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• 31. NOMEK09_Report

  Hermannsdóttir (Media) Sveinn Brynjólfsson (Avalanche) Theodor Freyr Hervarsson (Aviation) Guðrún Nína Petersen (Forecast errors) SMHI: Andreas Carlsson (Dynamics/NWP) Anna Eronn (Satellite) met.no: Bjørn Røsting (Dynamics) FMI: Vesa Nietosvaara (Online sessions and website) Elena Saltikoff (Radar-distans teaching) DMI: Claus Petersen (Slippery Roads) EUMETSAT: Jochen Kerkmann (Satellite /media/vedurstofan/utgafa/skyrslur/2009/NOMEK09_Report.pdf

• 32. Climatic-Change-2012---Personality-type-differences-between-Ph.D.-climate-experts-and-general-public---implications-for-communication

  RM, Capraro MM (2002) Myers-Briggs Type Indicator score reliability across studies: a meta-analytic reliability generalization study. Educ Psychol Meas 62:590–602 Carlson JG (1985) Recent assessments of the Myers-Briggs Type Indicator. J Personal Assess 49:356–365 Carlyn M (1977) An assessment of the Myers-Briggs Type Indicator. J Personal Assess 41:461–473 Clack GB, Allen J, Cooper D et al (2004 /media/loftslag/Climatic-Change-2012---Personality-type-differences-between-Ph.D.-climate-experts-and-general-public---implications-for-communication.pdf

• 33. Climate and Modeling Scenarios

  & Ruosteenoja, K. (2009). Comparing regional risks in producing turnip rape and oilseed rape - Impacts of climate change and breeding. Acta agriculturae Scandinavica 59B:2, 129-138. doi:10.1080/09064710802022895 (http://www.ingentaconnect.com/content/tandf/sagb/2009/00000059/00000002/art00004). Pryor, S.C., Barthelmie, R.J., Clausen, N.E., Drews, M., MacKellar, N. & Kjellström, E. (2010). Analyses /ces/publications/nr/1680

• 34. 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

• 35. Public-Choice-2012---McGuiness-and-Walker---Foundations-of-the-Ostrom-workshop---institutional-analysis,-polycentricity,-and-self-governance-of-the-commons

  The well-being of any human community is critically dependent on continued access to natural and man-made resources. Decades before sustainability became a popular slogan, the Ostroms began to investigate the conditions under which natural resources can be man- aged in a sustainable manner. The origins of the Workshop approach can be traced back at least as far as “State Administration of Natural /media/loftslag/Public-Choice-2012---McGuiness-and-Walker---Foundations-of-the-Ostrom-workshop---institutional-analysis,-polycentricity,-and-self-governance-of-the-commons.pdf

• 36. VI_2013_006

  stability, e.g. CAPE, in global numerical weather prediction models to mask out potential thunderstorm areas. Such a global system might be useful to monitor large areas of poorly monitored volcanoes, e.g. the chain of volcanoes in the Aleutian Islands, with its heavy air-traffic above. Conclusions An automatic monitoring system has been developed that estimates eruption site location based /media/vedurstofan/utgafa/skyrslur/2013/VI_2013_006.pdf

• 37. VI_2014_005

  lines). Additionally, the aver- age profiles for offshore distances to the coast of up to 30 km are shown by the black lines. For temperature, the dashed lines indicate linear projection from the two lowest model levels to 2 m above ground. A comparison of monthly averages of simulated 2-m temperature with station measurements, both for SURFEX and projected values, is shown in Figure 8. On average /media/vedurstofan/utgafa/skyrslur/2014/VI_2014_005.pdf

• 38. 2010_005_

  Century control runs, as well as 21st Century forecast runs, submitted by various institutions to the Intergovernmental Panel on Climate Change (IPCC) for their Forth 11 Table 1. General circulation and regional climate models that were considered in this study. Model Version Model Name, Institute BCCR BCM 2.0 Bergen Climate Model, Bjerknes Centre for Climate Research, Bergen, Norway CCCMA CGCM 3.1 /media/ces/2010_005_.pdf

• 39. Water_resources_man_Veijalainen_etal

  range 90% 93.15–93.29 93.27–93.36 75.43–75.56 94.59–94.71 2040–2069 range 90% 93.09–93.31 93.28–93.40 75.41–75.56 94.72–94.83 2070–2099 range 90% 93.02–93.32 93.24–93.43 75.38–75.61 94.79–94.91 Lowest water level (in the 30 year period) (m) Reference period 92.86 92.72 75.20 94.27 2010–2039 range 90% 92.62–92.91 92.96–93.13 75.24–75.33 94.47–94.59 2040–2069 range 90% 92.55–92.82 92.88–93.12 75.19 /media/ces/Water_resources_man_Veijalainen_etal.pdf

• 40. 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