added value of convection resolving climate simulations (crcs) · large scale features. t32-cgcm....

Added Value of Convection Resolving Climate Simulations

(CRCS)

Prein Andreas, Gobiet Andreas, Katrin Lisa Kapper, Martin Suklitsch, Nauman Khurshid Awan, Heimo Truhetz

Wegener Center for Climate and Global Change andInst. for Geophysics, Astrophysics, and Meteorology (IGAM)/ Inst. of Physics, University

of Graz, Austria

CCLM Community Assembly 2010, Berlin, 02. September 2010

nach bedarf füllen 1/XY

Overview

Introduction Categories of Added Value Conclusion

MotivationPros and Cons

Data

Examples …where to search

how to detect

Thinks to keep in mind

Introduction

nach bedarf füllen 2/XY

Better representation of topography and surface fieldsExplicitly resolved deep convectionFiner resolved land-surface interactionsAtmospheric flows related to topography and land-sea contrastBetter localization of maxima values (e.g., precipitation, wind gusts…)

Advantages of CRCS

Problems with CRCSComputational expensiveAvailability of high resolved reference datasetsMissing high resolution surface boundary conditionsNumerical instabilities in mountainous regionsMissing components for CRCS simulations:

• 3D turbulence scheme• 3rd order vertical advection• 3D radiation – cloud interactions• Orographic shading• Sub grid snow model…

The NHCM-1 Project

• CCLM, MM5, & WRF • 27 RCM simulations• Resolutions of:

10 km (red)3 km (blue)1 km (green)

• 2 MonthsJuly 2007January 2008

nach bedarf füllen 3/XY

The Non Hydrostatic Climate Modeling (NHCM-1) Project

Hilly region in south east Styria

• Reference dataset INCA (1 km res.)• Ensembles evaluations for detection of added

value

The NHCM-1 Project

nach bedarf füllen 4/XY

10 km HorizontalResolution

3 km HorizontalResolution

1 km HorizontalResolution

Altitude [m]

nach bedarf füllen 5/XY

Overview

Introduction Categories of Added Value Conclusion

MotivationPros and Cons

Data

Examples …where to search

how to detect

Thinks to keep in mind

Categories of added Value

nach bedarf füllen 6/XY

Categories Methods

Temporal Mean (Climate) Bias, RMSE,PDF, Trend…

Categories of added Value

nach bedarf füllen 7/XY

Temporal Mean (Climate)Resolution: 10 km 3 km 1 km

Bia

s P

R, 0

7.2

00

7CL

M-I

NCA

Bia

s T2

M, 0

1.2

00

8CL

M-I

NCA

Categories of added Value

nach bedarf füllen 8/XY

Categories Methods

Temporal mean (Climate) Bias, RMSE, PDF, Trend…

Temporal characteristicsCorrelation, Time series analysis, diurnal circle…

Mann et al, 1999.

Categories of added Value

nach bedarf füllen 9/XY

Temporal characteristicsT2

M [

°C]

High resolution simulations represent certain weather events better

Categories of added Value

nach bedarf füllen 10/XY

Temporal characteristicsPointwise averaged temporal Taylor PlotsT2M, 01.2008, hourly

Nearly no improvements in temporal- correlation or

variability

Categories of added Value

nach bedarf füllen 11/XY

Categories Methods

Temporal mean (Climate) Bias, RMSE, PDF, Trend…

Temporal characteristics Correlation, Time series analysis…

Spatial characteristics Correlation

Categories of added Value

nach bedarf füllen 12/XY

Spatial characteristicsSpatial Taylor PlotsT2M, January, 2008 (hourly)

Slight improvement of spatial correlation

Categories of added Value

nach bedarf füllen 13/XY

Categories Methods

Temporal mean (Climate) Bias, RMSE, PDF, Trend…

Temporal characteristics Correlation, Time series analysis…

Spatial characteristics Correlation

Fine scaleScale Separation, Spatial- temporalpatterns

Categories of added Value

nach bedarf füllen 14/XY

Fine Scale Features

Karper et al. 2010

CRCS are able to simulate spatially small scaled variability.

Scale Separation

Categories of added Value

nach bedarf füllen 15/XY

Categories Methods

Temporal mean (Climate) Bias, RMSE,PDF, Trend…

Temporal characteristics Correlation, Time series analysis…

Spatial characteristics Correlation

Fine scale

Scale Separation, Spatial- temporalpatterns, river runoff

Large scaleScale Separation, Spatial- temporalpatterns

Categories of added Value

nach bedarf füllen 16/XY

Large Scale Features

T32-CGCM 45km-CRCMObservation (Willmott

and Matsuura)

Winter precipitation [mm/d]

Laprice, 2010

Improvement in precipitation patterns in mountainous regionShadowing effects behind Rocky Mountains large Scale effect due to

fine scale forcing

Categories of added Value

nach bedarf füllen 17/XY

Categories Methods Added Value

Temporal mean (Climate) Bias, RMSE,PDF, Trend…

Temporal characteristics Correlation, Time series analysis…

Spatial characteristics Correlation

Fine scale

Scale Separation, Spatial- temporalpatterns, river runoff

Large scaleScale Separation, Spatial- temporalpatterns

Combinations Fuzzy verificationmethods

Categories of added Value

nach bedarf füllen 18/XY

Combined Characteristicse.g., Fractional Skill Score (FSS)

Compares fractional coverage in forecast with fractional coverage in observations.

1 km Simulations have a higher FSS especially for medium and heavy precipitation events

1 km 10 km

Observation Hindcast

Spat

ial S

cale

[km

]

Threshold [mm/h] Threshold [mm/h] Threshold [mm/h]

Fractional Skill Score Difference for CCLM(1 km – 10 km), July 2007

- =

nach bedarf füllen 19/XY

Overview

Introduction Aspects of Added Value Conclusion

MotivationPros and Cons

Data

Examples …where to search

how to detect

Thinks to keep in mind

Conclusion

nach bedarf füllen 20/XY

Categories Added Value of CRCS

Temporal mean (Climate) • Cold bias January• Dry bias in July

Temporal characteristics • Single events better represented• Diurnal precipitation circle (Hohenegger, et al. 2008)

Spatial characteristics• Slightly improved T2M correlation in Jannuary• PR pattern in summer (Grell et al. 2000,

Hohenegger et al. 2008)

Fine scale • Higher variability in fine scales (Karper et al. 2010)

Large scale

Combinations • FSS improved for medium and strongprecipitation on small and large scales

Literature

Literature:Laprise, R., 2010, Where and when should one hope to find added value from dynamical downscaling of GCM

data, WCRP Regional Climate Workshop: Facilitating the production of climate information and its use in impact and adaptation work, Lille (France)

Kapper, L. et al., 2010, Determination of the Effective Resolution of Climate Models by Spectral Analysis, Journal of Geophysical Research, in Preperation

Dankers, R. et al., 2007, Evaluation of very high-resolution climate model data for simulating flood hazards in the Upper Danube Basin, Journal of Hydrology, Vol. 347, 319– 331

Hohenegger, et al. 2008, Towards climate simulations at cloud-resolving scales, Meteorologische Zeitschrift, Vol. 17, No. 4, 383-394

Grell, A. G. et al., 2000, Nonhydrostatic climate simulations of precipitation over complex terrain, Journal of Geophysical research, vol. 105, No. D24, 595–608

nach bedarf füllen 21/XY