Atmospheric Thermal Effect vs Greenhouse Effect

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

A recent paper of 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 July 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 July 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 (yX) for Excel 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.

Prediction of CO2 Concentration till 2030

Mon, 27 Jun 2011 10:25:47 +0200

The obtained, simple predictive model describes 99% of the observed monthly Mauna Loa CO2 records measured at an altitude of 3400 m. It shows a current average CO2 growth rate of 0.47 % per year (fig. 1):

CO2(t) = 1.018 * CO2(t-12) - 4.8.

Figure 1. Observed and predicted CO2 concentration from June 1960 to June 2030.

The annual fluctuations are due to seasonal variations in CO2 consumption by land plants. In the northern hemisphere are many more forests than in the south which is why more CO2 is removed from the atmosphere during northern hemisphere summer.

For simplicity and visibility, it is possible to smooth the data by the approximation model shown in fig. 2. This model is a common quadratic trend function of time which is valid for the displayed period of time, i.e., it should not be used for extrapolation purposes:

CO2(t) = a * (t - t0) 2 + b * (t - t0) + c,

with t - t0: number of months since June 1960 (t0), a, b, c: estimated parameters.

Figure 2. Approximated trend function describing the period June 1960 to June 2030.

Any impressions and thoughts when viewing these charts?

Prediction of Aerosol Index

Fri, 24 Jun 2011 11:17:29 +0200

Aerosols like smoke, oceanic haze, air pollution, or smog are suspensions of fine solid particles or liquid droplets in a gas. They play a key role in building clouds and are supposed to have impacts on rainfall and the climate system. Some scientists like Svensmark see a relationship between solar activity, aerosols, cloud cover, and global warming.

For describing and predictiing the global aerosol index the same data set as for ozone concentration modeling has been used for self-organizing modeling:

‣ Global Ozone concentration [DU] (Dobson Units) (x1),
‣ Global Radiative Cloud Fraction (x2),
‣ Global Aerosol Index (x3),
‣ Global CO2 concentration [ppm] (x4),
‣ Sunspot Numbers (x5).

The model shown in the image below was developed from data of the period Nov 1978 to Oct 2008 using a maximum time lag of 36 months. The data till Dec 2010 has been used ex post (out-of-sample) for model evaluation. The model represents a non-linear difference equation of 9 self-selected input variables:

x3(t) = f(x1(t-i), x2(t-j), x5(t-k)),

with i = {18, 28, 35, 36}, j = {6, 16}, and k = {29, 34, 36}. In other words, global aerosol index at a time t is described by ozone concentration, radiative cloud fraction and sun activity at certain previous points in time.

The accuracy of this best model is 81% (R2, coefficient of determination, using leave-one-out cross-validation) at a Descriptive Power of 40% and a high model robustness within the forecast horizon of Jan 2011 to Oct 2017.

The data are available on request.

Prediction of Radiative cloud fraction

Thu, 23 Jun 2011 10:18:37 +0200

The radiative cloud fraction characterizes the fraction of the incoming radiation that is scattered by clouds. It is an essential part or the complex atmospheric system so it is worth building a predictive model for it.

Again, this model was developed in a self-organizing way by extracting knowledge about the system‘s behavior from observational data, objectively. The same data set as for ozone concentration modeling has been used for this model:

‣ Global Ozone concentration [DU] (Dobson Units) (x1),
‣ Global Radiative Cloud Fraction (x2),
‣ Global Aerosol Index (x3),
‣ Global CO2 concentration [ppm] (x4),
‣ Sunspot Numbers (x5).

The model shown below was developed from data of the period Nov 1978 to Oct 2008 using a maximum time lag of 36 months. The data till Dec 2010 has been used ex post (out-of-sample) for model evaluation. The model represents a non-linear difference equation of these self-selected input variables:

x2(t) = f(x1(t-i), x3(t-j)),

with i = {1, 5, 11, 17, 19, 23, 25, 29}, j = {7, 32}. In other words, global radiative cloud fraction at a time t is described by ozone concentration and aerosol index at certain previous points in time.

The accuracy of this best model is 81% (R2, coefficient of determination, using leave-one-out cross-validation) at a Descriptive Power of 41% and a very high model robustness within the forecast horizon of Jan 2011 to Oct 2017.

The data are available on request.

Ozone Concentration Prediction

Wed, 22 Jun 2011 09:14:19 +0200

This is the first result of global ozone concentration modeling through self-organizing knowledge mining from historical data. This data set, as the only source of information, has been built from public NASA satellite data, namely the Nimbus-7, Earth Probe, and OMI spacecrafts. Overall, they cover the observational period from Nov 1978 to Dec 2010 in form of zonal, hemispheric, and global monthly mean values. In addition to these satellite data observed and predicted sunspot numbers data and observed CO2 concentration data recorded by NOAA at Mauna Loa, Hawaii, has been used for modeling so that the entire data set is composed of these 5 variables:

‣ Global Ozone concentration [DU] (Dobson Units) (x1),
‣ Global Radiative Cloud Fraction (x2),
‣ Global Aerosol Index (x3),
‣ Global CO2 concentration [ppm] (x4),
‣ Sunspot Numbers (x5).

The satellite data contain missing values, especially the entire period from May 1993 to July 1996 is missing. These data have been predicted/ interpolated by self-organized models that show an accuracy of 90% and above which is evidence that missing data is approximated well. Nonetheless, this adds noise and uncertainty to the data. However, we even have to consider the observed data as noisy data which the modeling algorithm has to deal with appropriately so the approximated missing data should not bias the results too much. (In fact, we did build models on different time periods and the results are similar.)

The model displayed in the graph below was developed from data of the period Nov 1978 to Oct 2008 using a maximum time lag of 36 months. The data till Dec 2010 has been used ex post (out-of-sample) for model evaluation. The model represents a non-linear difference equation of these self-selected input variables:

x1(t) = f(x2(t-i), x3(t-j), x5(t-2)),

with i = {0, 6, 30, 35, 36}, j = {0, 1, 4, 12, 23, 30}. In other words, global ozone concentration at a time t is described by radiative cloud fraction, aerosol index, and sunspot numbers at current and/or previous points in time.

The accuracy of this best model is 82% (R2, coefficient of determination, using leave-one-out cross-validation) at a Descriptive Power of 43% and a high model robustness within the forecast horizon of Jan 2011 to Oct 2017.

The underlying data are available on request.

Sunspot number prediction (2)

Mon, 20 Jun 2011 17:38:02 +0200

This is an update of the initial sunspot number prediction about one year ago. In this update, the actually observed sunspot numbers of the past 12 months have been added for evaluation purposes while the prediction itself remains completely unchanged. The new data observed ex post are displayed in the graph by white squares.

The prediction is a composite of three models obtained by self-organizing knowledge mining from data. Example data and models in Excel are included in this software which can be downloaded free here.

Monthly Predictions: April 2011 to March 2017

Sun, 8 May 2011 12:35:53 +0200

Beginning with this month we extend the predictions to 72 months. The overall observation from these predictions is that temperatures in most latitudinal bands will remain almost constant at the current level. Rather strong warming, however, is expected on the land mass of the pole regions (northern parts of Greenland, Canada and Russia, Antarctica).

TRENDS
Global temperature anomalies are marginally increasing from 0.4 to 0.5°C in average (0.01°C per year).
Largest expected warming is seen for the polar bands (90N-70N_lat and 90S-70S_lat) with 0.11°C and 0.067°C per year starting from high average temperature anomalies of 2.4°C for the North Pole region.
Largest expected cooling: Only marginal cooling is predicted for few bands.
Land air temperatures, exclusive the pole regions, are in average remain constant.
Sea surface temperatures are also change only slightly up or down with trend to warm, especially in the southern hemisphere.
On the northern hemisphere, except the north pole region, temperatures are almost stay constant with a light tendence to cool.
On the southern hemisphere temperatures are almost stay constant with a light tendence to warm.

Show all predictions...

About the Data
About the Prediction Models

Prediction Scenarios World Oil Price

Mon, 29 Nov 2010 14:12:15 +0100

We publish a model which predicts based on different scenarios the crude oil price from world oil consumption and total oil reserves till the year 2025. The data for this model are taken from the BP Statistical Review of World Energy 2010. The fully functional simulation running in an Excel sheet can be downloaded here free to experiment with.

We show 3 different scenarios here: A more optimistic, a more realistic, and a status-quo based, apparently more pessimistic - not worst case - one (or is this the realistic one?).

Scenario 1 - optimistic
Assumption 1: The world oil consumption will decrease by 0.1 % yearly. Note, for
comparison, that the oil consumption has been rising at a rate of 1.4 % per year
in the past 40 years.
Assumption 2: New oil reserves of 10 thousand million barrels will be exploited every year.

Figure 1.

The oil reserves are still slightly melting faster than the consumption is decreasing. So even in this scenario the era of oil comes - more slowly - to an end. The oil price is moderately rising within the ranges seen in the past. The remaining oil reserves in 2025 will last for additional 36 years.

Scenario 2 - realistic
Assumption 1: The world oil consumption will increase by 0.3 % yearly.
Assumption 2: New oil reserves of 5 thousand million barrels will be exploited every year.

Figure 2.

The gap between remaining oil reserves and oil consumption is increasing every year. The oil price is expected to reach a basic level of $80 per barrel in 2016 and $160 in 2024 without considering other influences or events. The remaining oil reserves in 2025 will last for additional 31 years.

Scenario 3 - status quo
Assumption 1: The world oil consumption will increase by 1.0 % yearly.
Assumption 2: New oil reserves of 5 thousand million barrels will be exploited every year.

Figure 3.

The outcome of this scenario, which is not worst case, seems unpredictable. In 2014, the average oil price per barrel will hit the $100 line and is exploding exponentially. This scenario assumes that the demand for oil can and will be satisfied to 100 %. If not, this would build a different case. The remaining oil reserves in 2025 will last for additional 27 years.

The bottom line is obvious: World oil consumption has to decrease from now...
If you are interested to check and try different simulations yourself download the Excel file or send us a message if you have questions.

Frank Lemke
KnowledgeMiner Software

Monthly Predictions: August 2010 to July 2013

Thu, 9 Sep 2010 09:27:44 +0200

TRENDS
Global temperature anomalies are marginally increasing from 0.4°C.
Largest expected warming is seen for the Equator bands (10N-10S_lat and 10N-10S_sst) with 0.08°C per year (0.5°C in 6 years) starting from rather lower averages of 0.4°C and 0.2°C respectively.
Largest expected cooling is seen for the North Polar bands (90N-70N_lat and 90N-70N_sst) with -0.08°C per year (-0.5°C in 6 years) from current high averages of 2.1°C respectively 1.0°C.
Land air temperatures are in average slightly growing (5 out of 9 bands show a warming, 3 a cooling).
Sea surface temperatures are in average more strongly growing (7 out of 9 bands show a warming).
On the northern hemisphere temperatures are almost stay constant with a light tendence to cool.
On the southern hemisphere temperatures in all regions are rising, with one exception. Temperatures strongly grow in the oceans including the antarctic sea.

Show all predictions...

About the Data
About the Prediction Models

Sunspot number prediction

Thu, 1 Jul 2010 14:42:48 +0200

There is no clear and final understanding if and to which extent sun activity is connected to global warming. To be able to use sun activity as one external, potential factor of influence for modeling and predicting the earth climate system a prediction of sun activity several years ahead is required, first. One common indicator used to express sun activity is the sunspot number.

The presented results below show prediction of sunspot numbers of the coming sun activity cycle till the end of year 2020. The set of dynamic, non-linear prediction models and similar patterns where autonomously developed by self-organizing knowledge extraction from data technologies. The data used for modeling originates from NASA representing monthly observations of sunspot numbers back to the year 1749.

The prediction from NASA for the same period can be found here.

DOWNLOADS:
High-resolution image (PDF 1.9 MB)

Predictions (Tab separated text file 5 KB)

Does current global temperature significantly depend on the oceans temperature in 1980 and before?

Tue, 22 Jun 2010 10:07:53 +0200

We publish a set of 21 self-organized models used for the most recent monthly predictions (Jan. 2010 to Dec. 2012). They are implemented in Excel sheets so that you can not only view and analyze them but also get an idea of the complex interdependencies and dynamics between the different region models, and you can, for example, do what-if type predictions yourself right in the sheets.
These models are result of high-end knowledge extraction from data technologies, which means that the only source for the models has been the information inherently stored in historical temperature measurements. In other words, the models show what the - incomplete and therefore noisy - observed temperature DATA can tell us.

One most interesting finding is the very long memory of most of the influencial variables of the models both for land air and sea surface temperatures. They show that the current temperatures around the globe can be well described by the temperature of the oceans, especially, 360 months or more back in history (= 30+ years ago). So the state of the oceans in 1980 and before seems to have an major impact on today's climate in general. Other influences and relations are expected to exist, too, like green house gas emissions, or sun activity, but this data has not been used for these models (or has not been available to us) so such influences couldn't be expressed in the available models.

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View models and graphs (PDF 2.1 MB) - best for mobile devices

Full functional models in Excel (zip 4.6 MB) - best for desktop computers

Note: This is free copyrighted work.

Predicting global, northern and southern hemispheric means 84 months ahead.

Fri, 26 Feb 2010 17:38:42 +0100

The first results from a small set of self-organized non-linear prediction models along with the self-selected relevant inputs used in the models. The models (the equations) are also available on request.

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About the Data
About the Prediction Models