Contents:

1

Page 2: April 2018 global surface air temperature overview
Page 3: Comments to the April 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: 1975: Advancing glaciers in Zillertaler Alpen, Austria

April 2018 global surface air temperature overview

2

April 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 April 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 geographicalpattern of 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 thecold period 1945-1980. Most comparisons with thistime period will automatically appear as warm, andit 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
approach also corresponds well to the typicalmemory horizon for many people.
3

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 toboth the beginning of satellite observations and theonset 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.

April 2018 global surface air temperatures
General: For April 2018 GISS supplied 15248
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 monthly temperature anomaly was near the
average for this month during the previous 10
years.

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 thelast previous 10 years is more useful.

The Northern Hemisphere was characterised bymarked regional differences (see p. 2). EspeciallyNorth America and western Russia were relatively
cold, while Europe and eastern Siberia had
temperatures above average. Most of the Arctic
had temperatures below average, although the
GISS surface air temperatures north of 80o N still
appears to be affected by interpolation artefacts.

The different air temperature records have been
divided into three quality classes, QC1, QC2 andQC3, respectively, as described on page 7.

Near the Equator temperatures were, on average,
below the 10-year average, and especially over theoceans. In the eastern Pacific Ocean, temperatures
are displaying the effects of a La Niña episode.

In many diagrams shown in this newsletter the thinline represents the monthly global average value,
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

The Southern Hemisphere temperatures were nearor below the average for the previous 10 years.
There were few regions showing strong anomalies
compared to the average of the previous 10 years.
In the Antarctic, however, most of the continent
was clearly warmer than the average.

Temperature quality class 1: Lower troposphere temperature from satellites, updated to April 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 March 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 April 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 beginningof 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 muchearlier (in 1850 and 1880, respectively), as can beinspected on www.climate4you.com.

7

For all three surface air temperature records, but
especially NCDC and GISS, administrative changes
to anomaly values are quite often introduced, evenfor observations 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 averagevalues. It is clearly impossible to evaluate thevalidity of such administrative changes for theoutside user of these records; it is only possible tonote that such changes appear very often (seexample 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 geographicallyduring their existence, and their instrumentation
changed, and they are influenced by changes intheir surroundings (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 May 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 March 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 the troposphere records. The reason(s) for this is not entirely clear but can presumably at least partly be
explained by the recurrent administrative adjustments made to the surface records (see p. 7-8).

Global air temperature linear trends updated to March 2018

Diagram showing the latest 5, 10, 20 and 30-yr linear annual global temperature trend, calculated as the slope of thelinear 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 March 2018

11

Superimposed plot of Quality Class 1 (UAH and RSS) global monthly temperature estimates. As the base period differs forthe individual temperature estimates, they have all been normalised by comparing with the average value of the initial 120
months (30 years) from January 1979 to December 2008. The heavy black line represents the simple running 37 month (c. 3year) mean of the average of 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 rightcorner 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 the average value of the initial 120 months (30 years) from January 1979 to December 2008. The heavy black line
represents the simple running 37 month (c. 3 year) 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 satellite records (seep. 9).
The average of all five global temperature
estimates presently shows an overall stagnation, atleast since 2002-2003. There has been no
significant increase in global air temperature since1998, which however was affected by theoceanographic 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
the coming years. Again, time will show which ofthese possibilities is correct.

Global sea surface temperature, updated to April 201813

Sea surface temperature anomaly on 29 April 2018. Map source: National Centers for Environmental Prediction (NOAA).

Because of the large surface areas near Equator,
the temperature of the surface water in theseregions is especially important for the global
atmospheric temperature (p. 4-6).

major heat exchanges takes place between the
Pacific Ocean and the atmosphere above,
eventually showing up in estimates of the global air
temperature.

Relatively cold water is now beginning to dominatemuch of the oceans near the Equator and is
influencing global air temperatures now and in themonths to come.

However, this does not reflect similar changes inthe total heat content of the atmosphere-oceansystem. In fact, global net changes can be small andsuch heat exchanges may mainly reflect
redistribution of energy between ocean andatmosphere. What matters is the overalltemperature development when seen over several
years.

The significance of any short-term cooling or
warming reflected in air temperatures should not
be overstated. Whenever Earth experiences cold La
Niña or warm El Niño episodes (Pacific Ocean)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). Baseperiod 1955-2010.

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). Baseperiod 1955-2010.

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 December 2017

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 December 201720

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 December 201721

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 20042008 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 April 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 April 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 April 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 April 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 April 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 March 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. Because of the relatively small number of Arctic stations
before 1930, month-to-month variations in the early part of the temperature record are larger than later. The period fromabout 1930 saw the establishment of 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.

29

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 April 201830

Sea ice extent 28 April 2018. The median limit of sea ice (orange line) is defined as 15% sea ice cover, according to the average ofsatellite observations 1981-2010 (both years inclusive). 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 28 April 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 28 April 2018, by courtesy of Japan Aerospace Exploration Agency (JAXA).

32

Diagrams showing Arctic sea ice extent and thickness 29 April 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 causedby recurrent 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 theslowly changing configuration of land and oceans. Anothereffect may be the slow rise of basins due to isostatic offloading
by deglaciation after an ice age. The floor of the Baltic Sea andthe Hudson Bay are presently rising, causing a slow net

transfer of water from these basins into the adjoining oceans.
Slow changes of very big glaciers (ice sheets) and movementsin the mantle will affect the gravity field and thereby thevertical position of the ocean surface. Any increase of the total
water mass as well as sediment deposition into oceansincrease the load on their bottom, generating sinking byviscoelastic flow in the mantle below. The mantle flow is
directed towards the surrounding land areas, which will rise,
thereby partly compensating for the initial sea level increaseinduced by the increased water mass in the ocean.
Mechanism 3 (temperature-driven expansion) only affects theuppermost part of the oceans on human time scales. Usually,
temperature-driven changes in density are more important
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 tolateral displacement of water, but only lift the ocean surface
locally. Near the coast, where people are living, the depth ofwater approaches zero, so no temperature-driven expansion
will take place here (Mörner 2015). Mechanism 3 is for thatreason not important for coastal regions.
Mechanism 4 (changes in glacier mass balance) is an importantdriver for global sea-level changes along coasts, for humantime scales. Volume changes of floating glaciers – ice shelves –has no influence on the global sea-level, just like volume
changes of floating sea ice has no influence. Only the massbalance of grounded or land-based glaciers is important for the
global sea-level 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 December 201735

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 201636

Holgate-9 monthly tide gauge data from PSMSL Data Explorer. Holgate (2007) suggested the nine stations listed in the diagram tocapture the variability found in a larger number of stations over the last half century studied previously. For that reason, average valuesof the Holgate-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 April 2018

Northern hemisphere snow cover (white) and sea ice (yellow) 29 April 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.

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

Atmospheric specific humidity, updated to April 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 April 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 March 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 April 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 inMarch 1958, and 1958 was therefore chosen as starting year for the diagrams. 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 withdirect atmospheric measurements. The dotted grey line indicates the approximate linear temperature trend, and the boxesin the lower 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 March 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 ona monthly or annual basis, but for a longer time, as
other effects (oceanographic, etc.) may welloverride the potential influence of CO2 on shorttime scales such as just a few years. It is of cause
equally
inappropriate
topresent
newmeteorological record values, whether daily,
monthly or annual, as support for the hypothesis
ascribing high importance of atmospheric CO2 for
global temperatures. Any such meteorologicalrecord value may well be the result of otherphenomena.
What exactly defines the critical length of a
relevant period length to consider for evaluating
the alleged importance of CO2 remains elusive andrepresents a theme for discussion. However, thelength of the critical period must be inversely
proportional to the temperature sensitivity of CO2,
including feedback effects. If the net temperatureeffect of atmospheric CO2 is strong, the critical
period will be short, and vice versa.
However, past climate research history provides
some clues as to what has traditionally been
considered the relevant length of period overwhich 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 theirunderstanding of climate dynamics was sufficient45

to conclude 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 ofincreasing
atmosphericCO2
onglobaltemperatures.
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 April 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
and demonstrates the differences between two
often used statistical approaches to determine
recent temperature trends. Please also note thatsuch fits only attempt to describe the past, andusually have limited predictive power. In addition,
before using any linear trend (or other) analysis oftime series a proper statistical model should bechosen, 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
have trends 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 timeseries 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 April 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

1975: Glaciers in Zillertaler Alpen, Austria, begins to advance in response to climatic
cooling

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The glacier Waxeckkees in Zemmgrund, Zillertaler Alpen, Austria, in August 1921 (left; photo by S.
Ulrich or R. Finsterwalder) and in 8 September 1973 (right; photo by H. Rentsch). The large 1850
moraines are clearly seen on either side of the glacier. Reproduced from Heuberger 1977.

Heuberger (1977) described the climatic responseof three valley glaciers (Waxeckkees, Hornkees and
Schwarzensteinkees) in the southern Zillertaler Alpsin Austria. In general, all glaciers attained theirmaximum Little Ice Age position around 1850 andhave since been retreating between 800 and 2200
m (see diagram below). A period of rapid retreat
for all three glaciers commenced around 1920.

climatic changes than the two other neighbouring
glaciers. In 1975, all three glaciers were advancing
in response to cooling since around 1945-50. Dueto its steeper longitudinal profile and somewhat
shorter length, Waxeckkees began to advancealready around 1960. In 1975 the glacier hadadvanced almost 200 m beyond the frontal positionin 1960 (see diagram below).

One of the glaciers, Waxeckkees, has a rather steep
long profile, and is known to react more rapid on.

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Diagram showing frontal retreat (in meters) of the three glaciers Waxeggkees, Hornkees and
Schwarzensteinkees in the valley Zemmgrund, Zillertaler Alpen, Austria, from the maximum Little Ice
Age position reached around 1850. A period of rapid frontal retreat began around 1920, followed by aperiod of beginning advance from 1960-1970. Diagram reproduced from Heuberger 1977, originallypublished by Hoinkes et al. 1975.

REFERENCES
Heuberger, H. 1977. Gletscher- und klimageschichtliche Untersuchungen im Zemmgrund. AlpenvereinsJahrbuch 1977. Deutschen und Österreichischen Alpenverein, München und Innsbruck, 39-50.
Hoinkes, H.C. 1969. Surges of the Vernagtferner in the Ötztal Alps since 1599. Canadian Journal of Earth
Sciences 6, 853-861.

*****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)
May 20, 2018.

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