Added Value Generated by Regional Climate Models

H. von Storch, F. FeserInstitute of Coastal Research, Helmholtz Zentrum Geesthacht, Germany

29 May 1 - June 2012 - 46th Congress of the Canadian Meteorological and Oceanographic Society (CMOS), Montreal

Feser, F., B. Rockel, H. von Storch, J. Winterfeldt, and M. Zahn, 2011: Regional climate models add value. Bull. Amer. Meteor. Soc. 92: 1181–1192

Climate = statistics of weather

The genesis of climateCs = f(Cl, Φs)

with Cl = larger scale climateCs = smaller scale climateΦs = physiographic detail at smaller scale

von Storch, H., 1999

“downscaling”

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

4

Pattern correlation coefficients for V at 500 hPa between the global reanalyses and the

RCM with standard forcing via the lateral boundaries and the

RCM with spectral nudging

09.-12.03.93

global

regional

Nudging of the large scales

5NCEP - RCM standard NCEP - RCM nudge

March 12, 1993, 0:00

Berliner Wetterkarte

RCM standard NCEP RCM nudge

Bea

te M

ülle

r, pe

rs. c

omm

.DWD analysis

))

Added value in reconstructions using spectral nudging

Improved representation of

variability at medium scales. effect of physiographic detail (coasts) of sub-synoptic phenomena (polar lows, medicanes,

typhoons) forcing fields for impact models (ocean waves, storm

surges)

Improved presentation of variability at medium scales

NCEP-driven multidecadal simulation with RCM REMOEmploying spectral nudging (wind above 850 hPa, for scales > 800 km) Usage of German Weather Service (DWD) regional

analysis for a few years as reference to determine skill Considering ratios 2

DWD/2NCEP and 2

DWD/2RCM

Determining mean spatial correlation patterns between DWD, NCEP and RCM representations, for different spatial scales.

9

Ratio of standard deviations of 2m temperature DWD-analysis / NCEP reanalysis [%], JJA 1992 – 1999, at the regional scale.

Added Value

Ratio of standard deviations of 2m temperatureDWD-analysis / regional simulation [%], JJA 1992 – 1999, at the regional scale.

10

Positive values show added value of the regional model.95% significant deviations are marked by a *.

PCC DWD and NCEP

PCC improvement/ deteriorationREMO Nudge

PCC improvement/ deterioration

REMO Standard

Pattern correlation coefficients [PCC, %]

Feser, MWR 2006

Improved presentation of effect of physiographic detail

NCEP-driven multidecadal simulation with RCM REMOEmploying spectral nudging (wind above 850 hPa, for scales > 800 km) Usage of Quikscat-windfields (Q) over sea. Determining Brier Skill score

B = 1 – (RCM-Q)2 / (NCEP-Q)2

for all marine grid boxes

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Brier skill score using QuikSCAT level 2B12 as the “truth”, global reanalysis (NCEP reanalysis) as the reference forecast, and a regional model (SN-REMO) as forecast, Winterfeldt et al. (2010).

Added Value

Open Ocean: No value addedby dynamical downscaling

Coastal region:Added Valuein complexcoastal areas

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

Improved representation of sub-synoptic phenomena

NCEP-driven multidecadal simulation with RCM CLMEmploying spectral nudging (wind above 850 hPa, for scales > 800 km)Simulation of sub-synoptic phenomena Polar lows in the Northern North Atlantic Polar lows in the North Pacific Medicanes in the Mediterranean Sea Typhoons in the West Pacific

Density of North Atlanticpolar low genesis

Genesis in RCM simulation constrained by NCEP reanalsysis

Bracegirdle, T. J. and S. L. Gray, 2008 Zahn, M., and H. von Storch, 2008

Annual frequency of past North Atlantic polar lows

Zahn and von Storch, 2008

Set-up of multi-decadal simulation

NCEP/NCAR reanalysis 1/ CLM 2.4.6

Initialised: 1.1.1948 finishing: 28.2.2006

spectral nudging of scales > 700 km

σ=13

Projected changes in polar low frequency and vertical atmospheric stability

A2

C20

A1BB1

Zahn and von Storch, 2010

Differences of the area and time-averaged ice-

free SST and T500-hPa over the maritime northern

North Atlantic as proxy for frequency of

favorable polar low conditions

(CMIP3/IPCC AR4)

North Pacific Polar Low on 7 March 1977(Chen et al., 2011)

NOAA-5 infrared satellite image at 09:58UTC 7th March 1977

Cavicchia and von Storch, 2011

Medicane of January 16, 1995

ECMWF analysis

CLM-COSMO, 10 km grid

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Brier Skill Score between JMA best track data and NCEP or CCLM

Sea Level Pressure

10m Wind Speed

> 0 : CLM closer to best track

0 : CLM and NCEP equally close to best track

< 0 NCEP closer to best track

Feser and von Storch, MWR 2008 b

Improved representation of forcing fields for impact models

NCEP-driven multi-decadal simulation with RCM REMOEmploying spectral nudging (wind above 850 hPa, for scales > 800 km)1948-2010 simulation Wind and air pressure used to drive models of sea level

and circulation of marginal seas (not shown) for describing currents and sea level (North Sea “CoastDat”; not shown)

Wind used to drive models of the statistics of surface waves (ocean waves) in coastal seas (North Sea).

Red: buoy, yellow: radar, blue: wave model run with REMO winds

wave direction

significant wave height

[days]

[days]

Gerd Gayer, pers. comm., 2001

Annual mean winter high waters Cuxhavenred – reconstruction, black – observations

Interannual variability of mean water levels

(Weisse and Plüß 2006)

Summary: Added value …

… in medium scales. Medium scales are determined by both the large scale dynamics and the regional physiographic details (Cs =

f(Cl,Φs)) 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.

Present analysis refers to reconstructions, where we can compare the results with a “truth”. For scenario simulations other approaches are needed (e.g., big brother-type)