GHCN San Francisco – CO2?

The problem with averaging things together is that it hides what is really going on. Average a hot dog with a chunk of fried chicken and what have you got? Nothing that makes sense…

So we are told that CO2, a “well mixed gas”, is causing a consistent, global, and gradual rise in temperatures of about 1/2 C over the historical record.

We get this from averaging a bunch of thermometers together and then saying “Look! It is 1/2 C Hotter!”.

Yet looking at San Franciso, it HAS warmed. But is that due to a diffuse gradual CO2 warming?

Here is July and January in San Francisco California.

GHCN v3.3 San Francisco July

Look at those data slew from about 12 to 14 C in 1860 to about 17 C to 19 C now. Why that’s a good 5 C of HOTTEST EVAAAHHH!!!!

But wait! Isn’t that rather a lot more than the 1/2 C we’re supposed to find? Hmmm…. So maybe it is “something else”, like that the San Francisco airport has grown from a grass field next to the water in the 1800s to be acres of concrete, tarmac, and buildings consuming MW of electricity and burning tons of kerosene in jets and Diesel & Gasoline in cars and trucks?

GHCN v3.3 San Francisco January

Winter has less going on. Perhaps because in winter, here, we get more wind blowing the heat away and with lots of cloud cover and rain, sunshine on asphalt and concrete can’t do as much as the sunshine never reaches the ground?

So as I look at the unadjusted data for San Francisco, I don’t see a well diffused “Greenhouse Gas” causing a gentle slow warming. What I see is a large airport with lots of Urban Heat Island effect from being miles of pavement in all directions; but where that doesn’t do much under our common clouds and fog in winter.

So why would you average that in with all the other stations and think it means CO2 is doing anything?

The Technical Bits

Here’s the station:

mysql> SELECT stationID, name FROM invent3 WHERE name LIKE '%ISCO%';
+-------------+-------------------------------+
| stationID   | name                          |
+-------------+-------------------------------+
| 30984691000 | PISCO                         |
| 40371730003 | BISCOTASING,ON                |
| 40778460003 | SAN FRANCISCO DE MACORIS D    |
| 41476423001 | FRANCISCO I. MADERO, DURANGO  |
| 41476556001 | MASCOTA, JALISCO              |
| 41476612001 | CHAPALA, JALISCO              |
| 41476612002 | TEPATITLAN, JALISCO           |
| 42572494000 | SAN FRANCISCO                 |
| 42574506002 | SAN FRANCISCO/MISSION DOLORES |
| 50998437000 | SAN FRANCISCO                 |
+-------------+-------------------------------+

The Code:

# -*- coding: utf-8 -*-
import datetime
import pandas as pd
import numpy as np
import matplotlib.pylab as plt
import math
import MySQLdb

plt.title("GHCN v3.3 San Francisco 42572494  July")
plt.ylabel("Year")
plt.xlabel("Temp C")
#plt.xlim(0,20)
plt.ylim(1850,2020)

try:
    db=MySQLdb.connect("localhost","root","OpenUp!",'temps')
    cursor=db.cursor()

    30382586000
    sql="SELECT T.deg_c, T.year FROM invent3 AS I 
    INNER JOIN temps3 as T on I.stationID=T.stationID 
    WHERE T.stationID LIKE '42572494%' AND T.deg_c>-90 
    AND T.deg_c<60 AND T.month='JULY' ;"

    print("stuffed SQL statement")
    cursor.execute(sql)
    print("Executed SQL")
    stn=cursor.fetchall()
    data = np.array(list(stn))
    print("Got data")
    xs = data.transpose()[0]   # or xs = data.T[0] or  xs = data[:,0]
    ys = data.transpose()[1]
    print("after the transpose")

    plt.scatter(xs,ys,s=1,color='blue',alpha=1)
    plt.show()

    plt.title("GHCN v3.San Francisco 342572494  JAN")
    plt.ylabel("Year")
    plt.xlabel("Temp C")
#    plt.xlim(5,25)
    plt.ylim(1850,2020)
    
     
    sql="SELECT T.deg_c, T.year FROM invent3 AS I 
    INNER JOIN temps3 as T on I.stationID=T.stationID 
    WHERE T.stationID LIKE '42572494%' AND T.deg_c>-90 
    AND T.deg_c<60 AND T.month=' JAN' ;"

    print("stuffed SQL statement")
    cursor.execute(sql)
    print("Executed SQL")
    stn=cursor.fetchall()
    data = np.array(list(stn))
    print("Got data")
    xs = data.transpose()[0]   # or xs = data.T[0] or  xs = data[:,0]
    ys = data.transpose()[1]
    print("after the transpose")

    plt.scatter(xs,ys,s=1,color='red',alpha=1)

    plt.show()

except:
    print "This is the exception branch"

finally:
    print "All Done"
    if db:
        db.close()

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