General database for ground water site information

In most cases, analysis and modeling of flow and transport dynamics in ground water systems require long-term, high-quality, and multisource data sets. This paper discusses the structure of a multisite database (the H+ database) developed within the scope of the ERO program (French Environmental Research Observatory, http://www.ore.fr). The database provides an interface between field experimentalists and modelers, which can be used on a daily basis. The database structure enables the storage of a large number of data and data types collected from a given site or multiple-site network. The database is well suited to the integration, backup, and retrieval of data for flow and transport modeling in heterogeneous aquifers. It relies on the definition of standards and uses a templated structure, such that any type of geolocalized data obtained from wells, hydrological stations, and meteorological stations can be handled. New types of platforms other than wells, hydrological stations, and meteorological stations, and new types of experiments and/or parameters could easily be added without modifying the database structure. Thus, we propose that the database structure could be used as a template for designing databases for complex sites. An example application is the H+ database, which gathers data collected from a network of hydrogeological sites associated with the French Environmental Research Observatory.     

Cold Dust and Very Cold Excess Emission in the Galaxy

We provide insight in the origin of the far-IR to mm emission from galaxies by presenting a decomposition of the Galaxy emission where we separate the contributions from dust in the atomic gas and dust associated with quiescent molecular gas as a function of Galacto-centric distance. This decomposition leaves a puzzling very cold emission excess with a brightness independent of Galactic longitude that might be pointing at interstellar matter in the outer Galaxy not traced by H I nor CO emission. This revised version was published online in September 2006 with corrections to the Cover Date.     

Assessment of climate information needs in the Argentinean Agro-business sector

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.     

3D modelling and sensitivity in DC resistivity using charge density

A three‐dimensional (3D) electrical resistivity modelling code is developed to interpret surface and subsurface data. Based on the integral equation, it calculates the charge density caused by conductivity gradients at each interface of the mesh, allowing the estimation of the potential everywhere without the need to interpolate between nodes. Modelling generates a huge matrix, made up of Green's functions, which is stored by using the method of pyramidal compression. The potential is compared with the analytical and the numerical solutions obtained by finite‐difference codes for two models: the two‐layer case and the vertical contact case. The integral method is more accurate around the source point and at the limits of the domain for the potential calculation using a pole‐pole array. A technique is proposed to calculate the sensitivity (Jacobian) and Hessian matrices in 3D. The sensitivity is based on the derivative with respect to the block conductivity of the potential computed using the integral equation; it is only necessary to compute the electrical field at the source location. A direct extension of this technique allows the determination of the second derivatives. The technique is compared with the analytical solutions and with the calculation of the sensitivity according to the method using the inner product of the current densities calculated at the source and receiver points. Results are very accurate when the Green's function that includes the source image is used. The calculation of the three components of the electric field on the interfaces of the mesh is carried out simultaneously and quickly, using matrix compression.     

CHAC: a weather pattern classification system for regional climate downscaling of daily precipitation

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.     

The Trident Pacific model. Part 1: simulating surface ocean currents with a linear model during the 1993–1998 TOPEX/POSEIDON period

 Zonal advection by long equatorial waves has been shown to be an important process in the evolution of sea surface temperature in the central Pacific on ENSO time scales. The present study aims at investigating how well an oceanic model whose dynamics are based on long equatorial waves can simulate the large-scale surface zonal current variability. Thus an ocean linear model which can be run with two or three layers is validated against several sets of observations in the Pacific ocean (TOPEX/POSEIDON sea level, TAO zonal currents, surface current climatology). The surface layer (mixed-layer) has a constant depth. Therefore the layer model is equivalent to considering a shear layer solution and either one or two baroclinic modes. It allows evaluation of the impact of adding a second baroclinic mode on the simulation of surface currents. This evaluation is done for different friction parametrizations: a weak linear Rayleigh friction (24 months⁻¹), a strong linear Rayleigh friction (6 months⁻¹), and a new parametrization using quadratic friction in the momentum equation only. It is shown in all simulations using various Rayleigh friction parametrizations that the addition of a second baroclinic mode always improves the simulation of both the sea level and the surface currents, especially in the central western Pacific. In that region, there is a reduction of the propagating long Rossby waves whose amplitude is much too large when only one baroclinic mode is used. Despite this reduction, the use of a weak friction (24 months⁻¹) always yields results which compare only poorly to observations confirming results from previous studies. The use of strong friction (6 months⁻¹) improves the model simulation, but surface current variability still remains too large. Finally, the use of quadratic friction as proposed in the present study considerably improves the simulation of zonal currents and its comparison to all data sets. This result gives more confidence in the choice of such a simple model to further explore the role of zonal advection by long equatorial waves on ENSO time scales. Received: 28 May 1999 / Accepted: 18 May 2000     

A Europe–South America network for climate change assessment and impact studies

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.     

Downscaling extreme month-long anomalies in southern South America

We investigate the performance of one stretched-grid atmospheric global model, five different regional climate models and a statistical downscaling technique in simulating 3 months (January 1971, November 1986, July 1996) characterized by anomalous climate conditions in the southern La Plata Basin. Models were driven by reanalysis (ERA-40). The analysis has emphasized on the simulation of the precipitation over land and has provided a quantification of the biases of and scatter between the different regional simulations. Most but not all dynamical models underpredict precipitation amounts in south eastern South America during the three periods. Results suggest that models have regime dependence, performing better for some conditions than others. The models’ ensemble and the statistical technique succeed in reproducing the overall observed frequency of daily precipitation for all periods. But most models tend to underestimate the frequency of dry days and overestimate the amount of light rainfall days. The number of events with strong or heavy precipitation tends to be under simulated by the models.