Global Warming Thermodynamics

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Open Access Library Journal
2022, Volume 9, e8945
ISSN Online: 2333-9721
ISSN Print: 2333-9705
DOI:
10.4236/oalib.1108945 Jul. 26, 2022 1
Open Access Library Journal
Global Warming Thermodynamics
—Thermodynamic Simulation and Analysis of Global Warming Indicators
Qinghan Bian
Victoria, British Columbia, Canada
Abstract
Historical data since 1880 shows that no correlation exists between air tem-
perature anomalies and atmospheric CO2
concentrations. However, that
strong pseudo-correlation from 1965 does exist implies
that there could be a
strong correlation between
the warming and waste heat because the latter
and CO2 are concurrent by-
products of fossil fuel combustion. Global
warming is a basic thermodynamic problem driven by huge amount of waste
heat from human activities that is about 80% of globally consumed en
ergy.
This article presents a climate change thermodynamic model of a quaternary
system consisting of air, land, oceans, and ices to investigate the warming
phenomena through a thermodynamic approach. Unique, definitive rela-
tionships exist between warming
or sea level rise and the amount of waste
heat allocated to each of the components according to their respective spe-
cific heat capacities. Simulation results of past temperature changes in air,
land, and seawaters as well as sea level rise are very well consistent with ob-
served anomalies and sea level rise measurements. The results suggest that
waste heat dominates global warming. This approach can also be used to
forecast future warming. Additionally, the climate system experienced a
transition from a cold to a warm era around 1980, before that time the sys-
tem was “heat” hungry. Reducing thermal emissions, increasing energy con-
version efficiency, and recover
ing and reusing waste heat are important
measures to effectively mitigate climate change.
Subject Areas
Atmospheric Sciences
Keywords
Climate Change Thermodynamic Model, Global Warming, Ice Melting, Sea
Level Rise, Simulations, Temperature Changes, Waste Heat
How to cite this paper:
Bian, Q.H. (2022
)
Global Warming Thermodynamics
.
Open
Access Library Journal
,
9
: e8945.
https://doi.org/10.4236/oalib.1108945
Received:
May 30, 2022
Accepted:
July 23, 2022
Published:
July 26, 2022
Copyright
© 2022 by author(s) and Open
Access Library Inc
.
This work is licensed under the Creative
Commons Attribution International
License (CC BY
4.0).
http://creativecommons.org/licenses/by/4.0/
Open Access

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1. Introduction
Global warming, or climate change, and extreme weather events threaten sus-
tainability. They occur more evidently at accelerated rates recently, and our
knowledge about them is still very limited.
1.1. GHG-Based Theory Unable to Well Explain Global
Warming/Climate Change
It is stated that about 90 percent of the infrared heat reflected upwards by the
earth’s surface after absorbing sunlight is then absorbed by atmospheric green-
house gases (GHGs) and radiated back to the earth’s surface [1]. This is referred
to as “heat trapping” or greenhouse effect. Human activities have increased at-
mospheric GHGs by over 48% since industrialization, which is believed to ex-
pand the greenhouse effect. It is also stated that “there’s a better than 95 percent
probability that human-produced greenhouse gases such as carbon dioxide, me-
thane and nitrous oxide have caused much of the observed increase in Earth’s
temperatures over the past 50-plus years” [1]. From these, it seems that GHGs
are the culprit and solar energy is the only energy source that GHGs trap and re-
flect back to the earth’s surface in the form of infrared radiation, leading to the
warming, but neglects the fact that waste heat (including residual heat) enters
the environment from human activities in the context.
Although the mainstream perceives that GHGs should be held accountable for
the warming based on the assumption that they form a “blanket” covering the
earth, exerting greenhouse effect and preventing heat radiations from escaping
to outer space, thus triggering global warming, this doesn’t seem to be supported
by the facts: 1) GHGs only share about 0.04%, a very trace amount, in the at-
mosphere; 2) GHGs have different specific gravities (air = 1, CO2 = 1.5189, CH4
= 0.5537, N2O = 1.530, O2 = 1.1044 [2]), randomly distribute in the atmosphere
spatially with CO2 and N2O mostly staying at the ground level, meaning that
they cannot form a “blanket” at a certain level with such a trace quantity; 3) ab-
sorbing and reflecting infrared radiations and other wavelengths of light are not
the unique characteristics of GHGs, everything with absolute temperature over
zero degree can absorb and emit radiations at certain wavelengths. Therefore,
other compositions in the atmosphere can also absorb and reflect radiations.
Additionally, GHGs should also absorb certain wavelengths of solar radiations.
They then emit and reflect radiations equally upwards and downwards because
of their statistically symmetrical distribution in the atmosphere, thus the solar
energy reaching the earth’s surface should be reduced after GHGs absorb its
infrared, causing a cooling effect eventually, if the theory were valid
i.e.
, GHGs
trap and reflect heat.
Further, if GHGs were responsible for the warming, they would articulate
quantitively the warmth in surface air, in land surface and sea surface as well as
the sea level rise,
i.e.
, a unique relation Δ
tgw
(or Δ
Ls
) =
f
(
Cghg
) could exist between
the atmospheric GHG concentration (
Cghg
) and the global (air, land, ocean)

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warming (Δ
tgw
) as well as sea level rise (Δ
Ls
). However, the fact is that no single
model can well simulate the air temperature changes, thus assembling all “satis-
factory” models’ simulations together, and taking their mean become an ap-
proach, although such an approach doesn’t make common scientific sense
(because if variables
x
and
y
correlate, theoretically only one relationship
y
=
f
(
x
) exists), the simulated results still cannot align well with the observations
(refer to Figure 9.8 in Reference [3]). Quantitatively explaining the temperature
changes in land surface and sea surface as well as sea level rise faces more chal-
lenges.
On the other hand, exploring the relation between CO2 concentration and the
surface air temperature anomalies finds that no correlation exists based on the
datasets from [4] [5] as shown in Figure 1, which exhibits the CO2 concentra-
tion (in ppm) and annual mean air temperature anomalies from 1880 to 2011.
The time span between 1880 and 2011 can be divided into four periods ac-
cording to the changes in annual mean anomalies: 1) 1880-1909 anomalies de-
creasing period; 2) 1910-1944 anomalies slight increasing period, 3) 1945-1964
anomalies very slow increasing period and 4) 1965-2011 anomalies rapid in-
creasing period as shown in the embed small figures on top of Figure 1. Clearly,
the air temperature anomalies and atmospheric CO2 concentration don’t corre-
late. However, a strong pseudo-relation does exist in the last period since 1965.
This finding is similar to Gosselin’s analysis [6]. Therefore, it is unlikely that
there is a single cause-effect between CO2 (also GHGs) and the global warming,
and even just from the pseudo-relation during the last period since 1965 to draw
that CO2 has incurred the warming is farfetched because CO2 is a concurrent
by-product with waste heat from the increased fossil fuel combustions. Adverse-
ly, CO2 and waste heat should correlate well (their relationship will be explored
later). The strong pseudo-relation between annual mean anomalies and CO2
since 1965 implies that waste heat may correlate to the air anomalies and may be
the real contributor to the temperature changes, or the global warming.
The GHG-based climate change theory has been facing strong challenges, al-
though a large number of models have been developed.
In addition, water vapor is a far more concentrated gas than GHGs in the at-
mosphere, it retains or traps much more heat than GHGs due to its greater spe-
cific heat capacity (SHC). Water on the earth and its vapor in the atmosphere
largely regulate the temperature of the environment we live in.
The climate system consists of four components: air, land, oceans, and ice. In
the previous study [7] [8], though sea ices are taken into consideration by re-
trieving their melted quantity from reported sea level rise, it is better to accu-
rately contemplate sea level rise reversely from ice melting.
Global warming is a basic thermodynamic phenomenon. Waste heat from
human activities can induce direct warming and ice melting. There should be
unique determinative relations between the waste heat and the warming in the
air, land, oceans, and ices melting as well as the sea level rise.

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Figure 1. Changes of CO2 concentrations [4] and annual surface air temperature anomalies
(
i.e.
, land-ocean) [5] from 1880 to 2011. In different periods, their correlations are shown in
the top small embed figures from which it is clear that no definitive correlation exists. In the
early three periods, correlations are very weak, while from 1965 the correlation is strong.

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1.2. Energy Conservation and Waste Heat from Human Activities
According to the Law of Conservation of Energy or the First Law of Thermody-
namics, energy can be transformed from one form to another or others (e.g.,
coal through combustion to heat, electricity, and chemical energy contained in
new products), but cannot be created nor destroyed. It is obvious that while be-
ing used, part of the energy is inevitably transformed into a non-useful form as
residual or waste energy that dissipates into the environment.
Usually, we only consider how and how much energy is used, but are less
concerned about where the energy goes eventually. In fact, in every energy ap-
plication, it experiences a form transformation: from one form to another or
others. For example, when burning coal to boil water, the energy released from
combusting coal is converted to: 1) heat energy that heats up and boils the water,
2) heat energy that heats up the boiler case that eventually escapes into the envi-
ronment and 3) the energy that disperses into the environment along with flue
gas and water vapor. Among these three parts, the last two are obviously a kind
of waste heat (or waste energy) that is useless. Even for the first part, the heat
energy retained in the hot water is eventually released into the environment too
as residual heat, whether it is used for heating or for drinking. Thus, all these
three parts enter the climate system, and are referred to as waste heat.
Similarity exists in burning natural gas in the furnace, running air condition-
ers, cooking/baking, etc. in residential and commercial sectors from which the
amount of waste heat is almost the same as the amount of energy consumed [7]
[8].
In transportation, only a small part of consumed energy is used to move loads
and vehicles themselves for useful work, whereas the rest is dispersed into envi-
ronment through friction (which is then converted to heat) and sensible waste
heat from radiator, hot hood, and tail gas, etc. It’s estimated that about 75% of
the used fossil fuel energy in transportation is dispersed into the environment as
waste heat [7] [8].
In industries, the energy consumed is converted to several forms such as elec-
tricity, chemical energy retained in products and heat in addition to waste heat.
The widely used concept of energy efficiency cannot tell exactly how much
energy is ultimately converted to useful work or retained in products, it only in-
dicates how much of the input energy can be produced out during a process. For
example, when boiling drinking water using a modern kettle with an efficiency
of 90% (obviously it is very efficient), 90% of the consumed energy is used to
heat the water. However, it is unclear how much energy will be still contained in
the boiled water at its final state. Few people realize that almost all of this very
90% of the consumed energy is also released into the environment eventually, in
addition to the 10% already entered the environment even during the boiling
process. Cooking is another similar example. Thus, a new concept to describe
how much of the consumed energy is really converted to useful work or energy
retained in products is necessary, which is referred to as energy’s effective con-

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version efficiency (EECE) [7] [8]. It is the EECE that can clearly tell how much
consumed energy is dissipated to the environment during a process.
As analyses shown in [7] [8], in the residential and commercial sectors, al-
most all the energy consumed is eventually dissipated into the environment. In
transportation, only about 25% of the consumed fossil fuels is converted to
useful work by considering the different levels of technologies across the
world, and the rest,
i.e.
, 75%, is wasted into the environment through friction
and sensible heat. In industries, it is assumed that the entire EECE is about
30% across all sectors.
According to BP [9], industries consume about 51% of the global energy, res-
idential and commercial 29% and transport 20% (these shares may vary from
time to time, but the variance should be reasonably small). Thus, it is estimated
that the global EECE is about 20%, which means about 80% of the energy con-
sumed globally enters the environment as waste heat [7] [8]. This is reasonable if
considering the different technology levels in the world, and even in the USA
with the most advanced technologies, the waste heat is reported to be about 67%
of the consumed energy because of the inefficiencies in energy application
processes, equipment [10].
BP has adjusted its estimate of a standard power plant’s thermal efficiency
from 36% in 2000 to 40.4% in 2019 [11] [12] in the last two decades, while re-
maining 36% for the period of 1965-2000, to reflect the technology advance-
ments. Nevertheless, from the perspective of global energy application, this ad-
justment would not affect much on the global EECE since the basics of the
energy application processes have not been changed radically. Therefore, assume
the EECE is 20%, although during earlier years (say, 1965 to 1979, only after that
point climate change has become more recognized) a lower EECE might exist
because of less advanced technologies, especially in developing countries.
Besides, countless flaring and spontaneous firing of surface coal and methane
from the natural reservoirs, volcanic eruptions, forestry wildfires and various
chemical fuel applications (such as space explorations, military activities, etc.)
also send a huge amount of heat to the climate system, contributing to the global
warming.
2. Climate Change Thermodynamics
The climate system consists of land, oceans, air, and ices. It receives energy from
sun, interior earth, and moon, etc. to sustain the earth environment human lives
in. The energy reaching the earth surface retains at a dynamic energy budget
balance as expressed below, which provides enough energy for the entire system.
in net air water land bio consum
E EE E EE
−
=+ + ++
(1)
where,
E
in-net is the net energy reaching the earth surface from sun (
i.e.
, solar energy),
interior earth and moon (
i.e.
, tidal energy) (energies from other celestial bodies
can be ignored). Generally, it’s considered that solar energy reaching the earth

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surface is quite stable although a small temperature change of about 0.05˚C -
0.1˚C has been detected due to the variance in total solar irradiance during the
11-year solar cycle [13] [14], while the energy from moon is less studied and
very limitedly known, compared to the solar one, but consideredly remains sta-
ble in the past long history.
E
air is the energy absorbed by and stored in air;
E
water is the energy absorbed by and stored in waters including ices;
E
land is the energy absorbed by and stored in land;
E
bio is the bioenergy
i.e.
, energy absorbed by and stored in plants or biomass as
a result of photosynthesis;
E
consum is the energy consumed by humans and animals, including the solar
energy collected through photovoltaic process, etc.
As described in [8], it is this dynamic energy budget balance that enabled the
climate system to maintain the temperatures of air, land, and seawaters relatively
stable over a long period. Any extra energy entered the system from human ac-
tivities such as combusting fossil fuels, using geothermal or nuclear energy will
certainly shift the above energy budget balance to the right side, making the air,
land, and seawaters warmer, and thawing ices. Deforestation decreases Ebio con-
verted from solar energy, thus the corresponding excessive energy contributes to
the warming. However, details on deforestation’s effect need further studies. It
also needs attention that combusting biomass releases long-term accumulated
energy back to the system concentratedly, breaking the local energy budget bal-
ance, and thus contributing to the warming.
Global warming is a basic thermodynamic problem. Warming a house by us-
ing heat energy not only warms the air in the house, but also the waters in con-
tainers, the floor, and walls, etc. Similarly, the globe warms from the surface lev-
el,
i.e.
, at surface air, land surface, seawaters surface, and ices. As indicated in the
earlier articles [7] [8], the huge amount of waste heat entered the climate system
induces the warming, following the thermodynamic laws.
As well known, heat always rises along with the air. In the climate system heat
extends towards the poles along with the vortex of air that forms around the
earth surface as the earth rotates. This attracts much heat energy to the polar
areas, especially the North since the north hemisphere is much more populated,
more energy consumed, and consequently more waste heat dissipated than in
the South. This may help understand why the “Arctic Amplification” [15],
i.e.
the evident Arctic warming happens in the last decades, where more glaciers, ice
sheets and covers melt, permafrost thaws and increasing heat waves than ever
before, as Walsh said “The Arctic has been warming twice as fast as the rest of
the world. In some seasons, it has warmed three times faster than the rest of the
globe [16].
2.1. Equivalent Climate Change Model
Equivalent Climate Change Model (ECCM) has been developed to explain the

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warming phenomena, consisting of respective equivalent climate change boun-
dary layers to simulate temperature changes in surface air, land surface, and
seawaters surface [7] [8]. The results show that the simulated temperature
changes are well consistent with those observed anomalies in air, land, and
oceans, respectively. However, ice melting was not considered in the same way,
instead, it was considered through the observed sea level rise based on NASA’s
assumption that one-third of the rise is from sea water warming and two-thirds
are from ice melting [15] [17]. In fact, this is not an appropriate approach and
cannot fully explain how ices respond to human activities and how ices melt and
contribute to sea level rise.
2.2. Climate Change Thermodynamic Model
In the present study, melting of both ices (glaciers, icebergs, and ice shelves) and
sea ices (
i.e.
, ice sheets and covers) is considered thermodynamically based on
the SHC and latent heat of fusion. Incorporating this into the existing ECCM [7]
[8] constitutes a new climate change thermodynamic model (CCTM) that in-
cludes a thermodynamically equivalent climate change surface air layer (or col-
umn), a thermodynamically equivalent climate change land surface layer, a
thermodynamically equivalent climate change seawaters surface layer, and the
melting of glaciers and sea ices. They absorb the waste heat from human activi-
ties according to their respective SHC.
The temperature change in surface air:
( )
33
00
3
4
a
a
a pa
H
tRh R C
ρ
∆
∆= 
π + − ⋅⋅

(2)
The temperature change in land surface:
l
ll l l pl
H
tSD C
ρ
∆
∆=
(3)
The temperature change in seawaters surface:
w
ww w w pw
H
tSD C
ρ
∆
∆=
(4)
The amount of sea ices melted due to the absorbed heat:
si
si psi
H
ML
∆
=
(5)
The amount of glaciers melted due to the absorbed heat:
gl
gl pgl
H
ML
∆
=
(6)
where,
R
0 Earth radius, 6371 km
h
The depth of the surface air layer in atmosphere from the earth surface
Sw
Seawaters surface area, 361,800,000 km2

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Dw
The depth of the seawaters surface layer
ρa
Air density under normal pressure
Cpa
Air SHC under normal pressure, or the isobaric heat capacity
∆
H
a The heat absorbed in the surface air layer corresponding to the tem-
perature change ∆
ta
∆
ta
The temperature change in the surface air layer after experiencing heat
change ∆
Ha
ρw
The seawaters density
∆
Hw
The heat change in seawaters surface layer corresponding to the tem-
perature change ∆
tw
∆
tw
The temperature change in the seawaters surface layer after experienc-
ing heat change ∆
Hw
Cpw
Seawaters SHC under normal pressure
ρl
The land (soil) density
∆
Hl
The heat change in land surface layer corresponding to the tempera-
ture change ∆
tl
∆
tl
The temperature change in the land surface layer after experiencing
heat change ∆
Hl
Cpl
Land (soil) SHC under normal pressure
Sl
Land area on the earth surface
Dl
Depth of land surface layer
Mgl
Annal amount of melted glaciers (
i.e.
, pure ice)
Cpgl
Glaciers (
i.e.
, pure ice) SHC under normal pressure, 2.11 kJ∙kg−1∙K−1
Lpgl
Glaciers (
i.e.
, pure ice) latent heat of fusion at constant pressure,
333
.
4
KJ∙Kg−1
Msi
Annual amount of melted sea ices
∆
Hsi
Annual heat amount absorbed by melted sea ices
Cpsi
Sea ices effective SHC
Lpsi
Effective latent heat of fusion of sea ices at constant pressure.
Glaciers, including icebergs and ice shelves floating on oceans but originated
on land [18], and the landfast ice, formed from pure water, melt at zero degree
Celsius (0˚C), have a SHC of 2.11 kJ∙kg−1∙K−1
and latent heat of fusion of
333
.
4
KJ∙Kg−1 [19]
.
2.3. Methods
As described in [7] [8], the feature of this study is using SHC to allocate heat en-
tered the climate system from human activities to the components: air, land,
oceans, sea ices and glaciers.
Sea ices (including ice sheets and covers) behave very differently than pure ice
because they contain brine and salts. They melt at different temperatures than
they form. Seawaters freeze at about −2.0˚C [20] - −1.8˚C [21] to form sea ices
due to the salinity. The resulting ices trap brine that either precipitates or drains
out of the ice crystal lattice [22], forming brine pockets and eventually flowing

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into the oceans again. As a result, the bulk salinity in sea ices is much lower than
that in seawaters (34 - 35 parts per thousand,
i.e.
, ppts). For old and matured sea
ices it is estimated the salinity ranges from 4 to 12 ppts (4 ppts is widely used for
bulk sea ices) although some have lower or greater values, while for new ices or
those at the bottom of ice sheets (or covers) the salinity may be greater, for ex-
ample, new ices can be of between 12% - 20% [23] when growing from seawaters
with normal salinity of 32 - 35 ppts or greater. Further cooling during winter
reduces salinity. Therefore, it is reasonable to consider that the salinity of sea
ices melted during the melting season has reduced gradually from early years
when more new ice was formed around the matured ones, and the amount of
new ice reduces recently as the global warming proceeds.
Similar to pure ice, sea ices are resistant to melting because it needs more heat
(
i.e.
, latent heat of fusion) to go through the phase transformation. Because of
very limited knowledge and data on sea ices behavior and physicochemical
properties, details of the formation and ablation are not very clear yet, and much
less is known about how sea ices melt in the real ocean environment, however
the process is very complicated. Sea ices melt at distinguishingly higher temper-
atures than the freezing points (−2.0 [20] - −1.8˚C [21]). From Spring to Au-
tumn seawaters and surface air over them become warmer, it is hypothesized
that when the in-situ temperature in the bulk sea ices exceeds a point at which
the ice - brine pocket remains at microscopic level equilibrium at the interface
[19], the melting process initiates, meltwater diluting the brine solution in the
pocket, enlarging the brine flux channel and accelerating the ice melting, as a
result, increasing sea ices melt. Towards summer, temperature becomes higher
and the melting speeds up. Sea ices can melt completely at a temperature (
Tf
)
determined by the following equation [19],
f m si
T mS= ∗
(7)
For example, the potentially complete melting temperature is about –0.22˚C
when the salinity (
Ssi
) is 4 ppts and enough heat energy is available. However,
due to the vast volume of sea ices and relatively limited heat availability, only a
part of the ices can melt before getting into winter to re-form ices.
As such, it is anticipated that no evident phase transformation (or fusion)
from sea ices to seawaters can be observed, at a certain temperature, that can be
expected by a certain value of latent heat of fusion, instead, a transitional process
exists that merges the phase transformation into a melting process. Under such a
situation, using sea ices’ effective specific heat capacity (ESHC) [19] [22] is more
appropriate. ESHC includes the latent heat for phase transformation (
i.e.
, fu-
sion) that accompanies temperature change in sea ices [19]. Furthermore, the
latent heat of fusion is a function of salinity and temperature [19] [22] [24].
According to Ono [24], sea ices’ ESHC can be expressed as below:
0.505 0.0018 4.3115 0.0008 0.00002 4.1868
si
psi si si
S
C T S TS
T

=++ − + ∗


(8)

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Similarly, using effective latent heat of fusion to describe sea ices heat re-
quirement during phase transformation is advisable. Sea ices’ effective latent
heat of fusion can be expressed as below according to Petrich
et al.
[19]:
si
psi pgl pgl pgl m si m pgl
S
L L C T C mS mL T
=−+ −
(9)
Or simply,
333.4 2.11 0.1142 18.0403
si
psi si
S
L TS T
=−− +
(9a)
where
,
mm
Water’s freezing point depression as a function of salinity
Ssi
,
−0.054
K/ppt,
Ssi
Salinity of sea ices, in parts per thousand (ppts),
T
Temperature of sea ices in degrees Celsius (˚C),
T
< 0˚C.
Assuming the values of salinity of sea ices are as indicated in Table 1 for this
study period concerned, where they are decreasing gradually due to less forma-
tion of new sea ices, and this decrease escalates in recent decades as warming
accelerates.
Meanwhile, the ESHC of sea ices at −2˚C (the most conservatively highest
temperature at which it is in a steady state), the melting point temperature for
potentially melting completely as well as the latent heat of fusion at the melting
point that are calculated from Equations (7)-(9a) are also included in Table 1.
For air, its SHC depends on the temperature and the moisture content, and
the latter can be derived from relative humidity. Air SHC is calculated by the
equation in [25] with the procedures prescribed in [8],
i.e.
, obtaining the abso-
lute humidity from relative humidity and then water vapor’s SHC at the corres-
ponding temperature. Table 2 shows the surface air’s temperature, relative hu-
midity, absolute humidity, water vapor’s SHC and finally the air’s SHC. The wa-
ter vapor’s SHC at 20˚C is obtained by simple linear interpolation between 2˚C
(
i.e.
, 275 K, 1.859 KJ∙Kg−1∙K−1) and 27˚C (
i.e.
, 300 K, 1.864 KJ∙Kg−1∙K−1) [26]. The
global average of air relative humidity and temperatures are obtained from the
respective datasets [27] and [28] on an annual basis.
For seawaters, its SHC is determined by its surface temperature that is aver-
aged from the datasets [29] annually, and the corresponding SHC is about 4.004
KJ∙Kg−1∙K−1 at the corresponding temperature range of 13˚C - 14˚C [30] as
shown in Table 3.
The land’s SHC is assumed to be 0.83 KJ∙Kg−1∙K−1 from [31].
Therefore, the relative strengths of SHCs (or simply SHC shares) of air, sea-
waters, land, sea ices, and glaciers in the climate system are summarized in Ta-
ble 4 for the study period, which determines how much heat will be allocated to
each component respectively.
3. Result Analyses and Discussions
According to BP Global [32], the global total energy consumption, global total

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10.4236/oalib.1108945 12
Open Access Library Journal
Table 1. Sea Ice salinity, Effective Specific Heat Capacity (SHC), Melting Point Temperature, and Latent Heat of Fusion at Melting
Point.
Year
1965
1966
1967
1968
1969
1970
1971
1972
1973
1974
1975
1976
Sea ice salinity, ppt
12
12
12
12
12
12
12
12
12
12
11
11
Sea ice ESHC @ −2˚C,
steady state, KJ/Kg/K
56.2112 56.2112 56.2112 56.2112 56.2112 56.2112 56.2112 56.2112 56.2112 56.2112 51.7019 51.7019
Sea ice (complete)
melting point, ˚C
−0.649 −0.649 −0.649 −0.649 −0.649 −0.649 −0.649 −0.649 −0.649 −0.649 −0.595 −0.595
Sea ice fusion heat,
KJ/Kg at melting point
334.434 334.434 334.434 334.434 334.434 334.434 334.434 334.434 334.434 334.434 334.32 334.32
Year
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
Sea ice salinity, ppt
11
11
11
11
11
11
11
11
10
10
10
10
Sea ice ESHC @ −2˚C,
steady state, KJ/Kg/K
51.7019 51.7019 51.7019 51.7019 51.7019 51.7019 51.7019 51.7019 47.1926 47.1926 47.1926 47.1926
Sea ice (complete)
melting point, ˚C
−0.595 −0.595 −0.595 −0.595 −0.595 −0.595 −0.595 −0.595 −0.541 −0.541 −0.541 −0.541
Sea ice fusion heat,
KJ/Kg at melting point
334.32 334.32 334.32 334.32 334.32 334.32 334.32 334.32 334.206 334.206 334.206 334.206
Year
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
Sea ice salinity, ppt
10
9
9
9
9
9
8
8
8
8
8
7
Sea ice ESHC @ −2˚C,
steady state, KJ/Kg/K
47.1926 42.6832 42.6832 42.6832 42.6832 42.6832 38.1739 38.1739 38.1739 38.1739 38.1739 33.6646
Sea ice (complete)
melting point, ˚C
−0.541 −0.487 −0.487 −0.487 −0.487 −0.487 −0.433 −0.433 −0.433 −0.433 −0.433 −0.379
Sea ice fusion heat,
KJ/Kg at melting point
334.206 334.092 334.092 334.092 334.092 334.092 333.978 333.978 333.978 333.978 333.978 333.864
Year
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
Sea ice salinity, ppt
7
7
7
7
6
6
6
6
6
5
5
5
Sea ice ESHC @ −2˚C,
steady state, KJ/Kg/K
33.6646 33.6646 33.6646 33.6646 29.1552 29.1552 29.1552 29.1552 29.1552 24.6459 24.6459 24.6459
Sea ice (complete)
melting point, ˚C
−0.379 −0.379 −0.379 −0.379 −0.325 −0.325 −0.325 −0.325 −0.325 −0.271 −0.271 −0.271
Sea ice fusion heat,
KJ/Kg at melting point
333.864 333.864 333.864 333.864 333.75 333.75 333.75 333.75 333.75 333.636 333.636 333.636
Year
2013
2014
2015
2016
2017
2018
2019
Sea ice salinity, ppt
5
5
4
4
4
4
4
Sea ice ESHC @ −2˚C,
steady state, KJ/Kg/K
24.6459 24.6459 20.1366 20.1366 20.1366 20.1366 20.1366
Sea ice (complete)
melting point, ˚C
−0.271 −0.271 −0.216 −0.216 −0.216 −0.216 −0.216
Sea ice fusion heat,
KJ/Kg at melting point
333.636 333.636 333.522 333.522 333.522 333.522 333.522

Q. H. Bian
DOI:
10.4236/oalib.1108945 13
Open Access Library Journal
Table 2. Surface air relative and absolute humidity; temperature, density, specific heat capacities of water vapor, dry air, and sur-
face air.
Year
1965
1966
1967
1968
1969
1970
1971
1972
1973
1974
1975
1976
1977
1978
Global average of air
relative humidity, % [27]
79.277 79.420 79.622 79.181 79.502 79.403 78.719 78.738 79.009
79.027
79.094
79.155
79.172
79.178
Global average of air
mean temperature [28]
20.471 20.132 19.119 19.166 18.983 18.853 18.921 18.996 18.952
19.029
18.887
18.723
19.003
18.903
air density, Kg/m
3
1.202
1.204
1.208
1.208
1.208
1.209
1.209
1.208
1.208
1.208
1.209
1.209
1.208
1.209
Air absolute humidity,
g/Kg
0.117 0.115 0.108 0.108 0.107 0.106 0.106 0.106 0.106 0.107 0.106 0.105 0.107 0.106
Dry air specific heat
capacity, KJ/(Kg*K) [34]
1.006 1.006 1.006 1.006 1.006 1.006 1.006 1.006 1.006 1.006 1.006 1.006 1.006 1.006
Water vapor specific
heat capacity, KJ/(Kg*K)
1.863 1.863 1.863 1.863 1.863 1.863 1.863 1.863 1.863 1.863 1.863 1.863 1.863 1.863
Surface air specific heat
capacity, KJ/(Kg*K)
1.224 1.220 1.208 1.207 1.206 1.204 1.203 1.204 1.204 1.205 1.203 1.201 1.205 1.204
Year
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
Global average of air
relative humidity, % [27]
79.207 79.251 79.440 78.942 78.862 78.940 79.093 78.714 79.185
79.249
79.022
79.023
78.946
78.978
Global average of air
mean temperature [28]
18.929 19.005 18.928 19.143 19.315 19.033 19.009 19.317 19.083
18.689
18.944
19.407
19.109
19.162
air density, Kg/m3
1.209
1.208
1.209
1.208
1.207
1.208
1.208
1.207
1.208
1.210
1.208
1.207
1.208
1.208
Air absolute humidity,
g/Kg
0.106 0.107 0.107 0.107 0.108 0.107 0.107 0.108 0.107 0.105 0.106 0.109 0.107 0.108
Dry air specific heat
capacity, KJ/(Kg*K) [34]
1.006 1.006 1.006 1.006 1.006 1.006 1.006 1.006 1.006 1.006 1.006 1.006 1.006 1.006
Water vapor specific
heat capacity, KJ/(Kg*K)
1.863 1.863 1.863 1.863 1.863 1.863 1.863 1.863 1.863 1.863 1.863 1.863 1.863 1.863
Surface air specific heat
capacity, KJ/(Kg*K)
1.204 1.205 1.205 1.206 1.208 1.205 1.205 1.208 1.206 1.201 1.204 1.210 1.206 1.206
Year
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
Global average of air
relative humidity, % [27]
79.022 79.092 79.143 79.323 79.170 79.326 79.369 79.413 78.895
78.837
78.908
78.787
79.032
79.018
Global average of air
mean temperature [28]
19.077 19.222 19.107 18.803 19.247 19.553 18.760 18.649 18.611
18.722
18.834
18.665
18.737
19.034
air density, Kg/m
3
1.208
1.207
1.208
1.209
1.207
1.206
1.209
1.210
1.210
1.209
1.209
1.210
1.209
1.208
Air absolute humidity,
g/Kg 0.107 0.108 0.107 0.106 0.108 0.111 0.105 0.105 0.104 0.104 0.105 0.104 0.105 0.107
Dry air specific heat
capacity, KJ/(Kg*K) [34]
1.006 1.006 1.006 1.006 1.006 1.006 1.006 1.006 1.006 1.006 1.006 1.006 1.006 1.006
Water vapor specific
heat capacity, KJ/(Kg*K)
1.863 1.863 1.863 1.863 1.863 1.863 1.863 1.863 1.863 1.863 1.863 1.863 1.863 1.863
Surface air specific heat
capacity, KJ/(Kg*K)
1.205 1.207 1.206 1.203 1.208 1.212 1.202 1.201 1.199 1.201 1.202 1.200 1.201 1.205

Q. H. Bian
DOI:
10.4236/oalib.1108945 14
Open Access Library Journal
Continued
Year
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
Global average of air
relative humidity, % [27]
78.947 78.859 78.775 79.136 78.915 79.030 78.696 78.487 78.608
78.716
78.671
78.896
78.565
79.003
Global average of air
mean temperature [28]
18.756 18.708 18.854 18.664 18.706 18.955 18.616 19.142 19.554
19.494
19.219
19.102
19.286
19.433
air density, Kg/m
3
1.209
1.209
1.209
1.210
1.209
1.208
1.210
1.208
1.206
1.206
1.207
1.208
1.207
1.206
Air absolute humidity,
g/Kg
0.105 0.104 0.105 0.104 0.104 0.106 0.104 0.107 0.110 0.109 0.108 0.107 0.108 0.109
Dry air specific heat
capacity, KJ/(Kg*K) [34]
1.006 1.006 1.006 1.006 1.006 1.006 1.006 1.006 1.006 1.006 1.006 1.006 1.006 1.006
Water vapor specific
heat capacity, KJ/(Kg*K)
1.863 1.863 1.863 1.863 1.863 1.863 1.863 1.863 1.863 1.863 1.863 1.863 1.863 1.863
Surface air specific heat
capacity, KJ/(Kg*K)
1.201 1.201 1.202 1.201 1.201 1.204 1.199 1.205 1.210 1.210 1.206 1.205 1.207 1.210
Note: Global averages of air relative humidity and air temperatures are obtained from the NOAA’s respective datasets [27] [28] on
annual basis.
Table 3. Sea surface temperature, seawater specific heat capacity (SHC).
Year
1965
1966
1967
1968
1969
1970
1971
1972
1973
1974
1975
1976
1977
1978
Global sea surface
temperature, ˚C [29]
13.61 13.7 13.76 13.72 13.84 13.84 13.81 13.84 13.94 13.82 13.85 13.79 13.93 13.91
Seawater SHC,
KJ∙Kg
−1
∙K
−1
[30]
4.004 4.004 4.004 4.004 4.004 4.004 4.004 4.004 4.004 4.004 4.004 4.004 4.004 4.004
Year
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
Global sea surface
temperature, ˚C [29]
13.97 13.93 13.96 13.89 13.96 13.93 13.96 13.96 14 14.1 14.03 14.09 14.06 13.94
Seawater SHC,
KJ∙Kg
−1
∙K
−1
[30]
4.004 4.004 4.004 4.004 4.004 4.004 4.004 4.004 4.004 4.004 4.004 4.004 4.004 4.004
Year
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
Global sea surface
temperature, ˚C [29]
13.93 13.99 14.03 14.04 14.03 14.19 14.14 14.09 14.17 14.16 14.15 14.11 14.16 14.14
Seawater SHC,
KJ∙Kg
−1
∙K
−1
[30]
4.004 4.004 4.004 4.004 4.004 4.004 4.004 4.004 4.004 4.004 4.004 4.004 4.004 4.004
Year
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
Global sea surface
temperature, ˚C [29]
14.2 14.21 14.25 14.32 14.31 14.31 14.32 14.35 14.38 14.46 14.4 14.39 14.33 14.34
Seawater SHC,
KJKg
−1
K
−1
[30]
4.004 4.004 4.004 4.004 4.004 4.004 4.004 4.004 4.004 4.004 4.004 4.004 4.004 4.004

Q. H. Bian
DOI:
10.4236/oalib.1108945 15
Open Access Library Journal
Table 4. Relative shares of specific heat capacities of air, seawater, land, sea ices and glaciers (%).
Year
1965
1966
1967
1968
1969
1970
1971
1972
1973
1974
1975
1976
1977
1978
Atmospheric air, %
1.90
1.90
1.88
1.88
1.87
1.87
1.87
1.87
1.87
1.87
2.01
2.01
2.01
2.01
Seawaters, %
6.22
6.22
6.22
6.22
6.22
6.22
6.22
6.22
6.22
6.22
6.69
6.69
6.69
6.69
Vegetated land, %
1.29
1.29
1.29
1.29
1.29
1.29
1.29
1.29
1.29
1.29
1.39
1.39
1.39
1.39
Glaciers, %
3.28
3.28
3.28
3.28
3.28
3.28
3.28
3.28
3.28
3.28
3.53
3.53
3.53
3.53
Sea Ice, %
87.31
87.32
87.34
87.34
87.34
87.34
87.34
87.34
87.34
87.34
86.39
86.39
86.38
86.39
Year
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
Atmospheric air, %
2.01
2.01
2.01
2.02
2.02
2.01
2.18
2.18
2.18
2.17
2.18
2.38
2.37
2.37
Seawaters, %
6.69
6.69
6.69
6.69
6.69
6.69
7.24
7.23
7.23
7.24
7.24
7.88
7.88
7.88
Vegetated land, %
1.39
1.39
1.39
1.39
1.39
1.39
1.50
1.50
1.50
1.50
1.50
1.63
1.63
1.63
Glaciers, %
3.53
3.53
3.53
3.53
3.53
3.53
3.81
3.81
3.81
3.81
3.81
4.15
4.15
4.15
Sea Ice, %
86.39
86.38
86.38
86.38
86.38
86.38
85.28
85.27
85.27
85.28
85.28
83.96
83.97
83.97
Year
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
Atmospheric air, %
2.37
2.38
2.60
2.60
2.61
2.62
2.60
2.87
2.87
2.87
2.88
2.87
3.22
3.23
Seawaters, %
7.88
7.88
8.64
8.64
8.64
8.64
8.64
9.58
9.58
9.58
9.58
9.58
10.73
10.73
Vegetated land, %
1.63
1.63
1.79
1.79
1.79
1.79
1.79
1.99
1.99
1.99
1.99
1.99
2.23
2.22
Glaciers, %
4.15
4.15
4.55
4.56
4.55
4.55
4.56
5.05
5.05
5.05
5.05
5.05
5.66
5.66
Sea Ice, %
83.97
83.96
82.41
82.41
82.40
82.40
82.41
80.52
80.52
80.52
80.52
80.52
78.16
78.16
Year
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
Atmospheric air, %
3.22
3.22
3.22
3.66
3.66
3.67
3.66
3.67
4.28
4.28
4.26
4.26
4.27
4.28
Seawaters, %
10.73
10.73
10.73
12.21
12.21
12.21
12.21
12.21
14.15
14.15
14.15
14.16
14.15
14.15
Vegetated land, %
2.23
2.23
2.23
2.53
2.53
2.53
2.53
2.53
2.93
2.93
2.93
2.93
2.93
2.93
Glaciers, %
5.66
5.66
5.66
6.43
6.43
6.43
6.44
6.43
7.46
7.46
7.46
7.46
7.46
7.46
Sea Ice, %
78.16
78.16
78.16
75.16
75.16
75.15
75.17
75.15
71.18
71.18
71.19
71.19
71.19
71.18
non-renewable energy consumption, the energy entered the climate system as
waste heat from human activities (EECE = 20% as discussed earlier), and the
energy allocated to air, seawaters, land as well as sea ices and glaciers are shown
in Table 5. The allocation is calculated based on the waste heat multiplied by the
corresponding share percentage of each component’s SHC as shown in Table 4.
It is mentioned earlier that both waste heat and CO2 are concurrent by-products
of fossil fuel burning. Figure 2 shows their strong correlation. Clearly this cor-
relation is much stronger (correlation coefficient R2 = 0.9839) than that between
the temperature anomalies and CO2 (correlation coefficient R2 = 0.8677) shown
in the small figure at the top right in Figure 1 for the same period of 1965 ~
2011, implying that air temperature anomalies could be more reasonably due to
waste heat, as opposed to increased CO2 or GHGs. It is worth noting that the

Q. H. Bian
DOI:
10.4236/oalib.1108945 16
Open Access Library Journal
Table 5. Energy consumption and distribution of waste heat among the components in climate system.
Year
1965
1966
1967
1968
1969
1970
1971
1972
1973
1974
1975
1976
1977
1978
Global Total Energy Effective
Conversion Efficiency
0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2
Global Primary Energy
Consumption, 10
18
J
155.2 163.5 169.7 179.9 192 204.2 212.5 224 237.1 238.4 239.6 252.5 261.7 272.2
Global consumption of
non-renewable energy, E+18 J
145.8 153.4 159.4 169.1 180.5 192.2 199.9 210.8 223.7 223.7 224.7 237.6 246.3 255.6
Total energy entered climate
system, E+17 KJ
1.166 1.228 1.275 1.353 1.444 1.537 1.6 1.686 1.789 1.79 1.798 1.901 1.97 2.044
The surface air absorbed heat,
E+15 KJ
2.218 2.326 2.392 2.537 2.705 2.875 2.989 3.154 3.347 3.35 3.614 3.816 3.967 4.112
The sea surface waters
absorbed heat, E+15 KJ
7.253 7.635 7.931 8.415 8.983 9.564 9.952 10.49 11.13 11.13 12.03 12.72 13.18 13.68
The land surface absorbed
heat, E+15 KJ
1.503 1.583 1.644 1.744 1.862 1.983 2.063 2.175 2.308 2.308 2.493 2.636 2.733 2.835
Sea Ice absorbed heat, E+15 KJ
101.8
107.2
111.3
118.1
126.1
134.3
139.7
147.3
156.3
156.3
155.3
164.2
170.2
176.6
Glaciers absorbed heat, E+15
KJ
3.822 4.023 4.18 4.434 4.734 5.04 5.244 5.528 5.867 5.867 6.338 6.701 6.947 7.208
Year
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
Global Total Energy Effective
Conversion Efficiency
0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2
Global Primary Energy
Consumption, 1018 J 281.6 279.4 278.2 276.6 281 294.3 302 308.8 319.6 331.8 338.2 342.1 344.6 346.9
Global consumption of
non-renewable energy, E+18 J
264.1 261.5 259.8 257.8 261.4 273.9 281.2 287.6 298.1 309.5 315.9 319 320.9 323.1
Total energy entered climate
system, E+17 KJ
2.113 2.092 2.079 2.063 2.091 2.192 2.25 2.301 2.385 2.476 2.527 2.552 2.567 2.585
The surface air absorbed heat,
E+15 KJ
4.251 4.212 4.184 4.157 4.22 4.411 4.898 5.021 5.196 5.375 5.498 6.072 6.088 6.134
The sea surface waters
absorbed heat, E+15 KJ
14.14 13.99 13.91 13.8 13.99 14.66 16.28 16.65 17.25 17.92 18.29 20.1 20.22 20.36
The land surface absorbed
heat, E+15 KJ 2.93 2.901 2.883 2.86 2.9 3.039 3.374 3.451 3.576 3.714 3.791 4.166 4.191 4.22
Sea Ice absorbed heat, E+15 KJ
182.5
180.7
179.6
178.2
180.6
189.3
191.8
196.2
203.3
211.2
215.5
214.3
215.5
217
Glaciers absorbed heat, E+15
KJ
7.449 7.375 7.328 7.272 7.372 7.726 8.578 8.773 9.091 9.442 9.637 10.59 10.65 10.73
Year
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
Global Total Energy Effective
Conversion Efficiency
0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2
Global Primary Energy
Consumption, 1018 J 349.5 354.1 361.9 372.3 376 378.1 384.8 394.5 398.3 407.2 421.6 442.4 456.6 469.6
Global consumption of
non-renewable energy, E+18 J
324.3 328.6 335 345 348.1 349.9 356.3 365.3 369.9 378.1 392.5 411 424 435.4

Q. H. Bian
DOI:
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Continued
Total energy entered climate
system, E+17 KJ 2.594 2.629 2.68 2.76 2.785 2.799 2.851 2.922 2.959 3.024 3.14 3.288 3.392 3.483
The surface air absorbed heat,
E+15 KJ
6.152 6.244 6.979 7.167 7.262 7.325 7.4 8.396 8.49 8.685 9.028 9.436 10.92 11.25
The sea surface waters
absorbed heat, E+15 KJ
20.43 20.71 23.17 23.86 24.07 24.19 24.64 27.99 28.34 28.96 30.07 31.49 36.41 37.39
The land surface absorbed
heat, E+15 KJ
4.236 4.292 4.802 4.946 4.99 5.015 5.108 5.802 5.875 6.004 6.233 6.527 7.548 7.75
Sea Ice absorbed heat, E+15 KJ
217.8
220.7
220.9
227.5
229.5
230.7
234.9
235.3
238.3
243.5
252.8
264.7
265.1
272.2
Glaciers absorbed heat, E+15
KJ
10.77 10.91 12.21 12.57 12.68 12.75 12.99 14.75 14.94 15.26 15.85 16.59 19.19 19.7
Year
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
Global Total Energy Effective
Conversion Efficiency
0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2
Global Primary Energy
Consumption, 10^18 J
484.2 489.5 482 505.4 517.6 524.6 534.3 539.6 544.4 551.7 561.8 576.1 581.5 557.1
Global consumption of
non-renewable energy, E+18 J
448.9 451.4 443.1 463.5 473.9 478.2 485 487.8 490.9 495.3 502.2 512.9 515 487.2
Total energy entered climate
system, E+17 KJ
3.591 3.611 3.545 3.708 3.791 3.825 3.88 3.903 3.927 3.962 4.017 4.103 4.12 3.898
The surface air absorbed heat,
E+15 KJ
11.56 11.62 11.42 13.58 13.88 14.04 14.19 14.34 16.8 16.94 17.13 17.49 17.58 16.67
The sea surface waters
absorbed heat, E+15 KJ
38.55 38.76 38.05 45.28 46.29 46.71 47.38 47.65 55.59 56.07 56.87 58.08 58.32 55.17
The land surface absorbed
heat, E+15 KJ
7.99 8.035 7.887 9.386 9.596 9.682 9.821 9.877 11.52 11.62 11.79 12.04 12.09 11.44
Sea Ice absorbed heat, E+15 KJ
280.7
282.3
277.1
278.7
285
287.5
291.6
293.3
279.5
282
286
292.1
293.3
277.4
Glaciers absorbed heat, E+15
KJ
20.31 20.43 20.05 23.86 24.4 24.61 24.97 25.11 29.29 29.55 29.97 30.61 30.73 29.07
Note: The difference between global primary energy consumption and global consumption of non-renewable energy is the
amount of total renewable energy that includes solar, wind, hydro, and biomass—referred to as surface renewable energy (geo-
thermal should not be included in the renewable energy category, but in BP’s report [32] the geothermal is instead included in the
renewable energy. Due to its small amount this won’t affect the results).
CO2 here comes from various sources, not just combustion. However, if only
considering CO2 from fossil fuel burning, it is anticipated that the correlation
between them should be much stronger. Later, the effects of waste heat on sur-
face air, land and seawaters surface temperature anomalies and on sea level rise
will also be investigated and confirmed.
If this is true, then CO2 from combustion could serve as an indirect and easy
indicator to assess how much waste heat pumped into the system and conse-
quently how it impacts the system.
3.1. Surface Air Temperature Changes
Air temperature changes calculated by using Equation (2), the allocated waste

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heat energy entered the surface air boundary layer, the air density, and SHC in
Table 2, are shown in Figure 3 at different layer depths, in which NASA’s an-
nual mean of observed anomalies and 5-year moving Lowess smoothing [33] are
Figure 2. Correlation between waste heat entered climate system and CO2 concentration. The amount of waste heat entered
the environment is based on BP’s Energy Review data [32], and CO2 concentration is based on NASA’s data [4].
Figure 3. Simulations of global surface air temperature changes at different surface air boundary layer depths
during 1965-2019. NASA’s annual mean of observed anomalies (
i.e.
, NASA-Annual Mean) and 5-year moving
Lowess smoothing (
i.e.
, NASA_Lowess (5)) [33] are also shown for comparison.

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also exhibited.
From Figure 3, it is clear that in most of the time, especially the last four dec-
ades, the observed anomalies are very consistent with the calculated results at the
surface air boundary layer depth between 20 - 30 meters, and the Lowess
smoothing anomalies [33] are much closer to that calculated at the depth of 25
meters.
Therefore, it is reasonable to draw that a surface air column with a depth be-
tween 20 - 30 meters can be considered equivalent in simulating its warming
trend, with a 25-meter depth being a representative. However, before late 1970s
this is not true, observed anomalies are much less than those calculated.
Plotting the calculated results of air temperature changes at the surface air
boundary layer depth of 25 meters, NASA Annual Mean and Lowess smoothing
values [33] with the energy entered the surface air obtains the left in Figure 4, in
which the corresponding trend lines are also exhibited. The black solid line on
blue dots represents the strongest relation between the simulation results and the
energy entered the air with a correlation coefficient of 1, which is understanda-
ble. The green triangles and dash line represent NASA’s annual mean anomalies,
the red squares and dot line represent Lowess 5-year smoothing values. It is no-
ticeable that the red dot line and the green dash line are almost overlapped, with
their respective correlation coefficients being 0.9361 and 0.8769 (Figure 4, Left),
advising that the correlations between these observational values and the energy
quantities are very strong. Furthermore, these trend lines of observational values
are also very close to that of the simulations.
As indicated earlier, obvious differences exist between the observations and
the simulations until late 1970s. Excluding these data points and only taking the
rest points since 1980 is indicated at the right in Figure 4. It can be seen that
while the simulations with the energy entered the surface air still show the
strongest relation with the correlation coefficient of 1, NASA’s-Annual Mean
Figure 4. Correlations between surface air temperature changes (anomalies) and the energy entered the surface air layer. Blue dots
and the black line show the relation between temperature changes calculated by Equation (2) and the energy entered the air with a
correlation coefficient of 1; Green triangles and the green dash line represent NASA’s annual mean anomalies [33]; Red squares
and the red dot line represent NASA’s Lowess smoothing results of the observations [33]. Left: during the period of 1965-2019;
Right: during the period of 1980-2019.

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and NASA’s Lowess smoothing against the energy entered the air still exhibit
respectively strong relations with big correlation coefficients of 0.8669 and
0.9618, and their trend lines are much closer to that of the simulations, and even
they almost overlap each other, compared to those in the left figure for the pe-
riod of 1965-2019. This is not by accident.
All these suggest that the temperature changes or anomalies in the surface air
are strongly linked to the extra energy (
i.e.
, waste heat here) entered the air, pro-
viding direct evidence that energy beyond the earth’s energy budget balance en-
tered the surface air caused the air temperature increase. This approach can be
used to predict the future surface air temperature rises.
3.2. Land Surface Temperature Changes
Simulations of land surface temperature changes by using Equation (3) and the
energy entered the land in Table 5 are shown in Figure 5, in which NASA’s an-
nual mean and 5-year Lowess smoothing of observed land temperature anoma-
lies [33] are exhibited as well.
Clearly Figure 5 shows the similar trends to surface air temperature changes
as seen in Figure 3. Before late 1970s the observed anomalies deviate obviously
from the simulations, with more negative anomalies; during the 1980s and 1990s
the observed anomalies fluctuate between the simulations at a boundary layer
depth of 0.015 and 0.15 meters, while in the last two decades the observed ano-
malies fall within the simulations between boundary layer depths of 0.0175 and
0.03 meters, and a depth of 0.025 meters can be a good representative since 1980.
Figure 5. Simulations of global land surface temperature changes at different land surface boundary layer depths during
1965-2019. NASA’s annual mean of observed anomalies (
i.e.
, NASA_Land_Annual) and 5-year moving Lowess smoothing (
i.e.
,
NASA_Land_Lowess (5)) [33] are also shown for comparison.

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These variations may have reflected the changes in methods and technologies of
measurements as well as in natural conditions on the land surface such as vege-
tation covers as well as the effect of ice melting (the land is the most susceptible
to any heat changes because of its smallest SHC among the components).
Plotting the simulation results at the boundary layer depth of 0.025 meters,
observed anomalies (NASA_Land_Annual_mean and NASA_Land_Lowess)
against the energy entered the land surface are shown in Figure 6: Left—the data
from 1965-2019 and Right—the data from 1980-2019, revealing clearly that
strong relationships exist either for the NASA_Land_Annual_mean vs the ener-
gy or the NASA_Land_Lowess vs the energy, and both have big correlation coef-
ficients, while the simulation’s correlation coefficient is 1.
Again, after excluding the data before 1980, the observational trends
(NASA_Land_Annual_mean and NASA_Land_Lowess) in the right figure of
Figure 6 are much closer to the simulations than shown in the left. All these in-
dicate that strong relationship exists between the land surface temperature
change and the energy absorbed. This approach can be used to predict the future
land surface temperature rises.
3.3. Sea Surface Temperature Changes
Simulations of sea surface temperature changes by using Equation (4) and the
energy entered sea surface waters in Table 5 are shown in Figure 7, in which
NASA’s annual mean and 5-year Lowess smoothing of observed sea surface
temperature anomalies [33], NOAA’s annual anomalies [35] are exhibited too.
Similarly, before late 1970s the observed anomalies deviate obviously from the
simulations, with more negative anomalies. During 1980s and 1990s the ob-
served anomalies fluctuate between the simulations at boundary layer depth of
0.03 and 0.1 meters, while in the last two decades the observed anomalies fall
Figure 6. Correlations between land surface temperature changes (anomalies) and the energy entered the land surface layer. Blue
dots and the blue solid line show the relation between temperature changes calculated by Equation (3) and the energy entered the
land surface with a correlation coefficient of 1; Orange squares and the orange dash line represent NASA_Land_Annual_mean
[33]; Black triangles and the black solid line represent the 5-year NASA_Land_Lowess smoothing of observations [33], respective-
ly. Left: during the period of 1965-2019; Right: during the period of 1980-2019.

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Figure 7. Simulations of global sea surface temperature changes at different sea surface boundary layer depths during 1965-2019.
NASA’s annual mean of observed anomalies (
i.e.
, NASA_Ocean_Annual) and 5-year moving Lowess smoothing (
i.e.
,
NASA_Ocean_Lowess (5)) [33] and NOAA’s annual mean anomalies (NOAA_Ocean_Annual) [35] are also shown for comparison.
within the simulations between boundary layer depth of 0.04 and 0.08 meters,
and a depth of 0.06 meters can be a good representation since 1980. These varia-
tions may have reflected the changes in methods and technologies of measure-
ments, in natural conditions of the sea surfaces such as the chemical and physi-
cal properties of surface seawaters as well as the effect of ice melting.
Plotting the simulation results at the seawaters boundary layer depth of 0.06
meters, observed anomalies (NASA_Ocean_Annual, NASA_Ocean_Lowess and
NOAA_Ocean_Annual) against the energy entered the seawaters are shown in
Figure 8: Left contains the data from 1965-2019 and Right contains data from
1980-2019, revealing clearly that strong relationships exist either for the
NASA_Ocean_Annual vs the energy or the NASA_Ocean_Lowess vs the energy,
or NOAA_Ocean_Annual vs the energy. All these observed anomalies trend
lines have big correlation coefficients (about 0.80 or greater), and the simula-
tion’s correlation coefficient is 1.
Additionally, it is very interesting that the NOAA_Ocean_Annual trend line is
almost parrallel to the simulation one (
cf.
their slopes), which is important and
supports that the temperature changes are due to the contribution of energy. In
general, all these indicate that strong relationship exists between the sea surface
temperature changes and the energy absorbed.
This approach can be used to predict the future sea surface temperature rises.
3.4. Sea Level Rise
In this study, the relationship between sea level rise and the intake energy asso-

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ciated with ices melting and seawaters warming has been explored. The ices in-
clude sea ices (
i.e.
, ice sheets and covers), glaciers (including landfast ices).
Based on energy absorbed respectively by sea ices and glaciers as listed in Ta-
ble 5, the amounts of melted sea ices and glaciers by Equations (5) and (6) are
shown in Figure 9.
Figure 8. Correlations between sea surface temperature changes (anomalies) and the energy entered the sea surface layer. Blue
dots and the blue solid line show the relation between temperature changes calculated by Equation (4) and the energy entered the
sea surface with a correlation coefficient of 1; Orange dots and the orange dot line represent NASA’s sea surface annual mean of
observational anomalies (NASA_Ocean_Annual) [33]; Black squares and the black dash line represent the 5-year Lowess smooth-
ing of NASA’s observational anomalies (NASA_Ocean_Lowess (5) [33]; Purple triangles and the purple dot line represent
NOAA’s sea surface annual mean of observational anomalies [35]. Left: during the period of 1965-2019; Right: during the period
of 1980-2019.
Figure 9. Melted ices amount (E+13 Kg) during 1965-2019. The top line (yellow) is the amount of melted sea ices, and the bottom
one (blue) is the amount of melted glaciers.

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Sea level rise consists of the following: 1) added depth from melted ices (or
meltwaters), 2) added depth from meltwaters due to thermal expansion from
melting point to the average of global sea surface temperatures, 3) added depth
of meltwaters due to thermal expansion from seawaters warming and 4) added
depth due to thermal expansion of bulk seawaters warming.
Specifically, the contribution from sea ices melting can be calculated as below:
1) added depth from melted sea ices;
2) added depth from melted sea ices due to thermal expansion from melting
point (Table 1) to the average of global seawater surface temperature. The aver-
age seawater surface temperature during the melting season (May ~ Sept) is de-
termined from the NCEI’s ERSST V4 datasets [29] as shown in Table 3;
3) added depth from melted sea ices due to thermal expansion from the sur-
face seawaters warming,
i.e.
, seawater surface temperature rising as discussed in
the foresaid section “Sea Surface Temperature Change”. The value of tempera-
ture rising is that calculated at the sea surface boundary layer depth of 0.06 me-
ters.
The contribution from glaciers can be calculated as below:
1) added depth from melted glaciers;
2) added depth from melted glaciers due to thermal expansion from melting
point (
i.e.
, 0˚C) to the corresponding global average seawater surface tempera-
ture (Table 3). The corresponding average seawater surface temperature at the
melting seasons (May ~ Sept) is determined from the NCEI’s ERSST V4 datasets
[29];
3) added depth from melted glaciers due to thermal expansion from the sur-
face seawater warming,
i.e.
, seawater surface temperature rising as discussed in
the foresaid section “Sea Surface Temperature Change”. The value of tempera-
ture rising is that calculated at the seawater surface boundary layer depth of 0.06
meters.
Seawaters (including the deep waters) are subject to thermal expansion due to
the cumulated entrained heat energy. According to Luann D. and Rebecca L.
[36], “the 1993-2020 heat-gain rates (in oceans) were 0.37 - 0.41 Watts per
square meter for depths from 0 - 700 meters, depending on which research
group’s analysis you consult. Meanwhile, heat gain rates were 0.15 - 0.31 Watts
per square meter for depths of 700 - 2000 meters. For depths between 2000 -
6000 meters, the estimated increase was 0.06 Watts per square meter for the pe-
riod from June 1992 to July 2011”, the average temperature rise due to this heat
gain is estimated about 3.23446E−05˚C per year for the full ocean depths. Sup-
pose this temperature rise applies to the entire period of this study and a depth
of 2000 meters is taken as a representative for the entire ocean depths, seawaters’
thermal expansion coefficient is 1.57E−4 K−1 under the pressure of 2000 decibars
(in oceans one meter depth is about equivalent to 1 decibar) and 5˚C at a salinity
of 35 ppts [37].
The simulated global sea level rise (GSLR) that accumulates annually is shown

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in Figure 10, where the measured global mean sea level rise (GMSL, mm) [38] is
also exhibited. Clearly, they have similar trends. Since the GMSL is an accumu-
lated record since 1880, and in 1965 its value is 86.8 mm, adjusting the GMSL by
subtracting 85 mm can easily compare with the simulations which start from
1965. The adjusted GMSL (
i.e.
, GMSL-85) is also shown in Figure 10. Clearly at
an early time the simulation is very close to the measurements, however, an in-
creasing departure from the simulation appears with time especially in the last
two decades. The potential reasons for this departure may be attributed to 1)
volcanic eruptions, wildfires, coal fires [39], natural gas fires and explosions [40]
[41], flares as well as various chemical fuels used in military and space explora-
tions, etc.; 2) increase in the amount of melted multiple-year sea ices in which
salinity is very low and even close to zero (0), compared to simulations with
higher salinity.
Increased volcanic eruptions have been reported since 2000 [42], although the
actual number may be greater and possibly remain unchanged due to inaccessi-
bility and technological difficulties. Volcanic eruptions eject substantial energy
into the climate system that can change the climate, for example, the eruption of
Mount St. Helens in 1980 released 24 megatons of thermal energy, equivalent to
1600 times the size of the atomic bomb dropped on Hiroshima [43], that is about
0.1E+15 KJ. However, huge amount of volcanic dust, etc. may also shelter solar
radiations, leading to a decrease in the temperature in the system afterwards.
Additionally, wildfires occur recently more frequently and intensively such as
those in California and Australia. According to Jonathan [44], the annual energy
released from wildfires in Western United States is about 1.4E+15 KJ. Although
these wildfires are fueled by the biomass that is included in the earth’s energy
budget balance during a long growing time, the concentrated burning and
Figure 10. Cumulated simulation of sea level rise from annual melted sea ices and glaciers as well as seawa-
ters warming, measured global mean sea level rise (
i.e.
, GMSL) [38] and adjusted GMSL (
i.e.
, Adjusted GMSL
(GMSL-85)).

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release of this cumulated energy in a short time will impact the system’s behavior.
These compounding effects are significant on the system.
Figure 11 plots the sea level rise (both simulated sea level rise,
i.e.
, cumulated
calculated GSLR and GMSL-85) against the energy associated with ice melting
(
i.e.
, the sum of the energies melting sea ices and glaciers respectively). Poly-
nomial regressions show clearly that both the measurements and the simulations
have strong correlations with the energy.
According to the regression of GMSL-85 in Figure 11, the energy that melted
ices (
i.e.
, sea ices and glaciers) in 2019 should be 339.84E+15 KJ, compared to
the allocated waste heat of 324.02E+15 KJ, the difference of 15.82E+15 KJ could
be the extra energy coming from those forementioned energy sources. It is
known that in 2019 the energy allocated to melting sea ices and glaciers is about
78.65% of total waste heat entered the climate system, thus this extra energy
melting the ices corresponds to a total extra energy of 20.12E+15 KJ. That would
contribute to extra temperature rises in air, land, and oceans of about 0.021˚C
(depth: 25 m), 0.028˚C (depth: 0.025 m) and 0.014˚C (depth: 0.06 m), respec-
tively. These correspond to adjusted simulation results of 0.97˚C, 1.34˚C and
0.67˚C, being much closer to their corresponding observations: 0.99˚C, 1.44˚C
and 0.68˚C [33].
Furthermore, Figure 12 shows the contributions to sea level rise of sea ices
melting, glaciers melting and bulk seawaters warming on an annual basis (top)
and cumulated basis (bottom) when sea ice salinity changes from 12 (1965) to 4
ppts (2019). Clearly, on an annual basis (top), the melting of sea ices is the do-
minant contributor to sea level rise, sharing about 90.2% - 95.6%, and glaciers
contributing about 3.6% - 9.5% while bulk seawaters warming only contributes
about 0.4% - 1.2%.
Figure 11. Relation between sea level rise and the energy associated with ice melting. Blue dots and the blue line represent the
simulated results (GSLR), while orange dots and the orange line represent the measurement results less 85 mm (GMSL-85).

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Figure 12. Contributions to sea level rise of sea ice melting, glaciers melting and bulk seawater warming on an annual and cumu-
lated basis when sea ices salinity changes from 12 to 4 ppts from 1965 to 2019. Upper: On annual basis; Bottom: on a cumulated
basis since 1965.
On the cumulated basis (bottom), the melting of sea ices still dominates the
sea level rise, with its contribution ranging from 93.6% - 95.5%, glaciers melting
contributes about 3.6% - 5.8% and bulk seawaters warming only shares about
0.5% - 1.2%.
We can therefore forecast future sea level rise if the salinity in sea ices and the
global EECE as well as global energy consumption, etc. are known.
3.5. Miscellaneous
3.5.1. Cold Era and Warm Era
According to NOAA [45], the Earth has experienced a gradual warming since
the early 1900s as indicated in Figure 13 in which before 1900 the globe was
cooling gradually from 1880. The fluctuations in temperature anomalies between

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Figure 13. Global land and ocean (
i.e.
, surface air) temperature anomalies from 1880 to 2020 based on NOAA’s analyses [45].
Earth was in a gradual cooling period until the early 1900s, then turned to gradually warming although there were fluctuations in
the temperature anomalies between the 1940s and late 1970s, which might be attributed to ENSO cycles: El Niño and La Niña, etc.
the early 1940s and late 1970s suggest that this is a transitional period and sus-
ceptive to any disturbance due to the ENSO cycles,
i.e.
, El Niño and La Niña, etc.
Alternatively, when looking at the heat content in oceans (Figure 14), a simi-
lar phenomenon is observed that before around mid-1980s the heat energy in
the top half-mile depth of the oceans is negative compared to the average be-
tween 1955 and 2006 [36], which indicates that the oceans were heat energy
“hungry”,
i.e.
, “aggressively” absorbing heat, and after that the oceans are “rich”
in heat and storing more heat.
These suggest that the globe has experienced a change from a “cold” era to a
warm one as the warming proceeds. These changes are generally consistent with
the changes in sea surface temperatures as shown in Figure 15 [29], where, si-
milarly, the temperature was declining before 1904 and thereafter turning to an
increasing trend journey, which coincides with Figure 1. This can be attributed
to the industrialization that has consumed increasingly more fossil fuels and dis-
sipated more waste heat into the climate system, driving the warming.
However, a time lag (several decades) exists between the two phenomena
shown in Figure 13 and Figure 14, which is dependent on the baseline used and
due to the difference between their medium’s SHC. Although the time points at
which the values of temperature anomalies and heat content turn from negative
to positive are different, it is reasonable to believe that the Earth entered a warm
era from around 1980.
This cold-to-warm-era transition can easily explain what we’ve seen in the

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Figure 14. Global ocean heat content changes with time compared to the 1955-2006 average, more than
ninety percent of the heat trapped in the climate system entered the oceans [36].
Figure 15. Sea surface temperature change from 1854 to 2020 based on NOAA’s datasets of ERSST v4 [29].

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simulations of temperature changes in surface air, land surface, and seawaters
surface in the early years where the simulation results are always greater than the
measurements. This is due to in the cold era the system (including air, land,
seawaters, especially ices and possibly permafrost) needs great deal of heat to
balance its energy requirement. However, we need more knowledge about the
system then to better understand its behaviors.
Additionally, permafrost may also contribute to the early “abnormal” beha-
viors in air, land, and seawaters’ temperature anomalies by absorbing much heat
before exhibiting evidence of “thawing”. As discussed above, large amount of
waste heat has been cumulated towards the polar areas, especially the North be-
cause of the densified population, increased fossil fuel consumption and thus in-
creased associated waste heat release, via the atmospheric vortexes.
3.5.2. Global Warming Forecast
Figure 16 shows the waste heat energy distribution in the climate system where
the steps indicate the change of salinity in sea ices. Among the total energy that
entered the climate system, about 92.8% - 96.8% entered the oceans through
melting sea ices and glaciers, warming surface seawaters (this aligns well with
the estimate by Luann D. and Rebecca L. [36], and Figure 14 above), 1.9% -
4.3% absorbed by air, with the remainder (about 1.3% - 2.9%) absorbed by the
land. Oceans therefore become the primary energy storage, the buffer and regu-
lator of temperature changes in the system.
If the energy (mainly non-renewable energy) consumption, energy effective
conversion efficiency and the salinity in sea ices are known, it is possible to
Figure 16. Waste heat energy distribution in the climate system. Energy entered surface air and land surface are shown by the
secondary vertical axis, and energy entered oceans is indicated by primary vertical axis.

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forecast the temperature increases in surface air, land, and oceans as well as sea
level rise with very good certainty and reliability.
There are two methods that can be used for forecasting future temperature
rises in air, land and oceans, and sea level rise. One is based on the thermody-
namic equations as discussed earlier; and another is based on the empirical re-
gression equations found in the corresponding regression figures above.
Using the thermodynamic equations and the 2020 global energy consumption
[32], the calculated temperature rises in surface air, land, and oceans are 0.90˚C,
1.24˚C, 0.62˚C, respectively, and the sea level rise is 104.7 mm. They are close to
the observations: surface air 0.98˚C, land surface 1.58˚C, sea surface 0.75˚C [46],
sea level rise 91.3 mm [47], although differences exist that can be attributed to
the extra heat energy from natural sources and chemical fuels as discussed earli-
er. If considering these extra energy contributions, these simulated values will be
much closer to what observed. Remember that 2020 was the second warmest
year in the 141-year record for air surface, and the hottest one for land surface
[48] (large scale wildfires might have contributed to the hottest land surface),
this may help to understand why such differences exist.
BP’s 2020 Energy Outlook [49] imagines three possible scenarios
i.e.
, Rapid,
NetZero and Business-as-usual (BAU), regarding how the energy economy
would be transitioned in future. The energy consumptions in 2050 are estimated
in Table 6 where renewables consist of wind, solar and biofuels. It is noted that
the biofules also include geothermal. The latter is a non-surface renewable ener-
gy and should be considered in the simulation, but its amount is very small, thus
its impacts on the earth’s energy budget balance can be negligeable. Besides, the
non-combusted fossils such as feedstocks for petrochemicals, etc. are excluded
from this calculation.
Assuming that in 2050 the salinity in sea ices remain 4 ppts, global sea surface
temperature about 14.5˚C and the polar areas’ sea surface temperature in the
melting season is about 0.2˚C, the EECE is 20% under BAU, 30% under Rapid
Table 6. Energy Outlook at 2050 (E+18 J) [49].
Scenario
Rapid
Net Zero
BAU
Total
625
625
725
Oil
89
42
172
Natural Gas
134
81
187
Coal
24
12
123
Nuclear
44
57
31
Hydro
57
62
51
Renewables (incl. biofuels)
277
370
161
Non-Combusted
44
28
53
Net Non-renewables
247
165
460

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and 25% under NetZero scenarios. The forecast values of the climate change in-
dicators (temperature risings of surface air, land surface, sea surface and sea level
rise) are shown in Table 7. The forecast for temperature changes is performed by
the two aforesaid methods: theoretical methods with Equations (2)-(4); Regression
equations of simulated results and regression equations of measurements in Fig-
ure 4, Figure 6, Figure 8 and Figure 11, respectively. With the endeavors and
measures to be taken, the climate change indicators are remarkably suppressed
under the forecast energy consumption scenarios compared to the current levels.
The results produced by different methods are very close for temperatures.
With respect to sea level rise, the situation is little different as shown in Table
7, because decreased fossil fuels and increased surface renewables will be con-
sumed. Using the theoretical method produces similar results under the three
scenarios, which is very reliable by comparing to the current rise in sea level.
Under BAU using three methods creates slightly different results, with the big-
gest one from Adjusted GMSL regression, while under Rapid and NetZero sce-
narios, the two regression methods produce much less results than those by
theoretical method. Among the three methods, using Cumulated Calculated
GSLR regression produces the smallest results under all three scenarios. Conse-
quently, using the theoretical approach provided by this thermodynamic model
to forecast sea level rise is highly effective and recommended. Using empirical
regression methods largely underestimates concentratedly-consumed surface
renewable energies’ impacts
3.5.3. Challenges in Future Studies
Accurate data are very limited on global energy’s effective conversion efficiency
Table 7. Forecast climate change indicators under different scenarios by different methods.
Climate Change Indicator
Forecast Method
BAU
NetZero
Rapid
Surface air temperature change at 25 m (˚C)
Theoretical, Equation (2)
0.38
0.14
0.20
Surface air temperature change at 25 m (˚C)
Calculated regression, Figure 4 Right
0.38
0.14
0.20
Surface air temperature change (˚C)
NASA Lowess Regression, Figure 4 Right
0.39
0.15
0.22
Land surface temperature change at 0.25 m (˚C)
Theoretical, Equation (3)
0.51
0.18
0.20
Land surface temperature change at 0.25 m (˚C)
Calculated regression, Figure 6 Right
0.51
0.18
0.28
Land surface temperature change (˚C)
NASA Lowess Regression, Figure 6 Right
0.54
0.17
0.27
Sea surface temperature change at 0.06 m (˚C)
Theoretical, Equation (4)
0.26
0.09
0.14
Sea surface temperature change at 0.06 m (˚C)
Calculated regression, Figure 8 Right
0.26
0.09
0.14
Sea surface temperature change (˚C)
NASA Lowess Regression, Figure 8 Right
0.28
0.14
0.18
Sea surface temperature change (˚C)
NOAA Regression, Figure 8 Right
0.34
0.19
0.23
Cumulated sea level rise (mm)
Theoretical
110
107
106
Cumulated sea level rise (mm)
Cumulated Calculated GSLR regression, Figure 11
103.5
1.7
19.0
Cumulated sea level rise (mm)
Adjusted GMSL Regression, Figure 11
135.4
3.0
21.5

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(EECE) at various processes, sea ices salinity, melting point and thermodynamic
properties of permafrost (density, SHC, melting points, etc). Therefore, collect-
ing more accurate and reliable data will be crucial to allow accurate future mod-
elling.
Additionally, greater attention should be paid to the relationship between ex-
treme weather events and the heat accumulated in the environment, especially in
oceans.
4. Conclusions
From these analyses, it is clear that there is no correlation between atmospheric
CO2 or GHGs and the surface air temperature anomalies, which means GHGs
and global warming don’t have a cause-effect relation. CO2 (and other GHGs)
and waste heat are concurrent by-products of fossil fuel combustions, they cor-
relate strongly.
Global warming correlates strongly with the waste heat. A huge amount of
waste heat from human activities provides enough energy to warm the climate
system: arising temperatures of air, land, and oceans as well as melting ices (sea
ices and glaciers) to raise sea level. Waste heat distributes among the compo-
nents of the climate system according to their specific heat capacities, about
92.8% - 96.8% entered oceans through melting ice and warming seawaters, 1.9%
- 4.3% into the air and 1.3% - 2.9% into the land. The climate system had transi-
tioned from a cold to a warm era around 1980.
Thermodynamic simulations by means of the climate change thermodynamic
model are well consistent with the observations in air, land and oceans’ temper-
ature anomalies and sea level rise, providing direct evidence that the warming is
caused dominantly by the waste heat. Other extra energies entered the system
also contribute to the warming. Both approaches of using thermodynamic model
and using regression equations can be used for future temperature change fore-
casting.
Sea level rise comes from 1) meltwater, 2) meltwater thermal expansion from
melting point to the average surface seawater temperature, 3) meltwater thermal
expansion due to the surface seawater warming and 4) thermal expansion of
seawaters by the retained heat energy. Using the thermodynamic approach to
forecast future sea level rise is recommended.
Based on these, efficiently fighting global warming needs to control and re-
duce waste heat from human activities. Increasing energy’s effective conversion
efficiency (EECE), rather than only increasing energy efficiency, changing so-
cietal and personal behaviors, and reducing energy consumption through tech-
nology advancements and retrofits, as well as largely developing and prudential-
ly planning the use of surface renewable energies are vital. Recovering and reus-
ing waste heat from the environment will also help greatly mitigate climate
change.
These findings invite the international communities to further review and

Q. H. Bian
DOI:
10.4236/oalib.1108945 34
Open Access Library Journal
re-investigate the real root cause of the current climate change from different
angles: is it GHGs or waste heat or other factors? So that the right, effective and
efficient solutions can be developed.
Acknowledgements
The author is grateful for his family’s unconditional support in conducting this
hobby research. He also wants to extend his thanks to Jennifer Roebuck at Na-
tional Snow & Ice Data Center (NSIDC) who directed him to the right place for
data searching.
Conflicts of Interest
The author declares no conflicts of interest.
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