Greenhouse Gases Not Behind The End Of The Warming Pause

During the years 2000-2014, the global temperature hardly increased, and that period has been called the temperature pause or hiatus.

The debate among the climate community has resulted in more than 200 research studies in some cases with opposite results about the reasons.

This amount of papers can be compared to the research studies of Earth’s energy balance and the greenhouse effect. I have found about 10 publications for both subjects.

During the years 2000-2014, the emissions of carbon dioxide were 126 gigatons carbon (GtC) being 31% of the total emission after 1750, but the greenhouse (GH) gases were not able to increase the temperature.

According to the IPCC, the temperature increase should have been 0.4°C from 2000 to 2014 (Ref. 1).

It looks like that the pause ended to the super El Nino 2015-2016 because the temperature has been thereafter about 0.2 °C above-the-pause average.

Research study about the pause and the ENSO

The impulse for my research study came from a story figure on WUWT that showed shortwave (SW) radiation variations during the pause.

A curve showed increased values around El Nino 2015-16 and thereafter. I decided to find out what could be the impact of this finding on the temperatures.

In Fig. 1, I have depicted the total solar irradiance (TSI), SW radiation and LW radiation from 2000 onward. This data is available from the CERES databank maintained by NASA.

Fig.1. TSI, SW radiation and LW radiation trends normalized to the altitude of 20 kilometers.

TSI has been in decreasing mode since 2000 but it is surprising that SW radiation has an increasing trend with a maximum during El Nino 2015-16.

I decided to carry out a research study about the factors which should explain the temperature variations during the pause including ENSO events.

I have used two dynamic models with the same structure naming them as the Ollila model and the IPCC model. In my own model, I have used the results of my earlier research studies. This newest study has been published, Ref.2.

Both models include the same terms

dT = dTSW + dTENSO+ dTANTR + dTCLOUD, (1)

where dT is the total temperature change, dTSW is the temperature impact of SW radiation, dTENSOis the temperature impact of ENSO event, dTANTR is the temperature impact of greenhouse (GH) gases and other anthropogenic factors, and dTCLOUD the temperature impact of cloudiness changes.

The term dTCLOUD has not been used in the IPCC model because this effect has been integrated into the term dTANTR.

The temperature effects of SW radiation and anthropogenic factors have been calculated using the equation applied both in the IPCC model and my model

dT = λ*RF, (2)

where λ is the climate sensitivity parameter having a value of 0.5 K/(W/m2) in the IPCC model and in my model 0.27 K/(W/m2) and RF means radiative forcing (W/m2).

The IPCC has normalized the anthropogenic factors to correspond to SW radiation changes at the top of the atmosphere.

Therefore, SW radiation changes and anthropogenic factors per the IPCC can be applied directly in Eq. (2). I have used the total RF values of the IPCC reports.

In my own model, I have used only the carbon dioxide effects, because of the impacts of other GH gas changes are below 0.001°C during the pause, Ref. 3.

dTANT= 0.27 * 3,12 * ln (C/280), (3)

where C is the concentration of carbon dioxide (ppm). The temperature effects of ENSO events I have calculated in both models using Eq. (4)

dTENSO = 0,1 * ONI, (4)

where ONI is the Oceanic El Nino Index used for reporting and assessing the magnitude of ENSO effects. ONI is the three months running-average value of the seawater temperature in the area of Nino3.4.

The coefficient value of 0.1 is based on the empirical data between global temperature and ONI.

Trenberth et al. (Ref. 1) have used the same method and the same value. By using the correlation analysis, I found out that there was a delay of 6 months between the ONI index and the global temperature.

The temperature effects of cloudiness change I have calculated using the relationship from my earlier research study (Ref. 4)

dT = – 0,11 * CL-%, (5)

where CL-% is the change of cloudiness in percentages.

The equations above do not include any dynamic factors, which means that they do not describe in which way the changes would have their temperature effects during shorter time intervals when all-climate drivers variate continuously.

I have used the climate drivers – i.e. the input data – in monthly steps. The residence time of the mixing layer of the oceans is 2.74 months and the same of the land is 1.04 months (Ref. 4).

The accurate simulation method for stepwise changes of inputs can be found in my research study (Ref. 2).

The results of the study

The results showed some surprises and new findings. I have depicted the essential results of my model and the observed satellite temperature of the UAH in Fig. 2.

Fig. 2. The results of the Ollila model.

In Fig.2 we can see that the model calculated temperature (a black curve) follows well the observed global temperature. The ENSO effects and the SW radiation changes are the main reasons for temperature changes.

During the super El Nino 2015-16, the temperature impact is slightly more than 50% about the total temperature change. This kind of observation has not been reported before.

Another important observation is that La Nina 2017 was very weak, but the temperature remained at about 0.2°C higher level in comparison to the average pause temperature of 2000-2014.

The reason can be identified directly, and it is the SW radiation. The overall effect of cloudiness is small, but its impact is in the right direction.

Fig. 3. The results of the IPCC model. The curve ”Forcing by all anthropogenic factors per IPCC” includes other factors than GH gases, and therefore its temperature impact is lower than the impact of GH gases only.

I have depicted the results of the IPCC model in Fig. 3. The black curve is the model calculated temperature and the red one is the GISTEMP. The IPCC model has a rather great error during the El Nino 2015-16 and thereafter.

I have calculated the Mean Absolute Error (MAE) in four different cases for the period of 2000-2018:

• The Ollila model versus UAH 0.075°C

• The Ollila model versus GISTEMP 0.082°C

• The IPCC model versus UAH 0.191°C

• The IPCC model versus GISTEMP 0.128°C.

The error of my model is relatively small with respect to both temperature curves and it is about 100 % smaller than the same as the IPCC model the correlation is 0.82.

The reason for the IPCC model greater errors is pretty clear and it is the positive water feedback duplicating the impacts of anthropogenic (GH gases) changes and the SW radiation changes.

The increase in temperature from the beginning of 2001 to the average of 2018 was according to UAH 0.29 °C and according to Gistemp 0.50 °C.

Respectively the increase per the Ollila model was 0.23 °C and per the IPCC model 0.73 °C. The error of my own model was thus +0.06 °C with respect to the UAH temperature and -0.31 °C with respect to the Gistemp.

The IPCC model error was +23 °C with respect to the Gistemp and 0.44 °C with respect to the UAH. The conclusion is that the IPCC model runs too hot due to the too great radiative forcing of carbon dioxide and the positive water feedback. Thinking these errors for 20 years only, we can imagine how great errors might be to the end of 2100.

We have a new climate change factor in the form of an SW radiation increase since 2014. We will see in which way the IPCC shall take this matter into account.

By knowing the practices of the IPCC, I would not be surprised if this change will be assessed to be anthropogenic by nature. It would be interesting to see, what are the pieces of evidence.

One may also observe in Fig. 3 a great difference between the impacts of GH gases and the total anthropogenic factors about 0.2°C in 2011.

In order to concretize this issue, I use the numbers of the year 2011 in AR5 for the change from 1750 to 2011.

The anthropogenic term includes the impacts of aerosols (-0.27 W/m2), the albedo changes due to land use (-0.15 W/m2) and the cloud adjustment (-0.55 W/m2); totally -0.97 W/m2 corresponding to the temperature impact of -0.5 °C. The RF of GH gases in 2011 per the IPCC was 3.18 W/m2 corresponding to the temperature impact of 1.6°C.

A question is that what is the scientific method and data that the IPCC knows the radiative forcings of aerosols, land-use, and the cloud adjustments of the year 1750? I think that they do not know these figures even today.

For me, these factors are really pure adjustments to tamper down the over-all temperature impacts and to cover up the sky-high impacts of GH gases.

The observational data between the temperature and the absolute amount of the atmospheric water do not show this mechanism, Fig. 4.

Fig. 4. Short- and long-term effects of water vapor on a global scale.

It can be noticed that for 1982–2003, the global temperature anomaly has been increasing but long-term water vapor amount has been decreasing destroying the theory of positive water feedback.

This figure illustrates very well that the global temperature effects of ENSO events happen about 50 % through the absolute humidity changes in the atmosphere.

It means that the positive water feedback works in short-term events like ENSO but in the long-term trends, this effect disappears.

Water is about 12 times stronger GH gas than carbon dioxide.

The great impact of SW radiation in the El Nino 2015-16 came as a big surprise. Therefore, I looked for the radiative information during the earlier super El Nino 1998-98.

The CERES data started in 2001 but the ERBE data was available and the results are depicted in Fig. 5.

Fig. 4. Super El Ninos of 1997-98 and 2015-16.

Fig. 4 shows that also during super El Nino 1997-98 the temperature impact of SW radiation has been slightly over 50 %.

The retrospective analysis reveals that the pause ended by 2014. The most important factor has been the abnormal positive SW radiation anomaly.

I sent this paper to six different journals in category 2. I did not expect positive publication decisions, but I gathered information about the rejection comments of reviewers.

The collection of rejection comments would be another story. It is a well-known fact among the contrarian researchers that they do not get papers through in the categories 1 (Science and Nature) and in category 2 (more than 100 journals) if the content is clearly against the IPCC science.

In the final phase of this process, I received a useful comment from a reviewer that there is a research study of Loeb et al. published in 2018 (Ref. 5).

Some readers may know that Dr. Loeb is a responsible person for the CERES data in NASA and he surely follows the observational data trends. There is a comprehensive analysis of the CERES data in this study.

The researchers showed that the SW radiation change was due to the end of hiatus and it is also the dominant reason for the “increased temperature tendency during the post-hiatus period” as they have formulated this matter.

As an example, about the rejection comments, there was a comment that a reviewer did not accept the SW radiation changes to have any impact on El Nino temperatures or thereafter. Maybe this reviewer would have rejected also the paper of Loeb et al. – do you think so?

So, I cannot say that I was the first one who found out the role of SW radiation in the temperature changes after pause even though I did my work independently without knowing the study of Loeb et al.

What I can still say is that I have observed the role of SW radiation as an important part of super El Nino temperature impacts. I have not been able to suggest the mechanism and it may be a pure coincidence.

We can see in Fig. 2 that the El Nino 2010 was in a category “strong”. It could have developed into “very strong” super El Nino but the SW radiation anomaly was in the wrong phase.

I also carried out the analysis between “the anti-IPCC model” and the IPCC model showing that the IPCC model does not work properly.

Loeb et al. found that the correlation of SW radiation flux anomalies to the low-level cloud cover was 0.66. They did not go further to analyze what factors could cause variations to the low-level cloudiness changes.

As we know, Dr. Henrik Svensmark has proposed a theory about the cosmic radiation modulating the cloud formation process and having major impacts on the surface temperature through cloudiness changes.

This is, of course, a forbidden subject to any researcher wanting to belong to the climate establishment, because the IPCC wants to avoid any theories about the cosmic forces having a role in the climate change.

References:

1. Trenberth KE, Zhang Y, Fasullo JT. Relationships among top‐of‐atmosphere radiation and atmospheric state variables in observations and CESM. J. Geophys. Res. Atmos. 2015;120:10074–10090. https://doiorg/101002/2015JD023381

2. Ollila, Antero. The Pause End and Major Temperature Impacts During Super El Niños are Due to Shortwave Radiation Anomalies. Physical Science International Journal, 24(2), 1-20, 2020. http://www.journalpsij.com/index.php/PSIJ/article/view/30174/56612

3. Ollila, Antero. The Potency of Carbon Dioxide (CO2) as a Greenhouse Gas, 2014, URL: https://www.researchgate.net/publication/274956207_The_potency_of_carbon_dioxide_CO2_as_a_greenhouse_gas

4. Ollila, Antero. Dynamics between Clear, Cloudy, and All-Sky Conditions: Cloud Forcing Effects, 2014. URL: https://www.researchgate.net/publication/274958251_Dynamics_between_clear_cloudy_and_all-sky_conditions_Cloud_forcing_effects.

5. Loeb NG, Thorsen TJ, Norris JR, Wang H, Su W. Changes in earth’s energy budget during and after the “pause” in global warming: an observational perspective. Climate. 2018;6:62. DOI:103390/cli6030062.

Dr. Antero Ollila is an Adj. Ass. Prof. (Emeritus) at Aalto University in Finland and runs the website ClimatExam.