AGW and Extreme Heat in the US

As part of his 2016 Congressional testimony, John Christy presented this chart:

slide65

I sought to reproduce the plot. I used the USHCN. The results I found match the pattern quite well, though the absolute numbers differ, I suspect, because of station selection. Rather than assemble the various transient stations, I used all the stations. And I excluded any year from a station in which more than 30 days of data were missing. Here is the result:GT_100

The station data used looks like this:

USHCN_STATION_TIMELINES

Where stations are one line each from top to bottom and years are left to right, from 1895 through 2014. Green indicates a year of good data and grey indicates no data ( or no station ). Obviously, station selection could conceivably change the temperature frequency. However, running the same analysis, but excluding all transient stations and all stations that were not already online by 1930 yielded quite similar results. The spatial distribution of stations in the US has been reasonably good and the spatial coverage has been reasonably dense, so for the US, there is some confidence in this distribution. For the globe, spatial coverage in 1930 was very poor and a global analysis of this sort may not be meaningful.

The number of stations included by year:

STATIONS

What’s interesting also is examining not just extreme heat ( max greater than 100F ) by also moderate heat ( max greater than 80F or 90F ):

HEAT

Also, minimum temperatures:

COLD

Here is an animation of all the stations reaching 100F:STATIONS100

 

 

The year 1936 is a peak year of the Dust Bowl, but also drought in the MidWest. The effect on 100F days is visible:

MAP_1936

GHCN

There were some questions as to whether the USHCN data was suitable compared to the GHCN data. The resulting plot using the CONUS stations in the GHCNv3.22 data set produces very similar results:

GHCN_Christy

The different stations are evident in the year by year animation of 100F stations:

GHCN_CONUS_STATIONS100

 

The CONUS GHCN stations do appear more numerous and continuous:

 

 

Cold days from CONUS GHCN stations:CONUS_GHCN_COLD

 

 Correlations

Here is the comparison of average 100F days versus global average temperature from NCDC:

CONUS_GHCN_GMAT_CORRELATION

For the continental US, extreme high temperatures, defined as the average number of days with high temperatures exceeding 100F, are very weakly anti-correlated with global mean surface temperature anomalies.

 

CONUS_GHCN_PRECIP_CORRELATION

For the contiguous US (CONUS), extreme high temperatures, defined as the average number of days with high temperatures exceeding 100F for a given year, are strongly anti-correlated with total precipitations falling from May through August for that year.

 

TOBS

The Time of Observation (TOBS) bias may be present in the USHCN and GHCN TMAX data sets. If the time of observation coincides with the time of maximum temperature, two days may indicate the 100F threshold. And there was a systemic shift from evening TOBS to morning TOBS in the data. For consecutive 100F days, this is not significant, but the bias  persist, but TOBS can have an effect. There are two available measures to obviate or minimize this effect.

Station Count

One way to obviate TOBS influence is to count the stations which exceed a threshold at least once. This approach is possible because the number of stations for CONUS is relatively constant. This measure indicates area of occurrence more than frequency. The patterns exhibited over time are similar to frequency. Below is the plot of the number of 100F stations plotted over the Christy graphic.

 

COUNT_GHCN_Christy

The number of 100F stations also indicates very weak anti-correlation with global average temperature:

CONUS_GHCN_COUNT_GMAT_CORRELATION

And strong correlation with drought:

CONUS_GHCN_COUNT_PRECIP_CORRELATION

Exclusive TOBS

Another way to remove TOBS bias is to select for only stations which have night or morning TOBS. Below is the same chart comparison of 100F days using only US stations with a TOBS specified and between 10PM and 10AM.GHCN_Christy

The results are much the same as for the analysis using all stations and for the counts of single occurrence, though before 1930, there are fewer 100F days. That’s likely because there were few TOBS stations meeting the criteria then:

Similarly, the weak anti-correlation with global temperature and the strong correlation with drought reflect the patterns above:

CONUS_GHCN_GMAT_CORRELATION

CONUS_GHCN_PRECIP_CORRELATION

 Comments

  • For CONUS, there is not evidence of global warming causing extreme heat
  • For CONUS, there is evidence that droughts cause extreme heat

The GHCN CONUS TMAX data are contrary to claims of extreme heating from AGW.  But when one considers the US MidWest average July latent heat flux( as seen below from NCAR reanalysis), upward latent heat flux ranges from 50 to 150 W/m^2. As a drought ensues and soil moisture tends toward zero, so too does latent heat flux tend toward zero. Anthropogenic greenhouse forcing, on the other hand,  is on the order of 4W/m^2 for a CO2 doubling. The capacity of natural drought to cause extreme heat will always be much greater than the anthropogenic signal of global warming.

latentheatflux_jul

 

Finally, about attribution, extreme events attributed to AGW carry the emotional weight of the entirety of the event, not the margins. Radiative Forcing is a real phenomenon, and might contribute to margins of a heatwave, but the scale of RF, compared to natural variability, as demonstrated above, is not significant.

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