intercomparison of arctic regional climate simulations: case studies of january and june 1990

JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 105, NO. D24, PAGES 29,669-29,683, DECEMBER 27, 2000

Intercomparison of Arctic regional climate simulations: Case studies of January and June 1990

Annette Rinke

Alfred Wegener Institute for Polar and Marine Research, Potsdam, Germany

Amanda H. Lynch Program in Atmosphere and Ocean Sciences, University of Colorado at Boulder Cooperative Institute for Research in Environmental Sciences, Boulder, Colorado

Klaus Dethloff

Alfred Wegener Institute for Polar and Marine Research, Potsdam, Germany

Abstract. Advances in regional climate modeling must be strongly based on analysis of physical processes in comparison with data. In a data-poor region such as the Arctic; this procedure may be enhanced by a community-based approach, i.e., through collaborative analysis by several research groups. To illustrate this approach, simulations were performed with two regional climate models, HIRHAM and ARCSyM, over the Arctic basin to 65øN, laterally driven at the boundaries by observational analyses. It was found that both models are able to reproduce reasonably the main features of the large-scale flow and the surface parameters in the Arctic. Distinct differences in the simulations can be attributed to specific characteristics of the boundary layer and surface parameterizations, which result in surface flux differences, and to the lateral moisture forcing, both of which affect moisture availability in the atmosphere. Further disparities are associated with the additional degrees of freedom allowed in the coupled model ARCSyM. Issues of model configuration and experimental design are discussed, including domain size, grid spacing, boundary formulations, model initialization and spin-up, and ensemble approaches. In order to reach definitive conclusions in a regional climate model intercomparison, ensemble simulations with adequate spin-up and equivalent initialization of surface fields will be required.

1. Introduction

An increasingly popular approach for understanding and modeling the climate system is the development and application of regional climate models (RCMs). The rationale for constructing a high-resolution regional model is that the treatment of orography and the physical processes are limited in global climate models by both vertical and horizontal model resolution. Parameterizations of

physical processes specific to the region in question can be tested and implemented to assess their interaction with and importance to the regional climate system. When driven by analyzed lateral boundary conditions, an RCM has the additional advantage of min- imizing the impact of model biases or errors from outside the region of interest. McGregor [1997] and Giorgi and Mearns [1999] give short reviews of the underlying principles and limitations of the method and discuss future prospects. A useful course for im- proving regional climate simulations is the comparison of simulations produced by different models with each other as well as with available observations. Strengths and weaknesses of model struc- tures, numerics, and parameterizations can be assessed side by side. RCM intercomparison projects performed for midlatitudes (RACCS project [Christensen et al., 1997; Machenhauer et al., 1998], MERCURE project, PIRCS project [Arritt et al., 1999; Takle et al., 1999]) provide examples for frameworks which eval-

Copyright 2000 by the American Geophysical Union.

Paper number 2000JD900325. 0148-0227/00/2000JD900325 $09.00

uate the strengths and weaknesses of RCMs and their component parameterizations through systematic, comparative simulations. These projects concluded that in order to be able to produce suffi- ciently reliable regional climate simulations in the future it is essential that the reduction of systematic errors in the driving (global) models, when used, and local errors due to defects in the parameterizations of physical processes in the RCMs is addressed in concert.

This community-based approach is particularly valuable in a data-poor region such as the Arctic. Until recent climate modeling results [IPCC, 1996] and observational studies [Serreze et al., 2000] highlighted the Arctic as a region of particular importance and vulnerability to global climate change, there has been little mo- tivation to focus on the physical processes occurring in the Arctic climate system, which are in many ways unique when compared with other regions of the globe. Despite the climatic significance of the Arctic, many physical processes occurring in this region and the complex interactions among atmosphere, sea ice, ocean, and land surface are still not well understood. Up to now, regional climate modeling has been successfully applied for simulating the Arctic climate at high spatial resolution with the Arctic Region Climate System Model ARCSyM [Walsh et al., 1993; Lynch et al., 1995, 1999] and the regional atmospheric climate models HIRHAM [Dethloff et al., 1996; Rinke et al., 1997, 1999a] and REMO [Jiirrens, 1999]. Recent experiments using ARCSyM and HIRHAM have been conducted using lateral forcing provided by analyzed atmospheric fields for the year 1990 [Rinke et al., 1999a, 1999b; Lynch and Cullather, 2000] which presents an opportunity for intercomparison.

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29,670 RINKE ET AL.: ARCTIC REGIONAL CLIMATE SIMULATIONS

These experiments with so-called "perfect" boundary forcing provide a good method of isolating local biases arising from RCM physical parameterization deficiencies. Because the RCMs include some physical parameterizations that are also used in global models, deficiencies observed in global simulations may be reflected in simulations of the region. Using the AMIP (Atmospheric Model Intercomparison Project) simulations, Tao et al. [1996] found zonally and seasonally averaged surface temperatures simulated by a range of global models to differ by up to 8øC during summer and 17øC during winter near the North Pole. Chen et al. [ 1995] examined the longitudinally and annually averaged total cloud coverage and found values ranging from 40 to 90% for the different models. Kattsov et al. [1998] showed modeled Arctic Ocean June surface insolation values in the AMIP results to range from 85 to 185 W/m 2.

The purpose of this study is to examine simulations performed by two different models with reference to observations with the aim of identifying an optimal experimental design for more comprehensive model intercomparison projects employing a larger number of models. The primary goal of a model intercomparison project is to identify local errors in certain physical parameterization schemes which contribute directly to the biases and their differences between the models. In addition, systematic errors in the simulated mean flow fields can be detected. Thus this paper reports on a pilot study for a planned series of collaborative intercomparison experiments using different RCMs to simulate the climate of the Arctic using the data-rich case period of 1997-1998. This period is notable for the extensive field programs taking place in the western Arctic, including the Surface Heat Budget of the Arctic Ocean (SHEBA) project, the First ISCCP Regional Experiment- Arctic Cloud Experiment (FIRE-ACE) project, the Arctic Transi- tions in the Land-Atmosphere System (ATLAS) project, and the implementation of the North Slope of Alaska/Adjacent Arctic Ocean (NSA/AAO) site for the Atmospheric Radiation Measure- ment (ARM) program.

The models examined in this study are described in section 2; the experiments and the observational data employed are described in section 3. The intercomparison for January and June 1990 simulations are discussed in the following section 4. All model experiments were performed using a 100 km horizontal resolution. Although some GCMs, particularly operational weather prediction models, use comparable grid spacing, lower resolutions are more routine for global models of climate in the research environment [e.g., Frederiksen et al. 1999; Boville and Gent, 1998]. To investigate the influence of horizontal resolution on the model results, simulations have been rerun using HIRHAM at 50 km resolution. The results of this sensitivity study are shown in section 5. Finally, section 6 summarizes the main conclusions.

2. Regional Climate Model Descriptions As noted, the models employed in this study are HIRHAM

[Christensen et al., 1996], which has been applied on the Arctic Basin [Dethloff et al., 1996, 2000; Rinke et al., 1997, 1999a, 1999b; Rinke and Dethloff, 2000] and at midlatitudes [Christensen et al., 1998; Machenhauer et al., 1998; Pan et al., 2000] and ARCSyM, which has been applied on the western Arctic [Lynch et al., 1995, 1998, 1999], over the Arctic Ocean [Maslanik et al., 2000; Lynch and Cullather, 2000], and in the Antarctic [Bailey and Lynch, 2000a, 2000b]. HIRHAM is an atmospheric model with strongly constrained and largely specified lower boundary, whereas ARCSyM is a coupled atmosphere-ocean-sea ice model with greater degrees of freedom.

The models can be implemented at a range of grid resolutions (generally from 10 to 100 km) and a variety of domains. Both models are grid-point hydrostatic primitive equation models and use a staggered Arakawa C grid in the horizontal. HIRHAM is formulat- ed in hybrid coordinates (19 levels) which reduce to sigma coordinates near the surface and pressure coordinates at the top of atmosphere, and ARCSyM uses vertical sigma coordinates (23 levels). The models differ in the aspects of the numerical solution of adiabatic processes, boundary forcing approaches, and the parameterization of the physical processes. The following sections de- scribe briefly the main model characteristics (for more detailed descriptions see the quoted references).

2.1. HIRHAM Model

HIRHAM includes sophisticated atmospheric parameterizations, including land surface processes. The version discussed in this study is HIRHAM4 which consists of the following components: (1) a hydrostatic primitive equation regional atmospheric model based on the adiabatic formulation of the HIRLAM system [Machenhauer, 1988; Gustafsson, 1993] and the physical parameterizations of the global model ECHAM4 [Roeckner et al., 1996]; horizontal grid: rotated latitude/longitude coordinates with North Pole at 0øN and 0øE, resolutions 0.5 ø by 0.5 ø or 1 ø by 1ø; lateral boundary scheme: simplified Davies [ 1976] relaxation over 10 grid points; a semi-implicit leapfrog time-stepping scheme with Asselin time filtering; a radiation scheme according to Morcrette [1989] with improved parameterization of the water vapor continuum [Giorgetta and Wild, 1995]; a planetary boundary layer (PBL) parameterization based on the eddy diffusivity concept [Brinkop and Roeckner, 1995]; stratiform clouds [Sundqvist, 1978; Roeckner et al., 1991], and cumulus convection [Tiedtke, 1989; Nordeng, 1994]; and a gravity wave drag formulation [Palmer et al., 1986; Miller et al., 1989]; (2) a land-surface parameterization package which comprises a five-layer soil with predicted temperature, a bucket model for the soil water, vegetation effects, and a runoff scheme [Damenil and Todini, 1992], with soil characteristic fields from Claussen et al. [1994] and Roeckner et al. [1996]; and (3) a simple sea ice treatment based on the sea surface temperature (SST), simply setting the sea ice mask when the SST is below 271.37 K, and calculating the skin temperature according to a lin- earized heat balance equation; a fixed sea ice thickness of 2 m is used; the SST is taken from the European Center for Medium- Range Weather Forecasts (ECMWF) analyses, updated daily.

2.2. ARCSyM Model

ARCSyM is a numerical model that includes comprehensive treatments of the atmosphere, ocean, sea ice, and the land surface for application over a limited region. The version of ARCSyM used in this study is ARCSyM 3.2.2, which consists of the following components: (1) a hydrostatic primitive equation regional atmospheric model based on the National Center for Atmospheric Research (NCAR) Regional Climate Model Version 2 (RegCM2) [Giorgi et al., 1993]; horizontal grid: equal area azimuthal grid projection, 100 km by 100 km; lateral boundary scheme: a wave absorbing relaxation scheme [Grell et al., 1994] with eight grid- point boundary zone; a split-explicit time-stepping scheme; the NCAR Community Climate Model (CCM2) shortwave radiative scheme [Briegleb, 1992]; the RRTM longwave radiation scheme [Mlawer et al., 1997; Pinto et al., 1999]; the Holtslag et al. [1990] PBL parameterization; the "explicit" cloud microphysics parameterization of Dudhia [1989], and the cumulus scheme of Grell [1993]; (2) the NCAR Land Surface Model [Bonan, 1996], which

RINKE ET AL.: ARCTIC REGIONAL CLIMATE SIMULATIONS 29,671

includes a seasonally dependent vegetation/canopy layer, a six- layer soil with parameterized hydrologic and thermal processes, a multiple surface-type mosaic, permafrost, lakes, and carbon diox- ide exchange processes; (3) a sea ice model based on the Hunke and Dukowicz [1997] elastic-viscous-plastic ice dynamics, with Parkinson and Washington [1979] ice thermodynamics with mod- ifications following Schramm et al. [1997]; and (4) a sigma-coordinate, free-surface primitive equation ocean model [Mellor and Kantha, 1989] with a high-resolution mixed layer model able to resolve vertical ocean convection [Kantha and Clayson, 1994; Bailey et al., 1997].

For the experiments described in this paper, ARCSyM was constrained as much as possible to conform to the HIRHAM configuration. The ocean model was replaced by a simple "swamp" ocean with a constant oceanic heat flux. The sea ice area was constrained

to the Special Sensor Microwave/Imager (SSM/I)-derived ice area, and the dynamical component of the sea ice model was not employed. SST in open ocean areas was constrained to the observations of Shea et al. [1992], which are very close to the observational data set employed in the HIRHAM experiments. Ice and lead temperatures were calculated using the sea ice thermodynamics scheme but with ice growth and melt partially constrained to maintain the observed ice area. For these simulations, the EASE grid (Equal Area Scalable Earth grid [M.J. Brodzik, unpublished data, 1997]) was employed at a resolution of 100 km by !00 km.

3. Simulations and Data

Simulations of January and June 1990 from month-long integrations are presented. More precisely, the experiments were performed for 6-week integrations, with the first two weeks constituting a minimal spin-up time. Thus the January experiment started on December 15, and the June experiment started on May 15. At the lateral boundaries the models are forced by ECMWF analyses, updated every 6 hours in the HIRHAM model and every 12 hours in the ARCSyM model. For the study presented here, both models are applied over the whole Arctic region north of 65øN, in which HIRHAM uses both 50 km and 100 km horizontal resolution

and ARCSyM uses a 100 km horizontal resolution. In this pilot study, the intercomparison of the models with each

other and with the observations is conducted in a way that only evaluates the mean fields focusing on the large-scale circulation and surface parameters (surface air temperature, precipitation). While it is clear that the use of mean monthly fields only in this intercomparison can qualitatively influence the assessment of model skill [e.g., Mearns et al., 1995], it was found that monthly mean differences between the models are large enough that more detailed comparisons would be precipitate.

Observed surface air temperature fields were obtained from the Polar Exchange at the Sea Surface (POLES) project [Martin and Munoz, 1997]. The gridded 2 m air temperatures in this data set are interpolated from station data and drifting buoys. Comparisons of circulation are made with ECMWF operational analyses. For the purposes of this study, the 100 km EASE grid has been used to compare gridded fields. The ARCSyM and POLES data are mapped to this grid already, but for the HIRHAM output, it was necessary to interpolate to this grid.

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Figure 1. Monthly mean 850 hPa geopotential height in meters for January 1990. (a) HIRHAM, (b) ARCSyM, (c) ECMWF analyses. Contour intervals, 25 m.


4. General Performance of the Models

4.1. Large-Scale Circulation

In this section, the simulated monthly mean 850 hPa and zonal average geopotential heights are compared with the driving ECMWF analyses. In January 1990 the observed monthly mean pressure pattern in 850 hPa is characterized by low heights over Iceland and high heights over the eastern Arctic (Figure 1 c), which is connected with a tropospheric vortex in 500 hPa over Canada and higher 500 hPa heights over the Arctic Ocean (not shown). This pattern is associated with a prevailing flow from the Barents to the Beaufort Sea. Both HiRHAM and ARCSyM capture this cross-Arctic flow with a good degree of accuracy (Figure 1). The dual lobed high with centers in the East Siberian and Kara Seas in the ECMWF analysis is not captured by either model, although the tongue of high pressure along the Siberian coast extending from the Kara Sea into the East Siberian Sea is captured by both models. At 500 hPa the elongated structure remains with the pressure maximum in HIRHAM shifted more toward the Laptev Sea and the ARCSyM high over the Kara Sea. The observed closed high over Greenland is captured in HIRHAM and is only indicated in ARCSyM.

The summer season shows more variation between the models

and in comparison with the ECMWF analyses. Prominent in the June height fields at 850 hPa (Figure 2) is a strong low over the East Siberian Sea, which both models capture, but the level of accuracy is reflected in quite different flow patterns. The HIRHAM simulation (Figure 2a) captures the flow from the Beaufort to the Barents Seas more precisely than the ARCSyM simulation, but the ARCSyM simulation (Figure 2b) captures the closed low extending deeply into the atmosphere with greater success. Furthermore, both models exhibit underestimated heights over Greenland.

Given these results, a set of ensemble simulations [e.g., Toth et al., 1997] for the summer months of 1990 was integrated using ARCSyM in order to investigate the variability of the model and hence the extent to which, on these timescales [Pielke et al., 1999], it is advisable to ascribe model biases to shortcomings in physical parameterizations. In each of the six members of the ensemble the initial atmospheric conditions were perturbed by advancing or re- treating the initial data by one day, following $ivillo et al. [1997]. Following this perturbation, the simulations were integrated as for the control simulation. Figure 3a shows the ensemble standard deviations in 850 hPa height for June and July 1990. Immediately ap- parent is the fact that the ensemble variability is much larger in July than in June. In fact, the month of July 1990 shows much higher variability in all aspects of the circulation than the other summer months tested (from May to September, not shown). Maximum July standard deviation is 20 m over the Arctic Ocean, compared to a maximum of 12 m in June. Rinke and Dethloff[2000] applied the ensemble technique to January and July 1990 HIRHAM simulations. In this study it was shown that the weaker large-scale forcing leads to a stronger sensitivity with respect to uncertainties in the initial conditions. The calculated July ensemble standard deviation in 850 hPa height is shown in Figure 3b. A comparison of Figures 3a and 3b shows that both models have a similar pattern (more or less a two-wave pattern with maximum values over the Beaufort Sea and eastern Arctic) and a similar order for their inter- nal mean model variability. Additionally, the maximum standard

Figur e 2. Monthly mean 850 hPa geopotential height in meters for June 1990. (a) HIRHAM, (b) ARCSyM, (c) ECMWF analyses. Contour intervals, 25 m.

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deviations are of the same order and in about the same location as the model biases. While a full analysis of this behavior is beyond the scope of this paper, it is the subject of ongoing investigations.

Figure 4 shows the zonally averaged cross sections of geopotential height bias, compared to ECMWF analyses, for January and June. The zonally averaged circulation through the atmosphere is an important diagnostic tool for determining meridional momen- tum, heat, and moisture transports in the Arctic basin domain [Lynch and Cullather, 2000]. Both models perform quite well in simulating the geopotential height variation through the atmosphere, but the biases are quite different, and the differences are not constrained to the near-surface environment. In January, HIRHAM shows generally very small biases, with a slight systematic underestimation of height in the stratosphere. ARCSyM shows higher biases toward the pole in the midtroposphere and an underestimate of heights near the surface, but with minimal biases in the stratosphere. In June, ARCSyM maintains its tendency for positive midtropospheric biases over the pole and develops moderate nega- tive biases in the stratosphere, whereas in HIRHAM, the height is underestimated throughout the entire domain and shows a marked tendency for underestimation in the stratosphere. A January experiment, which omitted the parameterization of gravity wave drag in HIRHAM, showed a rather small influence of that parameterization on the model error. Without gravity wave drag, the zonally averaged geopotential height errors are by 10% greater (not shown). Thus while the surface differences are likely to be due to variations in physical treatments, the stratospheric differences can be attributed to variations in the treatment of radiation and the upper boundary condition. The geopotential height bias arising from the treatment of upper boundary conditions is discussed extensively for ARCSyM by Lynch and Cullather [2000].

4.2. Physical Processes

As noted in section 3, the performance of the models with regard to surface parameters, while largely driven by the large-scale flow, is also likely to be strongly influenced by the initialization of

surface parameters. Further, the largest differences between HIRHAM and ARCSyM remain in the surface model and, particularly, over the sea ice. While an attempt was made to constrain the sea ice model of ARCSyM, it is not possible to constrain it to the extent that HIRHAM is constrained, and hence larger differences are to be expected in the fields to be considered in this section.

Considering first the simulated precipitation fields in January (Figure 5), we see an agreement in the geographical locations of the precipitation maxima at the southeast coast of Greenland, the Rocky Mountains of North America, and at the coast of Norway. Generally, ARCSyM produces more orographic precipitation than HIRHAM. This can be explained by differences in upslope diffusion of moisture along the terrain following coordinate surfaces. In HIRHAM, the fourth-order horizontal diffusion is switched off near steep orography, which causes a reduction of orographic precipitation. The ARCSyM behavior is consistent with simulations with the RegCM2 and other similar models. Simulation of high winter precipitation in the vicinity of mountains is noted in simulations of the northwestern United States [Giorgi and Shields, 1999; Leung and Ghan, 1999] and of central Asia [Small et al., 1999].

Giorgi and Shields [1999] also note that in mountainous regions there is a tendency to underobserve winter precipitation due to a lack of high-elevation stations and the influence of undercatch due to the effects of wind and sublimation. Legates and Willmott [ 1990] suggest that winter precipitation may be underestimated by as much as 40% in such regions of the globe. The Arctic is a particularly challenging region to study the hydrological cycle due to the above observational difficulties being combined with sparse data networks and extreme temperatures. Nevertheless, Kattsov et al. [2000] suggest that the HIRHAM model produces credible simulation of Arctic precipitation in recent monthly (January 1985- 1995) and yearly (1990) experiments. Alternative approaches to estimating precipitation, such as glaciological observations [Bromwich et al., 1998] and moisture convergence calculations [Bromwich et al., 1999; Calanca and Ohmura, 1994; Masuda,


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Figure 4. Difference between model and ECMWF of zonal average monthly mean height in meters: (a) HIRHAM, January' b) HIRHAM, June; (c) ARCSyM, January; (d) ARCSyM, June.

1990], have met with greater success in assessing the accuracy and consistency of global analysis products and station observations. Brornwich et al. [ 1998] found that nearly all of the data sets considered (including ECMWF and NCEP reanalyses) are overly dry for high-elevation areas, as seen from comparisons with glaciological observations from Summit in Greenland. $erreze and Hurst [2000] found that these data sets underestimate precipitation over the Atlantic side of the Arctic in all seasons in a comparison with an improved gauge-based climatology. The consensus at this time in- dicates that the ECMWF reanalysis (ERA) precipitation is the most reliable, although somewhat dry, gridded data set, pending the re- lease of new reanalysis products with enhanced polar station infor- mation (R.I. Cullather, personal communication, 1999). With the shortcomings described above kept in mind, in comparison with the ERA precipitation for January 1990 (not shown), both models

are performing adequately and within the (large) bounds of observational error with regard to winter precipitation.

In addition to the differences in moisture advection discussed

above, further differences in the local moisture regime may be seen by examining the surface energy balances of the models. Figure 6 shows the monthly mean net radiation and turbulent fluxes for the two models for the entire domain and separated into land, ice, and ocean regions. ARCSyM shows larger latent heat fluxes over ice (Figure 6c), due to the presence of leads and open water in the ice pack in this model, particularly in the marginal ice zone during this season (not shown). The sea ice model in ARCSyM requires a min- imum open water fraction in every grid cell containing sea ice due to ice strength and compaction limitations, and hence ARCSyM maintains a comparatively warm moisture source to the cold Arctic atmosphere throughout the winter season. The importance of leads


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Figure 5. Monthly totals of precipitation in millimeters for January 1990. (a) HIRHAM and (b) ARCSyM. Contour intervals, 25 mm.

to high-latitude climate have been noted in global [Simmonds and Budd, 1991] and regional [Walsh and McGregor, 1996] climate models. The HIRHAM model does not allow for leads in the sea ice but rather specifies a continuous sea ice cover according to sea surface temperature only.

However, the much larger surface turbulent fluxes over ocean in the HIRHAM model •r:i ..... t•,4) are also closely adjacent to the zones of winter precipitation. Over the total domain, both models appear to underestimate latent heat fluxes compared to the NCEP reanalyses (ARCSyM less than HIRHAM, Figure 6a), and it is

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Figure 6. Monthly mean surface energy balance components in W/m 2 for January 1990, averaged over (a) the whole domain, (b) land, (c) sea ice, and (d) ocean grid points, in comparison with the NCEP reanalysis of the surface energy balance. Positive net radiation values indicate heating of the surface, whereas positive turbulent flux components indicate cooling of the surface.

29,676 RINKE ET AL.' ARCTIC REGIONAL CLIMATE SIMULATIONS

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Figure 7. Monthly totals of precipitation in millimeters for June 1990. (a) HIRHAM and (b) ARCSyM. Contour intervals, 25 ram.

clear that the large oceanic latent heat fluxes in HIRHAM do not influence a sufficient area to counteract the larger latent heat fluxes over land and ice in the ARCSyM simulation, leading to greater net moisture flux from the surface in the ARCSyM domain. In addition, both models underestimate radiational cooling and sensible

heat fluxes compared to the NCEP reanalyses (HIRHAM less than ARCSyM). However, Gupta et al. [1997] estimated that climatological winter radiational cooling is too high in the NCEP reanalyses compared to the Langley 8-year SRB data set [Gupta et al., 1996], and station-by-station comparisons of the full surface ener-

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Figure 8. Monthly mean surface energy balance components in W/m 2 for June 1990, averaged over (a) the whole domain, (b) land, (c) sea ice, and (d) ocean grid points, in comparison with the NCEP reanalysis of the surface energy balance. Positive net radiation values indicate heating of the surface, whereas positive turbulent flux components indicate cooling of the surface.


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gy balance for particular years have not been undertaken for the pan-Arctic. Thus such a comparison must be qualitative at best given the limited validation of these analyzed fluxes.

In June the general patterns of simulated precipitation are somewhat similar, but the differences in monthly totals between HIRHAM and ARCSyM are much larger (Figure 7). In comparison with ERA precipitation (not shown), HIRHAM undersimulates summer precipitation, whereas ARCSyM strongly oversimulates precipitation at the boundaries. The differences between the ERA precipitation and the ARCSyM simulation are large enough that data set problems alone do not explain the discrepancy. It is prob- able that this difference is due in part to a flaw in the boundary forcing of moisture in the ARCSyM model. Tests of the standard 10-grid-point relaxation technique [Davies, 1976] in the HIRHAM model have indicated that such a procedure on the specific humidity field produces spurious precipitation in the relaxation zone. This is also seen in the ARCSyM experiments. The HIRHAM simulations shown here use an inflow/outflow formulation for specific humidity [Christensen et al., 1996] which produces results that seem qualitatively more realistic. It is intended to implement such a formulation in ARCSyM in the future.

In addition to the effects of boundary relaxation, within-domain differences in summer are likely to be due to different convection and large-scale precipitation schemes. Lynch et al. [1995] and Giorgi and Shields [1999] discussed the impact of moisture parameterization on grid and subgrid scales and showed a substantial impact in precipitation. Moreover, RCM studies over Europe have indicated the sensitivity of summertime precipitation with respect to different physical parameterizations such as the radiation scheme [Giorgi, 1991] and soil moisture [Schaer et al., 1999]. Finally, the two models use different land surface and atmospheric boundary layer parameterizations, which results in some significant surface flux differences affecting moisture availability in the atmosphere. Figure 8 shows the surface energy balance components for June, for the various surfaces in the model. Note that

climatological summer downward longwave fluxes in the NCEP reanalyses (Figure 8a) was been estimated to be strongly overesti- mated in high latitudes [Gupta et al., 1997]. Stronger latent heat fluxes are indeed seen over land in the ARCSyM model (Figure 8b), due to the presence of lakes and wet tussock tundra in this model. These land surface types are based on observations in Alaska which suggest a much wetter surface than the standard GCM tundra type, and the ubiquity of thermokarst lakes [e.g., Lynch et al., 1999]. Over ice and ocean the surface energy balances of the two models are very similar. Overall, more moisture is available to the boundary layer scheme in ARCSyM than in HIRHAM, although both models underestimate total latent heat fluxes for the domain compared to the NCEP reanalyses (Figure 8a).

Comparing the simulated near-surface air temperature during January (Figure 9), the ARCSyM temperatures are higher than the HIRHAM simulation, of the order of 5ø-15øC. The simulation of near-surface air temperatures has been extensively validated over land in both models [e.g., Rinke et al., 1999a; Lynch et al., 1999; Wu and Lynch, 2000]. The comparison of both simulations with the POLES estimation over the sea ice and ocean (Figure 9c) shows that the HIRHAM temperatures agree with the POLES data, but the model is somewhat colder (up to 5øC) over Novaya Zemlya than the data. ARCSyM temperatures agree in the marginal seas with

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Figure 9. Simulated monthly mean air temperatures in degrees Celsius for January 1990. (a) HIRHAM, (b) ARCSyM, and (c) POLES. Contour interval, 5øC.

29,678 RINKE ET AL.' ARCTIC REGIONAL CLIMATE SIMULATIONS

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Figure 10. Simulated monthly mean air temperature profiles in degrees Celsius for (a) January and (b) June 1990. Shown are profiles for each surface type for each model, and NCEP and ECMWF reanalyses. Note that for HIRHAM, leads are not represented, and for ARCSyM, the ice profile represents ice-covered regions with greater than 50% con- centration, and hence cannot be compared directly with the 100% ice cover of HIRHAM, NCEP and ECMWF over- ice profiles.

the POLES data, but show a positive bias over the central Arctic of up to 15øC. An examination of the net longwave radiative flux at the surface (Figure 6, which comprises primarily longwave fluxes during this season) shows that ARCSyM simulates smaller upward fluxes over the whole Arctic in comparison to HIRHAM. The smaller net longwave surface fluxes in ARCSyM are due primarily to the excessive cloud cover. In off-line calculations the RRTM longwave scheme in ARCSyM gives errors of less than 5 W m -2 (J. O. Pinto, personal communication, 1999). This excessive Arctic Ocean cloud cover is due to two interacting factors in the design of the model. The first is that the ARCSyM model imparts more moisture to its boundary layer scheme through the higher surface latent heat fluxes discussed earlier. However, these fluxes, as noted, are of reasonable size compared to large-scale reanalyses. The second factor is that the atmospheric boundary layer scheme implemented in ARCSyM is not capable of simulating the shallow stable boundary layer of the polar winter (Figure 10a), in part because it is designed for efficient vertical moisture transport in midlatitude simulations [e.g., Giorgi et al., 1993], and in part because the destabilizing influence of the leads is felt throughout the entire grid cell. These factors, when combined, lead to a neutral boundary

layer profile and excessive boundary layer clouds. In the case of HIRHAM, Abegg et al. [1998] and Dethloff et al. [2000] studied the influence of the boundary layer parameterization on Arctic simulations and found that a simpler (lower-order closure) scheme, which is designed to reproduce shallow stable boundary layers, has a superior performance in Arctic cases to a higher-order scheme that does not. An additional issue in these profiles is the fact that the NCEP and ECMWF reanalyses specify 100% ice over, as HIRHAM. The more realistic approach using observed ice concen- trations in ARCSyM leads to warmer temperatures and higher humidity (not shown). It should also be noted that the ice profile shown in Figure 10 includes all grid points containing >50% ice cover. No 100% ice cover exists in the ARCSyM experiments due to ice strength and compaction requirements. This highlights the challenges of pursuing highly complex, physically realistic coupled models in contrast to a more strongly constrained approach.

During June (Figure 10b) the two models and the large-scale analyses show much closer concurrence throughout the entire column for each surface type. It is not surprising that the models behave similarly during the summer season, during which time temperatures are constrained by melting ice, and parameterizations

RINKE ET AL.' ARCTIC REGIONAL CLIMATE SIMULATIONS 29,679

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Figure 10. (continued)

designed for global or midlatitude applications are operating at more appropriate temperatures. Thus the melt process constrains the lower levels of models and analyses. The exception to this is over land, where warmer temperatures are analyzed, in this case, the HIRHAM model exhibits a slightly cold tendency throughout the atmospheric column. Figure 11 shows the simulated surface temperatures, which are quite similar and agree with the observed POLES data. The 5øC isotherm runs near the coastline around the

Arctic Ocean in both models and in the data. HIRHAM is, over the

Beaufort Sea, slightly colder than POLES (up to 5øC). Over the North Atlantic, ARCSyM temperatures are slightly higher (up to 5øC) than POLES estimates. The radiative processes leading to these balances are quite different. ARCSyM simulates a higher net shortwave radiation than HIRHAM in the North Atlantic (up to 60 W/m2), where the largest differences in net radiation balance are observed (not shown). Over land, HIRHAM exhibits more longwave cooling (see Figure 8b), but with a larger net shortwave radiation than ARCSyM, the net radiation balance is quite similar for both models.

The ECHAM4 radiation scheme was validated globally [e.g., Wild et al., 1995a, 1995b] and in the Arctic by Rinke at al. [1997]. They showed that the simulated radiative fluxes agree well with observations and are sensitive to the cloud properties. Similarly, the radiative schemes incorporated into ARCSyM have been vali-

dated globally [Mlawer et al., 1997; Briegleb, 1992] and in the Arctic by Pinto and Curry [1997] and Pinto et al. [1999]. Thus interactions and feedbacks between these different schemes and

other aspects of the models are likely to be responsible for the differences seen here and to fully diagnose the important factors, a more constrained column modeling approach is suggested.

5. Influence of Horizontal Resolution

To examine the influence of resolution on the results, the more

computationally efficient HIRHAM model simulations were re- peated with a higher horizontal resolution of 50 km. In previous studies, additional resolution in the horizontal has a positive impact on the details of RCM simulations. For example, Christensen et al. [1998] used a double nesting approach, applying an 18 km horizontal resolution RCM over Scandinavia, and showed that local effects

playing a role in the hydrological budget start to be resolved and can be more realistically simulated.

In this case, the increased model resolution has not appreciably affected the simulated large-scale circulation (not shown). Figure 12 shows the surface temperature simulated by HIRHAM using 50 km for January and June (compare to Figures 9a and 1 la). Because of the higher resolution, more small-scale features appear in the temperature field, especially over land. In order to verify that this


(b)

- I

additional detail represents an improvement, in the absence of gridded observational fields at this resolution, it would be necessary to

undertake a point-by-point validation using available station records, which is beyond the scope of this paper but has been addressed by Rinke et al. [1997], Dethloff et al. [2000], Pinto et al. [1999], and Wu and Lynch [2000]. Turning to the precipitation field (Figure 13, compare with Figures 5a and 7a) significant changes in the precipitation are evident. The higher resolution produces more precipitation (up to 75 mm), so the totals are closer to those simulated by the ARCSyM model (for different reasons), although the geographical distribution seen in the HIRHAM 100 km resolution experiment is maintained. The improvement in the orographic precipitation with increasing resolution is explained by the increasing realism of the mountains with increasing resolution, and by the fact that with increasing resolution there is an overall in- tensification of the hydrological cycle and increased vertical circulation [Jones at al., 1995; Machenhauer et al., 1998]. The locations of maximaJminima are the same in both simulations, but addition-

ally many small-scale features are now visible. As noted in section 4.2, a verification of precipitation, however, is extremely problematic.

6. Summary

Two alternative model configurations have been compared with each other and with observations, whereby the main focus was to compare and validate the monthly mean fields. We find that both models are able to reproduce reasonably the main features of the large-scale flow and the surface parameters in the Arctic. The model performance biases are strongly regional in character. To some extent, it was possible to identify attributes of physical parameterization schemes which contribute directly to the model biases and to the differences between the models. During January over the Arctic Ocean, ARCSyM simulates higher near-surface air temperatures, which seems to be primarily connected with excessive cloud cover. This may be explained by the coupling to the sea ice and ocean models, and the way in which the boundary layer schemes in ARCSyM and HIRHAM enhance or suppress vertical moisture transport. By constraining the sea ice, ocean, and boundary layer, HIRHAM is able to maintain cooler winter temperatures near the surface. During June the simulated temperatures are quite similar and agree with the POLES data. The verification of precipitation is extremely problematic, but it was possible to identify a specific improvement in HIRHAM (lateral boundary forcing of water vapor) which can have an immediate impact on the ARCSyM model skill when implemented. Finally, however, the two models use different parameterizations, different initialization of surface fields, and different model configurations (HIRHAM is an atmospheric model largely specified at the lower boundary, whereas ARCSyM is a coupled atmosphere-ocean-sea ice system), and therefore it is difficult to reach definitive conclusions in this

first step of model intercomparison. The discussion of the differences shows once more that the fol-

lowing parameterizations are primarily important for realistic Arctic simulations: land surface and stable boundary layer parameterizations, radiative transfer, and treatment of cloud. Using the now available observations of a broad range of observational projects (SHEBA, FIRE-ACE, ATLAS, ARM) from the datarich case period of 1997-1998 presents an unprecedented opportunity to

Figure 11. Simulated monthly mean air temperatures in degrees Celsius for June 1990. (a) HIRHAM, (b) ARCSyM, and (c) POLES. Contour interval, 5øC.


(a)

(b) I I

I I

Figure 12. Monthly mean surface temperature of HIRHAM at 50 km for (a) January and (b) June 1990.

Figure 13. Monthly totals of precipitation of HIRHAM 50 km in millimeters for (a) January and (b) June 1990. Contour intervals, 25 mm.

improve the treatments of these processes. The paper presented here should be understood as a first pilot study for a planned series of collaborative intercomparison experiments using different RCMs to simulate the Arctic climate. In the next step, intercomparison will be conducted in a way that not only evaluates the mean fields but also evaluates relationships between variables in more strongly constrained simulations so that specific processes and feedbacks can be accurately diagnosed. Finally, it is recommended that any model intercomparison should employ a carefully initial- ized ensemble experimental design.

Acknowledgments. Comments of two anonymous reviewers aided greatly in the preparation of this paper. Thanks to Jim Maslanik of the National Snow and Ice Data Center, Boulder, Colorado for POLES and SSM/I data used in this study. We are grateful to B. Weisheimer, W. Wu, and R. Cullather for programing support and preparing the graphics. We benefited from discussions with J.H. Christensen. This is AWI contribution

1711. This work was supported in part by NSF grant OPP-9732461 and NASA grant NAG5-6820.

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A. Rinke and K. Dethloff, Alfred Wegener Institute for Polar and Ma- rine Research, Telegrafenberg A43, D-14473 Potsdam, Germany. ([email protected])

(Received December 20, 1999; revised April 6, 2000; accepted May 16, 2000.)

intercomparison of arctic regional climate simulations: case studies of january and june 1990

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