Global Warming This page shows my early global warming investigations using
Data workbooks and charts made with Excel by D
Kelly O'Day; available for download
the climate is unequivocal, as is now evident from
observations of increases in global average air and ocean
temperatures, widespread melting of snow and ice, and rising
global mean sea level."
Topics: Here's my current list of global warming topics,
with links have been completed, unlinked topics show
what I am hoping to get to. Feel free to suggest topics
or comment on my posts.
To Contact me
Global warming, increasing atmospheric CO2, ice age, fossil
fuels, rising sea levels, El Nino, hurricanes, climate models, Gulf
Stream, “Inconvient Truth”, Kyoto … !
Global warming is a very serious, complex and controversial subject.
I’m really curious about these topics. I really want to
understand what’s going on with our climate. I’d like to see the
numbers, chart the trends, understand the science. I want to
separate the facts from the opinions and hype.
This page will document
my journey from a climate science ignoramus to hopefully a better
informed citizen who can help solve the climate challenges. I'll
point to videos and web sites that I have found useful in my
This page will use Excel charts
and other desktop tools to help assess the climate trends. Each post
will include a link to the data sources and where appropriate, sites
that provide the scientific/theoretical underpinnings to the
EPA's site provides a
good explanation of climate and the greenhouse effect. A summary of
EPA's discussion is reproduced below:
"Energy from the sun drives the earth’s weather and climate, and
heats the earth’s surface; in turn, the earth radiates energy back
into space. Atmospheric greenhouse gases (water vapor, carbon
dioxide, and other gases) trap some of the outgoing energy,
retaining heat somewhat like the glass panels of a greenhouse.
this natural “greenhouse effect,” temperatures would be much lower
than they are now, and life as known today would not be possible.
Instead, thanks to greenhouse gases, the earth’s average temperature
is a more hospitable 60°F. However, problems may arise when the
atmospheric concentration of greenhouse gases increases."
Since the natural greenhouse effect
is so important to the climate on earth, we need to be sure that we
do not adversely effect it by increasing greenhouse gases and
altering our climate in unanticipated ways.
"Since the beginning of the industrial revolution, atmospheric
concentrations of carbon dioxide have increased nearly 30%, methane
concentrations have more than doubled, and nitrous oxide
concentrations have risen by about 15%. These increases have
enhanced the heat-trapping capability of the earth’s atmosphere.
Sulfate aerosols, a common air pollutant, cool the atmosphere by
reflecting light back into space; however, sulfates are short-lived
in the atmosphere and vary regionally."
NASA's Goddard Institute of Space Studies (GISS)
tracks atmospheric global temperature climate trends as part of its
long term climate assessment efforts. Temperature changes by
location, day of year and time of day. To provide meaningful year to
year comparisons, climatologists calculate the global mean annual
land and ocean temperature.
To facilitate assessments of long term trends, climatologists
compare the mean for a base period with the annual mean. Differences
between the annual mean and baseline mean are called anomalies.
GISS uses the 1951 - 1980 period for their baseline period. They use
the difference between the annual mean and the baseline mean to
determine the global temperature anomaly for the year.
This Excel based chart, using GISS data available
here, shows the annual global
temperature anomalies for the period 1880 - 2006. GISS uses the
1951-1980 period mean to establish the baseline.
In the 1880 - 1935 period, the temperature anomaly was
consistently negative. In contrast, the 1980 - 2005 period has
had a consistently positive temperature anomaly. The 1917
temperature anomaly (-0.47oC) was the lowest year on
record. Since 1917, global temperature has warmed, with the most
recent years showing the highest anomalies of +0.4/ 0.6 oC
in the past 120 years.
This Excel based chart shows the Northern Hemisphere
temperature anomaly - F for the period 1880 - 2006. Trend
lines have been added to compare trends over the 126 year
Trend Line Change Rate & r2
Change Rate oF/Year
There have been at least 4 distinct N Hemisphere temperature
anomaly periods in the 1880 - 2006 period. The rate of change has
varied from a low to -0.017 oF per year in the 1939 -
1972 period to a high of 0.054 oF in the 1973 - 2006
Open Mind Blog's 4/3/07
post presents an analysis of 5 Swiss temperature datasets for the
period 1901 - 2004. The source data was obtained from the European
Climate Assessment And Dataset project (ECA).
Open Mind smoothed the daily data sets with a 1-year timescale
wavelet transform. Open Mind's chart is shown on the right.
The interpretation is difficult because of the static nature and
size of the display. The charted points are the wavelet
transformation values, not the actual measurements.
Several Open Mind readers had questions about the data set and
proper interpretation of the data. ProcessTrends.Com has developed
an Excel workbook which provides the source data as well as an
interactive chart tool so that users can toggle series on/off and
move a cursor to get exact values for all series for a particular
year. The ProcessTrends.Com analysis used mean annual temperature
trends without smoothing.
This workbook reproduces the Open Mind analysis by:
1. Documenting Swiss Station identification codes, lat/long
locations and elevations
2. Calculating mean annual temperature values for the 5 stations
3. Charting 1901 - 2004 mean temperature trends for the 5 stations
4. Calculating 1975 - 2004 temperature increase rates based on mean
5. Calculating projected 100 year mean temperature increase rates
for the 5 stations
6. Comparing projected 100 year mean temperature increase rates with
Open Mind's wavelet approach
Users can download the Excel workbook for these 5 Swiss stations
at this link.
Several Open Mind blog readers raised questions about the temperature
trends, noting potential trend shifts in several series. To address
these questions, I performed a Change Point Analysis (CPA) based on
techniques described by Dr. Wayne Taylor (
Variation.Com). CPA combines the use of
CuSum charts and a bootstrapping technique to compute 1,000 or
more iterations of the CuSum chart.
The 5 Swiss station CuSum charts are shown to the right. Notice
that all 5 series had change points in 1988. Geneva had change
points 4 years, 1920, 1943, 1962 in addition to 1988. Lugano
had change points in 1919, 1940, and 1988, Saentis had
change points in 1920 and 1988, and Zurich had change points in
1943, 1951 as well as 1988.
While the 5 stations are physically quit distant, and the
temperature values also differ, the patterns of the 5 CuSum charts
are comparable. For example, the 1920 change points in Saentis and
Geneva are close to the 1919 change point for Lugano. Zurich's 1919
- 1920 CuSum chart shows a change in direction, it is simply to
small a change to be classified as a change point.
This table shows change point years for each station.
5 Swiss Station Temperature Series (1901 - 2004)
Change Point Analysis Summary:
Change Pt Year
No CP Yrs
CuSum charts and change point analysis provide comparative
information that can be useful in analysis of multiple temperature series.
The Excel workbook, with the CuSum analysis for these 5 Swiss stations,
at this link.
"Carbon dioxide is the most important
anthropogenic greenhouse gas. The global atmospheric concentration
of carbon dioxide has increased from a pre-industrial value of about
280 ppm to 379 ppm in 2005. The atmospheric concentration of carbon
dioxide in 2005 exceeds by far the natural range over the last
650,0000 years (180 to 300 ppm) as determined from ice cores."
Policymakers, Feb. 2007
air samples have been collected at Mauna Loa Observatory, Hawaii
since 1958 (data
link). This CO2 data and a series of Excel charts are
included in this workbook.
The CO2 data can be plotted as a trend chart, as shown
to the right. The CO2 data has increased from 315 parts
per million - volume (ppmv) in 1958 to 380 ppmv in 2006. The
data also exhibits a significant seasonal effect.
Respiration by land vegetation causes the seasonal cycle. Most of
the land vegetation is in the northern hemisphere. During
northern-hemisphere summer, plants use atmospheric CO2 to grow,
extracting CO2 from the air and lowering its atmospheric
concentration. As plant matter decays in the winter months, the CO2
is returned to the atmosphere.
Monthly cycle charts
can be used to analyze seasonal cycles.
The CO2 monthly cycle chart for the Mauna Loa observatory
is presented in the chart to the right. It shows the 1958 to 2004
data trends for each month (Jan - Dec) as well as the monthly
average CO2 levels. Two key points are clear from this
monthly cycle chart:
CO2 levels have increased for every month over
the 1958 - 2004 period.
CO2 varies by month, with the maximum CO2 level
in May-June and minimum in September - October.
Continuous temperature records based on thermometers only go back
about 100 years.
Paleoclimatology, the study of past climate, uses several
proxy data techniques to estimate climate conditions over
geologic time scales. These techniques include:
Historical records - farmer logs, travel logs,
newspaper accounts can be used to construct climate estimates
Corals calcium carbonate composition - oxygen
isotopes and trace metals are used to reconstruct climate during
that period of time that the coral lived
Fossil Pollen - pollen grains are well
preserved in the sediment layers of water bodies, estimates of
climate conditions can be based on the types of pollen found in
Tree Rings - tree-ring widths, density, and
isotopic composition can be used to estimate climate conditions
Ice Cores - ice in glaciers and ice caps over many
centuries. Ice cores contain dust, air bubbles, or isotopes of
oxygen, that can be used to interpret the climate at the time
the ice formed
Ocean & Lake Sediments- ocean and lake
sediments include tiny fossils and chemicals that can be used to
interpret past climate
The collaborative ice-core project between Russia, the United
States, and France at the Vostok station in Antarctica reached a
depth of 3,623 meters, extending back approximately 420,0000 years
in time. The chart below was made in Excel using panel chart
The parallel nature of the methane (CH4), CO2 and
temperature difference is striking.
From a chart making standpoint, there are several interesting
aspects to this paleoclimate chart:
Time scale is thousands of years before present (Kyr BP)
The vertical panel chart uses a single x axis scale for the
three time lines
The Y Axis labels alternate to avoid axis scale data label
Etheridge et al reconstructed atmospheric CO2 and CH4
levels from thee ice cores taken from Law Dome, East Antarctica
between 1987 and 1993. The Law Dome CO2 data
extends from 1006 AD until 1978 AD, a 972 year period.
The Law Dome atmospheric CO2 trends show a relatively
stable trend between 1006 and 1750. After 1750, the atmospheric CO2
level increased, with an accelerating trend in the 20th century.
The Vostok ice core data shows
that the atmospheric CO2 levels ranged from 182 - 299
ppmv in the past 420,000 years before present. The Law Dome Ice Core
data set (period 1006 - 1978) shows that atmospheric CO2
levels ranged from a minimum of 274 in 1604 to a maximum of 335 in
1978.The annual Mauna Loa Observatory,
Hawaii data set shows a continual rise in atmospheric CO2; 1959
levels were 316
ppmv increasing to 381.9 ppmv in 2006.
The figure below consolidation of the Vostok and Law Dome Ice
Core data and Mauna Loa Observatory data.
Several important points are apparent:
All Vostok CO2 levels were less than or equal to
299 ppmv over the 420,0000 years before present
All Law Dome pre industrial revolution (circa 1750) CO2 data
was below 300 ppmv.
All Law Dome readings after 1912 exceeded 300 ppmv
All Mauna Loa Observatory readings after 1958 exceed 300 ppmv
Law Dome and Mauna Loa CO2 trends show and clear
increasing pattern after 1912
The use of fossil fuels, clearing and burning of forests and
production of cement release CO2 into the
atmosphere. The Carbon
Dioxide Information Analysis Center (CDIAC)
estimates of global CO2 emissions from 1751 to 2003 (trends) are shown below. Global CO2 emissions have
increased from an estimated 3 million metric tons per year in
1751 to nearly 7,300 million metric tons per year in 2003, a 2,433 fold
Rollover to see
breakdown by source
By rolling your cursor over the Global CO2 Emissions
chart you can see the breakdown by source. The source of emissions
has evolved over time, with solid (wood, coal) sources representing
nearly 100% until the late 1800's when liquid and gas sources became
more common. Notice the role of cement production, which
converts calcium carbonate to lime, releasing CO2.
The Excel based dot plot below compares the
population, CO2 emissions and CO2 emission per
capita for the USA, Europe, china, India and the rest of the world
(ROW) for the year 2000 (CDIAC)
The USA, with a population of 280 million, emitted 5.8 billion
metric tons in 2000, equivalent to 20.6 metric tons per capita.
India, with a population of 1.0 billion, emitted 1.0 billion
metric tons, equivalent to 1.0 metric tons/capita. An average US
resident emitted 20.6 times as much CO2 as an Indian resident.
European residents, with a standard of living comparable to the US
level, emitted 7.7 metric tons per capita, only 37% of the US rate.
The per capita US rate of CO2 emissions is the
highest, 2.6 times the rate for Europe and nearly 8.6 times the rate
for China. Current CO2 emissions have increased
atmospheric CO2 concentrations and global temperatures.
As the lesser developed countries like China and India, with their
large populations, expand economically, their CO2
emissions per capita will increase, adding even more CO2
to the atmosphere.
"Over the last
100 years, the global sea level has risen by about 10 to 25 cm."
Sea level change
is difficult to measure. Relative sea level changes have been
derived mainly from tide-gauge data. In the conventional tide-gauge
system, the sea level is measured relative to a land-based
It is likely
that much of the rise in sea level has been related to the
concurrent rise in global temperature over the last 100 years."
warming and the consequent thermal expansion of the oceans may
account for about 2-7 cm of the observed sea level rise"
".. the observed
retreat of glaciers and ice caps may account for about 2-5 cm. "
are more difficult to quantify. The rate of observed sea level rise
suggests that there has been a net positive contribution from the
huge ice sheets of Greenland and Antarctica, but observations of the
ice sheets do not yet allow meaningful quantitative estimates of
their separate contributions. The ice sheets remain a major source
of uncertainty in accounting for past changes in sea level because
of insufficient data about these ice sheets over the last 100 years.
The New England
Integrated Sciences and Assessment (NEISA)
has an Excel workbook of North Atlantic sea level trends from 1856
to 2004. The source data, maintained by the Permanent Service for
Mean Sea Level (PSMSL),
uses a Revised Local Reference (RLR) datum. The RLR datum for each
station is defined to be approximately 7000mm below mean sea level
to eliminate negative numbers in the resulting RLR monthly and
annual mean values.
workbook includes the
source seal level data and a charting tool that allows the user to
select the location and generate a trend chart of the seal level
data for that location.
post presents an analysis of the beginning day of snowmelt for the
Red River of the North, using river flow data from Fargo, ND. Open
Mind's post is based on data collected by
Patrick Neuman, a retired snow hydrologist.
This post and downloadable
workbook review Neuman's original data and the Open Mind
analysis to confirm the results and provide the data set to others
who might wish to analyze trends in snowmelt.
To evaluate trends in the beginning day of snowmelt each year,
Neuman analyzed river flow data for three basins in the Midwest, as
shown on this Google Earth map.
estimated the beginning day of snowmelt for each year in the
period 1910 - 2003. The beginning day was calculated
Open Mind used this data for the Red River to produce a
series of charts that show the trend in beginning day of
snowmelt, 10 year moving average for beginning day and a 2
period regression model for the beginning day. Open Mind's
charts are shown below.
Mind Charts: Red River of the North Beginning Day of
Snowmelt, 1910 - 2003 Click chart to see full size
1910 - 2003
Trend Chart with 10 Year Moving Average
(1919-1964 & 1965-2003)
These charts show a significant change in the beginning
day of snowmelt in the Red River basin. Open Mind states: "
"It appears that up to
1964, snowmelt runoff timing
for the Red River at Fargo,
ND, had its ups and downs
but remained constant over
the long haul. Since 1965,
snowmelt runoff into the Red
River has been happening
sooner. In fact, the slope
of the line for the recent
data indicates that snowmelt
is coming earlier by about
0.62 days per year. From
1965 to 2003, its timing is
about three and a half weeks
That’s not a trivial
difference, it’s quite
Original Figure 1: 10 Year
Moving Average Chart for 3
While Neuman's Figure 1 shows the same dramatic change in
the Red River (Fargo) snowmelt beginning as Open Mind's
chart, Neuman's moving average trends for Scanlon and
St Cr F were less dramatic, prompting me to compare the
trends in the 3 basins rather than just rely on the Red
ProcessTrends Analysis of 3 Basins
Noting the differences in Neuman's moving average trends
for the 3 basins, I redid Neuman's 10-year moving average
chart to make it more readable and did
Change Point analysis to look at the differences between
the 3 basins in more detail.
My 10 year moving average chart, comparable to Newman's,
is shown below:
10 Year Moving Average
Beginning of Snowmelt - 3 Basins
This 10 year moving average chart, similar to Neuman's
chart, clearly shows that the Red River at Fargo exhibited a
more rapid downward trend than St Louis at Scanlon and
St Croix at St Croix Falls. While Red River beginning day
continued to decline in the 1990s, the Scanlon and St Croix
beginning days stabilized. While all three basins showed a
decline, the magnitude of the decline was more pronounced in
the Red River than in the St Louis and St Croix basins.
Open Mind found 1965 to be a change point in the Red
River regression analysis, with the trend from 1910 to 1964
nearly random and the trend from 1965 to 2003 significant. I
used the 1965 change point year to calculate regression line
slopes for the 3 basins. The results are summarized in the
Regression Results: 1965 - 2003 Period
River @ Fargo
St Louis R @
St Croix R @
The Red River slope for the 1965 - 2003
was -0.62 days per year, with an r2 of 0.39.
The slope results for the St Louis and St Croix rivers
were considerably less (-0.14 and -0.29 per year)
and the r2 values were very low (0.01 and 0.05).
To further evaluate the differences among the basins, I
prepared CuSum charts and conducted a
change point analysis,
as summarized in the graphic below:
Comparison of CuSum Charts
- 3 Basins (ProcessTrends.Com)
The St Louis and St Croix CuSum charts are similar to each other
and quite different than the Red River CuSum chart . Change Point
Analysis (CPA) of these stations showed that Red River had two
change points (1971 and 1994) while the St Louis and St Croix
stations had one change point each in 1983.
The CuSum and change point analysis confirm that the Red River
snowmelt beginning date trend is different than the St Louis and St
Croix river trends.
My analysis confirms the Neuman and Open Mind analysis results,
however, like many analyses, it raises additional questions:
Why is the Red River snowmelt beginning day trend decline so
much more dramatic than the St Louis and St Croix river trends?
Why do St Louis and St Croix river trends in 1995 - 2003
appear to have leveled off while Red River continues to decline?
Is there another factor at play that affects the St Louis
and St Croix basins differently than the Red River basin?