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Aurélien Ribes, Soulivanh Thao, and Julien Cattiaux


Describing the relationship between a weather event and climate change—a science usually termed event attribution—involves quantifying the extent to which human influence has affected the frequency or the strength of an observed event. In this study we show how event attribution can be implemented through the application of nonstationary statistics to transient simulations, typically covering the 1850–2100 period. The use of existing CMIP-style simulations has many advantages, including their availability for a large range of coupled models and the fact that they are not conditional to a given oceanic state. We develop a technique for providing a multimodel synthesis, consistent with the uncertainty analysis of long-term changes. Last, we describe how model estimates can be combined with historical observations to provide a single diagnosis accounting for both sources of information. The potential of this new method is illustrated using the 2003 European heat wave and under a Gaussian assumption. Results suggest that (i) it is feasible to perform event attribution using transient simulations and nonstationary statistics, even for a single model; (ii) the use of multimodel synthesis in event attribution is highly desirable given the spread in single-model estimates; and (iii) merging models and observations substantially reduces uncertainties in human-induced changes. Investigating transient simulations also enables us to derive insightful diagnostics of how the targeted event will be affected by climate change in the future.

Open access
Soulivanh Thao, Laurence Eymard, Estelle Obligis, and Bruno Picard


The wet tropospheric path delay is presently the main source of error in the estimation of the mean sea level by satellite altimetry. This correction on altimetric measurements, provided by a dedicated radiometer aboard the satellite, directly depends on the atmospheric water vapor content. Nowadays, water vapor products from microwave radiometers are rather consistent but important discrepancies remain. Understanding these differences can help improve the retrieval of water vapor and reduce at the same time the error on the mean sea level.

Three radiometers are compared: the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E), Jason-1 microwave radiometer (JMR), and Envisat microwave radiometer (MWR). Water vapor products are analyzed both in terms of spatial and temporal distribution over the period 2004–10, using AMSR-E as a reference. The Interim ECMWF Re-Analysis (ERA-Interim) data are also included in the study as an additional point of comparison. Overall, the study confirms the general good agreement between the radiometers: similar patterns are observed for the spatial distribution of water vapor and the correlation of the times series is better than 0.90. However, regional discrepancies are observed and a quantitative agreement on the trend is not obtained. Regional discrepancies are driven by the annual cycle. The JMR product shows discrepancies are highly dependent on water vapor, which might be related to calibration issues. Furthermore, triple collocation analysis suggests a possible drift of JMR. MWR discrepancies are located in coastal regions and follow a seasonal dynamic with stronger differences in summer. It may result from processing of the brightness temperatures.

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Pascal Yiou, Julien Cattiaux, Davide Faranda, Nikolay Kadygrov, Aglae Jézéquel, Philippe Naveau, Aurelien Ribes, Yoann Robin, Soulivanh Thao, Geert Jan van Oldenborgh, and Mathieu Vrac
Free access