precipitation is generally increased in those areas and over topography due to a better representationof surface heterogeneities when increasing resolution. Large-scale atmospheric circulation is substantially changed whenhorizontal resolution is increased. Feedback processes occur between surface air temperature change over heterogeneoussurfaces and atmospheric circulation. High-resolution simulations develop a stronger polar vortex. The mean sea-levelpressure increases over the western Arctic and Iceland and decreases over the eastern Arctic. This circulation leads to asubstantial cooling of the eastern Arctic and enhanced synoptic activity over the Arctic associated with an intensificationof the baroclinic zone. Aerosol mass loading, which is simulated explicitly in this model, is significantly altered by thegrid resolution change with the largest differences in aerosol concentration over areas where precipitation and atmosphericcirculation are the most affected. The implications of this sensitivity study to the evaluation of indirect radiative effectsof anthropogenic aerosols are discussed. Copyright 2005 Royal Meteorological Society.

KEY WORDS: regional climate modelling; arctic; aerosols; clouds

1. INTRODUCTION

The Arctic is vulnerable to anthropogenic emission in mid-latitudes. Radiative active gases such as CO2

and anthropogenic aerosols emitted in the mid-latitudes are likely to cause substantial changes to the Arcticclimate (IPCC, 2001). Arctic climate change projection could also have strong effects on the climate at lowerlatitudes. It is therefore important to better simulate high-latitude climate and the effects of radiative activegases and anthropogenic aerosols.

Traditionally, global climate models (GCMs) have been used to simulate the current and temporal evolutionof the Arctic climate. GCMs use horizontal resolutions between 300 and 500 km (IPCC, 2001). The choiceof the horizontal resolution generally depends on model physical sophistication and simulation length. GCMspatial resolution is sufficient to properly simulate large-scale evolution of the atmosphere. However, higher

* Correspondence to: Eric Girard, Regional Climate Modelling Laboratory, Department of Earth and Atmospheric Sciences, Universityof Quebec at Montreal, P.O. Box 8888, Montreal (Quebec) H3C 3P8, Canada; e-mail: [email protected]

Copyright 2005 Royal Meteorological Society

1456 E. GIRARD AND B. BEKCIC

spatial resolutions are often required to resolve stationary forcing due to surface inhomogeneities (McGregor,1997; IPCC, 2001).

The sensitivity of GCMs to horizontal grid resolution has been investigated previously (Boer and Lazare,1988; Tibaldi et al., 1990; Boyle, 1993; Williamson et al., 1995; Brankovic and Gregory, 2001; Pope andStratton, 2002). Results show a decrease of systematic errors and an increase of spatial and temporal variability.The most important differences are found in the detailed spatial pattern obtained for most variables. Physicalprocesses related to topography, such as precipitation, also benefit from higher resolution. However, physicalparameterizations that have been developed for GCM spatial resolution can deteriorate the simulation whenused at higher resolutions (IPCC, 2001).

The mean circulation over the Arctic is not well simulated by GCM with wintertime mean sea-level pressurebias of up to ±20 hPa. Large bias also characterizes surface air temperature simulated by GCM in winter.Errors are particularly high near shores and ice boundary (Tao et al., 1996; Chen et al., 1995; Walsh, personalcommunication). To better evaluate regional and local feedbacks, one needs models that are more elaborated.In addition, to improve the physics of the models, particular attention must also be put on the spatial resolutionof the model grid.

Several solutions have been proposed to solve this problem: variable resolution used with GCM (Deque andPiedelievre, 1995), high-resolution GCM in which simulations are performed in short time slices (Cubaschet al., 1995), and limited-area (or regional) climate models (RCM) (Dickinson et al., 1989; Giorgi, 1990).Given the computational limitations associated with the use of high-resolution GCM, RCM are a valuableapproach. However, one must be aware of the intrinsic limitations of such models. Giorgi and Mearns (1999)mention that the horizontal resolution, the domain size, and the chosen area are among the parameters relatedto the model configuration that can alter the RCM solution. Some criteria should be accounted for whenchoosing horizontal grid spacing. The grid resolution should be high enough to represent the forcings ofinterest and should correspond to the application the user wants to make of the results. Finally, it should beadapted to the scale (temporal and spatial) of the phenomena investigated.

RCM needs to be driven at its boundaries by a coarser grid model like a GCM or by observation analyses.The spatial resolution of the driving model (or analyses) at the lateral boundaries of the RCM domain is alsoimportant. Owing to potential interpolation errors and numerical noise, the ratio between these two resolutionsis usually limited between 2 and 5 (Denis et al., 2002). For higher resolution ratios, multiple nesting approachis needed (Christensen et al., 1998).

Few studies have been performed to assess the sensitivity of RCM to horizontal resolution in the Arctic.Rinke et al. (2000) found that higher-resolution simulations do not differ significantly from lower-resolutionsimulations for large-scale circulation over the Arctic. However, surface air temperature and precipitation overland, particularly where topography is important, such as over Greenland, change significantly with increasingresolution. Inasmuch as observation of those variables is not available at the 50 km resolution used, they couldnot conclude whether high-resolution simulation verifies better than low-resolution simulation.

An important aspect of high-resolution simulations is the potential change of the model internal variability(Christensen et al., 2001). The internal variability comes from the non-linear nature of the fluid mechanicsequations of the model and is associated with its freedom to generate its own circulation at smaller spatialscales. The internal variability must be taken into account to detect the desired signal in sensitivity studies.According to Rinke and Dethloff (2000), pan-Arctic domains are characterized by a much larger variabilitythan those over mid-latitudes of the same size or smaller Arctic domains. The lateral boundary control on thecirculation inside the domain is weaker for a circumpolar domain compared to a domain over mid-latitudes.Therefore, the model has more freedom to diverge from the forcing field. This effect is amplified during thecold season when the circulation over the Arctic is stronger. It allows for smaller-scale perturbations to grow ata faster rate. In addition to the atmospheric internal variability, one must also consider inter-annual variabilityin any investigations aiming at detecting a climate signal. The Arctic is characterized by particularly stronginter-annual and inter-decadal variabilities, which have to be taken into account (Dorn et al., 2000; Battistiet al., 1997).

The regional aspect of the aerosol radiative forcing over the Arctic is the main motivation of thisinvestigation. Aerosols have a relatively short lifetime and their concentration over a given area is the end

Copyright 2005 Royal Meteorological Society Int. J. Climatol. 25: 1455–1471 (2005)

ARCTIC REGIONAL CLIMATE SIMULATION AND HORIZONTAL RESOLUTION 1457

result of the atmospheric circulation, cloud cover, cloud microphysical properties, and precipitation. Previousinvestigations have shown the regional aspect associated with aerosol climate forcing over the Arctic (e.g.Rinke et al., 2004; Girard and Stefanof, 2004). Both the mean state and the variability of the atmosphereare likely to play a major role in the spatial distribution and magnitude of the aerosol radiative forcing.Investigations of the sensitivity of RCM with aerosol as a prognostic variable have not been performedthus far for the Arctic region. However, the sensitivity of GCM with prognostic aerosol to the horizontalresolution has been investigated recently. Ghan et al. (2001) have found that the indirect radiative effect ofanthropogenic aerosols is particularly sensitive to GCM horizontal grid resolution. They have shown that theindirect radiative forcing varies from −2.4 to −1.7 W m−2 when increasing the resolution from 4.5° by 7.5°

to 2.8° by 2.8°. Their results indicate a decrease of the total cloud water path and an increase of aerosol massloading with increasing horizontal resolution.

The purpose of this sensitivity study is to investigate the effect of horizontal resolution on the simulationof a month of January using an RCM with prognostic aerosol, the Northern Aerosol Regional ClimateModel (NARCM). This study is not aimed at evaluating the performance of the model. The main goal isto investigate the ability of the model to develop smaller-scale features, which are not present at lowerresolution, and to assess the impact of those small-scale features on clouds, precipitation, and the large-scaleatmospheric circulation, from which depends aerosol transport. Two horizontal resolutions typically used inregional climate simulations (50 and 100 km) will be compared. A particular attention is given to variablesand physical processes related to the estimation of the indirect radiative effect of anthropogenic aerosols:temperature, cloud phase, precipitation, and aerosol. This is an important issue, given the fact that aerosoldirect and indirect radiative effects strongly vary on the regional scale.

A short description of NARCM is given in Section 2. The experimental design is described in Section 3and results are shown in Section 4. Finally, Section 5 summarizes the main conclusions.

2. MODEL DESCRIPTION

NARCM is a limited-area non-hydrostatic climate model with a dynamic size-distributed aerosol schemeCAM (Canadian Aerosol Module). The numerical formulation of NARCM is derived from the CanadianRegional Climate Model (Caya and Laprise, 1999). It is based on the fully elastic, non-hydrostatic Eulerfield equations solved with a state-of-the-art semi-implicit and semi-Lagrangian (SISL) marching algorithmsadequate for computing atmospheric flow at all spatial scales. CRCM atmospheric variables are discretizedon an Arakawa C-type staggered grid on a polar-stereographic projection in the horizontal and Gal-Chenterrain-following scaled height coordinate in the vertical (Gal-Chen and Somerville, 1975).

NARCM simulates aerosol explicitly with 12-size bins from 0.005 to 20.48 µm (Gong et al., 2003).The aerosol module CAM is a size-segregated multi-component algorithm that treats five major typesof aerosols: sea-salt, sulphate, black carbon, organic carbon, and soil dust. It includes major aerosolprocesses in the atmosphere: production, growth, coagulation, nucleation, condensation, dry deposition,below-cloud scavenging, activation, a cloud module with explicit microphysical processes to treat aerosol-cloud interactions, and chemical transformation of sulphur species in clear air and in clouds. Each of theseaerosol processes is strongly dependent on particle size, thus requiring an explicit representation of the sizedistribution. Although still demanding in computer resources, the numerical solution is optimized to efficientlysolve the complicated size-segregated multi-component aerosol system and make it feasible to be included inglobal and regional models. Application of CAM in the Canadian GCM has resulted in a very reasonable globalsea-salt and sulphate aerosol distributions compared to observations. Previous experiments have shown thatCAM is able to predict relatively well aerosol size and concentration in marine and continental area (Covertet al., 1996; Quinn et al., 1996; Gong et al., 1997). With proper anthropogenic emissions surrounding theArctic, CAM is able to predict the spatial and temporal aerosol compositions and size distributions in theArctic atmosphere.

Surface processes are simulated with the Canadian Land Surface Scheme (CLASS). The microphysicsscheme is from Lohmann and Roeckner (1996). The other physical parameterizations are from the Canadian



GCM (McFarlane et al., 1992). NARCM can be implemented at a range of grid resolutions (generally from10–100 km) and a variety of domains. The model has 22 Gal-Chen vertical levels with high vertical resolution(50 m) in the lower troposphere. The vertical resolution gradually decreases to 1750 m at 500 hPa and remainsconstant from this point to 34 120 m.

3. DESIGN OF THE EXPERIMENT

Simulations of the month of January 1990 at 100 km and 50 km horizontal resolution are performed.Simulations start on December 1 to allow for 1-month spin-up time. An ensemble mean of five sim-ulations for each horizontal resolution is considered. The first ensemble of simulations is performed at100 km horizontal resolution (hereafter NARCM100), while the second ensemble of simulations is per-formed at a horizontal resolution of 50 km (hereafter NARCM50). Each simulation within an ensem-ble is initialized differently by advancing or retreating the initial data by one day following Rinke andDethloff (2000). Ensemble simulations are required to distinguish the model internal variability from thedifferences between the two simulated cases due to the change of horizontal resolution. In the analy-sis of the results, the confidence on differences found in one given field over the domain is evaluatedusing a Student’s t-test with a confidence level of 95%. The uncertainty associated with the differencebetween the averaged values of a variable of NARCM50 and NARCM100 ensembles is calculated asfollows:

± tN100+N50−2;α/2

√(1

N100+ 1

N50

)(N100 − 1)s2

100 + (N50 − 1)s250

N100 + N50 − 2

where t is the student parameter, α is the confidence level, N100 and N50 are the number of simulations inthe NARCM100 and NARCM50 ensembles respectively, and s2

100 and s250are the unbiased sample variance

of a given variable within the NARCM100 and NARCM50 ensembles respectively. When this uncertaintyis smaller than the difference between two means, it is considered to be significant at the confidence levelα. Areas where differences are significant are shaded in the figures presenting the results for the differencesbetween NARCM50 and NARCM100.

The domain size is 6800 km by 6800 km and includes most of Europe, northern Canada, Siberia, andNorth Atlantic Ocean. The domain is centred at 360°E, 79°N. The slight eastward location of the domain isa compromise between the need to include source area of aerosols and the computational cost of the model.Results are analysed on a sub-domain of 4900 km by 4900 km to eliminate the nine-grid point sponge of thedomain boundaries.

Initial and boundary conditions for atmospheric fields are provided by the National Center for EnvironmentalPrediction (NCEP) analyses on a 2.5° by 2.5° longitude/latitude grid. Monthly mean sea surface temperature(SST) and sea ice concentration are from the CCC (Canadian Climate Centre) world climatology on a 1° by1° grid. These values are interpolated on the two grids (50 km and 100 km horizontal resolution) used forsimulations.

In this experiment, sulphate and sea-salt aerosols are considered. Emission of sea-salt aerosols is calculatedfollowing a function developed by Gong et al. (1997). Natural sources of sulphate (DMS and H2S) areprovided by dataset of Bates et al. (1992) for DMS and by Kettle et al. (1999) for H2S. Anthropogenicemission of sulphur dioxide and sulphate (SO2 and SO4

−2) is from the Global Emission InventoryActivity (GEIA) 1985 (Benkovitz et al., 1996). Seasonal emission rate on two levels is considered inthe dataset. Initially, the aerosol concentration is set to 0 everywhere in the domain. At the lateralboundaries, the aerosol concentration is maintained to 0 during the whole simulation. The model generatesits own aerosol and simulates the transport, source, and sink of aerosols. The aerosol spatial distributionand concentration reach background observed value within 1 month, which is the spin-up time in ourexperiment.



4. RESULTS

4.1. Internal variability of the model

One of the objectives of increasing the horizontal resolution of an RCM is to better simulate smaller-scale processes. It is also important to be able to evaluate feedback processes associated with small-scaleprocesses. To reach this goal, the RCM must have enough freedom to develop its own regional circulation,which can feedback on larger scales. The RCM internal variability is important for the detection of a climatesignal in sensitivity studies. Indeed, when the model internal variability is reduced compared to the realclimate variability, the detection and the evaluation of the magnitude of a climate signal are likely to beunderestimated. Among the factors contributing to the change of the model internal variability, Christensenet al. (2001) mentioned the domain size, model physics, and grid horizontal resolution. The extent to whichthe internal variability of the RCM is increased over a given area as a result of increasing horizontal resolutionis directly related to the gain in the model ability to simulate realistically the climate of this area. The internalvariability of NARCM has been investigated in this context.

Figure 1 shows the ensemble standard deviation of the mean sea-level pressure, temperature at 1000 hPa,and aerosol mass loading for NARCM50 and NARCM100. Results show that the internal variability of theNARCM50 ensemble is generally larger over most of the domain. The largest increase occurs over sea iceborders and Greenland. These results support the hypothesis formulated by Rinke and Dethloff (2000) onthe link existing between sea ice border and topography and the internal variability of RCMs. The betterresolution of topography and sea ice in the NARCM50 ensemble appears to increase substantially the internalvariability of the model over these areas. This increase of internal variability is particularly strong for allexamined variables over the Canadian Archipelago, Greenland, and northern Europe. On the other hand, themodel internal variability remains small over southern Europe and Beaufort Sea. These regions are underthe influence of an atmospheric flow that comes from outside the domain. A strong forcing by the drivingobservation analysis exists over these areas. As a result, the model cannot diverge much from the NCEPobservation analysis. This is why these areas show little variability. In the absence of topography and sea iceborders, the increase of the model internal variability is smaller, although not negligible. Feedback processesrelated to better surface resolution over sea ice borders and topography at lower latitudes explain, in part,the larger variability over central Arctic. Increasing resolution from 100 to 50 km increases significantly theability of the model to generate its own circulation, as shown by these results. The extent to which the modelcan increase its variability is not known a priori. One has to compare the RCM simulation to a GCM havingthe same physics and horizontal resolution.

4.2. Large-scale circulation and surface air temperature

Figure 2 shows the January 1990 ensemble mean sea-level pressure (MSLP) obtained with NARCM100 andNARCM50 and the difference between the two cases. The model compares well with the MSLP climatologyfor January (not shown) derived from the NCEP 30-year reanalysis. NARCM100 and NARCM50 reproducewell the cyclone centred just southwest of Iceland with an associated through stretching over the Barents Sea.The anticyclone over Siberia is also well simulated by the model. However, NARCM100 and NARCM50differ on the magnitude and slightly on the location of these pressure centres.

When compared to NARCM100, NARCM50 generally simulates lower MSLP in the eastern Arctic andsub-Arctic, while the opposite occurs in the western Arctic. NARCM50 develops a secondary low pressure(associated with the Icelandic low) southeast of Svalbard, while this secondary low is located southwest ofSvalbard in NARCM100. This contributes to MSLP differences up to −10 hPa over the Kara Sea and +8 hPaover the North Atlantic Ocean between NARCM50 and NARCM100. The anticyclone and its associated ridgeover Siberia have a different orientation in NARCM50 with a west-east axis, while this high pressure has asouth-north axis in NARCM100. As a result, the pressure over northern Canada is higher in NARCM50 byup to 7 hPa.

Figure 3 presents the monthly mean surface air temperature for the two simulations, the difference betweenthe two, and the surface wind direction for NARCM50 and NARCM100. As expected, NARCM50 simulates



(a) (b)

(c) (d)

(e) (f)

Figure 1. Ensemble standard deviation for (a) NARCM100 MSLP (hPa), (b) NARCM50 MSLP (hPa), (c) NARCM100 surface airtemperature (K), (d) NARCM50 surface air temperature (K), (e) NARCM100 aerosol mass loading (×10−7 kg m−2), and NARCM50

aerosol mass loading (×10−7 kg m−2)

finer scale temperature variation when compared to NARCM100. This is particularly true over Greenland,Canadian Northern Islands, and the sea ice border over the North Atlantic and the Barents Sea. Increasinghorizontal resolution from 100 to 50 km produces large differences over small islands, particularly overnorthern Canada, where NARCM50 is colder by 2 to 6 K. Similar results are obtained around the NovayaZemlya Russian Island and over rivers in northern Russia, which discharge their water into the Kara Sea.



(a) (b)

(c)

Figure 2. January mean MSLP (in hPa) for (a) NARCM100, (b) NARCM50, and (c) difference between NARCM50 and NARCM100(NARCM50 minus NARCM100). Areas where differences between NARCM50 and NARCM100 are statistically significant are shaded

The sea ice climatology used in the simulations has a resolution of 10 by 10, which corresponds to a spatialresolution of about 55 km (east-west direction) at 60°N. NARCM100 horizontal resolution is not large enoughto resolve relatively narrow straits between Canadian Arctic islands and large rivers in northern Russia. Theinterpolation of the sea ice climatology to NARCM100 grid reduces artificially sea ice extent. As a result,NARCM100 simulates higher surface air temperature over these areas since islands and continental surfacesare generally warmer than sea ice. Over Greenland, NARCM50 has a better representation of its topography.As a result, it simulates colder temperature.

Temperature differences between NARCM50 and NARCM100 are not confined to sea ice borders and largetopography. Indeed, large differences appear over the Arctic with NARCM50 being generally colder by upto 6 K. Differences also reach large values over the unfrozen part of the Barents Sea with values up to 10 K.When averaged over time and space, NARCM50 is colder than NARCM100 by 2.1 K. The air temperaturedifference between the two simulations decreases upward to reach 0 at about 700 hPa (not shown). Over the



(a) (b)

(c) (d)

Figure 3. January mean surface air temperature (K) for (a) NARCM100, (b) NARCM50, and (c) differences between NARCM50 andNARCM100 (NARCM50 minus NARCM100). Statistically significant differences are shaded. (d) Winds at 1000 hPa for NARCM100

(grey) and NARCM50 (black)

Arctic and free water of the Barents Sea, the surface is much more homogeneous. Discrepancies between thetwo ensembles are due to feedback processes occurring between large-scale circulation and surface temperaturedifferences over areas of surface discontinuities such as sea ice and continental borders and topography. InNARCM50, the new orientation of the Siberian high creates a larger pressure gradient over the eastern Arctic.As a result, cold temperature advection is enhanced in NARCM50 compared to NARCM100 over this areasince the dominant winds comes from Siberia, which is colder than the eastern Arctic. This contributes tosignificantly cool the eastern Arctic. The eastern Arctic cooling is therefore due to a feedback process, whichoriginates from the difference between NARCM50 and NARCM100 over the Canadian Northern Islands.Colder surface air temperature over Svalbard is also due to the change in the circulation pattern near thesurface. In NARCM50, winds in this area are from the northeast compared to winds from the southeast inNARCM100. Consequently, the surface air temperature is substantially colder over this area in NARCM50simulations.



Figure 4 shows the geopotential height at 500 hPa as simulated by NARCM50 and NARCM100 and thedifferences between the two. NARCM50 geopotential heights at 500 hPa are generally lower than NARCM100by 37 m on average. The largest differences between NARCM50 and NARCM100 are located over the easternArctic (−120 m) and northern Canada (−60 m). These differences are due to the colder lower tropospherictemperature simulated by NARCM50 over most of the domain. The decrease of the geopotential heights at500 hPa over Canada, eastern Arctic, and Barents Sea induces a change in the 500-hPa circulation pattern.Results show a stronger isohypse gradient north of eastern Siberia and a decrease of the isohypse gradientnorth of western Siberia. This change has some important implications for the aerosol transport, cloud cover,and precipitation. This will be discussed in the next section.

Increasing horizontal resolution has also an impact on the climate variability over some areas. Figure 5shows the standard deviation for surface air temperature, and MSLP for NARCM50 and NARCM100. Resultsshow that the spatially averaged monthly standard deviations of MSLP and surface air temperature are

(a) (b)

(c)

Figure 4. January mean geopotential height (gpm) at 500 hPa for (a) NARCM100, (b) NARCM50, and (c) difference between the twosimulations (NARCM50 minus NARCM100). Areas where differences between NARCM50 and NARCM100 are statistically significant

are shaded



(b)(a)

(c) (d)

Figure 5. Surface air temperature standard deviation (K) for (a) NARCM100 and (b) NARCM50. MSLP standard deviation (hPa) for(c) NARCM100 and (d) NARCM50

respectively 11.9 hPa and 5.8 K for NARCM50 and 10.4 hPa and 5.1 K for NARCM100. The largest increasesof variability for these variables are over sea ice borders, continental borders, and high topography. It appearsthat these surface discontinuities, better resolved by NARCM50, allow for better simulating stationary eddies.Over these locations, MSLP and surface air temperature standard deviations increase by up to 300%.

Larger MSLP and surface air temperature standard deviations are also obtained over areas characterized byfew surface discontinuities such as the central Arctic and northern Siberia. The increase of MSLP and surfaceair temperature standard deviations over these areas is related to the increase of the atmospheric baroclinicity.The surface air temperature gradient is enhanced in NARCM50 between the Arctic and the mid-latitudes. Inthese conditions, the frequency and amplitude of transient eddies increase and contribute to the variabilityover the surface air temperature and MSLP. Transient eddies transport the heat energy from the mid-latitudesto the Arctic. This transport is especially important during winter over Eurasia. The increase of variability ofMSLP and surface air temperature over this area is substantial. Figure 6 shows that the meridional componentof the wind is larger for NARCM50 over most of the domain. The only exceptions are northern Canada,



Figure 6. Differences between NARCM50 and NARCM100 (NARCM50 minus NARCM100) for the January ensemble mean meridionalwind at 1000 hPa (m s−1)

northeast of Siberia, and east of Greenland. Therefore, energy transport between mid-latitudes and the Arctic issignificantly strengthened in NARCM50 and contributes in increasing the variability of surface air temperatureand MSLP in the Arctic.

Differences between NARCM100 and NARCM50 for MSLP, geopotential heights, and temperature aresimilar to the differences found by Dorn et al. (2000) between two distinct circulation regimes in the Arcticduring January. They have analysed 12 cold and warm months of January from a high-resolution RCMforced by a GCM. They found two distinct circulation patterns, which were identified by the authors as awarm and cold circulation type. NARCM50 corresponds to a cold January, while NARCM100 correspondsto a warm January. Dorn et al. found that warm (cold) Januaries were associated with small (large) sea iceconcentration. Differences found between NARCM50 and NARCM100 suggest that a small difference in seaice concentration due to the model horizontal resolution may have a substantial impact on the atmosphericcirculation. In the case simulated in this study, this small difference is enough to change the circulation typefrom a warm January (NARCM100) to a cold January (NARCM50). These results highlight the potentialimportance of sea ice on the atmospheric circulation over the Arctic during January. Further research will benecessary to clarify this aspect.

4.3. Clouds, radiation, precipitation, and aerosols

The estimation of the indirect effect of aerosols on the arctic climate strongly depends on the model ability toproperly simulate cloud microphysical properties, precipitation, aerosol transport, and the interaction betweenclouds and aerosols. The effect of increasing the model horizontal resolution on the simulation of aerosolsand their effects on clouds and climate has been investigated in this study.

During January, the sub-Arctic air mass is very dry with low precipitation frequency (Ryaboshapko et al.,1994). Further, wintertime large-scale circulation patterns are favourable to the transport of mid-latitudeaerosols and gaseous precursors into the Arctic. This transport is controlled by a strong anticyclone centredover Siberia and two low pressures centred in Iceland and southwest of Alaska (Barrie et al., 1989; Bigg,1996). The latter is not visible in Figure 2 since it is confined at the limit of our domain of analysis. Under theseconditions, anthropogenic emissions in highly populated areas of northern Europe and northeastern Asia areefficiently transported into the Arctic with limited washout by precipitation (Raatz, 1984). According to Iversen(1996), anthropogenic sources in Europe contribute for more than 70% of the Arctic anthropogenic aerosolconcentration, while sources in Asia contribute about 30%. Limited precipitation and favourable atmospheric



circulation explain the seasonal variability of anthropogenic aerosol concentration over the Arctic, with amaximum during winter.

As opposed to greenhouse effect, radiative direct and indirect effects of aerosols may be very regionalin some cases. This is due to the fact that aerosol sources are not uniformly distributed in space and time.Furthermore, aerosols have a relatively short lifetime (less than 2 weeks), which depends on dry and wetremoval processes. Therefore, their concentration varies substantially in time and space and depends on theatmospheric circulation and cloud and precipitation spatial distribution. As a result, the direct and indirectradiative effects of aerosols also vary substantially over time and space. In turn, aerosols can also modifycloud microphysical properties, thus modifying clouds, precipitation, and atmospheric circulation throughfeedback processes. The effect of increasing the model horizontal resolution on aerosols is investigated inthis context.

Figure 7a shows the aerosol mass loading (aerosol mass integrated in the vertical) difference betweenNARCM50 and NARCM100. The monthly aerosol mass loading averaged over the domain decreases slightlyby less than 1% in NARCM50 when compared to NARCM100. However, strong differences appear locally.Over Europe, eastern Greenland, and Barents Sea, the aerosol mass loading decreases substantially inNARCM50. On the other hand, it strongly increases over most of the Eastern Arctic just north of Siberia.Over the western Arctic, differences between NARCM50 and NARCM100 are not statistically significant.Over Europe, the decrease of aerosol mass loading in NARCM50 is explained by the enhanced precipitation,which contributes to increase in aerosol wet removal. Figure 7b shows that precipitation over Europe is muchhigher in NARCM50 compared to that in NARCM100. Larger precipitation rate by NARCM50 over Europeis associated with areas with important topography, which is better resolved by NARCM50. Results show thatthe precipitation rate is enhanced over other regions with high topography such as Greenland. Consequently,the aerosol mass loading over Greenland is also decreased.

The increase of the monthly mean aerosol mass loading over the eastern Arctic originates from the differenceof the atmospheric circulation between NARCM50 and NARCM100. Stronger zonal circulation and enhancedmeridional component of the wind over northwestern Russia in NARCM50 favours the transport of aerosolsto the eastern Arctic. This effect is amplified by the fact that this pathway is the one that contributes the mostto the anthropogenic aerosol concentration in the Arctic (Tarascon and Iversen, 1996). The decrease of themonthly mean aerosol mass loading over the Barents Sea also arises from the atmospheric circulation pattern,which is more zonal over this area in NARCM50.

(a) (b)

Figure 7. Differences between NARCM50 and NARCM100 (NARCM50 minus NARCM100) for the (a) January mean aerosol massloading (×10−6 kg m−2) and (b) January mean precipitation rate (mm day−1). Areas where differences between NARCM50 and

NARCM100 are statistically significant are shaded



Clouds largely control the radiative budget at the surface in the Arctic during winter. In January, solarradiation is negligible over the Arctic. Therefore, the main effect of clouds on the radiative budget is to warmthe surface through infrared radiation. The amount of infrared radiation emitted by clouds largely depends ontheir microphysical properties and lifetime. Aerosols may act as either cloud condensation or cloud ice nucleiand determine to some extent cloud microphysical properties and lifetime. Therefore, a change of the aerosolcomposition and concentration over a given area may change significantly the cloud radiative effect at thesurface and the local climate. Other factors also affect clouds such as temperature (thermodynamic phase),humidity, and atmospheric circulation.

Figure 8 shows that large cloud cover characterizes both simulations with values exceeding 90% overmost areas of the domain with the exception of northern Canada and Greenland with values between 60 and70%. NARCM50 and NARCM100 simulations produce similar cloud coverage. Differences are generallysmall except for the Canadian Arctic, where NARCM50 simulates much less clouds. Large differences overthe eastern Canadian Northern Islands can be attributed to high topography, which is better resolved byNARCM50. Over the rest of the domain, results show that NARCM50 produces more clouds over most ofthe Arctic. Enhanced circulation and storm activity of NARCM50 explain in part the differences between thetwo ensembles over the Arctic. This is the case in the eastern Arctic, which is characterized by a strengthenedcirculation in NARCM50. Cloud cover over this area increases by more than 10%. Larger aerosol concentrationover the eastern Arctic north of Siberia in NARCM50 also contributes to the increase of the monthly meancloud cover. Cloud lifetime is enhanced owing to the increase of cloud droplet number concentration, whichresults from the increase of cloud condensation nuclei.

Figure 9 shows the difference between NARCM50 and NARCM100 for the monthly mean cloud liquidand ice water path. NARCM50 produces significantly more cloud ice and about the same amount ofcloud liquid water than NARCM100 over the eastern Arctic. Over the western Arctic, both the ice andliquid water paths decrease significantly. At lower latitudes, the liquid water path increases substantiallyin NARCM50. Differences between the two simulations can be explained as follows. The eastern Arcticis much colder in NARCM50, thus the formation of cloud ice is favoured at the expense of cloud liquidwater. However, because the atmospheric circulation is stronger over this area in NARCM50, the moistureadvection from lower latitude is enhanced so that cloud liquid water does not change much and even increasesat about 70°N. The increase of aerosol concentration also contributes in maintaining the cloud liquid pathby increasing cloud lifetime. Over the western Arctic, both cloud ice and cloud water decrease. This isdue to the high-pressure ridge over northern Canada, which is stronger when compared to NARCM100,and brings dryer conditions over this area. Over most areas south of 70°N, cloud liquid water path isincreased as a result of enhanced storm activity in NARCM50. It should be noted here that, as opposed

100

95

90

85

80

75

70

65

60

(a)100

95

90

85

80

75

70

65

60

(b)

Figure 8. January ensemble mean cloud cover (%) for (a) NARCM100 and (b) NARCM50



(b)(a)

(c)

Figure 9. Difference between NARCM50 and NARCM100 (NARCM50 minus NARCM100) for the January ensemble mean (a) cloudliquid water path (×10−2 kg m−2), (b) cloud ice water path (×10−2 kg m−2), and (c) cloud radiative forcing at the surface (W m−2).

Areas where differences between NARCM50 and NARCM100 are statistically significant are shaded

to cloud condensation nuclei, cloud ice nuclei concentration does not depend on aerosols in the microphysicsscheme. Therefore, the effects of the change of aerosols concentration and composition on heterogeneous icenucleation are not accounted for here. Although potentially important, the lack of knowledge on the effect ofanthropogenic aerosols on ice nucleation properties does not allow accounting for this feedback process inthis investigation.

Cloud radiative effect (CRE) at the surface is defined as the difference between the net terrestrial radiativeflux at the surface for cloudy and clear sky. Solar radiation is not accounted for here since it is very small overthe Arctic in January. Both simulations show similar values of CRE ranging from 5 W m−2 to 75 W m−2

(not shown). Small values of CRE are found over the Canadian Archipelago and in the western Arctic. HigherCRE are found over the North Atlantic and eastern Arctic, with a maximum over the north-western coast ofNorway. Averaged over time and space, both simulations have similar CRE with 33.2 and 34.9 W m−2 forNARCM50 and NARCM100 respectively.



Although mean values of CRE are similar between the two simulations, CRE varies substantially locally.Figure 9c shows the CRE difference between NARCM50 and NARCM100. Differences can be as large as27 W m−2. Largest differences are located over the Barents Sea, which is a region where cloud liquid and icewater paths, and temperature is much lower in NARCM50 when compared to NARCM100. Over northernCanada, CRE is decreased by up to 10 W m−2 and is mostly explained by the reduction of cloud coverin NARCM50 over this area. Despite the substantial decrease of the lower tropospheric temperature overeastern Arctic, CRE differences are slightly positive but remain generally very small with values of lessthan 5 W m−2, except for a very localized maximum of 27 W m−2 over Zemlya Island. In NARCM50, theincrease of the liquid and ice water paths and cloud cover associated with the enhanced meridional circulationand larger aerosol concentration compensate the decrease of downwelling infrared radiation at the surface dueto lower tropospheric temperature. Although the eastern Arctic is the region where the effect of increasingresolution is the strongest for temperature, aerosols, and cloud microphysical properties, compensating effectsbetween optically thicker clouds and lower temperature allow the CRE to remain almost unchanged.

5. SUMMARY AND CONCLUSION

The investigation of the radiative effects of aerosols on climate is a difficult task due to the spatialheterogeneities characterizing aerosol concentration. In addition, to improve model physics that govern aerosolinteraction with the climate system, one also needs to gain a better understanding of the intrinsic limitationsassociated with current numerical modelling approaches. In this research, we address one model limitation,which is the horizontal grid resolution. The effect of horizontal grid resolution on the simulation of the monthof January 1990 over the Arctic is investigated using a limited-area numerical model. This sensitivity study isdone in the context of climate change studies involving aerosols and the indirect radiative effects of aerosols.

A sensitivity study is performed using a limited-area climate model (NARCM), which simulates aerosolprognostically. Two ensembles of 1-month simulations are performed at a horizontal resolution of 50 km(NARCM50) and 100 km (NARCM100) over a pan-Arctic domain. Results show that the effects of increasingthe horizontal resolution from 100 to 50 km are substantial over most of the domain. The January-meansurface air temperature averaged over the domain is lower when horizontal resolution is increased. The largestdifferences range between 5 and 10 °C and occur over eastern Arctic and Barents Sea, where NARCM50 ismuch colder than NARCM100. MSLP is also modified substantially with differences reaching up to 10 hPalocally.

As expected, increasing horizontal resolution from 100 to 50 km improves the realism of temperature andprecipitation simulations particularly over areas with large surface heterogeneities such as large topography,islands, straits, and sea ice borders. Results show, however, that differences over these areas also have animpact on other areas characterized with a homogeneous surface through feedback process. These feedbackprocesses occur between the surface air temperature change over northern Canada and the atmosphericcirculation at larger scale. Straits between islands over northern Canada are a few tens of kilometres wide.As a result, increasing the horizontal resolution from 100 to 50 km allows resolving these frozen straits. Thisis sufficient to alter significantly the surface temperature over a large area over northern Canada since seaice is colder than surface islands. Colder temperature in the lower atmosphere decreases geopotential heightsand intensifies the polar vortex over this area. As a result, a negative NAO circulation type develops withhigher MSLP over Canada and Iceland and lower MSLP over the eastern Arctic. Large-scale circulation overnorthern Russia and eastern Arctic is thus slightly changed. The modification of the large-scale circulationcontributes in initiating the cooling over homogeneous areas such as the eastern Arctic. It also contributesin intensifying the atmospheric baroclinicity and enhancing temperature and MSLP variability over easternArctic.

Aerosols strongly depend on the atmospheric circulation and precipitation. The modified atmosphericcirculation and enhanced precipitation in NARCM50 substantially change the concentration and spatialdistribution of aerosols. The aerosol mass loading change in NARCM50 mostly arises from the change ofatmospheric circulation. Local effects usually associated with high-resolution simulations, such as the better



simulation of precipitation over large topography, appear to significantly affect the aerosol mass loading onlyover Greenland and Europe, and remains confined to these area. Cloud microphysical properties and cloudradiative effects strongly depend on aerosols, atmospheric circulation, humidity, and temperature. They aretherefore substantially modified over most of the domain.

Results obtained in this study are not in agreement with a similar investigation done by Rinke et al. (2000),who have simulated the same month over a similar domain with the same horizontal resolutions with theregional climate model HIRHAM. The only difference, other than the model, was the sea ice and SST. In theirsimulations, they have used observed SST and sea ice, while CCC climatology was used in our investigation(in January 1990, observed sea ice was smaller than the CCC climatology). These divergent results suggestthat the sensitivity of the wintertime Arctic climate simulation to the horizontal resolution depends uponlower boundary conditions and model internal variability, which largely depends on domain configuration,model physics, and atmospheric circulation pattern. In NARCM simulations, increasing horizontal resolutionwas enough to change the circulation pattern from a warm to a cold January, which were identified byDorn et al. (2000). A thorough investigation should be undertaken to investigate the sensitivity of the modelhorizontal resolution to the simulated climate as a function of months, seasons, years, circulation type, andlower boundary conditions. This study has shown that a small change in sea ice extent, which appears asthe model horizontal resolution is increased, can have dramatic consequences on the Arctic circulation andclimate. Hence, the effect of sea ice should be investigated for various circulation types to determine itsimportance. A comparison with observations should also be done to determine to what extent increasingresolution improves (or degrades) the simulations.

In this research, horizontal resolutions of 100 and 50 km were chosen because they are currently thestandard values used in RCM simulations. However, the effect of increasing horizontal resolution is likely todepend on the absolute values of resolution. For instance, increasing resolution from 25 km to 10 km mayhave smaller effects on results. More research is needed to find the optimal horizontal resolution given thecomputational limitations.

ACKNOWLEDGEMENTS

The authors would like to thank the Canadian Foundation for Climate and Atmospheric Sciences (CFCAS),the Consortium OURANOS, and the Fonds du Quebec pour la Recherche sur la Nature et la Technologie(FQRNT) for support funding.

REFERENCES

Barrie LA, Olson MP, Oikawa KK. 1989. The flux of anthropogenic sulphur into the Arctic from mid-latitudes. Atmospheric Environment23: 2502–2512.

Bates TS, Lamb BK, Guenther A, Dignon J, Stoiber RE. 1992. Sulfur emission to the atmosphere from natural sources. Journal ofAtmospheric Chemistry 14: 315–337.

Battisti DS, Bitz CM, Moritz RE. 1997. Do general circulation models underestimate the natural variability in the arctic climate? Journalof Climate 10: 1909–1920.

Benkovitz CM, Scholtz MT, Pacyna J, Tarrason L, Dignon J, Voldner EC, Logan Pa, Graedel TE. 1996. Global gridded inventories ofanthropogenic emissions of sulphur and nitrogen. Journal of Geophysical Research 101: 29 239–29 253.

Bigg EK. 1996. Ice forming nuclei in the Arctic. Tellus 48B: 223–233.Boer GJ, Lazare M. 1988. Some results concerning the effect of horizontal resolution and gravity wave drag on simulated climate.

Journal of Climate 1: 789–806.Boyle JS. 1993. Sensitivity of dynamical quantities to horizontal resolution for a climate simulation using the ECMWF (cycle 33)

model. Journal of Climate 6: 796–815.Brankovic C, Gregory D. 2001. Impact of horizontal resolution on seasonal integrations. Climate Dynamics 18: 123–143.Caya D, Laprise R. 1999. A semi-implicit semi-Lagrangian regional climate model: the Canadian RCM. Monthly Weather Review 127:

341–362.Chen B, Bromwich DH, Hines KM, Plan X. 1995. Simulations of the 1979-1988 polar climates by global climate models. Annals Of

Glaciology 21: 83.Christensen OB, Christensen JH, Machenhauer B, Botzet M. 1998. Very high-resolution regional climate simulations over Scandinavia-

present climate. Journal of Climate 11: 3204–3229.Christensen OB, Gaertner MA, Prego JA, Polcher J. 2001. Internal variability of regional climate models. Climate Dynamics 17:

875–887.Covert DS, Kapustin VN, Bates TS, Quinn PK. 1996. Physical properties of marine boundary layer aerosol particles of the mid-Pacific

in relation to sources and meteorological transport. Journal of Geophysical Research 101(D3): 6919–6930.



Cubasch U, Waszkewitz J, Hegerl D, Perlwitz J. 1995. Regional climate changes as simulated in time-slice experiments. ClimaticChange 31: 273–304.

Denis B, Laprise R, Caya D, Cote J. 2002. Downscaling ability of one-way nested regional climate models: the big-brother experiment.Climate Dynamics 18: 627–646.

Deque M, Piedelievre JP. 1995. High resolution climate simulation over Europe. Climate Dynamics 11: 321–339.Dickinson RE, Errico RM, Giorgi F, Bates GT. 1989. A regional climate model for the western United States. Climatic Change 15:

383–422.Dorn W, Dethloff K, Rinke A, Botzet M. 2000. Distinct circulation states of the Arctic atmosphere induced by natural climate variability.

Journal of Geophysical Research 105: 29 659–29 668.Gal-Chen T, Somerville RC. 1975. On the use of a coordinate transformation for the solution of Navier-Stockes. Journal of

Computational Physics 17: 209–228.Ghan SJ, Easter RC, Chapman E, Abdul-Razzak H, Zhang Y, Leung R, Laulainen N, Saylor R, Zaveri R. 2001. A physically-based

estimate of radiative forcing by anthropogenic sulfate aerosol. Journal of Geophysical Research 106: 5279–5294.Giorgi F. 1990. Simulation of regional climate using a limited area model nested in a general circulation model. Journal of Climate 3:

941–963.Giorgi F,Mearns LO. 1999. Introduction to special section: regional climate modeling revisited. Journal of Geophysical Research 104:

6335–6352.Girard E, Stefanof A. 2004. Effect of sulphuric acid aerosols on wintertime low-level ice crystals, humidity, and temperature over the

Arctic. Preprints of the 14th International Conference on Clouds and Precipitation , vol 1, 18–23 July 2004, Bologna, 444–448.Gong SL, Barrie. LA, Prospero JM, Savoie DL, Ayers GP, Blanchet J-P, Spacek L. 1997. Modeling sea-salt aerosols in the atmosphere:

2. atmospheric concentration and fluxes. Journal of Geophysical Research 102: 3819–3830.Gong SL, Barrie LA, Blanchet J-P, Salzen Kv, Lohmann U, Lesins G, Spacek L, Zhang LM, Girard E, Lin H, Leaitch R, Leighton H,

Chylek P, Huang P. 2003. CAM: a size segregated simulation of atmospheric aerosol processes for Climate and Air Quality Models1. Module development. Journal of Geophysical Research 108: 4007–4023.

IPCC. 2001. In Climate Change 2001: The Scientific Basis, Contribution of Working Group I to the Third Assessment Report of theIntergovernmental Panel on Climate Change, Houghton JT, Ding Y, Griggs DJ, Noguer M, vanderLinden PJ, Dai X, Maskell K,Johnson CA (eds). Cambridge University Press: Cambridge and New York; 881.

Iversen T. 1996. Atmospheric transport pathways for the Arctic. In Chemical Exchange Between the Atmosphere and Polar Snow, GlobalEnvironmental Change, NAIO ASI series I, vol. 43, Wolff E, Bales RC (eds). Springer-Verlag: Berlin and Heidelberg: 71–92.

Kettle AJ, Andreae MO, Amouroux D, Andreae TW, Bates TS, Berresheim H, Bingermer H, Bonforti R, Curran MAJ, diTullio GR,Helas G, Jones GB, Keller MD, Kiene RP, Leck C, Levasseur M, Malin G, Maspero M, Matrai P, McTagart AR, Mihalopoulos N,Nguyen BC, Novo A, Putaud JP, Rapsomanikis S, Roberts G, Schebeske G, Sharma S, Simo R, Staubec R, Turner S, Uher G. 1999.A global database of sea surface dimethylsulfide (DMS) measurements and a procedure to predict sea surface DMS as a function oflatitude, longitude and month. Global Biochemical Cycles 13: 399–444.

Lohmann U, Roeckner E. 1996. Design and performance of a new cloud microphysics scheme developed for the ECHAM generalcirculation model. Climate Dynamics 12: 557–572.

McFarlane NA, Boer GJ, Blanchet J-P, Lazare M. 1992. The Canadian Climate Centre second-generation general circulation model andits equilibrium climate. Journal of Climate 5: 1013–1044.

McGregor JL. 1997. Regional climate modeling. Meteorology and Atmospheric Physics 63: 105–117.Pope VD, Stratton RA. 2002. The processes governing horizontal resolution sensitivity in a climate model. Climate Dynamics 19:

211–236.Quinn PK, Kapustin VN, Bates TS, Covert DS. 1996. Chemical and optical properties of marine boundary layer aerosol particles of

the mid-Pacific in relation to sources and meteorological transport. Journal of Geophysical Research 101(D3): 6931–6951.Raatz WE. 1984. Tropospheric circulation patterns during the Arctic Gas and Aerosol Sampling Program (AGASP), March/April 1983.

Geographysical Research letters 11: 449–452.Rinke A, Dethloff K. 2000. On the sensitivity of a regional Arctic climate model to initial and boundary conditions. Climate Research

14: 101–113.Rinke A, Lynch AH, Dethloff K. 2000. Intercomparison of Arctic regional climate simulations: case studies of January and June 1990.

Journal of Geophysical Research 105: 29 669–29 683.Rinke A, Dethloff K, Fortmann M. 2004. Regional climate effects of Arctic Haze. Geophysical Research Letters 31: L16202.Ryaboshapko AG, Sukhenko VV, Paramono SG. 1994. Assessment of wet sulfur deposition over the former USSR. Tellus 46B: 205–219.Tao X, Walsh JE, Chapman WL. 1996. An assessment of global climate model simulations of Arctic air temperatures. Journal of Climate

9: 1060–1076.Tibaldi S, Palmer TN, Brankovic E, Cubasch U. 1990. Extended-range predictions with ECMWF models: influence of horizontal

resolution on systematic error and forecast skill. Quarterly Journal of the Royal Meteorological Society 116: 835–866.Williamson DL, Kiehl JT, Hack JJ. 1995. Climate sensitivity of the NCAR Community Climate Model (CCM2) to horizontal resolution.

Climate Dynamics 11: 377–397.


Documents

Sensitivity of an Arctic regional climate model to the horizontal resolution during winter: implications for aerosol simulation