Knowability and No Ability in the Earth and Climate Sciences


Week 7: Abrupt Climate Change, Part I



Download 0.54 Mb.
Page16/17
Date conversion17.07.2018
Size0.54 Mb.
1   ...   9   10   11   12   13   14   15   16   17

Week 7: Abrupt Climate Change, Part I

  1. The Agenda/Task

Case study: rapid climate change
So we bow to your democratic demands, and will do rapid climate change as a case study. (It was the winner by a wide margin, except for a recount in Florida)
We were a little reticent to pick this for a couple of reasons a) it is all too easy to be negative, and b) not everybody may have the background. On the other hand any problem probably has these properties. So let's strive to be constructive:
• How do we move forward to make progress if we declare this to be our 'big problem?'

• How does thinking about a practical problem change our 'check lists'?

It is a big messy problem that has many aspects. None of us has enough background in all of these aspects. What we really want to do is focus on the lessons we can learn about how to tackle complex problems, as much as it is about this particular issue.
Lets just see how it goes.
Next week will hopefully star Eric Steig, who will give us the benefit of his wisdom on the issue (or it will be me doing a shoddy impersonation). Then in 9th week we can brainstorm on a recipe for research going forward on this issue.
1. So, for this weeks meeting, please review/email comments

• David's effort to sythesize a checklist (link). Right now the list is quite long. Can it be distilled more succinctly? What is missing/wrong/not generally applicable?


2. Rapid climate change overview articles/email comments
• Alley et al., 1999: making sense of millennial-scale climate change (pdf)

• Rahmstorf., 2002: ocean circulation and climate during the past 120,000 years (pdf)

If you have read the above before, as many of you have, also try to get to this Seager and Battisti production

• Seager and Battisti, 2006: Challenges to our understanding of the general circulation: abrupt climate change (pdf)



    1. Summary

    2. In Class Discussion

    3. Student Comments (delivered prior to class)



  1. Week 8: Abrupt Climate Change, Part II

    1. The Agenda/Task


Background papers:
• Alley et al., making sense (pdf)

• Rahmstorf, role of ocean (pdf)

• Seager and Battisti (pdf)
The Game:
So here's the game we all agreed to play: Bill Gates has rowed across from Redmond, and has given you 100 million dollars to study abrupt climate change. He does not care about the details of what you do but it must be in the general area. You have a conscience and you want to do the best science you can.
In this game, what seems like the best way to make progress and to enhance understanding?

• What problems do you pick to tackle?

• How do you tackle those problems?

• What do you expect to be able to learn?


In other words, we are taking out Polya checklists out for a test drive, and we want to see what we learn by doing this. Lets try and get fairly detailed about the plans.
Justin suggested picking something quite specific like D/O events and thinking your way up or down the Polya 'tree of possibilities' and, I think, trying to evaluate that specific properties of that problem. That would be great
I also wanted to make sure that, in contrast to trying to explain a particular observation, that we also come at it from the perspective of understanding fundamental dynamics questions That is, are rapid changes in "circulation regime" (where you get to choose what this means) possible?
As ever, please send emails by Wednesday morning.

    1. Summary

    2. In Class Discussion

    3. Student Comments (delivered prior to class)


  1. Week 9: The Complex Polya List for problem X

    1. The Agenda/Task


So for our last hurrah, before a glorious summing up.
By Thursday evening, please email us a half dozen or so random topics in climate that you think are interesting (i.e. role of clouds in climate uncertainty; droughts; severe weather). We'll collate them and then set the task of taking 4 of 5 out of these 20 or so topics, and finding a precise, specific, and interesting question to ask, and briefly articulating why it is a good one. Be bold and don't just pick from the subset of alternatives that you yourself suggested!
We are deliberately being slightly harsh task masters on this. The point, we hope, is to internalize (a little) the importance of searching out well posed and doable questions, which seems an important element in what we have talked about so far. Doable and interesting means having a fairly complete sense of what the recipe for proceeding to the solution will look like. We'll do it too.
David and I had the sense that, while the problems suggested in class today were interesting, many were not terribly precisely formulated. Obviously this is somewhat a function of background and experience, but it seems to us that there should always be a striving to ask tractable and realistic questions. Sorry if I said this clumsily in class, or am saying this clumsily now. You are all, obviously, a complete bunch of stars.
I bet that brainstorming with other people is the way to go on this, so definitely pair up and bang heads. We'll see how it goes, declare victory whatever happens, and then go have a BBQ.
Cheers,
Gerard (& David in absentia)
      1. Gerard’s Example


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. The Ideas the Students Came Up With


Hi Gerard,
Off the top of my head, sorry they are fairly ice-centric:
- Controls on fast-flow of ice sheets

- Glacier-climate interactions

- El Nino and global warming

- Controls on hurricane intensity

- Changes in Antarctica since the LGM

- Methane hydrate release

- Modelling Heinrich events

- Recent major drought in the Sahel (1968-1974)


From Mike:

*the effect of aerosols on climate (change)

*west antarctic ice sheet vulnerability to climate change

*future changes in precipitation in a warmer climate

*thermohaline circulation role in climate

*general methods of equator to pole heat transport in other climates (glacial, warmer, snowball earth...)

From Rei:

Here are some of my ideas for the (very broad) topics for next week:

- natural vs. anthropogenic climate variability

- thermohaline circulation (its role, changes in past climate)

- (quantifying) climate sensitivity and feedback

- teleconnection mechanisms

related to the above maybe...

- role of tropics in climate variability (e.g., ENSO issues)


At the moment, these are all I can come up with...

      1. The Ideas they Picked (and their Polya lists to solve the problems)


larissa

convectively coupled waves

tropical precipitation and stratification

controls of hurricane intensity


jimmy

role of thc in climate

land use effect on climate
ken

general controls of heat transport

drought

enso and global warming


mike t

cloud feedbacks and climate change

drought (2)

volcanoes and decadal climate

enso during the glacial period
rob

enso and global warming (2)

clathrates and abrupt climate change
kat

glacier climate interaction

green sahara

land surface feedback and abrupt climate change

land use effects on climate (2)
gerard and david

changes in precip in a warmer climate

volcanoes and decadal climate (2)

solar variability and holocene climate

meridional energy transport - general controls (2)
Discussion of a 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).



Larissa:
My scrawled outline of questions (I've stuck to things I know enough

about to feel I have doable, useful, "inexpensive" plans- ie I think I

could do myself w/some assistance, and in some cases plan to do so).

While I haven't stuck to our list per se, I think the sort of work I

outline applies to many of the things on our list.

1.) General research area- a big problem in tropical meteorology/climate is

understanding why convectively coupled wavespeeds are as observed? ie

how is convection slowing them down from dry theory

subqestion/possible recipe- how do radar (TRMM PR) statistics

systematically change as waves pass through a given region (since radar

is probably the best measure of convection we have)?

Tools that make this tractable problem: tons of observations from PR (7

years worth), a systematic filtering methodology to get at waves

(Wheeler and Kiladis). Field campaigns have already tried to do this to

some extent, but not in the systematic way I'm suggesting and I think

something like this could be done by a computer-programming competent

person in a very reasonable amount of time.

Issues: picking signal out of statistics, geographic variations?

Why a good problem? Fundamental piece of data would constrain "wild

speculation." Results would be interesting to a variety of communities

from more mesoscale to climate-based to tropical dynamics. Useful for

moving towards understanding MJO, incorporating convection/moisture into

dynamical understanding. While I haven't stuck to our list per se, I

think this applies to many of the things on our list.
2.) General research area- a simple way of understanding climatological

precipitation in the tropics w/o forcing model with latent heating. In

particular, the zonally asymmetric component.

a) subquestion: how do thermodynamic equilibrium profiles depend on

vertical structure of heating? (which is observed to vary dramatically)

Tools/recipe: cloud-resolving model forced with time-invariant idealized

vertical motion profiles (from reanalyses), try to understand

differences

Why good problem: understanding applicable to a range of important

problems, relatively doable, interesting theoretically as well as useful

for cumulus parameterization and understanding of convection

b) subquestion: to what extent can we think of frictionally boundary

layer convergence (ie Lindzen and Nigam) as causing convection and being

relevant to determining structure of latent heating

subsubquestion: I have an idea for a simple mixed-layer model than can

be used to quantify this - is it useful? [if not, more thinking

necessary to call this a tractable problem]

Why good problem: understanding can lead to relevant simple models of

tropical circulation, which can address many of the issues on our list.

Issues with a) and b): data, statistical significance, applicability of

models to real world, only a small part of big problems- thus is this

worth doing?

c) Is a spectrum of entraining plumes enough to reasonably reproduce

time-mean massflux-height relationship cloud resolving model shows.

related to understanding convection/simple model, etc. If not, why?

(other than "things are complicated") In the interest of time, I'm not

going to bore folks w/all the details of this one, which I've already

sort of done, though not in publishable form.

3) Controls on Hurricane intensity (admittedly not something I know a

lot about)

Kerry Emmanuel has looked at changes in integral of v^3 because it's

well correlated with dollars of destruction. I'm interested in what

more fundamental physical quantities (energy released, etc.) this is

well-correlated with and not well-correlated. In other words, what do

his correlations physically tell us about hurricanes and climate

change.


Recipe: pick statistical quantities you think have been measured well

enough in some smaller dataset and correlate them with integral of v^3.

Issues: has this already been done? Data availability- this could

become quite time-intensive if data isn't in a reasonable format. I

don't know much about hurricanes. Do we care enough to do this? As a

non-hurricane person I have no idea.


Jimmy B.
1) Topic: Thermohaline circulation role in climate
Subquestion: What is the heat transport budget at 53 North for the Atlantic Ocean.
Possible Recipe: Three teams of researchers coordinate together to take data along 53N, all starting at the same time. The teams take section data at equal distances so as the cover the entire ocean at the fixed latitude. This study is carried out every spring for 10 years. The temperature plus velocity data is analyzed to calculate an approximate heat budget across this latitude.

Justification: The deep convection that feeds the meridional overturning circulation (MOC) is believed to occur north of 53. If we assume that the North Atlantic is a closed box at the northern boundary, this set of data will give an estimate for the net heat flux into/out of the North Atlantic. Using satellite data we can approximate the atmosphere-ocean heat exchange. The result will be a semi-closed box for the heat cycle in the North Atlantic. This will help to explain the magnitude of the MOC. It may also give insight into the role of sea-ice in the heat budget for the North Atlantic.

Motivation: Bob Pickart did a similar study using existing section data for the Labrador Sea. The section was about one-third the length of the Atlantic Ocean at 53 N. Although he could not nail down the processes involved in convection, he was able to approximate the heat flux across a section at the southern edge of the Labrador Sea. With this information, he was able to approximate the role of the Labrador Sea in the MOC.
Backfires: The ocean could be in a transient state right now, meaning that the ten years of data will not lend itself to any of the present theories.
2) Topic: Land use effects on climate
Subquestion: Does urban/business park sprawl create a change in local summer weather conditions that is more significant than white-noise.
Possible Recipe: The Research Triangle Park (RTP) in North Carolina has experienced rapid large-scale land use changes since 1986. A large area of forest has been converted into businesses, housing and roads. My plan is to compare temperature and precipitation data for 20 summers before 1986, which that from 1986-2006. Additionally, I would compare the data with that from a forested area nearby that was not developed substantially. My hope is to find changes in small-scale variability, so I would remove the long-term trends. Finally, I would take the pre-development data and add white noise to the data (the amplitude of which I would base on the amplitude of small scale variability in the data).
Justification: Sprawl like this is occurring all over the U.S. It would be interesting to note if these changes have effects on local climates. If there are significant changes, then the next step would be to look at health and agriculture effects. If there are not significant changes, weather forecasters would have one less degree of uncertainty.

Motivation: We know that large-scale weather phenomenon is not affected by something like the RTP land development. However, in the summer, short-lived flashfloods and thunderstorms occur over very small areas. It seems possible that this type of atmospheric phenomena could be influenced by local albedo and/or urban heat-island effects.

Backfires: The influence of land-use change could be unique to this area. The influence of large-scale events (such as ENSO, NAO) over the past 40 years may not by distruted evenly over the first and last 20 years, leading to differences in the data that large enough to smear-out the local or small scale changes.


Ken T:
"General controls on equator-to-pole heat transport"
Specific problem:

What controls the effective emission temperature in the atmosphere?


Hypothesis:

The strong insensitivity of total meridional energy transports in the atmosphere to what processes are responsible for them (e.g. steady vs transient eddies, latent vs sensible heat transports) suggest that they are strongly constrained by the radiative budget at the TOA. It has been shown (Stone, 197?) that, as long as the atmosphere is an efficient redistributor of energy, the meridional shape of the energy transports is determined by the geometry of the planet. The magnitude of the transports,

however, depends on albedo and the effective emission temperature of the atmosphere. Since, in the tropics, the net cloud radiative forcing due to deep convective clouds is close to zero, we may neglect them as a first approximation. My hypothesis is that, in a model under those conditions, it will be difficult to change the observed effective emission temperature in the atmosphere and, therefore, meridional energy transports.

Plan:

- Setup an aquaplanet atmospheric GCM in which cloud radiative effects are neglected, coupled to an ocean mixed layer with fixed "heat transports" (Q-fluxes).

- Perform runs in which a horizontally homogeneous radiative forcing is imposed at the TOA and assess the sensitivity of the OLR and meridional energy transports.

- Analyze how the OLR is changed (or not) through changes in effective emission height, emission temperature, emissivity. These effects should cancel out to yield low sensitivity of OLR.

- Assuming the cancelation above occurs, try to determine what is the fundamental constraint that produces this. Hartmann's FAT hypothesis is an example of what such a constraint might look like.

Caveats:

- Although convective clouds have CRF~0 at the TOA, they have a strong cooling effect at the surface. Since we have an interactive ocean mixed layer, we can not constrain what SST will do. This will complicate the interpretation of the results somewhat.

------------------------------------------------------
"Drought"
Specific problem:

Understand the processes affecting interannual variability in precipitation in the Andes of Peru.


Hypothesis:

Sea surface temperature (SST) variability (ENSO and Atlantic) and/or land surface conditions in the Amazon (both taken as "external" forcings to the atmosphere) will have a significant impact on the variability in precipitation.


Plan:

- Determine rainfall variability from rain-gauge data and establish patterns of such variability (EOFs).

- Compare to similar results based on satellite-based estimates (e.g. GPCP or OLR), which can then be used to determine the associated large-scale patterns of the rainfall variability.

- Perform statistical tests of the hypothesis that the rainfall variability (as determined above) is related to SST and to soil moisture (there are some products out there).

-Similarly, identify how the atmospheric flow mediates this influence from the forcing region to the Andes.

- Perform a long run with a full atmospheric GCMs with realistic ("observed") forcings (SST, soil moisture) and determine whether the model reproduces the statistical results from above.

- If the model is well validated, perform idealized experiments in which the important forcings are varied to assess their marginal effect on rainfall variability. The way the atmosphere mediates this effect has to be validated as well.

-----------------------------------------------------


"El Nino and Global Warming" & "Future changes in precipitation in a warmer climate"

Specific question:

To what extent will ENSO variability exacerbate or ameliorate the impacts in the northwestern coast of Peru of changes in the mean climate expected in the future?

Background :

The northwestern coast of Peru is a desert except when El Nino brings torrential rains. In that case, rainfall occurs in the warm season (Dec-Apr) and there seems to be a threshold SST of around 26C for rainfall to occur. The main effect of high SST appears to be increasing the low-level moist static energy (MSE) to levels that allow deep convection (comparable to MSE aloft).


Hypothesis:

Changes in the mean climate will bring the climatological SST closer to the threshold. On the other hand, the threshold might change due to changes in MSE aloft. These effect may or may not cancel out.

However, even if it does and even if El Nino variability does not change (as measured by Nino1+2 variance) the non-linearity of the Clausius-Clapeyron equation might lead to an enhanced effect of this variability on that of rainfall.

Plan:

- Characterize the ensemble of projections for future change in the mean SST climatology (annual mean and seasonal cycle) in the Nino 1+2 region is from different climate models.

- Do the same for the interannual variability.

- On the basis of the above and the currently observed SST threshold for rainfall, as well as current statistical models for rainfall amounts based on SST, predict how the rainfall characteristics (annual amounts, length of rainy season, interannual variability) would change.

- To test the robustness of the statistical models for future conditions (e.g. against changes in mean free tropospheric temperature distribution) and to assess other changes in rainfall characteristics (e.g. frequency of extreme rainy events), run a regional climate model (e.g. MM5 or RegCM2) with boundary conditions provided by a representative GCM under the following configurations:

a) 20 years from the baseline (current conditions) with lateral boundary

conditions from the GCM and observed SST climatology with

interannual anomalies from the GCM.

b) Similar to a), but adding the projected changes in SST climatology

for 100 years into the future.

c) Similar to b), but replacing interannual SST anomalies by those

projected for 100 years into the future.

d) Similar to c), but replacing the lateral boundary conditions by those

projected for 100 years into the future.

- Comparing runs a) and d) tells us how rainfall will change under global climate change.

- Comparing runs a) and b) tells us how much changes in the climatology affects rainfall.

-Comparing runs b) and c) tells us how changes in interannual variability changes the impacts of the changes in the climatology.

- Comparing runs d) and c) gives us a crude estimate of how changes in tropical atmospheric thermal structure affects the relation of rainfall to SST.

----------------------------------------------------------

Mike T:

subject 1:

cloud feedbacks in climate change.
Address the question of basic cirrus cloud formation. We need to understand when cirrus clouds form in order to predict their presence and feedback on the atmosphere. Their radiative feedbacks are well-characterized relative to their formation mechanisms.
I see this project as a basic data collection excercise. There are several projects/efforts out there to address this issue. Thus, there is academic support for this project in terms of interested parties in the field campaign and modeling camps.
This project will address the different hypothesis for cirrus cloud formation (CCN, ice nuclei (IN), humidity, temperature, wind speed, wind shear, turbulence, ...). Basically, plot everything vs. everything. It would be best to have a controlled environment that isolates a few of these variables at a time. So, one site will probably not do for this field campaign. We will need two or three adventures.
Pick a pristine place with cirrus clouds so that IN are not a factor. Predictably, I pick the South Pole. It is clean (low CCN, low IN, no chance of mixed phase clouds in winter, ...). The clouds are low and can be reached with accurate instrumentation by balloon and kite. Remote sensing from the surface is also possible. Only changes in temperature, humidity, and wind shear/turbulence to contend with. Of course, we need to measure CCN and IN in order to prove that ice crystal concentration is not related to CCN or IN concentration. But, based on previous measurements of IN there (not necessarily correlated with cloud cover) it is not likely that they are correlated.

Next, move to a site with more variable conditions. SGP. It is well-instrumented. It is more difficult to reach the cirrus clouds there. The inferences about atmospheric humidity and temperature surrounding the clouds will be less exact. However, based on data from the South Pole we might be able to narrow down some of the issues related to IN, temperature, humidity, and turbulence.

Are there general controlling factors in the lifetime of a cirrus cloud? surrounding temperatures, surrounding humidity, wind speeds, crystal sizes?
These factors can be characterized and generalized into a format that can be input into a model.

Subject 2.

Drought.
Drought is an anthropocentric word. What we really care about are extremes in precipitation. Drought implies a prolonged extreme in precipitation.
Question: Will a drought in the Arctic cause a change in THC?

This question is more of a 'characterize the system' type question, rather than a 'what happens in real life' type question. At least it is the way I want to go about it.


Check the precipitation data north of 60 deg. Probably not very good data. Get some idea for the extremes and temporal extent of extremes. Proceed to of ocean with minimal atmosphere with some idea of how much water to input into the arctic, where to put it (rain, snow, river runoff). Perturb the model with realistic variations on precip north of the arctic circle. See what happens to the THC. There are a matrix of intial conditions (precipitation amount, precipitation type, precipitation location, duration of drought).
This is a decent question because it is one factor of many that will affect the THC in the future. If a matrix of solutions exist then this link in the chain of climate change feedbacks can be referenced when it is encountered in other simulations. It is also a decent question because it incorporates about 3 of the suggested questions into one.
subject 3:

Volcano influence on decadal climate variability.


Specific question: Are there regions of the world where a volcano of some reference size have more impact on mean global surface temperature than others?

Pick as the reference size volcano Pinatubo. We have data characterizing the effect of the Pinatubo on the atmosphere (change in albedo, aerosol amount, aerosol lifetime, distribution, changes in surface temperature).

Explode volcanoes of the same size at different locations around the globe in a GCM. See how the circulation patterns carry the dust/aerosol in the troposphere and stratosphere. Determine the affect of the aerosols on future surface temperatures due to changes in albedo and downward IR forcings.
This question is as relevant to present climate as to paleoclimates. The continental distribution does not have to be as it is in the present. This will call into question the circulation patterns generated by the GCM. It will be up to the researcher how much uncertainty is introduced by trying to simulate extremely old contiental orientations. The GCM can be checked against the Pinatubo erruption.
Subject 4:

Does El Nino exist during Glacial Periods?


Specific question: If El Nino exists in a Glacial period, will it matter to more than the tropics?
Force an El Nino-type circulation in the tropics (with weaker easterly winds over the pacific) in a GCM. See how the affects propagate to the higher latitudes.
Does the El Nino affect the jet? Is the jet too strong to allow a 'teleconnection' to higher latitudes? Are there other circulation changes affected that the model is capable of resolving (MJO, PNA)? Are there any unexpected results? Are these results due to pure physics of the atmosphere or artifacts of the model construction?

Rob N.

Or is it the other way around? I had a pretty tough time with this; here are two attempts, though...

(1a) big question: How does the frequency and intensity of ENSO change with global mean temperature?
(1b) more focused question: What phenomena, variations in which are associated with variations in global mean temperature according to our best current understanding, can induce changes in the frequency or intensity of ENSO?

(1c) possible recipe: Identify some basic (almost certainly

large-scale) phenomena that should be assocated with slow changes inglobal mean temperature (e.g. changes in ocean mixed layer depth); these might be identified from data or models. Now take two or three models that do the best job of predicting the frequency and intensity of ENSO events (it would be nice to have at least one coupled AOGCM and one empirical model) and force them with the changes identified above. Do it for each change independently and for combinations of the expected changes. Compare the the results. Where do the models agree? Where do they disagree? Is there any data from the instrumental or paleo records that can be used to assess the reasonableness of the various model results?

(1d) why this might be a good question: GCMs vary widely on what should happen to ENSO under greenhouse warming scenarios. Getting down to individual phenomena is one way to simplify this problem. And ENSO matters.
(1e) possible problems with this approach: Models that do the best job getting ENSO for the current climate may not work well at all under different conditions. Hasn't somebody tried this already?

(2a) big question: How do dissociation of clathrates contribute to abrupt warming events?


(2b) more focused question: What are the timescales for "large" changes in global mean temperature and relaxation to normal conditions when a "big" pluse of methane is released into the atmosphere?
(2c) possible recipe: Use available data to estimate total methane tied up in clathrates. Pick two models: an EBM coupled to a simple scheme for tracking the chemical evolution of the released methane and an AOGCM with full-blown chemistry. Run them both with all the methane dumped into the atmosphere in a single, short pulse (one year?). Are the results the same or different? If the results are very different, try it again but use the same chemical scheme (maybe even something empirical) for both. Check ice core records to see if there are events that can be used to corroborate model results.
(2d) why this might be a good question: If you're confident in your models, this should be pretty straightforward. It's also nice for comparison to other "big pulse" experiments like dumping a bunch of freshwater in the North Atlantic.
(2e) possible problems with this approach: Instantaneous, catastrophic release of methane probably isn't realistic. We probably don't have much confidence in the coupling of these models. Using paleo data to corroborate is tricky because we can't really isolate the methane effect; I suspect there may not even be any pulse-like releases of methane in the ice core record.
Kat:

1. Glacier-climate interactions

a. Subquestion:

In general, glaciologists think of climate affecting glaciers. Which is reasonable. Also, work has been (namely by you, Gerard) regarding the way large ice sheets affect climate patterns on a grander scale. I think that an interesting question would ask when the threshold between affecting and not affecting a region’s climate could be reached. A small mountain glacier, like South Cascade or a maritime glacier like Blue, contributes minimally to the elevation of the mountains upon which they rest. But the three kilometer thick Greenland ice sheet will divert wind, clouds, precip, decrease temperatures at the surface, and thusly create its own sustainable environment.

What is the tipping point such that ice will grown naturally, because it is in and of itself causing the climate required to grow a glacier/ice cap/ice sheet? What kind of climate forcings would make it flounder out of that state and cause it to shrink back?
b. Possible Recipe:

This question requires modeling, of course. Maybe it would be wise to try to squeeze the glaciers. Start with a smaller glacier and keep cooling it and/or pumping snow onto it until it is able to force only snowfall, even in summertime. On the other hand, maybe take a model of a large ice sheet and increase the T, decrease the precip, and until it reaches a breaking point, and retreat is rapid.


c. Why is this an interesting question?

This is a good question because it provides insight as to what kind of climatic change may actually melt away Greenland and Antarctica, and some sort of timescale for how long that would take. On the other hand, it might provide insight as to why there is not as much ice cover on pieces of land that at latitudes similar to Greenland.


d. Motivation?

Glaciers are the canary in the climate mine. Also interesting to think of how ice sheets spilled all over N America and Europe during the iceages, and at what rate they retreated

e. Problems

Modelling this will be no walk in the park

Probably will be a huge nebulous range between ice sheet formation and just mountained glaciers.

Deciding if T or P is more important where and when?

*****************

2. Why was the Sahara wet during a portion of the Holocene?

a. Subquestion:

Does sufficient paleoevidence exist to tell us that there was indeed a huge regional shift in the climate of the Sahara 5.5 kyrs BP, or does the evidence that we see merely point to pockets of smaller scale climate fluctuations?
b. Possible recipe:
to attack this question it is important to:

1. inspect current climate in the Sahara. Is the rgion somewhat homogeneous? Is precip originating from one source/driving force? How important is vegetation in the current climate?

2. Come from the other side. Is it possible to describe the paleoevidence with change local to where the evidence was taken? Or will the changes that we see necessitate a larger scale shift in a model?

3. Drink beer. Start a “hippos in the sahara” class. Get confused anyway.


c. Why is this a good question?

Paleoproxies are very difficult to come by in the desert. It is important that models will provide some guidance as to what conclusions can indeed be drawn from paleoclimatic evidence in such arid areas.


d. Motivation

Besides the obvious fame and glory, it will be interesting to know what kind of changes we can expect from the Sarah in the future, or to use it as a model of what could possibly happen elsewhere, in a currently vegetated region. There are also cool links to humanity and civilization in the area.


e. Problems with this approach

Such a large area to model. Many many uncertainties. Will the answer be ambiguous?


**************************
3. What is the role of land-sfc feedback in abrupt climate change?
a. Subquestion:

What on earth would happen if, say, the South American rainforest did not exist? How and how quickly would the rest of the world respond if it was instantly wiped out?

b. Possible recipe

This is another “obviously need a model.” Impose an instant change on a GCM, one with enough complexity to take into account vegetation. Replace the rainforest with pampas, or shiny white sand, or even water.

c. Why is this a good question?

It can tell us something about a worst-cast scenario for large scale clear cutting. It is quite possible to do with models that we do already have and is a fun exersice.


d. Motivation:

Tree-hugging. Tells us about limits

e. Problems:

We won’t know how many details to put into the GCM, and I’m assuming increasing or decreasing the number of variables will have huge impacts on the outcome.

******************
4. Land Use effects on climate
a. Subquestion

I’ve always thought that it was interesting to look at how cows affect regional climate was interesting. Does large-scale grazing have a noticeable impact on climate in the US?


b. Possible Recipe:

Find out how much methane a cow releases in its lifetime. How much vegetation is munched up by these cows? What other resources are used in processing and shipping the meat/milk? How is groundwater affected by this as well. Basically, synthesize a lot of data, and multiply it by the number of cows.


c. Why is it an interesting question?

It is relevant, and relatively easy to constrain. Doesn’t try to explain what the GHGs will do once released, but provides lots of interesting information for counting emissions and how to cut down.


d. motiviation?

Hippie
e. Problems

I don’t actually know how to collect data like that. Maybe it’s quite difficult. Plus, the man would not like it.

      1. Gerard’s Picks and Solutions:



Future changes in precipitation in a warmer climate:

What is the subproblem, what is the background knowledge, how confident are we in it? What is the recipe?, what is the simplest statement of the problem? Can you see articulate why progress will be made? What are the unknowns, Have you used all the information available?

The aspect that we’d like to go after is to understand what it would mean to predict changes in precipitation. What would a clean, confident answer look like?
We are interested in the midlatitude storm tracks, so our focus will be wintertime midlatitude precipitation. We should narrow it down still further to something like a particular region (i.e., PNW).
The simplest thing would be to throw many GCMs at the problem. We know that people have done this and that there is a huge spread in their predicted changes, so how do we find the good question to ask?
What do we expect?


  • in a warmer world Clausius-Clapeyron tells us to expect more water vapour in the atmosphere, on average

  • still have Pacific storm track!

How confident are we?



  • The C-C argument depends on relative humidity not changing. Not totally confident about this.

  • There will be a storm-track, its shape, size, and location are uncertain




1   ...   9   10   11   12   13   14   15   16   17


The database is protected by copyright ©hestories.info 2017
send message

    Main page