GISTEMP: High Alt, High Lat, Rural
In the Summary for Policy Makers in Watts’ and D’Aleo’s
Surface Temperature Records: Policy Driven Deception, there is the following claim:
5. There has been a severe bias towards removing higher-altitude, higher-latitude, and rural stations, leading to a further serious overstatement of warming.
Earlier, I examined the effect of removing high altitude, high latitude and rural stations by using the GHCN v2.mean data set and applying the published CRU station gridding and averaging code. Here the exercise is repeated using GISTEMP inputs and methods.
The method will be described in some detail in the hope that this will reveal some fresh light on a process that is widely misunderstood.
The following files were updated via retrieval from FTP and HTTP sites.
The following files are not updated and are included in the GISTEMP source.
combine_pieces_helena.in: contains parameters for special handling of station 147619010 (St Helena)
t_hohenpeissenberg_200306.txt_as_received_July17_2003: contains an unusually long lived record for central Europe (begins in 1781)
Ts.discont.RS.alter.IN: contains parameters for special handling of station 425911650 (LIHUE, KAUAI)
Ts.strange.RSU.list.IN_full: contains information for the exclusion of 65 records from 63 stations
v2.inv: similar to GHCN v2.temperature.inv but includes brightness information and Antarctic stations
9641C_200907_F52.avg: USHCN average of fully-adjusted monthly mean maximum and minimum temperatures (with estimates for missing values) (This file is OBE but is the one that the public code is hard coded to use. You can get a copy at the Clear Climate Code Project http://code.google.com/p/ccc-gistemp/downloads/list ccc-gistemp-test-2009-12-28.tar.gz (45mb))
GISTEMP Step 0
Step 0 takes the Antarctica files, parses them into a GHCN v2 format and merges them into the v2.mean file (v2.meanx). It then removes pre 1880 data (v2.meany). USHCN stations are parsed out of the GHCN data sets and replaced with USHCNv2 stations. (v2.meanz). Finally, the Hohenpeissenberg record is updated and the final file, v2.mean_comb, is placed into a directory for use in the next step.
It can be noted that the GISTEMP process to replace the US data has been changing over the last several years as described on the GISTEMP web page.
GISTEMP Step 1
Step 1 takes the multiple records found for many individual station ids and merges them into a single record. This process is handeled differently for overlapping and discontinuous records. GHCN v2 files listed Ts.strange.RSU.list.IN are excluded. Input from Ts.discont.RSU.list.IN, and combine_pieces_helena.in is used in this processing. The output is a single file, Ts.txt, with one data table for each station which includes a header line with some meta data.
GISTEMP Step 2
Step 2 is the homogenization step which includes the urban-rural adjustments as well as the ‘brightness’ adjustment. The file Ts.txt is parsed into 6 latitudinal zones and each is processed separately. Stations with less than 20 years of data are dropped. The result is six zonal files with individual station records before the urban adjustments, and six zonal files with individual station records after the urban adjustments.
GISTEMP Step 3
Step 3 is the global gridding and averaging step, the output of which includes the GLB.Ts.GHCN.CL.PA.txt file which is used in the following discussion.
More information regarding this process can be found at the GISTEMP sources web page:
Establishing a Baseline
GISTEMP Step 0 was run using the latest data available at the FTP and HTTP sites listed above. The v2.mean_comb file was saved to a separate directory for use in the following steps. GISTEMP Steps 1-3 were run to completion. The output file of a global average of gridded anomalies for surface stations, GLB.Ts.GHCN.CL.PA.txt was then saved for later comparisons. The baseline output from this step is compared to the official NASA GISS site:
High Altitude Stations
To test the effect of removing high atitude stations, the v2.inv file was parsed for a list of stations which were located at more than 1000′. These stations were then removed from the v2.mean_comb file. GISTEMP Steps 1-3 were run to completion. The global average of gridded anomalies for surface stations at low altitude (less than 1000m) are compared to the baseline. (unknowns are left in the baseline)
High Latitude Stations
Testing the effect of removing high latitude stations is more problematic. In a previous version of this test using the public CRU gridding and averaging methods, all stations above 60N and below 60S were removed from the baseline. GISTEMP fails when this method is attempted due to the use of zonal gridding. Instead, 90% of the files above 60N and below 60S were randomly chosen from the v2.inv file. These stations were then removed from the v2.mean_comb file. GISTEMP Steps 1-3 were run to completion. The global average of gridded anomalies for surface stations at low latitude (100% below 60deg and 10% above) are compared to the baseline.
The Gridded Population of the World data version 3 (gpwv3) was used to identify stations located in regions with more than 100 people per square mile. This is a significantly lower threshold for ‘urban’ than is used by the US Census bureau which defines urban as 1000 people per square mile and an urban cluster as urban areas and surrounding regions with 500 people per square mile. All stations located in the regions above 100 people per square mile were then removed frome the v2.mean_comb file. GISTEMP Steps 1-3 were run to completion. The global average of gridded anomalies for surface stations at low population density (< 100 people per square mile) are compared to the baseline.
Low Station Count
A corollary to the ‘dropping of high latitude, high altitude, and rural’ stations claim is the claim that the loss of stations in the 1990s has caused a spurious warming trend in the last two decades. A list of stations with data available in this decade was extracted from the v2.mean_comb file. The converse of this, a lit of stations with data only prior to the year 2000 was also prepared. The post 2000 stations were then pulled from the v2.mean_comb and saved into v2.mean_comb-post2000. The original v2.mean_comb file without any of the post-2000 stations is then retained as v2.mean_comb-pre2000. Each file is then used to feed GISTEMP Steps 1-3 and the output is compared.
There is no evidence that removing high altitude, high latitude, or rural (as defined by population density) stations from the data input used by GISTEMP produces extra warming. Indeed, if any trend is discernible, it is that a loss of high latitude stations might introduce a slight cooling effect to the GISTEMP globally gridded mean anomaly trend. Neither is there any evidence for extra warming when comparing stations available after 2000 with those that are only available before 2000. However, the early 1990s drop did cause a bit of discontinuity in the difference for that time – probably worth a closer look.
Zeke Hausfather @ The Blackboard (guest post)
Lucia @ The Blackboard (a spherical cow)
drj @ Clear Climate Code
Tamino @ Open Mind
Tim Lambert @ Deltoid
I would also like to acknowledge D. Kelly O’Day @ Climate Charts and Graphs from whose site I cribbed the foundation R code for creating these charts.