Summary. Should the public be able to recognize that climate is changing, despite the notorious variability of weather and climate from day to day and year to year? We investigate how the probability of unusually warm seasons has changed in recent decades, with emphasis on summer, when changes are likely to have the greatest practical effects. We show that the odds of an unusually warm season have increased greatly over the past three decades, but also the shape of the frequency distribution has changed so as to enhance the likelihood of extreme events. A new category of hot summertime outliers, more than three standard deviations (3σ) warmer than climatology, has emerged, with the occurrence of these outliers having increased 1-2 orders of magnitude in the past three decades. Thus we can state with a high degree of confidence that extreme summers, such as those in Texas and Oklahoma in 2011 and Moscow in 2010, are a consequence of global warming, because global warming has dramatically increased their likelihood of occurrence.

We illustrate observed variability of seasonal mean surface air temperature anomalies in units of standard deviations, including comparison with the normal distribution (“bell curve”) that the lay public may appreciate. We take 1951-1980 as an appropriate base period, because temperatures then were within the Holocene range to which humanity and other planetary life are adapted. In contrast, we infer that global temperature is now above the Holocene range, as evidenced by the fact that the ice sheets in both hemispheres are shedding mass (1) and sea level is rising at a rate [more than 3 mm/year or 3 m/millennium (2)] that is much higher than the rate of sea level change during the past several millennia.

The frequency of occurrence of local summer-mean temperature anomalies was close to the normal distribution in the 1950s, 1960s and 1970s in both hemispheres (Fig. P1A, B). However, in each subsequent decade the distribution shifted toward more positive anomalies, with the positive tail (hot outliers) of the distribution shifting more than the negative tail. The temporal change of the anomaly distribution for the contiguous United States (Fig. P1C) is similar to the global change, but much noisier because the contiguous U.S. covers only ~1.5% of the globe.

Winter warming exceeds that in summer, but the standard deviation of seasonal mean temperature at middle and high latitudes is much larger in winter (typically 2-4°C) than in summer (typically ~1°C). Thus the shift of the anomaly distribution, in the unit of standard deviations, is less in winter than in summer (Fig. P1D).

A concept of “climate dice” was suggested (3) to describe the stochastic variability of local seasonal mean temperature, with the implication that the public should recognize the existence of global warming once the dice become sufficiently “loaded” (biased). Specifically, the 10 warmest summers (Jun-Jul-Aug in the Northern Hemisphere) in the 30-year period ofclimatology (1951-1980) define the “hot” category, the 10 coolest the “cold” category, and the middle 10 the “average” summer. Thus it was imagined that two sides of a six-sided die were colored red, blue and white for these respective categories. The divisions between “hot” and “average” and between “average” and “cold” occur at +0.43σ and -0.43σ for a normal distribution of variability.

Temperatures simulated in a global climate model reached a level such that four of the six sides of the climate dice were red in the first decade of the 21st century for greenhouse gas scenario B (3), which is an accurate approximation of actual greenhouse gas growth [(4), updates at http://www.columbia.edu/~mhs119/GHG_Forcing/]. We find that actual summer-mean temperature anomalies over global land during the past decade averaged about 75% in the “hot category”, thus midway between four and five sides of the die were red, which is reasonably consistent with expectations.

A more important change is the emergence of a subset of the hot category, extremely hot outliers, defined as anomalies exceeding +3σ. The frequency of these extreme anomalies is about 0.13% in the normal distribution, and thus a typical summer in the period of climatology would have only about 0.1-0.2% of the globe covered by such hot extremes. We show that during the past several years the portion of global land area covered by summer temperature anomalies exceeding +3σ has averaged about 10%, thus an increase by about a factor of 50 compared to the period of climatology. Recent examples of summer temperature anomalies exceeding +3σ include the heat wave and drought in Oklahoma, Texas and Mexico in 2011 and a larger region encompassing much of the Middle East, Western Asia and Eastern Europe, including Moscow, in 2010.

The question of whether these extreme hot anomalies are a consequence of global warming is commonly answered in the negative, with an alternative interpretation based on meteorological patterns. For example, an unusual atmospheric “blocking” situation resulted in a long-lived high pressure anomaly in the Moscow region in 2010, and a strong La Nina in 2011 may have contributed to the heat and drought situation in the southern United States and Mexico. However, such meteorological patterns are not new and thus as an “explanation” fail to account for the huge increase in the area covered by extreme positive temperature anomalies. Specific meteorological patterns help explain where the high pressure regions that favor high temperature and drought conditions occur in a given summer, but the unusually great temperature extremities and the large area covered by these hot anomalies is a consequence of global warming.

This attribution is important, because we can project with a high degree of confidence that the area covered by extremely hot anomalies will continue to increase during the next few decades and even greater extremes will occur. The decade-by-decade shift to the right of the temperature anomaly frequency distribution (Fig. P1) will continue, because Earth is out of energy balance, more solar energy absorbed than heat radiation emitted to space (5), and it is this imbalance that drives the planet to higher temperatures. Even an extremely optimistic scenario, with fossil fuel emission reductions of 6%/year beginning in 2013, results in global temperature rising to almost 1.2°C relative to 1880-1920, which compares to a current level ~0.8°C.

We argue that it is important to keep the base period defining climatology fixed. Shifting the base period continually to the most recent three decades hides the increasing variability that we found. A base period prior to 1980 avoids this problem and yields a climatology within the global temperature range of the Holocene, to which nature and human civilization are adapted.

Practical effects of the increasingly loaded climate dice are likely to occur via amplification of extremes of the water cycle. Higher temperatures exacerbate hot dry conditions, but higher temperatures also increase the amount of water vapor that the atmosphere can hold. Increased water vapor leads to heavier rainfall and floods as well as the potential for stronger storms driven by latent heat including thunderstorms, tornadoes and tropical storms. We cite data suggesting that such climate impacts are already underway, but because of the small spatial scale of many of these phenomena it is necessary to gather more extensive homogeneous hydrologic data to assess ongoing global change. Such assessment is important because of potential effects on humans and other species, as it has been estimated that continued business-as-usual fossil fuel emissions and global warming could result by the end of the century in 21-52% of the species on Earth being committed to extinction IPCC (6).

Climatic Change

in press, doi:10.1007/s10584-011-0383-3

ABSTRACT:  Ocean acidification is increasingly recognized as a major global problem. Yet economic assessments of its effects are currently almost absent. Unlike most other marine organisms, mollusks, which have significant commercial value worldwide, have relatively solid scientific evidence of biological impact of acidification and allow us to make such an economic evaluation. By performing a partial-equilibrium analysis, we estimate global and regional economic costs of production loss of mollusks due to ocean acidification. Our results show that the costs for the world as a whole could be over 100 billion USD with an assumption of increasing demand of mollusks with expected income growths combined with a business-as-usual emission trend towards the year 2100. The major determinants of cost levels are the impacts on the Chinese production, which is dominant in the world, and the expected demand increase of mollusks in today’s developing countries, which include China, in accordance with their future income rise. Our results have direct implications for climate policy. Because the ocean acidifies faster than the atmosphere warms, the acidification effects on mollusks would raise the social cost of carbon more strongly than the estimated damage adds to the damage costs of climate change.

Nature Geoscience

in press, doi:10.1038/ngeo1375

ABSTRACT:  Global climate change results from a small yet persistent imbalance between the amount of sunlight absorbed by Earth and the thermal radiation emitted back to space¹. An apparent inconsistency has been diagnosed between interannual variations in the net radiation imbalance inferred from satellite measurements and upper-ocean heating rate from in situ measurements, and this inconsistency has been interpreted as ‘missing energy’ in the system². Here we present a revised analysis of net radiation at the top of the atmosphere from satellite data, and we estimate ocean heat content, based on three independent sources. We find that the difference between the heat balance at the top of the atmosphere and upper-ocean heat content change is not statistically significant when accounting for observational uncertainties in ocean measurements³, given transitions in instrumentation and sampling. Furthermore, variability in Earth’s energy imbalance relating to El Niño-Southern Oscillation is found to be consistent within observational uncertainties among the satellite measurements, a reanalysis model simulation and one of the ocean heat content records. We combine satellite data with ocean measurements to depths of 1,800 m, and show that between January 2001 and December 2010, Earth has been steadily accumulating energy at a rate of 0.50±0.43 Wm⁻² (uncertainties at the 90% confidence level). We conclude that energy storage is continuing to increase in the sub-surface ocean.

Nature Climate Change

in press, doi:10.1038/nclimate1372

ABSTRACT:  Since the beginning of the Industrial Revolution humans have released ~500 billion metric tons of carbon to the atmosphere through fossil-fuel burning, cement production and land-use changes^{1, 2}. About 30% has been taken up by the oceans³. The oceanic uptake of carbon dioxide leads to changes in marine carbonate chemistry resulting in a decrease of seawater pH and carbonate ion concentration, commonly referred to as ocean acidification. Ocean acidification is considered a major threat to calcifying organisms^{4, 5, 6}. Detecting its magnitude and impacts on regional scales requires accurate knowledge of the level of natural variability of surface ocean carbonate ion concentrations on seasonal to annual timescales and beyond. Ocean observations are severely limited with respect to providing reliable estimates of the signal-to-noise ratio of human-induced trends in carbonate chemistry against natural factors. Using three Earth system models we show that the current anthropogenic trend in ocean acidification already exceeds the level of natural variability by up to 30 times on regional scales. Furthermore, it is demonstrated that the current rates of ocean acidification at monitoring sites in the Atlantic and Pacific oceans exceed those experienced during the last glacial termination by two orders of magnitude.

Paleoceanography

27, PA1204, doi:10.1029/2011PA002163.

ABSTRACT:  We established a new high-resolution carbonate carbon isotope record of the Albian interval of the Marne a Fucoidi Formation (Central Apennines, Italy), which was deposited on the southern margin of the western Tethys Ocean. Bulk carbonate sampled with 10–15 cm spacing was used for the construction of a continuous carbon isotope curve through the Albian stage. Spectral analyses reveal prominent 400 kyr cyclicity in the δ¹³C curve, which correlates with Milankovitch long eccentricity changes. Cycles occurring in our record resemble those observed in several Cenozoic δ¹³C records, suggesting that a link between orbital forcing and carbon cycling existed also under mid-Cretaceous greenhouse conditions. Based on comparisons with Cenozoic eccentricity-carbon cycle links we hypothesize that 400 kyr cycles in the mid-Cretaceous were related to a fluctuating monsoonal regime, coupled with an unstable oceanic structure, which made the oceanic carbon reservoir sensitive to orbital variations. In the Tethys these oceanographic conditions lasted until the Late Albian, and then were replaced by a more stable circulation mode, less sensitive to orbital forcing.

Journal of Geophysical Research – Atmospheres

in press, doi:10.1029/2011JD017013

ABSTRACT:  During the 20th century solar activity increased in magnitude to a so called `grand maximum’. It is probable that this high level of solar activity is at or near its end. It is of great interest whether any future reduction in solar activity could have a significant impact on climate that could partially offset the projected anthropogenic warming. Observations and reconstructions of solar activity over the last 9000 years are used as a constraint on possible future variations to produce probability distributions of total solar irradiance over the next 100 years. Using this information, with a simple climate model, we present results of the potential implications for future projections of climate on decadal to multi-decadal timescales. Using one of the most recent reconstructions of historic total solar irradiance, the likely reduction in the warming by 2100 is found to be between 0.06 and 0.1K, a very small fraction of the projected anthropogenic warming. However if past total solar irradiance variations are larger and climate models substantially underestimate the response to solar variations then there is a potential for a reduction in solar activity to mitigate a small proportion of the future warming, a scenario we cannot totally rule out. While the Sun is not expected to provide substantial delays in the time to reach critical temperature thresholds, any small delays it might provide are likely to be greater for lower anthropogenic emissions scenarios than for higher emissions scenarios.

Climatic Change
Volume 33, Number 2, 145-149, DOI: 10.1007/BF00140245

Full text (PDF format) available.

ABSTRACT:

“The models used in the assessment of the effects of global climate change are based on limited knowledge of the fundamental phenomena, for instance, the role of the clouds and of the oceans (IPCC, 1996). Although a general consensus seems to exist among the scientists involved, the very existence of this consensus does not
constitute proof that it represents nature’s reality. Uncertainties remain and many of the generally accepted assumptions need to be revisited. The effects of some of these uncertainties are represented in the results, but often under the form of confidence intervals. Yet, all parts of these intervals do not have the same probability, and many points outside of these intervals do not have a zero probability. Much work needs to be done to improve the information used for public policy in response to a threat of global climate change. Some of it is fundamental research, some of it is better representation of the information that already exists.

When science can progress quietly, independently from the pressures of public policy making, the scientific community has ample time to fight its internal battles and to prove or disprove each element of the problem. There is no need to synthesize the state of knowledge until the problem is considered resolved by most. In that context, errors may not matter much. The speed of light, for example, was measured over many years with different levels of accuracy (Henrion and Fischhoff, 1986) until available instruments and methods allowed general confidence in the results.

When decisions need to be made along the way, based on partial and incomplete information for private purposes or public sector regulations, one does not have the luxury of taking the time to reach a complete, unquestioned consensus. In that case, the available information, imperfect as it is, must be synthesized at a particular stage to represent as closely as possible the state of knowledge at that time.”

Science, 18 November 2011:
Vol. 334 no. 6058 pp. 908-909
DOI: 10.1126/science.1211886

Subscription NOT required with these links:  <Summary> <Full text> <pdf>

At the 2010 Cancun Conference of the Parties to the United Nations Framework Convention on Climate Change (UNFCCC), the international community agreed in principle to one of the largest development programs in history. The developed nations pledged to mobilize U.S.$100 billion per year by the year 2020 to “address the needs of developing countries” in responding to climate change (1). The funds, which may apply to adaptation and mitigation, are proposed to flow through multiple channels, including existing development banks, official development assistance, bilateral programs, international private investment flows (e.g., carbon markets), and other public and private mechanisms. Recommendations provided by a transitional committee for the management and operation of the proposed climate change financing will be considered by the parties to the UNFCCC at the upcoming conference in Durban, South Africa (2).

Q:  Thanks for talking to us about your paper. How would you summarize it, if you had to do it in one sentence?

It estimates the influence of carbon dioxide on the climate using temperature data from the Last Glacial Maximum, and finds a weaker influence (and with less uncertainty) than many previous studies.

Q: Can you put that into a little bit of a broader context for us? 

The paper uses temperature data from a glacial period about 20,000 years ago to estimate what is known as the equilibrium climate sensitivity to a doubling of atmospheric carbon dioxide (CO₂).  This quantity is also referred to as “climate sensitivity” or “ECS” for short.  It is a standard measure of how much the climate can be changed with respect to some change in the Earth’s energy balance, such as the greenhouse effect of CO₂.  ECS is defined through a kind of hypothetical “thought experiment”.  

If we had the ability to precisely double the amount of CO₂ in the atmosphere, ECS is the average amount of global warming that would result, assuming we could wait a long time for the warming to fully take effect.   (We could equally well consider the warming that would result from a tripling, or some other change, of CO₂, but doubling has been adopted as a standard reference measure.  From that we can estimate the warming from other amounts of CO₂.)

I don’t want to give a tutorial on climate sensitivity here; for that, see my earlier interview on the Azimuth Project and Planet 3.0 blogs.  In particular, I recommend reading the discussion of “feedback effects”, which can combine with greenhouse or other warming to cause more (or less) warming than the greenhouse effect alone.

Q:  Why should we care about climate sensitivity?

Climate sensitivity tells us about how much global warming we will see from the greenhouse effect, in the long run.

To measure climate sensitivity, we’d have to experiment on the Earth by doubling CO₂ in the atmosphere and waiting a long time (at least hundreds of years for the climate to approach a new equilibrium), holding everything else constant.  But this isn’t really feasible.  We can’t hold every other factor constant, and we would like to have some idea of what’s coming ahead of time, instead of just waiting to see what happens.

Instead of measuring climate sensitivity, which we can’t do, we want to estimate it, based on data that we can already measure today.  With that estimate in hand, we can in turn estimate how much global warming we’re likely to see from future fossil fuel emissions.

Q:  How might someone go about trying to estimate climate sensitivity?

We look for periods in the Earth’s history when CO₂ levels were higher or lower than today, and see how much warmer or colder the climate was.  From this we can estimate ECS by applying a physical theory relating carbon dioxideto planetary temperatures.

The period of time we consider is the Last Glacial Maximum (LGM), about 19,000 to 23,000 years ago, which was the deepest and coldest phase of what is colloquially known as “the last ice age”.  We obviously weren’t around to measure the Earth’s temperature back then, but we can infer it based on geological evidence.  We call this a “reconstruction” of  temperatures, rather than a measurement, based on “proxy” evidence.  Proxies are measurements that tell us indirectly about temperatures back then, such as levels of chemical isotopes found in core samples.

Part of the reason glacial periods were cold is because CO₂ levels were lower then, by about 100 parts per million.  This allows us to relate CO₂ to climate change.  Higher climate sensitivities (i.e., stronger influence of carbon dioxide on climate) imply a colder LGM, because there is strong cooling from a weakened greenhouse effect.  We can therefore work backward from LGM temperatures to the implied climate sensitivity.

The physical theory we use in our paper is a climate model — a computer simulation of the climate system.  The University of Victoria “UVic” model has a three dimensional circulation model of the ocean, and a simplified “energy balance” model of the atmosphere.  (This means that it doesn’t simulate the atmospheric circulation directly, only the approximate transfer of energy.)  We simulate the LGM climate assuming different hypothetical climate sensitivities, and see which of the simulations agree with LGM temperatures, and which do not.  We assign probabilities to climate sensitivities in proportion to how closely they agree with the temperature data.

Below is a comparison of the proxy temperature data with the predictions of our best-fitting climate model simulation.

Some people object to using climate models in studies such as these, favoring what they call “empirical” or “data-driven” approaches.  In my opinion, there is no such thing as a “purely empirical” estimate of climate sensitivity.  Climate sensitivity is not an observable quantity.  It has to be inferred indirectly from quantities that can be observed.  The only way to do this is to use a physical model that links the observed quantities to the inferred quantity.

Analyses that are often passed off as empirical, such as direct comparisons of global average temperature and radiative forcing data, implicitly use climate models, even if the “model” is nothing more than a ratio or linear regression.   This usually amounts to using a zero-dimensional linear energy balance climate model in disguise, whose physical assumptions are much cruder than the model we use.

There is no escaping using a model, it’s just a matter of how realistic a model you want to use.  I do like simple climate models, and there can be advantages to using models that don’t make complex physical assumptions.  But we shouldn’t fool ourselves into thinking that simple models are not models at all, or that they necessarily produce superior estimates just because they make fewer assumptions.  I advocate working up and down the hierarchy of model complexity to improve understanding and test the robustness of conclusions.

Q:  You recommend working with a “hierarchy” of both simple and complex models.  Why did you choose to use the UVic model for this study?

The simple answer is that the lead author, Andreas Schmittner, chose UVic because he is one of the model developers.  Given a hammer, you then look for nails:  there are certain problems that particular models are suited to study.

We wanted to choose a somewhat complex model because simple models have already been studied, and because we wanted the ability to analyze spatial patterns in the data.  The simplest models don’t simulate regional climate changes very well.

On the other hand, UVic isn’t the most complex model either.  It has a simplified atmosphere, which is an advantage and disadvantage.  The disadvantage is that it has a very approximate representation of atmospheric processes.  The advantage is that this makes the simulations run faster.  It is less computationally expensive.  (There is a new version of the model, called OSUVic, that has a dynamic atmospheric circulation model, but it is 10 or 20 times slower.)

This speed advantage makes UVic a good choice for paleoclimate studies, when you have to run the model for long periods of simulated time.  It also makes UVic suitable for uncertainty analysis, because you can afford to run multiple simulations to study the model’s response to different assumptions (e.g., to different climate sensitivities). The more complex the model, the fewer simulations are affordable within a given computational budget, and the fewer uncertainties can be explored.

Within the class of simplified-atmosphere models, UVic has a better ocean model than most, so it’s a good choice when sea surface temperatures and changes in ocean circulation are important, as we believe they are during the LGM.  UVic also has terrestrial and ocean carbon cycle models, and so simulates the response of the carbon cycle to changes in climate and CO₂ levels.  (For example, colder temperatures can cause vegetation patterns to change, which change the reflectivity of the planet’s surface and its temperature.)  We have not yet analyzed in detail what effect carbon cycle feedbacks have on our findings, but our model simulates them.

Q:  Why does the study consider climate data from so far in the past?

The main alternative is to consider historic temperature measurements from the last century or so.  Instrumental data is more accurate and numerous than indirect geological reconstructions of temperatures 20,000 years again.  However, a limitation of instrumental data is that it’s only available for a relatively short period of time (about a century).  This makes it difficult to separate out the climate effect of CO₂ from other causes of climate variability.  Also, the last century hasn’t seen as much climate change as was present during the Last Glacial Maximum; with the latter, there is a stronger climate signal to analyze.

Q:  How does this paper improve on existing research?

Other studies have estimated climate sensitivity from LGM temperatures before.  Our new study has several main features:

1. It uses a new reconstruction of LGM temperatures from geologic evidence.

2. It considers a number of climate model simulations (an “ensemble” of simulations) each with a different climate sensitivity, each with a different climate sensitivity, to evaluate the uncertainty in our ECS estimate.

3. It uses a more realistic climate model than has commonly been used in such perturbed physics ensemble studies.

4. It estimates ECS using the spatial pattern of LGM temperatures, as opposed to a single average temperature.

Earlier studies have done some of the above, but not the full combination.  We also have made some other methodological improvements on past work.

Q:  What did you find, and what is the significance of your findings?

The scientific community generally believes that the climate sensitivity is likely to lie between 2 and 4.5 degrees Celsius per doubling of atmospheric CO₂, with a best estimate of 3 °C.  I will call this the “IPCC” or “consensus” estimate, since it is based on a review of the scientific literature found in the latest report of the Intergovernmental Panel on Climate Change (IPCC).

In our LGM study we find that ECS is “likely” (66% probability) to lie between 1.7 and 2.6 degrees, and “very likely” (90% probability) to lie between 1.4 and 2.8 degrees, with a best estimate of around 2.2 or 2.3 °C.  Our estimate of the warming effect of CO₂ is therefore on the low end of, and less uncertain than, the currently accepted IPCC range.

Our low sensitivity is interesting, but within the range of previous studies.  What is probably more significant is the fact that our analysis seemingly rules out the higher sensitivities (above the IPCC “best” estimate of 3 °C) which other studies have been unable to exclude.  (Note the word “seemingly”:  more on that later.)

Q:  Looking at your Figure 2, it looks like your data implies the LGM was only about 2°C colder than the modern climate.  This seems like a small change.  How do you reconcile this with the large changes in ice sheets, sea level, and vegetation during the LGM?

First, Figure 2 is a little misleading in this context, because it shows the temperature averaged only over the locations where we have proxy data.  This doesn’t give the same number as the average global temperature. In our best-fitting climate model, if we average over the entire Earth’s surface, we get a global average surface air cooling of about 3.3°C. This is 33% less cooling than the ~5°C figure that people often cite.

Still, with only 3.3°C of global cooling, one might wonder whether it is possible to grow the large ice sheets that existed at that time, with the accompanying large fall in sea level. For that, global averages can be deceiving. You have to look at how cold the ice sheets are, not the planetary average. If we look specifically at land temperatures north of 40°N latitude, our model simulates a cooling of 7.7°C. We compared this to the scientific literature and found a study which reported that a cooling of 8.3 ± 1°C is sufficient to generate the LGM ice sheets. So our study appears consistent with glaciological constraints.

That being said, our LGM temperature reconstruction is quite different from what has been commonly assumed, and our study may prove inconsistent with other evidence that we have not yet considered. This is something that will have to be sorted out by further debate and research.

Q:  Why does this study find a lower estimate of climate sensitivity?

Probably the main reason is because our new temperature reconstruction suggests the Last Glacial Maximum was warmer than previously thought.  The warmer the glacial period, the weaker must be the cooling effect of diminished glacial CO₂ levels.  We find that the cooling between today and the LGM is about 30-40% less than previously thought.  Roughly, this implies a 30-40% lower climate sensitivity.

If we take the IPCC 2–4.5 °C range and subtract a third from it to account for the weaker LGM cooling that we find, we get 1.3-3 °C.  This is similar to (but wider than) our 1.7–2.6 °C “likely” range.  This suggests that our new temperature reconstruction explains a lot of the difference between our climate sensitivity estimate and previous estimates.

(This isn’t an entirely fair comparison, though, since the IPCC range takes into account data other than the LGM temperatures we studied.  It also assumes that LGM cooling and climate sensitivity are strictly proportional.)

It is important to note that we are not the first to find climate sensitivities on the low end of the IPCC range. The paper that published most of the sea surface temperature data we use found a range of climate sensitivities between 1 and 3.6 °C, similar to but somewhat wider than our range, using simpler methods. Our contribution is to reanalyze this data with the addition of land data and some other sea surface data, studying the spatial pattern of temperature change in a formal model-based uncertainty analysis.

Q:  Does this study overturn the IPCC’s estimate of climate sensitivity?

No, we haven’t disproven the IPCC or high climate sensitivities.  At least, not yet.  This comes down to what generalizations can be made from a single, limited study.  This is why the IPCC bases its conclusions on a synthesis of many studies, not relying on any particular one.

While our statistical analysis calculates that high climate sensitivities have very low probabilities, you can see from the caveats in our paper (discussed further below), and my remarks in this interview, that we have not actually claimed to have disproven high climate sensitivities.  We do claim that our results imply “lower probability of imminent extreme climatic change than previously thought”, and that “climate sensitivities larger than 6 K are implausible”, which I stand by.  I do not claim we have demonstrated that climate sensitivities larger than 3 K are implausible, even though we calculate a low probability for them, because our study has important limitations.

It is rare that a single paper overturns decades of work, although this is a popular conception of how science works. Many controversial results end up being overturned, because controversial research, almost by definition, contradicts large existing bodies of research. Quite often, it turns out that it’s the controversial paper that is wrong, rather than the research it hopes to overturn. Science is an iterative process.  Others have to check our work.  We have to continue checking our work, too.  Our study comes with a number of important caveats, which highlight simplifying assumptions and possible inconsistencies.  These have to be tested further.

There is a great quote from an article in the Economist that sums up my feelings, as a scientist, about the provisional nature of science.

“In any complex scientific picture of the world there will be gaps, misperceptions and mistakes. Whether your impression is dominated by the whole or the holes will depend on your attitude to the project at hand. You might say that some see a jigsaw where others see a house of cards. Jigsaw types have in mind an overall picture and are open to bits being taken out, moved around or abandoned should they not fit. Those who see houses of cards think that if any piece is removed, the whole lot falls down.”

Most scientists I know, including myself, are “jigsaw” types.  We have to see how this result fits in with the rest of what we know, and continue testing assumptions, before we can come to a consensus about what’s really going on here.  The rest of the Economist article, by the way, is well worth reading.

Q:  Tell me about these caveats you keep mentioning.

Let’s get into details.  One major caveat has to do with unexplained differences in the climate sensitivities implied by land vs. ocean temperatures.  First, some of our results in graphical form:

This figure shows the uncertainty analysis for our climate sensitivity estimate.  The black curve is our main result, which is the result of our analysis of both land and sea surface temperature data.  The height of the curve is proportional to the probability that ECS has a given value.  You can see that most of the probability weight lies between 1 and 3 °C, concentrating around 2.2 or 2.3 °C.

There are, however, two other curves in this figure.  These show our estimates if we look at only the land data or only the ocean data.

Two things are immediately apparent from these curves.  First, the sea surface temperature data support lower climate sensitivities and the land surface temperature data support higher sensitivities.  There isn’t a great deal of overlap between these curves, so this suggests a possible inconsistency between the land and ocean analyses.  Second, when we combine the land and ocean data, the ocean data dominate the result (the black and blue curves are very similar), “overruling” what the land data have to say.  I think this is, at least in part, because there are more ocean data than land data.

This discrepancy between land and ocean is one of our biggest caveats.  (I originally mentioned this directly in the abstract of our paper, but it was cut in editing for space reasons, so you have to read the body of the paper to find this out.)  If the ocean and land analyses really are inconsistent with each other, which one should we trust?  Maybe neither one?  How can we reconcile these results?

Ultimately, the discrepancy occurs because at low sensitivities, the climate model predicts land temperatures that are warmer than the proxy data, but at high sensitivities, the model predicts ocean temperatures that are colder than the proxy data. Actually, at low sensitivities the model does generate cold land temperatures, but most of them don’t occur where the cold proxy temperatures are found.

There are many hypotheses for what’s going on here.  There could be something wrong with the land data, or the ocean data.  There could be something wrong with the climate model’s simulation of land temperatures, or ocean temperatures.  The magnitudes of the temperatures could be biased in some way.  Or, more subtly, they could be unbiased, on average, but the model and observations could disagree on where the cold and warm spots are, as I alluded to earlier.  Or something even more complicated could be going on.

Until the above questions are resolved, it’s premature to conclude that we have disproven high climate sensitivities, just because our statistical analysis assigns them low probabilities.  The uncertainty analysis is only as good as the data and models that go into it, and we need to continue studying the relationships between and quality of the data and model we used.

Q: Are there any other caveats to this study, other than the discrepancy between land and ocean-based estimates? 

Yes.  Obviously the quality of the reconstructed proxy temperature data is important, since a lot of our conclusions depend on our new reconstruction of past temperatures.  I defer to my coauthors about that, since it is outside my area of expertise.

Beside the data quality, the model quality is also important. One limitation of our study is that we assume that the physical feedbacks affecting climate sensitivity occur through changes in outgoing infrared radiation (e.g., through the greenhouse effect of water vapor). In reality, feedbacks affecting reflected visible light are also important (e.g., cloud behavior). Our study did not account for these feedbacks explicitly. Also, as I mentioned earlier, our simplified atmospheric model does not represent these feedbacks in a fully physical manner. Finally, there is the “state dependence” of climate sensitivity which we have not fully addressed with our model. The extent to which these model limitations influence our findings depends on how much they affect the spatial pattern of temperature change considered in the data-model comparison. It is hard to know what the net effect would be without actually redoing the study with a more complex model. There is some evidence in the literature that LGM climate sensitivity estimates are model-dependent, and complex models may simulate the same amount of LGM cooling with different climate sensitivities.

There are also caveats related to the statistical data-model comparison, which could either bias our estimates or mischaracterize the level of certainty we find.

One way in which our climate sensitivity estimate could be biased is because of missing data.  We don’t have temperature proxies everywhere on the Earth’s surface, as you can see from the first figure in this interview.   Our study is based on only 435 temperature proxies, 322 ocean proxies and 113 land proxies.  The coverage is spotty.  The data we have may be present in colder- or warmer-than-average locations, compared to the locations where we don’t have data, due to choices scientists have made of where to sample, or due just to chance.  If so, our results could be biased toward too-low or too-high climate sensitivities.

Our calculated uncertainty range could also be either overconfident (too narrow) or underconfident (too wide).  Usually studies are more often overconfident than underconfident.  The only physical uncertainty we formally considered is the uncertainty in climate sensitivity.  There can be other uncertainties, such as in the dust present during the LGM (which can have a non-greenhouse cooling effect), or in LGM wind stresses (which our model cannot calculate, due to its simplified atmosphere).  We did explore the robustness of our results to some of these assumptions, but it wasn’t a full-fledged uncertainty analysis, and it’s always possible we could have neglected some important source uncertainty, which would tend to make our results overconfident.

There is a more subtle way in which our results could be overconfident.  Earlier I said that one of the advantages of this study is that it analyzes the spatial pattern of LGM temperatures, instead of just working with global averages or other highly aggregated data representations.  This allows us to compare the data to the model in a more refined way that uses the information contained in regional patterns of climate.  This tends to reduce uncertainty, because it uses more data, or more structured data.

On the other hand, this could causes us to accidentally rule out some possibilities, reducing uncertainty more than is warranted (overconfidence), if we don’t properly account for regional biases in the data or model.  For example, suppose that a particular climate sensitivity causes the model to agree with the data extremely well except in one particular location, where the model fits terribly because either the data or the model is particularly bad.  In that case, we might not want to be too hasty in rejecting that climate sensitivity, even though it superficially causes poor data-model agreement, if we don’t completely trust the data or model in every region.

You can think of this as an “outlier” problem.  It is well known in statistics that outliers can have undue influence on conclusions if the analysis doesn’t somehow account for their presence.  This is a specific case of a more general phenomenon that statisticians call “discrepancy”, which is a catch-all term for structured sources of error in the data or model.

It is very difficult to account for discrepancy in a statistical analysis unless you know ahead of time how and where the data and model have errors.  We explored the possibility that the model or data have some global bias as a simple representation of discrepancy, but it is still possible that our analysis is ruling out possibilities that it shouldn’t. I think there is some evidence this is the case in some of our side analyses exploring alternate assumptions. Some of those analyses produce extremely narrow uncertainty distributions, more narrow than I actually believe. I suspect this is due to a too-simple treatment of discrepancy in these situations, although I don’t know if this carries over to our main analysis.

Q: Some earlier studies have found that the sensitivity of the climate during the Last Glacial Maximum was different than the present-day climate sensitivity. Does this affect your results?

There are obvious differences between the LGM and modern climates. The LGM had major continental ice sheets in the Northern Hemisphere, for one. Differences in the state of the climate, or “state dependence”, can affect how the climate responds to CO2, e.g., by having more ice around that can be melted.

Q: Is the UVic model you used capable of addressing this issue?

The way the UVic model parameterizes feedbacks is not complex enough to show such state- and forcing-dependent nonlinearities.

That said, we don’t assume that doubling CO2 during the LGM would have the same effect on the climate as doubling CO2 today. But we do have to assume some common physics between the two periods of time in order to compare them. What we assume is that both periods of time obey the same relationship between temperature and the amount of infrared radiation that escapes to space — the same “outgoing longwave feedback”. This assumption may not be entirely true either, but it is better than assuming that the LGM and modern climates have exactly the same temperature response to CO2. To do better than this we would need a better climate model that is capable of simulating the various climate feedback processes at a more physical level.

Q:  Given all these caveats, how robust are the results of your study?

I think our lower climate sensitivity estimate will hold up, provided the reconstructed LGM temperature data on which it is based hold up.  Our finding of a warmer LGM will prove controversial among the scientific community and the data will be subject to much scrutiny.  It remains to be seen whether this temperature data is consistent with everything else we know about that period of time (its climate, its vegetation, the size of its ice sheets, etc.).

I am less confident that our narrow uncertainty range really does exclude climate sensitivities above 3 °C.  This is something that could be overturned by future work.  It certainly would stimulate a lot of rethinking among scientists if the result isn’t overturned.  I can’t say I’m rooting strongly for either outcome, though.  I’d be pleased to see our findings confirmed, but if they’re disproven, I’ll learn something from the way in which they are disproven, and this will improve my own research.  Who knows, maybe I will disprove them myself.

Q: Can you briefly summarize which aspects of the study you and you coauthors contributed to?

I developed and conducted the statistical data-model comparison, in collaboration with lead author Andreas Schmittner. This corresponds to Figure 3 of the paper and most of sections 5, 6, and 7 of the supporting online material. Andreas designed and carried out the model simulations. Other coauthors worked on the temperature reconstructions, the assumptions about dust forcings, etc. I can’t tell you the exact partitioning of responsibility because I entered this project relatively late, after all the proxy reconstructions and model simulations had been completed.

Q: Your paper got a lot of positive attention from climate skeptic blogs like “Watts Up With That?”. What’s your reaction to all that? 

I haven’t followed these blogs too closely, but I skimmed the comments on a few that were pointed out to me.  The responses I saw were fairly predictable, veering from uncritical acceptance of our findings, to uncritical dismissal of any study that involves computer models or proxy data. But some comments did seem to find an appropriate middle ground of, well, skepticism.

Q. It’s a little funny, to me, that your paper was receiving such positive comments from skeptics while many of those same skeptics also support claims by Richard Lindzen and Roy Spencer purporting to find an essentially insensitive (~1°C or less) or self-stabilizing climate. Does your paper support such incredibly low values for ECS?

Our analysis found a lower bound of 1.35 °C for climate sensitivity (less than 5% probability of being below this bound). We tried a range of statistical and physical assumptions, and found sensitivities as low as 1.15 °C, and as high as 4.65 °C (if we analyze the land data). I don’t think sensitivities lower than our bound are consistent with either our study or paleoclimatic evidence in general.

Q: Any other thoughts on the skeptics’ reception of your paper?

One blog did surprise me. World Climate Report doctored our paper’s main figure when reporting on our study.  This manipulated version of our figure was copied widely on other blogs.  They deleted the data and legends for the land and ocean estimates of climate sensitivity, and presented only our combined land+ocean curve:

Upper: World Climate Report’s manipulated image removing the Land and Ocean data.

 Lower: The actual figure as it appears in Science, with the Land and Ocean curves included.

They did note that their figure was “adapted from” ours, and linked to our paper containing the real figure.  On the other hand, Pat Michaels duplicated this doctored version of our figure again in an article at Forbes, and didn’t mention at all that it had been altered.    (A side note with respect to the Forbes article:  Science didn’t “throw a tantrum” about posting our manuscript on the web.  They never contacted us about that.  I took it down myself as a precaution, due to the journal’s embargo policy.)

I find this data manipulation problematic.  When I created the real version of that figure, it occurred to me that it would be reproduced in articles, presentations, or blog posts.  Because I find the difference between our land and ocean estimates to be such an important caveat to our work, I made sure to include all three curves in the figure, so that anyone reproducing it would have to acknowledge these caveats.  I didn’t anticipate that anyone would simple edit the figure to remove our caveats.  I can’t say why they deleted those curves.  If you were to ask them, I’d guess they’d say it was to “clarify” the figure by focusing attention on the main result we reported.

Regardless of their intent, I find the result of their figure manipulation to be very misleading, especially since their blog post strongly implies that our study eliminates the “fat right tail” of the climate sensitivity distribution, and has proven the IPCC’s climate sensitivity range to be incorrect.  Our land temperature curve, which they deleted, undermines their implication.  They intentionally took our figure out of the context in which it was originally presented, a form of “selective quotation” which hides data that does not support their interpretation.

In summary, I find World Climate Report’s behavior very disappointing and hardly compatible with true skeptical inquiry.  I can only imagine how they would respond if they found a climate scientist intentionally deleting data from a figure, especially if they deleted data that undermined the point of view they were presenting.

Q: What are some other topics in climate science that you’re interested in? What’s next for you?

I’d like to follow up on this LGM project, looking into the caveats in more detail. This includes expanding the uncertainties considered, upgrading the statistical treatment of data-model discrepancies, and finding a way to handle state-dependence better. I am also interested in estimating climate sensitivity using data from periods of time in the Earth’s history other than the LGM. Another question I’d like to study more is whether slow carbon-cycle feedbacks can amplify global warming beyond what the direct climate feedbacks would predict (e.g., if warming can cause the release of additional carbon from permafrost or ocean clathrates). Currently and in the near future, though, I am starting to focus more on ice sheet dynamics and sea level rise.

Beyond uncertainty quantification, I’m moving toward quantifying learning rates. How quickly will we be able to reduce our uncertainties in the future, by acquiring more data or better synthesizing existing data? What effect will this have on climate policy? Finally, I’d like to learn more about climate adaptation policy and how uncertainties affect adaptive decision making.

Thanks very much to Nathan, for talking to Planet 3.0 about his latest paper. We hope to check in again with him about his future research.

UPDATE: RC weighs in, and links to other valuable discussion including a couple by James.

Image courtesy of Flickr user "marsi", used under Creative Commons

A contribution to attribution of recent global warming by out-of-sample Granger causality analysis

ABSTRACT:  The topic of attribution of recent global warming is usually faced by studies performed through global climate models (GCMs). Even simpler econometric models have been applied to this problem, but they led to contrasting results. In this article, we show that a genuine predictive approach of Granger analysis leads to overcome problems shown by these models and to obtain a clear signal of linear Granger causality from greenhouse gases (GHGs) to the global temperature of the second half of the 20th century. In contrast, Granger causality is not evident using time series of natural forcing.

Growth and Development Rates Have Different Thermal Responses

ABSTRACT:  Growth and development rates are fundamental to all living organisms. In a warming world, it is important to determine how these rates will respond to increasing temperatures. It is often assumed that the thermal responses of physiological rates are coupled to metabolic rate and thus have the same temperature dependence. However, the existence of the temperature-size rule suggests that intraspecific growth and development are decoupled. Decoupling of these rates would have important consequences for individual species and ecosystems, yet this has not been tested systematically across a range of species. We conducted an analysis on growth and development rate data compiled from the literature for a well-studied group, marine pelagic copepods, and use an information-theoretic approach to test which equations best describe these rates. Growth and development rates were best characterized by models with significantly different parameters: development has stronger temperature dependence than does growth across all life stages. As such, it is incorrect to assume that these rates have the same temperature dependence. We used the best-fit models for these rates to predict changes in organism mass in response to temperature. These predictions follow a concave relationship, which complicates attempts to model the impacts of increasing global temperatures on species body size.

Estimating the effects of ENSO upon the observed freshening trends of the western tropical Pacific Ocean

ABSTRACT:  A significant surface freshening trend and an eastward expansion of fresh surface waters have been documented in the western tropical Pacific, consistent with the expected effects of climate change. The highest El Niño Southern Oscillation (ENSO) variability in Sea Surface Salinity (SSS) has been also documented in that region, with different quantitative signatures for the Eastern and Central Pacific ENSO events (EP and CP ENSO, respectively). This study hence analyses to what extent have the EP and CP ENSO events contributed to the long-term freshening trends, relying on 1955–2008 in situ SSS data and on EP and CP ENSO main features. We show the influence of EP ENSO events to be negligible, while CP El Niño events contribute to enhance the long-term freshening trend (up to 30%) in the far western equatorial Pacific and moderately reduce that freshening (up to 10%) in the South Pacific Convergence Zone (SPCZ). Our results thus suggest that the observed eastward expansion of the surface covered by low-salinity waters in the western half of the tropical Pacific is mostly due to climate change rather than to the documented possible increased occurrence and intensity of CP El Niño events. The sensitivity of the trend estimates to the different periodicity of the SSS data records is also discussed.

Simulating the impacts of disturbances on forest carbon cycling in North America: Processes, data, models, and challenges

ABSTRACT:  Forest disturbances greatly alter the carbon cycle at various spatial and temporal scales. It is critical to understand disturbance regimes and their impacts to better quantify regional and global carbon dynamics. This review of the status and major challenges in representing the impacts of disturbances in modeling the carbon dynamics across North America revealed some major advances and challenges. First, significant advances have been made in representation, scaling, and characterization of disturbances that should be included in regional modeling efforts. Second, there is a need to develop effective and comprehensive process-based procedures and algorithms to quantify the immediate and long-term impacts of disturbances on ecosystem succession, soils, microclimate, and cycles of carbon, water, and nutrients. Third, our capability to simulate the occurrences and severity of disturbances is very limited. Fourth, scaling issues have rarely been addressed in continental scale model applications. It is not fully understood which finer scale processes and properties need to be scaled to coarser spatial and temporal scales. Fifth, there are inadequate databases on disturbances at the continental scale to support the quantification of their effects on the carbon balance in North America. Finally, procedures are needed to quantify the uncertainty of model inputs, model parameters, and model structures, and thus to estimate their impacts on overall model uncertainty. Working together, the scientific community interested in disturbance and its impacts can identify the most uncertain issues surrounding the role of disturbance in the North American carbon budget and develop working hypotheses to reduce the uncertainty.

Methane emissions from the surface of the Three Gorges Reservoir

ABSTRACT:  After our previous study about methane (CH₄) emissions from littoral marshes of the Three Gorges Reservoir (TGR), Chinese dams have raised a world-wide concern. Through measurements from the surface of the TGR, a CH₄ emission rate was recorded as 0.26 ± 0.38 mg CH₄ m⁻² h⁻¹ (Mean ± SD), relatively low compared with those from other hydropower reservoirs. We also recorded CH₄ emission rate from the surface of downstream water, which was also relatively low (0.24 ± 0.37 mg CH₄ m⁻² h⁻¹). Such result may indicate that TGR is not a great CH₄ emitter (not “CH₄ menace”). One possible reason for such a low emission rate is that measures to maintain water quality and protect environment and ecosystem decrease the input of organic materials (for methanogenesis), which in turn limits the CH₄ production in the sediment of the TGR. We also found that CH₄ emission from the flooding drawdown area (0.29 ± 0.37 mg CH₄ m⁻² h⁻¹) was higher than other permanently flooded sites (0.23 ± 0.38 mg CH₄ m⁻² h⁻¹). Because of annual vegetation re-growth, the drawdown zone is the especially important carbon source for methanogenesis in flooding season. Interestingly, we also observed that mean CH₄ emission was significantly higher in winter than in spring and summer. This was partly due to seasonal dynamics of hydrology. In order to estimate the net CH₄ emissions caused by the reservoir and reservoir operation, the best approach would be Life Cycle Analysis.

Protracted storage of biospheric carbon in the Ganges–Brahmaputra basin

ABSTRACT:  The amount of carbon stored in continental reservoirs such as soils, sediments and the biosphere greatly exceeds the amount of carbon in the atmosphere¹. As such, small variations in the residence time of organic carbon in these reservoirs can produce large variations in the atmospheric inventory of carbon dioxide. One such reservoir is the Ganges–Brahmaputra system draining the Himalayas, which represents one of the largest sources of terrestrial biospheric carbon to the ocean². Here, we examine the radiocarbon content of river sediments collected from the Ganges–Brahmaputra drainage basin to determine the residence time of organic carbon in this reservoir. We show that the average age of biospheric organic carbon in the drainage basin ranges from 0.5 to 17 thousand years. The radiocarbon age of plant-derived fatty acids—a proxy for labile terrestrial vegetation—ranges from just 0.05 to 1.3 thousand years. We propose that the bulk ages can be explained by the existence of a refractory, slowly cycling component of the organic carbon pool that is mixed with a younger labile pool. We estimate that this refractory component has an average age of over 15,000 years, and represents up to 20% of total biospheric carbon exported by the Ganges–Brahmaputra system. We suggest that global warming might destabilize this ancient pool of carbon, if warming stimulates microbial decomposition of organic carbon reserves.

Abundant Early Palaeogene marine gas hydrates despite warm deep-ocean temperatures

ABSTRACT:  Abrupt periods of global warming between 57 and 50 million years ago—known as the Early Palaeogene hyperthermal events—were associated with the repeated injection of massive amounts of carbon into the atmosphere^{1, 2, 3, 4}. The release of methane from the sea floor following the dissociation of gas hydrates is often invoked as a source⁵. However, seafloor temperatures before the events were at least 4–7 °C higher than today¹, which would have limited the area of sea floor suitable for hosting gas hydrates^{6, 7}. Palaeogene gas hydrate reservoirs may therefore not have been sufficient to provide a significant fraction of the carbon released. Here we use numerical simulations of gas hydrate accumulation⁸ at Palaeogene seafloor temperatures to show that near-present-day values of gas hydrates could have been hosted in the Palaeogene. Our simulations show that warmer temperatures during the Palaeogene would have enhanced the amount of organic carbon reaching the sea floor as well as the rate of methanogenesis. We find that under plausible temperature and pressure conditions, the abundance of gas hydrates would be similar or higher in the Palaeogene than at present. We conclude that methane hydrates could have been an important source of carbon during the Palaeogene hyperthermal events.

Sensitivity of the attribution of near surface temperature warming to the choice of observational dataset

ABSTRACT:  A number of studies have demonstrated that much of the recent warming in global near surface temperatures can be attributed to increases in anthropogenic greenhouse gases. While this conclusion has been shown to be robust in analyses using a variety of climate models there have not been equivalent studies using different available observational datasets. Here we repeat the analyses as reported previously using an updated observational dataset and other independently processed datasets of near surface temperatures. We conclude that the choice of observational dataset has little impact on the attribution of greenhouse gas warming and other anthropogenic cooling contributions to observed warming on a global scale over the 20th century, however this robust conclusion may not hold for other periods or for smaller sub-regions. Our results show that the dominant contributor to global warming over the last 50 years of the 20th century is that due to greenhouse gases.

High variability of Greenland surface temperature over the past 4000 years estimated from trapped air in an ice core

ABSTRACT:  Greenland recently incurred record high temperatures and ice loss by melting, adding to concerns that anthropogenic warming is impacting the Greenland ice sheet and in turn accelerating global sea-level rise. Yet, it remains imprecisely known for Greenland how much warming is caused by increasing atmospheric greenhouse gases versus natural variability. To address this need, we reconstruct Greenland surface snow temperature variability over the past 4000 years at the GISP2 site (near the Summit of the Greenland ice sheet; hereafter referred to as Greenland temperature) with a new method that utilises argon and nitrogen isotopic ratios from occluded air bubbles. The estimated average Greenland snow temperature over the past 4000 years was −30.7°C with a standard deviation of 1.0°C and exhibited a long-term decrease of roughly 1.5°C, which is consistent with earlier studies. The current decadal average surface temperature (2001–2010) at the GISP2 site is −29.9°C. The record indicates that warmer temperatures were the norm in the earlier part of the past 4000 years, including century-long intervals nearly 1°C warmer than the present decade (2001–2010). Therefore, we conclude that the current decadal mean temperature in Greenland has not exceeded the envelope of natural variability over the past 4000 years, a period that seems to include part of the Holocene Thermal Maximum. Notwithstanding this conclusion, climate models project that if anthropogenic greenhouse gas emissions continue, the Greenland temperature would exceed the natural variability of the past 4000 years sometime before the year 2100.

Varying boreal forest response to Arctic environmental change at the Firth River, Alaska

ABSTRACT:  The response of boreal forests to anthropogenic climate change remains uncertain, with potentially significant impacts for the global carbon cycle, albedo, canopy evapotranspiration and feedbacks into further climate change. Here, we focus on tree-ring data from the Firth River site at treeline in northeastern Alaska, in a tundra–forest transition region where pronounced warming has already occurred. Both tree-ring width (TRW) and maximum latewood density (MXD) chronologies were developed to identify the nature of tree growth and density responses to climatic and environmental changes in white spruce (Picea glauca), a dominant Arctic treeline species. Good agreement was found between the interannual fluctuations in the TRW chronology and summer temperatures from 1901 to 1950, whereas no significant relationships were found from 1951 to 2001, supporting evidence of significant divergence between TRW and summer temperature in the second half of the 20th century. In contrast to this unstable climatic response in the TRW record, the high frequency July–August temperature signal in the MXD series seems reasonably stable through the 20th century. Wider and denser rings were more frequent during the 20th century, particularly after 1950, than in previous centuries. Finally, comparison between the tree-ring proxies and a satellite-derived vegetation index suggests that TRW and MXD correlate with vegetation productivity at the landscape level at different times of the growing season.

Are secular correlations between sunspots, geomagnetic activity, and global temperature significant?

ABSTRACT:  Recent studies have led to speculation that solar-terrestrial interaction, measured by sunspot number and geomagnetic activity, has played an important role in global temperature change over the past century or so. We treat this possibility as an hypothesis for testing. We examine the statistical significance of cross-correlations between sunspot number, geomagnetic activity, and global surface temperature for the years 1868–2008, solar cycles 11–23. The data contain substantial autocorrelation and nonstationarity, properties that are incompatible with standard measures of cross-correlational significance, but which can be largely removed by averaging over solar cycles and first-difference detrending. Treated data show an expected statistically-significant correlation between sunspot number and geomagnetic activity, Pearson p < 10⁻⁴, but correlations between global temperature and sunspot number (geomagnetic activity) are not significant, p = 0.9954, (p = 0.8171). In other words, straightforward analysis does not support widely-cited suggestions that these data record a prominent role for solar-terrestrial interaction in global climate change. With respect to the sunspot-number, geomagnetic-activity, and global-temperature data, three alternative hypotheses remain difficult to reject: (1) the role of solar-terrestrial interaction in recent climate change is contained wholly in long-term trends and not in any shorter-term secular variation, or, (2) an anthropogenic signal is hiding correlation between solar-terrestrial variables and global temperature, or, (3) the null hypothesis, recent climate change has not been influenced by solar-terrestrial interaction.

Recent global trends in atmospheric fronts

ABSTRACT:  An automated, objective method is used to identify atmospheric fronts in four independent reanalysis data sets for the period 1989–2009 and to calculate changes in their frequency. The analysis highlights several coherent regions of statistically significant change in the frequency of fronts. The front frequency in the North Atlantic storm track has decreased by about 10–20%, whereas changes observed over the Southern Ocean are relatively small. In the subtropical Pacific the front frequency has increased significantly, which is consistent with an expansion of the dry subtropics. The sensitivity of these trends to the detection method is tested and the results are found to be robust. The results provide a concise summary of the recent changes in a major component of synoptic weather conditions, providing a benchmark for climate models as well as an additional tool for interpreting climate change predictions.

Just spotted this (http://www.sciencemag.org/content/334/6057/794):

Aerosol Indirect Effect on Biogeochemical Cycles and Climate

Abstract: The net effect of anthropogenic aerosols on climate is
usually considered the sum of the direct radiative effect of
anthropogenic aerosols, plus the indirect effect of these aerosols
through aerosol-cloud interactions. However, an additional impact of
aerosols on a longer time scale is their indirect effect on climate
through biogeochemical feedbacks, largely due to changes in the
atmospheric concentration of CO2. Aerosols can affect land and ocean
biogeochemical cycles by physical forcing or by adding nutrients and
pollutants to ecosystems. The net biogeochemical effect of aerosols is
estimated to be equivalent to a radiative forcing of –0.5 ± 0.4 watts
per square meter, which suggests that reaching lower carbon targets
will be even costlier than previously estimated.

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A slightly longer description, seeming from the author, is at:
http://www.greencarcongress.com/2011/11/mahowald-20111111.html

This “new” forcing is not something we need, obviously, so it will be
interesting to see what follow-up work and commentary this paper
sparks.

Image courtesy of Flickr user "bionicteaching", used under Creative Commons.

Correlation between climate sensitivity and aerosol forcing and its implication for the “climate trap”

ABSTRACT:  Climate sensitivity and aerosol forcing are dominant uncertain properties of the global climate system. Their estimates based on the inverse approach are interdependent as historical temperature records constrain possible combinations. Nevertheless, many literature projections of future climate are based on the probability density of climate sensitivity and an independent aerosol forcing without considering the interdependency of such estimates. Here we investigate how large such parameter interdependency affects the range of future warming in two distinct settings: one following the A1B emission scenario till the year 2100 and the other assuming a shutdown of all greenhouse gas and aerosol emissions in the year 2020. We demonstrate that the range of projected warming decreases in the former case, but considerably broadens in the latter case, if the correlation between climate sensitivity and aerosol forcing is taken into account. Our conceptual study suggests that, unless the interdependency between the climate sensitivity and aerosol forcing estimates is properly considered, one could underestimate a risk involving the “climate trap”, an unpalatable situation with a high climate sensitivity in which a very drastic mitigation may counter-intuitively accelerate the warming by unmasking the hidden warming due to aerosols.

California coastal management with a changing climate

ABSTRACT:  With over 2,000 miles (3,218 km) of ocean and estuarine coastline, California faces significant coastal management challenges as a result of climate change-induced sea level rise. Under high emission scenarios, recent models predict 1.4 m or more of sea level rise by 2100, accompanied by increasing storm surges. This article investigates the most important issues facing coastal managers, explores the policy tools available for adapting to the impacts of climate change, assesses institutional constraints to adaptation, and identifies priorities for future research and policy action. We find that adaptation tools exist for dealing with anticipated increases in coastal erosion and flooding, but they involve significant costs and tradeoffs. In particular, coastal armoring, such as seawalls, can protect developed coastal lands, but destroys beaches and habitat. Although California already has policies and institutions that aim to balance the competing objectives for coastal development, management agencies are at the early stages of understanding how to facilitate adaptation. Research priorities to inform coastal adaptation planning include: (i) inventorying coastal resources to provide a firmer basis for balancing decisions on property and habitat protection, (ii) identifying opportunities for coastal habitat migration, (iii) assessing the vulnerabilities of existing and planned coastal infrastructure, and (iv) experimenting with alternatives to armoring as a way of managing the changing coastline.

Recent summer precipitation trends in the Greater Horn of Africa and the emerging role of Indian Ocean sea surface temperature

ABSTRACT:  We utilize a variety of climate datasets to examine impacts of two mechanisms on precipitation in the Greater Horn of Africa (GHA) during northern-hemisphere summer. First, surface-pressure gradients draw moist air toward the GHA from the tropical Atlantic Ocean and Congo Basin. Variability of the strength of these gradients strongly influences GHA precipitation totals and accounts for important phenomena such as the 1960s–1980s rainfall decline and devastating 1984 drought. Following the 1980s, precipitation variability became increasingly influenced by the southern tropical Indian Ocean (STIO) region. Within this region, increases in sea-surface temperature, evaporation, and precipitation are linked with increased exports of dry mid-tropospheric air from the STIO region toward the GHA. Convergence of dry air above the GHA reduces local convection and precipitation. It also produces a clockwise circulation response near the ground that reduces moisture transports from the Congo Basin. Because precipitation originating in the Congo Basin has a unique isotopic signature, records of moisture transports from the Congo Basin may be preserved in the isotopic composition of annual tree rings in the Ethiopian Highlands. A negative trend in tree-ring oxygen-18 during the past half century suggests a decline in the proportion of precipitation originating from the Congo Basin. This trend may not be part of a natural cycle that will soon rebound because climate models characterize Indian Ocean warming as a principal signature of greenhouse-gas induced climate change. We therefore expect surface warming in the STIO region to continue to negatively impact GHA precipitation during northern-hemisphere summer.

The relative contributions of radiative forcing and internal climate variability to the late 20th Century winter drying of the Mediterranean region

ABSTRACT:  The roles of anthropogenic climate change and internal climate variability in causing the Mediterranean region’s late 20th Century extended winter drying trend are examined using 19 coupled models from the Intergovernmental Panel on Climate Change Fourth Assessment Report. The observed drying was influenced by the robust positive trend in the North Atlantic Oscillation (NAO) from the 1960s to the 1990s. Model simulations and observations are used to assess the probable relative roles of radiative forcing, and internal variability in explaining the circulation trend that drove much of the precipitation change. Using the multi-model ensemble we assess how well the models can produce multidecadal trends of realistic magnitude, and apply signal-to-noise maximizing EOF analysis to obtain a best estimate of the models’ (mean) sea-level pressure (SLP) and precipitation responses to changes in radiative forcing. The observed SLP and Mediterranean precipitation fields are regressed onto the timeseries associated with the models’ externally forced pattern and the implied linear trends in both fields between 1960 and 1999 are calculated. It is concluded that the radiatively forced trends are a small fraction of the total observed trends. Instead it is argued that the robust trends in the observed NAO and Mediterranean rainfall during this period were largely due to multidecadal internal variability with a small contribution from the external forcing. Differences between the observed and NAO-associated precipitation trends are consistent with those expected as a response to radiative forcing. The radiatively forced trends in circulation and precipitation are expected to strengthen in the current century and this study highlights the importance of their contribution to future precipitation changes in the region.

Impact of abrupt climate change in the tropical southeast Atlantic during Marine Isotope Stage (MIS) 3

ABSTRACT:  High resolution planktonic foraminifera Mg/Ca paleotemperatures and oxygen isotopes of seawater of Ocean Drilling Program (ODP) Site 1078 (off Angola) have been reconstructed and reveal insights into the seasonal thermal evolution of the Angola Current (AC), the Angola-Benguela Front (ABF), and the Benguela Current (BC) during the last glacial (50–23.5 ka BP). Special emphasis is put on time intervals possibly associated with the North Atlantic Heinrich Stadials (HS), which are thought to lead to an accumulation of heat in the South Atlantic due to a reduction of the Atlantic Meridional Overturning Circulation (AMOC). Within dating uncertainties, Globigerinoides ruber (pink) Mg/Ca-based sea surface temperature (SST) estimates that represent southern hemisphere summer surface conditions show several warming episodes that coincide with North Atlantic HS, thus supporting the concept of the bipolar thermal seesaw. In contrast, the Mg/Ca-based temperatures of Globigerina bulloides, representing the SST of the ABF/BC system during southern hemisphere winter, show no obvious response to the North Atlantic HS in the study area. We suggest that surface water cooling during the winter season is due to enhanced upwelling or upwelling of colder water masses which has most likely mitigated a warming of the ABF/BC system during HS. We further speculate that the seasonal asymmetry in our SST record results from seasonal differences in the dominance of atmospheric and oceanic teleconnections during periods of northern high latitude cooling.

Impact of ocean acidification on benthic and water column ammonia oxidation

ABSTRACT: Ammonia oxidation is a key microbial process within the marine N-cycle. Sediment and water column samples from two contrasting sites in the English Channel (mud and sand) were incubated (up to 14 weeks) in CO₂-acidified seawater ranging from pH 8.0 to pH 6.1. Additional observations were made off the island of Ischia (Mediterranean Sea), a natural analogue site, where long-term thermogenic CO₂ ebullition occurs (from pH 8.2 to pH 7.6). Water column ammonia oxidation rates in English Channel samples decreased under low pH with near-complete inhibition at pH 6.5. Water column Ischia samples showed a similar though not statistically significant trend. However, sediment ammonia oxidation rates at all three locations were not affected by reduced pH. These observations may be explained by buffering within sediments or low-pH adaptation of the microbial ammonia oxidizing communities. Our observations have implications for modeling the future impact of ocean acidification on marine ecosystems.

Historical and potential future contributions of power technologies to global warming

ABSTRACT:  Using the mathematical formalism of the Brazilian Proposal to the IPCC, we analyse eight power technologies with regard to their past and potential future contributions to global warming. Taking into account detailed bottom-up technology characteristics we define the mitigation potential of each technology in terms of avoided temperature increase by comparing a “coal-only” reference scenario and an alternative low-carbon scenario. Future mitigation potentials are mainly determined by the magnitude of installed capacity and the temporal deployment profile. A general conclusion is that early technology deployment matters, at least within a period of 50–100 years. Our results conclusively show that avoided temperature increase is a better proxy for comparing technologies with regard to their impact on climate change, and that numerous short-term comparisons based on annual or even cumulative emissions may be misleading. Thus, our results support and extend the policy relevance of the Brazilian Proposal in the sense that not only comparisons between countries, but also comparisons between technologies could be undertaken on the basis of avoided temperature increase rather than on the basis of annual emissions as is practiced today.

Solution to the paradox of climate sensitivity

ABSTRACT:  Most countries endorse a limit of either 2°C or 1.5°C global warming above pre-industrial levels. However, for several reasons, there is still a significant uncertainty in the climate sensitivity parameter, which relates greenhouse gas concentration (or other forcings) to steady-state temperature. One key source of uncertainty is the disagreement about the appropriate prior for Bayesian estimation. A common choice is the uniform distribution, often thought to contain no information. However, when used to estimate sensitivity it leads to paradoxical results, which have been interpreted as revealing an inherent indeterminacy in the prior of choice. If this were the case, part of the uncertainty would be irreducible. Here I develop an objective Bayesian approach to this problem. I show that both Jaynes’ invariant groups criterion and a new criterion based on information theory lead to the conclusion that there is a uniquely defined non-informative prior of climate sensitivity, which is distinct from the uniform and solves the paradox. This prior distribution is the log-uniform. Furthermore, this result is supported empirically by the observation that other comparable non-equilibrium parameters display a scale-invariant, log-uniform-like frequency distribution. Rather than advocating a direct use of this prior, I recommend to refine it with a limited use of expert elicitation or other methods. A sound prior is a key ingredient in the process to reach a consensus low-uncertainty estimate of climate sensitivity to inform climate policy.

Climate warming and natural disaster management: An exploration of the issues

ABSTRACT: This paper explores two issues that have been receiving increasing attention in recent decades, climate change adaptation and natural disaster risk reduction. An examination of the similarities and differences between them reveals important linkages but also significant differences, including the spectrum of threats, time and spatial scales, the importance of local versus global processes, how risks are perceived, and degree of uncertainty. Using a risk perspective to analyze these issues, preferential strategies emerge related to choices of being proactive, reactive, or emphasizing risk management as opposed to the precautionary principle. The policy implications of this analysis are then explored, using Canada as a case study.

Communicating global climate change using simple indices: an update

ABSTRACT:  Previous studies have shown that there are several indices of global-scale temperature variations, in addition to global-mean surface air temperature, that are useful for distinguishing natural internal climate variations from anthropogenic climate change. Appropriately defined, such indices have the ability to capture spatio-temporal information in a similar manner to optimal fingerprints of climate change. These indices include the contrast between the average temperatures over land and over oceans, the Northern Hemisphere meridional temperature gradient, the temperature contrast between the Northern and Southern Hemisphere and the magnitude of the annual cycle of average temperatures over land. They contain information independent of the global-mean temperature for internal climate variations at decadal time scales and represent different aspects of the climate system, yet they show common responses to anthropogenic climate change. In addition, the ratio of average temperature changes over land to those over the oceans should be nearly constant for transient climate change. Hence, supplementing analysis of global-mean surface temperature with analyses of these indices can strengthen results of attribution studies of causes of observed climate variations. In this study, we extend the previous work by including the last 10 years of observational data and the CMIP3 climate model simulations analysed for the IPCC AR4. We show that observed changes in these indices over the last 10 years provide increased evidence of an anthropogenic influence on climate. We also show the usefulness of these indices for evaluating the performance of climate models in simulating large-scale variability of surface temperature.

Arctic methane sources: Isotopic evidence for atmospheric inputs

ABSTRACT:  By comparison of the methane mixing ratio and the carbon isotope ratio (δ¹³C_CH4) in Arctic air with regional background, the incremental input of CH₄ in an air parcel and the source δ¹³C_CH4 signature can be determined. Using this technique the bulk Arctic CH₄ source signature of air arriving at Spitsbergen in late summer 2008 and 2009 was found to be −68‰, indicative of the dominance of a biogenic CH₄ source. This is close to the source signature of CH₄ emissions from boreal wetlands. In spring, when wetland was frozen, the CH₄ source signature was more enriched in ¹³C at −53 ± 6‰ with air mass back trajectories indicating a large influence from gas field emissions in the Ob River region. Emissions of CH₄ to the water column from the seabed on the Spitsbergen continental slope are occurring but none has yet been detected reaching the atmosphere. The measurements illustrate the significance of wetland emissions. Potentially, these may respond quickly and powerfully to meteorological variations and to sustained climate warming.

Climate change effects on stream and river temperatures across the northwest U.S. from 1980–2009 and implications for salmonid fishes

ABSTRACT:  Thermal regimes in rivers and streams are fundamentally important to aquatic ecosystems and are expected to change in response to climate forcing as the Earth’s temperature warms. Description and attribution of stream temperature changes are key to understanding how these ecosystems may be affected by climate change, but difficult given the rarity of long-term monitoring data. We assembled 18 temperature time-series from sites on regulated and unregulated streams in the northwest U.S. to describe historical trends from 1980–2009 and assess thermal consistency between these stream categories. Statistically significant temperature trends were detected across seven sites on unregulated streams during all seasons of the year, with a cooling trend apparent during the spring and warming trends during the summer, fall, and winter. The amount of warming more than compensated for spring cooling to cause a net temperature increase, and rates of warming were highest during the summer (raw trend = 0.17°C/decade; reconstructed trend = 0.22°C/decade). Air temperature was the dominant factor explaining long-term stream temperature trends (82–94% of trends) and inter-annual variability (48–86% of variability), except during the summer when discharge accounted for approximately half (52%) of the inter-annual variation in stream temperatures. Seasonal temperature trends at eleven sites on regulated streams were qualitatively similar to those at unregulated sites if two sites managed to reduce summer and fall temperatures were excluded from the analysis. However, these trends were never statistically significant due to greater variation among sites that resulted from local water management policies and effects of upstream reservoirs. Despite serious deficiencies in the stream temperature monitoring record, our results suggest many streams in the northwest U.S. are exhibiting a regionally coherent response to climate forcing. More extensive monitoring efforts are needed as are techniques for short-term sensitivity analysis and reconstructing historical temperature trends so that spatial and temporal patterns of warming can be better understood. Continuation of warming trends this century will increasingly stress important regional salmon and trout resources and hamper efforts to recover these species, so comprehensive vulnerability assessments are needed to provide strategic frameworks for prioritizing conservation efforts.

On the long-term context of the 1997–2009 ‘Big Dry’ in South-Eastern Australia: insights from a 206-year multi-proxy rainfall reconstruction

ABSTRACT:  This study presents the first multi-proxy reconstruction of rainfall variability from the mid-latitude region of south-eastern Australia (SEA). A skilful rainfall reconstruction for the 1783–1988 period was possible using twelve annually-resolved palaeoclimate records from the Australasian region. An innovative Monte Carlo calibration and verification technique is introduced to provide the robust uncertainty estimates needed for reliable climate reconstructions. Our ensemble median reconstruction captures 33% of inter-annual and 72% of decadal variations in instrumental SEA rainfall observations. We investigate the stability of regional SEA rainfall with large-scale circulation associated with El Niño–Southern Oscillation (ENSO) and the Inter-decadal Pacific Oscillation (IPO) over the past 206 years. We find evidence for a robust relationship with high SEA rainfall, ENSO and the IPO over the 1840–1988 period. These relationships break down in the late 18th–early 19th century, coinciding with a known period of equatorial Pacific Sea Surface Temperature (SST) cooling during one of the most severe periods of the Little Ice Age. In comparison to a markedly wetter late 18th/early 19th century containing 75% of sustained wet years, 70% of all reconstructed sustained dry years in SEA occur during the 20th century. In the context of the rainfall estimates introduced here, there is a 97.1% probability that the decadal rainfall anomaly recorded during the 1998–2008 ‘Big Dry’ is the worst experienced since the first European settlement of Australia.

Sources of uncertainty in future changes in local precipitation

ABSTRACT:  This study considers the large uncertainty in projected changes in local precipitation. It aims to map, and begin to understand, the relative roles of uncertain modelling and natural variability, using 20-year mean data from four perturbed physics or multi-model ensembles. The largest—280-member—ensemble illustrates a rich pattern in the varying contribution of modelling uncertainty, with similar features found using a CMIP3 ensemble (despite its limited sample size, which restricts it value in this context). The contribution of modelling uncertainty to the total uncertainty in local precipitation change is found to be highest in the deep tropics, particularly over South America, Africa, the east and central Pacific, and the Atlantic. In the moist maritime tropics, the highly uncertain modelling of sea-surface temperature changes is transmitted to a large uncertain modelling of local rainfall changes. Over tropical land and summer mid-latitude continents (and to a lesser extent, the tropical oceans), uncertain modelling of atmospheric processes, land surface processes and the terrestrial carbon cycle all appear to play an additional substantial role in driving the uncertainty of local rainfall changes. In polar regions, inter-model variability of anomalous sea ice drives an uncertain precipitation response, particularly in winter. In all these regions, there is therefore the potential to reduce the uncertainty of local precipitation changes through targeted model improvements and observational constraints. In contrast, over much of the arid subtropical and mid-latitude oceans, over Australia, and over the Sahara in winter, internal atmospheric variability dominates the uncertainty in projected precipitation changes. Here, model improvements and observational constraints will have little impact on the uncertainty of time means shorter than at least 20 years. Last, a supplementary application of the metric developed here is that it can be interpreted as a measure of the agreement amongst models of their projected local precipitation change. Results differ from, but are complementary to, those of the more usual approach.