Computers and robots have helped automate science dramatically over the last 70 years.  In most cases, this involved so-called ‘brute force’ applications, where the scope of calculations involved sucked up a lot of time and required a lot of people.  Computers helped displace the legions of human calculators – usually female -  employed to do astronomical or ballistic calculations by hand (read When Computers Were Human).  Computers and robotics helped make genome mapping something that can be done in months rather than decades.

In what could prompt another shift in scientific human resources, there are reports (H/T Wired Science) of a robot actually forming and testing hypotheses.  Working in the baker’s yeast genome, the robot Adam worked to fill in gaps in understanding of the yeast’s metabolism.  By scanning a database of similar genes and enzymes from other organisms, Adam utilized algorithms to determine possible genes in the yeast genome that would correspond to orphan enzymes – enzymes that had no known gene coding for them.  After forming an hypothesis, Adam would conduct the experiment, analyze the data, and refine the hypothesis.  The robot’s designers recently confirmed by hand Adam’s discovery of three genes that coded for an orphan enzyme.

At the moment, designing these robots will continue to be a specialized affair – one robot for a particular area of research will likely be different from a robot for a different area of research.  But if there is the potential of standardizing, or at least spreading, such robot development, how and where science can be conducted could change dramatically.

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MSNBC reported last week that warnings and trends connected to climate change were included in the recently released “Global Trends 2025″ report issued by the National Intelligence Council.  I put this on par with the determination during the Clinton Administration that the AIDS pandemic in Africa qualified as a national security issue.  As I’ve heard before that wars over water and resources were likely in the future, the kinds of risks outlined by the National Intelligence Council are perhaps more updated than completely new.

My first reaction upon seeing this story was that someone was having some fun. But it doesn’t seem like benthic bacteria . . . So this article from the AFP comes as a surprise, and a reminder that forecasting the future remains a perilous business. With news like this, it seems premature to dismiss skepticism about climate science as fading away, far from it, expect skeptics of all sorts to have a bit more bounce in their steps.

DHAKA (AFP) – New data shows that Bangladesh’s landmass is increasing, contradicting forecasts that the South Asian nation will be under the waves by the end of the century, experts say.

Scientists from the Dhaka-based Center for Environment and Geographic Information Services (CEGIS) have studied 32 years of satellite images and say Bangladesh’s landmass has increased by 20 square kilometres (eight square miles) annually.

Maminul Haque Sarker, head of the department at the government-owned centre that looks at boundary changes, told AFP sediment which travelled down the big Himalayan rivers — the Ganges and the Brahmaputra — had caused the landmass to increase.

The rivers, which meet in the centre of Bangladesh, carry more than a billion tonnes of sediment every year and most of it comes to rest on the southern coastline of the country in the Bay of Bengal where new territory is forming, he said in an interview on Tuesday.

The United Nations Intergovernmental Panel on Climate Change (IPCC) has predicted that impoverished Bangladesh, criss-crossed by a network of more than 200 rivers, will lose 17 percent of its land by 2050 because of rising sea levels due to global warming.

The Nobel Peace Prize-winning panel says 20 million Bangladeshis will become environmental refugees by 2050 and the country will lose some 30 percent of its food production.

Director of the US-based NASA Goddard Institute for Space Studies, professor James Hansen, paints an even grimmer picture, predicting the entire country could be under water by the end of the century.

But Sarker said that while rising sea levels and river erosion were both claiming land in Bangladesh, many climate experts had failed to take into account new land being formed from the river sediment.

“Satellite images dating back to 1973 and old maps earlier than that show some 1,000 square kilometres of land have risen from the sea,” Sarker said.

“A rise in sea level will offset this and slow the gains made by new territories, but there will still be an increase in land. We think that in the next 50 years we may get another 1,000 square kilometres of land.”

Mahfuzur Rahman, head of Bangladesh Water Development Board’s Coastal Study and Survey Department, has also been analysing the buildup of land on the coast.

He told AFP findings by the IPCC and other climate change scientists were too general and did not explore the benefits of land accretion.

“For almost a decade we have heard experts saying Bangladesh will be under water, but so far our data has shown nothing like this,” he said.

UPDATE: At Dot Earth Andy Revkin labels an excerpt from this op-ed the “quote of the day.”

I have an invited op-ed in today’s Financial Post (a Canadian newspaper with a skeptical editorial perspective on climate change). I argue that even though many scientists oversell the predictive capabilities of climate models, action on climate change still makes sense. Here is an excerpt:

So in the debate on what to do about climate change, what are we to make of the overstated claims of predictive accuracy offered by many scientists?
Not surprisingly, the reason for overstated claims lies in the bitter and contested politics of climate change. Myanna Lahsen, an anthropologist who has studied climate modelers, finds that many of these scientists are acutely aware of the fact that any expressed “caveats, qualifications and other acknowledgements of model limitations can become fodder for the anti-environmental movement.” She documents how, more than a decade ago, a prominent climate scientist warned a group of his colleagues at the National Center for Atmospheric Research, home of one of the main U.S. climate modeling efforts that informs the IPCC, to “Choose carefully your adjectives to describe the models. Confidence or lack of confidence in the models is the deciding factor in whether or not there will be policy response on behalf of climate change.”

I witnessed this dynamic in practice while I was waiting to testify on climate policy before the U.S. Congress in 2006. A prominent climate scientist testifying on the panel appearing before mine was asked by a member of Congress about uncertainties in predictions from climate models. The scientist replied, enthusiastically and accurately, that there are a range of important uncertainties coming from scenario inputs and choices in parameterization schemes, instantly overwhelming his congressional audience with technical detail. Much later, and after a long break, the scientist requested an opportunity to clarify his earlier comments, and this time he said, “I would like to give you a little more direct answer to the question on reliability of climate models. I think they are reliable enough to be a very useful guide into the future.”

Lost in the Manichean debate over climate change is the real significance of what climate models really are telling us: We should act on climate mitigation and adaptation not because we are able to predict the future, but because we cannot.

See it all here. Comments and reactions welcomed.

[UPDATE]Real Climate did not like the figure shown below, so I responded to them with the following request, submitted as a comment on their site:

If these guys know what is “wrong” then they must have an idea about what is “right”.

Real Climate writes an entire post responding to Steve McIntyre’s recent discussions of buckets and sea surface temperatures, explaining why the issue doesn’t really matter, but for some weird reason they can’t seem to mention him by name or provide a link to what they are in fact responding to. (If the corrections don’t matter, then one wonders, why do them? Thompson et al. seemed to think that the issue matters.)

Real Climate does seem have mastered a passive voice writing style, however. Since they did have the courtesy to link here, before calling me “uninformed” (in deniable passive voice of course), I though a short response was in order.

Real Climate did not like our use of a proposed correction suggested by He Who Will Not Be Named. So Real Climate proposed another correction based on a graphic printed in The Independent. Never mind that the correction doesn’t seem to jibe with that proposed by Thompson et al., but no matter, we used the one suggested by Mr. Not-To-Be-Named so lets use Real Climate’s as well and see what difference it makes to temperature trends since 1950. Based on what Real Climate asserts (but oddly does not show with numbers), you’d think that their proposed adjustment results in absolutely no change to mid-20th century trends, and indeed anyone suggesting otherwise is an idiot or of ill-will. We’ll lets see what the numbers show.

The graph below shows a first guess at the effects of the Real Climate adjustments (based on a decreasing adjustment from 1950-60) based on the graphic in The Independent.

What difference to trends since 1950 does it make? Instead of the about 50% reduction in the 1950-2007 trend from the first rough guess from you-know-who, Real Climate’s first guess results in a reduction of the trend by about 30%. A 30% reduction in the IPCC’s estimate in temperature trends since 1950 would be just as important as a 50% reduction, and questions of its significance would seem appropriate to ask. But perhaps a 30% reduction in the trend would be viewed as being “consistent with” the original trend

Try again Real Climate. And next time, his name is STEVE MCINTYRE — and his blog is called CLIMATE AUDIT. There is a lot of science and civil discussion there, with a healthy mix of assorted experts and a range of ordinary folks. Questioning scientific conclusions is a lot healthier for science than rote defense, but we all learned that in grad school, didn’t we?

The graph above shows three trend lines.

BLUE: Temperature Trend prediction from the 1990 IPCC report
RED: Temperature Trend prediction from the 2007 IPCC report
GREEN: Observed Trend for 2001-2007 (from average of four obs datasets)

All data is as described in this correspodence (PDF).

Your assignment:

Which IPCC prediction is the trend observed 2001-2007 more consistent with and why? Show your work!

You are free to bring in whatever information and use whatever analysis that you want.

The May 1, 2008 issue of New Scientist magazine has an interesting article that parallels some of the discussions that we’ve had on this site lately. Here is an interesting excerpt:

“Politicians seems to think that the science is a done deal,” says Tim Palmer, “I don’t want to undermine the IPCC, but the forecasts, especially for regional climate change, are immensely uncertain”.

Palmer is a leading climate modeller at the European Centre for Medium-Range Weather Forecasts in Reading, UK, and he does not doubt that the Intergovernmental Panel on Climate Change (IPCC) has done a good job alerting the world to the problem of global climate change. But he and his fellow climate scientists are acutely aware that the IPCC’s predictions of how the global change will affect local climates are little more than guesswork. They fear that if the IPCC’s predictions turn out to be wrong, it will provoke a crisis in confidence that undermines the whole climate change debate.

The IPCC’s forecasts could be wrong in many different ways, over different time periods and spatial scales, including underestimating future changes. And it is not even clear that scientists involved with the IPCC have a collective view on what it would even mean for the IPCC to be “wrong”. As we’ve argued here often, action on climate change makes sense even if the predictions of the IPCC are not yet perfect. But this is a hard case to make when defenders of those predictions allow no room for imperfections to be seen, or questions to be asked.

Readers following recent threads know that I’ve been looking for instances where scientists make claims that some observations are “inconsistent with” the results from climate models. The reason for such a search is that it is all too easy for modelers to claim that anything and everything under the sun is “consistent with” their predictions, sometimes to avoid the perception of a loss of credibility in the political battle over climate change.

I am happy to report that claims of “inconsistent with” do exist. Here is an example from a paper just out by Knutson et al. in Nature Geoscience:

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[UPDATE] Roger Pielke, Sr. tells us that we are barking up the wrong tree looking at surface temperatures anyway. He says that the real action is in looking at oceanic heat content, for which predictions have far less variability over short terms than do surface temperatures. And he says that observations of accumulated heat content over the past 4 years “are not even close” to the model predictions. For the details, please see for your self at his site.]

“Skill” is a technical term in the forecast verification literature that means the ability to beat a naïve baseline when making forecasts. If your forecasting methodology can’t beat some simple heuristic, then it will likely be of little use.

What are examples of such naïve baselines? In weather forecasting historical climatology is often used. So if the average temperature in Boulder for May 20 is 75 degrees, and my prediction is for 85 degree, then any observed temperature below 80 degrees will mean that my forecast had no skill. In the mutual fund industry stock indexes are examples of naive baselines used to evaluate performance of fund managers. Of course, no forecasting method can always show skill in every forecast, so the appropriate metric is the degree of skill present in your forecasts. Like many other aspects of forecast verification, skill is a matter of degree, and is not black or white.

Skill is preferred to “consistency” if only because the addition of bad forecasts to a forecasting ensemble does not improve skill unless it improves forecast accuracy, which is not the case with certain measures of “consistency,” as we have seen. Skill also provides a clear metric of success for forecasts, once a naïve baseline is agreed upon. As time goes on, forecasts such as those issued by the IPCC should tend toward increasing skill, as the gap between a naive forecast and a prediction grows. If a forecasting methodology shows no skill then it would be appropriate to question the usefulness and/or accuracy of the forecasting methodology.

In this post I use the IPCC forecasts of 1990, 2001, and 2007 to illustrate the concept of skill, and to explain why it is a much better metric that “consistency” to evaluate forecasts of the IPCC.

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The IPCC will be using new scenarios for its future work, updating those produced in 2000, the so-called SRES scenarios. This would be good news, since, as we argued in Nature last month, the IPCC scenarios contain some dubious assumptions (PDF). But from the looks of it, it does not appear that much has changed, excpet the jargon. The figure below compares the new scenarios as presented in a report from a meeting of the IPCC held last month (source: PDF) with those from the 2000 IPCC SRES report. I have presented the two sets of scenarios on the same scale to facilitate comparison. Do they look much different to you?