by Judith Curry
Jonathan Leake asks in the Sunday Times: “Why has it warmed so much less than the IPCC predicted?“
The article provides a good overview on the debate. Some summary excerpts:
Is it really true that global temperatures have not risen since 1997?
The simple answer is: they have risen, but not by very much. “Our records for the past 15 years suggest the world has warmed by about 0.051C over that period,” said the Met Office. In layman’s terms that is 51 thousandths of a degree.
One [dataset], held at the National Climate Data Centre (NCDC), run by America’s National Oceanic and Atmospheric Administration, suggests that global temperatures rose by an average of 0.074C since 1997. That’s small, too — but it is another rise.
A third and very different data set is overseen by John Christy. . . “From 1997-2011 our data show a global temperature rise of 0.15C,” he said.
Overall, then, the world has got slightly warmer since 1997. Perhaps the real question is: why has it warmed so much less than was predicted by the climate models?
For the critics of climate science this is a crucial point — but why? The answer goes back to the 2001 and 2007 science reports from the Intergovernmental Panel on Climate Change that had predicted the world was likely to warm by an average of about 0.2C a decade. The implication was that temperatures would rise steadily, not with 15-year gaps. The existence of such gaps, the critics argue, implies the climate models themselves are too flawed to be relied on.
Some scientists appear to be warning we will fry, while other sources fear we will freeze.
How we interpret the 20th century temperature data has implications for how we project future temperature variability and change.
Climate trend statistics and graphs
So, how should we analyze the recent time series of temperature global or local temperature? Various blog posts have attempted to instruct us on this matter:
- Christopher Monckton: criticizing the trend lines in AR4 graph
- Tamino: The Real Global Warming Signal
- Skeptical Science: Going up the down escalator
- Bob Grumbine: Results on deciding trends
- William Briggs: How to cheat and fool yourself
- Peter Gleick: How to Fool People Using Cherry Picked Climate Data
- Doc Snow: Cherry picking
- Curry: Pause (?)
Josh encapsulates all this with a cartoon.
An argument for change-point analysis and analysis of partial time series is provided by Raymond Sneyers: Climate Chaotic Instability: Statistical Determination and Theoretical Background [sneyers environometrics].
Abstract. The paper concerns the determination of statistical climate properties, a problem especially important for climate prediction validation. After a brief review of the times series analyses applied on secular series of observations, an appropriate method is described for characterizing these properties which finally reduces itself to the search for existing change-points. The examples of the Jones North Hemispheric land temperature averages (1856±1995) and of the Prague Klementinum ones (1771±1993) are given and results discussed. Relating the observed chaotic character of the climatological series to the non-linearity of the equations ruling the weather and thus climate evolution, and presenting the example of a solution of the Lorenz non-linear equations showing that non-linearity may be responsible for the instability of the generated process, it seems justified to conclude that there are severe limits to climate predictability at all scales.
Three competing hypotheses
Consider the following three hypotheses that explain 20th century climate variability and change, with implied future projections:
I. IPCC AGW hypothesis: 20th century climate variability/change is explained by external forcing, with natural internal variability providing high frequency ‘noise’. In the latter half of the 20th century, this external forcing has been dominated by anthropogenic gases and aerosols. The implications for temperature change in the 21st century is 0.2C per decade until 2050. Challenges: convincing explanations of the warming 1910-1940, explaining the flat trend between mid 1940′s and mid 1970′s, explaining the flat trend for the past 15 years.
II. Multi-decadal oscillations plus trend hypothesis: 20th century climate variability/change is explained by the large multidecadal oscillations (e.g NAO, PDO, AMO) with a superimposed trend of external forcing (AGW warming). The implications for temperature change in the 21st century is relatively constant temperatures for the next several decades, or possible cooling associated with solar. Challenges: separating forced from unforced changes in the observed time series, lack of predictability of the multidecadal oscillations.
III: Climate shifts hypothesis: 20th century climate variability/change is explained by synchronized chaos arising from nonlinear oscillations of the coupled ocean/atmosphere system plus external forcing (e.g. Tsonis, Douglass). The most recent shift occurred 2001/2002, characterized by flattening temperatures and more frequent LaNina’s. The implications for the next several decades are that the current trend will continue until the next climate shift, at some unknown point in the future. External forcing (AGW, solar) will have more or less impact on trends depending on the regime, but how external forcing materializes in terms of surface temperature in the context of spatiotemporal chaos is not known. Note: hypothesis III is consistent with Sneyers’ arguments re change-point analysis. Challenges: figuring out the timing (and characteristics) of the next climate shift.
There are other hypotheses, but these three seem to cover most of the territory. The three hypotheses are not independent, but emphasize to varying degrees natural internal variability vs external forcing, and an interpretation of natural variability that is oscillatory versus phase locked shifts. Hypothesis I derives from the 1D energy balance, thermodynamic view of the climate system, whereas Hypothesis III derives from a nonlinear dynamical system characterized by spatiotemporal chaos. Hypothesis II derives from climate diagnostics and data analysis.
Each of these three hypotheses provides a different interpretation of the 20th century attribution and has different implications for 21st century climate. Hypothesis III is the hypothesis that I find most convincing, from a theoretical perspective and in terms of explaining historical observations, although this kind of perspective of the climate system is in its infancy.
Cherry picking data, or testing alternative hypotheses?
Back to the issue of cherry picking data, and interpreting the temperature time series for the past two decades.
Is the first decade+ of the 21st century the warmest in the past 100 years (as per Peter Gleick’s argument)? Yes, but the very small positive trend is not consistent with the expectation of 0.2C/decade provided by the IPCC AR4. In terms of anticipating temperature change in the coming decades, the AGW dominated prediction of 0.2C/decade does not seem like a good bet, particularly with the prospect of reduced solar radiation.
Has there been any warming since 1997 (Jonathan Leake’s question)? There has been slight warming during the past 15 years. Is it “cherry picking” to start a trend analysis at 1998? No, not if you are looking for a long period of time where there is little or no warming, in efforts to refute Hypothesis I.
In terms of projecting what might happen in coming decades, Hypothesis III is the best bet IMO, although it is difficult to know when the next change point might occur. Hypothesis III implies using 2002 as the starting point for analysis of the recent trend.
And finally, looking at global average temperatures makes sense in context of Hypothesis I, but isn’t very useful in terms of Hypothesis III.
And none of this data analysis is very satisfying or definitive owing to deficiencies in the data sets, particularly over the ocean.
IMO, the standard 1D energy balance model of the Earth’s climate system will provide little in the way of further insights; rather we need to bring additional physics and theory (e.g. entropy and the 2nd law) into the simple models, and explore the complexity of coupled nonlinear climate system characterized by spatiotemporal chaos.