Regional climate modeling – added value and utility

Hans von Storch Institute of Coastal Research - Helmholtz Zentrum Geesthacht

and KlimaCampus Hamburg

28. April 2011 - 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: 849-860

Climate = statistics of weatherThe genesis of climate

Cs = f(Cl, Φs)

with

Cl = larger scale climate

Cs = smaller scale climate

Φs = physiographic detail at smaller scalevon Storch, H., 1999: The global and regional climate system. In: H. von Storch and G. Flöser: Anthropogenic Climate Change, Springer

Verlag, ISBN 3-540-65033-4, 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.

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Concept of Dynamical DownscalingRCM 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

Spectral nudging vs. standard formulation

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9 Feser, MWR 2006

DWD

NCEP REMO Nudge

REMOStandard

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 addedby dynamical downscaling

Coastal region:Added Valuein complexcoastal 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 (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.

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 (1948-2010)- 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 1958-2001

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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]

[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](1958-1998; HIPOCAS, 2003)

Ocean wave height reconstruction

Annual mean winter high waters Cuxhavenred – 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 (2070-2100) statistics of North Sea storm surges

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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 (2070-2100) 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 offshoreconditions• 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

www.coastdat.de

Wave Energy Flux [kW/m]

Currents Power [W/m2]

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

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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 2.4.6

Initialised: 1.1.1948 finishing: 28.2.2006

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 (1960-1989)

A2, B1, A1B1 (2070-2099)

… run with ECHAM5/MPIOM1

… downscaled with CLM, employing spectral nudging.

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 favourable polar low conditions (CMIP3/IPCC AR4)

Past changes according to dynamical downscaling of NCEP/NCAR re-analysis 1949-2005 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 sense2.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).