by Judith Curry
Moreover, it appears likely that essentially all of this increase results from the human emission of greenhouse gases. These findings are stronger than those of the Intergovernmental Panel on Climate Change, the United Nations group that defines the scientific and diplomatic consensus on global warming. In its 2007 report, the I.P.C.C. concluded only that most of the warming of the prior 50 years could be attributed to humans. – Richard Muller, NYT op-ed
Muller bases his ‘conversion’ on the results of their recent paper. So, how convincing is the analysis in Rohde et al.’s new paper A new estimate of the average surface land temperature spanning 1753-2011? Their analysis is based upon curve fits to volcanic forcing and the logarithm of the CO2 forcing (addition of solar forcing did not improve the curve fit.)
I have made public statements that I am unconvinced by their analysis. I do not see any justification in their argument for making a stronger attribution statement than has been made by the IPCC AR4. I have written MANY posts that critique the IPCC’s attribution analysis. Here I try to give a sense of the challenges in attributing climate change to causal factors.
Guidelines from the IPCC attribution workshop
Lets first take a look at how the IPCC approaches the attribution of climate change. A good summary is provided by the 2009 IPCC Expert Meeting on Detection and Attribution Related to Anthropogenic Climate Change:
Attribution “of causes of climate change is the process of establishing the most likely causes for the detected change with some defined level of confidence” . As noted since the SAR, unequivocal attribution would require controlled experimentation with our climate system. Since that is not possible, in practice attribution is understood to mean demonstration that a detected change is “consistent with the estimated responses to the given combination of anthropogenic and natural forcing” and “not consistent with alternative, physically-plausible explanations of recent climate change that exclude important elements of the given combination of forcings” (Mitchell et al., 2001). Information about the expected responses to external forcing, so called ‘fingerprints’, is usually derived from simulations by climate models, although the use of simple or conceptual models is possible as well. The consistency between an observed change and the estimated response to a forcing can be determined by estimating the amplitude of a ‘fingerprint’ from observations and then assessing whether this estimate is statistically consistent with the expected amplitude of the pattern from a model. If the response to a key forcing, such as greenhouse gas increases, is also distinguishable from that to other forcings, this strengthens confidence in the attribution assessment. Often, results are based on multiple regression of observations onto several fingerprints representing climate responses to different forcings, and in many cases, the estimate involves a metric that increases the signal-to-noise ratio by suppressing internal climate variability.
Global scale surface temperature is recorded by an instrumental record of 150 years and reconstructed from palaeo data over several centuries. Both compare well with climate model simulations if driven with estimates of external forcing, even on the scale of large regions. This comparison, attribution studies and physical energy considerations led to the assessment that it ‘is extremely unlikely (<5%) that the global pattern of warming during the past half century can be explained without external forcing’. Results from fingerprint studies show that the response to greenhouse gases can be well separated from that to other forcings, and that the recent warming requires a significantly positive and substantial response to greenhouse gas forcing, irrespective of the model used and robust to a variety of technical choices. The fingerprint does not require significant rescaling to match the observed change. All this led to the assessment that ‘greenhouse gas forcing has very likely caused most of the observed global warming over the recent 50 years’. Results for individual continents for the same timeframe show that ‘it is likely that there has been a substantial anthropogenic contribution to surface temperature increases over every continent except Antarctica’.
The AR4 presented strong evidence that recent multi-decadal trends in global near-surface temperatures were very unlikely to have been caused by natural internal variability or natural external forcings from changes in solar output and explosive volcanic eruptions. Since then, the fact that neither 2007 or 2008 has broken the record for warmest year in the instrumental record has been used by some to claim that global warming has stopped or slowed down.
However papers by Easterling and Wehner (2009) and by Knight et al. (2009), have demonstrated that decade long trends with little warming or cooling are to be expected under a sustained long-term warming trend, as a result of multi-decadal scale internal variability. These results underscore the importance of understanding the effects of variability, in addition to external drivers of climate.
Detection of the anthropogenic and natural fingerprints of near-surface temperature change has enabled robust observationally constrained quantification of the contributions of different forcings to global temperature trends and likely ranges of future warming, assuming particular emissions scenarios (Stott et al, 2006). By including multiple climate models to provide estimates of the uncertainty in response patterns, more comprehensive estimates of attributable changes are obtained (Christidis et al, 2009).
Change in most variables of interest has multiple causes, whether in the climate system itself or downstream in natural or human systems. Therefore, attribution to the external forcing of interest must take into account the other forcings and drivers that affect the variable of interest. The effects of external forcings and drivers may be masked or distorted by the presence of confounding influences or factors. Expert judgement based on as complete an understanding as possible of the data, response processes and potential confounding factors and their possible ef fects should be used to carefully assess the likelihood that the detection and attribution results are substantially affected by confounding factors.
Non-climate drivers can have a significant influence on many natural or human systems. For example, the impact of mass coral bleaching events may be affected by the presence or absence of non-climate related drivers such as fishing pressure and pollution. To the extent that the response to greenhouse gas forcing can be separated from the responses to other external forcings and drivers, the change attributable to greenhouse gas forcing can be assessed and further used to produce probabilistic projections of future change.
Confounding factors may lead to false conclusions within attribution studies if not properly considered or controlled for. Examples of possible confounding factors for attribution studies include pervasive biases and errors in instrumental records; model errors and uncertainties; improper or missing representation of forcings in climate and impact models; structural differences in methodological techniques; uncertain or unaccounted for internal variability; and nonlinear interactions between forcings and responses.
JC’s criticism of the IPCC Ar4 detection and attribution arguments
In my published Uncertainty Monster paper, we argued that that AR4 attribution statement was overconfident for the following reasons:
- uncertainties in the models
- failure to account for uncertainties in external forcing (particularly solar and aerosols) and the use of inverse modeling in determining aerosol forcing
- inadequacy of the climate models in simulating natural internal variability on multidecadal (>30 years) timescales
- bootstrapped plausibility and circular reasoning in the detection and attribution arguments
Observation – based analyses
For those of you that think climate models aren’t useful for attribution studies and/or prefer observation-based analyses, lets take a look at some of the better analyses.
On the Trends, Changepoints, and Hypotheses thread, I described three hypotheses that explain 20th century climate variability and change, that have provided frameworks for observation based attribution analysis:
I. IPCC AGW hypothesis: 20th century climate variability/change is explained by external forcing, with natural internal variability providing high frequency ‘noise’. Best in class: Lean and Rind. They conclude: Empirical models that combine natural and anthropogenic influences (at appropriate lags) capture 76% of the variance in the CRU monthly global surface temperature record, suggesting that much of the variability arises from processes that can be identified and their impact on the global surface temperature quantified by direct linear association with the observations.
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). Best in class: Wu et al. They conclude: Depending upon the assumed importance of the contributions of ocean dynamics and the time-varying aerosol emissions to the observed trends in global-mean surface temperature, we estimate that up to one third of the late twentieth century warming could have been a consequence of natural variability.
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). From Tsonis et al.:
The above observational and modeling results suggest the following intrinsic mechanism of the climate system leading to major climate shifts. First, the major climate modes tend to synchronize at some coupling strength. When this synchronous state is followed by an increase in the coupling strength, the network’s synchro- nous state is destroyed and after that climate emerges in a new state. The whole event marks a significant shift in climate. It is interesting to speculate on the climate shift after the 1970s event. The standard explanation for the post 1970s warming is that the radiative effect of greenhouse gases overcame shortwave reflection effects due to aerosols [Mann and Emanuel, 2006]. However, comparison of the 2035 event in the 21st century simulation and the 1910s event in the observations with this event, suggests an alternative hypothesis, namely that the climate shifted after the 1970s event to a different state of a warmer climate, which may be superimposed on an anthropogenic warming trend.
Specific issues with the Rhode, Muller et al. analysis
Judged by standards set by the IPCC and the best of recent observation-based attribution analyses, in my opinion the Rhode, Muller et al. attribution analysis falls way short. The closest in approach is the Lean and Rind analysis, which considers all of the external forcings (with units, not just curve fits) and discusses their uncertainties. Looking at regional variations provides substantial insights into the attribution.
Both global and regional attribution studies have been done, but what are we to make of the global land attribution study done by Rhode, Muller et al.? Land has warmed substantially more than the oceans; it does not seem that their same model would explain the ocean temperature changes. Also, given the regional variations in attribution, going back to the 18th and 19th centuries tells us what is going on in western europe and eastern north america, which is dominated by ocean circulation patterns in the North Atlantic and high latitude volcanoes. While I like what they have done going back further in time, these regional data are of little use for a global attribution study.
Attribution of the recent warming remains a challenging problem. The model-based methods used by the IPCC have numerous problems, but the main advantage is that hypotheses regarding causal mechanisms can be tested (by turning off or enhancing various processes).
Observation based methods are gaining more traction, and increasing recognition is being given to multidecadal natural variability. The challenge here is that there are lags and nonlinear shifts in the system, making the attribution to external forcing agents challenging.
No one that I listen to questions that adding CO2 to the atmosphere will warm the earth’s surface, all other things being equal. The issue is whether anthropogenic activities or natural variability is dominating the climate variability. If the climate shifts hypothesis is correct (this is where I am placing my money), then this is a very difficult thing to untangle, and we will go through periods of rapid warming that are followed by a stagnant or even cooling period, and there are multiple time scales involved for both the external forcing and natural internal variability that conspire to produce unpredictable shifts.
Maybe the climate system is simpler than I think it is, but I suspect not. I do know that it is not as simple as portrayed by the Rhode, Muller et al. analysis.
However, this does not stop the team from cheering Muller’s conclusion, see especially the thinkprogress post and the comments. If the attribution problem was as simple as Muller makes it out to be (curve fitting to CO2 concentration), then why are others wasting all their time with complex modeling studies, data analyses etc as described above? At least William Connolley and Eli Rabett have stated this analysis is oversimplistic. As an example, Ken Caldeira seems to think that getting the ‘right’ answer for whatever reason is ok:
Ken Caldeira: I am glad that Muller et al have taken a look at the data and have come to essentially the same conclusion that nearly everyone else had come to more than a decade ago. The basic scientific results have been established for a long time now, so I do not see the results of Muller et al as being scientifically important. However, their result may be politically important. It shows that even people who suspect climate scientists of being charlatans, when they take a hard look at the data, see that the climate scientists have been right all along.