Analysis of NOAA Arctic Sea Ice extent since 1979

For climate analysis we consider the average monthly extents for March and for September of each year in the satellite record, and the differences (the melt extent). Though we would prefer a longer record, these are currently the most popular data. Several observations:

March averages (annual maximums) do not vary greatly: 15.48 M Km2 is the average extent, with a range of 16.45 to 14.43 M Km2. 2/3 of the years are between 15 and 16M.

September averages (annual minimums) vary much more: 6.40 M Km2 is the average, with a range of 7.88 to 3.63 M Km2. Standard deviation is +/- 1.07 M Km2.

 

Note: The largest September extent (7.88) in the record occurred in 1996, the same year of the smallest melt extent: 7.25. And the smallest September extent (3.63) occurred in 2012, due to the largest melt in the record, 11.8M. The March extents of those two years were nearly the same.

The Arctic ice extent time series appears to consist of three periods:
1979 to 1996 Annual minimums mostly above average
1997 to 2006 Annual minimums around average
2007 to 2014 Annual minimums below average

Averages March Sept Diff (Melt)
1979 to 1996 15.8 7.2 8.6
1997 to 2006 15.3 6.2 9.0
2007 to 2014 15.0 4.5 10.5

Since 2005 the combination of below average March extents, combined with above average melts has produced September extents below 6 M Km2 each year.

It is now evident that 2012 was an outlier (probably due to the unusual storm activity). That year’s melt of 11.8 was 28% above the average melt of 9.09 and more than 1 M km2 larger than the second largest melt in 2008.

The pivotal decade was 1997 to 2006, preceded by slightly declining extents, and followed by much lower extents. What any of this has to do with CO2 and air temperatures is not obvious.

Data is here:
ftp://sidads.colorado.edu/DATASETS/NOAA/G02135/Mar/N_03_area.txt
ftp://sidads.colorado.edu/DATASETS/NOAA/G02135/Sep/N_09_area.txt

Comparing NOAA to MASIE Arctic Ice Extent 

Some might be interested to compare MASIE results with NOAA Sea Ice Index, since NOAA is a typical reference for Arctic Ice news. NOAA uses only passive microwave readings, while MASIE includes other sources, such as satellite images and field observations.

For comparison, MASIE shows about 700,000 Km2 more ice extent than NOAA both at maximum and minimum. This is usually explained by microwave sensors seeing melt water on top of ice the same as open water.

For the years 2007 to 2014 inclusive, each year MASIE shows higher maximums than NOAA, on average 5% higher. In each of those same years MASIE shows higher minimums than NOAA, on average 15% higher. The melt extent is more comparable: NOAA shows an average annual loss of 70.5 %, while MASIE shows an average loss of 67.5%.

NIC Ice Charts

 

 

Climatologists say the NIC estimates are “conservative.” By this they mean NIC’s priority is shipping safety, and so, when in doubt, under mixed ice and water conditions, ice charts show ice. NIC people do not make predictions about sea ice, they only report what is there, according to their multiple sources.

On the other hand, principals at both NASA and NOAA have said on the record that the Arctic will soon be ice-free, and it will be the fault of CO2. Could it be when in doubt, under mixed conditions, they report water in places where NIC shows ice? That would explain the discrepancies in estimates of ice extent

Note: NOAA has bureaucratic authority over NIC and advises against using NIC records for climate analysis. Last year, NIC results became available only on a rolling 30-day basis, so that estimates older than the current period are no longer available. Noticing this policy change, I began building a spreadsheet to capture the history for my own analysis. Since mid November, 2014, NIC ice extent reports have been unavailable at the MASIE webpage.

Update on April 2: NSIDC now says MASIE will be back after April.

Update November  2015: MASIE dataset is now available from January 1, 2006 to the present.

 

Everything You Wanted to Know about Measuring Arctic Ice (But Were Afraid to Ask)

There are several research centers that monitor Arctic ice extent, especially NSIDC (USA), DMI (Denmark), JAXA (Japan), and NANSEN (Norway). All start with the same data from passive microwave sensors on NASA satellites. Slight differences arise from different algorithms used to process the inputs into ice extent estimates.

Operational ice charts are an alternative measure of Arctic sea ice extent. These are prepared daily by Canada, Russia and US maritime authorities to assist ships navigating in Arctic waters. For example, the US National Ice Center (NIC) provides an index called MASIE (Multisensor Analyzed Sea Ice Extent). NIC charts are based upon not only passive microwave numbers, but also satellite imagery and reports from planes and ships operating in the regions. Operational ice charts are the most detailed pre-satellite records, with the Russian archives being the oldest.

What’s the Issue?

On another blog, a climate person put it to me this way:

“Based on what I’ve read if I had no other source, or were heading out in a boat, I would most certainly use the operational indices such as NIC. For climate change trends, I’d go with NSIDC, JAXA … Do you know of any climate scientists that prefer NIC?”

Measuring anything in the Arctic is problematic due to the conditions. And any technology has limitations and uncertainties. Thus it is useful to have more than one estimate of ice extent. Comparisons of the two types of data show the passive microwave results underestimate ice extent, especially during the late summer minimum. The difficulty is mistaking surface melt water for open water, failing to discern the ice underneath.

“Passive microwave sensors from the U.S. Defense Meteorological Satellite Program have long provided a key source of information on Arctic-wide sea ice conditions, but suffer from some known deficiencies, notably a tendency to underestimate ice concentrations in summer. With the recent release of digital and quality controlled ice charts extending back to 1972 from the U.S. National Ice Center (NIC), there is now an alternative record of late twentieth century Northern Hemisphere sea ice conditions to compare with the valuable, but imperfect, passive microwave sea ice record.”

“This analysis has been based on ice chart data rather than the more commonly analyzed passive microwave derived ice concentrations. Differences between the NIC ice chart sea ice record and the passive microwave sea ice record are highly significant despite the fact that the NIC charts are semi-dependent on the passive microwave data, and it is worth noting these differences. We compare the ice chart data to ice concentrations from the NASA Team algorithm which, along with the Bootstrap algorithm [Comiso, 1995], has proved to be perhaps the most popular used for generating ice concentrations [Cavalieri et al.,1997]. We find a baseline difference in integrated ice concentration coverage north of 45N of 3.85% ± 0.73% during November to May (ice chart concentrations are larger). In summer, the difference between the two sources of data rises to a maximum of 23% peaking in early August, equivalent to ice coverage the size of Greenland.“ ( My Bold)

http://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1058&context=usdeptcommercepub

The differences are even greater for Canadian regions.

“More than 1380 regional Canadian weekly sea-ice charts for four Canadian regions and 839 hemispheric U.S. weekly sea-ice charts from 1979 to 1996 are compared with passive microwave sea-ice concentration estimates using the National Aeronautics and Space Administration (NASA) Team algorithm. Compared with the Canadian regional ice charts, the NASA Team algorithm underestimates the total ice-covered area by 20.4% to 33.5% during ice melt in the summer and by 7.6% to 43.5% during ice growth in the late fall.”

From: The Use of Operational Ice Charts for Evaluating Passive Microwave Ice Concentration Data, Agnew and Howell
http://www.tandfonline.com/doi/pdf/10.3137/ao.410405

Or, if you don’t like what the US or Canada puts in their ice charts, you can get a third, independent perspective from Russia. The AARI has been studying and mapping the polar regions for a very long time, currently one of them chairs the ETSI and they maintain a global sea ice database (the other one is at NSIDC). Their ice charts can be accessed here:

http://www.aari.ru/main.php?lg…

A warning note: The Russians are not alarmed by what they see in the Arctic.

“In winter, the newly formed ice actively grows up to a 1.2 meter thick layer, while the coastal ice grows up to 2.0 meters. Consequently, the Arctic sea ice layer does not change significantly. Moreover, according to Genrikh Alekseev, in the summer, ice melts in various seas unequally. This year, the seas through which the Northern Shipping Route passes are covered with an unusually thicker ice layer. The Barents Sea is covered by a thin ice layer, but the amount of ice in the Kara, Laptev, East-Siberian and Chukotskiy seas exceeds the level of 2007. The conditions in the Arctic in the warm summer can be considered abnormal, but the Northern Shipping Route has not been completely freed from ice yet. This means icebreakers will be needed in the future, says the scientist.”

The extreme melting of ice in the summer 2012 is most likely the last gesture that the warming is ending. In fact, ice is a product of climate, and when comparing the graphs of the air temperature and melting ice, one can see that they coincide, Genrikh Alekseev said.
http://sputniknews.com/voiceofrussia/2012_08_31/Arctic-sea-ice-rejuvenating/

Conclusion:

Operational ice charts are more variable due to human error in their production.

Climatologists prefer passive microwave indices of ice extent because they are consistently wrong.

One wonders about their preference if satellites were overestimating ice. For myself I am glad for two mostly independent measures, one done by people who only want to get it right that day.

Note: As of March 2015, NSIDC has been showing MASIE off-line since mid November 2014. They say NIC results will be back in April.

Update on April 2: NSIDC now says MASIE will be back after April.

Arctic Sea Ice Factors

Early-spring sunlight hits ice in the Chukchi Sea near Barrow, Alaska, in March 2009. UCAR

Alarmists are always claiming the Arctic Sea Ice is the “canary in the coal mine.” Wrong. Arctic ice extent varies a lot for a lot of reasons. Predictions of its disappearing because of rising CO2 are another attempt to use a natural process as proof that global warming is dangerous and linked to fossil fuel emissions.

The Long View of NH Sea Ice

First some historical context for how NH ice extent varies over decades and centuries.


Figure 16-3: Time series of April sea-ice extent in Nordic Sea (1864-1998) given by 2-year running mean and second-order polynomial curves. Top: Nordic Sea; middle: eastern area; bottom: western area (after Vinje, 2000). IPCC Third Assessment Report

“The extent of ice in the Nordic Seas measured in April has been subject to a reduction of ~33% over the past 135 yr. Nearly half of this reduction is observed over the period ~1860–1900, prior to the warming of the Arctic. Decadal variations with an average period of 12–14 yr are observed for the whole period. The observation series indicates that less than 3% of the variance with respect to time can be explained for a series shorter than 30 yr, less than 18% for a series shorter than 90 yr, and less than 42% for the whole 135-yr long series. While the mean annual reduction of the April ice extent is decelerating by a factor of 3 between 1880 and 1980, the mean annual reduction of the August ice extent is proceeding linearly.”

“The August ice extent in the Eastern area has been more than halved over the past 80 yr. A similar meltback has not been observed since the temperature optimum during the eighteenth century. This retrospective comparison indicates accordingly that the recent reduction of the ice extent in the Eastern area is still within the variation range observed over the past 300 yr.”

Anomalies and Trends of Sea-Ice Extent and Atmospheric Circulation in the Nordic Seas during the Period 1864–1998 by TORGNY VINJE, Norwegian Polar Institute, Oslo, Norway

http://journals.ametsoc.org/doi/pdf/10.1175/1520-0442(2001)014%3C0255%3AAATOSI%3E2.0.CO%3B2

Multiple Factors Affecting Sea Ice Extent

The references below, among many others, show that the factors causing Arctic Ice to lessen, when that was happening, have nothing to do with air temperatures which is the only way CO2 could have an effect (theoretically). The melting is much more the result of water circulations, especially when warm Atlantic water from the south is able, or not, to get into the Arctic Ocean.

Arctic Ocean Circulations at Depth

“Regional Arctic sea ice variations result from atmospheric circulation changes and in particular from ENSO and North Atlantic Oscillation (NAO) events. Patterns of Arctic surface air temperature changes and trends are consistent with regional changes in sea ice extent. A dominant mode of Arctic variability is the Arctic Oscillation (AO), and its strong positive phase during the 1990s may account for much of the recent decrease in Arctic ice extent. The AO explains more than half of the surface air temperature trends over much of the Arctic.”
http://onlinelibrary.wiley.com/enhanced/doi/10.1029/2003GL018031/

“The variation in the ice extent caused by a 1C change in the ocean temperature since 1860 compares with about 90% of the concurrent total ice extent variation observed in the eastern area. The net effect of atmospheric temperatures seems accordingly to be relatively small over the same period of time. This concurs with the large difference in the individual heat capacity.”

http://journals.ametsoc.org/doi/pdf/10.1175/1520-0442(2001)014%3C0255%3AAATOSI%3E2.0.CO%3B2

“So why does circulation matter? Two reasons. First off, you can see warm water entering on the Pacific and Atlantic connections and cold water leaving via Canada and Greenland / Fram Strait. During a Glacial, that circulation stops. With a mile of ice over Canada, that exit is closed. With ocean levels 100 meters lower, folks can walk from Russia to Alaska. (Well, they do it sometimes now over the ice, but it will be easier and less seasonal during a Glacial).”

“So look again. No Bering Sea warm intrusion. No Canadian cold drain. No Beaufort Gyre when the ice is deep, since there will be no wind driven circulation under the ice. The Asian current toward the Bering Sea will end. The entire Asian warm river drain into the Arctic likely freezes up and doesn’t happen – which raises the interesting question of where does it go then? But that is for another day. Like asking where the Alaskan rivers drain then, or are they just glaciers at that point?”

“In short, what is left is just the North Atlantic Drift (aka Gulf Stream for Americans) warming a small patch near Europe and some cold water near Greenland. As Scotland was under ice in the last Glacial, even that North Atlantic Drift circulation likely didn’t get very far north.”
https://chiefio.wordpress.com/2014/05/04/arctic-flushing-and-interglacial-melt-pulses/

In addition to water circulation effects, sea ice extent is influenced by clouds and winds.

“Researchers have found that the high amounts of cloud in the early summer lead to low concentrations of sea ice in the late summer. This relationship between cloud cover and sea ice is so strong that it can explain up to 80 per cent of the variation in sea ice over as much as 60 per cent of the the sea ice area.”

http://www.reportingclimatescience.com/news-stories/article/high-cloud-levels-drive-low-arctic-sea-ice.html

The Great Arctic Cyclone of 2012

“We have shown evidence that low level winds over the Arctic, play an important role in mediating the rate of retreat of sea ice during summer. Anomalous anticyclonic flow over the interior of the Arctic directed toward the Fram Strait favors rapid retreat and vice versa. We have argued that the relative rankings of the September SIE for the years 2007, 2010 and 2011 are largely attributable to the differing rates of decrease of SIE during these summers, which are a consequence of year-to-year differences in the seasonal evolution of summertime winds over the Arctic. . . It is not clear why anticyclonic wind anomalies have been prevalent in recent years. ”

http://www.jamstec.go.jp/res/ress/masayo.ogi/2012GL051330.pdf

Conclusion:

Like most things in the climate, Arctic sea ice extent is determined by many interacting factors.  Among those many influences, the weakest case is claiming CO2 as a driving force.

Do-It-Yourself Climate Analysis

This article was first posted at Watts Up With That on July 12, 2014

People in different places are wondering: What are temperatures doing in my area? Are they trending up, down or sideways? Of course, from official quarters, the answer is: The globe is warming, so it is safe to assume that your area is warming also.

But what if you don’t want to assume and don’t want to take someone else’s word for it. You can answer the question yourself if you take on board one simplifying concept:

“If you want to understand temperature change,
  you should analyze the changes, not the temperatures.”

Analyzing temperature change is in fact much simpler and avoids data manipulations like anomalies, averaging, gridding, adjusting and homogenizing. Temperature Trend Analysis starts with recognizing that each micro-climate is distinct with its unique climate patterns. So you work on the raw, unadjusted station data produced, validated and submitted by local meteorologists. This is accessed in the HADCRUT3 dataset made public in July 2011. Of course, there are missing datapoints which cause much work for climatologists. Those are not a big deal for trend analysis.

The dataset includes 5000+ stations around the world, and only someone adept with statistical software running on a robust computer could deal with all of it. But the Met Office provides it in folders that cluster stations according to their WMO codes.
http://www.metoffice.gov.uk/research/climate/climate-monitoring/land-and-atmosphere/surface-station-records

I am not the first one to think of this. Richard Wakefield did similar analyses in Ontario years ago, and Lubos Motl did trend analysis on the entire HADCRUT3 in July 2011. With this simplifying concept and a template, it is possible for anyone with modest spreadsheet skills and a notebook computer to answer how area temperatures are trending. I don’t claim this analysis is better than those done with multimillion dollar computers, but it does serve as a “sanity check” against exaggerated claims and hype.

The method involves creating for each station a spreadsheet that calculates a trend for each month for all of the years recorded. Then the monthly trends are averaged together for a lifetime trend for that station. To be comparable to others, the station trend is presented as degrees per 100 years. A summary sheet collects all the trends from all the sheets to provide trend analysis for the geographical area of interest.

I have built an Excel workbook to do this analysis, and as a proof of concept, I have loaded in temperature data for Kansas . Kansas is an interesting choice for several reasons:

1) It’s exactly in the middle of the US with little change in elevation;
2) Kansas has a manageable number of HCN stations:
3) It has been the subject lately of discussion about temperature processing effects;
4) Kansas legislators are concerned and looking for the facts; and
5) As a lad, my first awareness of extreme weather was the tornado in OZ, after which Dorothy famously said: “We’re not in Kansas anymore, Toto.”

For the Kansas example, we see that BEST shows on its climate page that the State has warmed 1.98 +/-0.14°C since 1960. That looks like temperatures will be another 2°C higher in the next 50 years, and we should be alarmed.

Well, the results from temperature trend analysis tell a different story.
From the summary page of the workbook:

Area State of Kansas, USA
History 1843 to 2011
Stations 26
Average Length 115 Years
Average Trend 0.70 °C/Century
Standard Deviation 0.45 °C/Century
Max Trend 1.89 °C/Century
Min Trend -0.04 °C/Century

So in the last century the average Kansas station has warmed 0.70+/-0.45°C , with at least one site cooling over that time. The +/- 0.45 deviation shows that climate is different from site to site even when all are located on the same prairie.

And the variability over the seasons is also considerable:

Month °C/century Std Dev
Jan 0.59 1.30
Feb 1.53 0.73
Mar 1.59 2.07
Apr 0.76 0.79
May 0.73 0.76
June 0.66 0.66
July 0.92 0.63
Aug 0.58 0.65
Sep -0.01 0.72
Oct 0.43 0.94
Nov 0.82 0.66
Dec 0.39 0.50

Note that February and March are warming strongly, while September is sideways . That’s good news for farming, I think.

Temperature change depends on your location and time of the year. The rate of warming here is not extreme and if the next 100 years is something like the last 100, in Kansas there will likely be less than a degree C added.

Final points:

When you look behind the summary page at BEST, it reports that the Kansas warming trend since 1910 is 0.75°C +/-0.08, close to what my analysis showed. So the alarming number at the top was not the accumulated rise in temperatures, it was the Rate for a century projected from 1960. The actual observed century rate is far less disturbing. And the variability across the state is considerable and is much more evident in the trend analysis. I had wanted to use raw data from BEST in this study, because some stations showed longer records there, but for comparable years, the numbers didn’t match with HADCRUT3.

Not only does this approach maintain the integrity of the historical record, it also facilitates what policy makers desperately need: climate outlooks based on observations for specific jurisdictions. Since the analysis is bottom-up, micro-climate trends can be compiled together for any desired scope: municipal, district, region, province, nation, continent.

This example analyzed monthly average temperatures at a set of stations. This study used HADCRUT3, but others are done with CRUTEM4 and GHCN. The same technique can be applied to temperature minimums and maximums, or to adjusted and unadjusted records. And since climate is more than temperatures, one could also study precipitation histories, or indeed any weather measure captured in a time series.

The trend analysis workbook is provided below. It was the first iteration and the workbook was refined and enhanced in subsequent studies, also posted at this blog.

HADCRUT3 Kansas TTA

Analyzing Temperature Change using World Class Stations

This article was first posted on July 28, 2014 at Watts Up With That.

This is a study to see what the world’s best stations (a subset of all stations I selected as “world class” by criteria) are telling us about climate change over the long term. There are three principle findings.

To be included, a station needed at least 200 years of continuous records up to the present. Geographical location was not a criterion for selection, only the quality and length of the histories. 247 years is the average length of service in this dataset extracted from CRUTEM4.

The 25 stations that qualified are located in Russia, Norway, Denmark, Sweden, Netherlands, Germany, Austria, Italy, England, Poland, Hungary, Lithuania, Switzerland, France and Czech Republic. I am indebted to Richard Mallett for his work to identify the best station histories, to gather and format the data from CRUTEM4.

The Central England Temperature (CET) series is included here from 1772, the onset of daily observations with more precise instruments. Those who have asserted that CET is a proxy for Northern Hemisphere temperatures will have some support in this analysis: CET at 0.38°C/Century nearly matches the central tendency of the group of stations.

1. A rise of 0.41°C per century is observed over the last 250 years.

Area WORLD CLASS STATIONS
History 1706 to 2011
Stations 25
Average Length 247 Years
Average Trend 0.41 °C/Century
Standard Deviation 0.19 °C/Century
Max Trend 0.80 °C/Century
Min Trend 0.04 °C/Century

The average station shows an accumulated rise of about 1°C over the last centuries. The large deviation, and the fact that at least one station has almost no warming over the centuries, shows that warming has not been extreme, and varies considerably from place to place.

2. The warming is occurring mostly in the coldest months.

The average station reports that the coldest months, October through April are all warming at 0.3C or more, while the hottest months are warming at 0.2C or less.

Month °C/Century Std Dev
Jan 0.96 0.31
Feb 0.37 0.27
Mar 0.71 0.27
Apr 0.33 0.28
May 0.18 0.25
June 0.13 0.30
July 0.21 0.30
Aug 0.16 0.26
Sep 0.16 0.28
Oct 0.34 0.27
Nov 0.59 0.23
Dec 0.76 0.27

In fact, the months of May through September warmed at an average rate of 0.17C/Century, while October through April increased at an average rate of 0.58C/Century, more than 3 times higher. This suggests that the climate is not getting hotter, it has become less cold. That is, the pattern suggests milder winters, earlier springs and later autumns, rather than hotter summers.

3. An increase in warming is observed since 1950.

In a long time series, there are likely periods when the rate of change is higher or lower than the rate for the whole series. In this study it was interesting to see period trends around three changepoints:
1.1850, widely regarded as the end of the Little Ice Age (LIA);
2.1900, as the midpoint between the last two centuries of observations;
3.1950 as the date from which it is claimed that CO2 emissions begin to cause higher temperatures.

For the set of stations the results are:

°C/Century Start End
-0.38 1700’s 1850
 0.95 1850 2011
-0.14 1800 1900
 1.45 1900 1950
 2.57 1950 2011

From 1850 to the present, we see an average upward rate of almost a degree, 0.95°C/Century, or an observed rise of 1.53°C up to 2011. Contrary to conventional wisdom, the aftereffects of the LIA lingered on until 1900. The average rate since 1950 is 2.6°C/Century, higher than the natural rate of 1.5°C in the preceding 50 years. Of course, this analysis cannot identify the causes of the 1.1°C added to the rate since 1950. However it is useful to see the scale of warming that might be attributable to CO2, among other factors.

Conclusion

Of course climate is much more than surface temperatures, but the media are full of stories about global warming, hottest decade in history, etc. So people do wonder: “Are present temperatures unusual, and should we be worried?” In other words, “Is it weather or a changing climate?” The answer in the place where you live depends on knowing your climate, that is the long-term weather trends.

Note: These trends were calculated directly from the temperature records without applying any adjustments, anomalies or homogenizing. The principle is: To understand temperature change, analyze the changes, not the temperatures.

Along with this post I provide below the World Class TTA workbook for readers to download for their own use and to check the data and calculations.

World Class TTA

Climate Thinking Out of the Box

CMIP5 vs RSS

It seems that climate modelers are dealing with a quandary: How can we improve on the unsatisfactory results from climate modeling?

Shall we:
A.Continue tweaking models using classical maths though they depend on climate being in quasi-equilibrium; or,
B.Start over from scratch applying non-equilibrium maths to the turbulent climate, though this branch of math is immature with limited expertise.

In other words, we are confident in classical maths, but does climate have features that disqualify it from their application? We are confident that non-equilibrium maths were developed for systems such as the climate, but are these maths robust enough to deal with such a complex reality?

It appears that some modelers are coming to grips with the turbulent quality of climate due to convection dominating heat transfer in the lower troposphere. Heretofore, models put in a parameter for energy loss through convection, and proceeded to model the system as a purely radiative dissipative system. Recently, it seems that some modelers are striking out in a new, possibly more fruitful direction. Herbert et al 2013 is one example exploring the paradigm of non-equilibrium steady states (NESS). Such attempts are open to criticism from a classical position, but may lead to a breakthrough for climate modeling.

That is my layman’s POV. Here is the issue stated by practitioners, more elegantly with bigger words:

“In particular, it is not obvious, as of today, whether it is more efficient to approach the problem of constructing a theory of climate dynamics starting from the framework of hamiltonian mechanics and quasi-equilibrium statistical mechanics or taking the point of view of dissipative chaotic dynamical systems, and of non-equilibrium statistical mechanics, and even the authors of this review disagree. The former approach can rely on much more powerful mathematical tools, while the latter is more realistic and epistemologically more correct, because, obviously, the climate is, indeed, a non-equilibrium system.”

Lucarini et al 2014
http://arxiv.org/pdf/1311.1190.pdf

Here’s how Herbert et al address the issue of a turbulent, non-equilibrium atmosphere. Their results show that convection rules in the lower troposphere and direct warming from CO2 is quite modest, much less than current models project.

“Like any fluid heated from below, the atmosphere is subject to vertical instability which triggers convection. Convection occurs on small time and space scales, which makes it a challenging feature to include in climate models. Usually sub-grid parameterizations are required. Here, we develop an alternative view based on a global thermodynamic variational principle. We compute convective flux profiles and temperature profiles at steady-state in an implicit way, by maximizing the associated entropy production rate. Two settings are examined, corresponding respectively to the idealized case of a gray atmosphere, and a realistic case based on a Net Exchange Formulation radiative scheme. In the second case, we are also able to discuss the effect of variations of the atmospheric composition, like a doubling of the carbon dioxide concentration.

The response of the surface temperature to the variation of the carbon dioxide concentration — usually called climate sensitivity — ranges from 0.24 K (for the sub-arctic winter profile) to 0.66 K (for the tropical profile), as shown in table 3. To compare these values with the literature, we need to be careful about the feedbacks included in the model we wish to compare to. Indeed, if the overall climate sensitivity is still a subject of debate, this is mainly due to poorly understood feedbacks, like the cloud feedback (Stephens 2005), which are not accounted for in the present study.”

Abstract from:
Vertical Temperature Profiles at Maximum Entropy Production with a Net Exchange Radiative Formulation
Herbert et al 2013
http://arxiv.org/pdf/1301.1550.pdf

In this modeling paradigm, we have to move from a linear radiative Energy Budget to a dynamic steady state Entropy Budget. As Ozawa et al explains, this is a shift from current modeling practices, but is based on concepts going back to Carnot.

“Entropy of a system is defined as a summation of “heat supplied” divided by its “temperature” [Clausius, 1865].. Heat can be supplied by conduction, by convection, or by radiation. The entropy of the system will increase by equation (1) no matter which way we may choose. When we extract the heat from the system, the entropy of the system will decrease by the same amount. Thus the entropy of a diabatic system, which exchanges heat with its surrounding system, can either increase or decrease, depending on the direction of the heat exchange. This is not a violation of the second law of thermodynamics since the entropy increase in the surrounding system is larger.

Carnot regarded the Earth as a sort of heat engine, in which a fluid like the atmosphere acts as working substance transporting heat from hot to cold places, thereby producing the kinetic energy of the fluid itself. His general conclusion about heat engines is that there is a certain limit for the conversion rate of the heat energy into the kinetic energy and that this limit is inevitable for any natural systems including, among others, the Earth’s atmosphere.

Thus there is a flow of energy from the hot Sun to cold space through the Earth. In the Earth’s system the energy is transported from the warm equatorial region to the cool polar regions by the atmosphere and oceans. Then, according to Carnot, a part of the heat energy is converted into the potential energy which is the source of the kinetic energy of the atmosphere and oceans.

Thus it is likely that the global climate system is regulated at a state with a maximum rate of entropy production by the turbulent heat transport, regardless of the entropy production by the absorption of solar radiation This result is also consistent with a conjecture that entropy of a whole system connected through a nonlinear system will increase along a path of evolution, with a maximum rate of entropy production among a manifold of possible paths [Sawada, 1981]. We shall resolve this radiation problem in this paper by providing a complete view of dissipation processes in the climate system in the framework of an entropy budget for the globe.

The hypothesis of the maximum entropy production (MEP) thus far seems to have been dismissed by some as coincidence. The fact that the Earths climate system transports heat to the same extent as a system in a MEP state does not prove that the Earths climate system is necessarily seeking such a state. However, the coincidence argument has become harder to sustain now that Lorenz et al. [2001] have shown that the same condition can reproduce the observed distributions of temperatures and meridional heat fluxes in the atmospheres of Mars and Titan, two celestial bodies with atmospheric conditions and radiative settings very different from those of the Earth.”

THE SECOND LAW OF THERMODYNAMICS AND THE GLOBAL CLIMATE SYSTEM: A REVIEW OF THE MAXIMUM ENTROPY PRODUCTION PRINCIPLE
Hisashi Ozawa et al 2003
http://www.knmi.nl/~laagland/cursus/presentaties_voorjaar11/Ozawa.pdf

Energy and Poverty

Energy and Poverty are obviously tied together.

“Access to cleaner and affordable energy options is essential for improving the livelihoods of the poor in developing countries. The link between energy and poverty is demonstrated by the fact that the poor in developing countries constitute the bulk of an estimated 2.7 billion people relying on traditional biomass for cooking and the overwhelming majority of the 1.4 billion without access to grid electricity. Most of the people still reliant on traditional biomass live in Africa and South Asia.

The relationship is, in many respects, a vicious cycle in which people who lack access to cleaner and affordable energy are often trapped in a re-enforcing cycle of deprivation, lower incomes and the means to improve their living conditions while at the same time using significant amounts of their very limited income on expensive and unhealthy forms of energy that provide poor and/or unsafe services.”

http://www.iiasa.ac.at/web/home/research/Flagship-Projects/Global-Energy-Assessment/GEA_Chapter2_development_hires.pdf

The moral of this is very clear. Where energy is scarce and expensive, people’s labor is cheap and they live in poverty. Where energy is reliable and cheap, people are paid well to workl and they have a better life.

Temperatures According to Climate Models

In December 2014, Willis Eschenbach posted GMT series generated by 42 CMIP5 models, along with HADCRUT4 series, all obtained from KNMI.

CMIP5 Model Temperature Results in Excel
The dataset includes a single run showing GMT from each of 42 CMIP5 models. Each model estimates monthly global mean temperatures in degrees Kelvin backwards to 1861 and forwards to 2101, a period of 240 years. The dataset from CMIP5 models includes 145 years of history to 2005, and 95 years of projections from 2006 onward.

The estimated global mean temperatures are considered to be an emergent property generated by the model. Thus it is of interest to compare them to measured surface temperatures. The models produce variability year over year, and on decadal and centennial scales.

These models can be thought of as 42 “proxies” for global mean temperature change. Without knowing what parameters and assumptions were used in each case, we can still make observations about the models’ behavior, without assuming that any model is typical of the actual climate. Also the central tendency tells us something about the set of models, without necessarily being descriptive of the real world.

What temperatures are projected by the average model?

Periods HADCRUT4 ALL SERIES ALL MINUS HADCRUT4
1850-1878 0.035 0.051 0.016
1878-1915 -0.052 0.024 0.076
1915-1944 0.143 0.050 -0.093
1944-1976 -0.040 -0.008 0.032
1976-1998 0.194 0.144 -0.050
1998-2014 0.053 0.226 0.173
1850-2014 0.049 0.060 0.011

The rates in the table are C/decade. Over the entire 240 years time series, the average model has a warming trend of 1.26C per century. This compares to UAH global trend of 1.38C, measured by satellites since 1979.

However, the average model over the same period as UAH shows a rate of +2.15C/cent. Moreover, for the 30 years from 2006 to 2035, the warming rate is projected at 2.28C. These estimates are in contrast to the 145 years of history in the models, where the trend shows as 0.41C per century.

Clearly, the CMIP5 models are programmed for the future to warm more than 5 times the rate as the past.

Is one model better than the others?

In presenting the CMIP5 dataset, Willis raised a question about which of the 42 models could be the best one. I put the issue this way: Does one of the CMIP5 models reproduce the temperature history convincingly enough that its projections should be taken seriously?

I identified the models that produced an historical trend nearly 0.5K/century over the 145 year period, and those whose trend from 1861 to 2014 was in the same range. Then I looked to see which of the subset could match the UAH trend 1979 to 2014.

Out of these comparisons the best performance was Series 31, which Willis confirms is output from the INMCM4 model. Rates in the table below are C/decade.

Periods HADCRUT4 SERIES 31 31 MINUS HADCRUT4
1850-1878 0.035 0.036 0.001
1878-1915 -0.052 -0.011 0.041
1915-1944 0.143 0.099 -0.044
1944-1976 -0.040 0.056 0.096
1976-1998 0.194 0.098 -0.096
1998-2014 0.053 0.125 0.072
1850-2014 0.049 0.052 0.003

Note that this model closely matches HADCrut4 over 60 year periods, but shows variances over 30 year periods. That is, shorter periods of warming in HADCrut4 run less warm in the model, and shorter periods of cooling in HADCrut4 run flat or slightly warming in the model. Over 60 years the differences offset.

It shows warming 0.52K/century from 1861 to 2014, with a plateau from 2006 to 2014, and 0.91K/century from 1979-2014. It projects 1.0K/century from 2006 to 2035 and 1.35K/century from now to 2101. Those forward projections are much lower than the consensus claims, and not at all alarming

In contrast with Series 31, the other 41 models typically match the historical warming rate of 0.05C by accelerating warming from 1976 onward and projecting it into the future. For example, while UAH shows warming of 0.14/decade from 1979-2014, CMIP5 models estimates average 0.215/decade, ranging from 0.088 to 0.324/decade.

For the next future climate period, 2006-2035, CMIP5 models project an average warming of 0.28C/decade, ranging from 0.097 to 0.375/decade.

The longer the plateau continues, the more overheated are these projections by the models.

What’s different about the best model?

Above, I showed how one CMIP5 model produced historical temperature trends closely comparable to HADCRUT4. That same model, INMCM4, was also closest to Berkeley Earth and RSS series.

Curious about what makes this model different from the others, I consulted several comparative surveys of CMIP5 models. There appear to be 3 features of INMCM4 that differentiate it from the others.

1.INMCM4 has the lowest CO2 forcing response at 4.1K for 4XCO2. That is 37% lower than multi-model mean.

2.INMCM4 has by far the highest climate system inertia: Deep ocean heat capacity in INMCM4 is 317 W yr m^-2 K^-1, 200% of the mean (which excluded INMCM4 because it was such an outlier)

3.INMCM4 exactly matches observed atmospheric H2O content in lower troposphere (215 hPa), and is biased low above that. Most others are biased high.

So the model that most closely reproduces the temperature history has high inertia from ocean heat capacities, low forcing from CO2 and less water for feedback. Why aren’t the other models built like this one?

Conclusions:

In the real world, temperatures go up and down. This is also true of HADCRUT4. In the world of climate models, temperatures only go up. Some variation in rates of warming, but always warming, nonetheless.

Not all models are created equal, and the ensemble average is far from reality and projects unreasonable rates of future warming. It would be much better to take the best model and build upon its success.

Excel workbook is here: CMIP5 VS hADCRUT

Lawrence Lab Report: Proof of Global Warming?

It’s important to deconstruct this study because it is touted in the press as silencing “Climate Deniers” and as giving scientific proof of the greenhouse gas effect, once and for all. For example, a CBC article said this:

“A recent experiment at the Lawrence Berkeley National Laboratory in California has directly measured the warming effect of our carbon emissions, using data from instruments that measure the infrared radiation being reflected back to the ground by the atmosphere – the so-called greenhouse effect.
They found that the amount of radiation coming down increased between 2000 and 2010 in step with the rise of carbon dioxide in the atmosphere. So, the effect is real. And since we are continuing to increase our carbon emissions, change will continue to happen, like it or not, both warm and cold.”

The media was agog over this paper, saying that it measures the warming effect of CO2 in the atmosphere, and is proof of the greenhouse gas effect.

This paper claims to prove rising CO2 in the atmosphere increases down-welling infra-red radiation (DWIR), thereby warming the earth’s surface. The claim is based on observations from 2 sites, in Alaska and Oklahoma. Let’s examine the case made.

Observation: In Alaska and Oklahoma CO2 and DWIR are both increasing.
Claim: Additional CO2 is due to fossil fuel emissions.
Claim: Higher DWIR is due to higher CO2 levels.
Claim: Global DWIR is rising.
Claim: Global surface temperatures are rising.
LL Conclusion: Fossil fuel emissions are causing Global surface temperatures to rise

There are several issues that undermine the report’s conclusion.

Issue: What is the source of rising CO2?
Response: Natural sources of CO2 overwhelm human sources.

The sawtooth pattern of seasonal CO2 concentrations is consistent with release of CO2 from the oceans. Peaks are in March when SH oceans are warmest (60% of world oceans), and valleys are in September when NH oceans are warmest. In contrast biosphere activities peak in January in SH and July in NH.

CO2 content of the oceans is 10 times that of the atmosphere, resulting in the sawtooth extremes. Human emissions are ~5 to 7 Gigatons compared to ~150 Gigatons from natural sources.

Issue: What is the effect of H2O and CO2 on DWIR?
Response: H2O provides 90% of IR activity in the atmosphere.

The long term increase in DWIR can be explained by increasing cloudiness, deriving from evaporation when the sunlight heats the oceans. A slight change in H2O vapor overwhelms the effect of CO2 activity, and H2O varies greatly from place to place, while the global average is fairly constant.

Issue: What is the global trend of DWIR?
Response: According CERES satellites, DWIR has decreased globally since 2000, resulting in an increasing net IR loss upward from the surface.

Globally, Earth’s surface has strongly strengthened its ability to cool radiatively from 2000 to 2014 (by about 1.5 W/m2 or ~1 W/m2 per decade) according to CERES. The increased upward heat loss from the surface is matched by decreasing trend of DWIR globally. And this is in spite of significantly increasing atmospheric content of both CO2 and H2O (WV & clouds) + allegedly rising temps since 2000.

Conclusion:
The rise in CO2 is almost all from natural sources, not fossil fuel emissions.
IR activity is almost all from H2O, not from CO2.
Global DWIR is lower this century, and the surface heat loss is less impeded than before.
Global surface temperatures are not rising with rising fossil fuel emissions.

In fact, you need only apply a little critical intelligence to this paper, and it falls like a house of cards. Are there no journalists with thinking caps allowed to write about this stuff?