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• 11. CES_D2_2_poster_3x3

  (median) changes from the model-based hindcast. Bottom: the location of the observed change within the hindcast probability distribution. Blue (red) shading indicates areas where the observed change was above the 95th percentile (below the 5th percentile) of the hindcast distribution. These maps show that there was no detailed agreement between the best-estimate precipitation change hindcast /media/ces/CES_D2_2_poster_3x3.pdf

• 12. 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

• 13. Isskyrsla_20100407

  N66°44.92' W025°08.94' 79. N66°45.01' W025°08.50' 80. N66°46.32' W025°10.27' 81. N66°46.73' W025°07.19' 82. N66°46.01' W025°04.86' 83. N66°46.22' W025°04.33' 84. N66°47.12' W025°06.45' 85. N66°47.26' W025°04.36' 86. N66°48.44' W025°03.60' 87. N66°49.10' W025°04.45' 88. N66°51.68' W025°03.27' 89. N66°53.36' W024°59.25' 90. N66°55.39' W024°58.89' 91. N66°55.82' W024°58.13' 92. N66°56.15' W025°01.15 /media/hafis/skyrslur_lhg/Isskyrsla_20100407.pdf

• 14. CES_D2.4_VMGO

  the territories of European Russia (ER) and Europe (EU) an ensemble of 9 CMIP3 [1] comprehensive global (coupled atmosphere-ocean) climate models is used in this assessment (Table 1); so called A2 scenario is considered. Both A2 and A1B scenarios are similar in terms of resulting global mean warming up to the mid-21st century IPCC [2]. The projected changes in ER and EU in many cases continue /media/ces/CES_D2.4_VMGO.pdf

• 15. PhD_course-Programme_26Aug2011-final

  Christian Refsgaard, GEUS All week AP Adriaan Perrels, FMI All week SK Sigrún Karsldóttir, IMO All week FU Frederik Uldal, University of Copenhagen Logistic support Course material Papers and book chapters - recommended reading Henriksen HJ, Barlebo HC (2008) Reflections on the use of Bayesian belief networks for adaptive management. Journal of Environmental Management, 88, 1025- 1036. doi:10.1016 /media/vedurstofan/PhD_course-Programme_26Aug2011-final.pdf

• 16. 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

• 17. The weather in Iceland in 2021

  89 54.1 210 129 Sauðanesviti 944.7 107 101 41.8 222 127 Akureyri 636.4 111 98 29.5 172 103 Grímsstaðir 400.4 103 89 14.3 189 97 Skjaldþingsstaðir 1112.4 # 84 61.2 184 115 Dalatangi 1437.2 87 81 47.6 232 145 Höfn í Hornafirði 1327 # 83 /about-imo/news/the-weather-in-iceland-in-2021

• 18. BIGJ_windrose_2005-2014

  1 2 1 1 Wind rose BIGJ April 2005−2014 90 80 70 60 50 40 30 2010360350340 330 320 310 300 290 280 270 260 250 240 230 220 210 200 190 180 170 160 150 140 130 120 110 100 2 4 6 8 10 Frequency of wind direction (%) Aerodrome Total observations: 114 Calm: 0.88% Variable winds: 0% Average wind speed for wind direction Wind direction (°) Wind speed (knots ) 0 5 10 15 0 45 90 135 180 225 270 315 /media/vedur/BIGJ_windrose_2005-2014.pdf

• 19. VI_2022_006_extreme

  77 91 109 Hraunaveita 132 116 136 159 117 140 169 Kvíslaveita 48 42 49 58 42 51 61 Sultartangi 66 57 68 80 58 69 84 Þingvallavatn 96 84 99 117 85 102 123 Þórisvatn 47 41 49 57 42 50 60 Tungnaá 76 67 79 92 67 80 98 Ufsarlón 104 92 108 126 93 112 134 36 Figure 19 – 1M5 maps for catchment Hálslón based on the ICRA dataset without projection (top left), with RCP 2.6 and 10th percentile /media/vedurstofan-utgafa-2022/VI_2022_006_extreme.pdf

• 20. Outline_for_the_case_Road_maintenance_in_a_changing_climate

  & Hautala, R. 2009. Benefits and value of meteorological information services – the case of the Finnish Meteorological Institute, Meteorological Applications, 16, pp. 369–379. Meyer, M.D., Amekudzi, A., O’Har, J.P. (2009), Transportation Asset Management Systems and Climate Change: An Adaptive Systems Management Approach, paper for Transportation Research Board 2010 Annual Meeting, CD-ROM /media/loftslag/Outline_for_the_case_Road_maintenance_in_a_changing_climate.pdf