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  • 1. VanderKeur_etal-2008-Uncertainty_IWRM-WARM

    cia l sy ste m ): th re e m ai n gr ou ps o fthreat s ar e at th e scene : cl im at e, so ci o -e co n o m ic s an d gover nanc e 1: M ai nl y ep ist em ic pl us som e onto logi ca l 2:S cenari o Pa rti ci pa to ry pr oc es s (PP ) imp ortan tt o de al w ith un certaint y Pa rti cip at o ry pr o ce ss (PP )im po rta n tt o de al w ith u n ce rta in ty : Pa rti ci pa to ry go al se tti ng :c o n /media/loftslag/VanderKeur_etal-2008-Uncertainty_IWRM-WARM.pdf
  • 2. IPPC-2007-ar4_syr

    2000 to 2100 in the absence of additional climate policies Global GHG emissions (Gt C O 2 -eq / yr ) post-SRES (max) post-SRES (min) Purchasing Power Parity, PPP) does not appreciably affect the pro- jected emissions, when used consistently.11 The differences, if any, are small compared to the uncertainties caused by assumptions on other parameters in the scenarios, e.g. technological change /media/loftslag/IPPC-2007-ar4_syr.pdf
  • 3. Huntjens_etal-2010-Climate-change-adaptation-Reg_Env_Change

    power delivery Finances and risk Financial resources concentrated in structural protection (sunk costs) Financial resources diversified using a broad set of private and public financial instruments Climate change adaptation in European river basins 267 123 Tabl e 2 Overvie w o fvariable s an d indicator s fo rAIW M Dimensio n Variabl e Indicato r Literatur e (A )Agenc y 1. Typ e o fleadershi p /media/loftslag/Huntjens_etal-2010-Climate-change-adaptation-Reg_Env_Change.pdf
  • 4. ces_risk_flyer

    opportunity, it is essential for any new methods to be able to account for future potential. The Risk Assessment Framework also accounts for the identification of opportunities, and subsequently also th i id ti i th d i i kirepresen , or e ec s on ma ers or o er stakeholders, the risk analysis results in a efficient and compact way. encourages e r cons era on n e ec s on‐ma ng process. Fig.2. Fourfold /media/ces/ces_risk_flyer.pdf
  • 5. 2012-Refsgaard_etal-uncertainty_climate-change-adaptation-MITI343

    frame s Ambiguit y Reduce d wee d cutting .(structural ) Me d P, A A, C S L, W Multipl e frame s Ambiguit y Mitig Adapt Strateg Glob Change Tabl e 4 Ex am pl es o fc ha ng e ad ap tat io n iss ue s re la te d to w at er in fra st ru ct ur e in ru ra la re as in D en m ar k. Se e n o te s o n ad ap ta tio n m ea su re s be lo w Ta bl e 2 Climat e chang e impac t Adaptatio n Typ e o fproble m Consequenc /media/loftslag/2012-Refsgaard_etal-uncertainty_climate-change-adaptation-MITI343.pdf
  • 6. Dataseries and components

    ), quantity (mm), pH Heavy metals in aerosol: Pb, Cd, Cu, Zn, Cr, Ni, Fe, Mn, V, As, Al (ng/m3) Cl, NO3-N, SO4-S (µg/m3) Hg (pg/m3) Persistent organic pollutants (POPs), same for precipitation (ng/l) and air (pg/m3): alfa-HCH, beta-HCH, gamma-HCH p,p'-DDE p,p'-DDD o,p'-DDT p,p'-DDT dieldrin HCB cis-chlordan, trans-chlordan, trans-nonachlor, PCB-28, -31, -52, -101, -105, -118, -138, -153, -156 /pollution-and-radiation/pollution/components/
  • 7. VAT_newsletter_2018_06

    retreated by tens of metres in . Kalda lónsjökull and E-Hagafellsjökull hold the  record in the terminus variations dataset of the Iceland Glaciological Society, retreating by – m in a year. The Breiðamerkurjökull out let glacier of the Vatnajökull ice cap retreats even faster, where it calves into Jökulsárlón lagoon, with an annual rate of retreat in recent years up to –m /media/Eplicanámskeið/VAT_newsletter_2018_06.pdf
  • 8. NONAM_1st_workshop_summary_v3

     peer review  separate from stakeholders Technical input from own  experts and stakeholders  Review of principal options Possible support measures Acceptability & Trade‐offs Deliberation with and  possible mandate from  stakeholders Process & Actions by Core Team    Actor / Source      Policy cycle(s)  Incl. learning  Le ar ni ng fr om e ar lie r c yc le s an d fe ed ba ck fr om o pe ra tio ns /media/vedurstofan/NONAM_1st_workshop_summary_v3.pdf
  • 9. CES_D2.4_task2_CMIP3_winds

    % 6,0 % 8,0 % 10,0 % 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 Fr eq ue nc y (% ) Geostrophic wind (m/s) September-April 1971-2000 2046-2065 -0,5 % -0,4 % -0,3 % -0,2 % -0,1 % 0,0 % 0,1 % 0,2 % 0,3 % 0,4 % 0,5 % 0,6 % 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 C ha ng e in % Wind class in m/s September-April change 46-71 Fig. 6. Top: 10-GCM mean frequency distributions of daily /media/ces/CES_D2.4_task2_CMIP3_winds.pdf
  • 10. Perrels-CBA

    of magnitude of influence factors • Model validation; data quality • Parameter level: adequate estimates of parameters in selected functions in the models A re w e fo cu si ng o n th e rig ht is su es ? 26.8.2011Adriaan Perrels/IL 17 Uncertainty 2 • Natural science related: e.g. limitations to downscaling of modeled future climate features, inherent chaotic processes underlying weather & climate /media/loftslag/Perrels-CBA.pdf

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