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Results:

• 51. 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

• 52. 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

• 53. 2010_003rs

  ................ 91 Figure B.5. Rake distribution for mapped faults/clusters in boxes E and F, Brennisteinsfjöll-Bláfjöll. ................................................................................. 92 Figure B.6. Rake distribution for mapped faults/clusters in box G, Ölfus. ......................... 93 Figure B.7. Rake distribution for mapped faults/clusters in box H, Hengill, active between January /media/vedurstofan/utgafa/skyrslur/2010/2010_003rs.pdf

• 54. CES_D2.4_task1

  is projected to approach 90%. The impact of anthropogenic climate change on precipitation is still estimated to be very small at present. In the middle of this century, typically about 60% of all months are projected to have above-median precipitation in northern Europe, although with a substantial variation with the time of the year. An on-line appendix of this report provides detailed tables /media/ces/CES_D2.4_task1.pdf

• 55. GA_2009_91A_4_Andreassen

  (alb: AWS) MODELLING LONG-TERM SUMMER AND WINTER BALANCES ? The authors 2009 Journal compilation ? 2009 Swedish Society for Anthropology and Geography 241 ed temperatures relative to –20°C to account for de-cay of snow albedo at temperatures below the melt-ing point, following a study by Winther (1993). Wetested both approaches, and chose to use –5°C asthe minimum for the accumulated temperature /media/ces/GA_2009_91A_4_Andreassen.pdf

• 56. Reykholt-abstracts

  in July 2016 on a Delta II rocket from Vandenberg Air Force Base in California. ICESat-2 will carry the Advanced Topographic Laser Altimeter System (ATLAS) and collect data to a latitudinal limit of 88 degrees. In contrast to Geoscience Laser Altimeter System (GLAS) on ICESat, ATLAS employs a 6-beam micro-pulse laser photon-counting approach. It uses a high repetition rate (10 kHz; 70 cm /media/vatnafar/joklar/Reykholt-abstracts.pdf

• 57. 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

• 58. 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

• 59. 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

• 60. 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