Archive for the ‘temperature’ Category

Another ABC Fail

February 5, 2017

Viewers of ABC-TV news, and followers of ABC News Online, were treated to a story on Friday night about “Turtle hatchlings dying in extreme heat at Mon Repos”, as it was headlined at ABC News Online:

Piles of dead turtle hatchlings are lining Queensland’s famous Mon Repos beach amid a heatwave which has pushed the sand’s temperature to a record 75 degrees Celsius.

While the majority of hatchlings break free from their nests at night when the sand is cooler, those escaping in the day face overheating.

“They can’t sweat, they can’t pant, so they’ve got no mechanism for cooling,” Department of Environment and Heritage Protection chief scientist Dr Col Limpus said.

….

The extreme heat is also conducted down to the turtle’s nest, pushing the temperature to about 34C, which is approaching the lethal level for incubation.

That is the hottest temperature recorded in a nest in more than a decade.

A record 75 degrees sand temperature? Hottest nest temperature in more than a decade?

Time for a reality check.

I have no data on temperatures inside turtle nests, but I do have data on temperature at nearby Bundaberg Aero (Hinkler Airport), which is an ACORN site.

Using monthly Acorn data, here is a plot of all January maxima at Bundy.

bundy-jan-max

January’s mean maximum of 31.6 degrees C was equalled or exceeded in 1924, 1931, 1969, 1998, 2002, 2006, 2013, and 2014.  While monthly mean doesn’t tell us about individual days, it does give us a clue about daily temperatures in hot years.  For that I also use ACORN daily data- adjusted, homogenised, and world’s best practice apparently.

How do temperatures at this time of year compare with those of previous years?  The next figures show data for the first 45 days of every year, that is from January 1 to February 14.

bundy-jan-max-daily-45

The past three weeks at Bundaberg have been at the high end of the range, but no records have been broken, and no days have been even close to 35C.  What about previous years?  The next plot shows the number of consecutive days above 35 degrees: very likely to raise sand temperature above what it has been this year.

bundy-jan-max-daily-45-over-35

No days this year above 35C, but at least 27 occasions in previous years of single days reaching 35C, at least 6 of 2 days in a row, and one of 3 days in a row above 35C.

A 7 day running mean will show whether temperatures have been consistently high.

bundy-jan-max-7d-av-45

As you can see 2017 is high but not extreme.  2002 had a 7 day average just under 35C.

This graph plots temperatures of the first 45 days of years with similarly hot January temperatures.  2017 is the thick black line.

bundy-jan-max-daily-45-hot-yrs

On one day- January 20- 2017 was hotter than the other years.  Note how in several years the temperature drops to the mid 20s when heavy rain falls.  Note also the temperature reached the high 30s in February 2002.

The final graph shows the 7 day average of the same period of similarly hot years.

bundy-jan-max-7d-av-45-hot-yrs

Several previous periods were hotter than so far this year.

Once again we see misleading claims being made and reported by the ABC as gospel, without any attempt at fact checking.  A simple check shows that, while it may be true that the reported temperatures are the hottest recorded by these researchers, it is extremely unlikely that these were as high as they were in past years.  On every count- daily, monthly mean, 7 day mean, consecutive hot days- it can be shown that this year, while hot, is not as hot as many previously, and it follows that sand temperatures would similarly have been hotter in the past.

And that’s without considering the Holocene Optimum and the Eemian.

Another ABC fail.

Dig and Delve Part III: Temperate Regions

February 1, 2017

In this post I draw together ideas developed in previous posts- Poles Apart, Pause Updates, Dig and Delve Parts I and II– in which I lamented the lack of tropospheric data for the regions of the northern and southern hemispheres from 20 to 60 degrees North and South.  These regions between the Tropics and Polar regions I shall call Temperate regions, as that’s what I was taught in school.

A commenter of long standing, MikeR, who has always endeavoured to keep me on the straight and narrow, suggested a method of estimating temperature data for these regions using existing Polar and Extra-Tropical data.  I’ve finally got around to checking, and can now present the results.

The correct formula is:

T (20 to 60 degrees) = 1.256 x TexT ( 20 to 90 degrees) – 0.256 X T pole(60 to 90 degrees).

This gives an approximation for these regions in lieu of UAH data specifically for them.

And the results are very, very interesting.  Hello again, Pause.

All data are from the University of Alabama (Huntsville) (UAH) lower troposphere, V.6.0.

First of all, here are plots showing the Extra-Tropics (20-90), compared with  the corresponding Temperate regions (20-60).

Fig. 1:  Monthly UAH data for Northern Extra-Tropics (20-90N) and Estimate for Northern Temperate Region (20-60N)

 nth-temp-v-next

Fig. 2:  Monthly UAH data for Southern Extra-Tropics (20-90S) and Estimate for Southern Temperate Region (20-60S)

sth-temp-v-sext

As expected, the result of very slight differences is a slight cooling of the Northern Extra Tropics trend, and a slight warming for the Southern.   No surprise there.

The real surprise is in the Land and Ocean data.  In the Northern Temperate region, CuSum analysis reveals a large regime change which occurred at the beginning of 1998.  The following plots show trends in the data up to January 1998 and from February 1998 to December 2016.

Fig. 3: Estimated Northern Temperate data trends to January 1998 and from February 1998 to December 2016.

nth-temp-2-trends

Fig. 4: Estimated Northern Temperate data trends to January 1998 and from February 1998 to December 2016: Ocean areas.

nth-temp-2-trends-ocean

Fig. 5: Estimated Northern Temperate data trends to January 1998 and from February 1998 to December 2016: Land areas.

nth-temp-2-trends-land

Say hello to the Pause again.  Northern Temperate land areas- most of North America, Asia, Europe, and North Africa, containing the bulk of the world’s population, agriculture, industry, and CO2 emissions- has had zero trend for 18 years and 11 months.  While the trend for the whole record is +1.8C per 100 years, the record is clearly made of two halves, the first with a much milder +0.7C trend, then after an abrupt step change, the second half is flat- in spite of the “super El Nino” and the “hottest year ever”.

Compare this with the Extra-Tropics data, 20-90N.

Fig. 6: Northern Extra-Tropics data (20-90N) trends to January 1998 and from February 1998 to December 2016: Land areas.

next-land-2-trends

The step change is still there, but the trends are virtually unchanged- only 0.1C different +/- 0.1C.

Why the difference?  Northern Extra Tropics data (20-90N) includes the North Polar data (60-90N).  The major change in the North Polar region occurred in early 1995, as the next two figures show:

Fig. 7: Northern Polar data (60-90N) trends to February 1995 and from March 1995 to December 2016: Land areas.

np-land-2-trends

Fig. 8: Northern Polar data (60-90N) trends to February 1995 and from March 1995 to December 2016: Ocean areas.

np-ocean-2-trends

Massive changes in trend.  Note the change apparently occurred in land data before ocean, which is peculiar, and both in the dead of winter.  Polar regions, though much smaller, have a large impact on trends for the Extra-Tropics.

In the Southern part of the globe, once again say hello to the Pause.

Fig. 9: Estimated Southern Temperate data trends to January 1998 and from February 1998 to December 2016.

sth-temp-2-trends

While the step change is much smaller, using the same dates the Pause is still undeniable.

Fig. 10: Estimated Southern Temperate data trends to January 1998 and from February 1998 to December 2016- Land areas.

sth-temp-2-trends-land

Fig. 11: Estimated Southern Temperate data trends to January 1998 and from February 1998 to December 2016- Ocean areas.

sth-temp-2-trends-ocean

Most of the Southern Hemisphere is ocean, so it follows that a Pause in the ocean leads to a Pause overall.

It is important to stress that the figures I show for Northern and Southern Temperate regions are estimates, not actual data from UAH.  However, they are pretty good estimates, and until we have data from UAH, the best available.

Of the world’s regions, South Polar and Southern Temperate regions are paused, as is the Northern Temperate Land region, which is arguably the most important.  The Tropics fluctuate with ENSO.  Only the Arctic is strongly warming.

The Temperate regions are arguably the most important of the globe.  Together they cover more than half the surface area, and contain the bulk of the world’s population, agriculture, industry, and emissions.  I hope that Dr Spencer will be able to provide datasets for these regions as soon as possible.

Putting Temperature in Context: Pt 2

December 14, 2016

To show how handy my Excel worksheet is, here’s one I did in the last 15 minutes.

Apparently Sydney has had its warmest December minimum on record at 27.1 C.  The record before that was Christmas Day, 1868 at 26.3C.

The following seven plots show this in context.

Fig. 1:  The annual range in Sydney’s minima:

whole-yr-sydney-min

Extremes in minima can occur any time between October and March.

Fig. 2:  The first 2 weeks of December

14d-sydney-min

Plainly, a new record was set this morning, but apart from Day 340 the other days are within the normal range.

Fig. 3:  7 day mean of Tmin in this period

7d-avg-sydney-min

Extreme, but a number of previous years had warmer averages.

Fig. 4:  Consecutive days above 20C Tmin.

days-over-20-sydney

But there have been longer periods of warm minima in the past.

Now let’s look at the same metric, but for all of December.

Fig. 5:  All Decembers (including leap years).

december-sydney-min

A record for December, with 1868 in second place.

Fig. 6:  7 day mean of Tmin for Decembers

7d-avg-sydney-min-december

Seven day periods of warm nights are not new.  The horizontal black line shows the average to this morning (20.6C) is matched or exceeded by a dozen other Decembers.  (Of course this December isn’t half way through yet.)  Also note what appears to be a step change about 1970.

Fig. 7:  Consecutive days above 20C Tmin in December.

days-over-20-sydney-december

I doubt if 15 December will be as warm as today, but could still be over 20C.

This is weather, not global warming.

 

Putting Daily Temperature in Context

December 14, 2016

In this post I demonstrate a simple way of comparing current temperatures for a particular location with those previously recorded.  In this way it is possible to show the climatic context.

Using data from Climate Data Online, I plot maximum temperature for each day of the year, and then for a particular short period: in this case the last week of November and the first week of December, which coincides with the recent very warm spell here in Queensland.  To account for leap and ordinary years this period is 15 days.  In ordinary years 24th November is Day 328 and 7th December is Day 341, while in leap years this same calendar period is Day 329 to 342.  I also calculate the running 7 day mean TMax for this period, and the number of consecutive days above 35C.

To put the recent heatwave in context, I have chosen six locations from Central and Southern Queensland which regularly feature on ABC-TV weather: Birdsville, Charleville, Roma, Longreach, Ipswich (Amberley RAAF), and Rockhampton.

Birdsville:

Fig. 1

whole-yr-birdsville

The Police Station data are from 1954 to 2005, and the Airport from 2000.  This shows the range of temperatures throughout the year.  The red arrow indicates the current period.   The next plot shows data only for the period in question.

Fig. 2:  24 November- 7 December: Airport data

14d-comp-birdsville-air

Note there were three days where the temperature this year was the highest for those days since 2000, but didn’t exceed the highest in this time period, which was in November.  The other days were well within the historic range.

For interest, let’s now see how this year compares with the Police Station record.  (The average difference in TMax during the overlap period was 0.0 to 0.3C.)

Fig. 3:  24 November- 7 December: Police Station data

14d-comp-birdsville-police

In a similar range.

Fig. 4

7d-avg-birdsville

This heatwave was the third hottest since 2000 and fifth overall.

Fig. 5

days-over-35-birdsville-air

Five previous periods had more consecutive days above 35C.  2006 had 22.

Charleville:

Fig. 6: Charleville Aero since 1942

whole-yr-charleville-aero

Temperatures in this period reached the extremes of the range on three days.

(Although the Post Office record begins in 1889, there are too many errors in the overlap period so the two records can’t be compared.)

Fig. 7:

14d-charleville-aero

A new record for early December was set, but note this was the same temperature as 29th November 2006.

Fig. 8:

7d-avg-charleville-aero

Definitely the hottest for this period since 1942.

Fig. 9:

days-over-35-charleville-aero

Note this was not the longest warm spell by a mile: there were many previous periods with up to 26 consecutive days above 35C.

Roma

Fig. 10:

whole-yr-roma

Although there is not one day of overlap so the two records can’t be compared, you can see that Airport (from 1992) and Post Office records are similar.

Fig. 11:

14d-comp-roma-air

A new record for this time of year was set: 44.4C, and six days in a row above 40C.  Pretty hot….

Fig. 12:

days-over-35-roma-air

…but there were longer hot periods in the past (since 1992).

Longreach

Fig. 13:  Longreach Aero since 1966.

whole-yr-longreach-aero

Fig. 14:

14d-longreach-aero

Hot, but no record.

Although there is good overlap with the Post Office, temperatures for this period differ too much: from -1 to +0.7C.

Fig. 15:

7d-avg-longreach-aero

Fifth hottest period since 1966.

Fig. 16:

days-over-35-longreach-aero

And in the past there have been up to 47 consecutive days above 35C at this time of year.

Ipswich (Amberley RAAF):

Fig. 17:

whole-yr-amberley

Fig. 18:

14d-amberley

Not unusually hot for this time of year.

Fig. 19:

7d-avg-amberley

Ninth hottest since 1941.

Fig. 20:

days-over-35-amberley

Hotter for longer in the past.

Rockhampton:

Fig. 21:

whole-yr-rocky

Fig. 22:

14d-rocky-air

Very hot, but no records.  (The heat lasted another two days, with 36.6 and 37.3 on 8th and 9th.)

Fig. 23:

7d-avg-rocky

Fourth hottest 7 day average on record (since 1939).

Fig. 24:

days-over-35-rocky-air

Again, a number of hot days, but there were as many and more in the past.

To conclude: the recent heatwave was very hot certainly, and was extreme in southern inland Queensland.  While Charleville had the highest seven day mean temperature on record, NO location had as many consecutive hot days (above 35C) as in the past.

This is a handy method for showing daily data in context.  It can used for any period of the year, can be tuned to suit (I chose TMax above 35C, but temperatures below a set figure could be found), and can be used for any daily data.

If you would like a comparison done for a location that interests you, let me know in comments including time period and parameters of interest (e.g. Sydney, first 2 weeks of December, TMax above 30C say, or Wangaratta, September, daily rainfall over 10mm say.)

Land and Sea Temperature: South West Australia Part II: TMin

November 29, 2016

This is a quick follow up to my last post, as an update:  I’ve been reminded to show Tmin as well.   My apologies.

In this post I examine minimum temperature for Winter in South-Western Australia, and Sea Surface Temperature data for the South West Region, all straight from the Bureau of Meteorology’s Climate Change time series page .

All temperature data are in degrees Celsius anomalies from the 1961-90 average.

Fig. 1:   Southwestern Australia Winter TMin Anomalies & SST

sw-tmin-sst

Note that TMin roughly matches SSTs, but there are differences from TMax.  CuSums will show this:

Fig. 2:  CuSums of Winter TMin and SST compared:

sw-tmin-cusums

Note that TMin has completely different change points, marked in red.  The major different ones are at 1949, 1956, 1964, 1990, 2000, and 2010.  There is a barely discernible point at 1976 (not 1975), so the next plots will use 1976 to show trends since then.

Fig. 3:  Trends in TMin:

sw-tmin-trends

Cooling since 1976 at -0.36C/100 years.

Detrending the data allows us to see where any of the winters “bucks the trend”.  In the following plots, the line at zero represents the trend as shown above.

Fig. 4:  TMin Detrended:

sw-tmin-detrended

2016 winter TMin is 0.5C below trend, and 0.38C below average, however winter this year in southwest WA was not as cold as 1986, 1990, 2001, 2006, 2008, or 2010- according to Acorn of course.

The action is with TMax.

Poles Apart

November 4, 2016

Satellite data from UAH (University of Alabama- Huntsville) are estimates of temperature in the Lower Troposphere, and thus a good indicator of whether greenhouse warming is occurring.  My next post about the length of The Pause in various regions will be ready in a few days’ time.  Meanwhile, I’ve been looking at the data in a different way.

In this post I will be examining how and when temperatures have changed in discrete regions of the globe, including over land and over oceans.  There are no startling revelations, but a different approach reinforces the need to understand climate variability in different regions.  The important regions of course are the Tropics and the Poles, and fortunately UAH data is available separately for just these three regions.

Firstly, Figure 1 shows the regions for which UAH has atmospheric data.

Fig. 1:  UAH Data Regions

regions

The Northern and Southern Extra-Tropics include the Polar regions, so there are three discrete regions which do not overlap: Tropics, North Polar, and South Polar.  It would be very helpful if Dr Spencer provided data for the Extra Tropical regions excluding the Polar Regions.

For this analysis I use CuSum, which is a simple test of data useful for detecting linearity or otherwise, and identifying sudden changes in trend, or step changes.  It can be used for any data at all- bank balance, car accidents, rainfall, GDP, or temperature.  It is simple to use:  find the mean of the entire data, calculate differences for every data point from this mean, then calculate the running sum (Cumulative Sum) of the differences.  If done correctly, the final figure will be zero.  Plot the CuSum usually by time and identify points of any sudden change in direction.  A generally straight or smoothly curving line indicates linearity, but points of sudden change mean a change in trend or a step change.  (Further, data series with identical start and end points, exactly the same number of data points, and anomalies from the same period- such as UAH- should produce directly comparable CuSums.)  These points, and ranges between them, are then checked in the original data. The usefulness of CuSums will become obvious as we go, especially as they are compared.

The next figures show CuSum plots for various regions.

Fig. 2:  UAH CuSums for all regions

cusums-all

Points to note:

The brown line at the top is the South Polar region.  The line wobbles about zero, indicating little relative change in temperature from the mean.  Contrast this with the North Polar region (the blue line at the bottom.)  The Polar regions are conspicuously different from the other regions and from each other.

The spaghetti lines clustered in the middle are CuSums for (in order from top to bottom): Southern Extra-Tropics; Southern Hemisphere; Tropics; Globe; Northern Hemisphere; Northern Extra-Tropics.

The red arrows point to wobbles coinciding with major ENSO events.  These changes in direction indicate trend changes or step changes in the original data.  There are other changepoints, notably 2002-2003.

The vertical red line joins changepoints in all the CuSums in mid-1991 following the eruption of Mt Pinatubo.

Fig. 3: UAH CuSums for the Tropics, South Polar, and North Polar regions

cusums-np-sp-tropics

Note there is little similarity between CuSums for the only regions with discrete data, and you have to look carefully to see North Polar CuSums changing some months after Tropics, but not always.

The next plots show the differing responses of Land and Ocean areas.

Fig. 4:   UAH CuSums for the Globe, Land and Ocean

cusums-land-ocean

Note that Land areas have greater relative temperature changes than the Oceans, and that the Global mean closely mirrors the Ocean CuSums (as the Globe is mostly Ocean).  The major turning point is in 1997-98.

Fig. 5:  UAH CuSums for the Tropics, Land and Ocean

cusums-tropics-land-ocean

Note once again the mean CuSums closely follow that of the Ocean as 20 degrees North to 20 degrees South is mostly water.  The changepoints are very distinct.

Fig. 6:  UAH CuSums for the North Polar region, Land and Ocean

cusums-np-land-ocean

Note that all CuSums are close, but after 1982 Ocean CuSum changes relatively more than Land- the blue line has switched to below the mean.  The main changepoints are 1991, 1993-94, 2002, 2009, and 2015.

Fig. 7:  UAH CuSums for the South Polar region, Land and Ocean

cusums-sp-land-ocean

Now that is interesting.  Note all three CuSums have similar changepoints, but Land varies more than Ocean and after 1992 Land is largely negative, Ocean is largely positive.  The Land CuSum range is about half of the North Polar equivalent.

Remember CuSums in Figure 4 showed Land temperatures must vary more than Ocean (though not in the North Polar region).  The next figures show plots of UAH original data (not CuSums).

Fig. 8:  UAH original data for the Globe, Land and Ocean

graphs-globe-land-ocean

I find a visual representation demonstrates greater relative variation in Land temperatures well.

Fig. 9:  UAH original data for the Tropics, Land and Ocean

graphs-tropics-land-ocean

Note much greater fluctuation with ENSO, and Land varying a little more that Ocean.

Fig. 10:  UAH original data for the North Polar region, Land and Ocean

graphs-np-land-ocean

Note the much greater variation, but Land is more often than not masked by Ocean.

Fig. 11:  UAH original data for the South Polar region, Land and Ocean

graphs-sp-land-ocean

Note the much greater range in Land data, with large non-linear multi-year swings- calculate a linear trend for Land at your peril.

Having found changepoints, we can now analyse periods between them.  One way is to calculate means, and step changes between periods.

Fig. 12:  UAH original data for the Tropics based on CuSum changepoints

steps-tropics

I deliberately ignored the 2001 changepoint- it made very little difference to means and appears to be a continuation of the series starting in 1997.  Note the step changes are very small, and the final step change is reliant on current data and will change.  While I have shown means and steps, the data are decidedly non-linear with sharp spikes and multi-year rises and falls.

Fig. 13:  UAH original data for the North Polar region based on CuSum changepoints

steps-np

Note the large step change in the mid-1990s occurs before the 1997-98 El Nino.  The range is much greater than the Tropics.

As the Land data for the South Polar region looks more interesting, I decided to use Land instead of the mean.

Fig. 14:  UAH original data for the South Polar region (Land data) based on CuSum changepoints

steps-sp-land

Up and down like a toilet seat!

Conclusions:

The data series are characterised by step changes and multi-year rises and falls.

The Polar regions are “poles apart” in their climate behaviours.  Explanations might include: different geography (an ocean almost surrounded by land but subject to warming and cooling currents vs a continent isolated from the rest of the world by a vast ocean); different snow and ice albedo responses; different cloud influences.

The Global mean combines data from regions with very different climatic behaviour.  Averaging hides what is really going on.  The Tropics are governed by ENSO events, and the Poles are completely different.

Please Dr Spencer can you provide separate data for 20-60 degrees North and South?

Comments and interpretations are most welcome.

When Tmax and Tmin Are Poor at Describing Weather

October 25, 2016

Last Sunday was a miserable day in Rockhampton- overcast with drizzling rain and cold all day.  Mean maximum for October is 29.7 degrees, so the maximum reported by the Bureau of 20.4 was 9.5 degrees below average, as expected.  However, that does not tell you anything like the whole story.

Here is the temperature graph from the Bureau for the period midday Saturday to midday Tuesday.  The solid horizontal line shows the duration of Sunday 23rd, and the thin black vertical lines show 9.00 a.m., which is the time when the daily minimum and the previous day’s maximum are recorded.   Temperatures at recording times are circled.

rocky-temp-23-oct

On fine, clear days, minima usually occur around sunrise and maxima in the early afternoon: you can see this on the 22nd, 24th, and (almost) on the 25th.  Sunday 23rd was wet.  As you can see the temperature was falling fairly steadily from Saturday afternoon until Monday morning.   The maximum for Sunday was 22.7 at midnight, and the coldest temperature on Sunday was 14.3 from 7.30 p.m. to 9.00 p.m. on Sunday night- not the 17.6 at 9.00 a.m.   The official maximum for Sunday of 20.4 degrees was actually the temperature at 9.00 a.m. on Monday!

So what was the Diurnal Temperature Range?  Was 20.6 (or 22.7) a good representation of how high the temperature “rose”?  The temperature in the early afternoon varied between 15 and 16.4, and this was about 14 degrees below normal for this time of the year (and two to three degrees below the official lowest maximum of 18.1 on 10th October 1982).

Which is one reason I don’t take a lot of notice of claims of hottest or coldest extremes.

The Pause Update: September 2016

October 18, 2016

The complete UAH v6.0 data for September have just been released. I present all the graphs for various regions, and as well summaries for easier comparison. The Pause has finally ended globally and for the Northern Hemisphere, and the Tropics, but still refuses to go away in the Southern Hemisphere.

These graphs show the furthest back one can go to show a zero or negative trend (less than 0.1 +/-0.1C per 100 years) in lower tropospheric temperatures. I calculate 12 month running means to remove the small possibility of seasonal autocorrelation in the monthly anomalies. Note: The satellite record commences in December 1978- now 37 years and 10 months long- 454 months. 12 month running means commence in November 1979. The y-axes in the graphs below are at December 1978, so the vertical gridlines denote Decembers. The final plotted points are September 2016.

[CLICK ON IMAGES TO ENLARGE]

Globe:

pause-sep16-globe

The Pause has ended. A trend of +0.18 C/100 years (+/- 0.1C) since March 1998 is about one sixth of the trend for the whole record.

And, for the special benefit of those who think that I am deliberately fudging data by using 12 month running means, here is the plot of monthly anomalies:

pause-sep16-globe-monthly

Northern Hemisphere:

pause-sep16-nh

The Northern Hemisphere Pause has ended.

Southern Hemisphere:

pause-sep16-sh

For well over half the record, the Southern Hemisphere still has zero trend.  The Pause may end shortly.

Tropics:

pause-sep16-tropics

As expected, the Pause in the Tropics (20N to 20S) has ended.

Tropical Oceans:

pause-sep16-tropic-oceans

The Pause remains (just) for ocean areas.

Northern Extra Tropics:

pause-sep16-next

The minimal trend is creeping up- how high will it go before decreasing again?

Southern Extra Tropics:

pause-sep16-sext

The Pause is one month longer.

Northern Polar:

pause-sep16-np

Another big increase in temperature in this region but the minimal trend is still one seventh that of the whole record.

Southern Polar:

pause-sep16-sp

The South Polar region has been cooling for the entire record- 36 years 11 months.

USA 49 States:

pause-sep16-usa49

One month shorter.

Australia:

pause-sep16-oz

No change.

The next graphs summarise the above plots. First, a graph of the relative length of The Pause in the various regions:

pause-length-sep16

Note that the Pause has ended by my criteria in all regions of Northern Hemisphere, and consequently the Globe, and the Tropics, but all southern regions have a Pause for over half the record, including the South Polar region which has been cooling for the whole record.

The variation in the linear trend for the whole record, 1978 to the present:

trends-78-now

Note the decrease in trends from North Polar to South Polar.

And the variation in the linear trend since June 1998, which is about halfway between the global low point of December 1997 and the peak in December 1998:

trends-jun-98-sep-16

The imbalance between the two hemispheres is obvious. The lower troposphere over Australia has been strongly cooling for more than 18 years- just shy of half the record.

The next few months will be interesting. The Pause may disappear from the Southern Hemisphere soon. How long will the Pause last in the Southern Extra Tropics and South Polar regions?  ( I would like to see separate data for the Extra-tropical regions from 20 to 60 degrees north and south.)

Temperature Variation Due to ENSO

July 25, 2016

In this post I use the Multivariate ENSO Index (MEI) supplied by NOAA at http://www.esrl.noaa.gov/psd/enso/mei/index.html and lower tropospheric temperature data supplied by UAH to show how much of temperature variation over the past 20 years is due to ENSO and how little is due to CO2.  I will keep words brief and let graphics do the talking.

Firstly, here is the MEI data from 1950:

Fig. 1:  Monthly MEI from 1950

mei monthly

As an aside, this is how it compares with SOI data.  The SOI is inverted and both are scaled for comparison.

Fig. 2:  MEI compared with SOI inverted

mei vs soi

Now compare scaled MEI with Global UAH:

Fig. 3: MEI (scaled) and UAH

mei monthly w uah

Notice tropospheric temperatures appear to lag the MEI by some 5 months:

Fig. 4: MEI advanced 5 months and UAH

mei monthly advd 5m w uah graph

Notice both datasets are noisy, and there is a clear discrepancy in the early 1990s.  12 month running means show this more clearly:

Fig. 5:  12 month means of UAH and MEI advanced 5 months:

mei advd w uah 12m

The slump in UAH data is shown by the arrow.  Mt Pinatubo’s main eruption was in June 1991. (Without El Chichon in 1982, there may well have been a much higher spike in the mid-1980s).

Now let’s look at the correlation between monthly MEI and UAH.  Firstly, the whole period from December 1978:

Fig. 6:  UAH vs MEI advanced 5 months 1978 – 2016

mei monthly advd 5m w uah

About 13% of temperature variation is associated with MEI variation.  Doesn’t tell us much does it.  What if we exclude the UAH data for two years from April 1982, and from July 1991 to December 1995?

Fig. 7:  UAH vs MEI advanced 5 months 1978 – 2016 with periods after volcanic eruptions excluded

mei monthly advd 5m v uah excl volcanoes

Considering the fluctuations in both datasets, that shows a fairly strong correlation.

Next, we examine the periods, before, during, and after the Pinatubo influence.

Fig. 8:  :  UAH vs MEI advanced 5 months December 1978 – June 1991, excluding April 1982 to March 1984

mei monthly advd 5m w uah 78-91

Again we see a similar correlation.

Fig. 9:  UAH vs MEI advanced 5 months July 1991 – December 1995

mei monthly advd 5m w uah 91-95.jpg

The strong positive correlation of the previous plots has broken down.

Fig. 10:  UAH vs MEI advanced 5 months January 1996 – June 2016

mei monthly advd 5m w uah 96-16

The correlation is even higher.  Over half of temperature variation is associated with ENSO variation five months previously.  Here is the same 1996-2016 plot but with 12 month running means:

Fig. 11  UAH vs MEI advanced 5 months January 1996 – June 2016, with 12 month running means

mei  advd 5m w uah 96-16 12m

74% of temperature variation for the past 20 years and 6 months can be explained by previous ENSO variation alone.  In the same period, carbon dioxide concentration at Mauna Loa has increased by 44.77 ppm, which is more than 49% of the entire increase from 1958, and Global temperature as measured by UAH has increased by a little over 0.1 degree C.

No wonder Global Warming Enthusiasts were pinning their hopes on the 2015-16 El Nino to put an end to the Pause, but they must also hope for the ENSO- temperature correlation to break down shortly, as a deep La Nina will mean cooler temperatures and further embarrassment for them.  However, the correlation breaks down when volcanoes cause lower temperatures in El Nino conditions as we have seen, but what mechanism could there be for higher temperatures in La Nina conditions?  Perhaps that magical greenhouse gas CO2?  That would indeed be spectacular- there are no outliers at the low end of any of the above plots.  The most UAH has been higher than expected with low MEI is about +0.2C to +0.3C, and those values cannot be described as outliers.  Besides, UAH for June is already down to +0.34C, and we are only four months past the peak- the cooling has barely begun.

Finally, this is a plot of the centred 37 month mean MEI (because La Ninas can last for three years).

Fig. 12: 37 month centred mean MEI

mei 37m avg

Notice that before 1975 the 37 month average never exceeded +0.5, the majority of the time was in negative territory, and in the 1950s and 1970s reached below -1.0.  Since 1975 the MEI has dropped below -0.5 only once in 2000 and approached -0.5 in 2012, but has been in positive territory for the vast majority of the time, exceeded +0.5 in six events, and was above +1.0 in the early 1990s.  It would be surprising if global temperatures had not seen a large increase.

How low will the monthly MEI go with the coming La Nina, and how low will the following global temperatures go?  All depends on La Nina’s length and strength, but the monthly MEI data are falling fast.  Stand by.

Temperature and Mortality

May 24, 2016

We are all going to die, nothing is surer. “Nobody knows the day or the hour”, but one thing is clear: we are more likely to die in winter than in summer.

Death by unnatural causes (suicide, accident, bushfire, disaster, even acute illness) can come to otherwise healthy people of any age. Death by natural causes is more predictable.

Those vulnerable to death are the elderly, very young babies, those with chronic illness (e.g. asthma, diabetes) and weakened immunity, and those with respiratory and circulatory illness.

Analysing mortality is made difficult because the sample population is always changing. Excess deaths in one month may be followed by further excess deaths in the following month, or because so many vulnerable people have already died, there will be fewer than expected deaths in the next month or months, or even the next couple of winters. Similarly, if fewer than expected deaths occur, there will be a larger cohort of the vulnerable in the following months, getting older and with probably poorer health. Population growth, aging, migration, improved vaccines, and public education programs all play a part as well.

In this analysis, I use mortality and population data from the Australian Bureau of Statistics (ABS), and temperature data from the Bureau of Meteorology (BOM), for Victoria, as it is a small and compact state which is subject to large temperature changes and also severe heat waves. Monthly mortality data are difficult to find, so this study is restricted to the period January 2002 to December 2011. A 10 year period is hardly sufficient for meaningful averages, however some useful insights can be found.

Mortality statistics are available by month, but population figures are by quarter, therefore I interpolated estimated monthly population figures based on three month growth.

Firstly, this plot shows the total deaths for every month from January 2002 to December 2011.

Fig. 1:

act D per mnth
Note the seasonal spikes and dips. The apparent increase in deaths can be compared with Victoria’s population increase:

Fig.2:

Population Vic
By dividing the total deaths by the population in thousands we can calculate the death rate:

Fig. 3:

Death rate per yr

Note the mortality rate has decreased, and that, in spite of heatwaves, bushfires, and flu pandemics, 2009 had a lower death rate than 2008.

Because months have varying numbers of days, a better analysis can be made by calculating the Daily Death Rate for each month (by dividing each monthly rate by 31, 30, 29, or 28 days).

Fig. 4:

mortality per month

For the state of Victoria for the 10 years to 2011, on average more deaths occurred for each day in August than for any other month. The lowest Daily Death Rate was in February.

Now compare with monthly averages (2002 to 2011) for maximum and minimum temperatures:

Fig. 5:

Tmax Tmin avg

The death rate peak lags July temperature by about a month. Cooler months (June to September) are deadlier than warmer (December to April).

The relationship with temperature can be shown with scatter plots:

Fig. 6:

DDR v Tmax

Fig. 7:

DDR v Tmin

Which merely reinforce that deaths are more likely in winter!

Now we look at the question of estimating how many deaths are likely in a given period, by multiplying the average daily death rate for each month by the number of days in each month and by the estimated total population for each month. By subtracting this figure from the actual number of deaths we get a mortality “anomaly”.  The following graph shows this anomaly for each year:

Fig. 8:

Act minus exp deaths per year

And each month:

Fig. 9:

Diff act minus exp Deaths per mnth

Note the peaks in the winters of 2002 and 2003, and also in the summer of 2008-2009. Note also that both graphs show that in spite of a killer heatwave, the Black Saturday bushfire, and the swine flu pandemic, deaths in 2009 were below what could be expected.

To put the anomaly for January 2009 into context, we can compare actual daily deaths per 1,000 population for all months from 2002 to 2011:

Fig. 10:

act daily D per mnth

Note that the extreme figure for January 2009, while extremely high for January, is still below those of the lowest extremes of June, July, and August.

Perhaps higher mortality in the winter months is coincidence and due to some other factor than temperature- seasonal flu incidence for example. I now look at the month of August with the highest average mortality rate:

Fig. 11:

Act minus exp deaths vs Tmin August

There is fairly decent correlation showing that for every degree warmer in minima, the August death toll will be around 150 less than expected.

February, with the lowest rate:

Fig. 12:

Act minus exp deaths vs Tmin Feb

Even in summer, warmer minima mean fewer deaths.

In summer, do higher maxima cause more deaths?

Fig. 13:

Act minus exp deaths vs Tmax Feb

Even including the 173 deaths in the Black Saturday bushfires in the 200 extra deaths for February 2009, there is no trend.

January, whose data include the 2009 heatwave:

Fig. 14:

Act minus exp deaths vs Tmax Jan

A very small trend, but the 2009 heatwave outlier is obvious and skews the data. (Victorian health authorities say there were 374 excess deaths in the week to 1 February 2009).

Extreme heatwaves are indeed killers. Normal hot summers up to two degrees above average are not.

Conclusion:

Improved public health measures, influenza vaccines, and improved public awareness – plus warmer winters- have led to a decrease in the Victorian mortality rate in the period 2002-2011.

Extreme heatwaves are dangerous in Victoria and cause hundreds of extra deaths especially amongst the elderly (>75 years old). However, these are rare events. Severe and Extreme Heatwaves are newsworthy precisely because they are unusual.

Normal Victorian winters are even more dangerous with on average 17.5% more deaths in winter than summer every year, but because this is normal and expected, this regular annual spike in deaths is unremarkable and not newsworthy- much less regarded as a natural disaster. While 374 excess deaths in a week in a heatwave is shocking, even with these included, the highest January’s Daily Death Rate (in 2009) is below that of the lowest of any winter month.

Warmer minimum temperatures are associated with lower death rates at all times of the year, but especially in August in Victoria, where for every degree of extra warmth, about 150 fewer deaths can be expected. I hope, for the sake of those who are sick or elderly, that we have a warm winter this year.