1. Introduction. mountain; temperature; trends; radiosonde; re-analysis. Received 9 September 2006; Revised 26 February 2007; Accepted 17 March 2007

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1 INTERNATIONAL JOURNAL OF CLIMATOLOGY Int. J. Climatol. 27: (2007) Published online 15 June 2007 in Wiley InterScience ( Short Communication A comparison of surface and free-air temperature variability and trends at radiosonde sites and nearby high elevation surface stations N. C. Pepin a *andw.duane b a Department of Geography, Buckingham Building, Lion Terrace, University of Portsmouth, PO1 3HE, UK b Department of Geography, University Brunei Darussalam, Jalan Tungku Link, Gadong BE 1410, Negara Brunei Darussalam, Brunei ABSTRACT: Previous research has illustrated differences in temperature trends as measured by high elevation surface stations versus free-air temperatures interpolated to the same locations from the National Centers for Environmental Prediction (NCEP)/National Center for Atmospheric Research s (NCAR) Re-analysis R1. This paper examines the extent to which the decision to use R1 rather than radiosonde data influenced these results. Temperatures at selected high elevation surface sites ( ) are paired with nearby homogeneity adjusted radiosonde data from Lanzante, Klein and Seidel (LKS). For each station pair, four mean monthly temperature anomaly time series are examined, consisting of (1) surface Global Historical Climate Network (GHCNv2)/Climate Research Unit (CRUv2) station anomalies (SF), (2) LKS radiosonde anomalies, and R1 anomalies interpolated to (3) the surface (RNSF) and (4) radiosonde (RNLKS) locations respectively. Analyses demonstrate the extent of common variance, the mean climatology of each of the four series, and differences or similarities in trends. The surface record is decoupled from the other three series especially in locations of incised topography. In 15 out of 18 pairs RNSF shows greater affinity with LKS than with SF and there is a high degree of common variance between LKS and RNSF. There is a high degree of correlation between secular trends in the two R1 series, both of which are much more similar to radiosonde than to surface trends. Trends in raw temperature and in T (the surface/free-air temperature difference) both therefore show limited sensitivity to the choice of LKS radiosonde versus R1 (RNSF), apart from in a few locations in the Eurasian continent. Copyright 2007 Royal Meteorological Society KEY WORDS mountain; temperature; trends; radiosonde; re-analysis Received 9 September 2006; Revised 26 February 2007; Accepted 17 March Introduction There is continuing debate about estimates of global temperature change over recent decades estimated from surface, radiosonde and satellite data (Jones, 1994; National Research Council, 2000; Chelliah and Ropelewski, 2000; Christy et al., 2000, 2003; Lanzante et al., 2003a,b; Jones and Moberg, 2003; Mears et al., 2003; Seidel et al., 2004; Karl et al., 2006). Many recent studies have shown that on a global scale surface temperatures are increasing slightly faster than free-air tropospheric temperatures, but there is regional variation in this relationship and this is not universally true. There is particular concern about enhanced sensitivity of mountain sites to global warming (Diaz et al., 2003; Bradley et al., 2006) and study of temperature trends at these locations offers a unique regional * Correspondence to: N. C. Pepin, Department of Geography, Buckingham Building, Lion Terrace, University of Portsmouth, PO1 3HE, UK;. nicholas.pepin@port.ac.uk perspective on the surface/free-air contrast since mountains potentially show characteristics of both the surface and free atmosphere. A variety of datasets have been used to quantify free air conditions in past research including radiosonde, satellite and re-analysis products. This paper examines the extent to which the choice between re-analysis or radiosonde data influences trends, specifically concerning comparisons with equivalent surface trends at high elevation stations, and in the quantification of any trend in the surface/free air temperature difference ( T), defined in Pepin and Seidel (2005). The sensitivity analysis is achieved through comparison of both radiosonde data from the Lanzante, Klein and Seidel (LKS) dataset Lanzante et al., 2003a) and interpolated NCEP/NCAR re-analysis R1 (Kistler et al., 2001) with high elevation surface data at a subset of mountain locations with high-quality records. Section 2 discusses past studies and the rationale for this research in more detail. Section 3 outlines datasets and methods. Section 4 (analyses) is divided into three sections, examining common patterns of variance, mean seasonal Copyright 2007 Royal Meteorological Society

2 1520 N. C. PEPIN AND W. DUANE patterns and finally long-term trends. Section 5 concludes a summary of findings and discussion concerning future research. 2. Past studies Surface temperature trends are widely held to be significant and average 0.17 C/decade over the last two decades of the twentieth century (Jones and Moberg, 2003). Free-air trends are less consistent and depend on atmospheric level and dataset (Seidel et al., 2004; Free and Seidel, 2005). Global mean tropospheric trends range from to C/decade using Radiosonde Atmospheric Temperature Products for Assessing Climate RATPAC (Free et al., 2005) and HadAT2 (Thorne et al., 2005) datasets depending on atmospheric level or exact time period (Karl et al., 2006). Different authors have obtained various figures for satellite trends (Christy et al., 2003; Mears et al., 2003; Vinnikov and Grody, 2003) depending on how factors such as orbital decay, inter-satellite calibration, diurnal bias and changes in local sampling times are accounted for, but generally trends are slightly weaker than at the surface. Fu et al. (2004) showed that the tropospheric temperature trend as measured by microwave sounding unit (MSU) channel 2 was weak since tropospheric warming was partly offset by stratospheric cooling. Trends in re-analysis data have also been analysed but there are concerns about homogeneity, (Kalnay and Cai, 2003; Sturaro, 2003). In most of the above comparisons the satellite, surface and radiosonde data are not measuring the same parameter at the same time and place. This is assumed to be not important when calculating mean global trends but it could explain different trends on a local scale, particularly in areas of complex relief. In addition there is the important issue of whether surface trends at remote mountain locations can be approximated by equivalent free air trends, since reliable high elevation surface records are limited (Diaz et al., 2003; Bradley et al., 2006). Therefore, among studies which have examined location specific comparisons, Seidel and Free (2003) compared temperatures for pairs of high and low elevation radiosonde sites, and found that at the elevation of the higher station, surface warming was often greater than the warming at the same absolute elevation above the lower station. Pepin and Seidel (2005) investigated surface temperatures at 1084 high elevation sites in the Global Historical Climate Network (GHCNv2) and Climate Research Unit (CRUv2) datasets (Peterson and Vose, 1997; Jones et al., 1999; Jones and Moberg, 2003). Their trends and variability were compared with freeair temperatures interpolated to the same location from the NCEP/NCAR re-analysis R1. The majority of sites showed enhanced surface warming ( ) in comparison with R1. Pepin and Norris (2005) showed much of the difference between the surface and free-air datasets to be correlated with meteorological factors such as snow cover, cloud and wind vectors, illustrating the importance of local surface radiative exchange at mountain locations. Similar detailed comparisons between high elevation surface records and radiosonde (rather than re-analysis) data are desirable but the relatively sparse homogenous radiosonde network limits the possible spatial extent of any such study. An analysis of sensitivity of the surface/free-air temperature difference ( T) and its trend to the choice of R1 versus radiosonde data, as in this paper, is therefore important using locations where both are available. 3. Data and methods High elevation surface sites from GHCNv2 and CRUv2 (>500 m) were paired with radiosonde stations from the LKS dataset. Typical spacing between the 87 LKS sites is 1000 km. Suitable GHCN/CRU sites are much more numerous (n>1000), and the re-analysis can be interpolated to any location. Thus LKS sites, being most coarsely spread, determine possible locations for dataset comparison. Insignificant anomaly correlations based on mean monthly temperature anomalies for a subset of three adjacent western U.S. LKS stations spaced around 1000 km apart (Table I) show that it is not advisable to use the LKS stations themselves to interpolate horizontally representative free-air temperatures between them. Therefore only the closest surface GHCN/CRU surface sites to each LKS radiosonde site can be compared with any validity. Figure 1 shows the correlations between GHCN/CRU mean monthly surface temperature anomalies and equivalent LKS radiosonde temperature anomalies (at the nearest pressure level to the GHCN/CRU surface elevation from a choice of 1000, 850, 700 and 500 mb) versus distance between the two locations. The most frequent LKS height was 850 mb. The left panel represents the day and right panel the night, classified based on local solar times (LST). The threshold inter-site distance below which the common anomaly variance is above 0.7 (r2 = 0.49 or approximately half of the variance) is 592 km during the day and 313 km at night. A compromise distance of 450 km was chosen and site pairs with lower separations were selected. The 18 resulting station pairs are listed in Table II and mapped in Figure 2. Some LKS stations are included Table I. Mean monthly temperature anomaly correlations for adjacent LKS radiosonde stations in the western U.S. Time Level Brownsville (vs) Miramar Miramar (vs) Great Falls Great Falls vs Brownsville 12 UTC Surface UTC Surface UTC 500 mb UTC 500 mb

3 A COMPARISON OF SURFACE AND FREE-AIR TEMPERATURE VARIABILITY 1521 Figure 1. Anomaly correlation versus inter-site distance for GHCN/CRU surface temperatures (SF) versus LKS radiosonde temperatures (at the most appropriate vertical level), (a) daytime, (b) nighttime. This figure is available in colour online at Figure 2. Map showing location of 18 chosen station pairs. Insets (not all to the same scale) show the local topography around each station pair. Radiosonde sites are shown as circles, and high elevation surface sites as squares. more than once. Munich radiosonde is paired with 3 sites; Sniezka (1613 m), Zugspitze (2962 m) and Sonnblick (3109 m). None of the station pairs is tropical. None is at high latitudes. There is a bias towards mid-latitudes in the Northern Hemisphere, partly the consequence of avoiding low elevation sites below 500 m. However there are pairs on most continents. Most LKS sites are in lowland areas even though the paired site is at high elevation. Quite a few pairs contain a continental contrast, the radiosonde site being at the coast and the GHCN/CRU surface site inland, indicated by an asterisk in Table II. The topography of the chosen surface sites varies from locations in incised mountain valleys (Naryn and Dease Lake) to isolated mountain summits (Zugspitze and Sönnblick). The height value in the right hand part of the table represents the difference in elevation between the paired sites. In all cases this is greater than 400 m. Thus any free-air readings above LKS taken at the same height as the surface station are well above the mean planetary boundary layer height (McIlveen, 1992).

4 1522 N. C. PEPIN AND W. DUANE Table II. Details of SF vs LKS station pairs. Topographical classification, V = valley, IV = incised valley, P = plateau, S = summit. These station pairs show a continental/maritime contrast, the radiosonde site being situated near the coast and the surface station being inland. Surface station (SF) Radiosonde station (LKS) Code Name Lat deg Lng deg Elev m Topo X N Code Name Lat deg Lng deg Elev m Dist km Height m O UTC 12 UTC YON Yongala V y y AD Adelaide y y DSL Dease Lake IV y y AI Annette Island y y TOR Torbate V n n AS Ashabad y y JER Jerusalem P n n BD Bet Dagan y y SLT Saltillo V n n BV Brownsville y y CED Cedara P y y DB Durban y y WSP Warm Springs V y y GF1 Great Falls y y LKY Lake Yellowstone V y y GF2 Great Falls y y AKQ Akqi V y y KA1 Kashi y y NAR Naryn IV n n KA2 Kashi y y ASO Asosan S y y KG Kagoshima y y SNZ Sniezka S y y MC1 Munich y y ZUG Zugspitze S n n MC2 Munich y y SON Sonnblick S n n MC3 Munich y y WUS Wushaoling P y y MQ Minqin y y CUY Cuyamaca P y y MR Miramar y y MKN Meknes P n n NF North Front y y HER Herberton P n n TV Townsville y n

5 A COMPARISON OF SURFACE AND FREE-AIR TEMPERATURE VARIABILITY 1523 For each of the 18 station pairs, four different monthly temperature anomaly time series are derived. Letters in bold are the shorthand code for each time series. a. High elevation surface temperatures at the mountain site (homogeneity adjusted records from the (GHCNv2) or Climate Research Unit (CRU) datasets) were used to represent surface conditions (SF) b. Radiosonde-derived free-air temperatures interpolated vertically to the same elevation as the surface site but above a nearby radiosonde site were used to represent free-air conditions (LKS) c. NCEP/NCAR Re-analysis (R1) interpolated vertically and horizontally from free-air pressure level data to the same location as a) (RNSF) replicates the free air variable at the mountain site used in previous studies (Pepin and Seidel, 2005) d. NCEP/NCAR Re-analysis (R1) interpolated vertically and horizontally from free-air pressure level data to the same location as b (RNLKS) was used to compare re-analysis with radiosonde in a similar way to the past re-analysis/surface (RNSF/SF) comparison performed in Pepin and Seidel (2005). A schematic diagram illustrating the relative locations of these four series and their derivation is given in Figure 3. The period of record is , determined by the LKS record length. All comparisons are horizontal, temperatures in series b, c and d being interpolated vertically to the same elevation asthe surfacesite. Further details of data sources and manipulation required to derive each series are given in Appendix 1. Summit Site S SF Mountain AET Free Air C Radiosonde Site 500 mb 700 mb LKS Sounding Figure 3. Schematic illustration of surface and free-air temperatures derived from a variety of sources. SF represents the surface temperature record at location S, LKS is vertically interpolated from the radiosonde sounding to point C, the same elevation as the surface site, to create an Air Equivalent Temperature or AET. Both RNSF and RNLKS are interpolated from the re-analysis R1 to locations S and C respectively. This figure is available in colour online at 4. Analyses 4.1. Common patterns of variance R1 is a combination of radiosonde and satellite data. Thus we expect that mean monthly temperature anomaly correlations between LKS and RNLKS will be higher than those between SF and RNSF (where there is no common data source). The former comparison is also well above temporally variable boundary layer effects. Table III, with correlations based on mean monthly temperature anomalies and also mean monthly maximum and minimum temperature anomalies (where available), shows that this is true for 15 out of 18 station pairs (mean data) and in all cases except the Dease Lake/Annette Table III. Anomaly correlations for surface and free-air temperature comparisons, based on daily mean temperatures, and day/night comparisons where available. SF vs RNSF LKS vs RNLKS SF vs LKS RNSF vs LKS Station Daily Mean Daily Max Daily Min Station Daily Mean Daily Max Daily Min Daily Mean Daily Mean YON AD DSL AI TOR AS JER BD SLT BV CED DB WSP GF LKY GF AKQ KA NAR KA ASO KG SNZ MC ZUG MC SON MC WUS MQ CUY MR MKN NF HER TV Mean Mean

6 1524 N. C. PEPIN AND W. DUANE Island pair for maximum and minimum temperature anomalies. This indicates that the re-analysis is more representative of the free-air than of surface conditions at most locations. However, differences in correlation strength are often small because many mountain summits, (e.g. Zugspitze, Sönnblick and Asosan) show a high affinity with free-air conditions anyway (high SF/RNSF anomaly correlations). There is much more between-location variability in the SF/RNSF anomaly correlation than for the LKS/RNLKS anomaly correlation. This is a result of topography, the lowest daily mean SF/RNSF anomaly correlations being at Saltillo (0.661) and Naryn (0.482), the latter being in a mountain valley prone to strong local temperature inversions. A crude topographic index was calculated from the GTOPO30 global digital elevation model (DEM) from the United States Geological Survey (USGS) ( gtopo30.html) for each location by subtracting the station elevation from the mean elevation of the surrounding 21 grid cells (30 arc second resolution). There is a fair relationship between the degree of topographic incision and the reduction of the SF/RNSF anomaly correlation (Figure 4) although this relationship is not linear. Naryn is the most incised station and additionally is on the opposite side of the Tien Shan mountains to Kashi (itself in the Tarim basin). This explains the exceptionally low correlation here. Such low values do not as a rule transfer to the equivalent LKS versus RNLKS comparison. Nearly all LKS/RNLKS correlations are above 0.85 and many above 0.9, showing welcome affinity between radiosonde and re-analysis. The exceptions involve Kashi, where the influence of the localized environment of the Tarim basin can still be seen (r = 0.725), and Minqin (r = 0.759). Poor SF/RNSF anomaly correlations are often accentuated at night, substantiating the importance of local inversions. At Yongala the correlation is in the day but at night. Other less dramatic contrasts are seen at Cedara, Lake Yellowstone, Warm Springs and Cuyamaca. A few sites do not show a diurnal contrast, Correlation between SF and RNSF anomalies ZUG Topography vs SF/RNSF anomaly correlation SON SNZ ASO DSL MKN CUY CED WSP TOR LKYYON JER WUS HER SLT AKQ NAR Topographic Exposure Index Figure 4. Relationship between SF/RNSF anomaly correlation and topographic index at the 18 surface sites. The topographic index is derived from subtracting the station elevation from the mean elevation of the 21 surrounding grid cells. Stations are identified by their station code listed in the first column of Table II. particularly Dease Lake, Akqi, Asosan and Sniezka. The latter two of these are isolated mountain top locations but the reasons for the lack of contrast at Dease Lake and Akqi are unclear. At Dease Lake winter inversions are likely to persist for weeks due to the high latitude and degree of topographic screening and it may be that this has influenced all data sources equally. It is expected that SF and LKS will be different and lower anomaly correlations support this (column 3). The mean correlation is (the lowest among all paired comparisons). However RNSF and LKS are more similar (mean correlation 0.866). In 11 out of 18 pairs, the anomaly correlation between RNSF and LKS (column 4) is higher than that between RNSF and SF despite the additional spatial contrast (sometimes hundreds of km) inherent within the former comparison. Thus the reanalysis interpolated to the surface site is often more strongly correlated with the LKS radiosonde data a distance away, than with temperatures at the surface site itself, illustrating the credentials of RNSF as a freeair variable and as a sensible alternative to radiosonde data (LKS). Where values of the RNSF/LKS anomaly correlation are high there should be limited sensitivity of trends to the choice of radiosonde (LKS) or re-analysis free-air data (RNSF) Seasonal changes At each location mean monthly values of SF, LKS, RNSF and RNLKS were examined to assess seasonal climatology. Four examples are shown in Figure 5. In the Dease Lake/Annette Island pair (Figure 5(a)), there is a large contrast in continentality and topography, the surface site being sited inland in a mountain valley and the radiosonde site being on the coast. Thus SF is much colder than all other measurements in winter, a result of persistent temperature inversions not modeled by RNSF or RNLKS. In summer SF becomes warmer than all other values. This continentality signal remains in the RNSF vs RNLKS comparison but is much subdued. In contrast the Torbate/Ashabad pair (Figure 5(b)) shows very little seasonal contrast between SF and LKS. Both SF and RNSF are always warmer than LKS and RNLKS respectively. This consistent difference is dominated by latitude, the sites being 306 km apart and Torbate being much further south (therefore warmer). In South Africa (Cedara/Durban: Figure 5(c)) there is again a continentality influence but on a mean annual basis differences between SF and RNSF and between LKS and RNLKS are small. Finally for the Sönnblick/Munich pair all measurements are very similar, not surprising since the high summit site is extremely exposed to freeatmospheric advection at this mid-latitude location. Generally the mountain surface (SF) is colder than the radiosonde measurement (LKS) averaged over the year (13 out of 18 sites). Frequent snow cover could explain the mean heat deficit. The exceptions tend to be at low latitudes where great surface heating maybe expected (e.g. Saltillo in Mexico, and Herberton in Australia). This

7 A COMPARISON OF SURFACE AND FREE-AIR TEMPERATURE VARIABILITY 1525 Figure 5. Mean monthly values of SF, LKS, RNSF and RNLKS at a selection of station pairs, (a) Dease Lake/Annette Island (Canada), (b) Torbate/Ashabad (Turkmenistan), (c) Cedara/Durban (South Africa) and (d) Sonnblick/Munich (Germany). climatology confirms past knowledge concerning freeair/surface temperature differences (von Hann, 1913; von Hänsel, 1962; Richner and Phillips, 1984) and shows the importance of surface radiation balance in controlling instantaneous free-air/surface temperature contrasts (Pepin and Norris, 2005). Differences between RNSF and RNLKS are much smaller, and the magnitude of the mean difference is largely accounted for by the distance between the two points (r = 0.645, p<0.01). The smooth re-analysis grids show minimal influence of topography and continentality. The above results both make sense physically and confirm that (1) SFandLKSaredifferent. (2) SF and RNSF are almost as different. (3) RNSF behaves more like RNLKS and/or LKS than SF. (4) Re-analysis temperatures are relatively insensitive to exact location Trends Raw temperatures For each of the four series, SF, RNSF, LKS and RNLKS, significance of temporal trends was assessed using the significance levels of Santer et al. (2000), taking lag 1 temporal autocorrelation into account through the calculation of an effective (smaller) sample size. All trend magnitudes were derived from simple least squares linear regression and are expressed in C/decade. The purpose here is to examine the influence of free-air data source (LKS or RNSF) on trends obtained for this subset of stations, and not to present globally representative trend figures. Figure 6 shows how the pattern of trend magnitudes alters for different datasets. In Figure 6(a) the influence of dataset (re-analysis vs LKS) on free-air trends is shown, through plotting RNSF trends versus LKS trends. There is a correlation of between the two trend magnitudes and only in 3 cases does the trend change sign. If one outlier is removed (the Ashabad/Torbate pair) the correlation increases to Thus there is a high degree of similarity in free-air trends whether reanalysis or radiosonde data is used. The major difference is that the highest positive LKS trends appear to be weaker in the re-analysis, probably because R1 is a smoothed representation of reality. Figure 6(b) shows the comparison of trend magnitudes corresponding to the datasets compared in Pepin and Seidel (2005) (RNSF vs SF). In this case the trends are completely different (r = 0.383), the re-analysis does not agree with the surface dataset (when interpolated to the same location). There is a negative relationship between trend magnitudes and 10 sites change sign. This difference remains when LKS and SF trends are compared (Figure 6(c), r = 0.147). RNSF and RNLKS trends, coming from the same dataset, are highly correlated (Figure 6(d), r = 0.965). These relationships increase the confidence that past analyses using RNSF as a surrogate for radiosonde data (LKS) on a global scale have identified real differences in surface and free-air temperature trends (Pepin and Seidel, 2005) and

8 1526 N. C. PEPIN AND W. DUANE Figure 6. Scatterplots of temperature trend magnitudes for for various dataset comparisons, (a) RNSF vs LKS, (b) RNSF vs SF, (c) LKS vs SF and d) RNLKS vs RNSF. the similarity between Figure 6(b) and (c) implies that such differences would have remained had radiosonde data been used. The next section examines the effect of dataset on trends in derived T (the instantaneous surface/free-air temperature difference) Differences in free-air and surface temperatures The following instantaneous temperature differences were created: (1) SF minus RNSF (2) SF minus LKS (represents surface/radiosonde instrumental contrast) DeltaT trend with Reanalysis: deg C/decade MQ MC1 MC3 BV AI AS KA2 KA1 MC2 GF1MR GF2 TV KG BD NF Delta T trend with Radiosonde: deg C/decade AD DB The differences were then converted to anomalies. The resultant time series are anomalies of the differences (NOT differences in the anomalies from the previous section). The first calculation (SF minus RNSF) is equivalent to the surface versus free-air temperature difference ( T) investigated in Pepin and Seidel (2005). The second calculation (SF minus LKS) is the equivalent estimate of T using radiosonde instead of re-analysis data. Figure 7 compares trends in the two estimates of T at these 18 locations. While there is a weak positive correlation between trend magnitudes, disparate trends appear at several locations on the Eurasian continent including Munich vs Sniezka (MC1), Minqin (MQ) and Kashi vs Naryn (KA2). In each of these cases the original SF/RNSF anomaly correlation was low. Ignoring these three examples the correlation between trend magnitudes Figure 7. Scatterplot of trend magnitudes in T using re-analysis R1 (vertical axis) versus local LKS radiosonde data (horizontal axis). Trend magnitudes ( ) are plotted in C/decade. Letters represent the station pair as identified by the radiosonde station (key listed in LKS part of Table II). This figure is available in colour online at is (p<0.01). In 14 cases T trends using the reanalysis are more positive than the equivalent ones using radiosonde data but in most cases the difference is small (<0.025 C/decade). Although there are limitations in speculating about global results from this small number of station pairs, this analysis suggests that in this subset of high quality radiosonde locations there is significant correspondence between the use of homogeneity adjusted radiosonde and

9 A COMPARISON OF SURFACE AND FREE-AIR TEMPERATURE VARIABILITY 1527 re-analysis data R1 when examining trends in T. The deliberate policy of limiting comparison to nearby pairs of high quality homogeneity-adjusted records limits the size of the dataset. Thus it would be dangerous to assume that these results apply globally. In a few locations the correspondence in trends breaks down and further investigation into this spatial variability and the reasons behind it are required. 5. Summary and discussion Previous work has suggested that surface and free-air temperature trends over the past 50 years are substantially different (Pepin and Seidel, 2005). Much of this work involved comparing free-air temperatures interpolated from the NCEP/NCAR Re-analysis R1 with surface observations. It was not known how much the use of re-analysis as opposed to radiosonde data influenced the results. Because radiosonde data are not available in all locations (unlike re-analysis products) the dataset influence was examined here at fixed locations where reliable radiosonde data are available. The initial results are encouraging in that there is a fair degree of similarity in both anomaly variance and the trend estimates obtained in the two cases. In particular both the re-analysis and radiosonde series are decoupled from the surface, and trends in radiosonde- derived T are significantly correlated with those in re-analysis derived T. However, the choice of free-air dataset (LKS vs re-analysis) does have a small impact on the pattern of temperature trend magnitudes over the last 50 years in comparison with the difference between RNSF (free-air) and SF (surface) trends. Summary data (e.g. mean trend magnitudes, the number of significant trends and their signs) are given in Table IV. Too much emphasis can be given to such figures when derived from only 18 pairs of sites, so our discussion is brief. The range of figures is not too dissimilar to global mean values reported in other studies (Fu et al., 2004; Karl et al., 2006). The large degree of statistical uncertainty with such a small dataset explains why only the mean LKS trend is significantly greater than zero, and the relatively high LKS mean trend magnitude is influenced by the inclusion of Munich radiosonde three times (i.e. the mean figures are not statistically robust). The small size of the dataset excludes discussion of regional trend patterns. Demonstrating that surface and free-air trends are different, and that this difference is largely consistent whether radiosonde or re-analysis data is used for the free-air variable, does not prove necessarily that the difference is real (e.g. a physical change in the global climate system). It could result from inaccuracies in any or all of the datasets. To an extent any explanations are partly a matter of interpretation. However two points suggest that much of the decoupling is real. First, there is concern that solar heating of radiosondes could cause anomalous heating during the day. Yet T follows the surface energy balance, in being most positive at this time (the radiosonde/re-analysis temperatures are relatively cool during the day and in summer) so any radiative error is minor in comparison with the climatology. Second, past work (Pepin and Norris, 2005) has shown a significant proportion of the difference T to be correlated with snow, wind and cloud cover, and therefore the contrasting surface and free-air trends are supported by changes in other elements. Further work is underway to attempt to reduce inhomogeneities in radiosonde datasets (Free et al., 2004; Free and Seidel, 2005; Thorne et al., 2005). This offers further opportunity for temperature trend comparison and validation. However, the fact that free-air and surface trends at mountain sites are different, and that this difference is largely irrespective of the use of re-analysis or radiosonde data, means that maintenance and creation of long-term surface high elevation stations is critically important to increase our understanding of present and future climate change in mountains. Acknowledgements The authors thank the Climate Variability and Trends Group at the NOAA Air Resources Laboratory in Silver Spring, MD for their guidance, in particular the advice of Dian Seidel in the preparation of this work. Thanks also to John Lanzante for access to the LKS dataset and advice on methodology. Phil Jones and Tom Peterson provided the CRU and GHCN data respectively. Mike Hartman provided help with reanalysis data manipulation. Table IV. Summary of free-air and surface temperature trends ( ) for the 18 pairs of stations. All figures in C/decade. SF RNSF LKS RNLKS Mean / / / / 0.07 Max Min Median Number +ve trends Number ve trends Sig. +ve trends Sig. ve trends Total significant trends

10 1528 N. C. PEPIN AND W. DUANE Appendix 1: Details of Derivation of Individual Time Series. a. GHCN/CRU Surface Temperatures. Sites above 500 m in elevation were selected from the GHCNv2 (Global Historical Climate Network) and CRUv2 (Climatic Research Unit) climate datasets, details of which are given in Peterson and Vose (1997) and Jones and Moberg (2003) respectively. The elevational threshold was applied to avoid stations near sea-level where a comparison with free-air radiosonde data at the same elevation would be strongly influenced by boundary layer effects. Availability of mean monthly surface maximum and minimum temperatures (rather than solely monthly means) was preferred. All mean monthly maximum, minimum and mean temperatures were converted to anomalies with respect to b. LKS Radiosonde. The Lanzante, Klein and Seidel (LKS) Radiosonde dataset consists of 87 high quality radiosonde sites upon which there have been adjustments made to improve homogeneity (Lanzante et al., 2003a). Since the focus of their work was trend examination, all time series were available as monthly anomalies. In this study, absolute temperatures are also required to calculate T (seesection 4.3.2). The calculation method follows the principles that any absolute temperature time series should retain the same trend as the anomaly series and that absolute instrumental calibration has improved over time. Therefore any adjustments that had been made by LKS on the basis of homogeneity should be minimal (in absolute terms) towards the end of the record. Original homogeneity adjustments had been made by LKS through dividing the record into homogenous segments based on metadata. In this research a difference series representing the adjustments made by LKS was calculated by subtracting the unadjusted (UNADJ) from liberal/conservative (LIBCON) datasets (for details see Lanzante et al., 2003a). These difference series were usually step functions, although there was added noise representing differential adjustments made for individual months. The start and end times of each segment were defined by examining metadata and the mean adjustment for each segment was calculated. The whole difference series was then shifted by a constant value to make the most recent segment have a mean adjustment of zero. This cleaned-up difference series was then added to the raw data to create an absolute temperature time series which would be (1) similar to the raw unadjusted one during the last segment, and (2) have the same trend as the homogeneity adjusted anomaly record produced by LKS. In some cases where no adjustment had been made by LKS the new absolute series was the same as the raw radiosonde record. The LKS time series are taken at fixed pressure levels (e.g. 700 mb) rather than fixed elevations, and as a consequence are difficult to compare with fixed surface measurements. Trends in geopotential height could cause temperature trends. To eradicate such problems multiple LKS time series at various pressure levels were used to interpolate vertically to the same elevation as the surface site with which it was to be compared. In normal circumstances the two nearest pressure levels (above and below) to the required elevation were used but in a few cases where the lower level would be near the surface or was 1000 mb (this level has known problems) the calculation was extrapolated down from higher pressure levels to avoid excessive boundary layer effects. Geopotential heights for the relevant pressure levels were unavailable from LKS so were obtained from an independent integrated global radiosonde archive (IGRA) operated by the National Oceanic and Atmospheric Administration (NOAA), index.php. LKS only contains standard levels (1000, 850, 700 and 500 mb) and vertical interpolation could be quite crude in the presence of inversions. To quantify possible errors, data from Great Falls, Kagoshima and North Front stations (which report additional significant levels) were used to interpolate with and without these additional levels. Mean errors in the interpolated temperature were small (<0.25 C) inallcases, andtherewerenosignificant temporal trends in these errors. Using LKS homogenized temperature readings along with heights from IGRA as a basis for vertical interpolation ignores possible inhomogeneities in height measurements and could lead to violations of the hydrostatic equation. It would be possible to correct IGRA heights based on the assumption that surface temperatures, humidities and the surface elevation are correct and thereafter progressing up from the surface, but reliable humidity measurements are unavailable and this was not done. Since the original LKS series themselves had been homogenized independently by pressure level and corrections for each level considered in isolation, the hydrostatic assumption may already have been violated and was not a consideration in the original LKS work (Lanzante pers. Comm. 2005). It would introduce more uncertainty to attempt to adjust for this here. c and d. Re-analysis R1. Interpolated free air temperatures RNSF and RNLKS at the surface and radiosonde locations respectively were derived from the R1 NCEP/NCAR 2.5 degree resolution re-analysis. For a discussion of this dataset and its known problems see Kalnay et al. (1996), Kistler et al. (2001). None of the specific problems with R1 (Kanamitsu et al., 2002) is expected to influence temperatures well above the surface at given pressure levels greatly. Interpolation was carried out using individual mean monthly temperature and pressure height fields. Vertical interpolation to the relevant elevation was done first for the four surrounding re-analysis grid points, based on a linear lapse

11 A COMPARISON OF SURFACE AND FREE-AIR TEMPERATURE VARIABILITY 1529 rate between the two nearest pressure levels. Horizontal interpolation followed based on weighting of the four grid points dependent upon the relative distances of these four grid points to the GHCN/CRU surface or LKS radiosonde site. In the case of RNLKS the surface topography is well below that of the four R1 grid points used in the interpolation. This is less consistent for RNSF and is dependent on surface topography, although the majority of sites are mountain summits so this often applies. Timing Differences. Most analyses are concerned with mean monthly temperatures alone. However, daily maximum and minimum temperatures were available at a subset of 10 surface sites. This raises the question of timing differences inherent in the data. For any RNLKS/LKS comparison there are no timing differences, both the re-analysis and LKS data being available at 0 and 12 UTC. 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