1. Modelling - last I heard, all the computer power in the world couldn't accurately model a mouse. Well, I calculate the whole world's probably a mite more complex than a mouse, so my question is can you verify the accuracy and comprehensiveness of your model?
There's a common phrase among people who use models. No models are correct, some are useful. We don't have another planet to experiment with, and science is a combination of observation and experimentation. So what we're left with is modelling experiments for things like investigating new hypotheses on the global climate, and looking at the importance of factors and variables of interest. As for accuracy of models, there is a fairly large list of outcomes that were predicted from models, before the observations could confirm them. They include things like polar amplification, i.e. it's warming faster at the poles than at the equator. They include other things like the decreasing diurnal temperature trend, the night time temperatures are warming faster than the daytime temperatures. Models predicted that the troposphere would warm while the stratosphere cools, and this one is particularly relevant because that can't happen without an enhanced greenhouse effect. Lots of others, like expanding Hadley cells, the amount of water vapour in the atmosphere, poleward migration of storm tracks, even hindcasting of sea surface temperatures during the last glacial maximum.
So, yeah, no models are correct, but
some are useful. I say some, and that's important, because it doesn't do any good to lump them all together. Lots of fields use modeling experiments, and anyone who does will tell you it's not exactly what you should expect in the field, because we can't model reality, we can't predict the random variations in the real world, but we can model it. I use models everyday at work, disease models. My lab experiments aren't good enough though for a regulating agency to accept on their own. We require field studies to confirm the effects we observe in the lab.
If we could model reality, it wouldn't be a model anymore.
2. Sampling - how good, widespread, and comprehensive is your sampling? I'm asking because, best I know, we've only had thermometers for a smidge over a hundred years, and Mr. Fahrenheit's six-foot tubes of water weren't exactly precision instruments. I mean, considering the Earth is exactly 6017 years, two months, thirty days, eleven hours, and 47 minutes old, ain't a century of observations kinda like looking at a Tuesday afternoon from 2:00 to 4:00 p.m. and predicting the year's weather from just that? And how many thermometers y'all got? I hear folks talk about ocean temperatures. How many thermometers y'all got down on the seabed?
This requires a bit of statistical savvy. How many samples do you need to adequately represent the population? If you buy a package of food, how certain are you that say, a bag of chips that says it contains 220 grams actually has 220 grams of potato chips? If you sample multiple bags as they come off the line, you'll get a range of values. Sample enough and you'll get a sample average that is indicative of the population mean. For climate observations, there's an extra bit of information to throw in there. One of the other climate threads I saw this morning, the lightning one, posters are talking about temperatures and the temperatures in nearby and farther away cities. The other bit I'm talking about is auto-correlation, where observations taken close together in time or space tend to measure the same effect, so the random bit of sampling is not really random anymore. Normally when you sample from a population, you want to claim that your samples are independent measurements. But with temperature, the value you measure in Brooklyn is very likely going to be similar to the measurement you get in Queens. If you followed those two stations over time, you'd see that they both tend to move in concert with one another. That's auto-correlation. So for measuring temperature on the globe, it's not really about how many thermometers you have, but what kind of spatial coverage do you have. The various agencies tracking global surface temperture have various methods for dealing with this, and they've published the results. Have a look below, at the difference between the complete US historical climate network dataset using all thermometers, and the dataset used:
Sometimes stations move, or the station owners change the method that they report temperatures by, or large scale land changes around the station occur. The meteorological agencies involved look at all of this, and make adjustments to their network to ensure that these changes aren't biasing the end result, and looking at those two time series above, that's clearly true.
As for the seabed, well that would only give you sea floor measurements! The ARGO network of floats goes around the worlds ocean, and rises and falls in the water column, so it's reporting temperature in 3 dimensions. Here's a global map showing you as of yesterday, where all the ARGO floats were:
Pretty significant global coverage.
3. Compensatory factors - OK, I get it. If you dump megatons of carbon dioxide into the atmosphere, planet's gonna warm up. Any fool can see that. But what about plants? They eat carbon dioxide, and seems to me if there's more carbon dioxide, the plants'll be all well-fed and happy. And when they're well-fed and happy, they reproduce a lot (don't we all?). Which'd tend to bring down the CO2 some. Now, being an Oklahoma redneck, I'd never use words like "self-correcting systems" or "homeostatsis," but I figured you smart guys might could. Got that in your model?
I've covered this in other posts here before. It's not so straightforward. Not all plants use the same biochemical pathways. For the photsynthesis reaction, there are two types of plants, C3 and C4 plants. The difference is in how efficient the plant is at enzymatically converting the carbon as a substrate, into sugars. Rather than type it all out again, I'll just cut and paste it here:
"Do you know what the difference is between a C4 and C3 carbon fization by plants? In the latter case, higher temperatures and drought will erase gains made by more carbon dioxide. More CO2 does not necessarily mean more food. The plants utilizing the C3 carbon fixing pathways lose roughly 25-30% of the fixed carbon. The enzyme
RuBisCO (read the products paragraph in the section on Enzymatic activity) is responsible for two reactions, carboxylation, and oxegenation. When plants are stressed by heat and drought, more carbon is utilized by RuBisCo in the oxeganation pathway, losing more than the 25-30% of carbon I mentioned above.
If the world is growing more tropical plants, then yes, generally more CO2 means more plant food. In the developed world, where we grow temperate climate cultivars, the end result is far more uncertain, because of those other factors like droughts and heat stress."
As for the reproduce more, the plants will still need space. That measn competition to fill it, and that's really where we run into problems. Our society is built on infrastructure that was built for the climate we have. Plants are part of our infrastructure, a big part of food production, and resources. We may find that what we would call weeds beats out the plants we have built supply chains and products around. The winners in the plant competition might not fit so well. Of course we can adapt, but that's not to say things will be better, or worse. And it will certainly be a cost to adapt, to change our infrastructure.
4. Closed systems - I hear a lot about treating the Earth as a closed system. But the helmsman of Starship Earth just reported a fair-sized thermonuclear reactor 'bout 150,000,000 kilometers off the starboard bow. And the one thing we know even from our Tuesday-afternoon observations is its output ain't steady. Don't vary much, but with a million-mile-wide wildcatting fusion reactor, how much y'all need? I'd never use big ol' words like "insolation," but maybe y'all should think on it some.
Insolation is part of the picture. Those climate models we talked about up above, none would be useful if they didn't account for insolation. They'd all produce a snowball earth if they ignored that part of the equation. Closed and open systems, conceptually they don't change the physics of a greenhouse gas molecule vibrating, rotating, absorbing energy at a bandwidth and re-emitting it to get back to the molecules rest state.
5. History - Ice ages? Do we know how they happen? Might that have some effect?
Insolation
Milankovitch cycles dominate our ice age cycle, changes in our orbits eccentricity and precession, axial tilt. The cycles alone don't explain it, you need to account for other factors as well. Like feedbacks, including greenhouse feedbacks.
There's a really good talk given by Richard Alley a few years ago at the 2009 American Geophysical Union Fall meeting conference. I'd highly recommend it if you're interested in this topic, the talk is called "The Biggest Control Knob: Carbon Dioxide in Earth's Climate History". It's a great talk, a bit technical, but he explains it all very well I think.
Richard Alley: "The Biggest Control Knob: Carbon Dioxide in Earth's Climate History" - YouTube
Damn, that was longer than I thought! In Praxius' league.