Regional climate modeling – added value and utility Hans von Storch Institute of Coastal Research - Helmholtz Zentrum Geesthacht and KlimaCampus Hamburg 28. April School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta Feser, F., B. Rockel, H. von Storch, J. Winterfeldt, and M. Zahn, 2011: Regional climate models add value. Bull. Amer. Meteor. Soc. (in press) Weisse, R., H. von Storch, U. Callies, A. Chrastansky, F. Feser, I. Grabemann, H. Günther, A. Plüss, T. Stoye, J. Tellkamp, J. Winterfeldt and K. Woth, 2009: Regional meteo-marine reanalyses and climate change projections: Results for Northern Europe and potentials for coastal and offshore applications, Bull. Amer. Meteor. Soc. 90:

Climate = statistics of weather The genesis of climate C s = f(C l, Φ s ) with C l = larger scale climate C s = smaller scale climate Φ s = physiographic detail at smaller scale von Storch, H., 1999: The global and regional climate system. In: H. von Storch and G. Flöser: Anthropogenic Climate Change, Springer Verlag, ISBN , 3-36  “downscaling”

Regional climates do not create global climate. Instead, regional climate should be understood as the result of an interplay of global climate and regional physiographic detail. The local processes are important for the formation of the global climate not in terms of their details but through their overall statistics. Implications: Planetary scale climate can be modeled with dynamical models with limited spatial resolution the success on planetary scales does not imply success on regional or local scales.

4 Model variance as a function of spatial scales. The rectangles show well and insufficiently resolved spatial scales of the global and regional model.

Concept of Dynamical Downscaling RCM Physiographic detail 3-d vector of state Known large scale state projection of full state on large-scale scale Large-scale (spectral) nudging

standard formulation large-scale nudging Similarity of zonal wind at 850 hPa between simulations and NCEP re-analyses large scales medium scales Spectral nudging vs. standard formulation

Beate Müller, pers. comm. DWD analysis ) )

9 Feser, MWR 2006 DWD NCEP REMO Nudge REMO Standard Anomalies of medium-pass filtered 2m temperature [K] (shaded) and unfiltered SLP isobars [hPa] for August 30, 1998, 06:00 a.m.

10 DWD-analysis/ NCEP reanalysis DWD-analysis / regional simulation Ratio of standard deviations of 2m temperature DJF 1992 – 1999, at the regional scale, % Feser, MWR 2006

11 Positive values show added value of the regional model. 95% significant deviations are marked by a *. PCC DWD and NCEP PCC improvement/ deterioration REMO Nudge Pattern correlation coefficients [PCC, %] PCC improvement/ deterioration REMO Standard Feser, MWR 2006

12 Wind Speed 1998: Distribution a) Open Ocean: buoy RARH b) Coast: Light Ship Channel Percentile-percentile plots (qq-plots) of wind speed: The 99 dots represent the wind speed percentiles in steps of 1 percent. Winterfeldt and Weisse, MWR 2009

13 QuikSCAT: Added Value - BSS Open Ocean: No value added by dynamical downscaling Coastal region: Added Value in complex coastal areas Winterfeldt and Weisse, MWR 2009

Added value … … in medium scales. Medium scales are determined by both the large scale dynamics and the regional physiographic details (C s = f(C l,Φ s )) More added value with large-scale constraint (spectral nudging) Little improvement over driving large-scale fields for SLP, which is a large-scale variable, but significant improvement for structured fields like 2 m temp or coastal wind. Dynamical downscaling works … - Large scales are hardly affected but smaller scales respond to regional physiographic detail.

Coastdat Application Dynamical downscaling to obtain high- resolution (10-50 km grid; 1 hourly) description of weather stream. - use of NCEP or ERA re-analysis allows reconstruction of regional weather in past decades ( ) - when global scenarios are used, regional scenarios with better description of space/time detail can be downscaled. Meteorological data are fed into dynamical models of weather-sensitive systems, such as ocean waves, catchment hydrology, long-range pollution etc. Integration area used in HZG reconstruction and regional scenarios

Weisse, pers. comm. Extreme value analysis of wind speed at platform K13 (southern North Sea) January 1980-January 1997

Lars Bärring, pers. comm., 2003

Stormcount Weisse et al., J. Climate, 2005 Change of # Bft 8/year t ≤ T t ≥ T

Skill in representing wave conditions Red: buoy, yellow: radar, blue: wave model run with REMO winds wave direction significant wave height [days] Gerd Gayer, pers. comm., 2001

Solid: HIPOCAS RCM/NCEP reconstruction Dashed: WASA/DNMI SLP reconstruction Red: Local observations 50%, 90%, 95% and 99%iles. Southern North Sea: Hs 99% Change/Year [cm] ( ; HIPOCAS, 2003) Ocean wave height reconstruction

Annual mean winter high waters Cuxhaven red – reconstruction, black – observations Interannual Variability Water Levels (Weisse and Plüß 2006)

20-yr running trends in SST at Helgoland (German Bight) (Meyer et al. 2009)

Differences in inter-annual percentiles of surge / A2 - CTL: HIRHAM Differences in inter-annual percentiles of surge / A2 - CTL: RCA Woth, pers.comm. Scenarios of future ( ) statistics of North Sea storm surges

(Grabemann and Weisse 2007 ) Change of annual 99%ile of significant wave height in 2085-present Average over several models and scenarios colour: areas in which all changes from all models and scenarios have at least the same sign Scenarios of future ( ) statistics of North Sea significant wave height

The CoastDat data set: Long (50 years) and high-resolution reconstructions of recent offshore and coastal conditions mainly in terms of wind, storms, waves, surges and currents and other variables in N Europe Scenarios (100 years) of possible consistent futures of coastal and offshore conditions extension – ecological variables, SE Asia Clients: Governmental: various coastal agencies dealing with coastal defense and coastal traffic Companies: assessments of risks (ship and offshore building and operations) and opportunities (wind energy) General public / media: explanations of causes of change; perspectives and options of change

Wave Energy Flux [kW/m] Currents Power [W/m 2 ] Some applications of - Ship design - Navigational safety - Offshore wind - Interpretation of measurements - Oils spill risk and chronic oil pollution - Ocean energy - Scenarios of storm surge conditions - Scenarios of future wave conditions

© Dundee Satellite Receiving Station ~300 km Scandinavia Spitz- bergen Polar lows mesoscale (< 1000 km) sized maritime storms ● intense/ strong winds (>13.9 m/s) severe weather ● occur poleward the Polar Fronts in both hemispheres during winter ● typically induced by disturbances in the air flow ● typically driven by convective processes ● Here: only Northern North Atlantic

Set-up of multi-decadal simulation NCEP/NCAR reanalysis 1/ CLM Initialised: finishing: spectral nudging of scales > 700 km

Annual frequency of past polar lows PLS: Polar Low Season (Jul-Jun) Zahn and von Storch, 2008

Downscaling vs. “obs” C=0,72 NCEP-based downscaling (black) and observations (red) of MetNo (Noer, pers comm) Monthly comparison of NCEP- downscaling (in black) with analysed observed data (in red; Blechschmidt, 2008)

Bracegirdle, T. J. and S. L. Gray, 2008 Density of polar low genesis Genesis in NCEP downscaling RCM simulation

Downscaling scenarios Downscaling scenarios and simulations C20 ( ) A2, B1, A1B1 ( ) … run with ECHAM5/MPIOM1 … downscaled with CLM, employing spectral nudging.

Projected changes in polar low frequency and vertical atmospheric stability A2 C20 A1B B1 Zahn and von Storch, 2010 Differences of the area and time-averaged ice- free SST and T 500-hPa over the maritime northern North Atlantic as proxy for frequency of favourable polar low conditions (CMIP3/IPCC AR4)

Past changes according to dynamical downscaling of NCEP/NCAR re- analysis  Strong inter annual variability  Frequency remains on a similar level – no systematic trend Polar lows become less frequent in the Northern North Atlantic in the coming 100 years according to  regional modeling (dynamical downscaling) - control and scenario simulations with one GCM, and by  analysis of vertical stability in a large set of (CMIP3) global climate simulations; all simulations show an increase in projected stability. The genesis regions shift northward. Past and future occurrence of North Atlantic Polar Lows

Mediterranean hurricanes are strong mesoscale cyclones with some resemblance with tropical cyclones and polar lows (warm core, cloud-free eye, winds up to hurricane speed) that develop occasionally over the Mediterranean Sea. Medicanes 15 Jan 1995 Leone Cavicchia, pers. comm Dynamically downscaled

Take-Home Message 1.The downscaling paradigm makes sense 2.Spectral downscaling is a manifestation of the downscaling concept. 3.Reconstruction of detailed weather stream of past decades possible – useful for running impact models (say: sea state; transport of substances), for detection of anthropogenic signals; studying processes; assessing risks. 4.Construction of scenarios possible. 5.Added value in terms of more realistic detail and enhanced variability at medium scales – spatial detail, such as physiographic effects (e.g., coasts), but also dynamical processes (e.g., polar lows; typhoons; medicanes).