Norwegian Meteorological Institute met.no Regional Climate Development under Global Warming ( Norges Forskningsråd: Seminar om bruk av klimascenarier i virkningsstudier, Oslo, RegClim: Present results and future plans relevant for climate impact studies E.J.Førland, R.E.Benestad, I.Hanssen-Bauer, T.E.Skaugen, J.E. Haugen, D. Bjørge, M. Ødegård, E.Støren, A.Sorteberg, B.Ådlandsvik Uncertainty and confidence in present RegClim scenarios Downscaling scenarios for impact studies: Tools and examples Tailoring scenarios for specific impact assessments:  What RegClim can and cannot produce

Norwegian Meteorological Institute met.no Regional Climate Development under Global Warming regclim.met.no RegClim’s Overall Aim Phase III to produce scenarios for regional climate change – suitable for impact assessments in Northern Europe, bordering sea areas and major parts of the Arctic (our region), given a global climate change; and to quantify uncertainties – due to choice of methods, global scenarios, and due to poorly understood processes influencing our region's climate, in particular: those causing the warm and ice-free Nordic Seas, and the effects of aerosols. Keywords: Risks and uncertainties

Norwegian Meteorological Institute met.no Regional Climate Development under Global Warming regclim.met.no RegClim: 5 Principal Modules (PM) PM1: Atmospheric interpretation for regional climate. Eirik Førland, met.no. Impacts PM2: Regional interpretation for oceanic and Arctic climate. Bjørn Ådlandsvik, IMR. PM3: Global and regional significance of the Nordic Seas. Nils Gunnar Kvamstø, UiB PM4: Climate response of regional contaminants. Jon Egill Kristjansson, UiO. Regional risk and uncertainty Global-scale response Basic theory PM5: Optimal forcing structures for atmospheric flows & regional climate predictability. Trond Iversen, UiO

Norwegian Meteorological Institute met.no WARNING ! For interpretation of climate scenarios (e.g. for impact assessments) it is important to keep in mind that: A climate scenario is an internal consistent realisation of the future climate development under certain assumptions Climate model simulations with slightly different initial conditions may exhibit different characteristics in each evolution (  Large variability and some uncertainties…!) A climate scenario is not a forecast ! Meteorologisk Institutt met.no Regional Climate Development under Global Warming / regclim

Norwegian Meteorological Institute met.no Large uncertainty North of 60 o N J. Räisänen: Increased Temperature by 2080 rel. to Global average for 19 CMIP2 runs Red curve is the average over all 19.

Norwegian Meteorological Institute met.no Uncertainties in future climate projections I. Global System Unpredictability (“climate forcings”): – Natural forcing: Solar radiation, volcanoes – Anthropogenic release of gases and particles – Changes in land-use II. Climate System Unpredictability: – Internal variations in the climate system (“Natural variability”) III. Model Deficiencies: – Imperfect knowledge about forcings and processes – Imperfect physical and numerical treatment of processes – Poor resolution in the global models – Imperfect downscaling procedures

Norwegian Meteorological Institute met.no I. Global System Unpredictability

Norwegian Meteorological Institute met.no RegClim Regional Climate Model (HIRHAM) Precipitation response winter DJF (mm/day) MPI (GSDIO, ) Hadley (A2, ) Is this due to model uncertainty only? Can sample uncertainty be an explanation too? II. Climate System Unpredictability / III. Model Deficiencies:

Norwegian Meteorological Institute met.no AMO MEAN = 17 Sv PM3: Bergen Climate Model Control Run 300 yrs: Atlantic Meridional Overturning (AMO). CMIP2: 4 runs starting from different AMO-strength: Notice: E77: AMO=19Sv ; E78: AMO= 16 Sv. Nils Gunnar Kvamstø and Asgeir Sorteberg, Univ. of Bergen Uncertainties II & III ctd.:

Norwegian Meteorological Institute met.no Winter delta Precip. mm/day At 2 x CO2 E77 Start from strong AMO ”MPI-type” E78 Start from weak AMO ”Hadley-type” ANSWER: Yes it may be due to sample uncertainty. Uncertainties II & III ctd.:

Norwegian Meteorological Institute met.no

Downscaling in RegClim Poor spatial resolution in global models Dynamical downscaling (regional modelling). Empirical downscaling (statistical downscaling). How? Why?

Norwegian Meteorological Institute met.no Spatial resolution 55x55 km 2 1x1 km 2

Norwegian Meteorological Institute met.no Downscaling techniques applied by RegClim (“et al.”) Dynamical downscaling (Regional Climate Models, RCM) – Grid points (55x55km, will be refined to 22x22 km) – ”Δ-Change” – Interpolation to specific sites (different elevation, distance to coast, local topography, …) – Interpolation + adjustment to observed climatology – Empirical downscaling based on RCM-values Empirical downscaling (Statistical downscaling) – Linear techniques (regression [simple + multiple, CCA, SVD, etc]) – Non-linear techniques (Analogues [Weather types], Weather Generator, Neural networks, etc)

Norwegian Meteorological Institute met.no Dynamical downscaling (RegClim-contact: – A “Regional Climate Model (RCM)” is run with high spatial resolution for a limited area – Results from a global model are used as input at the boarders of the RCM – The RCM-simulations are performed for present (“control”) and future (“scenario”) climate – A number of climate elements are calculated for every 6 hours, e.g.: Sea level air pressure Temperature (2 m): mean, maximum, minimum Wind-speed and –direction (10 m) Precipitation-amount (rain & snow) Snow depth Evaporation Wind and geopotential height for the 500 hPa-level Meteorologisk Institutt met.no

Norwegian Meteorological Institute met.no Scenarios from dynamical downscaling ”MPI”: ECHAM4/OPYC3 GSDIO, IS92a – & – 3 different domains ”Hadley”: HadAM3h, A2 – & – Domain 3 Several meteorological elements are available with a time resolution of 6 hours in a 55x55 km grid and for 19 levels

Norwegian Meteorological Institute met.no Projected change, winter temperature Small differences between simulations MPI (IS92a) Hadley(A2)

Norwegian Meteorological Institute met.no Projected change, winter precipitation Substantial differences between simulations MPI IS92a Hadley A2

Norwegian Meteorological Institute met.no Jan Erik Haugen, PM1: Combined statistics of HIRHAM downscaled scenarios, 55x55 km ECHAM4 IS92a, and x20 years, 50 year response HadAM3H A2, and x30 years, 110 year response Scaling procedure The Hadley response over 110 year was scaled to the 50 year MPI response period from the global temperature of the Hadley simulation -> Factor 0.32 for Hadley data (Christensen et al, 2001, Geophys. Res. Letters, 28,1003-6) The two (scaled) scenarios were treated as equally valid realizations i.e. it was assumed that the difference is due to natural climate variability

Norwegian Meteorological Institute met.no Precipitation response winter DJF MPI Hadley (unscaled) Combined (after scaling)

Norwegian Meteorological Institute met.no Monthly projections from empirical downscaling by regression models MPI: ECHAM4/OPYC3 GSDIO, IS92a MPI: ECHAM4/OPYC3 GSDIO, IS92a – Temperature and precipitation for ca 50 Norwegian stations are downscaled for the period

Norwegian Meteorological Institute met.no Empirically downscaled temperature scenario for Longyearbyen Winter temperature, Longyearbyen Temperature, deg C obs obs mod mob mod

Norwegian Meteorological Institute met.no Monthly projections from empirical downscaling by regression models MPI: ECHAM4/OPYC3 GSDIO, IS92a MPI: ECHAM4/OPYC3 GSDIO, IS92a – Temperature and precipitation for ca 50 Norwegian stations are downscaled for the period ”Multimodel-experiments”: ”Multimodel-experiments”: – Several global models are downscaled for a number of stations in Northern Europe – The downscalings are based on different choices of domains and predictors Selected scenarios are available through NOSerC Selected scenarios are available through NOSerC ( Contact person: Egil Støren

Norwegian Meteorological Institute met.no Projected warming in Oslo in January, results from 48 scenarios (17 global models): Uncertainty in global models & downscaling Robust signal:  Warming

Norwegian Meteorological Institute met.no Empirical downscaling of various GCMs Larger spread for precipitation than for temperature Uncertainty in global models & downscaling

Norwegian Meteorological Institute met.no Projected change in annual mean temperature during 50 years: Results from dynamical (DD) and empirical downscaling (ED) (MPI ECHAM4/OPYC3 GSDIO, IS92a) – Uncertainty related to downscaling techniques  Rather similar values

Norwegian Meteorological Institute met.no Projected change in annual precipitation during 50 years: Results from dynamical (DD) and empirical downscaling (ED) (MPI ECHAM4/OPYC3 GSDIO, IS92a) –Uncertainty related to downscaling techniques  Some deviations.

Norwegian Meteorological Institute met.no Quantifying sources of risks and uncertainties by: Downscaling simulations with the same emission scenario from different global models (uncertainty in global models); Increasing available downscalings through Nordic/European collaboration (uncertainty in regional models); Empirical vs dynamical downscaling (methodological uncertainty); mapping regional natural variability by: – Downscaling ”ensemble runs” from the Bergen Climate Model (PM3) which is focussing the Atlantic Ocean currents; – Downscaling global scenarios from the Oslo GCM (PM4) which is focussing the influence of aerosols; RegClim PM1: Present activities/future plans

Norwegian Meteorological Institute met.no Length of the growing season in south-eastern part of Norway a) b) ( )-( ) Meteorologisk Institutt met.no

Norwegian Meteorological Institute met.no Average change in the snow storage by 1.April The snow storage is expected to increase at altitudes above 800 m in Eastern Norway as a result of increasing winter precipitation Large spring floods are therefore possible even in a warmer climate (NVE & met.no)

Norwegian Meteorological Institute met.no RegClim: Downscaling of daily values Main elements: Temperature and precipitation SimulationEmissionDomainMethodPeriodState ERA-15 ( )-D1,D2,D3D:RCM F ECHAM4/OPYC3IS92aD1D:RCM , F ECHAM4/OPYC3IS92aD2D:RCM F ECHAM4/OPYC3IS92aD3D:RCM , R ECHAM4/OPYC3 T106B2D2D:RCM , P HadAm3HA2D3D:RCM , R HadAm3HB2D3D:RCM , P ECHAM4/OPYC3IS92aD2E:Analog F ECHAM4/OPYC3IS92aD2E: Linear , F HadAm3HA2D3E:Analog , P Domains: D1: Large, D2: Medium, D3: Small Method: D ynamical/RCM, E mpirical: Linear regression & Analogs State: F: Finalised, R: Downscaled, but not finally quality-checked, P: Planned downscaling

Norwegian Meteorological Institute met.no RegClim PM2: Info on marine impact studies RegClim contact-person: To study effects on marine ecosystems, it is not sufficient to know the state on wind, air temperature and precipitation. Coupled global atmosphere-ocean models may be used, but the resolution is too coarse to resolve the topography on the continental shelves In RegClim PM2 regional marine scenarios will be established, with emphasis on currents, salinity and temperature from the sea surface to the bottom in the ocean areas around Norway This is a new activity, and presently few results are finalized. Available at the moment is a scenario with ocean level (storm surge) and waves

Norwegian Meteorological Institute met.no Nature, January 11, 2004: Schär et al.: The role of increasing variability in European summer heatwaves

Norwegian Meteorological Institute met.no Various users may have different needs for climate data for impact studies Climatology (Mean values, extremes, frequencies, etc.) Internal consistency (spatial, temporal [day- to-day] and between elements) Optimal historical time series (observations) to ”calibrate” empirical models High resolution in time and space Representative for local climate conditions

Norwegian Meteorological Institute met.no High resolution in Repr. local Internal consistency SpaceTimeconditionsStatisticsday-to-day betw. elements Multiple scenarios DYNAMICAL Grid points (RCM) NY NY Y Y N D-change NY N Y ? ? N Interpolation NYN Y Y Y N Interpol+adjust ?Y Y Y Y YN Emp.downsc. EMPIRICAL Simple regr. N N Y Y N N Y Multiple regr. ? N Y Y N ? Y Analogues Y N Y Y N Y Y Weather Gen. Benefits and drawbacks of different downscaling methods YES NO MAYBE ?

Norwegian Meteorological Institute met.no Produce the type of scenario data needed for impact assessments: Implementing RCMs with higher spatial resolution; Empirical downscaling of daily values; Improve methods for estimating changes in “climatic risks”, i.e. changes in frequency distributions and extremes of temperature, precipitation, wind, waves, etc; Improve scenarios for changes in snow conditions; Develop user-friendly tools for empirical downscaling; Make results available on the web: ( NB! Tailoring of climate scenarios for specific impact studies is NOT a part of the planned RegClim activities  A dialogue between users &RegClim is important to ensure: -That appropriate data for impact assessments are focussed -That interpretation of the results is optimal RegClim Phase III: Present activities/future plans, PM1

Norwegian Meteorological Institute met.no Overview: Main features of downscaled scenarios for Norway (MPI IS92a & HadCM A2) Temperature: MPI og Hadley in average give similar warming per decade. The scenarios indicate larger warming during winter than summer, larger warming in inland than at the coast, and larger in the northern than southern regions Temperature: MPI og Hadley in average give similar warming per decade. The scenarios indicate larger warming during winter than summer, larger warming in inland than at the coast, and larger in the northern than southern regions Rare extremes (compared to present climate) probably will occur more frequent for high temperatures and less frequent for low temperatures Rare extremes (compared to present climate) probably will occur more frequent for high temperatures and less frequent for low temperatures Precipitation: Locally there are rather large differences between the MPI and Hadley simulations. This may be caused by natural variability Precipitation: Locally there are rather large differences between the MPI and Hadley simulations. This may be caused by natural variability High intensity events probably will occur more frequent in the future High intensity events probably will occur more frequent in the future Snow: Both models indicate less snow in the lowland parts, particularly in the coastal regions. In the inland areas and particularly in the high- mountains the models indicate risk of increasing snow amounts during winter Snow: Both models indicate less snow in the lowland parts, particularly in the coastal regions. In the inland areas and particularly in the high- mountains the models indicate risk of increasing snow amounts during winter Atmospheric circulation / Storm tracks: Substantial differences between MPI and Hadley in projected changes over Norway. This implies differences for e.g. local scenarios of precipitation and wind Atmospheric circulation / Storm tracks: Substantial differences between MPI and Hadley in projected changes over Norway. This implies differences for e.g. local scenarios of precipitation and wind