Eli Got a Brand New Combine Harvester and He's Gonna Pull Some Carrots

 

UPDATES: See below 

Let us continue our exploration of Fall, et al. the al. being played by Watts, Nielsen Gammon, Jones, Niyogi, Christy and Pielke Sr. Credit where credit is due of course, but Eli thinks that after he drives his combine harvester through their carrot patch they may not be so happy. To his credit John N-G has been posting a lot on this, describing what he thinks is worthwhile about the paper. Here is how he differentiates between Fall, et al. and Menne, et al.

This is probably a good time to roll out a comparison of the Menne et al. abstract and our corresponding results. Menne et al. is in italics, including agreements and disagreements. Some agreements, some disagreements. Not shown are additional results from our paper.

There is a mean bias associated with poor exposure sites relative to good exposure sites.

Confirmed.

This bias is consistent with previously documented changes associated with the widespread conversion to electronic sensors in the USHCN during the last 25 years.

The evolution of the bias shows a major contribution at the time of sensor conversion roughly consistent with but not entirely attributable to the sensor change, plus other bias changes over time.

Associated instrument changes have led to an artificial negative bias in maximum temperatures.

Siting differences and associated instrument changes have led to an artificial negative bias in maximum temperature trends (same finding, different interpretation).

Associated instrument changes have led to only a slight positive bias in minimum temperatures.

Siting differences and associated instrument changes have led to an artificial positive bias in maximum temperature trends, similar in magnitude to the negative bias in maximum temperature trends.

Adjustments applied to USHCN Version 2 data largely account for the impact of instrument and siting changes.

The adjustments for instrument and siting changes tend to reduce the impact by about half but do not eliminate it.

A small residual negative bias appears to remain in the adjusted maximum temperature series.

A substantial residual negative bias remains in the adjusted maximum temperature trend, and a substantial residual positive bias remains in the adjusted minimum temperature trend.

We find no evidence that the CONUS average temperature trends are inflated due to poor station siting.

Neither do we, but important questions remain regarding the effect of the adjustments and the different effects of siting and instruments that may bear on the CONUS average temperature trends.

So Eli being a RTFR kinda bunny asked where the data was, and John pointed. Many thanks, and Eli went and got and extracted the Excel file with the results. Now to be honest, Eli was not looking for what he found, but what he found has implications both for Fall et al, and elsewhere (tho not so much for GISSTEMP). When Eli unzipped the Final List.xls he sorted it by Watts Rank (1-5, with 5 being the worst stations) and by location: Rural, Suburban and Urban. Then, thanks to Gatesian logic, the Rabett compared the number of stations in each Watts Rank by location and count,

 

rank	1	2	3	4	5
rural	0.43	0.52	0.68	0.68	0.53
suburb	0.21	0.24	0.21	0.25	0.24
urban	0.29	0.21	0.10	0.07	0.16
Total	14	67	222	662	68

It was an ah choo moment, because clearly rural stations are relatively under-represented in categories 1 and 2, but relatively over represented in the worst three rankings.

The implication of this is that Fall and Co. (and Menne) can and should not simply compare results from categories with each other, but should first look and see how the rural, suburban and urban distributions vary within categories, and indeed they do. Let the bunnies look at this for a couple of categories (gets very Tamino like) starting with Category 2

and Rabett Labs sees pretty much the same thing for the trends in Tmin and Tman, with what looks like two classes of rural stations. This is equally clear in WR3. How about Watts Rank 4? UPDATE: This was originally switched with Tmax for WR 3. John N-G pointed this out. The asymmetry between the urban and rural remains, but the suburban is more like the rural

It's a little harder but there is a bump on the right hand side of the rural distribution which you can see more clearly in the trend for Tmin for WR4 between the urban and the rural UPDATE: Same as for above for WR4

Tmean for WR3

Tmax for WR3

So, where does this leave us. 1. Fall, et al. fell off the carrot truck into the harvester because they did not correct for location bias which is a hoot and a half given how Watts and Pielke have gone on for centuries about the UHI, urban heat island effect, but this appears to be the RRE, the rural refrigerator effect. 2. If Eli compares the list of stations GISS uses with those used by Fall, et al., Fall appear to omit some urban and some airport stations although he was too foul to look at what was in the USHCN and what not 3. Without the RRE stations, trends in Tmean, Tmax and Tmin appear to match pretty well within Watts Ranks and across them and for rural, suburban and urban stations (Eyeball Stats). 4. The RRE stations appear to have pretty damn close to zero trends in Tmean, Tmax and Tmin. 5. What differentiates the RRE stations is not clear to Eli. Probably requires digging deep into the metadata. 6. There is at least one paper in there. Please acknowledge Rabett Labs, E. Rabett Prop. 7. If you want a copy of the Excel spreadsheet, put a note in the comments UPDATE: John N-G points out that for determining a US wide trend proper area weighting has to be used. True enough, but to average something, it helps to average apples, not apples and pears.