The Regional Arctic System Model version 1 (RASM1) has been developed to provide high-resolution simulations of the Arctic atmosphere–ocean–sea ice–land system. Here, we provide a baseline for the capability of RASM to simulate interface processes by comparing retrospective simulations from RASM1 for 1990–2014 with the Community Earth System Model version 1 (CESM1) and the spread across three recent reanalyses. Evaluations of surface and 2 m air temperature, surface radiative and turbulent fluxes, precipitation, and snow depth in the various models and reanalyses are performed using global and regional datasets and a variety of in situ datasets, including flux towers over land, ship cruises over oceans, and a field experiment over sea ice. These evaluations reveal that RASM1 simulates precipitation that is similar to CESM1, reanalyses, and satellite gauge combined precipitation datasets over all river basins within the RASM domain. Snow depth in RASM is closer to upscaled surface observations over a flatter region than in more mountainous terrain in Alaska. The sea ice–atmosphere interface is well simulated in regards to radiation fluxes, which generally fall within observational uncertainty. RASM1 monthly mean surface temperature and radiation biases are shown to be due to biases in the simulated mean diurnal cycle. At some locations, a minimal monthly mean bias is shown to be due to the compensation of roughly equal but opposite biases between daytime and nighttime, whereas this is not the case at locations where the monthly mean bias is higher in magnitude. These biases are derived from errors in the diurnal cycle of the energy balance (radiative and turbulent flux) components. Therefore, the key to advancing the simulation of SAT and the surface energy budget would be to improve the representation of the diurnal cycle of radiative and turbulent fluxes. The development of RASM2 aims to address these biases. Still, an advantage of RASM1 is that it captures the interannual and interdecadal variability in the climate of the Arctic region, which global models like CESM cannot do.
The late 20th and early 21st centuries have been marked by dramatic changes
in the northern high latitudes. Most notable was the rapid decline in sea ice
cover (e.g., Serreze et al., 2007; Comiso and Hall, 2014), that accelerated
during the first decade of the 21st century (e.g., Comiso et al., 2008;
Stroeve et al., 2012; Swart et al., 2015). Since then, sea ice extent
partially recovered in 2013–2015 (Swart et al., 2015), followed by further
declines in 2016–2017 (
Because of the region's increased sensitivity to global warming, the Arctic is an important region for global climate models (GCMs) and Earth system models (ESMs) to model correctly. Yet even though GCMs and ESMs capture the general large-scale and long-term temperature trends in the Arctic, they have difficulty capturing other climatic trends in the region (Serreze and Francis, 2006). For instance, while these models generally simulate the overall decline in sea ice extent and area, there is a large spread in the simulated sea ice decline among the various models (Stroeve et al., 2007; Zhang and Walsh, 2006) and many fail to capture the recent acceleration in that decline (Stroeve et al., 2007; Zhang, 2010). Such biases lead to a large range in the simulated polar amplification from these models due to variations in the sea ice state caused by differences in the representation of physical processes (Holland and Bitz, 2003) and due to errors in simulated atmospheric circulation (Maslowski et al., 2012; DeRepentigny et al., 2016). The latter is partly due to errors in the phase of the Arctic Oscillation and North Atlantic Oscillation (Moritz et al., 2002; Stroeve et al., 2007), which are not expected to be portrayed accurately.
The improvement of GCMs and ESMs in the Arctic may be facilitated by an
Arctic regional system model as was proposed by Roberts et al. (2010). Such a
regional model would provide a stepping stone toward the development of
high-resolution fully coupled global models with sophisticated polar
representations. Many physical and biogeochemical processes in the Arctic are
contingent upon interfacial exchanges at fine spatial scales and short
timescales that may be better represented by a regional coupled model
(Roberts et al., 2011). The development of such a new regional coupled model,
the Regional Arctic System Model (RASM) presented here, incorporates
high-resolution atmosphere, ocean, sea ice, and land surface components and
accommodates expansion to mountain glaciers, ice sheets, dynamic vegetation,
and biogeochemistry modules (Maslowski et al., 2012). The first version of
RASM (RASM1) incorporates the Weather Research and Forecasting (WRF) model as
the atmospheric model, the Variable Infiltration Capacity (VIC) land surface
model, a streamflow routing model (RVIC), the Parallel Ocean Program (POP)
ocean model, and the Los Alamos Community Sea Ice Model (CICE). The latter
two are also used in the global Community Earth System Model (CESM), and the
development of RASM has contributed to refinements in the CICE version 5
(Hunke et al., 2015). Along with the use of CESM's ocean and sea ice models,
coupling between the various components is performed by the CESM coupler,
CPL7 (Craig et al., 2012;
The development of the version of WRF used in RASM for long-term climate simulations for a pan-Arctic domain (Cassano et al., 2011) was motivated by the adaptation of WRF for polar applications (Polar WRF; Hines and Bromwich, 2008; Bromwich et al., 2009), which is being used to produce the Arctic System Reanalysis (ASR; Bromwich et al., 2016). This grew out of the previous development of a polar version of the fifth-generation Mesoscale Model (Polar MM5; Bromwich et al., 2001; Cassano et al., 2001). In developing RASM1, lessons were also heeded from the existing lineage of Arctic-centric models like the Arctic Climate System Model (ARCSyM; Lynch et al., 1995, 1998, 2001; Lynch and Cullather, 2000), the coupled ocean–atmosphere models of the Rossby Centre Atmosphere–Ocean RCM (RCAO; Döscher et al., 2002, 2010), and HIRHAM (Dethloff et al., 1996) coupled to the North Atlantic–Arctic ocean–sea ice model (NAOSIM) or the Modular Ocean Model (MOM) (Dorn et al., 2007; Rinke et al., 2003).
RASM1 and its simulations evaluated here are described in more detail in Sect. 2.1. These or similar simulations have also been evaluated in Hamman et al. (2016, 2017) and Cassano et al. (2017). The former focused exclusively on the land surface climatology and hydrology, and the latter compared the near-surface atmospheric climate in RASM to a single reanalysis. What is presented here is an evaluation of the capability of these simulations in regards to atmosphere–land–ocean–sea ice interface processes by comparing with observational data and using three reanalyses and an ESM as baselines for the performance of RASM1. It should be noted that it is not the goal of RASM1 to always be comparable to the ESM and reanalyses, as these may not always compare well with the observational data. Instead, RASM1 should be better than the ESM for quantities that the ESM does not simulate well and should be comparable for quantities that the ESM simulates well. The focus here is on evaluating RASM and providing pathways for improving this particular model, which will be a useful tool for gaining an improved understanding of the Arctic climate system. The ESM used here, CESM1, is also described in Sect. 2.1, and the reanalyses used are described in Sect. 2.2. The observational data, both globally gridded data and surface observations, are described in Sect. 2.3 and 2.4, respectively. The evaluation is given in Sect. 3. Finally, conclusions are given in Sect. 4.
RASM is run over a pan-Arctic domain that encompasses the entire Arctic
Ocean and the surrounding river basins (Fig. 1). The atmosphere and land
models are run on the same
The RASM1 domains for the atmosphere (WRF) and land models (VIC) and for ocean (POP) and sea ice (CICE) models. The tracks of the ocean ship cruises (Moorings '99, CATCH, and FASTEX) and SHEBA are included. The locations of the flux towers used in this study are also indicated by the symbols in the legend. The solid brown circle indicates the location of the Manitoba cluster and the three other towers that are shown in Figs. 8 and 11. The other symbols are the other flux towers. The two regions for snow depth evaluation with upscaled surface observations are also demarcated.
RASM includes version 3.2 of the Advanced Research WRF (Skamarock et al.,
2008) modified for use in the Arctic (Cassano et al., 2011,
2017). In order to successfully couple within RASM, WRF's boundary layer,
surface layer, and radiation parameterizations have been adapted. Details on
these changes and other information on the WRF configuration used in RASM
can be found in DuVivier and Cassano (2015) and Cassano et al. (2017). Of
particular relevance to this paper is the use of spectral nudging to reduce
biases in the regional simulation (Glisan et al., 2012; Cassano et al.,
2011). The nudging of temperature and winds starts from zero at
Version 4.04 of the land model VIC (Liang et al., 1994, 1996) used in RASM is modified for coupling to the other components and to include a broadband snow albedo that depends on vegetation cover (Barlage et al., 2005). Other modifications include an increase in the bare surface albedo to simulate bare land ice at very high latitudes and a decrease in land surface emissivity throughout the region to 0.97 to be consistent with the other components. Hamman et al. (2016) describe this version of VIC in more detail. RASM also includes a model to route streamflow from the land to the river outlets into the ocean. This river routing model, RVIC, is described in more detail in Hamman et al. (2017).
RASM uses version 2 of the ocean model POP (Smith et al., 1992, 2010;
Dukowicz and Smith, 1996) modified for a regional closed boundary domain on a
The current baseline simulation, RASM1, is as described above using the Mellor–Yamada–Nakanishi–Niino (MYNN; Nakanishi and Niino, 2006) boundary layer and Kain–Fritsch (KF; Kain, 2004) convection schemes in WRF. The MYNN and KF schemes were found to produce a more realistic boundary layer height, liquid water path, and downward shortwave radiation in stratocumulus (Jousse et al., 2016) such as those prevalent over the subpolar oceans. This is very similar to the “RASM_atm_ice” simulation assessed in Cassano et al. (2017) with improvements to the ice–ocean coupling. The ice–ocean coupling improvements mostly affected sea surface temperature and salinity but had a minimal impact on sea ice concentration or thickness (not shown).
The initial conditions for POP and CICE were provided from a spin-up using CORE-2 forcing and runoff (Large and Yeager, 2009) from 1948 and those for VIC from a spin-up from January 1948 to August 1979 using the forcing dataset of Sheffield et al. (2006). The European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim; Dee et al., 2011) was used as lateral boundary conditions for the atmospheric model and to nudge the upper atmosphere of the model. The Climate Forecast System Reanalysis (CFSR; Saha et al., 2010) was also used for lateral atmosphere model boundary conditions and to nudge the model upper atmosphere (while continuing to use the PHC climatology for the ocean model boundary conditions), producing results that are generally not significantly different from those using ERA-Interim over most of the domain (Supplement Fig. S1). Differences along the edge of the domain are produced by differences in the boundary conditions.
The RASM1 simulation was run fully coupled for 1979–2014. The period 1979–1989 is not analyzed here, as the ocean and sea ice needed to relax into the climatological state. For instance, while domain average sea surface temperature (SST) is stable throughout the simulation, sea surface salinity slightly decreased from 1979 into the 1980s in RASM1 (Supplement Fig. S2). Thus, analysis is made for results from 1990 onwards, generally focusing on the period up to 2009 to have a consistent comparison with the period available from all of the reanalyses used here.
To provide a baseline for the capability of RASM1 in simulating interface processes, we compare the climate from RASM1 to that of CESM, the modeling system from which portions of RASM were branched. Output from the 30-member CESM large ensemble (LE) (Kay et al., 2015) is used here, since the CESM-LE output in the NCAR database includes 6-hourly, daily, and monthly means of many of the quantities investigated here. We refer to CESM-LE as CESM1 henceforth and use output from 1990 to the end of the simulations in 2005.
To further evaluate RASM1 simulations, we compare them to the spread in the latest generation of reanalyses: the Modern Era Retrospective Analysis for Research and Applications version 2 (MERRA-2; Gelaro et al., 2017), ERA-Interim (Dee et al., 2011), and National Centers for Environmental Prediction (NCEP) CFSR (Saha et al., 2010). These reanalyses have been shown to be the most consistent with independent observations in the Arctic (Lindsay et al., 2014). The last two have been used for lateral and internal boundary conditions for RASM with similar results (Fig. S2).
The temporal and horizontal resolutions of the reanalyses used in this study
are summarized in Table S1 in the Supplement. The MERRA-2 data used
here include the surface turbulent flux, surface radiation, and single-level
diagnostics data collections given at the reanalysis model's native
horizontal resolution of 0.5
The simulated monthly means are first evaluated using several global monthly mean gridded datasets. This is done by regridding the model and reanalysis data to the various product resolutions for comparison in Sect. 3.1.
Monthly mean 2 m land surface air temperature (SAT) is compared to the
dataset generated by Wang and Zeng (2013, hereafter WZ13). WZ13 includes
adjusted hourly 2 m air temperature on a
Sea surface temperature (SST) is evaluated using version 3 of the Hadley
Centre SST (HadSST3.1.1.0; Kennedy et al., 2011a, b) dataset on a
Sea ice concentration and extent are important quantities to be assessed in such a regional climate model for the Arctic. These were preliminarily evaluated in Cassano et al. (2017) and will be more thoroughly evaluated in a subsequent paper about CICE as used in RASM. Still, we will briefly assess this to understand some of the model biases over and around the margins of the sea ice through use of the National Oceanic and Atmospheric Administration (NOAA) climatic data record (CDR) sea ice concentration product (Peng et al., 2013; Meier et al., 2014).
To understand the biases in 2 m air temperature or surface temperature, we
evaluate the surface energy balance in the models. Surface radiation is
evaluated using the measurements from the Clouds and the Earth's Radiant
Energy System (CERES) satellite for 2001–2009. CERES's level 3B Energy
Balanced and Filled (EBAF) surface product (Li et al., 1993; Li and Kratz, 1997;
Gupta et al., 1997) provides surface radiative fluxes on a
Finally, we use NCEP's Climate Prediction Center Merged Analysis of
Precipitation (CMAP; Xie and Arkin, 1997) to evaluate precipitation over the
period 1990–2009. Monthly mean values on a
We use point observations to further evaluate RASM. First, we use observations of 2 m surface air temperature (SAT) from five automated weather stations (Supplement Table S2 and the map in the lower right of Fig. 11) from the Greenland Climate Network (GC-Net) on the Greenland Ice Sheet. These stations have been operational since the 1990s (Steffen and Box, 2001), and the five chosen for this study have some of the longest records in the accumulation zone of the ice sheet above 2300 m. We compare the SAT observations from these stations distinctly with the individual model or reanalysis grid cell values containing these stations.
Over land elsewhere, we use tower observations from FLUXNET (Baldocchi et
al., 2001), a global network of more than 100 locations where fluxes of
RASM snow depth over land is evaluated with upscaled in situ observations
using the methodology of Dawson et al. (2016). The upscaling within
Over sea ice, we use meteorological and flux observations from the Surface Heat Budget of the Arctic (SHEBA; Uttal et al., 2002; Persson et al., 2002) between October 1997 and October 1998. These include measurements made at the 20 m tower at the main camp and from four portable automated mesonet (PAM; Militzer et al., 1995) stations surrounding the main camp. On the tower, measurements were made at several levels. Here, we use the sensible heat fluxes derived from fast measurements of temperature and wind made by sonic thermometers and anemometers and latent heat fluxes derived from measurements from a fast hygrometer at 8.1 m. Upward and downward shortwave and longwave radiation were measured by pyranometers and pyrgeometers on nearby masts at 1.5–2 m of height. Surface temperature was measured nearby by a downward-pointing radiation thermometer. At the PAM stations, we use sensible heat fluxes, surface radiation, surface temperature, and near-surface air temperature from similar measurements. Further discussion of these instruments and their uncertainties is provided by Brunke et al. (2006) and Persson et al. (2002). We compare the average of the tower and PAM stations with the values from the model or reanalysis grid cell containing the combined observations at the corresponding day.
The bias in precipitation rate (mm day
Over ocean, we use flux and meteorological observations made aboard ships in three field campaigns that fall within the RASM domain: the Fronts and Atlantic Strom Track Experiment (FASTEX) from December 1996–January 1997, followed by Couplage avec l'Atmosphère en Conditions Hivernales (CATCH) from January–February 1997 in the North Atlantic, and the National Oceanic and Atmospheric Administration's cruise to service its moorings in the North Pacific (Moorings '99) in September and October 1999 (Fig. 1). We use the eddy covariance latent and sensible heat fluxes from the US cruises (FASTEX and Moorings '99), while only inertial dissipation fluxes were available for CATCH. Flow distortion, ship motions, and environmental conditions were accounted for as in Brunke et al. (2003). We only use observations deemed far enough within the active ocean domain (Fig. 1). Still, the location of the CATCH and FASTEX observations used are close enough to the edge of the active domain for the model state and fluxes to be influenced by the boundary conditions. We still use them because of the lack of high-latitude ocean observations. We compare the daily averages of the cruise data to the daily mean model or reanalysis grid cell value containing the daily average observations.
In this study, latent and sensible heat fluxes are considered positive in
the upwards direction. The magnitude of the radiation components is
considered (i.e., always positive) such that a net radiative flux
We first evaluate RASM1 across the pan-Arctic domain for the period
1990–2009 (2001–2009 for CERES). In Fig. 2, the biases in RASM1's simulated
precipitation relative to CMAP (the mean values of which are presented in
Supplement Fig. S3 for reference) are compared to those of
ERA-Interim and CESM1 in January and July. We pick these months to represent
snow-covered and relatively snow-free periods, respectively, over most of
the domain. We focus on biases poleward of 50
This is further illustrated by the mean annual cycle averaged over the Ob River basin indicated by the brown region in Fig. 3a. This basin is representative of all of the river basins within the domain except the Amur (the red region in Fig. 3a). RASM1's precipitation rate lies between that of CMAP and GPCP before May and after July and is within the spread in the reanalyses from January to May (with the exception of March). Additionally, RASM1 precipitation is within CESM1's ensemble spread for every month except June. This suggests that RASM1 simulates precipitation fairly well in the Ob River and other similar basins. In the Amur basin, GPCP and CMAP are more consistent with each other. GPCP in this basin is at or near the bottom of the reanalysis spread until August. RASM1 precipitation here is lower than GPCP and CMAP throughout the year and only barely within the CESM1 ensemble spread in March and April.
The similarity of precipitation in RASM1 to that of ERA-Interim could be due
to the lateral boundary conditions (BCs) or to the spectral nudging imposed
from the reanalysis. Using a different reanalysis for BC and nudging could
produce different simulated precipitation. To test the impact of the choice
of reanalysis used for the BCs and for spectral nudging, we compare the
RASM1 run using ERA-Interim BCs with that using CFSR BCs. Figure S1 in the
Supplement shows that the precipitation differences between
RASM1 using ERA-Interim BCs and CFSR BCs are minimal (differences of
In contrast, surface temperatures were shown to have large biases in a very
similar version of RASM that preceded RASM1 (referred to as
RASM_atm_ice) when compared to ERA-Interim in
Cassano et al. (2017). The land SAT biases are further substantiated here by
comparing to WZ13 in January and July in Fig. 4. For reference, the mean
values in WZ13 for these two months are shown in Supplement Fig. S4. Besides over Greenland, RASM1 land SAT in January is also much colder
over the low-lying land areas with the coldest biases in northern European
Russia (NRU; the dark blue box bordered by 60–75
The bias in 2 m surface air temperature (SAT,
Reeves Eyre and Zeng (2017) have shown that CRU (and hence WZ13) is biased high in winter compared to Greenland automatic weather stations. Here, interior SAT relies on interpolating data from the few coastal stations vertically to the top of the ice sheet. Therefore, RASM1 and the reanalyses appear to be colder than WZ13 in January over most of Greenland. A warm bias for related reasons over areas of higher terrain elsewhere in the domain results from WZ13 being too cold. Thus, RASM1 and the reanalyses appear to be too warm when compared to WZ13. These biases are further discussed below.
We return to the cold biases over the flatter terrain of the domain. The mean annual cycle in land SAT for all land in NRU where the coldest biases are in January (the blue box in Fig. 4a) is given in Fig. 5a–c. The cold bias in RASM1 from WZ13 is clearly evident in this region in winter and fall, while this model's SAT is too warm in June and July. The reanalysis spread indicated by the gray shading tightly surrounds the WZ13 mean throughout the year; RASM1 is outside of this for most of the year. The CESM1 ensemble mean is also too cold in winter. We can further evaluate RASM1's biases by comparing with the spread (minimum to maximum) of the CESM1 ensemble member means. RASM1 is within the CESM1 ensemble spread only in March, April, August, and December.
Cassano et al. (2017) suggested that the SAT biases in RASM1 are the result
of cloud errors as evidenced by surface incident downward radiation biases.
In winter, downward incident shortwave (SW) radiation is near zero in NRU
(Fig. 5b). Downward incident longwave (LW) radiation is much more
substantial (
Regional mean of
RASM1 downward incident SW radiation in NRU is much higher in summer with a
maximum of 298 W m
We further illustrate WZ13's wintertime cold bias over higher terrain
outside of Greenland by looking at the mean annual cycle shown in Fig. 5d–f
for the northeastern Siberia (NSIB) region demarcated by the red box
(60–75
RASM1 SSTs are compared to HadSST in Fig. 6. There are large differences in
SST in the marginal ice zones, including the largest biases (in excess of
The bias in sea surface temperature (
We now use in situ observations over land to further explore several of the biases discussed above. To substantiate the above comparisons of RASM1 with global reference data elsewhere over the land, we further compare the modeled surface meteorology, fluxes, and radiation to in situ observations made at the FLUXNET towers. There is some uncertainty in comparing point measurements to grid cell mean quantities from a model simulation or a reanalysis. We use a cluster of eight FLUXNET towers in northern Manitoba (CA-Man and CA-NS1 through 7) that happens to span a RASM1 grid cell within the boreal forest as an example of the possible uncertainty of such a comparison. This is not the only cluster to do so, but most clusters cover only a small area with approximately two or three towers to sample vegetation diversity. The towers in this Manitoba cluster are more spread out throughout a RASM1 or typical reanalysis grid cell. Therefore, this would be a better sample with which to investigate uncertainty arising from evaluating grid cell means in the models to these point measurements, especially since the terrain here is relatively flat (tower elevation ranging from 245 to 291 m). CA-NS1 through CA-NS7 were only operational from 2001 or 2002 to 2005, while CA-Man was a long-term site that was operational from 1994–2008 (Supplement Table S2). Figure 7 shows that the CA-Man SATs and net radiation annual cycles are very similar to the eight-tower mean throughout the year, while latent heat (LH) and sensible heat (SH) fluxes may be substantially different from the mean. This suggests that there is more uncertainty in using single-point measurements of turbulent fluxes than in SAT and net radiation over a region that is relatively flat.
We can use the range in tower observations to evaluate the RASM1 simulation.
If the simulated value falls outside of this range, then the simulation
might be problematic. Even with the large winter cold biases and warm biases
in summer, SAT is generally within the observational spread in this region
except in January, November, and December when it is below the observational
minimum (Fig. 7a). Net radiation is also below the observational spread in
these months, while it is above the observational maximum from June to
August (Fig. 7b) with a maximum that is 33 W m
Another measure of how well RASM1 simulates the mean annual cycle in these
quantities is to compare it with the spread in the reanalyses. Reanalyses
have been previously evaluated through comparisons to surface in situ
observations (e.g., Decker et al., 2012; Betts et al., 2006; Zhou and Wang,
2016; Du et al., 2018), but this is not the focus here. Instead, we assess
whether or not the reanalysis spread is within the observational spread
at this Manitoba cluster. In such cases when they are not and RASM1 is, the
model is better than the reanalyses. The reanalyses fall within the
observational spread for SAT throughout the year (Fig. 7a), but not
necessarily for radiation or SH and LH fluxes (Fig. 7d, e), quantities that
are not assimilated. Thus, the RASM1 autumn and winter cold biases are also
below the reanalysis spread (Fig. 7a), while simulated net radiation is
within the reanalysis spread during this time (Fig. 7b). On the other hand,
net and downward incident SW radiation is higher than the reanalysis spread
in summer (Fig. 7b, c). Model LH and SH flux is even above the reanalysis
spread during the summer maximum. However, the reanalysis spread falls
outside the observational spread in autumn and winter, whereas RASM1
compares well with the observations of
Mean annual cycle in
To evaluate how well RASM1 performs across the domain, we look at the other single FLUXNET towers (Fig. 8). The model winter cold bias is evident at all locations, especially at the more northern sites. However, simulated SATs at the tundra sites are biased high from late winter into summer, while they are better simulated across boreal Canada and at the temperate stations (Fig. 8a).
Monthly biases in RASM1 from flux tower observations for
Monthly biases in RASM1 from flux tower observations for
downward incident
The cold biases are generally associated with negative net radiation biases,
and warm biases are generally associated with positive net radiation biases
To further understand these monthly means, we analyze the monthly mean
diurnal cycles at CA-Man and two other sites, one in the boreal forest of
northern Europe (FI-Hyy) and another in the Alaskan tundra (US-Ivo) in Fig. 10. We focus on July when monthly mean SAT is biased quite highly positive
in RASM1, but the mean diurnal cycle in SAT differs slightly among the
three sites. For example at CA-Man, RASM1 SAT is biased low within the
observational interannual variability (
Mean diurnal cycles for July in
Similarly, the mean diurnal cycles in the surface turbulent and radiative
fluxes provide some explanation for their mean monthly values. The mean
diurnal maximum net radiation in RASM1 is similar (531 and 509 W m
In light of the problems in WZ13 over Greenland due to the use of CRU, we compare simulated temperature to in situ observations from five Greenland automated weather stations (Fig. 11). The reanalyses generally encompass the in situ observations at all sites, whereas WZ13 has much warmer temperatures than observed from October to March at all locations. This further confirms that the use of CRU introduced warm biases over Greenland in winter in WZ13. On the other hand, RASM1 generally compares well with observations at all locations except NGRIP (Fig. 11a) and Summit (Fig. 11c) where RASM1 is too cold. From July to August, RASM1 is too warm compared to observations and reanalyses at all sites.
The mean annual cycle in 2 m surface air temperature
(SAT) observed at automated weather stations across Greenland along with
those from the Wang and Zeng (2013, WZ13) dataset (black), RASM1 (red), and
the spread in the reanalyses indicated by the gray shading at
Snow is a very important component of the Arctic system. Newly fallen snow
has a much higher albedo than bare ground or vegetation. Additionally, snow
insulates the ground from the cold air above in the winter. We compare RASM1
snow depth to the upscaled in situ observations in Fig. 12. Upscaled snow
depth is higher in the mountainous ALASKA SOUTH region than in the flatter
ALASKA MID region with maximum snow depths of
The mean annual cycle over a water year
(October–September) of snow depth averaged for the
Some of the model land biases are similar to biases over the neighboring central Arctic Ocean (Cassano et al., 2017). Since there are not many global gridded or in situ data for the central Arctic, we choose to rely on surface observations made during the year-long SHEBA field campaign (Fig. 13). Observed LH flux is near zero in autumn but is a little higher in summer (Fig. 13a), while observed SH flux is near zero throughout the year (Fig. 13b). The observed LH flux is less reliable, as the mean is based on only one location (at the central tower). Also, no LH flux measurements were made in February 1998. RASM1 SH flux compares well with observations during autumn and winter, but is higher than the observational range from May to July (Fig. 13b). RASM1 compares better to observations than the reanalyses, which are largely outside of the observational range (Fig. 14b).
Comparison of monthly mean
The SW and LW radiation components are also compared. Downward incident SW radiation in RASM1 is within the small observational spread from October 1997 to July 1998. Upward SW radiation is also within the observational spread from autumn to spring but peaks too low and early. Downward incident SW radiation is too low in late summer (Fig. 13c, d). On the other hand, downward LW radiation in RASM1 is generally slightly lower than the observational spread in winter but compares well to observations from March to August 1998 (Fig. 13e). Interestingly, simulated upward LW radiation is within the observational spread throughout the year (Fig. 13f). Here, we also find that the reanalyses do not necessarily fall within (totally or partially) the observational spread for upward SW and LW radiation for part of the year.
The CESM1 ensemble mean does not consistently fall within the observational
spread for the turbulent fluxes and LW radiation, and the ensemble spread
may only partially fall within the observational spread. Because of this,
the comparison to the observational spread is more relevant than a
comparison to the CESM1 ensemble spread. Surprisingly, despite the biases in
the SW radiation components in RASM1 in summer, the net SW radiation is
within the observational spread in June, August, and September 1998. The
CESM1 ensemble mean is slightly above the observational spread from July to
October 1998, but the ensemble spread is partially within it (Supplement Fig. S7a). Interestingly too, all model and reanalysis net LW
radiation mostly falls outside of the observational spread throughout the
campaign (Supplement Fig. S7b). Over ice, the surface energy
balance dictates that the sum of the net radiation and turbulent heat fluxes
be balanced by the conductive flux through the snow (Maykut and Untersteiner,
1971; Maykut, 1978). In winter, this means that the strong LW radiative
cooling is due almost exclusively to this conductive heat flux, since the SW
radiation and LH and SH fluxes are practically zero. The larger radiative
cooling in the models and reanalyses suggests that they produce more
conductive heat flux than observed in winter. Sturm et al. (2001) found that
snow–ice interface temperatures implied from SHEBA observations were often
much warmer (as much as 15
As would be expected from the upward LW radiation, surface temperature is generally within the observational spread throughout the year (Fig. 14a). Reanalysis surface temperatures are generally too high in autumn and winter, in agreement with their upward LW radiation (Fig. 13f). Wind speed in RASM1 is too high from February to August (Fig. 14b). This may explain the model overestimate of LH and SH fluxes in summer. The reanalysis spread is partially outside of the observational spread for wind speed from February 1998 onward.
Comparison of monthly mean
As expected from the regional comparisons made above, RASM1's SSTs are slightly colder than ship observations during CATCH/FASTEX in the wintertime North Atlantic and slightly warmer during Moorings '99 in the autumnal North Pacific (Table 1). The SATs and specific humidity are similarly biased. Wind speed during the Atlantic cruises is underestimated in RASM1, while it is overestimated during Moorings '99. The reanalysis spread includes the observed mean for CATCH/FASTEX but is too high in all but wind speed during Moorings '99.
Comparison of cruise mean observations and simulated values of surface meteorology and turbulent fluxes.
These surface conditions result in SH and LH fluxes that are slightly
overestimated in RASM1. On the other hand, the reanalyses can be less
biased, and CESM1 is slightly higher than observed with the exception of LH
flux during CATCH/FASTEX. The reason for the difference between RASM1 and
CESM1 biases becomes clear when we look at a scatter plot of model fluxes to
ship observed fluxes. Despite RASM1 mean fluxes being higher than observed,
the spread in this model's SH fluxes, for instance, is better than CESM1's,
which produces nearly constant fluxes at
Daily mean model (
In this study, we evaluate the newly developed version 1 of the Regional Arctic System Model (RASM1), a fully coupled atmosphere–land–ocean–sea ice model for improved high-resolution simulation of climate in the northern high-latitude region. The model is run over a pan-Arctic domain with WRF for the atmosphere, VIC for the land surface, POP for the ocean, and CICE for simulating sea ice. The model simulation is evaluated by using a coarser-resolution global model (CESM1) and the spread in recent reanalyses of similar resolution to RASM as baselines of performance.
Overall, precipitation is similarly simulated in RASM1 as in CESM1 and the
reanalyses. RASM1 precipitation compares better to GPCP and CMAP in every
river basin except the Amur. RASM1 precipitation using ERA-Interim for BCs
and spectral nudging is remarkably similar to the reanalyses, but the change
in simulated precipitation by switching to CFSR for BCs is generally
Monthly (lines and shading) and annual (dots) mean 2 m
surface air temperature (SAT) over the central Arctic (70
Snow in RASM1 is underestimated by both simulations but is better simulated with a higher annual maximum in the flatter box in central Alaska (ALASKA MID) than in the more mountainous southern Alaska (ALASKA SOUTH) box. This is consistent with what Dawson et al. (2016) found when using these same data to evaluate the NCEP models. Broxton et al. (2016) found that a version of VIC utilizing a snow elevation band parameterization simulated snow the best out of the other land models used in the Global Land Data Assimilation System (GLDAS; Rodell et al., 2004). This parameterization is currently not used in RASM1 but is being explored for use in RASM2.
There are mean biases in RASM1's land surface air temperature resulting from biases in surface radiation. In winter, SAT is too cold over much of the land within the RASM domain. Cassano et al. (2017) suggest that this is due to cloud biases. This cannot be confirmed here, as cloud variables were still unable to be included in the model output of these simulations. Such problems with simulating clouds have been noted before (e.g., Bromwich et al., 2009, 2016; Porter et al., 2011). Clouds and their effect on interface processes might be better simulated if a newer version of WRF had been used. Notably, WRF version 3.2 as used in RASM1 neglects the radiative impacts of convective clouds. This effect will be incorporated with the inclusion of the latest version of WRF in RASM2.
The above monthly mean biases are a result of biases evident in the monthly mean diurnal cycles. For example, the monthly mean RASM1 warm SAT biases in July are derived from more prominent warm biases during the day or consistently warmer SATs throughout the diurnal cycle. The surface turbulent flux and radiation are also similarly biased diurnally. Therefore, the key to advancing the simulation of SAT and the surface energy budget would be to improve the representation of the diurnal cycle of radiative and turbulent fluxes. The upcoming inclusion of WRF 3.2 may alleviate some of these diurnal cycle biases.
The comparison to the SHEBA observations from October 1997–October 1998 reveals that the reanalyses and the CESM1 ensemble spread do not always fall within the observational uncertainty. Therefore, the RASM1 comparison to the observational uncertainty is a better baseline in this instance. The surface temperature generally falls within the observational uncertainty for most months, consistent with the upward longwave radiation. However, RASM1 wind speed is above the observational uncertainty during the spring and summer, which may help to explain why the simulated latent and sensible heat fluxes are biased high in summer.
An advantage of using RASM1 is that it captures the interannual and
interdecadal variability in the climate of the Arctic region, which global
models like CESM cannot do. This is shown in Fig. 16 for the SATs averaged
over the central Arctic region, defined as 70
The RASM output is archived at the U.S. DoD HPCMP, which requires security clearance to access but can be made available upon request. For ERA-Interim and the NOAA sea ice CDR please see the European Centre for Medium-Range Weather Forecasts (2012) and Meier et al. (2017).
The RASM1 model code is archived on the Subversion server
at the Naval Postgraduate School (
The supplement related to this article is available online at:
MAB performed most of the analyses and prepared the paper with contributions from all of the other authors. Additionally, XZ provided suggestions towards the development of the analyses, ND provided the snow surface evaluations, and JRE provided support for the Greenland surface evaluation.
The authors declare that they have no conflict of interest.
This multi-institutional work was funded by the U.S. Department of Energy
(DE-SC0006693, DE-SC0006178, DE-SC0006643, DE-FG02-07ER64460, DE-SC0006856,
DE-SC0005783, and DE-SC0005522), by the U.S. National Science Foundation
(PLR-1107788, PLR-1417818, and ARC1023369), and by the National Aeronautics
and Space Administration (NNX14AM02G). Computing resources were provided via
a Challenge Grant from the U.S. Department of Defense (DoD) High Performance
Computing Modernization Program (HPCMP). MERRA-2 data were downloaded from
the Goddard Earth Sciences Data and Information Services Center
(