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• 21. D2.3_CES_Prob_fcsts_GCMs_and_RCMs

  1 Probabilistic forecasts of temperature and precipitation change by combining results from global and regional climate models (CES Climate Modelling and Scenarios Deliverable D2.3) Jouni Räisänen* and Leena Ruokolainen Department of Physics, P.O. Box 48, FI-00014 University of Helsinki, Finland 18 August 2009 *Corresponding author Email: jouni.raisanen@helsinki.fi /media/ces/D2.3_CES_Prob_fcsts_GCMs_and_RCMs.pdf

• 22. ces_geus_paakitsoq_full_report

  ) is shown. At the margin the grid boxes of the RCAO RCM are visible. The area between Illulisat and Swiss camp is commonly called Paakitsôq. respectively. The Automatic Weather Stations (AWS) Swiss Camp and Crawford are located on the ice sheet and are operated by the Greenland Climate Network (GC-net) (Steffen and Box, 2001). The locations of the stations are indicated in Fig. 1, further details /media/ces/ces_geus_paakitsoq_full_report.pdf

• 23. The weather in Iceland in 2019

  111 Lambavatn 744.4 76.6 81.6 44.6 152 119 Hólar í Dýrafirði 897.6 # 80.7 48.9 215 119 Litla-Ávík 723.7 # # 35.6 243 138 Litla-Hlíð 349.3 # 89.7 21.8 155 74 Sauðanesviti 708.5 # 76.0 43.8 210 133 Akureyri 692.8 133.6 110.9 40.6 195 110 /about-imo/news/the-weather-in-iceland-in-2019

• 24. The weather in Iceland 2014

  Hornafirði 6.5 1.9 1.2 Fagurhólsmýri 6.4 1.8 1 112 0.9 Stórhöfði 6.2 1.4 2 138 0.4 Hveravellir 0.9 1.9 2 50 0.6 Árnes 5.3 1.7 2 134 0.7 Eyrarbakki 5.6 1.5 3 123 0.4 Annual temperature at a few stations and deviation from the 1961 to 1990 and 2004 to 2013 averages. The highest annual temperature was registered in Surtsey (off the South coast), 7.2 /about-imo/news/nr/3082

• 25. ved-eng-2014

  5.8 2.1 1 142 1.1 Höfn í Hornafirði 6.5 1.9 1.2 Fagurhólsmýri 6.4 1.8 1 112 0.9 Stórhöfði 6.2 1.4 2 138 0.4 Hveravellir 0.9 1.9 2 50 0.6 Árnes 5.3 1.7 2 134 0.7 Eyrarbakki 5.6 1.5 3 123 0.4 Annual temperature at a few stations and deviation from the 1961 to 1990 and 2004 to 2013 averages. The highest annual temperature was registered in Surtsey (off the South coast), 7.2°C /media/vedurstofan/utgafa/skylduskil/ved-eng-2014.pdf

• 26. Traffc-maintenance_expenditures

  1965 7 549 30 414 3 555 86 331 - 813 - 1 065 - - - - 129 728 1 823 901 1964 5 392 27 038 1 611 70 991 - 1 090 - 2 043 - - - - 108 166 1 603 671 1963 4 275 24 368 1 682 54 879 - 462 - 2 492 - - - - 88 158 1 382 441 1962 3 632 22 117 2 313 52 079 - 168 - 3 633 - - - - 83 941 1 396 905 1961 3 249 18 138 2 313 52 276 - 118 - 3 934 - - - - 80 028 1 388 467 1960 2 800 16 168 1 514 53 793 - 128 - 3 491 /media/loftslag/Traffc-maintenance_expenditures.pdf

• 27. CES_D2.4_solar_CMIP3

  the period 1971-2000 to 2020-2049 under the A1B greenhouse gas scenario, derived from simulations performed with 18 global models. The yellow box covers the interval between the 25th and 75th percentage points, with the median (50th point) shown by a black line. Whiskers represent the in- terval from the 5th to the 95th percentage point. Top-left: south-western Greenland (65◦N, 50◦W); top-right /media/ces/CES_D2.4_solar_CMIP3.pdf

• 28. CES_BioFuels_Flyer_new

  Participants and organization of project For queries, please contact to: Prof. Seppo Kellomäki (Co-ordinator) Ashraful Alam (Researcher) Faculty of Forest Sciences University of Joensuu P.O. Box 111, FI-80101 Joensuu, Finland. Email: firstname.lastname@joensuu.fi Bioenergy production potential increased due to the climate change both in energy wood thinnings and in final fellings (FF). In addition /media/ces/CES_BioFuels_Flyer_new.pdf

• 29. ces_risk_flyer

   Energy (Risø DTU, Denmark) � VTT Technical Research Centre of Finland (VTT, Finland) With assistance from: � Finnish Environment Institute (SYKE) � Finnish Meteorological Institute (FMI) Contact: Jari Schabel, Research scientist VTT Technical Research Centre of Finland, P.O. Box 1300, 33101 Tampere, Finland jari.schabel@vtt.fi       � University of Eastern Finland /media/ces/ces_risk_flyer.pdf

• 30. Nikulin_Grigory_CES_2010

  the Barents Sea (reduction in sea ice ) checkbld a tendency to strengthening of wind extremes over the Baltic Sea checkbld ensemble mean is sensitive to the number of simulations in the ensemble Conclusions checkbld strong underestimation of warm extremes (open-land observation and grid box average model, not sensitive to driving GCMs) checkbld the key role of driving GCMs in the simulated cold extremes /media/ces/Nikulin_Grigory_CES_2010.pdf