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A Comparative Study of Maximum and Minimum Temperatures Over Argentina: NCEP±NCAR Reanalysis Versus Station Data

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A Comparative Study of Maximum and Minimum Temperatures Over Argentina: NCEP±NCAR Reanalysis Versus Station Data 1AUGUST 2002 RUSTICUCCI AND KOUSKY 2089 A Comparative Study of Maximum and Minimum Temperatures over Argentina: NCEP±NCAR Reanalysis versus Station Data MATILDE M. RUSTICUCCI Departamento de Ciencias de la AtmoÂsfera y los OceÂanos, Universidad de Buenos Aires, 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 2.58 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 38C 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- q 2002 American Meteorological Society Unauthenticated | Downloaded 10/02/21 12:47 AM UTC 2090 JOURNAL OF CLIMATE VOLUME 15 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 (8S) Lon (8W) (m MSL) Lat (8S) Lon (8W) Azul AZU 36.75 59.83 132 37.14 60.00 BahõÂa Blanca BHB 38.73 62.17 25 39.05 61.88 Comodoro Rivadavia CRV 45.78 67.50 46 46.67 67.50 Concordia CON 31.30 58.02 38 31.43 58.13 Corrientes 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 Unauthenticated | Downloaded 10/02/21 12:47 AM UTC 1AUGUST 2002 RUSTICUCCI AND KOUSKY 2091 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. Unauthenticated | Downloaded 10/02/21 12:47 AM UTC 2092 JOURNAL OF CLIMATE VOLUME 15 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 28C, 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).
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