An assessment of ECMWF analyses and model forecasts over the North Slope of Alaska using observations from the ARM Mixed-Phase Arctic Cloud Experiment

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1 JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 111,, doi: /2005jd006509, 2006 An assessment of ECMWF analyses and model forecasts over the North Slope of Alaska using observations from the ARM Mixed-Phase Arctic Cloud Experiment Shaocheng Xie, 1 Stephen A. Klein, 1 John J. Yio, 1 Anton C. M. Beljaars, 2 Charles N. Long, 3 and Minghua Zhang 4 Received 15 July 2005; revised 12 November 2005; accepted 2 December 2005; published 11 March [1] European Centre for Medium-Range Weather Forecasts (ECMWF) analysis and model forecast data are evaluated using observations collected during the Atmospheric Radiation Measurement (ARM) October 2004 Mixed-Phase Arctic Cloud Experiment (M-PACE) at its North Slope of Alaska (NSA) site. It is shown that the ECMWF analysis reasonably represents the dynamic and thermodynamic structures of the large-scale systems that affected the NSA during M-PACE. The model-analyzed near-surface horizontal winds, temperature, and relative humidity also agree well with the M-PACE surface measurements. Given the well-represented large-scale fields, the model shows overall good skill in predicting various cloud types observed during M-PACE; however, the physical properties of single-layer boundary layer clouds are in substantial error. At these times, the model substantially underestimates the liquid water path in these clouds, with the concomitant result that the model largely underpredicts the downwelling longwave radiation at the surface and overpredicts the outgoing longwave radiation at the top of the atmosphere. The model also overestimates the net surface shortwave radiation, mainly because of the underestimation of the surface albedo. The problem in the surface albedo is primarily associated with errors in the surface snow prediction. Principally because of the underestimation of the surface downwelling longwave radiation at the times of singlelayer boundary layer clouds, the model shows a much larger energy loss ( 20.9 W m 2 ) than the observation ( 9.6 W m 2 ) at the surface during the M-PACE period. Citation: Xie, S., S. A. Klein, J. J. Yio, A. C. M. Beljaars, C. N. Long, and M. Zhang (2006), An assessment of ECMWF analyses and model forecasts over the North Slope of Alaska using observations from the ARM Mixed-Phase Arctic Cloud Experiment, J. Geophys. Res., 111,, doi: /2005jd Introduction [2] Arctic clouds and their influences on radiative feedback processes are very important to global climate change [e.g., Intergovernmental Panel on Climate Change, 2001]. Accurately representing Arctic clouds and interactions between clouds and radiation in general circulation models (GCMs) has been a challenging task in the modeling community. This is mainly because of our limited knowledge of the cloud-associated processes due to a lack of sufficient observations and basic cloud studies in the Arctic [Curry et al., 1996]. Previous studies show that most GCMs have difficulties in correctly predicting the Arctic climate and clouds [e.g., Tao et al., 1996; Walsh et al. 2002]. This 1 Lawrence Livermore National Laboratory, Livermore, California, USA. 2 European Centre for Medium-Range Weather Forecasts, Reading, UK. 3 Pacific Northwest National Laboratory, Richland, Washington, USA. 4 Marine Sciences Research Center, Stony Brook University, State University of New York, Stony Brook, New York, USA. Copyright 2006 by the American Geophysical Union /06/2005JD poor prediction is also true for GCMs that are used for weather prediction, such as the model of the European Centre for Medium Range Weather Forecasts (ECMWF) [Beesley et al., 2000]. The lack of adequate observational data also results in few numerical modeling studies and model evaluation activities in high latitudes compared to those in middle and low latitudes, which further hampers model improvements. The United States Department of Energy s Atmospheric Radiation Measurement (ARM) program seeks to redress this problem by maintaining a longterm monitoring station at the North Slope of Alaska (NSA). [3] The data situation in high latitudes has been improved with several major field experiments conducted in the Arctic in recent years to gather the needed data for model evaluations and improvements. These field experiments are the Surface Heat Budget of the Arctic Ocean (SHEBA) project founded by the National Science Foundation (NSF) and the Office of Naval Research (ONR) [Perovich et al., 1999; Uttal et al., 2002], the First International Satellite Cloud Climatology Project (ISCCP) Regional Experiment (FIRE) Arctic Clouds Experiment (ACE) founded by the National Aeronautics and Space Administration (NASA) [Curry et 1of17

2 Figure 1. (a) M-PACE sounding network at the ARM NSA site. The locations of the four ARM sounding stations are shown as circles, and their surface elevations are given in parentheses. (b) Data analysis domain. The locations of the six analysis grid points are displayed as circles, pluses are the ECMWF model output grid points, and asterisks are the ARM sounding stations. al., 2000], and the Atmospheric Radiation Measurement (ARM) program sponsored by the Department of Energy (DOE) [Stokes and Schwartz, 1994; Ackerman and Stokes, 2003]. These field campaigns all share the common goal of improving our understanding and model simulations of cloud-radiation interactions in the Arctic but with somewhat differing scientific focus. SHEBA was a yearlong field experiment from October 1997 to October 1998, based on an ice-breaker ship that was frozen into the perennial pack ice of the Alaska Ocean and left to drift with the ice for a full year. It collected data necessary to understand the physical processes that determine the surface energy budget and the sea ice mass balance in the Arctic. FIRE-ACE was coordinated with SHEBA but used research aircraft to obtain measurements of cloud properties over the SHEBA ship and at Barrow, Alaska for the period from April to July ARM has made continuous measurements of clouds, radiation, and atmospheric state over the North Slope of Alaska (NSA) region, using surface-based radiometers and remote sensing instruments among others at its Barrow site since 1997 and its Atqasuk site since Data collected from these experiments have advanced our understanding of the fundamental physics related to interactions between clouds and radiation in the Arctic [Pinto et al., 1999; Persson et al., 2002; Intrieri et al., 2002a, 2002b; Shupe et al., 2001; Shupe and Intrieri, 2004; Morrison et al., 2005; Zuidema et al. 2005]. [4] Among all ARM Arctic field experiments, the most comprehensive one is the Mixed-Phase Arctic Cloud Experiment (M-PACE) that was conducted over NSA in October 2004 with the goal of advancing our understanding of the dynamical and physical processes in mixed-phase Arctic clouds [Harrington and Verlinde, 2004; Verlinde et al., 2005]. In the experiment, a sounding network of four radiosonde stations (Figure 1a) was used to measure the time evolution and three-dimensional structure of the Arctic atmosphere four times per day. The ARM cloud radar, lidars, and three instrumented aircrafts provided detailed information about Arctic clouds and cloud microphysics. Basic surface meteorology fields, surface radiative fluxes, and cloud liquid water path were measured at the ARM Barrow and Atqasuk sites and the Pacific Northwest National Laboratory (PNNL) Atmospheric Remote Sensing Laboratory (PARSL) ARM-like remote sensing facility at Oliktok Point. In addition, the NASA Terra and the National Oceanic and Atmospheric Administration (NOAA) 15 and 16 satellites provided measurements of clouds and the top of the atmosphere (TOA) broadband radiative fluxes. During the experiment, a variety of common Arctic cloud types occurred, such as multilayered clouds, persistent boundary layer clouds, and deep frontal clouds. Furthermore, changes between cloud types were closely connected with changes in the large-scale meteorology affecting the NSA region. The comprehensive information of clouds and radiation obtained from M-PACE provides a unique data set to assess how well GCMs simulate these observed Arctic clouds over the North Slope of Alaska region. [5] For the M-PACE period, the European Centre for Medium-Range Weather Forecasts (ECMWF) generously provided their six-hourly analysis and model forecast data over a region that covers the M-PACE domain, which is enclosed by the four sounding stations (Figure 1a). Prior studies [Pinto et al., 1999; Morrison and Pinto, 2004] have used the ECMWF forecast and analysis to derive the required large-scale vertical velocity and advective tendency terms necessary for integrations of Single-Column and Cloud-Resolving Models (SCMs and CRMs). In addition, the large-scale atmospheric state variables from the ECMWF analysis are used as the first guess background field in a recently completed objective analysis of M-PACE observations (S. C. Xie et al., Developing large-scale forcing data for single-column model and cloud-resolving model from the Mixed-Phase Arctic Cloud Experiment, submitted to Journal of Geophysical Research, 2006, hereinafter referred to as Xie 2of17

3 et al., submitted manuscript, 2006). However, because of the paucity of in situ observations, the ECMWF analysis in high latitudes is largely dependent on the accuracy of its model dynamics and physical parameterizations. This motivates the current study which assesses the quality of those data over the Arctic region. [6] One prior study which evaluated the quality of the ECMWF analysis and forecasts with observations is the study of Beesley et al. [2000]. Using observations collected at SHEBA ice camp, they examined the ECMWF shortrange predictions of cloud and boundary layer variables over the Arctic Ocean during November and December of In their study, the SHEBA soundings were assimilated into the ECMWF analysis system in an attempt to constrain the overall model atmospheric state variables close to observations. They found that the ECMWF model could reasonably reproduce many SHEBA observed surface fields and cloud structures. However, the model showed rather large errors in the surface temperature and sensible heat fluxes, which were mainly due to deficiencies in the ECMWF sea ice model used at that time. Under low-cloud conditions, the model underestimated the surface downwelling longwave radiation. [7] In this paper, we use the recently available ARM M- PACE data to assess the quality of the ECMWF analyses and model forecasts over NSA. Over the region, there is one National Weather Service (NWS) observation station at Barrow that provides 12-hourly soundings and higherfrequency surface meteorology data to the ECMWF data assimilation system. However, the ARM M-PACE soundings and surface observations were not assimilated in the ECMWF model. Thus these data can serve as an independent data set for us to assess the quality of the ECMWF model analysis. The model-predicted cloud variables, radiation, surface precipitation, and surface sensible and latent heat fluxes, are dependent on the accuracy of its physical parameterizations, provided that the ECMWF analysis can reasonably represent the large-scale fields. A thorough assessment of these physical fields could help suggest potential deficiencies in the model parameterizations and hopefully lead to further model improvements. [8] The paper is organized in the following order. In section 2, we provide a detailed description of the ECMWF data assimilation and forecasting system, M-PACE data, and our model-observation comparison strategy. The quality of the ECMWF analyses and forecasts is evaluated using M- PACE observations in section 3. Section 4 gives a summary of the study. 2. Model, Data, and Comparison Strategy [9] The ECMWF operational data assimilation and forecasting system at the time of the M-PACE experiment included a forecast model with T-511 horizontal resolution (about 40 km) with 60 vertical levels and a four-dimensional variational (4DVAR) data assimilation system that operates in 12-hour cycles. The vertical model levels are terrain following near the surface with the lowest model level at about 10 m. The distance between levels increases gradually with height with typically about 10 levels to resolve the boundary layer. The top of the model is at 0.1 hpa. The model s physical parameterization includes (1) the Rapid Radiative Transfer Scheme (RRTM) for longwave radiation [Mlawer et al., 1997], (2) a shortwave radiation scheme adapted from Fouquart and Bonnel [1980], (3) a mass flux convection scheme for shallow, midlevel and deep convection [Tiedtke, 1989; Gregory et al., 2000], (4) a cloud scheme with prognostic variables for cloud water/ice and cloud cover [Tiedtke, 1993; Jakob and Klein, 2000], (5) a firstorder boundary layer scheme with prescribed diffusion coefficient profiles for the unstable boundary layer and a stability-dependent coefficient for the stable boundary layer [see Beljaars and Viterbo, 1999] and (6) the Tiled ECMWF Scheme for Surface Exchanges over Land (TESSEL) [van den Hurk et al., 2000]. The latter has 6 tiles over land namely: bare soil, low vegetation, high vegetation, wet surface, exposed snow and snow under high vegetation. Ocean and land are distinguished by a mask. Ocean points can have two tiles: open water and ice. [10] The six-hourly ECMWF data used in this study contain both analyses and forecasts from the ECMWF operational data assimilation and forecasting system, which is initialized at 00Z and 12 Z every day. The forecast model is an integral part of the data assimilation. In the 4DVAR data assimilation, the initial condition of the 12-hour forecast (model trajectory) is adjusted by the variational methods to minimize a cost function. The horizontal wind components, temperature, and humidity above the surface are taken from this data assimilation. This is from the model trajectory that is considered to be optimal with respect to observations and first guess (the first guess is 12-hour forecast from the previous cycle). Also the cloud parameters are taken from this model trajectory, but they are obviously dependent on the model parameterizations, because no cloud observations are used in the assimilation. The surface winds, temperature, and relative humidity are from a separate surface analysis that is run every six hours at 00Z, 06Z, 12Z, and 18Z. Over the NSA region, soundings and surface meteorology observations are only available from the NWS Barrow station and are assimilated in the ECMWF analysis system. Given the paucity of observations, the analyzed fields are clearly influenced by the model dynamics and physical parameterizations and the data assimilation schemes. The fluxes, for example, precipitation, radiative and turbulent fluxes, are computed in the first guess forecasts by the model physics and are therefore not constrained by observations. In summary, one might say that an assessment of cloud variables, precipitation and fluxes tests the model physics, provided that the large-scale fields of wind, temperature and humidity are well represented. [11] The data used to evaluate the ECMWF model are based on the ARM M-PACE observations but are processed using the objective variational analysis method developed by Zhang and Lin [1997]. In the experiment, sounding balloons at the four sounding stations were launched every 6 hours to measure the vertical profiles of temperature, relative humidity, and horizontal winds during two radiosonde Intensive Operational Periods (IOPs). The first radiosonde IOP was from 00Z 5 October to 00Z 10 October 2004 and the second one was from 00Z 14 October to 12Z 22 October Between the two IOPs, ARM sounding data were available once a day at the Barrow and Atqasuk sites. [12] Surface measurements were only available at Barrow, Atqasuk and Oliktok Point. These data include surface 3of17

4 precipitation, wind components, temperature, relative humidity, and upwelling and downwelling radiation. The ARM Microwave Radiometer (MWR) at these three stations provided measurements of column precipitable water and cloud liquid water path. At Barrow and Oliktok Point, information about clouds and cloud properties was available from the Millimeter-Wavelength Cloud Radar (MMCR), Micropulse (MPL) Lidars, and laser ceilometers. The NASA Terra satellite and the NOAA 15 and NOAA 16 satellites provided measurements of clouds and TOA broadband radiative fluxes over NSA. Note that cloud properties measured from aircrafts are not used in current study. [13] One common weakness in comparing model simulations with field measurements is that model results, which are representative of an area of tens to hundreds of kilometers, must be compared with single point measurements. The original ECMWF analysis and forecast data, which are represented on the ECMWF T511 grid, are interpolated onto a regular 0.5 latitude-longitude output grid (pluses in Figure 1b). Model quantities on the output grid near the coastline could contain information from both ocean grid points and land grid points, over which the surface characteristics can be very different. It is seen from Figure 1b that the closest ECMWF model output grid point to the Barrow site (A1) is actually an ocean grid point. As we will show later, this will result in large discrepancy in some fields between the ECMWF model and the M-PACE observations at the Barrow site. Therefore the model-observation comparison needs to be conducted carefully in order to correctly interpret discrepancies between model results and observations. Fortunately, the availability of M-PACE observations from multiple sites provides an opportunity to perform a variational analysis to compute a large-scale average of the state of the atmosphere over the region encompassed by the multiple observational sites. Thus a better model-observation comparison can be made over an area that model results and observations are reasonably comparable to each other. [14] Here we provide some details of the variational analysis of M-PACE data which will be described more fully in a forthcoming article (Xie et al., submitted manuscript, 2006). The analysis domain is the area enclosed by the solid black lines shown in Figure 1b, and the surface and top-of-the-atmosphere constraints needed by the variational analysis approach are averages over this area. Note that this region differs from the area enclosed by the original sounding network (Figure 1a) which contains a sounding station at Toolik Lake that has much higher surface elevation (760m) than the other three stations (less than 30m). Moreover, there were no surface measurements taken at the Toolik Lake site during M-PACE. Therefore the original sounding network (Figure 1a) is not optimal for deriving the required domain-averaged surface variables because of the inhomogeneous surface elevations and the lack of surface measurements at Toolik Lake. To reduce the problem, Xie et al. (submitted manuscript, 2006) slightly modified the M- PACE domain by not using the Toolik Lake site as one of the analysis grid points but adding one extra analysis point A4 with the surface elevation of 50m (Figure 1b). Note that the sounding information collected from the Toolik Lake site is still used in the analysis method through the interpolation approach described by Cressman [1959]. Since the surface elevation at A4 is about 50m, the domain encompassed by A1, A2, A4, and A5 is now more homogeneous than the original M-PACE domain. Following Zhang et al. [2001], Xie et al. (submitted manuscript, 2006) added another two auxiliary grid points at the middle of the two long sides of the domain (i.e., A3 and A6 in Figure 1b) to improve the linear assumption in the line-integral flux calculations into or out of the analysis domain. The horizontal area of the domain is approximately km 2, with approximately 230 km in the longitudinal direction and 100 km in the latitudinal direction. [15] Note that the Cressman interpolation scheme used in the variational analysis requires a background field for filling missing soundings and interpolating data onto the analysis grid points that do not overlap with the sounding stations. The background field used in the current objective analysis is from the ECMWF analysis. Thus a comparison between the ECMWF analysis and the objective analysis using the ECMWF analysis may not reveal the extent of the errors in the ECMWF analysis. To quantify the extent to which the background analysis field impacts the objective analysis, we generated another objective analysis data set using the background data from the National Center for Environmental Prediction (NCEP) ETA-12 (12 km and 60 levels) analysis (courtesy of Eric Rogers of NCEP). Figures 2a 2d show the root-mean-square (RMS) differences between the two objective analysis data sets generated using the two different first guess fields (solid lines) and between the two model analysis data sets (dashed lines) for horizontal winds, temperature, and relative humidity (RH ice ), respectively. RH ice was calculated with respect to ice for temperatures beneath freezing. It is seen that the difference between these two objective analysis data sets is typically less than 1 m s 1 in horizontal winds, 0.3 K in temperature, and 5% in relative humidity. These numbers are close to the typical uncertainties in the sounding measurements and are much smaller than the differences between the two model analysis data sets (Figure 2), suggesting that the objective analysis is largely independent of the background analysis used. In the following discussions, we use these numbers as a crude estimate of the uncertainties in the objective analysis caused by the first guess field. [16] To obtain the domain-averaged surface and TOA quantities, we first interpolate observations onto the prescribed analysis grid points using the interpolation scheme described by Barnes [1964] and then a simple arithmetic averaging is used to obtain the domain means. It should be noted that there is no background field used in deriving the observed surface and TOA domain-averaged fields. The domain-averaged model data are obtained by averaging only the model land grid points within the analysis domain. Note that there are several model ocean grid points within the analysis domain. We have found that these ocean grid points can largely affect the domain-averaged surface quantities, such as the surface sensible and latent heat fluxes and surface albedo, because of the strong contrast in surface characteristics between ocean and land. Therefore these ocean grid points are not used to get model domain-averaged quantities. 3. Results [17] In this study, the evaluation is made for the period from 00Z 5 October to 12Z 22 October This is a 4of17

5 Figure 2. RMS difference of (a) horizontal wind u component, (b) horizontal wind v component, (c) temperature, and (d) RH ice during M-PACE. Solid lines show the RMS difference between the objective analysis using the ECMWF analysis as the first guess and the one using the NCEP ETA analysis as the first guess. Dashed lines are the RMS difference between the ECMWF analysis and the NCEP ETA analysis. period where conditions could be expected to be in a transition from summer to winter. However, as described by Yannuzzi et al. [2005] and Verlinde et al. [2005], the year 2004 was an unusually high melt year and for the entire M- PACE period the sea ice did not freeze to the coast until after the experiment. During the first stage of the experiment (5 14 October), the NSA was mainly affected by a high-pressure system centered to the northeast of the Alaskan coast. The east-northeasterly flow coming out of the strong cold high continued to impinge on the Alaska coast and resulted in an 8 10 C temperature drop at the ARM NSA sites. Persistent mixed-phase boundary layer clouds formed over the open ocean and advected into NSA during this period. These clouds were typically topped by liquid water that precipitated ice. Continuous light snowfalls were observed at both the NWS Barrow station and the ARM ground-based remote sensing sites. After 14 October, the high-pressure system moved toward the southeast, and strong low-pressure centers that formed to the southwest near Kamchatka started to affect the NSA. Southerly and southwesterly flows prevailed over NSA. Scattered deep clouds were observed during this period, associated with the passages of warm frontal systems. A rather strong snow event was observed on 19 October at the time of a strong frontal passage ECMWF-Analyzed Fields [18] As described above, the observed cloud systems were closely related to the large-scale synoptic state over the NSA. Thus it is important to examine the extent to which the ECMWF analysis captured the variations in the synoptic state. Figure 3 shows the time-pressure cross section of the domain-averaged observed and ECMWFanalyzed horizontal wind fields and their difference. Note that in this section, the observations are the data from the variational analysis and that the difference between the ECMWF analysis and the variational analysis is termed error under the assumption supported by the evidence presented in section 2 that the variational analysis is much more close to the actual state of the atmosphere (as represented by the available soundings) than is the ECMWF analysis despite the use of the ECMWF analysis as a firstguess background field. From 5 to 14 October, the observed upper air circulations (Figures 3a 3b) were typically characterized by the west-northwesterly flow in the middle and upper troposphere (above 500 hpa) and the east-northeasterly flow in the lower troposphere over the North Slope of Alaska. After 14 October, southwesterly flow prevailed in the entire troposphere except on 19 October when there was an abrupt wind direction change from the southwest to the southeast corresponding to the strong frontal passage mentioned earlier. The temporal evolution of the observed upper circulation is represented remarkably well by the ECMWF analysis (Figures 3c 3d). The model analysis error is generally less than 2 m s 1 except on 19 October where the relatively large error is due to an incorrect timing of the frontal passage. [19] Figure 4 displays the domain-averaged temperature and RH ice fields. There was a substantial temperature decrease below 600 hpa from 7 to 14 October shown in 5of17

6 Figure 3. Domain-averaged time-pressure cross sections of the horizontal wind components and their difference between the variational analysis (VA) and the ECMWF analysis (ECMWF): (a d) Contour interval is 5 and contours larger than 0 are shaded and (e f) contour interval is 2 and contours less than 0 are shaded. Units are m s 1. Solid lines are for contours greater than or equal to 0, and dotted lines are for contours less than 0. the observations associated with an upper level cold trough and the strong surface high over the pack ice to the north of the open ocean adjacent the coast (Figure 4a). From 9 to 14 October, temperature at 865 hpa had dropped below 10 C, suggesting that the boundary layer clouds observed during this period at this level may include both liquid and ice. Note that prior studies indicated that mixed-phase clouds could occur at temperature as warm as 5 C [Hobbs and Rangno, 1998; Pinto, 1998] and ARM aircraft observations confirmed that the boundary layer clouds before 14 October were indeed mixed phase. After 14 October, the upper air started to warm up when a warm ridge moved into the NSA and a significant temperature increase was observed on 19 October. The warmer and moister air aloft on 19 October (Figures 4a 4b) was the result of the strong warm frontal passage. It is worth noting that the NSA air was often supersaturated with respect to ice in the clouds (shaded area in Figure 4b). [20] The observed temporal evolution in both temperature and RH ice is generally represented well by the ECMWF analysis although the model-analyzed RH ice is smoother than the observed (Figures 4c 4d). The substantial temperature decrease below 865 hpa during the period from 8 14 October where the persistent mixed-phase boundary layer clouds were observed is slightly underestimated in the model analysis, which leads to a small warm bias (less than 2 C) near the boundary layer top at 865 hpa (Figure 4e). This warm bias may be due to the underpredicted cloud top 6of17

7 Figure 4. Same as Figure 3 but for temperature ( C) and RH ice (%): (a and c) Contour interval is 5; (b and d) contours are (0, 20, 40, 60, 80, 90, 100, 110, 120) and contours larger than 100 are shaded; (e) contour interval is 1 and contours less than 0 are shaded; and (f) contour interval is 10 and contours less than 0 are shaded. Solid lines are for contours greater than or equal to 0 and dotted lines are for contours less than 0. radiative cooling associated with the model clouds containing too little liquid water as will be shown later. Another possibility for this error is that the model underestimated the height of the temperature inversion which tops the boundary layer. Above 850 hpa, the model analysis generally shows a cold bias. This cold bias may be related to the stronger than observed easterly or northeasterly flow in the model analysis as discussed earlier, which brings colder air from the northeast into the NSA region. For RH ice, the model analysis typically shows a dry bias during the M-PACE period (Figure 4f). In addition, note that the analysis does not have any ice supersaturation. This is probably related to the treatment of the saturation water vapor pressure in the ECMWF model. In the ECMWF model, the saturation vapor pressure is set equal to the vapor pressure with respect to ice at temperatures lower than 23 C and for temperatures between 0 C and 23 C is equal to a temperaturedependent average of the liquid and ice saturation vapor pressures. Any vapor in excess of this model defined vapor pressure is condensed out of the atmosphere. In general, the model analysis error is less than 2 C in temperature and 10% in RH ice. [21] Figure 5 presents both the root-mean-square (RMS) errors (thick lines) and the mean errors (thin lines) of the ECMWF analyses during M-PACE at Barrow, Atqasuk, Oliktok Point, and averaged over the analysis domain. The model values at the individual observation sites are obtained by interpolating data from the nearby model output grid points. It is seen that the RMS error is typically less than 2 m s 1 in the horizontal wind components, 1 K in the 7of17

8 Figure 5. Errors in the ECMWF model analysis of (a) horizontal wind u component, (b) v component, (c) temperature, and (d) relative humidity during M-PACE. Thin lines show the mean error in the ECMWF analysis, and the thick lines present the RMS difference between the ECMWF analyses and the M-PACE observations. Solid, dashed, dash-dotted, and dotted lines denote the errors at Barrow, Atqasuk, and Oliktok Point and errors averaged over the analysis domain, respectively. temperature, and 10% in the RH ice within the troposphere (Figures 5a 5d). The RMS error of the horizontal winds and the RH ice varies little in the vertical except for the levels below 900 hpa where relatively large errors are seen in the wind fields and above 250 hpa where rather small errors are seen in the RH ice because there is not much water vapor in the atmosphere above 250 hpa. It should be noted that the small RH ice errors could lead to errors in the upper level model clouds as shown later. Another noteworthy feature in Figure 5 is that the domain-averaged model results show the smallest RMS errors in comparison with those at the three individual sounding sites, especially for the horizontal winds and the RH ice fields. This suggests that the discrepancy between model and observations could be overestimated if one compares model results with point measurements. [22] The mean error of the horizontal winds is very small except in the upper levels where the model overestimates the observed westerly and southerly flows at all the three sounding stations. For temperature, the model shows a cold bias up to 1 C in the middle and lower troposphere except for the level around 865 hpa where it has a small warm bias. For RH ice, the model displays a systematic dry bias in almost the entire troposphere with the magnitude of the error less than 5%. The exception is at Atqasuk where the model shows a moist bias in the levels between 765 hpa and 565 hpa. As discussed earlier, the dry bias is partially related to the assumption that the ECMWF model does not allow ice supersaturation. [23] Figures 6a 6d show a comparison of the modelanalyzed 10-m horizontal wind components, 2-m temperature, and 2-m relative humidity (with respect to water) with the ARM observations, averaged over the analysis domain. Note that there is no first guess field used in obtaining the domain-averaged surface observations. There is a good overall agreement between the observations and the model analyses in terms of the magnitude and temporal variability of these near-surface variables. However, some disagreements are seen in the horizontal wind fields where the model analysis slightly underestimates the easterly winds and overestimates the northerly winds for the period from 5 to 14 October. The model-analyzed 2-m temperature is colder than the observed from 10 to 14 October which may be related to the stronger than observed northerly winds and/or the negative bias in the surface energy balance to be seen later. The model analysis slightly underestimates the 2-m relative humidity and shows a relatively large error during the strong frontal passage. Similar results can be seen in the comparison made at the three individual ARM ground-based remote sensing sites except they exhibit slightly larger model-observation discrepancies (not shown). 8of17

9 Figure 6. Time series of the domain-averaged observed and model-produced (a) 10-m horizontal u component (m s 1 ), (b) 10-m horizontal v component (m s 1 ), (c) 2-m temperature ( C), and (d) 2-m relative humidity (with respective to water, %). Solid lines are for the ECMWF analyses, and pluses are for M-PACE observations. [24] The above discussions indicate that the ECMWF analysis reasonably represents the dynamic and thermodynamic structures of those large-scale systems that affected the NSA during M-PACE. The analyzed near-surface winds, temperature, and relative humidity are also in a good agreement with the M-PACE surface measurements. The error in the state variables is typically comparable to the uncertainties in the observations. The good quality of the ECMWF analysis is presumably because the data obtained from the NWS Barrow station were assimilated in the ECMWF data assimilation system, although it is noteworthy that the ECMWF analysis is in much better agreement with M-PACE observations in comparison with the NCEP ETA analysis, which also assimilates the NWS Barrow observations (Xie et al., submitted manuscript, 2006) ECMWF-Predicted Fields Clouds and Cloud Properties [25] Figure 7 shows the observed and model-generated clouds and their difference at the Barrow site. The observed clouds are obtained by integrating measurements collected from the ARM millimeter wavelength cloud radar, micropulse lidars, and laser ceilometers using the algorithm described by Clothiaux et al. [2000], that is, the so-called the Active Remotely-Sensed Clouds Locations (ARSCL) products. As indicated by the ARSCL data, Barrow was covered with multilayered clouds in the middle and low levels with the cloud top up to 550 hpa from 6 to 8 October. Persistent boundary layer clouds with the cloud top around 850 hpa occurred during the period from 8 to 14 October. The aircraft measurements taken during M-PACE indicate that these boundary layer clouds were typically topped by liquid water with precipitating ice [McFarquhar et al., 2005] as has been found in other boundary layer clouds [Hobbs and Rangno, 1998; Pinto, 1998]. The boundary layer clouds started to disappear as a warm front moved through the area on October and a deep ridge moved over the NSA [Yannuzzi et al. 2005]. For the period October, a strong warm frontal system that formed to the southwest near Kamchatka approached and passed over the site and brought deep prefrontal and frontal clouds over the NSA on 18 and 19 October. 9of17

10 determined by the ARM laser ceilometers and micropulse lidars, which are usually insensitive to ice precipitation (if the concentration of precipitation particles is not sufficiently large) or clutter and can provide quite accurate cloud base measurements [Clothiaux et al., 2000]. It is worth noting that cloud radar tends to underestimate the cloud top heights for high-altitude clouds because it will not be able to detect cloud particles if they are sufficiently small, for example, cloud ice water content is less than 10 6 kg m 3. We have found that the model produced ice water content is typically smaller than kg m 3 for the levels above 300 hpa and smaller than kg m 3 near the model high-cloud tops (not shown). This may partially explain the overestimation of the high-altitude clouds as shown in Figure 7c. [27] Figure 8 shows a comparison of the total cloud fraction between ECMWF and the observations at Barrow. The total cloud fraction is calculated from the ARSCL products and the modeled clouds assuming maximum cloud overlap. The observations typically showed a persistent almost 100% cloud cover when the multilayered clouds and mixed-phase boundary clouds occurred except on 7 8 October and 11 October where the cloud cover decreased slightly. After 14 October, the observed total cloud cover showed a large temporal variability associated with several frontal systems that passed over NSA. The model does a decent job in capturing the observed total cloud cover both in the temporal variability and in the magnitude during the period where the multilayered clouds and deep high frontal clouds were observed; however, it considerably underestimates the total cloud fraction in the presence of the boundary layer clouds. During this period, the modelpredicted cloud cover gradually decreases from 100% on 8 October to 60% on 14 October, indicating difficulty in simulating the boundary layer mixed phase clouds. [28] Cloud liquid water and ice water contents have large impact on the surface and TOA radiation. To examine how well the model simulates the cloud properties, Figure 9 shows the model cloud liquid water/ice contents averaged Figure 7. Time-height cross sections of (a) the ARSCL clouds (%), (b) model clouds (%), and (c) model cloud errors (%) at Barrow during M-PACE. [26] The cloud types observed during M-PACE are generally captured by the ECMWF model as shown in Figure 7b, which displays the model-produced clouds at Barrow. The model reasonably generates the multilayered clouds, the boundary layer clouds, and the frontal deep high clouds. However, there are considerable differences in detailed structures of the clouds between the observations and the model simulations. For example, the model substantially underestimates the observed boundary layer clouds. For those deep frontal clouds, the model tends to overestimate the clouds at high levels and underestimate them at middle and low levels. These are common model biases in simulating frontal cloud systems [e.g., Klein and Jakob, 1999; Ryan et al., 2000; Tselioudis and Jakob, 2002; Zhang et al., 2005; Xie et al., 2005]. It is interesting to see that the model-produced cloud base is lower than the observed for the period 5 10 October. Note that the cloud base in the ARSCL products is Figure 8. Time series of the total cloud fraction (%) derived from ARSCL (pluses) and ECMWF (solid line) by assuming maximum cloud overlap at Barrow during M-PACE. 10 of 17

11 [30] Figures 10a 10b show the observed and modeled cloud liquid water path (LWP) at Barrow and over the analysis domain, respectively. For information, the modelpredicted cloud ice water path (IWP) is also given in Figure 10 although the observed value is not available. The instrument uncertainty of LWP is typically about 20 g m 2. Similar results are seen for Atqasuk and Oliktok Point (not shown). Consistent with the underestimate of cloud amount, the model LWP, as well as the sum of the LWP and IWP, are significantly smaller than the observed in the presence of mixed-phase boundary layer clouds. The less total cloud condensate in the model clouds results in a weaker cloud top radiative cooling compared to the observations, which may be related to the warm bias shown in the model temperature analysis as shown earlier Surface Fluxes [31] Figures 11a 11b show the model-predicted surface precipitation rates with the measurements at Barrow and Atqasuk, respectively. The observed precipitation data at Figure 9. Vertical distribution of model-predicted cloud liquid water content (solid line) and ice water content (dashed line) averaged over the period 8 14 October where the mixed-phased boundary layer clouds were observed. Units are g kg 1. over the period 8 14 October where the mixed-phase boundary layer clouds occurred. The model cloud liquid water/ice contents are predicted by using one prognostic equation described in detail by Tiedtke [1993]. The distinction between water and ice phase is made as a function of temperature. The fraction of liquid water in the total condensate is defined as a ¼ 0 if T T ice a ¼ ððt T ice Þ= ðt 0 T ice ÞÞ 2 if T ice < T < T 0 a ¼ 1 if T T 0 where T is temperature, T 0 = K (0 C), and T ice = K ( 23 C). For the period 8 14 October, the temporally averaged model temperature between 965 hpa and 815 hpa is in a range of 8 C to 14 C. This implies that 57% to 85% of the total model condensate could be in ice phase during this period. [29] Consistent with the above discussions, it is seen that the model-predicted ice water content is much larger than the liquid water content. Both model cloud liquid water and ice contents increase with height through the cloud layer with a maximum occurring near model cloud top at 890 hpa where the maximum clouds are produced. This vertical distribution of cloud ice water content in the model is different from that observed in M-PACE. Preliminary results from analyzing the M-PACE aircraft data on 10 October indicated that the cloud ice content was generally larger near cloud base where greater numbers of large crystals have settled to, and was not a simple function of temperature [McFarquhar et al., 2005]. This is consistent with the findings of Pinto [1998], which showed that the ice water content generally decreased with height through the cloud layer with a maximum value near cloud base. Figure 10. Time series of the observed (pluses) and modelproduced (solid lines) cloud liquid water path (g m 2 ) during M-PACE: (a) Barrow and (b) domain. For comparison, the ECMWF model-produced ice water path (g m 2 ) (dashed lines) is also shown. 11 of 17

12 Figure 11. Time series of the observed and modelproduced surface precipitation rates (mm d 1 ) during M- PACE: (a) Barrow and (b) Atqasuk. Solid lines are for the ECMWF model, and pluses are for M-PACE observations. Barrow and Atqasuk were obtained using a Vaisala FD12P Present Weather Sensor. They are the liquid water equivalent by melting snow with a claimed instrument accuracy of ±30% for 0.5 to 20 mm hr 1 liquid precipitation. Note that the actual uncertainty in the surface precipitation measurements during M-PACE should be larger than the reported instrument accuracy mainly because of blowing snow (M. Ritsche, Argonne National Laboratory, personal communication, 2005). Since there were no measurements made at Oliktok Point, we did not generate the domainaveraged values for this field. It should be noted that the observed values at the Barrow station in Figure 11a are scaled to those reported by the NWS Barrow station (Atqasuk data are not scaled) since the original ARM observed precipitation data are contaminated by blowing snow. The precipitation data at the NWS Barrow station are thought to be more reliable (J. Pinto, University of Colorado, personal communication, 2005) and more consistent with the information recorded in the experiment synoptic logs during M-PACE. No attempt has been made to quantitatively validate the model-produced precipitation because of the large uncertainty in the magnitude of the observed values; rather, the emphasis here is to qualitatively examine if the model can correctly reproduce the occurrence or nonoccurrence of the snow events that were observed during this period. This will have important implications to the surface condition in the model, which directly determines the model surface albedo, and thereby affects the model surface energy budget. [32] Both the precipitation measurements and synoptic logs indicated that the Barrow and Atqasuk sites experienced light snow almost every day for the period from 5 to 14 October and a strong snow event associated with the strong front passage on 19 October. The model typically captures these weak snow events at the Barrow site while the duration of these snow events lasts shorter than the observed. The model misses the weak snow events at Atqasuk. It is possible that the observed precipitation might contain blowing snow. The less frequent occurrence of the snow in the model can have large impact on the model surface albedo as we will show later. It is seen that the model captures well the strong snow event on 19 October at both sites. [33] Since there are no direct and reliable measurements currently available for the surface sensible and latent heat fluxes (SHFLX and LHFLX), these fields in the model are compared with those calculated from using the bulk flux algorithm developed by Fairall et al. [1996] with some modifications so that it can be suitable for use over a surface covered by snow or ice. All the required inputs for the bulk flux algorithm, such as surface temperature, moisture, horizontal winds, and pressure, are from M-PACE observations. In particular, the snow surface temperature is estimated from measured downwelling and upwelling longwave radiative fluxes with a broadband infrared radiative emissivity of 0.98 for snow/ice. The surface specific humidity is obtained from the surface temperature, assuming ice-saturated conditions. A momentum roughness length of mis assumed for the snow surface and the stability corrections have been used as in the Fairall et al. [1996] algorithm. This algorithm has been successfully used in deriving the surface fluxes for the Tropical Ocean-Global Atmosphere Coupled- Ocean Atmosphere Response Experiment (TOGA-COARE) [Fairall et al., 1996] and the SHEBA experiment [Persson et al., 2002]. [34] Sensible and latent heat fluxes are good examples to demonstrate potential problems in comparison of model data with point measurements near the coastline because they are very sensitive to surface characteristics. Figure 12 presents the model and the bulk fluxes at the Barrow station. The bulk SHFLX indicates a sensible heat transport from the atmosphere to the surface (negative) for most part of the period, consistent with the observed near-surface temperature inversion (not shown). In contrast, the model exhibits a large heat transport from the surface to the atmosphere (positive) for the period from 5 to 14 October, reflecting the fact that the model output grid point contains an area covered by the warm open water along the Alaska coast, which has much higher surface skin temperature than that over land. For the same reason, the model-produced LHFLX is much larger than the bulk algorithm calculated (Figure 12b). [35] Figure 13 is the same as Figure 12 except for the domain-averaged values. Although a noticeable discrepancy 12 of 17

13 from surface radiometer measurements using the algorithm described by Long and Ackerman [2000] and Long [2001]. Figure 14a shows that model largely underpredicts the observed downwelling SW except for the period 9 14 October where the model captures the downwelling SW surprisingly well despite the fact that it substantially underestimates the observed boundary layer clouds and cloud liquid water path. The underestimation of the downwelling SW could be related to the smaller model surface albedo (to be discussed below), which leads to a weaker multiple scattering between the surface and cloud layers than the observations. In the presence of the mixed-phase boundary layer clouds, this problem is offset by the overestimation of the downwelling SW due to the underestimation of the cloud fraction and cloud liquid water. In addition, as shown in Figure 7c, the model overestimates the multilayered clouds and frontal clouds near the cloud tops. This could also lead to less SW reaching at the surface. For the surface upwelling SW (Figure 14b), Figure 12. Time series of the bulk algorithm calculated and model-produced (a) sensible heat flux (W m 2 ) and (b) latent heat flux (W m 2 ) at Barrow during M-PACE. Solid lines are for the ECMWF model, and pluses are for the bulk fluxes. still exists between the model and the bulk fluxes, the large disagreement shown in Figure 12 has been significantly reduced when the comparison is made over a comparable land area. The SHFLX in both data sources shows that the surface is obtaining heat energy from the air for most part of the period because of the near surface temperature inversion. They also agree well with each other in the LHFLX. However, it is noticed that the model data show larger temporal variability in both sensible and latent heat fluxes than the bulk fluxes. For the entire period, the model surface receives 4.7 W m 2 more sensible heat flux and releases 2.4 W m 2 more latent flux than the bulk fluxes (Table 1). [36] The radiative fluxes are closely related to the cloud fields. A comparison of the domain-averaged surface downwelling and upwelling shortwave radiative fluxes (SW) between the model and the observations is given in Figures 14a 14b, respectively. Similar results are seen at the individual stations. The observed data are derived Figure 13. Same as Figure 12 but for the domainaveraged values. 13 of 17