cˇ. brankovic´ Æ f. molteni seasonal climate and variability of … · 2010. 3. 10. · cˇ....

C. Brankovic Æ F. Molteni

Seasonal climate and variability of the ECMWF ERA-40 model

Received: 14 May 2003 / Accepted: 8 October 2003 / Published online: 3 February 2004� Springer-Verlag 2004

Abstract The climate and variability of seasonalensemble integrations, made with a recent version ofECMWF model (used for ERA-40 production) at rel-atively high horizontal resolution (TL159), have beenstudied for the 10-year period, 1980–1989. The modelsystematic error over the Atlantic-European region hasbeen substantially improved when compared with theearlier model versions (e.g. from the PROVOST andAMIP-2 projects). However, it has worsened over thePacific-North American region. This systematic errorreduces the amplitude of planetary waves and has anegative impact on intraseasonal variability and pre-dictability of the PNA mode. The signal-to-noiseanalysis yields results similar to earlier model versions:only during relatively strong ENSO events do someparts of the extratropics exhibit potential predictability.For precipitation, there is more disagreement betweenobserved and model climatologies over sea than overland, but interannual variations over many parts of thetropical ocean are reasonably well represented. Thesouth Asian monsoon in the model is severely weak-ened when compared to observations; this is seen inboth poor climatology and interannual variability.Overall, comparing the ERA-40 model with earlierversions, there seems to be a balance between modelimprovements and deteriorations due to systematic er-rors. For the seasonal time-scale predictability, it is notclear that this model cycle constitutes an advantageover the earlier versions. Therefore, since it is not al-ways possible to achieve distinct improvements inmodel climate and variability, a careful and detailed

strategy ought to be considered when introducing anew model version for operational seasonal forecasting.

1 Introduction

The continuous development of general circulationmodels (GCMs) used for seasonal predictions requiresa periodic assessment of models’ capacity to reproducepotentially predictable atmospheric variations on sea-sonal time scales. In order to make the best possibleuse of seasonal forecasts, it is important to knowwhether, for example, the systematic errors of a givenmodel have been reduced, or whether a model’s abilityto reproduce natural variability has improved. Suchassessments are usually demanding exercises; however,they help to better judge the quality of the ever-evolving modelled climatology, whose validation is anintegral part of any operational seasonal predictionprogramme.

In collaboration with the European Centre forMedium-Range Weather Forecasts (ECMWF), theAbdus Salam International Centre for TheoreticalPhysics (ICTP) carried out an experimental programmeto assess the potential for seasonal predictability of arelatively recent version of the ECMWF atmosphericmodel, namely the version used for the production of theECMWF 40-year re-analysis (ERA-40). In the experi-ments described and discussed, the atmospheric modelwas forced with observed sea surface temperatures(SSTs), i.e. with the best available estimate of the oceanboundary conditions. The seasonal time scale experi-ments were made in ensemble mode and over a relativelylong period of years. The experimental design essentiallycorresponds to that of the PROVOST, a EuropeanUnion project on seasonal predictability in a multi-model context (see, e.g., Brankovic and Palmer 2000 fordetails), and the results presented in this study are oftendiscussed in view of and compared with those from

C. Brankovic (&) Æ F. MolteniThe Physics of Weather and Climate Group,The Abdus Salam International Centre for Theoretical Physics(ICTP), Strada Costiera 11, 34014 Trieste, ItalyE-mail: [email protected]

Present address: C. BrankovicCroatian Meteorological and Hydrological Service,Gric 3, 10000 Zagreb, Croatia

Climate Dynamics (2004) 22: 139–155DOI 10.1007/s00382-003-0370-0

PROVOST. (The ECMWF model version used inPROVOST was operational in 1995.)

The model systematic errors are also compared withthose from ECMWF model versions later than PRO-VOST, in particular with the AMIP-2 climate experi-ments (made with a model version operational in 1998/99; see Brankovic et al. 2002 for details on AMIP-2experiments). This was necessary in order to ascertainimprovements made with recent model versions, andalso to emphasise still existing problems or to indicatenew ones. Such a comparison is not always ‘‘clean’’,because our experiments may differ from earlier ones interms of horizontal and/or vertical resolution, the lengthof integrations, initial dates, ensemble sizes and the yearscovered. Nevertheless, they are indicative of the impactthat changes to the model formulation may induce uponmodel climatology and variability.

Many present-day GCMs are capable of reproducingreasonably well the observed seasonal-mean climate (e.g.Gates et al. 1999). However, a correct simulation of theinteractions between internal variability and forcing fromanomalous surface conditions must also be attained ifseasonal prediction is to be deemed successful. In view ofthe still relatively large biases that exist in coupledatmosphere-ocean GCMs (e.g. Goddard et al. 2001),understanding the relationship between errors in thetime-mean state and deficiencies in the simulation of in-terannual variation bears a significant weight. Further-more, reliable estimates of seasonal predictability dependon a correct simulation of the links between interannualand intraseasonal variability (e.g. Ferranti et al. 1997;Straus and Shukla 2000). This is particularly importantfor mid-latitudes, where due to predominantly chaoticproperties of the atmosphere, forcing at the lowerboundary is reflected by (sometimes subtle) changes tothe statistical properties of large-scale circulation pat-terns. Even in the tropics, which exhibits an intrinsicallyhigher predictability than the extratropics, many GCMshave difficulties in accurately simulating interannualvariations in regions where intraseasonal variability playsan important role (e.g. Sperber and Palmer 1996). This isoften reluctantly demonstrated when evaluating GCMs,partly because it is much harder to achieve, and partlybecause it may reveal models’ deficiencies in more details.

Due to considerable computational requirements,seasonal-scale integrations are usually run at relativelower horizontal and/or vertical resolutions than nor-mally used for shorter time scales. Though Brankovicand Gregory (2001) found no dramatic advantage of avery high horizontal resolution (of the order of TL319)being applied to seasonal integrations, many meteoro-logical variables (in particular those sensitive to regionalorographic variations) are better simulated when a GCMis integrated at a relatively high horizontal resolution. Inthis study, the ECMWF model was integrated at theTL159 spectral truncation, which may be considered asrelatively high for the purpose of seasonal predictions.

To summarise, the purpose of this study is twofold, toassess the seasonal climate and variability: (1) for a

relatively recent version of ECMWF model, i.e. themodel version used for ERA-40 production, and (2) forrelatively higher horizontal resolution than is normallyused in an operational seasonal forecasting environ-ment. We attempt to address different aspects that arerelevant to seasonal predictability: the seasonal-meanstate of the model, as well as the simulation of interan-nual and intraseasonal variations. In the following sec-tion, a brief description of the experiments is given. Themodel climate and seasonal-scale systematic errors arediscussed at some length in Sect. 3. The comparison ofthe observed and modelled variability, both interannualand intraseasonal is presented in Sect. 4. Summary andconclusions are given in Sect. 5.

2 Model and experiments

The experiments discussed were made with the ECMWF model,cycle 23R4 at the TL159 horizontal spectral resolution and with 40levels in the vertical. This model version was in the ECMWFmedium-range operations from June 2001 to January 2002 (atTL511 and with 60 levels) and, as mentioned, is being used for the40-year ECMWF re-analysis, ERA-40. The number of verticallevels in our experiments was constrained by the vertical domain ofinitial conditions. These were taken from the previous 15-yearECMWF reanalysis archive (ERA-15; see Gibson et al. 1997). Theuppermost pressure level in ERA-15 was at 10 hPa.

The sea surface temperatures (SSTs) as the model’s lowerboundary condition were also taken from ERA-15 and were up-dated daily in the model. The experiments were run for winter andsummer seasons over the period of 10 years, 1980–1989. Eachexperiment was of approximately five months duration. For bothseasons, ensembles of integrations were made, six-member ensem-bles for winter and three-member ensembles for summer. Thewinter initial conditions were defined for November 1–6, and thesummer ones were for May 1–3. At the time of running anddiagnosing the experiments discussed here, ERA-40 was stillunavailable for many years from the 1980–1989 period.

A number of changes to ECMWF model formulation has beenintroduced between model versions used for PROVOST andAMIP-2 projects and the one used for ERA-40 production. Mostof the changes relate to model physics (convection, boundary layer,clouds, radiation, surface land/ice exchanges; see, e.g., Viterbo andBetts 1999; Viterbo et al. 1999; Morcrette and Jakob 2000; Jakoband Klein 2000; Van den Hurk et al. 2000; Gregory et al. 2000),however, some of them are related to model dynamics as well (e.g.Temperton et al. 2001). It is very difficult to ascribe or associatechanges in the model response to SST forcing or in systematicerrors between different versions to any particular modificationintroduced in the model formulation. It is usually a combination ofvarious modifications that leads to an overall improvement inmodel performance. However, and what is more important here,improvements usually seen in ECMWF operational (medium-range) forecasts are not always carried over to seasonal or longertime scale integrations.

3 Model climate and systematic errors

The model wintertime climate was computed from 60integrations (six-member ensembles over ten years,1980/81–1989/90), and summer climate was derivedfrom 30 integrations. For most parameters, the modelclimate was assessed with respect to the corresponding10-year climate derived from ERA-15. For the

140 Brankovic and Molteni: Seasonal climate and variability of the ECMWF ERA-40 model

verification of precipitation, the Xie and Arkin (1997)data were used. The discussion is focused on the lastthree months of integrations, i.e. January to March(JFM) for winter, and July to September (JAS) forsummer.

Obviously, both verification datasets are not freefrom deficiencies. One of the main problems found inERA-15 is a warming below 500 hPa during the 1980s,in particular in the tropics. This was revealed whenERA-15 data were compared with satellite observationsand with the NCEP-NCAR reanalysis (National Centersfor Environmental Prediction, NOAA/NWS, CampSprings, MD, USA and National Center for Atmo-spheric Research, Boulder, CO, USA; Trenberth et al.2001). This deficiency in temperature field caused anincrease in tropospheric specific humidity as well. Also,biases in high-latitude land surface temperature werefound to be related to an incorrect representation ofalbedo over snow covered forests and to soil moisturefreezing (Viterbo and Betts 1999; Viterbo et al. 1999).

3.1 Errors in upper-air fields

3.1.1 Geopotential height

In winter, the error pattern for the 500 hPa geopotentialheight (model minus analysed climatology) indicates a

relative strong overestimation as well as displacement ofthe zonal flow over the northern Pacific and towardsNorth America (Fig. 1c). The ridging over the NorthAmerican western seaboard and over Alaska has almostdisappeared in the model when compared to ERA-15.The Pacific jet does not penetrate sufficiently enoughinto the central and northeastern parts of the ocean, thuscausing a large positive error over these regions.Whereas the amplitude of the height error over the Pa-cific is larger than in some earlier ECMWF model ver-sions, the error over the northern Atlantic/Europeanregion is alleviated, indicating a reasonable model sim-ulation of the diffluent flow there. However, despitebeing reduced, the error still amounts to more than6 dam over the northern Atlantic. In the SouthernHemisphere, the JFM (southern summer) errors arecomparatively small. The largest error, reaching –6 damis found between New Zealand and Antarctica (notshown).

During the northern summer (JAS), a weak NorthernHemisphere circulation is associated with relativelysmall errors. Similar to the PROVOST experiments,positive errors dominate over the mid-latitude conti-nents; however over northern Asia there is a relativelarge negative error of about –5 dam (Fig. 1d). Theamplitude of this negative error is somewhat larger thanin PROVOST, but it is only about a half of that seen inAMIP-2 experiments. The southern hemisphere heighterrors are similar to those from the TL159 AMIP-2

Fig. 1 The 10-year climatologyof JFM 500 hPa geopotentialheight: a from modelintegrations, b from ERA-15, cthe difference model minusERA-15 (systematic error), andd the difference model minusERA-15 for JAS. Contours are10 dam in a and b, and 2 damin c and d

Brankovic and Molteni: Seasonal climate and variability of the ECMWF ERA-40 model 141

experiment – they are predominantly negative, reachingjust over –6 dam in the same position as in JFM (notshown).

3.1.2 Zonally averaged fields

The JFM zonally averaged u-wind errors indicate anincrease of the northern midlatitude westerlies (north of40�N) throughout the troposphere, and a decrease ofwesterlies in the northern subtropics (Fig. 2c). Such anerror pattern has hardly changed when compared withthe earlier ECMWF model cycles. The error amplitudeshown in Fig. 2c is marginally smaller than in the pre-vious ECMWF seasonal integrations, however, it ismuch reduced when compared with the AMIP-2 exper-iments. The largest errors are found in the tropicalstratosphere; however, such estimates might be affectedby deficiencies in ERA-15 due to insufficient verticalresolution, as discussed in Brankovic et al. (2002). InJAS, errors in u-wind are generally confined to thestratosphere. There, the largest errors are associatedwith a strengthening of the southern subtropical jet, and

with a slight northerly displacement of the southernpolar night jet (not shown).

In both winter and summer seasons, the zonallyaveraged temperature errors are characterised by rela-tively large cooling at high latitudes (Fig.3). In summerhemispheres, the strongest cooling is mostly confined toa narrow layer around 200 hPa, amounting to about –6 K. This stratospheric cooling is a long-standingmodelling problem, not only in ECMWF model (e.g.,comments in Brankovic and Gregory 2001). In winterhemispheres, the cooling is spread throughout the tro-posphere with maxima near the ground.

In the meridional wind component, v-wind, a relativelarge error is found during the boreal summer (Fig. 4).The maximum in the modelled southward branch of theHadley cell near the tropopause is displaced from theequator to about 15�S when compared with ERA-15,and is reduced in amplitude by nearly 50%. In thenorthward near-surface branch, the error is mainly dueto the displacement of the v-wind maximum, again fromthe equator to near 15�S. In JFM, a similar error patternin the zonally averaged upper-level v-wind is seen, butwith the opposite sign. However, in contrast to JAS, it ismainly due to a reduction in wind magnitude, whilst themaximum has been located correctly by the model (notshown).

In terms of spatial distribution, the largest JASv-wind errors are located at 200 hPa over the northernIndian Ocean and the maritime continent (Fig. 5b). Thedominant positive error (with amplitude of over 7 ms–1)indicates a too weak northerly wind in the model whencompared with ERA-15 (Fig. 5a). On the other hand,

Fig. 2 Same as Fig. 1 a–c but for zonally averaged u-wind.Contours are 5 ms–1 in a and b, and 1 ms–1 in c

Fig. 3 Systematic errors for zonally averaged temperature ina JFM, and b JAS. Contour interval is 1 degree


the modelled northerly component of the 300 hPa windover the Indian Ocean is of a comparable magnitudewith that at 200 hPa (not shown), while it is muchweaker in the analysis. This indicates an overall‘‘spreading’’ of the model northerly wind within a rela-tive thick vertical layer, instead of being concentrated toa very thin layer, as shown in Fig. 4b. These errors are inthe sense of reducing the meridional circulation at thedivergence level, thus weakening the local Hadley cell. Ina number of studies on intraseasonal monsoon vari-ability (e.g. Ferranti et al. 1997), a weakening of theHadley cell over the monsoon region was associatedwith monsoon break periods. It could be speculated,therefore, that relatively large errors in summer tropicalv-wind are (at least partly) associated with a decrease inthe frequency (and/or the intensity) of active monsoonperiods in the model (see also the discussion on mon-soon precipitation later).

In JFM, the largest errors are also found oversoutheastern Asia, as well as over Central America andthe northern parts of South America, again indicating

the weakening of the local Hadley circulations (notshown).

When assessing the Hadley circulation, one shouldalso consider the associated large-scale vertical motion.Though differences between the modelled and analysedvertical motion do exist (and are broadly coherent withthe errors in meridional wind), they are not discussedhere because vertical velocity estimates are stronglysusceptible to the formulation of model physics (see alsocaveats in Brankovic and Gregory 2001). For example,when seasonally averaged vertical velocity from severalECMWF model cycles was compared against ERA-15,the smallest errors were found in integrations made bythe same model cycle used for ERA-15.

The same applies to humidity, the model’s atmo-sphere is generally drier than in ERA-15; however, it isnot clear to what extent this reflects the impact of modelchanges introduced into physical parametrisation be-tween the model cycle used for ERA-15 and cycle 23R4.

Fig. 4 Same as Fig. 2 but for v-wind in JAS. Contours are 0.5 ms–1

in a and b, and 0.2 ms–1 in c

Fig. 5 JAS 200 hPa v-wind: a ERA-15 climate, and b model error:model minus ERA-15. Contours every 2 ms–1 in a and 1 ms–1 in b


3.2 Precipitation climate

3.2.1 Global precipitation

The spatial distribution and zonally averaged values ofthe modelled and verifying precipitation are shown inFigs. 6 and 7 (extended to 60�S only because of limita-tions in the Xie and Arkin 1997 data). In JFM, morerainfall than observed is found to the north of theequator and over the northern oceans (Fig. 6, bottomleft panel). The zonal averages clearly show that thelargest contribution to the model error comes from seapoints, and amounts to 2 mm day–1 at around 5�N(Fig. 6, middle right). The model error in high latitudesappears larger than in the tropics in relative terms, al-though the Xie and Arkin (1997) estimates are less

reliable in such regions. In the tropics, the peaks to thenorth and to the south of the equator (mostly corre-sponding to the inter-tropical convergence zone, ITCZ,and to the south Pacific convergence zone, SPCZ,respectively) are reversed in the model when comparedto the Xie and Arkin (1997) data. In addition, an east-ward shift in the location of the precipitation maxima inthe equatorial Pacific is evident in the model simula-tions. The tendency towards enhancing the ITCZ peakand reducing the SPCZ peak was also noticed in theprevious ECMWF model cycles. This enhancement isassociated with a relatively strong rising motion in thecentral Pacific (not shown).

In JAS, the apparent closer agreement between themodel and the observations shown by zonal averages isunfortunately due to the compensation of strong

Fig. 6 The 10-year climatology of JFM total precipitation (inmm day–1). Left: for model (top), for verifying Xie-Arkin data(middle), and the difference between the model and verification

(bottom). Right: zonal averages for Xie-Arkin precipitation (solidblack) and model (dotted red) for all points (top), sea points only(middle), and land points only (bottom)


regional errors (Fig. 7). Over sea, the peak to the southof the equator is overestimated, and again can be linkedto a stronger than observed vertical motion, similar to itsJFM northern counterpart (Fig. 7, middle right). How-ever, in addition to enhanced SPCZ precipitation, alarge proportion of the model overestimation comesfrom the Indian Ocean, and is associated with deficien-cies in the simulation of the Asian summer monsoon(Fig. 7, bottom left; see also the discussion later).

Over land, in both seasons, there is generally a higherdegree of agreement between the model and observa-tions than over oceans. Some differences over land couldbe associated with large-scale orographic features, likethe Himalayas, Rockies and the Andes. These differ-ences may have been partly caused by the differenthorizontal resolutions at which observed and modelprecipitation were defined (though in Figs. 6 and 7 they

are interpolated to the same grid), and should not beconsidered solely as modelling errors.

Relatively large precipitation errors are also seen overthe northern parts of South America, namely, theAmazon region. An underestimated model rainfall overthe Amazon basin is associated with an excess of rainfallover the tropical Atlantic. Since a similar error pattern isseen in both JFM and JAS, it is likely to have a commoncause. An additional set of extended range experimentshas been designed in order to investigate whether thismodel deficiency may depend on model’s land surfaceparametrisations. The results will be reported in duecourse.

Despite the unquestionable nature of most errorsdiscussed, a better simulation of the precipitation cli-mate over land than over sea may also reflect possibleproblems in the observational climatology over the

Fig. 7 Same as Fig. 6 but for JAS


oceans. Indeed, Adler et al. (2001) found that someprecipitation products that mostly rely upon satelliteestimates tend to underestimate precipitation over theoceans. Also, Trenberth et al. (2002) claim that the Xie-Arkin precipitation within the ITCZ is underestimated(see also the discussion on monsoon precipitation later).It is therefore likely that, besides some genuine model-ling problems, some discrepancies between the modeland the Xie and Arkin (1997) data over sea are enhancedby uncertainties in the observational climatology.

3.2.2 South Asian summer monsoonand African precipitation

Because of its importance, the climate of the south Asiansummer monsoon is discussed separately. The climate iscomputed for a longer season, June to September(JJAS), and the circulation pattern is discussed first,before focusing on precipitation. The modelled low-levellarge-scale flow is generally too weak when comparedagainst ERA-15 climate (Fig. 8). The error increasesfrom west to east, the Somali jet is weakened, and moreso the westerly flow from southern India to the SouthChina Sea. Wind errors reach a magnitude of 8 ms–1 inthe southern part of the Bay of Bengal (Fig. 8c). Areduction in the strength of the lower-troposphericmonsoon circulation was already noticed in some earlierversions of the ECMWF model, however not as severeas seen in Fig. 8. The overall weakening of the modelledmonsoon flow is also found at upper levels. The erroramplitude in easterlies at 200 hPa is more than 10 ms–1

over the Bay of Bengal (not shown).In an idealised baroclinic dynamical framework, the

monsoon circulation could be described as the follow-ing. The relatively weak low-level westerly flow intosoutheast Asia in the model reduces the moisture fluxinto region. Compared to the relatively strong observedlow-level convergence, (in particular over Indochina andthe South China Sea) the weakened moisture conver-gence in the model is associated with a weaker risingmotion over the southeastern Asia (not shown). This inturn suppresses divergence at upper levels, eventuallyensuing much weaker 200 hPa easterlies than analysed.Thus, the overall large-scale east-west overturning in themonsoon region is weaker in the model than in ERA-15,in a dynamically consistent pattern. As discussed earlier,one ought to accommodate the different physical para-metrisations used in the model cycle 23R4 and in ERA-15, particularly when considering vertical motions; evenso, the interpretation of dynamical processes wouldessentially be unchanged.

The model deficiencies in monsoon circulation arereflected in the precipitation climatology. When com-pared with the Xie and Arkin (1997) data, model datashow a severe underestimation of precipitation over arelative large area, extending from the western Indiancoast and the Bay of Bengal into the western tropicalPacific, and towards Japan (Fig. 9). This may be

considered the most serious deficiency in the model’ssummer precipitation climatology. The pattern in rain-fall error over the Indian subcontinent (insufficient rainin the southwest and too much rain in the central part,with a reduction of the Western Ghats rain shadow)could also be associated with the differences between themodelled and observed low-level flow. However, it isworth noting the (possibly correct) precipitation maxi-mum over the Himalayas in the model, due to oro-graphic effects and not seen in observed climatology.

In the equatorial Indian Ocean, the model yields toomuch precipitation (Fig. 9c). The causes of such anexcessive precipitation rate in the model are difficult todiagnose, even if the criticism of the verification data isaccepted (i.e. the underestimation of precipitation withinthe ITCZ, see comments in the previous section). Thelow-level circulation to the south of the equator and the

Fig. 8 Climate of JJAS 850 hPa wind over the tropical Africa andthe Asian monsoon area for a model, b ERA-15, and c modelsystematic error. Contours are 5 ms–1 in a and b, and 2 ms–1 in c


cross-equatorial flow show small error amplitude(Fig. 8). However, the increased convergence along theequator (as inferred by wind arrows of the low-levelcirculation in Fig. 8c), is consistent with an increasedconvective activity. A longer (or more frequent) resi-dence of convective systems over the ocean can beinterpreted as a ‘‘shift’’ of the modelled precipitationfrom the south Asian continent to the equatorial IndianOcean (Fig. 9c). This might be associated with longerthan observed break periods within the monsoon seasonover the continent; however, an explicit conclusion canonly be made by analysing the evolution of daily pre-cipitation over all individual years considered. The fre-quency of occurrence of the break and active periodsdetermines interannual variations of the monsoon (e.g.Ferranti et al. 1997) and eventually affects the modelclimate.

Another region with prominent low-level wind andprecipitation errors in Figs. 8 and 9 is over Africa,extending from the west coast into the Sahel and furtherinto Sudan and the Arabian Peninsula. This is an areawith relatively weak winds, as seen in ERA-15 (Fig. 8,middle panel). The moisture is advected into this semi-arid region through the northern extension of theAtlantic ITCZ. The northernmost position of the ITCZmay vary widely from one year to another, and conse-quently the interannual variation of rainfall can be verylarge. In the mean, the modelled ITCZ is located a littlefurther north than observed, and an increased moistureconvergence into the African continent is causing toomuch rain over this region. Associated with this, asubstantial increase in the modelled specific humiditywith respect to ERA-15 is found between 10�N and25�N (up to 5 g kg–1 at 925 hPa, not shown).

Given the limited size of our summer ensembles(three-member only), some of the discrepancies in themonsoon circulation and the associated precipitationbetween the model and ERA-15 could be partly attrib-uted to inadequate sampling of the model climatology.However, as it will be demonstrated in the next section,even with a doubling of the ensemble size (though in adifferent context), model estimates of precipitation forthe Indian summer monsoon have not significantlyimproved.

4 Observed and simulated variability

4.1 Interannual variations

In the 10-year period covered by our experiments, tworelatively strong and two moderately strong ENSOevents occurred. The strong events were El Nino in1982/83 and La Nina in 1988/89, and moderately strongevents were El Nino in 1986/87 and La Nina in 1984/85.During summer (JJAS), El Nino was active in 1982 and1987, and La Nina in 1985 and 1988. As in the‘‘canonical’’ ENSO cycle, the summer events had weakeramplitude than winter ones; however, a notable excep-tion was the El Nino in JJAS 1987, which was strongerthan the one in the previous winter, DJFM 1986/87.

4.1.1 Model signal and noise

In an ensemble of integrations with atmospheric model,it is often useful to evaluate the effect of prescribed SSTforcing on atmospheric development relative to the ex-tent of unpredictable nonlinear effects imposed bymodel’s internal dynamics. The former is usually calledsignal and the latter is called noise. There are severalways how to define signal and noise in an ensemble (e.g.Shukla et al. 2000). A simple approach is adopted here;the signal is defined as the mean squared anomaly of allensemble means �Fjwith respect to the climatologicalmean �F

Fig. 9 Climate of JJAS precipitation for the same region as inFig. 8 for a model, b Xie-Arkin data, and c difference between themodel and verifying data. Contours are at 1, 2, 3, 5 … mm day–1


r2s ¼

1

N

XN

j

ð�Fj � �F Þ2 ;

where

�Fj ¼1

M

XM

i

Fij

is the ensemble mean for year j, and Fij is the i-thensemble member in the j-th year (M = 6 ensemblemembers, N = 10 years). Likewise, the noise is definedas the mean squared distance within an ensemble aver-aged over all ensembles (years):

r2n ¼

1

N

XN

j

1

M

XM

i

ðFij � �FjÞ2" #

:

Figure 10 shows the signal-to-noise ratio for the200 hPa geopotential height during JFM and computedfor different periods, for the years 1981–1990, for ENSOyears only and for non-ENSO years. Irrespective ofyears considered, the signal-to-noise ratio is largest inthe tropics. When all years are taken into account(Fig. 10a), there are only a few regions outside the tro-pics (30�N–30�S) where the magnitude of the signal-to-noise ratio exceeds unity, the threshold that indicatespotential predictability. They include Japan, the north-ern Pacific, southern part of North America, and someparts in the Southern Hemisphere including westernAustralia. This is because the noise dominates in theextratropics, in particular in the winter hemisphere, andis virtually non-existent in the tropical belt. Though thesignal is relatively large at high latitudes (not shown),Fig. 10a indicates a poor predictability over the polarregions because of a considerable level of noise there.

When only the ENSO years are considered (the ElNinos of 1983 and 1987, and the La Ninas of 1985 and1989), the signal-to-noise ratio has considerably in-creased in the tropics (Fig. 10b). This makes little dif-ference however, since the predictability in the tropicsimplied from Fig. 10a was already quite high. Moreimportantly, the area of potential predictability in manyparts of the extratropics has become larger than inFig. 10a. It now includes parts of Asia around theCaspian Sea and a great deal of the southeastern andeastern Asia as well as the whole of northern Canada.Also, some increase is seen in the Antarctica region.Clearly, this signifies the impact of ENSO lowerboundary forcing on predictability at seasonal timescales. (Interestingly, the signal-to-noise ratio for theENSO years has been reduced over the southern half ofAustralia.) When only non-ENSO years are included (6out of 10 years), there is very little predictability outsidethe tropics (Fig. 10c). But perhaps surprisingly, a smallpart of southeastern Europe has now the ratio greaterthan unity. Though only ten years of model integrationswere at our disposal, these results may be assumed to berobust, in particular for ENSO years.

The spatial extension of potential predictability forthe 850 hPa u-wind (shaded areas in Fig. 11) is essen-tially similar to that of the 200 hPa geopotential height,however, a relatively high ratio of signal-to-noise isconfined to the three distinct and relatively small re-gions: the Asian maritime continent, the central equa-torial Pacific, and the Amazon-equatorial Atlantic(Fig. 11a). These may be identified with strong semi-permanent trades that maintain convergence withinITCZ, in particular over the Pacific. In addition,Fig. 11c indicates that, even in the non-ENSO years, the

Fig. 10 JFM 200 hPa geopotential height signal-to-noise ratio for:a the whole 1981–1990 period, b ENSO years in the same period,and c the non-ENSO years. Contours are at 0.1, 0.5, 1, 5, 10 …


lower branch of the Walker circulation seems to bepotentially predictable.

4.1.2 Geopotential height

Because of a relative small number of ENSO events inthe 10-year period considered, composites for the twoEl Nino and two La Nina years were made. Thisalleviates somewhat the sampling problem associatedwith simulating a single natural realisation, since the

analysis is only one of many possible realisations withthe same boundary forcing. The signature in the JFM500 hPa height composites shown in Fig. 12 comesmainly from the dominant year, since the two eventsthat make up the composite in each ENSO phase werenot of the same intensity. For example, for the El Ninocomposite (Fig. 12, left panels), the year 1982/83 isdominant, for La Nina the signature comes from theyear 1988/89.

From the composites in Fig. 12, the model perfor-mance looks reasonably good over the Pacific/NorthAmerica (PNA) region, where the patterns of both ElNino and La Nina anomalies agree rather well withthose analysed (Fig. 12, bottom panels). The modelanomalies are generally weaker than the observed ones,and the El Nino composite is closer to the verifyinganalysis than the La Nina composite. The positiveanomaly over eastern Canada and the northern Atlanticis represented in the model, albeit with much reducedamplitude. On the other hand, the negative anomalyover the southern US and Mexico is too strong in themodel when compared with ERA-15.

Outside the PNA region, the model does not correctlyreproduce the height anomalies. This is partly because,even for ENSO events of the same sign, observedanomalies may differ from one case to another. Forexample, during the 1984/85 La Nina, a rather strongpositive anomaly prevailed over Greenland, whereasduring the 1988/89 event, a negative anomaly of almostthe same amplitude was dominant (not shown); in thecomposite, they nearly cancel each other (Fig. 12, bot-tom right). The model composite for the same regionyields the negative anomaly, which comes primarilyfrom a strong La Nina forcing in JFM 1989. OverEurope, the model simulations are not successful; thenegative El Nino anomaly over Scandinavia and positiveLa Nina anomaly over the British Isles are both mis-placed when compared with analysis.

4.1.3 Precipitation

For precipitation, the interannual variations are dis-cussed in terms of area-averaged anomalies over variousregions. In both seasons, the time series of ensemble-mean precipitation anomalies over the tropical Pacific(the Nino3 and Nino4 areas) agree quite well with ver-ifying anomalies computed from the Xie-Arkin data(Fig. 13). Such a high correlation between observed andsimulated precipitation anomalies reflects an accurate(direct) model response to the underlying forcing. In theNino3 region, the model overestimates the amplitude ofJFM interannual variations. The most evident case is forJFM 1983, where model precipitation exceeds the ob-served one by nearly 5 mm day–1, or almost 100% in-crease (Fig. 13, bottom left). This overestimation of the1983 Nino3 precipitation makes a significant contribu-tion to the positive model error in the eastern Pacificseen in Fig. 6, bottom panel.

Fig. 11a–c Same as Fig. 10 but for 850 hPa u-wind


Fig. 12 JFM 500 hPageopotential height compositeanomalies for the model (top)and ERA-15 (bottom) during ElNino (left) and La Nina (right).Contours every 2 dam

Fig. 13 Time series of area averaged precipitation anomalies for the Nino4 (top) and Nino3 (bottom) regions for JFM (left) and JAS(right). Model data red dashed (open circles), Xie-Arkin data solid black (open squares). Unit in mm day–1


For the Indian monsoon, the four-month average(JJAS) is considered and area averages relate to landpoints only (Fig. 14, top). Clearly, the model perfor-mance is much poorer than over the tropical Pacific.There is some consistency between the model andobservations after major ENSO events (i.e. in 1983 and1989). However, some large discrepancies are found aswell: for example, the opposite model and observedanomalies for 1981 and 1982, and again for 1987 and1988. The latter is illustrated in difference maps between1987 and 1988 for both model and the Xie and Arkin(1997) data (Fig. 14, bottom panels). Whereas overmuch of India the model simulates more rain in 1987than 1988, the opposite is true for the verification(Fig. 14, bottom, left and right panels). In fact, manystudies indicate that Indian monsoon was poor in 1987,and returned to near normal conditions in 1988 (e.g.Parthasarathy et al. 1994; Ju and Slingo 1995; Arpe et al.1998). Another check is performed by using the ERA-4012–24 hour accumulated forecast data, from both 00Zand 12Z. The difference for ERA-40 accumulations isvery similar to that obtained by using the Xie and Arkin(1997) data (not shown).

Simulation of monsoon interannual variability is awell recognised modelling challenge. Sperber and Palmer(1996) found little coherence among the simulations ofmany models, even in 1987 and 1988, the two years withrelatively large and opposite ENSO SST anomalies. In

addition to a genuine modelling problem, our experi-ments might have also been affected by poor sampling.Estimates of the 1987-minus-1988 rainfall differencebased on three-member ensembles have a standard errorof about 2 mm day–1 over central India, as diagnosedfrom the variance of individual ensemble members.When the size of the 1987 and 1988 ensembles is in-creased to six members, the positive difference over In-dia is reduced somewhat (Fig.14, middle bottom panel),but the improvement is very modest indeed. Despite thereduction in expected amplitude (by a factor of �2),sampling error may still affect our six-member simula-tions of monsoon variations. Brankovic and Palmer(1997) found that at least 10-member ensembles are re-quired to attain a sufficient level of reproducibility forthe Indian monsoon.

The interannual variations of precipitation for someextratropical regions were also analysed. The bestagreement between observed and simulated interannualvariations is found for the western USA (25�N–50�N,125�W–100�W) in JFM and for the southern Europe(35�N–50�N, 10�W–30�E) during JAS (Fig.15). For theformer, where a relative large positive anomaly duringthe 1983 El Nino is clearly dominant in the observations,the model shows a tendency to flatten the variation. Forsouthern Europe, the two peaks (largest positive anom-alies), in JAS 1982 and 1987, may be associated with ElNino events, whereas the largest negative anomaly in

Fig. 14 Top: same as Fig. 13but for the JJAS Indianmonsoon precipitationanomalies over land pointsonly. Bottom: the differencebetween 1987 and 1988 JJASprecipitation over the Indianregion for 3-member ensemble,6-member ensemble and for theXie-Arkin data respectively.Units in mm day–1, contours at1, 2, 3, 5, … mm day–1


1985 may be related to a moderate La Nina. The modelfollows closely (but not exactly) the observed variation.

4.2 Intraseasonal variations

The total intraseasonal variability of the NorthernHemisphere 500 hPa height during JFM, is representedin Fig. 16 in terms of standard deviation computed withrespect to each winter’s average. The observed vari-ability, estimated from ERA-15 data (Fig. 16a), is foundto be largest over the northern Atlantic/European andnorth Pacific regions. The difference between the mod-elled and ERA-15 variability (i.e. the error in the sim-ulated variability) indicates decreased model variabilityover the mid-to-low latitude oceans, especially over thePacific, and over the polar region (Fig. 16b). An increasein the total modelled variability is seen over the northernedge of the Pacific, the northeastern Atlantic and a mid-latitude band extending from the Mediterranean intocentral Asia.

The largest part of the differences in total variabilitycan be ascribed to low-frequency (or low-pass) vari-ability (Fig. 16, middle). This variability accounts fortime scales longer than seven days, and corresponds to arange of fluctuations, from atmospheric blocks to har-monics of the seasonal cycle. A relative small part of thechange in total variability can be related to high fre-quencies, i.e. to time scales shorter than seven days(Fig. 16, bottom). These are normally associated withstorm tracks and related synoptic-scale activity.

The relatively large errors in the modelled intrasea-sonal variability over the Pacific are of particular inter-est, because this region coincides with one of the centresof dynamical activity in the Northern Hemisphere. Infact, the low-frequency errors present some intriguingaspects, as can be appreciated from the following con-sideration.

It has long been recognised that the boundary forcingexerted by SST anomalies makes an important impacton atmospheric low-frequency variability. Based onobservational data, Chen and Van den Dool (1995)found that during cold ENSO episodes, low-frequencyvariability over the north Pacific is much increased,leading to a reduction of the signal-to-noise ratio in theprediction of monthly to seasonal-scale anomalies.During warm ENSO events, the opposite is the case, i.e.relatively small variability associated with low-pass fre-quencies is observed over the north Pacific, thus con-tributing to an enhanced extended-range predictability.Even relatively simple dynamical models (e.g. Molteniand Corti 1998) have reproduced the impact of tropicalforcing on low-frequency variability.

Now, if model systematic error of the 500 hPa height(Fig. 1c) is compared with the observed (and modelled)response to La Nina SST anomalies (Fig. 12, rightpanels), a similarity between the two patterns over thenorth Pacific region can be clearly seen. Based on theobserved relationship between ENSO episodes and low-frequency variability over the north Pacific, one wouldexpect the model to overestimate low-frequency vari-ability in this region. On the contrary, in Fig. 16dnegative differences between modelled and observedlow-frequency variability prevail, and the positive dif-ference is confined to a narrow region south of theKamchatka peninsula.

A closer inspection of the 500 hPa height patterns inFig. 1 and Fig. 12d shows that the La Nina response hasa weak amplitude over the western side of the NorthAmerican continent, in the region where the climato-logical stationary waves have a large positive amplitude(seen as a strong ridging in Fig. 1b). Therefore, coldENSO events change the phase, rather than the ampli-tude, of the observed stationary wave pattern. On theother hand, the model systematic error shows a largenegative bias in the region of the observed stationarywave ridge, thereby reducing its amplitude considerably(Fig. 1a). The relationship between planetary waveamplitude and the instability of large-scale anomaliesmay explain the different effects of the model bias andthe observed La Nina response on low-frequency vari-ability. Whereas the observed flow associated with LaNina events was found to have stronger than averagebarotropic instability (e.g. Palmer 1988), the reductionof the modelled planetary wave amplitude in the Pacificregion has an opposite effect, according to a number ofstudies on the instability of the planetary-scale flow (e.g.Simmons et al. 1983; Branstator 1990).

In fact, the model error in planetary wave amplitudeshown in Fig. 1c has an impact on the relationship

Fig. 15 Same as Fig. 13 but for a the western USA during JFMand b southern Europe during JAS


Fig. 16 Variability of the JFM500 hPa heights for ERA-15(left) and the difference betweenthe model and ERA-15 (right).Total standard deviation (top),low-frequency variations withperiod longer than 7 days(middle), and high-frequencyvariations with period shorterthan 7 days (bottom). Units indam, contours for full fieldsevery 2 and 1 dam, fordifferences every 0.5 dam

Fig. 17 Difference in the JFM500 hPa height low-frequencyvariability between thecomposite of the two La Ninayears and the composite of thesix non-ENSO years for: aERA-15, and b the model.Contours every 1 dam


between ENSO episodes and low-frequency variabilityin the Pacific. Figure 17 shows the difference of the500 hPa low frequency standard deviation during LaNina and non-ENSO years for both model simulationsand ERA-15. While, during the La Nina years, low-frequency variability in ERA-15 data is strongly en-hanced over the Pacific (consistently with the resultsfrom Chen and van den Dool 1995), it is slightly reducedin the model. On the other hand, when the difference inlow-frequency variability between El Nino and non-ENSO anomalies are compared, a better similarity be-tween the model and ERA-15 is found. In this case, bothreanalysis and the model show a reduction in low-fre-quency variability over the PNA region as implied fromobservational studies (not shown). Therefore, as far asthe simulation of the extratropical response to ENSO isconcerned, the negative impact of the model systematicerror shows up much more strongly on intraseasonalvariability patterns than on time-mean anomalies.

5 Summary and conclusions

The climatology and variability of seasonal ensembleintegrations made with the ECMWF model cycle 23R4was assessed in this study. This cycle was in ECMWFmedium-range operations until January 2002, and is alsoused for the new 40-year ECMWF re-analysis (ERA-40). The experimental design was similar to that of thePROVOST project (Brankovic and Palmer 2000); how-ever, our experiments were run over a smaller number ofyears (10 versus 15) and with smaller ensemble size (6 or3 versus 9). On the other hand, both horizontal andvertical model resolutions were higher than in PRO-VOST (TL159 versus T63, and 40 versus 31 levels).Despite having a smaller set of experiments, the datasetwas sufficient to evaluate model systematic errors andinterannual and intraseasonal variability.

The model wintertime systematic error in the upper-air flow over the north Atlantic/European region ismuch reduced when compared with PROVOST. On theother hand, a large positive error (worse than in PRO-VOST) is seen over the north Pacific. Though this errorprojects strongly onto the La Nina anomaly pattern overthe north Pacific, it does not contribute, as might beexpected, to an enhancement in low-frequency varia-tions in the model. On the contrary, this error, togetherwith non-negligible negative error over the western partof North America, contributes to an overall reduction ofthe planetary wave amplitude in the north Pacific regionand strongly influences the modelled intraseasonalvariability.

The La Nina-like upper-air error pattern found inJFM over the PNA region is not associated, however,with a La Nina-like error in tropical precipitation (al-though the reduction in the north Pacific planetarywaves is consistent with decreased rainfall over themaritime continents, as simulated e.g. in the study byFerranti et al. 1994). An explanation for this apparent

inconsistency between tropical and extratropical modelerrors is sought through additional experimentation,and is beyond the scope of this study.

For zonal averages, largest errors occur in or near thestratosphere. For some fields (like zonal wind), theperceived error may also reflect an inadequacy in thevertical resolution in the verification data, ERA-15. Astrong erroneous stratospheric cooling at high latitudes,irrespective of season, continues to remain a robustfeature on seasonal time scales, little affected by theintroduction of newer model cycles. A relatively strongweakening of the modelled v -wind at the divergencelevel over southeastern Asia and the northern IndianOcean is associated with a weakened local Hadley celland, in summer, with a poor monsoon circulation.

For precipitation climatologies, there are relativelylarger discrepancies between the model and verifyingdata over the oceans than over land. Besides revealinggenuine modelling errors, these may also be affected byuncertainties with observational analysis based on sa-tellite estimates over sea. Despite the differences betweenobserved and modelled climatologies, the interannualvariations in the tropical Pacific precipitation are wellsimulated, and reflect an appropriate model response torealistic boundary forcing.

On the other hand, for the Asian summer monsoon, asevere weakening of the low-level flow over southernIndia and the Bay of Bengal yields both poor precipi-tation climatology and poor interannual variations. Inthe region where the monsoon flow impinges against theIndian subcontinent there is too little rain, so that eventhe well-known rain-shadow effect of the Western Ghatsmountains is absent from the model climatology. Ingeneral, a strong underestimation of rainfall occurs onthe southeastern edge of the Asian continent.

Potential predictability, as estimated by a conven-tional signal-to-noise ratio, has not changed much sincePROVOST. Only for moderate and strong ENSO years,do some regions in the extratropics acquire potentialpredictability.

In the northern extratropics, 500 hPa height com-posites of ENSO events are reasonably well representedby the model only over the PNA region. Outside thisregion, the poor composite patterns may be indicative ofweakened teleconnections in the model. Despite thisdeficiency, in some extratropical regions (western USA,southern Europe), the modelled interannual variation ofprecipitation is relatively close to the observed one.

Whilst most recent ECMWF model cycles undoubt-edly represent better model versions for short andmedium range forecasts (Simmons and Hollingsworth2002), benefits from cycle 23R4 are not quite obviouswhen it comes to seasonal time scales. It could be arguedthat recent changes to the physical parametrisationshave been tuned for the substantially higher resolutionused in medium range forecasting. However, Brankovicand Gregory (2001) have shown that seasonal time scalesystematic errors do not differ substantially across var-ious resolutions (T63, T159 and T319). For the 23R4


version, it was found that some improvements in themodel climatology are evident when compared withearlier model cycles; however, many known problemsare still present, and new ones emerged. Benefits fromchanges to model formulation seen at shorter time scalesdo not necessarily extend to seasonal time scales.Therefore, a careful analysis and a detailed strategyought to be considered when planning the introductionof a modified model version into operational seasonalforecasting.

Acknowledgements We are grateful to Nils Wedi for his patience inhelping to set up model experiments in the remote mode. We alsothank ECMWF scientists from the DEMETER project for theircomments and suggestions on the earlier version of the work, whilereviews of the two anonymous referees improved its final version.This work was performed as a part of the ICTP contribution to thePROMISE project of the European Union.

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