Future projections in extreme wind statistics over Europe Grigory Nikulin, Erik Kjellström and Colin Jones Rossby Centre Swedish Meteorological and Hydrological.

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Presentation transcript:

Future projections in extreme wind statistics over Europe Grigory Nikulin, Erik Kjellström and Colin Jones Rossby Centre Swedish Meteorological and Hydrological Institute

Objectives What is our confidence is the projected climate change in wind extremes compared to temperature and precipitation extremes ? Sources of uncertainties in regional projections: different driving GCMs different RCMs natural variability Starting point: regional climate projections in wind extremes is much more sensitive to driving GCMs than temperature and precipitation extremes (Nikulin et al., Tellus A 2011)

Ensembles of simulations 1. One RCM driven by different GCMs RCM: RCA3, SMHI (50 km) GCMs: ECHAM5-r3 (MPI, Germany) HadCM3-ref (MOHC, UK) BCM (NERSC, Norway) CCSM3 (NCAR, USA) CNRM (CNRM, France) IPSL (IPSL, France) 3.Natural variability - one RCM driven by one GCM with different initial conditions RCM: RCA3, SMHI (50 km) GCMs: ECHAM5 (3 members: r1, r2, r3) 2. Different RCMs driven by one GCM RCMs: RCA3, SMHI; RACMO, KNMI; REMO, MPI; (25 km) GCM: ECHAM5-r3, MPI

Data and method daily max 10m gust wind Extreme events the 50-year return values of winter (October-March) maximum gust wind; the generalised extreme value (GEV) distribution fitting the GEV: stationary model, L-moments 30-yr time slices : , , , yr moving GEV (gust wind averaged over a region) Confidence intervals parametric bootstrap

Projected change in warm extremes Moving GEV: 50yr ret. val. of T2max (ONDJFM ) common gradual increase role of driving GCMs

Climate change in precipitation extremes Moving GEV: 50yr ret. val. of winter max precipitation a tendency to intensification of precipitation extremes role of driving GCMs

strengthening of extreme gust winds over the Barents Sea (reduction in sea ice ) a tendency to strengthening of wind extremes over the Baltic Sea large spread among the simulations (magnitude, spatial patterns) Climate change in wind extremes

role of driving GCMs diverse behaviour of individual projections no common gradual increase; large decadal variability Moving GEV: 50yr ret. val. of winter (ONDJFM) max gust wind

Climate change in wind extremes role of natural variability: one driving ECHAM5 with different initial conditions some tendency to an increase in wind extremes natural variability or forced signal ?

Climate change in wind extremes role of natural variability Moving GEV: 50yr ret. val. of winter (ONDJFM) max gust wind r2-3 show a large increase from 2060 but a small increase for r1 only natural variability or forced signal masked by natural variability ? Are 3 members enough to conclude ?

Climate change in wind extremes Different RCMs RCA3 RACMO2 REMO some similarities between RCA3 and REMO noisy patterns for RACMO2

Climate change in wind extremes Moving GEV: 20yr ret. val. of winter max gust wind – ( ) different RCMs difference in magnitude; time series are often not "synchronized";

Conclusions Projected changes in Wind Extremes Natural variability is very large and can easily mask the forced signal; 3 members with different initial conditions may not be enough to separate natural and forced signals Driving GCMs very critically define the projected regional change in wind extremes: different magnitudes, diverse spatial patterns RCMs: different parameterization of gust wind and internal RCM dynamics show a spread among the results comparable to the spread related to natural variability