interannual variabilty of the earth's radiation budget: some regional studies

INTERNATIONAL JOURNAL OF CLIMATOLOGY, VOL. 17, 929±951 (1997)

INTERANNUAL VARIABILITY OF THE EARTH'S RADIATION BUDGET:SOME REGIONAL STUDIES

MARK A. RINGER*

Department of Meteorology, University of Reading, UK

Received 5 March 1996Accepted 26 November 1996

ABSTRACT

A detailed study is presented of the interannual variability of the Earth's radiation budget (ERB) at the top of the atmospherein three equatorial regions: the western Paci®c over Indonesia, the eastern Paci®c off the west coast of South America and theeastern Atlantic off the west coast of Africa. The objective is to try and understand the cloud, surface and ERB interactionsthat occur in these areas, each of which shows a large interannual signal in the net radiation balance. The period studiedextends from March 1979 through to April 1985, for which coincident ERB and cloud data are available from the Nimbus-7ERB and Cloud Climatology data sets. This period encompasses the exceptional 1982±1983 ENSO as well as the 1984Atlantic warming event. Additionally, sea-surface temperature data from the Global Ocean Surface Temperature Atlas(GOSTA) data set are used in combination with the ERB data to partition the outgoing longwave radiation into surfaceemission and greenhouse-effect components. The methodology involves examination of the time series of anomalies in all ofthese quantities together with the formulation of statistical relationships amongst them. These methods ®rstly allow theshortwave (SW) and longwave (LW) components of the ERB anomalies to be identi®ed. The SW and LW anomalies arethemselves seen to be made up of different components and, much like the net balance, are often the residuals of twocompeting effects. This is exempli®ed in the eastern Paci®c and Atlantic regions, which both experienced periods ofanomalously high SSTs. In such areas the SW anomaly can arise due to an imbalance between the cooling effect of increasedhigh cloudiness and warming due to reduction of low level cloud, whilst the LW anomaly is composed of warming due to theincrease in high cloud amount and cooling due to increased surface emission. The result is that anomalies in the net balanceterm, which is ultimately the most important with regard to climate variability, are seen to arise as a result of complexinteractions between the atmosphere, cloudiness and surface. # 1997 by the Royal Meteorological Society. Int. J. Climatol.,17: 929±951 (1997).

(No. of Figures: 8 No. of Tables: 11 No. of Refs: 31)

KEY WORDS: climate variability; satellite data; cloud±climate interaction; ENSO; Paci®c Ocean; equatorial; Atlantic Ocean; equatorial.

1. INTRODUCTION

The availability of radiometric measurements from successive generations of satellite-based experiments over the

last 40 years has enabled the variability of the Earth's radiation budget (ERB) at the top of the atmosphere (TOA)

on nearly all time-scales of climatological interest to be assessed (e.g. House et al., 1986). However, the often

short-lived nature of individual experiments has restricted the number of studies of the interannual variations of

the ERB. The multi-year data sets now available from the Nimbus-7 ERB and ERBE experiments allow reliable

estimates of the interannual variability of the radiation budget to be made. Moreover, given that clouds play such

an important role in modulating the radiation budget (Arking, 1990; Harrison et al., 1990), the development of

the Nimbus-7 and ISCCP cloud climatologies, coincident with those from Nimbus-7 ERB and ERBE

respectively, now provides an opportunity to study the effects of interannual cloudiness variations on the

radiation budget. Previous studies of interannual ERB variability that were performed (e.g. Ohring and Clapp,

1980; Smith and Smith, 1987) had to assume, rather than demonstrate, that these variations arose in association

with those in cloudiness.

CCC 0899-8418/97/090929-23 $17.50

# 1997 by the Royal Meteorological Society

*Present af®liation: Laboratoire de MeÂteÂorologie Dynamique du CNRS, Ecole Polytechnique, F-91128 Palaiseau Cedex, France.

The interannual variability of the ERB over the Nimbus-7 observational period has been presented by Ardanuy

et al. (1992) and Ringer (1994), who also calculated the interannual variability of the ERB and cloudiness for the

ERBE±ISCCP period. Ringer and Shine (1997) have estimated the sensitivity of the ERB to interannual changes

in cloudiness using both pairs of data sets. The aim of the present work is to try and understand the processes (in

particular the cloud and surface in¯uences) that lead to the large interannual variations in the TOA radiation

budget observed in certain particular regions of the globe. The study of these regions is based on the analysis of

radiation budget, cloud and sea-surface temperature data sets over the period from April 1979 to March 1985 over

which the Nimbus-7 ERB and cloud data sets are coincident.

The analysis begins with the identi®cation of three tropical regions shown to demonstrate a large interannual

signal in the TOA net radiation (NET), i.e.

R � S ÿ F �1�where S is the absorbed solar radiation (ASR) and F is the outgoing longwave radiation (OLR). An examination

of the annual mean ERB parameters in these regions is then followed by a detailed study of the monthly mean

time series of anomalies in the ERB, cloud parameters, SST and the greenhouse effect (de®ned below). This is

followed by the formulation of relationships between the different parameters leading to the estimation of local

sensitivity parameters.

As well as cloudiness variations, changes in SST are also of fundamental importance in relation to long-period

atmospheric phenomena and consequently to ERB and cloud ¯uctuations on these time-scales. This is

exempli®ed during El NinÄo ± Southern Oscillation (ENSO) events, when, in the central equatorial Paci®c, for

example, anomalously high SSTs are associated with anomalous convective activity, leading to the formation of

high cloud and consequent longwave warming of the earth±atmosphere system, which is then observable as a

negative top-of-the-atmosphere OLR anomaly. The greenhouse effect, G, as de®ned by Raval and Ramanathan

(1989), is given by

G � E ÿ F �2�where E is the surface longwave emission and F is the TOA OLR, for the particular region of interest. When the

surface emissivity is unity (an approximation generally applied to the open oceans) and the surface is regarded as

a black body, G can written as

G � sT4s ÿ F �3�

where s is the Stefan±Boltzman constant and Ts is the SST. The value of G thus represents the amount of

longwave radiation emitted from the surface that is absorbed by clouds and atmospheric constituents such as

water vapour. Equation (2) also shows how the OLR can be thought of as comprising a purely surface component

and an atmosphere-cloud component.

2. DESCRIPTION OF DATA SETS

The Nimbus-7 ERB data are taken from the wide-®eld-of-view (WFOV) instrument. The full details of the

experiment are described in Jacobowitz et al. (1984). The data sets used here are described in Bess and Smith

(1987) and Smith et al. (1990) and consist of monthly mean values of the shortwave and longwave components of

the ERB on a global, 5� latitude by 5� longitude grid.

The Nimbus-7 cloud data are taken from the Nimbus-7 Cloud Climatology described by Stowe et al. (1988).

The cloud coverage is derived from THIR 11�5mm radiances and TOMS 0�37 mm re¯ectivities. Estimates are

provided of the total cloud amount (TCA) and the fractional low (below 2 km), middle (between 2 and 7 km) and

high (above 7 km) level cloud cover. The monthly mean cloudiness measurements were spatially interpolated on

to the same regular grid as the ERB measurements from their original, equal-area grid format.

These two data sets are coincident over the 6-year period extending from April 1979 to March 1985. It is

important to note that the derivation of the Nimbus-7 Cloud Climatology did not use data from the ERB

instruments, so the two data sets can be considered as independent.

930 M. RINGER

The SST data are taken from the Global Ocean Surface Temperature Atlas (GOSTA) produced by Bottomley

et al. (1990). This data set provides monthly mean SSTs, also on a 5� latitude by 5� longitude grid, which are

based on the Meteorological Of®ce Marine Data Bank coupled with the Consolidated Data Set of the US Fleet

Numerical Oceanography Center. In this study, data coincident with the Nimbus-7 observation period is utilized,

although the SST data set itself extends over forty years, beginning in 1951.

3. ANALYSIS OF ANNUAL MEAN QUANTITIES

3.1. Identi®cation of regions

The standard deviation of the annual mean TOA radiation budget and total cloud amount as derived from the 6

years of coincident Nimbus-7 data sets are presented in Figures 1 and 2 respectively. The shortwave and

longwave ERB components of this statistic are seen to be dominated by maxima in the tropical Paci®c Ocean,

whereas the variability of the NET balance term highlights several regions of large variability where the signal in

the ASR and OLR appears to be relatively small. The standard deviation of the annual mean total cloud amount is

largest over the equatorial Paci®c, with the maxima corresponding to those in either the ASR, OLR, or both. It is

also worth noting that the maxima over the eastern Paci®c and Indonesia correspond to the maxima in the NET

variability in these regions, whereas no such maxima is seen off the west coast of Africa, for example. The

analyses presented here begin with a more detailed look at the annual mean radiation budget in these regions of

high annual mean NET variability.

The three speci®c regions chosen for this particular analysis, together with their annual mean variability

characteristics, are shown in Table I, the names being chosen for ease of reference. These three areas were

identi®ed previously by Randel and Vonder Haar (1990), who presented time series of the ERB and cloud amount

anomalies at three selected Nimbus-7 grid points falling within the regions selected here. The present paper can

thus be considered as an extension of their study, with the emphasis being to understand the physical mechanisms

that lead to the observed variability. In the present analysis, spatial means over regions of 15� by 15� will be

considered. If the uncertainties in the original monthly mean Nimbus-7 ASR and OLR are considered to be

around 10 Wmÿ2 (e.g. Cess, 1990), then the uncertainty in the regional (i.e. 15� by 15�), annual mean NET is

approximately 1�4 Wmÿ2. The values of the standard deviation of the annual mean NET in these three regions,

which reach values in excess of 5 Wmÿ2, can thus be considered to be robust and worthy of investigation.

3.2. Climate variability during the Nimbus-7 observation period

The observed ERB variability in these regions is related to the major climatic events which occurred during the

period encompassed by the Nimbus-7 data. A brief summary is presented here. Over this period the most

important large-scale climatic episode was the well-documented 1982±1983 ENSO event (see En®eld (1989) for

a complete description). This particular ENSO event was unusual owing to both its unprecedented intensity and

timing (Cane, 1983; Gill and Rasmusson, 1983; Philander and Rasmusson, 1985). Over the tropical oceans, large-

scale convection generally tends to occur when the SST exceeds around 300 K (e.g. Graham and Barnet, 1987).

This is the norm over the western Paci®c but is certainly not usual in the Paci®c eastward of the dateline. During

an ENSO episode the region of highest SSTs expands eastwards along the Equator from the western Paci®c

`warm pool'. In this respect, the 1982±1983 event was typical. In early 1983, as anomalous convective activity

moved eastwards following the highest SSTs, the trade winds began to diminish. Although this eastward

progression was typical, the collapse of the winds was unprecedented. En®eld (1989) suggests that this was

probably due to the unusual timing of the 1982±1983 anomalies. In the so-called `canonical' ENSO event these

usually occur during the Southern Hemisphere summer, when the SSTs in the eastern Paci®c are at their annual

minimum. Thus, the SSTs themselves do not reach those necessary to `trigger' convective activity, despite the

fact that the SST anomalies are large. However, in the early part of 1983 the SSTs were at the peak of their annual

cycle and the large anomalies consequently increased them over and above 300 K. This seasonal dependence of

the atmospheric response to SST anomalies has been discussed in relation to the 1986±1987 event by Kousky and

Leetmaa (1989) and in the context of GCM simulations of ENSO by Lau (1985).

VARIABILITY OF EARTH'S RADIATION BUDGET 931

The 1982±1983 `warm' ENSO phase in the tropical Paci®c collapsed in mid-1983 and subsequently reversed

into a `cold' ENSO phase (Horel et al., 1986), with similarly anomalous, but opposite, conditions occurring

during 1984: lower than normal SSTs in the central equatorial Paci®c and increased SSTs in the tropical Atlantic;

strengthened Paci®c trade winds and weakened Atlantic trades; dry conditions over the central Paci®c and

anomalously high rainfall over Indonesia and the north-east of Brazil. The anomalously high SSTs in the tropical

Atlantic during 1984 were associated with an unusually southward displacement of the ITCZ at this time

Figure 1. Interannual variability of ASR, OLR, and NET (Wmÿ2) derived from the Nimbus-7 ERB WFOV data

932 M. RINGER

(Philander, 1986). This type of displacement of the ITCZ also occurs during a warm ENSO phase in the Paci®c.

The primary difference between these Atlantic warming events, such as occured in 1984, and an ENSO event in

the Paci®c is that the Atlantic events are normally not accompanied by an eastward migration of convective

activity, i.e. the dominant changes are meridional rather than zonal.

In what follows it will be seen that the interannual variability of the net radiation in the three chosen regions is

related to these climatic phenomena: the reduced convective activity over Indonesia associated with the 1982±

1983 ENSO; the unusual migration of convection into the Peru region in 1983; the displacement of the ITCZ into

the Angola region during the 1984 Atlantic warming event.

3.3. Time series of annual mean ERB parameters

Figure 3 shows the 6-year time series from 1979 to 1984 of the annual mean ERB anomalies for these regions.

In view of the dipole-like structure of the ERB variability in the tropical Paci®c seen in Figure 1, these parameters

are also shown for a region in the central Paci®c Ocean centred to the east of the dateline at (215�E, 5�S). The

annual mean anomalies for each year are de®ned as the difference between the annual mean value for that year

and the long-term (i.e. 6-year) mean value of the parameter in question.

3.3.1. Indonesia and the central Paci®c. Figure 3(a and b) show the annual mean ERB anomalies for the

Indonesian and central Paci®c regions respectively. Clearly, the largest perturbations to the annual mean values

of these quantities occurred in the ENSO dominated year of 1982 (i.e. the mean over April 1982±March 1983). In

the Indonesian region, the 1982 annual mean departures of the ASR and OLR are approximately 8 Wmÿ2 and

18 Wmÿ2 respectively, leading to a NET perturbation of around ÿ10 Wmÿ2, which is then the dominant

contribution to the NET variability. The magnitude of the anomalies in other years is less than 3 Wmÿ2. In the

central Paci®c the anomalies in the annual mean ASR, OLR and NET in 1982 are of similar magnitude, but of

opposite sign, to those over the Indonesian region, as might have been expected given the eastward migration of

Table I. Regions selected on the basis of annual mean NETvariability. The standard deviations of the annual mean ASR,OLR and NET are given in units of Wmÿ2. The regions are 15�

by 15�, centred on the locations indicated.

Region Location ASR OLR NET

Indonesia 5�S, 125�E 4�04 8�90 4�95Angola 5�S, 5�E 2�91 3�23 5�71Peru 5�S, 275�E 1�41 4�97 5�72

Figure 2. Interannual variability of the total cloud amount (per cent) derived from the Nimbus-7 Cloud Climatology


Figure 3. Time series of annual mean ERB anomalies for the (a) Indonesia, (b) central Paci®c, (c) Peru and (d) Angola regions

934 M. RINGER

convection into this region during the ENSO. The changing cloudiness patterns over tropical Paci®c regions such

as these are often described as giving rise to large positively correlated variations in the ASR and OLR, leading to

a much smaller residual perturbation in the NET (e.g. Kiehl, 1994). On the basis of this annual mean ERB

analysis this would appear to be somewhat of an oversimpli®cation. The NET perturbations in both these regions

clearly arise as a result of the perturbations in the OLR being roughly twice the magnitude of those in the ASR.

This is also re¯ected in the interannual standard deviations shown in Table I. This larger variability of the OLR

could be related to perturbations in the cloud height, the SST or atmospheric water vapour accompanying the

large-scale total cloud amount perturbations in these areas.

3.3.2. Peru. Figure 3(c) shows the 6-year time series of the annual mean ASR, OLR and NET departures for

the Peru region, to the west of South America. The largest perturbation to the NET, of 10�5 Wmÿ2, occurs in

1983 and arises primarily as a result of a perturbation of ÿ9�8 Wmÿ2 to the OLR, the ASR perturbation being

less than 1 Wmÿ2. As the annual means are averages from April to March of the following year, this OLR

perturbation may perhaps be attributed to the extreme eastern migration of anomalous convection during the

1982±1983 ENSO event (En®eld, 1989). The very much smaller ASR anomaly, however, suggests that the

situation might be less straightforward than a simple reduction or increase in one particular type of cloud.

3.3.3. Angola. Figure 3(d) shows the annual mean ERB departures for the Angola region to the west of Africa.

Here a perturbation to the NET of 10 Wmÿ2 occurs in 1984, which is comparable to that noted in 1982 over the

Indonesian region and in 1983 over the Peru region. This arises due to the re-enforcement of anomalies in the

ASR and OLR of 5�4 Wmÿ2 and ÿ4�6 Wmÿ2 respectively, which are of opposite sign. Again, if cloudiness

variations are responsible for the NET variation in this region it does not appear that a simple increase (or

decrease) of the total cloud coverage (or in a dominant cloud type) is responsible. In fact, the annual mean total

cloud amount anomaly is less than 1 per cent.

3.3.4. Summary. A summary of the ERB, total cloud amount and SST annual mean anomalies for these three

regions for the years when they experienced large NET perturbations is presented in Table II.

Clearly, even though all three of these regions experienced years with large annual mean NET perturbations,

the physical processes leading to such behaviour appears to be different in each case. Furthermore, although it

might appear reasonable to suggest that the previously described major convective cloudiness variations arising

during the 1982±1983 ENSO event are responsible for the NET perturbations over Indonesia, such an apparently

straightforward explanation for the NET perturbations in the Peru and Angola regions does not seem applicable.

It has been noted, however, that both these areas experienced periods of large SST anomalies; during the 1982±

1983 El NinÄo event in the Peru region and during the 1984 Atlantic warming event in the Angola region. In the

following section an analysis of the monthly mean anomalies in the ERB, cloudiness, SST and greenhouse effect

is presented which seeks to more clearly identify the evolution of the monthly perturbations that lead to the

annual mean radiation budget anomalies described here.

Table II. Annual mean anomalies of the radiation budget (Wmÿ2), total cloudamount (per cent) and SST (K) for the three regions in the years when the

magnitude of the NET anomaly was largest.

Region NET ASR OLR TCA SST

Indonesia (1982) ÿ10�0 8�0 18�1 ÿ9�3 ÿ0�2Angola (1984) 10�0 5�4 ÿ4�6 0�6 1�3Peru (1983) 10�5 0�8 ÿ9�7 5�6 1�9


4. TIME SERIES OF MONTHLY MEAN ANOMALIES

4.1. Indonesia

Figure 4 shows the 6-year time series over the period March 1979 to February 1985 of the monthly mean

anomalies in the ERB, cloudiness, SST and greenhouse effect for the Indonesian region. To aid clarity of

presentation a 3-month running mean has been applied to these and all subsequent time series. Consider the ERB

anomaly time series ®rst (Figure 4(a)). These are dominated by the ENSO related perturbations over the period

from June 1982 to March 1983. Throughout this period OLR anomalies of greater than 10 Wmÿ2 are maintained,

with maxima in excess of 30 Wmÿ2 during December 1982 and January and February 1983. At the start of this

period the ASR and OLR anomalies are roughly equal. From September 1982, however, the OLR anomalies

exceed those in the ASR by 10 Wmÿ2 or more, leading to the sustained period of NET anomalies of around

10 Wmÿ2. Outside of this period the NET anomalies only rarely exceed a magnitude of 5 Wmÿ2. Examination of

the entire time series also shows that the ASR and OLR anomalies are almost always of the same sign.

Figure 4(b) shows the time series of the Nimbus-7 derived total cloud amount for this region, and Figure 4(c)

those of the individual low, middle and high cloud amounts. The largest (negative) anomalies in TCA clearly

Figure 4. Time series of monthly mean anomalies in (a) the ERB, (b) total cloud amount, (c) high-, middle- and low-level cloud, (d) SST and(e) greenhouse effect for the Indonesia region. The SST anomaly is shown together with the SST annual cycle. The greenhouse effect

(GHE) anomaly is shown together with minus the OLR anomaly

936 M. RINGER

occur during the ENSO period, corresponding to the period of largest (positive) ASR and OLR anomalies. These

TCA anomalies are the result of anomalies in both middle-level and high-level cloudiness, the low-level cloud

anomaly being less than 5 per cent throughout the whole time period. The peak anomalies in the total and high

cloud amounts occur in January 1983, when the ASR and OLR anomalies also attain their maximum values. The

anomalies for this month represent decreases from 86 per cent to 53 per cent in TCA, 49 per cent to 21 per cent in

high cloud amount and 34 per cent to 25 per cent in middle-level cloud, compared with the mean values for the

three previous years. This corresponds to increases from 277 Wmÿ2 to 315 Wmÿ2 and 190 Wmÿ2 to 240 Wmÿ2

in the ASR and OLR respectively. Such decreases in middle- and high-level cloudiness result in the exposure of

lower, warmer layers of the atmosphere and thus leads to positive OLR anomalies. The exposure of the darker

ocean surface also leads to positive ASR anomalies, and although it often may be dif®cult to predict the resultant

NET anomaly (because these two effects lead to longwave and shortwave perturbations of comparable

magnitude), it seems clear that during the period of the extremely large ENSO related cloudiness perturbations of

the longwave effect dominated.

Figure 4(d) shows the time series of SST anomalies for this region. Also shown on this ®gure is the annual

cycle of the SST, calculated by simply subtracting the long-term mean SST, of 301�5 K, from each monthly mean

value. Immediately noticeable is the fact that the maximum (negative) SST anomalies, which occur during

August to November 1982, precede the maximum ASR, OLR and high-cloud anomalies by around 3 months. It is

also apparent that the largest NET anomalies occur both during the period when the magnitude of the SST

anomalies are greatest and also after they have they died away. In fact, the ASR, OLR and high-cloud-amount

anomalies attain their maximum values when the SST itself reaches its largest values (of around 302�5 K).

In an attempt to gauge the separate surface and cloud±atmosphere contributions to the OLR anomalies, the

time series of the all-sky greenhouse effect anomalies for this region is presented in Figure 4(e). These are shown

together with (minus) the OLR anomalies. From equation (2) it can be seen that anomalies in the OLR are related

to those in the surface emission and greenhouse effect by

DF � DE ÿ DG �4�Where the two time series are almost coincident the pure surface component to the OLR anomaly is negligible,

when they diverge signi®cantly the surface emission perturbation component is an important factor. The OLR

Figure 4. (continued )


anomaly will be zero when, for example, increased SSTs lead to increased surface emission and therefore

longwave cooling of the system, which is balanced by the resulting increased greenhouse trapping of clouds or

the atmosphere. Clearly, the greenhouse effect and OLR anomalies are of comparable magnitude throughout most

of this particular time period with the maximum differences reaching around 8 Wmÿ2 and occurring in the few

months, July to October 1982, when the SST anomaly was around ÿ1 K and the SST itself dropped below 300 K.

At the time of the maximum cloud amount anomalies, the OLR and greenhouse effect are almost equal, implying

that it is these cloudiness changes that directly determine the OLR perturbations.

4.2. Peru

Figure 5 shows the time series of ERB, cloud, SST and greenhouse-effect anomalies for the Peru region. Firstly

consider the ERB anomalies (Figure 5(a)). These time series, particularly those of the OLR and NET, are

dominated by anomalies occurring between March and September 1983. As with the Indonesian region, these

anomalies are presumed to be related to phenomena associated with the 1982±1983 ENSO event. In particular,

the highly unusual eastward migration of convective activity across the equatorial Paci®c and the extreme SST

anomalies that occurred in the eastern Paci®c (see below).

Throughout this 7-month period, the NET anomaly was greater than 10 Wmÿ2, reaching a maximum value of

20 Wmÿ2 in June 1983. What is especially noteworthy is the way in which these NET anomalies arise. The OLR

anomaly remains negative throughout the period, ranging between ÿ10 Wmÿ2 and ÿ22 Wmÿ2. The ASR

anomalies, however, display a far different behaviour, being negative in the ®rst half of the period, approximately

zero in June 1983 and positive for the remainder of the period. Thus, over these 7 months the large positive NET

anomalies in this region arise for three different reasons: incomplete cancellation between SW and LW anomalies

of the same sign (March±May); almost purely due to LW variability (June 1983); re-enforcement of SW and LW

anomalies of opposite sign (July±September 1983). The implication is thus of three different physical

mechanisms, each of which lead to comparable increases in the top of the atmosphere NET heating. The possible

nature of these mechanisms is explored below. Examination of the complete time series for this region also shows

that the SW and LW anomalies can be either of the same or opposite sign, with the NET anomalies arising due to

cancellation or re-enforcement accordingly. In particular, note the period of negative anomalies between July and

December 1979, when the SW and LW anomalies were of opposite sign.

Next consider the cloud amount anomalies for the Peru region, shown in Figure 5(b) (total cloud amount) and

Figure 5(c) (high, middle and low cloud), focusing particularly on the period of large NET anomalies. The TCA

anomaly shows a steady increase from September 1982 to April 1983, when it reaches a maximum value of

around 20 per cent. It then declines much more rapidly over the next 3 months, becoming negative in August

1983. The months of highest TCA anomalies are also those of maximum high- and middle-level cloud anomalies

and correspond to the maximum (negative) OLR perturbations. The ASR anomalies at this time are, as already

noted, much smaller than those in the OLR. During the period of the largest NET anomalies, the positive high-

and middle-level cloud anomalies on their own would lead to negative ASR anomalies. It can, however, also be

seen that negative low-cloud anomalies also occur during this period, reaching their maximum values of around

12 per cent in June, July and August 1983. These low-cloud anomalies by themselves would lead to positive ASR

anomalies. The observed ASR anomalies are therefore the residual of these two components of opposite sign,

which in June 1983, when the ASR anomaly is close to zero, appear to balance almost completely. Prior to this

month, as the high- and middle-level cloud anomalies are increasing, their effect on the ASR dominates.

Subsequently, the high- and mid-cloud anomalies decline to zero, the low-cloud anomalies remain negative and

the ASR anomalies change sign. Such low-cloud ¯uctuations have a very much smaller effect on the OLR and the

OLR anomalies closely follow those in high-cloud amount. The evolution of the sustained period of the large

NET anomalies that occur in this region during 1983 thus arise as a result of the imbalance of two quite separate

ERB±cloud interactions in the LW and SW spectral regions. It is also important to note that the relationship

between these NET perturbations and those in cloudiness could not simply be deduced from the total cloud

amount alone and that height-resolved cloud data are essential.

The SST anomalies and annual cycle for this region are shown in Figure 5(d). The unusual timing of the largest

SST anomalies during the 1982±1983 ENSO event has already been noted. Its effects can clearly be seen in these

938 M. RINGER

Figure 5. As Figure 4 but for the Peru region


time series. The extremely large SST anomalies that occur at the peak of the annual cycle in the ®rst half of 1983

lead to SSTs approaching 301 K. It can be seen that the maximum high- and middle-level (and also total) cloud

amount anomalies occur during February to May 1983, when the SSTs reach in excess of 300 K. In the period

from July 1982 to January 1983 the SST anomaly increases steadily from zero up to 3 K. Over this period there

are positive middle-level cloud and negative low-cloud anomalies, with the high-cloud anomalies remaining

close to zero. The positive anomalies in high cloud begin when the SST exceeds 300 K. In this respect, it also

noticeable that the high-cloud anomalies experience a rapid decrease when the SST drops below 298 K in August

1983, whereas the SST anomalies are still above 2 K at this time.

The greenhouse effect and OLR anomaly time series are shown in Figure 5(e). Consider speci®cally the period

from August 1982 to September 1983. The separation of the surface and cloud±atmosphere components of the

OLR anomalies, together with examination of the cloud-amount anomalies, demonstrates how the anomalous

appearance of high cloud in this region leads to net longwave warming of the surface±atmosphere system. The

OLR anomalies are in fact the residual resulting from the imbalance between warming by increased cloudiness

and cooling due to increased surface emission, which itself depends on both the SST and the SST anomaly. From

August 1982 to January 1983, when the high-cloud-amount anomalies are negligible, these two effects are of

comparable magnitude and the OLR anomalies are consequently small. However, as the amount of high cloud

increases to its maximum value its increased warming effect, compared with that of lower level cloudiness,

comes to dominate that of the surface cooling resulting in longwave warming of the system as a whole.

4.3. Angola

Figure 6 shows the ERB, cloud, SST and greenhouse-effect anomaly time series for the Angola region.

Consider the ERB time series ®rst (Figure 6(a)). Of particular interest is the period of sustained positive NET

anomalies of greater than 10 Wmÿ2 extending from March to December 1984. The evolution of the ASR and

OLR anomalies that lead to these NET anomalies is again interesting. At the beginning of this period, large

negative OLR anomalies are uncompensated in the ASR. Subsequently, the magnitude of the OLR anomalies

decreases (remaining negative) and stabilizes at around 5 Wmÿ2, whereas the positive anomalies in the ASR

increase to a maximum in August 1984, when the maximum NET anomaly occurs. The ASR anomalies then start

to decrease but large NET anomalies are maintained because those in the OLR are of opposite sign. Aside from

this period, NET anomalies of around 10 Wmÿ2 occur in 1982 (due largely to ASR ¯uctuations, although re-

enforced by a small LW contribution), whereas at the beginning of 1983 the ASR and OLR anomalies are of the

same sign and comparable magnitude, leading to minimal perturbations to the NET.

Figure 6(b) and (c) shows the cloud amount anomaly time series for this region. In general it can be seen that

the high-cloud variations are very small and the TCA anomalies are primarily a result of those in middle- and

low-level cloud amounts. This results in largely uncompensated ASR anomalies which then comprise the major

part of those in the NET (e.g. April±September 1982). During the period of largest NET anomalies (March±

December 1984), anomalies in low-, middle- and high-cloud amount occur. The combined effect of these

anomalies results in anomalies in the total cloud amount, which are at ®rst positive (March±May), then negative

(June±September) and ®nally close to zero (October±December). Noting that the ASR (positive), OLR (negative)

and NET (positive) anomalies each remain of the same sign throughout this period, it is again clear that

information on the vertical distribution of cloud is essential for understanding the ERB variations in this region.

As with the Peru region, the evolution of the LW and SW components of these NET anomalies appears to result

from separate high and low cloud interactions respectively. The maximum (negative) OLR anomalies occur in

March, April and May 1984 and correspond to those in the high-cloud amount, with the magnitude of the OLR

anomalies then declining as the high cloud anomalies decrease. This is in contrast to the ASR anomaly, which is

actually positive at the time of high cloud maximum and then proceeds to increase, presumably in response to the

increasing (negative) low-cloud anomalies. Again it can be seen that there are two opposite in¯uences on the

ASR due to increased high cloud and reduced low cloud (which are in fact augmented in turn by the middle-level

cloud anomalies), with the low-cloud effect appearing to be greater.

Figure 6(d) shows the SST anomalies and annual cycle for this region. Firstly, it is clear that the largest high-

cloud anomalies occur when the SST is at the peak of its annual cycle and the SST anomaly (of around 1 K) is

940 M. RINGER

Figure 6. As Figure 4 but for the Angola region


such as to push the SST itself over 300 K. Following this period of maximum SSTs, the SST anomaly continues

to increase, peaking at around 2 K in July 1984. This corresponds to the minimum in the annual SST cycle and

results in the SST only increasing to approximately 297 K. Although not appearing suf®cient to trigger the

formation of anomalous high cloud, these enhanced SSTs coincide with the period of maximum low-cloud

anomalies.

The greenhouse effect and OLR anomalies for the Angola region are presented in Figure 6(e). A similar

balance between cloud and surface effects to that described over the Peru region is here in evidence during 1984.

From March to May 1984, the high-cloud anomalies result in longwave warming, which dominates the cooling

due to the increased SSTs and positive SST anomalies. However, as these high-cloud anomalies die away, the

continuing SST anomalies (although now combined with lower SSTs) result in positive surface emission

anomalies, which come closer to balancing the longwave warming due to the cloud perturbations. Also worthy of

remark at this time is the apparently much larger effect of small high-cloud anomalies on the OLR, compared

with that of both larger low-cloud anomalies of opposite sign and of increased surface emission.

4.4. Discussion

The preceding examination of the ERB, cloud, SST and greenhouse-effect time series over the period from

March 1979 to February 1985 for these three regions has demonstrated many interesting features with regard to

ERB±cloud±surface interactions. The initial reason for highlighting the variability in these particular regions was

the fact that they each showed a large interannual signal in the NET of comparable magnitude. On close

examination, however, the physical reasons for these NET anomalies was seen to vary quite markedly from

region to region and also within the same region at different times.

Initial analysis of the SW and LW components shows that these NET ¯uctuations can arise due either to

residual imbalances between SW and LW anomalies of the same sign, re-enforcement of SW and LW anomalies

of opposite sign or as a result of anomalies occurring in one component but not in the other. Although

intrinsically interesting in itself, this classi®cation of the NET anomalies does not constitute a physical

explanation. This requires the analysis of cloud and surface parameters coincident with the ERB data.

The analysis of the Nimbus-7 Cloud Climatology data together with the ERB data demonstrated how such

perturbations can arise due to ¯uctuations in total cloud amount and also in the cloud amounts at different

altitudes. Several interesting phenomena were noted. When high-cloud ¯uctuations occur due to variations in

convective activity, their effect on the NET depends primarily on the balance between ASR and OLR anomalies

of the same sign. This imbalance usually arises because the longwave effect is greater than that of the shortwave

effect. When ¯uctuations to the amounts of cloud at different vertical levels occur simultaneously the situation is

more complex. It is possible that the SW and LW anomalies can be decoupled such that the ASR anomalies

follow those in low-level cloudiness and the OLR anomalies follow those in the high-cloud amount.

By calculating the all-sky greenhouse effect for these oceanic regions it is also possible to separate the OLR

perturbations into a component arising solely due to SST changes and one which is due to the absorption of LW

radiation by both clouds and the atmosphere. Analysis of these parameters shows that the OLR anomalies

themselves are often the residual imbalance of surface and non-surface contributions. For example, when SST

anomalies lead to SSTs high enough to induce anomalous deep convective cloud, there is a balance between the

warming effect of the anomalous high cloud and the cooling effect of increased surface emission. Whether or not

one of these effects necessarily limits the other has become a subject of much current debate (Ramanathan and

Collins, 1991; Wallace, 1992).

5. STATISTICAL RELATIONSHIPS BETWEEN ERB, CLOUD AND SST ANOMALIES

In Ringer and Shine (1997) global distributions of the sensitivity of the ERB to variations in cloudiness were

estimated from linear regression relationships formed between ERB and cloud amount anomalies. Here, similar

methods are used to study the relationships amongst the ERB, cloudiness and SST variations in the regions whose

anomaly time series were described in detail in the previous section.

942 M. RINGER

5.1. ERB and total cloud amount

The examination of the anomaly time series for the Indonesian region suggested that a good, at least

qualitative, relationship existed between the ASR and OLR anomalies and those in total cloud coverage. In the

Peru and Angola regions such relationships were not clearly apparent. The quantitative nature of these

relationships between ERB and cloud anomalies can be examined from regression analyses, which in effect lead

to the estimation of regional ERB sensitivities to total cloud amount.

Figure 7 shows scatter diagrams of ASR, OLR and NET anomalies against those in total cloud amount (shown

in the time series of Figure 4(a and b), derived from the two Nimbus-7 data sets for the Indonesian region. Also

shown is the scatter plot of the ASR anomalies against the OLR anomalies. The solid line represents the least-

squares linear regression ®t to the data. The regression parameters are summarized in Table III. Note that the

slope shown for the regression of ASR against OLR anomalies is the mean of the lower and upper bound

estimates. These are the slope of the regression line with the OLR anomalies taken as the independent variable

and the reciprocal of the slope when the ASR anomalies are assumed to be the independent variable. The

regressions of ERB against cloud-amount anomalies are motivated by an assumption that the ERB variations are

dependent primarily on those in the total cloud amount.

A clear linear relationship exists between the ASR and OLR anomalies and those in total cloud coverage. The

NET anomalies are seen to be much less well correlated with the cloud amount anomalies. It is probably worth

noting, however, that a moderate positive correlation does appear to exist. The Nimbus-7 data indicate that the

longwave warming effect of increases in cloudiness dominates that of their shortwave cooling effect in this

region. This can also be deduced from the regression of the ASR and OLR anomalies. As the ASR and OLR

Figure 7. Scatter diagrams of the (a) ASR, (b) OLR and (c) NET anomalies versus those in total cloud amount and (d) the ASR versus the OLRanomalies for the Indonesia region


anomalies are both strongly related to those in cloud amount, the slope of this line should also be a good

approximation for the ratio of the shortwave and longwave effects, i.e.

@S

@F� @S=@AC

@F=@AC

�5�

The ratio of the SW and LW sensitivities is 0�74 which, within the uncertainty limits, is comparable to the value

of 0�79 obtained from the ASR±OLR regression. The time series of Figure 4 show that during the 1982±1983

ENSO period, when the largest ERB and cloud amount anomalies occurred, the OLR anomalies were generally

larger than those in ASR, leading to the observed NET cooling at this time. To see if this period is signi®cantly

in¯uencing the regression analyses a `non-ENSO' set of anomalies was calculated for this region and the

regressions reformulated. In order to do this it is ®rst necessary to in some way de®ne the temporal limits of the

ENSO period. Examination of the ERB and cloudiness anomaly ®elds over the the Nimbus-7 period (Ringer,

1994), together with descriptions of the 1982±1983 event from the literature (e.g. Gill and Rasmusson, 1983) led

to the months from July 1982 to July 1983 being de®ned as the `ENSO period'. These 13 months were then

removed from the ERB and cloud time series, and a second set of anomalies was derived from the remaining 59

months of data. The regression calculations were repeated using these `non-ENSO' anomalies. The results are

shown in Table IV.

It can be seen that the implication is still one of NET warming due to increased cloudiness, with the magnitude

of the NET sensitivity being practically unchanged, although the ASR and OLR sensitivities are both reduced

slightly. The ratio of the ASR and OLR sensitivities in this case is also 0�74, which again agrees (within the

uncertainty bounds) with the value obtained from the ASR±OLR regression. The Nimbus-7 data sets thus imply

that in this region variations in total cloud amount are a good predictor of those that can be expected in the ASR

Table III. Regression parameters for thelinear least-squares ®ts shown in Figure 7.The slope of the regression line is in units ofWmÿ2 per 1 per cent anomaly in total cloudamount for the ERB±CLOUD regressionsand is unitless for the ASR±OLR regression.

r is the linear correlation coef®cient.

Regression Slope r

ASR±TCA ÿ0.87� 0.06 ÿ0.87OLR±TCA ÿ1.17� 0.06 ÿ0.91NET±TCA 0.29� 0.07 0.44ASR±OLR 0.79� 0.07 0.86

Table IV. Regression parameters for theIndonesian region derived for the Nimbus-7data sets from anomalies calculated from thetime series with the ENSO period, July

1982±July 1983, removed.

Regression Slope r

ASR±TCA ÿ0�76� 0�08 ÿ0�78OLR±TCA ÿ1�03� 0�09 ÿ0�85NET±TCA 0�27� 0�09 0�37ASR±OLR 0�82� 0�06 0�79

944 M. RINGER

and OLR, with the OLR effect generally being dominant. Furthermore, these conclusions remain unaltered

regardless of whether or not the large ENSO perturbations are included in the analysis. The ENSO anomalies of

1982±1983 might therefore be regarded as an ampli®cation of the general net cooling effect due to a decrease in

cloudiness in this region.

As might have been anticipated from the examination of the anomaly time series, such clear linear

relationships between the ERB and total cloud amount anomalies do not exist in the other two regions of high

NET interannual variability. As an example, Figure 8 shows scatter diagrams of the ERB and total cloud amount

anomalies, derived from the Nimbus-7 data sets, for the Peru region. The regression parameters calculated for this

region are summarized in Table V.

Inspection of the regression analyses reveals some interesting features. Firstly, the Nimbus-7 data seem to

indicate a net warming effect due to increases in the total cloudiness. This is somewhat unexpected because this is

an area which, in general, is dominated by low-level stratus cloud (Hanson, 1991; Klein and Hartmann, 1993).

Thus, it seems possible that the highly unusual ENSO related cloud and ERB anomalies of 1982±1983 are

signi®cantly in¯uencing the regressions in this case. With the ENSO period removed, the recalculated ASR and

OLR sensitivities are ÿ0�66 Wmÿ2 and ÿ0�24 Wmÿ2 respectively, which then imply a net cooling of

ÿ0�43 Wmÿ2. Unlike the Indonesian region, therefore, the inclusion or removal of the ENSO months alters the

inference of either net warming or cooling due to increases in cloud amount. The Angola region is also an area

that is generally dominated by low-level cloudiness and a comparison of the two regression analyses reveals a

similar picture to the Peru region, as shown in Table VI.

Figure 8. As Figure 7 but for the Peru region


5.2. ERB and multiple cloud types

It is now of interest to see if the use of the vertical distributions of cloudiness can provide improved regression

analyses in these regions. The time series analyses of the Nimbus-7 anomalies showed that cloud-type

information was particularly important when attempting to make qualitative inferences about the ERB anomalies

in both the Peru and Angola regions. It is also interesting to try and ascertain if this information can lead to useful

direct predictors of the NET anomalies, which are often poorly determined by the total cloud amount anomalies

alone. Using anomalies in high-, middle- and low-level cloudiness from the Nimbus-7 Cloud Climatology

multiple linear regression relationships of the form, e.g. for the OLR anomalies,

DF � aHDAH � aMDAM � aLDAL �6�were derived for each of these regions. Here DAH, DAM and DAL are the anomalies in high-, middle- and low-

cloud amount and the regression coef®cients aH, aM and aL are interpreted as the local sensitivities to each cloud

type.

One important limitation of this method concerns the nature of satellite-derived vertical cloud distributions. As

the satellite can only observe the highest cloud layer it is possible that variations in cloud amounts at lower levels

are due simply to the covering and uncovering of these cloud layers according to the presence or absence of high

cloudiness. Such correlations between the different layer cloud amounts have been noted by Weare (1992) and

Ringer (1994) for both seasonal and interannual variations in the Nimbus-7 cloud estimates. Similar correlations

were also noted by Ringer (1994) in the ISCCP data. Such a systematic relationship between the vertical cloud

amount variations means that the sensitivities derived from this method must be interpreted with caution because

the variables may not be truly independent. However, the time series shown in the preceding section seem to

indicate that the cloud-amount variations are consistent with those in the ERB in the three regions of interest here.

The utility of cloud-type information and of this multiple regression approach can be determined by

considering if it leads to an improvement in the amount of explained ERB variance compared with a regression

on the total cloud amount anomalies alone. Table VII summarizes this information for the three regions. Without

exception, the multiple regression using the vertical distribution of cloud leads to an increase in the amount of

ERB variance explained. There are, however, important differences depending on both the particular region and

ERB parameter in question. Furthermore, the importance of cloudiness at different levels also varies with these

things.

In the Indonesia region, high-cloud variations are the dominant in¯uence on those in the ASR and OLR. A

simple one-parameter regression on the high-cloud anomalies alone explains almost as much of the explained

Table V. Regression parameters for thelinear least-squares ®ts to the ERB and totalcloud amount anomalies for the Peru region.

Other details as Table III.

Regression Slope r

ASR±TCA ÿ0�37� 0�09 ÿ0�42OLR±TCA ÿ0�63� 0�10 ÿ0�61NET±TCA 0�26� 0�14 0�22

Table VI. Regression parameters for thelinear least-squares ®ts to the ERB and totalcloud amount anomalies for the Angola

region. Other details as Table III.

Regression Slope r

ASR±TCA ÿ0�55� 0�12 ÿ0�47OLR±TCA ÿ0�37� 0�10 ÿ0�42NET±TCA 0�18� 0�10 ÿ0�13

946 M. RINGER

variance as the full multiple regression and, interestingly, more than the regression on the total cloud amount. The

NET variations do not appear to be strongly related to any particular cloud type and the explained variance from

the multiple regression is only half that of either the ASR and OLR. The in¯uence of the ENSO period anomalies

in this region is also particularly apparent with regard to the NET, the direct determination of NET variations

from cloud-amount information appearing to be practically impossible unless the cloud anomalies are

exceptionally large.

The results obtained for the Peru and Angola regions are similar in many respects. In both cases there is a

dramatic increase in the amount of OLR and NET explained variance with the multiple regression. For the OLR

this is due primarily to the in¯uence of high cloud, whereas both the high- and low-cloud variations appear to be

important in¯uences on the NET. Comparison with the non-ENSO explained variances in the Peru region

suggests that the anomalous periods of high cloudiness associated with exceptional SSTs noted previously,

strongly in¯uence the regression formulations in these areas. The non-ENSO explained NET explained variance

in the Peru region still represents a signi®cant improvement on the total cloud amount regression. The ASR

multiple regressions for both these regions, although doubling the explained variance compared with that on total

cloud amount, do not lead to increases as large as those for the OLR and NET. It might reasonably be argued that

information on cloud height would be more useful in determining OLR rather than ASR variations because cloud

albedos (and consequently the ASR) are more dependent on the optical thickness rather than the height of the

cloud layer. The high correlations between the ASR and high-cloud anomalies are probably indicative of the

much greater occurrence of optically thick cloud associated with convective systems in that region.

The sensitivity parameters obtained from the multiple regressions in each of these regions are summarized in

Table VIII. These sensitivities are largely in agreement with the qualitative deductions from the study of the time

series in Section 4. For example, in the Indonesian region, variations in high cloud have a large effect on both the

ASR and OLR, with the net effect being a slight warming. In the Peru and Angola regions, high cloud has a very

strong greenhouse warming effect, which dominates its in¯uence on the NET. In these regions the large

shortwave cooling effect of both middle- and low-level cloudiness is apparent. This SW cooling dominates the

in¯uence of these cloud layers on the NET and opposes the LW warming of the high-cloud variations. Also note

the general qualitative agreement of the sensitivities in the Indonesian and Peru regions when the regressions are

calculated from the non-ENSO set of anomalies.

5.3. ERB, cloud and SST

Having established the coincidence of SST, cloud and resulting ERB anomalies it is of interest to see if any

quantitative relationships can be determined between the observed variations in both the radiation budget and

cloudiness and those in the SST. Firstly, Table IX shows the amount of variance of the anomalies in total cloud

Table VII. Comparison of the explained variance obtained from regressions of the Nimbus-7 ERBand cloud amount anomalies for the Indonesia, Peru and Angola regions. r2(DAH), r2(DAM),r2(DAL) and r2(DAC) are the explained variances (in per cent) obtained from linear regressionsagainst the high-, middle-, low- and total-cloud amount anomalies alone. r2(MUL) (also in percent) is that obtained from multiple linear regressions of the form of equation 4. For theIndonesian and Peru regions the numbers in parentheses are the explained variances for the

equivalent regressions with the non-ENSO sets of anomalies.

Region r2(DAH) r2(DAM) r2(DAL) r2(MUL) r2(DAC)

Indonesia ASR 87(68) 69(49) 29(3) 90(73) 76(61)OLR 89(73) 74(44) 43(5) 91(74) 83(72)NET 34(1) 32(0) 36(1) 14(4) 19(14)

Peru ASR 6(6) 5(14) 5(2) 44(54) 18(25)OLR 78(10) 20(7) 26(0) 84(36) 37(6)NET 44(0) 8(18) 33(0) 70(57) 5(8)

Angola ASR 1 15 16 45 22OLR 75 19 31 75 18NET 42 0 48 69 2


amount and the three cloud types that is explained by the SST anomalies in the three regions. It is understood that

the relationship between cloud cover is in reality very complex and depends strongly on the dynamics of the

particular region in question. This is probably re¯ected in the apparently weak relationship between the total

cloud amount and SST variations in all three regions. The Peru and Angola regions, however, which both

experienced times of large SST anomalies, show quite strong relationships between the variations in both low-

level and high-level cloudiness and those in SST. The sign of the correlation coef®cients indicates that increased

SSTs are associated with increased amounts of high cloud and decreases in low-level cloud in both these regions.

The anomaly time series for these regions clearly showed this to be the case at the times of large SST anomalies.

Using data from the Comprehensive Ocean±Atmosphere Data Set (COADS), both Hanson (1991) and Klein and

Hartmann (1993) have also suggested that above average SSTs are associated with reductions in the amount of

marine stratocumulus cloud that normally prevails in these areas. It was seen above that inclusion of high- and

low-cloud amount information greatly increased the amount of explained NET interannual variance in these two

regions. It might then be reasonable to suggest that this strong relationship between these cloud types and SST

variability could be manifested as a similarly strong relationship between the SST and NET variations in these

areas. This is, in fact, demonstrated in Table X, which shows the results of a regression of NET and SST

anomalies in the three regions.

In the Peru and Angola areas, these regression analyses, together with the examination of the anomaly time

series, suggest the following mechanisms by which anomalously high SSTs can lead to a net warming of the

atmosphere:

(i) reduction of low-level stratiform cloudiness, the consequent large SW warming effect of which dominates

the small LW cooling;

(ii) increases of high-level cloudiness, the greenhouse warming effect of which exceeds the SW coolingÐthis is

likely to occur when convection is induced by SSTs in excess of 300 K;

Table VIII. Comparison of the sensitivity parameters obtained from multiple regressionsof the Nimbus-7 ERB and cloud-amount anomalies for the Indonesia, Peru and Angolaregions. aH, aM and aL are the high-, mid- and low-cloud amount sensitivitiesrespectively, in units of Wmÿ2 per 1 per cent anomaly in cloud amount. For theIndonesian and Peru regions the numbers in parentheses are the sensitivity parameters

obtained from the equivalent regressions with the non-ENSO sets of anomalies.

Region aH aM aL

Indonesia ASR ÿ1�30 (ÿ1�13) ÿ0�38 (ÿ0�43) ÿ1�24 (ÿ1�08)OLR ÿ1�35 (ÿ1�34) ÿ0�74 (ÿ0�16) 0�21 (ÿ0�84)NET 0�05 (0�21) 0�37 (ÿ0�27) ÿ1�45 (ÿ0�24)

Peru ASR ÿ0�27 (ÿ0�49) ÿ0�48 (ÿ0�72) ÿ0�87 (ÿ1�11)OLR ÿ1�74 (ÿ2�51) 0�42 (0�47) 0�47 (0�33)NET 1�48 (2.02) ÿ0�89 (ÿ1�19) ÿ1�34 (ÿ1�45)

Angola ASR ÿ0�03 ÿ0�89 ÿ0�88OLR ÿ1�68 ÿ0�07 0�03NET 1�65 ÿ0�83 ÿ0�91

Table IX. Comparison of the explained variance (in percent) obtained from linear regressions of the Nimbus-7cloud anomalies and the GOSTA SST anomalies in the

three selected regions.

Region DAH DAM DAL DAC

Indonesia 18 35 11 20Angola 64 37 43 39Peru 40 0 59 10

948 M. RINGER

(iii) a combination of (i) and (ii) such as occurred in the Peru region during 1983 and in the Angola region during

1984.

The regression slopes obtained from these various analyses suggest the possibility of estimating the effect on

the TOA radiation budget due to SST increases and the consequent cloud perturbations, in effect a sort of

tentative cloud feedback estimate, in these two regions. For example, the NET anomaly arising due to high cloud

variations associated with 1 K SST increase can be estimated as

DR�AH� � DAH

DTS

DR

DAH

� ��7�

where the two terms on the right-hand side are the slopes derived from the regression analyses presented above.

The effect on the NET of the resulting low-cloud anomalies, DR(AL), can be similarly calculated. The results

obtained for the two regions are shown in Table XI. The NET perturbations are shown, together with their

dominant SW and LW contributions. Hanson (1991), using 40 years of COADS data from four marine

stratocumulus regions (two of which included the smaller regions studied here), estimated that a 1 K SST rise

would lead to an average 1�5 per cent decrease in stratocumulus cloud and a resulting ASR increase of

1�5 Wmÿ2, which, in fact, he assumed to be the sole contribution to the NET perturbation. The values shown in

Table XI each have an uncertainty of approximately 0�5 Wmÿ2 and the mean of the two regional DS(AL)

estimates is 2�3 Wmÿ2. There is thus agreement on the positive feedback effect of low-level cloudiness in such

areas. The results shown in Table XI also indicate the positive feedback effect associated with high-cloud

variations in these regions, which is primarily due to increased greenhouse warming.

6. CONCLUSIONS

This paper has presented a detailed examination of three tropical regions identi®ed as areas of large variability in

the top-of-the-atmosphere net radiation balance. It is important to study such regions because it is variations in

the net radiative balance that ultimately can be described as `climate variations'. These studies examined the time

series of the ERB and cloud-amount anomalies in these regions, identifying the periods when the largest NET

anomalies occurred. The anomalies in high-, middle- and low-level cloudiness suggest how the shortwave and

longwave components of the NET anomalies arise due to variations in different types of cloud. The cloudiness

variations themselves can be related to those in SST. The variations in SST also can be used to calculate those in

Table XI. The TOA radiative perturbations (Wmÿ2) arising dueto cloudiness changes associated with a 1 K SST increase in the

Peru and Angola regions.

Region DR(AH) DF(AH) DR(AL) DS(AL)

Peru 2�7 ÿ3�2 2�9 1�5Angola 2�8 ÿ2�9 3�2 3�1

Table X. Regression parameters for thelinear least squares ®ts to the Nimbus-7NET and the GOSTA SST anomalies forthe three selected regions. The slopes arein units of Wmÿ2 per 1 K increase in

SST.

Region Slope r2 (per cent)

Indonesia 4�13 10Angola 3�64 63Peru 7�07 71


the greenhouse effect, thus allowing the OLR anomalies to be broken down into their surface- and cloud-related

components. The time series analysis was augmented by estimating regional sensitivity parameters to cloud and

SST variations.

In the Indonesian region the largest NET variations arise principally due to the reduction of high cloud

associated with circulation changes during the 1982±1983 ENSO event. The NET variations are positive,

indicating the dominance of LW cooling over the SW warming resulting from the high-cloud amount reduction.

In the Peru and Angola regions the situation is more complex. Both regions experienced periods of anomalously

high SSTs, which lead to signi®cant variations of cloud at all levels in the atmosphere. In the Peru region, SST

anomalies in excess of 4 K lead to SSTs above 300 K during the 1982±1983 ENSO. This leads to anomalous

convective activity and high-level cloud and also to a considerable reduction in cloud at lower levels. These SST

and cloud variations have a complicated effect on the ERB. The ASR anomalies are seen to be the residual

between cooling due to increased high cloud and warming due to reduced low-level cloud, although the OLR

anomalies arise due to the imbalance between increased surface emission and the greenhouse warming effect of

high cloud. The situation in the Angola region during 1984 was similar, although the SST anomalies were

somewhat smaller.

Given the still relatively short length of available satellite data sets, interannual variability studies of the ERB

are probably best facilitated by regional studies of this kind. Similar methods could be used to study the period

covered by the ERBE and ISCCP data sets and also could be applied to output from extended GCM runs, so

enabling the observed and modelled variability to be compared.

ACKNOWLEDGMENTS

This work was supported by the Joint Environmental Programme of National Power and PowerGen. I would like

to thank Keith Shine for his advice, comments and suggestions during the course of this work.

REFERENCES

Ardanuy, P. E., Kyle, H. L. and Hoyt, D. 1992. `Global relationships between the earth's radiation budget, cloudiness, volcanic aerosols, andsurface temperature', J. Climate, 5, 1120±1139.

Arking, A. 1990. `The radiative effects of clouds and their impact on climate', Bull. Am. Meteorol. Soc., 71, 795±813.Bess, T. D. and Smith, G.L. 1987. Atlas of Wide-®eld-of-view Outgoing Longwave Radiation Derived from Nimbus-7 Earth Radiation Budget

Data Set Ð November 1978 to October 1985. NASA Reference Publication 1186.Bottomley, M., Folland, C. K., Hsuing, J., Newell, R. E. and Parker, D. E. 1990. Global Ocean Surface Temperature Analysis `GOSTA',

Meteorological Of®ce Technical Report, Bracknell.Cane, M. A. 1983. Òceanographic events during El NinÄo', Science, 222, 118±1195.Cess, R. D. 1990. Ìnterpretation of an 8-year record of Nimbus-7 wide-®eld-of-view infrared measurements', J. Geophys. Res., 95, 16 653±

16 657.En®eld, D. B. 1989. Èl NinÄo, past and present', Rev. Geophys., 27, 159±189.Gill, A. E. and Rasmusson, E. M. 1983. `The 1982±83 climate anomaly in the equatorial Paci®c', Nature, 306, 229±234.Graham, N. E. and Barnet, T. 1987. `Sea surface temperature, surface wind divergence and convection over tropical oceans', Science, 238,

657±659.Hanson, H. P. 1991. `Marine stratocumulus climatologies', Int. J. Climatol., 11, 147±164.Harrison, E. F., Minnis, P., Barkstom, B. R., Ramanathan, V., Cess, R. D. and Gibson, G. G. 1990. `Seasonal variation of cloud radiative

forcing derived from the Earth Radiation Budget Experiment', J. Geophys. Res., 95, 18 687±18 703.Horel, J. D., Kousky, V. E. and Kagano, M. T. 1986. Àtmospheric conditions in the Atlantic sector during 1983 and 1984', Nature, 322, 248±

251.House, F. G., Gruber, A., Hunt, G. E. and Mercherikunnel, A. T. 1986. À history of satellite missions and measurements of the earth radiation

budget (1957±1984)', Rev. Geophys., 24, 351±356.Jacobowitz, H. W., Soule, H. V., Kyle, H. L., House, F. B. and the Nimbus-7 ERB Experiment Team. 1984. `The Earth Radiation Budget

(ERB) Experiment: an overview', J. Geophys. Res., 89, 5021±5038.Kiehl, J. T. 1994. Òn the observed near cancellation between longwave and shortwave cloud forcing in tropical regions', J. Climate, 7, 559±

565.Klein, S. A. and Hartmann, D. L. 1993. `The seasonal cycle of low stratiform cloud', J. Climate, 6, 1587±1606.Kousky, V. E. and Leetmaa, A. 1989. `The 1986±87 Paci®c warm episode: evolution of oceanic and atmospheric anomaly ®elds', J. Climate,

2, 254±267.Lau, N.-C. 1985. `Modeling the seasonal dependence of the atmospheric response to observed El NinÄos in 1962±76', Mon. Wea. Rev., 113,

1970±1996.Ohring, G. and Clapp, P. 1980. `The effect of changes in cloud amount on the net radiation at the top of the atmosphere', J. Atmos. Sci., 37,

447±454.

950 M. RINGER

Philander, S. G. H. 1986. Ùnusual conditions in the tropical Atlantic Ocean in 1984', Nature, 322, 236±238.Philander, S. G. H. and Rasmusson, E. M. 1985. `The Southern Oscillation and El NinÄo', Adv. Geophys., 28A, 19215.Ramanathan, V. and Collins, W. 1991. `Thermodynamic regulation of ocean warming by cirrus clouds deduced from observations of the 1987

El NinÄo', Nature, 351, 27±32.Randel, D. L. and Vonder Haar, T. H. 1990. Òn the interannual variation of the Earth Radiation Balance', J. Climate, 3, 1168±1173.Raval, A. and Ramanathan, V. 1989. Òbservational determination of the greenhouse effect', Nature, 342, 758±761.Ringer, M. A. 1994. Interannual variability of the Earth's radiation budget and cloudiness: a satellite view. PhD thesis, University of

Reading.Ringer, M. A. and Shine, K. P. 1997. `Sensitivity of the Earth's radiation budget to interannual variations in cloud amount', Climate Dyn., in

press.Smith, E. A. and Smith, M. R. 1987. Ìnterannual variability of the tropical radiation balance and the role of extended cloud systems', J.

Atmos. Sci., 44, 3210±3234.Smith, G. L., Rutan, D. and Bess, T. D. 1990. Atlas of Albedo and Absorbed Solar Radiation Derived from Nimbus-7 Earth Radiation Budget

Data SetÐNovember 1978 to October 1985. NASA Reference Publication 1231.Stowe, L. L., Wellemeyer, C. G., Eck, C. G., Yeh, H. Y. M. and the Nimbus-7 Cloud Data Processing Team. 1988. `Nimbus-7 global cloud

climatology. Part 1: algorithms and validation', J. Climate, 1, 445±470.Wallace, J. M. 1992. Èffect of deep convection on the regulation of tropical sea surface temperature', Nature, 357, 230±231.Weare B. C. 1992. `Variations in Nimbus-7 cloud estimates. Part II: regional changes', J. Climate., 5, 1506±1513.


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