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Reducing uncertainty in the prediction of global warming some pesky cloud obstacles. Brian Mapes doubting reductionist University of Miami. Sources. Radiation: Robin Hogan, ECMWF Ann. Seminar Sep 2008 available on web: presentation and writeup

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

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

Brian Mapes

doubting reductionist

University of Miami


Sources
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
    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
    Univariate conceptual model

    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.)

    • Relevant for interpreting what parts of DQ are “F(DTs)”


    Feedbacks and sensitivity
    Feedbacks and sensitivity

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

      • Largely Planck feedback

        • -3.8 = d/dT(sT4) at global Teff = 255K

    • Sensitivity a 1/(Sfeedbacks -3.2)

    • 0  unstable climate (infinite sensitivity)


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

    Runaway warming!!

    3.2

    Bony et al. 2006



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

    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


    Changes in cloudiness

    multimodel

    net cloud feedback

    Soden Held...

    2008

    where

    d cloud causes

    less emission,

    darker albedo, or both

    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.


    Outline1
    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
    Radiation budget: a vast integral

    • global warming =

    • [TOArad] =

      ∫∫dfdl ∫dn ∫dz ∫∫dW [R]


    Knowns
    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
    Blankly: how do we compute an integral?

    • Reductionist extreme:

      • Model the integrand R explicitly and precisely

        • From fundamental physics

          • right values, for right reasons

            • (so sensitivity to perturbations is right too)

  • Holistic extreme:

    • Go measure the answer

      • or more importantly for GW, changes of the integral for a known perturbation of the integrand(forcing)

  • Wise: a mixed approach

    • 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


  • Steps in integrating
    Steps in integrating

    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


    The scale problem
    The Scale Problem

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

    • both intellectually on firm ground, if hard to reconcile

  • Micro:

    • Basic units obey locallaws of interaction

      • physics: “air parcel” jostlings, thermo

        • humanities: human nature, drives, responses to stimuli

  • Macro:

    • Whole system constrained by integrallaws 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
    Yechh – take me back to physics

    • 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


    Elementary and rigorous
    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 continuum
    From particles to continuum

    Maxwell E,B for .

    ensemble

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

    Robin Hogan ECMWF Seminar 2008


    From particles to continuum1
    From particles to continuum

    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)

      • Longwave: mostly absorption (& emission)

        • proportional to condensate volume (mass)

          • no re (droplet size) dependence!

          • typically ~2 times greater than SW scattering extinction


    Angle integral
    Angle integral

    Maxwell

    eqs.

    particle ensemble

    angle

    integral

    Robin Hogan ECMWF Seminar 2008


    Wavelength integral can be done
    Wavelength integral can be done

    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

    • clouds mercifully gray


    Unreasonable assumptions
    “Unreasonable” assumptions

    Maxwell

    eqs.

    particle ensemble

    angle

    integral

    Robin Hogan ECMWF Seminar 2008


    Unbiased vs accurate
    Unbiased vs. accurate

    • 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


    Locally wrong but unbiased
    Locally wrong, but unbiased

    • 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)


    Hooray for atmospheric physics
    Hooray for atmospheric physics!

    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)


    Outline2
    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 learned even as uncertainty fails to shrink


    The problem of scales in practice x y t is really x y t scales
    THE PROBLEM OF SCALES in practice, (x,y,t) is really (x,y,t,scales)


    Almost without limit but remember the independent column approximation
    Almost without limit...but remember the independent column approximation!


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

    the ISCCP 2D space for characterizing cloudy columns


    Net crf in that space
    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
    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
    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


    Outline3
    Outline population regimes’

    • 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


    Stuck with 3d models
    Stuck with 3D models population regimes’

    • 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


    First off cloud water is a precision nightmare
    First off, cloud water is a precision nightmare population regimes’

    • only a few % of the water is condensed

    0.3

    60

    mm = kg/m2


    Problems with sums and integrals
    Problems with sums and integrals population regimes’

    • 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


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

    courtesy Robin Hogan population regimes’


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

    Magic number population regimes’

    courtesy Robin Hogan


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

    courtesy Robin Hogan population regimes’


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

    courtesy Robin Hogan population regimes’


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

    courtesy Robin Hogan population regimes’


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

    courtesy Robin Hogan population regimes’


    Yet all these effects are secondary
    Yet all these effects are secondary! population regimes’

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

    courtesy Robin Hogan


    Outline4
    Outline population regimes’

    • 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


    Tuning
    Tuning population regimes’

    • 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
    Back to the integral -- tolerances population regimes’

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

    Hansen et al. 2004

    Science Express


    Outline5
    Outline population regimes’

    • 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


    What could change systematically with climate
    What could change population regimes’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
    What could change with climate? population regimes’

    • 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 climate1
    What could change with climate? population regimes’

    • 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
    All connected population regimes’

    • 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
    Careful with “hunger” population regimes’

    • Global mean well defined but how meaningful?

      • What is the phys/phil status of a math. average?


    Conceptual connection closure
    Conceptual connection/ closure population regimes’

    • example


    Begat quantitative work n box model bony and dufresne wyant et al
    Begat quantitative work (n-box model) population regimes’(Bony and Dufresne, Wyant et al.)

    w500

    long time means


    Sw effects of low clouds under subsidence are the key to climate sensitivity uncertainty
    SW effects of low clouds under subsidence population regimes’are the key to climate sensitivity uncertainty

    in GCMs

    high sens models

    low sens models

    Bony and Dufresne 2006


    But is stratocu subsidence really so linked to the ascending tropics
    But is stratocu subsidence really so linked to the ascending tropics?

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







    Analogue to global warming
    analogue to global warming? just slice cartoons

    • 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
    One special just slice cartoonsmodel

    • NICAM – a 7km mesh globally

      • and 50+ vertical levels

    • Verrrrry expensive

    • Climate sensitivity estimated by SST+2 run

    • Result:



    Summary
    Summary high-thin cloud incr., unlike GCMs!

    • 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!