Post on 08-Jul-2020
Do We Know the Temperature of Earth?
CERES Earth Radiation Budget
CALIPSOIR and Earth Observing
Yes
Average Radiant Space Temperature ~ 254 K
Average Surface Temperature ~ 287 K (~14 C)
How Well Do We Know the Surface Air Temperature of Earth?
How Well Do We Know the Surface Air Temperature of Earth?
Land Surface Temperature Record
Thermometers, Sensors, and Measurement Error
Sea Surface Temperature Record
Ships, Buoys, and Measurement Error
How Well We Know the Surface Air Temperature of Earth.
The Surface Air Temperature Anomaly Record
Climatic Research Unit, University of East Anglia and Hadley Centre for Climate, UK February, 2011 data set http://cdiac.ornl.gov/ftp/trends/temp/jonescru/global.txt
Land Surface Air Temperature Record
±0.2 C
±(0.2-0.05) C
Random instrumental errorError Due to Changes in:
•Station Siting•Measurement time•Instrumentation•Instrumental exposure
Urban Heat Islands
Published Sources of Error
P. Brohan, et al. (2006) "Uncertainty estimates in regional and global observed temperature changes: A new data set from 1850" J. Geophys,
Res. 111, D12106
But nothing about systematic sensor error
Instrumental Error in the Temperature Record
(including in the recent Berkeley Expert Systems Technologies (BEST) compilation)Instrumental measurement error has never been evaluated
Systematic measurement error
T Min
T Max
T dry bulb T wet bulb
“Liquid in Glass” (LiG) ThermometersBy far the most common surface air temperature measurement instrument used
globally over the entire 20th century
LiG-CRS instrument has never been field-calibrated
Butte County Fire Station #41 near Nord, CA.
Hg thermometers in Stevenson Screens (CRS)
The Ideal
Sensor Shield Calibration Experiment: Univ. of Nebraska, Lincoln
K. Hubbard and X. Lin (2002) Realtime data filtering models for air temperature measurements Geophys. Res. Lett. 29(10), 1425
Systematic Measurement Error
Major Impacts on Accuracy
1.Solar irradiance2.Ground albedo3.Wind speed
• Too low4.Electronic Instruments
• Self-heating• Voltage errors• Response drift
Stevenson ScreenR. M. Young aspirated probe
Standard ReferenceHMP45C platinum resistance thermometer (PRT)
Accuracy in Surface Air Temperature SensorsThe Hubbard and Lin Experiment:
How Effective are Radiation Screens Against the Effects of Sun and Wind?University of Nebraska, Lincoln
Screen-Induced Error Test temperature
minusR. M. Young temperature
Bias = 0.8 Cσ = ±0.3 C
Bias = 0.1 Cσ = ±0.2 C
Bias = 0.4 Cσ = ±0.4 C
Bias = 0.4 Cσ = ±0.2 C
Bias = 0.2 Cσ = ±0.2 C
Bias = 0.3 Cσ = ±0.3C
Data taken: April through August 2000Protocol:
1. Aspirated R. M. Young ref. ~(±0.1 C)
2. Experimental screens; PRT.3. Simultaneous Measurement
Side-by-Side Comparison of LiG/Stevenson and MMTSCarried out to obtain a “transfer function” for when the LiG instrument is replaced by the MMTS.
The “TF” scales current temp. trend to past temp. trend.
Transfer function is not a
calibration
No Published Record
Field calibration of a LiG thermometer in a
Stevenson Screen:
Accuracy in Surface Air Temperature SensorsHow Accurate are LiG thermometers in Stevenson screens?
T LiG = Ttrue +εsysLiG
T MMTS = Ttrue +εsysMMTS
T LiG −T MMTS = [(Ttrue +εsysLiG ) − (Ttrue +εsys
MMTS )]
= [(Ttrue −Ttrue ) + (εsysLiG −εsys
MMTS )
= εsysLiG −εsys
MMTS + εsysMMTS = εsys
LiG
Figure 3. MMTS – LIG temperature differences (Deg F) by month for the period Jan. 2002 through Dec. 2004 for Fort Collins, Colorado.
Parallel measurementsCO State Univ. Ft. Collins
Nolan J. Doeskin (2005) The National Weather Service MMTS (Maximum-Minimum Temperature System) -- 20 years after in 13th Symposium on Meteorological Observations and Instrumentation, Baker, C.B., Ed. (Amer. Meteor. Soc., Savannah, GA)
Accuracy in Surface Air Temperature SensorsHow Accurate are LiG thermometers in Stevenson screens?
LiG minus MMTS (Doesken, 2005) MMTS Systematic Error (Hubbard & Lin, 2002)
+
Systematic Error:LiG Thermometer in a
Stevenson Screen
Mean bias = +0.26 CSys. Error = ±0.39 C=
Mean bias = +0.25 CSys. Error = ±0.31 C
Systematic Error:PRT in a Stevenson
Screen
The Huwald, et al. ExperimentNon-aspirated R. M. Young Sensor vs. sonic anemometer
Accuracy in Surface Air Temperature Sensors
5 February through 10 April 2007; Plaine Morte Glacier, Switzerland
Bias = 2.0 Cσ = ±1.3 C
Bias = 0.03 Cσ = ±0.3 C
Bias = 0.7 Cσ = ±0.9 C
H. Huwald, C. W. Higgins, M.-O. Boldi, E. Bou-Zeid, M. Lehning, and M. B. Parlange (2009) Albedo effect on radiative errors in air temperature measurements Wat. Resour. Res. 45, W08431
What About Sea-Surface Temperatures (SSTs)?World Ocean = ~70% of the Global Surface
= ~70% of Global Temperature In situ SST Measurements
•Prior to ~1970: mostly bucket-dipped thermometers•~1970 to ~1990: mostly ship engine intake thermometers•After ~1990 : floating buoys and ship engine intakes
Charles Franklin Brooks
Prof. Of Meteorology, Harvard University(1931-1958), principal founder and first secretary (1919-1954) of the American Meteorological Society.
SST Calibration ExperimentFebruary-March 1924
RMS Empress of Britain
This 1926 study is the only comprehensive calibration of shipboard bucket SST measurementsever published
Fuess surface thermometer ~1900
Traditional Sea Surface TemperaturesCanvas Bucket
~1880-1970
Wooden Bucket19th century
35°
30°
25°
20°
15°
Canvas Bucket Errors
C.F. BrooksRMS EoB
Caribbean
Lt. Cmdr. E.H. Smith
Int’l Ice Patrol Modoc & Tampa
Grand Banks
Mid-Twentieth Century Sea Surface TemperaturesShip Intakes
Twelve US military ships, 2½ years, 6825 measurements, Eastern and Western Pacific Ocean
(1963) J. Applied Meteorology 2, 417-425
Precision (±0.1 C) insulated bucket
thermometer
Fuess Thermometer
No other comprehensive study of measurement error in ship SSTs
C.F. Brooks RMS EoB 1926Measurement Error: Engine Intake Temperature
n=56
One trip of a Military Sea Transport Ship,June-July 1959
n = 48
All trips, All ships
Avg. bias: 0.33 C; avg. σ=±0.89 C; “without improved quality control, the sea temperature data reported currently and in the past are for the most part adequate only for general climatological studies.”
Annual average standard deviation:
±0.16 C
Buoy separation: < 5 km
Figure 5: Buoy minus buoy SST difference as a function of separation distance (March 1996)
Since 1979: Satellite infrared SST measurements are calibrated to floating buoys
Late-Twentieth Century Sea Surface TemperaturesShip Intake and Buoys
Ship separation: < 5 km
Figure 11: Ship minus ship SST difference as a function of separation distance (March 1996)
Annual average standard deviation:
±0.54 C
Emery, W. J., Baldwin, D. J., Schlossel, P., and Reynolds, R. W. (2001) Accuracy of in situ sea surface temperatures used to calibrate infrared satellite measurements J. Geophys. Res. 106(C2), 2385-2405.
William J. Emery, et al., investigated the temperature difference between paired ships or paired buoys at 0-50 km separation distance
For d < 10 km, SST measurements are considered replicates.
SST Measurement Methods 1850-2010
E. C. Kent, et al., (2005) “Effects of instrumentation changes on sea surface temperature measured in situ” WIREs Climate Change 1, 718-728
1850 through 1980: ship sea surface temperatures
•1850-1880: wooden buckets
•1880-1940: canvas buckets
•1940-1970: canvas buckets and engine intakes
•1970-1980: engine intakes and canvas buckets
•1980-2010: buoys and engine intakes
Figure 3a: Number of SST observations and measurement methods excluding drifters and buoys
Figure 2b Annual number of SST observations per year by platform type expressed as a fraction of the total.
Ship SST Measurements
Weighted Systematic Measurement Error Algorithm
1850-18990.3×LiG-CRS + 0.7×wooden (canvas) bucket
1900-19390.3×LiG-CRS + 0.7×canvas bucket
1940-19690.3×LiG-CRS+0.7×(0.5×canvas bucket + 0.5×Engine intake)
1970-19790.3×LiG-CRS +0.7×(0.25×canvas bucket + 0.75×Engine intake)
1980-19900.3×LiG-CRS+.7×(0.75×Engine intake +0.25×buoy)
1991-20000.3×(0.75×LiG-CRS +0.25×MMTS)+0.7×(0.25×Engine intake+0.75×buoy)
2001-20110.3×(0.5×LiG-CRS+0.5×MMTS)+0.7×(0.1×Engine intake+0.9×buoy)
Progress in Accuracy
1850-1899: ±0.73 C
1900-1939: ±0.73 C
1940-1969: ±0.65 C
1970-1979: ±0.60 C
1980-1990: ±0.50 C
1991-2000: ±0.36 C
2001-2011: ±0.29 C
Accuracy in the 130-Year Surface Air Temperature Trend131-year anomaly Record
Average Systematic Error (∆C)Official 0.8±0.11Corrected 0.8±0.64
We literally do not know the shape of the true temperature trend line within the limits of the systematic error bounds
Official RecordCorrected Record
These systematic error bars reflect a lower limit of physical uncertainty
At the End of the Journey
Large systematic physical errors in GCMs make predictions of future Earth climate...
Large systematic measurement errors make claims of an unprecedented increase in surface air temperature since 1850 …
It is clear that:
…entirely unreliable.
Systematic errors have been systematically neglected by the AGW guild of climate scientists
…entirely unreliable
No scientific case establishing a human cause for recent global air temperature change
Acknowledgements
Under the Table Oil Company Slush Funds
None
Pat Frank’$ Deep Pocket$
The whole ball of wax
Thank-you for your kind interest and attention
Funding Agencies
None
Foundational Grants
None
Business Contracts
None
$upportFor Reviewing parts of this work
Prof. David Legates University of Delaware
Dr. David StockwellUniversity of California San Diego
Prof. Demetris KoutsoyiannisNational Technical University of Athens
Bias = 2.0 Cσ = ±1.3 C
Bias = 0.03 Cσ = ±0.3 C
Bias = 0.7 Cσ = ±0.9 C
5 February through 10 April 2007.
Bias = 0.2 Cσ = ±0.6 C
Bias = 3.1 Cσ = ±1.2 C
H. Huwald, C. W. Higgins, M.-O. Boldi, E. Bou-Zeid, M. Lehning, and M. B. Parlange (2009) Albedo effect on radiative errors in air temperature measurements Wat. Resour. Res. 45, W08431
What is the IPCC actually communicating about future global average temperature?
What do we finally know about the future of Earth climate?
Nothing
(Almost) Nothing
What is the IPCC able to say about recent global average air temperature changes?
Very Little
The Scientific View of Recent and Future Climate( as opposed to the political view)
Amanda Staudt, Nancy Huddleston, Sandi Rudenstein, Michele de la Menardiere
http://dels.nas.edu/basc/
p. 2: “In the judgment of most climate scientists, Earth’s warming in recent decades has been caused primarily by human activities that have increased the amount of greenhouse gases in the atmosphere”
p. 3: “However, much higher concentrations of greenhouse gases than naturally occur—mostly from burning fossil fuels—are trapping excess heat in the atmosphere and are warming Earth’s surface faster than at any time in recorded history.”
p. 5: ““changes in [global-average surface temperature] observed over the last several decades are likely mostly due to human activities”...”
From the U.S. National Academy of Sciences
Figure 4. Simulations of past temperature more closely match observed temperature when both natural and human causes are included in the models. The gray lines indicate model results. The red lines indicate observed temperatures. Source: [IPCC}.
US National Academy of Sciences “Understanding Climate Change”
How NAS/IPCC Figure 4, Panel 3 might have looked if the NAS or the IPCC had decided to include the propagated temperature uncertainty from a 2.8 W m–2 cloud forcing error.
It makes little sense to claim an explanatory fit is impossible without man-made causes, when in fact an explanatory fit is impossible, period.
The Global Thermohaline Conveyor Belt
“The global conveyor belt thermohaline circulation is driven primarily by the formation and sinking of deep water (from around 1500m to the Antarctic bottom water overlying the bottom of the ocean) in the Norwegian Sea.”
IPCC 2AR, 1996: http://www.grida.no/climate/vital/32.htm
Off the coast of Brazil at 2000-3000 m, a cool dense thermohaline current is flowing south
As adapted in C. Wunsch Ocean observations and the Climate Forecast Problem In: Meteorology at the Millennium, R. P. Pearce, ed., London:Academic Press, 2002, pp. 233-245, Figure 5
800 Days of Laminar North Atlantic Deep Water Thermohaline FlowN. G. Hogg and W. B. Owens (1999) Direct measurement of the deep circulation within the Brazil Basin
Deep-Sea Research II 46 (1999) 335–353
“[I]t seems clear that our existing ideas of how the subthermocline regions work will have to be rethought. For example, the expectation that the deep flow might conform to simple Stommel-Arons dynamics with associated poleward interior flow seems unrealistic. Instead flows are more zonal than meridional and no consistent polewardcomponent emerges … at either the NADW or AABW levels.”
Neutrally buoyant floats measured current flow at 2500 m (North Atlantic Deep Water) and 4000 m (Antarctic Bottom Water).
p. 244: "Examples [of important phenomena neglected in oceanography until actually observed include] temperature and velocity micro-structure, the intricate current regime near the equator, the dominance of high-latitude barotropic fluctuation, and the recent realization that the ocean probably mixes primarily at its boundaries -- in flagrant conflict with almost all GCMs."
p. 245: “In general, ocean models are not numerically converged, and questions about the meaning of nonnumerically converged models are typically swept aside on the basis that the circulations of the coarse resolution models “look” reasonable.”
Carl Wunsch
In: Ocean observations and the Climate Forecast Problem In: Meteorology at the Millennium, R. P. Pearce, ed., London:Academic Press, 2002, pp. 233-245
“The conveyor belt picture is a wonderful cocktail party metaphor for nonscientists.”*
* p. 236
Global Average CloudinessAR4 page 601, FAQ 8.1: “Significant uncertainties, in particular, are associated with the representation of clouds, and in the resulting cloud responses to climate change. Consequently, models continue to display a substantial range of global temperature change in response to specified greenhouse gas forcing.”
What is the average cloud error in GCMs? What is its effect on projected global average temperature?
W. L. Gates, et al. (1999) An Overview of the Results of the Atmospheric Model Intercomparison Project (AMIP I) Bulletin of the American Meteorological Society 80, 29-55.
Observed: 1983-1990; Predicted: 1979-1988
Cloud Error Estimation1. Integrate the global average cloudiness retrodicted by each GCM.2. Integrate the observed global average cloudiness across the identical
latitude ranges.3. Calculate the r.m.s. average error.
Ob s e rve d and GCM Re trod ic ted Gl o bal Ave rage Clou di ne s s Inte gr atedOve r the S am e Pair-W ise Latit udi na l Rang e s .
GCM GCMAver ag e
Clou di nes s
Ob s e rve dAver ag e
Clou di nes s
Abs o lut eFraction al
Error
Lag-1 ErrorAuto corr el ati on
[R]LMD 10629 9648 0 .1017 0 .9631DERF 10389 10291 0 .009516 0 .9595BMRC 90501 10346 0 .1252 0 .9881CNRM 10659 10306 0 .03422 0 .9766NRL 11710 10329 0 .1337 0 .9850MPI 11353 10313 0 .1008 0 .9767MRI 11709 10435 0 .1221 0 .9639DNM 10389 10291 0 .009516 0 .9595SUNGEN 10322 10268 0 .005232 0 .9411YONU 11972 10436 0 .1471 0 .9704
Average r.m.s. error = ±10.1%
Global net cloud forcing (satellite): = –27.6 W m-2.
Global average r.m.s. cloud error = ± 2.8 W m-2.
Global average r.m.s. cloud error = ±100% of the extra forcing due to all human-produced GHG’s.
Cloud error in GCMs is not random but systematic
The Structure of GCM Global Cloudiness Error III
It is inherent in the GCMs and almost certainly reflects theory-bias
How does theory-bias error propagate in a time-wise projection?
T. S. Saitoh and S. Wakashima Energy Conversion Engineering Conference and Exhibit, 2000. (IECEC) 35th Intersociety , vol.2,, pp.1026-1031
In a time-wise climate projection every year Yn-1 provides the initial conditions for every year Yn.
C0F,T T1
T2-e T2
T2+eT2
T1-eT1
T1+e T1
T2
T2h+e T2hT2h
T2h-e T2h
T2l
T2l+eT2l
T2l-e T2l
And produces an increasing uncertainty in predictions of future global average temperature.
Theory-bias error does not cancel but accumulates
T0
Tn+et(Tn)
Tn-et(Tn)
Tn(m)
time
Uncertainty Propagation in Time-wise Projections of Global Average Surface Temperature III
Figure SPM-5 when ±2.8 W m-2 propagated uncertainty is included and plotted at full scale
SRES AB1: 2.8 ±109 CSRES B1: 1.8 ± 95 CSRES CCC: 0.54±105 C
SRES A2: 3.7 ±111 CAt Year 2100
SRES A2
Figure 5 from the IPCC 4AR Summary for
Policy-Makers