Factors affecting DLWIR - NREL data (re-analysis)

Data from NREL re-analysed - spread sheet corrected, ULWIR nulling removed as this is basically the same as temperature, latest data added.

Data is dependent on cloud cover. Unfortunately cloud data is only available during daylight. Hours of darkness therefore are not included in this analysis

 The first plot shows the variation with date  (2004 to 2013) Unfortunately the calibration of the pyrgeometer (including device swapping on every calibration) shows up as a signal greater than any trend. The calibration dates are shown as dotted blue lines.

2004 - 2006 31192F3 large error
2006 - 2008 31194F3 10 w/sqm drop
2008 - 2009 31192F3 random 8w/sqm p-p
2009 - 2011 31194F3 10 w/sqm drop
2011 - 2012 31192F3 random 13w/sqm p-p
2012 - 2013 31194F3 random 10w/sqm p-p
It appears that 31194F3 has a drift with time (now possibly corrected)
Also it seems that +-5w/sqm is the expected accuracy for this type of pyrgeometer

Luckily this is the only data extraction that is synchronous with date. Other extraction will tend to remove the drift by averaging. It is significant that the calibration adjustments show up indicating that this spreadsheet successfully sees valid changes of <5w class="goog-spellcheck-word" span="" style="background: none repeat scroll 0% 0% yellow;">sqm

This next plot shows the expected variation of dlwir with temperature.

It should be noted that the drop in value at the high temperature end is most likely due to the small number of results returned and is therefore not valid.

The net plot shows the effect of increasing absolute humidity

 

The following pair show the change with day of the year. Note that temperature effects should have been nulled so the peak should not be due to summer temperature. The second plot compares La Jolla CO2 with the dwlir. The dwlir seems to show the inverse of what would be expected!

Station pressure below, has very little effect on dwlir

Opaque cloud cover increases the dwlir!

Data from:
http://www.nrel.gov/midc/srrl_bms/

Temperature dependence - more analysis of NREL data

This analysis shows the interdependence of temperature and other data.
Temperature may be the cause or the effect!
The second curve on each plot shows the number of results returned. Obviously the more results the more likely the data returned is valid.
All data is averaged with the top and bottom 30% discarded to remove outliers

Temperature is affected negatively by the absolute humidity (gms h2o/cu m). To heat air and water vapour takes more energy than air alone hence the negative slope.

Using the nulling technique produces a plot with little day of year dependence (no annual peak or dip is obvious).
The slope of the line is =0.0001602 per day. This equates to 0.585°C per decade and this is over a period that people say the warming has stopped!

As expected with opaque cloud cover the temperature is negatively correlated.

Temperature with day of year is as expected with a peak at day 200 (19th July) and a minimum at day 40 (9th February). These dates are of course offset from longest/shortest day.

Plots of the nulled variables:

Note that the nulled portion is sometimes limited to less than whole range. In this case a limit is used to only accept data for that nulled range on that variable

Also the nulling process is only used to produce a line of zero slope for each variable - the offset from zero is not relevant as only anomalies are plotted.

Data from:
http://www.nrel.gov/midc/srrl_bms/

Cycle Mania and Hadcrut3

From the fun school of posts here are a couple of plots that reconstruct hadcrut3v from a series of sine waves.
One shows reconstruction from cycles only; this has problems getting a good fit in the 1800s but shows rhat the next few years should be a period of reducing temperatures. The long period controlling the plot is 317 year long
The other is constructed round a smooth increasing trend. A better fit in the 1800s and still shows that despite the trend the temperatures will be flat for a few more years before increasing with a vengance. The underlying trend is defined by this polynomial
y = 2.40389E-07x3 - 1.34093E-03x2 + 2.49320E+00x - 1.545547E+03

Do either have any predictive skills. = NO

The most importasnt thing shown is in the the trending plot where  despite an ever increasing trend there is still a period where temperatures appear not to increase - from 1998 to 2018. this is due to an underlying 60year period being on a down part of the cycle. This is something that the "skeptics" cannot seem to grasp - CO2 is increasing so why is temperature static?.

The all cycle:

317 year and 60.1 year cycles controlling the "trend"

The trend+cycle plot

Trend and 59.75 year cycle controlling trend

So what curve are we "following" - only another 4 or so years will tell!.

Earlier posts:
http://climateandstuff.blogspot.co.uk/search/label/simulation

Some more analysis of u/d lwir and clouds

Total / opaque cloud vs Temperature

No slope on the opaque cloud but a definite dip when teperatures are between 16 and 22C

 

The following plots limit RH to 20 to 40%. Day refers to time that cloud can be measured Night to when cloud is not measured.

D/U LWIR vs Temperature (night values - 0-100% cloud)

Both upward and downward LWIR linear proportional to temperature

D/U LWIR vs Temperature (day values cloud 0-100%)

Very similar to night - slopes are a bit different.

D/U LWIR vs Temperature (day values but limiting cloud to 20 to 40%) (note change in humidity limits

D/U LWIR vs Humidity Temp 22-24C cloud 40-50%

By constraining the temperature to a 2C band The temperature effects on IR are minimised whilst still returning a reasonable number  of results. Note that the ULWIR falls with increasing cloud but the DLWIR rises by 100w/sq m 

D/U LWIR vs Opaque Cloud cover temp 22-24C RH 35-40%

Temperature RH are constrained to minimise these effects. The DLWIR increases by approx 80 w/sqm

 

I think these last two plots conclusively prove relative humidity and cloud cover have a positive effect on the downward long wave ir (ir increases if cloud and/or RH increase)

 

Now how do you do this for CO2?

Data available from:
http://www.nrel.gov/midc/srrl_bms/

 

USCRN Average vs Mean data

From the downloaded data from uscrn

        Average temperature, in degrees C, during the 24 hours of the day.
        Note: USCRN/USRCRN stations have multiple co-located temperature
        sensors that record independent measurements. This value is a single
        temperature that is calculated by averaging 24 full-hour averages
        derived from the multiple independent measurements of 5-minute intervals
        during each hour

        Mean temperature, in degrees C, calculated using the typical historical
        approach of (T_DAILY_MAX + T_DAILY_MIN) / 2.

 

Mean temperature gives twice the slope of average

The Effect on slope using base period from 1931 to 1995

As requested for WUWT here is a plot of linear curve fit to plots of the same data referenced to 30 year periods from 1931 to 1995
eg. base periods
1931 to 1961
1945 to 1975
1995 to 2012* THIS DOES NOT GIVE SUFFICIENT YEARS BUT IS PLOTTED.

Only stations returning over 15  reference base years were used as noted on the normal plot. The stations in this data set are from the UK

So it looks as if the slope changes by over 10% but 1961 to 1991 gives one of the lower slopes. Choosing 1931 the slope (deg C per year) is near the maximum!

NOAA/NCDC and BEST compared to Watts Favourite

Now found some US data (presumed ALL US not just CONUS) up to 2012 from NOAA
 http://www7.ncdc.noaa.gov/CDO/CDODivisionalSelect.jsp

These are monthly (like BEST) and so to fit with USCRN/USRCRN daily dat I have assumed a months worth of constant temperature for both these sources. This data is then passed through the same processing as the USCRN/USRCRN to produce the plot.

As can be seen the NOAA data for June is significantly higher than USCRN so Tony's claim of "not the warmest July" may be correct. However the overall trend of NOAA is significantly downwards compared tio the upwards trend of USCRN.

This being the case Tony may be backing the wrong horse in this race. USCRN (his ACCURATE) data stream show continual warming over the last decade. Time will tell (hopefully before disater strikes!).

 

uscrn 60 days average

noaa 200 day average

Best 80 day average

 

The Effect Of Anomaly reference period on Temperature Plots

Much rubbish is talked in some locations about how the reference period for anomaly plots is chosen to to create worse temperature rises than reality.

Here is a plot showing monthly data from UK station temperatures. These are converted into anomalies by taking 30 Januarys from the start year, averaging the temperature and then subtracting this from each January to create an anomaly. This is repeated for each month to create the full plot.

This method shows how met. stations are warming and allows a reasonable average anomaly to be calculated over dissimilar (envirnonmentally) stations. It also removes the annual fluctuation in temperature reducing the need to filter this out.

The plot shows 5 different start years from 1951 to 1991 (the latter only having 20 years averaged for the anomaly calculation.

As can be seen all that happens is the plot gets shifted up and down the chart. The wiggles and the slope are constant.

NO ADDITIONAL WARMING is created by changing the reference period. The reference period is not relevant unless the distribution of temperatures throughout the year changes.

USCRN compared to Best

Plot showing Best and USCRN data.
Best is monthly data Average
USCRN is daily Average

Quite a good match (bearing in mind Best is7500 stations and USCRN is 40)

The match becomes better as more uscrn stations come on line

The slope of linear trend is -ve in Best +ve in uscrn mainly due to Best data terminating in 2011

Berkley Data Here

http://berkeleyearth.lbl.gov/regions/united-states

USCRN/USRCRN - The CONUS data

Data from the contiguous states of USA.

This is derived from all locations that have reported data from 2003 onwards. The more rescently commissioned sites therefore do not appear

The average plot shows a temperature increase of 0.3K per decade
The maximum plot shows a temperature increase of 0.6K per decade
The minimum plot shows a temperature increase of 0.14K per decade

The main plots are averaged over 10 days
The spaghetti plot is averaged over 100 days

It is interesting to note that the temperature increase since 2010 is much greater than that for the whole plot the final plot here shows a temperature increase of 6.8K per decade (PS. I realise that this is more weather than climate!)

How will watts handle this?!!!!!!!

And a final a couple with longer averaging

1. showing a 200 day averaged max chart

2. showing a 400 day averaged spaghetti plot

The last 3 years:

USCRN/USRCRN - "perfect" data and how it compares

A quick look at a few CONUS (contiguous US states) data

"ITS WORSE THAN WE THOUGHT!"


http://wattsupwiththat.com/2012/08/08/an-incovenient-result-july-2012-not-a-record-breaker-according-to-the-new-noaancdc-national-climate-reference-network/

Tony has said that this date is as good as it is going to get as far as temperatures go.
He has invalidly compared USCRN with the older ghcn network using absolute values (does not get rid of any offsets between the data sets). And concludes this July is not a record breaker.

Leaving "records" aside this is the plot for 12 of the stations reporting from 2002 to current date (daily data)

A note: some of the stations reporting early in 2002 have much missing data that seems to give a high temperature anomaly in the first few months. This has been left in.

These are a simple arithmetic average of the 12 station data which individually have been averaged over 20days

This is a first stab at these plots so they may change if errors are found

Data from:
ftp://ftp.ncdc.noaa.gov/pub/data/uscrn/products/daily01
plots updated - more data - corrected averaging

Max temperature plot

Linear fit gives 0.774C/decade


Min temperature plot

Linear fit gives 0..22C per dacade


Average temperature plot

Linear fit gives 0.44C per decade


Spaghetti plot showing station names

As a comparison here is the plot for crutem3v from wft

Over the same period a negative slope!

I wonder if Tony will reject the USCRN data in the same manner as he tried to kill the Best data!

Revisionism in the satellite Temperatures

http://discover.itsc.uah.edu/amsutemps/
 Dr. Roy Spencer and Dr. Danny Braswell, NSSTC Control the data

No problem with this (it presumably corrects errors from an older satellite) But if this had been done by CRU/Giss etc. it would have headlined on WUWT and CA with statements that this proves that the data and its controllers cannot be trusted!

One interesting one is CH4 (final plot) thewhole of the last years data (not shown on plot) has been deleted - why?

Prediction(!!!) of future temperature using "cycles" + trend

This is an update of an earlier post and is just a "fun" thing

Get HADCRUT3V global temp record
Average over 6 months to remove some of the "noise"
Create a series of narrow band filters on the resultant temperature plot.
Tune each filter manually to isolate peaks in the output.
These SHOULD show peaks wherever there is a signal of, say, Scarfetta's 60 years.
Take the output of each narrow band filter and generate a cosine wave that is as near as possible the same amplitude and phase as the filtered signal.
Do this a number of times isolating each frequency.
Add together the generated cosines. Multiply the result by a factor (approx 3 in the plot below). If there is a suitable long period - low frequency - signal isolated the resultant should match the original signal. IT DID NOT so a trend was added.
y = 2.44231E-07x^3 - 1.36387E-03x^2 + 2.53884E+00x - 1.57576E+03 (not good as it deviates before 1850.
This is what I got :


Note
No 60 year signal
No massive TSI signal (there is some!)
The significant signals are all around 2 to 6 years
The plot shows prediction for the next few years!!!!

Get HADCRUT from CRU website
Get Excel from microsoft
Get bandpass filter from
http://www.web-reg.de/index.html
in general set the bandwidth to months/150 (e.g. period start 21.19 end 21.29 months ie. months/200 in this case)

How much warming

From post on WUWT:

What is evident from my plot is that the period from 1985 to present does not (yet !!) conform to the general linear trend. Adding a trend line to 1985 to present gives a warming of 4.4degC/century (got it right this time I think)


The current trend in CET is negative so there is a possibility that in a decade or so there will be a return to the .3C/100year average. But can we wait to find out?

Looking at satellite data:


data: http://discover.itsc.uah.edu/amsutemps/
the channel CHLT (no longer reported – too much of an incline??!!) gives a temp increase of 11C/century.
It would be interesting to know why this channel was dropped.