OCTOBER WAS SECOND WARMEST IN 126 YEARS
Roger Coppock
have made up many fanciful conjectures to explain global warming.
Meanwhile, global mean surface temperatures continue to rise.
These globally averaged temperature data come from NASA:
http://data.giss.nasa.gov/gistemp/tabledata/GLB.Ts+dSST.txt
They represent the results of tens of millions of readings
taken at thousands of land stations and ships around the globe
over the last 126 years. Yes, the land data are corrected for
the urban heat island effect. The sea data do not need to be.
There are few urban centers in the sea.
The Mean October temperature over the last 126 years is 13.992 C.
The Variance is 0.04658.
The Standard Deviation is 0.2158.
Rxy 0.772821 Rxy^2 0.597253
TEMP = 13.701111 + (0.004586 * (YEAR-1879))
Degrees of Freedom = 124 F = 183.885447
Confidence of nonzero correlation = approximately
0.9999999999999999999999999 (25 nines), which is darn close to 100%!
The month of October in the year 2005,
is linearly projected to be 14.279,
yet it was 14.61.
The sum of the residuals is 13.356085
Exponential least squares fit:
TEMP = 13.703536 * e^(.0003265 * (YEAR-1879))
The sum of the residuals is 13.326798
Rank of the months of October
Year Temp C Anomaly Z score
2003 14.63 0.638 2.95
2005 14.61 0.618 2.86 <--
2004 14.56 0.568 2.63
1997 14.50 0.508 2.35
2002 14.48 0.488 2.26
2001 14.43 0.438 2.03
1995 14.42 0.428 1.98
1998 14.42 0.428 1.98
1990 14.40 0.408 1.89
1994 14.37 0.378 1.75
1999 14.30 0.308 1.43
1988 14.27 0.278 1.29
1987 14.26 0.268 1.24
MEAN 13.992 0.000 0.00
1883 13.74 -0.252 -1.17
1913 13.74 -0.252 -1.17
1884 13.73 -0.262 -1.22
1898 13.73 -0.262 -1.22
1886 13.71 -0.282 -1.31
1910 13.71 -0.282 -1.31
1964 13.70 -0.292 -1.35
1892 13.69 -0.302 -1.40
1908 13.69 -0.302 -1.40
1882 13.68 -0.312 -1.45
1917 13.68 -0.312 -1.45
1887 13.65 -0.342 -1.59
1903 13.63 -0.362 -1.68
1912 13.55 -0.442 -2.05
The most recent 140 continuous months, or 11 years and 8 months,
on this GLB.Ts+dSST.txt data set are all above the 1951-1980
data set norm of 14 C.
There are 1510 months of data on this data set:
-- 645 of them are at or above the norm.
-- 865 of them are below the norm.
This run of 140 months above the norm is the result of a warming
world. It is too large to occur by chance at any reasonable level
of confidence. A major volcano eruption, thermonuclear war, or
meteor impact could stop this warming trend for a couple of years,
otherwise expect it to continue.
john fernbach
Fairly recently, or are we talking back in 1935 or 1898 or something?
z
"So what you're saying is that we're actually in a cooling period since
November."
Roger Coppock
-- John Fernbach
Please look at my analysis for the subtitle
"Rank of the months of October," there you
will find your answer.
Roger Coppock
report any cooling nor did I discuss the month of November.
Coby Beck
"john fernbach" <fernba...@yahoo.com> wrote in message
news:1131396065.7...@g49g2000cwa.googlegroups.com...
> Roger - just out of curiosity, what was the warmest October on record?
>
>
> Fairly recently, or are we talking back in 1935 or 1898 or something?
Rank of the months of October
Year Temp C Anomaly Z score
2003 14.63 0.638 2.95
2005 14.61 0.618 2.86 <--
2004 14.56 0.568 2.63
1997 14.50 0.508 2.35
2002 14.48 0.488 2.26
2001 14.43 0.438 2.03
1995 14.42 0.428 1.98
1998 14.42 0.428 1.98
1990 14.40 0.408 1.89
1994 14.37 0.378 1.75
(copied from Roger's post)
--
Coby Beck
(remove #\Space "coby 101 @ bigpond . com")
john fernbach
I missed it.
As a non-mathematician, and as an admirer of science rather than a
scientist myself,
I think it's actually pretty easy for many people to get lost in a
document that relies
exclusively on numbers. Most of us who have jobs that don't regularly
involve us in
examining numbers tend to blank out whenever topics like global warming
get discussed
largely in mathematical and technical terms.
I'm not complaining, mind you. Thank you for the information, which
looks like
it's the equivalent of a fire alarm ringing in the night.
Of the ten warmest Octobers on record, ALL of them have occurred since
1990. Of the six warmest Octobers, five have occurred in the past
five years. That looks significant to me.
Sorry if this is another dumb question, but does anybody here have an
explanation for what
happened to keep the years 2000, 1999, 1987 and 1986 from making the
top 10 list? Were they just
a little cooler, or did volcanoes etc. (or maybe smoke from burning
rainforests) interfere with the apparent warming trend in those years,
or don't we know, really?
Prick Bombo @ProfessionalPrick.com
Below is the list extracted from the data at the same website:
http://data.giss.nasa.gov/gistemp/tabledata/GLB.Ts.txt
It is sorted High to Low values, and numbered.
2000 is year # 25, 1999 is year # 11, 1987 is year #16, 1986 is year #
56.
Here's things in 1986 that may have affected the weather:
http://www.inthe80s.com/time1986.shtml
* Fox Network starts in America
* Return of Haley's Comet
* January 28, the Challenger explodes.
* Worst nuclear disaster ever in Chernobyl, USSR, April.
* In Nyos, Cameroon, a cloud of carbon dioxide rumbled out of the
nearby lake Nyos on August 21 and killed approximently 1,700 people.
* Mandatory recycling programs started in Rhode Island.
Rank Oct Year
1) 76 2003
2) 75 2005
3) 72 2004
4) 61 1995
5) 58 2002
6) 58 1998
7) 51 1990
8) 50 2001
9) 46 1988
10) 43 1997
11) 42 1999
12) 41 1938
13) 35 1994
14) 35 1943
15) 32 1989
16) 31 1987
17) 29 1947
18) 28 1937
19) 27 1996
20) 27 1927
21) 26 1991
22) 26 1944
23) 26 1885
24) 25 1893
25) 23 2000
26) 22 1983
27) 20 1979
28) 20 1932
29) 19 1900
30) 17 1961
31) 16 1993
32) 16 1984
33) 16 1896
34) 15 1973
35) 15 1967
36) 15 1941
37) 15 1914
38) 14 1980
39) 12 1960
40) 12 1951
41) 12 1931
42) 11 1958
43) 11 1929
44) 10 1953
45) 8 1981
46) 8 1936
47) 8 1919
48) 7 1969
49) 7 1945
50) 7 1940
51) 6 1985
52) 6 1968
53) 6 1963
54) 6 1934
55) 6 1889
56) 5 1986
57) 5 1970
58) 5 1962
59) 5 1955
60) 4 1954
61) 4 1949
62) 4 1935
63) 4 1918
64) 3 1952
65) 3 1933
66) 3 1928
67) 3 1915
68) 2 1948
69) 2 1942
70) 1 1957
71) 1 1923
72) -1 1978
73) -1 1982
74) -1 1992
75) -2 1888
76) -2 1895
77) -3 1901
78) -3 1906
79) -4 1921
80) -5 1959
81) -5 1971
82) -6 1890
83) -6 1894
84) -6 1926
85) -7 1939
86) -7 1974
87) -7 1977
88) -8 1909
89) -8 1924
90) -8 1965
91) -9 1975
92) -10 1972
93) -11 1882
94) -11 1946
95) -12 1922
96) -15 1883
97) -15 1911
98) -15 1916
99) -15 1950
100) -16 1881
101) -18 1956
102) -20 1930
103) -21 1913
104) -23 1910
105) -23 1925
106) -24 1907
107) -25 1966
108) -26 1887
109) -29 1899
110) -33 1964
111) -34 1905
112) -36 1920
113) -37 1902
114) -39 1892
115) -39 1976
116) -40 1880
117) -40 1908
118) -41 1884
119) -42 1897
120) -43 1904
121) -44 1898
122) -44 1917
123) -48 1912
124) -49 1903
125) -64 1891
126) -65 1886
Prick Bombo @ProfessionalPrick.com
Roger Coppock wrote:
> http://data.giss.nasa.gov/gistemp/tabledata/GLB.Ts+dSST.txt
>From the months of October, this is the rankings from that link
provided. They are sorted in high-to-low order same as yours. Nothing
has been done to them. They are the numbers on the page you cite as
your source data. Explain the slight variances from this list and
yours.
OCT YEAR
76 2003
75 2005
72 2004
61 1995
58 2002
58 1998
51 1990
50 2001
46 1988
43 1997
42 1999
41 1938
35 1994
35 1943
32 1989
31 1987
29 1947
28 1937
27 1996
27 1927
26 1991
26 1944
26 1885
25 1893
23 2000
22 1983
20 1979
20 1932
19 1900
17 1961
16 1993
16 1984
16 1896
15 1973
15 1967
15 1941
15 1914
14 1980
12 1960
12 1951
12 1931
11 1958
11 1929
10 1953
8 1981
8 1936
8 1919
7 1969
7 1945
7 1940
6 1985
6 1968
6 1963
6 1934
6 1889
5 1986
5 1970
5 1962
5 1955
4 1954
4 1949
4 1935
4 1918
3 1952
3 1933
3 1928
3 1915
2 1948
2 1942
1 1957
1 1923
-1 1978
-1 1982
-1 1992
-2 1888
-2 1895
-3 1901
-3 1906
-4 1921
-5 1959
-5 1971
-6 1890
-6 1894
-6 1926
-7 1939
-7 1974
-7 1977
-8 1909
-8 1924
-8 1965
-9 1975
-10 1972
-11 1882
-11 1946
-12 1922
-15 1883
-15 1911
-15 1916
-15 1950
-16 1881
-18 1956
-20 1930
-21 1913
-23 1910
-23 1925
-24 1907
-25 1966
-26 1887
-29 1899
-33 1964
-34 1905
-36 1920
-37 1902
-39 1892
-39 1976
-40 1880
-40 1908
-41 1884
-42 1897
-43 1904
-44 1898
-44 1917
-48 1912
-49 1903
-64 1891
-65 1886
> Rank of the months of October
> Year Temp C Anomaly Z score
> 2003 14.63 0.638 2.95
> 2005 14.61 0.618 2.86 <--
> 2004 14.56 0.568 2.63
> 1997 14.50 0.508 2.35
> 2002 14.48 0.488 2.26
> 2001 14.43 0.438 2.03
> 1995 14.42 0.428 1.98
> 1998 14.42 0.428 1.98
> 1990 14.40 0.408 1.89
> 1994 14.37 0.378 1.75
> 1999 14.30 0.308 1.43
> 1988 14.27 0.278 1.29
> 1987 14.26 0.268 1.24
76 2003
75 2005
72 2004
61 1995
58 2002
58 1998
51 1990
50 2001
46 1988
43 1997
42 1999
41 1938
35 1994
> MEAN 13.992 0.000 0.00
> 1883 13.74 -0.252 -1.17
> 1913 13.74 -0.252 -1.17
> 1884 13.73 -0.262 -1.22
> 1898 13.73 -0.262 -1.22
> 1886 13.71 -0.282 -1.31
> 1910 13.71 -0.282 -1.31
> 1964 13.70 -0.292 -1.35
> 1892 13.69 -0.302 -1.40
> 1908 13.69 -0.302 -1.40
> 1882 13.68 -0.312 -1.45
> 1917 13.68 -0.312 -1.45
> 1887 13.65 -0.342 -1.59
> 1903 13.63 -0.362 -1.68
> 1912 13.55 -0.442 -2.05
-36 1920
-37 1902
-39 1892
-39 1976
-40 1880
-40 1908
-41 1884
-42 1897
-43 1904
-44 1898
-44 1917
-48 1912
-49 1903
-64 1891
-65 1886
z
Roger Coppock wrote:
> I'm sorry Zombie, you must explain your question. I didn't
> report any cooling nor did I discuss the month of November.
Well, it's analogous to the "8 year cooling period since 1998". Some
folks see a recent heat record as a sign that we are warming, some see
it as us already being over the hump. Same goes for record heat in
October.
Roger Coppock
long term global mean surface temperature record that indicates
cooling. You're fanciful 8-year cooling period, is well within
the variance. Look at these land and sea yearly mean
temperatures from NASA GISS:
The mean yearly temperature over the last 126 years is 13.977 C.
These data are normalized at 14.0 C and represent hundreds of a K.
The Variance is 0.04661.
The Standard Deviation is 0.2159.
1997 40
1998 56
1999 33
2000 32
2001 47
2002 54
2003 52
2004 48
2005 59 (Average of first 10 months.)
Your "8-year cooling period" has an amplitude of little
more than 1 standard deviation in only two of the 8 years.
Roger Coppock
These data you have posted are not from the same page that I analyzed
and posted at the top of this thread.
The data you posted are the land data from:
http://data.giss.nasa.gov/gistemp/tabledata/GLB.Ts.txt
The data I analyzed are the land and sea data from:
http://data.giss.nasa.gov/gistemp/tabledata/GLB.Ts+dSST.txt
Alastair McDonald
The years 1986-87 and 1999-00 were both times of an La Nina which
followed a major El Nino. But I do not know if this idea is widely
accepted,
or has even been proposed already.
Cheers, Alastair.
Prick Bombo @ProfessionalPrick.com
from LAND & SEA Temperatures for Octobers.
2000 = #18
1999 = #11
1987 = #13
1986 = #35
In descending Order by rank:
Rank Oct Year
1. 63 2003
2. 61 2005
3. 56 2004
4. 50 1997
5. 48 2002
6. 43 2001
7. 42 1998
8. 42 1995
9. 40 1990
10. 37 1994
11. 30 1999
12. 27 1988
13. 26 1987
14. 24 1944
15. 23 1989
16. 23 1941
17. 22 1991
18. 21 2000
19. 20 1943
20. 20 1938
21. 18 1979
22. 16 1993
23. 15 1996
24. 15 1963
25. 14 1951
26. 14 1937
27. 13 1983
28. 11 1969
29. 11 1967
30. 10 1968
31. 10 1953
32. 9 1980
33. 9 1947
34. 9 1945
35. 8 1986
36. 8 1973
37. 8 1936
38. 7 1981
39. 7 1972
40. 7 1931
41. 7 1927
42. 6 1942
43. 5 1985
44. 5 1984
45. 5 1982
46. 5 1932
47. 4 1960
48. 4 1958
49. 4 1940
50. 4 1935
51. 3 1961
52. 3 1926
53. 2 1962
54. 2 1957
55. 1 1977
56. 1 1949
57. 1 1948
58. 1 1946
59. 1 1930
60. 1 1914
61. 1 1900
62. 0 1992
63. -1 1896
64. -1 1899
65. -1 1934
66. -1 1952
67. -1 1978
68. -2 1959
69. -2 1965
70. -2 1970
71. -3 1939
72. -4 1929
73. -4 1955
74. -5 1921
75. -5 1928
76. -5 1954
77. -7 1888
78. -7 1923
79. -8 1918
80. -8 1933
81. -8 1971
82. -10 1966
83. -11 1974
84. -12 1895
85. -12 1975
86. -13 1897
87. -14 1906
88. -15 1893
89. -15 1925
90. -15 1950
91. -16 1915
92. -16 1919
93. -17 1920
94. -18 1956
95. -19 1916
96. -20 1885
97. -20 1891
98. -20 1909
99. -20 1911
100. -21 1922
101. -21 1924
102. -22 1880
103. -22 1901
104. -23 1904
105. -23 1907
106. -24 1890
107. -24 1902
108. -24 1905
109. -25 1889
110. -25 1894
111. -25 1976
112. -26 1881
113. -26 1883
114. -26 1913
115. -27 1884
116. -27 1898
117. -29 1886
118. -29 1910
119. -30 1964
120. -31 1892
121. -31 1908
122. -32 1882
123. -32 1917
124. -35 1887
125. -37 1903
126. -45 1912
Roger Coppock
when I publish the list. The norm of all the GISS
temperature data is 14.0 C. A couple of years ago
the files used to say that. Also, it helps to view
the list as Z-scores, (score - mean) / StandardDeviation.
z
(I know, I was trying to satirize)
James Annan
>
> (I know, I was trying to satirize)
>
The problem with that is whatever is writen in jest, there are people
here who write more absurd stuff for real...
James
--
James Annan
see web pages for email
http://www.ne.jp/asahi/julesandjames/home/
http://julesandjames.blogspot.com/
H2-PV NOW
it into a spreadsheet (OpenOffice.org freeware distributable, M$ Excel
compatible), but if you have done that it can save typing. Also the ACE
scores and the HURDAT are begging for being in spreadsheet format.
Roger Coppock
a BASIC program I have written. That program
reads the data files as they come from NASS
GISS.
I also use another piece of open source freeware,
"The 'R' Statistical Program," which needs
a simple format like most spreadsheets read.
I HIGHLY recommend this program! The only
limitation I have encountered is that the free
version lacks numerical accuracy on large
(N>10,000) data sets. One can purchase a
solution for this, or write your own program with
a precision sumer, as I did.
Below are GISS land and sea data and HURDAT
numbers in one file, followed by the "R" script
that analyzes them.
=-=-=-=-=-=-=-=
Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec AnnMean Storms
1880 -20 -15 -20 -23 -26 -36 -22 -18 -19 -22 -26 -19
-22 11
1881 -26 -18 2 -4 -2 -27 -10 -12 -27 -26 -30 -21
-17 7
1882 -2 7 -5 -25 -20 -26 -26 -16 -23 -32 -23 -41
-19 6
1883 -38 -34 -17 -16 -21 -8 -11 -17 -26 -26 -26 -17
-21 4
1884 -15 -11 -27 -35 -26 -32 -25 -22 -28 -27 -29 -25
-25 4
1885 -52 -29 -15 -30 -29 -34 -25 -24 -21 -19 -19 -7
-25 8
1886 -32 -35 -28 -11 -11 -25 -4 -16 -17 -29 -22 -21
-21 12
1887 -53 -45 -32 -35 -22 -22 -8 -25 -24 -34 -30 -30
-30 19
1888 -35 -40 -39 -28 -19 -20 -21 -23 -17 -7 0 -14
-22 9
1889 -9 9 -1 -2 -4 -8 -17 -20 -20 -25 -30 -23
-12 9
1890 -37 -36 -26 -29 -38 -33 -30 -32 -36 -24 -45 -33
-33 4
1891 -41 -45 -16 -26 -16 -20 -24 -20 -15 -20 -33 -8
-24 11
1892 -32 -5 -32 -39 -25 -19 -32 -26 -20 -31 -39 -45
-29 9
1893 -81 -49 -13 -28 -34 -23 -13 -23 -22 -14 -19 -27
-29 12
1894 -43 -24 -18 -35 -29 -39 -21 -24 -34 -25 -32 -26
-29 7
1895 -52 -51 -25 -25 -26 -21 -21 -17 -10 -12 -7 -15
-23 6
1896 -14 -9 -22 -30 -10 -10 -11 -12 -11 -1 -14 -7
-13 7
1897 -21 -8 -13 -3 0 -12 -3 -5 -12 -13 -15 -13
-10 6
1898 10 -14 -44 -24 -28 -19 -18 -15 -19 -27 -32 -18
-21 11
1899 -17 -31 -22 -17 -15 -19 -13 -7 -10 -1 17 -25
-13 9
1900 -28 2 -3 -9 -3 -5 -11 -9 -4 1 -12 -4
-7 7
1901 -15 -3 2 -2 -9 -11 -18 -17 -23 -22 -20 -26
-14 12
1902 -7 3 -14 -26 -26 -25 -20 -26 -25 -24 -33 -38
-22 5
1903 -15 9 -14 -31 -29 -37 -30 -38 -40 -37 -30 -41
-28 10
1904 -45 -39 -32 -40 -31 -34 -31 -29 -37 -23 -8 -14
-30 5
1905 -25 -47 -21 -33 -22 -20 -14 -18 -18 -24 -4 -16
-22 5
1906 -22 -25 -18 -4 -18 -10 -21 -10 -22 -14 -31 -11
-17 11
1907 -40 -41 -23 -40 -45 -37 -35 -30 -24 -23 -38 -35
-34 5
1908 -33 -24 -46 -39 -30 -26 -23 -34 -25 -31 -36 -35
-32 10
1909 -51 -31 -41 -42 -34 -34 -34 -26 -23 -20 -16 -35
-32 11
1910 -22 -28 -30 -28 -33 -36 -23 -25 -29 -29 -38 -49
-31 5
1911 -44 -41 -44 -44 -37 -36 -28 -30 -24 -20 -13 -16
-31 6
1912 -19 -10 -30 -18 -18 -21 -34 -48 -47 -45 -30 -29
-29 7
1913 -30 -33 -31 -34 -37 -38 -32 -29 -29 -26 -10 -1
-28 6
1914 2 -6 -16 -24 -16 -19 -15 -13 -15 1 -11 -15
-12 1
1915 -16 -1 -11 3 4 7 1 -7 -4 -16 -6 -11
-5 5
1916 -13 -13 -29 -21 -25 -32 -29 -23 -25 -18 -34 -56
-27 14
1917 -37 -50 -46 -37 -52 -35 -20 -23 -13 -32 -24 -62
-36 3
1918 -39 -35 -24 -41 -37 -28 -30 -32 -24 -8 -15 -29
-28 5
1919 -16 -12 -14 -5 -15 -13 -21 -19 -12 -16 -39 -31
-18 3
1920 -9 -19 2 -11 -10 -16 -21 -18 -18 -17 -25 -42
-17 4
1921 4 -12 -10 -12 -13 -13 -2 -20 -17 -5 -18 -14
-11 6
1922 -32 -29 -15 -22 -23 -22 -16 -22 -27 -21 -17 -16
-22 4
1923 -19 -32 -21 -32 -30 -22 -25 -27 -21 -7 4 1
-19 7
1924 -20 -22 -11 -23 -16 -17 -15 -19 -21 -21 -15 -30
-19 8
1925 -24 -22 -15 -15 -20 -22 -19 -10 -12 -14 1 15
-13 2
1926 19 15 20 -3 -9 -7 -8 -1 -4 4 -5 -19
0 11
1927 -17 -3 -24 -18 -16 -13 -5 -10 -3 7 3 -28
-11 7
1928 6 0 -16 -18 -16 -20 -10 -12 -7 -5 2 -7
-9 6
1929 -33 -45 -14 -22 -26 -23 -22 -17 -18 -4 2 -39
-22 3
1930 -18 -7 3 -12 -14 -8 -2 -1 -7 1 18 0
-4 2
1931 4 -17 4 -9 -7 8 8 1 -5 7 -1 2
0 9
1932 21 -9 -12 -2 -7 -9 -6 -9 2 6 -10 -10
-4 11
1933 -17 -16 -15 -13 -14 -11 -8 -8 -16 -8 -18 -34
-15 21
1934 -15 5 -22 -20 0 -1 -3 -4 -13 0 8 2
-5 11
1935 -21 27 -2 -23 -19 -13 -8 -8 -9 4 -20 -14
-9 6
1936 -16 -25 -12 -12 -6 -4 8 1 1 8 6 12
-3 16
1937 -2 21 -7 -7 2 4 8 10 16 15 15 -4
6 9
1938 14 10 24 19 9 -3 6 10 14 20 14 -12
10 8
1939 4 2 -11 -6 0 6 6 5 2 -3 6 36
4 5
1940 -23 0 5 9 3 4 15 -3 5 4 6 13
3 8
1941 13 19 9 17 10 7 8 3 -10 23 15 24
12 6
1942 17 -10 5 4 10 6 -2 -3 4 6 5 5
4 10
1943 -12 12 -6 6 18 -1 10 4 3 20 15 24
8 10
1944 34 25 21 11 20 16 19 16 27 24 14 7
20 11
1945 12 9 15 21 2 8 7 16 3 9 11 -7
9 11
1946 15 15 -2 10 -6 -13 -4 -17 -2 1 0 -27
-2 6
1947 -5 -6 12 6 1 -2 -3 -3 -7 9 7 -11
0 9
1948 15 -5 -4 -1 8 3 -7 -4 -6 1 -2 -16
-1 9
1949 16 -8 -3 0 0 -13 -6 -2 -5 1 -4 -12
-3 13
1950 -24 -21 -3 -16 -6 -3 -8 -13 -10 -15 -30 -12
-13 13
1951 -26 -36 -17 -12 1 -2 2 10 11 14 4 19
-3 10
1952 17 14 -6 9 6 7 7 7 5 -1 -15 -5
4 7
1953 13 20 20 18 13 7 6 7 10 10 0 16
12 14
1954 -15 -3 -9 -10 -17 -10 -21 -12 -6 -5 8 -13
-9 11
1955 16 -10 -26 -18 -18 -12 -10 4 -8 -4 -20 -26
-11 12
1956 -16 -29 -22 -22 -24 -19 -13 -24 -18 -18 -17 -9
-19 8
1957 -8 0 -1 1 9 13 3 10 5 2 11 21
6 8
1958 38 23 10 4 12 0 11 3 3 4 6 10
11 10
1959 11 12 23 17 8 7 9 2 1 -2 -8 3
7 11
1960 1 19 -27 -8 -6 3 4 3 7 4 -7 17
1 7
1961 6 20 16 11 16 13 1 5 4 3 4 -10
7 11
1962 9 16 9 8 -5 1 0 -1 0 2 6 2
4 5
1963 -3 19 -3 -3 3 4 13 22 21 15 17 3
9 9
1964 2 -9 -19 -22 -17 -4 -10 -20 -31 -30 -19 -30
-17 12
1965 -8 -19 -13 -19 -8 -10 -18 -5 -12 -2 -5 -3
-10 6
1966 -9 3 9 -10 -7 0 8 -3 -1 -10 -2 -8
-2 11
1967 -7 -19 4 1 10 -7 0 -2 -3 11 -4 -5
-2 8
1968 -20 -15 19 -5 -11 -1 -4 -4 -9 10 -1 -10
-4 8
1969 -12 -11 3 19 16 10 3 6 7 11 17 23
8 15
1970 11 25 3 7 -1 1 -2 -7 6 -2 0 -14
2 10
1971 -2 -20 -20 -11 -12 -22 -11 -7 -5 -8 -4 -9
-11 13
1972 -24 -21 -1 -1 -1 4 1 13 1 7 4 18
0 7
1973 26 31 26 20 18 14 8 0 4 8 0 -6
12 8
1974 -17 -25 -8 -9 -9 -10 -5 2 -11 -11 -11 -13
-11 11
1975 3 -4 6 -2 8 -5 -5 -16 -6 -12 -17 -20
-6 9
1976 -8 -10 -27 -13 -24 -14 -10 -15 -10 -25 -13 -3
-14 10
1977 -3 14 18 17 20 17 15 10 4 1 15 3
11 6
1978 4 5 12 9 -2 -6 4 -14 3 -1 8 1
2 12
1979 4 -10 9 4 -3 8 -1 11 16 18 18 38
10 9
1980 18 22 13 20 25 14 15 13 10 9 23 11
16 11
1981 40 30 37 24 14 16 19 24 11 7 15 28
22 12
1982 3 6 -6 5 11 0 12 -1 4 5 6 33
6 6
1983 45 39 39 23 25 15 12 26 28 13 29 12
25 4
1984 22 9 17 1 22 -2 12 9 11 5 -4 -19
7 13
1985 9 -13 10 8 9 7 -5 8 0 5 -2 4
3 11
1986 24 28 23 16 12 7 7 8 2 8 1 8
12 6
1987 23 39 13 21 21 26 36 18 34 26 22 45
27 7
1988 51 32 44 35 30 32 24 28 26 27 5 22
30 12
1989 11 25 26 16 7 6 22 24 25 23 14 29
19 11
1990 33 34 65 43 32 31 34 28 23 40 45 39
37 14
1991 35 43 29 41 31 42 43 30 36 22 16 20
32 8
1992 38 37 34 19 22 15 0 1 -6 0 -7 13
14 7
1993 28 27 30 18 18 10 9 3 2 16 0 14
14 8
1994 28 -6 25 26 22 29 20 19 25 37 37 29
24 7
1995 44 70 44 34 10 33 46 37 24 42 37 26
37 19
1996 23 43 25 20 21 18 30 32 16 15 28 31
25 13
1997 27 32 46 33 31 43 28 36 45 50 55 53
40 8
1998 55 84 56 58 59 59 67 60 43 42 40 51
56 14
1999 45 64 27 28 23 29 26 27 25 30 32 37
33 12
2000 21 49 46 50 30 31 28 37 29 21 21 19
32 15
2001 38 40 56 42 47 45 46 45 45 43 68 47
47 15
2002 74 75 81 54 51 44 54 43 46 48 51 33
54 12
2003 63 48 49 46 48 39 44 56 54 63 49 68
52 16
2004 51 67 58 52 36 32 25 40 47 56 63 49
48 15
2005 64 49 68 62 53 58 58 54 60 61 NA NA NA
26
=-=-=-=-=-=-=-=-=-=
setwd("/Users/rogercoppock/Desktop")
aframe <- read.table("GLB.Ts+dSST.txt", header = TRUE)
aframe$Jan=14.0+aframe$Jan/100
aframe$Feb=14.0+aframe$Feb/100
aframe$Mar=14.0+aframe$Mar/100
aframe$Apr=14.0+aframe$Apr/100
aframe$May=14.0+aframe$May/100
aframe$Jun=14.0+aframe$Jun/100
aframe$Jul=14.0+aframe$Jul/100
aframe$Aug=14.0+aframe$Aug/100
aframe$Sep=14.0+aframe$Sep/100
aframe$Oct=14.0+aframe$Oct/100
aframe$Nov=14.0+aframe$Nov/100
aframe$Dec=14.0+aframe$Dec/100
aframe$AnnMean=14.0+aframe$AnnMean/100
aframe$AnnMean[126]=(aframe$Jan[126]+aframe$Feb[126]+aframe$Mar[126]+aframe$Apr[126]+aframe$May[126]+aframe$Jun[126]+aframe$Jul[126]+aframe$Aug[126]+aframe$Sep[126]+aframe$Oct[126])/10
aframe$Nov[126]=aframe$AnnMean[126]
aframe$Dec[126]=aframe$AnnMean[126]
aframe$AnnMean[126]
summary(aframe, digits=6)
options(digits=7)
sd(aframe)
fitted.model <- lm(AnnMean ~ Year, data = aframe)
quartz(display = "1", width=7, height=5, pointsize=14,
family="Helvetica", antialias=TRUE, autorefresh=TRUE)
plot(AnnMean ~ Year, data=aframe, pch=20, xlab="Year",
ylab="Temperature C")
title("LAND AND SEA YEARLY MEAN TEMPERATURE")
abline(fitted.model$coefficients)
formula(fitted.model)
print(fitted.model)
summary(fitted.model)
fitted.model <- lm(AnnMean ~ Storms, data = aframe)
quartz(display = "1", width=7, height=5, pointsize=12,
family="Helvetica", antialias=TRUE, autorefresh=TRUE)
plot(AnnMean ~ Storms, data=aframe, pch=20, xlab="Storms",
ylab="Temperature C")
title("GLOBAL MEAN TEMP. Vs. ATLANTIC STORMS (1880-2005)")
abline(fitted.model$coefficients)
formula(fitted.model)
print(fitted.model)
summary(fitted.model)
fitted.model <- lm(Storms ~ Year, data = aframe)
quartz(display = "1", width=7, height=5, pointsize=14,
family="Helvetica", antialias=TRUE, autorefresh=TRUE)
plot(Storms ~ Year, data=aframe, pch=20, xlab="Year", ylab="Number of
Storms")
title("YEARLY ATLANTIC STORMS")
abline(fitted.model$coefficients)
formula(fitted.model)
print(fitted.model)
summary(fitted.model)
residuals(fitted.model, type=c("working", "response", "deviance",
"pearson", "partial"))
quartz(display = "2", width=7, height=5, pointsize=14,
family="Helvetica", antialias=TRUE, autorefresh=TRUE)
plot(resid(fitted.model), fitted(fitted.model))
abline(h=0, lty=2, col = 'gray')
qqnorm(residuals(fitted.model))
Sine.mod <- nls(AnnMean ~ beta1 + beta2 * Year + beta3 * sin( beta4 +
(Year * 6.2831853)/beta5), data=aframe, start=list(beta1 =
fitted.model$coefficients[1], beta2 = fitted.model$coefficients[2],
beta3=1., beta4=0, beta5=50), trace=T)
quartz(display = "8", width=10, height=7, pointsize=12,
family="Helvetica", antialias=TRUE, autorefresh=TRUE)
plot(AnnMean ~ Year, data=aframe, pch=20, xlab="Year",
ylab="Temperature C")
title(main = "58-Year Temperature Cycle: Five Degrees of Freedom")
lines(aframe$Year, fitted.values(Sine.mod), lwd=2)
residuals(Sine.mod)
fitted.model <- lm(Jan ~ Year, data = aframe)
quartz(display = "3", width=7, height=5, pointsize=14,
family="Helvetica", antialias=TRUE, autorefresh=TRUE)
plot(Jan ~ Year, data=aframe, pch=20, xlab="Year", ylab="Temperature
C")
title("LAND AND SEA JANUARY MEAN TEMPERATURE")
abline(fitted.model$coefficients)
formula(fitted.model)
print(fitted.model)
summary(fitted.model)
fitted.model <- lm(Feb ~ Year, data = aframe)
quartz(display = "4", width=7, height=5, pointsize=14,
family="Helvetica", antialias=TRUE, autorefresh=TRUE)
plot(Feb ~ Year, data=aframe, pch=20, xlab="Year", ylab="Temperature
C")
title("LAND AND SEA FEBRUARY MEAN TEMPERATURE")
abline(fitted.model$coefficients)
formula(fitted.model)
print(fitted.model)
summary(fitted.model)
fitted.model <- lm(Mar ~ Year, data = aframe)
quartz(display = "5", width=7, height=5, pointsize=14,
family="Helvetica", antialias=TRUE, autorefresh=TRUE)
plot(Mar ~ Year, data=aframe, pch=20, xlab="Year", ylab="Temperature
C")
title("LAND AND SEA MARCH MEAN TEMPERATURE")
abline(fitted.model$coefficients)
formula(fitted.model)
print(fitted.model)
summary(fitted.model)
fitted.model <- lm(Apr ~ Year, data = aframe)
quartz(display = "6", width=7, height=5, pointsize=14,
family="Helvetica", antialias=TRUE, autorefresh=TRUE)
plot(Apr ~ Year, data=aframe, pch=20, xlab="Year", ylab="Temperature
C")
title("LAND AND SEA APRIL MEAN TEMPERATURE")
abline(fitted.model$coefficients)
formula(fitted.model)
print(fitted.model)
summary(fitted.model)
fitted.model <- lm(May ~ Year, data = aframe)
quartz(display = "7", width=7, height=5, pointsize=14,
family="Helvetica", antialias=TRUE, autorefresh=TRUE)
plot(May ~ Year, data=aframe, pch=20, xlab="Year", ylab="Temperature
C")
title("LAND AND SEA MAY MEAN TEMPERATURE")
abline(fitted.model$coefficients)
formula(fitted.model)
print(fitted.model)
summary(fitted.model)
fitted.model <- lm(Jun ~ Year, data = aframe)
quartz(display = "8", width=7, height=5, pointsize=14,
family="Helvetica", antialias=TRUE, autorefresh=TRUE)
plot(Jun ~ Year, data=aframe, pch=20, xlab="Year", ylab="Temperature
C")
title("LAND AND SEA JUNE MEAN TEMPERATURE")
abline(fitted.model$coefficients)
formula(fitted.model)
print(fitted.model)
summary(fitted.model)
fitted.model <- lm(Jul ~ Year, data = aframe)
quartz(display = "9", width=7, height=5, pointsize=14,
family="Helvetica", antialias=TRUE, autorefresh=TRUE)
plot(Jul ~ Year, data=aframe, pch=20, xlab="Year", ylab="Temperature
C")
title("LAND AND SEA JULY MEAN TEMPERATURE")
abline(fitted.model$coefficients)
formula(fitted.model)
print(fitted.model)
summary(fitted.model)
fitted.model <- lm(Aug ~ Year, data = aframe)
quartz(display = "10", width=7, height=5, pointsize=14,
family="Helvetica", antialias=TRUE, autorefresh=TRUE)
plot(Aug ~ Year, data=aframe, pch=20, xlab="Year", ylab="Temperature
C")
title("LAND AND SEA AUGUST MEAN TEMPERATURE")
abline(fitted.model$coefficients)
formula(fitted.model)
print(fitted.model)
summary(fitted.model)
fitted.model <- lm(Sep ~ Year, data = aframe)
quartz(display = "11", width=7, height=5, pointsize=14,
family="Helvetica", antialias=TRUE, autorefresh=TRUE)
plot(Sep ~ Year, data=aframe, pch=20, xlab="Year", ylab="Temperature
C")
title("LAND AND SEA SEPTEMBER MEAN TEMPERATURE")
abline(fitted.model$coefficients)
formula(fitted.model)
print(fitted.model)
summary(fitted.model)
fitted.model <- lm(Oct ~ Year, data = aframe)
quartz(display = "12", width=7, height=5, pointsize=14,
family="Helvetica", antialias=TRUE, autorefresh=TRUE)
plot(Oct ~ Year, data=aframe, pch=20, xlab="Year", ylab="Temperature
C")
title("LAND AND SEA OCTOBER MEAN TEMPERATURE")
abline(fitted.model$coefficients)
formula(fitted.model)
print(fitted.model)
summary(fitted.model)
fitted.model <- lm(Nov ~ Year, data = aframe)
quartz(display = "13", width=7, height=5, pointsize=14,
family="Helvetica", antialias=TRUE, autorefresh=TRUE)
plot(Nov ~ Year, data=aframe, pch=20, xlab="Year", ylab="Temperature
C")
title("LAND AND SEA NOVEMBER MEAN TEMPERATURE")
abline(fitted.model$coefficients)
formula(fitted.model)
print(fitted.model)
summary(fitted.model)
fitted.model <- lm(Dec ~ Year, data = aframe)
quartz(display = "14", width=7, height=5, pointsize=14,
family="Helvetica", antialias=TRUE, autorefresh=TRUE)
plot(Dec ~ Year, data=aframe, pch=20, xlab="Year", ylab="Temperature
C")
title("LAND AND SEA DECEMBER MEAN TEMPERATURE")
abline(fitted.model$coefficients)
formula(fitted.model)
print(fitted.model)
summary(fitted.model)
Coby Beck
news:3tckqaF...@individual.net...
>z wrote:
>
>>
>> (I know, I was trying to satirize)
>>
>
> The problem with that is whatever is writen in jest, there are people here
> who write more absurd stuff for real...
Exactly. I supress many a wry comment because the number of smileys
required to distinguish it from 90% of the sincerely absurd would quite
spoil the fun!
Coby Beck
news:Vzccf.96728$y_1.70245@edtnps89...
>> here who write more absurd stuff for real...
>
> Exactly. I supress many a wry comment because the number of smileys
> required to distinguish it from 90% of the sincerely absurd would quite
> spoil the fun!
In fact, here comes one now...
It looks like Ray is right...in one post Roger is saying this October was
the warmest in 126 years and in another he says the second warmest in 126
years!
"Further research is needed" as obviously we are not certain enough to take
any action.
James Annan
And by this time next year, this October might even have been revised
downwards to only the 3rd warmest October in 127 years, which would
obviously demonstrate that the much-heralded cooling had started...
James
Roger Coppock
I have two posts of this month's GISS data
1) Warmest in the Northern Hemisphere.
2) Second warmest in the global land and sea.
Yes, Coby, they are different.
No, Coby, no further research is need. Action should begin immediately.
Roger Coppock
He just must find any month cooler than that which
preceded it. Being a fossil fool means you never
have to connect with all of reality, you just cherry
pick enough facts to sell your lie.
z
I note a substantial cooling trend from noon October 15 until 2 AM this
morning.
Roger Coppock
For the Loop-the-loops of this world, that's enough to
challenge all of climate science. Some crackpot
carbon industry 'scientists' will publish it in a comic
book journal, and Steve Schuln will quote mine it.