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Atmospheric Research 72 (2004) 329–351

Radiative energy partition and cloud radiative

forcing at a Po valley site

C. Gallia, M. Nardinoa,b, V. Levizzania, R. Rizzic, T. Georgiadisb,d,*

a ISAC-CNR, Institute of Atmospheric Sciences and Climate, via Gobetti 101, I-40129 Bologna, Italyb IBiMet-CNR, Institute for Biometeorology, via Gobetti 101, I-40129 Bologna, Italy

cDepartment of Physics, University of Bologna, viale Berti Pichat 6/2, I-40127 Bologna, ItalydDESA, University of Sassari, via E. de Nicola, I-07100 Sassari, Italy

Received 30 July 2003; received in revised form 7 January 2004; accepted 31 March 2004

Abstract

Values of downward and upward flux densities of solar and terrestrial radiation were continuously

recorded between 1 December 2001 and 30 November 2002 using a four-components radiometer at

S. Pietro Capofiume (SPC) in northern Italy (44j39VN, 11j37VE, alt. 11 m a.m.s.l.), which is

characterized by a weakly-reflective surface. The aim of the study was to investigate the effects of

clouds on surface radiation balance (SRB); the cloud fraction (N) has been retrieved through the

inverted form of the parameterization proposed by Kasten and Czeplak [Solar Energy 24 (1980) 177]

and cloud types estimated following the methodology of Duchon and O’Malley [J. Appl. Meteorol.

38 (1999) 132]. The cloud radiative forcing (CRF) was evaluated through the Bintanja and Van den

Broeke [Int. J. Climatol. 16 (1996) 1281] formula and then associated with cloud type. Experimental

results showed that during the measuring period the net shortwave (Sw) balance always decreased

with increasing N, whereas the net longwave (Lw) balance increased in all seasons. The net radiation

available at the surface decreased with increasing N in all seasons except in winter, where no

significant dependency was detected.

The analysis of the cloud radiative forcing indicates that all seasons were characterized by a net

cooling of the surface except winter, where clouds seem to have no effects on the surface warming or

cooling. Taking into account the dependence on solar radiation cycle, an intercomparison between

the retrieved cloud types seems to indicate that the effect of stratus was a slight cooling whereas that

of cumulus clouds was a stronger cooling of the surface. On the contrary, cirrus clouds seem to have

slight warming effect on the surface.

The annual trends of mean monthly values of shortwave and longwave radiation balances

confirmed that the measurement site is characterized by a temperate climate. Moreover, in spite of

0169-8095/$ - see front matter D 2004 Elsevier B.V. All rights reserved.

doi:10.1016/j.atmosres.2004.03.022

* Corresponding author. IBiMet-CNR, Institute for Biometeorology, via Gobetti 101, I-40129 Bologna, Italy.

Tel.: +39-51-639-9006; fax: +39-51-639-9024.

E-mail addresses: c.galli@isac.cnr.it (C. Galli), m.nardino@ibimet.cnr.it (M. Nardino), v.levizzani@isac.cnr.it

(V. Levizzani), r.rizzi@adgb.df.unibo.it (R. Rizzi), t.georgiadis@ibimet.cnr.it (T. Georgiadis).

C. Galli et al. / Atmospheric Research 72 (2004) 329–351330

the cooling effect of clouds, a monthly radiative energy surplus is available all year long for surface–

atmosphere energy exchanges. The analysis is also instrumental for the detection of SRB variations.

D 2004 Elsevier B.V. All rights reserved.

Keywords: Global climate changes; Solar radiation; Atmospheric radiation; Surface radiation balance; Cloud

radiative forcing

1. Introduction

Climate changes as a multifaceted global problem are receiving growing attention

especially as regards to future impacts on the planet and mankind (IPCC, 2001). Due to the

chaotic nature of the atmospheric continuum, which inherently limits the skills of climate

variation predictions, we can only project climate changes in the future in terms of

scenarios. The main tools are represented by climate models (e.g., Collins and Senior,

2002) whose predictive accuracy of global warming scenarios through the increase of CO2

has not changed much during the last two decades. One of the main reasons for this fact is

the crude representation of cloud radiative properties in climate models (e.g., Webster and

Stephens, 1983; Cess et al., 1989; Senior and Mitchell, 1993; Haigh, 2002).

Clouds alone may account for more than 40% of the incoming and outgoing radiation

fluxes, let alone the latent heat flux, which might add an additional 24%, (e.g., Peixoto and

Oort, 1992). For this reason, clouds can be considered one of the main regulating factors of

the Earth’s climate; they exert a cooling effect by reflecting a portion of the incoming solar

radiation and a simultaneous warming effect by decreasing the Earth-emitted longwave

(Lw) radiation.

A way to weigh the effects of clouds on the surface–atmosphere system is the cloud

radiative forcing (CRF), defined as the difference between the radiation budget in all-sky

and clear-sky conditions. In recent years, several studies have attempted to quantify the

CRF at the top of the atmosphere (TOA) (Su et al., 2000; Inoue and Ackerman, 2002) and

at the Earth surface (Tarasova and Cavalcanti, 2002; Tian and Ramanathan, 2002; Dong

and Mace, 2003; Wang and Key, 2003).

Clouds have an important role in the vertical energy partition and consequently their

impact on the net radiation budget at TOA may not tell their total impact on the Earth

climate system. A particular value of the net outgoing radiation flux at TOA need not

represent a unique state of the column below (Stephens and Webster, 1981), a fact that

calls for a more thorough understanding of the role of clouds on the surface–atmosphere

radiative energy partition.

The methodologies used to retrieve the surface CRF are manifold (satellite radiances,

model-simulated values, empirical parameterizations, field observations), but regardless of

the measurement or modeling approach the magnitude and sign of CRF depends on

available solar energy, clouds and surface properties, i.e. cloud albedo and emissivity,

surface temperature and reflectivity, and also on cloud base height (Harrison et al., 1990;

Stuhlmann, 1995; Tian and Ramanathan, 2002).

Using a one-dimensional radiative convective model, Stephens and Webster (1981)

determined that high thin clouds at low and mid-latitudes in all seasons and all cloud types

C. Galli et al. / Atmospheric Research 72 (2004) 329–351 331

at high latitudes in winter tended to warm the surface relative to a clear sky; all other

clouds tended to cool the surface. The summer high-latitude effects were similar to the

low- and mid-latitude situation. Their model also showed that, for a given cloud type, a

critical surface albedo may exist at which the cloud turns from cooling to warming the

surface when the surface albedo increases.

These results seem to be confirmed by other studies that found a positive value of

surface CRF over highly reflective surfaces and/or in the presence of cirrus-like cloudiness

(Bintanja and Van den Broeke, 1996; Duda et al., 2001; Marty et al., 2002; Nardino and

Georgiadis, 2003). The positive CRF values may be ascribed to the multiple reflection of

solar radiation between the highly reflective surface and the cloud bottom that enhances

the net surface shortwave (Sw) flux, and/or to the low shortwave optical depth of cirrus-

like clouds compared to other cloud types (Hahn et al., 2001; Sassen and Comstock,

2001). Two of the above-mentioned studies (Bintanja and Van den Broeke, 1996; Nardino

and Georgiadis, 2003) also showed that the surface CRF is very sensitive to surface albedo

and cloud type because the CRF becomes negative in the presence of other cloud types

and/or melting ice.

In partial agreement with Stephens and Webster’s (1981) model results, Stuhlmann

(1995) suggested that low clouds tend to cool the surface, whereas high and middle clouds

cause a surface warming. On the contrary, Tian and Ramanathan (2002) found in the

tropical Pacific a negative surface CRF both for high (cirrus, cirrostratus and deep

convective clouds) and low (stratus, stratocumulus and cumulus) clouds, and a net cooling

effect of middle clouds.

The present measurements come from a radiometric station situated on a grass-covered

(weakly reflective) surface at a site in northern Italy and are representative of the Po Valley

region. This region is very important for the Italian and European primary production both

for the great economic value of its agricultural products and the wide extension of the

cultivated area. In fact, the seasonal analysis of surface radiation/energy parameters

contributes to a better understanding of the impacts on agriculture and generally on the

ecosystems since it aims at quantifying the overall cloud effects on surface energy

availability in all seasons separately. The surface energy budget (SEB) is governed by

several surface and atmospheric processes, whose time scales go from minutes to several

consecutive years. The SEB at time scales of days to years is intimately linked to the water,

carbon and nutrient balances of the surface and influences the ecosystem developments

(Pielke et al., 1998), especially the state of the vegetation above and below ground. Note thus

the importance of quantifying seasonal changes in the framework of global climate changes.

There are three main objectives in this paper. The first is to determine the relationships

between the cloud fraction (N) and the surface radiation balances, Sw, Lw and net. For this

purpose, the cloud fraction is retrieved through the Kasten and Czeplak (1980) parame-

terization and then related with each kind of radiation balance. The second is to obtain an

estimation of surface CRF and try to relate it with cloud type in order to establish if a

certain type of cloud causes a warming or a cooling of the Earth–atmosphere system. This

is derived by computing the CRF according to Bintanja and Van den Broeke (1996) and

estimating the cloud type through the methodology proposed by Duchon and O’Malley

(1999). The third is to give a climate classification of the measurement site, by means of

the hodograph method proposed by Smith et al. (2002).

C. Galli et al. / Atmospheric Research 72 (2004) 329–351332

The site and the experimental setup are described in Section 2, while the analysis

methods are detailed in Section 3. Sections 4 and 5 present the results and conclusions,

respectively.

2. Experimental setup

2.1. Site description

Measurements were collected at S. Pietro Capofiume (SPC), 44j39VN, 11j37VE, 11 m

a.s.l. The site is a typical rural location in the northeastern Emilia-Romagna region (Italy)

in the Po valley (Fig. 1). Fig. 2 shows the annual mean values of air temperature and

relative humidity (a), and yearly precipitation amounts (b) recorded in SPC from 1987 to

2001.

The instrumentation used for the campaign was installed in a wide flat, grass-covered

area, situated in the proximity of the Regional Meteorological Service (ARPA/SMR)

operative meteorological centre (OMC), a baseline station of The World Meteorological

Organization (WMO).

Fig. 1. Location of the S. Pietro Capofiume (SPC) measurement site.

Fig. 2. (a) Annual mean values of air temperature and relative humidity, and (b) yearly sums of atmospheric

precipitation. Data were recorded in SPC from 1987 to 2001 by the Emilia-Romagna Regional Meteorological

Service.

C. Galli et al. / Atmospheric Research 72 (2004) 329–351 333

C. Galli et al. / Atmospheric Research 72 (2004) 329–351334

The importance of the site stems from its representativeness of meteoclimatic

conditions of Emilia-Romagna, which is very relevant from the agricultural point of

view. The influence of cloud cover is therefore twice as important since it affects both the

flux of radiation reaching the surface and the frequency and magnitude of precipitation

events.

Climatological studies of the region are available in the literature trying to find a trend

in the precipitation time series and detect changes in precipitation amounts (Tomozeiu et

al., 2000, 2002a). These studies revealed an increasing trend in summer precipitations for

the period 1922 to 1995 (Tomozeiu et al., 2000) and a decreasing one in winter between

1960 and 1995 (Tomozeiu et al., 2002a). Another recent study on mean regional maximum

and minimum temperatures from 1956 to 2000, whose results are only very preliminary,

seems to suggest a shift in the distribution of mean temperature towards higher values

(Tomozeiu et al., 2002b, in Italian).

2.2. Sensors

2.2.1. Radiometric station

The downwelling and upwelling surface fluxes were measured by means of a four-

component EKO radiometer, model MR-40. The radiometer measured separately the Sw

(0.3 to 3 Am) and Lw (3 to 50 Am) radiation components with two pyranometers and two

pyrgeometers, respectively. The field of view of the sensors is 180j and an estimated error

of 10% is attributed to daily sums. The radiometric station measured each component

every 15 s and all data were stored in a Campbell CR10 data logger that calculated the

mean values over a 10-min period. All radiation fluxes are expressed in W m� 2.

2.2.2. Meteorological station

The key meteorological parameters were measured by a standard meteorological

station: precipitation (tipping bucket rain gauge), air temperature (T-thermocouple),

relative humidity (solid state hygrometer), and incoming Sw radiation (Swin) at 3 m

height (thermopile pyranometer). Each parameter was measured every minute and a data-

logger calculated and stored the 20-min mean values. Precipitation is expressed in mm,

temperatures in jC and relative humidity in %.

2.2.3. Network camera

The monitoring of sky conditions was carried out by means of a network camera AXIS,

model 2100, mounted in an elevated position in order to obtain a hemispheric, obstacle-

free sky view. The camera was modified for remote control during the measurement

campaign. A gray filter was inserted between the fish-eye and the network camera lens

with the aim to avoid solar radiation reflection and the camera was mounted on a stout

mount (Fig. 3). The data acquisition system stored a sky image every 10 min.

2.3. Data set

From 1 December 2001 to 30 November 2002, more or less uninterrupted measure-

ments were taken of the four surface radiation components: Sw incoming (Swin), Sw

Fig. 3. The AXIS network camera, model 2100 in its configuration as all-sky camera for cloud cover

identification.

C. Galli et al. / Atmospheric Research 72 (2004) 329–351 335

outgoing (Swout), Lw incoming (Lwin) and Lw outgoing (Lwout). The all-sky images data

set started on 13 June 2002 and ended on 30 November 2002.

A monthly and seasonal analysis has been performed with the aim to investigate both

monthly and seasonal cloud influences on surface radiation balance (SRB). The latter

dwells on data from December 2001, January 2002 and February 2002 for winter, March,

April and May 2002 for spring, June, July and August 2002 for summer, and September,

October and November 2002 for autumn.

3. Methods

The first step of the analysis was to filter the data to reject physically incorrect

measurements, such as the values of Sw< 0. The SRB was then computed from radiation

time series as follows:

Swn ¼ Swin � Swout ðSw radiation balanceÞ ð1Þ

Lwn ¼ Lwin � Lwout ðLw radiation balanceÞ ð2Þ

Rn ¼ Swn þ Lwn ðnet radiation balanceÞ ð3Þ

C. Galli et al. / Atmospheric Research 72 (2004) 329–351336

The surface reflectivity for the solar wavelengths (albedo coefficient, a) was evaluatedthrough the ratio between the Sw radiation reflected by the surface and that incident on the

surface:

a ¼ Swout

Swin

ð4Þ

Because of possible directional response deviations of the sensors, a was computed

only for measurements corresponding to solar elevations angles (w) greater than 10j.The cloud fraction (N) was obtained using the parameterization proposed by Kasten and

Czeplak (1980) (hereafter KC, see Section 3.1) and is defined as the fraction of the sky

dome covered by any type of cloud. The cloud type estimation was done through the

methodology proposed by Duchon and O’Malley (1999) (hereafter DM, see Section 3.2)

and then related to the CRF. The Sw (SwCRF) and Lw (LwCRF) CRFs were estimated

according to Bintanja and Van den Broeke (1996) (hereafter BV, Section 3.3).

3.1. Estimation of cloud fraction

The KC formula to retrieve the cloud fraction is

SwinðNÞSwinð0Þ

¼ 1� b1N

8

� �b2

ð5Þ

in its inverted form

N ¼ 1

b11� SwinðNÞ

Swinð0Þ

� �� �1=b2ð6Þ

where b1 and b2 are site-dependent coefficients, N is the cloud fraction that varies between

0 for clear-sky and 1 for overcast conditions, Swin(N) the measured global radiation, and

Swin(0) the global radiation that would reach the surface in clear-sky conditions. The latter

parameter is very difficult to retrieve and several studies are available in the literature (e.g.,

Battles et al., 2000; Niemela et al., 2001). In the present study, Swin(0) was computed

through the determination of the monthly typical-clear-sky atmospheric transmittance,

which is a two-step process. First, for every month, all the completely clear-sky days are

singled out, and subsequently the mean value of all data corresponding to the same hour of

the day are computed. The identification of clear-sky days is possible by observing the

images of the network camera and/or the daily Swin time series.

The total effective atmospheric transmission (s) was evaluated for all Swin measure-

ments as follows:

s ¼ Swin

SwinðTOAÞð7Þ

where Swin(TOA) is the Sw flux incident at TOA. The typical-clear-sky total atmospheric

transmission (sclear ) is then obtained as the mean value of all data corresponding to the

same hour of all clear-sky days.

C. Galli et al. / Atmospheric Research 72 (2004) 329–351 337

The value of Swin(TOA) depends only on astronomical factors:

SwinðTOAÞ ¼ ScosðUÞ ð8Þ

where S is the solar constant that depends on the Sun–Earth distance, and U is the solar

zenith angle. In our computation, we considered S constant and equal to 1372 W m� 2.

Finally, the typical-clear-sky incoming Sw flux was obtained with the following

formula:

Swinð0Þ ¼ SwinðTOAÞsclear ð9Þ

Again, the computation of Swin(0), and consequently of N, was done only for w greater

than 10j.The parameterization coefficients b1 and b2 of Eqs. (5) and (6) were 0.75 and 3.4,

respectively, in the original formula, whereas for SPC they were computed from the

network camera images. The cloud fraction was estimated from the sky images through the

algorithm developed by Nardino et al. (2002) and then related to the daily mean values of

the ratio Swin(N)/Swin(0) (see Fig. 4). The SPC best fit parameters b1 and b2 take the

values 0.99F 0.06 and 1.3F 0.1, respectively.

The KC’s formula inversion (6) was verified in other studies in the past (Orsini et al.,

2002; Nardino and Georgiadis, 2003) with satisfactory results in polar sites. Moreover, the

direct observations of the network camera images confirmed the reliability of the

parameterization in Eq. (6), especially considering daily mean values of N.

3.2. Estimation of cloud type

Duchon and O’Malley (1999) developed a methodology for the classification of cloud

types based on incoming global radiation measurements only. The cloud types and sky

Fig. 4. Scatterplot between the ratio of observed and clear-sky solar irradiance and the cloud fraction estimated via

the network camera images.

C. Galli et al. / Atmospheric Research 72 (2004) 329–351338

conditions categorized in the study are: cumulus, cirrus, cirrus and cumulus, stratus, fog

and/or precipitation, and no clouds (clear sky). For the present application, the fog and/or

precipitation class is not used.

The first step of the DM methodology consists on scaling the clear-sky irradiance,

Swin(0), to the constant value of 1400 W m� 2 for each measurement value of each day,

obtaining a day-dependent set of scale factors. The second step is to multiply the observed

Sw irradiance, Swin, by the set of scale factors corresponding to the considered day. The third

step is to compute the mean and standard deviation of the scaled measured irradiance in a 21-

min moving window. The ratio of the running mean scaled irradiance to the scaled clear-sky

irradiance (R) and the running standard deviation of scaled observed irradiance (A) are thetwo parameters used in the cloud-type decision criteria. The window of 21 min is an em-

pirical choice and in this paper a 30-min moving windowwas selected to retrieve cloud type.

The running means were computed with the 1-min Swin time series instead of the 10-

min one. Table 1 shows the thresholds used for the classification.

3.3. Computation of CRF

SwCRF and LwCRF were quantified through the BV method:

SwCRF ¼ N tSwnðovÞ � SwnðclÞb ð10Þ

LwCRF ¼ N tLwnðovÞ � LwnðclÞb

where N is the monthly or seasonal (depending on the type of analysis) mean value of the

cloud fraction and Swn(cl), Swn(ov), Lwn(cl) and Lwn(ov) are Sw and Lw radiation balances

in clear-sky and overcast conditions, respectively. These terms were computed from linear

best-fit equations interpolating the dependences of Swn and Lwn on N. Because LwCRF is

normally >0, clouds warm the surface in the Lw region, whereas they cool the surface in

the Sw region (SwCRF < 0).

4. Results and discussion

4.1. Radiative characterization of the measurement site

Fig. 5 shows the daily mean values of the surface radiation components and the

radiative balances for the whole measurement period. Note in graph (a) the enhancement

Table 1

Cloud types and thresholds of the two parameters used in the cloud type classification

Cloud types R (W m� 2) R

Cirrus 10 <A<80 0.8 <R < 1.05

Cumulus 120 <A<800 0.5 <R < 1.15

Cumulus + cirrus 100 <A<740 0.45 <R < 0.95

Stratus 0 <A<100 0 <R < 0.4

Clear sky 0 <A<10 0.88 <R < 1.05

Fig. 5. SPC daily mean values of (a) surface radiation components, and (b) Sw and Lw balance and net radiation.

C. Galli et al. / Atmospheric Research 72 (2004) 329–351 339

of Swin in spring and summer months due to its dependence on the solar elevation angle

and a corresponding rise of Lwout values that originates from the increase in surface

temperature. Moreover, an increase in air temperature causes a weak shift towards higher

values for the Lwin series in proximity of the spring and summer months.

Sw and Lw radiation balances, shown in Fig. 5b, have opposite sign: Swn is positive

whereas Lwn is negative and in general assumes lower (absolute) values than Swn; this

implies a radiative gain in the shortwave part of the spectrum and a loss in the infrared (IR)

one, due to lower atmospheric temperature compared to surface temperature.

C. Galli et al. / Atmospheric Research 72 (2004) 329–351340

The sign and magnitude of Swn and Lwn determine the available energy for the

nonradiative components (latent and sensible heat) of the surface energy balance that is the

net radiation balance Rn (shown in Fig. 5b). Note that in general SPC is characterized by

positive daily mean values of Rn, which means a daily positive radiative availability for

surface–atmosphere energy exchanges. This availability is low in winter months,

increases in spring and summer and assumes negative values only when the cloud cover

causes a decrease in net Sw radiation that is not balanced by the correspondent

enhancement of Lwn.

Fig. 6 shows the daily mean values of the albedo and the total atmospheric transmissivity.

The albedo generally ranges between 0.1 and 0.2, the typical interval for the grass-covered

surfaces, except for a few days in December 2001 and January 2002 that have been

characterized by snowfall events. On the other hand, the values of s show a high variability

due to its strong dependence on cloud cover properties and varies roughly between 0 and 0.8.

4.2. The cloud fraction

Fig. 7a shows the daily mean values of N computed through Eq. (6) for the whole

measurement period. Note the high variability of the cloud fraction, which is typical of

mid-latitude sites.

For a better understanding of the seasonal features of N, the cloud fraction range has

been divided into five classes: the occurrence of data of every N class for every season is

shown in Fig. 7b.

The winter has been characterized by roughly 50% of ‘‘middle-low’’ + ‘‘middle’’ N

values followed by the same number of events (about 20%) in the classes ‘‘low’’ and

‘‘middle-high’’. Note also that the winter was the season with the highest number of data

in the class 0.8–1.0.

Spring N values were 32% ‘‘low’’ and 28% ‘‘middle’’ whereas in summer the highest

number of ‘‘low’’ cover events was reached, roughly 40%.

In autumn 2002, the N class ‘‘middle’’ contains the majority of cases, and the

percentage of high N values is only slightly lower with respect to winter.

4.3. The influence of cloud fraction on the SRB

Fig. 8 shows the seasonal dependences of the SRB components on N. The data have

been fitted with a first-order polynomial curve in order to quantify the variation between

overcast and clear conditions of the SRB components in terms of W m� 2.

In the first row of Fig. 8, the daily mean values of Swn are plotted against N for all

seasons. The Sw balance always decreases with the increase of N but the slopes of the

linear fits present a remarkable inter-seasonal variability. The steepest slope corresponds to

summer (� 229 W m� 2), followed by spring (� 195 W m� 2) and autumn (� 177 W

m� 2); the winter slope is an order of magnitude lower than those of the other seasons.

Since the surface albedo is almost constant during the year, the Sw balance depends

mainly on Swin, which in cloudy conditions is determined by cloud optical properties.

The second row of Fig. 8 shows the scatterplots of Lwn vs. N. Note that in all seasons

the Lw balance increases with increasing N, as opposed to what happens to the Sw

Fig. 6. SPC daily mean values of (a) surface albedo, and (b) total atmospheric transmissivity.

C. Galli et al. / Atmospheric Research 72 (2004) 329–351 341

balance, and that the inter-seasonal variability of the slopes is lower than in the case of the

Swn slopes. The increase of Lwn with increasing N is mainly due to the direct emission of

cloud in the IR region, which leads to an increase in Lwin. The fact that the slopes are

generally lower than in the Swn case is attributable to the dependence of cloud radiative

properties on liquid water path (LWP). An increase in LWP causes an increase both in

cloud albedo and cloud Lw emissivity, but above a certain value of LWP the emissivity

reaches unity, whereas the cloud albedo can further increase with LWP (Stephens and

Webster, 1981).

Fig. 7. (a) SPC daily mean values of cloud fraction N, and (b) seasonal data percentage of cloud fraction

classes.

C. Galli et al. / Atmospheric Research 72 (2004) 329–351342

The effect of N on the net radiation is shown in the third row of Fig. 8. In winter, the

cloud fraction effect is almost negligible, with a slope of � 8 W m� 2, whereas in all the

other seasons the surface radiative availability decreases with increasing N. The slopes

show a seasonal dependence that is due both to the combination of Sw and Lw cloud

radiative properties and available solar energy. From these graphs, the Sw radiative cooling

is seen predominating on the Lw radiative heating in all seasons except in winter, in which

Sw and Lw cloud radiative effects balance out.

Table 2 summarizes the seasonal features of the influence of cloud fraction

on the SRB components and the variances explained by the linear fits shown in

Fig. 8.

Fig. 8. Dependence of daily net Sw radiation, Lw radiation and net radiation on N. The lines indicate the linear

best fits whose slopes are reported in the individual graphs.

C. Galli et al. / Atmospheric Research 72 (2004) 329–351 343

Table 2

Seasonal variations of Swn, Lwn and net radiation as a function of N along with their variances

Season N dSwn/dN

(W m� 2)

r 2 dLwn/dN

(W m� 2)

r 2 dRn/dN

(W m� 2)

r 2

Winter 0.47 � 70F 8 0.52 60F 6 0.56 � 8F 6 0.01

Spring 0.37 � 195F 22 0.51 84F 5 0.78 � 111F 21 0.27

Summer 0.30 � 229F 16 0.72 70F 16 0.22 � 178F 21 0.51

Autumn 0.50 � 177F 13 0.68 63F 4 0.7 � 114F 12 0.51

C. Galli et al. / Atmospheric Research 72 (2004) 329–351344

4.4. Estimation of cloud type

Fig. 9 shows the monthly and seasonal cloud type classification based on the DM

methodology. From the monthly classification, the peak occurrence corresponds to cirrus

clouds in December, whereas in January and February the predominant cloud presence is

Fig. 9. (a) Monthly and (b) seasonal occurrence of cloud types.

C. Galli et al. / Atmospheric Research 72 (2004) 329–351 345

of stratiform clouds. The seasonal classification shows a predominance of stratus clouds,

but also a relevant number of cirrus cases.

March was characterized by the presence of cirrus, cumulus and cumulus + cirrus

whereas in April and May the cumulus clouds were the predominant cloud type. During

April and May also, the categories of stratus and cumulus + cirrus contain a relevant

number of cases. In the seasonal graph, the events are roughly equally distributed

between cirrus, cumulus and cirrus + cumulus classes. In the transition between winter

and spring, a decrease of stratus clouds was detected together with an increase of clear-sky

events.

Cirrus clouds were predominant in June, July and August but cumulus and cumu-

lus + cirrus are also relevant in July and August; this can also be inferred from the summer

classification. September, October and November show a decrease of cirrus clouds

occurrence and an increase in stratus cloud events that are the predominant cloud type

in the seasonal classification, together with the cumulus and the cumulus + cirrus classes.

4.5. Estimation of cloud radiative forcing

In Fig. 10, the monthly and seasonal scatterplots between Sw and Lw cloud radiative

forcing are reported (see Section 3.3 for the computation method). Note that the distance

of the symbols from the SwCRF=�LwCRF line represents the amount of the cooling or

warming (net CRF).

In December and January, the effect of clouds was roughly negligible whereas in

February clouds caused a slight cooling. On a seasonal point of view, in winter the net

CRF was nearly zero. Note also that the strong increase (in absolute value) in Sw and

LwCRF moving from December–January to February seems to indicate a change in cloud

radiative properties.

Spring was generally characterized by a negative net CRF as also indicated by the

single months, and an increase in absolute value of SwCRF is larger than LwCRF; this was

probably due to the increment of available solar radiation. The month with the greatest

cooling was May.

In summer, the effect of cloud was a net cooling of the surface, with a slight increase of

the cooling in August. The monthly differences in CRF concern mainly the Sw part of the

spectrum but they are smaller than in spring.

Among autumn months, September was characterized by the highest cooling. The

entity of the cooling decreases significantly moving from September to October and just a

little from October to November. The difference between September and October–

November is both in the Sw and in the Lw parts of the spectrum.

4.6. Discussion of the relationship between CRF and cloud type

Taking into account that the SwCRF has an annual/seasonal cycle, the monthly and

seasonal Sw cloud transmissivities (scloud) were computed in order to distinguish between

the effects of cloud type and solar insolation on CRF. Considering the linear fits reported

in Fig. 8, scloud have been computed as the ratio between Swn(ov) and Swn(cl) and the results

are reported in Fig. 11.

Fig. 10. (a) Monthly and (b) seasonal mean values of surface Sw and LwCRF.

C. Galli et al. / Atmospheric Research 72 (2004) 329–351346

Looking at Figs. 9 and 10, it can be seen that winter was characterized by the presence

of stratus and a net CRF near zero. Stratus clouds prevailed mainly in February (see

monthly graph in Fig. 9) where clouds slightly cooled the surface as shown in Fig. 10a and

had the lowest scloud value (see Fig. 11a). December was characterized by cirrus, a

negligible net CRF and a greater scloud with respect to February. January registered a

transition between the prevalent December cirrus cloud cover and stratus with a

correspondent net CRF value near zero. The increase of both SwCRF and LwCRF

between December and February is consistent with the change in cloud type, because

stratus are generally characterized by a lower Sw transmissivity and a higher LWC than

cirrus.

C. Galli et al. / Atmospheric Research 72 (2004) 329–351 347

In spring, the amount of stratus tended to decrease in favor mainly of cirrus, cumulus

and cirrus + cumulus; the net CRF indicated higher cooling. The change in SwCRF

between March and May can be mainly ascribed to an increase of available solar radiation,

but also to a slight change in Sw cloud properties, as shown in Fig. 11a, that displays a

decrease of scloud. In fact, during the transition between these two spring months, the

stratus occurrence increases while the cirrus one decreases (see Fig. 9a).

Among summer months, June was characterized by the highest number of cirrus, the

lowest total cooling effect, and the highest scloud. An intercomparison between June and

August seems to suggest that the increase in cumulus occurrence and the decrease in cirrus

one (Fig. 9a) caused a slight cooling effect (Fig. 10a) due mainly to the lower scloud of

cumulus than that of cirrus (see Fig. 11a). The seasonal effect of the predominance of

cirrus clouds (Fig. 9b) is an Sw cooling roughly equal and an Lw heating slightly lower

than that of spring. This result is consistent with the change in cloud type shown in Fig. 9b

that shows an increase in cirrus and a decrease in stratus occurrence in the transition

between spring and summer. The seasonal scloud values (Fig. 11b) confirm this hypothesis,

being the summer scloud greater than that of spring. Note also that the seasonal prevalence

of cirrus is consistent with a low cloud fraction as shown in Fig. 7b.

The transition between summer and autumn is characterized by a decrease of cirrus

presence and an increase in stratus one. Again the seasonal SwCRF shows an increase in

Fig. 11. (a) Monthly and (b) seasonal mean values of Sw cloud transmissivity.

C. Galli et al. / Atmospheric Research 72 (2004) 329–351348

absolute value caused by the strongly low value of autumn scloud. In the monthly graphs, it

can be noted a progressive increase of stratus occurrence, a decrease of both scloud and net

CRF and an increase of Lw heating. From the comparison of September with October–

November, differences in Sw and Lw radiative properties between cumulus and stratus

clouds are found, being cumulus characterized by a greater scloud and a lower LwCRF than

stratus.

An overall evaluation of these results seems to show that stratus clouds are character-

ized by a low net CRF, due to the balance between Sw and LwCRFs, while cumulus

clouds exert a stronger cooling effect.

In spite of the uncertainty of cloud type classification, the analysis points out that in

SPC during the period between 1 December 2001 and 30 November 2002 the effect of

cumulus clouds was a marked cooling of the surface whereas the effect of stratus was a

slight cooling. Cirrus clouds seem to have a slight warming effect that is not directly

visible, but can be inferred by the shift toward lower values of the net CRF (i.e. during

March and June months). This confirms previous studies, although carried out in Polar

sites, where a warming surface effect was observed during cirrus-like cloudiness events

(Nardino and Georgiadis, 2003; Stuhlmann, 1995; Stephens and Webster, 1981).

4.7. Climate classification and SRB hodograph

A climatic classification of the measurement site can be done following the method

proposed by Smith et al. (2002), based on the relationship between the SRB and the

region’s climate. The authors developed a set of parameters, such as mean net Lw flux and

Fig. 12. Annual trajectory of monthly mean values of Swn and �Lwn at the surface: December (black circle) and

November (black square).

C. Galli et al. / Atmospheric Research 72 (2004) 329–351 349

range of net Sw flux, which discriminate various climate classes on the basis of the surface

radiation.

According to the classification criteria of land regions, SPC falls in the temperate class

with annual mean Lwn (changed in sign) and Swn fluxes equal to 55 and 127 W m� 2,

respectively.

The yearly cycle of net Lw vs. Swn (SRB hodograph) for SPC is shown in Fig. 12. The

black circle corresponds to the month of December and the black square to November. The

points below the line Swn =�Lwn indicate a positive value of net radiation and negative

above the line.

Note that all points stay in the warming region of the graph. On a monthly mean, the

heat storage for land surfaces is small, so that the surplus is transferred to the atmosphere

as sensible or latent heat (Smith et al., 2002). The energetic availability is low in winter

months and increases in spring and summer, but it is always positive.

Finally, in spite of the cooling effect of clouds, SPC is always characterized by a

monthly energy surplus available for surface–atmosphere energetic exchanges.

5. Conclusions

The present study is based on 1 year of continuous measurements of the four surface

radiation components (Sw and Lw, incoming and outgoing radiation fluxes) at a mid-

latitude site of Northern Italy. The aim of the work was to investigate the effects of clouds

on surface radiation budget components using cloud fraction and cloud type to evaluate

cloud radiative forcing.

Results showed that in the period between 1 December 2001 and 30 November 2002

the net Sw balance always decreases and the net Lw balance always increases with

increasing cloud cover. The net radiation available at the surface decreases with increasing

N in all seasons except in winter, in which this dependence is negligible.

The analysis of the cloud radiative forcing indicates that all seasons were characterized

by a net cooling of the surface except winter, where clouds seem to have no effects on the

surface warming or cooling. Taking into account the dependence on solar radiation cycle,

an intercomparison between the retrieved cloud types seems to indicate that the effect of

stratus was a slight cooling whereas that of cumulus clouds was a stronger cooling of the

surface. On the contrary, cirrus clouds seem to have slight warming effect on the surface.

The annual trajectory of monthly mean values of Sw and Lw radiation balances showed

that the measurement site falls within the temperate class and that, in spite of the cooling

effect of clouds, SPC has an available monthly energy surplus during the year, which

enters the surface–atmosphere energy exchange mechanisms.

This kind of study can be very useful for the climatological monitoring of sites where

no observers are present and is relatively simple and straightforward since it is based on

the data collected by a simple four-components radiometer. The state-of-the-art radio-

meters are instruments characterized by good accuracy, minimum maintenance and

robustness, characteristics that recommend them also for extreme environmental con-

ditions. As regards to climate change issues, the simple observation of variations in the

slopes of the dependencies of SRBs on cloud fraction, the cloud type occurrence and/or the

C. Galli et al. / Atmospheric Research 72 (2004) 329–351350

Sw or LwCRF changes can be used as an early advice, because changes in cloud radiative

forcing reflect changes in surface temperature, surface albedo and cloud properties (Wang

and Key, 2003). The SRB hodograph is instrumental for the detection of SRB variations

since it allows for a direct comparison between cycles of consecutive years.

Acknowledgements

This work has been supported by Italian Space Agency (ASI) under the grant ‘‘Sinergia

GERB-SEVIRI nello studio del bilancio radiativo a scala regionale e locale’’.

Meteorological data from 1987 to 1991 were kindly supplied by the Emilia Romagna

Meteorological Service (ARPA-SMR, Bologna, Italy), whose personnel also contributed

essential data processing labor. Special thanks go to the CNR colleagues Ubaldo Bonafe,

Francescopiero Calzolari and Giuliano Trivellone for their invaluable help in solving

technical problems at the SPC site.

Finally, the authors wish to thank the two anonymous reviewers for their valuable

comments and suggestions.

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