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Byline: Jean-Philippe Boulanger (1), G. Brasseur (2), Andrea Fabiana Carril (3), Manuel de Castro (4), Nicolas Degallier (1), Carlos Ereno (5), H. Le Treut (6), Jose Antonio Marengo (7), Claudio Guillermo Menendez (3), Mario Nestor Nunez (3), Olga C. Penalba (5), Alfredo Luis Rolla (3), Matilde Rusticucci (5), Rafael Terra (8) Abstract: The goal of the CLARIS project was to build an integrated European--South American network dedicated to promote common research strategies to observe and predict climate changes and their consequent socio-economic impacts taking into account the climate and societal peculiarities of South America. Reaching that goal placed the present network as a privileged advisor to contribute to the design of adaptation strategies in a region strongly affected by and dependent on climate variability (e.g. agriculture, health, hydro-electricity). Building the CLARIS network required fulfilling the following three objectives: (1) The first objective of CLARIS was to set up and favour the technical transfer and expertise in earth system and regional climate modelling between Europe and South America together with the providing of a list of climate data (observed and simulated) required for model validations (2) The second objective of CLARIS was to facilitate the exchange of observed and simulated climate data between the climate research groups and to create a South American high-quality climate database for studies in extreme events and long-term climate trends (3) Finally, the third objective of CLARIS was to strengthen the communication between climate researchers and stakeholders, and to demonstrate the feasibility of using climate information in the decision-making process. Author Affiliation: (1) LOCEAN, UMR CNRS/IRD/UPMC, Tour 45-55/Etage 4/Case 100, UPMC, 4 Place Jussieu, Paris Cedex 05, 75252, France (2) National Center for Atmospheric Research (NCAR), Earth and Sun Systems Laboratory (ESSL), P.O. Box 3000, Boulder, CO, 80307, USA (3) CONICET, CIMA.Pabellon 2, Piso 2, Ciudad Universitaria, (1428), Buenos Aires, Argentina (4) Universidad de Castilla-La Mancha, Avda. Carlos III, 21., 45071, Toledo, Spain (5) Depto Ciencias de la Atmosfera y los Oceanos-UBA, Intendente Guiraldes 2160, Pab II, Ciudad Universitaria, C1428EGA, Buenos Aires, Argentina (6) Laboratoire de Meteorologie Dynamique, Institut-Pierre-Simon-Laplace et Ecole Doctorale 'Sciences de l'Environnement en Ile de France', Paris, France (7) Instituto Nacional de Pesquisas Espaciais INPE, Rodovia Dutra Km 40, 12630 000, Cachoeira Paulista, Sao Paulo, Brasil (8) IMFIA, Facultad de Ingenieria, Universidad de la Republica, Julio Herrera y Reissig 565, Montevideo, Uruguay Article History: Registration Date: 17/09/2009 Received Date: 23/06/2008 Accepted Date: 03/08/2009 Online Date: 14/11/2009

Byline: Nicolas Degallier (1), Charly Favier (2), Christophe Menkes (1), Matthieu Lengaigne (1), Walter M. Ramalho (3), Regilo Souza (3), Jacques Servain (1,4), Jean-Philippe Boulanger (1) Abstract: Dengue fever is the most prevalent mosquito-borne viral disease of humans in tropical lands. As an efficient vaccine is not yet available, the only means to prevent epidemics is to control mosquito populations. These are influenced by human behavior and climatic conditions and thus, need constant effort and are very expansive. Examples of succeeded prevention are rare because of the continuous reintroduction of virus or vector from outside, or growing resistance of mosquito populations to insecticides. Climate variability and global warming are other factors which may favour epidemics of dengue. During a pilot study in Claris EC project, a model for the transmission of dengue was built, to serve as a tool for estimating the risk of epidemic transmission and eventually forecasting the risk under climatic change scenarios. An ultimate objective would be to use the model as an early warning system with meteorological forecasts as input, thus allowing better decision making and prevention. Author Affiliation: (1) LOCEAN-IPSL, Institut de Recherche pour le Developpement (IRD), UMR 182, UPMC Tour 45-55, 4 place Jussieu case 100, 75252, Paris cedex 05, France (2) Institut des Sciences de l'Evolution (UMR CNRS 5554), Universite Montpellier II-pl. E. Bataillon, case courrier 061, 34095, Montpellier cedex 05, France (3) SVS-MS, Esplanada dos Ministerios, Bloco G, Edificio Sede, 1deg andar, 70.058-900, Brasilia, DF, Brazil (4) Fundacao Cearense de Meteorologia e Recursos Hidricos (FUNCEME), Av. Rui Barbosa, 1246, Fortaleza, Ceara, Brazil Article History: Registration Date: 17/09/2009 Received Date: 23/06/2008 Accepted Date: 30/12/2008 Online Date: 20/10/2009

Byline: T. d'Orgeval (1,2), Jean-Philippe Boulanger (3), M. J. Capalbo (4), E. Guevara (4), O. Penalba (1), S. Meira (4) Abstract: 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. Author Affiliation: (1) Centro de Investigacion del Mar y la Atmosfera, Buenos Aires, Argentina (2) Laboratoire de Meteorologie Dynamique, Paris, France (3) Laboratoire d'Oceanographie et du Climat, Experimentation et Analyse Numerique, Paris, France (4) Instituto Nacional de Tecnologia Agropecuaria, Pergamino, Argentina Article History: Registration Date: 17/09/2009 Received Date: 23/06/2008 Accepted Date: 03/08/2009 Online Date: 08/10/2009

Byline: Jean-Philippe Boulanger (1,2), Olga Penalba (2) Abstract: 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. Author Affiliation: (1) LOCEAN, UMR CNRS/IRD/UPMC, Tour 45-55/Etage 4/Case 100, UPMC, 4 Place Jussieu, 75252, Paris Cedex 05, France (2) Departamento de Ciencias de la Atmosfera y los Oceanos, Facultad de Ciencias Exactas y Naturales, University of Buenos Aires, Buenos Aires, Argentina Article History: Registration Date: 17/09/2009 Received Date: 23/06/2008 Accepted Date: 03/08/2009 Online Date: 02/10/2009

Byline: Jean-Philippe Boulanger (1), J. Aizpuru (2), L. Leggieri (2), M. Marino (3) Abstract: 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. Author Affiliation: (1) LOCEAN, UMR CNRS/IRD/UPMC, Tour 45--55/Etage 4/Case 100, UPMC, 4 Place Jussieu, 75252, Paris Cedex 05, France (2) Departamento de Ciencias de la Computacion, Facultad de Ciencias Exactas y Naturales, University of Buenos Aires, Buenos Aires, Argentina (3) Servicio Meteorologico Nacional, 25 de Mayo 658-(C1002ABN), Buenos Aires, Argentina (4) Departamento de Ciencias de la Atmosfera y los Oceanos, Facultad de Ciencias Exactas y Naturales, University of Buenos Aires, Buenos Aires, Argentina Article History: Registration Date: 17/09/2009 Received Date: 23/06/2008 Accepted Date: 03/08/2009 Online Date: 01/10/2009