Polar Record 31 (177): 199-210 (1995). Printed in Great Britain. 199

Atmospheric temperature variability in the Arcticas revealed in a TOVS data recordSiri Jodha Singh KhalsaApplied Research Corporation, 8201 Corporate Drive, Suite 1120, Landover, MD 20785, USAJeffrey R. KeyCooperative Institute for Research in Environmental Sciences, Campus Box 449,University of Colorado, Boulder, CO 80309-0449, USAReceived July 1994

ABSTRACT. The Earth's high-latitude regions are of critical importance in many climate-change scenarios, but a time-continuous, spatially complete, and well-calibrated record of tropospheric temperatures is needed in order to assess pastand future climate changes. Studies of recently compiled upper-air data sets show no evidence of CO2-induced warming,but the spatial pattern of tropospheric temperature variability in the Arctic has not been thoroughly examined. This studyanalyzes a 108-month segment of the data record from the TIROS Operational Vertical Sounder (TOVS) aboard NOAApolar-orbiting satellites to examine both the spatial and temporal variability of atmospheric temperature in the Arctic.

Temperature retrievals based on clear-column radiances archived at NOAA/NESDIS were done using algorithmstuned to Arctic conditions. The retrieved temperatures compared well with Arctic rawinsonde data, and include low-level inversions that are often problematic for satellite retrievals. The amplitude of the seasonal cycle in 500 mbartemperatures from the TOVS, NMC, and rawinsonde data generally agreed, whereas the phase comparisons producedmixed results. Principal component analyses of the TOVS and NMC temperatures revealed both monopole and dipolespatial patterns in the component loadings. Spatial patterns of the correlation between the upper-air data and the TOVSretrievals were similar to the principal component loading patterns. Whereas no significant trends were found in thestation data for the same period as the TOVS record, a significant negative trend could be seen in the first principalcomponent scores of the TOVS retrievals.

ContentsIntroductionDataAnalyses and resultsSummary and conclusionsAcknowledgementsReferences

Introduction

199199202208209209

Concern over possible climatic impacts of increased CO2

and other greenhouse gases has prompted increasing atten-tion to be focused on the Arctic. Many consider this regionto be an especially sensitive indicator of climate change(for example, Wetherald and Manabe 1988), a view that issupported by modeling studies. To date, observationalstudies of Arctic temperatures have relied on a relativelysparse network of surface and upper-air stations at coastaland inland locations, leaving vast areas of the Arctic Basinunsampled. Recent examinations of the observationalrecord of Arctic temperatures are, however, contradictory.Hansen and Lebedeff (1987) found that Arctic air tempera-tures have increased since 1880; Kahl and others (1993a;1993b) did not find evidence of greenhouse-induced warm-ing in the Arctic troposphere during the period 1958-1986.

Theoretical studies indicate that the expected climaticwarming in the Arctic will be strongest at the surface butwill extend throughout the lower troposphere with increas-ing intensity toward the pole. Manabe and others (1992)reported modeled surface temperature increases due togreenhouse warming on the order of 6° C in the Arctic,with mid-tropospheric increases on the order of 2.5° C.Interpretation of the surface record in Arctic climate-change studies is complicated by the high frequency ofstrong surface-based inversions that can cause a decoupling

of the surface from the dynamics of the mid and uppertroposphere. However, surface temperatures remainradiatively coupled to tropospheric conditions, principallythrough cloud cover. In any case, Arctic climate monitor-ing should include an examination of temperatures at alllevels of the troposphere and, additionally, the strato-sphere, where cooling is expected.

This paper assesses the suitability of data from thepolar-orbiting TIROS Operational Vertical Sounder(TOVS) for providing a detailed, spatially complete analy-sis of temperature variability in the northern hemispherenorth of 60° latitude. Comparisons of TOVS temperatureretrievals with rawinsonde data are first done in an attemptto assess their accuracy. Spatial patterns are then exam-ined through the use of principal components analysis(PCA) of TOVS-derived temperatures and National Mete-orological Center (NMC) fields. Lastly, trends in theprincipal component scores for both data sets during theperiod of record are presented. Analyses of spatial patternsand trends are restricted to the middle troposphere, atapproximately the 500 mbar level. Monthly mean tem-peratures for the period 1983-1991 are examined.

Data

The primary data set in this analysis consisted of tempera-ture profiles retrieved from TOVS radiances. Tempera-tures from two other data sets were used for comparison:in situ rawinsonde data and NMC analyses.

TOVS retrievalsThe TOVS system is comprised of three sensors: the high-resolution infrared radiation sounder (HIRS), the micro-wave sounding unit (MSU), and the stratospheric sound-ing unit (SSU). Only the HIRS and MSU sensors wereused in this study. HIRS measures radiation in 19 infrared

200 KHALSA AND KEY

Fig. 1. Locations of the Arctic profiles incorporated into the TIGR database.

3i retrievals at 68°N135°E

COQ_

COCOCD

300

400

500

600

700

800

900

1000

• i i I i i i

• NOAA10

iA

—** -r\

- 4 - 3 - 2 - 1 0 1 2 3 4deviation from HARA (°C)

Fig. 2. Difference between the mean January temperature profile retrievedusing 31 and the nearest rawinsonde data.

(IR) bands from 3.7 to 15 /imand one shortwave band at 0.7fim. The MSU measures radia-tion at four frequencies between50 and 58 GHz. Both instru-ments scan from nadir to 58° oneither side of nadir with a result-ing swath width of 2200 km.HIRS and MSU have nadir reso-lutions of 17 and 110 km, re-spectively.

The National Environmen-tal Satellite and Data Informa-tion Service (NESDIS) of theUS National Oceanographic andAtmospheric Administration(NOAA) has been producing anoperational sounding product forassimilation into numericalweather prediction models since1979. These temperatureretrievals were not used becausethe NESDIS algorithms havechanged over the years, the al-gorithms do not perform well inArctic conditions, and their sta-tistical retrieval methods arestrongly biased towards clima-tology. Instead, an intermediate

product of their retrieval process was used:the clear-column radiances (McMillin andNappi 1986).

An important first step in interpretinginfrared radiance data from satellites isdetermining whether the scene being ob-served contains clouds. NESDIS first testswhether there are any completely clearscenes. If not, the radiances from a group ofpartially cloud-filled scenes is adjusted togive what the radiances would be in theabsence of clouds. If this also fails, thescenes are declared cloudy, and retrievalsare done using only those channels whoseweighting functions are high enough in theatmosphere to be unaffected by clouds.Radiances from these type of soundingswere not used in this study.

Since the retrievals were performed inareas that were clear or partly cloudy, theresults could be biased towards colder sur-face conditions. This is because the sam-pling excluded regions under persistentcloud cover, which tend to have highersurface temperatures, particularly in win-ter. In the monthly averages, we assumedenough partly cloudy regions were sam-pled to minimize this effect.

ATMOSPHERIC TEMPERATURE VARIABILITY IN THE ARCTIC 201

•Level31-Station 23205

84 96 108Fig. 3. Time series of monthly mean temperature anomalies near the 500 mbar level from TOVS andan upper-air station, from June 1983 through December 1991.

Temperature profiles were retrieved from the clear-column radiances using the Improved Initialization Inver-sion method (31; Chedin and others 1985). The 31 methodcombines both physical and statistical techniques to obtainestimates of geophysical quantities from the HIRS andMSU radiances. One of the most important steps in theretrieval of a temperature profile, both in 31 and in otherTOVS processing systems, is the first guess profile. The31 system incorporates the TOVS Initial Guess Retrieval(TIGR) database of representative atmospheric profiles,based on a global set of more than 150,000 radiosondemeasurements. A forward radiative transfer model is usedto compute channel radiances and brightness tempera-tures, transmittances, and covariances between tempera-tures and radiances. This is done for each profile at 10

-10 -

0 2 4 6 8 10 12 14 16 18 20 22 24

monthFig. 4. Example of fitting the seasonal cycle to temperaturedata from one Arctic station.

viewing angles, 19 surface pressures, and two surfaceemissivities. Temperature profiles are classified into oneof five air mass types: one tropical, two mid-latitude, andtwo polar, according to their statistical characteristics.

Version 2 of TIGR contains 1761 atmospheric situa-tions, of which 441 come from soundings north of 60°N.Figure 1 shows the locations of these soundings. Whilemost were from coastal stations, the central ice pack wasalso represented by soundings collected at Soviet drifting(ice) stations (Serreze and others 1992). Comparisons ofTIGR profiles with rawinsonde data have shown it tocontain a reasonably complete representation of Arcticconditions (Key and others 1993).

The 31 processing was normally done in four distinctsteps, with the temperature retrievals occurring in the

fourth step. The first three steps of the 31 package werenot necessary when starting from the clear-columnradiances supplied from NESDIS. However, severaloperations done in the first three steps of 31 producedresults that were needed in the fourth, so some modifi-cation of the source code was necessary. Additionally,the polar-specific modifications of 31 described inFrancis (1994) were incorporated. These have alsobeen integrated into the latest official version of 31.The modifications include detection of sea ice, animproved estimate of sea-ice emissivity, and polar-night cloud detection.

At present, retrievals are only performed on datafrom the NOAA-8, NOAA-9, NOAA-10, and NOAA-11 satellites, those for which the radiance bias correc-tions factors are included in the 31 code. This coversthe period June 1983 through December 1991, withone gap of eight months when none of the abovesatellites was operational. A comparison of monthlymean 500 mbar temperatures for the different satelliteswas done. The amplitude of the seasonal cycle wasabout 20°. In the regions of overlap, the NOAA-10/NOAA-11 overlap being the longest, consistent biases

202 KHALSA AND KEY

TOVS 31 LEVEL31 - 498mb AMPLITUDES

4.7'C

Fig. 5. Amplitude of the seasonal cycle in the TOVS 500 mbarretrieved temperatures over the Arctic Basin. Letters mark thelocations of upper-air stations (see Fig. 7 for their amplitudes).

TOVS 3 1 - 438mb SEASONAL CYCLE PHASE (from summer sols%e)

49. days

47. days

^ , > % ^ 1 45. days

43. days

41. days

39. days

37. days

35. days

33. days

31 . days

29. days

27. days

25. days

Fig. 6. Phase of the seasonal cycle (days since the summersolstice) in the TOVS 500 mbar retrieved temperatures over theArctic Basin. Letters mark the locations of upper-air stations (seeFig. 7 for their phase values).

were observed. The six-hour difference in the times thatthese two satellites observe any given point on the globewas not expected to be important in the summer and winterseasons when there is little diurnal cycle. The biasesobserved were relatively constant, implying incorrect in-strument correction factors. These factors were computedfrom radiosonde matchups from a global set. The authorssuspect that these bias correction factors need to berecomputed for the Arctic, since the measurements havebeen made at the far end of the instruments' operatingrange (see Eyre 1987).

The sounding data were averaged onto an equal-areaazimuthal polar grid, with grid cells 240 km on a side. This

translated into 29 x 29 grid cells that encompass thearea north of 60°N. Grid cells outside the 60°Nparallel but within the outer bounds of the 29 x 29square array were always treated as missing. Dataat all sensor scan angles were included, since elimi-nating data at large scan angles leaves a data gapover the pole. However, data were weighted by theinverse of the scan angle. Thus, in data-rich re-gions, the less reliable data contributed little to theaverage. The mean of the weights for a given gridcell provided an indication of how much high-angle data were included in the average. Fourparameters were computed and saved for each gridcell at each of 39 levels. These were: the weightedmean, the total number of soundings going into theaverage, the weighted standard deviation of themean, and the mean weight of the soundings.

Ancillary dataThe Historical Arctic Rawinsonde Archive (Kahland others 1992) is a comprehensive collection ofmore than 1.2 million rawinsonde soundings. Formost stations the record begins in 1958 and extendsto 1987. Temperatures are interpolated to the first24 pressure levels used in 31, from the surface to 50mbar.

The same analyses performed on the TOVStemperature retrievals were performed on the 500mbar gridded data from the National Meteorologi-cal Center's (NMC) numerical forecast model. TheNMC data were regridded from an octagonal gridonto the equal-area azimuthal grid that was used forthe TOVS data. Twice-daily data were averagedinto monthly means for the same period of record asthe TOVS data.

Analyses and results

The analyses in this paper were based on monthlymean temperatures at 31 level 31, representing alayer from 472 to 525 mbar, for the period 1983—1991. This study was limited to a single level,although future work will examine other pressurelevels in the data set. The analyses performedincluded a comparison of TOVS-derived and insitu profiles, a principal components analysis ofspatial patterns, and an examination of trends in the

principal component scores.

RetrievalsThe strong, surface-based temperature inversions com-mon to the Arctic present a major challenge to satellitetemperature retrieval algorithms. Nonetheless, 31 wasoften successful in capturing surface inversions. Theretrieved inversions were typically weaker than thosefound in the in situ data, but this was expected since thelayer-averaged temperatures returned in the retrievalssmooth out the strong gradients near the surface.

Figure 2 shows the difference between TOVS-derivedprofiles and rawinsonde data for mean January 1988

ATMOSPHERIC TEMPERATURE VARIABILITY IN THE ARCTIC 203

conditions at one Arctic station. At the level closest to thesurface the retrieval is 3°C warmer than the station data,but the magnitude of the inversion is nearly 15°C. Duringsummer, when low-level inversions are less intense, dif-ferences are typically much less.

A time series of monthly mean temperature anomaliesfor the TOVS/3I-derived and in situ 500 mbar tempera-tures at Narjan Mar is shown in Figure 3, starting in June1983 and ending December 1991. While the overallagreement between the two was good (r- 0.75), there wereclearly instances where the retrieved temperatures did notcapture large deviations from one month to the next.

Seasonal cycleThe seasonal cycle of temperature Tat each grid point wascomputed by first finding the mean for each month for allyears, and then subtracting the mean for all months. Theresulting 12 values were fit with:

T(m) = A sin - 6 [1]V 12

where m = 0.5, 1.5, ..., 11.5. These fits return A as theamplitude and 0 as the phase. A sample showing twocycles of the zero-mean monthly averages from Sodankylaalong with the best fit sinusoid is given in Figure 4. Ingeneral, the amplitude was determined better than thephase, since the annual cycle was not always symmetricwith respect to the seasons; for example, the summerwarming may occur more rapidly than the winter cooling.

Figure 5 shows the amplitude of the seasonal cycle oftemperatures at 500 mbar, and Figure 6 shows the phase.The peak-to-peak amplitude values were double what isshown. The amplitude was greatest over Siberia, with apeak-to-peak value of more than 25°C. A secondarymaximum occurred over the Canadian archipelago, con-necting with the primary maximum with a ridge over thepole. Minima occurred over the North Atlantic Ocean andBering Sea. This pattern was in good general agreement

Table 1. Letter codes, WMO station numbers, and names of stations used forcomparison of upper air data with TOVS soundings.

ABCDEFGHIJKLMNPQRS

WMOID

115228361028

222712074423022200462150424125219652198270026719577107271924

420243604339

Station name

BodoSodankylaBjornoyaSojnaMalye KarmakulyAmdermaGmo im et KrenkelyaOstrov PreobrazheniaOlenekOstrov ChetyrekhstolboOstrov VrangelyaBarrowInuvikMould BayResolute BayThuleAngmagssalikScoresby Sund

Latitude Longitude(°N) (°E)

67.28 14.4267.37 26.6574.52 19.0267.88 44.1372.38 52.7369.77 61.6880.62 58.0574.67 112.9368.50 112.4370.63 162.4070.97 181.4771.30 203.2268.30 226.5276.23 240.6874.72 265.0576.53 291.2565.60 322.3770.48 338.03

with the amplitude of the NMC 500 mbar temperatures(not shown).

The phase of the temperature cycle is expressed as thenumber of days that the peak positive value occurs pastsummer solstice, June 21, or, equivalently, the number ofdays that the minimum occurs after the winter solstice,December 21. Figure 6 shows a maximum of about 50days in the Greenland and Norwegian seas. This meansthat the maximum temperatures at the 500 mbar leveloccurred around August 10 in that area. The shortest phaseoccurred over the pole, about 25 days. This is probablydetermined more by the minimum occurring soon after thewinter solstice. The phase is also short over the BeaufortSea.

The seasonal cycle from selected upper-air stationswas also computed and compared with TOVS values forthe grid cells containing the stations. The stations are listedin Table 1 and the results are shown in Figure 7. Theoverall agreement between the amplitudes of the 31retrievals and the upper-air stations was good (r = 0.94).One region of discrepancy between the TOVS and NMCseasonal cycles (not shown) was in the Beaufort Sea,where TOVS returned amplitudes of 16°C peak-to-peakand NMC has an amplitude of 18°C peak-to-peak.

The phase of seasonal cycle determined from NMCdata was similar to TOVS in having a maximum lag overthe Greenland and Norwegian seas. However, the maxi-mum was roughly five days earlier. The minimum over thepole and Beaufort Sea was later by about 4—5 days. Also,there were other minima of the same size, around 30 days,over both Siberia and the Urals in the NMC data, whichplaced them within a few days' agreement with the TOVSdata in these regions. Comparisons of TOVS and NMCphases with results from individual stations were mixed.For some stations, the match with TOVS was better, andfor others the results were closer to the phases for NMC.Unfortunately, the region of greatest discrepancy between

TOVS and NMC and of greatest inter-est, the Arctic Ocean, was also theregion where there were little stationdata to resolve these discrepancies.Spatial patterns of non-seasonalvariabilityOne of the primary motivations of thiswork was the desire to know if there issignificant variability in Arctic atmos-pheric temperatures that is not cap-tured in the conventional rawinsonderecord. The analysis of the seasonalcycle in the previous section suggestedthat this may be the case. To pursuethis further, the spatial and temporalpatterns of variability in the tempera-ture anomalies — that is, the depar-tures from the seasonal cycle — wereexamined. These departures wereanalyzed with principal components

204 KHALSA AND KEY

Fig. 7. Scatter plots of the seasonal cycles of 500 mbar tempera-ture from TOVS-31 and station data: (a) amplitude; (b) phase.

analysis (PCA). Various types of factor analysis (PCA,empirical orthogonal functions (EOF), and singular valuedecomposition (SVD)) have been used extensively in theanalysis of climatological data sets. Recently these meth-ods have also been applied to remotely sensed data (com-pare Yulaeva and Wallace 1994).

In this study, principal components analysis sought toreplace one set of variables with another set of statisticallyindependent variables. These new variables were theprincipal components, or empirical orthogonal functions.The temperature at each grid location was a separatevariable. The principal components were derived from the

correlation between these variables over time, whichwould generally be high for neighboring cells andlow for cells separated by large distances. Theloadings, which could be positive or negative, de-fined the principal components, and were the corre-lations between the temperatures at each spatiallocation and the components. The loadings of eachvariable on each component therefore defined a newset of spatial patterns, summarizing the dominantpatterns of correlation between spatial locationsthroughout the time period of the data set. Whilethere are as many components as there are originalvariables, the first few typically accounted for mostof the variance in the data. Principal componentscores were the sum of the products of the loadingsand the original, standardized variable values at agiven time step. So, each grid of temperature wasreplaced by a set of principal component scores,which expressed the relative importance of thespatial pattern, represented by component loadings,as well as the temperature magnitude at that timestep.

Several facts should be kept in mind when inter-preting the results. First, the principal componentsof a given data set were orthogonal, but they werenot unique; that is, a different set of principal com-ponents (PCs) may equally well have described thedata. Thus, a one-to-one correspondence of theprincipal components in data from NMC and TOVSwas very unlikely. Second, the patterns obtainedwould have been different if data from lower lati-tudes were included in the analysis. Third, only oneatmospheric level was being examined. The pat-terns would probably be different at other levels.

Time series of anomalies were computed bysubtracting from each monthly value the monthlymean for that grid cell, as computed through allyears in the record. The first three principal compo-nents were then examined; the percent of the vari-ance in the original data explained by each principalcomponent is shown as a scree diagram in Figure 8.Components beyond the third explained less than10% of the variance in the data and therefore werenot considered further.

The strongest agreement between the TOVS andNMC data was in the second principal component (PC2)from each data set. Results are given in Figure 9, withpositive and negative loadings shown separately. For theTOVS data this PC explained 12% of the variance. Thedipole pattern shown in the loadings had one center overthe Barents Sea and northern Ural mountains. Another,broader, center of opposite sign was spread out zonallyfrom southern Greenland and across Canada. The timeseries of the scores of the second principal component foreach data set correlated at r = 0.73, with excellent coinci-dence of the major extrema.

The third principal component of the TOVS data also

ATMOSPHERIC TEMPERATURE VARIABILITY IN THE ARCTIC 205

0.3

TOVS Level 31 Principle Component Analysis F'9- 8. Percent of the variance in the originaltemperature data explained by each principalcomponent.

0.0 -

10 20 30Eigen Number

40

31 LEVEL 31 Second Principol Component 1983-1992 ,

I

50

Fig. 9. Component loadings for the secondprincipal components of the TOVS 500 mbartemperature (top) and the NMC analysis (bot-tom). Positive loadings are shown on the left;negative loadings are on the right.

31 LEVEL 31 Second Principol Component 1983-1991 0 0

0.I—| Not Vi

NMC 500mb Second Principol Component 1983 -1991 , - NMC 500mb Second Pnnapol Component 1983-1991 QQ

0.0I—| Not Valid

206 KHALSA AND KEY

31 LEVEL 31 Third Principal Component 1983-1991 31 LEVEL 31 Third Principal Component 1983-1991 QQ

n

NMC 500mb First Principal Component 1983-1991 , 0

NMC 500mb First Principal Component 1983-1991

0.0I—| Not Valid

-1.0I—| Not Valid

Fig. 10. Component loadings for the third principal component of the TOVS 500 mbar temperature (top) and the firstprincipal component of the NMC analysis (bottom). Positive loadings are shown on the left; negative loadings are on theright.

had a dipole pattern (Fig. 10). It explained 10% ofvariance. One pole of this pattern was over Alaska andEast Siberia, the other over the Greenland and Norwegianseas. Neither PCI nor PC3 of the NMC 550 mbar datashowed dipole patterns. Instead they were monopoles,roughly corresponding to each of the poles in TOVS PC3.The first principal component of the TOVS-3I tempera-tures and the third component of the NMC analysis areshown in Figure 11.

To verify that the dipole patterns seen in the second andthird principal components of the TOVS data were actualmodes of temperature variability in the Arctic atmosphere,rawinsonde data from a variety of high-latitude stationswere correlated with the temperatures from the TOVSretrievals. The time series of departures from the seasonalcycle at each TOVS level 31 grid point were correlatedwith the departures for the 500 mbar level from theselected station. InFigure 12, the correlations with ScoresbySund and with Barrow are shown. A maximum linearcorrelation, r = 0.74, occurred in the grid cell adjacent to

the station location, shown by an 'X.' Positive correlationscovered most of the North Atlantic Ocean, Greenland, andthe region around Baffin Island. Negative values coveredmost of the Arctic Basin, Alaska, and all of northernEurasia, with maxima up to r = -0.44 over northwesternRussia. This pattern was nearly identical to TOVS PC2(Fig. 9). The correlations with Barrow showed that posi-tive maxima of r=0.79 were over Alaska, while negativevalues of up to r - -0.44 were spread over the NorwegianSea. This reproduced the pattern seen in TOVS PC3 (Fig.10).

Trend analysis in TOVS level 31 PCIThe time series of principal component scores for the firstthree components of the TOVS-31500 mbar temperaturesare shown in Figure 13. The scores for PC 1 show some ofthe major climatic events, such as the major coolings in the1988/89 (Walsh and Chapman 1990)and 1990/91 winters.

Least squares linear fits were performed on the TOVSprincipal component scores. Only PCI had a trend that

ATMOSPHERIC TEMPERATURE VARIABILITY IN THE ARCTIC 207

31 LEVEL 31 First Principal Component 1983-1991 31 LEVEL 31 First Principal Component 1983-1991

o.oNot Valid

,—i 0-0

-1.0I—| Not Valid

NMC 500mb Third Principal Component 1983-1991 NMC 500mb Third Principal Component 1983-1991

Fig. 11. Component loadings for the first principal component of the TOVS 500 mbar temperature (top) and the third NMCprincipal component (bottom). Positive loadings are shown on the left; negative loadings are on the right.

was significant above the 99% level. Since the loadings ofPCI were all of one sign, this implied a secular trend in thetemperature departures. (A trend in the scores for a PCwith a dipole pattern in the loadings could imply only thatthe pattern was strengthening or weakening.) The nega-tive trend in PCI indicated an interannual cooling overmuch of the Arctic, especially in the region of the ChukchiSea and the East Siberian Sea. Data from OstrovChetyrekhstolbo and several stations in the region wereexamined. No significant trend was found in the timeseries from any of the stations. The correlations between500 mbar temperatures from these stations and the TOVSlevel 31 data at the location of peak loading for PC 1 weresignificant, but arose mainly from extreme events, not asimilarity in trends. None of the scores from first three PCsof the NMC data had significant trends.

Kahl and others (1993b) examined the historical Arcticrawinsonde record for evidence of greenhouse warmingover the period 1958-1986. The temperature trends they

found mostly fell below a 90% significance level. Thosethat did exceed this significance level were mixed withrespect to sign and varied by season. Significant negativetrends during the northern autumn and winter were ob-served for the 500-400 mbar layer in the far northernCanadian archipelago and the coasts of Greenland. Whilethese results were in partial agreement with the trends seenin TOVS PCI, Kahl and others' analysis was limited tocoastal stations. In another study, Kahl and others (1993a)examined data from drifting Soviet ice islands anddropsondes released from weather reconnaissance air-craft. These data covered the western and central ArcticOcean. For the period 1950-1990 it was found thatsignificant cooling in surface air temperatures was accom-panied by a warming in the 700-850 mbar layer. Theseresults did not compare readily with the current study sincein the analysis of Kahl and others (1993a) the data were notfrom a fixed location and little could be said about thespatial distribution of temperature trends.

208 KHALSA AND KEY

TOVS Level31 - Station 4339 Correlation 70.48(°N) - 2 1 TOVS Level31 - Station 4-339 Correlation 70 48(°N) - 2 1 97( j °£ , - °W)

0.0I—| Not Valid

-1.0I—| Not Valid

TOVS Level31 - Stotion 70026 Correlation 71.30(0N)-156.78|;^0E,-0 ' TOVS Level31 - Station 70026 Correlation 71 30(°N)~156.76£-£°E,-°W)

0.0I—I Not Valid

-1.0I—| Not Valid

Fig 12. Linear correlations between the TOVS 500 mbar temperatures and those from two upper-air stations: (a)Scoresby Sund (#4339) at 70°N, 22°W, and (b) Barrow (#70026) at 71 °N, 157°W.

Summary and conclusions

The Earth's high-latitude regions are of critical impor-tance in many scenarios of climate change, but a time-continuous, spatially complete, and well-calibrated recordof tropospheric temperatures is needed in order to assesspast and future climate changes. Analysis of a portion ofthe 16-year data record of the TIROS Operational VerticalSounder (TOVS) aboard NOAA polar-orbiting satelliteshas allowed an examination of both the spatial and tempo-ral variability of mid-tropospheric temperatures in theArctic. Although the period of the TOVS record isrelatively short for climate studies, it is adequate foridentifying the principal modes of Arctic temperaturevariability. This helps determine whether the longer butspatially inhomogeneous rawinsonde record captures allsignificant features of this variability. This study alsoprovides a baseline against which future climate variabil-ity in the Arctic can be assessed.

Temperature retrievals based on clear-column radiancesarchived at NOAA/NESDIS were done using algorithms

tuned to the Arctic. Retrievals of the temperature profileusing the 31 (Improved Initialization Inversion) methodwith NOAA/NESDIS clear-column radiances were firstcompared to historical Arctic rawinsonde data for selectedcases. In general, retrievals were good, although strong,low-level inversions were problematic. Time series of 500mbar temperatures in the TOVS, NMC, and rawinsondedata were examined and the seasonal cycles were de-scribed in terms of amplitude and phase. The spatialdistribution of amplitudes determined for the various datasets agreed well but there was less agreement in the phasecomparisons. One of the areas of greatest discrepancybetween NMC and TOVS was over the pole, where thereare little in situ data to assist in resolving the differences.

Principal component analysis was used to examinespatial patterns in the temperature departures (seasonalmeans removed) for TOVS and NMC data. Both monopoleand dipole patterns were observed in the componentloadings, one pair of patterns being nearly identical be-tween the two data sets. Spatial patterns of the correlation

ATMOSPHERIC TEMPERATURE VARIABILITY IN THE ARCTIC 209

4

3

2

1

0

-1

-2

- 3

- 4

3

2

1

0

-1

- 2

- 3

- 4

3

2

1

0

- 1

- 2

- 3

- 4

PC1

PC2

r""!1""

PC3

—}

CO00en

Jul cCO

~3^00CD

Jul cCO

i n00en

Jul c

CO

CO00en

Jul

00en

Jul

•?0000en

Jul c

CO~3CD00

Jul c

CO

oenCD

Jul

enen

cCO~3cvjenCD

Fig. 13. Times series of principal component scores for the TOVS 500 mbar analysis(principal components 31 level 31). Also shown are the linear regression fits to the series(solid line) and the 95% confidence interval.

between the upper-air data and the TOVS retrievals re-vealed patterns similar to the principal component loadings.Finally, the time series of component scores were analyzedfor trends. No significant trend was found in the stationdata but a significant negative trend was found in the firstprincipal component scores of the TOVS retrievals.

The Arctic temperature data set derived from TOVSprovides an important adjunct to data currently availablefrom other sources. The land-based upper-air networkdoes not provide coverage of the Arctic Ocean, which theanalysis shows has significant features in the spatial pat-terns of temperature variability. Data from the drifting iceisland and dropsonde programs do not have the spatial ortemporal continuity needed to study patterns of variabilityin any detail. Data from NMC are spatially complete andcontinuous, but incorporate little, if any, data from the

central Arctic Ocean in themodel analysis. Agreementbetween NMC and TOVSresults is good in somecases, but TOVS appearsto provide more detail andperhaps greater accuracy.Future work will explorefurther the utility of thisdata set by examining otherlevels of the atmosphere.

Acknowledgements

This study was supportedby NOAA grant noNA26GP0186 and NASAgrant no NAGW-2407.Thanks are due to G.Mandler and H. Maybee,who performed most ofthe programming andgraphics work.

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