Do We Know the Temperature of Earth? Yes

CERES CALIPSO Earth Radiation Budget IR and Earth Observing Average Radiant Space Average Surface Temperature Temperature ~ 254 K ~ 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. Land Surface Air Temperature Record

The Surface Air Temperature Anomaly Record

±(0.2-0.05) C ±0.2 C

Published Sources of Error Random instrumental error Error Due to Changes in: •Station Siting •Measurement time •Instrumentation •Instrumental exposure 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 Urban Heat Islands

But nothing about systematic sensor 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 Instrumental Error in the Temperature Record “Liquid in Glass” (LiG) Thermometers By far the most common surface air temperature measurement instrument used globally over the entire 20th century

Hg thermometers in Stevenson Screens (CRS)

T Min

T Max

Butte County Fire Station The Ideal T dry bulb T wet bulb #41 near Nord, CA. LiG-CRS instrument has never been field-calibrated Instrumental measurement error has never been evaluated (including in the recent Berkeley Expert Systems Technologies (BEST) compilation) Systematic measurement error Systematic Measurement Error

Sensor Shield Calibration Experiment: Univ. of Nebraska, Lincoln

Major Impacts on Accuracy

1.Solar irradiance 2.Ground albedo 3.Wind speed • Too low 4.Electronic Instruments • Self-heating • Voltage errors • Response drift

R. M. Young aspirated probe Stevenson Screen Standard Reference HMP45C platinum resistance thermometer (PRT) K. Hubbard and X. Lin (2002) Realtime data filtering models for air temperature measurements Geophys. Res. Lett. 29(10), 1425 Accuracy in Surface Air Temperature Sensors The Hubbard and Lin Experiment: How Effective are Radiation Screens Against the Effects of Sun and Wind? University of Nebraska, Lincoln Data taken: April through August 2000 Protocol: 1. Aspirated R. M. Young ref. Bias = 0.8 C Bias = 0.4 C ~(±0.1 C) σ = ±0.3 C σ = ±0.2 C 2. Experimental screens; PRT. 3. Simultaneous Measurement

Bias = 0.1 C Bias = 0.2 C σ = ±0.2 C σ = ±0.2 C

Bias = 0.4 C Bias = 0.3 C Screen-Induced Error σ = ±0.4 C σ = ±0.3C Test temperature minus R. M. Young temperature Side-by-Side Comparison of LiG/Stevenson and MMTS Carried 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 Field calibration of a LiG thermometer in a Stevenson Screen:

No Published Record Accuracy in Surface Air Temperature Sensors How Accurate are LiG thermometers in Stevenson screens?

Parallel measurements CO State Univ. Ft. Collins

Figure 3. MMTS – LIG temperature differences (Deg F) by month for the Nolan J. Doeskin (2005) The National Weather Service MMTS (Maximum- period Jan. 2002 through Dec. 2004 for Fort Collins, Colorado. Minimum Temperature System) -- 20 years after in 13th Symposium on LiG MMTS LiG MMTS Meteorological Observations and T − T = [(Ttrue +εsys ) − (Ttrue +εsys )] Instrumentation, Baker, C.B., Ed. (Amer. Meteor. Soc., Savannah, GA) LiG MMTS = [(Ttrue − Ttrue ) + (εsys −εsys ) LiG LiG T = Ttrue +εsys MMTS MMTS T = Ttrue +εsys LiG MMTS MMTS LiG = εsys −εsys + εsys = εsys Accuracy in Surface Air Temperature Sensors How Accurate are LiG thermometers in Stevenson screens? LiG minus MMTS (Doesken, 2005) MMTS Systematic Error (Hubbard & Lin, 2002)

+

Systematic Error: Systematic Error: LiG Thermometer in a PRT in a Stevenson Stevenson Screen Screen Mean bias = +0.25 C Mean bias = +0.26 C Sys. Error = ±0.31 C = Sys. Error = ±0.39 C Accuracy in Surface Air Temperature Sensors The Huwald, et al. Experiment Non-aspirated R. M. Young Sensor vs. sonic anemometer 5 February through 10 April 2007; Plaine Morte Glacier, Switzerland

Bias = 2.0 C σ = ±1.3 C

Bias = 0.03 C σ = ±0.3 C

Bias = 0.7 C σ = ±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 Prof. Of Meteorology, Harvard University(1931- 1958), principal founder and first secretary (1919- 1954) of the American Meteorological Society.

SST Calibration Experiment February-March 1924

RMS Empress of Britain

Charles Franklin Brooks This 1926 study is the only comprehensive calibration of shipboard bucket SST measurements ever published Traditional Sea Surface Temperatures Canvas Bucket Errors Canvas Bucket Fuess surface ~1880-1970 thermometer ~1900 Caribbean C.F. Brooks RMS EoB

35°

30°

25°

20°

15°

Grand Banks Wooden Bucket Lt. Cmdr. E.H. Smith 19th century

Int’l Ice Patrol Modoc & Tampa Mid-Twentieth Century Sea Surface Temperatures C.F. Brooks RMS EoB 1926 Ship Intakes One trip of a Military Sea Transport Ship, Measurement Error: Engine Intake Temperature June-July 1959 Fuess Thermometer n = 48 n=56

Precision (±0.1 C) All trips, All ships insulated bucket thermometer

(1963) J. Applied Meteorology 2, 417-425

Twelve US military ships, 2½ years, 6825 measurements, Eastern and Western Pacific Ocean

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 No other comprehensive study of adequate only for general climatological studies.” measurement error in ship SSTs Late-Twentieth Century Sea Surface Temperatures Ship Intake and Buoys 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.

Ship separation: < 5 km Buoy separation: < 5 km Annual average Annual average standard deviation: standard deviation: ±0.54 C ±0.16 C

Figure 11: Ship minus ship SST difference as a function Figure 5: Buoy minus buoy SST difference as a of separation distance (March 1996) function of separation distance (March 1996)

Since 1979: Satellite infrared SST measurements are calibrated to floating buoys

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. SST Measurement Methods 1850-2010

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 2b Annual number of SST observations per year by platform type expressed as a fraction of the total.

Ship SST Measurements

Figure 3a: Number of SST observations and measurement methods excluding drifters and buoys E. C. Kent, et al., (2005) “Effects of instrumentation changes on sea surface temperature measured in situ” WIREs Climate Change 1, 718-728 Weighted Systematic Measurement Error Algorithm Progress in Accuracy 1850-1899 1850-1899: ±0.73 C 0.3×LiG-CRS + 0.7×wooden (canvas) bucket

1900-1939 1900-1939: ±0.73 C 0.3×LiG-CRS + 0.7×canvas bucket

1940-1969 1940-1969: ±0.65 C 0.3×LiG-CRS+0.7×(0.5×canvas bucket + 0.5×Engine intake)

1970-1979 1970-1979: ±0.60 C 0.3×LiG-CRS +0.7×(0.25×canvas bucket + 0.75×Engine intake)

1980-1990 1980-1990: ±0.50 C 0.3×LiG-CRS+.7×(0.75×Engine intake +0.25×buoy)

1991-2000: ±0.36 C 1991-2000 0.3×(0.75×LiG-CRS +0.25×MMTS)+0.7×(0.25×Engine intake+0.75×buoy)

2001-2011 2001-2011: ±0.29 C 0.3×(0.5×LiG-CRS+0.5×MMTS)+0.7×(0.1×Engine intake+0.9×buoy) Accuracy in the 130-Year Surface Air Temperature Trend 131-year anomaly Record Average Systematic Error (∆C) Official 0.8±0.11 Corrected 0.8±0.64

Corrected Record Official Record

These systematic error bars reflect a lower limit of physical uncertainty

We literally do not know the shape of the true temperature trend line within the limits of the systematic error bounds At the End of the Journey It is clear that: Large systematic measurement errors make claims of an unprecedented increase in surface air temperature since 1850 …

…entirely unreliable.

Large systematic physical errors in GCMs make predictions of future Earth climate...

…entirely unreliable

Systematic errors have been systematically neglected by the AGW guild of climate scientists

No scientific case establishing a human cause for recent global air temperature change Acknowledgements $upport For Reviewing parts of this work Funding Agencies Prof. David Legates None University of Delaware Foundational Grants Dr. David Stockwell None University of California San Diego Business Contracts Prof. Demetris Koutsoyiannis None National Technical University of Athens 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 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 5 February through 10 April 2007.

Bias = 2.0 C σ = ±1.3 C Bias = 3.1 C σ = ±1.2 C

Bias = 0.03 C σ = ±0.3 C

Bias = 0.2 C σ = ±0.6 C Bias = 0.7 C σ = ±0.9 C The Scientific View of Recent and Future Climate ( as opposed to the political view)

What is the IPCC able to say about recent global average air temperature changes?

Very Little

What is the IPCC actually communicating about future global average temperature?

Nothing

What do we finally know about the future of Earth climate?

(Almost) Nothing From the U.S. National Academy of Sciences

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 Amanda Staudt, Nancy Huddleston, Sandi Rudenstein, the last several decades are likely Michele de la Menardiere mostly due to human activities”...”

http://dels.nas.edu/basc/ 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.

Figure 4. Simulations of past temperature more closely match It makes little sense to claim an observed temperature when both natural and human causes are explanatory fit is impossible without included in the models. The gray lines indicate model results. The man-made causes, when in fact an red lines indicate observed temperatures. Source: [IPCC}. explanatory fit is impossible, period. The Global Thermohaline Conveyor Belt IPCC 2AR, 1996: http://www.grida.no/climate/vital/32.htm

“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.” Off the coast of Brazil at 2000-3000 m, a cool dense thermohaline current is flowing south 800 Days of Laminar North Atlantic Deep Water Thermohaline Flow N. 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

Neutrally buoyant floats measured current flow at 2500 m (North Atlantic Deep Water) and 4000 m (Antarctic Bottom Water).

“[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 poleward component emerges … at either the NADW or AABW levels.”

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 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

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.”

“The conveyor belt picture is a wonderful cocktail party metaphor for nonscientists.”*

* p. 236 Global Average Cloudiness AR4 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.”

Observed: 1983-1990; Predicted: 1979-1988

W. L. Gates, et al. (1999) An Overview of the Results of the Atmospheric What is the average cloud error in GCMs? Model Intercomparison Project (AMIP I) Bulletin of the American What is its effect on projected global average temperature? Meteorological Society 80, 29-55. Cloud Error Estimation 1. 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 trodic ted Gl o bal Ave rage Clou di ne s s Inte gr ated Ove r the S am e Pair-W ise Latit udi na l Rang e s . GCM GCM Ob s e rve d Abs olut e Lag-1 Error Aver ag e Aver ag e Fraction al Auto corr el ati on Clou di nes s Clou di nes s Error [R] LMD 10629 9648 0 .1017 0 .9631 DERF 10389 10291 0 .009516 0 .9595 BMRC 90501 10346 0 .1252 0 .9881 CNRM 10659 10306 0 .03422 0 .9766 NRL 11710 10329 0 .1337 0 .9850 MPI 11353 10313 0 .1008 0 .9767 MRI 11709 10435 0 .1221 0 .9639 DNM 10389 10291 0 .009516 0 .9595 SUNGEN 10322 10268 0 .005232 0 .9411 YONU 11972 10436 0 .1471 0 .9704 Global net cloud forcing (satellite): = –27.6 W m-2. Global average r.m.s. cloud error = ± 2.8 W m-2. Average r.m.s. error = ±10.1% Global average r.m.s. cloud error = ±100% of the extra forcing due to all human-produced GHG’s. The Structure of GCM Global Cloudiness Error III Cloud error in GCMs is not random but systematic It is inherent in the GCMs and almost certainly reflects theory-bias

How does theory-bias error propagate in a time-wise projection?

T2h+e T2h Tn+et(Tn) T2h

T2h-e T2h

T1+e T1 T +e 2 T2 F,T T Tn(m) C0 1 T2 T0

T1-eT T -e 1 2 T2

T2l+e T2l T2l In a time-wise climate projection Tn-et(Tn) every year Yn-1 provides the initial T2l-e time T2l conditions for every year Yn. Theory-bias error does not cancel but accumulates And produces an increasing uncertainty in T. S. Saitoh and S. Wakashima Energy Conversion Engineering Conference and predictions of future global average temperature. Exhibit, 2000. (IECEC) 35th Intersociety , vol.2,, pp.1026-1031 Uncertainty Propagation in Time-wise Projections of Global Average Surface Temperature III

SRES A2

At Year 2100 Figure 5 from the IPCC SRES A2: 3.7 ±111 C 4AR Summary for SRES AB1: 2.8 ±109 C Policy-Makers SRES B1: 1.8 ± 95 C SRES CCC: 0.54±105 C

Figure SPM-5 when ±2.8 W m-2 propagated uncertainty is included and plotted at full scale