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Contents:

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Page 3: August 2023 global surface air temperature overview
Page 4: August 2023 global surface air temperature overview versus August last 10 years
Page 5: August 2023 global surface air temperature compared to August 2022
Page 6: Temperature quality class 1: Lower troposphere temperature from satellites
Page 7: Temperature quality class 2: HadCRUT global surface air temperature
Page 8: Temperature quality class 3: GISS and NCDC global surface air temperature
Page 11: Comparing global surface air temperature and satellite-based temperatures
Page 12: Global air temperature linear trends
Page 13: Global temperatures: All in one, Quality Class 1, 2 and 3
Page 15: Global sea surface temperature
Page 18: Ocean temperature in uppermost 100 m
Page 20: Pacific Decadal Oscillation (PDO)
Page 21: North Atlantic heat content uppermost 700 m
Page 22: North Atlantic temperatures 0-800 m depth along 59N, 30-0W
Page 23: Global ocean temperature 0-1900 m depth summary
Page 24: Global ocean net temperature change since 2004 at different depths
Page 25: La Niña and El Niño episodes, Oceanic Niño Index
Page 26: Zonal lower troposphere temperatures from satellites
Page 27: Arctic and Antarctic lower troposphere temperatures from satellites
Page 28: Arctic and Antarctic surface air temperatures
Page 31: Long Arctic annual surface air temperature series NEW
Page 32: Long Antarctic annual surface air temperature series NEW
Page 33: Temperature over land versus over oceans
Page 34: Troposphere and stratosphere temperatures from satellites
Page 35: Sea ice; Arctic and Antarctic
Page 39: Sea level in general
Page 40: Global sea level from satellite altimetry
Page 41: Global sea level from tide gauges
Page 42: Snow cover; Northern Hemisphere weekly and seasonal
Page 44: Greenland Ice Sheet net surface mass balance
Page 45: Atmospheric specific humidity
Page 46: Atmospheric CO2
Page 47: Relation between annual change of atm. CO2 and La Niña and El Niño episodes
Page 48: Phase relation between atmospheric CO2 and global temperature
Page 49: Global air temperature and atmospheric CO2
Page 53: Latest 20-year QC1 global monthly air temperature change
Page 54: Sunspot activity and QC1 average satellite global air temperature
Page 55: Sunspot activity and average neutron counts
Page 56: Sunspot activity, ONI, and change rates of atmospheric CO2 and specific humidity
Page 57: Monthly lower troposphere temperature and global cloud cover
Page 58: Climate and history: Background for Napoleon's Russian 1812 campaign

August 2023 global surface air temperature overview

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General: This newsletter contains graphs anddiagrams showing a selection of key meteorologicalvariables, updated to the most recent past month, if
possible. All temperatures are given in degrees
Celsius.

However, several of the data series have a longer
record length, which may be inspected in greater
detail on www.climate4you.com.

Traditionally, a 30 -year reference period is often usedby various meteorological institutions for comparison
purposes and are supposed to be updated throughthe end of each decade ending in zero (e.g., 19511980, 1961-1990, 1971-2000, etc.). The concept of anormal climate goes back to the first part of the 20th
century. At that time, lasting to about 1960, it was
generally believed that for all practical purpose’s
climate could be considered constant, no matter how
obvious year-to-year fluctuations might have been.
On this basis meteorologist decided to operate withan average or normal climate, defined by a 30-year
period, called the normal period, assuming that it wasof sufficient length to iron out all intervening
variations. In fact, using a 30-yr ‘normal’ period is
genuinely unfortunate, as observations clearly
demonstrate that various global climate parameters(see, e.g., page 20) are influenced by periodic changesof 50-70 years. The often used 30-yr reference period
is roughly half this time interval and is therefore
extremely unsuited as a good reference time interval.
In the maps on page 4, showing the geographicalpattern of surface air temperature anomalies, the last
previous 10 years are therefore used as reference
period. This decadal approach corresponds well to thetypical memory horizon for many people and is also
adopted as reference period by other institutions,e.g., the Danish Meteorological Institute (DMI).

General: For August 2023, the GISS data portal(https://data.giss.nasa.gov/gistemp/) provided 16200
AIRS interpolated surface air data points, based onsatellite observations. According to the GISS and
NCDC surface air temperature records, the August2023 global temperature anomaly was higher than in
the previous month. The UAH and RSS lower
troposphere satellite records also show the August2023 temperature anomaly to be higher than in theprevious month. According to AIRS v6 the August2023 global average surface air temperature anomaly
was higher (+0.17oC) than the global August averagefor the last 10 years.

In many diagrams shown in this newsletter the thinline represents the monthly global average value, andthe thick line indicate a simple running average, inmost cases a simple moving 37-month average, nearlycorresponding to a three-year average. The 37-monthaverage is calculated from values covering a range
from 18 months before to 18 months after, with equal
weight given to all individual months.
The year 1979 has been chosen as starting point inmany diagrams, as this approximately corresponds toboth the beginning of satellite observations and theonset of the late 20th century warming period.

August 2023 surface air temperature

The Northern Hemisphere surface temperature
anomality pattern (p.4) was characterised by regional
contrasts, controlled by the dominant jet stream
position. Alaska, NW Canada, southern USA, Northern
Russia and W Siberia were warm relative to theaverage for the last 10 years. In contrast, much ofEurope, SE Canada and western USA were relatively
cold. Ocean wise, the Norwegian Sea was relatively
cold, while most of the remaining part of the North
Atlantic was warm. In the northern Pacific, most of
the ocean was cold. Arctic Ocean surface airtemperatures were near or below the 10-yr average,
with exception of the Alaska sector.
Near the Equator temperatures were generally above
the 10-year average. Especially the part of the Pacific
Ocean affected by the ongoing warm El Niño episodedisplay above average surface temperatures, as is also
the case for major parts of the Atlantic Ocean north of
the Equator.
Southern Hemisphere temperatures were near the 10yr average. Much of Australia was relatively warm, aswere central regions of South America. In contrast,
most of southern Africa and South America wererelatively cold. Much of the Antarctic continent was
cold, while regions with below-average August sea ice
cover were warm, probably due to heat release to thewinter-cold atmosphere above the sea surface.

August 2023 global surface air temperature overview versus average August last 10 years

4August 2023 surface air temperature compared to the average of August over the last 10 years. Green-yellow-red colours indicate areas withhigher temperature than the 10-year average, while blue colours indicate lower than average temperatures. Data source: Remote Sensed
Surface Temperature Anomaly, AIRS/Aqua L3 Monthly Standard Physical Retrieval 1-degree x 1-degree V006 (https://airs.jpl.nasa.gov/),
obtained from the GISS data portal (https://data.giss.nasa.gov/gistemp/maps/index_v4.html).

August 2023 global surface air temperature compared to August 2022

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August 2023 surface air temperature compared to August 2022. Green-yellow-red colours indicate regions where the present month was
warmer than last year, while blue colours indicate regions where the present month was cooler than last year. Variations in monthly
temperature from one year to the next has no tangible climatic importance but may nevertheless be interesting to study. Data source: Remote
Sensed Surface Temperature Anomaly, AIRS/Aqua L3 Monthly Standard Physical Retrieval 1-degree x 1-degree V006
(https://airs.jpl.nasa.gov/), obtained from the GISS data portal (https://data.giss.nasa.gov/gistemp/maps/index_v4.html).

Temperature quality class 1: Lower troposphere temperature from satellites, updated to August 2023
(see page 9 for definition of classes)

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Global monthly average lower troposphere temperature (thin line) since 1979 according to University of Alabama at Huntsville, USA. The thickline is the simple running 37-month average. Reference period 1991-2020.

Global monthly average lower troposphere temperature (thin line) since 1979 according to according to Remote Sensing Systems (RSS), USA.
The thick line is the simple running 37-month average.

Temperature quality class 2: HadCRUT global surface air temperature, updated to July 2023

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Global monthly average surface air temperature (thin line) since 1979 according to the Hadley Centre for Climate Prediction and Research andthe University of East Anglia's Climatic Research Unit (CRU), UK. The thick line is the simple running 37-month average.

Temperature quality class 3: GISS and NCDC global surface air temperature, updated to August 2023

Global monthly average surface air temperature since 1979 according to according to the National Climatic Data Center (NCDC), USA. The
thick line is the simple running 37-month average.

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Global monthly average surface air temperature (thin line) since 1979 according to according to the Goddard Institute for Space Studies (GISS),at Columbia University, New York City, USA, using ERSST_v4 ocean surface temperatures. The thick line is the simple running 37-monthaverage.

A note on data record stability and -quality:
The temperature diagrams shown above all have 1979
as starting year. This roughly marks the beginning of
the recent episode of global warming, aftertermination of the previous episode of global coolingfrom about 1940. In addition, the year 1979 also
represents the starting date for the satellite-based
global temperature estimates (UAH and RSS). For thethree surface air temperature records (HadCRUT,
NCDC and GISS), they begin much earlier (in 1850 and
1880, respectively), as can be inspected onwww.climate4you.com.

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For all three surface air temperature records, but
especially NCDC and GISS, administrative changes toanomaly values are quite often introduced, evenaffecting observations many years back in time. Somechanges from the recent past may be due to thedelayed addition of new station data or change ofstation location, while others probably have their
origin in changes of the technique implemented to
calculate average values from the raw data. It isclearly impossible to evaluate the validity of suchadministrative changes for the outside user of theserecords; it is only possible to note that such changes
quite often are introduced (se example diagram nextpage).
In addition, the three surface records represent a
blend of sea surface data collected by moving ships orby other means, plus data from land stations of partly
unknown quality and unknown degree ofrepresentativeness for their region. Many of the land
stations also has been moved geographically during
their period of operation, instrumentation have beenchanged, and they are influenced by changes in theirnear surroundings (vegetation, buildings, etc.). The
surface network is inherently heterogeneous (dense
over continents but sparse over oceans) and probably
contaminated by urbanization surrounding manymeasurement sites.
The satellite temperature records also have theirproblems, but these are generally of a more technicalnature and probably therefore better correctable. In

addition, the temperature sampling by satellites ismore regular and complete on a global basis than that
represented by the surface records. It is also
important that the sensors on satellites measuretemperature directly by microwave radiance (thereby
unobstructed by clouds), while most modern surface
temperature measurements are indirect, using
electronic resistance.
Everybody interested in climate science should
gratefully acknowledge the big efforts put into
maintaining the different temperature databases
referred to in the present newsletter. At the sametime, however, it is also important to realise that all
temperature records cannot be of equal scientificquality. The simple fact that they to some degree
differ shows that they cannot all be correct.
On this background, and for practical reasons,Climate4you therefore operates with three quality
classes (1-3) for global temperature records, with 1
representing the highest quality level:
Quality class 1: The satellite records (UAH and RSS).
Quality class 2: The HadCRUT surface record.
Quality class 3: The NCDC and GISS surface records.

The main reason for discriminating between the threesurface records is the following:
While both NCDC and GISS often experience quite
large administrative changes (see example on p.10),
and therefore essentially must be considered as
unstable records, the changes introduced to HadCRUT
are fewer and smaller. For obvious reasons, as thepast does not change, any record undergoing
continuing changes cannot describe the past correctly
all the time. Frequent and large corrections in a
database unavoidably signal a fundamental
uncertainty about what is likely to represent thecorrect values.
You can find more on the issue of lack of temporalstability on www.climate4you.com (go to: Global
Temperature, and then proceed to Temporal
Stability).

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Diagram showing the monthly adjustments made since May 2008 by the Goddard Institute for Space Studies (GISS),
USA, as recorded by published anomaly values for the two months January 1910 and January 2000. AR5 indicatestiming of publication of IPCC report AR5 Climate Change 2013: The Physical Science Basis.

The administrative upsurge of the temperature
increase from January 1915 to January 2000 has
grown from 0.45 (reported May 2008) to 0.67oC(reported September 2023). This represents an about
49% administrative temperature increase over thisperiod, meaning that a significant part (nearly half) of
the apparent global temperature increase fromJanuary 1910 to January 2000 (as reported by GISS) iscaused by administrative changes of the original data
since May 2008.

Comparing global surface air temperature and lower troposphere satellite temperatures;
updated to July 2023

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Plot showing the average of monthly global surface air temperature estimates (HadCRUT5, GISS and NCDC) andsatellite-based temperature estimates (RSS MSU and UAH MSU). The thin lines indicate the monthly value, while
the thick lines represent the simple running 37-month average, nearly corresponding to a running 3-yr average.
The lower panel shows the monthly difference between average surface air temperature and satellite
temperatures. As the base period differs for the different temperature estimates, they have all been normalised bycomparing to the average value of 30 years from January 1979 to December 2008.

Global air temperature linear trends updated to July 2023

Diagram showing the latest 5, 10, 20 and 30-yr linear annual global temperature trend, calculated as the slope of the linear
regression line through the data points, for two satellite-based temperature estimates (UAH MSU and RSS MSU).

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Diagram showing the latest 5, 10, 20, 30, 50, 70 and 100-year linear annual global temperature trend, calculated as the slopeof the linear regression line through the data points, for three surface-based temperature estimates (GISS, NCDC and
HadCRUT5).

All in one, Quality Class 1, 2 and 3; updated to July 2023

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Superimposed plot of Quality Class 1 (UAH and RSS) global monthly temperature estimates. As the base period differs for theindividual temperature estimates, they have all been normalised by comparing with the average value of the initial 120 months(30 years) from January 1979 to December 2008. The heavy black line represents the simple running 37 month (c. 3 year) meanof the average of both temperature records. The numbers shown in the lower right corner represent the temperature anomaly
relative to the individual 1979-2008 averages.

Superimposed plot of Quality Class 1 and 2 (UAH, RSS and HadCRUT) global monthly temperature estimates. As the base period
differs for the individual temperature estimates, they have all been normalised by comparing with the average value of the initial
120 months (30 years) from January 1979 to December 2008. The heavy black line represents the simple running 37 month (c. 3year) mean of the average of all three temperature records. The numbers shown in the lower right corner represent thetemperature anomaly relative to the individual 1979-2008 averages.

Superimposed plot of Quality Class 1, 2 and 3 global monthly temperature estimates (UAH, RSS, HadCRUT, GISS and NCDC). As
the base period differs for the individual temperature estimates, they have all been normalised by comparing with the averagevalue of the initial 120 months (30 years) from January 1979 to December 2008. The heavy black line represents the simple
running 37 month (c. 3 year) mean of the average of all five temperature records. The numbers shown in the lower right corner
represent the temperature anomaly relative to the individual 1979-2008 averages.

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Please see reflections on page 9 relating to the above three quality classes.

Satellite- and surface-based temperature estimates
are derived from different types of measurements
and comparing them directly as in the above diagrams
therefore may be somewhat ambiguous.
However, as both types of estimates often are
discussed together in various news media, the above
composite diagrams may nevertheless be of someinterest.
In fact, the different types of temperature estimatesappear to agree as to the overall temperature
variations on a 2-3-year scale, although on a shortertime scale there are often considerable differences
between the individual records. However, since about
2003 the surface records used to be drifting towards
higher temperatures than the combined satellite
record, but this overall tendency was much removedby the major adjustment of the RSS satellite series in2015 (see lower diagram on page 6).

The combined records (diagram above) suggest amodest global air temperature increase over the last
30 years, about 0.22oC per decade. It should be notedthat the apparent temperature increases since about
2003 at least partly is the result of ongoingadministrative adjustments (page 9-10). At the sametime, none of the temperature records consideredhere indicates any overall temperature decreaseduring the last 20 years.
The present temperature development does not
exclude the possibility that global temperatures may
begin to increase significantly later. On the other
hand, it also remains a possibility that Earth just now
is passing an overall temperature peak, and that
global temperatures may begin to decrease during
the coming 5-10 years.
As always, time will show which of these possibilities
is correct.

Global sea surface temperature, updated to August 2023

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Sea surface temperature anomaly on 18 August 2023 (upper map) and 2022 (lower map). Map source: Plymouth State Weather
Center. Reference period: 1977-1991.

Because of the large surface areas near Equator, thetemperature of the surface water in these regions is
especially important for the global atmospherictemperature (p. 6-8). In fact, no less than 50% of
planet Earth’s surface area is located within 30oN and30oS.
A mixture of relatively warm and cold water presently
dominates much of the global ocean surface, but withnotable variations from month to month. All suchocean surface temperature changes will be
influencing global air temperatures in the months tocome. A cold La Niña episode (Pacific Ocean) has
recently ended and is now being followed by a warm
El Niño episode (see also p. 25).

The significance of short-term cooling or warming
reflected in air temperatures should never beoverstated. Whenever Earth experiences cold La Niña
or warm El Niño episodes major heat exchanges takeplace between the Pacific Ocean and the atmosphereabove, sooner or later showing up in estimates of the
global air temperature.
However, this does not necessarily reflect similarchanges in the total heat content of the atmosphereocean system. In fact, global net changes can be smalland such heat exchanges may mainly reflect
redistribution of energy between ocean andatmosphere. What matters is the overall temperaturedevelopment when seen over several years.

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Global monthly average lower troposphere temperature over oceans (thin line) since 1979 according to University of Alabama at Huntsville,
USA. The thick line is the simple running 37-month average. Insert: Argo global ocean temperature anomaly from floats, displaced verticallyto make visual comparison easier. UAH reference period: 1991-2020.

Global monthly average sea surface temperature since 1979 according to University of East Anglia's Climatic Research Unit (CRU), UK. Baseperiod: 1961-1990. The thick line is the simple running 37-month average. Insert: Argo global ocean temperature anomaly from floats,displaced vertically to make visual comparison easier.

17

Global monthly average sea surface temperature since 1979 according to the National Climatic Data Center (NCDC), USA. Base period: 19012000. The thick line is the simple running 37-month average. Insert: Argo global ocean temperature anomaly from floats, displaced verticallyto make visual comparison easier.

Ocean temperature in uppermost 100 m, updated to June 2023

World Oceans vertical average temperature 0-100 m depth since 1955. The thin line indicates 3-month values, and the thick line represents
the simple running 39-month (c. 3 year) average. Data source: NOAA National Oceanographic Data Center (NODC). Base period 1955-2010.

18

Pacific Ocean vertical average temperature 0-100 m depth since 1955. The thin line indicates 3-month values, and the thick line represents thesimple running 39-month (c. 3 year) average. Data source: NOAA National Oceanographic Data Center (NODC). Base period 1955-2010.

Atlantic Ocean vertical average temperature 0-100 m depth since 1955. The thin line indicates 3-month values, and the thick line represents
the simple running 39-month (c. 3 year) average. Data source: NOAA National Oceanographic Data Center (NODC). Base period 1955-2010.

19

Indian Ocean vertical average temperature 0-100 m depth since 1955. The thin line indicates 3-month values, and the thick line represents thesimple running 39-month (c. 3 year) average. Data source: NOAA National Oceanographic Data Center (NODC). Base period 1955-2010.

Pacific Decadal Oscillation (PDO), updated to August 2023

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Monthly values of the Pacific Decadal Oscillation (PDO) since January 1979. The PDO is a long-lived El Niño-like pattern of Pacific climate
variability, and the data series goes back to January 1854. Base period: 1982-2002. The thin line indicates monthly PDO values, and the thickline is the simple running 37-month average. Data source: NOAA Physical Science Laboratory (version PDO ERSST V5 plotted above).

The PDO is a long-lived El Niño-like pattern of Pacificclimate variability, with data extending back to
January 1854. Causes for PDO are not currently
known, but even in the absence of a theoreticalunderstanding, PDO climate information improves
season-to-season and year-to-year climate forecasts
for North America because of its strong tendency for
multi-season and multi-year persistence. The PDO
also appears to be roughly in phase with global
temperature changes. Thus, from a societal impact’s
perspective, recognition of PDO is important because
it shows that "normal" climate conditions can varyover time periods comparable to the length of ahuman's lifetime.

The PDO illustrates how global temperatures are tied
to sea surface temperatures in the Pacific Ocean, thelargest ocean on Earth. When sea surface
temperatures are relatively low (negative phase
PDO), as it was from 1945 to 1977, global air
temperature decreases. When Pacific Ocean surface
temperatures are high (positive phase PDO), as from1977 to 1998, global surface air temperature
increases.
A Fourier frequency analysis (not shown here) showsthe entire PDO record since 1854 to be influenced by
a 5.7-year cycle, and possibly also by a longer about
53-year long cycle.

North Atlantic heat content uppermost 700 m, updated to June 2021

21

Global monthly heat content anomaly (1018 Joules) in the uppermost 700 m of the North Atlantic (60-0W, 30-65N; see map above) ocean since
January 1955. The thin line indicates monthly values, and the thick line represents the simple running 37-month (c. 3 year) average. Data
source: National Oceanographic Data Center (NODC).

North Atlantic temperatures 0-800 m depth along 59oN, 30-0W, updated to August 2020

Time series depth-temperature diagram along 59 N across the North Atlantic Current from 30oW to 0oW, from surface to 800 m
depth. Source: Global Marine Argo Atlas. See also the diagram below.

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Average temperature along 59 N, 30-0W, 0-800m depth, corresponding to the main part of the North Atlantic Current, using
Argo-data. Source: Global Marine Argo Atlas. Additional information can be found in: Roemmich, D. and J. Gilson, 2009. The
2004-2008 mean and annual cycle of temperature, salinity, and steric height in the global ocean from the Argo Program.
Progress in Oceanography, 82, 81-100.

Global ocean temperature 0-1900 m depth summary, updated to August 2020

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Summary of average temperature in uppermost 1900 m in different parts of the global oceans, using Argo-data. Source: Global
Marine Argo Atlas. Additional information can be found in: Roemmich, D. and J. Gilson, 2009. The 2004-2008 mean and annual
cycle of temperature, salinity, and steric height in the global ocean from the Argo Program. Progress in Oceanography, 82, 81100.

The global summary diagram above shows that, on
average, the temperature of the global oceans down to1900 m depth has been increasing since about 2011. It isalso seen that this increase since 2013 dominantly is due tooceanic changes occurring near the Equator, between 30oN

and 30oS. In contrast, for the circum-Arctic oceans north of
55oN, depth-integrated ocean temperatures have been
decreasing since 2011. Near the Antarctic, south of 55 oS,
temperatures have essentially been stable. At mostlatitudes, a clear annual rhythm is evident.

Global ocean net temperature change since 2004 at different depths, updated to August 2020

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Net temperature change since 2004 from surface to 1900 m depth in different parts of the global oceans, using Argo-data.
Source: Global Marine Argo Atlas. Additional information can be found in: Roemmich, D. and J. Gilson, 2009. The 2004-2008
mean and annual cycle of temperature, salinity, and steric height in the global ocean from the Argo Program. Progress in
Oceanography, 82, 81-100. Please note that due to the spherical form of Earth, northern and southern latitudes represent only
small ocean volumes, compared to latitudes near the Equator.

La Niña and El Niño episodes, Oceanic Niño Index (ONI), updated to August 2023

25
Warm (>+0.5oC) and cold (<0.5oC) episodes for the Oceanic Niño Index (ONI), defined as 3 month running mean of ERSSTv4 SST
anomalies in the Niño 3.4 region (5oN-5oS, 120o-170oW)]. For historical purposes cold and warm episodes are defined when thethreshold is met for a minimum of 5 consecutive over-lapping seasons. Anomalies are centred on 30-yr base periods updated
every 5 years.

The 2015-16 El Niño episode is among the strongest
since the beginning of the record in 1950. Considering
the entire record, however, recent variations

between El Niño and La Niña episodes do not appear
abnormal in any way. See also diagrams on pages 44
and 53.

Zonal lower troposphere temperatures from satellites, updated to August 2023

26

Global monthly average lower troposphere temperature since 1979 for the tropics and the northern and southern extratropics,
according to University of Alabama at Huntsville, USA. Thin lines show the monthly temperature. Thick lines represent thesimple running 37-month average, nearly corresponding to a running 3-year average. Reference period 1981-2010.
The overall warming since 1980 has dominantly been anorthern hemisphere phenomenon, and mainly played out
as a marked change between 1994 and 1999. However, thisrather rapid temperature change is probably influenced by
the Mt. Pinatubo eruption 1992-93 and the subsequent
1997 El Niño episode. The diagram also shows thetemperature effects of the strong Equatorial El Niño’s in
1997 and 2015-16, as well as the moderate El Niño in 2019.
Apparently, these effects were spreading to higher
latitudes in both hemispheres with some delay. Just now a
new El Niño has begun in the Pacific Ocean (p.25), as
indicated by tropics surface air temperatures.

Arctic and Antarctic lower troposphere temperature, updated to August 2023

27

Global monthly average lower troposphere temperature since 1979 for the North Pole and South Pole regions, based on satellite
observations (University of Alabama at Huntsville, USA). Thin lines show the monthly temperature. The thick line is the simple
running 37-month average, nearly corresponding to a running 3-year average. Reference period 1991-2020.

In the Arctic region, warming mainly took place 1994-96,
and less so subsequently. In 2016, however, temperatures
peaked for several months, presumably because of oceanic
heat given off to the atmosphere during the 2015-15 El
Niño (see also diagram on page 25) and subsequently
advected to higher latitudes.
This underscores how Arctic air temperatures may beaffected not only by variations in local conditions but also
by variations playing out in geographically remote regions.

A slight, but persistent, temperature decrease has
characterised the Arctic since the 2016 peak (see also
diagrams on page 28-30).
In the Antarctic region, temperatures have basicallyremained stable since the onset of the satellite record in
1979. In 2016-17 a small temperature peak visible in themonthly record may be interpreted as the subdued effectof the recent El Niño episode.

Arctic and Antarctic surface air temperature, updated to December 2021

Diagram showing area weighted Arctic (70-90oN) monthly surface air temperature anomalies (HadCRUT4) since January 2000,
in relation to the WMO normal period 1961-1990. The thin line shows the monthly temperature anomaly, while the thicker line
shows the running 37-month (c. 3 year) average.

28

Diagram showing area weighted Antarctic (70-90oS) monthly surface air temperature anomalies (HadCRUT4) since January
2000, in relation to the WMO normal period 1961-1990. The thin line shows the monthly temperature anomaly, while thethicker line shows the running 37-month (c. 3 year) average.

Diagram showing area weighted Arctic (70-90oN) monthly surface air temperature anomalies (HadCRUT4) since January 1957,
in relation to the WMO normal period 1961-1990. The thin line shows the monthly temperature anomaly, while the thicker line
shows the running 37-month (c. 3 year) average.

29

Diagram showing area weighted Antarctic (70-90oS) monthly surface air temperature anomalies (HadCRUT4) since January
1957, in relation to the WMO normal period 1961-1990. The thin line shows the monthly temperature anomaly, while thethicker line shows the running 37-month (c. 3 year) average.

Diagram showing area-weighted Arctic (70-90oN) monthly surface air temperature anomalies (HadCRUT4) since January 1920,
in relation to the WMO normal period 1961-1990. The thin line shows the monthly temperature anomaly, while the thicker line
shows the running 37-month (c. 3 year) average.

30

Because of the relatively small number of Arctic
stations before 1930, month-to-month variations in
the early part of the Arctic temperature record 19202018 are higher than later (diagram above).
The period from about 1930 saw the establishment ofmany new Arctic meteorological stations, first in
Russia and Siberia, and following the 2nd World War,also in North America, explaining the above
difference.
The period since 2005 is warm, about as warm as theperiod 1930-1940.
As the HadCRUT4 data series has improved highlatitude coverage data coverage (compared to the
HadCRUT3 series), the individual 5ox5o grid cells havebeen weighted according to their surface area. This
area correction is especially important for polar

regions, where longitudes converge rapidly. Thisapproach differs from the approach used by Gillet etal. 2008, which calculated a simple average, with nocorrection for the substantial latitudinal surface area
effect in polar regions.
The area weighted Arctic HadCRUT4 surface airtemperature anomalies (p.28-30) correspond rather
well to the lower troposphere temperature anomalies
recorded by satellites (p.27).

Literature:
Gillett, N.P., Stone, D.A., Stott, P.A., Nozawa, T.,
Karpechko, A.Y.U., Hegerl, G.C., Wehner, M.F. andJones, P.D. 2008. Attribution of polar warming tohuman influence. Nature Geoscience 1, 750-754.

Long Arctic annual surface air temperature series, updated to year 2022

31

Arctic annual surface air temperature series, selected because of their length of observation time. The thin blue line
represents the mean annual air temperature, and the thick blue line is the running 5-year average. Annual values
were calculated from monthly average temperatures. More info on Climate4you.

Long Antarctic annual surface air temperature series, updated to year 2022

32

Antarctic annual surface air temperature series, selected because of their length of observation time. The thin blueline represents the mean annual air temperature, and the thick blue line is the running 5-year average. Annual
values were calculated from monthly average temperatures. More info on Climate4you.

Temperature over land versus over oceans, updated to August 2023

33

Global monthly average lower troposphere temperature since 1979 measured over land and oceans, respectively, according toUniversity of Alabama at Huntsville, USA. Thick lines are the simple running 37-month average, nearly corresponding to arunning 3-year average. Reference period 1991-2020.

Since 1979, the lower troposphere over land has
warmed much more than over oceans, suggestingthat the overall warming is derived mainly fromincoming solar radiation. In addition, there may besupplementary reasons for this divergence, such as,e.g., variations in cloud cover and changes in land use.

Troposphere and stratosphere temperatures from satellites, updated to August 2023

34

Global monthly average temperature in different according to University of Alabama at Huntsville, USA. The thin lines
represent the monthly average, and the thick line the simple running 37-month average, nearly corresponding to a running 3year average. Reference period 1991-2020.

Arctic and Antarctic sea ice, updated to August 2023

35

Sea ice extent 19 August 2023. The median limit of sea ice (orange line) is defined as 15% sea ice cover, according to the average of satellite
observations 1981-2010 (both years included). Sea ice may therefore well be encountered outside and open water areas inside the limit shownin the diagrams above. Map source: National Snow and Ice Data Center (NSIDC).

Diagrams showing Arctic sea ice extent and concentration 20 August 2022 (left) and 2023 (right), according to the Japan Aerospace Exploration
Agency (JAXA).

36

Graphs showing monthly Antarctic, Arctic, and global sea ice extent since November 1978, according to the National Snow and Ice data Center(NSIDC).

Diagram showing daily Arctic sea ice extent since June 2002, to 20 August 2023, data courtesy of Japan Aerospace Exploration Agency (JAXA).

37

Diagrams showing Arctic sea ice extent and thickness 20 August 2022 (left) and 2023 (right and above) and the seasonal
cycles of the calculated total arctic sea ice volume, according to The Danish Meteorological Institute (DMI). The mean sea ice
volume and standard deviation for the period 2004-2013 are shown by grey shading. Please note that DMI on 7 December
2021 changed their sea ice calculation model. DMI’s description of the model version change can be read here:
http://polarportal.dk/en/sea-ice-and-icebergs/sea-ice-thickness-and-volume/38

12 month running average sea ice extension, global and in both hemispheres since 1979, the satellite-era. The October 1979 value
represents the monthly 12-month average of November 1978 - October 1979, the November 1979 value represents the average of December
1978 - November 1979, etc. The stippled lines represent a 61-month (ca. 5 years) average. Data source: National Snow and Ice Data Center(NSIDC).

Sea level in general
Global (or eustatic) sea-level change is measured relative to an
idealised reference level, the geoid, which is a mathematicalmodel of planet Earth’s surface (Carter et al. 2014). Global sealevel is a function of the volume of the ocean basins and thevolume of water they contain. Changes in global sea-level arecaused by – but not limited to - four main mechanisms:
1.
2.
3.
4.

Changes in local and regional air pressure and wind, andtidal changes introduced by the Moon.
Changes in ocean basin volume by tectonic (geological)
forces.
Changes in ocean water density caused by variations incurrents, water temperature and salinity.
Changes in the volume of water caused by changes in
the mass balance of terrestrial glaciers.

In addition to these there are other mechanisms influencing sealevel, such as storage of ground water, storage in lakes and rivers,
evaporation, etc.

39

Mechanism 1 is controlling sea-level at many sites on a time scalefrom months to several years. As an example, many coastal
stations show a pronounced annual variation reflecting seasonalchanges in air pressures and wind speed. Longer-term climaticchanges playing out over decades or centuries will also affectmeasurements of sea-level changes. Hansen et al. (2011, 2015)
provide excellent analyses of sea-level changes caused byrecurrent changes of the orbit of the Moon and otherphenomena.
Mechanism 2 – with the important exception of earthquakes andtsunamis - typically operates over long (geological) time scales
and is not significant on human time scales. It may relate tovariations in the seafloor spreading rate, causing volume changes
in mid-ocean mountain ridges, and to the slowly changing
configuration of land and oceans. Another effect may be the slow
rise of basins due to isostatic offloading by deglaciation after an
ice age. The floor of the Baltic Sea and the Hudson Bay arepresently rising, causing a slow net transfer of water from thesebasins into the adjoining oceans. Slow changes of excessively big
glaciers (ice sheets) and movements in the mantle will affect thegravity field and thereby the vertical position of the ocean surface.
Any increase of the total water mass as well as sedimentdeposition into oceans increase the load on their bottom,
generating sinking by viscoelastic flow in the mantle below. The
mantle flow is directed towards the surrounding land areas, which
will rise, thereby partly compensating for the initial sea level
increase induced by the increased water mass in the ocean.
Mechanism 3 (temperature-driven expansion) only affects theuppermost part of the oceans on human time scales. Usually,
temperature-driven changes in density are more important thansalinity-driven changes. Seawater is characterised by a relatively
small coefficient of expansion, but the effect should however not
be overlooked, especially when interpreting satellite altimetrydata. Temperature-driven expansion of a column of seawater will
not affect the total mass of water within the column considered
and will therefore not affect the potential at the top of the watercolumn. Temperature-driven ocean water expansion will
therefore not in itself lead to any lateral displacement of water,
but only locally lift the ocean surface. Near the coast, where
people are living, the depth of water approaches zero, so no
measurable temperature-driven expansion will take place here
(Mörner 2015). Mechanism 3 is for that reason not important forcoastal regions.
Mechanism 4 (changes in glacier mass balance) is an importantdriver for global sea-level changes along coasts, for human timescales. Volume changes of floating glaciers – ice shelves – has noinfluence on the global sea-level, just like volume changes offloating sea ice has no influence. Only the mass-balance ofgrounded or land-based glaciers is important for the global sealevel along coasts.
Summing up: Presumably, mechanism 1 and 4 are the mostimportant for understanding sea-level changes along coasts.

References:
Carter R.M., de Lange W., Hansen, J.M., Humlum O., Idso C., Kear, D., Legates, D., Mörner, N.A., Ollier C., Singer F. & Soon W. 2014.
Commentary and Analysis on the Whitehead& Associates 2014 NSW Sea-Level Report. Policy Brief, NIPCC, 24. September 2014, 44 pp.
http://climatechangereconsidered.org/wp-content/uploads/2014/09/NIPCC-Report-on-NSW-Coastal-SL-9z-corrected.pdf
Hansen, J.-M., Aagaard, T. and Binderup, M. 2011. Absolute sea levels and isostatic changes of the eastern North Sea to central Baltic region
during the last 900 years. Boreas, 10.1111/j.1502-3885.2011.00229.x. ISSN 0300–9483.
Hansen, J.-M., Aagaard, T. and Huijpers, A. 2015. Sea-Level Forcing by Synchronization of 56- and 74-YearOscillations with the Moon’s Nodal
Tide on the Northwest European Shelf (Eastern North Sea to Central Baltic Sea). Journ. Coastal Research, 16 pp.
Mörner, Nils-Axel 2015. Sea Level Changes as recorded in nature itself. Journal of Engineering Research and Applications, Vol.5, 1, 124-129.

Global sea level from satellite altimetry, updated to October 2022

40

Global sea level since December 1992 according to the Colorado Center for Astrodynamics Research at University of Colorado at Boulder. The
blue dots are the individual observations, and the purple line represents the running 121-month (ca. 10 year) average. The two lower panels
show the annual sea level change, calculated for 1 and 10-year time windows, respectively. These values are plotted at the end of the interval
considered.

Ground truth is a term used in various fields to refer toinformation provided by direct observation as opposed to
information provided by inference, such as, e.g., by satellite
observations.

aids in the interpretation and analysis of what is being
sensed or recorded by satellites. Ground truth sites allow
the remote sensor operator to correct and improve theinterpretation of satellite data.

In remote sensing using satellite observations, ground truth
data refers to information collected on location. Groundtruth allows the satellite data to be related to real features
observed on the planet surface. The collection of groundtruth data enables calibration of remote-sensing data, andFor satellite observations on sea level ground true data areprovided by the classical tide gauges (example diagram onnext page), that directly measures the local sea level many
places distributed along the coastlines on the surface of theplanet.

Global sea level from tide-gauges, updated to December 2020

41

Holgate-9 monthly tide gauge data from PSMSL Data Explorer. Holgate (2007) suggested the nine stations listed in the diagram to capture thevariability found in a larger number of stations over the last half century studied previously. For that reason, average values of the Holgate-9group of tide gauge stations are interesting to follow, even though Auckland (New Zealand) has not reported data since 2000, and Cascais(Portugal) not since 1993. Unfortunately, by this data loss the Holgate-9 series since 2000 is underrepresented with respect to the southernhemisphere and should therefore not be overinterpreted. The blue dots are the individual average monthly observations, and the purple line
represents the running 121-month (ca. 10 year) average. The two lower panels show the annual sea level change, calculated for 1 and 10-year
windows, respectively. These values are plotted at the end of the interval considered.

Data from tide-gauges all over the world suggest an
average global sea-level rise of 1-2 mm/year, while thesatellite-derived record (page 37) suggest a rise of about
3.3 mm/year, or more. The noticeable difference (about
1:2) between the two data sets is remarkable but has no

generally accepted explanation. It is however known thatsatellite observations are facing several complications inareas near the coast. Vignudelli et al. (2019) provide an
updated overview of the current limitations of classicalsatellite altimetry in coastal regions.

References:
Holgate, S.J. 2007. On the decadal rates of sea level change during the twentieth century. Geophys. Res. Letters, 34, L01602,
doi:10.1029/2006GL028492
Vignudelli et al. 2019. Satellite Altimetry Measurements of Sea Level in the Coastal Zone. Surveys in Geophysics, Vol. 40, p. 1319–1349.
https://link.springer.com/article/10.1007/s10712-019-09569-1

Northern Hemisphere weekly and seasonal snow cover, updated to August 2023

Northern hemisphere snow cover (white) and sea ice (yellow) 20 August 2022 (left) and 2023 (right). Map source: National Ice
Center (NIC).

42

Northern hemisphere weekly snow cover since January 2000 according to Rutgers University Global Snow Laboratory. The thin blue line is
the weekly data, and the thick blue line is the running 53-week average (approximately 1 year). The horizontal red line is the 1972-2022
average.

Northern hemisphere weekly snow cover since January 1972 according to Rutgers University Global Snow Laboratory. The thin blue line is
the weekly data, and the thick blue line is the running 53-week average (approximately 1 year). The horizontal red line is the 1972-2022
average.

43

Northern hemisphere seasonal snow cover since January 1972 according to Rutgers University Global Snow Laboratory.

Greenland Ice Sheet net surface mass balance, updated to August 2023

44

Left: Surface mass balance 20 August 2023. Right: Net surface mass balance anomaly since September 1, 2022. Courtesy of Danish
Meteorological Institute (DMI).

Atmospheric specific humidity, updated to August 2023

45

Specific atmospheric humidity (g/kg) at three different altitudes in the lower part of the atmosphere (the Troposphere) since January 1948
(Kalnay et al. 1996). The thin blue lines show monthly values, while the thick blue lines show the running 37-month average (about 3 years).
Data source: Earth System Research Laboratory (NOAA).

Water vapor is the most important greenhouse gas in the
Troposphere. The highest concentration is found within alatitudinal range from 50oN to 60oS. The two polar regionsof the Troposphere are comparatively dry.

variations superimposed on the falling trend. A Fourier
frequency analysis (not shown here) shows these variations
to be influenced especially by a periodic variation of about
3.7-year duration.

The diagram above shows the specific atmospherichumidity to be stable or slightly increasing up to about 4-5
km altitude. At higher levels in the Troposphere (about 9km), the specific humidity has been decreasing for theduration of the record (since 1948), but with shorter

The persistent decrease in specific humidity at about 9 km
altitude is particularly noteworthy, as this altitude roughly
corresponds to the level where the theoreticaltemperature effect of increased atmospheric CO2 is
expected initially to play out.

Atmospheric CO2, updated to August 2023

46

Monthly amount of atmospheric CO2 (upper diagram) and annual growth rate (lower diagram); average last 12 months minus average
preceding 12 months, thin line) of atmospheric CO2 since 1959, according to data provided by the Mauna Loa Observatory, Hawaii, USA. The
thick, stippled line is the simple running 37-observation average, nearly corresponding to a running 3-year average. A Fourier frequency
analysis (not shown here) shows the 12-month change of Tropospheric CO2 to be influenced especially by periodic variations of 2.5- and 3.8years’ duration.

The relation between annual change of atmospheric CO 2 and La Niña and El Niño episodes, updated toAugust 2023

47

Visual association between annual growth rate of atmospheric CO2 (upper panel) and Oceanic Niño Index (lower panel). See also diagrams
on page 40 and 22, respectively.

Changes in the global atmospheric CO2 is seen to varyroughly in concert with changes in the Oceanic Niño Index.
The typical sequence of events is that changes in the global
atmospheric CO2 to a certain degree follows changes in the
Oceanic Niño Index, but clearly not in all details. Many
processes, natural as well as anthropogenic, controls theamount of atmospheric CO2, but oceanographic processes
are clearly particularly important (see also diagram on nextpage).
Atmospheric CO2 and the present coronavirus pandemic
Modern political initiatives usually assume the human
influence (mainly the burning of fossil fuels) to represent

the core reason for the observed increase in atmospheric
CO2 since 1958 (diagrams on page 44).
The coronavirus pandemic since January 2020 resulted in amarked reduction in the global consumption of fossil fuels.
It is therefore enlightening to follow the effect of thisreduction on the amount of atmospheric CO2.
However, there is still no clear effect to be seen of theabove reduction in release of CO2 from fossil fuels.
Presumably, the main explanation for this is that thehuman contribution is too small compared to thenumerous natural sources and sinks for atmospheric CO2 toappear in diagrams showing the amount of atmospheric
CO2 (see, e.g., diagrams on p. 44-46).

The phase relation between atmospheric CO2 and global temperature, updated to July 2023

48

12month change of global atmospheric CO2 concentration (Mauna Loa; green), global sea surface temperature (HadSST4; blue) and global
surface air temperature (HadCRUT5; red dotted). Entire data series since 1958 in upper figure, and last 15 years in lower figure, to enhancemodern dynamics. All graphs are showing monthly values of DIFF12, the difference between the average of the last 12 month and theaverage for the previous 12 months for each data series.

The typical sequence of events is seen to be that changes
in the global atmospheric CO2 follow changes in global
surface air temperature, which again follow changes in
global ocean surface temperatures. Thus, changes in global

atmospheric CO2 usually are lagging 9.5–10 months behindchanges in global air surface temperature, and 11–12
months behind changes in global sea surface temperature.

Reference: Humlum, O., Stordahl, K. and Solheim, J-E. 2012. The phase relation between atmospheric carbon dioxide andglobal temperature. Global and Planetary Change, August 30, 2012.
http://www.sciencedirect.com/science/article/pii/S0921818112001658?v=s5

Global air temperature and atmospheric CO2, updated to August 2023

49

50

51

Diagrams showing UAH, RSS, HadCRUT5, NCDC and GISS monthly global air temperature estimates (blue) and the monthly
atmospheric CO2 content (red) according to the Mauna Loa Observatory, Hawaii. The Mauna Loa data series begins in March
1958, and 1958 was therefore chosen as starting year for all diagrams above. Reconstructions of past atmospheric CO 2
concentrations (before 1958) are not incorporated in this diagram, as such past CO 2 values are derived by other means (ice cores,
stomata, or older measurements using different methodology), and therefore are not directly comparable with direct
atmospheric measurements.

Most climate models are programmed to give thegreenhouse gas carbon dioxide CO2 significant influence on
the calculated global air temperature. It is therefore
relevant to compare different air temperature records withmeasurements of atmospheric CO2, as shown in the
diagrams above.
Any comparison, however, should not be made on amonthly or annual basis, but for a longer time, as othereffects (oceanographic, cloud cover, etc.) may override thepotential influence of CO2 on short time scales such as justa few years.
It is of cause equally inappropriate to present new
meteorological record values, whether daily, monthly, or
annual, as demonstrating the legitimacy of the hypothesis
ascribing high importance of atmospheric CO2 for global air
temperatures. Any such meteorological record value may

well be the result of other phenomena. Unfortunately,
many media repeatedly fall into this trap.
What exactly defines the critical length of a relevant period
length to consider for evaluating the alleged importance of
CO2 remains elusive and still represents a theme fordiscussions.
Nonetheless, the length of the critical period must be
inversely proportional to the temperature sensitivity ofCO2, including feedback effects. Thus, if the net
temperature effect of atmospheric CO2 is strong, thecritical period will be short, and vice versa.
However, past climate research history provides someclues as to what has traditionally been considered therelevant length of period over which to compare
temperature and atmospheric CO2.

After about 10 years of concurrent global temperature- andCO2-increase, IPCC was established in 1988. For obtaining
public and political support for the CO 2-hyphotesis the 10year warming period leading up to 1988 most likely wasconsidered important. Had the global temperature instead
been decreasing at that time, politic support for the
hypothesis probably would have been difficult to obtain in
1988.
Based on the previous 10 years of concurrent temperatureand CO2-increase, many climate scientists in 1988

52

presumably felt that their understanding of climate
dynamics was sufficient to conclude about the importanceof CO2 for affecting observed global temperatures.
Thus, it may with confidence be concluded that 10 years in1988 was considered a period long enough to demonstrate
the effect of increasing atmospheric CO2 on global
temperatures. The 10-year period is also basis for thetemperature anomality diagrams shown on page 4.

Latest 20-year QC1 global monthly air temperature changes, updated to August 2023

53
Last 20 years’ global monthly average air temperature according to Quality Class 1 (UAH and RSS; see p.6 and 9) global monthly
temperature estimates. The thin blue line represents the monthly values. The thick black line is the linear fit, with 95% confidence
intervals indicated by the two thin black lines. The thick green line represents a 5-degree polynomial fit, with 95% confidence
intervals indicated by the two thin green lines. A few key statistics are given in the lower part of the diagram (please note thatthe linear trend is the monthly trend).

In the enduring scientific climate debate, the following
question is often put forward: Is the surface airtemperature still increasing or has it basically remained
without significant changes during the last 15-16 years?
The diagram above may be useful in this context anddemonstrates the differences between two often usedstatistical approaches to determine recent temperature
trends. Please also note that such fits only attempt todescribe the past, and usually have small, if any, predictive
power.
In addition, before using any linear trend (or other) analysisof time series a proper statistical model should be chosen,
based on statistical justification.

For global temperature time series, there is no a priori
physical reason why the long-term trend should be linear intime. In fact, climatic time series often have trends forwhich a straight line is not a good approximation, as isclearly demonstrated by several of the diagrams shown in
the present report.
For an commendable description of problems often
encountered by analyses of temperature time series
analyses, please see Keenan, D.J. 2014: Statistical Analysesof Surface Temperatures in the IPCC Fifth Assessment
Report.
See also diagrams on page 12.

Sunspot activity (SIDC) and QC1 average satellite global air temperature, updated to August 2023

54

Variation of global monthly air temperature according to Quality Class 1 (UAH and RSS; see p.4) and observed sunspot number
as provided by the Solar Influences Data Analysis Center (SIDC), since 1979. The thin lines represent the monthly values, while
the thick line is the simple running 37-month average, nearly corresponding to a running 3-year average. The asymmetricaltemperature 'bump' around 1998 is influenced by the oceanographic El Niño phenomenon in 1998, as is the case also for 201516. Temperatures in year 2019-20 was influenced by a moderate El Niño. In summer 2023 a new El Niño episode has begun.

.

Monthly sunspot activity (SIDC) and average neutron counts (Oulu, Finland), updated to August 2023

55

Observed monthly sunspot number (Solar Influences Data Analysis Center (SIDC) since April 1964, and (lower panel) monthly
average counts of the Oulu (Finland) neutron monitor, adjusted for barometric pressure and efficiency.

Monthly sunspot activity (SIDC), Oceanic Niño Index (ONI), and change rates of atmospheric CO2 andspecific humidity, updated to August 2023

56

Visual association since 1958 between (from bottom to top) Sunspot Number, Oceanic Niño Index (ONI) and annual change rateof atmospheric CO2. and specific humidity at 300 mb (ca. 9 km altitude). Upper two panels: Annual (12 month) change rate of
atmospheric CO2 and specific humidity at 300 mb since 1959, calculated as the average amount of atmospheric CO2/humidity
during the last 12 months, minus the average for the preceding 12 months (see also diagrams on page 43+44). Niño index panel:
Warm (>+0.5oC) and cold (<0.5oC) episodes for the Oceanic Niño Index (ONI), defined as 3 month running mean of ERSSTv4 SST
anomalies in the Niño 3.4 region (5oN-5oS, 120o-170oW)]. For historical purposes cold and warm episodes are defined when thethreshold is met for a minimum of 5 consecutive over-lapping seasons. Anomalies are centred on 30-yr base periods updated
every 5 years. Thin vertical stippled lines indicate the visually estimated timing of sunspot minima. The typically sequence
following a sunspot minimum appears to be a warm El Niño episode followed by a cold La Niña episode. Effects on change rates
of atmospheric CO2 and atmospheric specific humidity are visually apparent, with ONI variations being followed by changes infirst humidity, and then (last) by CO2.

The above diagram is inspired by the Leamon et al. 2021 publication: Robert J. Leamon, Scott W. McIntosh, Daniel
R. Marsh. Termination of Solar Cycles and Correlated Tropospheric Variability. Earth and Space Science, 2021; 8 (4)DOI: 10.1029/2020EA001223

Monthly lower troposphere temperature (UAH) and global cloud cover, updated to April 2021

57

Lower tropospheric air temperature and global cloud cover. Upper panel: Global cloud cover according to Satellite Application Facility on Climate
Monitoring. Lower panel: Global monthly average lower troposphere temperature (thin line) since 1979 according to University of Alabama at
Huntsville, USA. The thick lines represent the simple running 37-month average. Reference period for UAH is 1991-2020.

Cloud cover data citation: Karlsson, Karl-Göran; Anttila, Kati; Trentmann, Jörg; Stengel, Martin; Solodovnik, Irina; Meirink, Jan
Fokke; Devasthale, Abhay; Kothe, Steffen; Jääskeläinen, Emmihenna; Sedlar, Joseph; Benas, Nikos; van Zadelhoff, Gerd-Jan; Stein,
Diana; Finkensieper, Stephan; Håkansson, Nina; Hollmann, Rainer; Kaiser, Johannes; Werscheck, Martin (2020): CLARA-A2.1: CM
SAF cLoud, Albedo and surface RAdiation dataset from AVHRR data - Edition 2.1, Satellite Application Facility on Climate
Monitoring,
DOI:10.5676/EUM_SAF_CM/CLARA_AVHRR/V002_01, https://doi.org/10.5676/EUM_SAF_CM/CLARA_AVHRR/V002_01.

Climate and history; one example among many
1811: Background for Napoleon's Russian 1812 campaign

The two emperors Napoleon (left) and Alexander I (right) negotiating the Tilsit Treaty in a pavilion set up
on a raft in the middle of the Niemen River, beginning 25 June 1807.

58

In 1806, Napoleon won a conclusive battle against
Austria at Wagram. Austria was then forced tosign the treaty of Vienna, which reduced it to astate of powerlessness. From a military point ofview, Napoleon had now gained control over mostof Europe and was beginning to create a European
Community, almost 200 years before it became a
reality. As was the case for Adolf Hitler 129 years
later, only two European nations stood between
him and the total political dominance: Britain andRussia.
In November 1806 the Holy Synod of the Russian
Orthodox Church issued a denunciation of
Napoleon, accusing him of conspiring with evil
people against the Christan Faith, due toNapoleons declaration of his regard for Islam.
Russia therefore launched a military crusadeagainst him. This initiative was cut short by

Napoleon routing the Russian army at Eylau(January 1807) and at Friedland (June 1807). Tsar
Alexander I of Russia the sensibly enoughsuggested peace and an alliance, which was
negotiated and signed 7 July 1807 at Tilsit.
Cracks in this alliance, however, rapidly began toshow. Especially Napoleon's creation of theGrande Duchy of Warsaw in 1807 had, in effect,
introduced the first material renewed conflict ofinterest between France and Russia. This newpolitical unit inevitably raised the possibility of a
restoration of the Kingdom of Poland. Such a
restoration would entail the loss of Russia of some
if not all previous land acquisitions at the expenseof Poland - an area of 463,000 km2 with apopulation of more than seven million. Napoleon
was beginning to fear that Russia would use thePolish question as an excuse to seek anunderstanding with Britannia. The French-Russian
relationship began to deteriorate. By 1811, there
was much open talk about the coming war in bothcountries, although probably both Napoleon andAlexander had no personal wishes to go down theroad to war.

would be large by any scale, including soldiersfrom almost every nation of Europe. The largest
non-French contingent were the Poles, who
numbered some 95,000. In total, the 'Grande
Armée' probably numbered around 450,000. Also
Alexander did everything he could to prepare his
armed forces for the expected confrontation, andin 1812 he had almost 600,000 men under arms.
Napoleons army, however, was fortified by the
reputation of the French arms: The common
belief that they were invincible made them almost
invincible.

Caught up by the internal dynamics of thisdevelopment, Napoleon decided to strike first,
and began a relentless build-up of forces throughthe autumn and winter of 1811 and into the springof 1812. The army Napoleon was assembling

*****

59

All diagrams in this report, along with any supplementary information, including links to data sources andprevious issues of this newsletter, are freely available for download on www.climate4you.com

Yours sincerely,
Ole Humlum (Ole.Humlum@gmail.com)
Arctic Historical Evaluation and Research Organisation, Longyearbyen, Svalbard
20 September 2023.
Planned publication of next newsletter: around 21 October 2023.