Added Value of Convection Resolving Climate Simulations (CRCS) · Large Scale Features. T32-CGCM. 45km-CRCM. Observation (Willmott and Matsuura) Winter precipitation [mm/d] Laprice,

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


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


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


10 km HorizontalResolution



Altitude [m]


Overview

Introduction Categories of Added Value Conclusion


Data


how to detect


Categories of added Value


Categories Methods

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



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 Methods

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

Temporal characteristicsCorrelation, Time series analysis, diurnal circle…

Mann et al, 1999.



Temporal characteristicsT2

M [

°C]

High resolution simulations represent certain weather events better



Temporal characteristicsPointwise averaged temporal Taylor PlotsT2M, 01.2008, hourly

Nearly no improvements in temporal- correlation or

variability



Categories Methods


Temporal characteristics Correlation, Time series analysis…

Spatial characteristics Correlation



Spatial characteristicsSpatial Taylor PlotsT2M, January, 2008 (hourly)

Slight improvement of spatial correlation



Categories Methods




Fine scaleScale Separation, Spatial- temporalpatterns



Fine Scale Features

Karper et al. 2010

CRCS are able to simulate spatially small scaled variability.

Scale Separation



Categories Methods

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



Fine scale

Scale Separation, Spatial- temporalpatterns, river runoff

Large scaleScale Separation, Spatial- temporalpatterns



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 Methods Added Value

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



Fine scale

Scale Separation, Spatial- temporalpatterns, river runoff

Large scaleScale Separation, Spatial- temporalpatterns

Combinations Fuzzy verificationmethods



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

- =


Overview

Introduction Aspects of Added Value Conclusion


Data


how to detect


Conclusion


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


Documents

Added Value of Convection Resolving Climate Simulations (CRCS) · Large Scale Features. T32-CGCM. 45km-CRCM. Observation (Willmott and Matsuura) Winter precipitation [mm/d] Laprice,