Climate Science: Roger Pielke Sr. Research Group News

The National Climate Data Center (NCDC) has responded to the excellent report

Watts, A. 2009: Is the U.S. Surface Temperature Record Reliable? 28 pages, March 2009 The Heartland Institute [hard copies available from The Heartland Institute 19 South LaSalle Street #903 Chicago Illinois 60603]

which I weblogged on at  “Is The U.S. Surface Temperature Record Reliable?” By Anthony Watts.

The NCDC “Talking Points” released on June 9, 2009  are available at

Talking Points related to: Is the U.S. Temperature Record Reliable?

Unfortunately, the author of the NCDC Talking Points cavalierly and poorly responded to Anthony Watts report. They did not even have the courtesy to cite the report! {UPDATE 7/3/09: They have now cited Anthony’s report, but retained the original date of the Talking Points of June 9 2009).

Below, I comment on their response.

NCDC Talking Point #1

Q. Do many U.S. stations have poor siting by being placed inappropriately close to trees, buildings, parking lots, etc.?

A. Yes. The National Weather Service has station siting criteria, but they were not always followed. That is one reason why NOAA created the Climate Reference Network, with excellent siting and redundant sensors. It is a network designed specifically for assessing climate change. http://www.ncdc.noaa.gov/oa/climate/uscrn/. Additionally, an effort is underway to modernize the Historical Climatology Network, though funds are currently available only to modernize and maintain stations in the Southwest. Managers of both of these networks work diligently to put their stations in locations not only with excellent current siting, but also where the site characteristics are unlikely to change very much over the coming decades.

Climate Science Response

Their answer confirms what Anthony Watts and colleagues have carefully documented.  An obvious question is why did not NCDC elevate this as a priority sooner? Moreover, if the current sites can be “adjusted” to be regionally representative, why does NOAA even need the new Climate Reference Network? The answer to that is that they have recognized for years that there is a problem with the siting of the surface stations, but deliberately attempted to bury this issue until Anthony Watts and colleagues confronted NCDC with the issue.

 NCDC Talking Point #2

Q. How has the poor siting biased local temperatures trends?

A. At the present time (June 2009), to the best of our knowledge, there has only been one published peer-reviewed study that specifically quantified the potential bias in trends caused by poor station siting: Peterson, Thomas C., 2006: Examination of Potential Biases in Air Temperature Caused by Poor Station Locations. Bulletin of the American Meteorological Society, 87, 1073-1080. Written by a NOAA National Climatic Data Center scientist, it examined only a small subset of stations – all that had their siting checked at that time – and found no bias in long-term trends. The linear trend in adjusted temperature series over the period examined was nearly identical between the stations with good siting and the stations with poor siting, with the stations having poor siting showing slightly less warming. The following questions address implications from that paper.

Climate Science Response

This is blatantly untrue and the author of these talking points know that. Tom Peterson, for example, was even a reviewer of the Pielke 2007a and 2007b papers, and was aware of the Pielke et al 2002 paper.

Pielke Sr., R.A., T. Stohlgren, L. Schell, W. Parton, N. Doesken, K. Redmond, J. Moeny, T. McKee, and T.G.F. Kittel, 2002: Problems in evaluating regional and local trends in temperature: An example from eastern Colorado, USA. Int. J. Climatol., 22, 421-434.

Pielke Sr., R.A. J. Nielsen-Gammon, C. Davey, J. Angel, O. Bliss, N. Doesken, M. Cai., S.  Fall, D. Niyogi, K. Gallo, R. Hale, K.G. Hubbard, X. Lin, H. Li, and S. Raman, 2007a: Documentation of uncertainties and biases associated with surface temperature measurement sites for climate change assessment. Bull. Amer. Meteor. Soc., 88:6, 913-928.

Pielke Sr., R.A., C. Davey, D. Niyogi, S. Fall, J. Steinweg-Woods, K. Hubbard, X. Lin, M. Cai, Y.-K. Lim, H. Li, J. Nielsen-Gammon, K. Gallo, R. Hale, R. Mahmood, S. Foster, R.T. McNider, and P. Blanken, 2007b: Unresolved issues with the assessment of multi-decadal global land surface temperature trends. J. Geophys. Res., 112, D24S08, doi:10.1029/2006JD008229.

In the second paper, we wrote

“Peterson’s approach and conclusions, therefore, provide a false sense of confidence with these data for temperature change studies by seeming to indicate that the errors can be corrected.”

The decision of the NCDC Talking Points to ignore these papers illustrates the state that NCDC is in with respect to Climate Science. NCDC, as led by Tom Karl, is not interested in an inclusive assessment of climate science issues (in this case the multi-decadal surface temperature trends), but are only interested in promoting their particular agenda and in protecting their particular data set.

NCDC Talking Point #3

Q. Does a station with poor siting read warmer than a station with good siting?

Not necessarily. A station too close to a parking lot would be expected to read warmer than a station situated over grass far from any human influence other natural obstructions. But a station too close to a large tree to the west, so that the station was shaded in the afternoon, would be expected to make the afternoon maximum temperature read a bit cooler than a station in full sunlight. Many local factors influence the observed temperature: whether a station is in a valley with cold air drainage, whether the station is a liquid-in-glass thermometer in a standard wooden shelter or an electronic thermometer in the new smaller and more open plastic shelters, whether the station reads and resets its maximum and minimum thermometers in the coolest time of the day in early morning or in the warmest time of the day in the afternoon, etc. But for detecting climate change, the concern is not the absolute temperature – whether a station is reading warmer or cooler than a nearby station over grass – but how that temperature changes over time.

Climate Science Response

The answer correctly reports on the variety of issues that affect surface temperatures. However, where we disagree is that the multi-decadal surface temperature trends and anomalies also depend on the details of the observing sites and how these details change over time.

This can be illustrated from our 2007 BAMS paper, where the set of relatively closely spaced stations shown in Figure 10 (reproduced belw) have significantly different long term trends, as summarized in Table 5 (reproduced below) from that paper. Despite being relatively close together, the variations in both the local enviroment and the station exposure result in distinctly different trends [Using the categories in the Watts, 2009 report, the stations had the following Trinidad (3); Cheyenne Wells (1); Las Animas (5); Eads (4) and Lamar (4)]. 

Even sites that are locally in a category 1 class, such as Cheyenne Wells, however, also have issues with the landscape in their local surroundings, as we documented for locations in northeastern Colorado in Figures 5, 7, 9, 10 and 12 of

Hanamean, J.R. Jr., R.A. Pielke Sr., C.L. Castro, D.S. Ojima, B.C. Reed, and Z. Gao, 2003: Vegetation impacts on maximum and minimum temperatures in northeast Colorado. Meteorological Applications, 10, 203-215.

Depending on wind direction, the air that reaches the observing site can have a different temperature. Changes in the wind directions over time can result in temperature trends that are due to this effect alone.

This local landscape variation as a function of azimith can be seen in the photographs for the Cheyenne Wells site in

Davey, C.A., and R.A. Pielke Sr., 2005: Microclimate exposures of surface-based weather stations - implications for the assessment of long-term temperature trends. Bull. Amer. Meteor. Soc., Vol. 86, No. 4, 497–504,

where depending on the wind direction and time of year, the air that the temperature sensor monitors may transit a dirt road, crops, or other land surface varations, each with a different surface heat budget., before reaching the temperature observing site.

The NCDC Talking Points ignore informing us why all of these local landscape effects on multi-decadal surface temperature trends would be random and average out.

NCDC Talking Point #4

Q. So a station moving from a location with good siting to a location with poor siting could cause a bias in the temperature record. Can that bias be adjusted out of the record?

A. A great dealof work has gone into efforts to account for a wide variety of biases in the climate record, both in NOAA and at sister agencies around the world. Since the 1980s, scientists at NOAA’s NationalClimatic Data Center are at the forefront of this effort developing techniques to detect and quantify biases in station time series. When a bias associated with any change is detected, it is removed so that the time series is homogeneous with respect to its current instrumentation and siting. The latest peer-reviewed paper which provides an overview the sources of bias and their removal (Menne et al., 2009 in press), including urbanization and nonstandard siting. At the time that paper was written, station site evaluations were too incomplete to conduct a thorough investigation (that analysis is forthcoming). However, they could evaluate urban bias and found that once the data were fully adjusted the 30% most urban stations had about the same trend as the remaining more rural stations.

Climate Science Response

The failure of NCDC to correct for all of the recognized biases has been documented in

Pielke Sr., R.A., C. Davey, D. Niyogi, S. Fall, J. Steinweg-Woods, K. Hubbard, X. Lin, M. Cai, Y.-K. Lim, H. Li, J. Nielsen-Gammon, K. Gallo, R. Hale, R. Mahmood, S. Foster, R.T. McNider, and P. Blanken, 2007: Unresolved issues with the assessment of multi-decadal global land surface temperature trends. J. Geophys. Res., 112, D24S08, doi:10.1029/2006JD008229;

a paper NCDC has chosen to ignore [another surface temperature analysis group has been open to scientific debate, however; see]. 

NCDC has also ignored

Lin, X., R.A. Pielke Sr., K.G. Hubbard, K.C. Crawford, M. A. Shafer, and T. Matsui, 2007: An examination of 1997-2007 surface layer temperature trends at two heights in Oklahoma. Geophys. Res. Letts., 34, L24705, doi:10.1029/2007GL031652,

where we document a bias in the use of a single level surface temperature (the minimum temperature, in particular) to monitor multi-decadal surface temperature trends.  

The NCDC talking points also mention the Menne et al (2009) paper, which, unfortunately, perpetuates the NCDC failure to adequately consider all of the biases and uncertainties in the surface temperature record. The Menne et al paper was weblogged in

Comments On The New Paper “The United States Historical Climatology Network Monthly Temperature Data – Version 2 By Menne Et Al 2009

Finally, we have several other papers in the review process, and look forward to communicating them to you when accepted for publication.

NCDC Talking Point #5

Q. What can we say about poor siting’s impact on national temperature trends?

A. We are limited in what we can say due to limited information about station siting. Surfacestations.org has examined about 70% of the 1221 stations in NOAA’s Historical Climatology Network (USHCN). According to their web site of early June 2009, they classified 70 USHCN version 2 stations as good or best (class 1 or 2). The criteria used to make that classification is based on NOAA’s Climate Reference Network Site Handbook so the criteria are clear. But, as many different individuals participated in the site evaluations, with varying levels of expertise, the degree of standardization and reproducibility of this process is unknown.

However, at the present time this is the only large scale site evaluation information available so we conducted a preliminary analysis.

Two national time series were made using the same gridding and area averaging technique. One analysis was for the full data set. The other used only the 70 stations that surfacestations.org classified as good or best. We would expect some differences simply due to the different area covered: the 70 stations only covered 43% of the country with no stations in, for example, New Mexico, Kansas, Nebraska, Iowa, Illinois, Ohio, West Virginia, Kentucky, Tennessee or North Carolina. Yetthe two time series, shown below as both annual data and smooth data, are remarkably similar. Clearly there is no indication for this analysis that poor current siting is imparting a bias in the U.S. temperature trends.

Climate Science Response

This is a cavalier response.  In order to show that there is little effect on surface temperature anomalies due to station siting, they need to assess the anomalies over time in the same region for each category of station siting. A national average which includes includes large regional variations (e.g. see Figure 20a in Pielke et al 2007a ) tells us little about the quality of the data.

Q. Is there any question that surface temperatures in the United States have been rising rapidly during the last 50 years?

A. None at all. Even if NOAA did not have weather observing stations across the length and breadth of the United States the impacts of the warming are unmistakable. For example, lake and river ice is melting earlier in the spring and forming later in the fall. Plants are blooming earlier
in the spring. Mountain glaciers are melting. And a multitude of species of birds, fish, mammals and plants are extending their ranges northward and, in mountainous areas, upward as well.

Menne, Matthew J., Claude N. Williams, Jr. and Russell S. Vose, 2009: The United States Historical Climatology Network Monthly Temperature Data – Version 2. Bulletin of the American Meteorological Society, in press.

Peterson, Thomas C., 2006: Examination of Potential Biases in Air Temperature Caused by Poor Station Locations. Bulletin of the American Meteorological Society, 87, 1073-1080. It is available from http://ams.allenpress.com/archive/1520-0477/87/8/pdf/i1520-0477-87-8-1073.pdf.

Climate Science Response

Their claim that temperatures have been “rising rapidly” over the past 50 years is based on the surface temperature record in which there are reported warm biases; e.g. see

Pielke Sr., R.A., C. Davey, D. Niyogi, S. Fall, J. Steinweg-Woods, K. Hubbard, X. Lin, M. Cai, Y.-K. Lim, H. Li, J. Nielsen-Gammon, K. Gallo, R. Hale, R. Mahmood, S. Foster, R.T. McNider, and P. Blanken, 2007: Unresolved issues with the assessment of multi-decadal global land surface temperature trends. J. Geophys. Res., 112, D24S08, doi:10.1029/2006JD008229.

NCDC also is misinformed with respect to the other climate metrics. For example, they write

“Plants are blooming earlier in the spring.”  

However, a new paper in press (see)

White, M.A., K.M. de Beurs, K. Didan, D.W. Inouye, A.D. Richardson, O.P. Jensen, J. O’Keefe, G. Zhang, R.R. Nemani, W.J.D. van Leeuwen, J.F. Brown, A. de Wit, M. Schaepman, X. Lin, M. Dettinger, A. Bailey, J. Kimball, M.D. Schwartz, D.D. Baldocchi, J.T. Lee, W.K. Lauenroth. Intercomparison, interpretation, and assessment of spring phenology in North America estimated from remote sensing for 1982 to 2006. Global Change Biology (in press),

writes

“Trend estimates from the SOS [Start of Spring] methods as well as measured and modeled plant phenologystrongly suggest either no or very geographically limited trends towards earlier spring arrival, although we caution that, for an event such as SOS with high interannual variability, a 25-year SOS record is short for detecting robust trends.”

IN CONCLUSION

NCDC would be a much more valuable resource in the climate community if they worked to be inclusive in presenting all peer reviewed perspectives in climate science. Currently, they are only reporting on information that supports their agenda and not communicating real world observational data that conflicts with that agenda. The fault for this failure in leadership is with Tom Karl who is Director of NCDC.

I am pleased to announce another peer reviewed paper with the senior author, Professor Chris Castro, on the faculty of the University of Arizona.

Castro, C.L. A. Beltrán-Przekurat, and R.A. Pielke Sr., 2009: Spatiotemporal variability of precipitation, soil moisture, and vegetation greenness in North America within the recent observational record. J. Hydrometeor., accepted.

The abstract reads

“Dominant spatiotemporal patterns of precipitation, modeled soil moisture, and vegetation are determined in North America within the recent observational record (late 20th century on). These data are from a gridded U.S.-Mexico precipitation product, retrospective long-term integrations of two land surface models, and satellite-derived vegetation greenness. The analysis procedure uses two statistical techniques. First, all the variables are normalized according to the Standardized Precipitation Index procedure. Second, dominant patterns of spatiotemporal variability are determined using multi-taper method, singular value decomposition for interannual and longer timescales. The dominant spatiotemporal patterns of precipitation generally conform to known and distinct Pacific SST forcing in the cool and warm seasons. Two specific timescales in precipitation at 9 years and 6-7 years correspond to significant variability in soil moisture and vegetation, respectively. The 9 year signal is related to precipitation in late fall to early winter, while the 6-7 year signal is related to early summer precipitation. Canonical correlation analysis is additionally used to confirm that strong covariability between land surface variables and precipitation exists at these specific times of the year. Both signals are strongest in the central and western U.S., and are consistent with prior global modeling and paleoclimate studies which have investigated drought in North America.”

As writtnen in the paper

“The main goal of the present study is to determine the dominant spatiotemporal patterns of precipitation that force long-term variability in soil moisture and vegetation.”

This study is a very significant advancement in our understanding the role of sea surface temperatures in the Pacific Ocean on precipitation and other weather variables in the central and western United States. It also reinforces that it is the regional atmospheric and ocean circulations, not a global average surface temperature trend, that dominate regional climate patterns such as drought and floods.

Ten years ago, the following paper was published.

Vinnikov et al., 1999: Global warming and northern hemisphere sea ice extent. Science. 286, 1934-1937.

In this paper, there is a presentation of the model predictions of sea ice extent along with observations up to 1998.  This weblog introduces the subject of how well have the model predictions done.

Their abstract includes the statement (referring to the GFDL and Hadley global climate models)

“Both models used here project continued decreases in sea ice thickness and extent throughout the next century.”

In the conclusion to their paper, they write

“Both climate models realistically reproduce the observed annual trends in NH sea ice extent. This suggests that these models can be used with some confidence to predict future changes in sea ice extent in response to increasing greenhouse gases in the atmosphere. Both models predict continued substantial sea ice extent and thickness decreases in the next century.”

In their paper (in Table 1) they have model predictions (in units of linear trend in 10⁶ square kilometers per decade) listed for the GFDL climate model from 1978-1998 of -0.34 (and -0.19 using a “smoothed model output“) and for the Hadley Centre climate model -0.18 (and -0.16 using a “smoothed model output“).

A value of -0.18 is the loss of sea ice area of 180000 square kilometers per decade, for example.

The first figure below is from the Vinnikov et al 1999 paper  with respect to the model predictions, while the second and third figures are the sea ice areal anomaly and the sea ice areal converage for the Northern Hemisphere up to the present from The Cryosphere Today.

From:http://arctic.atmos.uiuc.edu/cryosphere/IMAGES/current.anom.jpg

From: http://arctic.atmos.uiuc.edu/cryosphere/IMAGES/current.area.jpg

Until later in 2007, the sea ice areal extent continued to decrease in a manner which, at least visually, is consistent with the Vinnikov et al 1999 predictions (although the actual values of areal coverage differ substantially between the observations and the predictions, perhaps as a result of their formulation to compute areal coverage).

However, since 2006, the reduction has stopped and even reversed. Perhaps this is a short term event and the reduction of sea ice extent will resume. Nonetheless, the reason for the turn around, even if short term, needs an explanation.  Moreover, this data provides a valuable climate metric to assess whether the multi-decadal global models do have predictive skill as concluded in the Vinnikov et al 2009 paper.

Climate Progress has a weblog by Joesph Romm titled “Breaking: NOAA puts out “El Niño Watch,” so record temperatures are coming and this will be the hottest decade on record“.

This is an interesting and very bold forecast of record temperatures by Joe Romm, and, if this does occurs, it would substantially support his claims on the dominance of human-caused global warming. Only time will tell, of course, if this warming will occur.

However, unfortunately, he still does not understand that i) the appropriate metric to monitor global warming involves heat in Joules, most which occurs in the oceans (e.g. see),  and ii) that the accumulation Joules in the upper ocean has not occurred since 2003 (e.g. see and see). Even Jim Hansen agrees that the ocean is the dominant reservoir for heat accumulation (e. g. see).

In Joe Romm’s weblog, there is the text

“As a side note:  Roger Pielke, Sr.’s “analysis” of how there supposedly hasn’t been measurable ocean warming from 2004 to 2008 is uber-lame.  In the middle of a strong 50-year warming trend, any clever (but cynical) analyst can connect an El Niño-driven warm year to a La Niña-driven cool year a few years later to make it look like warming has stopped.  In fact, the latest analysis shows “that ocean heat content has indeed been increasing in recent decades, just like the models said it should.”

This text shows a failure to understand the physics of global warming and cooling. There are peer reviewed analyses that document that upper ocean warming has halted since 2003 (e.g. see and see).  Even the last few years of the Levitus et al 2009 paper shows this lack of wamring (see).

Joe Romm, since he disagrees with this, should present other observational analyses of the continued accumulation of heat content in Joules since 2003. He should also focus on this time period since the Argo network was established, as it is this data network which is providing us more accurate assessments of the heat content in the upper ocean than is found in the earlier data.

If he continues to use the global average surface temperature trends as the metric for global warming, he will convince us that he does not recognize i) that surface temperature, by itself, is not a meaasure of heat (e.g. see), and ii) that there are major remaining uncertainties and biases with the surface temperature data set (e.g. see, see and see).

He writes

“In the middle of a strong 50-year warming trend, any clever (but cynical) analyst can connect an El Niño-driven warm year to a La Niña-driven cool year a few years later to make it look like warming has stopped.”

He ignores that since 2003, global warming (the accumulation of Joules) has stopped. An objective scientist [as opposed to a "clever (but cynical) analyst"] would report this scientific observation.

He would find more appreciation and respect for his viewpoints if he properly presented the actual observational finding, and discussed its implications as to where we are with respect to the accumulation of Joules over time. I have proposed such an approach in my weblogs

A Litmus Test For Global Warming - A Much Overdue Requirement

http://climatesci.org/2009/02/09/update-on-a-comparison-of-upper-ocean-heat-content-changes-with-the-giss-model-predictions/.

An excellent new paper is “in press” for Geophysical Research Letters (GRL) which documents the major role of regional atmospheric/ocean circulation pattern changes on regional multi-decadal climate variability (e.g. see What is the Importance to Climate of Heterogeneous Spatial Trends in Tropospheric Temperatures?).

This paper supports the finding that long term variations in atmospheric/ocean circulations (such as the Atlantic Multidecadal Oscillation, the Pacific Decadal Oscillation, ENSO, etc)  cause regional changes in temperatures over this time period, and that these changes have a significant natural cause. Such a perspective supports the views of Joe D’Aleo (see); Bill Gray (see); and Roy Spencer (see). [also see].  [Added June 2 2009: Joe D'Aleo alerted me to another paper on this topic: Francis, J. A., and E. Hunter (2007), Drivers of declining sea ice in the Arctic winter: A tale of two seas, Geophys. Res. Lett., 34, L17503, doi:10.1029/2007GL030995.]

The paper is

Chylek Petr, Chris K. Folland, Glen Lesins, Manvendra K. Dubeys, and Muyin Wang: 2009: “Arctic air temperature change amplification and the Atlantic Multidecadal Oscillation”. Geophysical Research Letters (in press).

The abstract reads

“Understanding Arctic temperature variability is essential for assessing possible future melting of the Greenland ice sheet, Arctic sea ice and Arctic permafrost. Temperature trend reversals in 1940 and 1970 separate two Arctic warming periods (1910-1940 and 1970-2008) by a significant 1940-1970 cooling period. Analyzing temperature records of the Arctic meteorological stations we find that (a) the Arctic amplification (ratio of the Arctic to global temperature trends) is not a constant but varies in time on a multi-decadal time scale, (b) the Arctic warming from 1910-1940 proceeded at a significantly faster rate than the current 1970-2008 warming, and (c) the Arctic temperature changes are highly correlated with the Atlantic Multi-decadal Oscillation (AMO) suggesting the Atlantic Ocean thermohaline circulation is linked to the Arctic temperature variability on a multi decadal time scale.”

Text in this paper includes

“In the following analysis we confirm that the Arctic has indeed warmed during the 1970-2008 period by a factor of two to three faster than the global mean in agreement with model predictions but the reasons may not be entirely anthropogenic. We find that the ratio of the Arctic to global temperature change was much larger during the years 1910-1970.”

“We consequently propose that the AMO is a major factor affecting inter-decadal variations of Arctic temperature and explaining [the] high value of the Arctic to global temperature trend ratio during the cooling period of 1940-1970.”

“Our analysis suggests that the ratio of the Arctic to global temperature change varies on [a] multi-decadal time scale. The commonly held assumption of a factor of 2-3 for the Arctic amplification has been valid only for the current warming period 1970-2008. The Arctic region did warm considerably faster during the 1910-1940 warming compared to the current 1970-2008 warming rate (Table 1). During the cooling from 1940-1970 the Arctic amplification was extremely high, between 9 and 13. The Atlantic Ocean thermohaline
circulation multi-decadal variability is suggested as a major cause of Arctic temperature variation. Further analyses of long coupled model runs will be critical to resolve the influence of the ocean thermohaline circulation and other natural climate variations on Arctic climate and to determine whether natural climate variability will make the Arctic more or less vulnerable to anthropogenic global warming.”

In the assessment of weather predictions, I routinely access the excellent website RAP Real-Time Weather Data. There is one product on this site that is quite informative with respect to tropospheric temperature anomalies on multi-decadal time scale, and it is their “500 mb Z-Anomaly” plots on their GFS model plots.

An example of one of the plots for a 120 hour forecast is given below.

A Northern Hemisphere perspective of these anomalies can be viewed at the Climate Prediction Center of NOAA (see). The contour interval for the 500 mb height anomalies is 120 m. The anomalies are departures from the 1979-95 daily base period means. Above average heights correspond to a warmer than average lower troposphere, while below average heights correspond to a cooler than average troposphere.

What is quite informative about these plots, if you follow them day by day over the year, is that the regions of above and below average 500 mb heights (which is what is displayed in these figures) has not shown evidence of any preference for more above average regions, as would be expected if the troposphere were significantly warming. 

Snapshots as given below, of course, represent “just weather”, but if we examine this data over time, we should be seeing a movement since 1979 towards more regions of above average heights, if the troposphere is warming. The relatively small warming that has been reported (e.g. see), is swamped by the much larger regional variations in warming and cooling.

Our research group and collaborating colleagues have published several papers with major findings with respect to climate science. This weblog lists several of these findings, along with the peer reviewed papers in which they are based on:

• A conservative estimate of the warm bias resulting from measuring the temperature near the ground is around 0.21°C per decade (with the nighttime minimum temperature contributing a large part of this bias). Since land covers about 29% of the Earth’s surface, the warm bias due to just this one effect explains about 30% of the IPCC estimate of global warming. In other words, consideration of this one bias in temperature would reduce the IPCC trend to about 0.14°C per decade; still a warming, but not as large as indicated by the IPCC. [based on Lin, X., R.A. Pielke Sr., K.G. Hubbard, K.C. Crawford, M. A. Shafer, and T. Matsui, 2007: An examination of 1997-2007 surface layer temperature trends at two heights in Oklahoma. Geophys. Res. Letts., 34, L24705, doi:10.1029/2007GL031652; Klotzbach, P.J., R.A. Pielke Sr., R.A. Pielke Jr., and J.R. Christy, 2009: An alternative explanation for differential temperature trends at the surface and in the lower troposphere. J. Geophys. Res., submitted.] - for other uncertainties and biases in the monitoring of multi-decadal global average surface temperature trends; see).

• From observations of the spatial distribution of the human input of aerosols in the atmosphere in the lower latitudes, the aerosol effect on atmospheric circulations (through their diabatic heating effect on the three dimensional pressure field), can be 60 times greater than the effect due to the radiative heating effect of the human addition of well-mixed greenhouse gases [based on Matsui, T., and R.A. Pielke Sr., 2006: Measurement-based estimation of the spatial gradient of aerosol radiative forcing. Geophys. Res. Letts., 33, L11813, doi:10.1029/2006GL025974].

• Extensive peer-reviewed research has shown that the focus on just carbon dioxide as the dominate human climate forcing is too narrow. We have found that natural variations are still quite important, and moreover, the human influence is significant, but it involves a diverse range of first-order climate forcings, including, but not limited to the human input of CO2 [ based on Rial, J., R.A. Pielke Sr., M. Beniston, M. Claussen, J. Canadell, P. Cox, H. Held, N. de Noblet-Ducoudre, R. Prinn, J. Reynolds, and J.D. Salas, 2004: Nonlinearities, feedbacks and critical thresholds within the Earth’s climate system. Climatic Change, 65, 11-38; Pielke Sr., R.A., G. Marland, R.A. Betts, T.N. Chase, J.L. Eastman, J.O. Niles, D. Niyogi, and S. Running, 2002: The influence of land-use change and landscape dynamics on the climate system- relevance to climate change policy beyond the radiative effect of greenhouse gases. Phil. Trans. A. Special Theme Issue, 360, 1705-1719.

The acceptance of CO2 as a pollutant by the EPA , yet it is a climate forcing not a traditional atmospheric pollutant, opens up a wide range of other climate forcings which the EPA could similarly regulate (e.g., land use, water vapor). These other forcings, such as land-use change and from atmospheric pollution aerosols, may have a greater effect on our climate than the effects that have been claimed for CO2.

Our peer reviewed papers have not been refuted by any subsequent peer reviewed articles. Interested climate scientists are invited to contact me, if they are interested in posting a guest weblog as to what scientific reasons exist to reject any of the findings listed above.

Thanks to Anders Vallard for alerting us to this paper.

Levitus S., J. I. Antonov, T. P. Boyer, R. A. Locarnini, H. E. Garcia, A. V. Mishonov (2009), Global ocean heat content 1955–2008 in light of recently revealed instrumentation problems,Geophys. Res. Lett., 36, L07608, doi:10.1029/2008GL037155.

The abstract reads

“We provide estimates of the warming of the world ocean for 1955–2008 based on historical data not previously available, additional modern data, correcting for instrumental biases of bathythermograph data, and correcting or excluding some Argo float data. The strong interdecadal variability of global ocean heat content reported previously by us is reduced in magnitude but the linear trend in ocean heat content remain
similar to our earlier estimate.”

This paper is, of course, directly related to the recent guest weblog on Climate Science

Have Changes In Ocean Heat Falsified The Global Warming Hypothesis? - A Guest Weblog by William DiPuccio

Climate Science has a few comments regarding the new Levitus et al. paper:

First, while they report on the longer term trend of upper ocean warming, which everyone agrees did occur, they are silent on the lack of recent warming, which was discussed in the paper

Pielke Sr., R.A., 2008: A broader view of the role of humans in the climate system. Physics Today, 61, Vol. 11, 54-55

and on the weblog

Update On A Comparison Of Upper Ocean Heat Content Changes With The GISS Model Predictions

Their plot of upper ocean heat content over time, which illustrates large multi-annual variations in trend back to 1955, is presented at http://www.nodc.noaa.gov/OC5/3M_HEAT_CONTENT/, and is reproduced below.

Secondly, the authors did not covert their heat accumulation into Watts per meter squared.  This can straightforwardly be completed for each year. Since 2004 in the Levitus et al analysis given above, the global average radiative imbalance is close to zero which deviates significantly from

Hansen, J., L. Nazarenko, R. Ruedy, Mki. Sato, J. Willis, A. Del Genio, D. Koch, A. Lacis, K. Lo, S. Menon, T. Novakov, Ju. Perlwitz, G. Russell, G.A. Schmidt, and N. Tausnev, 2005: Earth’s energy imbalance: Confirmation and implications. Science, 308, 1431-1435, doi:10.1126/science.1110252

where they wrote

“Our climate model, driven mainly by increasing human-made greenhouse gases and aerosols among other forcings, calculates that Earth is now absorbing 0.85±0.15 W/m² more energy from the Sun than it is emitting to space. This imbalance is confirmed by precise measurements of increasing ocean heat content over the past 10 years.”

Since 2004, this imbalance has not occurred. The longer this lack of radiative heating occurs, the lower will become the multi-decadal trend of radiative forcing diagnosed by fitting a linear trend in the Levitus et al data starting in 1955. Of course, if the heating resumes, than this lack of recent warming will attract less attention among the climate community. Until (and if) it does start warming again, however, there needs to be an explanation for this recent behaviour of the climate system.

Levitus et al do correctly recognize that

“Because of the importance of OHC as a major component of earth’s heat balance it needs to be accurately monitored. Analyses using independent data types such as those provided by Dickey et al. [2008] are important in evaluating OHC estimates.”

From the weblog Update On A Comparison Of Upper Ocean Heat Content Changes With The GISS Model Predictions, I wrote

“The observed best estimates of the observed heating and the Hansen et al. prediction in Joules in the upper 700m of the ocean are given below:

OBSERVED BEST ESTIMATE OF ACCUMULATION Of JOULES [assuming a baseline of zero at the end of 2002].

2003 ~0 Joules
2004 ~0 Joules
2005 ~0 Joules
2006 ~0 Joules
2007 ~0 Joules
2008 ~0 Joules
2009 —— 
2010 —— 
2011 —— 
2012 ——     

HANSEN PREDICTION OF The ACCUMULATION OF JOULES [ at a rate of 0.60 Watts per meter squared] assuming a baseline of zero at the end of 2002].

2003 ~0.98 * 10** 22 Joules
2004 ~1.96 * 10** 22 Joules
2005 ~2.94 * 10** 22 Joules
2006 ~3.92 * 10** 22 Joules
2007 ~4.90 * 10** 22 Joules
2008 ~5.88 * 10** 22 Joules
2009 ~6.86 * 10** 22 Joules
2010 ~7.84 * 10** 22 Joules
2011 ~8.82 * 10** 22 Joules
2012 ~9.80 * 10** 22 Joules

Thus, according to the GISS model predictions, there should be approximately 5.88 * 10**22 Joules more heat in the upper 700 meters of the global ocean at the end of 2008 than were present at the beginning of 2003.

For the observations to come into agreement with the GISS model prediction by the end of 2012, for example, there would have to be an accumulation 9.8 * 10** 22 Joules of heat over just the next four years. This requires a heating rate over the next 4 years into the upper 700 meters of the ocean of 2.45 * 10**22 Joules per year, which corresponds to a radiative imbalance of ~1.50 Watts per square meter.

This rate of heating would have to be about 2 1/2 times higher than the 0.60 Watts per meter squared that Jim Hansen reported for the period 1993 to 2003.

While the time period for this discrepancy with the GISS model is relatively short, the question should be asked as to the number of years required to reject this model as having global warming predictive skill, if this large difference between the observations and the GISS model persists.”

The new Levitus et al. 2009 paper, while not discussing this issue, further confirms that global warming, using upper ocean heat content as the metric,  has stopped, at least for now. Moreover, the rate of heating in the last 5 years falls significantly below the amount of heating predicted by the  IPCC models, as shown in the above figure.

Finally, they do cite an approach to assess ocean heating and cooling, which should be updated to the present (it is only to 2005 in the paper below). The paper is

Dickey, J. O., S. L. Marcus, and J. K. Willis (2008), Ocean cooling: Constraints from changes in Earth’s dynamic oblateness (J2) and altimetry, Geophys. Res. Lett., 35, L18608, doi:10.1029/2008GL035115.

There is a new paper on the latest version of the United States Historical Climatololgy Network (USHCN). This data is used to monitor and report on surface air temperature trends in the United States. The paper is

Matthew J. Menne, Claude N. Williams, Jr. and Russell S. Vose, 2009: The United States Historical Climatology Network Monthly Temperature Data – Version 2. Bulletin of the American Meteorological Society (in press). [url for a copy of the paper added thanks and h/t to Steve McIntyre and RomanM on Climate Audit].

The abstract reads

“In support of climate monitoring and assessments, NOAA’s National Climatic Data Center has developed an improved version of the U.S. Historical Climatology Network temperature dataset (U.S. HCN version 2). In this paper, the U.S. HCN version 2 temperature data are described in detail, with a focus on the quality-assured data sources and the systematic bias adjustments. The bias adjustments are discussed in the context of their impact on U.S. temperature trends from 1895-2007 and in terms of the differences between version 2 and its widely used predecessor (now referred to as U.S. HCN version 1). Evidence suggests that the collective impact of changes in observation practice at U.S. HCN stations is systematic and of the same order of magnitude as the background climate signal. For this reason, bias adjustments are essential to reducing the uncertainty in U.S. climate trends. The largest biases in the HCN are shown to be associated with changes to the time of observation and with the widespread changeover from liquid-in-glass thermometers to the maximum minimum temperature sensor (MMTS). With respect to version 1, version 2 trends in maximum temperatures are similar while minimum temperature trends are somewhat smaller because of an apparent over correction in version 1 for the MMTS instrument change, and because of the systematic impact of undocumented station changes, which were not addressed version 1.”

I was invited to review this paper, and to the authors credit, they did make some adjustments to their paper in their revision. Unfortunately, however, they did not adequately discuss a number of remaining bias and uncertainty issues with the U.S. HCN version 2 data.

The United States Historical Climatology Network Monthly Temperature Data – Version 2 still contains significant biases.

 My second review of their paper is reproduced below.

Review By Roger A. Pielke Sr. of Menne et al 2009.

Dear Melissa and Chet

I have reviewed the responses to the reviews of the Menne et al paper, and, while they are clearly excellent scientists, and have provided further useful information, unfortunately, they still did not adequately respond to several of the issues that have been raised. I have summarized these issues below:

1. With respect to the degree of uncertainty associated with the homogenization procedure, they misunderstood the comment. The issue is that in the creation of each adjustment [time-of-observation bias, change of instrument], there is a regression relationship that is used to create these adjustments. These regression relationships have an r-squared associated with them as well as a standard deviation. These deviations arise from the adjustment regression evaluation. These values need to be provided (standard deviations, r-squared) for each formula that they use.

Their statement that

“Based on this assessment, the uncertainty in the U.S. average temperature anomaly in the homogenized (version 2) dataset is small for any given year but contributes to an uncertainty to the trends of about (0.004°C)”

is not the correct (complete) uncertainty analysis.

2.

i) With respect to their recognition of the pivotal work of Anthony Watt, while they are clear on this contribution in their response; i.e.

“Nevertheless, we have now also added a citation acknowledging the work of Anthony Watts whose web site is mentioned by the reviewer. Note that we have met personally with Mr. Watts to discuss our homogenization approach and his considerable efforts in documenting the siting characteristics of the HCN are to be commended. Moreover, it would seem that the impetus for modernizing the HCN has come largely as a reaction to his work. “

the text itself is much more muted on this. The above text should, appropriately, be added to the paper.

Also, the authors bypassed the need to provide the existing photographic documentation (as a url) for each site used in their study. They can clearly link in their paper to the website
http://www.surfacestations.org/ for this documentation. Ignoring this source of information in their paper is inappropriate.

ii) On the authors’ response that

“Moreover, it does not necessarily follow that poorly sited stations will experience trends that disagree with well-sited stations simply as a function of microclimate differences, especially during intervals in which both sites are stable. Conversely, the trends between two well-sited stations may differ because of minor changes to the local environment or even because of meso-scale changes to the environment of one or both stations..”

they are making an unsubstantiated assumption on the “stability” of well-sited and poorly-sited stations. What documentation do that have that determines when “both sites are stable”? As has been clearly shown on Anthony Watt’s website, it is unlikely that any of the poorly sited locations have time invariant microclimates.

Indeed, despite their claim that

“We have documented the impact of station changes in the HCN on calculations of U.S. temperature trends and argue that homogenized data are the only way to estimate the climate signal at the surface (which can be important in normals calculations etc) for the full historical record “

is not correct. Without photographs of each site (which now exists for many of them), they have not adequately documented each station.

iii) The authors are misunderstanding the significance of the Lin et al paper. They state

“Moreover, the homogenized HCN minimum temperature data can be thought of as a fixed network (fixed in both location and height). Therefore, the mix of station heights can be viewed as constant throughout the period of record and therefore as providing estimates of a fixed sampling network albeit at 1.5 and 2m (not at the 9m for which differences in trends were found in Oklahoma). Therefore, these referenced papers do not add uncertainty to the HCN minimum temperature trends per se. “

First, as clearly documented on the Anthony Watts website, many of the observing sites are not at the same height above the ground (i.e. not at 1.5m or 2m). Thus, particularly for the minimum temperatures, which vary more with height near the ground, the height matters in patching all of the data together to create long term temperature trends. Even more significant is that the trend will be different if the measurements are at different heights. For example, if there has been overall long term warming in the lower atmosphere, the trends of the minimum temperature at 2m will be significantly larger than when it is measured at 4m (or other higher level). Including minimum temperature trends together will result in an overstatement of the actual warming.

The authors need to discuss this issue. Preliminary analyses have suggested that this warm bias can overstate the reported warming trend by tenths of a degree C.

iv) While the authors seek to exclude themselves from attribution; i.e.

“Our goal is not to attribute the cause of temperature trends in the U.S. HCN, but to produce time series that are more generally free of artificial bias.”

they need to include a discussion of land use/land cover change effects on long term temperature trends, which now has a rich literature. The authors are correct that there are biases associated with non-climatic and microclimate effects in the immediate vicinity of the observation sites (which they refer to as “artificial bias”), and real effects such as local and regional landscape change. However, they need to discuss this issue more completely than they do in their paper, since, as I am sure the Editors are aware, this data is being used to promote the perspective that the radiative effect of the well-mixed greenhouse gases (i.e. “global warming”) is the predominate reason for the positive temperature trends in the USA.

iv) The neglect of using a complementary data analysis (the NARR) because it only begins in 1979 is not appropriate. The more recent years in the HCN analyses would provide an effective cross-comparison. Also, even if the NARR does not separate maximum and minimum temperatures, the comparison could still be completed using the mean temperature trends.

Their statement that

” Given these complications, we argue that a general comparison of the HCN trends to one of the reanalysis products is inappropriate for this manuscript (which is already long by BAMS standards)”

therefore, is not supportable as part of any assessment of the robustness of the trends that they compute. The length issue is clearly not a justifiable reason to exclude this analysis.

In summary, the authors should include the following:

1. In their section “Bias caused by changes to the time of observation”

the regression relationship used in

“…the predictive skill of the Karl et al. (1986) approach to estimating the TOB was confirmed using hourly data from 500 stations over the period 1965-2001 (whereas the approach was originally developed using data from 79 stations over the period 1957-64)”

should be explicitly included with the value of explained variance (i.e. the r-squared value) and standard deviation, rather than referring the reader to an earlier paper. This uncertainty in the adjustment process has been neglected in presenting the trend values with its +/- values.

2. In their section “Bias associated with other changes in observation practice”

the same need to present the regression relationship that is used to adjust the temperatures due to instrument changes; i.e. from

“Quayle et al. (1991) concluded that this transition led to an average drop in maximum temperatures of about 0.4°C and to an average rise in minimum temperatures of 0.3°C for sites with no coincident station relocation.”

What is the r-squared and the standard deviation from which these “averages” were obtained?

3. With respect to “Bias associated with urbanization and nonstandard siting”,

as discussed earlier in this e-mail, the link to the photographs for each site needs to be included and citation to Anthony Watt’s work on this subject more appropriately highlighted.

On the application of “In contrast, no specific urban correction is applied in HCN version 2″, this conclusion conflicts with quite a number of urban-rural studies. They assume “that adjustments for undocumented changepoints in version 2 appear to account for much of the changes addressed by the Karl et al. (1988) UHI correction used in version 1.”

The use of text that concludes that this adjustment process “appear” to account for the urban correction of Karl et al (1988) indicates even some uneasiness by the authors on this issue. They need more text as to why they assume their adjustment can accommodate such urban effects. Moreover, the urban correction in Karl et al is also based on a regression assessment with an explained variance and standard deviation; the same data Karl used should be applied to ascertain if the new “undocumented changepoint adjustment” can reproduce the Karl et al results.

The authors clearly recognize this limitation also in their paragraph that starts with

“It is important to note, however, that while the pairwise algorithm uses a trend identification process to discriminate between gradual and sudden changes, trend inhomogenieties in the HCN are not actually removed with a trend adjustment..”

and ends with

“This makes it difficult to robustly identify the true interval of a trend inhomogeneity (Menne and Williams 2008).”

Yet, despite this clear serious limitation of the ability to quantify long term temperature trends in tenths of a degree C with uncertainties, they present such precise quantitative trends; e.g.

“0.071°and 0.077°C dec-1, respectively” (on page 15).

They also write that

“…there appears to be little evidence of a positive bias in HCN trends caused by the UHI or other local changes”

which ignores detailed local studies that clearly show positive temperature biases; e.g.

Brooks, Ashley Victoria. M.S., Purdue University, May, 2007. Assessment of the Spatiotemporal Impacts of Land Use Land Cover Change on the Historical Climate Network Temperature Trends in Indiana.

Christy, J.R., W.B. Norris, K. Redmond, and K.P. Gallo, 2006, Methodology and results of calculating Central California surface temperature trends: Evidence of human-induced climate change?, J. Climate, 19, 548-563.

Hale, R. C., K. P. Gallo, and T. R. Loveland (2008), Influences of specific land use/land cover conversions on climatological normals of near-surface temperature, J. Geophys. Res., 113, D14113, doi:10.1029/2007JD009548.

4. On the claim that

“However, from a climate change perspective, the primary concern is not so much the absolute measurement bias of a particular site, but rather the changes in that bias over time, which the TOB and pairwise adjustments effectively address (Vose et al. 2003; Menne and Williams 2008) subject to the sensitivity of the changepoint tests themselves.”

this is a circular argument. While I agree it is the changes in bias over time that matter most, without an independent assessment, there is no way for the authors to objectively conclude that their adjustment procedure captures these changes of bias in time.

Their statment that

“Instead, the impact of station changes and non-standard instrument exposure on temperature trends must be determined via a systematic evaluation of the observations themselves (Peterson 2006).”

is fundamentally incomplete. The assessment of the impact “of station changes and non-standard instrument exposure on temperature trends” must be assessed from the actual station location and its changes over time! To rely on the observations to extract this information is clearly circular reasoning.

As a result of these issues, their section “Temperature trends in U.S. HCN” overstate the confidence that should be given to the quantitative values of the trends and the statistical uncertainty in their values.

If this paper is published, the issues raised in this review need to be more objectively and completely presented. It should not be accepted until they do this.

I would be glad to provide further elaboration on the subjects I have presented in this review of their revised paper, if requested.

Best Regards

Roger A. Pielke Sr.

The January 2009 issue of the Bulletin of the American Meteorological Society had the very informative article

Aberson, Sim D.: 2009: Regimes or Cycles in Tropical Cyclone Activity in the North Atlantic.  Bulletin of the American Meteorological Society Volume 90, Issue 1 (January 2009) pp. 39–43 DOI: 10.1175/2008BAMS2549.1

The abstract reads

“ A cautionary tale in which previously published results are shown to be invalid due to the
lack of statistical analyses in the original work.”

Text from this paper includes

“Kinsmen (1957) wrote, ‘the job of a scientist is to invent a story which accounts for a set of observations and then decide how likely the story is.’ In his 1957 work, ‘Proper and improper use of statistics in geophysics,’ he emphasized the role of the correct use of statistics in this decision. However, statistics
continue to be misused or altogether neglected in the refereed literature, with the inevitable result of misleading or erroneous conclusions.”

“Though the results of HW07 [Holland, G. J., and P. J. Webster, 2007: Heightened tropical cyclone activity in the North Atlantic: Natural variability or climate trend? Philos. Trans. Roy. Soc. London, 365A, 2695–2716] are unlikely to be correct, this does not necessarily suggest that the alternative hypothesis—that the time series shows a cyclical pattern—is correct. The time series only has two full oscillations and is too short to test the likelihood that it is a cycle. Unfortunately, even if the data were completely accurate, decades may pass before the series is long enough to make any definitive statements on this topic. Nevertheless, the clear need for timely scientific results should not be a reason for shortcuts in the scientific process; correct statistical analyses must be performed to determine the likelihood that the hypothesis tested is valid.”

This paper informs us that i) natural variations in climate metrics are quite large and ii) the non-temporal homogeneity of the climate data can result in the misinterpretation of statistical results.