Sunday, 20 December 2015

An unclear result vis-à-vis necessity

Today, the Animal Legal Defense Fund in the United States has ranked the states in order of animal rights.

Although I am sceptical of the arguments of extreme animal rights advocates, I understand that at least in hotter climates with poorer soils there certainly is need for restrictions on animal killing and possibly even on use in some cases (which probably are not found in most of the Western Hemisphere). Nonetheless, the soil map below, which shows lower-fertility and older soils in the South and nutrient-poor parent materials in the central west coast, does suggest that these hotter regions need more regulations on animal use:
This belief of greater regulation in hotter climates is supported by native cultures: generally there were greater taboos on animal food in hotter regions than in cooler ones, and the greatest taboos of all were found in arid desert regions and in southern Australia where poor soils and dry climates (which create alkaline soils that immobilise phosphorus, zinc and copper) severely limit the availability of animal protein. Many primitive peoples in these regions were de jure and/or de facto vegetarians as a means of conservation – it is ironic that vegetarianism is most “trendy” today in some extremely protein-rich regions such as the Pacific Northwest and Germany where diets based upon animal foods cause limited or negligible ecological costs and comparative disadvantages in plant-based food production tend to be very large.
As can be seen from the map above, there does not seem to be a strong relationship between laws and actual ecological need – though I will stress that this is better than the usual result of inverse relationship whereby the best ecological laws found precisely where there exists least need.

In general, the best animal protection laws would be needed in the low-nutrient Southern states and perhaps in the “southwest” (which ecologically includes California and Oregon as well as the states of Arizona, New Mexico, Utah and Nevada normally known as the “Southwest”). As can be seen, some of the relevant states, like Oregon, California, Arizona, Louisiana and Florida, are in the top third, but on the other hand New Mexico, Alabama and Mississippi have laws relatively much poorer than would be required. At the same time, numerous Northeastern states like Maine, Michigan, Illinois, Indiana and Massachusetts are relatively overregulated.

Wednesday, 16 December 2015

Two global temperature databases compared

Perhaps seeking to expand my knowledge of how Australia’s appalling record of the highest per capita greenhouse emissions in the world (in ecological terms, Australians would logically be permitted vastly smaller per capita emissions than Eurasians and Americans since Australia’s ecosystems are based on much slower metabolism) – I have in recent months studied global temperature data and then found two major sets of global temperature maps, both of which date back to 1880. I have shown a comparison for the northern hemisphere winter season of 1933/1934 as an illustration:
Comparison of the GISS and NOAA global temperature anomalies for the season of December 1933, January 1934 and February 1934
The GISS temperature setlist is generally preferable to the NOAA site as the reader can look at natural climate variability at least partially insulated from Australian-made greenhouse pollution. Being restricted to 1961-1990 and 1971-2000 means, the NOAA site cannot give figures relative to something even approaching a genuinely natural average, which is problematic when assessing temperature anomalies for stations with long records. The GISS site also has data for a wider range of stations in most cases, especially in earlier years which are the purest indication of natural variability before Australia’s coal and aluminum industries were developed and bloodlessly took control of the global climate:
Comparative NOAA and GISS temperature anomalies for the winter of 1885/1886. Note that in all three maps large areas have no data. Because Australian land clearing and fossil fuel burning was less developed or not at all, however, these early maps are very valuable as representing the most natural climate variability available.
A problem with the GISS setup that makes NOAA of some use, however, can be seen from their data for the winter of 1940/1941. Actual station data in northern interior British Columbia and northern Alberta (e.g. from Baldonnel) seem to verify the NOAA figures rather than the GISS ones. In Baldonnel, British Columbia the monthly anomalies from December 1940 to February 1941 via-à-vis 1971 to 2000 are:
  • December 1940: +0.542106˚C
  • January 1941: -4.62105˚C
  • February 1941: +2.08500˚C
whilst for Fairview, Alberta the figures are:
  • December 1940: +1.06842˚C
  • January 1941: -3.58˚C
  • February 1941: -0.28˚C
Yet, the GISS maps do not show colder-than-normal temperatures in northern Alberta and northern interior British Columbia in January 1941:
NOAA and GISS temperature data for December 1940, January 1941 and February 1941. Note the negative anomaly in January 1941 – verified by data over northeastern British Columbia and northwestern Alberta – is not shown in GISS.
It’s notable that the stations previously noted do not possess data for the hotter years after 1990, which would add to the negative anomalies.

The subsequent winter of 1941/1942 had a similar problem around the Gulf of Ob and Gulf of Yenisey, which can be verified for the station of Dudinka on the Yenisey River west of Norilsk:
  • December 1941: +1.51052˚C
  • January 1942: +9.47˚C
  • February 1942: +2.62˚C
Global temperature maps for December 1941, January 1942 and February 1942. Note the positive anomaly in the NOAA map but not the GISS map around the Gulf of Ob and Gulf of Yenisey
It’s notable that I have not so far been able to verify major errors in the GISS maps from earlier dates as I have for the World War II years, but this may be because of poorer NOAA data.

All in all, whilst despite these problems GISS is still the best site, NOAA nonetheless has considerable value for earlier years. Those interested in the weather need to know changes in temperature just as they do anthropogenically produced changes in rainfall in southwestern Australia and central Chile, because they demonstrate how unsustainable Australian energy, farming and transport policies have been for over half a century – and without protest from elsewhere in the world.

Sunday, 13 December 2015

Longman reveals – unconsciously – the Enriched World as “circle of exclusive clubs”

Percent of mean national per capita income for each region of the US, 1929-1979
The now-veteran demographer Phillip Longman (he turns sixty in April), whose The Empty Cradle remains the best look – if without likely remedies – at the Enriched and Tropical Worlds’ severe demographic problems, has today showed, without having that aim, just how the Enriched World is becoming nothing except an exclusive club caught in a whirlpool of demographic suicide.

In November’s Washington Monthly, Longman in his new article ‘Bloom and Bust’, has argued that “regional inequality is out of control” after, as the graph on the left shows, having fallen for almost a century and a half until the 1980s. His data show that income inequality in the United States is increasing as
“geography has come roaring back as a determinant of economic fortune, as a few elite cities have surged ahead of the rest of the country in their wealth and income”
and
“only the very rich can still afford to work in Manhattan, much less live there, while increasing numbers of working- and middle-class families are moving to places like Texas or Florida... even though wages in Texas and Florida are much lower.”
Percent of New York mean per capita income for outlying US regions, 1969 to date
Longman then points out that the cities with highest per capita incomes have tended, in fact, to see large net out-migration, whilst areas where per capita incomes are not growing at all have tended to attract most in-migrants.

Longman’s primary argument is that looser enforcement of antitrust legislation and large amounts of financial deregulation during the 1980s and 1990s has led to the consolidation of extremely wealthy businesses in a small number of major cities on the East and Pacific Coast, notably New York, Boston, the San Francisco Bay Area, Washington D.C., Los Angeles and the cities of the Pacific Northwest. He believed the dominance of what he calls “retail goliaths” has meant much less is invested in “flyover” cities of the Plains, Mountain West and the South, with the result that the economies of these cities have severely declined as even new entrepreneurs must move to technology centres like Silicon Valley. Longman quotes Bill Gates to the effect that patent holders’ monopoly power – which he notes was expanded in the 1980s before which the federal government refused to grant any patents for software – makes it more difficult for inventors not allies with the patent holders. (Whilst I understand the value of patents in the context of agriculture, where Australia’s farmers do not pay anything for fertiliser technologies patented overseas but used to farm inherently unsustainable and exceedingly ancient soils, Gates’ and Longman’s criticism has major value.)

The problem is that Longman gives much too little attention to how impossible it is for the middle class – let alone the working masses – to live in such wealthy cities as New York, Boston, the Bay Area, Seattle, Portland and Washington D.C. Demographers ought to know all too well that:
  1. lowest-low fertility is a consequence of family formation being unaffordable due to limited housing space and consequent:
    1. simple unaffordability of housing for all but the very rich
    2. extreme lack of space in housing that is uncomfortable for all but one- or two-person houses and cannot accommodate families
    3. it is clear to me that, despite minor criticisms I received years ago Wendell Cox, many indices like fertility would correlate much better with:
      1. cost of housing per unit of housing space (relative to income) rather than actual total cost (because cheap housing is not useful for families if it be too small for comfort)
      2. cost of housing relative to each individual worker’s income, rather than with total household income (because a single income allows the mother to take more care of children)
      3. such criteria would show more accurately the problems Enriched World cities have with housing space and the need for women to work to gain basic sustenance – and of course this work tends to require high levels of education
  2. that severe land-use restrictions – in lands devoid of unique biodiversity (ice-free only for 15,000 years) and/or low secondary productivity to justify restrictions – create a large part of this housing shortage
  3. that politics in big “imperial cities” tends to be very left-wing due to the concentration of wealthy entrepreneurs there and resultant extreme levels of class resentment
  4. a fourth insight, which Longman does give, is that as the public sector has retreated from providing transport, government regulation of land supply and roads in “imperial cities” has precluded the private sector doing anything to improve mobility
  5. a fifth insight is that many regulations and much government spending in “imperial cities” is designed to help the very poor but:
    1. exacerbates natural flat land scarcity by means of rent control, which often allows less wealthy people who initially lived there to pay very low rents compared to what the market would charge
    2. create a culture of welfare dependency amongst these cities’ less wealthy populations, who obtain more from welfare than they could from modest-paying employment locally
    3. reduces job and trade opportunities by placing wages far above theoretical market levels given the regions’ resource poverty and dense pre-industrial populations, and by means of extreme and usually unnecessary (vis-à-vis Australia or Africa) environmental regulations
Under these conditions, Enriched World cities have no choice but to compete for the most skill-intensive industries extant. Their lands are generally cool, mountainous and pre-industrially densely populated, so they have large comparative disadvantages in agriculture. Glaciers and the Alpine Orogeny have stripped the Enriched World of difficult-to-smelt lithophile metals and preindustrial mining stripped it of easily smelted chalcophile ores, ruling out mining as a long-term base. The dense population and demands for clean air make unskilled labour totally inadequate as an income even with two partners, so that labour- or capital-intensive manufacturing industries also cannot serve as a long-term base for Enriched World economies.

The educational requirements and demographic consequences of an economy based exclusively upon skill-intensive industries have been documented for over a century. In 1900, when among women generally fertility rates were five to six children per lifetime, those of educated women could be as low as a tenth of that: I recall that one survey estimated the few tertiary-educated women produced merely 0.47 children over their lifetime! The situation has changed little in modern times – the difference is that dependence upon skill-intensive industries is now no isolated phenomenon but characterises most corners of the Enriched World and many of the Tropical, making these regions exclusive clubs for the skilled 1 percent or, in the most mountainous or densely populated, much less than that. Even if they had large pre-industrial populations or rapid modern growth, these nations, as shows dramatically by Japan post-1990, will one by one decline in global importance.

Families – who form the next generation – are being forced to move to land-rich regions like the American South or suburban Australia, which is where the future of the world must lie. Despite these regions’ inherently low soil fertility and generally high species diversity, the former trait tends to enhance cooperation and solid families and minimise the class conflict that produces the excessive regulation in cool climates. This sense of community undoubtedly allows tolerance for much lower quality of life via greater emotional support during social or environmental crises, by avoiding heat-of-passion reactions that can disturb relationships even between those who deeply love each other. It is this family-friendly “community culture” that drives migration to places with poor economies, bad climates and low quality of life, and the politics of the cooler and more mountainous regions of the globe make it unlikely to change.

Saturday, 12 December 2015

The generality of precipitation/temperature patterns: North Pacific versus North Atlantic

In a series of earlier blog posts (here and here) it was demonstrated that the relationships between England and Wales Precipitation (EWP) and Central England Temperature (CET) show consistency across months, but that the hotter and cooler parts of the year show different relationships:
  1. a positive CET/EWP relationship exists from November to February and in a minor way for the fiscal year from July to June
  2. a negative CET/EWP relationship exists from April to September
  3. no significant relationship in March and October
Despite the rapid change the globe’s climate due to emissions of greenhouse gases by Australia, South Africa, the Gulf States and to a lesser extent other mineral- and fuel-producing nations, these relationships have not substantially changed since 1974.

In this post I will see if an analogous condition to that of the UK also holds in the only other analogous climate region of the globe – southeastern coastal Alaska. Whilst generally extremely similar to the UK in its ecology and environmental history, there are major geographic differences owing to he extreme height of the coastal mountains, which reach much further above the glacial equilibrium line than does Mount Everest.

At the beginning of this year, southeastern coastal Alaska was divided after an examination of long-term station records into four climate divisions:

  • AK 9: East Gulf (red, on right)
  • AK 10: North Panhandle (blue, on right)
  • AK 11: Central Panhandle (purple)
  • AK 12: South Panhandle (dark green, on right at bottom)
I have chosen to investigate only AK 9 (East Gulf) for this study, to simplify matters because it is the largest and most “central” of Alaska’s six “maritime” or “southern” climatic divisions – which also include AK 8 West Gulf (green, around Kodiak) and AK 13 Aleutians (purple, in far southwest).

Reliable temperature and precipitation data for Alaska go back only to 1925, a little more than a third the length of the EWP data. For this reason, I have decided not to separate years with and without the dominant influence of greenhouse gas emissions by mineral exporting countries like Australia, South Africa and the Gulf monarchies: they are too likely to dominate the sample and the UK experience is that the change in correlation prove insignificant even though the averages do change significantly.

So, here are the scatter plots by months of precipitation versus mean temperature for the East Gulf division of Alaska, extending from Valdez to Sitka:

July:


As we can see here, in this the hottest month of the year, a general negative precipitation versus temperature regime prevails, exactly as seen for EWP versus CET in the United Kingdom over a record three times as long. The major outlier is the very wet July 1958, which was the second wettest on record with an estimated district average of 473.46 millimetres but no cooler than average.

August:

August, still in the hotter part of the year, shows a similar trend to July, which is in strong agreement with our earlier results re the relationship between EWP and CET in the various months. The relationship is not tight, and I have not measured the correlation coefficient. Two Augusts:
  • 1969, the coolest on record but sixteenth driest of 91
  • 1981, the second wettest on record but 0.4˚C hotter than all-series average 
show very distinct departures from the pattern of hot/dry and cool/wet.

September:

Here, we see that the seasonal change from hotter weather being drier to warmer weather being wetter than normal appears to be occurring one month earlier than we saw for the EWP versus CET graph in our earlier blog post. The scatter plot for September in the East Gulf division is basically flat, and is flat even with the extremely cold outlier of September 1992, whose estimated district average precipitation total is near normal.
September 1992 500 millibar chart anomaly vis-à-vis 1880 to 1974 mean. Support for the Twentieth Century Reanalysis Project dataset is provided by the U.S. Department of Energy, Office of Science Innovative and Novel Computational Impact on Theory and Experiment (DOE INCITE) program, and Office of Biological and Environmental Research (BER), and by the National Oceanic and Atmospheric Administration Climate Program Office.
As one can see, the extremely cold polar air over Alaska’s northeastern Gulf coast does not really have a mean onshore flow component, so that the cold was not accompanied by excessive rainfall or snowfall.

October:

In contrast to the CET versus EWP graph, October in southern Alaska already shows very clearly the typical winter pattern whereby precipitation and temperature show a direct correlation. This pattern has long been known via the National Weather Digest (here) for the main city in the region – Juneau – and the figures for October clearly show this pattern extending generally in the region. Cold months of October have anomalous flow from the major cold-air source region of the Yukon.

November:

As the discussed 1986 article about the winter climate of Juneau – located since this year in the Central Panhandle climate division to the southeast of the region graphed – would imply, the direct precipitation/temperature relationship increases in intensity for November.

Two facts one will note with this graph is that there are very few outliers, and that the shape is more curved than linear. The curved concave-down line of best fit implies that the temperature distribution is skewed due to the greater frequency of relatively mild and hyper humid maritime weather vis-à-vis frigid, dry continental conditions. The lack of outliers is such that even November 1956, on the “lower” right, was still warmer than average, and November 2002 at the extreme top was still wetter than average:
November 2002 500 millibar chart anomaly vis-à-vis 1880 to 1974 mean. Support for the Twentieth Century Reanalysis Project dataset is provided by the U.S. Department of Energy, Office of Science Innovative and Novel Computational Impact on Theory and Experiment (DOE INCITE) program, and Office of Biological and Environmental Research (BER), and by the National Oceanic and Atmospheric Administration Climate Program Office.
The key point from this November 2002 chart, which those only familiar with sea level charts might not instantly grasp, is that the powerful anticyclonic anomaly over southeast Alaska is still wetter than average on its warm western side because the anomalous flow is onshore.

December:

December, as the month where the winter solstice occurs, reflects clearly the pattern of mild, hyper-wet weather opposed always to frigid, dry weather. In fact, nothing approaching a moderate outlier can easily be seen here – which suggests much more powerful correlations than for EWP versus CET. The curved, concave-down line of best fit is also more clearly visible than for November, as is the record cold and dry December 1933 with its extremely strong flow from the frigid Yukon:
December 1933 500 millibar chart anomaly vis-à-vis 1880 to 1974 mean. Support for the Twentieth Century Reanalysis Project dataset is provided by the U.S. Department of Energy, Office of Science Innovative and Novel Computational Impact on Theory and Experiment (DOE INCITE) program, and Office of Biological and Environmental Research (BER), and by the National Oceanic and Atmospheric Administration Climate Program Office.
It is noticeable how strong the anomalous flow in December 1933 was vis-à-vis any of the other months whose flow patterns have been diagrammed here.

January:

Vis-à-vis the almost perfect relationship seen in December, January does not show quite so consistent a positive correlation, nor so curved a line of best fit. The line of best fit is much closer to the “familiar” linear shape than for December or even for November. More significantly, the famous month of January 1949 is an extremely powerful outlier being very close to the wettest on record, receiving 703.33 millimetres, but being no warmer than average at -7.9˚C:
January 1949 500 millibar chart anomaly vis-à-vis 1880 to 1974 mean. Support for the Twentieth Century Reanalysis Project dataset is provided by the U.S. Department of Energy, Office of Science Innovative and Novel Computational Impact on Theory and Experiment (DOE INCITE) program, and Office of Biological and Environmental Research (BER), and by the National Oceanic and Atmospheric Administration Climate Program Office.
This anomaly occurred because in January 1949 – as can be seen above – the flow anomaly was westerly (moist) but came from the cold Bering Sea and no warm source was accessible due to the powerful North Pacific anticyclonic anomaly.

This month was the snowiest January on record over Alaska as a whole, and the coldest on record over a large portion of the western United States, where it has been rivalled only by the Januaries of 1916, 1930, 1937, 1950, 1957 and 1969. It was extremely warm, however, over the eastern United States and Eurasia, being almost the “year without a winter” in the UK.

February:

February retains the basic winter scatter-plot pattern we have seen since October – mild and hyper humid versus frigid and dry. If anything, the line of best fit is more akin to the curved December shape than was seen for January.

Although not to the same extent as with December, there are no strong outliers. Even the record wet February 1964 with a strong high-level low pressure anomaly over Alaska itself was warmer than the all-series mean (which more than CET is distorted by greenhouse emissions from Australia and other resource-exporting nations), and the record dry February 1989 (driest for any month throughout this super-humid region) still very cold.
February 1989 500 millibar chart anomaly vis-à-vis 1880 to 1974 mean. Support for the Twentieth Century Reanalysis Project dataset is provided by the U.S. Department of Energy, Office of Science Innovative and Novel Computational Impact on Theory and Experiment (DOE INCITE) program, and Office of Biological and Environmental Research (BER), and by the National Oceanic and Atmospheric Administration Climate Program Office.
Even more than December 1933, we see extremely large heigh anomalies vis-à-vis mild, wet winter months. It is the extreme height anomalies combined with anomalous continental flow that made this month the driest of any month on record over almost all of climate divisions AK 9, AK 10, AK 11 and AK 12, whilst Barrow on the dry, frigid North Slope had its mildest month between November and March on record.

March:

In contrast to the EWP versus CET plot, March does not show any change from the winter months in its precipitation/temperature correlations over southern Alaska. As with October, the graph represents almost a straight line of positive slope, suggesting reduced skew in the temperature distribution but no change regarding the basic contrasts between wet and dry air masses.

April:

With the days becoming longer than the nights, and continental temperatures becoming hotter relative to maritime ones, we should expect that April would show a reversal or weakening of the consistent contrast of warn, hyper-humid maritime months versus frigid, dry continental months that are shown consistently over the East Gulf district between October and March.

In fact, even for April the correlation between precipitation and temperature (coefficient not measured) can be seen from the graph above to be positive. Nonetheless, it is weaker than the correlations we saw between October and March. However, the shape appears to show one key trait found for Juneau by Bradley Colman in the winter but not in the summer: a skewed temperature distribution with the median and mode warmer than the mean.

May:

Here at last we se a more definite transition to the typical hot-season pattern whereby hot months are drier and cool months wetter than the long-term mean.

What is noteworthy is that May gives no appearance of a transitional month, and outliers are not pronounced. Even the record hot and dry May of this year fits a line of best fit dating back to 1925 extremely well – indeed when you see the bullet in the top left, May 2015 fits the line as if there had been no radical man-made climate change as is demonstrated by rainfall and runoff data in southwestern Australia and parts of Chile.

June:

June, like May and July, behaves as one would anticipate from our earlier study of EWP/CET correlations like a typical summer month. Hotter-than-average Junes tend to be dry and cooler-than-average Junes wet over the East Gulf division.

As with May, there are almost no marked outliers, although the line of best fit is a little curved. this curved line, although predicted by Bradley Colman in 1986 for all months in the winter half-year, is emphatically not expected for a summer month. The differences between sea and land temperatures in the summer are less than in the winter months, and it stands tougher to get the southeasterly flow that would be needed for the hottest temperatures in summer, than it is to get frigid winter northeasterlies.

Conclusion:

Even without calculating Spearman ρ and/or Pearson r for each month, in the case of the Alaska East Gulf climate division – and almost certainly all of the maritime North Pacific – it can be concluded that:

  1. a very strong positive relationship between temperature and precipitation is observed in the winter half-yea between October and March
  2. a similarly strong negative relationship between temperature and precipitation is observed in the summer months from May to August
  3. September does not show a significant correlation between temperature and precipitation
  4. April appears to show a slight positive correlation, but it would be interesting to speculate whether longer records would show it as more of a transitional month
  5. Vis-à-vis the EWP and CET areas, climate division AK 9 is similarly located but further north.
  6. This more northerly location may explain why the reversal in correlation coefficients occurs earlier in autumn and later in spring.
  7. The patterns of monthly relationships between precipitation and temperature in southeastern Alaska (north Pacific) almost certainly are analogous to those over the United Kingdom (north Atlantic). The difference in (6) is almost certainly replicated over Scotland.
  8. Analogies between these two coastal regions are likely to be useful if topographic differences are taken into account.