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. 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

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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


    Why are all gcm cloud feedbacks positive

    Why are ~all GCM cloud feedbacks positive?


    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

    • 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

    • 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

    • only a few % of the water is condensed

    0.3

    60

    mm = kg/m2


    Problems with sums and integrals

    Problems with sums and integrals

    • 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


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

    Magic number

    courtesy Robin Hogan


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

    courtesy Robin Hogan


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

    courtesy Robin Hogan


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

    courtesy Robin Hogan


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

    courtesy Robin Hogan


    Yet all these effects are secondary

    Yet all these effects are secondary!

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

    courtesy Robin Hogan


    Outline4

    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

      • 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

    • 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

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

    Hansen et al. 2004

    Science Express


    Outline5

    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

    • 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 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?

    • 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?

    • 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

    • 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”

    • Global mean well defined but how meaningful?

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


    Conceptual connection closure

    Conceptual connection/ closure

    • example


    Begat quantitative work n box model bony and dufresne wyant et al

    Begat quantitative work (n-box model)(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 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?


    Need whole planet understandings general circulation not just slice cartoons

    Need whole-planet understandings (General Circulation), not just slice cartoons


    Very recent obs constraint attempt

    very recent obs constraint attempt


    Decadal in a box off california

    decadal, in a box off california


    Tied to subsidence and stability

    tied to subsidence and stability


    Consistent satellite surface obs

    consistent satellite & surface obs


    Analogue to global warming

    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

    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

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


    Summary

    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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