Comparison of AVHRR-derived and in situ surface albedo over the greenland ice sheet

Comparison of AVHRR-Derived and In Situ Surface Albedo over the Greenland Ice Sheet u ienne Jl

Stroeve,’ Anne Noh, ’ and Konrad Steflen’

T

his paper discusses a methodology for computing the clear-.yky .suvj&e albedo over the G&enland ice sheet b?y using advanced venJ high molution radiometer JAVHRR) global areu cowrage visible and near-infrared reflectmcex This approach is then used to map the rvw&hly changes in albedo owr the entire Greenland ice .sheet rlu ring the spm’ng and .summer months. The vvwthodology inch&~ correctiom j&- the intervening atmosphere hy wing the 6S radiative transfer vnoclel. Additional corrections for the misotropic natrm of srmc; r@ectcmce md the cmve~~ion .from N narrow- into a broadband alhedo m’ rrde based on in situ vviea.sureivients from the Swiss F&d Institute of Ti~chviologyllirziversitlJ (!f coh~rado CY~IIL~ crt 69”34’N, 4Y”lh”W. Comparison tc;itll .suv$uce dbcdo field vvZef~.~UrevvU~vlt.9 indicates that before the onset of .sxozc melt, agreervlerlt is good between the .sntellitetlcril;ed ad measured .suv-&ce cdbeh. After melt begins, it is d@cult to cornpare the satellite dues with thr jicdrl rhtcr owivig to melt uwter pondin, (J on the ice .sheet m r,filC(J. w i tl i 0ppm 1Mate vvudificatiovis, tlic 6s rdintiw tmnsj& model is suitable fivr cctrnos~~heric~[llllJ correcting AVHRR visible and netlr-infrtlreri radimccs ‘in p&r wgioris. OEl~wvier Science Inc., 1997

INTRODUCTION The variation of solar radiation absorbed and reflected by Earth is 11key factor in the understanding of climate change. This is particularly true for high albedo surfaces

such as SIKW ad ice. Snow is one of the most reflective naturally occurring materials, with shortwave (0.~3.0 pm) albedo values in excess of 0.85 for new snow (Warren, 1982). The high surface albedo of snow allows vcrv little solar energy to br absorbed by the snow pack. FG a reduction in the snow-covered area, the surfko albedo would decrease and the amount of outgoing long-wave radiation increase, creating a positive feedback &f&t that would flIrther increase the surf~lct~ air temperature. Even s1ml1 changes in alow-sllrface albedo WI double the amount of energy absorbed by the snowpack without any change in the amount of snow cover. Although thcl surface albedo remains high for most of the polar year, the Arctic albedo decreases substantially during SIII~IIIW~, dropping to values below 0.7 for melting snow (Warren, 1982). Because albedo is im iniportar~t boiinda~ condition for energy and heat exchange betwec~n thy midlatit&s and the polar regions. it is iiecrssary to untlerstmd the spatial and trmporal variations of the polar sltrfkc~ albedo. The most comprehensi\~r~ llleilIlS of clocllln~lltirrg chmges in surface albedo is a space-based obsr~ing systc3n. Satellites have significant xlvantag~s over C’OI~VI’IItional n~eteorologi:cal observations in polar rqjons, Ix,cause they provide temporally and spatially continuous data in a place that is Icry difficult to xctxs. Rcq$ar polar coverage is currently provided by the National Oceanographic and Atmospheric Administration (NOAA) satellites equipped with advanced very high r&ution radiometers (AVHRRs). Since October 1978, a series of satellites have carried the swmd AVHRR IIIO~C’IAVHRR/2. Therefixe, archives of data from AVIIRR art) of’sufficient leugth to be of siguificarit use in cliiu;itological studies. The AVHRR sensor consists of‘ one visible, one near-infrared, and two tht,rirlal-inf;aretl cl~~r~~wls,as wc~~~as oiw channr,l that is sensitive to a combination of‘ thermal and reflected solar fluxes (midinfrart:d) (Table 1). The visible and near-infrared radiances measured at the top of the atmosphert~ (TOA) can br ~~setl to con-

T&k 1. AVHRW2 Channel Spectral Range

Wanelength Region Chnnnrl

1 2

NOAA-C, -8. -10

NOAA-T, -9, -11, -12 0.5X-o.6H ,u,n

0.58~0.68 pn, 0.725~1.

0.725-l.

IOpm

IO ,/A,,,

of Electmnapetic Sp&71,n \‘isildr Near inti-ad

-

pute

the

surface albedo in polar regions under clear-sky conditions, provided a correction is made for the intervening atmosphere. Further corrections are needed to convert from narrow-band reflectance measurement into a broadband albedo (0.4-3.0 pm) and to account for the anisotropic nature of snow reflectance. Previous studies have had some success in computing surface albedo from AVHRR data in polar regions. However, these studies either assumed that snow reflection was isotropic and did not convert from narrow- into a broadband albedo (Haefliger et al., 1993) or used correction schemes based on measured radiances at the top of the atmosphere (De Abreu et al., 1994; Knap and Oerlemans, 1996). That snow does not reflect solar radiation isotropitally is well known (Steffen, 1987, 1996; Warren, 1982). Measurements of snow surface reflectance are affected by a number of variables, including the snow grain size. SJIOWdepth, impurities in the snow, illumination conditions such as the solar zenith angle, spectral composition of the incoming radiation, and the viewing geometry. Failure to account for the directional reflectance of snow for large viewing angles can lead to errors as large as SO% in the total outgoing flux (Steffen, 1987). Furthermore, conversion from narrow- into broadband surface albedo by using a scheme based on TOA radiances mav not be applicable. because the atmospheric attenuation in the visible and near-infrared channels will be diffrrent. This paper presents a nlethodology for extracting the broadband surface albedo over snow- a~nl ice-covered surfaces from AVHRR visible and near-infrared radiances. Measurements made on the Greenland ice sheet provide data for a surf&e-based narrow- into broadband albedo conversion algorithm. Modeled values of the snow surface bidirectional reflectance distribution function are used to account for anisotropy. In situ broarlband aIbedo measurements on the ice sheet are used fbl swface broadthe validation of the AVHRR-derived hand albrtlo. DATA Satellite Data

Data collected glol~ally from AVHRR are available in two formats: at 4-km pixel resolution, called global area cov-

erage (GAC), and at I.I-km pixel resolution, known as local area coverage (LAC). The GAC resolution is achieved by averaging four of every five pixels, along every third sea11 line. As discussed in Steffen et al. (1993), the difference between using the lower GAC and higher LAC resohltion AVHRR data for climate studies over the Greenland ice sheet is minimal. This is because, unlike sea ice and land areas, the major surface types of the ice sheet (glacier ice, wet snow, dry snow) encompass large areas. Thus, the application of lower-resolution satellite data is feasible for this study, and the GAC data set has been used. For a full description of the NOAA platform and AVHRR sensor, see Kidwell (1991). For the years 1990, 1991, and 1993, one satellite pass for each day (at approximately 1500 GMT) was processed and images centered over the Greenland ice sheet were extracted for analysis. To account for the geometric distortions due to the curvature of Earth and the scanning geometry, a navigation code developed by Baldwin et al. (1993) was used. The raw counts or digital numbers (DNs) for each pixel were converted into percentage of albedo by using the calibration method currently enrpfoyrd by the NOAA/NASA Pathfinder program, which calibrates the AVHRR visible and near-infrared channels as a function of days since launch (Rao, 1993). The accuracy of’the alldo is estimated to be of the order of‘ a few percentage points (Rao and C&en, 1995). Cloud-free pixels are then selected 011 the basis of synoptic observations from the study site that are available at 3-h inten&. In Situ Data

The study site chosen is the ETH/CU camp located at the equilibrium line altitude (ELA) on the midwestem slope of the Greenland ice sheet at 69”34’N, 49”18’W. This field station was established by the Swiss Federal Institute of Technology (ETH) in 1990 and is currently operated by the University of Colorado (CU). Since June 1990, the camp has provided spring-summer measureiilcWs of radiative fluxes, spectral reflectance, ice tenpcrature, air temperature, humidi~, pressure, wind speed and direction, and cloud observations. Surface broadband albedo was mrasrued by using a pair of’pvranometers, 011e upward- and the other down-

ward-looking. The pyranometers were calibrated before and in the course of field use aid provide measuremwrts 01’ the surface albedo averaged over IO-min intervals. Spectral albedo was measured with a Geophysical En& ronmental Research (GER) port&e field spectrolneter. The spectrometer measures the reflectance at 875 spectral bands from 0.3 to 2.5 pm. The field of view of the optical head is 7”Xl.ci”. To make measurements of the spectral reflectance of snow, an integration sphere was mounted in front of the optical head for hemispheric measurements that could be directed toward the zenith or perpendicular to the ground. With the use of a sun photometer, the total atmospheric optical depth at 0.55 pm was derived during the months of May and June 1993 in accord with the procedure outlined in Shaw et al. (1973). Aerosol optical depth was then estimated by subtracting from the total optical depth the contributions due to other atmospheric constituents, such as Rayleigh (molecular) scattering and absorption by atmospheric gases such as ozone, water vapor, carbon dioxide, and so forth. Rayleigh optical depth was calculated by using the basic equation of Penndorf (1957) and surface temperatures and pressures measured at the ETH/CU camp. This resulted in a Rayleigh optical depth of O.13,5 at a surface pressure of 900 mb and surface temperature of 270.0 K. Ozone was the only gas considered in the 0.55~pm wavelength range. Using values based on an atmospheric ozone amount of 0.33 c’natm, we calculated the optical depth of ozone absorption at 0.55 pm to be 0.01. Aerosol optical depths at the camp were found to range from 0.05 to almost 0.10. Values of the aerosol optical depth range from 0.05 to 0.12 in the Arctic (Binenko and Harshvardhan, 1993). This indicates that atmospheric optical depth at the ETH/CIJ camp is on the lower end of the values found in the rest of the Arctic, providing evidence that the atmosphere over the ice sheet is optically thin.

Although the, atmosphere is relatively thin iti thca polar regions, atmospheric attf~irriatioil of radiation is significant. Figure I shws s~~rfke and corrcsporltling TOA broadband Abedo for clear-sky C:NY tlmi~~g I HYO and 1991 at the ETH/CU camp. The TOA broadband alhedo was derived from narrowband AVHRR reflectanccs using the algorithm of Li and Leighton (1992). From these data, we find that, on average, 20% of tllr, reflected radiation at the surface is attenuated before reaching the satellite during the summer months and, in general. this attenuation varies seasonally. Less radiation is ;ittenuatrd during the early summer months when the atmosphere is still relatively thin (May-June, -l?%), and more during the later summer months (July, -20%. mtl August, -28%). Atmospheric correction of the AVHRR satellite radiances was accomplished by using the 6s radiative transfer model (Tanre et al., 1992). This radiativtl transfer model is considered accurate over a variety of surfaces (Mitchell and O’Brien, 1993). However, the model was not designed specifically for snow-covered surf&s and required the addition of a snow spectral albedo Inodel and Arctic summer atmospheric profile. Model accuracy declines substantially for solar zenith angles greater than 70” and so limits its use in polar regions to thosr periods when the sun is higher in the sky (solar zenith angks

less than ‘TO”). Using the 6s model, the surface albedo f;)r AVHRR channel 1 and channel 2 in percentage of alhedo (/Y(, i=l. 2) is found from the following relation:

(1)

(2)

METHODS Atmospheric

Correction

In the solar spectrum (0.25-3.0 pm), satellites measure the radtiance reflected by the Earth-atmosphere system as illuminated by the sun. Radiation received at the TOA depends not only on surface reflectance, but also on solar zenith angle, gas and aerosol absorption and scattering, and clouds (Riehland and Stuhlmann, 1993). For the AVHRR visible and near-infrared channels, absorption and scattering by the following gases are significant: O1 (significant absorption band between 0.55 and 0.65 pm), Hz0 (absorption band at 0.94 pm), and O2 (strong absorption band about 0.7 pm). Of these gases, ozone and water vapor are important because their concentrations may vary considerably in space and time. In general, absorption by Arctic aerosols is small and can be neglected (D’Ahneida and Koepke, 1988).

(3) P,.., is the satellite reflectance for each channel, $ is the mean Earth-sun distance, 2’(~;2n) and T(,LLu;~z) are the flux transmittance due to atmospheric scattering in the downward and upward directions, respectively, TgkI,is the atmospheric transmittance due to gaseous absorption, I);,~,,,is the atmospheric reflectance, S the spherical albedo, p,) the cosine of the solar zenith angle, p cosine the viewing zenith angle, and v the relative azimuth angle, defined as the angle between solar azimuth and viewing azimuth. All the foregoing parameters are obtained by running the 6s radiative transfer model. The atmospheric inputs used in the model include the following: (1) continental aerosol model with a “visiparameter bility” of 50 km (visibility I\ ‘: 3 meteorological

Swjkce Albeclo over the Greenland Ice Sheet

265

0 TOA Broadband Albedo A Surface Abedo

Jun Au4

May

Aug.

Jul

Jun

1

t

1991

1990

Figure 1. Comparison between observed surface broadband albedo and coincident AVHRR derived TOA broadband albedo for clear-sky cases only. Data are for summer months during 1990 and 1991. related to the optical depth) and (2) arctic summer atmoand humidity. spheric profile of pressure, temperature,

band channels,

The latter input was derived

ous work, linear relations between

at the ETH/CU

camp.

from radiosonde

At sea level, this profile

sponds to an air temperature column

of 275.0

water vapor; and 0.331

the camp elevation

launches corre-

K; 1.02 g crnp2

cm-atm

total ozone.

At

(1155 m), a visibility of 50 km corre-

sponds to an optical depth at 0.55 pm of approximately 0.1. To check

the sensitivity

of the model-estimated

al-

bedo to other model inputs, the 6s model was also run by assuming

(I)

subarctic

(air temperature

summer

atmospheric

at sea level 287.0

model

K; 2.10 g cn-’

col-

umn water amount; 0.480 cm-atm total ozone; and a visibility

of 50

km)

and

(2)

arctic

summer

atmospheric

model with a visibility of 25 km. For comparison, considered.

the Koepke

(1989)

albedo

in polar regions

(De Abreu et al., 1994). The Koepke model

Koepke (co&:

to

derived O.2-l.O),

very clear

is also

This method has had some success in deriv-

ing the surf&e ear

method

relate

surface

coefficients

from AVHRR

method uses a lin-

albedo

to TOA

for 16 solar zenith

two aerosol optical depths,

and somewhat

turbid

0.4), two ozone concentrations

atmospheres

albedo. angles

representing (0.05

and

(0.24 and 0.36 cm NTP-

normal temperatllre and surface pressure), and three water vapor amounts (0.5, 2.0, and 5.0 g cm-‘). In this study, the Koepke model is run with input variables to resemble the first set of inputs used in the 6s model. This is achieved by setting the total atmospheric water vapor amount at 0.6 g cm-” and the aerosol optical depth at 0.1 at the elevation of the ETH/CU camp. Broadband

Albedo

Parameterization

Because the AVHRR multispectral narrow-band radiometric scanners measure reflectance in a few narrow-

a conversion

may be used to estimate

is needed

the broadband

so that these data albedo. In previ-

planetary narrow- and broadband albedo were developed (Li and Leighton, for retrieving 1992; Wydick et al., 1987). However, broadband surface albedo, these schemes cannot be used, because the spectral behavior is very different at Earth’s surface and at the TOA. The relative fraction of direct and diffuse solar radiation varies with atmospheric turbidity and the snow spectral reflectivity, so the atmosphere attenuates the radiance differently in the visible AVHRR channels. and near-infrared Similar to the conversion into planetary AVHRR

satellite

albedo, a linear relation is sought, relating

the surface broadband rected

from narrowband

albedo to the atmospherically

channel

1 and 2 reflectances.

cor-

This new expression is based on surface-measured narrow- and broadband albedo data collected from the ETH/CU expedition camp. Daily hemispheric spectral albedo measurements made With a portable spectrometer serve as the narrow-band visible and near-infrared data set that is weighted by the corresponding AVHRR filter response function for each channel. Coincident solar measurements with a set of Eppley pyranometers provide the surface broadband albedo. Because two different instruItrents were used, the relative accuracy of the albedo values is estimated to be approximately 0.7% (Haefliger et al., 1993). Figure 2 shows the relation between 35 coincidental surface broad- and narrow-band albedo values representing AVHRR channel 1 and channel 2 reflectances from three field seasons: 1990, 1991, aud 199:3. The albedo vahles represent a variety of snow types, ranging from fine-grained new snow to coarse-grained melting snow,

601. . . 68

70

a

-

.

.

I

.

I

.

.

.

I

.

r

.

.

?? channel

2

’ A channel

1

.

72 74 76 78 80 82 84 Measured Broadband Albedo (%)

The correlation betweeu the broadband and channel 1 albedo (0.968) is slightly 111 ‘gller than that f;)r channel 2 (0.917). This means that channel 1 accounts for more of the explained variance than does chaunel 2. The reverse is true at the TOA, where Li and Leighton (1992) found a higher correlation between TOA broadband albedo and channel 2 (Table 2). Table 2 suuunarizes the regression results of using both channels as predictors of the surface broadband albedo, and Figure 3 compares the resulting model predicted with observed broadband albedo. Using both channels increases the accuracy of the broadbantl albetlo estimate (accuracy of 0.66% albedo) and explains roximately 98.5% of the variance associated with surace l~roadl~antl albedo. FPP The equation relating narrow-band (/I,,$ reflectances to the broadband albedo at the surface (m) is theref;m given by the following expression:

tance. As noted previously, the reverse was fo~~nd at the TOA by Li ant1 Leighton (1992). If Li ant1 Leighton’s TOA broadband relation is used instead, the estilnad surf& broadband albedo is on average 4.5%) lower than the observecl broadband albetlo (see Fig. 3). Thereforc~. the TOA conversion schen~ is not appropriate ti)r retrieving snow surf& albedo. Before Eq. (4) can be applied, it is iircrssary to correct for the atisotropic ilaturr of snow reflectance. This correction is tliscussc~tl in tile next section. Anisotropic

Correction

Satellite detectors, with narrow fields of view, tneiisurc’ the bidirectional reflrctancc~ at only one or ;L few a~~gles. Therefore, when snow surfaces are viewed front satdilr~, the angular relations between the satellite, thr sun d sensor rethe target point will govern the directional Ideally, tlicW flictors sponse froui that target stirfkv. a=4.123+0.655p,+O.216~~?. (4) nlust be taken into account wlrc~ using satellite cisible ant1 This uew relation will give the surf& broadband alheclo near-infrarrd data to retrieve accurate srirt&~ albc:tlos. from atmospherically corrected AVHHR visual and nearfrom isotropic reflection can 1x1 rrprf’Departure infrared reflectances. In Eq. (4), mm weight is given to scdetl by the anisotropic reflection function (f‘), to c’onthe vis~lal reflectance than to the near-infrared reflecpare the anisotropic rdlectance behavior of in;iterids with different albedos ((I,): ilid& 2. Regression Statistics Derived from l’yranonwtc~r Broacl- and Sprctrometer Narrow-band R&dances fief Snow

Surfaces

where R(O,,,(l’,O,l,) is the bidirectional reflectanct. filllction (BKDF) as a fiinction of solar zenith angle (8,J. angle (@), ant1 viewing zenith angle (H’), relative azindi wavelength (n). If .f- 1 at ;t partialtar sin-sensor g
267

S ;

87.5.’ ’ 85.. 82.5_

2

80-

. ’ . m- ’ . Greetidata: r2 = 0.985

m

’ . ’

r.m.s. = 0.66%

; 77.5

3

;;i

157

??Li and Leighton ?? Greenland Data

6

‘-

ii 72.5

68

70

.

.

’

-

’

8

72 74 76 78 80 Model Calculated Broadband Albedo (%)

Fip~ 3. (hmparison of albedo predicted by using the surface-based model and the model of Li and Leighton (1992) with pyranometer dbdo oherwtions from tlw ETWCX camp. Data are from 1990, 1x1, d 1993.

that would result in assuming the surface to be lambertian. For f>l, the satellite-observed albedo overestithe satellitemates the true alhedo and, for f
Snow v&es forf were derived by Steffen (19%) on the basis of bidirectional reflectance measurements made at the ETH/CU camp. Results indicate that the anisotropic reflection flmction of snow shows considerable variation with viewing and illumination conditions. For large solar zenith a~lgles, snow has a strong forward-scattering component and is greatest at relative azimuth angles of 180” (v&es exceeding 3.5 at &=W). In general, f> 1 for relative azimuth angles of 110-180” and viewing angles of 45-90’ (Steffen, 1996). However, at high solar zenith angles, f< 1 for a viewing angle of 45” and CD= 110-180”. It is also important to note that, compared with a dry snow surface, the reflectance of a melting snow surface has a larger fonvard-scattering conponent. For this study, a conversion scheme that incorporates a knowledge of snow BRDF developed for use with airborne and satellite-based sensors is used (Nolin and Stroc%ve, 1997). This cc~nversion is based on modr+

ing results from using the discrete-ordinate radiative transfer model (Disort), which is appropriate for modeling multiple scattering in particulate media. Formulated by Stamnes et al. (1988), Disort has been widely used to model the anisotropic distribution of reflected radiation in the atmosphere and, more recently, multiple scattering in snow (Nolin, 1995; Nolin and Stroeve, 1997). Here, we briefly describe this scheme as applied to remote sensing data from AVHRR Channels 1 and 2 over the Greenland ice sheet (Nolin and Stroeve, 1997). First, snow optical properties were calculated from Mie theor),. These spectral data were convolved with the channel response function for each channel. They are then used as input to Disort to generate a look-up table that relates snow surface reflectance to snow surface albrdo for a wide range of illumination conditions, viewing geometries, and snow grain sizes. For a given atmospherically corrected satellite image, the illumination and viewing geometries are calculated from a digital elevation model (DEM), solar zenith angle, and sensor viewing angles. Diffllse and direct surface irradiances are obtained from estimates of atmospheric conditions (using standard atmospheric profiles). Mod&d values off are-then used to convert surf&e reflectance into surface albedo for AVHRR data. These values agree well with field measurements over a small set of solar zenith angles (Nolin, 199s). The model results show that, for AVHRR channel 2, f is directly proportional to grain size. For AVHRR channel 1. f is insen-

were tneasured

at 16lfj

h GMT;

were

at 2110

11 (:MT:

meas1u-~cl The

AVHRR-derived

computed

thG arctic

Viewing

s1immer

derived

albedo

broadband

are

firon

60

the

5 compares

albedo

for each at the

albeclo

tween

the observed

visible

radiances

mosphere model

with

more

size.

forward

combe

The

physical

near-infrared scattering

and Warren,

with

of sensor

angle. Model beam

viewing inputs include

irradiance,

a solar

for this snow

larger

summer

grain

are respectively

angle

size shown

and relative

10% diffuse

zenith

result

is

(Wis-

angle

as a

azimuth

and 90% direct

of 40” and a grain

size radius

of SO0 pm. in the following discussion, a comparison is made between AVHRR-derived and in situ narrow-band visible and near-infrared surface albedo and broadband albedo. The

narrow-band

different Koepke corrected The time

comparisons

6s model

are

macle

runs, as discussed

fc)r the

earlier,

three

and for the

exact with

of’

summer

IIS~ of the, Koepke predicted

the mea-

sometimes

an arctic

summer

the 6S

profile

ns-

it was the arctic

of 25 km. Except

on 24

channel, the subarctic slunmer model never provided the brst fit, although, in this channel, the, predicted albedo was similar to that drrived b\, using the arctic summer profile when the same’ illput for the vi&lit)was used. This is expected bc~tusc~ w&r vapor

variations

in the visible

cl0 not siffnificantly

III genual, for AVHRR with an arctic

summer

of 50 km as input,

(23.67%);

2.7%

the

IInclerestiinates

model

is overestimated

sumtn~r

with

(t9.69%)

Koepke the

model

models

occasionally

measured

surface

the

b$

of 25

albetlo

tile surf&

1))

;Ilbedo

by the 6s model profile

us-

and visibility

of

by 7.Fj%j (+- 10.2% 1 llsiqg

a visibility

for the anisotropic nature of snow reflectance. of the satellite overpass at the camp location

different

albeclo

by 0.5% ( -+X70% ):

albedo

atmospheric

overestimated

6S ~noclt~l,

and a visibilitk

with a visibility

underclstimates

bv 5.9% sunlIner

50 km as input; the arctic

raclianct~s

the surface

summer

the surf&

Koclpke

ing an arctic

the

1, the

model

(+6.2S%). 1r1the near-infrared,

3.9%

from

channel

atmospheric

the arctic

km underestimates and

affect

channel.

the

is approximately 1600 h GMT. The in situ-measured narrow-band surface albedos during 1993 were measured between 1400 h and 1500 h GMT. On 4 June 1990, they

cases,

a visibility

though

values

subarctic

accurately.

have been

albedo

at-

(2) 6S

ant1 a visibility

with

correction

by cj.Y% (t9.!59%)

All narrow-band

summer

of *50 km [6S (S-SO)], and (4)

tirnated

model.

be-

the AVHRR

with arctic

atmosphere

more

using

al-

differences

of albedo) from

of 50 km, and sometimes

proAle

surface

surface

are the

derived

In some

albedo

Koepke

1991 in the visible

May

becomes

Shown

(3) 6s model

model.

was more

summer

4a and b, modeled

1980). In Figure

f values at 0.46 and 1.03 pm ftmction

basis

wavelenghs,

during

of 50 km [6S (A-SO)],

and a visibility

surface

model

Figure 4. Anisotropic reflectance factor for snow as a function of sensor viewing angle and relative azimuth for a snow grain radius of 500 /an and solar zenith of 40” for (a) 0.46pm and (II) I .03+m wavelengths.

to grain

from

May-June.

observed

in percentage

for the atmospheric

sured

60

camp.

a visibility

ing a visibility

in the

and

data

August;

and

the

and those

arctic

Koepke

model

with

for (1) 6S model

and

atmosphere the

sitive

and

f;)r 199:3, from

(given

25 km [6S (A-25)],

that,

by using

the 6S modeled channel

ETH/CU

in absohlte

40 Zenith

a visibilitv

Surface Albedo

Figure bedo

Viewing

with

IWO, dais f;)r the compar-

of June

and,

is

RESULTS Narrow-band

20

are made

months

May-July;

albeclo

only t;,r the run

betwee~ I measured

1990, 1991 , and 1993. During

40 Zenith

1991. lx-

surf&

atmosphere>

Comparisons

1991, front

20

broadband

of 50 km was used.

ison

1990, they

thlring

motlel output

bv using the 6S

in which

wavelength = 0.46 microns solar illumination angle = 40 deg

and,

I100 11and 1200 h GMT.

tweeu

50 -

OII 12 lunrl

using

of 2S km; and overclsthe Koepkr

do perform results albetlo

model. similarly,

in large (see

Althe,

deviations

Fig. iSa and

b).

(a) Channel 1: 0.56 - 0.68 pm

g

7.5

8 8

5

1

2.5

j

0

0 Sfc. ??Sfc. A Sfc. X Sfc.

- 6s (A-50) - 6s (A-25) - 6s (S-50) - Koepke

’ -2.5. -5-7.56/04/906/12/905/22/915/23/915/24/915/26/915/28l935/29/935/30/936/04/936/05/936/07/936/08/936/14/93

10 (b) Channel 2: 0.725 - 1.10 pm 5 0

I. _

g E 8

0 Sfc. - 6s (A-50)

-5

??Sfc. - 6s (A-25)

38

A Sfc. - 6s (S-50) X Sfc. - Koepke

n -10 3 5 -15

-20

-1

-25 ~/04/~6/12/905/22/91 II 5l23l91 5/24/915/26/915/28/935/29l935/30/936l04/936/05/936/M/936/08/936/14/93 Figuw 5. Differences in (a) visible and (b) near-infrared albedo (absolute albedo differences in %) between the observed and those derived from the AVHRR visible radiances for (1) 6s model using arctic summer atmosphere and a visibility of 50 km [6S (A-50)], (2) 6S model using arctic summer atmosphere and a visibility of 25 km [6S (A-25)], (3) 6s model using subarctic summer atmosphere and a visibility of 50 km [6S (S-SO)], and (4) the Koepke model.

.85s ; .82 1 .75-

.- 0 0.7 pm 0 1.0 pm

.7.6S.6.5.%.2

.

.3.

.

.4.

.

.51

-

.6,

-

.7,

.

.8,

, .9

.

-

, 1

.

r 1.1

cos(@o) the surface and satellite observations. The albedo in the near-infrared strongly depends on solar zenith angle (Fig. 6). However, except for 12 June 1990, the differencr in the solar zenith angles is minimal and does not havr~ a significant effect on the albeclo. More probable sources of error are the satellite calibration coefficients. The time-dependent coefficients of the NOAA Pathfinder program were used. These coefficients are considered to be more accurate than the prelaunch calibration coefficients but are still likely to cause the albedo to be in error on the order of few percentage points. The error in surface albedo does not appear to be model dependent. Use of the Streamer radiative transf& 1node1 (Key, 1994), a model specifically designed for polar regions, resulted in similar errors in the sllrfacc albedo. There could, however, still be problems in the inodeling of water vapor absorption by the radiative transfer nlodels, leading to the larger errors found in the near infrared. It is important to note that the errors f01 the two AVHRR channels are opposite in direction. Therefore, the total error in the surface broadband albrdo

might

Broadband

be less than

that for the individual

channels.

Surface Albedo

Figure,

7 canpares the AVHRR-computed surface albedo at the ETHKXI camp with the measured surf&c albedo for a total of :3X coincidental clear-sky nieasureJnents from 1990, 1991, and 1993. The figure shows that, in gemal, the AVHRR surface albedo underestinlates the olxcrved surface albedo by about 3.6%. However, in Augllst 1990 (total of .5 days), the surface albedo is UJIderestinrated by 11%. Including the data from August 1990 results in a correlation coefficient of 0.61 (RMS error=4.18%). Excluding the August days increases the correlation slightly to 0.71 (RMS error=2.56%) and results iii tlic, fbllowing relation: a(nle~ls)=:3.:39X+o.976

cl(calc).

(7)

Fiprc

6.

surk1cr

albedo at 0.7

IIk~pcnciericc

of arid

snow 1.0

fill1

It is very likely that the data from August 1990 at the camp arca not representative of the AVHRR GAC pixel. During July and August, there is extensive nlelting along the margins of the ice sheet, and melt ponds form on the ice sheet surface. For example, with the assump tion of a surface albedo at the camp of 0.80, the albedo can be underestimated by 11% for a melt pond occupying 20% of the satellite pixel. Therefore, the surface albedo for the satellite pixel will most likely be less than that measured at the camp. This is true duri~lg July as well, although the largest errors were found for the ii days in August 1990. It appears that there is still difficulty in obtaining accurate surface albedo front satellite visible rwdiances. However, it is important to remelnber that only :38 coiricident data points from one site were’ used and, during summer

melt,

between

thr

it is difficult satellite-derived

to make and

a direct

co~uprisorl

in situ slu+ace

albedo

More data points frown different locations art’ needed to lnake a complete assessnient of the accurac]c of the satellite-derived surface albedo over the c~ntirc ice sheet. With the present and future installation of I~OJ-e automatic weather stations around the ice sheet as part of the Progranl for Arctic Regional (;lilnate Assessnrent. SllJfXe albedo data will be available for this purpose’. Table 3 lists the mw11 Jtwnthl~avtages of the slurcan~p con~putc~d frOJU f:‘KY albcdo at the ETH/CU AVHRR satellite data, with and without the IIS’ of Eq. (7) to nonnalixr~ the surfkce albedo. Use of this cxquation significantly iinproves the accliracy in tlic, satellite surf&l ulbedo. Large differences are still found in the nronths of July and August, with the greatest differences fiord days were in July 1990. However, only three clear-s+ used to compute the monthly average su~-f’acc albc~lo front satellite in July, which, therefore, does not XXYrately rcpresrnt the all~tlo for that niontli. I argc‘r (‘rrors ilr the sllrfilc.(a nllwtlo for tile IllOJlthS of Jul, ;ultl Allgllsl at this site.

?? Excluding o

August Including August

0

501 62.5

.

’

65

.

’

.

’

.

’

.

’

.

-

.

m .

’

67.5 70 72.5 75 77.5 80 82.5 Surface Measured Broadband Albedo

are a result of comparing a d-km satellite albedo with a point measurement. In addition, in the months with less melt (May and June), the measured surface albedo at the camp is greater than that computed by rising AVHRR data. This is expected because the in situ-measured albedo includes cloudy conditions and, under cloud cover, the snow surfaw albrdo is known to increase slightly (Warren, 1982). Figure 8 shows the broadband surface albedo computed for May-July 1991. The surface broadband albedo remains high and relatively constant for the high elevation regions (4.82) and decreases substantially along the western and eastern coasts (10-207~ decrease). Large

Tnhk 3. MonthI! Averaged Surfiace Albedo Measured at the ETHKXJ (hnp and from AVHRR Satellitr Data

\Vithouc Eq. (7)

With Eq. (7)

70.9 (3.1)

z.fi

54.9 (14.1)

x5.9 (12.1)

a.9

64.8 (7.2,

(9.1)

(1.4)

Xl .:3 (3.7)

82.7 (2.3)

7:x.3 (3.7)

74.9 (2.1)

69.3 (3.7)

71.0 (2.0)

77.6 (4.4)

79.3 (1.7)

76.6 (1.4)

78.2 (0.2)

.

’

85

.

c

87.5

drops are also observed along the southern tip and the northeastern parts of the ice sheet (lO--lS% decrease). Maximum albedo values are found on the southern dome and a small area on the middle-western slope extending from approximately 69.5”N at an elevation of 2700 m above sea level to the area north of Melville Bay (71”N) at approximately 1500 m above sea level. This is the same area shown by Ohmura and Reeh (1991) to have high snow accumulation. In summer, this region receives substantial amounts of precipitation from up-slope advection from the west. Low values of albedo are found in northeastern Greenland and are consistent with this region being the lowest precipitation area of the ice sheet (Bromwich and Robash, 1993; Ohmura and Reeh, 1991). There also appears to be some melting in the northeastern comer near the coast (see July surface albedo image in Fig. 8). Surface melting in this region has been obsemed in passive microwave imagery (W. Abdalati, personal communication). Bromwich and Robasky (1993) also found that, from 1968 to 1988. significant decreases in the mean annual accumulation over most of the northern two-thirds of the ice sheet were observed. Roth surf&e melting and decreases in accumulation rates can account for the lower surface albrdo values in this region. Figure 9 shows the percentage of decrease in surface alb ec1o f rom May to July 1991. Although surface albedo remains relativelv constant during the summe‘~

JUllC Figure 8. Surface broadband

Jut!

albedo for May, June, and July 1991.

Figure 9. Difference (in %) in surfkc had band albedo between May and July 1991,

months at the higher elevations, there is a slight increase in albedo over the central part of the ice sheet. This increase is believed to be a result of new snowfall. Of significance are the large areas along the margins of the ice sheet where the surface albedo has dropped by IO-20% between May and July. In the ablation zone, where snow is melting away to reveal the bare ice substrate, the change in surface albedo may reach as much as 60%. These large drops in surface albedo allow a tremendous increase in the amount of solar radiation absorbed by the surface. If the Julv surface conditions were to occur earlier-say, in May-at the equilibrium line altitude, the amount of absorbed solar radiation would be doubled, from approximately SO W 111-l to 100 W n-l. The decrease in surface albedo as melt begins significantly affects the energy balance of the snowpack and changes the relative importance of solar radiation in the surface heat balance. Before the snowpack begins to melt, the net radiation balance is close to zero, and the turbulent fluxes dominate (Steffen, 1995). After the snowpack begins to melt, the amount of absorbed solar radiation nearly doubles, and net radiation becomes the dominant heat source for the surface. This additional solar input at the surface warms the snow, causing a corresponding increase in surface temperature.

ERRORS The accuracy of the atmospheric correction depends in part on the accuracy with which the atmospheric input variables can be determined and the sensitivity of the correction to uncertainty in these input variables. These input variables are atmospheric profiles for temperature, water vapor, ozone, and aerosols. The sensitivity of the atmospheric correction to uncertainties in these variables depends on both wavelength (channel integrated values) and the base value for the input variables. Estimates of the sensitivity of the satellite-measured surface albedo to water vapor, ozone, and aerosols were developed by using the 6S radiative transfer model. The sensitivity for each variable and model-atmosphere type was determined by changing the base value of each of the preceding three variables and noting the corresponding change in the calculated radiance. To assess the sensitivity of input parameter uncertainties, the following values were used: 1. aerosol: 50%; this large value was chosen because the amount of aerosol concentration is still largely unknown. 2. water vapor: 20%; the variance found in the rddiosonde column water vapor estimates. 3. ozone: SO%. The calculations were made at sea-level altitude. Table 4 summarizes the results of the sensitivity test. UncertaintitAs in aerosol amounts (*5O%) have the

greatest effect on the satellite-measured visible and nearinfrared radiances, with errors of as much as 3.3% for the visible channel. The 50% uncertainty in aerosol amount could be exceeded if stratospheric aerosol amounts were to increase during a volcanic eruption. Sensitivity to aerosol concentrations decreases with increasing surface elevation. For example, at roughly 1200 m (ETHKU camp), the change in satellite reflectance for a 50% change in visibility is only 1.1%. Ozone is the next most important atmospheric constituent to consider when estimating AVHRR satellite radiances in the visible channel. A SO% change in ozone concentration can lead to errors of as much as 1.5%. Uncertainties in the water vapor amount have the least effect on the visible and near-infrared satellite measurements. The sensitivity of the surface albedo to errors in the calibration coefficients, narrow- to broadband albedo weights, and anisotropic reflectance factors are given in Table 5. These parameters are seen to have a large effect on the broadband albedo. Because the postlaunch calibration coefficients are likely to be in error by about lo%, this is an important limiting factor in obtaining accurate surface and TOA albedos. This suggests that more effort is needed to ensure that the AVHRR satellite visible radiances are accurately calibrated if these data are to be used in climate studies. Accurate calibration of the AVHRR visible radiances remains an area of active research (Brest and Rossow, 1992; Che and Price, 1992; Rao and Chen. 1995). Surface anisotropic reflectance values derived from the modeling efforts could be in error by as much as 25% for very low sun angles, large viewing angles, and relative azimuths between 0” and 10”. Differences between modeled and measured data are largest at these angles, but it is not clear if these differences stem from model error or measurement error. However, for the illumination and viewing conditions of images used in this research, anisotropic reflectance differences between model results and measurements were typically less than 7%, with smallest differences for highest sun angles and smallest viewing angles. Because model-derived channel 2 anisotropic reflection function values are sensitive to grain size, the sensitivity of these values to erroneous grain size was tested. Anisotropic reflectance model simulations indicate that the maximum error resulting from incorrect choice of grain size also depends on viewing and illumination geometries. This is presented in Fig. 10, where the percentage of difference in f values between using a grain size of SO and using one of 1000 pm is shown. It is difficult to assess the accuracy of the broadband cornversion without more measurements of snow reflectance for a variety of snow types. The model, however, does not appear to be particularly sensitive to changes in snow grain size, and the error in the narrow-band

weights is estimated to be well below 10%. Only through additional field measurements will it be possible to improve on the model accuracy. Using the model based on TOA radiances does result in errors in the broadband albedo on the order of 5%. Therefore, the surface-based model is preferred to that of Li and Leighton (1992).

CONCLUSIONS The surface albedo was computed for the Greenland ice sheet by using output from the 6S radiative transfer model during the months of May-August and documents the progression of summer melt. In July, large drops in surface albedo were observed, with reductions of as much as 65% in coastal regions. Even in areas that experience little or no melt, albedo decreases of 10% were common. The albedo also remained low in August, indicating that there was extensive melt in late July and through early August. These large drops in surface albedo significantly alter the energy balance of the snowpack by doubling the amount of absorbed solar radiation. In the context of global warming with increased air temperatures, the area of wet-snow zone will increase significantly. For only a 1 K temperature increase, there will be not only a significant increase in ablation during

June-August, but also be a proportionally greater increase of ablation in May and September (Rraithwaite and Olesen, 1993). A 1-2 K temperature change can result from CO, doubling (Kuhn, 1989). If the July surface conditions were to occur earlier-say, in May-at the ELA, the amount of absorbed solar radiation would bc doubled, front approximately 50 to 100 W ne2. Therefore, it is necessav to monitor the spatial and temporal variability of surface albedo because small changes in the summer albedo directly affect the absorbed shortwave enera and the amount of snow melt. In general, using the 6S radiative transfer model and the Koepke (1989) method reslilts in similar accuracies in the retrieved narrow-band surf&e albedos. Comparison with surface measurements reveal that, with the 6S model, the narrow-band albedo was underestimated bv 8.7% in the \isible and mrerestiunated by 5.9%’ in thr near infrared. The Koepkc model underestimated the cisible albedo by 3.9% and overestimated the near-infiared albedo I)\, Fi.Y%. The 6S model, however, does off& an advantage ovtlr simple pararneterization, slich as that

Figuw 2 0. Difference (iii %) in anisotropic reflection lirnction between use of a grain size of 50 and that of 1000 purn for AVHRR clmmel 2. Model inputs include a solar zenith angle of 40”.

Tuhle 5. Sensitivity of’Retrieved Planetary and Surf&c, to Input Paralneters

Abedo

20

40 Viewing Zenith

60

of Koepke (1989), in that it is relatively simple to incorporate a DEM. Output from the 6s radiative transfer model. tabulated for discrete viewing and solar geometries and elevation, can then serve as a look-up table for the atmospheric correction of the AVHRR Lisible and near-infrared radiances in polar regions. The accuracy of the satellite-derived surface albedo depends in part on the accuracy of the primary atmospheric input variables used in the atmospheric correction. This is particularly true in regard to aerosol amounts, where uncertainties can lead to large errors in the estimated surface albedo. At the present time, aerosol retrievals over snow-covered surfaces are not possible from space. Comparisons between AVHRR-derived and surfacemeasured broadband albedos show that, before the snow surface begins to melt, agreement is good (less than 5%). In midsummer, when tnelt is occurring, comparison of the surface albedo derived from the AVHRR GAC data with field measurements at the ETH/CU camp is difficult. The camp is located at the ELA of the Greenland icr sheet and is therefore in a region where significant melt occurs in summer. Melt ponds, 1 km or larger in size, are known to form near the camp. Because of the location of the camp, the surface alhedo measured there does not represent the larger (d-km) pixel area of the AVHRR GAC data. The same conclusion was reached by Knap and Oerlelnans (1996) by using the AF’HRR LAC (l.l-km) data and suggests that the AVHRR GAC resolution is too coarsr to observe variations in albedo in the ablatiou area of the ice sheet. This also makes it difficult to firmly establish the accuracy of the AVHRR-derived surfacr albedo presented in this study, because there are currently- not enough in situ measurements available for comparison.

Present

lnents

at different

made

available

AVHKR temporal

and &ire locations

for this

purpose.

data is successfill albedo

surface about

the

albedo

However,

for identifying

ineasure-

ice sheet the

both

will be use

spatial

of and

changes.

REFERENCES Baldwin, 13. G., and Emery, W. J. (1993). A systematized approach to AVHKR image navigation. Ann. Clmiol. 17: 414-420. Binenko, \‘. I., md Harshvardhan (1993), Aerosol effect in radiation transfers. In Aeraso~ Effects on Chute, University of Arizona Press. Tucson, pp. 190-227. Rraithwaite, R., and Olesen, 0. (1993), Seasonal variation of ice ablation

at the margin

of the Greenland

ice sheet

and

its sensitivity

to climate

change. ~amanarssup

sermia. West

Greenland. J, Glacial. 39:201-209. Brest, C. L., and Rossow, W. B. (1992), Radiometric calibration and monitoring of NOAA AVHRR data for ISCCP. Int. J. Renwtc Sens. 13:235-273. Bronwicl~. 11. I-I., and Robasky, F. M. (19931, Recent precipitation trends over the polar ice sheet. Mctcorol. .4tmos. Phys. 51:359-274. Chr, N., and Price, J, C. (1992), Snrvey of radiometric calibration results and methods for visible and neitr infrared channels of NOAA-7, -9, and -11 AVHRRs. Remote Sens. Enuiro11. 41:19-27. D‘Almeida, G. A., and Koepke, P. (19X8), .411 approach to a glold optical aerosol climatology. In Aero.sol.u and Chate, i\. Derpak. Hampton, I’A, pp. 125-137. Dcx Abrrn, R. I)., Barber, D. G.. Misurak, K., and Le Drew, F;. F. (1994). Spectral albedo of snow-covered first-year and multi-year sea ice during spring melt. A,tll. Glacial. 21: %h-297. Haefliger, M., Steffen, K., and Fowler, (Z. (19Q3). AVHRR snrlicra temperature and narrowband albedo comparison with qrom~d measurements for the Greenland ice sheet. Ann. Chid. 17:49-54. Key. J. (1994). STREAMER user’s guide. Cooperative Institute for Krsearch in Environmental Sciences, University of Colorado at Boulder, May 1994. Kitlwt~ll, K. B. ( LQQl), NOAA polar orbiter data users guide. NOAA Information Service and Climate Data Center, Satellite Data Srnice Division, Washington, DC. Koepke, P. (1989), Removal of atmospheric effects from A\‘HRR alldos. J A&. Al&m-d. 28: 1341-1348. Knapp. IV. H.. and Oerlemans, J. (1996), The surface albedo of thll Greenland ice sheet: satellite derived and in situ measnrements in the Sondre Stromfjord area dnring the 1991 mc>lt season. J. Glacial. 42:364-374. Kuhn, M. (198Q). The role of land ice and snow in climate: nnderstandiug climate change. In Ccfyhysical Mfmfgraph =i2 (A. Brrger, R. E. Dickinson, and 1. \I’. Kidson, Eds.), IIJG
corrc~ctions. NOA, Trcllnicul Heport No. NOA.VNESIIIS, ~Vasllillgtort, I>(:. k10, (:. 11. Iv., and (Ilien. 1. (199.5), Iliter-satt~llitc cdit)ratioll linkages fbr th visit& ad nem--dramI cl~annels of the XIvar~cd vrn. high resolrltion radiomrter OII the NOAA-7, -9. and -1 1 sp&craft. 1111.1. Ho~ole Sfms. 16: 19:31-1942. Kiel~land, M., and Stuhlnranu, R. (1%X3),Toward th influence 01’clouds on the sliortwavc ratlietioil l)utlgrt of tlir Earthatmosphere systenl estimated hn satrllitc~ data. j. ;\$ Mr~tcwrol. :32:825-84:3. Shv, (G., Reagan, J., and Herman, 13. (19731, Ju\~estigations of atltlosplieric r~xtiriction llsirig direct solar radiation ni~‘iisurc’ments ma& with ii multiple wavelength radionirtrr. /. A& M&w-d. l&374-380. Stamut~s, K., Tsay, S., ~Viscombe, W., and Jayaweera, K. (1988), Numerically stable algorithm for discrete-ordirr~ite-Inethod radiative transfkr in multiple scattering and emittiq layered media. Appl, Opt. 27:2rjO2-2509. Stcffbl, K. (1987), Bidirrctional rdectancc of snow at rjOO-600 nt11. Proceedings of the Vancouver Symposium, August, 1987, Vancouver, R(:, Canada, pp. 415-425. tllcwtlatiolts

iVESIlIS-70,

for