A 10 year cloud climatology over Scandinavia ... - Wiley Online Library

INTERNATIONAL JOURNAL OF CLIMATOLOGY Int. J. Climatol. 23: 1023–1044 (2003) Published online in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/joc.916

A 10 YEAR CLOUD CLIMATOLOGY OVER SCANDINAVIA DERIVED FROM NOAA ADVANCED VERY HIGH RESOLUTION RADIOMETER IMAGERY ¨ KARL-GORAN KARLSSON* The Swedish Meteorological and Hydrological Institute, SE-601 76 Norrköping, Sweden Received 26 April 2002 Revised 19 March 2003 Accepted 19 March 2003

ABSTRACT Results from a satellite-based method to compile regional cloud climatologies covering the Scandinavian region are presented. Systematic processing of multispectral image data from the NOAA Advanced Very High Resolution Radiometer (AVHRR) instrument has been utilized to provide monthly cloud climatologies covering the period 1991–2000. Considerable local-scale variation of cloud amounts was found in the region. The inland Baltic Sea and adjacent land areas exhibited a large-amplitude annual cycle in cloudiness (high cloud amounts in winter, low cloud amounts in summer) whereas a weak-amplitude reversed annual cycle (high cloud amounts with a weak maximum in summer) was found for the Scandinavian mountain range. As a contrast, conditions over the Norwegian Sea showed high and almost unchanged cloud amounts during the course of the year. Some interesting exceptions to these patterns were also seen locally. The quality of the satellite-derived cloud climatology was examined through comparisons with climatologies derived from surface cloud observations, from the International Satellite Cloud Climatology Project (ISCCP) and from the European Centre for Medium-range Weather Forecasts ERA-40 data set. In general, cloud amount deviations from surface observations were smaller than 10% except for some individual winter months, when the separability between clouds and snow-covered cold land surfaces is often poor. The ISCCP data set showed a weaker annual cycle in cloudiness, generally caused by higher summer-time cloud amounts in the region. Very good agreement was found with the ERA-40 data set, especially for the summer season. However, ERA-40 showed higher cloud amounts than SCANDIA and ISCCP during the winter season. The derived cloud climatology is affected by errors due to temporal AVHRR sensor degradation, but they appear to be small for this particular study. The data set is proposed as a valuable data set for validation of cloud description in numerical weather prediction and regional climate simulation models. Copyright  2003 Royal Meteorological Society. KEY WORDS:

cloud climatology; NOAA AVHRR; Scandinavia; annual cycle; SCANDIA cloud classifications; ISCCP D2; ERA-40

1. INTRODUCTION Cloud observations from high-resolution imaging sensors onboard both geostationary and polar orbiting satellites have been available for nearly four decades. The utilization of these data has steadily increased in importance for meteorological applications during this period. In recent years, climatological applications have also been demonstrated based on systematic utilization of advanced cloud-analysis tools. The problems in maintaining a network of cloud-observing ground stations with a sufficiently high horizontal spacing and the fact that large oceanic and remote areas are always difficult to cover with surface observations have accelerated this development. Another important factor is the fast development of computer technology, now enabling the processing of very large data volumes such as those provided by satellite sensors. Many climate studies have stressed the importance of getting a complete picture of global cloudiness in order to assess correctly the impact on the climate due to cloud-related radiative feedback processes (e.g. Arking, 1991). Very small changes in global cloudiness may completely obscure or compensate for the anticipated * Correspondence to: Karl-Göran Karlsson, The Swedish Meteorological and Hydrological Institute, SE-601 76 Norrköping, Sweden; e-mail: [email protected]

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global warming due to an anthropogenically enhanced greenhouse effect (Ramanathan et al., 1989; Cracknell, 2001). This circumstance has caused an intense scientific debate on the actual climate impact of possible and observed cloudiness changes (e.g. Kristjánsson and Kristiansen, 2000; Lindzen et al., 2001; Lin et al., 2002; Hartmann and Michelsen, 2002). Consequently, it is of great importance that new and accurate methods capable of observing clouds over the entire globe with a high spatial resolution are developed. Current climate simulation models still have problems in correctly describing the present climate, in particular the global cloud climatology, which calls for further development of the cloud description in these models. Progress must be made in this area in order to increase the confidence in climate simulations. At the same time, a continued and refined compilation of global cloud observation data sets is also essential for getting a better understanding of the present cloud climatology and for supporting the climate model development. The best known example of such a global satellite-based cloud climate data set is produced by the International Satellite Cloud Climatology Project (ISCCP; Rossow and Garder, 1993a; Rossow and Schiffer, 1999). This data set consists of a complete and consistent set of global cloud and radiance parameters derived from sensors on both geostationary and polar orbiting satellites. Other data sets have also been proposed recently, such as that of Wylie and Menzel (1999). Even if the interest in global climate studies is continually high, an increasing attention on the effects of climate change on the regional and local scale has also been noticed lately. Many national meteorological services (NMSs) and other agencies have launched scientific programmes with the aim of downscaling and interpreting the effects of a globally predicted climate change (e.g. Rummukainen et al., 2001). Consequently, the need for high-resolution model-independent data sets for validation of regional climate model simulations has increased as well. Here, satellite observations have become increasingly important, since this is the only data source that can give a sufficiently good regional coverage and at the same time a high spatial resolution for some of the crucial cloud and radiation parameters. The increased interest in regional conditions has consequently led to national and international efforts to compile satellite-derived climatological data sets (Woick et al., 2000) as a complement to the existing coarse-resolution global data sets. Some examples of satellite-based high-resolution regional cloud climatologies have also been presented in recent years (Karlsson, 1997; Kästner and Kriebel, 2001). However, some limitations of these studies have been the coverage of relatively short periods (only 1 year for the first study) and the exclusive use of day-time satellite imagery (the second study). This paper addresses the task of compiling long-term (decadal) and high-resolution regional cloud climatologies from satellite imagery covering both day and night at high latitudes. Results are presented for the last decade (1991–2000) covering the Scandinavian region. Comparisons are made with three other cloud data sets: 1. Cloud climatologies based on conventional surface observations (synoptical cloud observations — SYNOP). 2. Results from the ISCCP D2 cloud climate data set. 3. Cloud information from the European Centre for Medium-range Weather Forecasts (ECMWF) re-analysis project (ERA-40). Sections 2 and 3 describe the basic methodology and the satellite data set used. Results are presented in Section 4, followed by comparisons with other data sets in Section 5. Finally, Section 6 discusses the limitations, the strengths and the potential use of this and future cloud climate data sets.

2. METHODOLOGY The cloud climate data set is based on results produced through a systematic processing of high-resolution multispectral imagery from the Advanced Very High Resolution Radiometer (AVHRR; Lauritson et al., 1979) instrument on the polar orbiting NOAA satellites. The basic cloud processing tool has been the SCANDIA model (the SMHI cloud analysis model using digital AVHRR data). SCANDIA utilizes information from Copyright  2003 Royal Meteorological Society

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all five spectral channels of the NOAA AVHRR instrument, which comprises two visible channels (denoted channels 1 and 2, at 0.6 µm and 0.9 µm respectively) and three infrared channels (denoted channels 3, 4 and 5 at 3.7 µm, 11 µm and 12 µm respectively). This is different to many previous cloud climatology data sets, which are often based on only a few spectral channels (typically one visible and one infrared channel). Of particular interest here is the fact that results have been produced using a fixed or frozen cloud classification scheme for the entire period from 1991 to 2000. In this way, the quality of the data set has not been influenced by any updates or changes of the algorithm, which means that the quality characteristics are the same for the entire data set as concerns pure model characteristics. The SCANDIA cloud classification model has been described in detail in several reports and papers. A general overview can be found in Karlsson (1995), whereas more detailed descriptions are given by Karlsson and Liljas (1990) and Karlsson (1996, 2001). Here, only a brief summary of the most important image features and their main use is given (Table I). A method for compilation of SCANDIA cloud climatologies has previously been described by Karlsson (1997). Here, a slightly modified version of this method has been used (explained in detail by Karlsson (2001)). The basic idea is to use AVHRR scenes with good viewing conditions exclusively (i.e. low satellite zenith angles) over the area in order to minimize problematic (anisotropic) reflection effects often encountered at large viewing angles. Consequently, only the satellite passages with the highest satellite elevation among several consecutive passages at descending and ascending passage nodes have been chosen. This means that, with the nominal two operational NOAA satellites, four useful AVHRR scenes per day could be exclusively chosen over the area at the reception site in Norrköping, Sweden. Table II shows an overview of the selected satellite scenes and their associated passage times over the particular region. Notice here that passage times are given in Central European Time (CET = UTC + 1 h) in order to correspond as closely as possible to the true solar time over the area. At least one passage with sufficiently low satellite zenith angles is normally guaranteed within the indicated time windows in Table II each day. However, the NOAA satellite orbits are not perfectly stable, which means that considerable deviations from these time windows occurred for some years in the period (as seen later in Figure 1). Cloud Table I. Cloud classification image features used by SCANDIA. Individual NOAA AVHRR channels are denoted CH1, CH2, CH3, CH4 and CH5. TEX4 means a local (in a 5 × 5 pixel window) variance in the CH4 measure of the small-scale variation of brightness temperatures Feature No.

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Main use for classifier Daytime separation of clouds and snow from land surfaces. Used coupled with feature 4 Daytime separation of land surfaces with vegetation from sea surfaces. Used also for snow detection Geographic map used for land–sea separation at night and for low sun elevations Separates clouds from land and sea surfaces during daytime. Important at night for fog, stratus and cirrus detection. Coupled with feature 1 during daytime Separates main cloud groups (low, medium and high clouds) by comparing with mean temperatures at 500 and 700 hPa Separates thin clouds (especially cirrus clouds) from thick clouds both night and day Separates clouds with high small-scale texture (e.g. cumulus) from more homogeneous clouds (e.g. stratus) Int. J. Climatol. 23: 1023–1044 (2003)

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Table II. Approximate time-windows (CET) valid for the satellite scenes used during the period 1991–2001 (compare with Figure 1). The NOAA satellites used during the period are also indicated Time of day

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Year Figure 1. Display of all the satellite scenes used and their corresponding passage times in Norrköping for the period studied, February 1991 until January 2001. Each dot indicates an individual satellite passage

classifications from the chosen satellite passages roughly describe cloud conditions at night, in the morning, in the afternoon and in the evening according to Table II. These four daily observations can be used to describe mean daily cloud conditions and provide a coarse estimation of the diurnal cycle of cloudiness. Cloud climatologies from surface stations (SYNOP) are often compiled in a similar way based on observations at 00 UTC, 06 UTC, 12 UTC and 18 UTC. Consequently, the method described here offers a possibility to extend a similar methodology to be applied over large areas with a homogeneous and constant spatial resolution as offered by satellite measurements. Actual comparisons between satellite- and SYNOP-derived data sets are given later in Section 5. For the estimation of mean total cloud amounts, the cloud climatologies from SCANDIA were compiled according to the following six steps: I. Cloud classification image results were resampled by use of a nearest-neighbour resampling technique in order to reduce the nominal spatial resolution from 1 to 4 km. II. Classification results for two different processing areas (one covering southern and the other northern Scandinavia) were merged into one image. III. Each pixel in the classified image was labelled cloudy or cloud-free, depending on the resulting cloud and surface types. IV. Pixels classified as sub-pixel clouds (cloud contaminated) were identified and given half the weight of pixels labelled as fully cloudy. Copyright  2003 Royal Meteorological Society

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V. Cloud frequencies for entire months were estimated by calculating the fraction of the total number of selected scenes within a month where the pixel was labelled as cloudy. VI. Conversion of cloud frequencies to total cloud amounts or total fractional cloud cover was finally accomplished by averaging over nine by nine pixels (representing an area of approximately 36 × 36 km2 ). The main reason for reducing the nominal pixel resolution from 1 to 4 km is explained by the fact that a standard method for operational (near-real-time) AVHRR image navigation has been used (Rosborough et al., 1994). Such methods are generally not able to provide a navigation accuracy within 1 km, and recently proposed more advanced methods (Brunel and Marsouin, 2000) have not been applicable to the AVHRR data set used. The method of treating pixels labelled as cloud contaminated in Step IV was chosen as the most appropriate way of handling this difficult problem. SCANDIA does not interpret any fractional cloud cover within a single pixel, it only indicates pixels that most likely contain sub-pixel-sized cloud elements or cloud edges. It is, in practice, impossible to apply one single reliable method for estimation of sub-pixel fractional cloud cover, since the method needs to be different depending on the actual cloud type, which generally is not known. Consequently, a compromise method giving these pixels a 50% weight in calculations of fractional cloud cover is used. This should be able to minimize errors in the calculations (as shown by Kidder and Vonder Haar (1995)). The conversion of cloud frequencies to fractional cloud cover in Step VI is evidently required if, for example, one wants to compare results with corresponding SYNOP observations. Furthermore, this quantity is probably one of the more valuable quantities to be used in comparison with results from numerical weather prediction (NWP) and climate simulation models. The choice of an averaging area size of 36 × 36 km2 was based on previous experiences obtained when comparing satellite observations with ground observations (Karlsson, 1995, 1996). Since SCANDIA provides information about various cloud and surface types, it is clear that, besides the estimation of the total fractional cloud cover, it should also be possible to estimate the contribution to the fractional cloud cover or the 4 km cloud frequency from individual cloud types. Consequently, a further grouping of the cloud types should be possible in order to investigate the contribution from different cloud types. Karlsson (2001) has presented results for several other cloud groups, but here we restrict ourselves only to demonstrating results for two sub-groups, denoted Water clouds and Ice clouds. The reason for this restriction is that it is generally very difficult to validate the results for individual cloud types. This is true also for the Water and Ice cloud groups, but because of their respective large and significantly different impact on atmospheric radiation conditions we show some results here to indicate the potential use of satellite observations for studying these aspects of radiation climatology. The basis for the capability of SCANDIA to separate the two groups is the fact that ice and water clouds have significantly different reflection and absorption characteristics in the visible and short-wave infrared AVHRR channels (Liou, 1980) affecting image features 1, 2 and 4 in Table I. Also, the differences in cloud transmittances for semi-transparent cirrus clouds at the two wavelengths in the pure thermal region (as measured in feature 6 in Table I) are utilized. However, in particular, it must be emphasized that the reflection characteristic differences utilized are largely influenced by differences in the effective radius of the cloud particles and not only by the actual difference in cloud phase. Consequently, the groups Water clouds and Ice clouds are, in this respect, better characterized as a separation of clouds with large and small effective cloud droplet/crystal radii. In most cases this distinction coincides with the distinction of true ice and water clouds, but occasionally (especially over oceanic regions) the water clouds may possess large cloud droplets near the cloud top and this could then lead to some misinterpretation here as an ice cloud.

3. THE SATELLITE DATA SET NOAA AVHRR data from a complete 10 year period have been used in this study. The data set period starts in February 1991 and ends in January 2001 and comprises archived results from operational SCANDIA Copyright  2003 Royal Meteorological Society

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cloud classifications. A fully successful archiving of results for all useful NOAA AVHRR scenes (according to Table II) would theoretically result in a total number of 14 336 cloud classifications during the period. Unfortunately, owing to reception problems, technical processing problems and unforeseen failures of operational NOAA satellites (e.g. NOAA-11 in September 1994 and NOAA-15 in July 2000), only 87% (12 470) of the theoretically available satellite scenes have been used here. The use of night and afternoon passages stayed at 86% of the theoretically available scenes, whereas the level of scenes used in the morning and in the evening was slightly higher: 89% and 88% respectively. The loss of the night-time and afternoon satellite (NOAA-11) between September 1994 and March 1995 explains the major part of this difference. Figure 1 gives an overview of the entire satellite data set during the period. Notice again that the passage times are here given in CET in order to give an indication of the true solar time during the satellite passages. To be noticed in Figure 1 are the following details: • NOAA-10 was used for the morning and evening passages until 4 September 1991, when it was replaced by NOAA-12. • NOAA-11 was used for the night and afternoon passages until 14 September 1994, when it was suddenly lost. It was not replaced by NOAA-14 until 28 February 1995. • NOAA-12 was used for the morning and evening passages between 5 September 1991 and 13 September 1998 and between 23 July 2000 and 31 January 2001 (due to the temporary loss of NOAA-15). In addition, data from the morning passage of NOAA-12 was also used in March–April 1999 during experiments with NOAA-15 (test transmission of a modified AVHRR channel 3 at 1.6 µm). • Archiving problems (tape failure) led to the loss of all data from June 1998. • NOAA-15 was used for the morning and evening passages between 14 September 1998 and 22 July 2000, when it was suddenly lost. The reappearance of useful data from NOAA-15 unfortunately occurred after the end of the study period (in February 2001). The instability of polar satellite orbits caused considerable variation of passage times during the period, for some periods even outside the targeted time windows described earlier in Table II. This especially concerned the night and afternoon passages, which quickly generated significant deviations compared with orbit characteristics at the time of launch. The situation became quite extreme at the very end of the period, where the loss of NOAA-15 overpasses forced the use of NOAA-12 overpasses, which were then sometimes overlapping night/afternoon passages of NOAA-14.

4. RESULTS Figures 2 and 3 show monthly and seasonal mean cloud amounts over Scandinavia for the study period at the chosen spatial resolution of 36 km. Cloud frequency results at the finer 4 km resolution can be found in Karlsson (2001). The four seasons in Figure 3 are defined as follows: December, January and February for winter; March, April and May for spring; June, July and August for summer; and September, October and November for autumn. Notice here that all months except January are taken from the period 1991–2000. For January, the period 1992–2001 has been used. This means that to get ten complete winter seasons, the tenth and last one has been composed by December 2000, January 2001 and February 1991. Monthly and seasonal values show a pronounced annual cycle in cloudiness over the area, especially over the waters of the Baltic Sea. A typical feature of the cloud climate in the region is the overall high cloud amounts in the winter and autumn seasons, ranging from 70 to 85% with only a small geographical variation. A weak wintertime minimum in cloudiness is found in an area around the Swedish coast of the Bothnian Sea in the centre of the region. This minimum appears to be correlated with the frequent occurrence of relatively warm winter months with strong westerly winds over the area during the 1990s. Weak wintertime maxima are found over the inner part of southern Sweden, over the Scandinavian mountain range and over the offshore parts of the Norwegian Sea. Cloud amounts appear also to be quite high over large areas in Finland and in the Baltic states. However, the high inland values in Finland and in northern Sweden are generally found to Copyright  2003 Royal Meteorological Society

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Figure 2. Mean monthly total fractional cloud cover for all months in 36 km horizontal resolution


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Figure 3. Mean total fractional cloud cover in 36 km horizontal resolution for (from left to right) the four seasons winter, spring, summer and autumn

be unrealistically high and caused by several factors (e.g. the non-separability of cloud-free very cold ground surfaces and mid- and high-level ice clouds). The maximum in the southeastern part is probably also an artefact caused by enhanced anisotropic reflection occurring, especially during the morning overpasses during the dark seasons (compare with Table II). These problems appear to be most pronounced during November and February according to Figure 1. A further discussion of these separability problems follows in Section 5. As a contrast to winter and autumn conditions, much less cloudiness and much larger geographical variations are found during the spring and summer seasons. The influence of seawater and major lakes is pronounced, causing drastic reductions in cloudiness, especially in summer. However, one remarkable exception from this pattern is found over the visible offshore parts of the Norwegian Sea. Here, cloud amounts continue to be high and they even increase slightly in summer compared with the darker and colder seasons. Since, at the same time, cloud amounts in the Scandinavian mountain range increase during spring and summer, a remarkable minimum in cloudiness appears in the inner part of the Norwegian Sea close to the coast. This minimum is most clearly seen during spring (most remarkable for April and May in Figure 1). Conditions in the outer offshore parts of the Norwegian Sea show less variation, with almost constantly high cloud amounts. From Figure 2 it is clear that, on average, July has been the least cloudy month during the period for most places except in the Scandinavian mountain range and over the outer regions of the Norwegian Sea, where we instead have summertime maxima of cloudiness. A cloud amount minimum in July with values below 40% is found in the Baltic Proper to the east of Gotland, and with individual cloud frequency minima at pixel resolution as low as 35%. November was the cloudiest month in the area during the period, except for the Scandinavian mountain range (where June was the most cloudy) and over the Norwegian Sea (where the highest cloud amounts were found both during summer and winter months). An interesting minimum in cloudiness was also found over the Norwegian coast in September. This was most probably correlated with the occurrence of several September months with prevailing southerly or southeasterly flow over the Scandinavian mountain range, causing a lee-effect with decreased cloudiness in the area mentioned. Another way of displaying the annual cycle of cloudiness over the area is to show the average annual evolution of cloudiness with a higher temporal resolution for some selected places. Figure 4 shows the Copyright  2003 Royal Meteorological Society

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Figure 4. Selected places for visualization of the detailed annual course of cloudiness (Figure 5). In addition, the positions for the SYNOP stations Falsterbo and Pajala (see Figure 10) are shown

location of six such locations and their corresponding annual cycles of cloudiness are shown in Figure 5. The selection of these places has been partly made to demonstrate the capability of showing cloud observations where, normally, no corresponding surface observations are available (e.g. positions Baltic Proper, Bothnian Bay, Kebnekaise and Norwegian Sea in Figure 4). In addition, the purpose of examining the differences between ocean locations and inland locations (positions Växjö and Malung in Figure 3) also influenced this choice. For each position, the mean cloud cover has been calculated in a 36 km horizontal resolution. Figure 5 shows the annual variation of cloud cover computed as daily means (thin line) and 5 day means (thick line with shading beneath) over the entire 10 year period for each selected position. Copyright  2003 Royal Meteorological Society

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K.-G. KARLSSON One day mean Mean over five days

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Figure 5. The annual course of cloud cover (%) for selected positions shown in Figure 4. The thick line shows the 5-day mean values and the thin line shows daily mean values computed over the whole 10 year period

In Figure 5 we notice the pronounced annual cycle of cloudiness over the Baltic and Bothnian Sea positions with a summertime minimum and a wintertime maximum. The largest amplitude is seen for the position in the Baltic Proper, which has winter cloud amounts close to 80% and summer cloud amounts close to 35%. Cloud conditions at the two inland positions of Växjö and Malung differ to some extent from the previously discussed positions. An annual cycle in cloudiness is clearly seen for Växjö, but the amplitude is now remarkably decreased (especially when compared with the position of the Baltic Proper). For Malung, the Copyright  2003 Royal Meteorological Society

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amplitude has decreased even further and is hardly visible. Here, cloud amounts in February are almost compatible with summertime cloud amounts. The short-term variation is very large for both positions, and seemingly with no significant correlation. Nevertheless, it is interesting to notice the cloudiness minimum in December, a feature that has been common for all four of the positions discussed so far. The results for the position in the Scandinavian mountain range in Figure 5 show a very different annual evolution of cloudiness compared with previously discussed positions. The annual cycle is reversed here, with a weak summertime maximum in cloudiness (in June and July). For the position over the Norwegian Sea, we can also see a summertime maximum in cloudiness very similar to the results found for the Scandinavian mountain range. However, this maximum is not pronounced for positions closer to the Norwegian coast, where instead a minimum in cloudiness can be seen earlier in spring (as mentioned previously). A pronounced cloudiness minimum is seen here for coastal parts of the Norwegian Sea, where cloud amounts are decreasing from approximately 70% in early spring to 55% in April and May to become higher again in June and July. To demonstrate the capability to give a crude estimate of the diurnal cycle of cloudiness in the area, Figure 6 shows the average cloud amounts in summer (June, July and August) for the four daily observations given by Table II. As expected, a pronounced diurnal cycle can be seen over land areas. Notice the high correlation of the shape of the cloudiness field and the coastlines in the afternoon. Notice also the high cloud amounts in the afternoon in the Scandinavian mountain range. Figure 7 shows the summertime distribution of Water and Ice clouds (according to their definition in Section 2). An interesting Ice cloud feature is the maximum eastward (leeward for the dominating westerly flow) of the Scandinavian mountain range in summer, indicating a frequent occurrence of lee-wave cirrus clouds. For the Water cloud results, we can identify a land–sea difference reflecting the more frequent formation of convective cumulus clouds over land (i.e. a higher frequency of small and medium-sized cumulus clouds). Similarly, a high cumulus convection activity is also indicated over the peaks of the Scandinavian mountain range. Here, Water clouds appear to persist during more than 50% of the time. In addition, Figure 7 emphasizes the large contribution to the total cloud amount from Water clouds over the Norwegian Sea (almost 60%) in summer. Ice cloud occurrence is seen to be generally higher over land areas, which is explained by

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Figure 6. The diurnal cycle of cloud cover (from left to right: night, morning, afternoon, evening — see Table II) for the summer season (June, July and August) Copyright  2003 Royal Meteorological Society

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Figure 7. Average amounts of Water clouds (left) and Ice clouds (right) in summer (June, July and August)

the gradual evolution of some of the convective clouds into precipitating cumulonimbus clouds with large horizontally extended cirrus anvils.

5. COMPARISONS WITH OTHER CLOUD CLIMATE DATA SETS 5.1. Cloud climatologies from surface stations The SCANDIA cloud climatologies have been compared with corresponding cloud analyses based on surface (SYNOP) observations over Sweden. In these comparisons, the spatial resolution of the satellite analyses was kept at 36 km instead of comparing with the fine resolution (4 km) representation. This would hopefully reduce some of the errors due to differences in the quantities compared (surface-observed sky cover versus satellite-observed earth cover — discussed by Rossow and Garder (1993b)). The averages used are assumed to correspond better to the mean cloud cover quantity than the derived high-resolution cloud frequencies at pixel resolution. A coarser resolution than the one proposed here could be justified, since highlevel cloud types are typically observed from ground at considerably larger distances in reality. However, the 36 km resolution has been chosen here to retain some of the characteristic small-scale cloudiness features. Additionally, the closest area should be the most important for the surface observer and distant clouds would only give small contributions to the total cloud amount. Thus, a best fit between the satellite-observed and the SYNOP-observed area is believed to be found somewhere in the range 30–40 km (following the discussion by Karlsson (1995)). Corresponding SYNOP analyses of the mean cloud cover over Sweden have been constructed here using four daily observations at 00, 06, 12 and 18 UTC. If compared with the corresponding satellite observation times (approximated in Table II and visualized in Figure 1), then it is clear that, generally, Copyright  2003 Royal Meteorological Society

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only small deviations from the SYNOP observation times are present, except for the 00 UTC observation. This comparison is limited to studies of the total cloud cover parameter, which is believed to be the most appropriate surface-observed parameter to be compared with satellite observations. The information on various cloud types in synoptic observations (e.g. indicating the presence of water and ice clouds) cannot easily be utilized in comparisons with satellite measurements. The reason is that the satellite observations are strongly biased towards the amounts of the topmost cloud layers (no information on underlying clouds is normally available), whereas the opposite is true for SYNOP observations. A selection from the 28 SYNOP stations, approximately evenly distributed over Sweden, has been used (see Karlsson (2001) for details). Unfortunately, major reductions in the synoptic network occurred in Sweden during the period studied. Many of the stations selected were closed in 1996, and only 15 of the stations cover the entire 10 year period. Consequently, the validation data set presented here is biased towards the first half of the observation period, when more observations were available. Figure 8 shows a year-by-year summary of validation results for the entire period and the entire validation data set (including 12 470 satellite scenes compared with more than 250 000 SYNOP observations). We conclude that the annual mean of cloud amount has not varied much throughout the period (all values are confined to the interval 60–70%) over the Swedish area. Both data sets show this feature; thus, no major bias in the satellite data set can be seen except for a possible negative bias of a few percent. In addition, both data sets show the same general behaviour of cloudiness over the Swedish area. The period starts with relatively high cloud amounts (1991–93), which is followed by some years with lower cloud amounts (1994–97) and it is finally ended with a new period of high cloud amounts (1998–2000). Despite this satisfying agreement between the two observation types, it is clear that the individual case-to-case variation is considerable, as indicated by the quite high root-mean-square (RMS) error (exceeding 30%). These results correspond rather well to results found in previous validation efforts (Karlsson, 1995) although RMS errors seem to be slightly higher here. The reason for this is probably the acceptance of a larger time difference between observation times for the two observation types. To highlight more details in the relation between SYNOP and satellite observations, Figure 9 shows monthly averages of validation results throughout the period, thus allowing an evaluation of the seasonal variation of error characteristics. In particular, we find a sinusoidal appearance of the bias error with a positive maximum in the winter season and a negative maximum in the summer season. Notice the effect of the loss of a

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large part of available SYNOP observations after 1996 giving a less pronounced and clear sinusoidal pattern. Also, upon separating the results for the night-time cases (not shown here) from the daytime cases (daytime meaning morning, afternoon and evening observations), it was found that for the night-time cases almost all bias error values were positive (for some months as high as 15%). The seasonal sinusoidal pattern was still visible but it was much less pronounced. For the daytime cases, the sinusoidal pattern of the bias error was again pronounced but all values were shifted towards negative values. Summertime daytime negative bias errors as low as −12% (i.e. one octa if translated to SYNOP-observed cloudiness units) were found for some months but, in general, the corresponding bias error for the winter season was still positive and between 5 and 10%. This could be interpreted as a consequence of the fact that most of the morning and evening observations during the winter season were actually made during dark conditions, with error characteristics more resembling the night-time case. A very interesting feature in Figure 9 is that the summertime negative peak of the bias error is generally accompanied by high values of the correlation coefficient and by a minimum of the RMS error. This could indicate that the existing negative bias might rather be caused by an overestimation of cloud amounts in the SYNOP observation than by a systematic underestimation by the satellite observation. It is well known that the surface observer encounters problems in describing the proper cloud amount in the case of convective cloud cover with scattered cloud elements with a rather high vertical extension (e.g. see Malberg (1973)). The effect of these vertically extended clouds will be to shield cloud-free portions between individual clouds when viewed at off-zenith angles. Thus, the observer will tend to overestimate the true total horizontal cloud cover. Another fact that supports this explanation of the bias error found in summer is that the multispectral method for discrimination of clouds in satellite imagery normally has its greatest capability in summer. The reason is that the optimal illumination conditions and the favourable temperature lapse rates in the atmosphere (free of near-surface temperature inversions) provide the maximum amount of useful information in the available spectral channels of the AVHRR instrument. This is further supported by many years of experience from visual inspection of cloud classification images showing no particular problems during summer. Consequently, it is concluded here that the negative bias in summer is an artefact caused by an overestimation of total cloud cover in SYNOP observations. In contrast, the situation in winter cannot possibly be explained more than partly by the problems encountered by the SYNOP observer under dark conditions (e.g. the difficulty in making an accurate Copyright  2003 Royal Meteorological Society

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observation of semi-transparent cirrus clouds at night, normally leading to an underestimation of the true cloud cover). We can see that the high positive bias is generally accompanied by a decrease in the correlation coefficient and an increase in the RMS error. Thus, it is obvious that there is not only a problem with overestimation of cloud amounts during winter. In addition, there is also evidently a frequent occurrence of underestimation of cloud cover explaining the dip in correlation and the increase of RMS errors. For some winter months (e.g. December 1997 and January 1998) we even have cases when cloud amounts are, on average, seriously underestimated, which is contradictory to the mean conditions found for other winter months. Some evidence of an underestimation of low-level clouds under twilight conditions has previously been reported (Karlsson, 1996) due to the loss of the typical day or night cloud signature in image feature 4 (see Table I). Also, night-time cases with superposed semi-transparent cirrus clouds over stratus clouds give the same result and might result in a failure in cloud detection due to the unfortunate mixing of the cloud signatures in image feature 4. This often results in a cancellation of the brightness temperature difference required for cloud detection. A closer look at the conditions during these months, including a visual inspection of cloud classifications, revealed that these months were warmer than normal and very cloudy. Thus, it is possible that the night-time and twilight problems of correctly estimating low-level cloudiness may have been dominant during these months, compared with previously mentioned, and often dominating the factors giving rise to overestimation of cloud amounts (i.e. cold land surfaces being mistaken for mid- or high-level ice clouds). To appreciate how important it is for the cloud analysis quality as to whether the analysis is performed in a warm or cold winter season, we can study conditions for two individually selected SYNOP stations: Falsterbo and Pajala (locations shown in Figure 4). Falsterbo is a coastal station at the southernmost tip of Sweden. Consequently, the wintertime ice-free conditions (prevailing here for all years in the study period) and the relatively warm sea surface temperatures in the surrounding Baltic Sea bring relatively mild winter seasons. In contrast, Pajala, in the inland part of northern Sweden, experiences generally very cold winter temperatures. Even during relatively warm winter seasons some periods with very cold winter weather generally occur here. Wintertime positive bias errors for Falsterbo were found to be at most approximately 10%, whereas at Pajala there were several months with bias errors exceeding 20% found. Similarly, wintertime RMS errors were generally below 35% at Falsterbo, whereas RMS errors exceeding 55% could be found at Pajala for some individual months. For the correlation coefficient, reasonable values (approximately 0.5–0.6) can be found for winter months at Falsterbo, but at Pajala the values drop to almost zero or even become slightly negative for some months. An illustration of these differences for the two locations is shown in Figure 10, where results for individual months are shown. The differences revealed between Falsterbo and Pajala were FALSTERBO

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also generally found when comparing the majority of the SYNOP stations in the southern part of the area with the majority of stations in the northern part.

5.2. Cloud climatologies from ISCCP and ERA-40 A comparison of SCANDIA total cloud amount results with corresponding results from the ISCCP and ECMWF ERA-40 cloud data sets is presented in this section. Since the latter two data sets are defined in coarse-resolution grids, the SCANDIA results had to be averaged over larger areas. The ISCCP data set used is the ISCCP D2 version (Rossow and Schiffer, 1999), representing a considerably revised ISCCP methodology compared with previous versions. For example, it includes the use of information from the AVHRR 3.7 µm channel (resembling the SCANDIA use of feature 4 in Table I) at high latitudes and in the polar areas as a complement to the use of traditional visible and infrared channels. Rossow and Schiffer (1999) give a detailed description of the revised ISCCP methodology. The ECMWF ERA-40 data set (Uppala, 2001) includes a cloud component generated as a mixed assimilation–forecast product. Through the assimilation of observations (both radiosonde and satellite derived), the basic thermodynamic variables (e.g. temperature and humidity) will be well constrained to follow observed conditions over the region of study. From this high-quality analysed model state, a short-range 6 h forecast is made. The cloud field resulting from this short-range forecast is the cloud field subsequently averaged into a monthly mean data set. Cloud amounts from the first 6 h forecast are used in the next assimilation cycle, along with newly introduced thermodynamic observations and a new 6 h forecast is made. In this manner, the basic state of the ERA-40 model is constrained to follow observations and the spin up in the forecast cloud field is minimized over the entire ERA-40 period (see Jacob (2000) for more details). For practical reasons, results from the comparison of SCANDIA with the ISCCP and ERA-40 data sets are here shown as spatial means over the whole or parts of the geographically covered SCANDIA domain. Furthermore, in order to avoid errors introduced through interpolating coarse-resolution results to higher resolution representations, the SCANDIA and ERA-40 results have been averaged and transferred to the corresponding ISCCP grid points (in total, 27 grid points with a grid resolution of approximately 280 km). Figure 11 shows mean monthly cloud cover for the study period 1991–2000 over the SCANDIA geographical domain. All data sets show a considerable seasonal and yearly variation over the area. Most of the time they agree reasonably well on the occurrences of individual maxima and minima in cloudiness, but differences exist in terms of the seasonal amplitude and also, in some cases, concerning actual cloud amounts. For the satellite-based data sets, we notice that the SCANDIA climatology shows a much larger seasonal amplitude in cloudiness than ISCCP. In particular, the summer season cloud amounts are significantly lower than for ISCCP and, in general, the lowest of the data sets studied. However, for the last 3 years in the series, the satellite data sets appear to agree better with each other. For ERA-40, cloud amounts are generally higher than SCANDIA, especially for the winter seasons (e.g. large deviations are found for winters 1994–95 and 1997–98). Figure 12 shows the results for a subset of ISCCP grid points valid for two geographic sub-regions, the Baltic Sea (defined by four ISCCP grid points) and the Norwegian Sea (defined by four ISCCP grid points), and for the period 1991–95 (basically the same features are seen for the period 1996–2000). Quite large individual differences between the data sets can be seen, features that were not seen when averaging over the whole SCANDIA domain. A striking feature over the Norwegian Sea is that SCANDIA, in general, shows approximately 10% lower cloud amounts than both ISCCP and ERA-40. In contrast, quite good agreement is seen for all data sets over the Baltic Sea. In particular, the SCANDIA and ERA-40 data sets show very good agreement, especially during the summer seasons. Figure 13 illustrates this further, showing the mean two-dimensional distribution of cloud amounts over the SCANDIA domain for the summer season (June, July, August) for SCANDIA and ERA-40. From this we may also conclude that the large deviation between SCANDIA and ERA-40 cloudiness in the winter seasons (as seen in Figure 11) is a typical problem mainly over land areas in the region. Copyright  2003 Royal Meteorological Society

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6. DISCUSSION 6.1. Typical cloud features over the Scandinavian region The SCANDIA cloud climatology has revealed a considerable local-to-regional-scale variation in cloudiness. When focusing on the annual cycle of cloudiness, three dominant cloud regimes were seen: 1. large-amplitude annual cycle (high cloud amounts in winter, low in summer) 2. weak-amplitude reversed annual cycle (high cloud amounts with a weak maximum in summer) 3. consistently high cloud amounts. Copyright  2003 Royal Meteorological Society

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The first category is seen over all parts of the Baltic Sea, and also over adjacent land areas (especially in the southern part of Scandinavia). The second category is typical for the Scandinavian mountain range, and the third category is found over the Norwegian Sea. Thus, we can see an interesting difference in prevailing cloud conditions over the Baltic Sea (a typical inland sea) and the Norwegian Sea (part of the North Atlantic Ocean). The comparatively colder sea surface temperatures in the Baltic Sea (especially in spring and early summer due to inflow of cold fresh water from melting snow) lead to a considerable stabilization of nearsurface layers of the troposphere. This could explain a large part of the observed cloudiness pattern in the area. Copyright  2003 Royal Meteorological Society

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SCANDINAVIAN CLOUD CLIMATOLOGY AVHRR JJA 82 79 76 73 70 67 64 61 58 55 52 49 46 43 40

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Figure 13. The mean two-dimensional distribution of cloud cover (%) over the Nordic region in the period 1991–98 for SCANDIA (top) and ERA-40 (bottom). This figure is available in colour online at http://www.interscience.wiley.com/ijoc

Particularly interesting is the formation of a local-scale minimum in cloudiness over the Norwegian Sea close to the coast during spring and early summer. The reason for the formation of this minimum is believed to be through a combination of several dynamical and surface-forcing mechanisms. Convection during the summer half of the year creates high cloud amounts in the Scandinavian mountain range (caused primarily by the slope-and-valley circulation mechanism — see Atkinson (1981)). The induced secondary circulation, together with the more normal sea-breeze circulation in the area, may lead to an area of enhanced subsidence that can form near the coast. Since at the same time the seawater here is relatively cold (mainly due to the large freshwater contributions from melting snow, particularly in spring), cloud formation may be suppressed even further. A weak minimum of sea surface temperatures normally forms near the Norwegian coast in spring (see Karlsson (1995)), supporting this theory. It is not likely that the cloudiness minimum is caused by large-scale circulation patterns (i.e. easterly winds causing leeward subsidence), since even during spring the main wind direction is from the southwest in this area. Copyright  2003 Royal Meteorological Society

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A search for more detailed information on cloud conditions, e.g. related to dominant flow regimes, is a logical next step to take when further analysing data sets such as the SCANDIA cloud climatology. For example, Karlsson (2001) tried to separate wintertime data into different NAO index categories in order to see the typical cloudiness behaviour for different winter flow regimes. However, the attempt showed that much development is still needed for improving and fine-tuning satellite-based multispectral cloud algorithms to allow reliable results also under difficult observation conditions, like in the winter season. Consequently, the present SCANDIA cloud climatology only allows more detailed and reliable studies (e.g. related to various cloud types or cloud phase) in the summer half of the year, when both visible and infrared data are available during most of the time. 6.2. Quality aspects The validation effort based on comparisons with surface observations has shown that the satellite-derived cloud climatology agrees to within one octa with the surface observation for most of the time. Particular problems, typically with an overestimation of cloud amounts, exist in the winter season. Consequently, if the winter season is excluded, then the SCANDIA cloud climatology could be assigned a quality comparable to conventional surface observations. It should be noted here that it is not judged as either possible or even desirable to reach a better agreement between the two data sources when taking into account the fundamental differences in basic observation conditions and the observation error for the surface observation. The much better spatial coverage and resolution for the satellite-based data set is considered as the major advantage of using the SCANDIA cloud climatology in comparison with traditional SYNOP-based climatologies. The comparison of SCANDIA results with the satellite-derived ISCCP D2 data set revealed a remarkable difference between SCANDIA and ISCCP for the summer seasons over the Nordic region. Here, ISCCP cloud amounts in the Scandinavian region generally exceeded SCANDIA values (more than 10% for some months) both over land and over sea points. Furthermore, a difference of almost 10% was found for all months over the Norwegian Sea. The explanation for this difference is not clear at this point. However, since the ISCCP methodology is based primarily on geostationary (METEOSAT) imagery in this region (thus being subject to rather large viewing angles), it is possible that the difference is explained by the fact that optically very thin clouds could be more easily detected by the ISCCP method. Thus, since the largest differences between SCANDIA and ISCCP often occurred for months with low cloud amounts (typical for summer months with dominant anticyclonic flow patterns), the difference could be due to a different sensitivity when detecting optically very thin cirrus clouds. The influence of the much denser horizontal sampling for SCANDIA, which should lead to a better capability of describing the variability in the local-scale cloudiness, is not easy to assess here. Future studies with longer data sets (including the use of ISCCP DX data with a finer horizontal resolution) are needed to pinpoint the exact causes of these differences. A reversed behaviour, i.e. lower ISCCP cloud amounts compared with SCANDIA, was found during the winter season, especially for the land areas. Consequently, the annual cycle in cloudiness for ISCCP was generally found to be much lower in amplitude than for SCANDIA. The comparison of the SCANDIA climatology and the model-assimilated/forecasted ERA-40 data set showed a remarkably good agreement, especially over the Baltic Sea region and during the summer season. However, large deviations with much higher cloud amounts for ERA-40 were seen during the winter seasons. This feature is believed to be trustworthy, since previous experience of SCANDIA results during winter seasons has shown a SCANDIA overestimation of wintertime cloudiness. There is one particularly weak point in the current method for compilation of cloud climatologies that must be discussed here. It is related to the basic preprocessing of the AVHRR sensor radiances and concerns the calibration of the visible spectral channels, namely errors due to degrading satellite sensors and the effects caused by the use of a multi-satellite data set (as discussed by Cracknell (2001)). Processing and archiving constraints at SMHI have not permitted an adequate compensation for these effects. SCANDIA was initially designed for exclusive use in operational weather forecasting applications (Karlsson, 1989), and the requirements for also using the results in quantitative cloud climate applications (thus requiring a high-quality calibration of visible radiances) were, therefore, given insufficient attention initially. However, the problem, Copyright  2003 Royal Meteorological Society

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as such, is very difficult to handle, and this has been obvious from several previous papers (e.g. Rao and Chen, 1999; Tahnk and Coakley, 2001). It is clear that it demands a long-term commitment to the monitoring of satellite-measured radiances. Furthermore, this task requires access to an almost global satellite data set and a vast amount of independent reference information. A good example of such an effort for ensuring a consistent treatment of data from several satellites and satellite sensors for the ISCCP project is described by Brest et al. (1997). For the SCANDIA cloud climatology study, the desirable monitoring and reprocessing capabilities have not been available. Thus, when using results it must be remembered that errors from calibration or sensor deficiencies have influenced results and that some of the deviations seen, when compared with surface observations, could be explained by this cause. On the other hand, these errors fortunately seem to be small for this particular data set (at least when comparing with results from conventional surface observation data sets). One reason for this might be that SCANDIA cloud detection relies to a great extent on the use of the 3.7 µm channel and not so much on the short-wave visible channels. The complete SCANDIA cloud climatology has been compiled on two CD-ROMs freely available for scientific use. The data set could be a valuable tool for local- and regional-scale cloud climate studies in the Scandinavian region. A use for validation of regional and local-scale NWP and climate simulation models is also possible. The availability of model-independent cloud observation data sets with a high horizontal resolution is still very limited. 6.3. Future advances The SCANDIA cloud climatology study has revealed interesting information about general cloud conditions in the Scandinavian region during one decade. However, the shortcomings of the method (mainly the wintertime non-separability problems and the lack of reprocessing and radiance monitoring capabilities) points to the need for a changed and improved methodology in future applications aimed at continuous monitoring of cloud conditions. In addition, for an adequate use of cloud climate information (e.g. in radiative impact studies), several other cloud parameters related to the optical thickness and micro-physical structure of clouds are needed. A dedicated action by the European organization for the exploitation of meteorological satellites (EUMETSAT) to organize a Climate Monitoring Satellite Application Facility (CM-SAF) offers a possibility to overcome these problems in the future. The CM-SAF will derive a large set of climate-related cloud and radiation products over the European region plus, at a later stage, the inner Arctic region and the tropical African region (Woick et al., 2000). Operational activities for the European area are planned to start in 2004. For the basic cloud products (fractional cloud cover and cloud type) extracted from polar orbiting satellite data, a successor to the SCANDIA method will be used. The new cloud algorithm (see Dybbroe et al. (2001)), initially developed within the context of the EUMETSAT Satellite Application Facility on Support to Nowcasting and Very-Short Range Forecasting applications — SAFNWC), will be adapted to the needs for climate monitoring purposes. At the same time, actions to handle the monitoring and correction of satellite-measured radiances will be taken to enable consistency when deriving climate-related products. ACKNOWLEDGEMENTS

This work was sponsored by the Swedish National Space Board (contracts 59/95, 113/96 and 152/98). The author is grateful for the assistance provided by Colin Jones, Adam Dybbroe and Camilla Andersson at SMHI during the preparation of the manuscript. REFERENCES Arking A. 1991. The radiative effects of clouds and their impact on climate. Bulletin of American Meteorological Society 71: 795–813. Atkinson BW. 1981. Meso-scale atmospheric circulations. In Slope and Valley Wind Circulation. Academic Press Inc.: London; 215–277. Brest CL, Rossow WB, Roiter MD. 1997. Update of radiance calibrations for ISCCP. Journal of Atmospheric and Oceanic Technology 14: 1091–1109. Brunel P, Marsouin A. 2000. Operational AVHRR navigation results. International Journal of Remote Sensing 21: 951–972. Cracknell AP. 2001. Remote Sensing and Climate Change — the Role of Earth Observation. Springer Praxis Books. Dybbroe A, Thoss A, Karlsson K-G. 2001. Validation of Nowcasting SAF Polar Platform products. In Proceedings of 2001 EUMETSAT Meteorological Satellite Data Users’ Conference, Antalya, Turkey, 1–5 October, EUM P 33; 444–451. Copyright  2003 Royal Meteorological Society

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