Program schedule

Keynote presentations Climate Reconstructions Challenges in Space and Time Johannes P. Werner and PAGES2k Reconstructing past climate is more than just an academic exercise. Knowing about the evolution of past climate can help us not only validating climate models, but also show what the future could bring things that happened in the past, like extreme droughts (Wetter et al. 2014), are valid states of the climate system that could occur again. It is also important to not only deriving a best estimate of past climate, but rather accounting for uncertainties in these climate reconstructions. Past climate has been recorded by human observations, in direct meteorological measurements and qualitative records, but mostly through natural biological or geochemical processes in what is called proxy records. These natural climate archives, such as tree rings or sediment layers, can then be used to estimate past climate. We present an overview over some of the most widespread climate reconstruction methods, and show their current limits based on experiments over the Arctic. One of the challenges in that region is the sparseness of the data and its quality not only from a signal content point of view, but also from uncertainties in the dating. Even layer counted data has uncertainties in the dating (Comboul et al. 2014), although it can sometimes be corrected for through cross-dating. We show how current methods deal with dating uncertainties, and how the more flexible (but sadly more demanding) Bayesian inference can help correctly propagating the uncertainties from the record to the final reconstruction product (Werner and Tingley, in prep.). Comboul, M et al. (2014), Clim. Past. 10, 825–841, doi:10.5194/cp-10-825-2014 Werner, JP and MP Tingley (in prep) Wetter, O et al. (2014), Clim. Change 125, 349-363, doi:10.1007/s10584-014-1184-2 Statistical analysis of heavy rainfall in France via multivariate extreme value theory G. Marcon, S. A. Padoan, P. Naveau and P. Muliere Analysing heavy rainfall in France is complex due to the high number of weather stations and the complexity of weather system patterns over the French territory. This leads to computational issues and classical Extreme Value Theory (ETV) cannot be directly applied. To bypass the computational hurdles, we perform a dimension reduction approach, based on EVT concepts, to create independent regional clusters. Within each cluster, we propose a nonparametric approach for estimating the maxima dependence function. Satellite fire monitoring based on low cost Rosa Lasaponara, Fortunato Desantis, Angelo Aromando, Antonio Lanorte Wildfire represents one of the main disturbance factors throughout the world bringing profound transformations which affect ecosystems, landscapes and environments. Fires cause fuel consumption, production of smoke and ash, soil heating, alteration in vegetation structure and composition, soil erosion, changes in nutrient, micro-climate, hydrology, vegetation succession, habitat. Remote sensing technologies can provide useful data to contrast fire phenomena, from risk estimation (including fuel mapping and fire spread simulation), fire detection to post fire monitoring and management (burn area mapping, fire severity, vegetation recovery, etc). This paper presents an overview of satellite remote sensing of forest fire as well as the successful results obtained in the context of the FIRE-SAT project, focused on the operative application of the satellite based tools for fire monitoring. FIRE-SAT has been funded by the Civil Protection of the Basilicata Region in order to set up a low cost methodology for fire danger monitoring and fire effect estimation based on satellite Earth Observation techniques. To this aim, data available free of charge such as, Moderate Resolution Imaging Spectroradiometer (MODIS), ASTER, Landsat TM were used. Novel data processing techniques have been developed by researchers of the ARGON Laboratory of the CNR-IMAA for the operative monitoring of fire. FIRE-SAT system is made up of the following modules (i) danger estimation to support and optimize fire fighting strategies from the alert to the management of resources including fire attacks. (ii) mapping of burned areas and fire severity to assess the damage on vegetation and soil and support post fire management (iii) estimation of landslide susceptibility after fire (conducted using both satellite time series and in situ analysis) to support the mitigation actions (iv) assessment of post fire recovery (using satellite time series) to evaluate fire resilience and assess the post fire vegetation recovery capability. The operative use of the integrated models we developed and tested from 2008 to 2014, pointed out that satellite data sets available free of charge as (MODIS, Landsat TM, ASTER) are very suitable for an effective, systematic (daily) and sustainable low-cost monitoring of large areas. FIRE-SAT system and tools we set up are capable to properly monitor spatial and temporal variations of fire susceptibility and provide useful information of both fire severity and post fire regeneration capability. Fitting and goodness-of-fit test of non-truncated and truncated power-law distributions