Cloud effect on Temperature limited to 3 hour window

As below but now liminted to a 3 hour window around midday. Note expanded Cloud cover scale (uneven)

Here's one with same RH and Months as previous example (diff scale)

The effect seems to be modified by the RH. At a high humidity the clouds have a more negative effect whilst a low humidity - maximum is cooled while minimum is heated.

All data and excel sheets available on request!

Just how sensitive is temperature to cloud coverage

Data is from the same place as the other plots below:
http://www.nrel.gov/midc/srrl_bms/

NREL Solar Radiation Research Laboratory
Baseline Measurement System BMS
Latitude: 39.742o North Longitude: 105.18o West
Elevation: 1828.8 meters AMSL

Since the previous plots I have downloaded another 4 years of hourly date from 2004 onwards. This of course will give better results
The following plots show : Temperature (max/min) variation with opaque cloud coverage. The data is only counted if :
1. It falls within the month selected.
2. The humidity is within selected limits
3. The cloud coverage is within 5% selected boundary
4. It is sufficiently light that cloud coverage is measurable optically (daylight!)

Each "returned data " count refers to 1 hour slots within the time period selected (months) Some data plots shown are for very sparse data. Any plot point with one point is just about irrelevant and certainly shows no difference in max and min!

The returned data is the max and min for the resuts returned for that period and could therefore show a spurios figure.

Further limitations on time of day would remove the pick-up of minimum at dawn / max after midday.

Dont Know where November went!

It seems that if it is cool then clouds warm even during the day

If it is hot then clouds cool.

Only one location, and very little data for each month but cloud effects on temperature seem not to be as negative (lower temps with more cloud) than others suggest.

Now if there was another 10 years of data from another location then a much better idea of the effect of clouds could be obtained.

Nenana Ice Classic - The River has moved!

The Nenana Ice Classic is a Non-Profit Charitable Gaming Organisation run to provide funds for local charities.
Read about it here:
http://www.nenanaakiceclassic.com

The object is to guess the date and time the Tanana river ice breaks up. The contest has been running since 1916.
Obviously there are many variables other than global warming that affect the break up - industry up stream, pollution,etc. However, there is no possibility of climate scientists changing the data and so is a true representation of changes in the local environment.

The last few years has seen an increase in the time since beginning of the year for the break up. This year has seen a large drop to 3rd lowest date.

Two plots are shown below. One shows a 2 line fit to the data showing fits from 1916 to 1965 and from 1965 to date.
The second is showing 3 splits that last year showed a possible up-tick from 2005 onwards (now reversed)

DLWIR Holding 2 parameters at the limit allowing sensible results

A couple of re-plots with less variation in the 2 other params. Also added a count of returned results for each measurement

Basically More cloud = less escaping radiation - not a lot of difference between total and opaque cloud coverage

But the biggest effect is from water vapour. With the small number of samples it looks as if the response is logarithmic with percentage relative humidity.

Backradiation - fixing the effect of 2 variables plotting the third

Up to now I've plotted the effect of cloud coverage, humidity and temperature on the difference between DLWIR and ULWIR.

However these plots are not a simple xy since there may be a correlation between temperature/humidity and clouds.

To improve the plots it would be best to plot for example humidity vs dlwir/ulwir at a fixed temperature and cloud cover. The problem is there are too few corresponding points to get a meaningful result.

The following plots were made by inspecting plots and choosing a range of values for each parameter where the dlwir/ulwir change is minimal (about 10% or less)

As a trial cloud coverage was replotted at a much closer variation in the other 2 parameters - this shows a good correspondance with the wider variation but with increased variability.

It should be pointed out that the dlwir as a % of ulwir is a combination of at least all the 3 parameters considered. All that can be gleaned from these plots is the effect of variation of  one parameter whilst holding the others static.

It should be noted that cloud cover is only measured during daylight. All the plots below are therefore only relevant for daylight.

From the above it can be seen that the:

temperature effect is inconsistent and small

relative humidity is the largest effect - more humidity more DLWIR

Cloud cover is significant - more clouds more DLWIR



The Effect of Humidity on Back Radiation

Continuing the same theme Here is the effect of changing Humidity (both relative and absolute) on the percentage of backradiation compared to upward radiation

Difficult finding a conversion between relative humidity (as measured) and absolute humidity.

the equation used for the above plot was:

abs humidity=1320.65/(273+T)*rh/100*10^(7.4475*(T+273-273.14)/(T+273-39.44))



So It looks as if the effects of cloud cover / temperature should only be made at fixed RH.

Yet more sums to do!

New Back Radiation Results

Continuing from the last 2 posts here is the effct of temperature on radiation.
Some at pseudo science stes suggest that all that is being measured by upward facing sensors is an effect of the local temperature.
If this were the case then warmer air would mean greater difference between upward and downward radiation - this is not seen

A repeat of the effect of cloud covage using DLWIR as a percentage of ULWIR instead of a simple difference

Diference between upward and downward IR vs Cloud cover

Using the same data source as below.
Here is the effect on downward/upward radiation of various cloud cover percentages (both total and opaque cloud)

Location of measurements

NREL Solar Radiation Research Laboratory

Baseline Measurement System BMS

Latitude: 39.742^o North
Longitude: 105.18^o West
Elevation: 1828.8 meters AMSL

Measurements Of ULWIR and DLWIR

A couple of plots from :
http://www.nrel.gov/midc/srrl_bms/

It is interesting to note:

•  that Upward LWIR is increasing faster than Downward LWIR over the very short period of available data.
• That often the downward LWIR is close to the upward LWIR during the night and presumably cloudy conditions

Final Plot but with cloud cover added