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The study of psychological barriers and the attribution of social meaning is a complex and subjective process, but essential for the development of strategies to adapt to climate change and land-use management. The objective of this paper is to point out and discuss aspects that comprise climate vulnerability, especially regarding the climate perceptions understood as an element for the development of community adaptation strategies. The first part presents two study cases and the second part presents propositions for developing climate adaptation strategies based on the results of these cases. The paper focuses on two aspects: the perception of climate dynamics and the relationship between vulnerability and local climate dynamics. In order to establish the relationship between climate dynamics and the level of vulnerability perceived, a qualitative approach was adopted. Field data collection consisted of observing local civil association meetings and conducting semi-structured interviews with local actors. Although both communities studied are strongly affected by climate events, their perception of climate influences is essentially different. The difference in perception arises mainly from the different context in which climate events acquire meaning, rather than any ability of assigning meaning to possible climate change. To understand the dynamics of establishing an adaptation strategy, the motivational problems would be a stimulus, a causative agent of reflections on the conditions of vulnerability to climate change and reflection about their limit-situations . The final propositions present “Adaptation learning process pre-steps framework” that emerges from the description of contrasts in perceptions/cognitions observed between the two study cases. It is a model for the visualization of different and better living conditions, the unknown-viable , which enable social actors to become aware of capacity of acting and governance. [ABSTRACT FROM AUTHOR]

Climate change impacts are already happening through the world, and it is now clear that there is the need for an adaptive response from global institutions down to the local level. Reducing vulnerability to cope with climate variability might be more challenging in tropical countries than in North America or Europe. The ten papers of this special issue were presented during the Adaptclim conference that was held by the Sinergia Project, the CLARIS LPB project, and the GeoData Institute in Asunción, Paraguay, in 2010. All papers, except one regarding the Brahmaputra Basin in South Asia, present studies from South America. These studies are first contextualized geographically and then are related one to another by a simplified vulnerability concept linking climate stress to sensitivity and adaptive capacity of natural and human systems. One half of the papers focus on actual or future climate change and the present-day causes of the vulnerability of natural and agrosystems. Droughts are and will be the main source of stress for agriculture in South America. Increasing fragmentation of forest of the center of this continent is aggravating their vulnerability to dry spells. Another half of the studies of this special issue deal with the adaptive capacity human populations to system perturbations produced or enhanced by climate change. The studies point out inclusion of traditional knowledge and involvement of local actors in their own vulnerability assessment to increase adaptive capacity. These elements of climate justice, giving voice to those less responsible for carbon emissions but bearing their most severe consequences, allow the particular needs of a community to be considered and can direct adaptation policy toward preserving or rebuilding their specific capabilities under threat from climate change. The special issue also made clear that a basin analysis of the climate change problem could provide information, results, and methods more readily of use for the local population and decision makers. [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]

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. [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]

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. [ABSTRACT FROM AUTHOR]

Evaluating the response of climate to greenhouse gas forcing is a major objective of the climate community, and the use of large ensemble of simulations is considered as a significant step toward that goal. The present paper thus discusses a new methodology based on neural network to mix ensemble of climate model simulations. Our analysis consists of one simulation of seven Atmosphere–Ocean Global Climate Models, which participated in the IPCC Project and provided at least one simulation for the twentieth century (20c3m) and one simulation for each of three SRES scenarios: A2, A1B and B1. Our statistical method based on neural networks and Bayesian statistics computes a transfer function between models and observations. Such a transfer function was then used to project future conditions and to derive what we would call the optimal ensemble combination for twenty-first century climate change projections. Our approach is therefore based on one statement and one hypothesis. The statement is that an optimal ensemble projection should be built by giving larger weights to models, which have more skill in representing present climate conditions. The hypothesis is that our method based on neural network is actually weighting the models that way. While the statement is actually an open question, which answer may vary according to the region or climate signal under study, our results demonstrate that the neural network approach indeed allows to weighting models according to their skills. As such, our method is an improvement of existing Bayesian methods developed to mix ensembles of simulations. However, the general low skill of climate models in simulating precipitation mean climatology implies that the final projection maps (whatever the method used to compute them) may significantly change in the future as models improve. Therefore, the projection results for late twenty-first century conditions are presented as possible projections based on the “state-of-the-art” of present climate modeling. First, various criteria were computed making it possible to evaluate the models’ skills in simulating late twentieth century precipitation over continental areas as well as their divergence in projecting climate change conditions. Despite the relatively poor skill of most of the climate models in simulating present-day large scale precipitation patterns, we identified two types of models: the climate models with moderate-to-normal (i.e., close to observations) precipitation amplitudes over the Amazonian basin; and the climate models with a low precipitation in that region and too high a precipitation on the equatorial Pacific coast. Under SRES A2 greenhouse gas forcing, the neural network simulates an increase in precipitation over the La Plata basin coherent with the mean model ensemble projection. Over the Amazonian basin, a decrease in precipitation is projected. However, the models strongly diverge, and the neural network was found to give more weight to models, which better simulate present-day climate conditions. In the southern tip of the continent, the models poorly simulate present-day climate. However, they display a fairly good convergence when simulating climate change response with a weak increase south of 45°S and a decrease in Chile between 30 and 45°S. Other scenarios (A1B and B1) strongly resemble the SRES A2 trends but with weaker amplitudes. [ABSTRACT FROM AUTHOR]

The present manuscript analyzes monthly equatorial Pacific indices by using a specific neural algorithm, the so-called “Self-Organizing Maps” (SOMs). The main result is a change found in the nature of the transitions between cold to warm and warm to cold extreme events from 1950 to present, around the late 1970s. SOM is an unsupervised clustering technique which allows one to reduce high-dimensional data space (in this case, three indices over 636 months) in terms of a smaller set of three-dimensional reference vectors (100) characterizing pertinent situations. These reference vectors, which are displayed on a two-dimension map, are closely related by a topological relationship leading us to discriminate La Niña conditions from the opposite El Niño conditions. In a second step, a Hierarchical Agglomerative Clustering (HAC) method is used to further group the reference vectors into a small number of clusters (12) whose spatial and temporal characteristics can be analyzed and interpreted in terms of physical parameters. Schematically, these 12 clusters can be divided into two “warm” clusters, six “neutral” or “transition” clusters and four “cold” clusters. In each particular group (warm, neutral, cold), the clusters mainly differ from each other by the amplitude of the anomalies, their spatial patterns and their temporal variability. Some clusters are found to be strongly linked to the boreal spring period, while others have barely any records during that season. Other clusters are associated with records mainly observed either prior to or after 1980. This suggests that the method is able to identify changes in the variability of the tropical Pacific basin observed on decadal time scales (1976 climate shift in our case). Each monthly record can be summarized by the cluster to which it belongs. The temporal evolution of this value during extreme ENSO events shows similar patterns (persistence in specific clusters and transition between groups of clusters) associated with comparable El Niño or La Niña events. The methodology described in the present study (SOM plus HAC) is suggested to be useful both for seasonal ENSO predictability and for the detection of decadal changes in ENSO behavior. [ABSTRACT FROM AUTHOR]

Projections for South America of future climate change conditions in mean state and seasonal cycle for temperature during the twenty-first century are discussed. Our analysis includes one simulation of seven Atmospheric-Ocean Global Circulation Models, which participated in the Intergovernmental Panel on Climate Change Project and provided at least one simulation for the twentieth century (20c3m) and one simulation for each of three Special Report on Emissions Scenarios (SRES) A2, A1B, and B1. We developed a statistical method based on neural networks and Bayesian statistics to evaluate the models’ skills in simulating late twentieth century temperature over continental areas. Some criteria [model weight indices (MWIs)] are computed allowing comparing over such large regions how each model captures the temperature large scale structures and contributes to the multi-model combination. As the study demonstrates, the use of neural networks, optimized by Bayesian statistics, leads to two major results. First, the MWIs can be interpreted as optimal weights for a linear combination of the climate models. Second, the comparison between the neural network projection of twenty-first century conditions and a linear combination of such conditions allows the identification of the regions, which will most probably change, according to model biases and model ensemble variance. Model simulations in the southern tip of South America and along the Chilean and Peruvian coasts or in the northern coasts of South America (Venezuela, Guiana) are particularly poor. Overall, our results present an upper bound of potential temperature warming for each scenario. Spatially, in SRES A2, our major findings are that Tropical South America could warm up by about 4°C, while southern South America (SSA) would also undergo a near 2–3°C average warming. Interestingly, this annual mean temperature trend is modulated by the seasonal cycle in a contrasted way according to the regions. In SSA, the amplitude of the seasonal cycle tends to increase, while in northern South America, the amplitude of the seasonal cycle would be reduced leading to much milder winters. We show that all the scenarios have similar patterns and only differ in amplitude. SRES A1B differ from SRES A2 mainly for the late twenty-first century, reaching more or less an 80–90% amplitude compared to SRES A2. SRES B1, however, diverges from the other scenarios as soon as 2025. For the late twenty-first century, SRES B1 displays amplitudes, which are about half those of SRES A2. [ABSTRACT FROM AUTHOR]