Annual Cycle of Arctic Cloud Forcing at SHEBA

J. M. Intrieri1, C. W. Fairall1, M. D. Shupe2, P. O. G. Persson 3, E. L Andreas 4, P. S. Guest 5, and R. E. Moritz 6

1 NOAA/Environmental Technology Laboratory

Boulder, CO

2 Science and Technology Corporation

Hampden, VA

3 Cooperative Institute for the Environmental Sciences

NOAA/University of Colorado

Boulder, CO

4 U.S. Army Cold Regions Research and Engineering Laboratory

Hanover, NH

5Naval Postgraduate School

Monterey, CA

6 Applied Physics Laboratory

University of Washington

Seattle, WA

Abstract.

We present an analysis of various aspects of forcing of surface fluxes by clouds from the Surface Heat Budget of the Arctic Experiment (SHEBA) conducted in the Beaufort Sea from November 1997 to October 1998. Cloud forcing is the difference in the mean flux and what it would be if clouds were removed from the system. We present a unique calculation of Arctic surface cloud forcing over an annual cycle exclusively utilizing ground-based remote and in situ sensors deployed as part of the SHEBA project. The measurements incorporated as part of this study included optical radiometer sets, turbulent fluxes, a depolarization lidar, ceilometer, a microwave water vapor/liquid radiometer, and radiosondes. The lidar and ceilometer are used to define cloud fraction and cloud base height. The lidar and microwave radiometer allow us to differentiate clouds with water from pure ice clouds. Simple clear-sky radiative flux models tuned to the observations are found to give estimates of cloud radiative forcing that are better than direct observation of fluxes in cloud-free conditions. This is because the cloud fractions are very high in summer so the statistical sampling errors are too large, particularly for shortwave flux.

Using these data, we have processed the time series in 20-day blocks to give the annual evolution of the components of the surface cloud forcing: upward, downward, and net longwave (LW) and shortwave (SW) radiative fluxes and the sensible and latent turbulent heat fluxes. Total cloud forcing (the sum of all components) is about +15 Wm-2 for most of fall, winter, and spring and dips to a minimum of -20 Wm-2 around July 1. The duration of the negative period is one to two months, depending on the choice of surface albedo. Our best estimates for annual average surface cloud forcings are -24 Wm-2 for SW, 35 Wm-2 for LW and -5 Wm-2 for turbulent fluxes. Our results agree more closely with the synthesis of data from Russian North Pole stations [Walsh and Chapman, 1998] than with some recent model simulations.

Using the lidar depolarization information to detect the presence of water versus ice in clouds, microwave radiometer liquid water path (LWP), and cloud base temperatures deduced from radiosondes, we investigated several aspects of the influence of cloud micro- and macrophysical properties on the surface cloud forcing. We found that clouds residing at altitudes less than 4 km in height and at temperatures warmer than -32 C had the greatest effects on the surface net LW flux. Low level water clouds, prevalent throughout the entire year including wintertime, have the strongest influence for both LW and SW. High level ice clouds (heights greater than 4 km) and cloudless ice precipitation crystals (diamond dust) were found to have almost negligible effects on the net surface LW flux. On an annual average basis we found cloud forcing to be significantly higher for water versus ice clouds: about a factor of two for LW and a factor of three for SW. A strong correlation of cloud forcing with LWP was found for both LW and SW fluxes.

1.	Introduction

Understanding climatic feedbacks in the Arctic is critically important not only because the climate can be locally affected but also because local feedbacks can have repercussions on the greater global climate system. According to some GCM simulations, the Arctic is predicted to show early-warning indicators from a change in climate and it is hypothesized that, with increasing levels of greenhouse gases, the polar regions will experience greater temperature changes than the tropical regions [Washington and Meehl, 1989]. This is in part attributed to the ice-albedo (IA) feedback mechanism [Barry et al., 1984]. However, not enough is known about the present Arctic climate, or about other feedback mechanisms not accounted for, that could drive the system in an opposite temperature direction. Various models account for polar processes in slightly different ways which, in some cases, yield vastly different climate simulations [Randall et al., 1998]. One point of consensus however, is that the feedbacks with the largest potential impact involve clouds and that clouds will make a significant difference in the way heat passes through the Arctic system.

The cloud-radiation (CR) feedback process is complex owing to myriad functional dependencies; correctly incorporating cloud and surface properties, both of which have been historically elusive, is critical. For example, a non-linear relationship exists between clouds and the total radiation flux since the net flux depends on surface conditions, a complicated fabric of ice, snow, pond and open ocean waters, which are all strongly affected by the presence of clouds and their macrophysical, microphysical, and optical properties. Additionally, clouds strongly affect the surface albedo and temperatures, sometimes in immediate and drastic ways [Persson et al., 1999], which indirectly leads to considerable effects on the boundary layer stability, latent and sensible heat fluxes [e.g., Ebert, 1984]. Thus far, model results have been our primary insight into the complexities and importance of understanding polar cloud radiative effects [Curry et al., 1993; Curry et al., 1996], and have illustrated our need for better observations to correctly quantify the effects. Investigators have reported modeled results indicating that the impact of Arctic clouds on the surface depends not only on cloud amount, but also on cloud base height, phase, particle size, optical depth and ice/water contents [e.g., Curry and Ebert, 1992; Curry et al., 1993; Francis, 1999].

The lack of extensive observational information on IA and CR feedback processes was the motivation for the Surface Heat Budget of the Arctic (SHEBA) field program [Curry et al., 1996; Randall et al., 1998; Perovich et al., 1999; Curry et al., 2000]. This program included measurements of all components of the heat balance of a multi-year ice floe over an annual cycle, plus continuous monitoring of atmospheric boundary layer and cloud properties with surface-
based remote sensors. A comprehensive set of instruments were used to measure turbulent and radiative heat fluxes at the snow/ice - air interface [Andreas et al., 1999]. Ground-based remote sensors included a depolarization lidar, a commercial ceilometer, and a microwave radiometer.

In this paper we will use these observations to examine the annual cycle of cloud forcing (CF) of the surface heat budget. While the annual cycle of the surface heat budget and mass balance is of primary importance in SHEBA, it is considered essential that models capture more dynamical detail than just reproduction of the mean flux components and simple cloud statistics such as mean cloud fraction [Curry et al., 1996; Curry et al., 2000]. The cloud forcing, i.e., the difference in the mean flux and that which would be obtained in the absence of clouds, has seen extensive application as an index of the importance of clouds in the global heat balance [e.g., Ramanathan et al., 1995 for the tropics; Walsh and Chapman, 1998 for the Arctic]. Cloud forcing can be inferred globally using satellite data at the top of the atmosphere (TOA) [Ramanathan et al., 1989] and, although it is more indirect, at the surface [Zhang et al., 1995]. For surface cloud forcing (SCF), surface-based methods are more direct and more accurate, but provide limited sampling. In the case of SHEBA, which followed the same ice floe for a year, our surface-based measurements will provide precisely the mean fluxes and CF for the measurements of mass balance for the SHEBA ice floe.

Previous studies in the tropics have shown that the heavy water vapor burden in the boundary layer masks the longwave (LW) signal from clouds [Stephens and Webster, 1981; Ramanathan et al., 1995] and SCF is dominated by the solar flux (LW SCF is about 5 to10 Wm-2 while shortwave (SW) SCF is -70 to -110 Wm-2). In the Arctic there is a strong annual cycle in the balance of SW and LW forcing that follows straightforwardly from the annual cycle of the solar flux. However, the annual-average balance is still controversial. Modeled surface cloud radiative forcing estimates from Curry and Ebert [1992], using their modeled annual cloud fraction, show that the average effect of polar clouds is a warming of the surface for all months over the annual cycle except in July. The warming is primarily due to the absence of incoming solar radiation from late fall to early spring and the high surface polar albedos. The SCF forcing becomes negative for only two weeks in the mid-summer when the clouds act to cool the surface by reflecting a greater portion of insolation. It is when the albedo of the surface is relatively low and solar heating is maximum that clouds exert a relative cooling effect. Satellite estimates of cloud forcing have also been reported and show a similar result of positive cloud forcing (warming) values for most of the year with negative values occurring during June and July [Key et al., 1999]. However, ambiguities associated with satellite determination of cloud make the details of these results uncertain.

This paper will begin with definitions and discussion of CF (section 2). In section 3 we will briefly discuss the measurements used in the study; in section 4 we describe models used to obtain radiation fluxes in the absence of clouds. In section 5 we present traditional analyses (i.e., cloud versus no-cloud) of the annual cycle of CF for radiative and turbulent fluxes. The results are compared with the comprehensive review by Walsh and Chapman [1998] using data obtained from two decades of Russian North Pole stations and the regional model of Curry and Ebert [1992]. In section 6 we examine the annual mean CF as a function of more sophisticated cloud properties - cloud-base height and whether clouds are pure ice or contain liquid water. Conclusions are given in section 7.

2.	Background

Within the past several decades new concepts regarding the analysis of our climate system have arisen. Flux forcings and feedback parameters are being used for understanding physical systems that are composed of internal processes which affect exchanges within a system [Curry et al., 1996]. We define the terms below which are used as part of this study. In this paper we will restrict our analysis to the surface cloud forcing.

The surface energy balance at the snow/ice-air interface can be written as,

Where the R are radiative fluxes (defined as positive quantities by the sign convention in the equation) with subscripts s and l on R denoting solar and longwave, d and u denoting downward and upward components. Hs is the sensible heat flux and Hl the latent heat flux for evaporation of water (we use the meteorological convention where these fluxes are positive when cooling the interface); these terms can be measured directly in the atmosphere using micrometeorological methods (see section 3). The conduction flux (positive upward), C , and net solar radiative flux, Rsn_ , are realized at some small depth, , just below the interface and we have assumed that the longwave flux does not penetrate significantly below the surface. The balance term, B, accounts for ice-water phase changes (positive for melting) at the interface. The net radiative flux is defined as the difference between the downwelling and the upwelling radiative fluxes. Over land the solar flux does not penetrate the surface and the heat balance is conventionally written as C=Hs + Hl -Rnet [e.g., page 253, Stull, 1988].

Downward and upward solar fluxes are related through the albedo, ,

Downward and upward longwave fluxes are related through the surface (interface) temperature, Ts, and the emissivity, ,

A forcing is an external or internal parameter that can act to influence the physical system, i.e. create a climate variation. Cloud forcing gives an indication of the effect clouds have on the surface in comparison to clear skies. For example, if more radiation reaches the surface when clouds are overhead versus clear skies, the cloud acts to warm the surface (i.e. the thermal effect) and the forcing value is positive. If less radiation reaches the surface when clouds are present versus clear skies then clouds act to cool the surface (negative values) and this is, in essence, the albedo effect. Cloud forcing provides a simplistic means for characterizing the bulk effect of clouds on the Earth's system. Surface LW and SW cloud forcing values presented in this study are computed according to Ramanathan et al. [1989], as follows;

where the brackets denote an average, F is the flux of interest (radiative or turbulent) and Fclear the expected flux if clouds are removed from the column. Cloud forcing can be computed for the TOA or the surface; cloud absorption is essentially the difference between the TOA and surface CF. Alternatively, cloud forcing can be defined in terms of the difference between the flux, F1, when skies are overcast (cloud fraction, f, equal to 1.0) versus clear (cloud faction equal to 0.0), F0. This approach is the conditional cloud forcing, i.e., the amount the flux changes when a cloud is present. We will follow Walsh and Chapman [1998] and refer to this as the maximum cloud forcing, MCF

If we consider a simple bimodal cloud distribution (i.e., either overcast or clear), then cloud fraction can be considered the fraction of time the sky is overcast. In that case, it is simple to show that,

True cloud fraction is the instantaneous fraction of the sky covered by clouds and is usually determined by observers or whole-sky imaging devices. This is distinct from vertical cloud fraction, f, which is usually determined from time or space averages of vertically-oriented, narrow field-of-view cloud sensors (microwave radiometer, lidar or ceilometer in the case of our analyses). We have chosen the latter approach because observers are subjective and prone to errors in dark conditions and present whole sky imaging technology does not provide information of cloud base heights or microphysical properties. We concede that our approach is imperfect, mainly because clouds can influence the surface radiation without passing directly in the field of view of the remote sensors. However, Arctic clouds do tend to have a highly bimodal distribution [Walsh and Chapman, 1998; Makshtas et al., 1999] so our underestimates of MCF will be modest in most cases. We illustrate the efficacy of the vertical cloud fraction in Fig.1 where clear periods (solid line) are shown to be highly correlated with minima in the downward LW flux (symbols). Maxima are also associated with overcast periods, but so are intermediate values of the flux, suggesting cloud microphysics are influencing observed downward LW flux. Note that CF does not rely on determining clear or overcast conditions, but relies on the specification of clear-sky flux; MCF can be based purely on measurements and does not require a parameterization but suffers from the sampling problem described above.

In this paper, we report Arctic region cloud forcing results based on observations made over almost an entire year during SHEBA. This cloud forcing study is unique in that we utilize an annual cycle of cloud measurements from a depolarization lidar, a cielometer, and a microwave radiometer as well as direct measurements of upward and downward LW and SW fluxes and direct measurements of sensible and latent heat fluxes near the surface. We use radiative flux models to estimate the flux for clear sky calculations, based on measured albedo, temperature, and humidity, where model coefficients were tuned to the radiative flux measurements during cloudless periods.

3.	Measurements and Instruments

Descriptions of the SHEBA experiment, the depolarization lidar and details regarding the determination of cloud occurrence, base heights and cloud phase are described in a companion paper which presents statistics on the annual cycle of cloud geometry and phase by Intrieri et al. [2000; this volume]. The radiative and turbulent flux measurements are described in a companion paper by the SHEBA Atmospheric Surface Flux Group (ASFG) on the surface energy budget [Persson et al., 2000; this volume]. In this section we will briefly sketch some of the experimental aspects and discuss the implications for determination of cloud forcing.

3.1	The Eppley Four-Component Radiometer

One of the main SHEBA project goals was to obtain multiple, varied and redundant measurements of all the components of the surface energy budget. As a result, seven groups provided radiometer measurements. In this study, Eppley Precision Infrared Radiometer (PIR) hemispheric flux pyrgeometers were used to measure the broadband (dome bandpass in the 4.0 - 50.0 m wavelength range) longwave component and Eppley Precision Solar Pryanometer (PSP) broadband radiometers were used to obtain the shortwave radiative fluxes (dome bandpass in the 0.29 - 2.80 m range). Note that while these instruments detect radiation within the stated bandpass, their calibration coefficients are set to return the entire SW or LW component. The instruments we use in this study were deployed and operated by the SHEBA ASFG [Andreas et al., 1999]. Two sets of each radiometer were mounted to look both upward and downward thus corresponding to the four components. The PIR and PSP measurements from the ASFG were chosen for this study because they appear to have been the most reliable of the conventional radiative flux instruments [Russell et al., 1999a] over the annual cycle. The ASFG radiometer domes were maintained relatively ice-free due to personal attention, proximity to the ship, and the installation of heaters and fans at the initial deployment.

The ASFG radiometers were located (nominally 1 m above the snow surface) near the base of the 20 m meteorological tower originally set approximately 100 m from the ship. Over the course of the year the ice shifted and by summertime the tower had changed it's bearing relative to the ship by ~ 90 degrees and was also displaced at a greater distance (~ 300 m). The radiometers were sited in an area that had fairly deep snow by the spring. During the summer, a small melt pond appeared in the field of view of the downward facing radiometers.

The radiometer thermopile outputs (and, in the case of PIRs, dome and case temperatures) were sampled at a rate of 10 s; means and standard deviations were stored at 1 min. intervals. These data were averaged to one hour intervals to produce time series of upward and downward radiance in Wm-2. The LW flux was computed from the PIR using the methods of Fairall et al. [1998]; the SW flux was calculated directly from the PSP thermopile values. All instruments were calibrated by the NOAA Climate Monitoring and Diagnostics Laboratory (CMDL) prior to and after SHEBA; the calibration coefficients were linearly interpolated in time over the experiment. The PIR's were calibrated in a temperature controlled black body chamber [e.g., Philipona et al., 1995 and 1998] and the PSP's on the roof of the NOAA building in Boulder, CO, against a cavity radiometer direct beam plus a shaded PSP diffuse beam [Michalsky et al., 1997]. Fairall et al. [1998] characterize the PIR accuracy as follows: when using laboratory calibrations, a typical unit will have mean bias of about 5 Wm-2 with an additional random scatter of 5 Wm-2 for 1 hr average values. The mean bias can be reduced by comparing against an absolute standard in the field or an ensemble of PIR's (the ensemble approach assumes that the basic PIR calibration method is unbiased). When compared against edited data from an ensemble of values from five SHEBA ice camp upward facing PIR's, the ASFG unit had a bias of 0.2 Wm-2 [Russell et al., 1999b]. The downward facing PIR's cannot be compared because they looked at different surfaces. Thus, we estimate that the mean values produced from the ASFG PIR's have absolute bias accuracies of about ±2.5 Wm-2 for Rld , ±5 Wm-2 for Rlu and ±6 Wm-2 for Rlnet.

Ruffieux et al. [1995] compared an ensemble of eight PSP at an Arctic ice camp in the spring and found and RMS disagreement of 2%. The Atmospheric Radiation Measurement (ARM) program recommends the use of a direct plus diffuse measurement for more accurate determinations of total downward solar flux [Michalsky et al., 1997]. This is done primarily to reduce the inaccuracies associated with poor cosine response of the PSP. The CMDL calibrations of the ASFG PSP's show cosine response within 1% of perfect for elevation angles from 10 to 65 degrees; the response was 90% of ideal at about 5 degrees elevation. Recently, Bush et al. [2000] have shown that Eppley PSP's are subject to a negative bias associated with some small transparency of the domes to LW radiation and direct LW coupling of the dome and thermopile (which may not be at the same temperature). The ASFG PSP's were completely enclosed in a radiation shield and the domes strongly ventilated; both upward and downward flux sensors showed a nighttime bias of -3±2 Wm-2. Negative SW values were set to 0 in post-
processing. Because the downward facing unit does not see the cold sky, it is expected to have smaller bias under strong solar flux. Bush et al. [2000] found biases of -6 to -17 Wm-2 for unventilated upward facing PSP's on sunny days in California. The 'cold bias' effect and cosine response (relevant to unscattered radiation) primarily affect the measurement of downward SW flux, causing an underestimation. Some hints of this can be seen in the data at very low elevation angles where inferred albedo exceeds 1.0. We estimate our uncertainty in mean downward SW to be ±3% with a bias from -3 to -10 Wm-2 and in mean upward SW to be ±3% with a bias of -2 to -5 Wm-2 ; mean net solar is uncertain by ±4.5% with a bias of -1 to -7 Wm-2 .

Because cloud forcing is essentially the difference in net radiative fluxes, the biases will tend to cancel, unless they are different in clear versus cloudy conditions. For LW flux we expect good bias cancellation and estimate the sensor-based uncertainty in mean LWSCF to be about 3 Wm-2. Cosine response errors contribute negligibly to our estimates of SWSCF because the summer is predominantly cloudy (i.e., the solar radiation is diffuse) and our clear sky SW model (see section 4) is tuned to fit to the peak of the peak of the solar flux during June (when elevation angles are well outside the error region of the PSP). We estimate our sensor-based uncertainty in SWSFC to be about ±4.5% with a bias of 4 Wm-2.

3.2	Turbulent Flux Measurements

The data used in this study were taken from a master file containing direct covariance turbulent fluxes and estimates of the fluxes based on a bulk transfer algorithm from the ASFG tower. Five levels (2 to 18-m nominal height above the surface) of sonic anemometer-
thermometers were used to computed covariance stress and heat fluxes. The data were sampled at 10 Hz and linearly de-trended. For each hour, a 54-min w-T cospectra was computed from 7 overlapping, Hamming windowed, 13.65 min blocks. The w-T covariance was computed by integrating the cospectrum in frequency. The w-q covariance was computed similarly from a single fast hygrometer at 9-m. Each sonic anemometer 1-hr time series was quality controlled based on validity limits for the streamwise and vertical velocity variances.

Bulk fluxes were computed from specifications of 1-hr mean surface temperature, air temperature, humidity, and wind speed. A modified form of the Coupled Ocean Atmosphere Response Experiment (COARE) sea-air flux algorithm [Fairall et al., 1996] was used. A velocity roughness length of 4.5 X 10-4 m was specified; this gave the best fit to the covariance stress measurements over the annual cycle. Temperature and moisture roughnesses were taken from the snow-ice parameterization of Andreas et al. [1987]. Bulk and covariance values agree well, on average, for sensible heat flux but the bulk values are about twice the covariance values for latent heat flux. Because the fast hygrometer had not been calibrated for Arctic conditions, we decided to discount those results and use bulk values for latent heat flux. For sensible heat flux we used the median of the five quality controlled covariance values; if no valid values were available, we used the bulk value. Ruffieux et al. [1995] determined that sonic anemometer covariance sensible heat fluxes were accurate to about ±2 Wm-2 for conditions similar to SHEBA. Mean latent heat fluxes are very small (maximum value of 5 Wm-2 in June) throughout SHEBA; the uncertainty is less than ±1 Wm-2.

3.3	Ancillary Measurements

Standard atmospheric profiles of temperature, relative humidity, pressure, wind direction and speed, etc. were obtained from the Geophysical Positioning System (GPS/LORAN) Atmospheric Sounding System (GLAS) radiosonde system. The soundings were routinely launched during the entire SHEBA experiment from the ice camp twice daily (1115 and 2315 UTC) nominally [Moritz et al., 1999] and four times daily (0515, 1115, 1715, and 2315 UTC) during the research aircraft overflights conducted from April through August, 1998. The system was developed by the National Center for Atmospheric Research (NCAR) and is based on a Vaisala sonde that has a 1-s sampling rate with a reported accuracy of ±0.2 C for temperature and 2-4% for relative humidity (although at very low temperatures this can actually be larger). The atmospheric temperature and humidity profiles were linearly interpolated to an hourly grid for the entire year and then correlated with the lidar-derived cloud base height and phase information. In this manner, we were able to identify cloud-layer-height and phase with temperature and assess the influence of these cloud properties on the surface LW radiation.

The ARM microwave radiometer [http://www.arm.gov/docs/instruments/static/mwr.html Greenwald et al., 1999] was used to obtain values for total precipitable water vapor (PWV) and integrated cloud liquid water path (LWP). The PWV was used in the clear sky SW flux models; the LWP was used in analysis of cloud forcing.

4.	Clear-sky Radiation Parameterizations

4.1	SW Flux

Simple bulk radiative parameterizations were used to estimate downward fluxes in clear sky conditions. The model from Iqbal [1998] was used for downward SW. Inputs for this model are latitude, longitude, GMT, PWV, and surface pressure. The ARM MWR values for PWV were rather noisy, so we fit a simple piecewise linear relationship in time for the ratio of PWV to near-surface specific humidity, q, from the ASFG tower. PWV was then estimated from measured values of q and the time-dependent ratio. The ratio was 1.0 cm kg g-1 at the beginning of the experiment, began increasing linearly about March 15, leveled off at 3.0 on May 15 after which it remained relatively unchanged. The model uses internal specifications of aerosol optical thickness k1, at 380 nm, and k2, at 500 nm, plus column ozone. We used the default value (0.3 cm NTP) for ozone and set k1 and k2 to 0.006 to get a good fit to peak downward solar flux measured under clear skies for several days in early May and in early June. An example from the May period is shown in Fig. 2 illustrating the measured (solid line) and model results (dots); we have also shown results (x's) using the model from Shine [1984], which performed best in the comparison done by Key et al. [1996]. Between May and August the Shine model varies about ±4% at peak solar relative to the Iqbal model. This relative variation is a combination of different zenith angle treatments and the use of near-surface water vapor by the Shine model verus PWV by Iqbal. On average, the Shine model was 3.5% higher, but it was not tuned to the SHEBA data.

Upward clear sky SW flux, Rsu_clr is computed from the downward

using the clear-sky albedo, clr , which tends to be lower than the cloud sky albedo because clouds pre-filter the more easily absorbed wavelengths. Walsh and Chapman [1998] found clear sky albedo to be about 0.02 - 0.03 lower in spring and 0.06-0.09 lower in summer and 0.02 lower to 0.02 higher in the fall. The ASFG tower results show clear sky albedo to be about 0.08 lower in the spring but widely variable in summer with essentially no usable values after June. Mean clear sky and mean cloudy sky ASFG SW fluxes yield a flux-weighted albedo difference of 0.05. Based on this, we have estimated the mean clear sky albedo as,

where is the mean all sky albedo.

4.2	LW Flux

We have used clear sky LW flux parameterizations of the form developed by Brunt [1932],

where x is u or d, b is the Stefan-Boltzmann constant, Ta the 10-m height air temperature, and x the effective emissivity,

where values for the coefficients are ad = 0.70, bd = 0.03 and au = 0.93, bu = 0.03. A time series of the clear sky model (solid line) and measurements (dots) when cloud fraction = 0.0 for downward LW flux is shown in Fig. 3; modeled versus measured is shown in Fig. 4. Outliers can be seen in both graphs, indicating the presence of clouds that were not detected by the vertically pointing sensors. This model fits the SHEBA ASFG data slightly better than the model of Effimova [1961], which performed best in the comparison study by Key et al. [1996]. The Effimova model is about 7 Wm-2 higher in winter and 9 Wm-2 higher in summer, but does fit the SHEBA data better than the other models tested by Key et al. [1996].

5.	Annual Cycle of Cloud Forcing

5.1	Radiative Fluxes

Using the lidar or ceilometer to determine f on an hourly time scale, downward and upward LW fluxes have been divided into categories and averaged in 20-day blocks as follows: measured flux when overcast (f=1.0, solid line in Fig. 5), measured flux when clear (f=0.0, circles), modeled clear sky flux when clear (f=0.0, x's), and measured and modeled flux regardless of sky condition (all sky, dashed line). In Fig. 5 the mean downward LW fluxes are shown from the beginning of November to the beginning of October; the difference between the mean all sky flux and the various estimates of the clear sky flux is the MSCF for just the downward component. There is a tendency for the model values in clear sky conditions to be slightly lower than the other two. The actual measured values in clear skies have some contamination by clouds that were not seen by the lidar. The all sky model values (remember it is a clear sky model, but we can compute it when the sky is overcast) also tend to be higher, except in the summer. This shows the radiative flux - surface temperature feedback effects on the near-surface air temperature [Fairall et al., 1999; Persson et al., 1999] used in the model. Thus, the clear sky model is indirectly feeling the effects of the clouds through their effects on the near-surface air temperature; for this reason, we contend that the clear sky model under clear sky conditions gives the best estimate of Rld_clr. A similar analysis for upward LW flux is shown in Fig. 6. Here the measured flux in clear sky conditions shows almost no undetected cloud contamination, suggesting the undetected clouds are too scattered to influence the snow surface temperature and cause the feedback effects.

Fig. 7 shows the net MSCF for LW flux using both measured fluxes (circles) and using the model values (x's) for the clear sky fluxes. The results from the two methods are similar except for September when the sampling of clear skies is so poor (there were almost no clear skies). The LW SCF is shown in Fig. 8 along with the time series of 20-day mean, f. Values for model-based SCF are given using equations (4) and (6); there is little difference between the two estimates. The LW MSCF peaks in July and August; this corresponds to the peak in LWP. The cloud base - surface temperature difference peaks in the winter, so the liquid water effect on cloud emissivity is dominating (see section 6).

The annual cycle for downward and upward SW flux (similar to LW analysis discussed above) is shown in Figs. 9 and 10. We have not included values for the clear sky model when f=0 because we account for the obvious feedback issues by using the clear sky albedo. The sampling variability of the measured clear sky fluxes is obvious in summer. SW MSCF is shown in Fig. 11 and SCF in Fig. 12. Also included in Fig. 12 is a result from using the albedo from the CRREL 200-m line of measurements (corrected to clear sky). This line samples a broad range of ice types, including melt ponds and a few small leads. It is generally lower than the ASFG albedo and leads to more significant SW CF. The annual mean SCF is 35 Wm-2 for LW and -16 Wm-2 for SW. If we use the mean ASFG albedo (instead of correcting to clear sky) the SW becomes -12 Wm-2 ; the clear sky corrected CRREL albedo yields -24 Wm-2. Net (sum of LW and SW) radiative cloud forcing (RCF) is shown in Fig. 13; the annual mean is 19 Wm-2.

5.2	Turbulent Fluxes

We sampled the turbulent fluxes for overcast, clear, and mean conditions. Note that no clear sky model exists for near-surface turbulent fluxes (Figs. 14 and 15). Unlike the radiative fluxes, they are primarily determined by the surface-air differences rather than an integral over the entire column. Sensible heat flux shows a MCF of about -10 Wm-2 in winter, decreasing in magnitude to only a few Wm-2 in summer. Latent heat flux MCF is about -1 Wm-2 all year.

5.3	Summary

In Fig. 16 we show the annual cycle of CF for each component of the surface energy budget measurable from the atmospheric side of the interface. Curiously, the turbulent component acts to oppose the LW component (diamonds) even in summer when net RCF is negative. This may be because much of the solar flux is penetrating the surface so the LW feedback effect on the surface temperature is still active. This is also consistent with anecdotal observations that clear sunny days often did not produce surface melt whereas some overcast days did [D. Perovich, private communication]. The sum of components is shown in Fig. 17a. In Fig. 17b we show the SHEBA results with model results from Curry and Ebert [1992] and the summary data from Walsh and Chapman [1998] from the Russian North Pole stations. We have also removed points from the non-model based values where the statistical sampling for clear sky conditions was poor. The statistical analysis is shown in Fig. 18 where we plot the annual cycle of the uncertainty in the mean estimate (standard deviation, , divided by the square root of the sample number, n) for all sky and clear sky downward LW and SW. The downward SW shows substantial sampling errors, particularly late in the summer when cloud fraction is highest. We excluded points from Fig. 17b where /n1/2 exceeded 30 Wm-2.

Fig. 17b presents four sets of observational results: SHEBA ASFG measurements only (circles), measurements and clear sky models that were tuned to the measurements (line), substituting the CRREL albedo (diamonds), and the Walsh and Chapman data from the entire polar region (astrices). These observational results agree within 5 Wm-2 in the winter months (November through March); they also produce a similar shape to the summer/fall transitions but diverge substantially in the depth of the summer forcing. The NP station data have a stronger annual cycle than the SHEBA results. Based on our analysis of instrumentation, the 35 Wm-2 disagreement between the NP stations and SHEBA in summer is not caused by SHEBA measurement inaccuracies. Model results (boxes) and observations all show a rather abrupt summer-fall transition. The model however compares less favorably with the observations with a mismatch in the spring transition and almost twice as much warming during winter. The annual average RCF is 19 Wm-2 for the ASFG data (11 Wm-2 using the CRREL albedo), 38 Wm-2 for the Curry and Ebert model, and 4.5 Wm-2 for the NP stations. The annual average turbulent flux contribution is -5 Wm-2.

6.	Cloud Property Analysis

In this section we examine surface cloud forcing in terms of the following cloud physical properties: cloud base height and temperature, LWP, and phase (i.e. the relative effects of clouds composed of liquid water versus ice). Because SHEBA represents the initial long-term deployment of a polarization-sensitive lidar [Alvarez et al., 1998] on the Arctic ice cap, we will first examine hourly values of LW fluxes in terms of inferred cloud blackbody properties. Next we will calculate annual mean CF for both LW and SW as functions of cloud properties.

6.1	Examples for LW Flux

The discussions in this section incorporate the synthesis of lidar cloud phase and height measurements, radiosonde temperatures interpolated to lidar-derived cloud heights, and hourly radiometer IR surface flux observations for the period between November 1997 and August 1998. Figure 19 shows the downwelling LW flux as a function of cloud base temperature for liquid (a) and ice phase clouds (b) as determined by the lidar depolarization ratios. (Note; optically thick water clouds can attenuate lidar signal and in effect obscure the detection of ice clouds above. For this discussion, however, there is more than adequate representation of both cloud types.) The line in Fig. 19a represents the theoretical blackbody emission curve for the range of cloud base temperatures. Points on or adjacent to this line give an indication that the clouds are effectively emitting as blackbodies while points above the line represent clouds emitting more like greybodies (i.e. emissivity significantly less than 1.0). The scattering of points below the line may be due to a) emission occurring higher within the cloud, and at potentially warmer temperatures than the cloud base height temperature, b) sampling problems inherent to comparing different instruments with different averaging times, or c) the intervening atmosphere emitting in addition to the cloud. To quantify this a bit more, consider the simple case of a cloud emitting as a pure blackbody (emissivity equal one) at cloud base temperature, Tb, with an intervening sub-cloud layer characterized by effective temperature, Tsc, and emissivity, sc. We can represent the downward LW flux at the surface as

We can expand (11) and estimate the apparent cloud emissivity when viewed from the surface

In the Arctic, it is typical for Tb to exceed Tsc (see below), so we anticipate eff will be slightly less than 1.0; sc is determined by the sub-cloud water vapor profile and will be smallest in winter.

When LWP exceeds 20-30 g m-2, liquid water phase clouds would emit essentially as blackbodies [Slingo and Schrecker, 1982] and from an inspection of Fig. 19a this is an obvious result. However, there is a fair subset of water clouds that have a lesser effect on the downwelling surface LW flux as well. Upon comparison with corresponding cloud base height data, these clouds most often reside at heights greater than 4 km. Also note from Fig. 19a that liquid water was detected by the lidar down to temperatures as cold as -34 C. These clouds, residing at very cold temperatures and composed of super-cooled liquid water droplets, are prevalent during wintertime and have a substantial effect on the surface radiation.

The ice clouds (Fig. 19b), which are appropriately bumped down in the temperature range (Note; no ice was detected by the lidar at temperatures greater than 0 C), show that, indeed, some are radiating as blackbodies but, in contrast to the water clouds, a larger subset are grey. In general, the ice clouds occurred at higher heights and were sometimes tenuous cirrus clouds. In these cases they contribute a negligible influence on the downwelling LW at the surface. There is also a greater spread of points below the blackbody line. This, in part, may be caused by differing sampling volumes between the lidar and the radiometers due to the inherent nature of ice as opposed to water clouds. The water clouds tended to be homogenous in extent as well as being typically persistent whereas the ice clouds were often more varied. The hemispheric versus point measurement mismatch perhaps has a noticeable impact here.

The net LW flux versus cloud base temperature and height are presented in Figs. 20a and b, respectively. Again, by initial inspection it is clear that the warmest and lowest clouds impart the greatest amount of warming at the surface. For all of the clouds observed during the SHEBA year by the lidar, 77% were deemed to have an appreciable LW effect at the surface (net LW flux greater than -40 Wm-2 ) which corresponds to the entire area right of the vertical lines in Figs. 20a and b. The horizontal lines creating an upper right box in Fig. 20a and a lower right box in Fig. 20b represent the limits of where 96% of these radiatively significant clouds reside which are at temperatures warmer than -32 C and at heights lower than 4 km. In wintertime (N,D,J,F), 47.5% of these radiatively significant clouds contain liquid water phase and in spring and summertime we found that 87% were water phase. However, note that there are some cases where low-level cloud and/or warm-temperature cloud show little influence on the net flux. We determined that a subset of these cases (low-level, cold temperature) correspond to cloudless ice crystal precipitation [Persson et al., 2000; this issue].

Although the relationships and bulk effects discussed above have generally been known, this data set allows us to determine the limits or boundaries that may be utilized for modeling cloud effects in the Arctic. For example, Curry and Ebert [1992] parameterize cloud liquid phase with temperatures warmer than -22 C. This underestimates the amount of liquid clouds present during winter and spring and thus would not account for a large influence on the net LW fluxes which is the dominant component in those seasons. It is also widely recognized that high altitude, cold-temperature clouds have little to no influence on the surface LW radiation and these observations also provide the corresponding upper height estimates (> 4 km) and lower temperature bounds for those qualitative assessments. As is an unfortunate reality, there will always exist clouds that defy characterization and the relative number of these cases is also illustrated.

We show the annual cycle of cloud base temperature (along with the 10-m air and the snow/ice-air interface temperatures) in Fig. 21 and the annual cycle of cloud LWP in Fig. 22. We show both the median and mean LWP. LWP tends to be log-normally distributed, so the median is probably a better estimator; also, the median rejects invalid outliers caused by precipitation. We can ratio the measured downward LW flux to the blackbody radiative fluxes computed from the cloud base temperatures to get a crude estimate of an effective emissivity (Fig. 23). The values generally range between 0.8 and 0.95 with a minimum in winter and the trend increasing with maximum emissivity values occurring during summertime. As we discussed in conjunction with Eqs. (11) and (12), this is not the true cloud emissivity because we have not accounted for the effects of the moisture profile.

6.2	Cloud Property Effects on Annual Average CF

The combination of the lidar/ceilometer and the MWR provide a unique opportunity to compute polar cloud dependencies on macro and microphysical properties for the first time. This work is still in its exploratory phase, but we will provide some interesting summaries. We investigated the dependence of MCF on cloud base height. We used the lidar/ceilometer to determine if clouds were present and estimate cloud base height, and the MWR liquid water data to separate water from ice clouds. The data were composited in water/ice and SW/LW categories and averaged in height bins for the entire experiment. The results (Fig. 24) show substantially greater magnitude of forcing for the water clouds; about a factor of two for LW and a factor of three for SW. Surprisingly, there were measurable LW MCF for very high (6 km) clouds.

The dependence on LWP was done similarly: the data were composited in low clouds (bases below 2 km) and high clouds (bases above 2 km) and SW/LW categories and averaged in LWP bins for the entire experiment. These results are shown in Fig. 25, where CF increases with increasing LWP. There is still finite MCF at zero LWP because of ice clouds.

Results such as Figs. 24 and 25 can offer new insights and modes of evaluation for climate models. For example, the recent single column model study by Beesley et al., [2000] broke down annual average total SRCF by cloud height categories: low clouds, 16 Wm-2, middle clouds, 7 Wm-2, and high clouds, 2 Wm-2. The SHEBA results noted above, which are MCF for the lowest cloud base, appear crudely similar but cannot be quantitatively directly compared to Beesley's; however, future model fields can be processed to duplicate the observational processing methods.

7.	Summary and Conclusions

Characterizing cloud radiative effects in the Arctic is a critical component for understanding the current polar climate and a step towards simulating potential polar region climate change impacts. A simplistic and effective means of quantitatively evaluating this is in terms of cloud forcing. In this study, we present a unique calculation of Arctic surface cloud forcing over an annual cycle exclusively utilizing ground-based remote and in situ sensors deployed as part of the SHEBA project. The measurements incorporated as part of this study included optical radiometer sets, a depolarization lidar, ceilometer, a microwave water vapor/liquid radiometer, and radiosondes.

The results show that over the course of the year the net affect of Arctic clouds is to predominantly warm the surface with a slight cooling affect only present for a short period during summertime. This is due to the reduction of the downwelling solar flux by the clouds in concert with the decreased summertime albedos. Our best estimates for annual average SCF are -24 Wm-
2 for SW, 35 Wm-2 for LW and -5 Wm-2 for turbulent fluxes. Different treatment of surface albedo would increase SW SCF to -12 Wm-2 . The measurements compared favorably in winter with North Pole drifting station data (within 5 Wm-2) reported by Walsh and Chapman [1998] and in capturing the summer-fall transition season but differed in the amplitude of the summertime cooling effect. Our results showed a less negative cloud forcing by approximately 30 Wm-2. Surface characteristic differences between the two measurement sites contribute to this large disparity. Modeled results [Curry and Ebert, 1992] compared less favorably with the measurements showing almost twice as much warming during winter and a mismatched trend in the spring transition season.

The characterization of Arctic cloud base heights, liquid water phase and temperature was also explored in terms of the influence and effect on surface radiation. Radiatively significant clouds (i.e. clouds with a net LW surface flux greater than -40 W/m2) were observed 77% of the time by the lidar over the entire year. Of this percentage, 96% resided at heights below 4 km and at temperatures warmer than -32 C. Results showed that low-level water clouds are prevalent during the Arctic winter and they impart a large influence on the downwelling LW radiation. Underestimating the occurrence of water in the Arctic winter (observed down to -34 C and in almost half of all the clouds observed) can thus have substantial error associated with it. Ice clouds at altitudes greater than 4 km in height have a negligible effect on wintertime LW flux. Cloudless ice crystal precipitation, which has been postulated to have a large effect on wintertime surface fluxes, was not observed to play a major role in this dataset.

With this baseline of measurements we can begin to extrapolate and experiment with different cloud scenarios, such as increasing or decreasing cloud amount or the percentage of clouds in liquid phase, for example. Additional microphysical information, provided by radar retrievals for example, could allow us to change particle concentrations or sizes to understand one possible feedback event. The complications involved with assessing a comprehensive cloud-
radiation feedback effect remain a challenge. Datasets such as these however provide a starting point for gauging the performance of models in capturing the seasonal trends with the correct sign.

Acknowledgments. The authors thank all of those involved in the impressive effort of obtaining a year long data set in the Arctic especially, Scott Sandberg, Jeff Otten, Raul Alvarez, Dave Costa, Kerry Claffey, Ann Keane, Dan Wolfe, and M.J. Post. We also appreciate the efforts of the Des Groseilliers crew, the SHEBA Project Office and all the staff associated with collecting the rawinsonde data. Thanks to Jim Liljegren for the ARM microwave radiometer and ceilometer data. This work was supported by the National Science Foundation, NASA FIRE program, and NOAA Office of Global Programs.

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Figure Captions.

Fig. 1.	Time series of cloud fraction (line, lower panel) and downward IR flux (symbols: x's are for cloud fraction =1, circles are for cloud fraction = 0; the dots otherwise).

Fig. 2.	Downward solar flux for several days in May: line - measured; dots - model clear sky.

Fig. 3.	Time series of downward IR flux: dots - measurements for cloud fraction=0.0; line - clear

sky model.

Fig. 4.	Downward IR flux: clear sky model vs measurements when cloud fraction=0.0.

Fig. 5.	Annual cycle of downward IR flux: solid line - measured when f=1.0; circles - measured

when f=0.0; x's - clear sky model when f=0.0; dashed line - clear sky model, all data.

Fig. 6.	As in Fig. 5, but for upward IR flux.

Fig. 7.	Annual cycle of net IR maximum cloud forcing: circles - using measured flux when f-
0.0; x's - using model clear sky flux when f=0.0.

Fig. 8.	Annual cycle of net IR cloud forcing using the model clear sky. The circles are using (1)

and the x's are using (2). A positive value means clouds warm the surface. The red line is the mean cloud fraction.

Fig. 9.	Annual cycle of downward solar flux: solid line - measured when f=1.0; circles -

measured when f=0.0; diamond's - clear sky model, all data.

Fig. 10. As in Fig. 5, but for upward solar flux.

Fig. 11. Annual cycle of net solar maximum cloud forcing: circles - using measured flux when f-
0.0; x's - using model clear sky flux when f=0.0.

Fig. 12. Annual cycle of net solar cloud forcing using the model clear sky.

Fig. 13. Annual cycle of net radiative cloud forcing: circles - measured clear sky; x's - model clear sky.

Fig. 14. Annual cycle of sensible heat flux for clear skies (green line) and cloud fraction = 1.0 (blue line).

Fig. 15. As in Fig. 14, but for latent heat flux.

Fig. 16. Annual cycle of surface heat budget cloud forcing components. Circles use (1) and x's use (2); blue and green - solar flux, red and cyan - IR flux. The turbulent flux (diamonds) is the sum of sensible and latent heat fluxes.

Fig. 17. Net total cloud forcing (sum of components).

(a) Analysis of SHEBA data. Solid line - cloud fraction, cirlces - measured clear and overcast fluxes, x's - modeled clear sky, diamonds - using CRREL albedo.

(b) Similar to (a) but including data from Walsh and Chapman [1998] - astrices; and model results from Ebert and Curry [1992] - boxes.

Fig. 18. Statistical uncertainty in mean estimates of downward radiative fluxes (standard deviation normalized by square-root on the number of samples) for all sky LW (diamonds), clear sky LW (circles), all sky SW (x's), and clear sky SW (boxes).

Fig. 19. Downwelling longwave surface radiation (W/m2) and vs. cloud base temperatures (C) for a) liquid water and b) ice clouds. The line represents a blackbody radiation emmission curve.

Fig. 20. Net longwave flux (W/m2) versus a) cloud base temperature (C) and b) cloud base height (km).

Fig.21. Annual cycle of cloud and boundary layer properties: surface temperature (diamonds), 10-m air temperature (circles), and cloud base temperature (x's).

Fig. 22. Annual cycle of cloud LWP: median (x's) and mean (solid line).

Fig. 23. Effect emissivity of cloud base inferred from the temperature and downwelling LW flux.

Fig. 24. Annual average maximum cloud forcing as a function of cloud base height. Solar flux, liquid cloud - circles; solar flux, ice clouds - triangles; IR flux, liquid clouds - x's; IR flux, ice clouds - diamonds.

Fig. 25. Annual average maximum cloud forcing as a function of LWP. Solar flux, cloud base below 2.0 km - circles; solar flux, cloud base above 2.0 km - triangles; IR flux, cloud base below 2.0 km - x's; IR flux, cloud base above 2.0 km - diamonds. The left (SW) and right (LW) pointing triangles are the result for clouds with less than 10 gm-2 liquid water (i.e., ice clouds).