D’Aleo at Heartland: An Apple a Day
Joseph D’Aleo made an appearance at the Heartland Institute’s recent conference. This post is my turn at the cracker barrel, a response mostly to D’Aleo’s Slide #50
Claim: 75% of the stations disappear, many from colder higher latitudes and elevations and in stable areas of lower latitudes
Response: If it is D’Aleo’s intent to imply that losing ‘colder stations’ necessarily imparts a warming trend, this is a mathematical fallacy. D’Aleo provides no estimates on the change in trend due to the station drop. There have been several independent attempts to quantify a change in trend due to the factors that D’Aleo outlines:
Zeke Hausfather, January 21, 2010 posts a graph comparing the simple average temperature anomaly from 1,017 thermometers with data available through 2000 to 402 that stopped providing data sometime between 1970 and 2000. He finds no significant difference between the two traces suggesting that station drop out is not an important source of bias.
Ron Broberg, Feb 1 2010 posts an analysis of the effect of excluding high latitude, high altitude, and rural (low pop density) stations from a globally gridded average anomaly using GHCN v2.mean (raw) data.
Roy Spencer Feb. 20, 2010. Roy Spencer computes trends using data drawn from the NOAA-merged International Surface Hourly (ISH) dataset, a ground based thermometer record. Using area weighting, he compared land based temperature anomalies for the northern hemisphere computed thermometers in operation from 1986-2010 to trends published by CRU (which may also affected by the GHCN station drop). He finds no difference in trend – although the monthly data from the ISH dataset appears noisier.
Tamino, Feb 23, 2010 presents preliminary GHCN temperature analysis comparing area weighted temperature anomalies for the Northern Hemisphere based on “cut-off” thermometers series and data from thermometers that remained in the record to the present time. He finds no significant difference between the two traces.
Clear Climate Code, Feb. 26, 2010 compares GISTEMP type calculations of global surface temperature anomalies based on the “full” and “cut-off” thermometer set. They find no major differences between the two traces.
Ron Broberg, March 3 2010, repeat the high alt, high lat, and rural analysis with GISTEMP.
Lucia Liljegren March 5 2010 starts short series using a small model (spherical cow) to demonstrate the effects of station loss. There is no loss of global trends, although she notes that there can be a loss of information when trends are associated with features that are related to the stations themselves.
Claim: Missing months increase tenfold, most rural and in winter
Response: D’Aleo again seems to imply that losing ‘colder stations’ necessarily imparts a warming trend. Still a mathematical fallacy. However, I am unaware of any attempt by D’Aleo or others to quantify the effects of this observation.
Claim: Urban adjustment removed or non-existent even as population grows 1.5 to 6.7B and most peer review finds significant contamination
Response: Urban adjustments are explicitly made in GISTEMP by comparisons with neighboring rural stations. In 2010, the use of nightlights was extended to include the ‘rest of the world’ as well as the US.
The urban adjustment in the current GISS analysis is a similar two-legged adjustment, but the date of the hinge point is no longer fixed at 1950, the maximum distance used for rural neighbors is 500 km provided that sufficient stations are available, and “small-town” (population 10,000 to 50,000) stations are also adjusted. The hinge date is now also chosen to minimize the difference between the adjusted urban record and the mean of its neighbors. In the United States (and nearby Canada and Mexico regions) the rural stations are now those that are “unlit” in satellite data, but in the rest of the world, rural stations are still defined to be places with a population less than 10,000. The added flexibility in the hinge point allows more realistic local adjustments, as the initiation of significant urban growth occurred at different times in different parts of the world.
The urban adjustment, based on the long-term trends at neighboring stations, introduces a regional smoothing of the analyzed temperature field. To limit the degree of this smoothing, the present GISS analysis first attempts to define the adjustment based on rural stations located within 500 km of the station. Only if these stations are insufficient to define a long-term trend are stations at greater distances employed. As in the previous GISS analysis, the maximum distance of the rural stations employed is 1000 km.
This homogeneity adjustment should serve to minimize the effect of nonclimatic warming at urban stations on the analyzed global temperature change. However, as discussed by Hansen et al. , it should not be assumed that the adjustment always yields less warming at the urban station or that it necessarily makes the result for an individual urban station more representative of what the temperature change would have been in the absence of humans. Indeed, in the global analysis we find that the homogeneity adjustment changes the urban record to a cooler trend in only 58% of the cases, while it yields a warmer trend in the other 42% of the urban stations. This implies that even though a few stations, such as Tokyo and Phoenix, have large urban warming, in the typical case, the urban effect is less than the combination of regional variability of temperature trends, measurement errors, and inhomogeneity of station records.
For the record, I believe that there may be better measures and methods for detecting and dealing with UHI. But D’Aleo’s claim that no method for dealing with it in the surface records ignores GISTEMP.
Claim: ‘Modernization’ instruments had warm bias or increased uncertainty
Menne 2009 documents that modernized instruments have raised daily minimum temperature readings but have lowered the daily maximum with an overall cooling effect of daily averages.
The pairwise results indicate that only about 40% of the maximum and minimum temperature series experienced a statistically significant shift (out of ~850 total conversions to MMTS). As a result, the overall effect of the MMTS instrument change at all affected sites is substantially less than both the Quayle et al. (1991) and Hubbard and Lin (2006) estimates. However, the average effect of the statistically significant changes (−0.52°C for maximum temperatures and +0.37°C for minimum temperatures) is close to Hubbard and Lin’s (2006) results for sites with no coincident station move.
In addition, a number of sites (about 5% of the etwork) converted to the Automated Surface Observation System (ASOS) after 1992. Like the MMTS, ASOS maximum temperature easurements have been shown to be lower relative to values from previous instruments (e.g., Guttman and Baker 1996).Such results are in agreement with the pairwise adjustments produced in HCN version 2; that is, an average shift in maximum temperatures caused by the transition to ASOS in the HCN of about −0.44°C. The combined effect of the transition to MMTS and ASOS appears to be largely responsible for the continuing trend in differences between the fully and TOB-only adjusted maximum temperatures since 1985. On the other hand, while the effect of ASOS on minimum temperatures in the HCN is nearly identical to that on maximum temperatures (−0.45°C), the shifts associated with ASOS are opposite in sign to those caused by the transition to MMTS, which leads to a network-wide partial cancellation effect between the two instrument changes. Undocumented changes, which are skewed in favor of positive shifts, further mitigate the effect of the MMTS on minimum temperatures.
Zeke Hausfather, April 8 2010 notes a slight cooling bias introduced by the shift from Liquid in Glass (LiG)/Cotton Region Shelters (CRS) measurement instruments to maximum-minimum temperatures system (MMTS) instruments.
Claim: ‘Modernization’ led to putting 90% stations in inappropriate locations where they have a distinct warm bias
Response: Menne 2010 looked for warming bias in USHCN due to station locations and found none.
Recent photographic documentation of poor siting conditions at stations in the U.S. Historical Climatology Network (USHCN) has led to questions regarding the reliability of surface temperature trends over the conterminous U.S. (CONUS). To evaluate the potential impact of poor siting/instrument exposure on CONUS temperatures, trends derived from poor and well-sited USHCN stations were compared. Results indicate that there is a mean bias associated with poor exposure sites relative to good exposure sites; however, this bias is consistent with previously documented changes associated with the widespread conversion to electronic sensors in the USHCN during the last 25 years. Moreover, the sign of the bias is counterintuitive to photographic documentation of poor exposure because associated instrument changes have led to an artificial negative (“cool”) bias in maximum temperatures and only a slight positive (“warm”) bias in minimum temperatures.
Claim: Homogenization and other adjustments blend the good with the bad usually cooling off early warm periods, producing a warming where none existed
Response: Either you correct for changing instrumentation or you don’t. Either you correct for location changes or you don’t. Homogenization helps correct those stations which experience induced sudden changes due to instrument changes, method of observation changes, station location changes, or station environment changes.
Homogenization algorithms dealing with ‘knees’ of discontinuity have a choice to raise one leg or lower the other. As I have read it, the choice to use the older leg, thus minimizing the changes in the most recent one, was deliberate to avoid confusion by creating the largest changes in the most recent data.
Claim: Each ocean estimate (changing inputs, Wigley’s cooling ‘1940s warm blip’, and removing cool satellite data) enhance ocean warming
Response: I have no comment on this claim since I have not studied sea temperature records.
Claim: Each version of the NOAA/NASA data sets warmer than the prior
Response: In this claim and the previous one, D’Aleo seems to imply that the since the adjustments have increased the calculated warming trend, those adjustments must be erroneously or intentionally biased. But NASA does not use the homogenized GHCN (NOAA) data (v2.mean_adj) but rather the relatively unprocessed ‘raw’ data (v2.mean). It is true that NOAA/NCDC does perform quality control checks on GHCN v2.mean data, but this falls more into the ‘toss out the outliers’ category rather than homogenization adjustments.
D’Aleo raises some valid issues but is unable or unwilling to drive those issues to a resolution. D’Aleo quotes Dr. Judith Curry.
In my opinion, there needs to be a new independent effort to produce a global historical surface temperature dataset that is transparent and that includes expertise in statistics and computational science….The public has lost confidence in the data sets …Some efforts are underway in the blogosphere to examine the historical land surface data (e.g. such as GHCN), but even the GHCN data base has numerous inadequacies.”
Dr Curry should be encouraged by several technical bloggers who have created their own surface-record global anomaly programs to examine these issues. These include Zeke Hausfather, Nick Stokes, JeffId/RomanM and Chad. These independent methods have confirmed the general trends presented by CRUTEM and GISTEMP. Indeed, they tend to show slightly more warming than the ‘official’ surface-records.