By Dr. Antero Ollila, member of the Scientific Board of the Norwegian Climate Realists
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. 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 science, the temperature increase should have been 0.4°C from 2000 to 2014 (Ref. 1). ENSO (El Niño Southern Oscillation) has strong impacts on the global temperature as can be estimated even by the eye in Fig. 1, where also ONI (Oceanic Niño Index) has been depicted. It looks like that the pause ended to the super El Nino 2015-2016 as shown in Fig. 1.
Fig.1. Trends of UAH, GISTEMP, and ONI (Oceanic Niño Index).
GISTEMP of 2019 was 0.35 °C above the average of 2001-14 and the same figure of UAH was 0.31 °C. The difference between GISTEMP and UAH has increased to about 0.2 °C from 2000 to 2020. Both UAH and GISTEMP values have been since January 2020 about at the same level as at the end of 2019. After 2016 there has only one weak El Niño event in 2019 and therefore it cannot be a cause for elevated temperatures since the super El Nino 2015-16.
The supporters of the AGW (Anthropogenic Global Warming) has explained (with satisfaction) that the warming impact of GH gases was delayed during the pause and now the surface temperature starts to follow the GCMs. The objective of this blog is to show that this is not the case, but there is a nonanthropogenic cause.
The recent trends of solar irradiance, shortwave, and longwave radiations
A possible reason can be detected in Fig. 2 where shortwave (SW) radiation, longwave (LW radiation and solar irradiance trends at the TOA (top of the atmosphere) have been depicted. This data is available from CERES data maintained by NASA.
Fig.2 SW, LW and solar irradiance fluxes at the TOA normalized to the altitude of 20 km from 2001 to 2020.
It is well-known that solar irradiance has been declining since 2000. Therefore, it is rather surprising that SW radiation has a strong positive trend since 2014. This phenomenon has been not been generally considered among climate researchers. The reason will be explained later. I am aware of two articles only analyzing SW radiation flux impacts and they are the article of Loeb et al. (Ref. 2) and the study of mine about the pause ending and super El Nino features (Ref. 3). Dr. Norman Loeb is the CERES Science Team Leader, and he has the best opportunity to study the CERES data. There has been more than 6 months delay in CERES data availability, but now NASA has improved this practice by updating data every second month. Because there is seasonal variability in the CERES data, the data must be processed with 12 months averaging procedure and it will cause a 6-month delay.
It looks like I am the only one who has published an article about the quantitative SW radiation impacts on the surface temperature during the pause and thereafter. I have used two ways to analyze the quantitative impacts of SW radiation changes and they are the IPPC way and my way. 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.
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, dTENSO is 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 in 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 TOA. 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
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)
assessing the magnitude of ENSO effects. ONI is the three months running average value of the seawater temperature at 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) has 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. 3)
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. 3). The accurate simulation method for stepwise changes of inputs can be found in my research study (Ref. 2).
The results of dynamic simulations
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. 3.
Fig. 3. The results of the Ollila model.
In Fig. 3 we can see that the model calculated temperature (a black curve) follows pretty 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. 4. 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 the correlation 0.82 to the UAH temperature being about 100 % smaller than the same as the IPCC model 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 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 SW radiation increase since 2014. We will see in which way the IPCC shall take this matter into account in its coming Assessment Report 6. 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.
There is a comprehensive analysis of the CERES data in this study of Loeb et al. (Ref. 2). 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. 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.
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. Because I have used my simple climate model, I can show the real quantitative temperature impacts and not only qualitative assessments. 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 in 1997-98 and 2015-16. I have not been able to suggest the mechanism and it may be a pure coincidence. We can see in Fig. 1 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.
- 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.
- 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/cli603006
- Ollila, A. The Pause End and Major Temperature Impacts During Super El Niños are Due to Shortwave Radiation Anomalies. Physical Science International Journal, 23(4), 1-19, 2020. http://www.journalpsij.com/index.php/PSIJ/article/view/30168
- Ollila, A. Dynamics between Clear, Cloudy, and All-Sky Conditions: Cloud Forcing Effects, 2014.