Posts Tagged ‘temperature’

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.

Land and Sea Temperature: South West Australia

November 29, 2016

This year, the south-west of Western Australia has recorded some unexpectedly low temperatures.  Has this been due to rainfall, cloud, winds, or the cooler than normal Leeuwin Current and Sea Surface Temperatures in the South West Region?

In this post I examine maximum temperature and rainfall data 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.

Figure 1 is a map showing the various Sea Surface Temperature monitoring regions around Australia.

Fig. 1

sst-regions

The Southwest Region is just to the west and southwest of the Southwest climate region, and winter south westerlies impact this part of the continent first.  2016’s winter has seen maxima drop sharply.  In fact, it was the coldest winter since 1993:

Fig. 2:  Southwestern Australia Winter TMax Anomalies

sw-tmax

There is a relationship between rainfall and Tmax- as rain goes up, Tmax goes down, so here south west rainfall is inverted and scaled down by 100:

Fig. 3:  TMax and Rain:

sw-tmax-rain

The next plot shows TMax and the South West Region’s Sea Surface Temperature anomalies (SST):

Fig. 4:  TMax & SST:

sw-tmax-sst

Again, related: both have strong warming from the 1970s.  Next I check for whether there was a real change in direction in the 1970s, and if so, when.  To do this I use CuSums.

Fig. 5:  CuSums of Winter TMax and SST compared:

sw-tmax-sst-cusums

Both have a distinct change point: 1975, with SST warming since, but TMax appears to have a step up, with another change point at 1993 with strong warming since.  Rainfall however shows a different picture:

Fig. 6:  CuSums of Winter Rainfall

sw-rain-cusums

Note the major change at 1968 (a step down: see Figure 3), another at 1975 with increasing rain to the next change point at 2000, after which rain rapidly decreases.

I now plot TMax against rainfall and SST to see which has the greater influence.  First, Rain:

Fig. 7:  TMax vs Rain:

sw-tmax-vs-rain

100mm more rain is associated with about 0.5C lower TMax, but R-squared is only 0.22.

Fig. 8:  TMax vs SST:

sw-tmax-vs-sst

A one degree increase in SST is associated with more than 1.1C increase in TMax, and R-squared is above 0.51- a much closer fit, but still little better than fifty-fifty.

TMax is affected by rain, but more by SSTs.

I now look at data since the major change points in the 1975 winter.  The next three figures show trends in SST, Rain, and TMax.

Fig. 9:  Trends in SST:

sw-sst-trends

Warming since 1975 of +1.48C/ 100 years.

Fig. 10:  Trends in Rainfall:

sw-rain-trends

Decreasing since 1975 at 89mm per 100 years (and much more from 2000).

Fig. 11:  Trends in TMax:

sw-tmax-trends

Warming since 1975 at +2.14C per 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. 12:  SST Detrended:

sw-sst-detrended-75-to-16

Fig. 13:  Rainfall Detrended:

sw-rain-detrended-75-to-16

Fig. 14:  TMax Detrended:

sw-tmax-detrended-75-to-16

Note that SST in 2016 is just below trend, but still above the 1961-90 average.  Rainfall is only slightly above trend, and still below average.  However TMax is well below trend, and well below average, showing the greatest 12 month drop in temperatures of any winter since 1975.

My conclusions (and you are welcome to comment, dispute, and suggest your own):

  • Maximum temperatures in winter in Southwestern Australia are affected by rainfall, but to a much larger extent by Sea Surface Temperature of the South West Region.
  • The large decrease in winter temperature this year cannot be explained by rainfall or sea surface temperature.  Cloudiness may be a factor, but no 2016 data are publicly available.  Stronger winds blowing from further south may be responsible.

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.

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.

The Pause Update: April 2016

May 9, 2016

The complete UAH v6.0 data for April were released on Friday.  I could have presented this earlier, but there are some more important things in my life, like grandkids’ sleepovers and Mothers’ Day.  Back to business.  I present all the graphs for various regions, and as well summaries for easier comparison.  The Pause still refuses to go away, despite all expectations.

These graphs show the furthest back one can go to show a zero or negative trend (less than +0.1C/ 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 5 months long- 448 months. 12 month running means commence in November 1979. The graphs below start in December 1978, so the vertical gridlines denote Decembers. The final plotted points are April 2016.

 [CLICK ON IMAGES TO ENLARGE]

Globe:

Apr 16 globe

The 12 month mean to April 2016 is +0.43C.  However, the Pause is still an embarrassing reality! For how much longer we don’t know.

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, which shows that The Pause is over by my rather strict criterion:

Apr 16 globe mthly

+0.22C/100 years since December 1997- not exactly alarming.  The Pause will return sooner with monthly anomalies than 12 month means of course.

Northern Hemisphere:

Apr 16 NH

The Northern Hemisphere Pause refuses to go quietly and remains at the same length. It may well disappear in the next month or two.

Southern Hemisphere:

Apr 16 SH

The pause has shortened by one month.  For well over half the record the Southern Hemisphere has zero trend.

Tropics:

Apr 16 Tropics

The Pause has shortened by 3 months.

Tropical Oceans:

Apr 16 Tropic Oceans

The Pause has shortened by 3 months.

Northern Extra Tropics:

Apr 16  NH ExtraTropics

The Pause by this criterion has ended in this region, however note that the slope since 1998 is +0.17 +/- 0.1C per 100 years compared with +1.56C for the whole period.  That’s not much above dead flat.

Southern Extra Tropics:

Apr 16  SH ExtraTropics

The Pause has lengthened by one month.

Northern Polar:

Apr 16 NP

No change.

Southern Polar:

Apr 16 SP

At -0.18C/ 100 years, this region is cooling for the entire record.

USA 49 States:

Apr 16 USA 49

No change

Australia:

Apr 16 Oz

One month longer.

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

Pause length

Apart from  the North Polar, whose Pause is shorter, and the Northern Extra Tropics, whose Pause has ended, all other regions have a Pause of 18 years 3 months (half the record) or longer- 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 1978 regions

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 1998 regions

The only region to show strong warming for this period is the North Polar region: the Northern Extra Tropics at +0.18C/ 100 years has very mild warming, and the Northern Hemisphere at +0.12C/ 100 years is virtually flat: all other regions are Paused or cooling.

12 month means will continue to grow for the next few months, so the Pause as  here defined may disappear shortly, and may not reappear until early 2018.  The impact of the coming La Nina will be worth watching.  Unless temperatures reset at a new, higher level and continue rising, very low trends will remain.

Antarctic Trends

April 17, 2016

Data from UAH Version 6.0 show the South Polar region to be unique in that it has a Pause, if not very mild cooling, for the whole of the satellite record, since December 1978. In this post I dig in a little deeper, and also look at surface data from Australia’s Antarctic bases.

Fig.1: Monthly TLT for the South Polar region (60- 85 S)

SP monthly

Fig. 2: Three Monthly TLT

SP 3m

Both plots show no evidence of any warming. However, Land areas are warming:

Fig. 3: SP Land: 3 month means

SP land 3m

While the Ocean area is cooling:

Fig. 4: SP Oceans: 3 month means

SP ocean 3m

Summers are warming:

Fig. 5: South Polar Summers (Yearly)

SP summer

While winters are cooling rapidly:

Fig. 6: South Polar Winters

SP winter

Especially Ocean winters, when the sea ice is at its greatest and thickest extent.

Fig.7:  SP Ocean Winters

SP ocean winter

Perhaps the sea ice insulates the atmosphere from the water below the ice? If so, in summer, with sea ice extent much reduced, the atmosphere above the ocean should be warmed much more than above the land, which is almost totally covered by ice. Let’s check:

Fig.8:  SP Ocean Summers

SP summer ocean

Fig.9:  SP Land Summers

SP summer land

Nope- TLT above land area is warming at four times the rate of ocean areas.

It’s not a great mystery. Here’s why.

We should not read too much into whether individual months create records or not, nor should we stress about the seasonal differences. Here’s an example of individual Octobers.

Fig.10: Octobers from 1979-2015

SP land october

Note the rising and falling pattern: a series of below average Octobers is followed by a series of above average Octobers.  A trend using only Octobers would show warming, as the record starts with below average Octobers and ends with above average. (Just like some global datasets!)

These patterns are evident, but with different values, in all months, which is why winters appear to be cooling and summers appear to be warming.

Fig.11:  SP Ocean Junes from 1979-2015

SP ocean junes

The most we can say is that the long term trend of ALL months shows no evidence of any warming, i.e. a Pause.

So is this just an artefact of the fairly short satellite record? We can check against surface data from Australia’s Antarctic stations at Mawson and Davis. (There is insufficient overlap to make a useful splice between closed and open sites at Casey.) These stations are on the coast far from the Antarctic Peninsula.

Fig. 12:  Monthly mean temperatures, Mawson Base

mawson mean

There is a Pause, or slight cooling, over the past 62 years.

Fig. 13: Monthly mean temperatures, Davis Base

davis mean

At Davis, a Pause, or slight warming, over the past 47 years.

The Pause in the South Polar region is real.

The Pause Update: March 2016 (Complete)

April 8, 2016

The complete UAH v6.0 data for March have been released. I present all the graphs for various regions, and as well summaries for easier comparison.  The Pause refuses to go away, despite greatly exaggerated rumours of its death.

These graphs show the furthest back one can go to show a zero or negative trend (less than +0.1C/ 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 4 months long- 448 months. 12 month running means commence in November 1979. The graphs below start in December 1978, so the vertical gridlines denote Decembers. The final plotted points are March 2016.

As I intimated in the previous post, there have been some small changes in the data. Some slope values have changed slightly.

[CLICK ON IMAGES TO ENLARGE]

Globe:

Mar 16B globe

Sorry, GWEs, The Pause, for more than half the record, is still an embarrassing reality! For how much longer we don’t know.

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, which shows that The Pause is over by my rather strict criterion:

global monthly B 2016 mar

I will continue posting these figures showing these scarey trends from monthly anomalies. The Pause will return sooner with monthly anomalies than 12 month means of course.

Northern Hemisphere:

Mar 16B NH

The Northern Hemisphere Pause refuses to go quietly and remains at more than half the record. It may well disappear in the next month or two.

Southern Hemisphere:

Mar 16B SH

For well over half the record the Southern Hemisphere has zero trend.

Tropics:

Mar 16B Tropics

Tropical Oceans:

Mar 16B Tropic Ocean

Northern Extra Tropics:

Mar 16B NExtraTropics

The Pause by this criterion has ended in this region, however note that the slope since 1998 is one tenth of the slope for the whole period.

Southern Extra Tropics:

Mar 16B SExtraTropics

Hmmm!

Northern Polar:

Mar 16B NP

The Pause here has shortened.

Southern Polar:

Mar 16B SP

As the trend exceeds -0.1, this region is cooling for the entire record.

USA 49 States:

Mar 16B USA

Australia:

Mar 16B Oz

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

Pause length var regions

Apart from  the North Polar, whose Pause is shorter, and the Northern Extra Tropics, whose Pause has ended, all other regions have a Pause of 18 years or longer- 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 1978 now mar 16

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 1998 now mar 16

The only region to show strong warming for this period is the North Polar region: the Northern Extra Tropics has very mild warming: all other regions are Paused or cooling.

12 month means will continue to grow for the next few months, so the Pause may disappear shortly, and may not reappear until early 2018.  The impact of the coming La Nina will be worth watching.