Clouds and their radiative impact as examples of histogram (binning) methods

1. Cloudsand their radiative impactas examples of histogram (binning) methods Brian Mapes

2. Global warming projectionsin terms of T

3. Remember this from class 5? • Climate heat budget over ocean + atm • ∫ (ρCp dT/dt ) dV = ∫ (Frad_TOA) dA     (+small) • Units: Watts • pert: = ∫ (-OLR’) dA + ∫ (ASR’) dA • outgoing longwave and absorbed solar Integrate over time (indefinite integral): • ∫ {∫ (ρCp dT/dt ) dV} dt: • units Joules • or YottaJoules (10^22 = Yotta I think) • Global warming due to increasing ASR (pdf)

4. Issue 1: integrating over area • dA = (df) (cosf dl) in ∫ (Frad_TOA) dA • weight by cos(f) when summing over lon bins • OR: dA = (dsinf) (dl) • Rebin latitude to equally spaced sinf bins • Then ou can just sum them up! • Related to map projection issue (equal area) but that’s just for “eyeball” integrals

5. Equal area map projections

6. Radiative imbalance • IPCC model ensemble (CMIP3) Cumulative longwave trapping by increasing GHGs (clear sky = broken lines) Effect is reduced somewhat by clouds (total sky = solid/shaded) Trenberth and Fasullo 2009 GRL

7. “Global warming due to increasing absorbed solar radiation” • All-sky mean longwave trapping quits by 2030 as skies clear (‘iris’ effect of clouds?) 2030 All sky Trenberth and Fasullo 2009 GRL

8. Global warming due to increasing absorbed solar radiation • From 2030, models warm largely by reduced albedo (clearing skies/ cloud reductions?) All sky 2030 Trenberth and Fasullo 2009 GRL

9. Cloud cover reductions – where? Non equal area Yellow overemphasized in perception? see colorbrewer.org

10. Cloud radiative forcing • “Stuff” (an additive scalar quantity): • B&W best! • Color is ambiguous among viewers • Wm-2 units • Area integration (or averaging) is what it’s all about • Can be distributed over “bins” • area bins matter (use sin(lat)) • but another dimension (like z) is free

11. 2007 Cloud Radiative Effect CRE (aka CRF) from CloudSat FLXHR product 19 LW global mean-55 SW (Wm-2) -55Wm-2 19Wm-2 Ztop (km) Caution: Simple average of 0130 and 1330 local time samples, not true diurnal mean estimate!

12. Distributions: each ink molecule corresponds to an equal amount of the Stuff (CRF) Ztop (km) -55 Wm-2 total total 19 Wm-2 LW 19 -55 SW LW

13. Decomposing CRE into cloud types Lowest possible base, high top: precipitating echo objects

14. “Storms” vs. “layer clouds” Echo Base < 3km AND Top > 3km AND Wider than 17km = storms All else: layer clouds

15. Decomposing the 19 and -55 -25W 8W -30W 11W >50% in “storms”

16. Latitude distributions Layers Have CRE impact everywhere Storms Impact at high latitudes (and equator) -25W 8W 43oS -30W 11W 53oN 53oS

17. SW CRE: Storms + sunshine -16W out of -30W SW are poleward of latitude 40 N/S Mostly in local summer Storms -30W SW CRE 53oN G. Alaska, Kamchatka 40 -14W in 40S-40N 40 53 Cape Horn 56S Day of year 2007

18. Half of tropical ‘storm’ area coverage is in echo objects >200km wide Half of midlat. ‘storm’ area coverage is in echo objects >500 km wide

19. SW CRE by latitude and size

20. Summary • Current clouds (cloudsat echo objects) have a shortwave effect of -55 Wm-2 and longwave effect of +19 Wm-2 according the (imperfect! 2xdaily) Cloudsat FLX-HR data set. • These total impacts can be distributed over latitude, cloud object size, season etc. • gray scale: total impact a amount-of-ink-on-page.

21. Other ways of binning area on the globe • methods used also in

22. Bony et al. method • summarized in wyant et al. these must be sin(lat) columns in order for simple sum (2) to be a true area x time average

23. Bony et al. method contd • 30N-30S

24. Omega500 maps

25. collapse into a 1D PDF

26. scatterplots, and bin averagesof CRF

27. Stretch bins so that dx on the page represents dA (area on globe) • T-Tbar: • RH:

28. cloud water changes when SST warms: • good use of color • p is the proper vertical coord (mass) • so the vertical sum is meaningful:

29. Decomposing changes into shift of bins vs. changes in bin means