Climatic Outliers, B. G. Hunt,-2006,Aug

INTERNATIONAL JOURNAL OF CLIMATOLOGYInt. J. Climatol. 27: 139–156 (2007)Published online 3 August 2006 in Wiley InterScience(www.interscience.wiley.com) DOI: 10.1002/joc.1379

Climatic outliers

B. G. Hunt*CSIRO Marine and Atmospheric Research, PMB1, Aspendale VIC 3195, Australia

Abstract:

A 10 000-year simulation for ‘present’ climate has been generated with the CSIRO Mark 2 coupled climatic model. Inthis paper the model output has been analysed to produce a climatology of climatic outliers and to assess the role of suchoutliers in climatic variability. An assessment of the frequency of occurrence of outliers, compared with the statisticalexpectation from a Gaussian distribution, indicates that over most of the globe the surface temperature conforms to thisdistribution, but this is not the case for rainfall. Among the issues considered are the spatial occurrence rates over theglobe of normalised climatic anomalies of various magnitudes, the temporal variability of outliers, the seasonal variabilityfor a given outlier year, the spatial characteristics of outliers and the question of precursors to outliers. The relationshipof outliers to the El Nino/Southern Oscillation, the Pacific Decadal Oscillation and the North Atlantic Oscillation is alsoconsidered. A case study examining the relative severities of an outlier drought to a severe drought is also undertaken.Finally, the role of outliers and the greenhouse effect is considered. It is concluded that outliers are a manifestation ofstochastic forcing. Copyright 2006 Royal Meteorological Society

KEY WORDS simulation; coupled model; multi-millenial analysis; climatic outliers

Received 3 August 2005; Revised 22 May 2006; Accepted 29 May 2006

1. INTRODUCTION

Climatic outliers are the most extreme anomalies occur-ring within a time series for any given climatic vari-able. As such they are of particular interest in view oftheir potential calamitous impacts. A pertinent example isthe extremely hot summer of 2003 in Europe, to whichsome tens of thousands of deaths have been attributed(Schar et al., 2004). This extreme event is now raisingthe question whether there was a greenhouse contribu-tion to this warming (Stott et al., 2004). An alternativeview is whether this was just a climatic extreme gen-erated exclusively by naturally occurring climatic vari-ability (see Burt, 2004). A further example of outliers isthe devastating droughts in the USA discussed by Fyeet al. (2003). Documentation of climatic outliers usingthe observational record is essentially circumscribed bythe brevity of this record. Proxy climatic records (Stahleet al., 2000; Jones and Mann, 2004; Briffa et al., 2004;among many others) provide valuable datasets, but theseare limited both in their spatial coverage and temporalextent. Consequently, at the current time there is a dearthof knowledge surrounding the climatology of outliers.

With the advent of multi-millennial runs with coupledclimatic models (Osborn et al., 1999; Stouffer et al.,2000; Hunt and Elliott, 2002), it is now possible touse the resultant model outputs to investigate climatic

* Correspondence to: B. G. Hunt, CSIRO Marine and AtmosphericResearch, PMB1, Aspendale VIC 3195, Australia.E-mail: [email protected]

outliers. For purposes of the present analysis outliers aredefined here to be intense and brief anomalies: thus theirmagnitudes should be either > +3 standard deviations(SD) or < −3 SD, with a duration of the order of oneyear. Among the questions of interest are: what is theglobal pattern of outliers (defined by specified ranges ofnormalised climatic outliers)? What is the frequency ofoccurrence of outliers, and are there quantifiable returnperiods? What is the spatial scale of an outlier? Dooutliers occur as isolated events or part of well-definedtemporal sequences? Is an outlier active for a whole yearor only isolated months within a year? Critically, are thereany identifiable precursors to an outlier?

A central issue, which is essentially undefined, iswhat is the magnitude of the maximum outlier that canbe expected at any one point? Our assessment of themagnitude of climatic outliers is greatly influenced byrecent events. For example, the US ‘dustbowl’ droughtsof the 1930s and the later droughts of the 1950s (see Fyeet al. (2003)) might be viewed as the typical extreme offuture droughts to be expected in the USA. However,Cook et al. (2004) have shown that these droughts weremodest compared to those that occurred between 900 and1300 A.D. The possibility exists that a drought of themagnitude of these earlier droughts could occur againgiven the lack of knowledge concerning the climatologyof outliers.

The present paper documents the climatology of anumber of characteristics of climatic outliers, as gener-ated in a 10 000-year simulation with the CSIRO Mark2 coupled climatic model. The aim is to answer, where

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possible, the questions raised above, and to provide someinsight into the properties of outliers, always remember-ing that only a simulation is involved.

Attention will be focussed on surface temperature andrainfall as the two principal climatic variables of inter-est, although the procedures used here can be readilyapplied to any variable. Owing to the relatively coarseresolution of the CSIRO Mark 2 model, outliers associ-ated with some specific climatic events cannot be repli-cated. These include extreme surface winds generated byhurricanes and tornadoes, intense rainfall produced bythunderstorms, etc. These are sub-grid scale features ofthe model. Thus, the outliers to be discussed are rel-atively large-scale phenomena that impact substantiallysized communities.

In addition to the temperature and rainfall, outliersassociated with El Nino/Southern Oscillation (ENSO)events, the Pacific Decadal Oscillation (PDO) and theNorth Atlantic Oscillation (NAO) will also be investi-gated to assess their potential association with outliers ingeneral.

Most results for temperature and rainfall will bepresented as normalised annual mean anomalies, i.e.anomalies as departures from the 10 000-year meandivided by the 10 000-year SD.

2. MODEL DESCRIPTION

The CSIRO Mark 2 coupled global climatic model wasused for the simulation. The model has been describedin detail by Gordon and O’Farrell (1997). The model hasan R21 horizontal spectral resolution (5.625° longitudeby 3.25° latitude) giving 3584 gridboxes per verticallevel. The atmospheric and oceanic components of themodel had 9 and 21 vertical levels, respectively. Thesecomponents are flux-corrected to prevent climatic driftwith the corrections varying monthly, but being invariantfrom year to year they do not influence interannualvariability. The model has dynamical sea-ice and a staticbiosphere, with a number of different soil and plant types.Diurnal and seasonal variability are included in the modeland the usual range of sub-grid parameterisations (seeGordon and O’Farrell, 1997 for more details).

The present simulation was commenced from a pre-vious 1000-year simulation, hence initial model condi-tions were fully developed. Model outputs were stored atmonthly intervals, as means or accumulations, for a widerange of atmospheric, sea-ice and oceanic variables. Themodel did not experience any climatic drift during the10 000-year run; annual mean, globally averaged indi-vidual climatic variables were time-invariant to within1–2% (see Hunt (2004) for an analysis of this aspect ofthe simulation).

3. SURFACE TEMPERATURE

In Figure 1, global distributions of the frequency ofoccurrence, over all 10 000 years of the simulation, are

presented for normalised annual mean surface tempera-ture anomalies for values greater than ±3 SD, ±4 SD and±5 SD. Normalised temperature anomalies of magnitude±3 SD occur over most of the globe in Figure 1, withlarge regions having ten or more occurrences over the tenmillennia. If any point had consecutive years with anoma-lies of the set magnitude, each year would have beencounted individually. Given the low occurrence rates inFigure 1, and the temporal distributions shown below,such consecutive counts would have been extremely rare.Lowest occurrence rates tend to be over land areas andthe Pacific Ocean, where an ENSO-like pattern can bediscerned. The North Polar region has markedly differentresponses for positive and negative temperature anoma-lies. As might be expected, negative anomalies have lowoccurrence rates of these magnitudes over the cold sea-ice, whereas positive anomalies occur quite frequently. Inthe Southern Hemisphere the opposite response occurs,presumably owing to fluctuations in the extent of thesea-ice.

The global distributions for ±4 SD and ±5 SDin Figure 1 reveal a sharp drop in occurrence rates,with the majority of the globe not even experiencinganomalies with a magnitude of 4 SD. Most gridboxeswere restricted to one or two occurrences over theten millennia, highlighting the rarity of these events.It appears from Figure 1 that most outliers will be inthe range of 3–4 SD, perhaps, a surprisingly low value.Gridboxes experiencing anomalies of ±4 SD and ±5 SDare mainly restricted to the oceans.

The remarkable reduction in the occurrence rate ofanomalies between ±3 SD and ±4 SD in Figure 1(a similar outcome exists for rainfall, see below) isof interest, given the ‘three-sigma fallacy’ discussedby Gumbel (1954). The latter is concerned with theview ‘That about three times the standard deviationshould be considered as the maximum for any statisticalvariate, for any number of observations’. While Gumbeldiscusses this statement, the results in Figure 1 suggestthat it is not entirely irrelevant for climatic variables,especially in view of the size of the dataset usedhere.

It is of interest to compare the values in Figure 1with expected outcomes associated with a Gaussian ornormal distribution. For ±3 SD there is an expecta-tion over the 10 000 years of the simulation for 27events, and a range of 12–42 events at the 95% con-fidence level. The majority of the gridboxes in the3 SD panels of Figure 1 meet this expectation. Mostof the gridboxes exceeding this upper range are asso-ciated with sea-ice, where the melting of the sea-icecan cause marked increases in surface temperature, andhence departure from Gaussian conditions. In the caseof the lower range, these regions were primarily associ-ated with very low SD, ∼0.2, indicating very constantconditions unrepresentative of a Gaussian distribution.Most of these regions are in the tropics and are spa-tially extensive, as can be seen from the related areas inFigure 1.

Copyright 2006 Royal Meteorological Society Int. J. Climatol. 27: 139–156 (2007)DOI: 10.1002/joc

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Figure 1. Global distributions of the frequency of occurrence over 10 000 years of normalised annual mean surface temperature anomalies. Theleft hand and right hand panels are for negative and positive anomalies, respectively. The upper, middle and lower panels are for normalisedanomalies greater than ±3 SD, ±4 SD and ±5 SD, respectively. The colour bars below the panels give the occurrence rates per 10 000 years.

For normalised SD of 4 and 5 in Figure 1, there isessentially a zero expectation of occurrence for a Gaus-sian distribution, even over 10 000 years. The simulatedclimate clearly meets this requirement over most of theglobe, although there are noticeable exceptions for −4SD. This situation was examined for the region of theSouth Atlantic Ocean in Figure 1, where an occurrencerate of about four events is shown for the −4 SD panel.A pdf was made of the normalised SD for a gridbox inthis region. This proved to be quite Gaussian in its char-acteristics, but with a very low frequency of occurrencenegative tail extending to just over −5 SD. This wasfound to be primarily associated with a period of foursuccessive years in the first 40 years of the simulation,and appears to be anomalous as regards the remainder ofthe run.

Thus a Gaussian distribution provides a surpris-ingly adequate representation of the occurrence rate fortemperature outliers over most of the globe. Exceptionsare associated with specific climatic features discussedabove.

The relatively high occurrence rates for surface temper-ature anomalies of ±3 SD over portions of the land areain Figure 1 have some implications regarding the green-house effect. These results indicate that naturally occur-ring climatic variability is capable of producing substan-tial positive temperature anomalies, albeit over limitedareas at any one time (see below), that could be mis-takenly attributed to greenhouse warming. On the otherhand, the occurrence of negative temperature anomaliesshows that the climatic system can also produce substan-tial cold spells, which could equally be used to discount


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Figure 2. Time series plots of normalised annual mean surface temperature anomalies for model gridboxes in USA (40 °N, 100 °W) and ASIA(40 °N, 110 °E). For USA only values greater than 3 SD are shown, while for ASIA only values less than −3 SD are shown..

any greenhouse warming. This is an issue discussed inmore detail in Hunt and Elliott (2004).

For selected model gridboxes, time series of normalisedanomalies were plotted for extreme events and outliers(Figure 2). Values are shown only for anomalies above±3 SD to reduce the clutter in the figure. Even with thiscutoff there were still numerous events, approximately 40over the 10 millennia at each of the points in Figure 2,which noticeably exceed the central expectation for aGaussian distribution. Periods of some centuries existedwithout any anomalies attaining the set criteria, althoughmany values below this limit did occur. In general, theoccurrence rates in Figure 2 appear to be random. Atother gridboxes (not shown) there was even more vari-ability. For example, a point in Central Asia had a fourmillennia interval between occurrences of anomalies of±3 SD, and subsequently there was about one occurrenceper millennium. In contrast, a point in Mozambique hadseveral occurrences per millennium with anomalies above±4 SD. Similar variability was obtained for negative sur-face temperature anomalies.

Figure 2 also highlights the problem of defining anoutlier. For example, around year 2000 in the ‘ASIA’panel, temperature anomalies exceeding −3 SD occurredwith relatively high frequency: thus while such events

were exceptional in magnitude they were not uncommon.In contrast, over the next 3000 years only ten such eventswere identified, justifying their description as outliers.Clearly, the definition of an outlier can depend on whichmillennium is being considered. If only results for the6th or 9th millennia in this panel were available, thenthe definition of an outlier might be greatly increased toa value of about −6 SD, given the extreme values atthese times. It is only when all ten millennia are viewedthat the great rarity of these extreme outliers can beplaced in perspective. How this perspective might changeif simulations of 20 000, 50 000 or 100 000 years wereavailable is an unresolved question.

Given the extreme brevity of the observational base,only a minor part of the outlier potential can have beenexplored at present, but Figure 1 nevertheless suggeststhat surface temperature anomalies outside the range ±3SD should be exceptional.

In Figure 3 the normalised surface temperature anoma-lies for years adjacent to two of the largest outliers inFigure 2 are illustrated. Similar outcomes were obtainedfor the other major outliers in Figure 2. Figure 3 revealsthat these outliers occur as discrete events, rather thanthe culmination of a series of steadily growing anoma-lies. Thus, there is no indication of any precursors,



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Figure 3. Time series plots of normalised annual mean surface temperature anomalies for the same model gridboxes as used in Figure 2,illustrating the values of the anomalies adjacent to outlier years.

suggesting that stochastic processes are the cause of theoutliers.

In Figure 4 global distributions of surface temperatureanomalies are illustrated for the three largest outliers ineach of the panels in Figure 2. Global distributions areused to provide an overall perspective of the temperatureanomalies associated with these outlier gridboxes.

For the outlier cases shown in Figure 4 the spatialpatterns are fairly extensive, as opposed to the tem-poral states in Figure 3. The exception is year 5619,where a very localised anomaly occurred in Asia. Sub-stantial surface temperature anomalies, with peak ampli-tudes above ±3K , are shown in Figure 4, not onlyin the vicinity of the gridboxes used in Figure 3 butalso for quite separate areas. Apart from the sea-icein the Antarctic, there was very little response in theSouthern Hemisphere associated with Northern Hemi-spheric anomalies. While there appears to be synchronic-ity in the spatial response over Eurasia and North Amer-ica to the presence of negative temperature anoma-lies in Figure 4, this was not the case for other yearsexamined, or for the positive temperature anomaliesin Figure 4. Thus, to a large extent, while an out-lier can have a substantial, local spatial response, there

does not appear to be any systematic activity in otherregions.

Returning to the greenhouse issue, the distribution ofthe positive surface temperature anomalies in Figure 4is clearly distinct from the global warming patternstypically associated with greenhouse simulations. Whilethe outlier anomalies are substantial in magnitude, theyare regionally restricted in space (with the exception ofyear 2879 in Figure 4) and are temporally constrained toa single year (Figure 3). It should therefore be possibleto distinguish between climatic warmings associated withthe greenhouse effect and outlier events. In this context,it would seem that the extreme warming in Europe in thesummer of 2003 could be categorised as an outlier event.This is not to claim that anthropogenic influences maynot have impacted on the observed European warmingin 2003. An analysis by Stott et al. (2004) suggests thatsuch influences may have increased the probability ofoccurrence of this warming by 30%.

In a separate study of this simulation concerned withheatwaves, it has been found that for year 5190 thecharacteristics of this European heatwave were replicatedin remarkable detail as regards amplitude and spatialcharacteristics. However, this was a 1 in 10 000 year


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Figure 4. Global distributions of annual mean surface temperature anomalies for the major outlier years shown in Figure 2. The left hand panelsare for years from the USA gridbox, and the right hand panels for the ASIA gridbox. The colour bars below the panels give the anomalies in K.

occurrence in the simulation, indicating the rarity of suchan event attributable solely to natural climatic variability.On this basis, a repetition of the observed Europeanheatwave in the next decade or so would certainly suggestthat anthropogenic activity rather than natural variabilitywas the cause of such heatwaves.

It is also necessary to put outlier results in context.Figure 4 reveals that in the south Atlantic sea, surfacetemperature anomalies are typically around 1 °C, yetFigure 1 shows normalised anomalies up to −5 SD. Thisis a consequence of the very small value of the SD ofsea surface temperature anomalies in this region. Hence,the attainment of large normalised anomaly values doesnot necessarily imply that the anomalies, per se, are ofclimatic importance.

The monthly mean surface temperature anomalies forthe USA gridbox for year 8546 and for the ASIA gridboxfor year 8291 are given in Figure 5. These were twoof the most extreme anomalies for the six outlier casesshown in Figure 4, and thus represent very exceptionalsituations. Nevertheless, such outliers can presumablybe expected to occur in reality. For USA a maximummonthly mean anomaly of almost 7 °C was attained inAugust, while for Asia the maximum value was −14 °Cin April. Obviously, very much larger daily anomalieswould have occurred on some days during these twomonths: unfortunately, daily values were not saved forthese millennia. Note that for both gridboxes not allmonths recorded anomalies of the same sign. Similaroutcomes were obtained for other gridboxes and years.



Thus, in these situations the temporal impact of theoutliers extends over much of the year. Again the resultsin Figure 5 highlight the large magnitude of surfacetemperature anomalies that can be generated by naturalclimatic variability.

4. RAINFALL

The global distributions of the occurrence rates, overall 10 000 years of the simulation, for normalised annualmean rainfall anomalies are given in Figure 6 for threeranges of standard deviations. For ±3 SD the spatialpatterns of the occurrence rates are almost reversed. Overthe low-latitude oceans and desert regions no rainfallanomalies reach −3 SD. This is a consequence of thelow rainfall amounts in these regions, as this means thatlarge negative deviations are difficult to achieve. On theother hand, positive deviations are attainable, and giventhe small SDs in these regions, high occurrence rates areachieved. In a like manner, regions with high rainfallamounts, the Congo, Indonesia and the Amazon, haverelatively few occasions where anomalies attain +3 SD.

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Figure 5. Monthly mean surface temperature anomalies for year 8546for the USA gridbox used in Figure 2 (top panel) and the corresponding

anomalies for year 8291 for the ASIA gridbox (bottom panel).

For rainfall anomalies of +4 SD and +5 SD (Figure 6),the occurrence rates decline very rapidly and are pre-dominantly over desert regions and low-latitude oceans,for reasons discussed above. Very few places over theglobe experience rainfall anomalies reaching +5 SD. Inthe case of the negative anomalies in Figure 6, the declinein occurrence rate is even faster at high SD. Only overessentially jungle regions, and a few isolated gridboxes,are outliers found, with most of these restricted to 3–4values over the 10 000 years of the simulation. In sum-mary, rainfall anomalies above the range of ±3 SD rarelyoccur in the simulation, as noted also by Hunt (2006).

When considering Figure 6 in relation to the expectedoutcomes from a Gaussian distribution, considerabledifferences are apparent compared to Figure 1. For −3SD practically all occurrence rates are below the expectedvalue, while the large areas with zero rates are obviouslyeven below the lower limit, for reasons discussed above.For −4 and −5 SD the occurrence rates are so low thatthe Gaussian expectation of zero events is achieved overvirtually the whole globe.

For positive SD in Figure 6 the outcomes are excep-tionally non-Gaussian. Over most of the globe the occur-rence rates are above the expected value for +3 SD,and the upper limit is exceeded over large regions of theIndian and Pacific oceans and desert areas. Even for +4SD about half of the globe registered at least one event,and in some areas considerably more, although the expec-tation for a Gaussian distribution is zero. The situation for+5 SD in Figure 6 is therefore even more exceptional.Thus a Gaussian distribution, overall, provides a poorrepresentation of expected outcomes as regards rainfallin this simulation.

In Figure 7 time series of normalised annual meanrainfall anomalies above +3 SD and below −3 SD areplotted for gridboxes in Australia and Brazil, respectively.For Australia outliers above +4 SD occur about onceper millennium on average, but after about year 6300there is an almost two millennia gap without such anoutlier. In the case of Brazil only a few outliers exceed−4 SD, and no such outliers are found for the lastfive millennia. Again, these outcomes indicate that thedefinition of an outlier could well depend upon thespecific timeframe for which data exist. Taking −3 SD asa more realistic definition of an outlier, Brazil still showsmany consecutive centuries without such outliers, whileclusters of outliers occur at other times.

As was found for surface temperature outliers (Fig-ure 3), the rainfall outliers also occurred as individual,discrete events with adjacent years having small anoma-lies of the same or opposite sign (not shown). Again thissuggests that stochastic processes generate these outliers.

Global distributions of annual mean rainfall anomaliesfor three of the largest outliers for each of the Australianand Brazilian time series shown in Figure 7 are plottedin Figure 8. Positive rainfall anomalies covered virtuallythe whole of Australia for the years shown in Figure 8,and in each case the Australian outlier appears tobe part of a larger ENSO-like pattern. The Brazilian


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Figure 6. Global distributions of the frequency of occurrence over 10 000 years of normalised annual mean rainfall anomalies. The left handand right hand panels are for negative and positive anomalies, respectively. The upper, middle and lower panels are for normalised anomalies

greater than ±3 SD, ±4 SD and ±5 SD, respectively. The colour bars below the panels give the occurrence rates per 10 000 years.

rainfall anomalies were more localised, particularly year8774. This localisation was also a feature of rainfalloutliers over the North Pacific, but other locations, withsmaller rainfall anomalies, had outliers as part of largersystems.

Both Australia and Brazil have rainfall that is influ-enced by ENSO events (Ropelewski and Halpert, 1987),with, in general, droughts associated with El Nino eventsand pluvial conditions with La Nina events.

In this regard it should be noted that an extensive anal-ysis of ENSO characteristics replicated by the presentmodel has been given by Hunt and Elliott (2003) for anearlier millennial-length simulation. This analysis con-firms that the model reproduces the main observed ENSOcharacteristics and teleconnections commendably well. Inaddition, AchutaRao and Sperber (2000) have conductedan intercomparison of ENSO behaviour in a range of

models, which again reveals the satisfactory performanceof the present model.

The three years in Figure 8 associated with pluvialconditions were all La Nina years of varying intensities.Year 5182 had a weak La Nina, while year 5181 had aweak El Nino with most of the preceding years forminga weak La Nina sequence. Years 8446 and 8447 werestrong La Nina years with a preceding sequence of LaNina years. Years 9313 and 9314 were modest La Ninayears preceded by an El Nino sequence.

In the case of Brazil the expected relationship withEl Nino events failed to materialise. Year 33 had analmost asymmetric pattern of sea surface temperatureanomalies in the Pacific, and was actually a weak LaNina year, although the preceding 5 years were El Ninoyears. Year 1589 was a strong La Nina year, precededby a sequence of weak La Nina years! Year 8774 was



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Figure 7. Time series of normalised annual mean rainfall for model gridboxes in Australia (27 °S, 124 °E) and Brazil (5 °S, 50 °W). For Australiaonly values greater than 3 SD are shown, while for Brazil only values less than −3 SD are shown.

a very weak La Nina year preceded by a weak El Ninosequence.

None of the years in Figure 8 was a peak El Ninoor La Nina year in the 10 000 year NINO3.4 sequence.Thus despite the ENSO connection, none of the prin-cipal outliers shown in Figure 7 was attributable toNINO3.4 outliers. While there was an ENSO influ-ence on some of these rainfall outliers, their devel-opment as outliers again suggests stochastic influ-ences. Stochastic input was noted as a possible con-tributor to a simulated mega drought in Mexico inthe present simulation (Hunt and Elliott, 2002), whileCole et al. (2002) have also noted the necessity forundefined factors to account for extended droughtin USA.

The monthly rainfall anomalies associated with outliersover the north Pacific Ocean and Brazil are shown inFigure 9; that for the north Pacific Ocean was selectedas it reveals that practically all the anomalous rainfallfor this particular year was concentrated in September.For other outlier years at this location anomalous heavyrainfall was distributed over a number of months, hencethe result in Figure 9 highlights how extreme rainfall canbe in outlier situations. The monthly mean anomalous

rainfall rate for September at the north Pacific gridboxin Figure 9 was over 20 mm/day, giving a monthlyanomalous total of over 670 mm, compared with itsannual climatological value of 1150 mm! This particularoutlier was an isolated, spatial event, unlike the situationsillustrated in Figure 8.

The corresponding negative rainfall anomalies forthe Brazilian outlier in Figure 9 reveal rainfall deficitsdistributed over a number of months, but with smallpositive anomalies still occurring in this extreme droughtsituation.

5. LARGE-SCALE CLIMATIC OSCILLATIONS

Given the influence of the Southern Oscillation, PacificDecadal Oscillation and North Atlantic Oscillation on cli-matic events (see, for example, Ropelewski and Halpert,1987; Gershunov and Barnett, 1998; Hurrell, 1995;respectively), the relationships of these phenomena tooutliers were evaluated.

The Southern Oscillation (SO) and NINO3.4 sea sur-face temperature are components of ENSO, and theirrelationship is illustrated in Figure 10 for a 100-year seg-ment of the simulation. The anti-correlation between the


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Figure 8. Global distributions of annual mean rainfall anomalies for major outlier years shown in Figure 7. The left hand panels are for yearsfrom the Australian gridbox, the right hand panels for the Brazil gridbox. The colour bars below the panels give the rainfall anomalies in mm/day.

two time series (r = −0.751 over all 10 000 years of thesimulation) is very apparent in this figure. From an out-lier perspective, the more important outcome is that themaxima of the two series do not necessarily coincide.Given this situation, it was decided to use the NINO3.4time series rather than that for the SO in evaluating therole of ENSO events in generating outliers, as sea sur-face temperature fluctuations in the low-latitude PacificOcean were considered to be the progenitors for ENSOimpacts.

A measure of the global impact of NINO3.4 seasurface temperature anomalies over the globe is givenin Figure 11. This figure shows the correlation betweenannual mean surface temperature anomalies at individualgridboxes and the NINO3.4 annual mean temperatureanomalies for years 9001–10 000 of the simulation. A

distinct ENSO-type pattern prevails in the low-latitudePacific Ocean, while correlation values vary markedlyover the globe, indicating the extent and limits of theinfluence of ENSO events. The correlation pattern inFigure 11 agrees well with the observed pattern givenin Figure 7(a) of Collins et al. (2001).

The issue of interest here is whether NINO3.4 outlieroccurrences coincide with those for surface temperatureoutliers at other gridboxes, implying a causative relation-ship as possibly suggested by Figure 11.

In Figure 12, eight 50-year time series of NINO3.4sea surface temperature anomalies, approximately centredon La Nina or El Nino outliers, are plotted togetherwith surface temperature anomalies for a number ofgridboxes. These gridboxes were selected to cover arange of geographical regions across the globe on the



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Figure 9. Monthly mean rainfall anomalies for year 6877 for a gridbox in the north Pacific Ocean (24°N, 185 °E) and for year 33 for a gridboxin Brazil (5 °S, 50 °W), plotted in the upper and lower panels, respectively.

basis of the correlation pattern in Figure 11. The largestNINO3.4 value in each panel of Figure 12 is an outlier,as judged from all 10 000 years of the simulation. (Notethat the ordinate scale differs between panels.) It is clearfrom this figure that apart from the top right hand panel,where the selected gridbox was located in the NINO3.4region, all other NINO3.4 outliers do not coincide withthe maximum value for the time series of the individualgridboxes. These latter values are not necessarily evenoutliers for these gridboxes. This outcome holds forboth La Nina events in the left hand panels and ElNino events in the right hand panels of Figure 12.Thus, despite the correlation relationships indicated inFigure 11, such correlations do not result in outliersfor individual gridboxes being contemporaneous withNINO3.4 outliers. Thus ENSO events are not progenitorsof outliers external to the NINO3.4 region. WhetherENSO events precondition the climatic system, owingto their large spatial signature, and thus influence thesubsequent occurrence of outliers is unknown.

The relationship of the PDO to ENSO outliers is shownin Figure 13 for two typical La Nina and El Nino cases.In general, there is an expectation that the PDO willhave the same sign as the concurrent ENSO event (see

the discussion by Newman et al. (2003)). This situationpartially prevails in Figure 13 and in other outlier casesexamined. Over all 10 000 years of the simulation, thecorrelation coefficient between these two time series was0.297. PDO outlier values were close to ±0.5 °C orgreater, a value attained only in the panel for year 5962in Figure 13, indicating that, in general, PDO and ENSOoutliers were not synchronous. Given the spatial extentover which the PDO is evaluated, which does not includethe NINO3.4 region, this result is understandable forextreme events such as outliers. In addition, PDO outlierswere not synchronous with the outliers for the variousgridboxes used in Figure 12. Thus, neither the PDO norENSO is associated with climatic outliers at individualgridboxes outside the NINO3.4 region.

The relationship of the NAO to climatic outliers,specifically surface temperature outliers, was investigatedfor two positive and two negative NAO outliers. Hurrell(1995) has identified three regions where surface tem-perature anomalies are probably influenced by the NAO.Time series of surface temperature anomalies from thesimulation were generated for gridboxes located in themiddle of each of those regions, i.e. central North Amer-ica, central north Atlantic Ocean and central Asia. Then,


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Figure 10. A 100-year time series of the Southern Oscillation Index and NINO3.4 sea surface temperature anomalies from the 10 000-yearsimulation. The ordinate scale is for the NINO3.4 time series only.

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Figure 11. Correlation pattern between the NINO3.4 sea surface temperature anomalies and surface temperature anomalies at individual modelgridboxes. Results are for years 9001–10 000 of the simulation. The colour bar gives the value of the correlation coefficient.

50-year time series for the NAO and each of the surfacetemperature anomalies centred on the NAO outlier wereplotted (not shown), similar to Figure 12. In no cases didsurface temperature outliers for these gridboxes coincidewith any of the four NAO outliers, indicating that theformer were not related to the NAO.

A brief discussion was given in the previous sec-tion concerning the relationship of rainfall outliers inFigure 8, to ENSO events. A more specific examina-tion of this relationship is undertaken here. In Figure 14,the annual mean surface temperature anomalies are illus-trated for the most positive NINO3.4 outlier (year 4888),and the most negative NINO3.4 outlier (year 9396). Dis-tinct El Nino and La Nina patterns were obtained in thePacific Ocean for these years. The corresponding annualmean rainfall anomalies are shown in the middle panels,and the normalised annual mean anomalies in the lowerpanels of Figure 14. The large-scale shifts in the rainfallpatterns associated with El Nino and La Nina events arewell defined in the figure, especially for the low-latitudePacific Ocean.

Examining the normalised rainfall anomalies in Fig-ure 14 reveals that for these, the largest ENSO outlierevents, rainfall outliers were not generated over Australiaor Brazil. Figure 7 shows that positive rainfall outliersover Australia, usually associated with La Nina events,are above +4 SD. For the La Nina year in Figure 14, lefthand panels, small negative normalised rainfall anomaliesoccurred over southeastern Australia, the opposite ofexpectations. Again, for the El Nino year in Figure 14,right hand panels, the expectation is for drought overBrazil. Drought did occur, but the normalised rainfallanomally was only about −1 SD, whereas in Figure 7such outliers for Brazil are below −3 SD. Whetherrainfall outliers were attained at other model gridboxesin Figure 14 would require examination at all 3584gridboxes, but a comparison of the limited range of thenormalised anomalies in Figure 14 with the results inFigure 6 would suggest that rather few rainfall outlierswould have been obtained. Hence, Figure 14, taken inconjunction with Figure 8, strongly indicates that majorENSO events do not generate corresponding rainfalloutliers.



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Figure 12. Fifty-year time series of NINO3.4 sea surface temperature anomalies and surface temperature anomalies for selected model gridboxes.The time series are approximately centred on ENSO outliers. La Nina and El Nino results are given in the left and right hand panels, respectively.The NINO3.4 outlier years are indicated on the individual panels. Note that different ordinate scales are used in some panels. The time seriesfor the individual gridboxes have been shifted half a year to the right for clarity. The selected model gridboxes are located as follows: AA1,

0°N, 180 °E; AA2, 0°N, 150 °E; AA3, 45°N, 90 °W; AA4, 45 °S, 90 °E; AA5, 15 °S, 30 °W.


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Figure 13. Fifty-year time series of NINO3.4 and PDO sea surface temperature anomalies. The time series are approximately centred on ENSOoutliers. La Nina and El Nino results are given in the left and right hand panels, respectively. The NINO3.4 outlier years are indicated on the

individual panels. The PDO time series has been shifted half a year to the right for clarity.

An analysis of the association of NAO outliers withrainfall outliers, similar to that for surface temperatureanomalies discussed above, also produced a null rela-tionship.

In conclusion, the deterministic processes associatedwith large-scale climatic oscillations had a minimalrole in exciting either rainfall or surface temperatureoutliers. This leaves stochastic processes as the mostlikely generator of climatic outliers.

6. OUTLIER VERSUS SEVERE CLIMATIC EVENTS

The above results are concerned primarily with docu-menting the characteristics of outlier events. It is alsonecessary to provide a perspective of the relative mag-nitudes of outlier and ‘severe’ climatic anomalies; thiscan be obtained from the probability density func-tions in Figure 15. These show the frequency of occur-rence, over all 10 000 years of the simulation, of nor-malised rainfall anomalies for a gridbox in Brazil (5 °S,50 °W) and normalised surface temperature anomaliesfor a USA gridbox (45°N, 100 °W). Both histograms areslightly skewed towards more extreme negative events,but are close to Gaussian distributions. For the purposesof the present discussion a severe event was defined,using Figure 15 for guidance, to be ±2 SD from themean.

For the Brazilian gridbox, monthly rainfall anomaliesare plotted in Figure 16 for year 1589, a marked outlieryear (Figure 7), and year 4087, a severe year. Theseyears had annual mean rainfall anomalies of −5 SDand −2 SD, respectively. The corresponding rainfalldeficiencies for these 2 years were 430 mm and 175 mm,respectively. As noted in relation to Figure 9, not everymonth of these drought years had below-average rainfall,but there were fewer positive anomalies for the outlieryear, and of course, more extensive negative anomalies(Figure 16).

The spatial characteristics associated with severe andoutlier years identified for the Brazilian and USA grid-boxes for annual mean rainfall and surface temperature,respectively, are shown in Figure 17.

For the Brazilian gridbox, the outlier year (1589) notonly had a larger rainfall deficit but also a more extensivespatial pattern than that for the extreme year (4087)(Figure 17). There was also substantial spatial variabilityin the monthly rainfall anomaly patterns for these 2 years.In general, there were larger rainfall anomalies over theglobe for the outlier year.

For the USA gridbox, the surface temperature outlierwas only 3.3 SD, year 4140, while for the severe case itwas 2 SD, year 5128. Despite this smaller difference com-pared to the normalised rainfall anomalies for the Brazil-ian gridbox, Figure 17 reveals distinctly different surfacetemperature anomaly patterns over the USA for these



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Figure 14. Global plots of surface temperature anomalies, rainfall anomalies and normalised rainfall anomalies for annual mean conditions (top,middle and bottom panels, respectively). The left hand panels are for year 9396 which had the largest NINO3.4 negative surface temperature

anomaly, while the right hand panels are for year 4888 which had the largest NINO3.4 positive surface temperature anomaly.

2 years. The outlier temperature anomaly was greaterin magnitude but more limited spatially, in contrast tothe Brazilian rainfall patterns in Figure 17. There werealso substantially different temperature anomaly patternsover Europe and Asia between the two years, which wasattributable to the vagaries of climatic variability. Whileoutlier years are infrequent, it is clear that they will havea very significant impact compared to the more typical‘severe’ years of common experience.

7. CONCLUSIONS

On the basis of a 10 000-year simulation of the CSIROMark2 climatic model, it has been shown that forannual mean values of surface temperature and rainfall

there are relatively few outliers exceeding the rangeof ±3 SD. Most outliers above this range tend to belocated over the oceans. At individual gridboxes, highvalues of normalised anomalies do not necessarily implyclimatically significant anomalies, as the associated SDmay be quite small. Hence individual cases need to beexamined carefully. ENSO-type patterns can be discernedin the ±3 SD normalised anomaly distributions overthe Pacific Ocean, but not necessarily for more extremeoutliers.

Examination of the temporal variations of outliers atindividual model gridboxes revealed a range of responses.In some centuries multiple outliers occurred, whilethousand year intervals without outliers of the specifiedmagnitude were also found. Thus the magnitude of what


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Figure 15. Probability density functions for all 10 000 years of the simulation illustrating the frequency of occurrence of normalised rainfallanomalies for a Brazilian gridbox (5 °S, 50 °W)(upper panel), and for surface temperature for a USA gridbox (40°N, 100 °W) (lower panel).

is perceived to be an outlier can vary from millennium tomillennium. This has the important implication that whatis currently considered to be the greatest observed out-lier to date may not be representative of the true, muchlarger, outliers that could occur in the future.

Examination of the time series of anomalies in thevicinity of an outlier (Figure 3) highlights the singularnature of outliers, with no apparent build up or subse-quent decay in anomaly values adjacent to an outlieryear. The lack of any precursor signal, in particular,emphasises the random, unpredictable nature of outliers,and that there is no predictive capability associated withthe occurrence of outliers.

For most of the outlier events examined, outlier-relatedanomalous activity occurred over a number of monthsin an outlier year, but with some months still havingopposite-signed anomalies, although of small magnitude.

Spatially, outliers had a substantial regional signatureindicating that they were part of a larger synoptic system.However, there was no apparent relationship with otherregions.

No compelling connections could be discerned betweenoutlier occurrences and ENSO, PDO or NAO outliers. In

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a month to the right for clarity.

particular, outliers associated with these phenomena wereshown not to be contemporaneous with outliers for grid-boxes in regions influenced by these oscillations, otherthan the NINO3.4 region.



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Figure 17. Global distributions of annual mean rainfall (left panels) and surface temperature (right panels) anomalies for outlier years and severeyears. The distributions for the outlier years are in the top panels, and for the severe years in the lower panels. For rainfall the outlier year wasidentified for a gridbox in Brazil, while for surface temperature the outlier year was identified for a gridbox in the USA. The severe years were

arbitrarily selected as years with anomalies of 2 SD for these two gridboxes.

On the basis of all the above analyses it is concludedthat outliers at individual gridboxes should be viewed asthe manifestation of stochastic forcing.

Finally, a case study was undertaken to illustratethe substantial quantitative differences between climaticevents associated with outliers (±5 SD) and severeanomalies (±2 SD), see Figure 16 and Figure 17.

This study of outliers is also of considerable relevanceconcerning the ongoing public debate about the realityof the greenhouse effect. As part of this debate thereis a tendency for any major drought or heatwave to beattributed to this effect. Given the centennial and mil-lennial temporal variability obtained in this simulation,any such attributions, as regards individual years, couldwell be spurious. The very limited observational time-frame is completely inadequate to quantify the possiblerange of climatic anomalies that can occur solely owingto naturally occurring climatic variability. If, for exam-ple, the past century of climatic observations embracesa relatively quiescent phase of outlier occurrence, thenwe might assign, erroneously, larger outlier magnitudeswhen they occur to the greenhouse effect rather than nat-ural variability.

As has been shown above, outliers occur essentiallyrandomly in time and have a regional spatial signature.In contrast, the present global warming is widespread spa-tially and, to a large extent, continuous in time. Outliers

will continue to occur as the greenhouse effect progressesbut their isolated nature should permit them to be seenas such, rather than as a constituent component of thegreenhouse warming. The ability of natural climatic vari-ability to locally overwhelm greenhouse-induced climaticchange has been demonstrated by Hunt and Elliott (2004)in the case of cold outbreaks. However, if, say, heat-waves, such as occurred in Europe in 2003, increase infrequency for a given region, then this would suggesta greenhouse influence rather than being attributable tooutliers.

REFERENCES

AchutaRao K, Sperber KR. 2000. El Nino Southern Oscillation incoupled models. PCMDI Report No. 16. PCMDI, LawrenceLivermore National Laboratory: Livermore, CA 95440.

Briffa KR, Osborn TJ, Schweingruber FH. 2004. Large-scale temper-ature inferences from tree rings: a review. Global and PlanetaryChange 40: 11–26.

Burt S. 2004. The August 2003 heatwave in the United Kingdom. Part1. Maximum temperatures and historical precedents. Weather 59:199–208.

Cole JE, Overpeck JT, Cook ER. 2002. Multiyear La Nina eventsand persistent drought in the contiguous United States. GeophysicalResearch Letters 29(13): 25-1, 25-4 DOI 10.1029/2001GLO13561.

Collins M, Tett SFB, Cooper C. 2001. The internal climate variabilityof HadCM3, a version of the Hadley Centre coupled model withoutflux adjustments. Climate Dynamics 17: 61–81.

Cook ER, Woodhouse CA, Eakin CM, Meko DM, Stahle DW. 2004.Long-term aridity changes in the western United States. Science 306:1015–1018.


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Fye FK, Stahle DW, Cook ER. 2003. Paleoclimatic analogs totwentieth-century moisture regimes across the United States. Bulletinof the American Meteorological Society 84: 901–909.

Gershunov A, Barnett TP. 1998. Interdecadal modulation of ENSOteleconnections. Bulletin of the American Meteorological Society 79:2715–2725.

Gordon HB, O’Farrell SP. 1997. Transient climate change in theCSIRO coupled climate model with dynamical sea ice. MonthlyWeather Review 125: 875–907.

Gumbel EJ. 1954. Statistical Theory of Extreme Values and SomePractical Applications, National Bureau of Standards AppliedMathematics Series No. 33 . US Government Printing Office:Washington, DC.

Hunt BG. 2004. The stationarity of global mean climate. InternationalJournal of Climatology 24: 795–806.

Hunt BG. 2006. Climatology of simulated annual mean rainfallextremes. Journal of Climate In press.

Hunt BG, Elliott TI. 2002. Mexican megadrought. Climate Dynamics20: 1–12.

Hunt BG, Elliott TI. 2003. Secular variability of ENSO events in a1000-year climatic simulation. Climate Dynamics 20: 689–703.

Hunt BG, Elliott TI. 2004. Interaction of climatic variability withclimatic change. Atmosphere-Ocean 42: 145–172.

Hurrell JW. 1995. Decadal trends in the North Atlantic Oscillation:regional temperatures and precipitation. Science 269: 676–679.

Jones PD, Mann ME. 2004. Climate over past millennia. Reviews ofGeophysics 42(1): 1–42.

Newman M, Compo GP, Alexander MA. 2003. ENSO-forced vari-ability of the Pacific Decadal Oscillation. Journal of Climate 16:3853–3857.

Osborn TJ, Briffa KR, Tett SFB, Jones PD, Trigo RM. 1999.Evaluation of the North Atlantic Oscillation as simulated by acoupled model. Climate Dynamics 15: 685–702.

Ropelewski CF, Halpert MS. 1987. Global and regional scaleprecipitation patterns associated with the El Nino/SouthernOscillation. Monthly Weather Review 115: 1606–1626.

Schar C, Vidale PL, Luthi D, Frei C, Haberll C, Liniger MA,Appenzeller C. 2004. The role of increasing temperature variabilityin European summer heatwaves. Nature 427: 332–336.

Stahle DW, Cook ER, Cleaveland MK, Thirrell MD, Meko DM,Grissino-Mayer HD, Watson E, Luckman BH, 2000. Tree-ring datadocument 16th century megadrought over North America. EOS 81:121, 125.

Stott PA, Stone DA, Allen MR. 2004. Human contribution to theEuropean heatwave of 2003. Nature 432: 610–614.

Stouffer RJ, Hegerl G, Tett S. 2000. A comparison of surface airvariability in three 1000-yr coupled ocean-atmosphere modelintegrations. Journal of Climate 13: 513–537.


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