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Contents:

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Page 3: March 2022 global surface air temperature overview
Page 4: March 2022 global surface air temperature overview versus March last 10 years
Page 5: March 2022 global surface air temperature compared to March 2021
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: Temperature over land versus over oceans
Page 32: Troposphere and stratosphere temperatures from satellites
Page 33: Sea ice; Arctic and Antarctic
Page 37: Sea level in general
Page 38: Global sea level from satellite altimetry
Page 39: Global sea level from tide gauges
Page 40: Snow cover; Northern Hemisphere weekly and seasonal
Page 42: Atmospheric specific humidity
Page 43: Atmospheric CO2
Page 44: Relation between annual change of atm. CO2 and La Niña and El Niño episodes
Page 45: Phase relation between atmospheric CO2 and global temperature
Page 46: Global air temperature and atmospheric CO2
Page 50: Latest 20-year QC1 global monthly air temperature change
Page 51: Sunspot activity and QC1 average satellite global air temperature
Page 52: Sunspot activity and average neutron counts
Page 53: Sunspot activity, ONI, and change rates of atmospheric CO2 and specific humidity
Page 54: Monthly lower troposphere temperature and global cloud cover
Page 55: Climate and history: Year 1909-1925: Changing sea ice conditions around SpitsbergenMarch 2022 global surface air temperature overview

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General: This newsletter contains graphs anddiagrams showing a selection of key meteorologicalvariables, if possible updated to the most recent
past month. All temperatures are given in degrees
Celsius. In the maps on page 4, showing thegeographical pattern of surface air temperature
anomalies, the last previous 10 years are used as
reference period.

to both the beginning of satellite observations andthe onset of the late 20th century warming period.
However, several of the data series have a much
longer record length, which may be inspected ingreater detail on www.climate4you.com.

The rationale for comparing with this recent period
instead of various ‘normal’ periods defined for partsof the past century, is that such reference periods
often will be affected by past cold periods, like, e.g.,
1945-1980. Most modern comparisons with suchreference periods will inevitably appear as warm,
and it will be difficult to decide if modern
temperatures are increasing or decreasing.
Comparing instead with the last previous 10 years
overcomes this problem and clearer displays themodern dynamics of ongoing change. This decadal
approach also corresponds well to the typicalmemory horizon for many people and is also
adopted as reference period by other institutions,e.g., the Danish Meteorological Institute (DMI).

General: For March 2022, the GISS data portal(https://data.giss.nasa.gov/gistemp/)
provided
16200 AIRS interpolated surface air data points,
based on satellite observations, and visualised hereon pages 4-5. According to the GISS surface airtemperature records, the March 2022 global
temperature anomaly was higher than in theprevious month. Also according to the UAH and RSS
lower troposphere satellite records the March 2022
temperature anomaly was higher than in theprevious month. According to AIRS the March 2022
average surface temperature was a little higher thanin March last year.

In addition, most temperature databases display
temporal instability for past data (see, e.g., p. 9). Anycomparison with such reference periods will
therefore be influenced by ongoing monthly
changes apparently of mainly administrative origin.
A fluctuating value is clearly not suited as reference
value. Simply comparing with the last previous 10
years is more useful as reference for modernchanges. Please see also additional reflections on
page 48-49. The different air temperature records
have been divided into three quality classes, QC1,QC2 and QC3, respectively, as described on page 9.
In many diagrams shown in the present newsletterthe thin line represents the monthly global averagevalue, and the thick line indicate a simple runningaverage, in most cases a simple moving 37-monthaverage, nearly corresponding to a three-year
average. The 37-month average is calculated fromvalues covering a range from 18 months before to
18 months after, with equal weight given to allindividual months.
The year 1979 has been chosen as starting point inmany diagrams, as this approximately corresponds

March 2022 surface air temperature

The Northern Hemisphere surface temperature
anomality pattern (p.4) was characterised byregional contrasts, largely controlled by thedominant jet stream configuration. Much of central
Russia and the Mediterranean region was cold,
compared to the average for the last 10 years. In
contrast, most of NW Europe and eastern Siberia
were relatively warm, as was southern China. Ocean
wise, much of the North Atlantic were relatively
warm south of 40oN, but relatively cold north of thislatitude. Much of the Pacific Ocean was relatively
warm. Over most of the Arctic Ocean surface airtemperatures were relatively high.
Near the Equator temperatures were mostly near or
below the 10-year average. Especially a region
between 0-20oS in the Pacific Ocean was relatively
cold. Parts of central Africa, southern India andsouthern Asia were relatively cold.
Southern Hemisphere temperatures were generally
near or below the average for the previous 10 years.
Much of South America, Africa and parts of southern
Australia had below average temperatures, while
relatively warm conditions characterised northern
Australia. East Antarctic was characterised byrelatively high temperatures, while the central parts
were relatively cold.

March 2022 global surface air temperature overview versus average March last 10 years

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March 2022 surface air temperature compared to the average of March over the last 10 years. Green-yellow-red colours indicate areas
with higher 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 V007
(https://airs.jpl.nasa.gov/), obtained from the GISS data portal (https://data.giss.nasa.gov/gistemp/maps/index_v4.html).

March 2022 global surface air temperature compared to March 2021

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March 2022 surface air temperature compared to March 2021. 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 V007
(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 March2022 (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
thick line 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 February 2022

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Global monthly average surface air temperature (thin line) since 1979 according to according to the Hadley Centre for Climate Prediction
and Research and the University of East Anglia's Climatic Research Unit (CRU), UK. The thick line is the simple running 37-month average.
Please note that HadCRUT5 is not yet updated beyond February 2022.

Temperature quality class 3: GISS and NCDC global surface air temperature, updated to March 2022

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.

8

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 37month average.

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,
after termination of the previous episode of global
cooling from about 1940. In addition, the year 1979
also represents the starting date for the satellitebased global temperature estimates (UAH and RSS).
For the three surface air temperature records(HadCRUT, NCDC and GISS), they begin much earlier(in 1850 and 1880, respectively), as can be inspectedon www.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.
Some changes from the recent past may be due tothe delayed addition of new station data or changeof station 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
next page).
In addition, the three surface records represent a
blend of sea surface data collected by moving ships
or by other means, plus data from land stations ofpartly unknown quality and unknown degree ofrepresentativeness for their region. Many of theland stations also has been moved geographicallyduring their period of operation, instrumentation
have been changed, and they are influenced bychanges in their near surroundings (vegetation,buildings, etc.). The surface network is inherently
heterogeneous (dense over continents but sparseover oceans) and probably contaminated byurbanization surrounding many measurement sites.
The satellite temperature records also have theirproblems, but these are generally of a more
technical nature and probably therefore better
correctable. In addition, the temperature samplingby satellites is more regular and complete on a

global basis than that represented by the surface
records. It is also important that the sensors on
satellites measure temperature directly by
microwave radiance (thereby unobstructed byclouds), 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 thethree surface 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 the past does not change, any record
undergoing continuing changes cannot describe thepast correctly all the time. Frequent and large
corrections in a database unavoidably signal a
fundamental uncertainty about what is likely torepresent the correct 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.

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 April 2022). This represents an about 49%
administrative temperature increase over thisperiod, meaning that half of the apparent global
temperature increases from January 1910 toJanuary 2000 (as reported by GISS) is due toadministrative changes of the original data since
May 2008.

Comparing global surface air temperature and lower troposphere satellite temperatures;
updated to February 2022

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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 normalisedby comparing to the average value of 30 years from January 1979 to December 2008.

Global air temperature linear trends updated to February 2022

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 theslope of 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 February 2022

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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. 3year) mean of the average of both 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 and 2 (UAH, RSS and HadCRUT) global monthly temperature estimates. As the baseperiod differs for the individual temperature estimates, they have all been normalised by comparing with the average valueof 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 three 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, 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 theaverage value of the initial 120 months (30 years) from January 1979 to December 2008. The heavy black line represents thesimple 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, theabove composite diagrams may nevertheless be ofsome interest.
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 muchremoved by the major adjustment of the RSS
satellite series in 2015 (see lower diagram on page
6).

The combined records (diagram above) suggest amodest global air temperature increase over the last
30 years, about 0.17oC per decade. It should be
noted that the apparent temperature increases
since about 2003 at least partly is the result of
ongoing administrative adjustments (page 9-10). At
the same time, none of the temperature records
considered here indicates any general temperature
decrease during 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 March 2022

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Sea surface temperature anomaly on 31 March 2022 (upper map) and 2021 (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
and 30oS.
A mixture of relatively warm and cold water
presently dominates much of the global ocean
surface, but with notable differences from month tomonth. All such ocean surface temperature changes
will be influencing global air temperatures in themonths to come. Just now a cold new La Niña
episode is playing out in the Pacific Ocean (see p.
24). Relatively warm surface water is mainly found
two bands centred around 30oN and 30oS,
respectively.

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 heatexchanges take place between the Pacific Ocean andthe atmosphere above, 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 theatmosphere-ocean system. In fact, global net
changes can be small and such heat exchanges may
mainly reflect redistribution of energy between
ocean and atmosphere. What matters is the overalltemperature development when seen over several
years.

16

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.
Base period: 1961-1990. The thick line is the simple running 37-month average. Insert: Argo global ocean temperature anomaly fromfloats, 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:
1901-2000. 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.

Ocean temperature in uppermost 100 m, updated to December 2021

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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 19552010.

Pacific 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 19552010.

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 19552010.

19

Indian 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 19552010.

Pacific Decadal Oscillation (PDO), updated to January 2022

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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 thethick line 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 importantbecause it shows that "normal" climate conditions
can vary over time periods comparable to the lengthof a human's lifetime.

The PDO illustrates how global temperatures aretied to sea surface temperatures in the Pacific
Ocean, the largest 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 sea
surface temperatures are high (positive phase PDO),
as from 1977 to 1998, global surface air temperature
increases.
A Fourier frequency analysis (not shown here)
shows the entire PDO record since 1854 to be
influenced by a 5.7-year cycle, and possibly also by alonger about 53-year long cycle.

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

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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.

22

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, 81-100.

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
to oceanic 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 55oS, temperatures have essentially been stable.
At most latitudes, 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 March 2022

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 definedwhen the threshold is met for a minimum of 5 consecutive over-lapping seasons. Anomalies are centred on 30-yr baseperiods updated every 5 years.

The subrecent 2015-16 El Niño episode is among thestrongest 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 March 2022

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 the simple running 37-month average, nearly corresponding to a running 3-year average. Reference period 19812010.

The overall warming since 1980 has dominantly been anorthern hemisphere phenomenon, and mainly playedout as a marked step change between 1994 and 1999.
However, this rather rapid temperature change isinfluenced by the Mt. Pinatubo eruption 1992-93 and thesubsequent 1997 El Niño episode. The diagram also
shows the temperature 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 tohigher latitudes in both hemispheres with some delay.

Arctic and Antarctic lower troposphere temperature, updated to March 2022

27

Global monthly average lower troposphere temperature since 1979 for the North Pole and South Pole regions, based onsatellite 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 figure 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 remoteregions. A small overall 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

28

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 thethicker line shows the running 37-month (c. 3 year) average.

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 thethicker 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 thethicker 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
1920-2018 are higher than later (diagram above).
The period from about 1930 saw the establishmentof many 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
have been 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.

Temperature over land versus over oceans, updated to March 2022

31

Global monthly average lower troposphere temperature since 1979 measured over land and oceans, respectively,
according to University of Alabama at Huntsville, USA. Thick lines are the simple running 37-month average, nearlycorresponding to a running 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 landuse.

Troposphere and stratosphere temperatures from satellites, updated to March 2022

32

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
3-year average. Reference period 1991-2020.

Arctic and Antarctic sea ice, updated to March 2022

33
Sea ice extent 2 April 2022. 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
shown in the diagrams above. Map source: National Snow and Ice Data Center (NSIDC).

Diagrams showing Arctic sea ice extent and concentration 31 March 2021 (left) and 2022 (right), according to the Japan Aerospace
Exploration Agency (JAXA).

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

34

Diagram showing daily Arctic sea ice extent since June 2002, to 31 March 2022, by courtesy of Japan Aerospace Exploration Agency (JAXA).

35

Diagrams showing Arctic sea ice extent and thickness 31 March 2021 (left) and 2022 (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/36

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 ofDecember 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,
and tidal changes introduced by the Moon.
Changes in ocean basin volume by tectonic(geological) forces.
Changes in ocean water density caused by variations
in currents, 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
sea-level, such as storage of ground water, storage in lakes andrivers, evaporation, etc.

37

Mechanism 1 is controlling sea-level at many sites on a timescale from months to several years. As an example, many
coastal stations show a pronounced annual variation reflecting
seasonal changes in air pressures and wind speed. Longer-termclimatic changes playing out over decades or centuries will also
affect measurements 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
and tsunamis - typically operates over long (geological) timescales and is not significant on human time scales. It may relate
to variations 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 are presently rising, causing a slow net transfer ofwater from these basins into the adjoining oceans. Slowchanges of excessively big glaciers (ice sheets) and movementsin the mantle will affect the gravity field and thereby the vertical
position of the ocean surface. Any increase of the total watermass as well as sediment deposition into oceans increase theload on their bottom, generating sinking by viscoelastic flow inthe mantle below. The mantle flow is directed towards thesurrounding land areas, which will rise, thereby partly
compensating for the initial sea level increase induced by theincreased 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
than salinity-driven changes. Seawater is characterised by arelatively small coefficient of expansion, but the effect should
however not be overlooked, especially when interpretingsatellite altimetry data. Temperature-driven expansion of acolumn of seawater will not affect the total mass of water within
the column considered and will therefore not affect thepotential at the top of the water column. 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 ofwater approaches zero, so no measurable temperature-driven
expansion will take place here (Mörner 2015). Mechanism 3 isfor that reason not important for coastal 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 – hasno influence 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, 124129.

Global sea level from satellite altimetry, updated to August 2021

38

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., bysatellite observations.

data, and 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 andimprove the interpretation of satellite data.

In remote sensing using satellite observations, groundtruth data refers to information collected on location.
Ground truth allows the satellite data to be related to realfeatures observed on the planet surface. The collection ofground truth data enables calibration of remote-sensing

For satellite observations on sea level ground true data
are provided by the classical tide gauges (example
diagram on next page), that directly measures the local
sea level many places distributed along the coastlines on
the surface of the planet.

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

39

Holgate-9 monthly tide gauge data from PSMSL Data Explorer. Holgate (2007) suggested the nine stations listed in the diagram to capture
the variability found in a larger number of stations over the last half century studied previously. For that reason, average values of theHolgate-9 group 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 tothe southern hemisphere and should therefore not be overinterpreted. The blue dots are the individual average monthly observations, andthe 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 March 2022

Northern hemisphere snow cover (white) and sea ice (yellow) 31 March 2021 (left) and 2022 (right). Map source: National
Ice Center (NIC).

40

Northern hemisphere weekly snow cover since January 2000 according to Rutgers University Global Snow Laboratory. The thin blue lineis the weekly data, and the thick blue line is the running 53-week average (approximately 1 year). The horizontal red line is the 19722021 average.

Northern hemisphere weekly snow cover since January 1972 according to Rutgers University Global Snow Laboratory. The thin blue lineis the weekly data, and the thick blue line is the running 53-week average (approximately 1 year). The horizontal red line is the 19722021 average.

41

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

Atmospheric specific humidity, updated to March 2022

42

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 thesevariations 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 45 km altitude. At higher levels in the Troposphere (about
9 km), 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 March 2022

43

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- and3.8-years’ duration.

The relation between annual change of atmospheric CO2 and La Niña and El Niño episodes, updated
to March 2022

44

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 alldetails. Many processes, natural as well as
anthropogenic, controls the amount of atmospheric CO2,
but oceanographic processes are clearly particularly
important (see also diagram on next page).
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 43).
The coronavirus pandemic since January 2020 resulted ina marked reduction in the global consumption of fossil
fuels. It is therefore enlightening to follow the effect of
this reduction 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 CO 2
to appear in diagrams showing the amount of
atmospheric CO2 (see, e.g., diagrams on p. 43-45).

The phase relation between atmospheric CO2 and global temperature, updated to February 2022

12-month change of global atmospheric CO2 concentration (Mauna Loa; green), global sea surface temperature (HadSST4; blue) andglobal surface air temperature (HadCRUT5; red dotted). All graphs are showing monthly values of DIFF12, the difference between the
average of the last 12 month and the average for the previous 12 months for each data series.

45

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 are lagging 9.5–10 months
behind changes in global air surface temperature, and11–12 months behind changes in global sea surface
temperature.

References:
Humlum, O., Stordahl, K. and Solheim, J-E. 2012. The phase relation between atmospheric carbon dioxide and global
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 March 2022

46

47

48

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 (icecores, 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 influenceon the calculated global air temperature. It is therefore
relevant to compare different air temperature records
with measurements 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
the potential influence of CO2 on short time scales suchas just a 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 atheme for discussions.
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 temperatureand CO2-increase, IPCC was established in 1988. For
obtaining public and political support for the CO2hyphotesis the 10-year warming period leading up to1988 most likely was considered important. Had theglobal temperature instead been decreasing at that time,politic support for the hypothesis probably would havebeen difficult to obtain in 1988.
Based on the previous 10 years of concurrent
temperature- and CO2-increase, many climate

49

scientists in 1988 presumably felt that theirunderstanding of climate dynamics was enough toconclude about the importance of CO2 for affecting
observed global temperatures.
Thus, it may with confidence be concluded that 10 years
in 1988 was considered a period long enough todemonstrate the effect of increasing atmospheric CO2 on
global temperatures. The 10-year period is also basis forthe temperature anomality diagrams shown on page 4.

Latest 20-year QC1 global monthly air temperature changes, updated to March 2022

50
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 that the 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)analysis of 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
in time. 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
Analyses of 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 March 2022

51

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
asymmetrical temperature 'bump' around 1998 is influenced by the oceanographic El Niño phenomenon in 1998, as is thecase also for 2015-16. Temperatures in year 2019-20 was influenced by a moderate El Niño.

.

Monthly sunspot activity (SIDC) and average neutron counts (Oulu, Finland), updated to March
2022

52

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
and specific humidity, updated to March 2022

53

Visual association since 1958 between (from bottom to top) Sunspot Number, Oceanic Niño Index (ONI) and annual change
rate of 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 monthrunning mean of ERSSTv4 SST anomalies in the Niño 3.4 region (5 oN-5oS, 120o-170oW)]. For historical purposes cold andwarm episodes are defined when the threshold is met for a minimum of 5 consecutive over-lapping seasons. Anomalies arecentred on 30-yr base periods updated every 5 years. Thin vertical stippled lines indicate the visually estimated timing ofsunspot minima. The typically sequence following a sunspot minimum appears to be a warm El Niño episode followed by acold La Niña episode. Effects on change rates of atmospheric CO2 and atmospheric specific humidity are visually apparent,with ONI variations being followed by changes in first 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

54

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
Year 1909-1925: Changing sea ice conditions around Spitsbergen and the opening of
the coalmine at Svea

Steamship Neptun in packice at Spitsbergen, summer 1909. Picture source: Anders Beer Wilse, Norsk
Folkemuseum.

DIFFICULT SEA ICE CONDITIONS 1909-1917
55

In connection with a detailed description of theSwedish mining and exploration activities (coal) in
Spitsbergen (Svalbard), Hoel (1966) provides
information on summer sailing conditions in themain fjords and along the west coast of Spitsbergen:••

July 7, 1909 : The Billefjord is blocked by ice.
First ship makes it to Pyramiden in
innermost Billefjord July 12.
September 1910: Geological expedition leadby Ernest Mansfield (The Northern
Exploration Co., Ltd., London) findKongsfjorden blocked by ice, and insteadseeksemergency
harbourinBraganzavågen, Van Milenfjorden. Here sea
ice makes it impossible to leave the fjord
and begin the return journey before early
October.

•••

August 11, 1912 : Braganzavågen in Van
Mijenfjorden is closed by ice. Also Bellsundis blocked by ice.
July 1915: An expedition lead by Birger
Johnsson finds the west coast of
Spitsbergen blocked by sea ice. Westerly
winds keep the ice in a state of compression.
The winter sea ice in the fjords is beginningto break up, but the ice along the west coast
fills the mouth of the fjords and keeps thewinter ice in place. Several vessels have toreturn to Tromsø in northern Norway
without reaching the coast of Spitsbergen.
The Birger Johnsson expedition for several
weeks’ attempts landing on Spitsbergen but
was forced to give up on August 17.
July-August 1917: Difficult sea ice conditions
in Van Milenfjorden made it impossible forthe steamship D/S Amsterdam to reachBraganzavågen, innermost Van Mijenfjord,
before early August

Steamship D/S Amsterdam in Braganzavågen, innermost Van Mijenfjorden, early August 1917 (right).
Photo by A. Reuterskiöld.

OPENING OF THE SVEA COAL MINE 1917
56

The First World War (The Great War; 1914-1918)
resulted in a global lack of coal for energy
production, and coal prizes increased rapidly. The
Swedishminingcompany
Aktiebolaget
Spetsbergens Svenska Kolfält was founded
September 4, 1916, with the purpose of opening acoal mine at Braganzavågen, innermost Van
Mijenfjorden, southern central Spitsbergen, wherepromising coal seams had been found. The company
rapidly decided to send an expedition with about
150 persons to Spitsbergen, to establish a coalmine
at this chosen site. The planned mine was given thename Sveagruvan. Coal production was planned to
begin during the winter 1917-1918, and a total
production of about 25,000 tonnes coal was
estimated for the first year of operation.
The expedition left Stockholm in Sweden early July
1917 on the steamship D/S Amsterdam, but difficult
sea ice conditions in Van Mijenfjorden made it
impossible to reach Braganzavågen before earlyAugust (see photo above). With little doubt thesummer of 1917 must have been cold compared toearly 21st century conditions, as is shown by the
many floes of sea ice. Today, the last floes of thewinter sea ice usually melts long before August.
Also, the fresh snow seen in the picture isnoteworthy. Snow must have been falling at low
altitudes shortly before the photo was taken. The
cold character of the year 1917 is clearly shown by
the official Svalbard temperature record since 1912,
which shows all seasons of the year 1917 to be coldin comparison with previous and following years.
Under direction of Director Granholm the firstbuildings in the mining settlement Svea wereconstructed, and parts of the coming harbour for
shipment of coal were established. Geologicalsurveying was carried out in the area around themine. About 50 persons stayed over winter alongwith Director Granholm, and 4,000 tonnes coal was
produced, somewhat below the initial estimate of
25,000 tonnes (Hoel 1966). A layer of clay stone justabove the coal layer often collapsed when the coal
was removed, and it proved difficult to avoid mixing
of clay stone and coal.

•

IMPROVING SEA ICE CONDITIONS 1919-1925
Hoel 1966 provides the following information on
summer sailing conditions in the main fjords andalong the west coast of Spitsbergen 1919-1925,
illustrating the effects of the rapid warming playingout after 1917 in this part of the Arctic.:
•

•
•

•

1919: The Swedish coal mine Sveagruvan in
innermost Van Mijenfjorden is able to shipno less than 20,000 tonnes of coal, partly
because of unusual fine sea ice conditions
during the summer of 1919.
1920: The harbour at Sveagruvan is open forshipping in 98 days.
1921: The harbour at Sveagruvan is open forshipping in only 85 days because of difficult
sea ice conditions.

••

1922: The harbour at Sveagruvan is open forshipping in 92 days. Sea ice conditions is
described as ‘normal’. Report on Arctic
Warming in the journal Monthly Weather
Review, October 10, 1922 (Ifft, 1922).
1923: The harbour at Sveagruvan is open forshipping in 97 days. Sea ice conditions is
described as ‘normal’.
1924: The harbour at Sveagruvan is open forshipping from 9 July to 21 October (105days). Sea ice conditions is described as‘normal’.
1925: The harbour at Sveagruvan is open forshipping from 3 July to 6 October (96 days).
This year the Van Mijenfjord is still free of
ice when the last ship leaves October 6.

References:
57

Hoel, A. 1966. Svalbard. Svalbards historie 1596-1965. Sverre Kildahls Boktrykkeri, Oslo, 1024 pp.
Ifft, G.N. 1922: The Changing Arctic. Monthly Weather Review, November 1922, p.589.
https://journals.ametsoc.org/view/journals/mwre/50/11/15200493_1922_50_589a_tca_2_0_co_2.xml?msclkid=6590dca8c00111eca743c6ac8c9eb183*****
All diagrams in this report, along with any supplementary information, including links to data sources
and previous 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
22 April 2022.
Planned publication day next newsletter: 28 May 2022.