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

Reducing uncertainty in the Reducing uncertainty in the prediction of global warming prediction of global warming

some pesky cloud obstaclessome pesky cloud obstacles

Brian MapesBrian Mapesdoubting reductionistdoubting reductionistUniversity of MiamiUniversity of Miami

Sources• Radiation:

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

– Consults with live-in radiation guru P. Zuidema• Cloud feedbacks:

– 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

Outline• 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?

• Prospects for understanding cloud changes– in models: so what? – analogues in observations? – via conceptualizations

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

Univariate conceptual model

from Stephens review - critique

• The system 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.)

• Relevant for interpreting what parts of Q are “F(Ts)”

Feedbacks and sensitivity

• ‘base’ negative feedback: ~ -3.2 W m-2 per K– Largely Planck feedback

• -3.8 = d/dT(T4) at global Teff = 255K

• Sensitivity 1/(feedbacks -3.2)

• 0 unstable climate (infinite sensitivity)

Bony et al. 2006

3.2

Runaway warming!!

Why are ~all GCM cloud feedbacks positive?

courtesy of I Heldwho credits B. Soden

Net Net cloud feedbackfrom 1%/ yr CMIP3/AR4simulations

SW

and

LW

clo

ud

feed

back

brighter

in

warm

er world

dark

er

changes in cloudiness

• 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.

multimodelnet cloud feedback

Soden Held...2008

wherecloud causesless emission,

darker albedo, or both

Outline

• 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

Radiation budget: a vast integral

• global warming =

• [TOArad] =

∫∫dd ∫d ∫dz ∫∫dR

knowns

• complete integral ===~ 0 over long integration times– and presumably in preindustrial Holocene

• must be maintained by overall negative feedback– Planck still king

• 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?

Blankly: how do we compute an integral?

Steps in integrating

micro meso macro

Maxwelleqs.

particleensemble

angleintegral

wavelengthintegral

small-scalestructure

geospace(lat, lon)

seasons,ENSO...

z up to TOA(overlap)

atmospheric radiation physics GCM grid sumssubgrid schemes

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

– both intellectually on firm ground, if hard to reconcile

• Micro: – Basic units obey local laws of interaction

• physics: “air parcel” jostlings, thermo– humanities: human nature, drives, responses to stimuli

• Macro: – Whole system constrained by integral laws of conservation

• physics: conservation of mass, energy, momentum– humanity: demographics (fertility, nutrition, etc.).

• Meso: in between: vast, important, but mushy– Only statistics... are they laws, or just descriptions?

Yechh – take me back to physics




• a lot is being learned as uncertainty fails to shrink

Elementary and rigorous

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

• Now just integrate all energy over all matter!

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

total “CRF”

From particles to continuumMaxwell E,B for .

ensemble

Robin Hogan ECMWF Seminar 2008Key bulk variable: Extinction (units: m-1)

From particles to continuum

• Bulk variable: Extinction (units: m-1)– Shortwave: ~all scattering, ~no absorption

• proportional to cross section (condensate volume(condensate volume/r/ree))– re is “effective radius” (3rd moment/2nd moment of DSD)

– Longwave: mostly absorption (& emission)• proportional to condensate volume (mass)condensate volume (mass)

– no rno ree (droplet size) dependence! (droplet size) dependence!– typically ~2 times greater than SW scattering extinction

Maxwell E,B for .

ensemble

Angle integralMaxwelleqs.

particle ensemble

angleintegral

Robin Hogan ECMWF Seminar 2008

Wavelength integral can be done

• 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

• clouds mercifully gray

Maxwelleqs.

particle ensemble

angleintegral

wavelengthintegral

“Unreasonable” assumptionsMaxwell

eqs.particle

ensembleangle

integral

Robin Hogan ECMWF Seminar 2008

Unbiased vs. accurate

• The vastness 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

Locally wrong, but unbiased• ICA:

– Neglect hor. photon flux Fhor (3D effects)– Wrong alm. ev. in inhomogeneous clouds– But unbiased 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)

Hooray for atmospheric physics!

∫∫∫∫∫∫∫ R (longlived GHGs,

T, qv, aerosol,

qcond, phase, re)

Maxwelleqs.

particle ensemble

angleintegral

wavelengthintegral

small-mesostructure

geo-space(lat, lon)

seasonsENSO...

z up to TOA(overlaps)

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

(x,y,z,t)(x,y,z,t)

(x,y,z,t)(x,y,z,t)

(x,y,z,t)(x,y,z,t)

Nice solid rules and tools!Nice solid rules and tools!(who remembers “anomalous absorption” ?)(who remembers “anomalous absorption” ?)

Outline




• a lot is learned even as uncertainty fails to shrink

THE PROBLEM OF SCALES THE PROBLEM OF SCALES in practice, (x,y,t) is really in practice, (x,y,t) is really

(x,y,t,(x,y,t,scalesscales))

Almost without limit...

but remember the independent

column approximation!

For a cloudy column, 2 things matter2 things matter: Emission temperature, and albedo

the ISCCP 2D space for

characterizing cloudy columns

net CRF in that space

• Kubar et al. (2007)

Project any cloud population (joint histogram in this space), and sum to get total CRF...presto!

Can do for any set of cloudy pixels – like these ‘cloud population regimes’

• from a cluster

analysis (aka self-

organizing maps) of

daily 5 degree joint histograms in the ISCCP

2-space

Outline


– Statistical descriptions from observations– Formulating GCMs


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

Stuck with 3D models

• The vastness vastness of our integral can be a pain pain too

• We only have laws to predict cloudiness in We only have laws to predict cloudiness in 3D3D– where air saturates, fundamentallywhere air saturates, fundamentally– where air almost-saturates, for scale-truncated where air almost-saturates, for scale-truncated

fluid dynamics/ thermodynamicsfluid dynamics/ thermodynamics• implying cloud in unresolved smaller-scale fluctuationsimplying cloud in unresolved smaller-scale fluctuations

• GCMs are stuck integrating partly-cloudy GCMs are stuck integrating partly-cloudy radiation over zradiation over z

First off, cloud water is a precision nightmare

• only a few % of the water is condensed

0.3 60mm = kg/m2

Problems with sums and integrals • The radiative impact of a local volume of cloudiness

is highly nonlinear• so it matters what’s above/below

condensate path/re

LW

SWopacity

courtesy Robin Hogan


Magic number





Yet all these effects are secondary!

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


Outline


– Statistical descriptions from observations– Formulating GCMs – tuning

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


Tuning

• 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

Back to the integral -- tolerances

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

Hansen et al. 2004Science Express

Outline


– Statistical descriptions from observations– Formulating GCMs to be constrainable from above– Tuning

• Prospects for understanding cloud changes– in models– in obs– via conceptualizations


What could change systematically with climate?

• Large-scale cloud coverage?– say low cloud incr. due to static stability increase?

– Miller 1997 negative feedback» but see Wood-Bretherton 2006 EIS recasting

What could change with climate?

• 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

What could change with climate?• Large-scale cloud coverage

– say low cloud incr. due to static stability increase– Miller 1997 negative feedback

» but see Wood-Bretherton 2006 EIS recasting– or say high cloud changes (anvil T or thickness)

• 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

• Effective radius– aerosol indirect effects – inadvertent or engineered!

• Overlap issues– say by shear...

All connected

• 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

Careful with “hunger”

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

Conceptual connection/ closure

• example

Begat quantitative work (n-box model)(Bony and Dufresne, Wyant et al.)

500

long time

means

SW effects of low clouds under subsidence

are the key to climate sensitivity uncertainty

Bony and Dufresne 2006

in GCMs

high sens models

low sens models

But is stratocu subsidence

really so linked to the

ascending tropics?

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

Need whole-planet understandings

(General Circulation), not

just slice cartoons

very recent obs constraint attempt

decadal, in a box off california

tied to subsidence and stability

consistent satellite & surface obs

analogue to global warming?

• 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

One special model

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

• Verrrrry expensive

• Climate sensitivity estimated by SST+2 run

• Result:

positive cloud feedback... but dominated by LW effects of high-thin cloud incr., unlike GCMs!

Summary• 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)

• 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!

• Prospects for understanding are better– slicing and dicing GCMs is actually informative

• if not “reducing uncertainty” exactly– especially outside model-attuned science community!

– conceptualization is still important and not cemented yet• an interesting time in any science

– if it turns out to be a science

• Reducing Uncertainty in GW as a Big Problem: I sure hope the paleo/ holistic constraints are stronger!

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