Contents:

1

Page 2: June 2018 global surface air temperature overview
Page 3: Comments to the June 2018 global surface air temperature overview
Page 4: Temperature quality class 1: Lower troposphere temperature from satellites
Page 5: Temperature quality class 2: HadCRUT global surface air temperature
Page 6: Temperature quality class 3: GISS and NCDC global surface air temperature
Page 9: Comparing global surface air temperature and satellite-based temperatures
Page 10: Global air temperature linear trends
Page 11: Global temperatures: All in one, Quality Class 1, 2 and 3
Page 13: Global sea surface temperature
Page 16: Ocean temperature in uppermost 100 m
Page 18: North Atlantic heat content uppermost 700 m
Page 19: North Atlantic temperatures 0-800 m depth along 59N, 30-0W
Page 20: Global ocean temperature 0-1900 m depth summary
Page 21: Global ocean net temperature change since 2004 at different depths
Page 22: La Niña and El Niño episodes
Page 23: Troposphere and stratosphere temperatures from satellites
Page 24: Zonal lower troposphere temperatures from satellites
Page 25: Arctic and Antarctic lower troposphere temperatures from satellites
Page 26: Temperature over land versus over oceans
Page 27: Arctic and Antarctic surface air temperatures
Page 28: Arctic and Antarctic sea ice
Page 34: Sea level in general
Page 35: Global sea level from satellite altimetry
Page 36: Global sea level from tide gauges
Page 37: Northern Hemisphere weekly and seasonal snow cover
Page 39: Atmospheric specific humidity
Page 40: Atmospheric CO2
Page 41: The phase relation between atmospheric CO2 and global temperature
Page 42: Global air temperature and atmospheric CO2
Page 46: Latest 20-year QC1 global monthly air temperature change
Page 47: Sunspot activity and QC1 average satellite global air temperature
Page 48: Climate and history: 1789: Storofsen – the largest historical flood in Norway

June 2018 global surface air temperature overview

2

June 2018 surface air temperature compared to the average of 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: Goddard Institute forSpace Studies (GISS) using ERSST_v4 ocean surface temperatures.

Comments to the June 2018 global surface air temperature overview
General: This newsletter contains graphs showing a
selection of key meteorological variables for the
past month. All temperatures are given in degrees
Celsius.
In the above maps showing the geographical patternof surface air temperatures, the last previous 10
years are used as reference period.
The rationale for comparing with this recent period
instead of the official WMO ‘normal’ period 19611990, is that the latter period is affected by the cold
period 1945-1980. Most comparisons with this time
period will automatically appear as warm, and it will
be difficult to decide if modern surface airtemperatures are increasing or decreasing.
Comparing instead with the last previous 10 years
overcomes this problem and displays the modern
dynamics of ongoing change. This decadal approachalso corresponds well to the typical memory horizon
for many people.
3

In addition, the GISS temperature data used for
preparing the above diagrams display distinct
temporal instability for data before the turn of the
century (see p. 7). Any comparison with the WMO‘normal’ period 1961-1990 is therefore influenced
by ongoing monthly mainly administrative changes,
and not suited as reference. Comparing with the last
previous 10 years is more useful.
The different air temperature records have been
divided into three quality classes, QC1, QC2 andQC3, respectively, as described on page 7.
In many diagrams shown in this newsletter the thinline represents the monthly global average value,
and the thick line indicate a simple running average,
in most cases a simple moving 37-month average,
nearly corresponding to a three-year average. The
37-month average is calculated from values
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 roughly corresponds to boththe beginning of satellite observations and the onsetof the late 20th century warming period. However,
several of the data series have a much longer record
length, which may be inspected in greater detail on
www.climate4you.com.

June 2018 global surface air temperatures
General: For June 2018 GISS supplied 16065
interpolated surface air data points; all publishedvalues are used to produce the diagrams shown on
page 2. According to the GISS data, the average
global June temperature anomaly was somewhat
lower than in the previous months. The overall
decrease of global surface air temperatures sincethe 2016-17 El Niño global temperature peakcontinues.
The Northern Hemisphere was characterised byrather marked regional differences (see p. 2). Parts
of northern Europe and especially western Siberia
were warm compared to the previous 10 years,
while northern North America, Greenland, western
Russia and eastern Siberia were cold. Most of the
Arctic had temperatures below the 10-year average,
although the GISS surface air temperatures north of
80o N still appears to have an issue withinterpolation in this region.
Near the Equator temperatures were near or below
the 10-year average. In the eastern Pacific Ocean,temperatures are still displaying the effects of a
weak La Niña episode.
The Southern Hemisphere temperatures were nearor below the average for the previous 10 years.
Especially South America was relatively cold. In
contrast, most of the Antarctic was warmer than the
average. South of 80oS there the GISS anomaly
pattern still is influenced by an interpolation
artefact.

Temperature quality class 1: Lower troposphere temperature from satellites, updated to June 20184

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.

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 May 20185

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.

Temperature quality class 3: GISS and NCDC global surface air temperature, updated to June 2018

6

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.

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.

A note on data record stability and -quality:
All temperature diagrams shown above have 1979
as starting year. This roughly marks the beginning of
the recent period of global warming, aftertermination of the previous period 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
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.

7

For all three surface air temperature records, but
especially NCDC and GISS, administrative changes toanomaly values are quite often introduced, even forobservations many years back in time. Somechanges may be due to the delayed addition of new
station data, while others probably have their origin
in a change of technique to calculate average values.
It is clearly impossible to evaluate the validity ofsuch administrative changes for the outside user ofthese records; it is only possible to note that suchchanges appear very often (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 have also moved geographically during
their existence, and their instrumentation changed,
and they are influenced by changes in theirsurroundings (vegetation, buildings, etc.).
The satellite temperature records also have theirproblems, but these are generally of a more
technical nature and therefore correctable. In
addition, the temperature sampling by satellites ismore regular and complete on a global basis thanthat represented by the surface records. Also

important is that the sensors on satellites measuretemperature directly by emitted radiation, whilemost surface temperature measurements areindirect, using electronic resistance.
Everybody interested in climate science should
gratefully acknowledge the 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 operates with three quality classes (13) 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.8),
and therefore essentially are unstable temperaturerecords, the changes introduced to HadCRUT are
fewer and smaller. For obvious reasons, as the past
does not change, any record undergoing continuingchanges cannot describe the past correctly all thetime.
You can find more on the issue of lack of temporalstability on www.climate4you.com (go to: Global
Temperature, followed by Temporal Stability).

8

Diagram showing the adjustment made since May 2008 by the Goddard Institute for Space Studies (GISS), USA,in anomaly values for the months January 1910 and January 2000.

Note: The administrative upsurge of the temperature increase from January 1915 to January 2000 has grownfrom 0.45 (reported May 2008) to 0.69oC (reported July 2018). This represents an about 53% administrative
temperature increase over this period, meaning that more than half of the apparent (as reported by GISS) global
temperature increase from January 1910 to January 2000 is due to administrative changes of the original data
since May 2008.

Comparing global surface air temperature and lower troposphere satellite temperatures;
updated to May 20189

Plot showing the average of monthly global surface air temperature estimates (HadCRUT4, 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.

NOTE: Since about 2003, the average global surface air temperature has been drifting away in positive direction
from the average satellite temperature, meaning that the surface records show warming in relation to thetroposphere records. The reason(s) for this is not entirely clear but can presumably at least partly be explainedby the recurrent administrative adjustments made to the surface records (see p. 7-8).

Global air temperature linear trends updated to May 2018

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).

10

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
HadCRUT4).

All in one, Quality Class 1, 2 and 3; updated to May 2018

11

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 HadCRUT4) 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, HadCRUT4, 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.

12

Please see notes on page 7 relating to the above three quality classes.

It should be kept in mind that satellite- and surfacebased temperature estimates are derived fromdifferent types of measurements, and thatcomparing them directly as done in the diagram
above therefore may be somewhat problematical.
However, as both types of estimate often are
discussed together, the above diagram maynevertheless be of some interest. In fact, thedifferent types of temperature estimates appear toagree as to the overall temperature variations on a
2-3-year scale, although on a shorter time scale
there are often considerable differences between
the individual records. However, since about 2003
the surface records are slowly drifting towards
higher temperatures than the combined satellite
record (see p. 9).
The average of all five global temperature estimatespresently shows an overall stagnation, at least since

2002-2003. There has been no significant increase inglobal air temperature since 1998, which however
was affected by the oceanographic El Niño event.
Also, the recent (2015-16) El Niño event may well
represent a relatively short-lived spike on a longer
development. The coming years will show if this is
the case or not. Neither has there been atemperature decrease since about 2002-2003.
The present temperature stagnation does not
exclude the possibility that global temperatures will
begin to increase again later. On the other hand, italso remains a possibility that Earth just now is
passing an overall temperature peak, and that global
temperatures will begin to decrease during thecoming years. Again, time will show which of these
possibilities is correct.

Global sea surface temperature, updated to June 201813

Sea surface temperature anomaly on 30 June 2018. Map source: Plymouth State Weather Center. Reference period: 19771991.

Because of the large surface areas near Equator, thetemperature of the surface water in these regions is
especially important for the global atmospherictemperature (p. 4-6).
Relatively cold water now dominates much of theoceans near the Equator. Also, part of the North
Atlantic is cooling. All this will be influencing global
air temperatures now and in the months to come.
However, the significance of any short-term coolingor warming reflected in air temperatures should not
be overstated. Whenever Earth experiences cold La

Niña or warm El Niño episodes (Pacific Ocean) major
heat exchanges takes place between the Pacific
Ocean and the atmosphere above, eventuallyshowing up in estimates of the global air
temperature.
However, this does not reflect similar changes in the
total heat content of the atmosphere-ocean system.
In fact, global net changes can be small and suchheat exchanges may mainly reflect redistribution ofenergy between ocean and atmosphere. Whatmatters is the overall temperature development
when seen over several years.

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.

14

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.

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.

15
June 18, 2015: NCDC has introduced several rather large administrative changes to their sea surface temperature record. The overall
result is to produce a record giving the impression of a continuous temperature increase, also in the 21st century. As the oceans coverabout 71% of the entire surface of planet Earth, the effect of this administrative change is clearly seen in the NCDC record for global
surface air temperature (p. 6).

Ocean temperature in uppermost 100 m, updated to March 201816

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 indicate 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 indicate 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.

17

Indian Ocean vertical average temperature 0-100 m depth since 1955. The thin line indicate 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.

North Atlantic heat content uppermost 700 m, updated to December 201718

Global monthly heat content anomaly (GJ/m2) 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 April 2018

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.

19

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 April 201820

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.

Global ocean net temperature change since 2004 at different depths, updated to April 201821

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, updated to May 201822

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.

Troposphere and stratosphere temperatures from satellites, updated to June 201823

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.

Zonal lower troposphere temperatures from satellites, updated to June 201824

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.

Arctic and Antarctic lower troposphere temperature, updated to June 201825

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 1981-2010.

Temperature over land versus over oceans, updated to June 201826

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 1981-2010.

Arctic and Antarctic surface air temperature, updated to May 201827

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 (70-90oN) 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.

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.

28

Diagram showing area weighted Antarctic (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.

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.

29
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 larger 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. The period since 2005 is
warm, about as warm as the period 1930-1940.
As the HadCRUT4 data series has improved highlatitude coverage data coverage (compared to the
HadCRUT3 series), the individual 5ox5o grid cells has
been weighted according to their surface area. This

contrasts with Gillet et al. 2008 which calculated asimple average, with no consideration of the effectof latitude on the surface area represented byindividual 5ox5o grid cells.

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.

Arctic and Antarctic sea ice, updated to June 201830

Sea ice extent 29 June 2018. 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 30 June 2017 (left) and 2018 (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).

31

Diagram showing daily Arctic sea ice extent since June 2002, to 30 June 2018, by courtesy of Japan Aerospace Exploration Agency (JAXA).

32

Diagrams showing Arctic sea ice extent and thickness 29 June 2017 (left) and 2018 (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.

33

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.

34

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 sea-floor 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 very big glaciers (ice sheets) and movements in 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 lateraldisplacement of water, but only lift the ocean surface locally.
Near the coast, where people are living, the depth of water
approaches zero, so no temperature-driven expansion will takeplace here (Mörner 2015). Mechanism 3 is for that reason notimportant 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: Mechanism 1 and 4 are the most important forunderstanding 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 January 201835

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. Data from the TOPEX/Poseidon mission have been used before 2002, and data from the Jason-1 mission (satellite
launched December 2001) after 2002.

Global sea level from tide-gauges, updated to December 201736

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. 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.

Reference:
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

Northern Hemisphere weekly and seasonal snow cover, updated to June 2018

Northern hemisphere snow cover (white) and sea ice (yellow) 30 June 2017 (left) and 2018 (right). Map source: National Ice
Center (NIC).

37

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 19722017 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 19722017 average.

38

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

Atmospheric specific humidity, updated to June 201839

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).

Atmospheric CO2, updated to June 201840

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.

The phase relation between atmospheric CO2 and global temperature, updated to May 2018

12-month change of global atmospheric CO2 concentration (Mauna Loa; green), global sea surface temperature (HadSST3; blue) andglobal surface air temperature (HadCRUT4; 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.

41

References:
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 June 2018

42

43

44

Diagrams showing UAH, RSS, HadCRUT4, GISS, and NCDC 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 the diagrams. Reconstructions of past atmospheric CO2
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. The dotted grey line indicates the approximate linear temperature trend, and the boxes in thelower part of the diagram indicate the relation between atmospheric CO 2 and global surface air temperature, negative or
positive. Please note that the HadCRUT diagram (p.42) is not yet updated beyond May 2018.

Most climate models are programmed to give thegreenhouse gas carbon dioxide CO2 important
influence on global temperature. It is therefore
relevant to compare different temperature records
with measurements of atmospheric CO2, as shownin the diagrams above.
Any comparison, however, should not be made on amonthly or annual basis, but for a longer time, as
other effects (oceanographic, etc.) may welloverride the potential influence of CO2 on short timescales such as just a few years. It is of cause equally
inappropriate to present new meteorological record
values, whether daily, monthly or annual, as support
for the hypothesis ascribing high importance of
atmospheric CO2 for global temperatures. Any suchmeteorological record value may well be the resultof other phenomena.
What exactly defines the critical length of a relevant
period length to consider for evaluating the alleged
importance of CO2 remains elusive and represents atheme for discussion. However, the length of thecritical period must be inversely proportional to thetemperature sensitivity of CO2, including feedback
effects. If the net temperature effect of atmospheric
CO2 is strong, the critical period will be short, andvice versa.
However, past climate research history provides
some clues as to what has traditionally been
considered the relevant length of period over whichto compare temperature and atmospheric CO2.
After about 10 years of concurrent global
temperature- and CO2-increase, IPCC wasestablished in 1988. For obtaining public andpolitical support for the CO2-hyphotesis the 10-year
warming period leading up to 1988 most likely was
important. Had the global temperature instead been
decreasing, politic support for the hypothesis would
have been difficult to obtain.
Based on the previous 10 years of concurrent
temperature- and CO2-increase, many climate
scientists in 1988 presumably felt that their45

understanding of climate dynamics was sufficient toconclude about the importance of CO2 for global
temperature changes. From this it may safely beconcluded that 10 years was considered a period
long enough to demonstrate the effect of increasing
atmospheric CO2 on global temperatures.
Adopting this approach as to critical time length (atleast 10 years), the varying relation (positive or
negative) between global temperature andatmospheric CO2 has been indicated in the lower
panels of the diagrams above.

Latest 20-year QC1 global monthly air temperature changes, updated to June 201846

Last 20 years’ global monthly average air temperature according to Quality Class 1 (UAH and RSS; see p.10) 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 ongoing climate debate the question if the
global surface air temperature still increases, or if
the temperature has levelled out during the last 1518 years, is often put forward.
The diagram above may be useful in this context anddemonstrates the differences between two often
used statistical approaches to determine recent
temperature trends. Please also note that such fits
only attempt to describe the past, and usually have
limited predictive power. In addition, before using
any linear trend (or other) analysis of time series aproper statistical model should be chosen, based on
statistical justification.

For 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 havetrends for which a straight line is not a good
approximation, as can clearly be seen from severalof the diagrams in the present report.
For an excellent 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.

Sunspot activity and QC1 average satellite global air temperature, updated to June 201847

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.

Climate and history; one example among many

1789: Storofsen – the largest historical flood in Norway

Memorial for the 1789 Storofsen flood in Gudbrandsdalen, eastern South Norway (left). Map showing
southern Norway, with the worst affected valleys indicated (right).
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Presumably still under influence of the 1784-1785
Laki volcanic eruption in Iceland, and the Little Ice
Age in general, the winter 1788-1789 became very
cold in Norway. Only little snow fell in the earlystages of the winter and freezing penetrated deep
into the ground. Later in the winter snow fell in hugequantities, and it now protected the frozen groundagainst thawing in the spring and early summer, as
most incoming solar energy initially was consumed
by melting snow. Furthermore, from a temperaturepoint of view, spring arrived late in 1789. At midsummer, in July, frozen ground presumably stillprevailed in many places with high ground insouthern Norway.
Heavy and persistent rain began in southern Norway
on July 20, 1789, especially affecting areas east of
the water divide. The rain continued throughout July21, 22 and 23, before the weather finally cleared on
July 24. It has been estimated that more than 300

mm w.e. fell during this period, a huge precipitationin this part of Norway.
The still frozen ground inhibited rapid percolation ofwater at many places, and on sloping groundinstable conditions arose on top of the still frozen
ground in thawed and now waterlogged sediments.
At other places, solid bedrock below loose
sediments may have acted in the same manner. The
result was a high number of mudflows andlandslides in eastern Norway, especially in areas
surrounding the valley Gudbrandsdalen. In addition,
the huge amount of water flooded the valley bottom
and the fertile river plain along many of the mainrivers in eastern South Norway (see map above). The
valley Gudbrandsdalen was described as one large
lake, and the water level in Mjøsa, the largest lake in
Norway, increased to about 10 m above normal.

Many houses and farms were destroyed by theflooding and by mud and stones from landslides andmudflows. It has been estimated by Dørum (2008)
that more than 950 farms were demolished. In

addition, valuable farming fields were covered bythick layers of fluvial sand and gravel. Storofsen was
indeed a national disaster for Norway.

REFERENCES
Dørum, E. 2008. Storofsen i 1789. Bøgda vår; Et dystert 200-årsminne. Oppdal historielag 1989, 5-13.

*****

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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)
July 20, 2018.