Towards cloud-resolving regional climate simulations over the Alpine region

Transcription

1 Towards cloud-resolving regional climate simulations over the Alpine region Hohenegger Cathy P. Brockhaus, C. Bretherton, C. Schär Institute for Atmospheric and Climate Science ETH Zurich

2 History of RCMs at ETH NCCR-I NCCR-II NCCR-III CHRM model, hydrostatic, Δ=50 km CLM/COSMO model, non-hydrostatic, Δ=25 km CCLM/COSMO, cloud-resolving, Δ=2 km PRUDENCE ENSEMBLES CSCS Swiss Alps Center for Climate Systems Modeling (C2SM) C2SM Schär ETH Zurich

3 History of RCMs at ETH C 2 SM Center for Climate Systems Modeling MeteoSchweiz Eidgenössische Technische Hochschule Zürich Swiss Federal Institute of Technology Agroscope Reckenholz Tänikon (ART) Cooperation with: - Oeschger Center, University Bern - MPI Hamburg -etc Schär ETH Zurich

4 History of RCMs at ETH C 2 SM Center for Climate Systems Modeling Overarching goal Increase our understanding and sharpen our predictive capability of climate variations and change on time scales from days to millennia. Unifying Research Theme Multi-scale interactions within the climate system 1 μm 1 m 1000 km [m] Schär ETH Zurich

5 History of RCMs at ETH NCCR-I NCCR-II NCCR-III CHRM model, hydrostatic, Δ=50 km CLM/COSMO model, non-hydrostatic, Δ=25 km CCLM/COSMO, cloud-resolving, Δ=2 km PRUDENCE ENSEMBLES CSCS Swiss Alps Center for Climate Systems Modeling (C2SM) C2SM Schär ETH Zurich

6 History of RCMs at ETH NCCR-I NCCR-II NCCR-III CHRM model, hydrostatic, Δ=50 km CLM/COSMO model, non-hydrostatic, Δ=25 km Swiss Temperature Series (mean of 4 stations) CCLM/COSMO, cloud-resolving, Δ=2 km PRUDENCE CSCS Swiss Alps ENSEMBLES Center for Climate Systems Modeling (C2SM) (Schär et al. 2004, Nature, 427, ) C2SM

7 History of RCMs at ETH NCCR-I NCCR-II NCCR-III CHRM model, hydrostatic, Δ=50 km CLM/COSMO model, non-hydrostatic, Δ=25 km CCLM/COSMO, cloud-resolving, Δ=2 km PRUDENCE ENSEMBLES CSCS Swiss Alps Center for Climate Systems Modeling (C2SM) C2SM Schär ETH Zurich

8 History of RCMs at ETH NCCR-I NCCR-II NCCR-III CHRM model, hydrostatic, Δ=50 km CLM/COSMO model, non-hydrostatic, Δ=25 km CCLM/COSMO, cloud-resolving, Δ=2 km PRUDENCE ENSEMBLES CSCS Swiss Alps Schär ETH Zurich Center for Climate Systems Modeling (C2SM) C2SM (Erich Fischer, ETH Zürich, ENSEMBLES)

9 History of RCMs at ETH NCCR-I NCCR-II NCCR-III CHRM model, hydrostatic, Δ=50 km CLM/COSMO model, non-hydrostatic, Δ=25 km CCLM/COSMO, cloud-resolving, Δ=2 km PRUDENCE ENSEMBLES CSCS Swiss Alps Center for Climate Systems Modeling (C2SM) C2SM Schär ETH Zurich

10 Outline 1. Why doing cloud-resolving climate simulations? 2. How to do it? 3. Do we need cloud-resolving resolution for climate applications? Desired properties I. Improved representation of mean climate characteristics: Can the cloud-resolving simulation beats its driving coarseresolution simulation with respect to observations? II. Improved representation of climate processes and feedbacks: Do the cloud-resolving and its driving coarse-resolution simulation differ in their representation of key climate feedbacks? 4. Conclusions

11 Why doing cloud-resolving climate simulations? Uncertainties exist in the simulation of : present-day climates (esp. summertime precipitation): e.g. Jacob et al. 2007, IPCC 2007 mm/h 0.2 OBS CLM 14 UTC 19 UTC 0.1 and future changes thereof: e.g. Deque et al. 2007, IPCC Brockhaus et al UTC Jacob et al. 2008

12 Why doing cloud-resolving climate simulations? Improvement expected because of : better representation of topography and surface fields Δx=25 km Δx=2.2 km

13 Why doing cloud-resolving climate simulations? Improvement expected because of : explicit representation of moist convection Δx=2.2 km Δx=25 km

14 How to do it? COSMO / CCLM model with a mesh size of 0.02 o (2.2 km) Doubly nested model chain CCCLM 25 km CCLM 2.2 km ECMWF Conv. parameterized Conv. explicit

15 Outline 1. Why doing cloud-resolving climate simulations? 2. How to do it? 3. Do we need cloud-resolving resolution for climate applications? Desired properties I. Improved representation of mean climate characteristics: Can the cloud-resolving simulation beats its driving coarseresolution simulation with respect to observations? II. Improved representation of climate processes and feedbacks: Do the cloud-resolving and its driving coarse-resolution simulation differ in their representation of key climate feedbacks? 4. Conclusions

16 High resolution needed? I. Mean climate July 2006 Picture: Roland Gnädinger, Wil

17 High resolution needed? I. Mean climate July 2006 Radar Rain gauge mm/h Precipitation Switzerland Gauge, desag. Radar CCLM25 CCLM2 CCLM25 CCLM2 h Overall precipitation pattern reproduced Precipitation peaks and diurnal cycle better captured in CCLM2 mm (Hohenegger et al. 2008, Meteorol. Z)

18 High resolution needed? I. Mean climate JJA 2006 Precipitation Rain gauge ENSEMBLES Observations mm/h Switzerland Gauge, desag. CCLM25 CCLM2 CCLM25 CCLM25 CCLM2 Model h [mm/d] Precipitation diurnal cycle much better captured in CCLM2

19 High resolution needed? I. Mean climate DJF 1999 Picture: Roland Gnädinger, Wil Guttannen (Switzerland)

20 High resolution needed? I. Mean climate DJF 1999 Precipitation [mm/d] CRU ENSEMBLES Alpine climatology Observations CCLM25 CCLM2 Model

21 Outline 1. Why doing cloud-resolving climate simulations? 2. How to do it? 3. Do we need cloud-resolving resolution for climate applications? Desired properties I. Improved representation of mean climate characteristics: Can the cloud-resolving simulation beats its driving coarseresolution simulation with respect to observations? II. Improved representation of climate processes and feedbacks: Do the cloud-resolving and its driving coarse-resolution simulation differ in their representation of key climate feedbacks? 4. Conclusions

22 High resolution needed? II. Climate feedback Climate Soil moisture-precipitation feedback Important for: European summer climate variability: e.g., Schär et al. 2004, Rowell et al. 2006, Seneviratne et al. 2006, Vidale et al Extreme events: e.g., Beljaars et al. 1996, Trenbert and Guillemot 1996, Fischer et al. 2007a,b Seasonal forecasting: e.g., Douville and Chauvin 2000, Ferranti and Viterbo 2006 Numerical weather predictions: e.g., Trier et al. 2004, Sutton et al. 2006

23 High resolution needed? II. Climate feedback Previous studies Atmospheric moisture transport More Moist convection Precipitation Latent heat flux More energy pro PBL air Sensible heat flux PBL height More precipitation over wet soils

24 High resolution needed? II. Climate feedback Experimental design 25-km simulations 2.2-km simulations % WET25 CTL25-30% DRY % WET2 CTL2-30% DRY2

25 High resolution needed? II. Climate feedback Precipitation response CTL25 CTL2 WET25-DRY25 WET2-DRY2 More rain over wet soils Positive feedback More rain over dry soils Negative feedback (Hohenegger et al. 2009, J. Climate)

26 High resolution needed? II. Climate feedback Precipitation response WET25 CTL25 DRY25 WET2 CTL2 DRY2 More rain over wet soils Positive feedback More rain over dry soils Negative feedback (Hohenegger et al. 2009, J. Climate)

27 High resolution needed? II. Climate feedback How can we understand the precipitation response at 2.2-km? Precipitation WET2 WET2 DRY2 DRY2 Tephigram 12 UTC Cloud liquid water content Sensible and latent heat SH LH PBL Cloud liquid water content (Hohenegger et al. 2009, J. Climate)

28 High resolution needed? II. Climate feedback WET2 Cloud cover More clouds DRY2 More clouds Clouds trapped in the PBL over wet soils due to missing surface warming. No full development! (Hohenegger et al. 2009, J. Climate)

29 High resolution needed? II. Climate feedback WET2 Cloud cover WET25 Cloud cover [%] More clouds More clouds DRY2 DRY25 More clouds Clouds trapped in the PBL over wet soils due to missing surface warming. No full development! (Hohenegger et al. 2009, J. Climate)

30 High resolution needed? II. Climate feedback Implications Soil moisture evolution Soil moisture [mm] 3 Uppermost layers (0.1 m) WET25 CTL25 DRY25 3 Uppermost layers (0.1 m) WET2 CTL2 DRY2 All 7 prognostic layers (1.9 m) All 7 prognostic layers (1.9 m) Soil moisture [mm] Day in July Day in July

31 High resolution needed? II. Climate feedback Implications Soil moisture evolution All 7 prognostic layers (1.9 m) WET25 DRY25 All 7 prognostic layers (1.9 m) WET2 DRY2 Day in July Day in July Half-life time assuming an exponential decay of initial anomaly: WET : 3 months DRY : >5 months 2 months 2 months (Hohenegger et al. 2009, J. Climate)

32 High resolution needed? II. Climate feedback Implications Precipitation mm km 2.2-km DRY CTL WET Less precipitation variability and no drought at 2.2-km

33 High resolution needed? II. Climate feedback Implications Temperature Temperature [ o C] WET25 CTL25 DRY o C WET2 CTL2 DRY2 2.5 o C UTC UTC Reduced surface warming! (Hohenegger et al. 2009, J. Climate)

34 Conclusions Cloud-resolving (2.2 km) climate simulations Promising prospects for the use of cloud-resolving resolution for climate applications! For the considered months and seasons, the cloud-resolving simulation improved the simulated precipitation, esp. its diurnal cycle Explicit Parameterized The use of uncertain parameterizations may misrepresent some of the fundamental feedbacks in our climate system The simulation of different feedback sign has implication for seasonal forecasting and climate change studies

35 Main question Can we trust these clouds? Reality is more complex!