Added Value of Convection Resolving Climate Simulations (CRCS)

Transcription

1 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 and Inst. for Geophysics, Astrophysics, and Meteorology (IGAM)/ Inst. of Physics, University of Graz, Austria CCLM Community Assembly 2010, Berlin, 02. September 2010

2 Overview Introduction Categories of Added Value Conclusion Motivation Pros and Cons Data Examples where to search how to detect Thinks to keep in mind 1/XY

3 Introduction Advantages of CRCS Better representation of topography and surface fields Explicitly resolved deep convection Finer resolved land-surface interactions Atmospheric flows related to topography and land-sea contrast Better localization of maxima values (e.g., precipitation, wind gusts ) Problems with CRCS Computational expensive Availability of high resolved reference datasets Missing high resolution surface boundary conditions Numerical instabilities in mountainous regions Missing components for CRCS simulations: 3D turbulence scheme 3 rd order vertical advection 3D radiation cloud interactions Orographic shading Sub grid snow model 2/XY

4 The NHCM-1 Project The Non Hydrostatic Climate Modeling (NHCM-1) Project CCLM, MM5, & WRF 27 RCM simulations Resolutions of: 10 km (red) 3 km (blue) 1 km (green) 2 Months July 2007 January 2008 Reference dataset INCA (1 km res.) Ensembles evaluations for detection of added value Hilly region in south east Styria 3/XY

5 The NHCM-1 Project 10 km Horizontal Resolution 3 km Horizontal Resolution 1 km Horizontal Resolution Altitude [m] 4/XY

6 Overview Introduction Categories of Added Value Conclusion Motivation Pros and Cons Data Examples where to search how to detect Thinks to keep in mind 5/XY

7 Categories Temporal Mean (Climate) Methods Bias, RMSE, PDF, Trend 6/XY

8 Temporal Mean (Climate) Resolution: 10 km 3 km 1 km Bias T2M, CLM-INCA Bias PR, CLM-INCA 7/XY

9 Categories Methods Temporal mean (Climate) Temporal characteristics Bias, RMSE, PDF, Trend Correlation, Time series analysis, diurnal circle Mann et al, /XY

10 Temporal characteristics High resolution simulations represent certain weather events better T2M [ C] 9/XY

11 Temporal characteristics Pointwise averaged temporal Taylor Plots T2M, , hourly Nearly no improvements in temporal- correlation or variability 10/XY

12 Categories Methods Temporal mean (Climate) Temporal characteristics Spatial characteristics Bias, RMSE, PDF, Trend Correlation, Time series analysis Correlation 11/XY

13 Spatial characteristics Spatial Taylor Plots T2M, January, 2008 (hourly) Slight improvement of spatial correlation 12/XY

14 Categories Methods Temporal mean (Climate) Temporal characteristics Spatial characteristics Fine scale Bias, RMSE, PDF, Trend Correlation, Time series analysis Correlation Scale Separation, Spatial- temporal patterns 13/XY

15 Fine Scale Features Scale Separation CRCS are able to simulate spatially small scaled variability. Karper et al /XY

16 Categories Methods Temporal mean (Climate) Temporal characteristics Spatial characteristics Fine scale Large scale Bias, RMSE, PDF, Trend Correlation, Time series analysis Correlation Scale Separation, Spatial- temporal patterns, river runoff Scale Separation, Spatial- temporal patterns 15/XY

17 Large Scale Features Improvement in precipitation patterns in mountainous region Shadowing effects behind Rocky Mountains large Scale effect due to fine scale forcing T32-CGCM Winter precipitation [mm/d] 45km-CRCM Observation (Willmott and Matsuura) Laprice, /XY

18 Categories Methods Added Value Temporal mean (Climate) Temporal characteristics Spatial characteristics Fine scale Large scale Combinations Bias, RMSE, PDF, Trend Correlation, Time series analysis Correlation Scale Separation, Spatial- temporal patterns, river runoff Scale Separation, Spatial- temporal patterns Fuzzy verification methods 17/XY

19 Combined Characteristics e.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 Fractional Skill Score Difference for CCLM (1 km 10 km), July 2007 Spatial Scale [km] - = Threshold [mm/h] Threshold [mm/h] Threshold [mm/h] 18/XY

20 Overview Introduction Aspects of Added Value Conclusion Motivation Pros and Cons Data Examples where to search how to detect Thinks to keep in mind 19/XY

21 Conclusion Categories Temporal mean (Climate) Temporal characteristics Spatial characteristics Added Value of CRCS Cold bias January Dry bias in July Single events better represented Diurnal precipitation circle (Hohenegger, et al. 2008) 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 strong precipitation on small and large scales 20/XY

22 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, Hohenegger, et al. 2008, Towards climate simulations at cloud-resolving scales, Meteorologische Zeitschrift, Vol. 17, No. 4, Grell, A. G. et al., 2000, Nonhydrostatic climate simulations of precipitation over complex terrain, Journal of Geophysical research, vol. 105, No. D24, /XY