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Reducing uncertainty in the prediction of global warming some pesky cloud obstacles

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Reducing uncertainty in the prediction of global warming some pesky cloud obstacles

Brian Mapes

doubting reductionist

University of Miami

- Radiation:
- Robin Hogan, ECMWF Ann. Seminar Sep 2008
- available on web: presentation and writeup

- Robin Hogan, ECMWF Ann. Seminar Sep 2008
- Consults with live-in radiation guru P. Zuidema

- Largely from reading list
- J. Clim. reviews by Stephens 2005 and Bony 2006

- Email correspondences and conversations
- Bruce Weilicki, NASA Langley (ERB matters)
- Larry DiGiralmo, Illinois (some scale issues)
- Brian Soden, Miami

- Preamble: clouds as a climate feedback
- A step backward: stating the problem flatly
- Integrals: triumphs of atm. RT physics
- Now about cloudiness (x,y,z,t)...
- Statistical descriptions from observations
- Formulating GCMs to be relatable to above
- Tuning
- compensating errors (better than some other kind!)
- any help for sensitivity?

- in models: so what?
- analogues in observations?
- via conceptualizations
... a lot is being learned even as uncertainty fails to shrink

from Stephens review - critique

- The system comprises a whole lot of things
- Global mean Ts well defined but how meaningful?
- What is the phys/phil status of a math. average?
- e.g. can be acausal (instant across space, nonlocal in time, etc.)

- What is the phys/phil status of a math. average?
- Relevant for interpreting what parts of DQ are “F(DTs)”

- ‘base’ negative feedback: ~ -3.2 W m-2 per K
- Largely Planck feedback
- -3.8 = d/dT(sT4) at global Teff = 255K

- Largely Planck feedback
- Sensitivity a 1/(Sfeedbacks -3.2)
- 0 unstable climate (infinite sensitivity)

Runaway warming!!

3.2

Bony et al. 2006

cloud changes cause

warmer world to

emit less

brighter in warmer world darker

SW and LW cloud feedback

Net cloud feedback

from 1%/ yr CMIP3/AR4

simulations

courtesy of I Held

who credits B. Soden

multimodel

net cloud feedback

Soden Held...

2008

where

d cloud causes

less emission,

darker albedo, or both

- IPCC ch 10
- The mid-level mid-latitude decreases are very consistent, amounting to as much as one-ﬁfth of the average cloud fraction simulated for 1980 to 1999.
- Much of the low and middle latitudes experience a decrease in cloud cover, simulated with some consistency. There are a few low-latitude regions of increase, as well as substantial increases at high latitudes.

- Preamble: clouds as a climate feedback
- A step backward: stating the problem flatly
- Triumphs of atm. RT column physics
- Now about cloudiness (x,y,z,t)...
- Statistical descriptions from observations
- Formulating GCMs to be constrainable from above
- Tuning = right mean answer for nonright reasons

- Prospects for understanding cloud changes
- in ensembles of runs of ensembles of GCMs
- via conceptualizations

- a lot is being learned as uncertainty fails to shrink

- global warming =
- [TOArad] =
∫∫dfdl ∫dn ∫dz ∫∫dW [R]

- complete integral ===~ 0 over long integration times
- and presumably in preindustrial Holocene
- must be maintained by overall negative feedback
- Planck still king

- must be maintained by overall negative feedback

- and presumably in preindustrial Holocene
- Cleanly separable into compensating LW and SW halves, each 235 Wm-2 in global mean
- equal and opposite
- depend on planetary albedo and Temis
- quiz: which was/is easier to guess/ measure?

- Reductionist extreme:
- Model the integrand R explicitly and precisely
- From fundamental physics
- right values, for right reasons
- (so sensitivity to perturbations is right too)

- right values, for right reasons

- From fundamental physics

- Model the integrand R explicitly and precisely

- Go measure the answer
- or more importantly for GW, changes of the integral for a known perturbation of the integrand(forcing)

- Model the integrand, but by broad based estimation
- physics, but also empiricism wherever can
- bracket uncertainties
- final accuracy depends on chain of judiciousness
- “uncertainty” is both physical and social

Maxwell

eqs.

z up to TOA

(overlap)

seasons,

ENSO...

small-scale

structure

geospace

(lat, lon)

particle

ensemble

angle

integral

wavelength

integral

micro meso macro

subgrid schemes

GCM grid sums

atmospheric radiation physics

“macro-” and “micro-” (physics, economics, etc.)

- both intellectually on firm ground, if hard to reconcile

- Basic units obey locallaws of interaction
- physics: “air parcel” jostlings, thermo
- humanities: human nature, drives, responses to stimuli

- physics: “air parcel” jostlings, thermo

- Whole system constrained by integrallaws of conservation
- physics: conservation of mass, energy, momentum
- humanity: demographics (fertility, nutrition, etc.).

- physics: conservation of mass, energy, momentum

- Only statistics... are they laws, or just descriptions?

- Preamble: clouds as a climate feedback
- A step backward: stating the problem flatly
- Triumphs of atm. RT column physics
- Now about cloudiness (x,y,z,t)...
- Statistical descriptions from observations
- Formulating GCMs to be constrainable from above
- Tuning = right mean answer for nonright reasons

- Prospects for understanding cloud changes
- in ensembles of runs of ensembles of GCMs
- via conceptualizations

- a lot is being learned as uncertainty fails to shrink

- Maxwell’s equations
- (from Robin Hogan, Reading U, ECMWF seminar 2008)

http://www.met.rdg.ac.uk/clouds/maxwell/

total “CRF”

Maxwell E,B for .

ensemble

Key bulk variable: Extinction b (units: m-1)

Robin Hogan ECMWF Seminar 2008

Maxwell E,B for .

ensemble

- Bulk variable: Extinction b (units: m-1)
- Shortwave: ~all scattering, ~no absorption
- proportional to cross section (condensate volume/re)
- re is “effective radius” (3rd moment/2nd moment of DSD)

- proportional to cross section (condensate volume/re)
- Longwave: mostly absorption (& emission)
- proportional to condensate volume (mass)
- no re (droplet size) dependence!
- typically ~2 times greater than SW scattering extinction

- proportional to condensate volume (mass)

- Shortwave: ~all scattering, ~no absorption

Maxwell

eqs.

particle ensemble

angle

integral

Robin Hogan ECMWF Seminar 2008

Maxwell

eqs.

particle ensemble

angle

integral

wavelength

integral

- Complicated for gases but
- Yields to precision laboratory (controlled) empiricism
- leveraged with physics

- Captured/ simplified in clever bundling
- ‘bands’ of abs. coeff. k

- Tuned up with final broadband empirical calibrations

- Yields to precision laboratory (controlled) empiricism
- clouds mercifully gray

Maxwell

eqs.

particle ensemble

angle

integral

Robin Hogan ECMWF Seminar 2008

- The vastness of our integral can be useful
- don’t need the integrand accurate and complete
- merely need a sufficiently large and unbiased sample, of an unbiased estimator of it!
- Example: McICA radiation
- Independent Column Approximation (ICA)
- Monte Carlo (Mc) treatment of wavelength integral

- ICA:
- Neglect hor. photon flux Fhor (3D effects)
- Wrong alm. ev. in inhomogeneous clouds
- But unbiased since

- MC:
- Send different wavelength bands through each subgrid cloud overlap realization
- Unbiased, and large-enough subsample of vast 2D space
- (even for weather forecasts)

Maxwell

eqs.

z up to TOA

(overlaps)

seasons

ENSO...

small-meso

structure

geo-space

(lat, lon)

particle ensemble

angle

integral

wavelength

integral

Nice solid rules and tools!

(who remembers “anomalous absorption” ?)

∫∫∫∫∫∫∫

R(longlived GHGs,

T, qv, baerosol,

qcond, phase, re)

Now for the problem of space-time integration...

(x,y,z,t)

(x,y,z,t)

(x,y,z,t)

- Preamble: clouds as a climate feedback
- A step backward: stating the problem flatly
- Triumphs of atm. RT column physics
- Now about cloudiness (x,y,z,t)...
- Statistical descriptions from observations
- Formulating GCMs to be constrainable from above
- Tuning = right mean answer for nonright reasons

- Prospects for understanding cloud changes
- in ensembles of runs of ensembles of GCMs
- via conceptualizations

- a lot is learned even as uncertainty fails to shrink

the ISCCP 2D space for characterizing cloudy columns

- Kubar et al. (2007)

- from a cluster analysis (aka self-organizing maps) of daily 5 degree joint histograms in the ISCCP 2-space

- Preamble: clouds as a climate feedback
- A step backward: stating the problem flatly
- Triumphs of atm. RT column physics
- Now about cloudiness (x,y,z,t)...
- Statistical descriptions from observations
- Formulating GCMs

- Prospects for understanding cloud changes
- in ensembles of runs of ensembles of GCMs
- via conceptualizations

- a lot is being learned even as uncertainty fails to shrink

- The vastness of our integral can be a pain too
- We only have laws to predict cloudiness in 3D
- where air saturates, fundamentally
- where air almost-saturates, for scale-truncated fluid dynamics/ thermodynamics
- implying cloud in unresolved smaller-scale fluctuations

- GCMs are stuck integrating partly-cloudy radiation over z

- only a few % of the water is condensed

0.3

60

mm = kg/m2

- The radiative impact of a local volume of cloudiness is highly nonlinear
- so it matters what’s above/below

LW

SW

opacity

t a condensate path/re

courtesy Robin Hogan

Magic number

courtesy Robin Hogan

courtesy Robin Hogan

courtesy Robin Hogan

courtesy Robin Hogan

courtesy Robin Hogan

- (cloud fraction at each model level, condensed water at each model level)

courtesy Robin Hogan

- Preamble: clouds as a climate feedback
- A step backward: stating the problem flatly
- Triumphs of atm. RT column physics
- Now about cloudiness (x,y,z,t)...
- Statistical descriptions from observations
- Formulating GCMs
- tuning

- Prospects for understanding cloud changes
- in ensembles of runs of ensembles of GCMs
- in observations
- via conceptualizations

- a lot is being learned even as uncertainty fails to shrink

- GCM cloudy radiation is tuned
- by several or 10s of Watts, I think
- to have net flux =0 for preindustrial control climate
- in each latitude belt
- to have right SW and LW individually?
(somebody correct me if wrong?)

Does this constrain sensitivity? no such luck

Global warming is driven by the imbalance [∫∫∫∫∫∫swR- ∫∫∫∫∫∫LwR] <1 Wm-2out of 235.

Hansen et al. 2004

Science Express

- Preamble: clouds as a climate feedback
- A step backward: stating the problem flatly
- Triumphs of atm. RT column physics
- Now about cloudiness (x,y,z,t)...
- Statistical descriptions from observations
- Formulating GCMs to be constrainable from above
- Tuning

- Prospects for understanding cloud changes
- in models
- in obs
- via conceptualizations

- a lot is being learned even as uncertainty fails to shrink

- Large-scale cloud coverage?
- say low cloud incr. due to static stability increase?
- Miller 1997 negative feedback
- but see Wood-Bretherton 2006 EIS recasting

- Miller 1997 negative feedback

- say low cloud incr. due to static stability increase?

- Small-scale cloud fraction at a given altitude?
- if vigor/ variance of w fluctuations changed?
- in a globally systematic way
- say via increased static stability

- in a globally systematic way

- if vigor/ variance of w fluctuations changed?

- Large-scale cloud coverage
- say low cloud incr. due to static stability increase
- Miller 1997 negative feedback
- but see Wood-Bretherton 2006 EIS recasting

- Miller 1997 negative feedback

- say low cloud incr. due to static stability increase
- or say high cloud changes (anvil T or thickness)

- if vigor/ variance of w fluctuations changed?
- in a globally systematic way
- say via increased static stability

- in a globally systematic way

- aerosol indirect effects – inadvertent or engineered!

- say by shear
...

- Dreaming up individual “possible effects” is not terribly fruitful
- Local effects tend to fade in the global average
- systematic or random cancellation?

- There is a hunger for whole-system results

- Global mean well defined but how meaningful?
- What is the phys/phil status of a math. average?

- example

w500

long time means

in GCMs

high sens models

low sens models

Bony and Dufresne 2006

- field impressions: connected to midlatitude influences more than tropical?

- The hard part of GW is the area average, not the time scale
- Patchy decadal variability seems to me no better than patchy interannual variability (or seasonal, or...)
- all infinitely slow relative to cloud time scales
- connected by circulation to compensating (or at least complicating) changes elsewhere

- NICAM – a 7km mesh globally
- and 50+ vertical levels

- Verrrrry expensive
- Climate sensitivity estimated by SST+2 run
- Result:

- Cloud changes are a positive feedback in GCMs
- LW: reduced emission in all (via high clouds?)
- like in NICAM 7km mesh model but weaker?

- SW: variable among models
- low clouds in subsiding regions are key (Bony and Dufresne)

- LW: reduced emission in all (via high clouds?)
- Prospects for fundamental GCM accuracy seem dubious
- RT physics is good, but 3D cloudiness and overlap seems a quagmire
- still lots of effort is being expended!

- RT physics is good, but 3D cloudiness and overlap seems a quagmire

- slicing and dicing GCMs is actually informative
- if not “reducing uncertainty” exactly
- especially outside model-attuned science community!

- if not “reducing uncertainty” exactly
- conceptualization is still important and not cemented yet
- an interesting time in any science
- if it turns out to be a science

- an interesting time in any science