THE CAUSE OF WARMING OVER NORWAY IN THE ECHAM4/OPYC3 GHG INTEGRATION

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1 INTERNATIONAL JOURNAL OF CLIMATOLOGY Int. J. Climatol. 21: (2001) DOI: /joc.603 THE CAUSE OF WARMING OVER NORWAY IN THE ECHAM4/OPYC3 GHG INTEGRATION RASMUS E. BENESTAD* The Norwegian Meteorological Institute, Oslo, Norway Recei ed 29 September 1999 Re ised 14 July 2000 Accepted 1 August 2000 Published online 22 February 2001 ABSTRACT The cause of warming over Norway in a future global climate model (GCM) scenario is examined. Analysis of historical observations indicate that the observed long-term temperature trends are not a result of systematic shifts in the North Atlantic Oscillation (NAO). The GCM prediction of past and future warming cannot be explained in terms of changes to the large-scale atmospheric flow. The climate model results may suggest that the model has a seasonal bias with respect to the time of the year when the fastest warming occurs. The NAO is associated with the warming after 1970, even though the temperature trends do not appear to be connected with the NAO. An accurate description of the NAO is, therefore, critical for forecasting short-term variability in the Norwegian winter climate and the results obtained here suggest that the model description of the NAO is generally good. Copyright 2001 Royal Meteorological Society. KEY WORDS: climate change; coupled global climate model; correlation; canonical correlation analysis; historical observations; North Atlantic Oscillation; Norway; regression 1. INTRODUCTION It is well known that the global climate models (GCMs) do not yet give a sufficiently accurate description of climatic details for useful studies on regional and local scales (IPCC, 1995). According to the study by Grotch and MacCracken (1991), the smallest skilful spatial scales (von Storch et al., 1993) may be about eight grid points, or about 20 with the current state of the art coupled GCMs (at T42 resolution). There are ways to overcome this shortcoming and both dynamical and empirical downscaling have been employed in local climate studies. Dynamical downscaling involves nested climate models with increasing spatial and temporal resolution (Christensen et al., 1998), but with reduced model domain (limited area models). Empirical models (von Storch et al., 1993; Heyen et al., 1996; Zorita and von Storch, 1997; Crane and Hewitson, 1998; Kidson and Thompson, 1998; Wilby et al., 1998; Benestad, 1999c) involve historical relationships between the local climate variability and variations in large-scale climatic features, such as circulation patterns. It is important to note the implicit assumption of empirical downscaling studies, that the historical relationships between the large and small spatial scales also hold for the future. Kaas E, Christensen OB, Christensen JH. (1998; Dynamical Versus Empirical Downscaling, personal communication) and Kidson & Thompson (1998) found similar skills for dynamical and empirical downscaling models over Scandinavia and New Zealand, respectively. Empirical downscaling methods have been used in previous studies to infer the local implications of climate scenarios given by GCMs and the downscaled scenarios are often different to those deduced directly from the GCM results. Schubert (1998) found insignificant changes in downscaled Australian temperatures under a global warming scenario, concluding that there may be insignificant changes in the local climate due to systematic changes in the atmospheric circulation over Australia. The GCM results * Correspondence to: The Norwegian Meteorological Institute, PO Box 43, 0313 Oslo, Norway. Copyright 2001 Royal Meteorological Society

2 372 R.E. BENESTAD did not indicate many systematic changes in the sea level pressure (SLP) field, but nevertheless described a non-negligible warming, which was attributed to enhanced radiative forcing. Benestad (1999c) examined the results from an empirical downscaling study of the ECHAM4/OPYC3 (Oberhuber, 1993; Roeckner et al., 1996) GHG run of the Max-Planck-für-Meteorologie (transient integration with greenhouse gas forcing but not aerosols) (Machenhauer et al., 1998), and reported inconsistencies between downscaled scenarios based on surface temperatures, SLP and 500 hpa geopotential heights ( 500 hpa ). Downscaling studies based on SLP maps for the Iberian peninsula have suggested smaller changes to the precipitation than GCM results predict (von Storch et al., 1993). Discrepancies between the historical precipitation trends and the results from GCM transient experiment were also reported by von Storch et al. (1993). A significant part of the historical climate variation in Norway can be related to the anomalous large-scale circulation (Benestad, 1998a, 1999a; Hanssen-Bauer, 1999) and especially the North Atlantic Oscillation (NAO). Empirical downscaling models based on SLP or geopotential heights describe the relation between the large-scale circulation and the local climate, but not how the temperatures directly relate to the radiative forcing. One central question in the Norwegian climate research programme RegClim (Regional Climate Development Under Global Warming) is therefore: which mechanism is responsible for the warming over Scandinavia in the global climate model enhanced greenhouse gas scenarios; enhanced radiative forcing or changes in the circulation? The answer to this question has implications for further regional climate studies, such as empirical downscaling of future climate scenarios. One objective of this study is also to see if the cause of the climate variations over Norway is the same in the model as in the real world. This paper will focus on both past trends and future scenarios. The outline of this paper is as follows: a section on the methodology is followed by the description of the results. The Results section is divided into the analysis of the relationship between the NAO, the past climate and future scenarios. The paper finishes with a Discussion and a Conclusion. 2. DATA AND METHODS The historical results from a transient integration with increased greenhouse gas concentrations, but with no effects from anthropogenic emissions of aerosols, were compared with the best available observations. The GCM results used in this study were taken from ECHAM4/OPYC3 GHG run of the Max-Planck-für-Meteorologie. In this transient integration, the GCM used the best estimates of historical greenhouse gas (CO 2,CH 4,N 2 O and industrial gases) concentrations from and the IS92a emission scenario after The model initial conditions were taken from year 100 of the control integration with present day forcing. Because the control integration is not really representative of the 1860s climate (since present-day conditions sea-surface temperatures (SSTs) and greenhouse gas concentrations are used), there is a warm bias in the GHG results. The greenhouse gas concentrations in the transient run were enhanced in order to obtain realistic values relative to the initial conditions and the ECHAM4/OPYC3 used flux correction to avoid an artificial climate drift under constant forcing conditions. The results from the control (CTL) run by the same coupled atmosphere ocean model have been evaluated by Benestad et al. (1999), who found a reasonably good reproduction of the most prominent climatic features over northern Europe. The CTL model results did, however, contain some serious systematic biases, such as too high mean SLP over the Arctic. The model calibration data included the University of East Anglia (UEA) Climate Research Unit s surface temperatures (Jones et al., 1998), National Meteorological Center (NMC; former National Center for Environmental Protection, NCEP) ds195.5 (NMC, 1996) SLP and 500 hpa analyses, and temperature records from the Norwegian Meteorological Institute s (Det Norske Meteorologiske Institutt, or DNMI) archives. The gridded 2-m UEA temperature record contains numerous holes with missing data and the data coverage is especially sparse in the high latitudes. A missing data mask that excludes all the grid points

3 WARMING OVER NORWAY 373 with an incomplete data record during the period was used in our analysis. This mask screened out the gridded temperature over a large part of Scandinavia. The NMC analysis data have been generated using a model that combines the observations in order to obtain a weather state as close as possible to the truth. It is difficult to make a good assessment of the quality of the NMC geopotential height fields, as there are few high-quality independent observations available for data evaluation. One solution is to compare the data with the more recent reanalysis data (the NCEP reanalysis (Kalnay et al., 1996) or the European Centre for Medium Range Weather Forecasting (ECMWF) reanalysis (Gibson et al., 1997)), but these are not completely independent either, as they are based on much of the same data. A canonical correlation analysis (Preisendorfer, 1988; Bretherton et al., 1992; Wilks, 1995) (CCA, not shown) between the older NMC and the NCEP reanalysis January 500 hpa field indicates a high degree of similarity between these two data sets, with almost identical CCA patterns and the ten highest correlations being close to unity: , , , , , , , , , Another solution is to evaluate surface quantities against measurements not used in the production of the analysis data, but such comparisons do not necessarily give a good measure of the errors in the upper atmosphere. Nevertheless, discrepancies in the surface fields give an indication of how representative the data are. Benestad (1998b) compared interpolated values of the SLP from the Comprehensive Ocean Atmosphere Data Set (COADS) (Slutz et al., 1985), UEA, National Center for Atmospheric Research (NCAR) (ds010.0) and NMC (ds195.5) with station values from Oksøy lighthouse and found that all the interpolated values were correlated with the station observations, but the NMC had the lowest correlation and the largest scatter. A third way of evaluating the data is to study the relationship between the geopotential field and the local climate variability through regression type models. Benestad (1998a, 1999a) showed that the NMC analysis 500 hpa and SLP fields are promising predictors for surface temperatures in Norway. It is, therefore, reasonable to assume that the large-scale NMC SLP and 500 hpa give a good description of the past climate, although they are not error-free. Nordli (1997) reported that the original temperature record from Bergen-Florida is inhomogeneous and the Bergen record may contain a small trend associated with the advancing urbanization. Systematic errors are due to changes in the observing system and only corrected temperatures (Nordli, 1997) have been used here. All data are represented in terms of monthly mean values. The surface temperatures used in regression analysis were obtained both from DNMI s archives and the UEA data set (Jones et al., 1998), interpolated to 60 N, 5 E. The historical Bergen record used in this study covers the period , but the Oksøy temperature only goes back as far as The NAO index (NAOI) is a crude description of the SLP conditions over the North Atlantic and the modelled NAOI has been estimated from gridded model SLP data by subtracting interpolated SLP anomalies over Iceland (22.44 W/65.50 N) from SLP anomalies over Portugal (9.10 W/38.70 N) and dividing this difference by its standard deviation. The observed NAOI record was obtained from an FTP site maintained by UEA (Jones et al., 1997). Regression was used in two types of analyses: (i) to estimate the best-fit linear temperature (temporal) trends associated with the model scenarios and past observations; and (ii) to examine the relationship between temperature variations and changes in the NAOI. A least squares method was used to estimate the best linear fit in both cases. The objective of the former type is of find a warming trend that can be used to describe the long-term (a century or longer) mean temperature change. This type of regression is carried along the time axis and the temperature is presumed to be a function of time. The latter type involves a regression between two quantities, which may not necessarily be related, and the point of this exercise is to find the relationship between the two, if such a relationship exists. In this respect, it is important to eliminate elements which can bias the results. Regression analysis aims to minimize the root mean-squared (RMS) error between two data series and, as a consequence, will always find a trend which gives the optimal RMS fit between the two curves. A best-fit with non-zero trend may not necessarily be representative of a physical link if either quantity is a function of more than one factor.

4 374 R.E. BENESTAD Figure 1. An example of biased and unbiased regression and corresponding trend estimates, based on the Oksøy lighthouse temperature. The figure shows the summer temperature (thick dark grey line) and the NAOI (thick light grey line). The thin lines show the same records, but after having been de-trended. The dark lines marked with diamonds show the results of a biased regression (reconstructed temperatures) where the linear trend has not been removed, whereas the stars denote the results from an unbiased analysis using de-trended data. Note that the regression results have ben scaled up by a factor of 10 and the y-axis for these results is given on the right hand side. The correlation between the de-trended NAOI and T 2m records is close to zero for this case The relationship between the two long-term trends may be due to coincidence and can, in worst case, lead to invalid conclusions as shown in the example given in Figure 1. Non-zero trend can also bias correlation analyses. Hence, best-fit analysis between two records that have non-zero trends may give a biased best-fit and the time series should be de-trended prior to the analysis in order to obtain an unbiased best-fit. If there is a real (and linear) relationship between the two quantities, then a regression model based on the de-trended series should also capture the relationship between their respective long-term trends. In simple mathematical terms, the time series can be expressed as the sum of a de-trended part and a linear trend: x(t)=x d (t)+x t (t), where x t (t) describes the linear trend in x(t) and x d (t) is the de-trended part. Hence the linear model y(t)=a x (t) implies that y d (t)+y t (t)=a(x d (t)+x t (t)) and that the coefficient a is the same for the de-trended records and the linear trends. Here, y is assumed to be the temperature record and x is the NAOI. The method for studying the coupling between two fields was based on the method described by Barnett and Preisendorfer (1987), where the data were prefiltered prior to the CCA by only including the leading empirical orthogonal functions (EOFs) (e.g. see North et al., 1982). The prefiltered Barnett and Preisendorfer (1987) method will henceforth be referred to just as CCA. Here only the 20 leading EOFs have been retained (accounting for 96, 99, 99 and 99% of the variance for ECHAM4/OPYC3 T 2m, ECHAM4/OPYC3 500 hpa UEA T 2m and NMC 500 hpa, respectively). The empirical downscaling models used in this study were constructed with a basis in a CCA and the construction of these models and their evaluation are described by Benestad (1998a). All the models described here were linear models, assuming a linear relationship between the large-scale climate patterns

5 WARMING OVER NORWAY 375 and the local climate variables. The calibration of the downscaling models was based on the 48-year period and a step-wise type regression method was used to select those EOFs which were to be included in the downscaling. All the 20 leading EOFs that contributed to the correlation score (of the predictant with the highest correlation skill) in a cross-validation analysis were included. The calibration data were de-trended before the EOF analysis and the model development, but the long-term trends have been kept in the scenario data. The models generally demonstrated good skill in cross-validation analysis (Wilks, 1995) with historical observations. A regression analysis was used to find matching spatial patterns in the model results and the observations, but in order to do this the observations were projected onto the model s grid using a bilinear interpolation scheme. The data used here are almost certainly affected by errors. Random observational errors may be present, as well as systematic errors due to changes in the measurement strategy and changes in the environment surrounding the climate stations (Nordli, 1997). Other likely errors may come from smoothing and gridding the data; such errors were reported by Benestad et al. (1999) who noted that the interpolated temperatures from the gridded UEA data have a smaller variance than the corresponding observational temperature records. Additional errors may also be introduced during the preprocessing of the data, for instance by interpolation onto a new grid. There are, furthermore, probable sampling errors associated with uneven and sparse data coverage as well as using finite data records. Although the GCM gives a complete data coverage, the results may still be affected by numerical errors, such as rounding of errors and numerical diffusion. Errors in the data may produce misleading results. A simple test was, therefore, conducted in order to examine the robustness of the correlation and CCA results. This test involved experiments with different time sections of the data, different spatial resolution, different spatial coverage and different data sets. These additional test results suggested that the main CCA features are sensitive to the length of the data record, spatial coverage and resolution. For this reason, the CCA results presented below were based on analysis using the same record length, spatial coverage and grid points. The fact that these results are not robust suggests that no conclusive evidence can be obtained from this analysis, but that they may merely give indications as to whether the model captured the coupling between the various fields. A Monte Carlo (Wilks, 1995) re-sampling test was used to assess the significance of the correlation scores. 3. RESULTS 3.1. Historical relationship between the NAO and Norwegian temperatures A close look at the past relationships between the circulation patterns and the local climate variations may advance our understanding of the causes of past warming over Norway. Figures 2 and 3 show the 10-year low-passed observed winter (Dec. Feb. (DJF)), spring (Mar. May (MAM)), summer (Jun. Aug. (JJA)) and autumn (Sep. Nov. (SON)) values of the NAOI and winter surface temperatures in Bergen and Oksøy fyr, respectively. Unbiased and biased estimates of the best-fit temperature trends, ŷ=mx+c, are presented in Figures 2 and 3. The biased values are merely shown to demonstrate how a simple regression analysis in some cases can lead to misleading conclusions if applied directly to the data. The unbiased estimates suggest that the best-fit linear (long-term) trends due to systematic shifts in the winter and springtime NAO over the period are small and negative. The more recent observations since the 1960s, however, do indicate recent winter and springtime warming associated with systematic changes in the NAO. The reason why the best-fit temperature trends are small is either because of the warm conditions during the 1930s (DJF) or weak long-term NAO trend (MAM). Figure 4 presents the corresponding results for the ECHAM4/OPYC3 GHG run. The low-passed wintertime NAO indices show prominent decadal variations in both the observational records and the model results, as do the temperature. But, there is little evidence suggesting a long-term strengthening of the modelled NAO, implying that the simulated long-term warming has other explanations than systematic changes in the NAO.

6 376 R.E. BENESTAD Figure 2. Observed NAOI (bars) and 2-m temperature (dotted) in Bergen (western Norway) for the winter (a), spring (b), summer (c) and autumn (d) seasons. The thick grey and black solid lines show the 10-year low-passed NAOI and temperatures, respectively, and the thin lines indicate the de-trended series. The linear trends are shown as straight lines (the observed temperature trend is marked with + s, the NAOI trend with stars and the unbiased reproduction with. ). The observations span the period A correlation analysis was applied to temperature records from various Norwegian locations and interpolated temperatures from the UEA data set (Table I). The observations suggested a positive correlation between the Jan. Feb. mean values of the NAOI and Norwegian temperatures at all locations and, hence, warmer winter mean temperatures are associated with strong NAO conditions. Similar correlation analysis for the pre-1960 Bergen data as for the whole Bergen record (Table I), gave correlation coefficients of 0.74, 0.37, 0.05 and 0.39 for winter, spring, summer and autumn, respectively, implying that there have been no major recent changes in the relationship between the NAO and the Bergen temperatures. Relatively small differences can be seen between the correlation coefficients from

7 WARMING OVER NORWAY 377 Figure 3. Same as Figure 2, but for Oksøy lighthouse in southern Norway different locations, despite the expectation of geographical variations in the NAO-temperature relationship. A slightly weaker wintertime correlation than for Bergen was found between the corresponding modelled NAOI and the surface temperature, but the GCM results exhibit a stronger NAOI-T (5 E, 60 N) correlation during the autumn season. Figure 5 shows the correlation maps between the observed and modelled SLPs and their respective NAOI, and both plots show similar features, suggesting that the GCM captures the main NAO SLP features. Figure 6 shows correlation maps between observed and modelled wintertime NAOI and surface temperatures. The model reproduces the large-scale features, however, there are also some differences in the detailed spatial structure.

8 378 R.E. BENESTAD Figure 4. The simulated 10-year low-passed NAOI (grey) and 2-m temperature (black) at 60 N, 5 E for the winter season (DJF). Also shown are the de-trended temperature record and the best-fit NAOI trend. The NAOI was estimated from the simulated SLP according to the description in Section GCM reconstruction of the past warming Best-fit linear warming trends were estimated for both model results and the historical data in order to examine past climatic trends. Benestad (1999c) argued that future warming estimated by subtracting the control integration from the scenario run is likely to contain a warm bias. The observed Norwegian spring temperatures show clear warming trends, whereas less significant trends have been found during the other seasons (Hanssen-Bauer and Nordli, 1998). Most significant does not always mean strongest warming. Best fit linear trends of interpolated values from the temperatures at Oksøy (Figure 3) indicate warming trends for the period of 0.03, 0.07, 0.03 and 0.07 C/decade, for the DJF, MAM, JJA and SON seasons, respectively. Similar analysis for the Kjøremsgrendi temperature record (not shown), which is also homogeneous, gave trends of: 0.06, 0.14, 0.04 and 0.09 C/decade. Strongest warming, therefore, appears to have taken place during spring (MAM). As the GHG integration is used in climate change studies, it is important to ask whether the GCM does reproduce the historical temperature trends. If it does not, then one has to account for the differences and relate these to the future scenarios. It is not certain that the historical temperature trends are primarily due to enhanced greenhouse gas warming, as long-term temperature variations may also be part of natural variability. It is unlikely that the model will be able to reproduce such natural variations exactly as seen in the historical records, because these are of a chaotic nature and unpredictable 1 (Lorenz, 1963).

9 WARMING OVER NORWAY 379 The natural variability part of the temperature variations will, henceforth, be referred to as noise, as distinct to the enhanced greenhouse warming which is the signal that we are trying to detect. The reconstruction of past temperature trends, according to the interpolated GCM results shown in Table II, indicates strongest warming during the winter, although springtime temperatures also have increased in certain locations. Nevertheless, seven of the spring temperature reconstructions indicate a significant trend (Table II), whereas only six winter trends are significant. The model appears to overestimate the past warming, but aerosols which may reduce the effect of the radiative forcing, have not been accounted for here. The temperature reconstructions from the downscaling models based on Jones et al. (1998) temperature (Table III) indicate that the fastest warming for took place during winter for most of Norway, although the warming rate in the northern Norway was highest during spring. The downscaled spring warming is not statistically significant, but all the reconstructions of the southern Norwegian winter temperatures exhibit clear warming trends. These results could suggest that there are some systematic seasonal biases in the GCM results associated with the warming and that these errors seem to propagate to the downscaled results. Alternatively, the past long-term climate variations are part of the natural variability that the GCM cannot reproduce in terms of timing the warming and cooling. In this case, one may expect, to the first order approximation, similar noise levels for the future and the local scenarios must take into account the associated uncertainty. The empirical models calibrated on 500 hpa indicate a maximum warming rate only during winter, but none of these are significant (Table IV). The 500 hpa models do not reproduce much springtime warming. Little of the reconstructed warming can, therefore, be explained in terms of systematic shifts in the atmospheric flow pattern. Table I. The correlation coefficients between seasonal mean values of the de-trended NAO index and surface temperature from the MPI model and observed temperatures in Norway a Location MPI Utsira Bergen Kjøremsgrendi fyr (H) Florida (C) Dombås (H) 100yrs Season 5.5 E 60.0 N 4.5 E 59.2 N 5.2 E 60.2 N 9.0 E 62.0 N DJF MAM 0.15* JJA 0.08* 0.12* 0.00* 0.08* SON Location Oksøy Oslo Værnes Tromsø fyr (H) Blindern (E) Trondheim (C) (T) Season 8.0 E 58.0 N 10.4 E 59.8 N 10.6 E 63.3 N 18.6 E 69.4 N DJF MAM JJA 0.02* 0.04* 0.09* 0.28 SON Location Vardø Røros Karasjok UEA (H) (I) (H) Jones et al. (1998) Season 31.5 E 70.2 N 11.2 E 62.3 N 25.3 E 69.3 N 5.0 E 60.0 N DJF MAM JJA * * SON a The Norwegian word fyr means lighthouse. The codes given in parentheses are: H=homogeneous record; C=originally inhomogeneous, but the data have been corrected; E=subject to environmental changes; T=tested, but not perfectly homogeneous; I=unadjusted inhomogeneous record. All estimates shown here are statistically significant at the 5% level, unless the estimates are given with superscript *. Different versions of NAOI were used with the model and observations: the former is estimated from model SLP field (Section 2) whereas the latter is taken from Jones et al. (1997).

10 380 R.E. BENESTAD Figure 5. The NAO correlation maps for historical SLP analyses/observations (a) and ECHAM4/OPYC3 GHG results (b) Figure 6. The correlation maps between the wintertime NAOI and surface temperatures for observations (a) and model (b)

11 WARMING OVER NORWAY 381 Table II. Past reconstruction ( ) and scenario ( ) trends ( T/decade) based on interpolated GCM temperatures a Jan. Apr. Jul. Oct. Reconstruction ( C 10 years 1 ) Vardø * 0.23* Karasjok * 0.14* Tromsø 0.28* * 0.07* Bodø * 0.05* Kjøremsgrendi 0.34* 0.27* 0.07* 0.01* Røros 0.38* 0.29* 0.06* 0.01* Ona 0.29* * 0.03* Bergen 0.27* 0.24* 0.27* 0.03* Oksøy * 0.08* 0.06* Ferder * 0.03* 0.03* Nesbyen 0.36* * 0.02* Oslo * 0.02* Scenario ( C 10 years 1 ) Vardø Karasjok Tromsø Bodø Kjøremsgrendi Røros Ona Bergen Oksøy Ferder Nesbyen Oslo a All estimates shown here are statistically significant at the 5% level, unless the estimates are given with superscript *. The different columns show the scenarios for the different seasons Future warming scenarios Although a reasonably good agreement was established between some of the model reconstructions of the past temperature trends and the observations, it is important to examine further the correspondence between the past reconstructions and the future climate predictions. Table II also presents future interpolated temperature trends from the GCM results, indicating warming rates of C/decade for the period. The GCM results indicate a shift in the trends from maximum warming during winter in the past to greatest warming in summer and autumn for the future. Downscaled scenarios based on large-scale 2-m temperature (Jones et al., 1998) patterns, presented in Table III, also show warming trends ( C/decade), although with greater geographical variations than the interpolated GCM results suggest (Table II). These downscaled results, however, do not suggest any change in the seasons of maximum warming, except for near Tromsø and Bodø. Some of these trends seem unrealistically large, which may be due to the limited area with valid data (only predictor data for southern Norway) or because of the short record length used for the model calibration. The downscaled scenarios based on 500 hpa (Table IV) give different accounts as to what to expect in Norway in a warmer global climate. Contrary to the GCM results and the downscaled scenarios based on the 2-m temperatures, these results hint at little change during winter, summer and autumn, and even cooling during the spring season. The 500 hpa fields give lower trend estimates than the 2-m temperature data. The spread in the scenarios based on different choice of predictor data illustrates the danger in uncritically basing empirical downscaling models on just one type of predictor. The differences apparent

12 382 R.E. BENESTAD Table III. Past reconstruction ( ) and scenario ( ) trends ( T/decade) based on CCA, DNMI temperature and Jones et al. (1998) gridded temperatures (from UEA) a Jan. Apr. Jul. Oct. Reconstruction ( C 10 years 1 ) Vardø 0.04* (74) 0.11* (85) 0.03* (80) 0.02* (85) Karasjok 0.16* (80) 0.10* (87) 0.03* (73) 0.05* (82) Tromsø 0.00* (84) 0.09* (82) 0.08 (66) 0.03 (84) Bodø 0.03* (90) 0.05* (71) 0.05* (69) 0.03 (87) Værnes 0.31* (87) 0.08* (72) 0.03* (69) 0.00* (86) Kjøremsgrendi 0.39 (84) 0.10* (81) 0.03* (62) 0.00* (78) Røros 0.38 (80) 0.09* (84) 0.03* (79) 0.01* (81) Ona 0.14 (89) 0.03* (69) 0.03 (51) 0.01 (87) Bergen 0.26 (92) 0.14* (73) 0.05 (81) 0.00* (78) Oksøy 0.26 (94) 0.09* (78) 0.03 (82) 0.00* (84) Ferder 0.24 (93) 0.09* (81) 0.03* (85) 0.01* (87) Nesbyen 0.39 (86) 0.11* (80) 0.02* (79) 0.02* (64) Oslo 0.27 (91) 0.11* (82) 0.03 (82) 0.01* (81) Scenario ( C 10 years 1 ) Vardø 0.16 (74) 0.52 (85) 0.23* (80) 0.46 (85) Karasjok 1.20 (80) 0.62 (87) 0.21* (73) 0.90 (82) Tromsø 0.27* (84) 0.43 (82) 0.20* (66) 0.56 (84) Bodø 0.39* (90) 0.33* (71) 0.03* (69) 0.70 (87) Værnes 0.80 (87) 0.30 (72) 0.43 (69) 0.41 (86) Kjøremsgrendi 1.04 (84) 0.25 (81) 0.46 (62) 0.50 (78) Røros 1.33 (80) 0.24 (84) 0.45 (79) 0.47 (81) Ona 0.49 (89) 0.09* (69) 0.29 (51) 0.48 (87) Bergen 0.78 (92) 0.17 (73) 0.58 (81) 0.42 (78) Oksøy 0.39 (94) 0.21 (78) 0.29 (82) 0.37 (84) Ferder 0.43 (93) 0.21 (81) 0.28 (85) 0.34 (87) Nesbyen 0.87 (86) 0.24 (80) 0.27 (79) 0.36 (64) Oslo 0.67 (91) 0.21 (86) 0.37 (82) 0.40 (81) a All estimates shown here are statistically at above the 5% level, unless the estimates are given with superiscript *. The numbers in the parentheses indicate correlation scores from a cross-validation analysis (in %). The different columns show the scenarios for the different seasons. here may be due to the 500 hpa models inability to describe warming due to enhanced radiative forcing. On the other hand, 500 hpa models capture circulation related climate variability. These results further suggest that the reconstructed past and predicted future warming are not primarily due to changes in the atmospheric circulation. Downscaling studies using large-scale 2-m temperature patterns as predictors may in principle capture temperature changes both due to large-scale circulation changes and radiative forcing The coupling between the 500 hpa and surface temperatures To better understand the divergence between scenarios in Table III and Table IV, the relationship between the 500 hpa and the near-surface temperatures was studied by applying CCA to both fields in the model results and observations. Figure 7 shows the leading CCA patterns between January 2-m temperatures (left) and 500 hpa (right). The upper panels show the results from an analysis applied to the 48 years of ECHAM4/OPYC3 GHG scenario (years , corresponding to , although the model years are fairly arbitrary). The lower panels present the corresponding results from analysis on historical data ( ). The temperature contours are only shown where there is valid data in the observations.

13 WARMING OVER NORWAY 383 Table IV. Past reconstruction ( ) and future scenario ( ) a temperatures trends ( T/decade) based on DNMI temperature and NMC 500 hpa Jan. Apr. Jul. Oct. Reconstruction ( C 10 years 1 ) Vardø 0.05* (62) 0.04* (76) 0.06* (69) 0.04* (92) Karasjok 0.28 (76) 0.09* (72) 0.10 (86) 0.05* (85) Tromsø 0.15* (78) 0.1* (78) 0.04* (87) 0.07* (93) Bodø 0.17* (82) 0.00* (72) 0.03* (78) 0.09* (88) Værnes 0.19 (84) 0.02* (72) 0.01* (69) 0.03* (80) Kjøremsgrendi 0.15* (77) 0.00* (78) 0.03* (87) 0.02* (87) Røros 0.13* (72) 0.01* (72) 0.03* (78) 0.05* (85) Ona 0.08* (90) 0.01* (65) 0.01* (16) 0.01* (88) Bergen 0.12* (92) 0.03* (77) 0.02* (87) 0.01* (75) Oksøy 0.16* (89) 0.08* (61) 0.07* (76) 0.03* (85) Ferder 0.12* (85) 0.07* (52) 0.06*(68) 0.06* (87) Nesbyen 0.26* (76) 0.02* (68) 0.05* (83) 0.07* (66) Oslo 0.18* (84) 0.05* (64) 0.07* (80) 0.07* (82) Scenario ( C 10 years 1 ) Vardø 0.08* (62) 0.05* (76) 0.02* (69) 0.13* (92) Karasjok 0.43 (76) 0.10* (72) 0.07 (86) 0.19* (85) Tromsø 1.15* (78) 0.14 (78) 0.15 (87) 0.05* (93) Bodø 0.08* (82) 0.22 (72) 0.11 (78) 0.02* (88) Værnes 0.08* (84) 0.13 (72) 0.08* (69) 0.13* (80) Kjøremsgrendi 0.9* (77) 0.25 (78) 0.08 (87) 0.08* (87) Røros 0.22* (72) 0.20 (72) 0.08 (87) 0.14* (81) Ona 0.11 (90) 0.14 (65) 0.06 (16) 0.04* (88) Bergen 0.16* (92) 0.21 (77) 0.05 (87) 0.04* (75) Oksøy 0.16 (89) 0.23 (61) 0.03* (76) 0.04 (85) Ferder 0.17 (85) 0.23 (52) 0.03 (68) 0.07 (87) Nesbyen 0.27 (76) 0.31 (72) 0.10 (83) 0.07 (66) Oslo 0.21 (84) 0.29 (64) 0.06 (80) 0.07 (82) a All estimates shown here are statistically significant at the 5% level, unless the estimates are given with superscript *. The numbers in the parentheses indicate correlation scores from a cross-validation analysis (in %). The different columns show the scenarios for the different seasons. It is evident from these plots that the coupling between the large-scale circulation pattern and the surface temperatures show some similarities. There are, however, important differences between the model and the observations in both the surface temperature structures and the 500 hpa fields, which may introduce errors into the downscaling analysis. However, the large-scale features in the leading CCA patterns are approximately similar, suggesting that the 500 hpa models should give reasonably reliable results. The CCA, therefore, underpins the earlier conclusion that the past and future warming over Norway in the GCM are predominantly non-circulation driven. 4. DISCUSSION Historical observations suggest that the NAO may have different effects on the land temperatures during different seasons (Table I). The winter temperatures are strongly influenced by the NAO state, whereas the summer temperature are generally insensitive to the NAO. Table I hints at smaller seasonal dependence of the NAOI temperature relationship for locations further north, and the NAO seems to have some influence on the summer climate in Tromsø. An accurate reproduction of the NAO is, therefore, required to predict winter, spring and autumn climate changes associated with fluctuations in the large-scale circulation.

14 384 R.E. BENESTAD Figure 7. Leading January mean CCA patterns of model (a b) and observed UEA-NMC (c d) temperature (left) and 500 hpa (right). The data were interpolated onto a grid and the CCA was applied to the last 48 years from the data records. Note that the model topography was given at a much lower resolution than the land mask shown here One interesting question is whether the observed long-term springtime warming trends are related to systematic changes in the NAO. The indication from the results obtained here do not give support to such an explanation, as there is little evidence of a long-term shift in the NAO. However, a correlation coefficient (r) of around 0.3 (Table I) leaves the possibility for other circulation patterns than the NAO to affect Norwegian temperatures. Hanssen-Bauer (1999) demonstrated that a regression model based on SLP could reproduce most of the recent observed springtime warming trends 2 after 1940, but not the earlier trends. Another likely explanation for some of the springtime warming may be enhanced radiative forcing. There is also a possibility that other types of circulation patterns or other mechanisms do affect the Norwegian climate, but this issue is outside the scope of this study. There has, despite the weak long-term trend in the NAOI, been a strengthening of the springtime NAO since the 1970s which can account for part of the most recent warming. This recent development can be

15 WARMING OVER NORWAY 385 regarded as a contribution to the long-term trend, although the trend is not very sensitive to this recent period. The springtime correlation between the Bergen temperature and the NAOI is modest (r 0.3), and the NAOI was recovering from a period of low values during the 1970s to reach a state of high values by the end of the record. The springtime NAO was weak just before 1890, during the 1930s and in the 1970s, and there was an extended period of high NAOI between the 1890s and 1930s and high values in the mid 1940s, the late 1960s and after The spring conditions in Bergen were warm both during two of the three most recent periods of high as well as low NAOI values (Figure 2(b)). The disagreement between modelled and observed seasonality of maximum warming may point to a seasonal bias in the model. The strong correlation between the modelled NAO and temperatures during autumn (Table I) corroborates this hypothesis, but these differences may also potentially be due to the presence of slow chaotic processes affecting the various seasons differently. The scenarios presented in this paper should not be regarded as the final word in this area of climate change research, but rather as tentative results from a pilot study on the long-term relationship between the NAO and Norwegian temperatures. The scenarios from the GHG integration are, furthermore, not considered as the most realistic ones to date, and integrations which include radiative effect of sulphate aerosols (GSDIO) should be used instead of GHG for making future climate scenarios. Benestad (1999c) argued that the downscaling models using Jones et al. (1998) temperatures as predictors are too sensitive and predict local variables with too high variance. It is uncertain, however, that the inclusion of aerosols will fix problems with problems with seasonal bias and misrepresentation of the large-scale circulation. There may be possibilities for reducing the discrepancies associated with the downscaled scenarios by adopting another strategy for matching the observed climatic patterns with those of the GCM. The use of more extensive data records, both in terms of length as well as spatial coverage, may further improve the downscaled results. Benestad (1999c) suggested that the empirical downscaling models are highly sensitive to a slight mismatch in the large-scale circulation patterns and this sensitivity may explain some of the discrepancies between the results in Table III and Table IV. 5. CONCLUSIONS The analysis of historical data and the results from the ECHAM4/OPYC3 GHG integration have suggested that a potential seasonal bias may be present in the predicted greenhouse gas warming with respect to the historical observations. It is not known whether this is a bias due to model shortcomings or whether the observed long-term temperature trends contain a strong component of slow natural variations that affect the trend-estimates. In either case, a significant part of the long-term temperature change may not be predicted by the ECHAM4/OPYC GHG model set-up, which must affect the uncertainty in the local climate scenarios for the future. The application of empirical downscaling models does not remove this bias, but merely alters its seasonality. Different downscaling models using different predictor data sets give diverging scenarios, which can be explained in terms of the limitation of the various model types. The 500 hpa models can describe warming related to changes in the atmospheric circulation, but not changes due to enhanced radiative forcing. It is, therefore, proposed that the warming in the ECHAM4/OPYC3 GHG was not primarily due to changes in the circulation. This explanation is supported by the analysis of the historical observations, which suggests that little of the linear warming trend between 1860 and 1997 can be attributed to systematic changes in the NAO. Since 1960, however, the NAO has strengthened resulting in milder Norwegian winters. Further work will elaborate on the downscaling using different GCM results (ECHAM4 GSDO/GSDIO, ECHAM3, HadCM2 (Cullen, 1993; Gordon et al., 2000), NCAR CSM (Meehl et al., 2000), CSIRO (Gordon and O Farrell, 1997), CCCma (Flato et al., 2000) and CCSR/NIES AOGCM (Emori et al., 1999)) and predictors such as T92m, SLP and 500 hpa as well as others. A different and more promising downscaling technique based on common EOFs (Flury, 1988; Sengupta and Boyle, 1993;

16 386 R.E. BENESTAD Barnett, 1999; Benestad, 1999b) will also be used. There is also a need for longer historical records of gridded data for more robust empirical downscaling results, as 48 years may be too short for proper model calibration. Evaluation studies of a number of GCM transient integrations are in progress and the historical T 2m and SLP records for the North Atlantic have been extended back to ACKNOWLEDGEMENTS The model data was kindly provided by The Max-Planck-Institute for Meteorology in Hamburg and was made available on local servers by Ole Vignes. The observed temperature series from the DNMI were quality controlled and preprocessed by Per Øyvind Nordli, who also gave valuable advice through the course of this research. Also thanks to Eirik Førland and Dr Inger Hanssen-Bauer for valuable discussions. The gridded temperature and the observed NAOI were obtained from the University of East Anglia Climate Research Unit s internet site. Thanks to my wife, for proof reading the manuscript and to the anonymous reviewer for useful comments. This work was carried out as part of the Norwegian climate research initiative, RegClim, and was funded by the Norwegian Research Council (Contract NRC-No /720) and the Norwegian Meteorological Institute. NOTES 1. In terms of its state, or the exact location on its attractor in the phase space. 2. Not based on analysis of de-trended records. REFERENCES Barnett TP Comparison of near-surface air temperature variability in 11 coupled global climate models. Journal of Climate 12: Barnett TP, Preisendorfer RW Origins and levels of monthly and seasonal forecast skill for United States surface air temperatures determined by canonical correlation analysis. Monthly Weather Re iew 115: Benestad RE. 1998a. CCA applied to statistical downscaling for prediction of monthly mean land surface temperatures: model documentation. Klima 28/98. DNMI, Oslo, Norway. Benestad RE. 1998b. Description and evaluation of the predictor data sets used for statistical downscaling in the RegClim. Klima 24/98. DNMI, Oslo, Norway. Benestad RE. 1999a. Evaluation of seasonal forecast potential for Norwegian land temperatures and precipitation using CCA. Klima 23/99. DNMI, Oslo, Norway. Benestad RE. 1999b. Evaluation of the common EOF approach in linear empirical downscaling of future ECHAM4/OPYC3 GSDIO climate scenarios. Klima 35/99. DNMI, Oslo, Norway. Benestad RE. 1999c. Pilot studies of enhanced greenhouse gas scenarios for Norwegian temperature and precipitation from empirical downscaling. Klima 16/99. DNMI, Oslo, Norway. Benestad RE, Hanssen-Bauer L, Førland EJ, Tveito OE, Iden K Evaluation of monthly mean data fields from the ECHAM4/OPYC3 control integration. Klima 14/99. DNMI, Oslo, Norway. Bretherton CS, Smith C, Wallace JM An intercomparison of methods for finding coupled patterns in climate data. Journal of Climate 5: Christensen OB, Christensen JH, Machenhauer B, Botzet M Very high resolution climate simulations over Scandinavia present climate. Journal of Climate 11: Crane RG, Hewitson BC Doubled CO 2 precipitation changes for the Susquehanna Basin: downscaling from the Genesis General Circulation Model. International Journal of Climatology 18: Cullen MJP The unified forecast/climate model. Meteorology Magazine 122: Emori S, Nozawa T, Abe-Ouchi A, Namaguti A, Kimoto M, Nakajima T Coupled ocean atmosphere model experiments of future climate change with an explicit representation of sulfate aerosol scattering. Journal of the Meteorological Society of Japan 77(6): Flato GM, Boer GJ, Lee WG, McFarlane NA, Ramsden D, Reader MC, Weaver AJ The Canadian Centre for Climate Modelling and Analysis global coupled model and its climate. Climate Dynamics 16: Flury B Common Principal Components and Related Multi ariate Models. Series in Probability and Mathematical Statistics. John Wiley and Sons. Gibson JK, Kallberg P, Uppala S, Hernandez A, Nomura A, Serrano E ERA Description. ERA Project Report Series. ECMWF. Gordon C, Cooper C, Senior CA, Banks H, Gregory JM, Johns TC, Mitchell JFB, Wood RA The simulation of SST, sea ice extents and ocean heat transports in a version of the Hadley Centre coupled model without flux adjustments. Climate Dynamics 16: Gordon HB, O Farrell SP, Transient climate change in the CSIRO coupled model with dynamic sea ice. Monthly Weather Re iew 125:

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