Knowability and No Ability in the Earth and Climate Sciences


First up, what is the game are we playing in?



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First up, what is the game are we playing in?

  1. how large are the predicted changes relative to observed interannual variability?


  2. we need to ask this question in a physics-based way – separately considering the intensity, duration, and frequency of the individual events comprising the climatology.

  3. we recognize there are some problems with models representing the physics of this correctly

Second, How might we bound the predictability of regional precipitation


From observations of what goes on now, what are the principal physical mechanisms controlling precipitation in the PNW – i.e. we know that ENSO, and the MJO play a role. If our predictions for the future are going to be of high quality, we must first understand the degree to which we can predict changes in these other things

  1. shopping list of everything we know that matters, using a storm-based (weather) understanding of contributing processes

  2. Lay out the issues in understanding how each of those might change, ball-park the uncertainties involved and cogitate the consequences for our problem

What does it feel like the answer will be?

In order to have confident predictions, the predicted changes must be large compared to current interanual variability OR there must be great confidence that we understand the causes of interannual variability well enough to anticipate the consequences.



      Summary


It is an ugly problem, but there would be tremendous value in articulating clearly why.

How has solar variability contributed to large-scale (continental,hemispheric, global) climate variations during the Holocene?
My subquestion based on this area: 

-Can we identify the places and mechanisms where we can clearly understand how atmospheric and ocean dynamics have played a significant role in affecting the climate (i.e. over and above the local energy balance changes), and can we reconcile that answer with the geologic record? 

Possible recipe:

Take an energy balance model, calibrated for today, and force it with Milankovitch insolation variations of the last 10 thousand years. Take a suite of coupled GCMs and do the same thing (would have to be ). Compare them. How are the GCMs different from the EBM? In what regions and seasons do all the GCMs disagree with the EBM in the same way (i.e. the difference has the same sign)? On the other hand, where and when do they not agree? For something where all the GCMs differ from the EBM in the same direction, it probably points to the role of atmospheric and ocean dynamics as explaining the difference. Moreover the model agreement means we can be fairly confident of the sign of the changes. Diagnose and understand the differences (i.e., talk to David). Find suitable paleorecords (talk to Eric, Julian, and Sandy). 


Why does it feel like a good question?

I am certain that some regions like the monsoons/maritime climates, we are going to have confident, consistent answers from the GCMs, and it will be progress to have nailed them down systematically. That is, we know we can answer some part of the problem. 


It would also be very valuable to identify areas of violent disagreement between models. It points to aspects of the climate are i) highly variable ii) hard to predict (unknowable?), and iii) might be best tackled by simply describing what happened using the best data possible. That is, the surprises along the way are likely to be interesting surprises.

Motivation:

Why it is interesting (from Julian Sachs) - the magnitude of Holocene solar variability changes in W/m2 are much larger seasonally and latitudinally than  the W/m2 from doubling of CO2 (albeit shortwave vs longave). In many ways, if done carefully we can look for analogues of specific possible changes for the future, or at least put them in much better context..

Addendum:

Note the whole exercise could be done from the perspective of the range of variability in GMC output. Without a compelling reason, it is awfully hard to reject a GCM scenario as being impossible. The range of GCMs therefore provide a not-silly first guess at bounding the answer of the problem. Where inter-GCM agreement is high, it suggests a good question, where agreement is low, it hints at a bad question (if your goal is to understand reality).


Problems with this approach:

1. What to do with the ocean?

2. It's probably all about the clouds anyway.

3. I'm not a climate modeler. Who's counting?


Possible recipe:

Take an energy balance model, calibrated for today, and force it with Milankovitch insolation variations of the last 10 thousand years. Take a suite of coupled GCMs and do the same thing (would have to be ). Compare them. How are the GCMs different from the EBM? In what regions and seasons do all the GCMs disagree with the EBM in the same way (i.e. the difference has the same sign)? On the other hand, where and when do they not agree? For something where all the GCMs differ from the EBM in the same direction, it probably points to the role of atmospheric and ocean dynamics as explaining the difference. Moreover the model agreement means we can be fairly confident of the sign of the changes. Diagnose and understand the differences (i.e., talk to David). Find suitable paleorecords (talk to Eric, Julian, and Sandy). 


Why does it feel like a good question?

I am certain that some regions like the monsoons/maritime climates, we are going to have confident, consistent answers from the GCMs, and it will be progress to have nailed them down systematically. That is, we know we can answer some part of the problem. 

It would also be very valuable to identify areas of violent disagreement between models. It points to aspects of the climate are i) highly variable ii) hard to predict (unknowable?), and iii) might be best tackled by simply describing what happened using the best data possible. That is, the surprises along the way are likely to be interesting surprises.

Motivation:

Why it is interesting (from Julian Sachs) - the magnitude of Holocene solar variability changes in W/m2 are much larger seasonally and latitudinally than  the W/m2 from doubling of CO2 (albeit shortwave vs longave). In many ways, if done carefully we can look for analogues of specific possible changes for the future, or at least put them in much better context..


Addendum:

Note the whole exercise could be done from the perspective of the range of variability in GMC output. Without a compelling reason, it is awfully hard to reject a GCM scenario as being impossible. The range of GCMs therefore provide a not-silly first guess at bounding the answer of the problem. Where inter-GCM agreement is high, it suggests a good question, where agreement is low, it hints at a bad question (if your goal is to understand reality).


Problems with this approach:

1. What to do with the ocean? 2. It's probably all about the clouds anyway.



  1. I'm not a climate modeler. Who's counting?



      1. David’s Picks and Solutions:


Volcanoes influence on decadal climate variability.
Issues:

  1. how well can we constrain the amplitude and spatial footprint of the decadal variability associated w/ volcanic forcing?

    1. Forcing vs. response




  1. Can we use volcanic events to estimate the climate sensitivity?



  1. Are the indices used for reconstructing empirical relationships between volcanic activity good enough to reconstruct reliable records of climate forcing? If not, isn’t it circular reasoning/curve fitting when one compares the time series of volcanic activity with that for global temperature?





  1. Can we reconcile the W/m2 necessary to explain the observed (proxy and instrumental) decadal variability in climate with the temporal reconstructions of aerosol forcing? Or are the aerosol indices massaged to match the proxy record of climate variability (analog: sun spots vs. temperature)?

Necessary subquestions that need to be answered:



    1. What are the dynamics of lofting the material:

      1. How does the size of material affect the elevation (profile) of the material ejected?

      2. How much is lofted?

    2. dynamics of mixing of the material;

      1. What affect does seasonality have on the duration that the aerosols stay in the stratosphere? On how quickly they are mixed horizontally away from the latitude of ejection? Does latitude of the volcano matter?

    3. Optical properties of material:

      1. Are the optical properties a function of the size of the blast? The type of volcano (shield vs. composit)?

    4. We need to know the time scale associated with the impact on climate (single event).

    5. how big of a climate response can you get? How small could it be? Take upper end of people’s estimate of the radiative forcing associated with a volcanic event. Can you explain the historical record of temperature variations (amplitude and pattern)? Use an EBM or GCM coupled to a slab ocean.

    6. data approach:

      1. use big know volcanoes to estimate spatial and temporal response (using data and models).

      2. In genera, too premature to reconstruct a time history of radiative forcing associated with volcanic events.

General controls on equator to pole heat transport in other climates (glacial, warmer, snowball earth...).

Subproblem: Can we bound the thermodynamic efficiency of the atmosphere ocean heat engine?

Subproblems: stick w/ atmosphere and use agcm+mixed layer; stick with aquaplanet - bald;


  1. thermdynamic sea ice

Subproblem: The present climate features strong hemispheric symmetry in total energy transport, yet there is large asymmetry in the mechanisms doing the transport and the forms of energy transported:




  • There is large hemispheric asymmetry in type of energy transport: latent vs sensible vs potential

  • There is large hemispheric asymmetry in the mechanisms of heat transport: stationary vs. transient

How and why do these compensations come about in the modern climate? [presumably the exploration of this question will help formulate a hypothesis for why there is symmetry in net transport, and whether that should be true under vastly different forcings (CO2, insolation, etc) and geometries (ice sheets, paleo land disturbtion)]


We will go about this by assuming that the basic control of net energy transport depends only on the TOA net insolation gradient, the TOA OLR and the albedo (as a function of latitutde).
Tool: an AGCM coupled to a slab ocean. Include no continents (aqua planet) and a thermodynamic sea ice model.
Experiments to do:

  • sensitivity to evaporation: change the latent heat of vaporization by a factor of 10 each way. This does not directly affect albedo or water vapor transport (equator to pole) but it will affect the portioning of latent and sensible heat transport. How does the total energy transport change?

  • the dry atmosphere case (set atmospheric water vapor to zero)

    • no clouds
    • keep albedo as a function of latitude and prescribed as in the control (wet atmosphere) case.


  • Make the clouds optically transparent (tests the relative importance of cloud radiative feedbacks)

  • Change the sea ice albedo (w/ and w/o transparent clouds): this probably wont make a difference because both hemispheres have relatively symmetric surface ice distributions (albeit w/ hugely different ice orographies).

  • Change the solar constant in a big way (up and down): (aqua planet; w/ and w/o fixed sea ice from the control integration; w and w/o fixed clouds – or fixed prescribed albedo and clouds that are optically transparent in the visible). This will change the magnitude of the TOA insolation gradient and we can see how the net energy transport changes (w/ and w/o albedo changes).

  • Change the CO2 concentration in a big way (up and down), holding sea ice fixed and w/ and w/o fixed clouds – or fixed prescribed albedo and clouds that are optically transparent in the visible). This will allow us to keep the TOA net insolation gradient fixed, while seeing how the TOA OLR and energy transport change due to a nearly uniform global warming (or cooling), which will be accompanied by an increase (decrease) in the equator to pole gradient of ambient moisture, (including partitioning between latent and sensible).

  • Change the value of the exchange coefficient in the surface evaporation term and the value of Lv such that the cooling of the tropical oceans remains the same, but the moisture flux to the tropical atmosphere increases (thereby increasing the mean meridional gradient of water vapor without directly changing the baroclinity or transient sensible heat flux). This tests whether the transient sensible heat flux will compensate for the presumed increased latent transient fluxes (might have to fix albedo and make clouds optically transparent in the visible).
  • Many other such experiments can look for compensation (when albedo is frozen), and see if compensation is not complete when albedo is allowed to change


We can use our results/thesis to anticipate how the net energy transport should change from summer to winter in the northern hemisphere as a function of the seasonal change in the net TOA energy imbalance


For example, if we conclude it is indeed the albedo that is controlling things, this will give us a hypothesis for what should happen to net energy transport (and how the components will change) during increased CO2, or due to Malankovitch forcing.
Note: we haven’t addressed in this plan the issue of asymmetry in mechanism of transport ( transient vs. stationary). Oh well. …
    1. Summary

    2. In Class Discussion

Gerard: helps to work in groups (keeps you focused).

Kat: found myself using the same template to attack each problem.

Ken: finds it is useful to write it down as you go – you find places where you have holes in your arguments/experimental plan.

Gerard: sometimes failing to make progress is because you haven’t asked a good question: are you asking an answerable question (is the question ill-posed? Too vague?)
The polya list acts somewhat like a good friend – making sure you ask yourself hard (thoughtful/probing) questions to make sure your work is rigorous/you are on the right (best?) track.
Is an intrinsic property of a “good question” a question that you believe other people are also interested in?
Good questions have several properties including: having relatively certain answers. But good questions also have the properties that their answers serve one of several possible purposes: reduce the uncertainty; falsify a hypothesis; bound the possibilities; illuminate a new path/hypothesis.
Discussions of particular question (in class). We choose drought:

Mike T leads the discussion: Interested in freshwater variability in the arctic and it’s impact on THC.


  • First, need to define drought. An excess of precipitation.

  • Target is to understand the variability in freshwater flux into the arctic.




  • Subquestion: understand the variability of the freshwater flux into the arctic ocean, due to variability in: river input; precipitation over the arctic ocean; salt flux into the arctic from the atlantic; freshwater variability in the bering sea input; …. Sea ice export vs. ocean export (which depends on thermodynamic and wind induced variability in ice thickness; etc. …

    • Understand variability in each component; understand the mean state budgets. This helps identify if the terms that have the biggest variances matter to the mean climatology (or how long/big an perturbation would have to be to get a significant impact on the total freshwater export from the arctic).

    • Use simple models to estimate impact on net freshwater export. Would you be confident that this would have relevance for the real world.

Ken T leads the discussion: Understand the processes affecting interannual variability in the Andes of Peru. (see list below).



    1. Student Comments (delivered prior to class)


  1. Week 10: Summary

    1. The Agenda/Task

    2. Summary

    3. In Class Discussion




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