The “uncertainty monster” strikes again
We’ve been highly critical for some time of the paper in summer 2015 by Karl et al. that claimed “the pause” or hiatus went away once “properly adjusted” ocean surface temperature data was applied to the global surface temperature dataset. Virtually everyone in the climate skeptic community considers Karl et al. little more than a sleight of hand.
No matter, this paper published today in Nature Climate Change by Hedemann et al. not only confirms the existence of “the pause” in global temperature, but suggests a cause, saying “…the hiatus could also have been caused by internal variability in the top-of-atmosphere energy imbalance“.
That’s an important sentence, because it demonstrates that despite many claims to the contrary, CO2 induced forcing of the planetary temperature is not the control knob, and natural variability remains in force.
Also of note, see the offset as designated by the two colored X’s in Figure 1:
Models and observations don’t even begin to match.
The subtle origins of surface-warming hiatuses
Christopher Hedemann, Thorsten Mauritsen, Johann Jungclaus & Jochem Marotzke
AffiliationsContributionsCorresponding author
Nature Climate Change (2017) doi:10.1038/nclimate3274
Received 12 July 2016 Accepted 17 March 2017 Published online 17 April 2017
During the first decade of the twenty-first century, the Earth’s surface warmed more slowly than climate models simulated1. This surface-warming hiatus is attributed by some studies to model errors in external forcing2, 3, 4, while others point to heat rearrangements in the ocean5, 6, 7, 8, 9, 10caused by internal variability, the timing of which cannot be predicted by the models1. However, observational analyses disagree about which ocean region is responsible11, 12, 13, 14, 15, 16. Here we show that the hiatus could also have been caused by internal variability in the top-of-atmosphere energy imbalance. Energy budgeting for the ocean surface layer over a 100-member historical ensemble reveals that hiatuses are caused by energy-flux deviations as small as 0.08 W m−2, which can originate at the top of the atmosphere, in the ocean, or both. Budgeting with existing observations cannot constrain the origin of the recent hiatus, because the uncertainty in observations dwarfs the small flux deviations that could cause a hiatus. The sensitivity of these flux deviations to the observational dataset and to energy budget choices helps explain why previous studies conflict, and suggests that the origin of the recent hiatus may never be identified.
http://www.nature.com/nclimate/journal/vaop/ncurrent/full/nclimate3274.html
(paywalled)
From the Introduction:
The surface temperature of the Earth warmed more slowly over the period 1998–2012 than could be expected by examining either most model projections or the long-term warming trend1. Even though some studies now attribute the deviation from the long-term trend to observational biases17, 18, the gap between observations and models persists. The observed trend deviated by as much as −0.17 °C per decade from the CMIP5 (Coupled Model Intercomparison Project Phase 5; ref. 19) ensemble-mean projection1—a gap two to four times the observed trend. The hiatus therefore continues to challenge climate science.
Key excerpts:
…
The coupled climate model MPI-ESM1.1 is forced with CMIP5-prescribed historical forcing from 1850 until 2005, and extended until 2015 with the RCP4.5 scenario (see Methods). When the red line lies above the grey line, at least one ensemble member is experiencing a hiatus, defined as a deviation of more than 0.17 °C per decade below the ensemble mean. This deviation is the same as the gap between the CMIP5 ensemble mean (black cross) and the observed (yellow cross) GMST trends for the period 1998–2012. Contours represent the number of ensemble members in bins of 0.05 °C per decade.
…
From our analysis of observational estimates, we are unable to exclude the TOA anomaly as a possible cause of the recent hiatus. Referencing the observations to an alternative energy budget (rather than that of the large ensemble) could shift the absolute position of the green and yellow crosses in Fig. 3c. However, their relative distance from one another and the size of their error bars would not change.
The role of the TOA and the ocean in each hiatus can be determined by comparing their relative contributions to the flux-divergence anomaly. For hiatuses in the large historical ensemble, the negative (cooling) anomaly is caused entirely by the TOA in 12% of cases and by the ocean in 24% of cases. In the remainder (64%), the negative anomaly is caused by the TOA and ocean acting together (bottom left quadrant of Fig. 3c). TOA variability is therefore involved in 76% of all hiatuses.
…
We conclude that the TOA may have been a source of significant internal variability during the hiatus. Our conclusions are not an artefact of model-generated TOA variability29—the large ensemble produces TOA variability that is similar to that in the observational record (Supplementary Fig. 7). Rather, our conclusions are based on a simple yet robust principle, namely that the Earth’s surface layer has a small heat capacity. The surface temperature can therefore be influenced by small variations in the large yet mutually compensating fluxes that make up this layer’s energy budget. Comparing the small variability in the TOA imbalance with the total TOA imbalance under global warming27, 30 obscures the significance of these small variations for the hiatus.
…
(bold mine)
This is the true dilemma at the heart of the hiatus debate: the variability in ocean heat content alone has no power to explain the hiatus, and the measure that can—the surface-layer flux divergence—is dwarfed by observational uncertainty. While there are attempts to fill the gaps in observations with ocean reanalyses such as ORAS4 (refs 9,23), the resulting data are of questionable integrity during the hiatus14, 21 and, as we show, disagree with the budget based on CERES21 and WOA22. Even if these disagreements could be reconciled, the process of anchoring satellite observations with ocean heat uptake makes the contributions from TOA and ocean difficult to disentangle, because their absolute difference is unknown. Therefore, unless the uncertainty of observational estimates can be considerably reduced, the true origin of the recent hiatus may never be determined.
Code availability.
The MPI-ESM1.1 model version was used to generate the large ensemble and is available at http://www.mpimet.mpg.de/en/science/models/mpi-esm.html. Computer code used in post-processing of raw data has been deposited with the Max Planck Society: http://pubman.mpdl.mpg.de/pubman/faces/viewItemFullPage.jsp?itemId=escidoc:2353695.
Data availability.
Raw data from the large ensemble were generated at the Swiss National Computing Centre (CSCS) and Deutsches Klimarechenzentrum (DKRZ) facilities. Derived data have been deposited with the Max Planck Society (http://pubman.mpdl.mpg.de/pubman/faces/viewItemFullPage.jsp?itemId=escidoc:2353695). Supplementary Fig. 7 uses TOA flux reconstructions provided by R Allan40 (http://www.met.reading.ac.uk/~sgs01cll/flux) and satellite observations provided by the NASA CERES project31 (http://ceres.larc.nasa.gov). For observational estimates in Fig. 3c, we make use of data provided by the NOAA World Ocean Atlas22(https://www.nodc.noaa.gov/OC5/3M_HEAT_CONTENT) and by the ECMWF Ocean Reanalysis System 4 (ref. 9; http://icdc.zmaw.de/projekte/easy-init/easy-init-ocean.html).
PDF files
Supplementary Information (3,616 KB)
Note: about 30 mins after publication, some grammatical and spelling errors were corrected, and a subtitle added.


From the inane comment above: “I see your acceptance (or liking) of models depends on what results they produce. ”
Actually that’s true. They either agree with the observations or they don’t. When they don’t good scientists/engineers either fix them to match obs. or discard them. Of course ‘model’ is too generalist a term: The general circulation models are demonstrably inadequate. Finite Element models are often within 1% error. Economic models are universally acknowledged to be the purest guesswork. Anyone using the argument that because one model is bad/good therefore they must all be bad/good is clearly innumerate.
And electronic design models and simulators have designed the modern world. Look around you and see what good models produce. They are the reason for the accelerating pace of advanced technology.
Yes, models in which every factor involved is known, and can be accurately programmed into it, can, and do, produce dependable results. Results that can be verified and replicated over and over again.
But as the IPCC stated- “The climate system is a coupled non-linear chaotic system, and therefore the long-term prediction of future climate states is not possible.”
But, you could design exactly that type of circuit with known components, and then explore that nonlinear space. Timing verifiers are specifically designed to allow stateless verification, while you can have control signals, data path can just define a stable/changing input. Thus generates an envelope of how change propagates. Btw transistors are nonlinear, and are coupled to billions of other nonlinear transistors. If we understood the components of climate, we could run ensembles and map the chaotic bounds. But those don’t get it right either. The component functions are wrong, they have their thumbs are water feedback with their parameterization of the air water boundary.
It’s not JUST a non-linear system, it’s nonlinear AND chaotic. You can explore that nonlinear space all you want to, but you’ll never be able to determine what the future will be with any accuracy.
I understand what chaotic means, let me quote myself “we could run ensembles and map the chaotic bounds”.
“Economic models are universally acknowledged to be the purest guesswork.”
Yes, but a slight edge in financial models can yield tremendous wealth if appropriately applied. So, low skill does not necessarily mean low value.
Reblogged this on Climatism and commented:
“The gap between observations and models persists. The observed trend deviated by as much as −0.17 °C per decade from the CMIP5 (Coupled Model Intercomparison Project Phase 5; ref. 19) ensemble-mean projection1—a gap two to four times the observed trend.
The hiatus therefore continues to challenge climate science.”
– Nature Climate Change (PEER-REVIEWED STUDY)
“This paper published today in Nature Climate Change by Hedemann et al. not only confirms the existence of “the pause” in global temperature, but suggests a cause, saying “…the hiatus could also have been caused by internal variability in the top-of-atmosphere energy imbalance“.
That’s an important sentence, because it demonstrates that despite many claims to the contrary, CO2 induced forcing of the planetary temperature is not the control knob, and natural variability remains in force.”
The “pause/hiatus” in Global Warming is now nearing 20 YEARS. This despite *record* man-made CO2 emitted over the same period.
Don’t be at all surprised if – CNN, BBC, ABC, CNBC, LATimes, NYTimes, WaPo, The Age, SMH and the rest of the “climate change” obsessed sycophant media don’t report this massively inconvenient climate news. – Too many reputations, jobs, govt grants and funding are now at stake.
Climatism
That’s not what this paper is saying. It’s referring specifically to surface data between the period 1998-2012, which it defines as a ‘hiatus’ because warming observed over that period didn’t reflect that expected by the average of the CMIP5 model ensemble. There ‘was’ warming over that period according to all the surface data sets; just not statistically significant or as strong as predicted by the multi-model average.
If you take the period 1998 to the present, then the warming is statistically significant in all surface data sets, as you can verify here: http://www.ysbl.york.ac.uk/~cowtan/applets/trend/trend.html
So the hiatus, such as it was, is no longer with us; at least not according to all the surface data producers. This latest study doesn’t attempt to claim that it is.
Hiatus theory is based upon a misleading use of language under which a straight line is fit to gtobal surface temperature data and each point along this line is called “the surface temperature” (TST).though they are not surface temperatures. At a given point in time, a surface temperature has a point value but TST has many values, the value being, dependent upon the interval containing the data to which the straight line is fit. Thus, given that it is true that the slope of the line is 0 it is also true that the slope of the line is not 0. To call TST “the surface temperature” is to create the misleading impression that TST is single valued. This phenomenon is one of many examples of application by climatologists of the equivocation fallacy.. In this application of the fallacy, the term that changes meaning in the midst of the argument is “the surface temperature.”
The hiatus is in the actual surface station records.
micro6500 on April 20, 2017 at 9:45 am
The hiatus is in the actual surface station records.
Well micro6.5K, wether or not this incredibly interesting matter called ‘hiatus’ existed is not my point today evening.
I lack the time (and the interest) to go into this Gsod data.
I’m wondering about the big discrepancy between the project’s data you display above and the GHCN V3 unadjusted data I have at home.
Here is a tmin-tmax chart for the Globe, period 1940-2016 (without the diff plot):
http://fs5.directupload.net/images/170420/lg4j4pw6.jpg
This confirms btw what is known: night minima increase faster than day maxima since around 2005.
It’s hard to detect in your chart because you didn’t superpose the tmin and tmax plots.
No idea where your drop-down in 1970 came from. Did you really publish Globe data?
Yep, global.
GSoD (I figured out today), is the Air Forces daily summary of about 20K stations over time. In ~2000 you can see dew points rise, min temp just follows dew point.
Rising = Max – Min, with calculated surfacing forcing from measured TSI
Max – Min
Climate researchers analyze the annual temperature anomaly data, which is transformed data and misses monthly cycles of temperature variations.
Our empirical time series modeling approach using ARIMA seasonal model for NOAA’s monthly global temperature data from 1950 to 2015 shows high R of 0.99. The prediction of monthly temperature for 2016 and its average value differs less than 2 percent from the measured value and it is highly accurate. In comparison, both the IPCC and EPA models overestimate their predictions for 2016. Our ARIMA seasonal model results until 2050 do not show any significant increase until 2050
An ARIMA model was developed for NOAA’s monthly atmospheric CO2 data with high R value of 0.98. It shows very poor crosscorrelation of 0.08 with global monthly temperature data. This clearly shows that global warming is not caused by CO2.
(This research is a part of my PhD student at the University of Mississippi who passed his defense last week.)
Waheed, I did something similar and came to the same conclusion
https://micro6500blog.wordpress.com/2016/05/18/measuring-surface-climate-sensitivity/?preview=true
A week ago, Alphan wrote, “Climate models currently do not mimic Earth’s systems. Period.” which apart form the word “currently” is spot on. My point is that they never will and that’s because climate models do not attempt to model the Earth system which is a much broader entity with fluxes of energy and materials that are fed from and feed into those of its components that are conventionally grouped into climate models.
It’s that odd word “paradigm”. Climate scientists are brought up into a paradigm where the climate is a free-standing object with its own inputs, state variables and outputs such as pressure and temperature fields and have little conception (or haven’t internalised it) of large “flywheels” involving carbon, nitrogen, water pools and fluxes etc with their own time constants that have a significant impact on what happens inside the “climate box” and in turn are affected by outputs from the box.
Some of these processes are on too long a time scale to be of interest within the time horizon of climate projections but many, especially those involving the biosphere, would be highly relevant to resetting state variables controlling land atmosphere interactions and hence boundary conditions of climate models. Issues also mentioned here such as “what causes natural variation” cannot possibly be answered without considering these excluded processes so appeals to climate scientists to divert attention to natural variation are doomed to failure as they are directed to the wrong people.