1. Evidence in ISCCP for regional patterns of cloud response to climate change Joel Norris Scripps Institution of Oceanography ISCCP at 30 Workshop City College of New York April 24, 2013

2. Collaborators Amato Evan (SIO) Bob Allen (UC Riverside) Steve Klein (LLNL) Mark Zelinka (LLNL) Chris O’Dell (Colorado State U.)

3. 30 Years of ISCCP Not originally intended for long-term climate monitoring But has provided first observational signal of cloud response to climate change

4. Satellite Cloud Record Local Correlation with Global Series Obvious artifacts associated with satellite view angles far from nadir and view areas of geostationary satellites Global Total Cloud Time Series Local Trends (%-Amount per Decade)

5. Nature of Satellite View Angle Artifact Systematic changes in satellite view angle occur over time (Evan et al. 2007) Longer path length for large  sat enables easier cloud detection Path length varies according to 1 / cos(  sat )  sat

6. Removing the Satellite View Angle Artifact No universally applicable physical theory for how cloud retrievals vary with satellite view angle  apply empirical procedure Do for each grid box Remove seasonal cycle and diurnal cycle to get anomalies Assume linear relationship between cloud C and  = cos(  sat ) C(x,t) = A(x)  (x,t) + R(x,t) Calculate A via linear regression Obtain residuals R from best-fit line R = C − A  corrected cloud anomalies (R) do not vary with satellite view angle

7. Before Satellite View Angle Artifact Removal Local Correlation with Global Series Global Total Cloud Time Series Local Trends (%-Amount per Decade)

8. After Satellite View Angle Artifact Removal Local Correlation with Global Series No more artifacts associated with satellite view angles far from nadir! Still some artifacts associated with satellite view area Global Total Cloud Time Series Local Trends (%-Amount per Decade)

9. Nature of Satellite Area Artifact Unidentified calibration/retrieval problem produces similar relative changes at every location viewed by a satellite from Norris and Wild (2007) Cloud property anomalies tend to be spatially correlated within each satellite view area ISCCP synop

10. Removing the Satellite View Area Artifact Unidentified problem affects cloud retrievals over entire area viewed by satellite  apply empirical procedure Do for each grid box in satellite view area Remove seasonal cycle and diurnal cycle to get anomalies Divide by standard deviation to get relative anomalies Assume spatially averaged relative anomaly C is due to artifact spatial anomaly time series

11. Removing the Satellite View Area Artifact Do for each grid box in satellite view area Remove seasonal cycle and diurnal cycle to get cloud anomalies C Assume artifact from calibration errors is linearly proportional to spatially averaged relative anomaly C C(x,t) = A(x) C(t) + R(x,t) Calculate A via linear regression Obtain residuals R from best-fit line R = C − A C corrected cloud anomalies (R) do not vary with spatial average over satellite view area

12. Before Satellite View Area Artifact Removal Synop Europe ISCCP Europe

13. After Satellite View Area Artifact Removal Synop Europe ISCCP Europe

14. Before Satellite View Area Artifact Removal Local Correlation with Global Series Global Total Cloud Time Series Local Trends (%-Amount per Decade)

15. After Satellite View Area Artifact Removal Local Correlation with Global Series Local Trends (%-Amount per Decade) Remaining artifacts appear negligible El Nino global correlation pattern Global Total Cloud Time Series

16. Before Artifact Removal Local Correlation with Global Series Global Total Cloud Time Series Local Trends (%-Amount per Decade)

17. After Artifact Removal Local Correlation with Global Series Local Trends (%-Amount per Decade) Global Total Cloud Time Series

18. Use of Statistically Corrected Cloud Data Empirical subtraction of variability common to entire satellite view area removes apparent artifacts Also removes any real variability common to entire satellite view area Cannot determine if global mean cloudiness has changed over ISCCP record! But can investigate regional patterns of cloud change relative to global mean cloud change

19. Robust Model Projections Simulations of 20 th century and 2xCO 2 -1xCO 2 differences show… Decrease in cloud amount at 25-45° latitude due to poleward shift of storm track cloudiness Increase in cloud optical thickness poleward of 45° latitude due to greater liquid water path/smaller particle size Increase in top height of highest clouds due to rise in level of zero radiative divergence/tropopause Global mean subtracted from model output for apples-to-apples comparison with ISCCP

20. CMIP3 2xCO 2 -1xCO 2 difference from 11 models CMIP3 20C1974-1999 trend from 19 models and 61 runs ISCCP and PATMOS 1983-2008 trends, 2003-10 CERES – 1985-89 ERBS Tropical Expansion and Storm Track Shift circles show 95% significance bars show 25-75% model trends

21. CMIP3 2xCO 2 -1xCO 2 difference from 11 models with “ISCCP simulator” ISCCP Jul 1983 – Jun 2008 trends Enhanced High Latitude Optical Thickness bars show 25-75% range of model changes circles show 95% significance

22. CMIP3 2xCO 2 -1xCO 2 difference from 11 models with “ISCCP simulator” CMIP3 “20 th Century simulations” from 8 models with 51 runs ISCCP and PATMOS-x Jul 1983 – Jun 2008 trends Rise of High-Level Cloud Top dots show 8 out of 11 models agree dots show 95% signif.

23. Summary Apparent artifacts exist in ISCCP beyond satellite view angle Empirical methods remove artifacts at the cost of removing real large-scale variability Regional cloud change relative to large-scale mean change remains ISCCP exhibits pattern of … poleward shift of storm track cloud higher-latitude cloud optical thickness increase increased height of highest cloud tops Agreement with other observational datasets and model projections

24. Thank You!