Author: Tom Fid

Realclimate has Martin Vermeer’s reflections on the making of his recent sea level paper with Stefan Rahmstorf. At some point I hope to post a replication of that study, in a model with the Grinsted and Rahmstorf 2007 structures, but I haven’t managed to replicate it yet. The problem may be that I haven’t yet tackled the reservoir storage issue.

At Nature Reports, Olive Heffernan introduces several sea level articles. Rahmstorf contrasts the recent set of semi-empirical models, predicting sea level of a meter or more this century, with the AR4 finding. Lowe and Gregory wonder if the semi-empirical models are really seeing enough of the dynamic ice signal to have predictive power, and worry about overadaptation to high scenarios. Mark Schrope reports on underadaptation – vulnerable developments in Florida. Mason Inman reports on ecological engineering, a softer approach to coastal defense.

From the Asilomar geoengineering conference, via WorldChanging:

Lesson two: Nobody has any clear idea how to resolve the inequalities inherent in geoengineering. One of the most quoted remarks at the conference came from Pablo Suarez, the associate director of programs with the Red Cross/Red Crescent Climate Centre, who asked during one plenary session, “Who eats the risk?” In Suarez’s view, geoengineering is all about shifting the risk of global warming from rich nations — i.e., those who can afford the technologies to manipulate the climate — to poor nations. Suarez admitted that one way to resolve this might be for rich nations to pay poor nations for the damage caused by, say, shifting precipitation patterns. But that conjured up visions of Bangladeshi farmers suing Chinese geoengineers for ruining their rice crop — a legalistic can of worms that nobody was willing to openly explore.

If geoengineering is a for-profit operation, it presumably also involves the public bearing the risk of private acts, because investors aren’t likely to have an appetite for the essentially unlimited liability.

The BBC today carries the headline, “US manufacturing output hits 6 year high.” That sounded like an April Fool’s joke. Sure enough, FRED shows manufacturing output 15% below its 2007 peak at the end of last year, a gap that would be almost impossible to make up in a quarter. The problem is that the ISM-PMI index reported by the BBC is a measure of growth, not absolute level. The BBC has confused the stock (output) with the flow (output growth). In reality, things are improving, but there’s still quite a bit of ground to cover to recover the peak.

China’s one child policy is at its 30th birthday. Inside-Out China has a quick post on the debate over the future of the policy. That caught my interest, because I’ve seen recent headlines calling for an increase in China’s population growth to facilitate dealing with an aging population – a potentially disastrous policy that nevertheless has adherents in many countries, including the US.

Here are the age structures of some major countries, young and old:

Vertical axis indicates the fraction of the population that resides in each age category.

Germany and Japan have the pig-in-the-python shape that results from falling birthrates. The US has a flatter age structure, presumably due to a combination of births and immigration. Brazil and India have very young populations, with the mode at the left hand side. Given the delay between birth and fertility, that builds in a lot of future growth.

Compared to Germany and Japan, China hardly seems to be on the verge of an aging crisis. In any case, given the bathtub delay between birth and maturity, a baby boom wouldn’t improve the dependency ratio for almost two decades.

More importantly, growth is not a sustainable strategy for coping with aging. At the same time that growth augments labor, it dilutes the resource base and capital available per capita. If you believe that people are the ultimate resource, i.e. that increasing returns to human capital will create offsetting technical opportunities, that might work. I rather doubt that’s a viable strategy though; human capital is more than just warm bodies (of which there’s no shortage); it’s educated and productive bodies – which are harder to get. More likely, a growth strategy just accelerates the arrival of resource constraints. In any case, the population growth play is not robust to uncertainty about future returns to human capital – if there are bumps on the technical road, it’s disastrous.

To say that population growth is a bad strategy for China is not necessarily to say that the one child policy should stay. If its enforcement is spotty, perhaps lifting it would be a good thing. Focusing on incentives and values that internalize population tradeoffs might lead to a better long term outcome than top-down control.

Somehow I forgot to mention our latest release:

The “Confirmed Proposals” emissions above translate into temperature rise of 3.9C (7F) in 2100. More details on the CI blog. The widget still stands where we left it in Copenhagen:

Are border carbon adjustments (BCAs) the wave of the future? Consider these two figures:

The first shows the scale of carbon embodied in trade. The second, even if it overstates true intentions, demonstrates the threat of carbon outsourcing. Both are compelling arguments for border adjustments (i.e. tariffs) on GHG emissions.

I think things could easily go this route: it’s essentially a noncooperative route to a harmonized global carbon price. Unlike global emissions trading, it’s not driven by any principle of fair allocation of property rights in the atmosphere; instead it serves the more vulgar notion that everyone (or at least every nation) keeps their own money.

Consider the pros and cons:

Advocates of BCAs claim that the measures are intended to address three factors. First, competitiveness concerns where some industries in developed countries consider that a BCA will protect their global competitiveness vis-a-vis industries in countries that do not apply the same requirements. The second argument for BCAs is ‘carbon leakage’ – the notion that emissions might move to countries where rules are less stringent. A third argument, of the highest political relevance, has to do with ‘leveraging’ the participation of developing countries in binding mitigation schemes or to adopt comparable measures to offset emissions by their own industries.

from a developing country perspective, at least three arguments run counter to that idea: 1) that the use of BCAs is a prima facie violation of the spirit and letter of multilateral trade principles and norms that require equal treatment among equal goods; 2) that BCAs are a disguised form of protectionism; and 3) that BCAs undermine in practice the principle of common but differentiated responsibilities.

In other words: the advocates are a strong domestic constituency with material arguments in places where BCAs might arise. The opponents are somewhere else and don’t get to vote, and armed with legalistic principles more than fear and greed.

In the 80s, my mom had an Audi 5000. It’s value was destroyed by allegations of sudden, uncontrollable acceleration. No plausible physical mechanism was ever identified.

Today, Toyota’s suffering from the same fate. A more likely explanation? Operator error. Stepping on the gas instead of the brake transforms the normal negative feedback loop controlling velocity into a runaway positive feedback:

… A driver would step on the wrong pedal, panic when the car did not perform as expected, continue to mistake the accelerator for the brake, and press down on the accelerator even harder.

This had disastrous consequences in a 1992 Washington Square Park incident that killed five and a 2003 Santa Monica Farmers’ Market incident that killed ten …

Given time, the driver can model the situation, figure out what’s wrong, and correct. But, as my sister can attest, when you’re six feet in front of the garage with the 350 V8 Buick at full throttle, there isn’t a lot of time.

Read more at the Washington Examiner

As a prelude to my next look at alternative fuels models, some thoughts on spreadsheets.

Everyone loves to hate spreadsheets, and it’s especially easy to hate Excel 2007 for rearranging the interface: a productivity-killer with no discernible benefit. At the same time, everyone uses them. Magne Myrtveit wonders, Why is the spreadsheet so popular when it is so bad?

Spreadsheets are convenient modeling tools, particularly where substantial data is involved, because numerical inputs and outputs are immediately visible and relationships can be created flexibly. However, flexibility and visibility quickly become problematic when more complex models are involved, because:

• Structure is invisible and equations, using row-column addresses rather than variable names, are sometimes incomprehensible.
• Dynamics are difficult to represent; only Euler integration is practical, and propagating dynamic equations over rows and columns is tedious and error-prone.
• Without matrix subscripting, array operations are hard to identify, because they are implemented through the geography of a worksheet.
• Arrays with more than two or three dimensions are difficult to work with (row, column, sheet, then what?).
• Data and model are mixed, so that it is easy to inadvertently modify a parameter and save changes, and then later be unable to easily recover the differences between versions. It’s also easy to break the chain of causality by accidentally replacing an equation with a number.
• Implementation of scenario and sensitivity analysis requires proliferation of spreadsheets or cumbersome macros and add-in tools.
• Execution is slow for large models.
• Adherence to good modeling practices like dimensional consistency is impossible to formally verify

For some of the reasons above, auditing the equations of even a modestly complex spreadsheet is an arduous task. That means spreadsheets hardly ever get audited, which contributes to many of them being lousy. (An add-in tool called Exposé can get you out of that pickle to some extent.)

There are, of course, some benefits: spreadsheets are ubiquitous and many people know how to use them. They have pretty formatting and support a wide variety of data input and output. They support many analysis tools, especially with add-ins.

For my own purposes, I generally restrict spreadsheets to data pre- and post-processing. I do almost everything else in Vensim or a programming language. Even seemingly trivial models are better in Vensim, mainly because it’s easier to avoid unit errors, and more fun to do sensitivity analysis with Synthesim.

Long ago, in the MIT SD PhD seminar, a group of us replicated and critiqued a number of classic models. Some of those formed the basis for my model library. Around that time, Liz Keating wrote a nice summary of “How to Critique a Model.” That used to be on my web site in the mid-90s, but I lost track of it. I haven’t seen an adequate alternative, so I recently tracked down a copy. Here it is: SD Model Critique (thanks, Liz). I highly recommend a look, especially with the SD conference paper submission deadline looming.

Not to be outdone by Utah, South Dakota has passed its own climate resolution.

They raise the ante – where Utah cherry-picked twelve years of data, South Dakotans are happy with only 8. Even better, their pattern matching heuristic violates bathtub dynamics:

WHEREAS, the earth has been cooling for the last eight years despite small increases in anthropogenic carbon dioxide

They have taken the skeptic claim, that there’s little warming in the tropical troposphere, and bumped it up a notch:

WHEREAS, there is no evidence of atmospheric warming in the troposphere where the majority of warming would be taking place

Nope, no trend here:

Satellite tropospheric temperature (RSS, TLT)

Skipping over a red herring about Erik the Red,

WHEREAS, the polar ice cap is subject to shifting warm water currents and the break-up of ice by high wind events. Many oceanographers believe this to be the major cause of melting polar ice, not atmospheric warming

Now, where could the energy for those warm water currents and high wind events be coming from?

Next we get the Oregon Petition (you can fact check the signatories yourself using google). Finally, we come to the point:

NOW, THEREFORE, BE IT RESOLVED, …, that the South Dakota Legislature urges that instruction in the public schools relating to global warming include the following:
(1)    That global warming is a scientific theory rather than a proven fact;
(2)    That there are a variety of climatological, meteorological, astrological, thermological, cosmological, and ecological dynamics that can effect world weather phenomena and that the significance and interrelativity of these factors is largely speculative

Shouldn’t knowing what a theory is and knowing the difference between astrology and astronomy, thermology and thermodynamics be prerequisites for making science education policy?

You can’t make this stuff up. I guess I’ll have to quit telling North Dakotan jokes now.

I linked some newish work on sea level by Aslak Grinsted et al. in my last post. There are some other new developments:

On the data front, Rohling et al. investigate sea level over the last half a million years and in the Pliocene (3+ million years ago). Here’s the relationship between CO2 and Antarctic temperatures:

Two caveats and one interesting observation here:

• The axes are flipped; if you think causally with CO2 on the x-axis, you need to mentally reflect this picture.
• TAA refers to Antarctic temperature, which is subject to polar amplification
• Notice that the empirical line (red) is much shallower than the relationship in model projections (green). Since the axes are flipped, that means that empirical Antarctic temperatures are much more sensitive to CO2 than projections, if it’s valid to extrapolate, and we wait long enough.

Looking at the temperature-sea level relationship, they find this:

The takeaway is:

Regardless of the uncertainties surrounding the use of any one of the specific scenarios in Fig. 2, it is clear that equilibrium sea level for the present-day TCO2U of 387 ppmv resides within a broad range between 0 and C25 (+/-5) m. The lower limit of that range derives from model projections3, whereas the upper limit derives from data describing the Earth system’s pre-anthropogenic behaviour over the past 0.5-3.5 Myr (this study).

In other words, if paleo analogies apply, sea level rise might not be fast, but it could get very big.

Siddall, Stocker and Clark develop a similar nonlinear relationship between temperature and sea level, and use that in yet another simple-model simulation experiment:

I haven’t had a chance to replicate this yet, but it looks like there are two innovations over the Grinsted approach. First, the equilibrium temperature-sea level relationship is nonlinear (s-shaped), as shown above. Second, the time constant of adjustment is asymmetric (potentially faster for ice sheet decay than for growth).

Siddall et al. find 21st century sea level rise for the A1FI scenario to be 0.82m – a little lower than the Rahmstorf (2007) and Grinsted et al. results I last considered, but still higher than AR4’s 0.59m.

Update: Whoops, the projections have just been retracted.

Vermeer and Rahmstorf revisit the 2007 Rahmstorf model, and extend it by including an instantaneous response term. The new model is:

dH/dt = a * (T – T0) + b * dT/dt

The first term, with parameter a, is the initial linear approximation of sea level’s exponential adjustment to a new equilibrium with a very long time constant. The second term is new, and represents fast processes. One interpretation of the “instantaneous” second term is as thermal expansion of the mixed ocean layer as it takes up heat.

V&R find that the slow/fast model fits data and GCM simulations better. But the fit is a bit weird: the b parameter winds up negative, though one can interpret that as the net effect of a positive value with a delay. The resulting sea level projections are generally higher than Siddall et al. – 1.43m for A1FI.

I’ll be taking a look at these new papers soon, with a few questions in mind:

• Are the Rohling and Siddall equilibrium relationships comparable? (This is basically just a matter of overlaying the figures and adjusting for polar amplification.)
• How well supported is the right tail of the nonlinear temp-sea level relationship in Siddall, and how does that matter to projections?
• Is there a more physical dual-time-constant version of Vermeer and Rahmstorf that solves the negative-b problem?
• Does the V&R dual model solve the problem of hindcasting inconsistency, identified by Grinsted?

I’m waaayyy overdue for an update on sea level models.

I’ve categorized my 6 previous posts on the Rahmstorf (2007) and Grinsted et al. models under sea level.

I had some interesting correspondence last year with Aslak Grinsted.

I agree with the ellipsis idea that you show in the figure on page IV. However, i conclude that if i use the paleo temperature reconstructions then the long response times are ‘eliminated’. You can sort of see why on this page: Fig2 here illustrates one problem with having a long response time:

http://www.glaciology.net/Home/Miscellaneous-Debris/rahmstorf2007lackofrealism

It seems it is very hard to make the turn at the end of the LIA with a large inertia.

I disagree with your statement “this suggests to me that G’s confidence bounds, +/- 67 years on the Moberg variant and +/- 501 years on the Historical variant are most likely slices across the short dimension of a long ridge, and thus understate the true uncertainty of a and tau.”

The inverse monte carlo method is designed not to “slice across” the distributions. I think the reason we get so different results is that your payoff function is very different from my likelihood function – as you also point out on page VI.

Aslak is politely pointing out that I screwed up one aspect of the replication. We agree that the fit payoff surface is an ellipse (I think the technical I used was “banana-ridge”). However, my hypothesis about the inexplicably narrow confidence bounds in the Grinsted et al. paper was wrong. It turns out that the actual origin of the short time constant and narrow confidence bounds is a constraint that I neglected to implement. The constraint involves the observation that variations in sea level over the last two millenia have been small. That basically chops off most of the long-time-constant portion of the banana, leaving the portion described in the paper. I’ve confirmed this with a quick experiment.

The multivariate random walk sensitivity analysis method is cool:

The inverse monte carlo makes a uniform random walk in the model space, except that some steps are rejected according to some rules depending on the relative likelihood of the previous and current model. The end result is that it samples the model space according to the likelihood function. I.e. a region that has twice as high likelihood will be twice as densily sampled. The actual algorithm is extremely simple.

Probability of accepting a given step in the random walk is

rand*L(previous_accepted_model)<L(suggested_model)

It is also called the metropolis algorithm. It is a very useful method for high dimensional model spaces, or model spaces with multiple minima. Klaus’ page has the derivations of why this works.

The Metropolis algorithm is more famous for its use in simulated annealing. A few other notes:

I also have some clarifications regarding the sea level record we used. We use the jevrejeva 2006 one from 1850 onwards. Prior to that we use the amsterdam record. Quote:
“We also extend the GSL reconstruction prior to 1850 using the record of annual mean sea level from Amsterdam since 1700 (van Veen, 1945) correcting it for the post glacial land submergence rate of 0.16 mm yr-1 (Peltier, 2004)”

You write: “Notice that during the period of disagreement, reported standard errors (in gray) are large. However, the magnitude of the discrepancy between series is larger than the reported standard error. Either the papers are measuring slightly different things, or there’s a significant systematic component that leads to underestimation of the error bounds.”
My comment: It is to be expected that the two curves are more than a single standard error apart sometimes. These deviations may last for a long-long time considering that the uncertainty is highly serially correlated. In the G paper I model the uncertainty with something that is quite close to a markov chain process. the reason why we chose amsterdam prior to 1850 is because I think that the errors in the GIA correction for this location is rather low. Basically I think that the negative trend 1807-1850 in the jevrejeva06 curve is artificially caused by slightly wrong GIA corrections.

On page VI you say: “It’s not clear to me how they handle the flip side of the problem, state estimation with correlated driving noise – I think they ignore that” – That is sort-of correct, in the sense that we treat the temperature as given (=error-free) in each of the experiments. But we do not ignore it: We address it by using different temperature reconstructions. So the true uncertainty should be gauged from all experiments.

Aslak has some other interesting comments and papers:

• Relationship between sea level rise and global temperature (a look at the paleo constraints in Rahmstorf’s RC critique, which I cited in my first installment)
• Relative importance of mass and volume changes to global sea level rise
• Recent global sea level acceleration started over 200 years ago?
• Anthropogenic forcing dominates sea level rise since 1850

Daniel Sarewitz has a recent column in Nature (paywall, unfortunately). It contains some wisdom, but the overall drift conclusion is bonkers.

First, the good stuff: Sarewitz rightly points out the folly of thinking that more climate science (like regional downscaling) will lead to action where existing science has failed to yield any. Similarly, he observes that good scientific information about the vulnerability of New Orleans didn’t lead to avoidance of catastrophe.

For complex, long-term problems such as climate change or nuclear-waste disposal, the accuracy of predictions is often unknowable, uncertainties are difficult to characterize and people commonly disagree about the outcomes they desire and the means to achieve them. For such problems, the belief that improved scientific predictions will compel appropriate behaviour and lead to desired outcomes is false.

Then things go off the rails.

… perhaps the best thing that ever happened in the field of earthquake research was the recognition that earthquake prediction was likely to be impossible. In recent decades, the priorities of the US Geological Survey’s earthquake-hazard programme have moved away from prediction and towards the assessment, communication and reduction of vulnerabilities. This evolution has demanded closer collaboration between scientists and diverse regional and state decision-makers, to provide information that can help improve construction practices, land-use decisions, disaster-response plans and public awareness.

…

This difficulty is on spectacular yet unacknowledged display in the climate-change arena. …. A central obstacle is that predictions of longterm doom have created a politics that demands immense costs to be borne in the near term, in return for highly uncertain benefits that accrue only in a dimly seen future.

Science could help untangle this politically impossible dilemma by moving away from its obsession with predicting the long-term future of the climate to focus instead on the many opportunities for reducing present vulnerabilities to a broad range of today’s — and tomorrow’s — climate impacts. Such a change in focus would promise benefits to society in the short term and thus help transform climate politics. Strange as it may seem, the right lessons for the future of climate science come not from the success in predicting thunderstorms, floods and hurricanes, but from the failure to predict earthquakes.

Earthquakes are a terrible analogy for climate, because their causes are strictly natural (except for small earthquakes from dams, mining, geothermal extraction, etc.). Therefore there’s no equivalent of emissions mitigation for earthquakes. Reducing vulnerability is a good idea, but Sarewitz is basically suggesting that we abandon consideration of the long term entirely, which is foolish, because climate is not entirely unpredictable.

It’s also unjustified, because the dilemma he poses (“A central obstacle is that predictions of longterm doom have created a politics that demands immense costs to be borne in the near term, in return for highly uncertain benefits that accrue only in a dimly seen future”) is at least partially a false framing and in any case more a matter of economics than of science. Why not start by combining no-regrets mitigation with vulnerability reduction, rather than abandoning mitigation altogether?

The proposed solution – focusing science on reducing vulnerability – also strikes me as misguided. To reduce vulnerability you don’t need much more science (especially if you believe that climate is fundamentally unpredictable). You need lots of economics and policy work. So, what to do with all those unemployed climatologists, now that we can ignore the long term climate? Turn them into Ag Economists?

If climate is completely unpredictable, then we’d have to plan for vulnerability to a zero-mean forecast of future geophysical factors. For some localities and variables that might actually make sense – for example, here in Bozeman winter snowpack could go down (a temp effect) or up (a precip effect). But designing ports for sea level to rise or fall (both) would be rather expensive. Planning for vulnerability while pretending that we know nothing about anthropogenic effects on climate strikes me as a silly abdication of intelligence.

Perhaps most importantly, there are surely some irreducible vulnerabilities to either geophysical surprises or knock-on effects of climate impacts like migration and war. You’d have to live a fatalist random worldview to plan for vulnerability yet fail to consider mitigation as a way of reducing your aggregate risk.

A focus on vulnerability does have two benefits. First, we are committed to some climate change, so reducing vulnerability reductions have an intrinsic payoff. Presumably there are also win-wins, like getting rid of flood insurance subsidies on development in coastal areas that are also environmentally sensitive. Second, the act of planning for future climate variability may be a good way to make the implications of possible impacts, uncertainty, and long-lived infrastructure vulnerability very real to people.

Paul Krugman documents an ongoing health care death spiral in California:

Here’s the story: About 800,000 people in California who buy insurance on the individual market — as opposed to getting it through their employers — are covered by Anthem Blue Cross, a WellPoint subsidiary. These are the people who were recently told to expect dramatic rate increases, in some cases as high as 39 percent.

Why the huge increase? It’s not profiteering, says WellPoint, which claims instead (without using the term) that it’s facing a classic insurance death spiral.

Bear in mind that private health insurance only works if insurers can sell policies to both sick and healthy customers. If too many healthy people decide that they’d rather take their chances and remain uninsured, the risk pool deteriorates, forcing insurers to raise premiums. This, in turn, leads more healthy people to drop coverage, worsening the risk pool even further, and so on.

A death spiral arises when a positive feedback loop runs as a vicious cycle. Another example is Andy Ford’s utility death spiral. The existence of the positive feedback leads to counter-intuitive policy prescriptions:

But here’s the thing: suppose that we posit, provisionally, that the insurers aren’t the main villains in this story. Even so, California’s death spiral makes nonsense of all the main arguments against comprehensive health reform.

For example, some claim that health costs would fall dramatically if only insurance companies were allowed to sell policies across state lines. But California is already a huge market, with much more insurance competition than in other states; unfortunately, insurers compete mainly by trying to excel in the art of denying coverage to those who need it most. And competition hasn’t averted a death spiral. So why would creating a national market make things better?

More broadly, conservatives would have you believe that health insurance suffers from too much government interference. In fact, the real point of the push to allow interstate sales is that it would set off a race to the bottom, effectively eliminating state regulation. But California’s individual insurance market is already notable for its lack of regulation, certainly as compared with states like New York — yet the market is collapsing anyway.

Finally, there have been calls for minimalist health reform that would ban discrimination on the basis of pre-existing conditions and stop there. It’s a popular idea, but as every health economist knows, it’s also nonsense. For a ban on medical discrimination would lead to higher premiums for the healthy, and would, therefore, cause more and bigger death spirals.

What would work?

By all means, let’s ban discrimination on the basis of medical history — but we also have to keep healthy people in the risk pool, which means requiring that people purchase insurance. This, in turn, requires substantial aid to lower-income Americans so that they can afford coverage.

In other words: the positive feedback from selective participation in the risk pool makes insurance an all-or-nothing proposition. Either we go for all-out Darwinism around health and genetics (the de facto outcome of collapse) or require universal participation as Krugman suggests.

Still, I can’t help wondering if there are alternatives that mitigate the self-selection problem while providing for a measure of individual choice. Is there a two-sided Vickrey auction that creates an incentive for everyone to reveal their true preference for health care over the long haul? Could such a system be made comprehensible and compatible with misperceptions of feedback, or would it just become a tax on ignorance?

The Utah House has declared that CO2 is harmless. The essence of the argument in HJR 12: temperature’s going down, climategate shows that scientists are nefarious twits, whose only interest is in riding the federal funding gravy train, and emissions controls hurt the poor. While it’s reassuring that global poverty is a big concern of Utah Republicans, the scientific observations are egregiously bad:

29 WHEREAS, global temperatures have been level and declining in some areas over the
30 past 12 years;
31 WHEREAS, the “hockey stick” global warming assertion has been discredited and
32 climate alarmists’ carbon dioxide-related global warming hypothesis is unable to account for
33 the current downturn in global temperatures;
34 WHEREAS, there is a statistically more direct correlation between twentieth century
35 temperature rise and Chlorofluorocarbons (CFCs) in the atmosphere than CO2;
36 WHEREAS, outlawed and largely phased out by 1978, in the year 2000 CFC’s began to
37 decline at approximately the same time as global temperatures began to decline;
…
49 WHEREAS, Earth’s climate is constantly changing with recent warming potentially an
50 indication of a return to more normal temperatures following a prolonged cooling period from
51 1250 to 1860 called the “Little Ice Age”;

The list cherry-picks skeptic arguments that rely on a few papers (if that), nearly all thoroughly discredited. There are so many things wrong here that it’s not worth the electrons to refute them one by one. The quality of their argument calls to mind to the 1897 attempt in Indiana to legislate that pi = 3.2. It’s sad that this resolution’s supporters are too scientifically illiterate to notice, or too dishonest to care. There are real uncertainties about climate; it would be nice to see a legislative body really grapple with the hard questions, rather than chasing red herrings.

First, check out SEED’s recent article, which asks, When it comes to scientific publishing and fame, the rich get richer and the poor get poorer. How can we break this feedback loop?

For to all those who have, more will be given, and they will have an abundance; but from those who have nothing, even what they have will be taken away.
—Matthew 25:29

Author John Wilbanks proposes to use richer metrics to evaluate scientists, going beyond publications to consider data, code, etc. That’s a good idea per se, but it’s a static solution to a dynamic problem. It seems to me that it spreads around the effects of the positive feedback from publications->resources->publications a little more broadly, but doesn’t necessarily change the gain of the loop. A better solution, if meritocracy is the goal, might be greater use of blind evaluation and changes to allocation mechanisms themselves.

The reason we care about this is that we’d like science to progress as quickly as possible. That involves crafting a reward system with some positive feedback, but not so much that it easily locks in to suboptimal paths. That’s partly a matter of the individual researcher, but there’s a larger question: how to ensure that good theories out-compete bad ones?

Now check out the work of John Sterman and Jason Wittenberg on Kuhnian scientific revolutions.