1AUGUST 2002 RUSTICUCCI AND KOUSKY 2089

A Comparative Study of Maximum and Minimum Temperatures over : NCEP±NCAR Reanalysis versus Station Data

MATILDE M. RUSTICUCCI Departamento de Ciencias de la AtmoÂsfera y los OceÂanos, Universidad de , Buenos Aires, Argentina

VERNON E. KOUSKY NOAA/NWS/NCEP Climate Prediction Center, Camp Springs, Maryland

(Manuscript received 16 August 2001, in ®nal form 23 January 2002)

ABSTRACT This paper compares surface-station temperature observations over Argentina with gridpoint analyses available in the NCEP±NCAR reanalysis dataset. The primary objective is to determine whether the maximum and minimum surface temperatures from the reanalysis can be used to compute statistics on the occurrence of extreme events. The extreme range of topography and geography of Argentina is viewed as a severe test for the reanalysis data. Good agreement, on both the daily and monthly timescales, between the station data and the reanalysis gridpoint data is found over the low-elevation regions in central and eastern Argentina. The agreement is relatively poor for summertime maximum temperatures over northern Argentina. The reanalysis data underestimate the intensity of extreme warm events over northern and southern Argentina and overestimate extreme cold events during winter over central Argentina. High-elevation areas in western Argentina have the poorest correspondence throughout the year. Thus, the NCEP±NCAR reanalysis data have to be used with caution for studies of the magnitude of day-to-day temperature changes. The results of this study indicate that the NCEP±NCAR reanalysis data are suf®cient for determining the timing of midlatitude events but are not suf®cient for determining the amplitude and frequency in the subtropics and in regions of high relief. The use of anomalies tends to improve the amount of agreement between the reanalysis data and station observations.

1. Introduction Centers for Environmental Prediction±National Center for Atmospheric Research (NCEP±NCAR) reanalysis Tremendous progress has been made in developing (Kalnay et al. 1996). These are model-derived quantities consistent long-term gridded datasets for use in climate that are computed from 6-hourly integrations of the studies. The efforts to reanalyze historical data using model. The NCEP±NCAR reanalysis data are available modern data assimilation systems (Schubert et al. 1993, on a 2.5Њ latitude±longitude grid. We address the issues 1995; Kalnay et al. 1996; Kistler et al. 2001) have of 1) how well the 2-m temperatures in the reanalysis played an important part in this progress. By using a data archive compare to station observations and 2) ®xed data assimilation system, jumps in the historical whether these reanalysis variables can be used to de- record that resulted from model improvements, such as termine a climatological description of extreme tem- increases in model resolution and changes in physical perature events. parameterizations, have been eliminated. However, jumps in the historical record remain because of non- homogeneous observational databases and imperfect 2. Methodology models into which the data are assimilated. In addition, certain reanalysis variables depend greatly on the phys- Our validation dataset consists of maximum temper- ical parameterizations in the model and the procedures atures (max T) and minimum temperatures (min T) for used to compute desired quantities. Therefore, it is nec- selected stations in Argentina for the 40-yr period of essary to validate the reanalysis, whenever possible, us- 1959±98. The location and altitude of the selected sta- ing independent observations. tions are shown in Fig. 1 and are listed in Table 1. The In this paper, we focus on the near-surface values of stations used were selected outside the main cities to maximum and minimum temperature in the National avoid the possible urban heat island effect. This effect produces mean differences of about 3ЊC between tem- peratures within and outside Buenos Aires (Rusticucci Corresponding author address: Matilde Rusticucci, Departamento de Ciencias de la AtmoÂsfera y los OceÂanos, FCEN, UBA, Ciudad and Vargas 1995). Universitaria Pab II, 1428 Buenos Aires, Argentina. Some potential problem areas that we will address in E-mail: [email protected] subsequent sections include 1) the use of gridded anal-

᭧ 2002 American Meteorological Society

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by comparing time series of the observed maximum and minimum temperatures at the stations with the correspond- ing time series at the reanalysis grid point nearest to the station locations. The daily, seasonal, and decadal vari- ability in the differences are investigated for each station. We also investigate the accuracy of the maximum (minimum) temperature anomalies in the reanalysis data. Anomalies for both the station data and the re- analysis data are computed for the periods of 1959±78 and 1979±98 by removing the respective 20-yr mean daily maximum (minimum) temperature from each of the datasets. We chose to break the entire 40-yr record into two 20-yr periods, one for the presatellite period of 1959±78 and the other for the satellite period of 1979±98. This allows us to assess the impacts of the change in the observational database on our results. For the analysis of extremes, threshold values were selected to de®ne the extreme events and the duration of warm and cold spells that occurred during the 40-yr pe- riod. All days during the year were used to calculate the percentiles. Maximum (minimum) temperature anoma- lies that ranked in the upper (lower) 25% of the distri- bution were used as thresholds for being included as warm (cold) spells. The spells start with a 1-day-long duration, but we used the longest duration. Extreme warm (cold) spells were de®ned as those whose length, based on spell duration, ranked in the upper 10% of the dis- tribution. The ranking and determination of extreme events were done for each month separately. For each month, there were approximately 10 extreme spells over FIG. 1. Location of stations used. Smoothed altitude is in meters. the 40-yr period. We applied the same criteria for deter- mining warm and cold spells, and extreme events, to the yses in comparison with point observations at selected reanalysis data. We then compared the results for extreme stations and 2) the effect of model versus real topog- events by counting the number of the events that had at raphy in producing temperature biases. least one day in common between the two datasets. We ®rst investigate the accuracy of the reanalysis data In addition, the agreement between the two datasets for

TABLE 1. List of stations, station abbreviations (shown in Fig. 1), their locations, and height (m) above sea level (MSL), plus the coordinates of the nearest Gaussian grid point in the NCEP±NCAR reanalysis archive. Station Station Altitude Gridpoint Gridpoint Station name Abbreviation Lat (ЊS) Lon (ЊW) (m MSL) Lat (ЊS) Lon (ЊW) Azul AZU 36.75 59.83 132 37.14 60.00 BahõÂa Blanca BHB 38.73 62.17 25 39.05 61.88 CRV 45.78 67.50 46 46.67 67.50 Concordia CON 31.30 58.02 38 31.43 58.13 CTE 27.47 58.82 62 27.62 58.13 Iguazu IGU 25.73 54.47 270 25.71 54.38 La Rioja LRJ 29.38 66.82 429 29.52 67.50 Lago Argentino LAG 50.20 72.18 220 50.48 71.25 Las Lomitas LOM 24.70 60.58 130 23.81 60.00 Mendoza MZA 32.83 68.78 704 33.33 69.38 NeuqueÂn NQN 38.95 68.13 271 39.05 67.50 Parana PAR 31.78 60.00 78 31.43 60.00 Pergamino PGM 33.93 60.92 65 33.33 60.00 Pilar PIL 31.67 63.88 338 31.43 63.75 Posadas POS 27.37 55.97 133 27.62 56.25 Salta SAL 24.85 65.48 1221 25.71 65.63 Santa Rosa SRS 36.62 64.32 191 37.14 63.75 Santiago del Estero SGO 27.77 64.30 199 27.62 63.75

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FIG. 2. Daily temperatures (upper panel: max T, lower panel: min T) for (a) Pergamino for 1969 (dashed line) and nearest grid point (solid line), and (b) Iguazu for 1993 (dashed line) and nearest grid point (solid line).

FIG. 3. Daily differences (reanalysis minus station data) averaged monthly, 1959±98, for max T (white bars) and min T (black bars) in four locations.

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FIG. 4. Daily differences (reanalysis minus station data) averaged over the indicated season for min T and max T. Signi®cant 95% differences evaluated by a Student's t test are marked with crosses. Contour interval is 2ЊC, shaded areas are negative values (reanalysis too cold). (a) Winter 1988 (1990) was the coldest (warmest) in record, and (b) summer 1975 (1972) was the coldest (warmest) in record. extreme-event cases was further analyzed by comparing for Iguazu (Fig. 2b) shows that there is good corre- the 24-h temperature changes in both datasets for every spondence between the reanalysis and station data only day of the year. Max T was used to calculate increments, for the minimum temperatures. The maximum temper- and min T was used to calculate drops in temperature. It atures at this station show substantial offsets between is assumed that the largest temperature changes are due the two curves, especially during the summer, autumn, to frontal passages, which is most likely the case for large and early winter months, with the reanalysis data being 24-h temperatures decreases (cold fronts). cooler than the station data. However, there is some Differences and correlation coef®cients have been agreement in the timing of day-to-day weather events tested using a Student's t test at a 95% con®dence level. between the two datasets, and the large difference be- tween summer and winter variability is well represented. The differences (reanalysis minus station) between si- 3. Results multaneous temperatures show large values, even as Comparisons of the reanalysis maximum and mini- monthly averages. Reanalysis data are relatively cold mum temperature with two selected stations are shown over and near the (Salta in the north, Comodoro in Figs. 2a,b. Each station represents a region with the Rivadavia in the south), warm in central Argentina (Per- same characteristics and biases. These years were se- gamino), and cold for max T over northern Argentina lected for a better representation of the differences. The (IguazuÂ; see these examples in Fig. 3). day-to-day variability associated with weather systems Some extreme seasonal cases were selected to show appears to be represented well in the reanalysis data, the differences and their statistical signi®cance. For the with good agreement in the timing of the maxima and winter season, 1988 (extremely cold) and 1990 (ex- minima in the two curves. At Pergamino (Fig. 2a), there tremely warm) are compared. For summer, a comparison is also good agreement in the magnitude of the day-to- is made between 1975 (cold) and 1972 (warm). The day changes and in the actual values of the extremes differences, reanalysis minus station data, and their 95% for both the maximum and minimum temperatures signi®cance level are drawn in Fig. 4. It is evident that throughout the annual cycle. In contrast, the time series there are no appreciable differences in the reanalysis

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FIG. 5. Correlations between station and reanalysis daily values for the whole period 1959±98 for (top) winter (JJA) and (bottom) summer (DJF) for (right) maximum and (left) minimum temperatures. Correlations are signi®cant at 95% level over 0.3. behavior for different extreme years. In winter (Fig. 4a), To examine the long-term daily correspondence be- reanalysis data are signi®cantly warmer over the east tween the reanalysis and station data, we ®rst calculated and in the south, especially in min T. For max T, the the daily correlations between the two datasets for the reanalysis data are closer to station data, and they cor- entire 40-yr record, for summer [December±January±Feb- rectly represent the warmest winter with very few sig- ruary (DJF)] and winter [June±July±August (JJA)]. These ni®cant differences. Summers (Fig. 4b) show different maps (Fig. 5) show that the best correspondence (locally) patterns depending on which variable is examined. Min of daily temperature variability between the two datasets T shows the same behavior in winter as in summer. Max is during winter for minimum temperatures over the east- T is signi®cantly colder almost everywhere, except for ern part of Argentina. Overall, winter maximum temper- central Argentina. ature is just as good as, if not better than, minimum tem-

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FIG. 6. Monthly maximum temperature correlations between reanalysis and station data over period 1959±98. Contour interval is 0.1. Shades: 0.5±0.7, light gray, 0.7±0.9, medium gray; over 0.9, dark gray.

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FIG. 7. Same as Fig. 6, but for monthly minimum temperature. perature with the exception of in the northeast. The cor- well, especially over eastern Argentina (La Plata relations generally decrease toward the west (Andes) at basin). Summer max T, which has the greatest interannual all latitudes, although all coef®cients are signi®cant. There- variability in its extremes (Rusticucci and Barrucand fore, the reanalysis data represent the daily variability very 2001), is not represented as well.

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FIG. 8. Maximum temperature anomalies, moving 10-yr averages starting in 1959±68, for (left) summer and (right) winter for station data (bold solid line) and reanalysis (dashed line).

The correlation patterns for monthly averaged tem- correlation coef®cients is 0.3, so the majority of stations peratures between the two datasets (Figs. 6 and 7) for have signi®cant correlation. the entire 40-yr record show similar patterns as those These correlations between the two datasets do not for the daily data (Fig. 5). For maximum temperatures show any signi®cant decadal variability. Yearly corre- (Fig. 6), there is considerable seasonality in the corre- lation values were computed and were found to oscillate lations over the northern portion of the region. For this around the long-term mean values without any sign of area, correlations are relatively low (less than 0.5, re- a trend that would indicate an improvement or wors- gions unshaded) during the late spring, summer, and ening of the correspondence between the reanalysis and early autumn months (October±April) and are uniformly station data (results not shown). high (greater than 0.7, medium gray shading) throughout Seasonal mean temperature anomalies were analyzed most of the domain during May±September. For mini- over moving 10-yr periods, running from 1959±68 to mum temperatures (Fig. 7), the correlation pattern is 1989±98, to investigate their long-term variability. The similar throughout the year. However, there is a marked station and reanalysis decadal mean anomalies at three west-to-east gradient in the correlations, with the highest points located near 60ЊW and spanning 15Њ of latitude values (greater than 0.7) over the eastern sections and are shown for summer (DJF) and winter (JJA) for max- the lowest values (less than 0.5) in the vicinity of the imum (Fig. 8) and minimum (Fig. 9) temperatures. The Andes. In this case, the 95% signi®cance limit for the best long-term correspondence is for min T in winter,

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FIG. 9. Same as Fig. 8, but for minimum temperature anomalies. especially during the last decades at Pergamino and Las Argentina. The coincidence percentage averaged over Lomitas. The min T time series for Bahia Blanca shows the country is from 78% (January) up to 92% (August) no trend and has good correspondence with the reanalysis for warm events and from 90% up to 96% for cold data. In general, the correspondence for decadal maxi- events. The extreme cold events are better represented mum temperature anomalies is poorer than for minimum in the reanalysis data than are the extreme warm events, temperatures, with results being particularly poor in at least from the point of view of the days involved in northern Argentina during the summer. In contrast to the the extreme event. very good correspondence observed at Bahia Blanca for For each event, the average departure from normal minimum temperature, the correspondence for maximum was determined (which we refer to as intensity of the temperatures is relatively poor throughout the series. event). The mean intensities averaged over the coinci- dent events in the station and reanalysis datasets were compared. In Fig. 10, the mean intensity differences a. Extreme events (calculated as station minus reanalysis) between extreme The criterion for the coincidence of extreme daily warm events were plotted for summer (December±Feb- events that we used (at least one day in common between ruary) and winter (June±August), and signi®cant dif- the two datasets) resulted in a coincidence of nearly ferences are shaded. It is evident that the observed ex- 100% every month for a majority of stations in central treme warm events have higher mean intensity (are

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FIG. 10. Mean intensity differences for extreme warm events. Positive values indicate station events stronger than reanalysis events for summer (DJF) and winter (JJA). Contour interval is 1ЊC. Shaded areas are signi®cant at 95% level. stronger) than reanalysis-based extreme events, and cases of negative and positive tendencies were identi®ed there is no marked seasonality. The cause for the weaker in each dataset. An analysis was made of the percentage warm events may be the amount of clouds produced in of the number of cases in agreement. For the analysis the reanalysis data (the same reason why our results of the agreement in magnitude of the tendencies, cases were not as good in the subtropics). Too much cloud- were selected for which the 24-h temperature changes iness would tend to keep temperatures down during the were either in the upper or lower 10% (largest 10% of day. positive and negative tendencies) of the total distribu- The same analysis was done for cold events. As tion. The threshold limit for negative changes was found shown in Fig. 11, the reanalysis-based extreme cold to be generally near Ϫ5ЊC, with some variability de- events generally have higher mean intensity (i.e., they pending on the time of year and on station location. are colder) than station-based extreme events, especially Changes of this magnitude or greater are assumed to be in winter months. Always, the southern region is poorly due to cold-frontal passages. In a similar manner, the represented in the reanalysis data, as is the northwest upper 10% limit was used to de®ne possible warm- portion of the domain. frontal passages. The percentage of cases for the ex- treme negative and positive 24-h temperature changes that is common to the two datasets was determined for b. 24-h temperature changes each station. These percentages are presented for the To assess the ability of the reanalysis to capture fron- following regions: (a) over the Andes, and (b) north- tal passages, the 24-h tendencies in maximum and min- eastern, (c) southern, and (d) central Argentina. imum temperature were calculated in both datasets, as The mean monthly percentages for agreement of the

Tday(i) Ϫ Tday(iϪ1). An analysis of the agreement was per- sign of the temperature tendencies for each region (Fig. formed, based on both the sign and the magnitude of 12) show that over 60% of the cases are common to the differences. In the analysis of sign agreement, all both datasets, with the highest values found over the

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FIG. 11. Same as Fig. 10, but for extreme cold events. Dashed contours are negative differences, reanalysis extreme events are stronger than station extreme events. Contour interval is 1ЊC. Shaded areas are signi®cant at 95% level. east and northeast. The agreement for the extreme events ness and convection could very well be the cause of the (magnitude of the tendency in the upper or lower 10% poor summertime agreement in that region. This is an of the distribution) in these regions is not high, with important result, because it gives the user of the reanal- 40% or less correspondence between the two datasets. ysis data information on where and when it is appropriate The poorest agreement is found over the northeastern to use the surface temperatures. stations for maximum-temperature tendencies, where The analysis of extreme-duration events revealed that some stations agree in less than 20% of the cases. the reanalysis data underestimate the intensity of ex- treme warm events over northern and southern Argen- tina and overestimate winter extreme cold events over 4. Discussion central Argentina. These results indicate a negative tem- There is good agreement between the station data and perature bias in the reanalysis data for extreme events. the reanalysis gridpoint data for low-elevation regions in Otherwise there are no differences in extreme-event in- central and eastern Argentina. The poorest correspon- tensity, with the exception of southernmost and higher- dence is in the vicinity of the Andes and, in general, at elevation locations. all low latitudes during summer. The poor correspon- The NCEP±NCAR reanalysis data correctly indicate dence over the Andes is probably due to the differences the sign of the 24-h temperature changes in about 60% between model topography and real topography. The of the cases, with results being best over eastern and poor results during summer (daily values in the reanalysis northeastern Argentina. However, when a comparison are colder than the station data) over northern Argentina was made for cases having the largest negative and pos- imply that there may be differences between model-pre- itive changes, there was less than 40% agreement and dicted cloudiness, possibly resulting from convection, in some cases less than 20% agreement. Thus, the and observed cloudiness. Because summertime cloudi- NCEP±NCAR reanalysis data have to be used with cau- ness is an important factor in determining temperature, tion for studies of the magnitude of day-to-day tem- weaknesses in the physical parameterizations of cloudi- perature changes.

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FIG. 12. Monthly percentages of coincidence in sign (solid lines) and in the top/bottom decile (dashed lines) for minimum (open circles) and maximum 24-h temperature change, for each of the four regions shown on the map below the graphs.

Our results indicate that the NCEP±NCAR reanalysis an important process, which usually results in an in- data are suf®cient for determining the timing of mid- version in the lowest layers. Perhaps the reanalysis does latitude events but are not suf®cient for determining the not allow for enough radiational cooling, because of too amplitude and frequency in the subtropics and in regions much cloudiness or too much wind. These are specu- of high relief. The use of anomalies tends to improve lations and need to be proven, but reanalysis can be the amount of agreement between the reanalysis data used in this region with the knowledge of differences and station observations. that has been established. Further diagnostic studies are necessary to verify the true causes for the differences we found. The topog- Acknowledgments. The visit of Matilde Rusticucci to raphy is too high in the reanalysis, which might lead to NCEP was supported by UBA Grants JX29, TW06 and the conclusion that temperatures should be too cold. For by the Departamento de Ciencias de la AtmoÂsfera FO- minimum temperatures, however, radiational cooling is MEC.

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