Regional climate modeling – added value and utility
Hans von Storch and Frauke FeserInstitute of Coastal ResearchHelmholtz Zentrum Geesthacht
and KlimaCampus Hamburg
28. April 2011 - School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta
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
Spectral nudging vs. standard formulation
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DWD analysis
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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
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
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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
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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
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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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Clim
ate,
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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
(Gra
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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
~300 km
Scan
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Spitz-
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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
Past occurrence of NA Polar Lows ● Polar Lows are simulated by 50 km grid resolution model● Strong inter annual variability● Frequency remains on a similar level – no systematic trend● Qualitative similarity with observations in terms of inter-annual, intra-annual variations and spatial distribution of genesis / other studies
Zahn, M., and H. von Storch, 2008: A longterm climatology of North Atlantic Polar Lows. Geophys. Res. Lett., 35, L22702, doi:10.1029/2008GL035769
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)
Polar lows become less frequent in the Northern North Atlantic according to● regional modelling (downscaling) control and scenario simulations with one GCM, and by● analysis of vertical stability in a large set of (CMPI3) global climate simulations; all simulations show a increase in projected stability.
The genesis regions shift northward.
Zahn, M., and H. von Storch, 2010: Decreased frequency of North Atlantic polar lows associated to future climate warming, nature 467, 309-312
Projected occurrence of NA Polar Lows
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).