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Global Warming which Some Call Climate Change.

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Part III F.  Global Warming which some call Climate Change.


D1. Introduction

D2. Climate Impacts

D3. Economic Impacts

D4. Reports from Around the World

D1. Introduction

To understand climate one needs to understand:

  • Secular trends such as Global Warming
  • Major climate cycles
  • Unusual events such as a volcanic eruption which can produce cooling for an extended period of time.

Global Warming is very important but we have a large number of scientists studying it and it is a topic that is of interest to the media so it is not likely to be ignored. But Global Warming does not proceed on a year by year basis at a predictable rate. We have on average a positive differential between solar energy reaching the Top of the Atmosphere (TOA) and the reflected or radiative energy escaping into outer space. The size of this imbalance is not very large but it is cumulative. It is in the range annually of about a half a watt per square meter out of perhaps 340 watts per square meter measured at the TOA. This creates what is called a secular warming trend. But the earth rotates and we have mountains and oceans. Thus for any spot on the Earth, the timeline of the path towards warmer is irregular until you analyze it and realize that it is following certain cyclical patterns which need to be superimposed on the secular warming trend to predict the path forward of temperatures as well as precipitation and other things that directly or indirectly result from or determine our climate.

Most cycles are pretty much orphans with respect to much of the academic community and do not captivate the public imagination. El Nino and La Nina which are phases of the ENSO Cycle are the notable exception to this. Most other cycles are rarely in the news and less than adequately integrated into weather forecasting models. And yet these cycles tend to have significant economic impacts because they impact different parts of the Earth differently at any one point in time and tend to be equal opportunity blessings or curses. The benefits they provide in one part of the World tend to be offset by misery somewhere else. So I have tried to focus attention on these cycles in Part II of my report.

In Part III I am mainly addressing impacts.

But first a discussion of the challenge of dealing with a changing climate both Low Frequency Cycles and the Secular Warming Trend.

Here is the write-up that describes the prior system which includes this sea surface temperature graphic.


And here is the write up on the revised system and the resulting sea surface temperature historic values.


So that is the change to the overall assessment of Sea Surface Temperatures (improved measurements and reanalysis) which then changes the assessment of anomalies. They are not major changes but enough to change calculations. These two articles Part I and Part II (Part I is of most interest) are the basis for the current change but are not available to the public without a payment.  But the abstracts are available and they are probably all one needs to read but I do not like the principle of government employees writing articles and using the results to modify information that is made available to the public and then having access to those articles only available to those with access via subscriptions of their employer or by paying a fee. Is that fee shared with the government employee? Just asking. In lieu of access to the full articles used this time, this article and this.article which relate to the prior revision might be of interest. The abstracts for the current supporting articles are available so here they are:

The Abstract for Part I

The monthly Extended Reconstructed Sea Surface Temperature (ERSST) dataset, available on global 2° x  2° grids, has been revised herein to version 4 (v4) from v3b. Major revisions include updated and substantially more complete input data from the International Comprehensive Ocean;Atmosphere Data Set (ICOADS) release 2.5; revised empirical orthogonal teleconnections (EOTs) and EOT acceptance criterion; updated sea surface temperature (SST) quality control procedures; revised SST anomaly (SSTA) evaluation methods; updated bias adjustments of ship SSTs using the Hadley Centre Nighttime Marine Air Temperature dataset version 2 (HadNMAT2); and buoy SST bias adjustment not previously made in v3b.

Tests show that the impacts of the revisions to ship SST bias adjustment in ERSST.v4 are dominant among all revisions and updates. The effect is to make SST 0.1° - 0.2°C cooler north of 30°S but 0.1°-2° C warmer south of 30°S in ERSST.v4 than in ERSST.v3b before 1940. In comparison with the Met Office SST product [the Hadley Centre Sea Surface Temperature dataset, version 3 (HadSST3)], the ship SST bias adjustment in ERSST.v4 is 0.1°-0.2°C cooler in the tropics but 0.1°- 0.2°C warmer in the midlatitude oceans both before 1940 and from 1945 to 1970. Comparisons highlight differences in long-term SST trends and SSTA variations at decadal time scales among ERSST.v4, ERSST.v3b, HadSST3, and Centennial Observation-Based Estimates of SST version 2 (COBE-SST2), which is largely associated with the difference of bias adjustments in these SST products. The tests also show that, when compared with v3b, SSTAs in ERSST.v4 can substantially better represent the El Nino/La Nino behavior when observations are sparse before 1940. [Editor's Note: That is why I would have liked to have had access to the full articles which I requested from NOAA but never received a reply. Do I need to file a FOIA 5 U.S.C. § 552 Request?] Comparisons indicate that SSTs in ERSST.v4 are as close to satellite-based observations as other similar SST analyses.

The Abstract for Part II

Described herein is the parametric and structural uncertainty quantification for the monthly Extended Reconstructed Sea Surface Temperature (ERSST) version 4 (v4). A Monte Carlo ensemble approach was adopted to characterize parametric uncertainty, because initial experiments indicate the existence of significant nonlinear interactions. Globally, the resulting ensemble exhibits a wider uncertainty range before 1900, as well as an uncertainty maximum around World War II. Changes at smaller spatial scales in many regions, or for important features such as Nino-3.4 variability, are found to be dominated by particular parameter choices.

Substantial differences in parametric uncertainty estimates are found between ERSST.v4 and the independently derived Hadley Centre SST version 3 (HadSST3) product. The largest uncertainties are over the mid and high latitudes in ERSST.v4 but in the tropics in HadSST3. Overall, in comparison with HadSST3, ERSST.v4 has larger parametric uncertainties at smaller spatial and shorter time scales and smaller parametric uncertainties at longer time scales, which likely reflects the different sources of uncertainty quantified in the respective parametric analyses. ERSST.v4 exhibits a stronger globally averaged warming trend than HadSST3 during the period of 1910 - 2012, but with a smaller parametric uncertainty. These global-mean trend estimates and their uncertainties marginally overlap.

Several additional SST datasets are used to infer the structural uncertainty inherent in SST estimates. For the global mean, the structural uncertainty, estimated as the spread between available SST products, is more often than not larger than the parametric uncertainty in ERSST.v4. Neither parametric nor structural uncertainties call into question that on the global-mean level and centennial time scale, SSTs have warmed notably.

I do not think the following change just happened but it is also important. For most purposes, climatology (average and distribution of conditions over a thirty-year period) is redefined the end of every decade. For purposes of calculating ONI, sea surface temperature climatology is recalculated every five years. You can read more about this here

Average SST

In one sense updating every five years should be a good thing. It means the base against which anomalies are calculated is adjusted every five years. Notice that over a 45 year period, SST in the Nino 3.4 region have increased by about 0.5C. The threshold for an El Nino condition is +0.5C. So without this adjustment to the base used for comparison, there would be a strong bias towards ONI's being El Nino-ish. The possible bad thing is the implied assumption that the base is changing due to Global Warming which excludes the impacts of the PDO and other low-frequency cycles. Given that most ocean cycles appear to be more or less of 60 year length, the definition of "climate" as being a "30" year slice of weather is problematic and then updating this for one purpose very decade and for another purpose very five years makes one wonder if this approach is sound.


And please do not interpret what I am saying in the most extreme way possible. The reason for characterizing the phases of the ENSO cycle as being El Nino, La Nina and Neutral, is the utility of these designations in predicting weather particular winter weather. It is not clear to me that these adjustments add to the predictive ability of these labels. In Asia, they measure the phases of ENSO differently and they have paid more attention to what might be called the flavors of El Nino defining what they call a Modoki which is probably about the same thing as what is called in the U.S. a Central Pacific El Nino or Date Line El Nino and they have gone even further to define two kinds of El Nino Modoki. The near El Nino of 2014/2015 which NOAA has now correctly "undeclared" was IMO a Modoki Type II and may have been a powerful Warm Event but impacting parts of the Equator that are not the Nino 3.4 measurement region where the ONI is calculated and also areas towards the Northeast  Pacific where it is difficult to separate the Tropical Event from a possible change in the PDO. BobTisdale has recently discussed part of this in this post  but I read the situation a little differently than he does. But he has started the conversation and that is very useful.

So I am concerned that the urge to measure may be distracting NOAA from the reason why we measure.  And it is important to understand that NOAA is charged with predicting weather in the U.S. and the Japanese Meteorological Agencies are mostly interested in Japan, Australian Meteorological Agencies in Australia etc and the best constructs for each may differ slightly. That is why the definition of ENSO events differs around the World. Different definitions have been found to be more useful for their geographies of interest.

Other Broader Impacts

I have to say that I think this change by NOAA and other meteorological agencies (some of which took place earlier this year or even late last year) although possibly very appropriate impacts many things. I think it compromises essentially all scientific research ever done on the Pacific (and other oceans)  in this sequence from most impacted to least impacted:

A. Any research that depends on what periods of time are designated as ENSO El Nino, La Nina or Neutral Events. Clearly those designations are not permanent but subject to revision every five years.

B. Any research that depends on ONI values ie. if an ONI value is an independent variable in any paper or model, that work has to be redone to be accurate.

C. Any research that depends on sea-surface-temperatures (SST's) from 60 degrees North Latitude to 60 degrees South Latitude in any ocean. So again to the extent historical sea surface temperatures were used in any research paper or computer model, they are no longer necessarily valid. I am not saying that everything is invalidated but I am saying that everything is impacted and might be either invalidated or alternatively further confirmed. .

In the private sector, there is a EPA process for making this type of change to the environment. Perhaps in government there needs to be a a similar process to determine if a change in a historical data series is sufficiently important to invalidate all scientific research that was based on the prior historical data series. One assumes that it is always better to have more accurate data than less accurate data. But is the incremental advantage always worth the incremental cost of redoing work or dealing with work that we know is less than as accurate as it could be? Not only is our climate changing, but our ability to make comparisons has gotten a whole lot more complicated. Thus we may need some rules for the road on how changes in data series are handled.

D2. Impact on Climate

Global Warming Update

I was not aware that the University of Alabama at Huntsville, one of three semi-independent parts of the University of Alabama, published temperature analyses but apparently they do and are about to issue a new set that is described as being a Beta Version at this point in time. Dr. Roy Spencer is part of this entity The National Space Science and Technology Center at UAH. You can find Dr. Spencer's full report here. It is interesting in a number of ways.

From the Abstract:

Version 6 of the UAH MSU/AMSU global satellite temperature dataset is by far the most extensive revision of the procedures and computer code we have ever produced in over 25 years of global temperature monitoring. The two most significant changes from an end-user perspective are (1) a decrease in the global-average lower tropospheric (LT) temperature trend from +0.140 C/decade to +0.114 C/decade (Dec. ’78 through Mar. ’15); and (2) the geographic distribution of the LT trends, including higher spatial resolution. The 0.026 C/decade reduction in the global LT trend is due to lesser sensitivity of the new LT to land surface skin temperature (est. 0.010 C/decade), with the remainder of the reduction (0.016 C/decade) due to the new diurnal drift adjustment, the more robust method of LT calculation, and other changes in processing procedures.

This is a very interesting graphic of temperature anomalies.

UOA Temperature Trend Map

This information is not in any way a challenge to Global Warming. It is simply an improvement in the interpretation of satellite imagery and removing the impact of surface (skin) temperature from the estimate of the temperature of near-surface lower troposphere (LT). The analysis comes with some some interesting commentary.

"The gridpoint trend map above shows how the land areas, in general, have warmed faster than the ocean areas. We obtain land and ocean trends of +0.19 and +0.08 C/decade, respectively. These are weaker than thermometer-based warming trends, e.g. +0.26 for land (from CRUTem4, 1979-2014) and +0.12 C/decade for ocean (from HadSST3, 1979-2014).

A number of things stand out.

  1. The greater warming of the Arctic Region than Antarctica which is a bit hard to explain but may be related to ocean cycles.
  2. The greater warming of land areas to ocean which is not a surprise but apparently there is a reinforcing mechanism so that there has been more warming in the Northern Hemisphere than in the Southern Hemisphere.
  3. This data does not adjust for Ocean Cycles but 1979 - 2015 (I think the 2015 is a typo and should be 2014) represents 36 years so it probably includes parts of different phases of ocean cycles and a further adjustment might move some of the warmer and cooler areas around a bit but will not change the overall conclusion. You can clearly see a PDO- signal in the Pacific which is a bit surprising as more of the period 1979 - 2014 was PDO+. But the number of years in the two phases is not very different. But it is still interesting remembering that this graphic reflects changes from "climatology" and the "climatology" used as the base might be biased to certain phases of the ocean cycles. I probably do not have the time or interest in sorting that out. 

It has been getting warmer! The rate may be less than some have previously estimated but it is still getting warmer or at least was getting warmer in the period analyzed. Some of the locations are a bit of a surprise. I expected to see the warming along 30N and 30S and that is not always the case and my guess is that land mass is again the factor. Where there is a lot of land mass above 30N or 30S, it has shifted the warming towards the greater land mass. This might be a factor in the difficulty the IPCC participants have had producing models that work even though it is well known that the ITCZ is impacted by land mass. I may discuss that topic in a future edition of the GEI Weather and Climate Report.

What is happening to our Oceans?

From Japan Met Global Environment and Marine Department Report 

"The annual mean annual global average sea surface temperature in 2013 was +0.13°C above the 1981-2010 average, making it the 2nd highest since 1891. The long-term trend of the global average sea surface temperature for 1891-2013 was about 0.51°C per century. Although positive anomalies above the long-term trend have frequently been observed since the late 1990s, the global average sea surface temperature has generally remained at similar levels since the early 2000s."

Global Sea Surface Temperature Trends

Here is another more short-term oriented chart.

Tisdale Sea Surface Temperatures

Notice the spike in August. This needs to be watched.

And this is a very interesting table (you can click on it to enlarge it) prepared by Bob Tisdale and you can find the full article here.

Table 1

It shows the results of running the latest IPCC models from 1981 (using the RCP 6.0 scenario which in my mind is probably the most realistic of the four) and compares the model results with the observed values. This provides a lot of useful information. 1981 was an ENSO neutral year and PDO had recently turned positive and the AMO was solidly negative. The starting point is very significant when comparing observed values with the IPCC model results because the IPCC models have no ability to integrate low-frequency cycles like the AMO and questionable ability to integrate medium frequency cycles like ENSO.

So you see that:

A. Overall the models have been high by about 100% re sea surface temperatures. That is a large forecasting error. 

B. Probably much of this has to do with the AMO which unlike many ocean cycles actually has a warm phase and a cold phase. This is different than the PDO which is really about the geospacial distribution of warm and cold water rather than the warming and cooling of the Pacific although the index every once in a while is adjusted to reflect the overall warming of the Pacific.

So you can see there is a lot of variability in the warming of the sea surface temperatures. There has been a lot of discussion about sub-surface temperatures but we have even less monitoring of that overall and weather is determined by the surface temperature. The subsurface temperature is a good guide to what future surface temperatures will be.

I found this graphic developed by Bob Tisdale quite interesing and his full article on this can be found here.

04 S. Atl.-Ind-W. Pac. SSTa

He and I strongly disagree on how to interpret the above. Bob Tisdale seems to be taking the position that ocean cycles not GHG are the cause of Global Warming. I disagree but do agree that ocean cycles influence the timing of both SST and atmospheric warming. The staircase phenomenon was discovered by British Met and I wrote about it at that time and my article can be found here.

What is Happening to Precipitation

Most scientists think of this in terms of the transfer between oceans and land. And the way to look at it is by considering E (evaporation) and P (precipitation). Like in economics where Savings must always equal Investment when looked at on a global basis, with water E must equal P except for increases in the amount of water stored in the atmosphere which can hold more water if warmer. In the short term we can assume that E = P. So one can then look at the oceans where E is always greater than P because clouds are driven by winds on shore. Thus we conclude that this shortfall between P and E over oceans implies that P will be greater than E over land to balance the equation. You will find this in all climate models. If there were no winds, then E would equal P over oceans and E would equal  P over land. Warming might cause E to increase but P would follow. The atmosphere as it warms can hold more moisture but other than that, which is important, there is no way that more evaporation will not result in more precipitation.

Now, where the precipitation falls is another and more complicated question. The simplistic notion is that those who get, get more but that is only a starting point for investigation. One can look at water availability a lot like a trade equation with imports and exports of moisture.  Since Warming will not occur evenly, the export of moisture will also not occur evenly. But you can not export that which you did not import unless you have glaciers or lakes. The surface of the earth does not store much water compared to the total amount of activity. So to a large extent, Global Warming models are attempting to address the question of changing moisture import and export patterns. It would be very helpful if those involved with water planning understood this very simple principle.

From a recent report on the causes of the three year California Drought which can be found here.

Seager RCP8.5 38 model mean

1. Geologic Time Perspective on Temperature

This is an interesting article.  It relates past episodes of sudden Global Warming to the current one.

Click here for a larger view.

The Hiatus

Before I understood this mechanism, I wrote the following article. Has Global Warming Ended? Of course it has not. It cannot end while we have more energy reaching the Top of the Atmosphere (TOA) than is escaping from the Top of the Atmosphere. But the vast majority of that energy is being absorbed by our oceans and only released by the El Nino Phase of the ENSO cycle. But in 1997/1998 we had a major release. If we understood that better, we might be able to make better medium-term predictions. One suggestion for further improving the author's correlation analysis (which is based on a four-month lag between the moving average of SOI and the twelve month moving average of the MGT) is to include the PDO or IPO Index. That index may be a surrogate for the proportion of Modoki events which may be a factor in the effectiveness of El Nino to release heat and thus the usefulness of the SOI to predict the release of heat.

Here is a graphic from my earlier article with this graphic coming from this source which was compiled jointly by the Climatic Research Unit and the UK Met. Office Hadley Centre.

You can see how the change in temperature has been a staircase type of thing with major changes followed by relative stability. What the authors above have done is show the relationship with the SOI so that is the new piece of information although it is a couple of years old but not widely reported on or discussed.

Global Air Temperatures

Perhaps the following from a paper by Chris R. de Freitas and John D. McLean (you can find the full paper here) might help in understanding some of the important differences between an El Nino and a La Nina. It does not address the variations in El Nino and to a lesser extent La Nina related to Modoki-ish forms of those events.

"During El Niño conditions, there is a decrease in Walker Circulation, an increase in meridional Hadley Cell Circulation and intensification of subtropical highs. A more vigorous overturning of the Hadley Cell Circulation leads to an increase in heat transfer from tropical to higher latitudes in both hemispheres. In contrast, during La Niña conditions the Hadley Cell Circulation diminishes and the Walker Circulation is enhanced, with well-defined and vigorous rising and sinking branches in the western and eastern extremes of the Pacific Ocean respectively. This results in stronger than normal easterly equatorial surface winds. In later work, [researchers] found that during La Niña conditions the Hadley Cell Circulation in both hemispheres weakens. The anomalies in the strength of the Hadley Cell Circulation are also strongly and inversely correlated with the anomalies in the strength in the Walker Circulation. As meridional circulation changes, there are global teleconnections, although the physical processes causing the linkages are often unclear. Through these variations in zonal and meridional transfer and the various teleconnections, the ENSO signal is correlated with global climate variation, which in turn is reflected in global temperature."

Will this change due to Global Warming?

Climate Change News

Food System Shock

This should be of interest.

Marine defaunation: Animal loss in the global ocean

Douglas J. McCauley1,*,    Malin L. Pinsky2,    Stephen R. Palumbi3,    James A. Estes4,    Francis H. Joyce1,    Robert R. Warner1


Comparing patterns of terrestrial and marine defaunation helps to place human impacts on marine fauna in context and to navigate toward recovery. Defauna­tion began in earnest tens of thousands of years later in the oceans than it did on land. Although defaunation has been less severe in the oceans than on land, our effects on marine animals are increasing in pace and impact. Humans have caused few complete extinctions in the sea, but we are responsible for many ecological, commercial, and local extinctions. Despite our late start, humans have already powerfully changed virtually all major marine ecosystems.


Humans have profoundly decreased the abundance of both large (e.g., whales) and small (e.g., anchovies) marine fauna. Such declines can generate waves of ecological change that travel both up and down ma­rine food webs and can alter ocean ecosystem functioning. Human harvesters have also been a major force of evolutionary change in the oceans and have reshaped the genetic structure of marine animal populations. Climate change threatens to accelerate marine defaunation over the next century. The high mobility of many marine animals offers some increased, though limited, capacity for marine species to respond to climate stress, but it also exposes many species to increased risk from other stressors. Because humans are intensely reliant on ocean ecosystems for food and other ecosystem services, we are deeply affected by all of these forecasted changes.

Three lessons emerge when comparing the marine and terrestrial defaunation experiences: (i) today’s low rates of marine extinction may be the prelude to a major extinction pulse, similar to that observed on land during the industrial revolution, as the footprint of human ocean use widens; (ii) effectively slowing ocean defaunation requires both protected areas and careful management of the intervening ocean matrix; and (iii) the terrestrial experience and current trends in ocean use suggest that habitat destruction is likely to become an increasingly dominant threat to ocean wildlife over the next 150 years.


Wildlife populations in the oceans have been badly damaged by human activity. Nevertheless, marine fauna generally are in better condition than terrestrial fauna: Fewer marine animal extinctions have occurred; many geographic ranges have shrunk less; and numerous ocean ecosystems remain more wild than terrestrial ecosystems. Consequently, meaningful rehabilitation of affected marine animal populations remains within the reach of managers. Human dependency on marine wildlife and the linked fate of marine and terrestrial fauna necessitate that we act quickly to slow the advance of marine defaunation.

The marine defaunation experience is much less advanced, even though humans have been harvesting ocean wildlife for thousands of years. The recent industrialization of this harvest, however, initiated an era of intense marine wildlife declines. If left unmanaged, we predict that marine habitat alteration, along with climate change (colored bar: IPCC warming), will exacerbate marine defaunation.

And here is a critique of this and similar articles. There is always a challenge to differentiate between the observed and the potential impacts of Global Warming. 

Biosphere may not be a GHG Sink   Interesting but potentially terrible news.

Re the above article the difference between GWP and GTP may be important. A good discussion can be found here 

From that discussion:

GWP  is  defined  as  the  increase  in  radiative  forcing  (RF)  of  the  emission  of  one  kilogram  of  the subject gas, relative to the increase in RF from release of one kilogram of carbon dioxide at the  same  time.  Changes  in  radiative  forcing  drive  climate  change  but  the  relationship  is not  simple,  there  are  many  environmental  interactions  (including  feedbacks),  and  the calculation   of   global   temperature   change   resulting   from   changes   in   radiative   forcing requires complex mathematical models.

Because there are many processes that remove carbon dioxide from the atmosphere, some of  which  have  environmental  lifetimes  in  excess  of  10,000  years,  GWP  is  integrated  over  a specific time horizon and the Kyoto Protocol uses the 100 year values.

Compared  to  GWP,  the  Global  Temperature  change  Potential  (GTP)  goes  one  step  further down   the   cause -effect   chain   and   is   defined   as   the   change   in   global   mean   surface  temperature at a chosen point in time in response to an emission pulse — relative to that of carbon dioxide. While GWP is integrated in time, GTP is an end-point  metric which is based on temperature change for a selected year. Thus GWP integrates the effects up to a chosen time horizon, giving equal weight to all times up to the horizon and zero weight thereafter, but GTP gives the temperature just for one chosen year with no weight on years before or after.

By accounting for the climate sensitivity and the exchange of heat between the atmosphere  and  the  ocean,  the  GTP  includes  physical  processes  that  the  GWP  does  not.  However,  this  makes  it  sensitive  to  atmospheric  modeling  assumptions  about  the  climate  sensitivity  and heat-uptake by the ocean. There are significant uncertainties related to both GWP and GTP and  the  relative  uncertainties  are  much  larger  for  GTP;  there  are  additional  contributions from  it  being  further  down  the  driver-response-impact  chain  and  from  the  inclusion  of  climate response

Here is part of the table from this analysis. 

GWP  versus GTP  presented in June 6, 2016 report.

As  you can see the big difference relates to methane because the GTP better takes into account the shorter half-life of Methane. On the other hand it shows that nitrous  oxide is not reduced in importance by the half life because the half life for nitrous oxide is quite long as compared to methane. This suggests that there is going to be pressure to reduce the use of nitrogen based fertilizers. 

One can find this information in many places including directly in the IPCC AR5 WGI, but this discussion from the European Fluorocarbons Technical Committee is easy to follow so I have used that for this discussion.


Sunspots. Do they impact climate?

I thought I would discuss something a little different. Sunspot Cycles. But first I should explain that sunspots although they represent cooler areas on the Sun are surrounded by brighter warmer areas called faculae. Sunspots are actually associated with more irradiation reaching the Earth. It is the opposite of what you might think.

The long sunspot cycle 23 predicts a significant temperature decrease in cycle 24

Jan-Eric Solheim, Kjell Stordahib, Ole Humlum Full report can be found here


Relations between the length of a sunspot cycle and the average temperature in the same and the next cycle are calculated for a number of meteorological stations in Norway and in the North Atlantic region. No significant trend is found between the length of a cycle and the average temperature in the same cycle, but a significant negative trend is found between the length of a cycle and the temperature in the next cycle. This provides a tool to predict an average temperature decrease of at least 1.0°C from solar cycle 23 to solar cycle 24 for the stations and areas analyzed. We find for the Norwegian local stations investigated that 25–56% of the temperature increase the last 150 years may be attributed to the Sun. For 3 North Atlantic stations we get 63–72% solar contribution. This points to the Atlantic currents as reinforcing a solar signal.


► A longer solar cycle predicts lower temperatures during the next cycle. ► A 1 °C or more temperature drop is predicted 2009–2020 for certain locations. ► Solar activity may have contributed 40% or more to the last century temperature increase. ► A lag of 11 years gives maximum correlation between solar cycle length and temperature.


In addition to the relation between solar cycle length and the amplitude of the next Rmax, it is reasonable to expect a time lag for the locations investigated, since heat from the Sun, amplified by various mechanisms, is stored in the ocean mainly near the Equator, and transported into the North Atlantic by the Gulf Stream to the coasts of Northern Europe. An example of time lags along the Norwegian coast is an advective delay between the Faroe-Shetland Channel and the Barents Sea of about 2 years determined from sea temperature measurements (Yndestad et al., 2008).

Formation of NADW represents transfer of upper level water to large depths. The water is transported and spread throughout the Atlantic and exported to the Indian and Pacific oceans before upwelling in Antarctic waters. The return flow of warm water from the Pacific through the Indian ocean and the Caribbean to the North Atlantic, a distance of 40,000 km, takes from 13 to 130 years (Gordon, 1986). There appears to be solar “fingerprints” that can be detected in climate time series in other parts of the world with each series having a unique time lag between the solar signal and the hydro-climatic response. Perry (2007) reports that a solar signal composed of geomagnetic aa-index and total solar irradiance (TSI) is detected with various lags from 0 years (Indian Ocean) to 34 years (Mississippi river flow) and 70 years (Labrador Sea ice). Mehl [sic G. A. Meehl] et al. (2009) have shown that two mechanisms: the top-down stratospheric response of ozone to fluctuations of shortwave solar forcing and the bottom-up coupled ocean–atmospheric surface response, acting together, can amplify a solar cyclical pulse with a factor 4 or more. Since our stations are located near or in the North Atlantic, solar signals in climatic time series may arrive with delays of decades. If we can detect a solar signal and measure the delay for individual regions, we may have a method for future climate predictions.

Recognizing that averaged temperature series from different meteorological stations of variable quality and changing locations may contain errors, and partially unknown phenomena derived from the averaging procedure, Butler (1994) proposed instead to use long series of high quality single stations. This might improve the correlation between SCL and temperature.

He showed that this was the case when using temperature series obtained at the Armagh Observatory in Northern Ireland 1844–1990. Since the Armagh series correlated strongly with the NH temperature, he concluded that this indicates that solar activity, or something closely related to it, has had a dominant influence on the temperature of the lower atmosphere in the Northern Hemisphere over the past 149 years (Butler, 1994). This investigation was later expanded to the period 1795–1995 by Butler and Johnston (1994), who found a relation tARM=14.42−0.5LSCtARM=14.42−0.5LSC, where LSC is the smoothed length of the solar cycle determined by the 1-2-2-2-1 filter. They concluded that the good fit over nearly 200 years indicated that solar activity had been the dominant factor for nearly two centuries."

I do not know what to make of this. Clearly these Norwegian researchers are challenging the Anthropogenic Global Warming Theory or at least that GHG is the primary cause of Warming at least with regards to parts of the North Atlantic. I have not gone into it, but these papers are related to the workings of the North Atlantic Oscillation (NAO). 

As an aside, a well known economic forecaster includes the sunspot cycle in his analysis. I am not sure how he sees this impacting the economy. But weather does impact the economy. It is on my list of things to pursue.

And here is another take which is based on the work of Dr. Judith Lean and is related to what is called the Maunder Minimum of solar activity (approximately 1645 - 1715 some say 1705) which is generally associated with the Little Ice Age (LIA) which occurred at about the same time perhaps with a slight lag. But this article also looked at more recent times.  It gives more weight to changes in solar irradiance impacting Warming than the IPCC. From that article.

"The Last 25 Years

Lean's study found that "solar forcing may have contributed about half of the observed 0.55°C surface warming since 1860 and one third of the warming since 1970". However, lest we take unwarranted comfort from the fact that the Sun seems most important and anthropogenic warming is less than originally estimated, keep in mind that if the Sun controls substantial climate fluctuations by changing its brightness by only 0.25%, a change of more than 1 percent in virtual brightness (from trace greenhouse gases like CO2 and CH4) could have a considerably greater impact. The fact is we do not know for sure which will have the greater effect, but it is well to remember that the reconstruction of sunspots, their relationship to solar energy output, and the link to overall background brightness are areas of science that are still changing. Thus, solar contributions may be much less (or a somewhat more) than those currently estimated. In any case, the conclusion that can be taken from this discussion is that the warming since 1975 is outside the range of a purely solar effect and may safely be ascribed to a strong anthropogenic component."

Here is the article SOLAR INFLUENCES ON CLIMATE  L. J. Gray, J. Beer, M. Geller J. D. Haigh, M. Lockwood, K. Matthes U. Cubasch, D. Fleitmann, G. Harrison, L. Hood, J. Luterbacher. G.A. Meehl, D. Shindel, B. van Geel, and W. White which I believe is the basis for the IPCC view on the matter.  I think it is both comprehensive and well prepared.  But it was published prior to the Solheim article and thus does not incorporate that information. It begins by focusing on the small variance in Total Solar Irradiance (TSI) at the Top of the Atmosphere (TOA) which according to this multi-author paper is in the order of 0.17 Watts/square-meter. The estimate in that paper of total solar incoming is 239 watts/square-meter. The variance of 0.17 is only 0.07% of 239 so that is pretty much what appears in the IPCC where sunspot cycles are declared to have minimal impact on climate based on the above calculation or similar. But the changes due to sunspot activity are not all in the same wavelength so those changes may be as much as 6% for certain wavelengths such as ultraviolet. And then you have what is called Solar Energy Particles (SEP) and Galactic Cosmic Rays (GCRs) which some believe can serve as cloud condensation nuclei (CNN) so it gets complicated really quick. It gets even more involved as you consider the impact or lack thereoff on ozone and other characteristics of the Stratosphere. The following graphic is simply to illustrate sunspot cycles. We are in Cycle 24 now.

Sunspot Numbers inversely related to cosmic rays

I am neither convinced that the Solheim et al article is valid or invalid but I observe that it is based on one geographical area. And I think the Gray et al article acknowledges that this type of impact has been observed in certain geographical areas especially in areas close to the Poles. Also the possible alternating impact of solar cycles is not surprising as a larger or smaller than average cycle is likely to be followed by one that reverts to the mean. And the 11 year nature of the solar cycle can easily be confused with the ENSO cycle. So I am neither buying into this concept or rejecting it but presenting it because it illustrates the complexity of climate.

I was originally not going to present the work of Richards et al which would appear to be predicting 75 years of reduced sunspot activity from current levels here. But I changed my mind and here is the Abstract. It certainly challenges the claim that all scientists believe that Global Warming is Anthropogenic.

The recent paucity of sunspots and the delay in the expected start of Solar Cycle 24 have drawn attention to the challenges involved in predicting solar activity. Traditional models of the solar cycle usually require information about the starting time and rise time as well as the shape and amplitude of the cycle. With this tutorial, we investigate the variations in the length of the sunspot number cycle and examine whether the variability can be explained in terms of a secular pattern. We identified long-term cycles in archival data from 1610 - 2000 using median trace analyses of the cycle length and power spectrum analyses of the (O-C) residuals of the dates of sunspot minima and maxima. Median trace analyses of data spanning 385 years indicate a cycle length with a period of 183 - 243 years, and a power spectrum analysis identifies a period of 188 ± 38 years. We also find a correspondence between the times of historic minima and the length of the sunspot cycle, such that the cycle length increases during the time when the number of spots is at a minimum. In particular, the cycle length was growing during the Maunder Minimum when almost no sunspots were visible on the Sun. Our study suggests that the length of the sunspot number cycle should increase gradually, on average, over the next ∼75 years, accompanied by a gradual decrease in the number of sunspots. This information should be considered in cycle prediction models to provide better estimates of the starting time of each cycle.

This is the key graphic in this paper. The third row is probably of most interest as that is described by the author as being the best fit with the data. Notice it does not fit with the Oort Minimum but does fit the others. That is the challenge of trying to fit a regular sinusoidal cycle to historical data. If you accept that there is predictive value to this approach, then the dashed line on the third row is a prediction of sunspot cycle length with is inversely related to number of sunspots which is in turn inversely related to the impact on the temperature of Planet Earth. So Richards et all are projecting a cooling influence for the next 75 years and actually longer since it will take longer to return to Year 2000 levels namely close to 188 years. I was reluctant to include this article because it so much stands apart from other research, but I concluded that it would be wrong to suppress information. Let the reader be the judge.

Richards Sunspot History

I generally am able to figure out the science behind the papers that I refer to in this weather and climate column. But I acknowledge that I am not enough of an astrophysicist to offer an opinion on the validity of these very complicated papers. But I find the premise very interesting. I suspect that the math (and some other aspects of this research) is too daunting for the Main Stream Media - even that part of the MSM that is science oriented. But I believe that there are some readers of this column who can post on what they think of these papers. It does not appear to me to be a small group of researchers who are involved in this sort of research but a fairly large group. As such their work can not simply be ignored.

D3. Observed Economic Impacts.

To set the stage it is useful to talk about population projections.

World Population Projections

It shows that there is a range of possible outcomes for population growth from 5 billion to 13 billion in this Century with 9 billion being the peak arrived at by this study which may be a bit dated. I have my own ideas as to where that process is going but my ideas on the most reasonable projection to use are not relevant today but are with respect to the book that I am writing. The original of this graphic can be found here. The point today is simply that understanding climate is very important. Weather impacts us today but climate will have a major impact on the history of the World this Century.

Impact of Climate Change on Food Security

This information comes from the latest IPCC Report on the Impacts, Adaptation and Vulnerability resulting from Climate Change and the draft report can be found here and the Final Report can be found here. The Table below is difficult to read as it often contains two scenarios (an alternative in parentheses both in the Yield and Scenario Identification columns).  Some data is based on models that do not reflect the increase in carbon dioxide level which generally stimulates plant growth and may also improve water utilization but the table is clear on which studies incorporate or do not incorporate the impact of carbon dioxide.

As you can see, there is a lot of variation in these estimates. So far I have not been able to sort out this data but according to the text in the full report, the correct answer is less wheat, much less corn and more rice. It seems that a key factor is the type of photosynthesis involved which would appear to change the C3/C4 ratio of plant types as carbon dioxide levels increase. But there are other factors. A good resource for understanding this can be found here or here. It is very complicated. But I am surprised that so far as I know there has not been a model created that integrates the climate forecasts of temperature, precipitation (including seasonal variations), and carbon dioxide levels to expected changes in the C3/C4 ratio for each geographical area. I think it is feasible to create such a model and then calibrate it with the historical record of what has grown where at different points in time with different values of the key parameters. This would provide estimates of crop yields and water requirements.

I hope that  no one concludes that I am being critical of the IPCC WGII effort as I am not. Their work is based on a large number of studies and they have done a reasonable job of abstracting from those studies and reporting on them in their report. The problem appears to be that the variation in study results so far has made it impossible to create a global picture of impacts. This is not the fault of the authors of the WGII Report. It is the nature of the problem that crop yields are vary specific to location, crops and varieties/cultivars  of the crop (farmers know what they are doing) and many other variables. So it is very difficult to develop a global model and yet that is what is needed.  I have purposely avoided reading the WGII Summary for Policy Makers but I am curious if they have acknowledged that they have no idea about how Climate Change impacts food security other than adaptation will be necessary. Farmers have adapted to climate variations from time immemorial.  It is what farmers do.

Looking just at the table below, on balance it would seem like a climate change is going to be a wash relative to food security and easily adapted to by advances in agricultural technology. We we also have the factor of population increases so climate change ends up being more about population increase than temperature increases when it comes to food security. Here is an interesting quote from the WG2 final accepted Draft Report 7.2.2

"Food production is an important aspect of food security (7.1), and the evidence that climate change has affected food production implies some effect on food security. Yet quantifying this effect is an extremely difficult task, requiring assumptions about the many non-climate factors that interact with climate to determine food security. There is thus limited direct evidence that unambiguously links climate change to impacts on food security."

Until recently, I had a better looking (but less well organized) table than the below from the Draft of the IPCC Report but had difficulty maintaining it as we made some changes in the Editor used to publish my work. Anyway there were changes from the Draft of the IPCC AR5 WG2 Report to the Final Edited Version so this is a better reference anyway although the type is smaller and thus harder to read. The most important thing to remember is that where they provide estimates with and without the impact of carbon dioxide, please ignore the estimates without the impacts of carbon dioxide. I have no idea what those researchers were "thinking" since if you are studying the impacts of higher GHG you can not just ignore them. Carbon dioxide is used in greenhouses to stimulate plant growth so any estimate that does not consider the impact of higher carbon dioxide levels is fairly useless. Photosynthesis is nature's way of feeding plants with carbon dioxide. So any study should consider the positive impact of more carbon dioxide versus the impacts of higher temperatures and more or less water as the case may be.

Wine Grape situation

This Table is from Chapter 7 of the AR5 WG2 Report and was labled Table 7-1 the last time I looked.

IPCC AR5 WGII Table 7 Plate 1 Cropped

IPCC AR5 WGII Table 7-1 Plate 2 Cropped

IPCC AR5 WGII Table 7-1 Plate 3IPCC AR5 WGII Table 7-1 Plate 4 Cropped

One reason I publish this report is because weather and climate are important. Not saying that weather has been the driver of food commodity prices but we can say that they have been on a tear at least until 2014 when some, particularly corn, moderated. You can find more details on that McKinsey study here.

Food Commodity Indexes McKinsey

Impact on U.S. Agriculture

The following may be useful in terms of relating all of these climate factors to some of the major U.S. crops. These are not specialty crops but they are some of the major crops grown in the U.S. and crops that are very impacted by weather.


It is a fairly concentrated area.


Soybeans overlap a lot with corn giving farmers a decision to make based on expected product pricing.


There are a lot of cotton growing areas thus a lot of places to be helped or hurt by the weather and climate. In the long term,  I guess we should be looking for Anthropogenic Climate Change to result in the cotton belt gradually expanding north. But today I am focusing on the short term.

Spring Wheat

I assume they got a late start with spring wheat this year.  Winter wheat is a crop where we will watch the impact of a possible El Nino closely.

Notice the maps are shown by climate division so it is easy to track the weather for those crops. Those climate divisions shown as growing areas for certain crops are not the only locations that those crops are grown. Cotton is grown in New Mexico but apparently it is not grown sufficiently consistently in any of the climate divisions in New Mexico to be shown. So these maps show the most important climate divisions for these crops.

How Weather Shocks can Cause Severe Food  Impacts  I have not read this article yet  but it looks very interesting. 

Carbon Dioxide and Water Use.

This is an interesting article.

D4. Reports from Around the World.

U.S. Too many to cover here. This will be covered in my book



South America

Nortern Europe

Southern Europe


From the Russian News Agency Tass this short assessment. Basically they like the idea of being warmer.

Middle East






Notice the maps are shown by climate division so it is easy to track the weather for those crops. Those climate divisions shown as growing areas for certain crops are not the only locations that those crops are grown. Cotton is grown in New Mexico but apparently it is not grown sufficiently consistently in any of the climate divisions in New Mexico to be shown. So these maps show the most important climate divisions for these crops.

F3. Reports from Around the World.

U.S. Too many to cover here. This will be covered in my book



South America

Nortern Europe

Southern Europe


From the Russian News Agency Tass this short assessment. Basically they like the idea of being warmer.

Middle East


Effect of Climate Change on Maize Production in Nigeria. Obasi IO and Uwanekwu GA Department of Agricultural Economics, Michael Okpara University of Agriculture, Umudike, Nigeria.

You can read the full report here.

From the Abstract

The study was conducted in Nigeria. The objective of the study was to examine the effect of climate change on maize. The data for the study was obtained from secondary sources. The result shows that the average rainfall and temperature statistics were 1288.311mm and 31.7173C in Nigeria within the period under study. The average maize output within the period was 4.84mt while hectarage and yield were 3.36mha and 1.44t/ha respectively. The result from the study equally shows that the area cultivated and productivity of maize increased as temperature and rainfall increased. However, there were deceleration of output and area of maize cultivated which may have been induced by the increase in temperature and rainfall over these period. Maize productivity accelerated. The climate change variables show significant effect on maize production with the period under review. Based on findings from the study, it is recommended that since temperature and rainfall are relatively beyond the control of farmers, there should be proper enlightenment of the farmers on the proper climate adaptation practices to employ in order to minimize the adverse effects of climate change on their output.

Quite frankly a 30 year study is not sufficient to show anything relative to Global Warming. 1980 - 2010. It is clear from reading the paper that the authors did not get the result they wanted as both precipitation and output increased. Too bad too sad. I am old school and believe it is best not to state the conclusion you want in the introduction but present it as a question to be answered by the study or experiment. The Climate of Nigeria is controlled mostly by the AMO which was in its positive phase for most of this study period and in general increasing precipitation and temperature increases crop yields. There may well be a point where the temperature goes beyond what C4 plants can tolerate. There is a seasonal aspect to this also. Again 30 years is not a sufficient period of time to observe that. This points out some of the issues with trying to detect the impact of Global Warming.  AR5 WG2 failed miserably and the organizers of AR5 WG2 should be embarrassed by their weak effort. Notice the Media have not been reporting on their report very much. Some of the key findings of AR5 WG2 are in Part III of my Weekly Weather and Climate Update so you can find them there. Re Nigeria, one would suspect that population growth might have been a driver of increasing the acreage used for corn production and should have been included in the model. I am not familiar with their situation but usually product prices impact crop mix. The authors did an excellent job of analyzing the data and their paper is well worth reading as I was quite impressed with their approach and it might be useful to others. They did the best they could with the data they had. Their hypothesis may not have been correct. Again it is best in science to look for answers rather than state in the beginning of a study what the answer should be and then try to show that it occurred. There is a place for that approach but it probably was not the proper approach in their situation.  



I have presented information on where Lower Troposphere (LT) Warming has taken place with the somewhat surprising (not new to me) information that the Arctic has seen a lot more Warming than the Antarctic. Perhaps this article is related to that. In some of these matters, cause and effect are difficult to separate. It does serve to show that Global Warming is not nearly the simple process that is often presented but a far more complex process. The differences between the two hemispheres is fairly substantial. Please do not interpret my reporting on this article as an indication that I do not believe in Anthropogenic Global Warming as I do. I do however believe the climate of Planet Earth is quite complex and there can be many surprises when studying it. As an example, warmer means wetter so you can have more polar ice even as it is getting warmer. The quantity of ice may not be a good surrogate for global temperature.






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