3.1. Temperature Trends
 Figure 2a compares time series of 40 years of monthly mean temperatures anomalies for a station (Villa Ortuzar) situated in a park within the city of Buenos Aires, the largest city of Argentina, including the average decadal difference between observations and the NNR. The correlation between both series is 0.91. To help visualize the difference in trends, we added a constant to make the temperature average for the 1980s the same for both station and NNR, but this does not affect the trends. It can be seen that the NNR captures very well the intraseasonal, interannual and interdecadal variability but there is a growing gap between the NNR estimate and the station observations, so that for the last two decades the OMR trend is 0.52°C/decade. Even before 1979 there is also good agreement between the two data sets but problems are observed over regions with topography (KC [Rusticucci and Kousky, 2002]). There is a large negative jump between the 1970s and 1980s (before and after the advent of satellite data) associated with the impact of the change in observing systems on the reanalysis climatology, which is much larger in the Southern than in the Northern Hemisphere. This negative jump in OMR of more than 0.2°C took place in essentially all stations north of 40°S, and is much larger in the reanalysis over South America than over the United States, where KC did not find a significant jump in the OMR trends. For this reason, we do not include the trend between the 1970s and the 1980s in the computations. Figure 3 shows that the 40-year correlation for all the stations located below 500 m averages 0.84. In mountainous regions (not shown), the correlation is lower than 0.7, so that these stations have not been included in the analysis.
Figure 2. Comparison of the monthly averaged temperature anomalies for the NNR (black) and stations (gray), shifted so that they have the same average during the 1980s, for Villa Ortúzar in the city of Buenos Aires. The observation minus reanalysis (OMR) on the right is the decadal average (valid nominally at the center of the decade), so that the OMR trend between the 1980s and the 1990s is 0.52°C/decade.
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Figure 3. Correlation between the surface temperatures anomalies with respect to the 40-year annual cycle for stations and for the NNR. The correlation for each of the 4 decades is averaged in order to avoid the jump between the 1970s and the 1980s due to addition of satellite data. The value 0.83 represents the average correlation.
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 Figure 4 shows the 40-year trend (in °C/decade) for the minimum (top) and maximum (bottom) temperatures for all the stations included in this study. Figure 4 (left) shows the trend of the station observations, the center panels show the NNR trend, and the right panels show their OMR differences, attributed at least partially to land surface properties and changes, including land use and precipitation. The decadal trends averaged over two separate 20-year periods (1981–2000 and 1961–1980) are computed for every station and averaged in circles centered in each station site with a cosine latitude weighted average computed over all the stations.
Figure 4. The 40-year (top) minimum temperature and (bottom) maximum temperature trends for Argentina (in °C/decade) over stations located below 500 m. Trends (left) from stations and (middle) from the NNR and (right) observations minus NNR trend. The number represents the average trend of all stations in the study.
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 The observations (Figure 4, left) indicate that in Argentina the minimum temperature increased slightly over these 40 years, on the average by about 0.05°C/decade, with some cooling in Patagonia. However, the NNR, which reflects the changes associated with atmospheric temperature changes (both due to changes in circulation and greenhouse warming) indicates that the lower atmosphere over Argentina underwent relative cooling of about −0.06°C/decade. The observation minus reanalysis (OMR) minimum temperature trends indicates strong warming (except in Patagonia) with an average of +0.11°C/decade, which would be attributable to changes in the land surface properties not included in the NNR.
 The maximum temperatures show a strong cooling trend in the observations of about −0.12°C/decade, stronger in the north than in Patagonia. The NNR trend (reflecting changes in circulation and greenhouse warming) shows warming south of 35°S, and strong cooling to the north, with an average of −0.15°C/decade. The OMR trend, which could be attributable to changes in the surface, shows warming in the maximum temperature in the northeast of Argentina, and cooling elsewhere, with an average of +0.03°C/decade.
 Figure 5 shows on the top the 40-year trend of the mean temperature, indicating an overall negative trend, moderate for the observations (−0.04°C/decade) and much stronger for the NNR (−0.11°C/decade). The trend in the observations is similar to that of Figure 1, even though in our case the trends do not include the difference between the 1980s and the 1970s (Figure 1c). The OMR trend suggests that surface changes have resulted in warming north of 40°S and cooling in the south, with an average of +0.07°C/decade.
Figure 5. The 40-year (top) mean temperature and (bottom) diurnal temperature range trends for Argentina (in °C/decade) over stations located below 500 m. Trends (left) from stations and (middle) from the NNR and (right) observations minus NNR trend. The number represents the average trend of all stations in the study.
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 The diurnal temperature range (DTR, Figure 5 (bottom)) has a very strong negative trend of about −0.17°C/decade in the observations, but an increase in DTR in Patagonia. The NNR trends are similar but the increase in DTR extends further north, with an average of −0.09°C/decade. The corresponding OMR trends are very negative over the wet pampas and mostly positive in Patagonia, with an average of −0.08°C/decade, suggesting that land changes and greenhouse warming contribute almost equally to the overall decrease in DTR.
 Table 1 presents the summer (DJF), fall (MAM), winter (JJA), spring (SON) and annual average trends for the observations, reanalysis and their OMR differences. The amplitude of the annual cycle in the trends is smaller than over the United States [Kalnay et al., 2006]. The observations show that Tmax has decreased very strongly in summer and Tmin increased in fall and winter, so that Tmean decreased in the summer and increased slightly in fall and winter. The observations show a decrease in the diurnal temperature range throughout the year, but it is strongest in the summer and fall.
Table 1. Summary of the 40-Year Trends (Computed as Discussed in the Text) for the Four Seasons and the Annual Averagea
| ||Total [(70 − 60) + (90 − 80)]/2|
 In the OMR trends, the annual cycle is also smaller than over the United States, and suggests that the surface effects have contributed positively to the temperature trend and to the decrease in diurnal temperature range throughout the year.
3.2. Precipitation Trends, Agricultural Changes, and Possible Relationship With the Temperature Trends
 Since precipitation varies widely with location, we present both an absolute trend and a normalized tendency showing the contribution of each decade to the total precipitation for different stations, allowing a comparison of precipitation trends in different stations. The absolute trends in Figure 6 are computed using observed precipitation, with the same method as indicated for temperature. The decadal percentage contributions of Figure 7 are computed by dividing at each station the decadal precipitation by the total precipitation over 40 years, so that normal decadal precipitation appears as about 0.25, in grey, and cold and warm colors represent below and above normal decadal precipitation respectively. The annual precipitation trend (mm/decade) presented in Figure 6 indicates an increase in most of the country with maximum values in the center of the country. The normalized precipitation contribution of each of the four decades, clearly indicates that the 1960s were considerably drier than the 1970s, not much change between the 1970s and the 1980s, and a further increase of rain in the 1990s, which had the highest level of precipitation throughout the country.
Figure 7. Relative contribution of each decade to the total precipitation. A value of 0.25 indicates that the precipitation over that decade was one quarter of the four decades total.
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 The South American low-level jet is a major source of moisture for northern and central Argentina [Vera et al., 2006]. We have checked for trends in this source of moisture using the NNR, and found that except in high latitudes the flux of moisture (vq)total = vmqm + v′q′ ≈ vmqm is dominated by the mean flux term vmqm, and that the eddy fluxes v′q′are an order of magnitude smaller. Figure 8 presents the vector of mean moisture flux trend for South America, computed, as in the temperature trends, without the trends from the 1970s to the 1980s. It indicates a very clear increase in moisture transport from the Amazon to northern and central Argentina, suggesting that this is a major cause of the observed increase in precipitation.
 This increase in precipitation has been accompanied by a very significant increase in agricultural production, especially the intensive cultivation of soy. In Argentina the border of the region devoted to crops has extended westward by more than 300 Km in the last years (see Figure 9 showing the displacement of the 800 mm/a isohyet between the 1960s to the 1990s), and the land productivity has also increased substantially in the 1990s, with the use of fertilizer increasing from about 5 Kg/hectare of cropland in the 1980s to over 30 Kg/hectare in the late 1990s. Figure 10 shows the areas planted with soy in Argentina (data corresponding to 2003) and Figure 11 shows the number of hectares planted with soy since 1970 in the country. The area with the maximum increase in precipitation generally coincides with the area with maximum total area planted with soy, as well as the area with the maximum estimated decrease of diurnal temperature range associated with surface changes using OMR (Figure 5, bottom right).
 These observations suggest that a possible explanation for the observed and OMR temperature trends can be related to the changes in precipitation and agricultural practices. The anomalous lack of warming in the center of Argentina over the last four decades (Figure 1a) can be associated with the increased precipitation, which, as shown in Figure 8, is probably associated with an increase in the inflow of moisture from the Amazon by the low-level jet. This is because the increase in precipitation should result in a decrease in maximum temperature (due to increased evaporation and more cloud cover during the day) and possibly an increase in minimum temperature (due to the increase in soil heat capacity). Nicholls  found similar negative correlations between observed maximum temperature and precipitation and smaller positive correlations for the minimum temperatures over Australia. Karoly and Braganza  found similar correlations in model simulations.
 Surface effects and changes in precipitation are not well represented in the NCEP-NCAR reanalysis. To the extent that OMR trends reflect these factors missing in the NNR, they suggest that the observed increase in precipitation and changes in agricultural productivity resulted in net warming and a strong reduction in diurnal temperature range.