Government Science

I’m seeing a few articles on Government-sponsored science lately that seem particularly applicable to the climate change research:

A short review of Economic Laws of Scientific Research links to an overview of the area, particularly the Cato Institute

Scientists may love government money, and politicians may love the power its expenditure confers upon them, but society is impoverished by the transaction.

Another in a similar vein on medical research reminds me of Craig Venter’s decoding of the Human Genome. I was at the San Diego Supercomputer at the time, and his use of innovative use of supercomputing to assemble pieces of DNA — called shotgun sequencing — made the Government-funded competitors look like clods. There was a prize offered, and it was decided to award the prize to both – how very droll.

A more balanced argument is presented here. Some infrastructural components, like large meteorological data sets, are better handled by government departments than others.

Professor Sinclair Davidson shows that the standard economic analysis supporting public expenditure on research is fundamentally and methodologically flawed.

The notion that throwing an infinite amount of money at public research will somehow, at some time, automatically lead to some benefit is a myth. The government spends a substantial amount on public science and innovation. It is not clear that any substantial benefit is derived from that expenditure.

He identifies the following ‘stepping stones':

  • R&D is not a public good.
  • The cost of public funds is not lower than the cost of private funds.
  • The returns to public science are low.
  • Governments have a poor track record of picking ‘winners’.
  • Publicly funded R&D has a negative impact on economic growth.
  • Economists are unable to explain how spillovers occur, or how valuable these spillovers are.

The main argument against government science, that “publicly-performed R&D crowds out resources that could be alternatively used by the private sector” needs to be strengthened in the case of climate science.

The push for taxes like the ETS, and subsidising impractical renewable energy schemes shows the impact of government climate science is regressive.

Climate science seems to particularly prone to the worst aspects of government science, from the UN IPCC process, to ClimateGate and through the enquiries, it’s like an season of ‘Yes, Minister’. If global warming is eventually shown to be non-existent or harmless, no doubt the climate scientists will declare victory and say they were sceptics all along.

Audit the BoM

Kenskingdom demonstrates again the wisdom of ‘trust, but verify':

I compared the adjusted [Australian Temperature] data with the raw data of these 34 stations.

Here are the results, and they are perplexing.

* I was expecting to find a stronger warming trend in the urban data than the 100 non-urban sites. WRONG.
* I was expecting to find BOM correcting for UHI, that is, reducing the trend. PARTLY RIGHT. But less often than with the non-urban sites.
* I was expecting the urban sites to have much better quality of data, with long records, few gaps, and good overlaps if stations’ data had to be combined. WRONG.

Fed up with c**p government science yet?

See JoNova for more.

Climate Models Falsified

From Roger Pielke Sr:

Writing in 2005, Hansen, Willis, Schmidt et al. suggested that GISS model projections had been verified by a solid decade of increasing ocean heat (1993 to 2003). This was regarded as further confirmation the IPCC’s AGW hypothesis. Their expectation was that the earth’s climate system would continue accumulating heat more or less monotonically. Now that heat accumulation has stopped (and perhaps even reversed), the tables have turned. The same criteria used to support their hypothesis, is now being used to falsify it.

It is evident that the AGW hypothesis, as it now stands, is either false or fundamentally inadequate. One may argue that projections for global warming are measured in decades rather than months or years, so not enough time has elapsed to falsify this hypothesis. This would be true if it were not for the enormous deficit of heat we have observed. In other words, no matter how much time has elapsed, if a projection misses its target by such a large magnitude (6x to 8x), we can safely assume that it is either false or seriously flawed.

Whether the anthropogenic global warning hypothesis is invalid or merely incomplete, the time has come for serious debate and reanalysis. Since Dr. Pielke first published his challenge in 2007, no critical attempts have been made to explain these failed projections. His blogs have been greeted by the chirping of crickets. In the mean time costly political agendas focused on carbon mitigation continue to move forward, oblivious to recent empirical evidence. Open and honest debate has been marginalized by appeals to consensus. But as history has often shown, consensus is the last refuge of poor science.

Failed science, corrupt institutions, and inadequate government science. Its a sad state.

Queensland Drought Comparisons

In 2009, the Queensland Climate Change Centre of Excellence prepared a series of reports detailing projected climate changes for 13 regions throughout Queensland. The reports provide a high-level summary of projected changes and an accessible overview of the potential impacts to a wide audience, including:

# a tendency for less rainfall, particularly in central and southern regions throughout winter and spring;
# more severe droughts, occurring with increasing frequency;

CO2 Science reviews a study of the United States’ Northern Great Plains which like Queensland, is a significant source of grain both locally and internationally, and like Queensland because of its location, it is also susceptible to extreme droughts. Because of this fact, it is probably as good a place as any to look for a manifestation of the climate-alarmist claim (Gore, 2006; Mann and Kump, 2008) that global warming will usher in a period of more frequent and intense drought.

The conclusions:

In light of climate-alarmist predictions of intensified drought conditions in a warming world, many people would assuredly claim that any new period of intensified drought on America’s Northern Great Plains would be a vindication of those prognostications … and probably of other climate-alarmist contentions as well. It is clear from the work of Fritz et al., however, that such need not be the case; for everything bad that happens need not be the result of rising atmospheric CO2 concentrations, as the study here described clearly demonstrates.

Here is the rainfall anomaly for the last 3 years (weather is not climate, yadda yadda). Almost no area has had below average rainfall.

Projected future runoff of the Breede River under climate change

More evidence of worthless model predictions from CO2 Science:

All of the future flow-rates calculated by Steynor et al. exhibited double-digit negative percentage changes that averaged -25% for one global climate model and -50% for another global climate model; and in like manner the mean past trend of four of Lloyd’s five stations was also negative (-13%). But the other station had a positive trend (+14.6%). In addition, by “examination of river flows over the past 43 years in the Breede River basin,” Lloyd was able to demonstrate that “changes in land use, creation of impoundments, and increasing abstraction have primarily been responsible for changes in the observed flows” of all of the negative-trend stations.

Interestingly, Steynor et al. had presumed that warming would lead to decreased flow rates, as their projections suggested; and they thus assumed their projections were correct. However, Lloyd was able to demonstrate that those results were driven primarily by unaccounted for land use changes in the five catchments, and that in his newer study the one site that had “a pristine watershed” was the one that had the “14% increase in flow over the study period,” which was “contrary to the climate change predictions” and indicative of the fact that “climate change models cannot yet account for local climate change effects.” As a result, he concluded that “predictions of possible adverse local impacts from global climate change should therefore be treated with the greatest caution,” and that, “above all, they must not form the basis for any policy decisions until such time as they can reproduce known climatic effects satisfactorily.”

Matt Ridley on AGW

Matt Ridley’s article sensibly concludes that the likely outcome is very mild AGW.

So I have concluded that global warming will most probably be a fairly minor problem – at least compared with others such as poverty and habitat loss – for nature as well as people.

After watching the ecologically and economically destructive policies enacted in its name (biofuels, wind power), I think we run the risk of putting a tourniquet around our collective necks to stop a nosebleed.

He suggests the most likely outcome is damage to the reputation of science by the unscrupulous who will milk it for fear and uncertainty for as long and as much as its worth.

Long-time temperature variations in Portugal over the last 140 years and the effect of the solar activity

A recipe for more reliable climate correlations with solar factors – use long temperature records such as Portugal for 140 years (from 1865 to 2005). Another study showing around half of decadal to centennial variations in temperature can be attributed to Cosmic Ray Flux.

Monthly averaged temperature series have been analyzed together with monthly North-Atlantic Oscillation (NAO) index data, sunspot numbers (W) and cosmic ray (CR) flux intensity. The absolute values of the correlation coefficients between the temperature and the CR are higher than those between the temperature and the sunspot numbers. Our results are consistent with some of the proposed mechanisms that relate solar activity to Earth climate and could be explained through the effect of the solar UV radiation and stratosphere-troposphere coupling or/and through the effect of the CR particles on clouds and stratospheric and tropospheric conditions.

More Tropical Inflow

Another example today of an upper atmosphere trough (note the kink in the isobars and the embedded high in the centre) dragging in tropical moisture at altitude. This is a major source of widespread rains.

Bucket Analogy for Spencer’s Feedbacks

The latest paper by Roy Spencer claiming negative feedback from AGW really has the alarmists choking on their baguettes, so I thought I would try to explain it with an analogy.

Feedbacks represent a secondary effect, but its not that much harder to understand with the following analogy.

Imagine the atmosphere as a bucket. Short-wave solar radiation pours in the top (yellow arrow), some splashes out (orange arrow), and long wave radiation out a hole in the bottom (red arrow). The level of water in the bucket is determined by the relative sizes of the flows, in turn determined by the size of the holes.

The level of water is the observable global surface temperature (green).

The primary effect is this. If the radiation pouring in the top increases, the level of water (temperature) in the bucket must increase until the flow out the hole in the bottom matches the flow coming in the top. This relationship is rigorously determined by laws of conservation.

Feedback represents a tendency for the water (temperature) to depart from this relationship. We can analogise this by varying the size of the holes in the top or the bottom of the bucket.

Positive feedback can be imagined to occur in two ways. Either the size of the top of the bucket increases as the water level increases, so less splashes out, or the size of the hole in the bottom decreases, so less flows out. The latter mechanism is represented by such effects as greenhouse gasses holding back the outgoing radiation.

Negative feedback is when the water level increases less than expected, and could be due to the hole in the top of the bucket decreasing, due to increasing albedo (SW reflection from cloud tops) or increasing size of the hole in the bottom (decreasing low level cloud letting more LW radiation out, as in the Lindzen Iris effect).

Now Spencer makes at least two interesting claims. The first is that most previous studies have ‘blundered’ because feedbacks are essentially unobservable from temperature only. This can be seen from the analogy. If the level of the water increased by 10 units, but feedback caused it to reduce by 9 units, then the net effect of an increase of one unit is indistinguishable from all of the other possibilities.

The second claim is that the only significant observable feedback from examining the most accurate satellite temperature and outgoing radiation data is a strong negative feedback. That is, when the water level increases, the size of the hole in the top decreases or the size of the hole in the bottom of the bucket increases to let more radiation out.

Now if Spencer is correct the only argument that alarmists have left is that positive feedback (and correspondingly high climate sensitivity) is necessary to explain glacial-interglacial transitions of 5C every 100,000 years or so. Well there are so many unknowns that far back, and changes in surface albedo from changing ice sheets are a stronger effect than CO2 anyway, so to base your argument on somethng so vague, and distant, just sounds silly.

And silly is how the alarmists are looking more and more these days. First they put their faith in an IPCC that might be put-down due to misrepresentation. Then they rely on models that have been known to be ‘running hot’. Cite unprecedented temperatures based on 1000 year thermometers that don’t work. And now, blundering into false interpretations of feedbacks that they didn’t understand properly in the first place.

CSIRO Mk3 Model Performance

On the comparisons of Climate Models from Douglass et al here is a table showing how well(?) the CSIRO Mark 3 model performed.

In layers near 5 km, the modelled trend is 100 to 300% higher than observed, and, above 8 km, modelled and observed trends have opposite signs.

The raw data are from (2007).

Table II. (a). Temperature trends for 22 CGCM Models with 20CEN forcing. The numbered models are fully identified in Table II(b).

Pressure (hPa)–>Surface 1000 925 850 700 600 500 400 300 250 200 150 100 Model Sims.∗ Trends (milli °C/decade)

1 9 128 303 121 177 161 172 190 216 247 263 268 243 40
2 5 125 1507 113 112 123 126 138 148 140 105 2 −114 −161
3 5 311 318 336 346 376 422 484 596 672 673 642 594 253
4 5 95 92 99 99 131 179 158 184 212 224 182 169 −3
5 5 210 302 224 215 249 264 293 343 391 408 400 319
6 4 119 118 148 175 189 214 238 283 365 406 425 393 −33
7 4 112 460 107 123 122 130 155 183 213 228 225 211 0
8 3 86 62 57 58 82 95 108 134 160 163 155 137 100
9 3 142 143 148 150 149 162 200 234 273 284 282 258 163
10 3 189 114 200 210 225 238 269 316 345 348 347 308 53
11 3 244 403 270 278 309 331 377 449 503 481 461 405 75
12 3 80 173 114 115 102 98 124 150 161 164 166 142 4
13 2 162 155 170∗∗ 182 225 218 221 282 352 360 340 277 −39
14 2 171 293 190 197 252 245 268 328 376 367 326 278 69
15 2 163 213 174 181 199 204 226 271 307 299 255 166 53
16 2 119 128 124 140 151 176 197 228 271 289 306 260 120
17 2 219 −1268 199 223 259 283 321 373 427 454 479 465 280
18 1 117 117 126 148 163 159 180 207 227 225 203 200 16
19 1 230 220 267 283 313 346 410 506 561 554 526 521 244
20 1 191 151 176 194 212 237 254 304 387 410 400 367 314
21 1 191 328 241 222 193 187 215 255 300 316 327 304 90
22 1 28 24 46 73 27 −26 −26 −1 20 24 32 −1 −136

Total simulations: 67

Average 156 198 166 177 191 203 227 272 314 320 307 268 78
Std. Dev. (σ) 64 443 72 70 82 96 109 131 148 149 154 160 124
∗ ‘Sims.’ refers to the number of simulations over which averages
CSIRO are number 15, with results compared to average of 67 runs on the line below

CSIRO 163 213 174 181 199 204 226 271 307 299 255 166 53
AVERAGE 156 198 166 177 191 203 227 272 314 320 307 268 78
Std. Dev. 64 443 72 70 82 96 109 131 148 149 154 160 124

h/t Geoff Sherrington