Scientific papers of the climate change
1. Changes in cosmic ray fluxes improve correlation to global warming, 2012
Journal: International Journal of the Physical Sciences, Vol. 7(5), pp. 822 - 826, 30 January, 2012
Abstract: In this study, it was found out that ion chamber measurements of cosmic ray fluxes during the last solar cycle ending in 2009 differ essentially from neutron measurements. The ion chamber measurements utilizing geomagnetic aa index as proxy for the years between 1868 and 1936 produced excellent correlation to the global temperature changes for the period of 1868 to 2009. These results indicate that solar activity changes may cause climate changes.
2. The Roles of Greenhouse Gases in Global Warming, 2012
Journal: Energy & Environment, Vol. 23, No. 2, 2012
url: no open access
Abstract: Scientists are still debating the reasons for “global warming”. The author questions the validity of the calculations for the models published by the Intergovernmental Panel on Climate Change (IPCC) and especially the future scenarios. Through spectral calculations, the author finds that water vapour accounts for approximately 87% of the greenhouse (GH) effect and only 10% of CO2. A doubling of the present level of CO2 would increase the global temperature by only 0.51 °C without water feedback. The IPCC claims that a temperature increase of 0.76 °C for 2005 was caused in part by water (about 50%), because relative humidity (RH) stays constant in their model. The calculations prove that CO2 would have increased the temperature by only 0.2 °C since 1750 and that the measured decrease in water since 1948 has compensated for this increase. This study has also produced results indicating a negative feedback for relative humidity. The simulations of this study propose that the IPCC’s model atmospheres could be approximately 50% too dry.
3. Earth’s Energy Balance for Clear, Cloudy and All-sky Conditions, 2013
Journal: Development in Earth Science, Volume 1, Issue 1, September 2013
Abstract: The researchers have published several studies on the radiation fluxes based on measurement data banks and radiative transfer models. The author has used available flux values and different methods to obtain the total of Earth’s energy balances for clear, cloudy and all‐skies. The calculation methods include balance equations, spectral calculations and the cloudiness factor in combining energy fluxes of three sky conditions. A new idea has been introduced that the surface albedo flux is partially absorbed in cloudy conditions, as with incoming shortwave radiation. The atmospheric albedo fluxes have been calculated separately for cloud reflection and air particles. Also the atmospheric absorption has been divided into cloud and clear air absorption fluxes.
4. Analyses of IPCC’s Warming Calculation Results, 2013
Journal: Journal of Chemical, Biological and Physical Sciences,
August 2013 - October 2013, Vol. 3, No. 4; 2912-2930
Abstract: Some researchers have noticed that the warming calculations of Intergovernmental Panel on Climate Change (IPCC) are not always based on the atmospheres, which use the global average values. CO2 effect of 26% in greenhouse phenomenon is based on the modified U.S. Standard Atmosphere 1976 (USST 76 atmosphere) containing only 50% of water in comparison to the true value. The calculations prove that the warming of 0.76 °C can be achieved if the USST 76 atmospheric model is applied and constant relative humidity (RH) assumed. The analysis also reveals that IPCC’s scenario presentation contains choices, which make the warming results looking higher than they should be. All the climate sensitivity values above 1.7 °C conflict with the explanation given by IPCC for the 1750 - 2005 periods. The global warming potential (GWP) values of CH4 and N2O are applicable only for small concentration changes and in higher concentrations these greenhouse (GH) gases are even weaker than CO2. The ultimate worst case scenario is the release of methane from the methane clathrates on the ocean floor. The calculations show that the release would cause 2.1 °C temperature increase, which is only 68% of the CO2 warming effect. The spectral analysis show that in the prevailing atmospheric conditions the warming potency of methane is about 14% from the potency of CO2, and the same of N2O is about 17%. The effect of water in the same conditions is 15.2 times greater than that of CO2.
5. Dynamics between Clear, Cloudy, and All-Sky Conditions: Cloud Forcing Effects, 2014
Journal: Journal of Chemical, Biological and Physical Sciences,
November 2013 - January 2014, Vol. 4, No. 1; 557-575
Abstract: The author has analyzed the dynamics of atmospheric changes between all-sky, clear and cloudy sky conditions. The basis of analyses is the calculation of flux values at the balance states. The analyses depend essentially on the time constants of basic processes, which can be analyzed separately. Two time constants are based on the former research results, and three time constants have been developed and estimated in this study. The basic processes in dynamic analyses have been the very rapid changes in cloudiness and cloud temperatures, the rapid change in upward atmospheric long wave radiation caused by solar insolation change, the slow change in temperature of the land and sea, and the transient change in the atmosphere temperature. This transient atmospheric process has an essential role in explaining why the surface temperature increases when at the same time the cloud forcing decreases. The dynamic simulations reveal that in all cases, two rapid changes in the atmosphere can bring the outgoing long wave radiation at the top of the atmosphere almost exactly (a difference of 0% to 0.3%) to the observed pseudo-balance values of clear and cloudy skies. Pseudo-balance values for clear and cloudy skies are not very time-sensitive because the values stay within ±1 W/m2 from one day to 13 days. According to the true energy balance values, the slightly nonlinear cloud forcing would be -0.56 Wm-2 per 1% increase in cloudiness and -0.1 °C per 1% increase in cloudiness over the normal cloudiness range variation from 60% to 70%. According to this study, the commonly used cloud forcing in the units of W/m2 yields effects that are about 40% too low for the long-term cloudiness changes. Cloudiness changes could alone explain the global warming.
6. The Potency of Carbon Dioxide (CO2) as a Greenhouse Gas, 2014
Journal: Development in Earth Science, Vol. 2, 2014, pp. 20-30.
Abstract: According to this study the commonly applied radiative forcing (RF) value of 3.7 Wm-2 for CO2 concentration of 560 ppm includes water feedback. The same value without water feedback is 2.16 Wm-2 which is 41.6 % smaller. Spectral analyses show that the contribution of CO2 in the greenhouse (GH) phenomenon is about 11 % and water’s strength in the present climate in comparison to CO2 is 15.2. The author has analyzed the value of the climate sensitivity (CS) and the climate sensitivity parameter (lambda) using three different calculation bases. These methods include energy balance calculations, infrared radiation absorption in the atmosphere, and the changes in outgoing longwave radiation at the top of the atmosphere. According to the analyzed results, the equilibrium CS (ECS) is at maximum 0.6 °C and the best estimate of l is 0.268 K/(Wm-2) without any feedback mechanisms. The latest warming scenarios of Intergovernmental Panel on Climate Change (IPCC) for different CO2 concentrations until the year 2100 include the same feedbacks as the 2011 warming i.e. only water feedback. The CS value of 3.0 °C would mean that other feedback mechanisms should be stronger than water feedback. So far there is no evidence about these mechanisms even though 40 % of the change from 280 ppm to 560 ppm has already happened. The relative humidity trends since 1948 show descending development which gives no basis for using positive water feedback in any warming calculations. Cloudiness changes could explain the recent stagnation in global warming.
7. Clear Sky Absorption of Solar Radiation by the Average Global Atmosphere
Journal: Journal of Earth Sciences and Geotechnical Engineering, Vol.5, No. 14, 2015, pp. 19-34.
Abstract: The author has analyzed shortwave (SW) absorption of the annual average global atmosphere (AGA) in the clear sky conditions utilizing spectral analysis methods. A modified zenith angle has been used in calculating the average zenith values for five atmospheric models covering three climate zones of the Earth. The absorption flux value of 67.71 Wm-2 of this study is very close to the value of 69 Wm-2 found in the energy balance analysis based on the observational data. It means that the absorption due to the aerosols would be 1.29 Wm-2, which is close to the values 1.6 – 2.4 Wm-2 calculated in different studies for aerosol absorption in the atmosphere. This result shows that there is no excessive absorption in the clear sky conditions when calculating annual global value. When the effective global zenith value of 51.38 degrees has been applied to the one dimensional (1D) model in the modified mid-latitude atmosphere corresponding to the AGA conditions, the results are similar to the results based on the five different atmospheric models applied in three climate zones of the Earth. The 1D model has been applied for finding basic relationships in SW absorption phenomenon. One result is that the contribution of water is 77.2 % and ozone’s contribution is 19.5 %. The results are comparable to the traditional radiation transfer models developed for SW absorption calculations.
8. Anthropogenic Carbon Dioxide (CO2) Amounts and Fluxes between the Atmosphere, the Ocean, and the Biosphere
Physical Science International Journal, Vol. 8, Issue 1, 2015, DOI: 10.9734/PSIJ/2015/18625.
Abstract: The author has developed one dimensional semi-empirical atmosphere-ocean-biosphere model (1DAOBM) based on the four-box presentation. Firstly, the author has analysed that the model development can be based on the two elements: 1) the four box-model containing two ideal mixing components (the atmosphere and the ocean), one plug flow component with four different residence times (the biosphere), and the outlet (the intermediate & deep ocean), 2) the ocean’s capacity to dissolve anthropogenic CO2 emissions of the present century. The surface ocean part is based on the known dissolution chemical equations. The net flux rate from the surface ocean into the deep ocean is based on the empirical data. The removal of the anthropogenic CO2 from the atmosphere is based on the huge carbon cycle flux rates of the dissolution pump and the biosphere carbon cycle, which remove yearly about 26 % of CO2 from the atmosphere to other reservoirs and, at the same time, recycle back natural and anthropogenic carbon. The simulations of the atmospheric net CO2 rate by 1DAOBM from 1960 to 2013 show fairly good similarity to the measured values: r2 = 0.75 and the standard error of estimate 0.68 GtC/y, which means the standard error of 12 % at the present emission rate of about 10 GtC/y. The simulations show that the present anthropogenic CO2 fraction in the atmosphere is 7.7 %, and it explains the observed δ13C value of -8.4 ‰ extremely well. Also, the reduction of δ13C in the ocean from 1900 to 2013, simulated to be -0.6 ‰, is close to the observed values. The 1DAOBM has been used also to simulate the fluxes and CO2 concentration trends corresponding to the projection RCP4.5 of IPCC. These results deviate from the IPCC’s results in the descending phase of the CO2 emissions. The mean residence time of the total atmospheric CO2 concentration change is 32 years and that of the anthropogenic CO2 change is 15 years, according to 1DAOBM simulations.
9. Cosmic Theories and Greenhouse Gases as Explanations of Global Warming
Journal of Earth Sciences and Geotechnical Engineering, vol. 5, no.4, 2015, 27-43 ISSN: 1792-9040 (print), 1792-9660 (online) Scienpress Ltd, 2015
Abstract: According to the IPCC’s simplest model based on the anthropogenic driving forcing factors, the temperature increase up to 2011 from 1750 is 1.15 °C, which is 35 % greater than the observed temperature 0.85 °C. In this study three other models have been analysed. The first model is a cosmic model, which is based on the galactic cosmic rays (GCR) changes and space dust amount. This model gives correlation r2=0.972. The second model is the combination of space dust changes, the calculated warming impacts of greenhouse gases and the Total Solar Irradiance (TSI) changes giving correlation r2=0.971. The third model is the combination of space dust and TSI changes giving correlation r2=0.948. All these models have negligible error in 2010. The atmospheric water has a decisive role in the real impacts of greenhouse gases. It remains uncertain, because the first global humidity measurements start from 1948. The final conclusion of this study is: the greenhouse gases cannot explain the ups and downs of the Earth’s temperature trend since 1750 and the temperature pause since 1998, but the space dust changes can do it extremely well.
10. Climate Sensitivity Parameter in the Test of the Mount Pinatubo Eruption
Physical Science International Journal, vol. 9, no. 4, 2016, pp. 1-14
Abstract: The author has developed a dynamic model (DM) to simulate the surface temperature change (ΔT) caused by the eruption of Mount Pinatubo. The main objectives have been 1) to test the climate sensitivity parameter (λ) values of 0.27 K/(Wm-2) and 0.5 K/(Wm-2), 2) to test the time constants of a simple first-order dynamic model, and 3) to estimate and to test the downward longwave radiation anomaly (ΔLWDN). The simulations show that the calculated ΔT of DM follows very accurately the real temperature change rate. This confirms that theoretically calculated time constants of earlier studies for the ocean (2.74 months) and for the land (1.04 months) are accurate and applicable in the dynamic analyses. The DM-predicted ΔT values are close to the measured value, if the λ-value of 0.27 K/(Wm-2) has been applied but the λ-value of 0.5 K/(Wm-2) gives ΔT values, which are about 100 % too large. The main uncertainty in the Mount Pinatubo analyses is the ΔLWDN flux, because there are no direct measurements available during the eruption. The author has used the measured ERBS fluxes and has also estimated ΔLWDN flux using the apparent transmission measurements. This estimate gives the best and most consistent results in the simulation. A simple analysis shows that two earlier simulations utilising General Circulation Models (GCM) by two research groups are depending on the flux value choices as well as the measured ΔT choices. If the commonly used minimum value of -6 Wm-2 would have been used for the shortwave anomaly in the GCM simulations, instead of -4 Wm-2, the ΔT values would differ from the measured ΔT values almost 100 %. The main reason for this error seems be the λ-value of 0.5 K/(Wm-2).
11. Timescales of Anthropogenic and Total Carbon Dioxide (CO2) in the Atmosphere
Physical Science International Journal, vol 11, no. 3, 2016,
Abstract: The author has enhanced the original one dimensional semi-empirical atmosphere-oceanbiosphere model 1DAOBM based on the four-box presentation. The improved 1DAOBM-2 contains two major parameters, which have been tuned to adjust the total CO2 net flux rate and the anthropogenic net flux rate from the surface ocean into the deep ocean based on the observed values. The surface ocean part is based on the known dissolution chemical equations according to Henry’s law depending on the atmospheric CO2 concentration and the surface ocean temperature. Simulations have been used to calculate the dynamic responses to the step changes from the actual fossil fuel rate to zero in 1964. The results show that the anthropogenic CO2 decay rate follows very accurately the observed decay rate of radiocarbon 14C having the residence time of 16 years. This is the expected result according to nature of anthropogenic CO2 in the system of the atmosphere, the ocean and the biosphere. The decay rate of the total CO2 in this system is much longer having the residence time of 55 years matching the adjustment time of 220 years. The simulations of the atmospheric net CO2 rate by 1DAOBM-2 from 1960 to 2013 confirms the earlier results that the coefficient of determination r2 = 0.75 (r2 = 0.81 eliminating the Pinatubo eruption effects). The simulations also show that the present anthropogenic CO2 fraction in the atmosphere is 8.0 %, and it explains the observed δ13C value of -8.4 ‰ extremely well. The problem of the sink between the ocean and the biosphere could not be solved totally. A mass balance study shows that before 1956, the ocean and/or the biosphere acted as a source for the total CO2 increase in the atmosphere and thereafter as a sink. This study suggests that the division ratio between the ocean and the biosphere is 60% / 40 % for the period from 1750 to 2013. The high correlation between the ocean uptake and the net increase of the total atmospheric CO2 strongly indicates that the ocean has been the sink after 1956.
12. Warming Effect Reanalysis of Greenhouse Gases and Clouds
Physical Science International Journal, vol 13, no. 2, 2017
Abstract. The author has reanalysed the warming effects of greenhouse (GH) gases utilising the latest HITRAN 2012 database and improved water continuum calculations in the spectral analysis tool. The contributions of GH gases in the GH effect in the all-sky conditions are found to be: H2O 81 %, CO2 13 %, O3 4 %, CH4 & N2O 1 %, and clouds 1 %. Because the total absorption is already 93 % from the maximum in the altitude of 1.6 km, which is the average global cloud base, the GH gas impacts are almost the same in the clear and all-sky conditions. The impacts of clouds are based on the normal cloudiness changes between the clear and cloudy skies. The positive impact of clouds is analysed and it is based on the warming impact of clouds during the night-time. The warming impact of CO2 is very nonlinear and it means that in the present climate the strength of H2O is 11.8 times stronger than CO2, when in the total GH effect this relationship is 6.2:1. The atmospheric Total Precipitable Water (TPW) changes during ENSO events are the essential parts of the ENSO process and they are not actually separate feedback processes. The TPW changes during the ENSO events almost double the original ENSO effects. On the other hand, during Mt. Pinatubo eruption and during the three latest solar cycles, the long-term water feedback effect cannot be found despite of rapid warming from 1980 to 2000. This empirical result confirms that the assumption of no water feedback in calculating the climate sensitivity of 0.6 ºC is justified. Because there is no long-term positive feedback, it explains why the IPCC model calculated temperature 1.2 ºC in 2015 is 44 % greater than the average 0.85 ºC of the pause period since 2000.
13. Semi Empirical Model of Global Warming Including Cosmic Forces, Greenhouse Gases, and Volcanic Eruptions
Physical Science International Journal, vol 15, no 2, 2017
In this paper, the author describes a semi empirical climate model (SECM) including the major forces which have impacts on the global warming namely Greenhouse Gases (GHG), the Total Solar Irradiance (TSI), the Astronomical Harmonic Resonances (AHR), and the Volcanic Eruptions (VE). The effects of GHGs have been calculated based on the spectral analysis methods. The GHG effects cannot alone explain the temperature changes starting from the Little Ice Age (LIA). The known TSI variations have a major role in explaining the warming before 1880. There are two warming periods since 1930 and the cycling AHR effects can explain these periods of 60 year intervals. The warming mechanisms of TSI and AHR include the cloudiness changes and these quantitative effects are based on empirical temperature changes. The AHR effects depend on the TSI, because their impact mechanisms are proposed to happen through cloudiness changes and TSI amplification mechanism happen in the same way. Two major volcanic eruptions, which can be detected in the global temperature data, are included. The author has reconstructed the global temperature data from 1630 to 2015 utilizing the published temperature estimates for the period 1600 – 1880, and for the period 1880 – 2015 he has used the two measurement based data sets of the 1970s together with two present data sets. The SECM explains the temperature changes from 1630 to 2015 with the standard error of 0.09°C, and the coefficient of determination r2 being 0.90. The temperature increase according to SCEM from 1880 to 2015 is 0.76°C distributed between the Sun 0.35°C, the GHGs 0.28°C (CO20.22°C), and the AHR 0.13°C. The AHR effects can explain the temperature pause of the 2000s. The scenarios of four different TSI trends from 2015 to 2100 show that the temperature decreases even if the TSI would remain at the present level.
14. Challenging the scientific basis of the Paris climate agreement
International Journal of Climate Change Strategies and Management, earlycite, 2018
The purpose of this paper is to analyze the scientific basis of the Paris climate agreement. The analyses are based on the IPCC’s own reports, the observed temperatures versus the IPCC model-calculated temperatures and the warming effects of greenhouse gases based on the critical studies of climate sensitivity (CS). The future emission and temperature trends are calculated according to a baseline scenario by the IPCC, which is the worst-case scenario RCP8.5. The selection of RCP8.5 can be criticized because the present CO2 growth rate 2.2 ppmy−1 should be 2.8 times greater, meaning a CO2increase from 402 to 936 ppm. The emission target scenario of COP21 is 40 GtCO2 equivalent, and the results of this study confirm that the temperature increase stays below 2°C by 2100 per the IPCC calculations. The IPCC-calculated temperature for 2016 is 1.27°C, 49 per cent higher than the observed average of 0.85°C in 2000.
Two explanations have been identified for this significant difference in the IPCC’s calculations: The model is too sensitive for CO2 increase, and the positive water feedback does not exist. The CS of 0.6°C found in some critical research studies means that the temperature increase would stay below the 2°C target, even though the emissions would follow the baseline scenario. This is highly unlikely because the estimated conventional oil and gas reserves would be exhausted around the 2060s if the present consumption rate continues.
15. Challenging the greenhouse effect specification and the climate sensitivity of the IPCC
Physical Science International Journal, vol 22, no 2, 2019
The greenhouse effect concept has been developed to explain the Earth’s elevated temperature. The prevailing theory of climate change is the anthropogenic global warming theory, which assumes that the greenhouse (GH) effect is due to the longwave (LW) absorption of 155.6 Wm-2 by
GH gases and clouds. The actual warming increase to 33°C of the Earth’s surface temperature according to the present GH effect definition is the infrared downward LW radiation of 345.6 Wm-2 emitted by the atmosphere. The atmosphere’s temperature is the key element behind this radiation. According to the energy laws, it is not possible that the LW absorption of 155.6 Wm-2 by the GH gases could re-emit downward LW radiation of 345.6 Wm-2 on the Earth’s surface. In this study, the GH effect is 294.5 Wm-2 including shortwave radiation absorption by the atmosphere and the latent and sensible heating effect. This greater GH effect is a prerequisite for the present atmospheric temperature, which provides downward radiation on the surface. Clouds’ net effect is 1% based on the empirical observations. The contribution of CO2 in the GH effect is 7.3% corresponding to 2.4°C in temperature. The reproduction of CO2 radiative forcing (RF) showed the climate sensitivity RF value to be 2.16 Wm-2 which is 41.6% smaller than the 3.7 Wm-2 used by the IPCC. A climate model showing a climate sensitivity (CS) of 0.6°C matches the CO2 contribution in the GH effect, but the IPCC’s climate model showing a CS of 1.8°C or 1.2°C does not.
16. The greenhouse effect definition
Physical Science International Journal, 23(2), 1-5, 2019
The greenhouse effect concept explains the Earth’s elevated temperature. The IPCC endorses the anthropogenic global warming theory, and it assumes that the greenhouse (GH) effect is due to the longwave (LW) absorption by GH gases and clouds. The IPCC’s GH definition lets to understand that the LW absorption is responsible for the downward radiation to the surface. According to the energy laws, it is not possible that the LW absorption of 155.6 Wm-2 by the GH gases could re-emit downward LW radiation of 345.6 Wm-2 on the Earth’s surface. When the shortwave (SW) absorption is decreased from this total LW radiation, the rest of the radiation is 270.6 Wm-2. This LW radiation downward is the imminent cause for the GH effect increasing the surface temperature by 33°C. It includes LW absorption by the GH gases and clouds in the atmosphere and the latent and sensible heating effects. Without the latent and sensible heating impacts in the atmosphere, the downward LW radiation could not close the energy balance of the surface. The contribution of CO2 in the GH effect is 7.4% corresponding to 2.5°C in temperature. This result does not only mutilate the image of CO2 as a strong GH gas, but it has further consequences in climate models. It turned out that the IPCC’s climate model showing a climate sensitivity (CS) of 1.2°C (caused by CO2 effects only) could not be fitted into the total GH effect of CO2. A climate model showing a CS of 0.6°C matches the CO2 contribution in the GH effect.
17. Analysis of the simulation results of three carbon dioxide (CO2) cycle models
Physical Science International Journal, 23(4), 1-19, 2020
The CO2 (carbon dioxide) circulation models referred by the IPCC (Intergovernmental Panel on Climate Change) show that the increase of atmospheric CO2 by 240 GtC (Gigatonnes of carbon) from 1750 to 2011 is totally anthropogenic in nature, which corresponds to the permille value of about -12.5‰, but the observed value is only -8.3‰. The author’s improved 1DAOBM-3 CO2 circulation model shows that the anthropogenic CO2 amount in 2011 is only 73 GtC, satisfying the observed atmospheric permille values from 1750 to 2017. The CO2 circulation between the ocean and the atmosphere has increased the amount of atmospheric CO2 by natural CO2 197 GtC from the ocean, and this explains why the net uptake rate is only 1.9 GtC yr-1. Together with the anthropogenic amount of 73 GtC, the total increase is 270 GtC by 2017, corresponding to the observed atmospheric CO2 concentration. The simulations for 1000 GtC emissions by 2100 have been carried out by three models, including 1DAOBM-3, Bern2.5CC, and the mean model of 15 circulation models (called Joos 2013). The residence time of 1DAOBM-3 is 16 years for anthropogenic CO2 impulse, the same as for the radiocarbon decay time. The decay time of 1DAOBM-3 for the impulse function is about 600 years meaning the residence time of about 150 years only. These values are much shorter than the residence times of two other models, which show that 25±9‰ of any anthropogenic CO2 is still found in the atmosphere after 1,000 years. The reasons have been analyzed. The advantage of 1DAOBM-3 over the other models is that its results are in line with the oceanic and atmospheric observations from 1750 to 2017 but the future simulations include uncertainties due to ocean and biosphere uptake rate models..
18. 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
The hiatus or temperature pause during the 21st century has been the subject of numerous research studies with very different results and proposals. In this study, two simple climate models have been applied to test the causes of global temperature changes. The climate change factors have been shortwave (SW) radiation changes, changes in cloudiness and ENSO (El Niño Southern Oscillation) events assessed as the ONI (Oceanic Niño Index) values, and anthropogenic climate drivers. The results show that a simple climate model assuming no positive water feedback follows the satellite temperature changes very well, the mean absolute error (MAE) during the period from 2001 to July 2019 being 0.073 °C and 0.082 °C in respect to GISTEMP. The IPCC’s simple climate model shows for the same period errors of 0.191 °C and 0.128 °C respectively. The temperature in 2017-2018 was about 0.2 °C above the average value in 2002–2014. The conclusion is that the pause was over after 2014, and the SW anomaly forcing was the major reason for this temperature increase. SW anomalies have had their greatest impacts on the global temperature during very strong (super) El Niño events in 1997-98 and 2015-16, providing a new perspective for ENSO events. A positive SW anomaly continued after 2015-16 which may explain the weak La Niña 2016 temperature impacts, and a negative SW anomaly after 1997-98 may have contributed two strong La Niña peaks 1998-2001. No cause and effect connection could be found between the SW radiation and temperature anomalies in Nino areas.
OTHER PERSONAL PUBLICATIONS
A. Scientific publications and articles in professional magazines
01. Ollila A, 1972. Mathematical Model of CO-conversion. Thesis of Master of Science, Engineering. Process Technology Department, the University of Oulu.
02. Uronen P. and Ollila A, 1972. Mathematical Model For a Water Gas Shift Reactor. Kemian Teollisuus Vol 29, 1972, no. 2, p.105-112.
03. Uronen P. and Ollila A, 1972. Some Examinations of Pulp Consistency Control. The University of Oulu, Report 2-72. Oulu.
04. Ollila A,1974. Approximation Methods of Transfer Functions. Thesis of Licenciate of Technology. Process technology department, the University of Oulu.
05. Ollila A, and Uronen P., 1975. Lumped Parameter Approximation for the Transfer Function of a Heat Exchanger. Acta Universitatis Ouluensis, Series C, Technica no. 8, Oulu.
06. Ollila A and Uronen P., 1975. The Dimensioning of a Pipe and Shell Type Gas-Gas Heat Exchanger. Kemia-Kemi 2 (1975) 1, p. 31-33.
07. Jutila E., Kauppinen S., Mensonen K., Ollila A, 1978. Special Features of Computer Control of a Continuous Digester and Diffuser Washer, Paperi ja Puu 10, p. 573-588.
08. Ollila A., 1978. Microprocessor for Control Tasks, uPA 1978,12, p. 25-28.
09. Ollila A, 1982. 90 Million Mark Automation Investments into Use at Neste Oy in 1982. Eletroniikka & Automaatio (1982) 10, p. 78-79.
10. Ollila A, 1983. Measurement and Control – cooperation of process and automation planning, Kemia-Kemi, no. 4, 1983, p. 286-290.
11. Ollila A, 1983. Operating Experiences with Distributed Digital Control. Hydrocarbon Processing, October 1983, p. 87-91.
12. Ollila A, 1985. Condition Monitoring - an Important Area of Maintenance. Tuottavuus 12, 1985, Helsinki, Finland.
13. Ollila A, 1986. Safecontrol - the Second Generation Condition Monitoring System. Konepajamies 1986, no 4, p. 6-12.
14. Ollila A, 1987. Maintenance - Unused Resource. Suomen Materiaalitalous 1987, no 4, p. 23-25.
15. Ollila A, 1989. The Predictive and Preventive Maintenance of Electrical Machinery. Maintenance, Vol. 4, 1989, no. 3, p. 13-18.
16. Jantunen E, Miettinen J. and Ollila A., 1993. Maintenance and Downtime Costs of Centrifugal Pumps in Finnish Industry. World Pumps, June 1993, p. 16-22.
17. Ollila A, 1994. Quality Improvements through ISO 9000 Standards, Thesis of Doctor of Technology, Helsinki University of Technology, ABB Service Oy, Helsinki, Finland, p. 145.
18. Ollila A,1998. One Approch for All. European Quality, Vol. 5,1998, no.6, p. 52-54.
18. Ollila A and Malmipuro M., 1999. Maintenance has a role in quality. The TQM Magazine, Vol. 11, No.1, p. 17-21.
19. Ollila A, 1999, Benchmarking in Finland. The Benchmarking Review,Issue 5, November/December 1999, p. 1-2.
20. Ollila A, 2007, A New Classification Paradigm for Companies – Product, Service and Product-Service Companies. Seventh International . Business Research Conference, 3-6 December, 2007, University of Technology, Sydney, Australia.
B. Books and learning material. Text books and teaching material
01. Ollila A, 1973. Real-Time Programming. The University of Oulu, The Duplicate 3-73. Oulu.
02. Ollila A, 1973. Control-Focal, The University of Oulu, The Duplicate 4-73. Oulu.
03. Ollila A, 1974. The Fundamentals of Promol Programming, The University of Oulu, The Duplicate 7-74. Oulu.
04. Ollila A and Aurasmaa H., 1983. Instrumentation and Automation in Pulp Industry. Pulp Manufacturing, the textbook for university students, Finnish Paper Engineers' Association, Helsinki, Finland.
C. Papers and presentations in scientific conferences
01. Ollila A and Uronen P., 1974. The Reduction of Complex Transfer Function Models Using Different Methods. 3rd Symposium on the Use of Computers in Chemical Engineering, September 9-13, 1974, Gliwice, Poland.
02. Ollila A, 1974. The Optimal Cooling of an Adiabatic Multiple Bed Reactor. The 15th Nordic Chemist Meeting, June 7-11, 1974, Tampere.
03. Uronen P., Ollila A. and Aurasmaa H., 1976. Modeling and Optimization of a CO-Shift Reactor. Zbornik Radova Jurema, 1976, Zagreb.
04. Ollila A, 1976. Computer Control of Bleach Plants - Latest News from Finland. 3rd International lFAC Conference on Instrumentation and Automation in the Pulp, Rubber and Plastics Industries, 24-26 May, 1976, Brussel, Belgium.
05. Ollila A, Meskanen A, and Lintunen K., 1981. Automation in Chemical Industries - The Group Work Outlining of Automation Development. The Automation Days 81, Finnish Society of Automatic Control, Publication No 2, p. 109-118, Helsinki 1981.
06. Ollila A, 1986. Vibration Measurement in Condition Monitoring. The Symposium of Measurement Technology, The State Research Center, September 3-4, 1986, Hameenlinna, Finland.
07. Ollila A, 1986. The Condition Monitoring Systems in Chemical Industry. The Process Technology Days 1986, The University of Oulu, Oulu, Finland.
08. Ollila A and Hagg B., 1987. Safecontrol- A Complete Condition Monitoring System for Large or Small Machinery Plants. SPCI, April 7-10, 1987, Stockholm, Sweden.
09. Ollila A, 1987. Safecontrol - A Mill wide Condition Monitoring System. Diagnostic and Preventive Maintenance Strategies in Manufacturing Systems, IFIP TC 5/Wg 5.3 Working Conference, September 1-4,1987, Dubrovnik.
10. Ollila, A, 1988. Automation Concepts in Maintenance Execution. Euromaintenance -88, EFNMS, May 24-27, 1988, Helsinki, Finland.
11. Ollila A, 1989. The Predictive and Preventive Maintenance of Electrical Machineries. The International Professional Conference on Development and Application of Up-To-Date Methods of Plant Maintenance, May 23-25, 1989, Beograd, Yugoslavia.
12. Ollila A, 1994. Industrial Requirements on Maintenance and Reliability Research and Development Work. The International Conference of Maintenance, The Research Center of the State, April 12, 1994, Espoo, Finland.
13. Ollila A, 2007. A New Classification Paradigm for Companies – Product, Service and Product-Service Companies. 7th International Business Research Conference, 2-6 December, 2007, Sydney, Australia.
14. Ollila A, 2008. Quality Management - Many Challenges and Opportunities for General Management. International Conference ‘Topical Issues of Rational Use of Natural Resources’. St. Petersburg State Mining Institute, 23-25 April 2008, St. Petersburg, Russia.
D. Lessons and presentations in professional seminars
01. Ollila A,1978. Minicomputer in Industrial Automation - The Computer Control of a Kamyr Digester. Blanko-78, The Information Management Days, Oct. 2, 1978, Oulu.
02. Ollila A, 1979. Hierarchical Systems and Their Influence Upon The Development of Automation. The Control and Measurement Technique Day in Oulu, Oct. 10, 1979.
03. Ollila A, 1980. The Measurements' Usability Needed in Production Management System. Production Management and Automation in Process Industries. The Training Central of Engineering Organizations, Helsinki 1980.
04. Ollila A, 1981. The Control Applications of The Digital Instrumentation Systems in Neste Oy. The Distillation Days 81, Vuoranta 1981.
05. Ollila A, 1982. Automation in Oil Processing - Experiences and Possibilities - The control room and the role of the operators. Nordic Refiners' Meeting 1982, Porvoo.
06. Ollila A, 1982. The Control and Automation of Processes. The seminar for Production Engineers, Rastor Institute, March 29-31, 1982, Helsinki, Finland.
07. Ollila A, 1984. The Process Measurements Today and in the Future. Measurement Technology for Process Engineers - The Possibilities of Today, the seminar of Training Center of The Engineers' Associations, April 12-13, Porvoo, Finland.
08. Ollila A, 1984. Automation Concepts of Maintenance. Maintenance 1984, the seminar of The Finnish Maintenance Society, September 5-6, Lahti, Finland.
09. Ollila A, 1985. The Influence of Maintenance for the Competitiveness of our Industry. Maintenance 1985, the seminar of The Finnish Maintenance Society, February 8-9, 1985, Lahti, Finland.
10. Ollila A, 1986. The Methods of Predictive Maintenance. Technical Diagnostic Days, the seminar 54-86 of Training Center of The Engineers' Associations, March 20, 1986, Helsinki, Finland.
11. Ollila A, 1986. Computer Based Condition Monitoring. Maintenance 1986, the seminar of The Finnish Maintenance Society, February 10-11, 1986, Jyvaskyla, Finland.
12. Ollila A, 1986. The Automatic Condition Monitoring of Rotating Machines. The Predictive Maintenance, the seminar of Training Center of The Engineers' Associations, January 21-22, 1987, Kuusankoski, Finland.
13. Ollila A, 1987. Development of Maintenance toward Automation. Automation Days 1987, The Finnish Automation Society, April 5-6, 1987, Helsinki, Finland.
14. Ollila A, 1987. The Technology Management of Medium Size Enterprise investing in Product Development. The Engineers' Days 1987, the seminar of Training Center of The Engineers' Associations, Espoo, Finland.
15. Ollila A, 1987. The On-line Condition Monitoring Systems - Safecontrol. The Maintenance 1987, the seminar of The Finnish Maintenance Society, March 10-11, 1987, Tampere, Finland.
16. Ollila A, 1988. The Role of Maintenance for the Competitiveness of our Industry. The Theme Days of Rationalization Society, September 7-9, 1988, Jyvaskyla, Finland.
17. Ollila A, 1988. The Favorable Atmosphere for Industry. The seminar of The Negotiation Days of Industrial Management, The Industrial Institute of Northern Finland, November 7-8, 1988, Oulu, Finland.
18. Ollila A, 1989. Total Maintenance Concept. Maintenance 1989, the seminar of The Finnish Maintenance Society, February 1-2, 1989, Helsinki, Finland.
19. Ollila A., 1989. The Trends and Visions of Maintenance. The maintenance seminar, Training Center of The Engineers' Associations, November 2930, 1989, Helsinki, Finland.
20. Ollila A, 1990. Maintenance Trends. Terotechnology seminar, The Royal Institute of Technology of Stockholm, April 18-20, 1990, Copenhagen, Danmark.
21. Ollila A, 1990. The Development Trends of Industrial Maintenance. The ensuring of competitiveness of production and risk management in the 90's of Finland, The seminar for decision makers, The Finnish Maintenance Society, May 5, 1990, Turku, Finland.
22. Ollila A, 1990. The Role of Maintenance in a Production Team. The Building Up of a Productive Team, Seminar of Institute for International Research (Finland) Oy, May 16-17, 1990, Helsinki, Finland.
23. Ollila A, 1991. The Role of Maintenance in Realization of Quality and JIT Principle. The Strategical Decisions in Maintenance, Seminar of Institute for International Research (Finland) Oy, March 12-13, 1991, Espoo, Finland.
24. Ollila A, 1992. The Service Stock, Maintenance Development and Re-engineering. The maintenance seminar, The Finnish Maintenance Society, March 16-17, 1992, Oulu, Finland.
25. Ollila A, 1992. Quality Certification, Quality Measurements and Customer Service as Profit Supporting Factors. Total service quality, seminar of Institute for International Research (Finland) Oy, October 21, 1992, Helsinki, Finland.
26. Ollila A, 1993. From Quality Systems into Total Quality Management Obstacles in the Finnish Enterprise Culture. Master of Quality, 1st Executive seminar, The Finnish Quality Society, September 2-3, 1993, Lahti, Finland.
27. Ollila A, 1993. Quality Assurance between the Buyer and the Seller. Maintenance 1993, the seminar of The Finnish Maintenance Society, September 17-18, 1993, Helsinki, Finland.
28. Ollila A, 1993. The Outsourcing of Maintenance as a Strategical Solution. The maintenance training program, Helsinki University of Technology, November 23, 1993, Espoo, Finland.
29. Ollila A, 1994. Summary About the Competitiveness of Maintenance and Business - Technical Development and Vision. The seminar for the management of maintenance, Tampere University of Technology, May 16, 1994, Tampere, Finland.
30. Ollila A, 1995. Enterprise Case -ISO 9001 Maintenance as a Part of Quality System of an Enterprise. The lessons in Quality Management course, Helsinki University of Technology, January 4, 1995, Helsinki, Finland.
31. Ollila A., 1995. The Influence of Quality and Availability of Maintenance on the Customer Satisfaction. Maintenance economy as a success factor for an enterprise, the seminar of Edutech, Tampere University of Technology, April 5-6, 1995, Tampere, Finland.
32. Hasi K. and Ollila A, 1995. The Winner of the Finnish Quality Award 1994. The Cavalcade of the Winners, The seminar of The Finnish Quality Society, April 24, 1995, Espoo, Finland.
33. Ollila A, 1995. Maintenance Service Enterprise and Customer Satisfaction. Quality seminar of maintenance, The Finnish Quality Society, May 15,1995, Helsinki, Finland.
34. Ollila A, 1995. Experiences from the Development of Quality. Quality in the services of hospital technology, the seminar of the University Hospital of Kuopio, May 16,1995, Vantaa, Finland.
35. Ollila A, 1995. Organizing and Managing Maintenance. Maintenance & Operation '95, Seminar of Institute for International Research (Finland) Oy, June 13-14, 1995, Helsinki, Finland.
36. Ollila A, 1995. The Winner of the Finnish Quality Award: Case ABB Service Oy. Master of Quality V Executive seminar, The Quality Awards as the Models of Leadership, The Finnish Quality Society, May 18-19, 1995, Helsinki, Finland.
37. Ollila A, 1996. Quality Management in Industrial Enterprise - Impact of Data, Information and Knowledge in Quality Management. The seminar of Industrial Data Warehouse, May 5, 1995, Helsinki, Finland.
38. Ollila A, 1996. The Researched Knowledge of Quality Tools. The seminar of Quality Tools, Rastor Institute, May 22-23, 1996, Vantaa, Finland.
39. Ollila A., 1997. Execution of Quality Management in Changing Work Environment. The Quality Days 1997, Seminar of The Finnish Society of Quality, April 24-25, 1997, Tampere.
40. Ollila A, 1997. Environmental Issues from the View Point of the Customer. Enterprise and Environment, the seminar of the Federation of Finnish Metal, Engineering and Electrotechnical lndustries, November 11, 1997, Helsinki, Finland.
41. Ollila A, 1998. The European Quality Award. Quality Days of the Finnish Defense Forces, April 22-23, Vantaa, Finland.
42. Ollila A, 1999. The Guidelines of Quality Management in the 2000's. The success factors, the seminar of The Finnish Quality Society, February 2, 1999, Helsinki, Finland.
43. Ollila A, 1999. Application of Quality Award Criteria in Improving Competitiveness. Master of Quality seminar, XVIII Executive, 9th September 1999, Helsinki, Finland.