What Affects DLWIR?

Using the same data source as before, the same parameter nulling gives this set of curves



This is the variation of DLWIR with day of the year (as before but low prob results retained)

This is absolute humidity effect - not linear

Interesting (night is disabled - no cloud information) but DLWIR is greater in mornings and evenings.  Why not midday?

Station Pressure - Possibly a problem with conversion between % hum and abs humidity causes this.

Linear effect with temperature as would be expected

Again a non linear relation with ULWIR

Wild errors are removed from the result by using the trimmean funcion disposing of 25% of highest and 25% lowest values.
Cloud values are measured using a visual light camera - hence no results will be returned for hours of darkness for this analysis.

===========UPDATE====================================================
Instrumentation
u/dlwir
PRECISION INFRARED RADIOMETER
Model PIR
The Precision Infrared Radiometer, Pyrgeometer, is intended for unidirectional operation in the measurement, separately, of incoming or outgoing terrestrial radiation as distinct from net long-wave flux. The PIR comprises a circular multi-junction wire-wound Eppley thermopile which has the ability to withstand severe mechanical vibration and shock. Its receiver is coated with Parson's black lacquer (non-wavelength selective absorption). Temperature compensation of detector response is incorporated. Radiation emitted by the detector in its corresponding orientation is automatically compensated, eliminating that portion of the signal. A battery voltage, precisely controlled by a thermistor which senses detector temperature continuously, is introduced into the principle electrical circuit.
Isolation of long-wave radiation from solar short-wave radiation in daytime is accomplished by using a silicone dome. The inner surface of this hemisphere has a vacuum-deposited interference filter with a transmission range of approximately 3.5 to 50 µm.
SPECIFICATIONS
Sensitivity: approx. 4 µV/Wm-2.
Impedance: approx. 700 Ohms.
Temperature Dependence: ±1% over ambient temperature range -20 to +40°C.
Linearity: ±1% from 0 to 700 Wm-2.
Response time: 2 seconds (1/e signal).
Cosine: better than 5%.
Mechanical Vibration: tested up to 20 g's without damage.
Calibration: blackbody reference.
Size: 5.75 inch diameter, 3.5 inches high.
Weight: 7 pounds.
Orientation: Performance is not affected by orientation or tilt.
-------------------------
This looks as if it is measuring the heating effect (thermopile) of radiation hitting the dome of the sensor (transmission 3.5 to 50um. The thermopile of course generates a voltage dependant on the temperature difference between one side and the other The non-dome side is not exposed to external radiation so no effect there. However, the nondome side temperature must be measured and compensated.
The instrument also compensates for its own generated IR.
No assumption of BB radiation is assumed. It is the ACTUAL heating effect of IR radiation of narrow or wide bandwith hitting the sensor that is the cause.

If the radiative "temperature" is less than the receiver temperature then the thermopile still measures - see series of posts about thermal imaging - the camera microbolometers sitting at 20+C shows temperatures down to -40C

======================================================================
Dry bulb temperature / wet bulb / relative humidity

HMP45C-L Specifications

• Supply Voltage: 12 Vdc nominal (typically powered by datalogger)
• Current Drain: ≤4 mA (active)
• Sensor Diameter: 2.5 cm (1 in.)
• Sensor Length: 25.4 cm (10 in.)
• Cable Diameter: 0.8 cm (0.3 in.)
• Weight: 0.27 kg (0.6 lb)

Relative Humidity

• Sensor: Vaisala’s HUMICAP® H-chip
• Measurement Range:
  0.8% to 100% RH, non-condensing
• Output Signal Range:
  0.008 to 1 Vdc
• Accuracy at 20°C (against factory reference): ±1% RH
• Accuracy at 20°C (field-calibrated against references):
  ±2% (0% to 90% RH);
  ±3% (90% to 100% RH)
• Temperature Dependence: ±0.05% RH/°C
• Long-Term Stability: Typically, better than 1% RH per year
• Response Time: 15 s with membrane filter (at 20°C, 90% response)
• Settling Time: 500 ms

Temperature

• Temperature Sensor: 1000 ohm Platinum Resistance Thermometer
• Measurement Range: -39.2° to +60°C
• Output Signal Range:
  0.008 to 1.0 V
• Accuracy:
  ±0.5°C (-40°C),
  ±0.4°C (-20°C),
  ±0.3°C (0°C),
  ±0.2°C (20°C),
  ±0.3°C (40°C),
  ±0.4°C (60°C)

====================================================================
Cloud - total and opaque

TSI-880 AUTOMATIC TOTAL SKY IMAGER

General Description The Total Sky Imager Model TSI-880 is an automatic, full-color sky imager system that provides real-time processing and display of daytime sky conditions. At many sites, the accurate determination of sky conditions is a highly desirable yet rarely attainable goal. Traditionally, human observers reported sky conditions, resulting in considerable discrepancies from subjective observations. In practice, the use of human observers is not always feasible due to budgetary constraints. The TSI-880 now replaces the need for these human observers under all weather conditions. An onboard processor computes both fractional cloud cover and sunshine duration, storing the results and presenting data to users via an easy-to-use web browser interface. The self-contained design makes it well suited for mission-critical applications such as aviation and military meteorology monitoring. It captures images into standard JPEG files that are analyzed into fractional cloud cover; if networked via TCP/IP (10/100BaseT) or PPP (modem) it becomes a sky image server to remote any user via the web. TSI-880 AUTOMATIC TOTAL SKY IMAGER General Description The Total Sky Imager Model TSI-880 is an automatic, full-color sky imager system that provides real-time processing and display of daytime sky conditions. At many sites, the accurate determination of sky conditions is a highly desirable yet rarely attainable goal. Traditionally, human observers reported sky conditions, resulting in considerable discrepancies from subjective observations. In practice, the use of human observers is not always feasible due to budgetary constraints. The TSI-880 now replaces the need for these human observers under all weather conditions. An onboard processor computes both fractional cloud cover and sunshine duration, storing the results and presenting data to users via an easy-to-use web browser interface. The self-contained design makes it well suited for mission-critical applications such as aviation and military meteorology monitoring. It captures images into standard JPEG files that are analyzed into fractional cloud cover; if networked via TCP/IP (10/100BaseT) or PPP (modem) it becomes a sky image server to remote any user via the web. Specifications Image Resolution: 352 x 288 color, 24-bit JPEG format Sampling rate: Variable, with max of 30 sec Operating Temperature: -40� C to +44� C Weight/Size: Approx.70 lbs.(32 kg); dims: 20.83"x18.78"; height is 34.19"; mounts on 16.75x12" 1/4-20 bolt square Power Requirements: 115/230 Vac; mirror heater duty cycle varies with air temperature: 560W with heater on / 60W off Software: None required for immediate real time display; uses Internet Explorer or Netscape Browsers on MS-Windows, Mac, UNIX (an optional DVE/YESDAQ package is available for data archiving, display, MPEG day movie creation and data reprocessing) Data Telemetry: LAN Ethernet (TCP/IP), telephone modem (PPP) or Data Storage Module option (for off grid sites) ==================================================================================== Precipitation: TE525-L Specifications Sensor Type: Tipping bucket/magnetic reed switch Material: Anodized aluminum Temperature: 0° to +50°C Resolution: 1 tip Volume per Tip: 0.16 fl. oz/tip (4.73 ml/tip) Rainfall per Tip: 0.01 in. (0.254 mm) Accuracy Up to 1 in./hr: ±1% 1 to 2 in./hr: +0, -3% 2 to 3 in./hr: +0, -5% Funnel Collector Diameter: 15.4 cm (6.06 in.) Height: 24.1 cm (9.5 in.) Tipping Bucket Weight: 0.9 kg (2.0 lb) ==================================================================================== Station Pressure CS105/CS105MD Barometric Pressure Sensor 1. General The CS105 analog barometer uses Vaisala’s Barocap silicon capacitive pressure sensor. The Barocap sensor has been designed for accurate and stable measurement of barometric pressure. The CS105 outputs a linear 0 to 2.5 VDC signal that corresponds to 600 to 1060 mb. It can be operated in a powerup or continuous mode. In the powerup mode the datalogger switches 12 VDC power to the barometer during the measurement. The datalogger then powers down the barometer between measurements to conserve power. 2. Specifications Operating Range Pressure: 600 mb to 1060 mb Temperature: -40 C to +60 C Humidity: non-condensing Accuracy Total Accuracy*** 0.5 mb @ +20 C 2 mb @ 0 C to +40 C 4 mb @ -20 C to +45 C 6 mb @ -40 C to +60 C Linearity*: 0.45 mb @ 20 C Hysteresis*: 0.05 mb @ 20 C Repeatability*: 0.05 mb @ 20 C Calibration uncertainty**: 0.15 mb @ 20 C Long-Term Stability: 0.1 mb per year * Defined as 2 standard deviation limits of end-point non-linearity, hysteresis error, or repeatability error ** Defined as 2 standard deviation limits of inaccuracy of the working standard at 1000 mb in comparison to international standards (NIST) *** Defined as the root sum of the squares (RSS) of end-point non-linearity, hysteresis error, repeatability error and calibration uncertainty at room temperature

Yearly CO2 variation Shown as Change in DLWIR?

Not sure about this post.
The data used is short
The data is noisy
Subtracting noisy signals does not improve accuracy!!

{UPDATE This data has now changed - I have nulled out the day of year changes and the long term variation(whole record) which significantly changes the results - the results will be posted at a later date]

Basically if CO2 is low then "back radiation" (DLWIR) should be lower than when CO2 is high
There is an annual cycly where CO2 dips in late spring and rises in autumn - see other posts.

So if you remove all factors changing downward long wave infrared radiation other than CO2 then what should be left is the yearly change in CO2 plus the long term increase.

The nulled data is inspected and a simple curve fit is applied and limits chosen that provide the best null for that factor.

Returned data that meets the criteria are averaged using a TRIMMEAN function to remove spurious high/low values

If the data is treated as a reapeated annual set then the long term becomes averaged and only the annual effect remains.

In the plots below the Nulled measurements are shown and CO2 at La Jolla is plotted for comparison.

The hourly measurement data is used

The analysis has been run many times each time there is always a dip starting at ~190 ( some ~60 days after the CO2 starts reducing)
Accuracy is nonsensical if less than 3 valid data are returned This unfortunately eliminates dec jan feb!.

However here are the final plots:

The raw data  (all points returning under 3 samples ignored) compared to La Jolla CO2

The smoothed data  (all points returning under 3 samples ignored) compare to La Jolla CO2

To pick sensible values for a number of variables the following limits are used.

Precipitation limit is set to eliminate any reading during "precipitation"
Cloud can only be measured during daylight
Only opaque cloud is considered
Humidity % is not used but is converted to absolute water vapour 

The Nulling Process

Each of the variables is nulled by plotting dlwir against the variable. Fitting a polynomial (order 1 to 6) to the resultant and then providing limits that deviate from the polynomial.  The polynomial is then applied to the extracted data.
Each variable is treated this way and then the process repeated until little change occurs. This produces the follwing limits.

start month	1
End month	12
hour min	11
hour max	15
Temp min	12.4
Temp max	29.4
Humidity Min	0
Humidity Max	1000
opaque Cloud Cover % min	2.8
opaque Cloud Cover % Max	30.9
cloud cover min	-999999
cloud cover max	1000
abs humid min	2.12
abs humid max	10.5
dlwir min	0
dlwir max	1000
ulwir min	445
ulwir max	595
dlwir as pc uplwir min	0
dlwir as pc uplwir max	100
start day	1
end day	19.2499
Pressure Min	809
Pressure Max	825
precipitation min	-1
precipitation Max	0.00001

These are the corrections applied:

	Temperature	opaque cld	ABS HUMIDITY	ULWIR	hour	Station pressure
x^6	-2.925607E-06	0.00E+00	0	0	0	0
x^5	4.16E-04	-1.30E-05	0	-1.73074E-09	0.01161075	0
x^4	-2.35E-02	1.05E-03	-0.01922243	4.37603E-06	-0.794129	0
x^3	6.78E-01	-3.04E-02	0.5449762	-0.004404108	21.56523	-0.001826181
x^2	-1.05E+01	3.83E-01	-5.604792	2.20558	-290.4116	4.45165
x	8.40E+01	-1.07E+00	29.91783	-549.6395	1938.646	-3617.085
c	3.22E+01	2.81E+02	108.4733	5.40E+04	-5133.338	979613.7

The nulling plots (not prettied up!)

Red plots are the result of nulling
blue lines are before nulling

Excel sheet is available (large)
Data is from (hourly):
http://www.nrel.gov/midc/srrl_bms/

Currently ~ 80,000 lines are analysed

The Complex World of Humidity and Temperature

During sunlight hours (minimal cloud) the water vapour in the air will absorb long wave IR (Green House Effect) from the sun and from the ground.
This will be radiated back in all directions.
The solar radiation will therefore be modified such that the wavelengths absorbed by the water vapour will be reduced depending on the effectiveness of the green house effect.
 However the same wavelengths radiated upwards will be more effectively "reflected" back down.

At night the solar input stops and the only radiation hitting the earth is that from GHGs So more water vapour = better "reflector"

Is this visible in the data previously used below.
Firstly data is limited and to get sensible results each point needs significantly more than one result to be significant.

Night time readings cannot include cloud coverage as this is not measured when dark
The former plots used either average or min/max values. The min max tend to plot one off anomalies. In the plots below 1st and 3rd quartile results have been used to improve this.

First a whole year all possible times with up to 9% opaque cloud cover

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

not sure why humidity so high!

Now in sequence 2 months at a time a couple of hours and up to 9% opaque cloud cover

Now for some night responses 2 months at a time

Conclusion?

• Well minimum temperature shows a increase with increasing water vapour (positive slope) over coldest period but turns to negative slope during warmer months
• The max temp during the night shows little change with water vapour.

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.

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.

Spencer: Clouds Dominate CO2 as a Climate Driver Since 2000
13

01

2010
By Dr. Roy Spencer, PhD.



Last year I posted an analysis of satellite observations of the 2007-08 global cooling event, showing evidence that it was due to a natural increase in low cloud cover. Here I will look at the bigger picture of what how the satellite-observed variations in Earth’s radiative budget compare to that expected from increasing carbon dioxide. Is there something that we can say about the relative roles of nature versus humanity based upon the evidence?

What we will find is evidence consistent with natural cloud variations being the dominant source of climate variability since 2000.

CERES Observations of Global Energy Budget Changes
The following graph shows the variations in the Earth’s global-average radiative energy balance as measured by the CERES instrument on NASA’s Terra satellite. These are variations in the imbalance between absorbed sunlight and emitted infrared radiation, the most fundamental quantity associated with global warming or global cooling. Also show (in red) are theoretically calculated changes in radiative forcing from increasing carbon dioxide as measured at Mauna Loa.


Since there is some uncertainty in the absolute accuracy of the CERES measurements, where one puts the zero line is also somewhat uncertain. Therefore, it’s the variations since 2000 which are believed to be pretty accurate, and the exact dividing line between Earth gaining energy and Earth losing energy is uncertain. Significantly, all of the downward trend is in the reflected sunlight portion, not the infrared portion of the variations. We similarly can not reference where the zero line should be for the CO2 forcing, but the reasons for this are more complex and I will not address them here.

In order to compare the variations in the CO2 forcing (in red) to the satellite observations, we need to account for the fact that the satellite observes forcing and feedback intermingled together. So, let’s remove a couple of estimates of feedback from the satellite measurements to do a more direct comparison.

Inferred Forcing Assuming High Climate Sensitivity (IPCC View)
Conceptually, the variations in the Earth’s radiative imbalance are a mixture of forcing (e.g. increasing CO2; clouds causing temperature changes), and feedback (e.g. temperature changes causing cloud changes). We can estimate the forcing part by subtracting out the feedback part.

First, let’s assume that the IPCC is correct that climate sensitivity is pretty high. In the following chart I have subtracted out an estimate of the feedback portion of the CERES measurements based upon the IPCC 20-model average feedback parameter of 1.4 W m-2 K-1 times the satellite AMSU-measured tropospheric temperature variations


As can be seen, the long-term trend in the CERES measurements is much larger than can be accounted for by increasing carbon dioxide alone, which is presumably buried somewhere in the satellite-measured signal. In fact, the satellite observed trend is in the reflected sunlight portion, not the infrared as we would expect for increasing CO2 (not shown).

Inferred Forcing Assuming Low Climate Sensitivity (”Skeptical” View)
There has been some published evidence (our 2007 GRL paper, Lindzen & Choi’s 2009 paper) to suggest the climate system is quite insensitive. Based upon that evidence, if we assume a net feedback parameter of 6 W m-2 K-1 is operating during this period of time, then removing that feedback signal using AMSU channel 5 yields the following history of radiative forcing:


As can be seen, the relative size of the natural forcings become larger since more forcing is required to cause the same temperature changes when the feedback fighting it is strong. Remember, the NET feedback (including the direct increase in emitted IR) is always acting against the forcing…it is the restoring force for the climate system.

What this Might Mean for Global Warming
The main point I am making here is that, no matter whether you assume the climate system is sensitive or insensitive, our best satellite measurements suggest that the climate system is perfectly capable of causing internally-generated radiative forcing larger than the “external” forcing due to increasing atmospheric carbon dioxide concentrations. Low cloud variations are the most likely source of this internal radiative forcing. It should be remembered that the satellite data are actually measured, whereas the CO2 forcing (red lines in the above graphs) is so small that it can only be computed theoretically.

The satellite observed trend toward less energy loss (or, if you prefer, more energy gain) is interesting since there was no net warming observed during this time. How could this be? Well, the satellite observed trend must be due to forcing only since there was no warming or cooling trend during this period for feedback to act upon. And the lack of warming from this substantial trend in the forcing suggests an insensitive climate system.

If one additionally entertains the possibility that there is still considerable “warming still in the pipeline” left from increasing CO2, as NASA’s Jim Hansen claims, then the need for some natural cooling mechanism to offset and thus produce no net warming becomes even stronger. Either that, or the climate system is so insensitive to increasing CO2 that there is essentially no warming left in the pipeline to be realized. (The less sensitive the climate system, the faster it reaches equilibrium when forced with a radiative imbalance.)

Any way you look at it, the evidence for internally-forced climate change is pretty clear. Based upon this satellite evidence alone, I do not see how the IPCC can continue to ignore internally-forced variations in the climate system. The evidence for its existence is there for all to see, and in my opinion, the IPCC’s lack of diagnostic skill in this matter verges on scientific malpractice.
--------------------------------------------------------
Thank you Dr Spencer for this.

So proof at last that Kevin Trenberth is correct in his travesty email
the energy in is greater than the energy out = warming (not seen!!!)

Kevin Trenberth:
" The fact is that we can't account for the lack of warming at the moment and it is a travesty that we can't. The CERES data published in the August BAMS 09 supplement on 2008 shows there should be even more warming: but the data are surely wrong. Our observing system is inadequate."
....
" We are not close to balancing the energy budget. The fact that we can not account for what is happening in the climate system makes any consideration of geoengineering quite hopeless as we will never be able to tell if it is successful or not! It is a travesty!"

Michael Mann wrote:
" Kevin, that's an interesting point. As the plot from Gavin I sent shows, we can easily account for the observed surface cooling in terms of the natural variability seen in the CMIP3 ensemble (i.e. the observed cold dip falls well within it). So in that sense, we can "explain" it. But this raises the interesting question, is there something going on here w/ the energy & radiation budget which is inconsistent with the modes of internal variability that leads to similar temporary cooling periods within the models.
I'm not sure that this has been addressed--has it?"

Perhaps an apology to Trenberth is in order?

Spencer attribute the warming to clouds - this does not seem to agree with Svensmark where increased clouds = cooling.
Also clouds should be increasing - causing cooling - due to increased GCRs with quiet sun.

Would it be fair to say it is a travesty?
..........
GCR-climate connection - GCR’s increase low cloud cover via increased CCN production (via increased atmospheric ionization), which acts as a cooling effect on the climate.
IPCC
6.11.2.2 Cosmic rays and clouds
Svensmark and Friis-Christensen (1997) demonstrated a high degree of correlation between total cloud cover, from the ISCCP C2 data set, and cosmic ray flux between 1984 and 1991. Changes in the heliosphere arising from fluctuations in the Sun's magnetic field mean that galactic cosmic rays (GCRs) are less able to reach the Earth when the Sun is more active so the cosmic ray flux is inversely related to solar activity.
boballab (16:49:58) :
Last year I posted an analysis of satellite observations of the 2007-08 global cooling event, showing evidence that it was due to a natural increase in low cloud cover.
Low level Clouds=COOLING
Lack of low level Clouds=WARMING
Not
more low level clouds=Warming
Spencer agrees with Svenmark more clouds, more cooling

Low solar activity = high gcrs
high gcr=more low cloud=cooling = svensmark

as solar minimum was approached (=high gcrs=cooling=svensmark) the plots above show earth gaining excess energy.
I do not, I admit, understand how more energy = cooling
The plots show CO2 line doing the correct thing more co2=earth gaining excess energy so the plots are named correctly.

To me svensmark does not equal spencer
but trenberth = honest:
The fact is that we can’t account for the lack of warming at the moment and it is a travesty that we can’t. The CERES data published in the August BAMS 09 supplement on 2008 shows there should be even more warming: but the data are surely wrong. Our observing system is inadequate"

svensmark says -ve
spencer says +ve
trenberth says our observing system is inadequate.

Who is being honest here?