98 results were found for [77AGG. COM]licin4 slot royal slot 88 ugdewa slot login dewacuan slot d4f.

Results:

• 21. VI_2009_012

  Mw(v) plotted against log distance. The coefficient of correlation between Mw(v) and log distance is 0.24. High correlation coefficients have been shown to affect parameter estimates in one step regression methods (see Fukushima and Tanaka, 1990). The Ci values were also calculated from the derived PGA values. Instead of constructing another magnitude scale based on acceleration it was decided /media/vedurstofan/utgafa/skyrslur/2009/VI_2009_012.pdf

• 22. ces-oslo2010_proceedings

  change over the North Atlantic and in some simulations also for Iceland. In all areas, including the North Atlantic and Iceland, a clear climate change signal compared to the spread between the simulations is seen. The standard deviation calculated from 17 of the simulations are less than 1°C in all areas apart from Iceland where it reaches between 1 and 2°C and in parts of the Barents Sea where /media/ces/ces-oslo2010_proceedings.pdf

• 23. VI_2017_009

  we take the corresponding data from the CMIP5 project. Table 1. All GCMs and RCMs used in this study. If a model is available for any of the domains Arctic-44, EURO-44, or EURO-11, it is marked with a v, but with an x if it is unavailable. Model name Type EURO-11 EURO-44 Arctic-44 CCCma-CanESM2 GCM x v v COSMO-CLM4-8-17 RCM v v x CNRM-CERFACS-CNRM-CM5 GCM v v x IHCEC-EC-Earth GCM v v v /media/vedurstofan-utgafa-2017/VI_2017_009.pdf

• 24. 2012-Refsgaard_etal-uncertainty_climate-change-adaptation-MITI343

  e in relatio n to climat echang eadapt ation .X ,X X ,XX X is a ge n era lguid eo n th e relativ e imp ortanc e leve lo fth e sourc es , alth oug h it mus tb e em phasise d tha tth e imp ortanc e o fth e indi vidua lsou rce s o fun certaint y is co n tex tspe cifi c St ep si n cl im at e ch an ge ad ap tat io n an al ys es (ch ain in u n ce rta in ty ca sc ad e, Fi g. 2) So ur ce s o fu n ce rta /media/loftslag/2012-Refsgaard_etal-uncertainty_climate-change-adaptation-MITI343.pdf

• 25. Public-Choice-2012---Teyssier---Inequity-and-risk-aversion-in-sequential-public-good-games

  individuals act like homo-œconomicus agents (see for example Andreoni 1988; Berg et al. 1995; Camerer 2003; Forsythe et al. 1994; Isaac et al. 1984). Recent developments in public-choice theory have taken a behavioral approach to broaden the analysis of collective action. The introduction of social preferences, such as altruism, inequity aversion or trust, may mean that optimal collective choices /media/loftslag/Public-Choice-2012---Teyssier---Inequity-and-risk-aversion-in-sequential-public-good-games.pdf

• 26. Kok_et_al._TFSC_published_2011

  ]. There is ample experience with backcasting, and consequently much has been said about the underlying principles (e.g. [20]), the methodological 838 K. Kok et al. / Technological Forecasting & Social Change 78 (2011) 835–851 Author's personal copy framework (e.g. [8,30–32]), and practical applications (e.g. [33–35]). Also the combination between backcasting and other types of scenarios has been /media/loftslag/Kok_et_al._TFSC_published_2011.pdf

• 27. Refsgaard_etal-2007-Uncertainty-EMS

  The advantage of Monte Carlo analysis is its general appli- cability and that it does not impose many assumptions on prob- ability distributions and correlations and that it can be linked to any model code. The key limitation is the large run times for computationally intensive models and the huge amount of outputs that are not always straightforward to analyse. 4.8. Multiple model simulation Multiple model /media/loftslag/Refsgaard_etal-2007-Uncertainty-EMS.pdf

• 28. D2.3_CES_Prob_fcsts_GCMs_and_RCMs

  Institution BCCR-BCM2.0 Bjerknes Centre for Climate Research, Norway CGCM3.1 (T47) Canadian Centre for Climate Modelling and Analysis CGCM3.1 (T63) same as previous CNRM-CM3 Météo-France CSIRO-MK3.0 CSIRO Atmospheric Research, Australia ECHAM5/MPI-OM Max Planck Institute (MPI) for Meteorology, Germany ECHO-G University of Bonn and Model & Data Group, Germany; Korean Meteorological Agency GFDL /media/ces/D2.3_CES_Prob_fcsts_GCMs_and_RCMs.pdf

• 29. raisanen_ruosteenoja_CES_D2.2

  Model Institution BCCR-BCM2.0 Bjerknes Centre for Climate Research, Norway CGCM3.1 (T47) Canadian Centre for Climate Modelling and Analysis CGCM3.1 (T63) same as previous CNRM-CM3 Météo-France CSIRO-MK3.0 CSIRO Atmospheric Research, Australia ECHAM5/MPI-OM Max Planck Institute (MPI) for Meteorology, Germany ECHO-G University of Bonn and Model & Data Group, Germany; Korean Meteorological Agency GFDL /media/ces/raisanen_ruosteenoja_CES_D2.2.pdf

• 30. Journal_of_Hydrology_Veijalainen_etal

  A second, but usually smaller, increase in runoff oc- curs in the autumn. In northern Finland more than 95% of annual maximum floods are caused by spring snowmelt (cf. Fig. 7a). Also the small upstream lakes in the northern part of the lake area and the northernmost of the coastal rivers fall mainly into this cat- egory. In most coastal rivers the major floods can be caused by either snowmelt /media/ces/Journal_of_Hydrology_Veijalainen_etal.pdf