Observations and Modeling of Atmospheric Profiles in the Arctic ...

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Observations and Modeling of Atmospheric Profiles in the Arctic Seasonal Ice Zone ZHENG LIU, AXEL SCHWEIGER, AND RON LINDSAY Polar Science Center, Applied Physics Laboratory, University of Washington, Seattle, Washington (Manuscript received 31 March 2014, in final form 15 September 2014) ABSTRACT The authors use the Polar Weather Research and Forecasting (WRF) Model to simulate atmospheric conditions during the Seasonal Ice Zone Reconnaissance Survey (SIZRS) in the summer of 2013 over the Beaufort Sea. With the SIZRS dropsonde data, the performance of WRF simulations and two forcing datasets is evaluated: the Interim ECMWF Re-Analysis (ERA-Interim) and the Global Forecast System (GFS) analysis. General features of observed mean profiles, such as low-level temperature inversion, low-level jet (LLJ), and specific humidity inversion are reproduced by all three models. A near-surface warm bias and a low-level moist bias are found in ERA-Interim. WRF significantly improves the mean LLJ, with a lower and stronger jet and a larger turning angle than the forcing. The improvement in the mean LLJ is likely related to the lower values of the boundary layer diffusion in WRF than in ERA-Interim and GFS, which also explains the lower near-surface temperature in WRF than the forcing. The relative humidity profiles have large differences between the observations, the ERA-Interim, and the GFS. The WRF simulated relative humidity closely resembles the forcings, suggesting the need to obtain more and better-calibrated humidity data in this region. The authors find that the sea ice concentrations in the ECMWF model are sometimes significantly underestimated due to an inappropriate thresholding mechanism. This thresholding affects both ERAInterim and the ECMWF operational model. The scale of impact of this issue on the atmospheric boundary layer in the marginal ice zone is still unknown.

1. Introduction The atmospheric boundary layer (ABL) is a crucial component of the Arctic atmosphere–ice–ocean system. The variability in temperature, water vapor, wind, and cloud in the ABL with changes in sea ice conditions is important for the surface energy budget (Ohmura 2001; Schweiger et al. 2008; Zygmuntowska et al. 2012; Kay and L’Ecuyer 2013). Understanding the structures of temperature, moisture, and winds in the ABL and their interactions with the changing sea ice underneath will lead to greater understanding of the physical processes involved in the declining Arctic sea ice in the last several decades (Serreze et al. 2007) and the projection of even more sea ice loss in future (Wang and Overland 2009). The temperature inversion is a frequently observed feature of the Arctic ABL in all seasons (e.g., Kahl 1990; Serreze et al. 1992; Tjernström and Graversen 2009;

Corresponding author address: Zheng Liu, Polar Science Center, Applied Physics Laboratory, University of Washington, 1013 NE 40th St., Seattle, WA 98105. E-mail: [email protected] DOI: 10.1175/MWR-D-14-00118.1 Ó 2015 American Meteorological Society

Devasthale et al. 2010; Zhang and Seidel 2011), with low-level and surface-based inversions dominant in winter and autumn and elevated inversions in spring and summer. Low-level jets (LLJ; e.g., Nilsson 1996; Andreas et al. 2000; Jakobson et al. 2013), featuring a low-level wind maximum in the vertical wind profile, are also frequently identified in the polar soundings. The LLJ and specific humidity inversions (e.g., Serreze et al. 1995; Devasthale et al. 2011) are often observed together with temperature inversions in polar regions. Temperature inversions, especially low-level inversions, tend to decouple the surface from the free troposphere, suppress turbulent mixing, and weaken the exchange of energy, moisture, and momentum. Because of anthropogenic greenhouse gas emission into the atmosphere, the global surface temperature is rising and the surface warming is significantly amplified in polar regions. This ‘‘polar amplification’’ is considered to be associated with the boundary layer mixing, especially the strength of the temperature inversion (e.g., Boé et al. 2009; Bintanja et al. 2011; Pithan and Mauritsen 2013). In the Arctic, the LLJ can be forced by various mechanisms, for example, inertial oscillations, baroclinicity,

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and wind gusts (e.g., Andreas et al. 2000; ReVelle and Nilsson 2008; Jakobson et al. 2013). In a stably stratified boundary layer, turbulence production by the wind shear associated with the LLJ is an important mechanism of turbulence production within the boundary layer (Mahrt 1999). Over land, the LLJ is known to be associated with regional weather and climate through the contribution to moisture transport and precipitation (e.g., Stensrud 1996; Cook et al. 2008; Barandiaran et al. 2013). The LLJ is also related to convective activities by providing favorable environments for the development of mesoscale convective systems (e.g., Maddox 1983; Zhang and Klein 2010; Blamey and Reason 2012). Although the LLJ is frequently observed in the Arctic ABL, its influence on the hydrological cycle and interactions with large-scale environments are not well understood in the context of the Arctic conditions, partly due to the difficulty in obtaining adequate observations in polar regions. The specific humidity inversion is frequently associated with low-level clouds in the Arctic (Sedlar et al. 2012) and important for the maintenance of Arctic mixed-phase clouds (e.g., Solomon et al. 2011; Morrison et al. 2011). Both the high humidity and the resulting cloud cover warm the underlying surface through longwave radiative heating. Despite the significant influences these ABL structures may have in the Arctic system, their correct representation in numerical models remain a challenge for the modeling community (e.g., Holtslag et al. 2013). In situ measurements of ABL profiles in the Arctic are mostly limited to observations near the coast. The Arctic Ocean and its marginal seas remain a data-sparse region, apart from surface pressure measurements from drifting buoys and occasional research missions (e.g., Uttal et al. 2002; Tjernström et al. 2004; Gascard et al. 2008; Lüpkes et al. 2010; Inoue et al. 2011; Tjernström et al. 2014). Satellite-borne remote sensors, such as the Atmospheric Infrared Sounder (AIRS; Chahine et al. 2006) provide continuous coverage over most of the Arctic and have been used in Arctic studies (e.g., Kay and Gettelman 2009; Devasthale et al. 2010; Pavelsky et al. 2011). However, they still cannot fully resolve the details of the boundary layer structure (Devasthale et al. 2011) and infrared-based sounding techniques are subject to sampling errors due to the ubiquitous cloud cover. Incorporating observations with data assimilation techniques, atmospheric operational analyses, and subsequent reanalyses (used hereafter together as ‘‘analyses’’) in the Arctic provide the best estimates of the state of the atmosphere continuously with complete spatial coverage. However, the performance of analyses depends on the quality of the underlying atmospheric model and the availability and accuracy of the

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observations used for assimilation. Recent studies showed that there are issues in analyses related to cloud, radiation, near-surface air temperature, moisture, and wind (e.g., Zib et al. 2012; Jakobson et al. 2012; Lindsay et al. 2014; Chaudhuri et al. 2014). The evaluation of analyses in the Arctic ABL with regional modeling studies and independent observations is critical for identifying deficiencies in analyses and their future improvement. A unique opportunity to obtain critical information for model validation and process studies is found in the Arctic Domain Awareness (ADA) flights that the U.S. Coast Guard routinely performs. These flights typically originate in Kodiak, Alaska, and frequently fly hundreds of kilometers north of the Alaskan coast. The Seasonal Ice Zone Reconnaissance Survey (SIZRS) takes advantage of the ADA flights over the Beaufort and the Chukchi Seas and air deploys various oceanographic instruments as well as atmospheric dropsondes. The dropsondes are launched along a flight path coordinated with Coast Guard objectives and other SIZRS experiments. The large spatial and temporal scopes of these flights assure a wide range of conditions and challenges for models. Here we analyze soundings obtained during the summer of 2013 over the Beaufort Sea. In this study we simulate the ABL conditions observed during SIZRS flights, using a polar adaptation of the regional atmospheric Weather Research and Forecasting (WRF) Model (Skamarock and Klemp 2008), commonly referred to as the Polar WRF (Hines and Bromwich 2008; Bromwich et al. 2009; Wilson et al. 2011, 2012). The Polar WRF is adapted to the particular conditions in polar regions, especially in the surface parameterizations. The Polar WRF has advantages over global analyses in that it can have a higher spatial resolution, more sophisticated treatments of atmospheric processes, higher vertical resolution in the boundary layer, and surface parameterizations tailored for the Arctic environment. We use the Interim European Centre for Medium-Range Weather Forecasts (ECMWF) ReAnalysis (ERA-Interim) and the Global Forecast System operational analysis to force the Polar WRF. The performance of the Polar WRF simulations, together with the forcing data are compared to the observed ABL structures under different summer synoptic conditions. We also discuss the potential causes and implications of the model biases.

2. Data and model description The dropsondes used in the SIZRS flights are the M10 GPSonde radiosondes from MétéoModem, converted to dropsondes by attaching a parachute. The dropsonde

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FIG. 1. Synoptic maps over the SIZRS domain for the (a) 19 Jun, (b) 16 Jul, and (c) 16 Aug flights. The nested domain boundaries are shown in thick black lines. The 850–1000-hPa thickness is shown in color shading, surface pressure contours in thin gray lines, and the surface winds in green vectors. The 15% sea ice concentration contours are shown in thick blue lines. The locations of dropsondes for the June, July, and August SIZRS flights are shown as light green dots in (a), (b), and (c) respectively.

sensors register temperature, moisture, horizontal winds, and height every second with a nominal vertical resolution of 5 m. Several of the early soundings have lower vertical resolutions (10–20 m) because of parachute failures. The SIZRS dropsondes are launched from around 2800 m above the sea surface and mostly along the 1408 and 1508W longitude lines over the Beaufort Sea, as shown in Fig. 1. In the summer of 2013, a total of 22 soundings out of 26 deployed dropsondes are analyzed from 6 SIZRS flights on 18, 19, and 20 June, 16 July, and 13 and 16 August, between 1700 and 2330 UTC. Although we plan to transmit future SIZRS soundings to the Global Telecommunication System of the World Meteorological Organization (WMO), current SIZRS soundings are not yet assimilated into analyses and can serve as an independent dataset for model evaluation. The large spatial extent of the dropsonde deployments provides the possibility of sampling different sectors of synoptic weather systems and determining the overall statistics of the ABL structure. The monthly mean and range of the SIZRS soundings are shown in Fig. 2. In general the temperature and specific humidity inversions are much weaker in August than in June and July, although not all soundings in June and July have a stronger inversion than the August soundings. The temperature inversion farther away from the coast is weaker when the influence of warm advection from Alaska weakens (not shown). The specific humidity profiles are highly variable in the vertical

and usually have multiple inversion layers. The mean profiles for each month, on the other hand, more clearly demonstrates the correspondence of the specific humidity inversion with the temperature inversion: the humidity inversion is either lower (June, by up to 500 m) or at about the same level (July and August) as the temperature inversion. Interestingly, the mean LLJ is also weaker in August, despite the large variability in the wind speed at most levels. Note that the SIZRS observations in each month are obtained within several hours on several particular days, so the weaker inversion and LLJ observed in the August 2013 flights should be interpreted with this limitation in mind. The Polar WRF, version 3.4.1, is used in this study to perform numerical simulations of the ABL over the Beaufort Sea, forced by global analysis or reanalysis. To assess the influence of different forcing, we use two forcing datasets: the ERA-Interim (hereafter ERAI; Dee et al. 2011) and the Global Forecast System (GFS) final (FNL) analysis (hereafter GFS; National Centers for Environmental Prediction 2000). For simplicity, we refer to them together as analyses in the following sections although we are aware that ERAI is a reanalysis instead of an operational analysis. To maintain consistency with the atmospheric forcings, the sea surface temperature (SST) and the sea ice concentration (SIC) from the analyses are adopted as the lower boundary forcing. The SST is updated every 6 h and the SIC is updated every 24 h. Fractional sea ice cover within

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FIG. 2. The mean and range of the observed SIZRS soundings for (top) June, (middle) July, and (bottom) August. (left) Temperature, (middle) specific humidity, and (right) wind speed profiles are shown. There are 7 observed soundings in June, 3 in July, and 11 in August. The black lines are the mean profiles and the range of observations is indicated by the gray shading. The SIZRS soundings with strongest and weakest temperature inversion in each month are shown in thin blue and green lines, respectively, as examples of what individual soundings look like.

a model grid box is allowed and contributions from the ice and the water fraction to surface fluxes and surface variables are aerially averaged. We create two small ensembles of simulations for each of the SIZRS flight days, with the two forcing datasets. For each forcing dataset, the ensemble of eight simulations consists of one baseline run, four runs with alternative parameterizations, and three runs with

different nudging. In the baseline runs, some of the most widely used parameterizations are adopted: the Mellor– Yamada–Janjic (MYJ; Janjic 1994) planetary boundary layer scheme, the Grell–Devenyi (Grell and Dévényi 2002) cumulus scheme, the Rapid Radiative Transfer Model for GCM (RRTMG; Mlawer et al. 1997) radiation scheme, and the Goddard Cumulus Ensemble Model (Tao et al. 1989) cloud microphysics scheme.

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TABLE 1. The WRF ensemble members in this study are constructed by changing one of the parameterizations or the nudging strategies from those adopted by the baseline runs. CAM: Community Atmosphere Model (CAM) radiation scheme; New SAS: New Simplified Arakawa-Schubert (SAS) cumulus parameterization. Member No. 1

2 3 4 5 6 7 8

Type Baseline

Boundary layer Cloud microphysics Radiation Cumulus Nudging Nudging Nudging

Parameterization/nudging choice Boundary layer: MYJ Cloud microphysics: Goddard Radiation: RRTMG Cumulus: Grell–Devenyi Nudging: T, qy, U, V, above 168 hPa YSU (Hong et al. 2006) Morrison (Morrison et al. 2009) CAM (Collins et al. 2004) New SAS (Han and Pan 2011) No nudging U, V, T, and qy above 700 hPa T and qy above 700 hPa, U and V at all levels

Above about 168 hPa, temperature, water vapor, and horizontal winds in model grids are nudged to the forcing analyses values with a nudging coefficient of 3 3 1024 s21. For each ensemble member, only one model parameterization scheme or the nudging strategy is substituted with an alternative compared to the baseline run as is listed in Table 1. The choice of alternative planetary boundary layer, cloud microphysics, radiation, cumulus parameterization, and nudging strategies are intended to explore potential influences of different physics parameterizations and nudging. For example, the boundary layer parameterization used in the baseline run is the MYJ (Janjic 1994) scheme, a local 1.5-order closure scheme, which is considered to be appropriate for stable conditions. The Yonsei University (YSU) scheme, a nonlocal first-order scheme, is updated in WRF 3.4.1 and has shown improved performance in stable boundary layers (Hu et al. 2013). For each flight day, simulations start at 0000 UTC and end at 2359 UTC, which corresponds to an average lead time of 20 h for the first sounding of each day. The model domain of SIZRS experiments and the nested domain boundaries are shown in Fig. 1. The horizontal resolution of the parent domain is 30 km and is reduced to 10 km for the nested domain. There are 54 vertical levels with 14 levels below 1 km. The half-hourly snapshots from WRF and the 6-hourly analyses fields are first interpolated temporally to the time of the SIZRS soundings then horizontally to the locations of soundings. Both the analyses fields and the SIZRS soundings are interpolated vertically to the WRF vertical levels to facilitate the comparisons. A comparison between the ECMWF pressure level analyses data and its sigma-level

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model data finds negligible differences (not shown) compared to the differences between analyses, WRF, and the SIZRS observations presented in the next section. For simplicity, the pressure level data are used in the analysis in the following sections because they are used to force the Polar WRF.

3. Results a. Temperature The mean profiles of the 22 SIZRS soundings are compared with the two analyses and WRF simulations in Fig. 3. The ERAI mean temperature profile deviates from the SIZRS observations by less than 0.3 K below 800 hPa except for the lowest levels. ERAI is warmer than the observed mean soundings with a maximum difference of 1.4 K near the surface. GFS has slightly larger deviations than ERAI between 1000 and 800 hPa, but it is still within 1 K at most levels. The WRF runs forced with ERAI (hereafter WRFE) have a similar mean temperature profile to ERAI and small deviations (,0.5 K) with respect to the SIZRS observations. The WRF runs forced with GFS (hereafter WRFG) is warmer than GFS, especially around 980 hPa, where the difference is over 1 K. Although the root-mean-square errors (RMSEs) of the WRF runs with respect to the SIZRS observations are larger than 1 K (not shown) at most levels, the ensemble spread of mean temperature profiles are much smaller in both WRFG and WRFE, suggesting that the average temperature structure in the ABL along the SIZRS transects is more sensitive to the large-scale forcing than the details of the model physics or the nudging scheme. During the June and July SIZRS flight days the advection of warm air from Alaska over the ice-covered Beaufort Sea (Figs. 1a,b) produced strong temperature inversions extending well into the Beaufort Sea, providing an ideal opportunity to study the stable summer ABL. In the observed mean temperature profile (Fig. 3a), there is an evident low-level temperature inversion from the surface extending all the way to 920 hPa. The temperature inversion strength (TIS), the temperature difference across inversion, of the mean temperature profile is 5 K. The observed low-level temperature inversion is captured well by both the analyses and their respective WRF runs. The identification of low-level temperature inversions in individual soundings follows that of Kahl (1990) and Tastula et al. (2012). Most of the June soundings have TIS larger than 5 K, with a mean of 9.5 K, while most of the August soundings have TIS smaller than 5 K, with a mean of 2.7 K. The mean TIS for the summer soundings is 5.9 K. A comparison of the WRF modeled and the analyses

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FIG. 3. (a) Mean temperature, (b) relative humidity, (c) specific humidity, (d) wind speed, (e) wind direction, and (f) turning angle of the 22 observed and modeled SIZRS 2013 summer soundings. The wind direction is defined as zero for northerly wind and its value increases clockwise. The turning angle at a given level is defined as the change of wind direction relative to the near-surface wind. The mean wind directions in (e) are the direction of mean horizontal winds (vector average) and the mean turning angles in (f) are the scalar average of the turning angles in individual soundings. The soundings obtained from GFS are shown in solid blue lines, ERAI are shown in solid green lines, and the corresponding WRF modeled ensemble mean soundings are shown in dashed lines with the same color as their forcing dataset. The color shading indicates the one standard deviation of the WRF ensemble spread about the ensemble mean profiles. The mean observed soundings are shown in solid red lines. The black dashed line in (a) shows the dry adiabatic lapse rate of an air parcel lifted from 920 hPa at 280 K.

TIS with observed TIS computed from the SIZRS soundings are shown in Fig. 4. TIS is underestimated in the analyses and overestimated by WRF. The GFS has a smaller bias, higher correlation, and smaller RMSE (2.4 K) than ERAI (3.3 K). Note that for some of the August soundings with a weak inversion the analyses fail to generate these inversions, especially ERAI, while the WRF runs are able to reproduce some of these inversions. Compared to their respective forcing analyses, both WRF ensembles have marginal improvement in the TIS characterization, with slightly larger correlation and smaller RMSE with respect to the SIZRS observations. Both WRFG and WRFE have a stronger

inversion than their forcing analyses by over 1 K, which is related to their colder near-surface temperature than the corresponding forcing analyses. The reason for the cold bias is discussed in section 3b. The mean profiles from the nested inner domain with higher horizontal resolution are almost exactly the same as the mean profiles at the same locations of the coarser domain (not shown), suggesting that TIS errors are not related to model resolution. Statistical tests of the temperature biases in the analyses with respect to the SIZRS observations are shown in Fig. 5a. It is evident that the low-level and nearsurface warm bias in ERAI (Fig. 3a) is statistically

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FIG. 4. The comparison of low-level temperature inversion strength (TIS) between the WRF ensembles mean and their forcing datasets vs observations: (a) GFS (blue circles) and WRFG ensemble mean (green stars) vs observations and (b) ERAI (blue circles) and WRFE (green stars) vs observations. The black dashed lines are the 1-to-1 lines. The differences between the modeled and observed TIS are demonstrated by the distance along either axis between the 1-to-1 lines and individual markers. The missing TIS in models are shown by markers on the zero line. Fitted lines for the WRF ensemble mean and reanalyses are shown in thin green and blue lines, respectively. The correlation coefficient R and bias of WRF ensemble mean and the analyses with respect to the SIZRS observations are also shown.

significant at the 95% level, using a nonparametric bootstrap test. Unlike the Student’s t test, the bootstrap method used here assumes no particular distribution for the data. The low-level and near-surface warm bias in ERAI was also reported in several recent studies. Lüpkes et al. (2010) showed a warm bias below 300 m and increasing near the surface of up to 1.78C in ERAI using the rawinsondes collected over the Atlantic and Asian sectors of the Arctic Ocean in the summers of 1996, 2001, and 2007. Jakobson et al. (2012) found ERAI has a warm bias of up to 2 K below 400 m compared to tethersonde observations in the central Arctic in April to August 2007. A warm bias of up to 1.3 K in ERAI below 200 m is found during the Arctic Summer CloudOcean Study (ASCOS; Tjernström et al. 2014) in the summer of 2008 over the Atlantic sector of the Arctic Ocean (Wesslén et al. 2014; de Boer et al. 2014). To further investigate the source of the warm bias we examine the SST and SIC from ERAI and find erroneous SIC compared to other SIC products. GFS and the Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave Imager (SSM/I) daily polar gridded sea ice concentrations from the National Snow and Ice Data Center (NSIDC; Maslanik and Stroeve 1999) show more extensive sea ice. An example is shown in Fig. 6. In the marginal seas along the Siberian Coast and much of the Chukchi Sea, GFS and SSM/I report high values of SIC, but ERAI has no ice at all. Even the SIC dataset, which reportedly provides the sea ice

boundary conditions for the ECMWF model (after February 2009), the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA; Donlon et al. 2012) shows quite different SICs than those used by ERAI. Inside the SIZRS model domain, the total sea ice area (SIC . 15%) calculated from GFS, SSM/I, and OSTIA is 3.64, 3.54, and 3.51 million km2, respectively, compared to only 2.62 million km2 from ERAI. We traced this discrepancy to a thresholding mechanism ECMWF applies to the SIC data to make them consistent with observed SST datasets. This thresholding mechanism sets SICs to 0 when SSTs exceed 274.26 K making the assumption that ice surface temperature cannot exceed the melting point by more than 1 K (H. Hersbach 2014, personal communication). On that particular day (Fig. 6), OSTIA SST over ice covered areas was in excess of 5 K above the melting point. The sources of the high OSTIA SST values over sea ice are not obvious at this point. Photographs taken from the SIZRS aircraft show the area to be heavily covered by meltponds. In situ measurements of radiative temperatures of melt-pond surfaces during SHEBA show extreme values of 48C during calm and clear conditions (T. Grenfell 2014, personal communication) and lead temperatures can substantially exceed the melting point. Though clear and relatively calm conditions prevailed over the study area it is difficult to imagine that area-integrated SST over sea ice would exceed the melting point of water by more than 5 K. Other SST

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FIG. 5. Mean bias of (a) temperature, (b) relative humidity, (c) horizontal wind speed, and (d) turning angle for GFS (blue) and ERAI (green) with respect to the 22 observed SIZRS soundings. The color shading indicates the 95% confidence intervals of the biases and the red dashed lines denote zero-bias lines. The confidence intervals are computed using a bootstrap resampling method with 5000 samples.

retrieval errors are likely responsible and a thresholding mechanism of SIC should take those into account and is planned to be implemented by ECMWF in the future. This issue not only affects ERAI, but also affects the

ECMWF operational model. The influence of the erroneous SIC in the seasonal ice zone during summertime on the ABL and ocean mixed layer and the extent of this issue is currently unknown, and requires further study.

FIG. 6. Comparison of sea ice concentration (SIC) map over the SIZRS domain on 18 Jun 2013 from GFS, ERAI, SSM/I, and OSTIA.

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To examine the role of the SIC error on the modeled SIZRS temperature profiles and the identified warm biases, we created another WRF ensemble using the ERAI atmospheric lateral forcing, the ERAI SST, and the GFS SIC. Results from this experiment (not shown) indicates that the WRF modeled soundings for the SIZRS area during summer are insensitive to the change in SIC and suggests that the ERAI SIC error is not directly responsible for the low-level warm bias in ERAI. We performed a further test of the lower boundary conditions using the ERAI atmospheric lateral forcing, the GFS SIC, and the GFS SST. The modeled SIZRS soundings in this ensemble (not shown) have almost identical near-surface temperature and moisture profiles as the ensemble forced by GFS atmosphere lateral forcing, the GFS SIC, and the GFS SST. These two tests suggest that the differences in SST, not SIC, between GFS and ERAI are responsible for the different nearsurface temperature and moisture profiles between Polar WRF ensembles forced by these two analyses. Are erroneous SSTs then possibly responsible for the bias with respect to observations? Although the ERAI SST over open water near the sea ice boundary does have a warm bias compared to the OSTIA and the Jet Propulsion Laboratory’s Multiscale Ultrahigh Resolution (MUR) SST (not shown), it might not be the only reason. The near-surface temperature bias over open water is 1.8 K while over sea ice it is 1.1 K. We suspect the thermodynamic sea ice surface model used in ERAI might be associated with the warm bias as well and lead to consistent low-level warm biases for soundings over both open water and sea ice. The exact causes for the warm bias are still under investigation.

b. Horizontal winds and the LLJ The SIZRS mean wind speed profile shows a LLJ below 1000 hPa and a secondary wind speed maximum around 870 hPa (Fig. 3d). The general structure of the observed mean wind speed profile is captured by both the analyses and the WRF ensembles. The mean horizontal wind speeds of GFS and ERAI are very close to each other, but weaker than the SIZRS observations by 0.5 m s21 except for near the LLJ and in a 100-hPa-deep layer around the 870-hPa wind speed maximum. As is shown in Fig. 5c, the underestimation of the horizontal wind speeds around 870 hPa and at the LLJ are statistically significant at the 95% level. The agreement between the WRF ensembles and observations is better, especially for wind speeds near the height of the maximum speed of the mean LLJ, at around 111 m (about 1004 hPa). The observed mean winds veer from southeasterly near the surface to almost westerly above 800 hPa. Both the analyses and the WRF runs capture

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the mean-wind direction change with height (Fig. 3e). Both analyses underestimate the turning angle, the average wind turning from the direction of the nearsurface wind, by over 208 below 950 hPa and these biases are statistically significant at the 95% level (Fig. 5d). The biases in the WRF runs, especially WRFG, are much smaller. Above 900 hPa, the turning angle is overestimated by both the analyses and the WRF runs. The LLJ observed in the mean SIZRS soundings peaks slightly below 1000 hPa, around 111 m, with a wind speed of 7.2 m s21. The LLJ is also shown in GFS and ERAI, but the jet is much broader and 1 m s21 weaker than the observations. Both WRF ensembles reproduce the observed LLJ remarkably well with little bias in the height and magnitude of the jet core. In addition, the underestimation of the turning angle in lower levels is also alleviated in the WRF runs compared to the analyses. Why do WRF simulations do much better in representing the LLJ in the mean wind speed profile? Around the jet core level and below, both the GFS and the ECMWF models have six sigma levels (below about 980 hPa) and they have enough resolution to resolve the LLJ. Below 980 hPa, the vertical grid for the Polar WRF runs has seven levels with vertical spacing of between 27 and 60 m, with just a slightly higher resolution than the GFS and the ECMWF models. To test the influence of coarser vertical resolution on the mean LLJ, we generate two additional WRF ensemble runs using half the number of vertical levels than in the previously discussed WRF ensembles. The mean wind profiles in coarse-resolution ensembles are very close to those of the higher-resolution ensembles and match the observed LLJ equally well. The only differences are a negligibly weaker jet and a slightly larger ensemble spread. An experiment with much higher vertical resolution was performed in a coastal LLJ study by Nunalee and Basu (2014), who showed that an increase of vertical resolution by doubling the number of vertical levels below 1 km from 18 to 36 in WRF also had no improvement in LLJ forecast and even weaker LLJ in the midlatitude nocturnal stable boundary layer. We, therefore, reject the idea that the coarser vertical resolution of the analyses is responsible for the weaker LLJ in this case. The deep and weak LLJ in GFS and ERAI may be related to overly diffuse boundary layer parameterizations in the stably stratified boundary layer. Without artificially enhanced vertical mixing, global models like GFS and ECMWF are known to have near-surface temperature cold biases in the stable boundary layer and artificially enhanced vertical diffusion is a handy fix. This practical solution, although not necessarily physical, also improves the development of synoptic-scale cyclones in these models (e.g., Holtslag et al. 2013). As

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a result, the simulated LLJs are broader and weaker than observations and the turning angle in the boundary layer is underestimated (Svensson and Holtslag 2009). Note that both WRF runs are colder near the surface than their respective analyses. This is consistent with recent modeling studies on the influence of mixing in the stable boundary layers using WRF (Hu et al. 2013) and ECMWF (Sandu et al. 2013). Both studies found near-surface cold biases when the artificially enhanced mixing is reduced in the stable boundary layer, even for state-of-the-art models like the recent versions of WRF and ECMWF. To test the above hypothesis that the LLJ bias in GFS and ERAI may be related to overly diffuse boundary layer parameterizations, we increased or decreased the mixing length scale of the MYJ boundary layer scheme by a factor of 5. Because of the nonsingularity constraints on the maximum MYJ mixing length scale (Janjic 2002), the actual allowed increase in the mixing length scale is smaller than expected. All ensemble members were run except the member using the YSU boundary layer scheme. As the mixing length scale increases, the LLJ gets slightly weaker by 0.1 m s21 compared to the ensemble using the original MYJ mixing length scale (not shown). As the mixing length scale decreases, the LLJ gets stronger, by 0.5 m s21 (forced by GFS) to over 1.0 m s21 (forced by ERAI). At the same time, the surface wind also decreases significantly due to the weaker mixing and the turning angle below 950 hPa increases significantly, by 58 (forced by GFS) to 108 (forced by ERAI). These changes are consistent with previous studies and our hypothesis that the improved mean LLJ in the Polar WRF simulations is related to the weaker boundary layer turbulent mixing compared to the GFS and ECMWF models, although changes in the depth of LLJ and the near-surface temperature are not significant in this experiment. In all previous WRF versions between 3.0 and 3.4, the YSU boundary layer scheme implementation is not consistent with the original published formulation and results in enhanced mixing compared to the original formulation (Hu et al. 2013; Sterk et al. 2013). In this study, using WRF 3.4.1, the YSU implementation has been corrected. The previous YSU scheme with artificially enhanced mixing is considered to be associated with weakening and deepening of LLJ in WRF simulations (Storm et al. 2009) and compared worse with observations than using the YSU implementation in WRF 3.4.1 (Hu et al. 2013). Using Polar WRF 3.4.1, we tested the influence of the artificially enhanced mixing by substituting the YSU implementation in WRF 3.4.1 with the YSU implementation in WRF 3.3.1. With artificially enhanced boundary layer mixing, the simulated SIZRS soundings show weaker and deep LLJ, smaller wind

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turning angle below 950 hPa, and warmer near-surface temperature (not shown). Results from these experiments in modifying the boundary layer mixing in both MYJ and YSU schemes confirms the idea that the improvement in the mean LLJ and wind turning angle in the Polar WRF simulations are related to the parameterization of boundary layer mixing. The vertical profiles of the correlation between the modeled and observed wind speeds are shown in Fig. 7a. Both GFS and ERAI wind speeds have higher correlation (.0.7) with the observations at most levels than WRFG and WRFE (correlation coefficient around 0.6). In addition, the wind speed RMSE of WRFG and WRFE are larger than GFS and ERAI by about 0.5 m s21 (Fig. 7c). It seems that the WRF simulations improve the mean LLJ but introduce larger variability around the mean state, which leads to worse correlation and larger RMSE, compared to their forcing analyses. Using the Fisher z transformation, we calculated the 95% confidence intervals for the difference of correlation coefficients between the WRF simulations and their respective forcing analyses. It is clearly shown in Fig. 7d that these differences in correlation coefficients are not statistically significant for any ensembles.

c. Moisture profiles The SIZRS mean relative humidity (RH) profiles show a steady decrease with height up to 900 hPa (Fig. 3b). The rate of decrease of RH with height is captured by ERAI and WRFE, but both of them are moister than the observations by about 10% in this layer. Lüpkes et al. (2010) reported a substantial (5%– 10%) moist bias in ERAI below 800 m and Jakobson et al. (2012) found a significant moist bias up to 9% in ERAI below 890 m. There is only a small moist bias in ERAI compared to the ASCOS radiosondes (Wesslén et al. 2014). Although the moist bias in ERAI compared to the SIZRS observations is consistent with previous studies over the Arctic Ocean, we should note that the water vapor measurements obtained by the MODEM M10 GPSonde used in the SIZRS might have a dry bias of 5%–7% RH compared to other sensors (Bock et al. 2013). Therefore, the actual RH moist bias in the analyses might be smaller than that shown in Fig. 3b. The RH in GFS and WRFG agrees well with the observations below 950 hPa but shows a slower rate of decrease with height and an increase in bias until 700 hPa. The moist biases of both GFS above 900 hPa and ERAI from surface to 750 hPa are statistically significant at the 95% level (Fig. 5b). A specific humidity (qy) inversion around 985 hPa is shown in the mean specific humidity profile. The specific humidity inversion is reproduced by both the analyses and the WRF runs. Note that the mean moisture profiles

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FIG. 7. (a) Correlation coefficient, (b) bias, and (c) RMSE of modeled horizontal wind speeds with respect to the SIZRS observations at WRF Model levels. (d) The 95% confidence intervals of the difference in correlation coefficient between the WRF ensembles and their respective forcing analyses are shown. In (a)–(c), the solid blue lines are for GFS, the solid green lines are for ERAI, the dashed blue lines are for the WRFG ensemble mean, and the dashed green lines are for the WRFE ensemble mean. In (d), the solid lines are the ensemble mean differences, the blue and green dashed lines show the ensemble mean of the upper and lower bound of the confidence intervals, and the zero-difference line is shown by the red dashed line. In all four panels, the color shading indicates the one standard deviation of the WRF ensemble spread about the ensemble mean profiles.

of the WRF runs resemble their respective forcing datasets rather than each other and the differences are far beyond the ensemble spread. This suggests that the largescale forcing is critical in establishing the moisture structure in the Arctic ABL in Polar WRF. The large differences between the two analyses and between the analyses and the SIZRS observations demonstrate the large uncertainties in our knowledge of moisture profiles over the Arctic marginal seas. Both the moisture advection and local surface evaporation are found to be important for the precipitation in the Arctic region (e.g., Glisan and Gutowski 2014; Bintanja and Selten 2014). The specific humidity inversion is found to be associated with Arctic low-level clouds (Sedlar et al. 2012) and is shown to be an important source of moisture to maintain mixed-phase clouds in large-eddy simulations (Solomon et al. 2014). The gap between the observed moisture profiles and the modeled profiles stresses the necessity and importance of obtaining more observations of the

vertical profile of water vapor in this region to improve the representation of the Arctic hydrologic cycle and cloud processes in analyses.

4. Summary and discussion This study evaluates the representation of the Arctic ABL in two analyses—the GFS and the ERAI—with the dropsonde observations obtained as part of the SIZRS Arctic Domain Awareness flights by the U.S. Coast Guard in the summer of 2013. We also explore the potential improvement in the representation of the Arctic ABL using a regional model, the Polar WRF, which is equipped with more sophisticated physics parameterizations and is computationally affordable to be run at higher resolution. We find that the analyses can capture the general structures of the observed mean profiles, but they have biases and fail to reproduce some finer structures in the

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ABL. In both analyses, the RH is too high and they are as different from each other as from the observations. The differences in RH highlight the large uncertainty of moisture content in the analyses. The horizontal winds, especially the LLJ are weaker than in the observations, and the wind turning with height is underestimated, likely due to excessive diffusion in the boundary layer. Both analyses, especially the ERAI, have difficulty in reproducing some of the weaker temperature inversions. The ERAI has a warm bias up to 1.4 K in the lowest levels of atmosphere over both open ocean and sea ice, although the warm layer is shallower than reported in previous studies (Lüpkes et al. 2010; Jakobson et al. 2012; de Boer et al. 2014; Wesslén et al. 2014). The bias in the analyses, such as the ERAI low-level temperature bias, the underestimation of the LLJ and the turning angle, and the moist bias in ERAI are all statistically significant at the 95% level. Note that the range of the confidence interval of the biases estimated using the bootstrap method is likely underestimated due to potential autocorrelation in the data (e.g., Wilks 1997), which is neglected here as in many previous studies (e.g., Lüpkes et al. 2010; Jakobson et al. 2012; de Boer et al. 2014; Wesslén et al. 2014). Although the effect of autocorrelation is difficult to quantify, we still want to stress that it should be considered for cases with better spatial and temporal sampling, using techniques such as the moving-block bootstrap (e.g., Liu et al. 2010). We also find that the ERAI has erroneous SIC in the seasonal ice zone, due to an inappropriate SIC thresholding process. In ECMWF, the SICs are set to zero when the SSTs are higher than 274.26 K, which can eliminate large areas of sea ice in marginal seas during the summertime. The impact of this bias on the ABL and marine mixed layer is still unknown. The WRF ensembles have comparable performance to that of their forcing analyses. The WRF ensembles can better represent some of the fine features than their forcing analyses, such as the temperature inversion and the LLJ, but they still have larger biases and inconsistencies with the observations in other aspects. The mean wind profiles, especially the LLJ are remarkably well reproduced by the WRF ensembles with little sensitivity to either vertical or horizontal resolution. This improvement in the representation of the LLJ is likely because in the WRF simulations with MYJ and the updated YSU schemes there is no artificially enhanced mixing, which is used by global analyses to compensate for other problems. The correlation and RMSE with respect to the observations are degraded, but the decrease in correlation coefficients is not statistically significant. The near-surface temperature in the WRF ensembles is colder than their respective forcing

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analyses, which was considered to be related to the reduced mixing (Hu et al. 2013; Sandu et al. 2013). However, in our experiments of changing boundary layer mixing in the MYJ scheme, the impact on near-surface temperature is insignificant. Comparing the analyses and the WRF ensembles with the moisture observations also shows interesting results. The WRF simulated RH profiles follow quite closely their forcing analyses, which suggests the WRF downscaling strategy cannot reproduce the observed RH profile better than the forcing analyses of WRF. Given the large differences in RH between the analyses, the only way to improve the modeled RH profiles is to constrain the models with more and better-calibrated observations of the atmosphere over the Arctic Ocean and its marginal seas. This is important because of the potential impact on cloud, radiation, and the energy budget of the underlying surface, especially on sea ice during the melting season. Consequently, current and future SIZRS observations can be a promising dataset to reduce the bias and uncertainty in the representation of the Arctic ABL in analyses over the Beaufort and the Chukchi Seas. The SIZRS flights in the summer of 2013 were still in an experimental stage and obtained only 22 soundings. Even so we have already shown some interesting results using the SIZRS observations. We expect that with more observations from the SIZRS flights, the Arctic ABL can be better understood and represented in analyses over the seasonal ice zone. For example, the Arctic System Reanalysis (ASR; Bromwich et al. 2010) is a recently developed reanalysis product focusing on the entire Arctic region and produced using the Polar WRF. Further Polar WRF modeling studies with the SIZRS observations may provide insights for the understanding of some ABL errors of the ASR (Wesslén et al. 2014) and aid its future development. Acknowledgments. This work is supported by the Office of Naval Research, the National Science Foundation, and the National Aeronautics and Space Administration. We express our gratitude to the U.S. Coast Guard for providing the C-130 platform for dropsondes deployment and their assistance in the SIZRS flights. Lt. Jesse Hyles in particular is thanked for piloting all of the approval processes as well as the C-130 on several SIZRS flights. We thank Hans Hersbach and Sarah Keeley (ECMWF) and Tom Grenfell (UW) for discussions on the ECMWF sea ice and sea surface temperature issue. Jamie Morison (UW) is thanked for his leadership of the SIZRS project and Roger Andersen (UW) for unwavering field engineering support. We thank David Bromwich and Keith Hines (Ohio State

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