Prediction of Monthly Global Temperatures - FINAL Update

Mon, 19 Feb 2018 09:13:52 +0100

In June 2011 we presented a monthly ex ante decadal forecast for global mean temperature for the period until October 2017. This forecast is based on a global system model developed by our Insights self-organizing modeling app, and it has been published at Climate Etc. in October 2011.

The model describes a non-linear dynamic system of the atmosphere for decadal forecasting consisting of 5 drivers: Ozone concentration, aerosol index, radiative cloud fraction, and global mean temperature as endogenous variables and sun activity as exogenous variable of the system. The model was built in May 2011 from observational data from October 1988 till April 2011 of up to 1000 input variables with time lags of up to 120 months, which is a typical input space dimension for complex dynamic systems modeling.

This post is an updated validation of the initial ex ante prediction for May 2011 to October 2017 (78 months) of this system model by a comparison of observed temperatures (black/withe square dots; HADCRUT3) vs predicted temperatures (red lines). Both model and ex ante prediction have not been changed since their publication. However, the HADCRUT3 dataset, a joint data product of the UK Met Office Hadley Centre and the Climate Research Unit at the University of East Anglia, which was used for model building is no longer supported by the providers. Instead, a new version, HADCRUT4, is maintained now, whose values differ from the previous version ones. In order to keep our system prediction up-to-date, we now have to transform HADCRUT4 into HADCRUT3 values, which introduces minor deviations from the original HADCRUT3 data, however.

As of December 2017, the prediction accuracy of the most likely prediction (solid red line) of the Insights model is 57%. The accuracy relative to the prediction range (pink area) is 93% (fig. 1).

Fig. 1: Ex ante forecast (most likely (red), high, low (pink); May 2011 - October 2017) of the system model (as of April 2011) vs observed values (black and white square dots; HADCRUT3) from May 2011 to December 2017. Since June 2014 HADCRUT3 is not maintained anymore and, therefore, has to be derived from HADCRUT4 data for compatibility reasons.

The high temperatures from October 2015 to August 2016 are attributed to a weather anomaly of the Pacific Ocean called El Niño, which has been of special intensity this time. El Niño is an irregularly occurring heat event in the central and east-central equatorial Pacific based on complex ocean-atmosphere interactions, which is not fully understood yet and which is affecting the coastal regions of South America, California, and Asia, directly, but also has effects globally.

In comparison, the expensive General Circulation Models (GCMs) which the IPCC AR4 and AR5 projections are based on and which rely on atmospheric CO2 as major climate driver (and which are long-term trend models for 100 years, to be fair), show a prediction accuracy of just 31% for the time period 2007 (the year of their publication) till today and 29% for the same time period as the Insights system model (fig. 2).

Fig. 2: Ex ante most likely forecast (red; May 2011 - October 2017) of the self-organized system model (as of April 2011) vs observed values (black and white square dots; HADCRUT3) from May 2011 to December 2017 vs IPCC A1B projection (yellow; until October 2017) vs CO2 concentration (light gray; until October 2017). Since June 2014 HADCRUT3 is not maintained anymore and, therefore, has to be derived from HADCRUT4 data for compatibility reasons.

The HADCRUT3/4 as well as the GISS (NASA Goddard Institute for Space Studies) datasets use station based temperature observations, exclusively. Currently, about 2000 ground stations are contributing to these data, and they cover only about 75% of the geographic surface of the earth. Also, these stations are not equally distributed over the globe, which is why complex interpolation methods are used to generate the final HADCRUT/GISS datasets of a regular grid structure of 5°x5° spatial resolution, which introduce considerable uncertainty in the provided temperature data.

In contrast, there are satellite based temperature observation data of the lower troposphere of which the RSS (Remote Sensing Systems) and UAH (University of Alabama in Huntsville) datasets are the most prominent ones. They show higher spatial resolution, cover over 98% of the earth's surface, and they do not use interpolation techniques for spatial grid correction (though they have to apply calibration methods for consistency of the datasets), which improves data quality and reliability.

A comparison of the ex ante system prediction with these observed satellite data (average of RSS and UAH) shows an even higher forecasting accuracy of currently 59% and almost no bias of the most likely prediction, with the exception of the extreme El Niño weather event in 2015/16 (fig. 3). Also, it is obvious that the moderate IPCC A1B forecast is above the observed temperature data most of the time, and it overestimated the temperature development over the past 10 years, except for few months of the El Niños of 2009/10 and 2015/16.

Fig. 3: Ex ante most likely forecast (red; May 2011 - October 2017) of the self-organized system model vs observed satellite data (black and white square dots; average of RSS and UAH) from May 2011 to December 2017 vs IPCC A1B projection (yellow; until October 2017). It shows a forecasting accuracy of currently 59%.

In 2013 the UK Met Office started to publish their decadal forecast based on yearly data, which they both model and forecast update and correct every year. This forecast can be found here.

Paper Series on Cosmic Climate Drivers (3)

Sun, 18 Feb 2018 09:13:52 +0100

In the last decade, GCM climate models of type PMIP2/3 and CMIP3/5 has been developed and widely used in climate science.

In the past years, many of these models were tested by independent model data comparisons showing underperformance in all climate aspects, i.e., in temperature evolution, in global atmospheric circulation and precipitation in the Holocene, and in modeling the last interglacial and aeolian dust. This low model performance over past centennial time frames is a result of insufficient a priori knowledge of the internal workings of the climate system, which is characteristic of complex systems in general and which requires to make often unjustified assumptions about the system. A logical development now is that alternative climate studies increasingly appear.

In a series of short papers, Seifert and Lemke introduce and present four major, mostly cosmic climate drivers over the multi-millennial period of the entire Holocene. Using the GISP2 ice-core temperature series (Greenland Ice Sheet Project), each paper describes the specifics of a certain time frame of the Holocene starting from 8500 BC. A recent paper covers the time span 1600-2050 AD.

Abstract
The time span 1600-2050 AD covers the most recent of a total of 30 cyclic sine half-wave periods, which developed since the beginning of the Holocene. The first half-wave cycle commenced in 8108 BC, with a periodicity of 238 years. This cycle is a growing cycle, which increments by 6.93 years, as all papers of the Holocene Climate Pattern Recognition analysis demonstrate. The present periodicity of the cycle is 439 years long, starting within the Little Ice Age (LIA) temperature bottom trough, 1590 to 1640 AD, at 1610 AD, and rising to the Current Warm Period (CWP) cycle top at 2049 AD. This Holocene paper series additionally serves as a 10,000 year empirical confirmation of astronomical-physical calculations of the cyclic nature of the climate. We showed that well-defined, regular wave periodicities led us out from the 500 BC Homerian Minimum into the Roman Warm Period, into the Late Antique Little Ice Age (LALIA) cold period, then into the Medieval Warm Period (MWP), followed by the cold LIA and now into the present warm CWP with its peak in 2049 AD and, thereafter, into the return of the next cold future “LIA”. As in seven previous recognition papers before, which cover the entire Holocene since 8500 BC, our obligatory pattern recognition grid was placed onto this time span 1600-2050 AD. We provide a comparison of nominal cyclic half-wave temperatures to actual measured GISP2 and GISS temperatures. It is demonstrated that the sine wave motion is modified by pulsations of the 62-year cosmic Solar-Planetary Oscillation (SPO; in previous parts called SIM) cycle (with the Atlantic Multidecadal Oscillation (AMO) and the Pacific Decadal Oscillation (PDO) as two observable oceanic temperature effects), which produces regularly spaced, consistent warm peaks along the entire Holocene. These warm peaks appear since 1818 AD in shapes of 4 upward moving staircase steps. The present staircase temperature peak is 2004 AD, from where on the flat step surface (today known as “The Pause”, “Hiatus”, “Plateau”) will persist until the year 2046 AD. The top of the sine wave temperature cycle is the year 2049 AD, from where on temperatures will enter into the 31st cycle, with a 439+6.93 year descent, until the future “LIA” temperature bottom will be reached. The final paper of this series, part 9, will cover the time span 2000 AD to the End of the Holocene. Such a forecast can be made, because existing five underlying cosmic, astronomical forcing mechanisms of climate change can be calculated.

The full paper can be downloaded free.

Prediction of Monthly Global Temperatures - Update (3)

Sat, 21 Jan 2017 09:13:52 +0100

In June 2011 we presented a monthly ex ante decadal forecast for global mean temperature for the period until October 2017. This forecast is based on a global system model developed by our Insights self-organizing modeling app, and it has been published at Climate Etc. in October 2011.

The model describes a non-linear dynamic system of the atmosphere for decadal forecasting consisting of 5 drivers: Ozone concentration, aerosol index, radiative cloud fraction, and global mean temperature as endogenous variables and sun activity as exogenous variable of the system. The model was built in May 2011 from observational data from October 1988 till April 2011 of up to 1000 input variables with time lags of up to 120 months, which is a typical input space dimension for complex dynamic systems modeling.

This post is an updated validation of the initial ex ante prediction for May 2011 to October 2017 (78 months) of this system model by a comparison of observed temperatures (black/withe square dots; HADCRUT3) vs predicted temperatures (red lines). Both model and ex ante prediction have not been changed since their publication. However, the HADCRUT3 dataset, a joint data product of the UK Met Office Hadley Centre and the Climate Research Unit at the University of East Anglia, which was used for model building is no longer supported by the providers. Instead, a new version, HADCRUT4, is maintained now, whose values differ from the previous version ones. In order to keep our system prediction up-to-date, we now have to transform HADCRUT4 into HADCRUT3 values, which introduces minor deviations from the original HADCRUT3 data, however.

As of December 2016, the prediction accuracy of the most likely prediction (solid red line) of the Insights model is 59%. The accuracy relative to the prediction range (pink area) is 93% (fig. 1).

Fig. 1: Ex ante forecast (most likely (red), high, low (pink); May 2011 - October 2017) of the system model (as of April 2011) vs observed values (black and white square dots; HADCRUT3) from May 2011 to December 2016. Since June 2014 HADCRUT3 is not maintained anymore and, therefore, has to be derived from HADCRUT4 data for compatibility reasons.

The high temperatures from October 2015 to August 2016 are attributed to a weather anomaly of the Pacific Ocean called El Niño, which has been of special intensity this time. El Niño is an irregularly occurring heat event in the central and east-central equatorial Pacific based on complex ocean-atmosphere interactions, which is not fully understood yet and which is affecting the coastal regions of South America, California, and Asia, directly, but also has effects globally.

In comparison, the expensive General Circulation Models (GCMs) which the IPCC AR4 and AR5 projections are based on and which rely on atmospheric CO2 as major climate driver (and which are long-term trend models for 100 years, to be fair), show a prediction accuracy of just 31% for the time period 2007 (the year of their publication) till today and 29% for the same time period as the Insights system model (fig. 2).

Fig. 2: Ex ante most likely forecast (red; May 2011 - October 2017) of the self-organized system model (as of April 2011) vs observed values (black and white square dots; HADCRUT3) from May 2011 to December 2016 vs IPCC A1B projection (yellow; until October 2017) vs CO2 concentration (light gray; until October 2017). Since June 2014 HADCRUT3 is not maintained anymore and, therefore, has to be derived from HADCRUT4 data for compatibility reasons.

The HADCRUT3/4 as well as the GISS (NASA Goddard Institute for Space Studies) datasets use station based temperature observations, exclusively. Currently, about 2000 ground stations are contributing to these data, and they cover only about 75% of the geographic surface of the earth. Also, these stations are not equally distributed over the globe, which is why complex interpolation methods are used to generate the final HADCRUT/GISS datasets of a regular grid structure of 5°x5° spatial resolution, which introduce considerable uncertainty in the provided temperature data.

In contrast, there are satellite based temperature observation data of the lower troposphere of which the RSS (Remote Sensing Systems) and UAH (University of Alabama in Huntsville) datasets are the most prominent ones. They show higher spatial resolution, cover over 98% of the earth's surface, and they do not use interpolation techniques for spatial grid correction (though they have to apply calibration methods for consistency of the datasets), which improves data quality and reliability.

A comparison of the ex ante system prediction with these observed satellite data (average of RSS and UAH) shows an even higher forecasting accuracy of currently 61% and almost no bias of the most likely prediction, with the exception of the extreme El Niño weather event in 2015/16 (fig. 3). Also, it is obvious that the moderate IPCC A1B forecast is above the observed temperature data most of the time, and it overestimated the temperature development over the past 10 years, except for few months of the El Niños of 2009/10 and 2015/16.

Fig. 3: Ex ante most likely forecast (red; May 2011 - October 2017) of the self-organized system model vs observed satellite data (black and white square dots; average of RSS and UAH) from May 2011 to December 2016 vs IPCC A1B projection (yellow; until October 2017). It shows a forecasting accuracy of currently 61% with almost no bias, with the exception of the strong El Niño weather event in 2015/16.

In 2013 the UK Met Office started to publish their decadal forecast based on yearly data, which they both model and forecast update and correct every year. This forecast can be found here.

Aerosol, Reflectivity and Ozone concentration prediction - Update

Tue, 17 Jan 2017 08:47:50 +0100

In June 2011 we published monthly ex ante decadal forecasts until October 2017 for aerosol index, reflectivity, and ozone concentration based on satellite data. Ex ante forecasts are most valuable, transparent and objective forecasts, because they cannot be fine tuned or otherwise manipulated after they have been published since everybody can evaluate the accuracy of the forecasts against the actually observed values later in time, which are usually provided independently of the forecasts by a third party. They are the only objective proof if an approach/method/model works and is useful or not. This comes at the cost that such a proof takes time, real-time.

Now, after five years have passed, it is time to make a first performance evaluation of the predictive models.

All three models are derived from satellite measurements. Satellite data cover over 98% of the earth’s surface at a high spatial resolution. A disadvantage, however, still is that the operational time especially of the early satellites is quite limited. They are replaced by new generations, which then use more sophisticated technologies and measurement instruments. The data they provide are usually not 100% compatible with their predecessor’s data. To get a consistent data set for a long period of time the data of the different satellites needs to be calibrated, which introduce some bias and uncertainty into the observation data. This has also been the case for the aerosol, reflectivity and ozone forecasts, where the satellites whose data were used for modeling and prediction are not operational anymore for several years.

Taking this into account, here are the performance evaluations of the three decadal, monthly forecasts. The forecast accuracy range from 45% (aerosols) to 85% (reflectivity) which is useful, good and highly competitive performance.

Aerosol index: The ex post forecast is from October 2008-May 2011, the ex ante forecast ranges from June 2011-October 2017. The overall forecast accuracy is 45%. For the aerosol forecast the difference in observation data provided by current [1] and previous satellite measurements has been quite large for the period 2004-2005 where both satellites operated in parallel. So the uncertainty of the calibrated ex post observation data used in this graph is supposed to be high.

Radiative cloud fraction: The ex post forecast is from October 2008-May 2011, the ex ante forecast ranges from June 2011-October 2017. The overall forecast accuracy is 85%. The data source is [1].

Ozone concentration The ex post forecast is from October 2008-May 2011, the ex ante forecast ranges from June 2011-October 2017. The overall forecast accuracy is 70%. The data source is [1].

The unaltered aerosol, reflectivity and ozone forecasts along with the forecast for sun activity (sunspot numbers) drive the ex ante decadal forecast of global mean temperature (also published in 2011) whose performance evaluation will be subject in a next post.

[1] Pawan K. Bhartia (Apri), OMI/Aura TOMS-Like Ozone, Aerosol Index, Cloud Radiance Fraction Daily L3 Global 1.0x1.0 deg, version 003, NASA Goddard Space Flight Center, Accessed May 14, 2016, 10.5067/Aura/OMI/DATA3001

Atmospheric CO2 concentration Prediction - Update

Tue, 17 Jan 2017 08:46:09 +0100

In June 2011 we published a long-term monthly ex ante forecast of atmospheric CO2 concentration. More than five years have passed now, and it is time to make a performance evaluation of this forecast compared to the actually observed data from Mauna Loa.

To make it short, with a very high forecast accuracy of 99% the model confirms that it is able to accurately and reliably predict atmospheric CO2 over a long forecast horizon.

Atmospheric CO2: The ex ante forecast ranges from June 2011-June 2030. The period for forecast evaluation is June 2011-December 2016 with an ex ante forecast accuracy of 99%. Atmospheric CO2 is continuously rising on a yearly basis. Will global temperature rise likewise?

Sunspot number prediction (5)

Sat, 14 Jan 2017 07:45:08 +0100

This is another update of the initial sunspot number prediction from June 2010. The new data are observed by NASA and by the Space Weather Services of the Bureau of Meteorology of the Australian Government, and they are displayed in the graph by white square dots. Recent observations are at the lower bottom of the prediction, which supposedly will keep being slightly too high for the rest of the current sun cycle. Over the past 79 months the monthly sunspot number predictions shows an overall accuracy of 75%.

The prediction is a composite of three models obtained by self-organizing knowledge mining from data. These predictions are unchanged since June 2010.

Paper Series on Cosmic Climate Drivers (2)

Sat, 25 Jun 2016 10:45:22 +0200

How forestry can help address global climate change

Sat, 27 Jun 2015 08:39:59 +0200

In their recent paper “How Forestry in the Southern Hemisphere Can Help Address Desertification and Global Climate Change”, García-Chevesich, Pizarro, and Valdes remind us to focus on the natural, ecologically and econimically most efficient, sustainable, and healthy way of consuming CO2: Reforestation. Using CO2 as nutrient to grow plants and to produce atmospheric Oxygen as the basis of life on earth should be our first - and certainly only - way to “geoengineer” the earth.

Paper summary: As we continue burning fossil fuels and releasing CO2 into our atmosphere, trees help us capturing tremendous amounts of the greenhouse gas through photosynthesis every year. Actually, in a given year, northern hemisphere forests are perfectly capable of removing the world's atmospheric CO2 between May and October (see Keeling Curve), when the mass of forests in the northern hemisphere are in growing period. However, between October and May of the following year, when northern hemisphere forests are dormant, global atmospheric CO2 concentrations rise again. After evaluating global deforestation rates, we realized that global lost of forests have occurred mostly in the southern hemisphere during the last few decades, at alarming rate. Such localized loss of global photosynthetic potential suggests that global warming might be occurring not so much because of the releasing of CO2, but rather because of the loss of southern hemisphere's forests. We suggest immediate mass reforestation plans in the southern hemisphere, to recover the lost Earth lung and, consequentially, cool down our planet in a few years.

The Keeling Curve
(see also: Prediction of CO2 Concentration till 2030)

For a full copy of the paper contact Pablo García-Chevesich, University of Arizona/Forest Institute of Chile (INFOR).

Paper Series on Cosmic Climate Drivers

Mon, 4 May 2015 17:53:07 +0200

Seifert and Lemke published a paper series on cosmic climate drivers for the entire Holocene. The study recognizes four distinct climate patterns: a multi-millennial pattern, two multi-centennial patterns and one short multi-decadal pattern. Special attention is given to peak temperature spikes. The analysis is able to distinguish different causes of climate change out from Holocene temperature graphs, such as the Milankovitch cycle, Earth orbit oscillations, cosmic meteor impacts on Earth and likely volcano mega-eruptions. The authors use the graphical version of the GISP2 (Greenland Ice Sheet Project) data set transformed into equidistant time intervals of 10 years for visual demonstration of climate patterns. Each up and down of the GISP2 temperature curve is explained in detail. A well-defined continuous multi-centennial Holocene cycle with 7-year growing periods is proven for the entire Holocene. Its exact timing of the cycle excludes an internal atmospheric-oceanic cycle cause. The pattern recognition method determines the indisputable celestial origin of cyclic patterns and is superior to GCM/PMIP/CMIP models, which all underperformed in recent 2014 model-data comparisons. The entire series contains 5 papers, which will be added successively.

The papers can be downloaded free.

Sunspot number prediction (4)

Thu, 25 Sep 2014 10:08:10 +0200

This is a new update of the initial sunspot number prediction from June 2010. The new data are observed by NASA and are displayed in the graph by white squares. They still confirm the predicted low sun activity of the current cycle 24.

The prediction is a composite of three models obtained by self-organizing knowledge mining from data. These models are unchanged since June 2010.

Still confirming forecast of Apr 2011 at 73% accuracy. IPCC forecast at 10%. What drives Global Warming? (Update 2)

Mon, 7 Oct 2013 07:25:01 +0200

This is an actual vs predicted update of the medium-term (79 months) quantitative forecast of monthly global mean temperatures based on an interdependent system model of the atmosphere developed by KnowledgeMiner Software based on observational data, exclusively. No prior assumptions, expectations or other subjective adjustments were used in the models.

This model describes a non-linear dynamic system of the atmosphere for short- to medium-term forecasting consisting of 5 drivers: Ozone concentration, aerosol index, radiative cloud fraction, and global mean temperature as endogenous variables and sun activity as exogenous variable of the system. Note that CO2, though provided as input data, has not been selected as relevant climate driver by self-organizing, inductive modeling. This is a CO2-free prediction model. The model was built from observational data from October 1988 till April 2011 of up to 1000 input variables with time lags of up to 120 months, which is a typical input space dimension for complex dynamic systems modeling.

As of August 2013, the OUT-OF-SAMPLE prediction accuracy of the most likely prediction (solid red line) of the self-organized model is 73%. The accuracy relative to the prediction range (pink area) is 98% (fig. 1).

Fig. 1: Ex-ante forecast (most likely (red), high, low (pink); April 2011 - November 2017) of the system model (as of March 2011) vs observed values (black and white square dots; HADCRUT3) from April 2011 to August 2013. These 29 months are used for verification of the out-of-sample predictive power of the system model.

In comparison, the highly expensive General Circulation Models (GCMs) which the IPCC AR4 projections are based on and which simplistically rely on atmospheric CO2 (and other greenhouse gases) as the major climate driver show a prediction accuracy of only 10% for the time period 2007 (the year of publication) till today. The tight connection between IPCC temperature projection (yellow) and CO2 concentration projection (gray) is clearly visible for the forecast horizon, too, as well as the growing gap between IPCC projected and observed temperatures (fig. 2).

Fig. 2: Ex-ante most likely forecast (red; April 2011 - November 2017) of the self-organized system model (as of March 2011) vs observed values (black and white square dots; HADCRUT3) from April 2011 to August 2013 vs IPCC A1B projection (yellow; until November 2017) vs CO2 concentration (light gray; until November 2017).

This one-sided drift of the IPCC projection seen in fig. 2 is not common for predictive models. A similar drift situation is not observed for the time before 2007, for the data the GCMs were developed on. Here, over- and underestimation of observed values is balancing within time periods of few years, as expected. In modeling, such a drift is seen as clear evidence of low (or decreasing) predictive power of the model, the lack of skill to describe the underlying phenomenon sufficiently. This evidence, for the IPCC model, is not surprising since the built-in simplistic linear cause-effect relationship „growing atmospheric CO2 concentration leads to proportionally growing global temperatures“ does not adequately describe the complex and interdependent nature of the atmosphere-ocean system and therefore does not sufficiently satisfy the adequateness law of modeling.

Additionally, our climate system is essentially influenced by external, cosmic climate drivers such as the Earth Orbit Oscillation in centennial time frames, the multidecadal tri-synodic Jupiter/Saturn cycle, or the well-known orbit eccentricity Milankovitch cycle, which causes glacial and interglacial ages on Earth. These cosmic climate drivers are responsible for most of the variation of solar radiation received on Earth, resulting in medium- to long-term warming and cooling periods, independently from the sun‘s own rather small changing activity and radiation.

The considerably higher predictive power of the discussed self-organized system model is clearly a result of the unique ability of the applied, proven inductive modeling technology to automatically and reliably extract more relevant information from the noisy observational data for identifying and modeling the internal workings of the ill-defined climate system than IPCC theory-driven modeling approaches can achieve based on incomplete and uncertain human knowledge.

Ivakhnenko 100th Anniversary

Sat, 28 Sep 2013 10:24:28 +0200

This year we are celebrating the 100th Anniversary of A.G. Ivakhnenko, the author of the unparalleled self-organizing, noise immune, inductive modeling and knowledge mining technology implemented in the Insights app, which has also been used for all the Global Warming forecasts presented here. He originated essential ideas found in many other data mining methods today, and having hosted Norbert Wiener, the father of cybernetics, after a major conference, Ivakhnenko authored more than 40 books and 500 scientific papers over his 65-year career, making significant contributions to informatics, cybernetics, artificial intelligence, intelligent control, and modeling.

He initiated the development of a proven noise immunity theory for inductive modeling not found in any other data mining technology today as a key feature of data analysis. It has been mathematically proven that models obtained by self-organizing, inductive modeling predict more accurately on noisy data than models based on physical principles and domain knowledge.

In his 1981 paper, Stanley J. Farlow effectively summarized the relevance of Ivakhnenko's GMDH, "A major difficulty in modeling complex systems in such unstructured areas as economics, ecology, sociology, and others is the problem of the researcher introducing his or her own prejudices into the model. Since the system in question may be extremely complex, the basic assumptions of the modeler may be vague guesses at best. It is not surprising that many of the results in these areas are vague, ambiguous, and extremely qualitative in nature.

"It was for this reason that in the mid 1960's the Ukrainian mathematician and cyberneticist, A.G. Ivakhnenko, introduced a method that allows the researcher to build models of complex systems without making assumptions about the internal workings. The idea is to have the computer construct a model of optimal complexity based only on data and not on any preconceived ideas of the researcher; that is, by knowing only simple input-output relationships of the system, Ivakhnenko's GMDH algorithm will construct a self-organizing model that can be used to solve prediction, identification, control synthesis, and other system problems."

Today, self-organizing inductive modeling is a proven and highly efficient knowledge extraction technology. Recent advances in research and development have made possible parallel implementations, multi-level self-organization for modeling high-dimensional data sets containing many thousands of input variables, cost-sensitive modeling, and new model evaluation techniques to improve the reliability and applicability of models. This technology has been employed successfully in various fields, from image recognition over biomarker detection and QSAR modeling, wastewater management and reuse questions, to Global Warming and micro and macro-economic forecasting problems.

Identification of an asteroid impact - The 4.2 kYear event

Wed, 15 May 2013 08:27:15 +0200

A new paper about the effects of a larger cosmic impact in Mesopotamia 4.200 years ago and its influence on global climate.

The destruction of the city of Akkad by a cosmic asteroid impact and the link to global climate change

Abstract.
We focus on one of the most important events in human history, the 4.2 kiloyear event, when great civilizations around the world collapsed into anarchy and social chaos. From this moment on, climate cooling and widespread aridification began, lowering agricultural food production and human living conditions. Various hypotheses exist about its cause; the most promising approach links the 4.2 kiloyear event to a cosmic asteroid crash into Mesopotamia. The asteroid landed in a densely populated area; we examine at first major translations of preserved Sumerian documents on details and progression of this catastrophic event. We quote major impact features as observed by historical Sumerian eyewitnesses. The impact, as a full strike, eradicated the Imperial city of Akkad. The impact damaged all other Sumerian towns to different degrees. Based on our findings, we identify the location of the missing city of Akkad. We analyze the onset of global cooling and severe aridification in the framework of our cosmic climate footprint analysis for a selected 1,000 year timeframe. This footprint analysis of Holocene climate change affirms the occurrence and date of the impact event. We also identify volcanic mega-eruptions, which are responsible for multi-decadal global temperature dips but which cannot cause centennial-long climate changes. The footprint analysis takes 5 climate macroforcings into account and explains global cooling and aridification based on impact-related causes.

The full paper is available here. (PDF, 1.4 MB)

What Drives Global Warming? - Update

Mon, 21 Jan 2013 11:48:00 +0100

In September 2011, we presented a medium-term (79 months) quantitative prediction of monthly global mean temperatures based on an interdependent system model of the atmosphere developed by KnowledgeMiner, which was also discussed at Climate Etc. in October 2011. This model describes a non-linear dynamic system of the atmosphere consisting of 5 major climate drivers: Ozone concentration, aerosols, radiative cloud fraction, and global mean temperature as endogenous variables and sun activity (sunspot numbers) as exogenous variable of the system. This system model was obtained from monthly observation data of the past 33 years (6 variables in total: the 5 variables the system is actually composed of (see above) plus CO2, which, however, has not been identified as relevant system variable), exclusively, by unique self-organizing knowledge extraction technologies.

Now, more than a year has passed, and we can verify what has been predicted relative to the temperatures, which have really been measured (fig. 1).

Fig. 1: Ex-ante forecast (most likely (red), high, low (pink); April 2011 - November 2017) of the system model as of March 2011 vs observed values (black and white square dots; HADCRUT3) from April 2011 to December 2012. These 21 months are used for verification of the out-of-sample predictive power of the system model.

Verifying the prediction skill of the system model from April 2011 to December 2012, the accuracy of the most likely forecast (solid red line) remains at a high level of 75%, and the accuracy relative to prediction uncertainty (pink area) is an exceptional 98%. Given the noise in the data (presumably incomplete set of system variables considered, noise added during measurement and preprocessing of raw observation data, or random events, for example), this clearly confirms the validity of the system model and its forecast.

How does the IPCC AR4 A1B Scenario compares to the recent observed data and the system model forecast (fig. 2)?

Fig. 2: Ex-ante most likely forecast (red; April 2011 - November 2017) of the system model as of March 2011 vs observed values (black and white square dots; HADCRUT3) from April 2011 to December 2012 vs IPCC A1B projection (yellow; until November 2017) vs CO2 concentration (light gray; until November 2017).

The IPCC A1B scenario is derived from a number of million-dollar General Circulation Models (GCMs), which depend on atmospheric CO2 as the major driver for Global Warming. Consequently, the IPCC A1B projection follows the development of CO2 concentration, which - in contrast to observed global temperatures - has only been rising in the past and which will continue to do so for the next future. This IPCC projection currently shows a prediction accuracy of 23% (September 2007 - December 2012, 64 months) and just 7% accuracy for the same forecast horizon as applied for the system model (April 2011 - December 2012, 21 months).

In Fig. 2, two different models - IPCC model and atmospheric system model - with two very different modeling approaches - theory-driven vs data-driven modeling - are compared. The IPCC model is based essentially on AGW theory by emission of greenhouse gases, namely CO2, the presented atmospheric system model on the other hand is a CO2-free prediction model. It is described by 5 other variables. The IPCC model shows a prediction accuracy of 7% and the atmospheric system model an accuracy of 75% for the same most recent 21 months of time...

It is only fair to mention that the objective of the IPCC models is long-term (centennial) qualitative projection of global temperatures while the proposed system model is for medium-term (decadal) quantitative forecasting purposes. It is a property of every long-term prediction model, by definition, that it does not necessarily fit to short-term variations of the observed variable. Therefore, it is not surprising that the IPCC model shows lower prediction accuracy on shorter time horizons (here, 21 and 64 months) than a dedicated short- to medium-term prediction model does.

However, for more than 6 years now observed global temperatures have been constantly below the IPCC projection. And the gap between observed global temperatures and projected IPCC scenario is expected to grow every month that passes given the confirmed system model forecast (fig. 2). By the end of 2017, within 10 years then, the prediction error of the IPCC A1B projection might have been accumulated to around 0.4 °C or 100%, already.

This one-sided drift of the IPCC projection seen in Fig. 2 is not common for long-term prediction models. A similar drift situation is not observed for the time before 2007, for the data the GCMs were developed on. Here, over- and underestimation of observed values is balanced, as expected. In modeling, such a drift is seen as clear evidence of low (or decreasing) descriptive power of the model, the lack of skill to describe the underlying phenomenon sufficiently. This evidence, for the IPCC model, is not surprising since the simplistic linear cause-effect relationship „growing atmospheric CO2 concentration leads to proportionally growing global temperatures“, which the model is based on, does not adequately describe the complex and interdependent nature of the atmosphere-ocean system.

Additionally, our climate system is essentially influenced by external, cosmic climate drivers such as the Earth Orbit Oscillation in centennial time frames, the multidecadal tri-synodic Jupiter/Saturn cycle, or the well-known orbit eccentricity Milankovitch cycle, which causes glacial and interglacial ages on Earth. These cosmic climate drivers are responsible for most of the variation of solar radiation received on Earth, resulting in medium- to long-term warming and cooling trends, independently from the sun‘s own rather small changing activity and radiation.

System model data and example models are available here for download.

What drives climate in the long run? A new paper

Mon, 8 Oct 2012 08:48:30 +0200

A new paper by Seifert and Lemke has been published that opens a new and wider view on which forces drive our climate in the long perspective. They show that cyclic cosmic processes, which have not been considered in the established climate models at all, play in fact a key role. Our life is determined by day and night, the seasons, or the moon phases, which are all cosmic cycles. These cosmic cycles are so present and self-evident for us that we don‘t spend a second of time on questioning or realizing this fact.

But there are other cosmic cycles that also drive our climate. One most important is the Earth Orbit Oscillation (EOO). The Earth orbits the planets system center of mass not in a plain ellipsoidal surface but in a three-dimensional, rising and shrinking spiral flight due to the influence of other cosmic cycles known as Milankovitch cycles and the motion of the sun (Solar Inertial Motion), which not sits fixed in the center of our planetary system. As the Earth oscillates in its orbital flight around the center of mass, the Earth-Sun distance oscillates too. Over the time frame of several hundred years the Earth, therefore, accumulates more or less solar energy, which results in swinging, corresponding warming and cooling climatic changes on Earth including glaciation. The variation in solar energy received on Earth by EOO is much higher than the Sun‘s own variation of solar irradiance emitted over the known sun cycles (for example, the 11-year Schwabe sunspot cycle).

A second major source of climate change is, as always, chance, namely stochastic cosmic impacts hitting planet Earth. The paper shows that these impacts also influence the orbital flight of the Earth resulting in a characteristic pattern of cooling, warming, and stabilization of global temperatures. The figure shows this cosmic impact pattern along with the Earth Orbit Oscillation curve on paleo-climatic temperature reconstructions (ice core; Greenland Ice Sheet Project GISP2) for the time period 37k - 27k yrs BP.

The abstract of the paper:
Five climate-forcing mechanisms govern 20,000 years of climate change

„We identify five macro-climatic mechanisms in our study that govern a long time span of 20,000 years. The state of the art in climate-forcing mechanism analysis is that presently available General Circulation Models (GCMs) underperform substantially in terms of predictive power. It is evaluated in the literature that all GCMs perform well for the first 500 years backwards from the present, but then lack skill for the previous 9,500 Holocene years. It is critical for climate models, however, that they also show their validity on time frames of more than 1,000 years.

The presented climate-forcing study proceeds with the selection of 10,000 years of the entire Holocene interglacial and, for comparison, of another 10,000 years of a purely glacial time span (37,000-27,000 BP) from the GISP2 data. It considers the effects of Milankovitch cycles, atmospheric CO2-concentrations, Solar Inertial Motions (SIM), the retrograde tri-synodic Jupiter/Saturn cycle, and of two major mechanisms, the Earth Orbit Oscillation (EOO) and the Cosmic Impact Oscillation (CIO). Detailed mechanisms for both oscillations are provided; their calculation methods are pointed out.

Concluding the study, we zoom in onto EOO and CIO forcing of the past 3,000 years and provide an outlook onto forcing mechanisms, which are expected to act within the future 500 years. The GISP2 proxy temperature curve and macro-forcing mechanisms are compared to the Hockey Stick temperature evolution pattern.

Details of demonstrated astro-climatic relations are as of today, 2012, new and original climate change knowledge. The IPCC has not been able to provide supplementary data on cycle mechanics. The identification of 5 macro-climatic drivers, missing in current GCMs, unmistakably proves that climate science is not settled yet. One missing driver may be excused, but not five. The notion of ,The science is settled‘, upheld since the days of Galileo, is a spiritual relict of the past. All GCMs will be rectified soon.“

A PDF summary of the paper can be downloaded here.

The full paper is available here.

Prediction Scenarios: World oil price, consumption, production (2)

Mon, 10 Sep 2012 17:28:02 +0200

This is an update of the crude oil price projections posted in November 2010. The data for this updated model are taken from the BP Statistical Review of World Energy 2011. The underlying models implemented in Excel are available in this package free. You can run different scenarios yourself and see how the oil price responds given different input conditions.

We would like to briefly show just two possible scenarios here, exemplarily.

1. Status-quo
If world oil consumption and production do not vary much from their development in the past 10 years, the yearly average oil price would continiously rise from its current price level up to over $180 per barrel in the year 2025 as shown in figure 1. Note also that beginning from 2005 the oil production has always been below oil consumption. This is important, because oil companies do a lot to discover new reserves and to rise production from oil sands, shale oil, and in the deep water. Still, following the published official numbers in the statistical review, this has not been enough to keep production and consumption balanced, at least. Always assuming that there are no other reasons for this fact. Extracting oil from tar sands or from the deep water is very cost intensive, and to do it economically reasonable a high oil price level is required.

Fig. 1: Status-quo prediction of oil price, consumption, and production.

1. Balanced growth rates
Given that the growth rate of oil production has been lower than the growth rate of oil consumption for several years in a row, assuming balanced growth rates for the coming 15 years would be an optimistic and ambitious scenario, already. Even in this case, however, the absolute oil production numbers would not catch up with world oil demand and, consequently, oil prices would rise (fig. 2). The only difference to the first scenario is that the price growth would expectedly be more moderate. This is of course only true if no serious international conflict comes up. The basic problem that demand is higher than production could not be solved in this way. It clearly would need higher growth rates of oil production over consumption to return to a positive balance. It is open if and to which extent this would really happen.

Fig. 2: Scenario for balanced growth rates of oil production and consumption

Take a look at the numbers published by BP and at this simulation. Apparently, oil is running short. No matter of whether there are indeed hard facts for it or if it is rather the result of some „special interests“ of certain market players, from a viewpoint of billions of consumers of oil, be it energy or every day consumer goods, this is not a good outlook. Are there really no alternatives, perspectively?

Atmospheric Thermal Effect vs Greenhouse Effect

Fri, 6 Apr 2012 19:14:15 +0200

A recent paper by Nikolov and Zeller and a accompanying document introduced the new concept of Atmospheric Thermal Effect (ATE). Like the Greenhouse Effect (GE), ATE describes why a celestial body with an atmosphere has a higher surface temperature than a celestial body without an atmosphere like the moon.

The GE theory is based on the the Stefan-Boltzmann-Law, which suggests that the surface temperature of an airless Earth would be -18°C (255K). Given the actual mean surface temperature of 15°C (288K) this results in a Greenhouse Effect of 33K caused essentially by the greenhouse gases in the atmosphere, according to GE theory.

In their paper Nikolov and Zeller clearly show that the Stefan-Boltzmann-Law (SB Law; Eq. (3) in their paper) has been (mathematically) incorrectly applied in the past and resulting from that, has drawn wrong conclusions about the Greenhouse Effect of 33K. According to their new ATE concept (Eq. (6) in their paper) as well as shown by current satellite temperature measurements of the entire moon as a proxy for an atmosphere-free Earth, the surface temperature of an airless Earth would be around 155K which is 100K lower than stated by the GE theory. This means that greenhouse gases would have to account for a temperature boost of 133K instead of 33K while ATE shows that this boost is due to the inner kinetic energy of the atmosphere, given by pressure and volume, according to the Ideal Gas Law.

They summarize :

"We have shown that the SB Law relating radiation intensity to temperature (Eq. 1 & 3) has been incorrectly applied in the past to predict mean surface temperatures of celestial bodies including Mars, Mercury, and the Moon. Due to Hölder’s inequality between non-linear integrals, the effective emission temperature computed from Eq. (3) is always significantly higher than the actual (arithmetic) mean temperature of an airless planet. This makes the planetary emission temperature Te produced by Eq. (3) physically incompatible with any real measured temperatures on Earth’s surface or in the atmosphere. By using a proper integration of the SB Law over a sphere, we derived a new formula (Eq. 6) for estimating the average temperature of a planetary gray body (subject to some assumptions). We then compared the Moon mean temperature predicted by this formula to recent thermal observations and detailed energy budget calculation of the lunar surface conducted by the NASA Diviner Radiometer Experiment. Results indicate that Moon’s average temperature is likely very close to the estimate produced by our Eq. (6). At the same time, Moon measurements also show that the current estimate of 255K for the lunar average surface temperature widely used in climate science is unrealistically high; hence, further demonstrating the inadequacy of Eq. (3). The main result from the Earth-Moon comparison (assuming the Moon is a perfect gray-body proxy of Earth) is that the Earth’s ATE, also known as natural Greenhouse Effect, is 3 to 7 times larger than currently assumed. In other words, the current GE theory underestimates the extra atmospheric warmth by about 100K! In terms of relative thermal enhancement, the ATE translates into NTE = 287.6/154.7 = 1.86.

This finding invites the question: How could such a huge (> 80%) thermal enhancement be the result of a handful of IR-absorbing gases that collectively amount to less than 0.5% of total atmospheric mass? We recall from our earlier discussion that, according to observations, the atmosphere only absorbs 157 - 161 W/m2 long-wave radiation from the surface. Can this small flux increase the temperature of the lower troposphere by more than 100K compared to an airless environment? The answer obviously is that the observed temperature boost near the surface cannot be possibly due to that atmospheric IR absorption! Hence, the evidence suggests that the lower troposphere contains much more kinetic energy than radiative transfer alone can account for! The thermodynamics of the atmosphere is governed by the Gas Law, which states that the internal kinetic energy and temperature of a gas mixture is also a function of pressure (among other things, of course). In the case of an isobaric process, where pressure is constant and independent of temperature such as the one operating at the Earth surface, it is the physical force of atmospheric pressure that can only fully explain the observed near-surface thermal enhancement (NTE)."

This is an essential finding which seriously questions GE and climate sensitivity.

NASA’s Diviner infrared measurements showing daytime maximum and nighttime minimum temperature fields (Source: Diviner Web Site)

Sunspot number prediction (3)

Thu, 22 Mar 2012 14:35:22 +0100

This is another update of the initial sunspot number prediction from June 2010. The new data observed ex post are observed by NASA and are displayed in the graph by white squares.

The prediction is a composite of three models obtained by self-organizing knowledge mining from data. These models are unchanged since June 2010.

What Drives Global Warming?

Tue, 13 Sep 2011 10:01:43 +0200

To say it upfront: It is NOT CO2. Not necessarily and not exclusively. Looking at observational data by high-performance self-organizing predictive knowledge mining, it is not confirmed that atmospheric CO2 is the major force of global warming. In fact, no direct influence of CO2 on global temperature has been identified for the best models. This is what the data are seriously telling us. If we believe them, it is the sun, ozone, aerosols, and clouds - and possibly other forces not considered in this model - that drive global temperature in an interdependent and complex way.

Models and software can be downloaded here free.

The self-organized model builds a dynamic system model - a system of nonlinear difference equations. The model shows a high accuracy of 77% given the fact that there is noise and uncertainty in the observational data. Figure 1 plots the observed vs predicted global temperature anomalies of this model retrospectively for the past 23 years and predictively for the next 6 years till October 2017. It is supplemented by the uncertainty of the predictions as a range where actual temperatures will most likely be observed in.

Concluding from that graph, no significant further global warming is expected in the coming 6 years. Temperatures rather remain at the current level of warming. This is confirmed, so far, by the most recent global warming observed ex post (April - July 2011; square dots in fig. 1). This does not contradict the fact that there still may be regions where temperature will continue growing since the global temperature represents the average of surface temps over the entire globe. In fact, recent warming predictions of 9 latitudinal bands show that there are very different regional developments.

Figure 1. Actual (black) vs predicted (dark and light red) plot of global temperature anomalies from October 1988 to October 2017. The square dots (April - July 11) show temperatures observed after the model has been built in April 2011. They confirm the accuracy of the predictions made by the system model at that earlier point in time.

Figure 2 outlines the interdependence structure of the dynamic system model obtained by model self-organization. Ozone concentration (x1), for example, affects cloud fraction (x2), aerosol concentration (x3), and global temperature (x6) while it is influenced in turn by cloud fraction, aerosols, and sun activity (x5) at certain earlier points in time. This interdependence applies to all other system variables, correspondingly, so that there is no clear, simple, single cause-effect chain in this system. Instead, dependencies between system variables become an interwoven pattern and it‘s hard to tell what is cause and what is effect. This is characteristic for complex real-world systems (Müller, 2000).

Such complex dynamic interdependence pattern has been automatically identified from data for all system variables except for CO2 (x4). The atmospheric CO2 at a time is described very well by the CO2 concentration observed 12 months before, exclusively (auto-regressive model). This model has been posted earlier. However - and this is a most important finding -, CO2 does also not influence any other of the system variables including global temperature. It remains completely autonomous (see f4-loop in fig. 2). This contrasts what has been communicated in the past years, but this is what the data are telling us when we are able to extract the hidden knowledge about the atmospheric system from that data appropriately, objectively and open in result.

Figure 2. Self-organized system model of global warming as a nonlinear system of difference equations
representing a network of interdependent input-output relationships. The models f1 to f6 are available
analytically and they show high dynamics by time lags of up to 120 months.

Why should it matter if CO2 do really drive Global Warming or not?
The current mental model of Global Warming that has been communicated worldwide is this (fig. 3): CO2 and other greenhouse gases cause global warming and if CO2 emissions are continuously growing global temperatures will do so, too, proportionally.

Figure 3. Communicated mental model of a supposed CO2-driven global warming as a linear chain cause-effect relationship.

If this is true, it will indeed have dramatic consequences. Believing that it is true, huge efforts has been propagated and also taken in many countries in recent years including the introduction of CO2 certificates trading as a questionable tool to mitigate CO2 emissions. To fight Global Warming we have to fight CO2 emissions. That‘s the conclusion.
But what if Global Warming has not been driven by greenhouse gas concentrations or not in the assumed way? Or what if Global Warming takes a different path than projected by the present communicated model due to other dependencies and effects that exist in reality than assumed and described by this model? Can we really afford failing in this matter? Wouldn‘t we have to take other actions in these cases? Maybe rather taking care of aerosol and ozone concentrations, for example?

Also, we have to remind us that we have quite incomplete knowledge and understanding about the complex behavior of the atmosphere and also comparatively short records of reliable observational data, only, so how can we be sure that we are not wrong? We cannot. This is part of the truth. Reality, only, decides if our explanations, expectations, assumptions, descriptions, models are right or not.

An obvious question that comes up now is how do this compare to Intergovernmental Panel of Climate Change (IPCC) projections?

In 2007, the IPCC in the Executive Summary of Chapter 10 on Global Climate Projections of Working Group 1 of its 4th Assessment Report writes:

„The future climate change results assessed in this chapter are based on a hierarchy of models, ranging from Atmosphere-Ocean General Circulation Models (AOGCMs) and Earth System Models of Intermediate Complexity (EMICs) to Simple Climate Models (SCMs). These models are forced with concentrations of greenhouse gases and other constituents derived from various emissions scenarios ranging from non-mitigation scenarios to idealised long-term scenarios. ...

All models assessed here, for all the non-mitigation scenarios considered, project increases in global mean surface air temperature (SAT) continuing over the 21st century, driven mainly by increases in anthropogenic greenhouse gas concentrations, with the warming proportional to the associated radiative forcing. There is close agreement of globally averaged SAT multi-model mean warming for the early 21st century for concentrations derived from the three non-mitigated IPCC Special Report on Emission Scenarios (SRES: B1, A1B and A2) scenarios (including only anthropogenic forcing) run by the AOGCMs (warming averaged for 2011 to 2030 compared to 1980 to 1999 is between +0.64°C and +0.69°C, with a range of only 0.05°C).“ (IPCC, 2007)

The original global warming projections of the three scenarios mentioned in the report, which are explicitly stated being greenhouse gas-driven models (fig. 3) based on different theories, are shown in figure 4.

Figure 4. Multi-model means of surface warming (relative to 1980–1999) for the scenarios A2, A1B and B1, shown as continuations of the 20th-century simulation.
(taken from AR4 IPCC, Global Climate Projections, (IPCC, 2007))

The citation above also points to a major methodological problem of theory-based modeling: Since we - society, science, individuals - have only few and incomplete knowledge about the complex behavior of the atmosphere (or simply, since there is no holistic theory at hand), we have to make many assumptions about the atmosphere, a priori, to fill these gaps to be able to explain, describe, model, predict it. If we do so, however, our assumptions more or less determine the result. If we make different assumptions we may get quite different results. In reverse, this means that to get, show, „proof“ a certain result we only have to find and set up the appropriate assumptions, which is sort of self-affirmation. In other words, if we are forced to make subjective, wild guesses on missing a priori information how reliable, adequate and accurate a predictive model then can be? This is a serious problem in theory-based modeling of complex systems, which the IPCC report is based on, however. An alternative approach has been proposed throughout this project by applying self-organizing knowledge extraction from noisy data (Ivakhnenko, 1968, 1970, 1971; Madala, 1994).

Choosing one scenario of the IPCC report, A1B, representative for the other scenarios and zooming it into the time scale of the presented system model (October 1988 - October 2017) gives a clearer view on what has been projected and what has been observed until now (fig. 5). Figure 5 adds to the observed and predicted temperature curves of figure 1 observed and predicted CO2 concentration and the IPCC A1B projection in the same temperature scale.

Figure 5. Observed (black) and predicted (red) global warming compared to atmospheric CO2 concentration (observed and predicted, white) and IPCC A1B Scenario (yellow) from October 1988 to October 2017.
There is a strong correlation between IPCC scenario and CO2 concentration while IPCC projection and observed (and predicted) global warming increasingly diverge over time.

The first observation of this chart is that the trend of the CO2 concentration curve and the IPCC projection are highly correlated, which is not surprising, because the IPCC projection is entirely based on assumed greenhouse gas-driven models (see description above). This projection reflects what was built into it. This is good. But if it‘s also correct is confirmed by actual temperature measurements, only.

The second finding is that IPCC projection and actual monthly global warming are starting to diverge: Actual warming of the past 5 years (summer 2006 to summer 2011) is lower than expected (average warming of 0.4°C compared to an IPCC projected average warming of 0.58°C on 1961-1990 base) and appears to move out of the projected trend. This may change again in the next several years since temperatures show a very high fluctuation, but it‘s the first time looking back 23 years that this is the case. It is also important to note that projections of the past 5 years are the first ones that has not been justified and based on historical data - it is true prediction in time as the IPCC report was published in 2007. It therefore is an indication of the true predictive power, accuracy and validity of this projection.

The observation of diverging IPCC projected A1B scenario based on CO2-driven models and actual warming amplifies when looking farther into the future using the predictions of the presented system model - which, of course, are open and have to be confirmed by future measurements, too. But we think that there is evidence, already, that makes it necessary and opportune to discuss these questions seriously and openly.

We proposed a powerful, proven and promising modeling by self-organizing knowledge extraction from data approach that has been applied to various real-world problems in the past (Farlow, 1984). The presented dynamic system model of global warming obtained by this self-learning modeling based on monthly data shows a high and reasonable accuracy on both historical and first predicted data, which up to now confirms its predictive power. It describes the complex behavior of global warming more adequately by interdependent, dynamic relationships between sun activity, ozone concentration, radiative cloud fraction, and aerosols. Atmospheric CO2 concentration has not been identified by the system model as major force of global warming. In fact, the model works without any direct impact of CO2 on warming.

We are providing these results free. Models implemented in Excel and the self-organizing modeling software KnowledgeMiner can be downloaded here. You can also leave a comment in our summary blog or contact us directly about more info.

Frank Lemke
KnowledgeMiner Software

Literature

Farlow, S.J. (ed.): Self-Organizing methods in Modeling. GMDH Type Algorithm. Marcel Dekker. New York, Basel. 1984

IPCC, 2007: Meehl, G.A., T.F. Stocker, W.D. Collins, P. Friedlingstein, A.T. Gaye, J.M. Gregory, A. Kitoh, R. Knutti, J.M. Murphy, A. Noda, S.C.B. Raper, I.G. Watterson, A.J. Weaver and Z.-C. Zhao, 2007: Global Climate Projections. In: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M. Tignor and H.L. Miller (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.

Ivakhnenko A.G.: Group Method of Data Handling as a Rival of Stochastic Approximation Method, Journal “Soviet Automatic Control”, Nо. 3 (1968), pp. 58-72.

Ivakhnenko A.G.: Heuristic Self-Organization in Problems of Automatic Control, Automatica (IFAC), No 6 (1970), pp. 207-219

Ivakhnenko A.G.: Polynomial theory of complex systems, IEEE Trans. Sys., Man and Cyb., 1 (1971), No 4, pp. 364-378.

Madala, H.R., Ivakhnenko, A.G.: Inductive Learning Algorithms for Complex Systems Modelling. CRC Press Inc..Boca Raton, Ann Arbor, London, Tokyo. 1994

Müller, J.-A., Lemke, F.: Self-Organising Data Mining. Libri, Hamburg, 2000

Data Sources:

Ozone, aerosols, clouds:
http://toms.gsfc.nasa.gov/ozone/
Sun activity:
http://solarscience.msfc.nasa.gov/SunspotCycle.shtml
CO2:
http://www.esrl.noaa.gov/gmd/ccgg/trends/
Global warming:
http://www.cru.uea.ac.uk/cru/data/temperature/

Self-organized Model of the Atmosphere

Wed, 29 Jun 2011 11:38:20 +0200

Now as there are predictive models for key characteristics of the atmosphere - ozone concentration, reflectivity, aerosols, and atmospheric CO2 - and for sun activity as its major force, we can go a step further and put all pieces together and build a system model of the atmosphere from these variables.

The atmosphere is a complex system and there is no single simple (linear) cause-effect relationship, but the system variables are interconnected and interdependent in a not completely known way with unknown dynamics building a complex relationship pattern where it is hard to tell cause from effect. This missing a priori knowledge is a major problem for modeling the climate system which leads to a lot of assumptions and often non-holistic approaches which introduces subjectivity into modeling and results. On the other hand, essential information about the complex behavior of the atmosphere is hidden in the observational data. This knowledge about the system only needs to be extracted appropriately from the data. And this is where self-organizing knowledge mining comes in as already shown in previous posts. The idea is to let the data, only, tell us what‘s happening, objectively and autonomously.

A still debated question of major interest and importance is: „What drives Global Warming?“ Is it CO2 alone and in the supposed way or is it a mix of influences or an other single driver, the sun? Adding a sixth system variable - Global Temperature Anomalies - leads to the nonlinear dynamic system model of the atmosphere shown in the figure below. With this system model it is possible now to self-organize a predictive model of global temperature from ozone, reflectivity, aerosols, CO2, and sun activity data. The outcome of this model self-organization, however, is open at this moment...

Self-organized system model of the atmosphere as a nonlinear system of difference equations.

This system model cannot be seen as a complete description of the atmosphere. It is a starting point and it may be extended by additional system variables over time, although we think it‘s already a good and powerful initial solution. Note that this project is open and independent and there is not any financial support behind it. We would love to hear about your comments, opinions, thoughts.