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The present paper describes the quality-control component of an automatic procedure (APACH: A Procedure for Automated Quality Control and Homogenization of Weather Station Data) developed to control quality and homogenize the historical daily temperature and precipitation data from meteorological stations. The quality-control method is based on a set of decision-tree algorithms analyzing separately precipitation and minimum and maximum temperature. All our tests are non-parametric and therefore are potentially useful in regions or countries presenting different climates as those observed in Argentina. The method is applied to the 1959–2005 historical daily database of the Argentine National Weather Service. Our results are coherent with the history of the Weather Service and more specifically with the history of implementation of systematized quality control processes. In temperature, our method detects a larger number of suspect values before 1967 (when there was no quality control) and after 1997 (when only real-time quality control had been applied). In precipitation, the detection of error in extreme precipitations is complex, but our method clearly detected a strong decrease in the number of potential outliers after 1976 when the National Weather Service was militarized, and the network was strongly reduced, focusing more on airport weather stations. Also in precipitation, we analyze in detail the long dry sequences and are able to identify potential long erroneous sequences. This is important for the use of the data for hydrological or agricultural impact studies. Finally, all the data are flagged with codes representing the path followed by the record in our decision-tree algorithms. While each code is associated to one of the categories (“Useful”, “Need-Check”, “Doubtful” or “Suspect”), the final user is free to redefine such category-assignment. [ABSTRACT FROM AUTHOR]

Several studies show that the El Niño-Southern Oscillation (ENSO) is an important factor in determining interannual rainfall variability in South America. This signal is detected in the region including Northeastern Argentina, Uruguay and part of southern Brazil, in the form of excessive rains and big floods in the regional rivers. To check that this relationship was similar in the past the objectives of this paper were to construct a time series of large floods in the Paraná River region from documentary records, during the sixteenth to eighteenth centuries, and to evaluate the relationship between that historical record of extreme floods, the ENSO documentary evidence (Ortlieb, The Documented Historical Record of El Niño Events in Perú: An Update of the Quinn Record (Ortlieb, sixteenth through nineteenth centuries, Diaz and Markgraf, (eds.), El Niño and the southern oscillation. Multiscale variability and global and regional Impacts. Cambridge University Press, pp. 207-295, 2001; Quinn and Neal, The historical record of El Niño events, Bradley and Jones (eds.), Climate since a.d. 1500, Routledges, pp. 623-648, 1992) and the temperature index of the Pacific Ocean (Mann ME et al., Global temperature patterns in past centuries: an interactive presentation, IGBP pages/world data center for paleoclimatology data contribution series #2000-075. NOAA/NGDC Paleoclimatology Program, Boulder CO, USA, 2000). Considering that the period 1904-2000, where 11 out of 16 floods occurred during El Niño events, it can be concluded that the proportion of years with exceptional flow volume in the Paraná River in years with El Niño events in the seventeenth and eighteenth centuries was relatively lower than that of the twentieth century. The reason for this difference is discussed. [ABSTRACT FROM AUTHOR]

The study considers the most important livestock regions of Argentina and the correlation between livestock and climate units. The conceptual scheme designed to understand these effects of climate elements on cattle describes a basic direct action by the air temperature in the environment in which they develop, considering temperature as the limit for the distribution of breeds and other action through its effects on vegetation and forage resources that will be available. The work done here shows a shift of the livestock regions southward and eastward simultaneously in the region under consideration. This is a consequence of the displacement towards the south of the isotherm of 26 °C and towards the east of the humidity indices, co-incidentally with the displacement of the isohyets of 600 and 1200 mm. As a consequence of the climate change, according to the CCSM4 climate model, in the near and far future under two emission scenarios, the regions suitable for tropical livestock (breeds with high heat tolerance as the Bos indicus) will extend to the southeast, displacing and reducing the regions suitable for European breed cattle. The displacement of the higher rainfall area mainly to the east could benefit livestock production by increasing forage and reducing livestock feed requirements. [ABSTRACT FROM AUTHOR]

A weather pattern clustering method is applied and calibrated to Argentinean daily weather stations in order to predict daily precipitation data. The clustering technique is based on k-means and is applied to a set of 17 atmospheric variables from the ERA-40 reanalysis covering the period 1979–1999. The set of atmospheric variables represent the different components of the atmosphere (dynamical, thermal and moisture). Different sensitivity tests are applied to optimize (1) the number of observations (weather patterns) per cluster, (2) the spatial domain size of the weather pattern around the station and (3) the number of members of the ensembles. All the sensitivity tests are compared using the ROC (Relative Operating Characteristic) Skill Score (RSS) derived from the ROC curve used to assess the performance of a predictive system. First, we found the number of observations per cluster to be optimum for values larger than 39. Second, the spatial domain size (~4° × 4°) was found to be closer to a local scale than to a synoptic scale, certainly due to a dominant role of the moisture components in the optimization of the transfer function. Indeed, when reducing the set of variables to the subset of dynamical variables, the predictive skill of the method is significantly reduced, but at the same time the domain size must be increased. A potential improvement of the method may therefore be to consider different domains for dynamical and non-dynamical variables. Third, the number of members per ensembles of simulations was estimated to be always two to three times larger than the mean number of observations per cluster (meaning that at least all the observed weather patterns are selected by one member). The skill of the statistical method to predict daily precipitation is found to be relatively homogeneous all over the country for different thresholds of precipitation. [ABSTRACT FROM AUTHOR]

The present investigation aims at identifying the needs of the Argentinean agro-business sector for climate research and information. Our approach is focused on any type of climate information from past observations to seasonal prediction and climate change projections. We tried, in particular, to evaluate the possible existence of specific research themes that would be of major interest for these agriculture end-users. We interviewed representatives of seventeen companies from three major agrobusiness sectors: (1) regional/national entities such as agricultural associations, cooperatives and commercial trade boards; (2) insurance companies in the Argentinean agro-insurance market; and (3) large national and international companies representing cereal producers, agro-chemistry and agro-seeds. While all the interviewees recognized the strong influence of climate on their activities, they all pointed out that, at the time of making decision, they considered the political and economic risks rather than the climate one. In many aspects, climate is often considered as a fatality against which it is difficult to be protected. An interesting result is the confidence of the private sectors in the climate information provided by public sources (INTA, SMN, Universities) although they contract private consultants for frequent reports and tendencies. Finally, we explain how such sectors could make a better use of climate information (seasonal prediction, climate change scenarios) that could be integrated in their business projections. In particular, the development of new financial contracts (climate derivatives) open in countries with a relatively poor insurance history new ways to protect the different agricultural sectors, from the producer to large companies. [ABSTRACT FROM AUTHOR]

The present article is a contribution to the CLARIS WorkPackage “Climate and Agriculture”, and aims at testing whether it is possible to predict yields and optimal sowing dates using seasonal climate information at three sites (Pergamino, Marcos Juarez and Anguil) which are representative of different climate and soil conditions in Argentina. Considering that we focus on the use of climate information only, and that official long time yield series are not always reliable and often influenced by both climate and technology changes, we decided to build a dataset with yields simulated by the DSSAT (Decision Support System for Agrotechnology Transfer) crop model, already calibrated in the selected three sites and for the two crops of interest (maize and soybean). We simulated yields for three different sowing dates for each crop in each of the three sites. Also considering that seasonal forecasts have a higher skill when using the 3-month average precipitation and temperature forecasts, and that regional climate change scenarios present less uncertainty at similar temporal scales, we decided to focus our analysis on the use of quarterly precipitation and temperature averages, measured at the three sites during the crop cycle. This type of information is used as input (predictand) for non-linear statistical methods (Multivariate Adaptive Regression Splines, MARS; and classification trees) in order to predict yields and their dependency to the chosen sowing date. MARS models show that the most valuable information to predict yield amplitude is the 3-month average precipitation around flowering. Classification trees are used to estimate whether climate information can be used to infer an optimal sowing date in order to optimize yields. In order to simplify the problem, we set a default sowing date (the most representative for the crop and the site) and compare the yield amplitudes between such a default date and possible alternative dates sometimes used by farmers. Above normal average temperatures at the beginning and the end of the crop cycle lead to respectively later and earlier optimal sowing. Using this classification, yields can be potentially improved by changing sowing date for maize but it is more limited for soybean. More generally, the sites and crops which have more variable yields are also the ones for which the proposed methodology is the most efficient. However, a full evaluation of the accuracy of seasonal forecasts should be the next step before confirming the reliability of this methodology under real conditions. [ABSTRACT FROM AUTHOR]

Heavy rainfall trends in a region of south-eastern South America during 1959–2002 were discussed using daily data of 52 meteorological stations of Argentina, Brazil and Uruguay. Changes in intensity and frequency were both studied with different statistical tests and approaches to check the significance of trends of single and regional aggregated rainfall series. There were predominant positive trends in the annual maximum rainfalls, as well as a remarkable increment in the frequency of heavy rainfalls over thresholds ranging from 50 to 150 mm. However, significant positive trends were not shown in the series of annual maximums and shown only in 15% to 30% of the series of frequencies over thresholds. This lack of significance is due to the high variability of heavy rainfalls in space and time, which makes difficult their capture by single rain gauges. Thus, when the assessment of the heavy rainfall indicators of intensity and frequency were conducted at the regional and sub-regional level, it showed significant trends, both in intensity and frequency over thresholds, with a clearer signal in central and eastern Argentina between 30° and 40° S. [ABSTRACT FROM AUTHOR]

The Puelo River is a watershed shared between Chile and Argentina with a mean annual streamflow of 644 m³ s-1. It has a high ecologic and economic importance, including introduced farmed salmon, tourism, sports fishing and projected hydroelectricity. Using Austrocedrus chilensis and Pilgerodendron uviferum tree-ring records we reconstructed summer-fall (December-May) Puelo River streamflow, which is the first of such reconstructions developed in the Pacific domain of South America. The reconstruction goes back to 1599 and has an adjusted r² of 0.42. Spectral analysis of the reconstructed streamflow shows a dominant 84-year cycle which explains 25.1% of the total temporal variability. The Puelo River summer-fall streamflow shows a significant correlation (P>0.95, 1943-2002) with hydrological records throughout a vast geographic range within the Valdivian eco-region (35 to 46°S). Seasonal Puelo River interannual streamflow variability is related to large-scale oceanic and atmospheric circulation features. Summer-fall streamflows showed a significant negative correlation with the Antarctic Oscillation (AAO), whereas winter-spring anomalies appear to be positively connected with sea surface temperature variations in the tropical Pacific. In general, above- and below-average discharges in winter-spring are related to El Niño and La Niña events, respectively. The temporal patterns of the observed and reconstructed records of the Puelo River streamflow show a general decreasing trend in the 1943-1999 period. Projected circulation changes for the next decades in the Southern Hemisphere would decrease summer-fall Puelo River streamflows with significant impacts on salmon production, tourism and hydropower generation. [ABSTRACT FROM AUTHOR]

Discusses the use of a new set of tree-ring records from the Andes of northern Patagonia, Argentina to evaluate recent regional trends in tree growth at upper treeline. Development of fifteen tree-ring chronologies; Influences of location site factors of elevation and exposure; Relationship between nothofagus growth and climate.