Using Satellite Simulators to Diagnose Cloud-Processes in CMIP5 Models

Transcription

1 Using Satellite Simulators to Diagnose Cloud-Processes in CMIP5 Models Stephen A. Klein Program for Climate Model Diagnosis and Intercomparison / LLNL Alejandro Bodas-Salcedo & Mark Webb United Kingdom s Met Office Sandrine Bony Laboratoire de Météorologie Dynamique/IPSL Additional Collaborators include Helene Chepfer (LMD), Jean-Louis DuFresne (LMD), John Haynes (CSU), Ben Hillman (UW), Viju John (UKMO), Jennifer Kay (NCAR), Roger Marchand (UW), Robert Pincus (U. Colorado), and Yuying Zhang (LLNL) LLNL-PRES December 9, 2011 American Geophysical Union Fall 2011 Meeting San Francisco, California

2 Stephen A. Klein, 9 December 2011, p. 2 Outline What is a satellite simulator and why is it needed to diagnose cloud processes in climate models? The Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package (COSP) Examples of science enabled by simulators Possibilities for COSP in CMIP5 Summary and future plans

3 Stephen A. Klein, 9 December 2011, p. 3 Why satellite simulators for clouds? Diagnosing cloud processes in climate models with observations is difficult and fraught with issues Correspondence of model quantities to available observations Limitations of satellite observations Scale of model grids (~100 km) vs. satellite pixels (~1 km) Simulators reduce the effects of these issues in order that comparisons between models and observations more likely are an evaluation of the model The simulator is a piece of diagnostic code that mimics the observational process by converting model variables into pseudo-satellite observations What would a satellite see if the atmosphere had the clouds of a climate model?

4 Stephen A. Klein, 9 December 2011, p. 4 Simulator conceptual diagram Climate Model Model Fields Agree? Retrieved Fields Retrieval Algorithm Satellite Instrument Observed Radiances

5 Simulator conceptual diagram Model Fields Simulated Fields Agree? Retrieved Fields Climate Model Satellite Simulator Downscaler & Forward Model Retrieval Algorithm Retrieval Algorithm Satellite Instrument Simulated Radiances Agree? Observed Radiances A simulator addresses issues of Cloud overlap (column-integrated τ and cloud-top pressure p ct of the high cloud in the column) Detection thresholds (τ >= 0.3, dbz > -25, SR > 5) Retrieval characteristics (different ways to calculate p ct ) Stephen A. Klein, 9 December 2011, p. 5

6 Stephen A. Klein, 9 December 2011, p. 6 CFMIP Observation Simulator Package COSP Flow Chart About 5 years ago, the CFMIP community came together to form a community software package of simulators COSP has simulators for 5 satellite cloud products ISCCP, MISR, MODIS, CloudSat, Calipso All major climate models use COSP Also used in global models with very-high resolution (~10 km) Most have put the code in-line to their model Bodas-Salcedo et al. (2011) A matching set of observations for each simulator has been specially prepared in ESG compatible format

7 Examples of science enabled by COSP Stephen A. Klein, 9 December 2011, p. 7

8 Comparisons to multiple satellites Total Cloud Cover Observations CAM4 with simulators CAM5 with simulators What model strengths and weaknesses are robust A comparison is meaningful only when total cloud cover is well-defined Stephen A. Klein, 9 December 2011, p. 8 Kay et al. (2011)

9 Fairer comparisons between models MISR cloud-top height τ histograms (40N-60N, 160E-125W) HADGem LMDZ In the past, it was difficult to compare models to each other due to their differing ways of parameterizing clouds MMF MISR Observations A simulator greatly facilitates model intercomparison! Stephen A. Klein, 9 December 2011, p. 9 Bodas-Salcedo et al. (2011) Credible model metrics may be computed (Williams and Webb 2009)

10 Common model biases Models overestimate cloud optical thickness Models underestimate total cloud cover Models underestimate middle level cloud cover Thin Medium Thick But there are signs of improvement High High Medium Low Observations Models Stephen A. Klein, 9 December 2011, p. 10 Zhang et al. (2005) Kay et al. (2011)

11 Stephen A. Klein, 9 December 2011, p. 11 Diagnosing cloud feedbacks Greenhouse gas-induced climate warming leads to: Increased optical thickness Increased high cloud altitude Decreased low and middle level cloud cover Coupling ISCCP simulator output with a radiative transfer code one can diagnose the contributions of different cloud types to cloud feedback Cloud Feedback (Wm -2 K -1 ) Ringer et al. (2006) Zelinka et al. (2011)

12 Stephen A. Klein, 9 December 2011, p. 12 Possibilities for COSP in CMIP5 In CMIP3 (CFMIP1), only ISCCP simulator output was available from the control and 2 x CO 2 equilibrium simulations of slab-ocean models For CMIP5 (CFMIP2), a much more extensive set of simulator output will be available ISCCP, Calipso and CloudSat simulators requested (MODIS, MISR informally) Multiple decades of simulator output requested from many experiments: picontrol, historical, amip, aqua, abrupt4xco2, 1pctCO2, aerosol, amip4k, amip4co2 Expected contributions from 8-10 models for each experiment Coupled with increased diagnostic output (high timefrequency, physical tendencies output, etc.), we will have an unprecedented opportunity to perform inter-model diagnoses of cloud processes in climate models!

13 Stephen A. Klein, 9 December 2011, p. 13 Summary and future plans The adoption of satellite simulators for clouds by the climate modeling community has greatly facilitated improved diagnosis of cloud processes in climate models better and wider use of satellite data by climate modelers credible inter-model comparisons of cloud properties and feedbacks What COSP improvements are being planned? diagnosis of aerosols and precipitation better sub-grid distributions and increased computational efficiency use by CRM/LES models You have opportunities to contribute using simulator output in your CMIP5 science studies contributing new diagnostics or simulator capabilities to COSP

14 Stephen A. Klein, 9 December 2011, p. 14 Thank you! Questions?

15 Stephen A. Klein, 9 December 2011, p. 15 Extra slides

16 COSP output variable list Stephen A. Klein, 9 December 2011, p. 16

17 Stephen A. Klein, 9 December 2011, p. 17 Simulator example (ISCCP) grid-box mean profiles sub-column generator sub-column cloud distribution p ct -τ histogram retrieved p ct calculate T b and τ retrieve p ct compute summary statistics retrieved τ

18 Stephen A. Klein, 9 December 2011, p. 18 Simulator example (Calipso) grid-box mean profiles lidar cloud fraction (SR>5) sub-column generator lidar scattering ratio sub-column cloud distribution calculate β e with a lidar forward model compute summary statistics retrieved τ

19 Simulators can mimic some of the differences between satellites p ct τ histograms (40S-60S, W) HADGem w/ ISCCP simulator ISCCP Observations Stephen A. Klein, 9 December 2011, p. 19 HADGem w/ MODIS simulator MODIS Observations Bodas-Salcedo et al. (2011) If models produce high thin over thick low clouds, the ISCCP simulator will produce a middle level cloud and MODIS simulator will produce a high-topped cloud Differences between ISCCP and MISR are an estimate of multilayer cloud (Marchand et al. 2010)