Human-induced atmospheric composition changes cause a radiative imbalance at
the top of the atmosphere which is driving global warming. This Earth energy imbalance (EEI) is the most critical number defining the prospects for continued global warming and climate change. Understanding the heat gain of
the Earth system – and particularly how much and where the heat is
distributed – is fundamental to understanding how this affects warming
ocean, atmosphere and land; rising surface temperature; sea level; and loss
of grounded and floating ice, which are fundamental concerns for society.
This study is a Global Climate Observing System (GCOS) concerted
international effort to update the Earth heat inventory and presents an
updated assessment of ocean warming estimates as well as new and updated estimates
of heat gain in the atmosphere, cryosphere and land over the period
1960–2018. The study obtains a consistent long-term Earth system heat gain
over the period 1971–2018, with a total heat gain of 358±37 ZJ,
which is equivalent to a global heating rate of 0.47±0.1 W m-2.
Over the period 1971–2018 (2010–2018), the majority of heat gain is reported
for the global ocean with 89 % (90 %), with 52 % for both periods in
the upper 700 m depth, 28 % (30 %) for the 700–2000 m depth layer and 9 % (8 %) below 2000 m depth. Heat gain over land amounts to 6 %
(5 %) over these periods, 4 % (3 %) is available for the melting of
grounded and floating ice, and 1 % (2 %) is available for atmospheric warming. Our
results also show that EEI is not only continuing, but also increasing: the EEI
amounts to 0.87±0.12 W m-2 during 2010–2018. Stabilization of
climate, the goal of the universally agreed United Nations Framework Convention on Climate
Change (UNFCCC) in 1992 and the Paris
Agreement in 2015, requires that EEI be reduced to approximately zero to
achieve Earth's system quasi-equilibrium. The amount of CO2 in the
atmosphere would need to be reduced from 410 to 353 ppm to increase heat
radiation to space by 0.87 W m-2, bringing Earth back towards energy
balance. This simple number, EEI, is the most fundamental metric that the
scientific community and public must be aware of as the measure of how well
the world is doing in the task of bringing climate change under control, and
we call for an implementation of the EEI into the global stocktake based on
best available science. Continued quantification and reduced uncertainties
in the Earth heat inventory can be best achieved through the maintenance of
the current global climate observing system, its extension into areas of
gaps in the sampling, and the establishment of an international framework for
concerted multidisciplinary research of the Earth heat inventory as
presented in this study. This Earth heat inventory is published at the German Climate Computing Centre (DKRZ, https://www.dkrz.de/, last access: 7 August 2020) under the DOI
10.26050/WDCC/GCOS_EHI_EXP_v2
(von Schuckmann et al., 2020).
Introduction
In the Paris Agreement of the United Nations Framework Convention on Climate
Change (UNFCCC), article 7 demands that “Parties should strengthen
[…] scientific knowledge on climate, including research,
systematic observation of the climate system and early warning systems, in a
manner that informs climate services and supports decision-making”. This
request of the UNFCCC expresses the need of climate monitoring based on best
available science, which is globally coordinated through the Global Climate
Observing System (GCOS). In the current Implementation Plan of GCOS, main
observation gaps are addressed and it states that “closing the Earth's
energy balance […] through observations remain outstanding
scientific issues that require high-quality climate records of Essential
Climate Variables (ECVs).” (GCOS, 2016). GCOS is asking the
broader scientific community to establish the observational requirements
needed to meet the targets defined in the GCOS Implementation Plan and to
identify how climate observations could be enhanced and continued into the
future in order to monitor the Earth's cycles and the global energy budget.
This study addresses and intends to respond to this request.
The state, variability and change of Earth's climate are to a large extent
driven by the energy transfer between the different components of the Earth
system (Hansen, 2005; Hansen
et al., 2011). Energy flows alter clouds, and weather and internal climate
modes can temporarily alter the energy balance on subannual to
multidecadal timescales (Palmer and McNeall,
2014; Rhein et al., 2013). The most practical way to monitor climate state,
variability and change is to continually assess the energy, mainly in the
form of heat, in the Earth system (Hansen et al., 2011).
All energy entering or leaving the Earth climate system does so in the form
of radiation at the top of the atmosphere (TOA) (Loeb et
al., 2012). The difference between incoming solar radiation and outgoing
radiation, which is the sum of the reflected shortwave radiation and emitted
longwave radiation, determines the net radiative flux at TOA. Changes of
this global radiation balance at TOA – the so-called Earth energy imbalance
(EEI) – determine the temporal evolution of Earth's climate: If the
imbalance is positive (i.e., less energy going out than coming in), energy in
the form of heat is accumulated in the Earth system, resulting in global
warming – or cooling if the EEI is negative. The various facets and impacts
of observed climate change arise due to the EEI, which thus represents a
crucial measure of the rate of climate change
(von Schuckmann et al., 2016). The EEI is the
portion of the forcing that has not yet been responded to
(Hansen, 2005). In other words,
warming will continue even if atmospheric greenhouse gas (GHG) amounts are
stabilized at today's level, and the EEI defines additional global warming
that will occur without further change in forcing (Hansen
et al., 2017). The EEI is less subject to decadal variations associated with
internal climate variability than global surface temperature and therefore
represents a robust measure of the rate of climate change
(von Schuckmann et al., 2016; Cheng et al.,
2017a).
The Earth system responds to an imposed radiative forcing through a number
of feedbacks, which operate on various different timescales. Conceptually,
the relationships between EEI, radiative forcing and surface temperature
change can be expressed as (Gregory and
Andrews, 2016)
ΔNTOA=ΔFERF-|αFP|ΔTS,
where ΔNTOA is Earth's net energy imbalance at TOA (in W m-2), ΔFERF is the effective radiative
forcing (W m-2), ΔTS is the global surface temperature anomaly (K)
relative to the equilibrium state and αFP is the net total feedback
parameter (W m-2 K-1), which represents the combined effect of the
various climate feedbacks. Essentially, αFP in Eq. (1) can be viewed
as a measure of how efficient the system is at restoring radiative
equilibrium for a unit surface temperature rise. Thus, ΔNTOA represents
the difference between the applied radiative forcing and Earth's radiative
response through climate feedbacks associated with surface temperature rise
(e.g., Hansen et al., 2011). Observation-based estimates of ΔNTOA are
therefore crucial both to our understanding of past climate change and for
refining projections of future climate change
(Gregory and Andrews,
2016; Kuhlbrodt and Gregory, 2012). The long atmospheric lifetime of carbon
dioxide means that ΔNTOA, ΔFERF and ΔTS will remain positive
for centuries, even with substantial reductions in greenhouse gas emissions,
and lead to substantial committed sea-level rise
(Cheng et al., 2019a; Hansen et
al., 2017; Nauels et al., 2017; Palmer et al., 2018).
However, this conceptual picture is complicated by the presence of unforced
internal variability in the climate system, which adds substantial noise to
the real-world expression of this equation
(Gregory
et al., 2020; Marvel et al., 2018; Palmer and McNeall, 2014). For example,
at timescales from interannual to decadal periods, the phase of the El
Niño–Southern Oscillation contributes to both positive or negative
variations in EEI (Cheng et al., 2019a; Loeb et al., 2018; Johnson and Birnbaum, 2017; Loeb
et al., 2012). At multidecadal and longer timescales, systematic changes
in ocean circulation can significantly alter the EEI as well
(Baggenstos et al., 2019).
Timescales of the Earth climate response to perturbations of the
equilibrium Earth energy balance at TOA are driven by a combination of
climate forcing and the planet's thermal inertia: the Earth system tries to
restore radiative equilibrium through increased thermal radiation to space
via the Planck response, but a number of additional Earth system feedbacks
also influence the planetary radiative response
(Lembo
et al., 2019; Myhre et al., 2013). Timescales of warming or cooling of the
climate depend on the imposed radiative forcing, the evolution of climate
and Earth system feedbacks, with ocean and cryosphere in particular leading
to substantial thermal inertia
(Clark et al., 2016; Marshall et al.,
2015). Consequently, it requires centuries for Earth's surface temperature
to respond fully to a climate forcing.
Contemporary estimates of the magnitude of the Earth's energy imbalance
range between about 0.4 and 0.9 W m-2 (depending on estimate method and
period; see also conclusion) and are directly attributable to increases in
carbon dioxide and other greenhouse gases in the atmosphere from human
activities (Ciais et al., 2013;
Myhre et al., 2013; Rhein et al., 2013; Hansen et al., 2011). The estimate
obtained from climate models (CMIP6) as presented by
Wild (2020) amounts to 1.1±0.8 W m-2.
Since the period of industrialization, the EEI has become increasingly
dominated by the emissions of radiatively active greenhouse gases, which
perturb the planetary radiation budget and result in a positive EEI. As a
consequence, excess heat is accumulated in the Earth system, which is
driving global warming (Hansen et al., 2005, 2011). The majority (about
90 %) of this positive EEI is stored in the ocean (Rhein et al., 2013) and
can be estimated through the evaluation of ocean heat content (OHC, e.g.,
Abraham et al., 2013). According to previous estimates, a small proportion
(∼3 %) contributes to the melting of Arctic sea ice and
land ice (glaciers, the Greenland and Antarctic ice sheets). Another 4 %
goes into heating of the land and atmosphere (Rhein et al., 2013).
Knowing where and how much heat is stored in the different Earth system
components from a positive EEI, and quantifying the Earth heat inventory, is
of fundamental importance to unravel the current status of climate change,
as well as to better understand and predict its implications, and to design
the optimal observing networks for monitoring the Earth heat inventory.
Quantifying this energy gain is essential for understanding the response of
the climate system to radiative forcing and hence to reduce uncertainties
in climate predictions. The rate of ocean heat gain is a key component for
the quantification of the EEI, and the observed surface warming has been
used to estimate the equilibrium climate sensitivity
(e.g., Knutti and Rugenstein, 2015). However, further
insight into the Earth heat inventory, particularly to further unravel
where the heat is going, can have implications on the understanding of the
transient climate responses to climate change and consequently reduces
uncertainties in climate predictions (Hansen et al., 2011). In this paper, we
focus on the inventory of heat stored in the Earth system. The first four
sections will introduce the current status of estimate of heat storage
change in the ocean, atmosphere, land and cryosphere, respectively.
Uncertainties, current achieved accuracy, challenges and recommendations
for future improved estimates are discussed for each Earth system component
and in the conclusion. In the last chapter, an update of the Earth heat
inventory is established based on the results of Sects. 1–4, followed by a
conclusion.
Heat stored in the ocean
The storage of heat in the ocean leads to ocean warming
(IPCC, 2020) and is a major contributor to sea-level rise through thermal
expansion (WCRP, 2018). Ocean warming alters ocean
stratification and ocean mixing processes
(Bindoff et al., 2020), affects ocean currents
(Hoegh-Guldberg,
2020; Rhein et al., 2018; Yang et al., 2016), impacts tropical
cyclones (Hoegh-Guldberg,
2020; Trenberth et al., 2018; Woollings et al., 2012), and is a major player
in ocean deoxygenation processes (Breitburg
et al., 2018) and carbon sequestration into the ocean
(Bopp et al., 2013; Frölicher et
al., 2018). Together with ocean acidification and deoxygenation, ocean
warming can lead to dramatic changes in ecosystems, biodiversity, population
extinctions, coral bleaching and infectious disease, as well as
redistribution of habitat
(García Molinos
et al., 2016; Gattuso et al., 2015; Ramírez et al., 2017). Implications
of ocean warming are also widespread across Earth's cryosphere
(Jacobs
et al., 2002; Mayer et al., 2019; Polyakov et al., 2017; Serreze and Barry,
2011; Shi et al., 2018). Examples include the basal melt of ice shelves
(Adusumilli et al., 2020; Pritchard et
al., 2012; Wilson et al., 2017) and marine-terminating glaciers
(Straneo and Cenedese, 2015), as well as the retreat and speedup
of outlet glaciers in Greenland (King et al., 2018)
and in Antarctica (Shepherd et al., 2018a) and of
tidewater glaciers in South America and in the High Arctic
(Gardner et al., 2013).
Opportunities and challenges in forming OHC estimates depend on the
availability of in situ subsurface temperature measurements, particularly
for global-scale evaluations. Subsurface ocean temperature measurements
before 1900 had been obtained from shipboard instrumentation, culminating
in the global-scale Challenger expedition (1873–1876)
(Roemmich and Gilson, 2009). From 1900 up to the
mid-1960s, subsurface temperature measurements relied on shipboard
Nansen bottle and mechanical bathythermograph (MBT) instruments
(Abraham et al., 2013), only allowing limited
global coverage and data quality. The inventions of the
conductivity–temperature–depth (CTD) instruments in the mid-1950s and the
expendable bathythermograph (XBT) observing system about 10 years later
increased the oceanographic capabilities for widespread and accurate (in the
case of the CTD) measurements of in situ subsurface water temperature
(Abraham et al., 2013; Goni et
al., 2019).
With the implementation of several national and international programs, and
the implementation of the moored arrays in the tropical ocean in the 1980s,
the Global Ocean Observing System (GOOS, https://www.goosocean.org/, last access: 7 August 2020) started
to grow. Particularly the global World Ocean Circulation Experiment (WOCE)
during the 1990s obtained a global baseline survey of the ocean from
top to bottom (King et al.,
2001). However, measurements were still limited to fixed point platforms,
major shipping routes, and naval and research vessel cruise tracks, leaving
large parts of the ocean undersampled. In addition, detected instrumental
biases in MBTs, XBTs and other instruments pose a further challenge for the
global scale OHC estimate
(Abraham
et al., 2013; Ciais et al., 2013; Rhein et al., 2013), but significant
progress has been made recently to correct biases and provide high-quality
data for climate research
(Boyer
et al., 2016; Cheng et al., 2016; Goni et al., 2019; Gouretski and Cheng,
2020). Satellite altimeter measurements of sea surface height began in 1992
and are used to complement in situ-derived OHC estimates, either for
validation purposes
(Cabanes et al., 2013)
or to complement the development of global gridded ocean temperature fields
(Guinehut et al., 2012; Willis et
al., 2004). Indirect estimates of OHC from remote sensing through the global
sea-level budget became possible with satellite-derived ocean mass
information in 2002
(Dieng
et al., 2017; Llovel et al., 2014; Loeb et al., 2012; Meyssignac et al.,
2019; von Schuckmann et al., 2014).
After the OceanObs conference in 1999, the international Argo profiling
float program was launched with first Argo float deployments in the same
year (Riser et al., 2016; Roemmich
and Gilson, 2009). By the end of 2006, Argo sampling had reached its
initial target of data sampling roughly every 3∘ between
60∘ S and 60∘ N. However, due to technical evolution, only
40 % of Argo floats provided measurements down to 2000 m depth in the year
2005, but that percentage increased to 60 % in 2010 (von
Schuckmann and Le Traon, 2011). The starting point of the Argo-based best estimate for near-global-scale (60∘ S–60∘ N) OHC is
either defined in 2005 (von Schuckmann and Le Traon, 2011) or in 2006
(Wijffels et al., 2016). The opportunity for improved OHC
estimation provided by Argo is tremendous and has led to major advancements
in climate science, particularly on the discussion of the EEI
(Hansen
et al., 2011; Johnson et al., 2018; Loeb et al., 2012; Trenberth and
Fasullo, 2010; von Schuckmann et al., 2016; Meyssignac et al., 2019). The
near-global coverage of the Argo network also provides an excellent test bed
for the long-term OHC reconstruction extending back well before the Argo
period (Cheng et al., 2017b). Moreover,
these evaluations inform further observing system recommendations for global
climate studies, i.e., gaps in the deep ocean layers below 2000 m depth, in
marginal seas, in shelf areas and in the polar regions (e.g., von Schuckmann
et al., 2016), and their implementations are underway, for example for deep
Argo (Johnson et al., 2019).
Different research groups have developed gridded products of subsurface
temperature fields for the global ocean using statistical models
(Gaillard
et al., 2016; Good et al., 2013; Ishii et al., 2017; Levitus et al., 2012)
or combined observations with additional statistics from climate models
(Cheng et al., 2017b). An exhaustive
list of the pre-Argo products can be found in, for example, Abraham et al. (2013), Boyer et al. (2016), WCRP (2018) and Meyssignac et al. (2019). Additionally,
specific Argo-based products are listed on the Argo web page
(http://www.argo.ucsd.edu/, last access: 7 August 2020). Although all products rely more or less on the
same database, near-global OHC estimates show some discrepancies which
result from the different statistical treatments of data gaps, the choice of
the climatology, and the approach used to account for the MBT and XBT
instrumental biases
(Boyer
et al., 2016; Wang et al., 2018). Argo-based products show smaller
differences, likely resulting from different treatments of currently
undersampled regions (e.g., von Schuckmann et al., 2016). Ocean reanalysis
systems have been also used to deliver estimates of near-global OHC
(Meyssignac et al.,
2019; von Schuckmann et al., 2018), and their international assessments show
increased discrepancies with decreasing in situ data availability for the
assimilation (Palmer et
al., 2017; Storto et al., 2018). Climate models have also been used to study
global and regional ocean heat changes and the associated mechanisms, with
observational datasets providing valuable benchmarks for model evaluation
(Cheng et al., 2016; Gleckler et al., 2016).
International near-global OHC assessments have been performed previously
(e.g., Abraham et al., 2013; Boyer et al., 2016; Meyssignac et al., 2019;
WCRP, 2018). These assessments are challenging, as most of the gridded
temperature fields are research products, and only few are distributed and
regularly updated operationally (e.g., https://marine.copernicus.eu/, last access: 7 August 2020). This
initiative relies on the availability of data products, their temporal
extensions and direct interactions with the different research groups. A
complete view of all international temperature products can be only achieved
through a concerted international effort and over time. In this study, we
do not achieve a holistic view of all available products but present a
starting point for future international regular assessments of near-global
OHC. For the first time, we propose an international ensemble mean and
standard deviation of near-global OHC (Fig. 1) which is then used to build
an Earth climate system energy inventory (Sect. 5). The ensemble spread
gives an indication of the agreement among products and can be used as a
proxy for uncertainty. The basic assumption for the error distribution is
Gaussian with a mean of zero, which can be approximated by an ensemble of
various products. However, it does not account for systematic errors that
may result in biases across the ensemble and does not represent the full
uncertainty. The uncertainty can also be estimated in other ways including
some purely statistical methods
(Levitus et al., 2012) or
methods explicitly accounting for the error sources
(Lyman and Johnson, 2013), but each method has
its caveats, for example the error covariances are mostly unknown, so
adopting a straightforward method with a “data democracy” strategy has
been chosen here as a starting point.
However, future evolution of this initiative is needed to include missing
and updated in situ-based products, ocean reanalyses and indirect
estimates (for example satellite based). The continuity of this activity
will help to further unravel uncertainties due to the community's collective
efforts on detecting/reducing errors, and it then provides up-to-date
scientific knowledge of ocean heat uptake.
Ensemble mean time series and ensemble standard deviation (2σ, shaded) of global ocean heat content (OHC) anomalies relative to the 2005–2017 climatology for the 0–300 m (gray), 0–700 m (blue), 0–2000 m (yellow) and 700–2000 m depth layer (green). The ensemble mean is an outcome of an international assessment initiative, and all products used are referenced in the legend of Fig. 2. The trends derived from the time series are given in Table 1. Note that values are given for the ocean surface area between 60∘ S and 60∘ N and are limited to the 300 m bathymetry of each product.
Linear trends (weighted least square fit; see for example von Schuckmann and Le Traon, 2011) as derived from the ensemble mean as presented in Fig. 1 for different time intervals, as well as different integration depth. The uncertainty on the trend estimate is given for the 95 % confidence level. Note that values are given for the ocean surface area between 60∘ S and 60∘ N and are limited to the 300 m bathymetry of each product. See text and Fig. 1 caption for more details on the OHC estimates.
Products used for this assessment are referenced in the caption of Fig. 2.
Estimates of OHC have been provided by the different research groups under
homogeneous criteria. All estimates use a coherent ocean volume limited by
the 300 m isobath of each product and are limited to 60∘ S–60∘ N since most observational products exclude high-latitude
ocean areas because of the low observational coverage, and only annual
averages have been used. 60∘ S–60∘ N constitutes
∼91 % of the global ocean surface area, and limiting to 300 m
isobath neglects the contributions from coastal and shallow waters, so the
resultant OHC trends will be underestimated if these ocean regions are
warming. For example, neglecting shallow waters can account for 5 %–10 % for
0–2000 m OHC trends (von Schuckmann et al., 2014). A first initial test using
Cheng et al. (2017b) data indicates that OHC 0–2000 m trends can be
underestimated by ∼10 % if the ocean warming in the area
polewards of 60∘ latitude is not taken into account (not shown).
This is a caveat of the assessment in this review and will be addressed in
the future.
The assessment is based on three distinct periods to account for the
evolution of the observing system, i.e., 1960–2018 (i.e., “historical”),
1993–2018 (i.e., “altimeter era”) and 2005–2018 (i.e., “golden Argo era”). In
addition, ocean warming rates over the past decade are specifically
discussed according to an apparent acceleration of global surface warming
since 2010 (WMO, 2020; Blunden and Arndt, 2019). All time series reach the
end in 2018 – which was one of the principal limitations for the inclusion
of some products. Our final estimates of OHC for the upper 2000 m over
different periods are the ensemble average of all products, with the
uncertainty range defined by the standard deviation (2σ) of the
corresponding estimates used (Fig. 1).
Linear trends of global ocean heat content (OHC) as derived from different temperature products (colors). References are given in the figure legend, except for IPRC (http://apdrc.soest.hawaii.edu/projects/Argo/, last access: 7 August 2020), CMEMS (CORA and ARMOR-3D, http://marine.copernicus.eu/science-learning/ocean-monitoring-indicators, last access: 7 August 2020), CARS2009 (http://www.marine.csiro.au/~dunn/cars2009/, last access: 7 August 2020) and NOC (National Oceanographic Institution, Desbruyères et al., 2016). The ensemble mean and standard deviation (2σ) are given in black. The shaded areas show trends from different depth layer integrations, i.e., 0–300 m (light turquoise), 0–700 m (light blue), 0–2000 m (purple) and 700–2000 m (light purple). For each integration depth layer, trends are evaluated over the three study periods, i.e., historical (1960–2018), altimeter era (1993–2018) and golden Argo era (2005–2018). In addition, the most recent period 2010–2018 is included. See text for more details on the international assessment criteria. Note that values are given for the ocean surface area (see text for more details).
The first and principal result of the assessment (Fig. 1) is an overall
increase in the trend for the two more recent study periods, e.g., the
altimeter era (1993–2018) and golden Argo era (2005–2018), relative to the
historical era (1960–2018), which is in agreement with previous results
(e.g., Abraham et al., 2013). The trend values are all given in Table 1. A
major part of heat is stored in the upper layers of the ocean (0–300 m and
0–700 m depth). However, heat storage at intermediate depth (700–2000 m)
increases at a comparable rate as reported for the 0–300 m depth layer (Table 1, Fig. 2). There is a general agreement among the 15 international OHC
estimates (Fig. 2). However, for some periods and depth layers the standard
deviation reaches maximal values up to about 0.3 W m-2. All products
agree on the fact that ocean warming rates have increased in the past
decades and doubled since the beginning of the altimeter era (1993–2018
compared with 1960–2018) (Fig. 2). Moreover, there is a clear indication
that heat sequestration into the deeper ocean layers below 700 m depth took
place over the past 6 decades linked to an increase in OHC trends over time
(Fig. 2). In agreement with observed accelerated Earth surface warming over
the past decade (WMO, 2020; Blunden and Arndt, 2019), ocean
warming rates for the 0–2000 m depth layer also reached record rates of 1.3
(0.9)±0.3 W m-2 for the ocean (global) area over the period
2010–2018.
For the deep OHC changes below 2000 m, we adapted an updated estimate from
Purkey and Johnson (2010)
(PG10) from 1991 to 2018, which is a constant linear trend estimate (1.15±0.57 ZJ yr-1, 0.07±0.04 W m-2). Some recent studies
strengthened the results in PG10
(Desbruyères et al., 2016; Zanna et al., 2019). Desbruyères et al. (2016) examined the decadal change of the deep and abyssal OHC trends below
2000 m in the 1990s and 2000s, suggesting that there has not been a significant
change in the rate of decadal global deep/abyssal warming from the 1990s to
the 2000s and the overall deep ocean warming rate is consistent with PG10.
Using a Green function method, Zanna et al. (2019) reported a deep ocean
warming rate of ∼0.06 W m-2 during the 2000s, consistent
with PG10 used in this study. Zanna et al. (2019) shows a fairly weak global
trend during the 1990s, inconsistent with observation-based estimates. This
mismatch might come from the simplified or misrepresentation of surface-deep
connections using ECCO reanalysis data and the use of time-mean Green
functions in Zanna et al. (2019), as well as from the limited spatial
resolution of the observational network for relatively short time spans.
Furthermore, combining hydrographic and deep-Argo floats, a recent study
(Johnson et al., 2019) reported an accelerated
warming in the South Pacific Ocean in recent years, but a global estimate of
the OHC rate of change over time is not available yet.
Before 1990, we assume zero OHC trend below 2000 m, following the methodology
in IPCC-AR5 (Rhein et al., 2013). The zero-trend assumption is made mainly
because there are too few observations before 1990 to make an estimate of
OHC change below 2000 m. But it is a reasonable assumption because OHC
700–2000 m warming was fairly weak before 1990 and heat might not have
penetrated down to 2000 m (Cheng et al., 2017b). Zanna et al. (2019) also
shows a near-zero OHC trend below 2000 m from the 1960s to 1980s. The derived
time series is used for the Earth energy inventory in Sect. 5. A
centralized (around the year 2006) uncertainty approach has been applied for
the deep (>2000 m depth) OHC estimate following the method of
Cheng et al. (2017b), which allows us to extract an uncertainty range over the
period 1993–2018 within the given [lower (1.15–0.57 ZJ yr-1), upper (1.15+0.57 ZJ yr-1)] range of the deep OHC trend estimate. We then extend the
obtained uncertainty estimate back from 1993 to 1960, with 0 OHC anomaly.
Heat available to warm the atmosphere
While the amount of heat accumulated in the atmosphere is small compared to
the ocean, warming of the Earth's near-surface air and atmosphere aloft is a
very prominent effect of climate change, which directly affects society.
Atmospheric observations clearly reveal a warming of the troposphere over
the last decades (Santer et
al., 2017; Steiner et al., 2020) and changes in the seasonal cycle
(Santer et al., 2018). Changes in
atmospheric circulation (Cohen et al., 2014; Fu et
al., 2019) together with thermodynamic changes
(Fischer and Knutti, 2016; Trenberth et al., 2015)
will lead to more extreme weather events and increase high-impact risks for
society (Coumou et al., 2018; Zscheischler et
al., 2018). Therefore, a rigorous assessment of the atmospheric heat content
in context with all Earth's climate subsystems is important for a full view
on the changing climate system.
The atmosphere transports vast amounts of energy laterally and strong
vertical heat fluxes occur at the atmosphere's lower boundary. The
pronounced energy and mass exchanges within the atmosphere and with all
other climate components is a fundamental element of Earth's climate
(Peixoto and Oort, 1992). In contrast, long-term heat
accumulation in the atmosphere is limited by its small heat capacity as the
gaseous component of the Earth system (von Schuckmann et al., 2016).
Recent work revealed inconsistencies in earlier formulations of the
atmospheric energy budget
(Mayer et al., 2017; Trenberth
and Fasullo, 2018), and hence a short discussion of the updated formulation
is provided here. In a globally averaged and vertically integrated sense,
heat accumulation in the atmosphere arises from a small imbalance between
net energy fluxes at the top of the atmosphere (TOA) and the surface
(denoted s). The heat budget of the vertically integrated and globally
averaged atmosphere (indicated by the global averaging operator
〈.〉) reads as follows (Mayer et al., 2017):
∂AE∂t=〈NTOA〉-〈Fs〉-〈Fsnow〉-〈FPE〉,
where, in mean-sea-level altitude (z) coordinates used here for integrating
over observational data, the vertically integrated atmospheric energy
content AE per unit surface area [J m-2] reads
AE=∫zszTOAρcvT+gz-zs+Leq+12V2dz.
In Eq. (2), AE represents the total atmospheric energy content, NTOA the net radiation at TOA, Fs the net surface energy flux
defined as the sum of net surface radiation and latent and sensible heat
flux, and Fsnow the latent heat flux associated with snowfall (computed
as the product of latent heat of fusion and snowfall rate). Here, we take
constant latent heat of vaporization (at 0 ∘C) in the latent heat
flux term that is contained in Fs, but variations in latent heat flux
arising from the deviation of evaporated water from 0 ∘C are
contained in FPE, which additionally accounts for sensible heat of
precipitation (referenced to 0 ∘C). That is, FPE expresses a
modification of Fs arising from global evaporation and precipitation
occurring at temperatures different from 0 ∘C.
Snowfall is the fraction of precipitation that returns originally evaporated
water to the surface in a frozen state. In that sense, Fsnow represents
a heat transfer from the surface to the atmosphere: it warms the atmosphere
through additional latent heat release (associated with freezing of vapor)
and snowfall consequently arrives at the surface in an energetic state
lowered by this latent heat. This energetic effect is most obvious over the
open ocean, where falling snow requires the same amount of latent heat to be
melted again and thus cools the ocean. Over high latitudes, Fsnow can
attain values up to 5 W m-2, but its global average value is smaller
than 1 W m-2 (Mayer et al., 2017). Although its global mean energetic
effect is relatively small, it is systematic and should be included for
accurate diagnostics. Moreover, snowfall is an important contributor to the
heat and mass budget of ice sheets and sea ice (see Sect. 4).
FPE represents the net heat flux arising from the different
temperatures of rain and evaporated water. This flux can be sizable
regionally, but it is small in a global average sense (warming of the
atmosphere ∼0.3 W m-2 according to Mayer et al., 2017).
Equation (3) provides a decomposition of the atmospheric energy content AE into
sensible heat energy (sum of the first two terms, internal heat energy and
gravity potential energy), latent heat energy (third term) and kinetic
energy (fourth term), where ρ is the air density, cv the specific
heat for moist air at constant volume, T the air temperature, g the
acceleration of gravity, Le the temperature-dependent effective latent
heat of condensation (and vaporization) Lv or sublimation Ls (the latter relevant below 0 ∘C), q the specific humidity of the
moist air, and V the wind speed. We neglect atmospheric liquid water droplets
and ice particles as separate species, as their amounts and especially their
trends are small.
In the AE derivation from observational datasets based on Eq. (3), we
accounted for the intrinsic temperature dependence of the latent heat of
water vapor by assigning Le to Lv if ambient temperatures are above
0 ∘C and to Ls (adding in the latent heat of fusion
Lf) if they are below -10∘C, respectively, with a gradual
(half-sine weighted) transition over the temperature range between. The
reanalysis evaluations similarly approximated Le by using values of
Lv, Ls, and Lf, though in slightly differing forms. The
resulting differences in AE anomalies from any of these choices are
negligibly small, however, since the latent heat contribution at low
temperatures is itself very small.
As another small difference, the AE estimations from observations neglected
the kinetic energy term in Eq. (3) (fourth term), while the reanalysis
evaluations accounted for it. This as well leads to negligible AE anomaly
differences, however, since the kinetic energy content and trends at a global
scale are more than three orders of magnitude smaller than for the sensible
heat (Peixoto and Oort, 1992). Aligning with the terminology of ocean heat
content (OHC) and given the dominance of the heat-related terms in Eq. (3), we hence refer to the energy content AE as atmospheric heat content (AHC)
hereafter.
Turning to the actual datasets used, atmospheric energy accumulation can be
quantified using various data types, as summarized in the following.
Atmospheric reanalyses combine observational information from various
sources (radiosondes, satellites, weather stations, etc.) and a dynamical
model in a statistically optimal way. This data type has reached a high
level of maturity, thanks to continuous development work since the early
1990s (Hersbach et al., 2018).
Especially reanalyzed atmospheric state quantities like temperature, winds
and moisture are considered to be of high quality and suitable for climate
studies, although temporal discontinuities introduced from the ever-changing
observation system remain a matter of concern
(Berrisford
et al., 2011; Chiodo and Haimberger, 2010).
Here we use the current generation of atmospheric reanalyses as represented
by ECMWF's fifth-generation reanalysis ERA5
(Hersbach et al., 2018, 2020),
NASA's Modern-Era Retrospective analysis for Research and Applications
version 2 (MERRA2) (Gelaro et al.,
2017) and JMA's 55-year-long reanalysis JRA-55 (Kobayashi
et al., 2015). All these are available over 1980 to 2018 (ERA5 also in 1979),
while JRA-55 is the only one covering the full early timeframe 1960 to 1979.
We additionally used a different version of JRA-55 that assimilates only
conventional observations also over the satellite era from 1979 onwards,
which away from the surface only leaves radiosondes as data source and which
is available to 2012 (JRA-55C). The advantage of this product is that it
avoids potential spurious jumps associated with satellite changes. Moreover,
JRA-55C is fully independent of satellite-derived Global Positioning System
(GPS) radio occultation (RO) data that are also separately used and
described below together with the observational techniques.
In addition to these four reanalyses, the datasets from three different
observation techniques have been used for complementary observational
estimates of the atmospheric heat content. We use the Wegener Center
(WEGC) multisatellite RO data record, WEGC OPSv5.6
(Angerer et al., 2017), as well as its radiosonde (RS) data
record derived from the high-quality Vaisala sondes RS80/RS92/VS41, WEGC
Vaisala (Ladstädter et al., 2015). WEGC OPSv5.6 and
WEGC Vaisala provide thermodynamic upper air profiles of air temperature,
specific humidity and density from which we locally estimate the vertical
AHC based on the first three integral terms of Eq. (3)
(Kirchengast et al., 2019). In atmospheric domains not
fully covered by the data (e.g., in the lower part of the boundary layer for
RO or over the polar latitudes for RS), the profiles are vertically completed
by collocated ERA5 information. The local vertical AHC results are then
averaged into regional monthly means, which are finally geographically
aggregated to global AHC. Applying this estimation approach in the same way
to reanalysis profiles subsampled at the observation locations accurately
leads to the same AHC anomaly time series records as the direct estimation
from the full gridded fields.
The third observation-based AHC dataset derives from a rather approximate
estimation approach using the microwave sounding unit (MSU) data records
(Mears and Wentz, 2017). Because the very
coarse vertical resolution of the brightness temperature measurements from
MSU does not enable integration according to Eq. (3), this dataset is
derived by replicating the method used in IPCC AR5 WGI Assessment Report
2013 (Rhein et al., 2013; chap. 3, Box 3.1 therein). We used the most
recent MSU Remote Sensing System (RSS) V4.0 temperature dataset (Mears and
Wentz, 2017), however, instead of MSU RSS V3.3 (Mears and Wentz, 2009a, b) that was used in the IPCC AR5. In order to derive global time series of AHC
anomalies, the approach simply combines weighted MSU lower tropospheric
temperature and lower stratospheric temperature changes (TLT and TLS
channels) converted to sensible heat content changes via global atmospheric
mass, as well as an assumed fractional increase in latent heat content according to water vapor content increase driven by temperature at a
near-Clausius–Clapeyron rate (7.5 % ∘C-1).
Figure 3 shows the resulting global AHC change inventory over 1980 to 2018
in terms of AHC anomalies of all data types (top), mean anomalies and
time-average uncertainty estimates including long-term AHC trend estimates
(middle), and annual-mean AHC tendency estimates (bottom). The mean anomaly
time series (middle left), preceded by the small JRA-55 anomalies over
1960–1979, is used as part of the overall heat inventory in Sect. 5 below.
Results including MSU in addition are separately shown (right column), since
this dataset derives from a fairly approximate estimation as summarized
above and hence is given lower confidence than the others deriving from
rigorous AHC integration and aggregation. Since MSU data were the only data for AHC change estimation in the IPCC AR5 report, bringing it into context is
considered relevant, however.
The results clearly show that the AHC trends have intensified from the
earlier decades represented by the 1980–2010 trends of near 1.8 TW
(consistent with the trend interval used in the IPCC AR5 report). We find
the trends about 2.5 times higher over 1993–2018 (about 4.5 TW) and about
3 times higher in the most recent 2 decades over 2002–2018 (near 5.3 TW), a period that is already fully covered also by the RO and RS records
(which estimate around 6 TW). Checking the sensitivity of these long-term
trend estimates to El
Niño–Southern Oscillation (ENSO) interannual variations, by comparing to trends fitted to ENSO-corrected AHC anomalies (with ENSO regressed out via the Nino 3.4
index), confirms that the estimates are robust (trends consistent within about 10 %, slightly higher with ENSO correction).
The year-to-year annual-mean tendencies in AHC, reaching amplitudes as high
as 50 to 100 TW (or 0.1 to 0.2 W m-2, if normalized to the global
surface area), indicate the strong coupling of the atmosphere with the
uppermost ocean. This is mainly caused by the ENSO interannual variations
that lead to net energy changes in the climate system including the
atmosphere (Loeb et al., 2012; Mayer
et al., 2013) and substantial reshuffling of heat energy between the
atmosphere and the upper ocean
(Cheng et al., 2019b; Johnson and Birnbaum, 2017; Mayer et al.,
2014, 2016).
Annual-mean global AHC anomalies over 1980 to 2018 of four different reanalyses and two (a, c, e) or three (b, d, f, plus MSU) different observational datasets shown together with their mean (a, b), the mean AHC anomaly shown together with four representative AHC trends and ensemble spread measures of its underlying datasets (c, d), and the annual-mean AHC change (annual tendency) shown for each year over 1980 to 2018 for all datasets and their mean (e, f). The in-panel legends identify the individual datasets shown (a, b and e, f) and the chosen trend periods together with the associated trend values and spread measures (c, d), with the latter including the time-average standard deviation and minimum/maximum deviations of the individual datasets from the mean.
Heat available to warm land
Although the land component of the Earth's energy budget accounts for a
small proportion of heat in comparison with the ocean, several land-based
processes sensitive to the magnitude of the available land heat play a
crucial role in the future evolution of climate. Among others, the stability
and extent of the continental areas occupied by permafrost soils depend on
the land component. Alterations of the thermal conditions at these locations
have the potential to release long-term stored CO2 and CH4 and
may also destabilize the recalcitrant soil carbon
(Bailey et al., 2019; Hicks Pries et al., 2017).
Both of these processes are potential tipping points
(Lenton et al., 2008, 2019;
Lenton, 2011) leading to possible positive feedback on
the climate system (Leifeld et al.,
2019; MacDougall et al., 2012). Increased land energy is related to
decreases in soil moisture that may enhance the occurrence of extreme
temperature events
(Jeong
et al., 2016; Seneviratne et al., 2006, 2014, 2010; Xu et al., 2019). Such extreme events carry negative health effects
for the most vulnerable sectors of human and animal populations and
ecosystems
(Matthews
et al., 2017; McPherson et al., 2017; Sherwood and Huber, 2010; Watts et
al., 2019). Given the importance of properly determining the fraction of EEI
flowing into the land component, recent works have examined the CMIP5
simulations and revealed that Earth system models (ESMs) have shortcomings
in modeling the land heat content of the last half of the 20th century
(Cuesta-Valero et al., 2016).
Numerical experiments have pointed to an insufficient depth of the land
surface models (LSMs)
(MacDougall
et al., 2008, 2010; Stevens, 2007) and to a zero heat-flow bottom
boundary condition (BBC) as the origin of the limitations in these
simulations. An LSM of insufficient depth limits the amount of energy that
can be stored in the subsurface. The zero heat-flow BBC neglects the small
but persistent long-term contribution from the flow of heat from the
interior of the Earth, which shifts the thermal regime of the subsurface
towards or away from the freezing point of water, such that the latent heat
component is misrepresented in the northern latitudes
(Hermoso de Mendoza et al., 2020). Although the heat
from the interior of the Earth is constant at timescales of a few
millennia, it may conflict with the setting of the LSM initial conditions in
ESM simulations. Modeling experiments have also allowed us to estimate the
heat content in land water reservoirs
(Vanderkelen et al., 2020), accounting
for 0.3±0.3 ZJ from 1900 to 2020. Nevertheless, this estimate has not
been included here because it is derived from model simulations and its
magnitude is small in relation to the rest of the components of the Earth's heat
inventory.
Borehole climatology
The main premise of borehole climatology is that the subsurface thermal
regime is determined by the balance of the heat flowing from the interior of
the Earth (the bottom boundary condition) and the heat flowing through the
interface between the lower atmosphere and the ground (the upper boundary
condition). If the thermal properties of the subsurface are known, or if
they can be assumed constant over short-depth intervals, then the thermal
regime of the subsurface can be determined by the physics of heat diffusion.
The simplest analogy is the temperature distribution along a (infinitely
wide) cylinder with known thermal properties and constant temperature at
both ends. If upper and lower boundary conditions remain constant (i.e.,
internal heat flow is constant and there are no persistent variations on
the ground surface energy balance), then the thermal regime of the
subsurface is well known and it is in a (quasi-)steady state. However, any
change to the ground surface energy balance would create a transient, and
such a change in the upper boundary condition would propagate into the
ground, leading to changes in the thermal regime of the subsurface
(Beltrami, 2002a). These changes in the ground surface energy
balance propagate into the subsurface and are recorded as departures from
the quasi-steady thermal state of the subsurface. Borehole climatology uses
these subsurface temperature anomalies to reconstruct the ground surface
temperature changes that may have been responsible for creating the
subsurface temperature anomalies we observe. That is, it is an attempt to
reconstruct the temporal evolution of the upper boundary condition. Ground
surface temperature histories (GSTHs) and ground heat flux histories (GHFHs)
have been reconstructed from borehole temperature profile (BTP) measurements
at regional and larger scales for decadal and millennial timescales
(Barkaoui
et al., 2013; Beck, 1977; Beltrami, 2001; Beltrami et al., 2006; Beltrami
and Bourlon, 2004; Cermak, 1971; Chouinard and Mareschal, 2009;
Davis et al., 2010; Demezhko and Gornostaeva, 2015; Harris and Chapman,
2001; Hartmann and Rath, 2005; Hopcroft et al., 2007; Huang et al., 2000;
Jaume-Santero et al., 2016; Lachenbruch and Marshall, 1986; Lane, 1923;
Pickler et al., 2018; Roy et al., 2002; Vasseur et al., 1983). These
reconstructions have provided independent records for the evaluation of the
evolution of the climate system well before the existence of meteorological
records. Because subsurface temperatures are a direct measure, which unlike
proxy reconstructions of past climate do not need to be calibrated with the
meteorological records, they provide an independent way of assessing changes
in climate. Such records are useful tools for evaluating climate
simulations prior to the observational period
(Beltrami
et al., 2017; Cuesta-Valero et al., 2019, 2016;
García-García et al., 2016; González-Rouco et al., 2006;
Jaume-Santero et al., 2016; MacDougall et al., 2010; Stevens et al., 2008),
as well as for assessing proxy data reconstructions
(Beltrami et al., 2017; Jaume-Santero et
al., 2016).
Borehole reconstructions have, however, certain limitations. Due to the
nature of heat diffusion, temperature changes propagated through the
subsurface suffer both a phase shift and an amplitude attenuation
(Smerdon and Stieglitz, 2006). Although subsurface
temperatures continuously record all changes in the ground surface energy
balance, heat diffusion filters out the high frequency variations of the
surface signal with depth; thus the annual cycle is detectable up to
approximately 16 m of depth, while millennial changes are recorded
approximately to a depth of 500 m. Therefore, reconstructions from borehole
temperature profiles represent changes at decadal-to-millennial timescales.
Additionally, borehole data are sparse, since the logs were usually recorded
from holes of opportunity at mining exploration sites. As a result, the
majority of profiles were measured in the Northern Hemisphere, although
recent efforts have been taken to increase the sampling rate in South
America (Pickler et al., 2018) and Australia
(Suman et al., 2017). Despite this uneven sampling, the
spatial distribution of borehole profiles has been able to represent the
evolution of land surface conditions at global scales
(Beltrami
and Bourlon, 2004; Cuesta-Valero et al., 2020; González-Rouco et al.,
2006, 2009; Pollack and Smerdon, 2004). Another factor that reduces the
number of borehole profiles suitable for climate analyses is the presence of
nonclimatic signals in the measured profiles, mainly caused by groundwater
flow and changes in the lithology of the subsurface. Therefore, all profiles
are screened before the analysis in order to remove questionable logs.
Despite all these limitations, the borehole methodology has been shown to be
reliable based on observational analyses
(Bense and
Kooi, 2004; Chouinard and Mareschal, 2007; Pollack and Smerdon, 2004;
Verdoya et al., 2007) and pseudoproxy experiments
(García Molinos et al., 2016;
González-Rouco et al., 2006, 2009).
Land heat content estimates
Global continental energy content has been previously estimated from
geothermal data retrieved from a set of quality-controlled borehole
temperature profiles. Ground heat content was estimated from heat flux
histories derived from BTP data
(Beltrami,
2002b; Beltrami et al., 2002, 2006). Such results have formed part of the
estimate used in AR3, AR4 and AR5 IPCC reports (see Box 3.1, chap. 3
Rhein et al., 2013). A continental heat content estimate was inferred from
meteorological observations of surface air temperature since the beginning
of the 20th century (Huang, 2006). Nevertheless, all
global estimates were performed nearly 2 decades ago. Since, those days,
advances in borehole methodological techniques
(Beltrami et al., 2015;
Cuesta-Valero et al., 2016; Jaume-Santero et al., 2016), the availability of
additional BTP measurements and the possibility of assessing the
continental heat fluxes in the context of the FluxNet measurements
(Gentine et al., 2020) require a comprehensive
summary of all global ground heat fluxes and continental heat content
estimates.
Ground surface heat flux and global continental heat content. Uncertainties in parenthesis.
ReferenceTimeHeat fluxHeat contentSource ofperiod(m W m-2)(ZJ)dataBeltrami (2002b)1950–2000337.1GeothermalBeltrami et al. (2002)1950–200039.1 (3.5)9.1 (0.8)GeothermalBeltrami et al. (2002)1900–200034.1 (3.4)15.9 (1.6)GeothermalBeltrami (2002b)1765–200020.0 (2.0)25.7 (2.6)GeothermalHuang (2006)1950–2000–6.7MeteorologicalGentine et al. (2020)2004–2015240 (120)–FluxNet, geothermal, LSMCuesta-Valero et al. (2020)1950–200070 (20)16 (3)GeothermalCuesta-Valero et al. (2020)1993–2018129 (28)14 (3)GeothermalCuesta-Valero et al. (2020)2004–2015136 (28)6 (1)Geothermal
Global mean ground heat flux history (black line) and 95 % confidence interval (gray shadow) from BTP measurements from Cuesta-Valero et al. (2020). Results for 1950–2000 from Beltrami et al. (2002) (green bar) are provided for comparison purposes.
Global cumulative heat storage within continental landmasses since 1960 CE (black line) and 95 % confidence interval (gray shadow) estimated from ground heat flux results displayed in Fig. 4. Data obtained from Cuesta-Valero et al. (2020).
The first estimates of continental heat content used borehole temperature
versus depth profile data. However, the dataset in those analyses included
borehole temperature profiles of a wide range of depths, as well as
different data acquisition dates. That is, each borehole profile contained
the record of the accumulation of heat in the subsurface for different time
intervals. In addition, the borehole data were analyzed for a single ground
surface temperature model using a single constant value for each of the
subsurface thermal properties.
Although the thermal signals are attenuated with depth, which may partially
compensate for data shortcomings, uncertainties were introduced in previous
analyses that may have affected the estimates of subsurface heat change. A
continental heat content change estimate was carried out using a gridded
meteorological product of surface air temperature by
Huang (2006). Such work yielded similar values to the
estimates from geothermal data (see Table 2). This estimate, however,
assumed that surface air and ground temperatures are perfectly coupled
everywhere, and it used a single value for the thermal conductivity of the
ground. Studies have shown that the coupling of the surface air and ground
temperatures is mediated by several processes that may influence the ground
surface energy balance and, therefore, the air–ground temperature coupling
(García-García
et al., 2019; Melo-Aguilar et al., 2018; Stieglitz and Smerdon, 2007). In a
novel attempt to reconcile continental heat content from soil heat-plate
data from the FluxNet network with estimates from geothermal data and a deep
bottom boundary land surface model simulation, Gentine et al. (2020)
obtained a much larger magnitude from the global land heat flux than all
previous estimates. Cuesta-Valero et al. (2020) has recently updated the
estimate of the global continental heat content using a larger borehole
temperature database (1079 logs) that includes more recent measurements and
a stricter data quality control. The updated estimate of continental heat
content change also takes into account the differences in borehole logging
time and restricts the data to the same depth range for each borehole
temperature profile. Such depth range restriction ensures that the
subsurface accumulation of heat at all BTP sites is synchronous. In addition
to the standard method for reconstructing heat fluxes with a single constant
value for each subsurface thermal property, Cuesta-Valero et al. (2020) also
developed a new approach that considers a range of possible subsurface
thermal properties – several models, each at a range of resolutions yielding
a more realistic range of uncertainties for the fraction of the EEI flowing
into the land subsurface.
Global land heat content estimates from FluxNet data, geothermal data and
model simulations point to a marked increase in the amount of energy flowing
into the ground in the last few decades (Figs. 4, 5 and Table 2). These
results are consistent with the observations of ocean, cryosphere and
atmospheric heat storage increases during the same time period as well as with EEI
at the top of the atmosphere.
Heat utilized to melt ice
The energy uptake by the cryosphere is given by the sum of the energy uptake
within each one of its components: sea ice, the Greenland and Antarctic ice
sheets, glaciers other than those that are part of the ice sheets
(“glaciers”, hereafter), snow, and permafrost. The basis for the heat uptake
by the cryosphere presented here is provided by a recent estimate for the
period 1979 to 2017 (Straneo et al., 2020). This
study concludes that heat uptake over this period is dominated by the mass
loss from Arctic sea ice, glaciers, and the Greenland and Antarctic ice
sheets. The contributions from thawing permafrost and shrinking snow cover
are either negligible, compared to these other components, or highly
uncertain. (Note that warming of the land in regions where permafrost is
present is accounted for in the land warming; however, the energy to thaw
the permafrost is not.) Antarctic sea ice shows no explicit trend over the
period described here (Parkinson, 2019).
Here, we extend the estimate of Straneo et al. (2020) backwards in time to
1960 and summarize the method, the data and model outputs used. The reader
is referred to Straneo et al. (2020) for further details.
Within each component of the cryosphere, energy uptake is dominated by that
associated with melting, including both the latent heat uptake and the
warming of the ice to its freezing point. As a result, the energy uptake by
each component is directly proportional to its mass loss (Straneo et al.,
2020). For consistency with previous estimates
(Ciais et al., 2013), we use a
constant latent heat of fusion of 3.34×105 J kg-1, a specific heat
capacity of 2.01×103 J/(kg ∘C) and an ice density of 920 kg m-3.
For Antarctica, we separate contributions from grounded ice loss and
floating ice loss building on recent separate estimates for each. Grounded
ice loss from 1992 to 2017 is based on a recent study that reconciles mass
balance estimates from gravimetry, altimetry and input–output methods from
1992 to 2017 (Shepherd et al., 2018b). For
the 1972–1991 period, we used estimates from
Rignot et al. (2019), which
combined modeled surface mass balance with ice discharge estimates from the
input/output method. Floating ice loss between 1994 and 2017 is based on
thinning rates and iceberg calving fluxes estimated using new satellite
altimetry reconstructions (Adusumilli et al., 2020). For
the 1960–1994 period, we also considered mass loss from declines in
Antarctic Peninsula ice shelf extent (Cook and Vaughan, 2010)
using the methodology described in Straneo et al. (2020).
To estimate grounded ice mass loss in Greenland, we use the Ice Sheet Mass
Balance Intercomparison Exercise for the time period 1992–2017 (Shepherd et
al., 2019) and the difference between surface mass balance and ice discharge
for the period 1979–1991
(Mankoff
et al., 2019; Mouginot et al., 2019; Noël et al., 2018). Due to a lack
of observations, from 1960–1978 we assume no mass loss. For floating ice
mass change, we collated reports of ice shelf thinning and/or collapse
together with observed tidewater glacier retreat (Straneo et al., 2020).
Based on firn modeling we assessed that warming of Greenland's firn has not
yet contributed significantly to its energy uptake
(Ligtenberg et al., 2018; Straneo et al.,
2020).
For glaciers we combine estimates for glaciers from the Randolph Glacier
Inventory outside of Greenland and Antarctica, based on direct and geodetic
measurements (Zemp et al., 2019), with
estimates based on a glacier model forced with an ensemble of reanalysis
data (Marzeion et al., 2015) and GRACE-based estimates
(Bamber et al., 2018). An
additional contribution from uncharted glaciers or glaciers that have
already disappeared is obtained from
Parkes and Marzeion (2018). Greenland
and Antarctic peripheral glaciers are derived from Zemp et al. (2019) and
Marzeion et al. (2015).
Finally, while estimates of Arctic sea ice extent exist over the satellite
record, sea ice thickness distribution measurements are scarce, making it
challenging to estimate volume changes. Instead we use the Pan-Arctic Ice
Ocean Modeling and Assimilation System (PIOMAS)
(Schweiger
et al., 2011; Zhang and Rothrock, 2003) which assimilates ice concentration
and sea surface temperature data and is validated with most available
thickness data (from submarines, oceanographic moorings, and remote sensing)
and against multidecadal records constructed from satellite (for example, Labe
et al., 2018; Laxon et al., 2013; Wang et al., 2016). A longer
reconstruction using a slightly different model version, PIOMAS-20C
(Schweiger et al., 2019), is used
to cover the 1960 to 1978 period that is not covered by PIOMAS.
These reconstructions reveal that all four components contributed similar
amounts (between 2 and 5 ZJ) over the 1960–2017 period, amounting to a total
energy uptake by the cryosphere of 14.7±1.9 ZJ. Compared to earlier
estimates, and in particular the 8.83 ZJ estimate from Ciais et al. (2013),
this larger estimate is a result both of the longer period of time
considered and, also, the improved estimates of ice loss across all
components, especially the ice shelves in Antarctica. Approximately half of
the cryosphere's energy uptake is associated with the melting of grounded
ice, while the remaining half is associated with the melting of floating ice
(ice shelves in Antarctica and Greenland, Arctic sea ice).
The Earth heat inventory: where does the energy go?
The Earth has been in radiative imbalance, with less energy exiting the top
of the atmosphere than entering, since at least about 1970, and the Earth has
gained substantial energy over the past 4 decades (Hansen, 2005; Rhein et
al., 2013). Due to the characteristics of the Earth system components, the
ocean with its large mass and high heat capacity dominates the Earth heat
inventory (Cheng et al., 2016, 2017b; Rhein et al., 2013; von Schuckmann et
al., 2016). The rest goes into grounded and floating ice melt, as well as warming
the land and atmosphere.
Earth heat inventory (energy accumulation) in ZJ (1 ZJ =1021 J) for the components of the Earth's climate system relative to 1960 and from 1960 to 2018 (assuming constant cryosphere increase for the year 2018). See Sects. 1–4 for data sources. The upper ocean (0–300 m, light blue line, and 0–700 m, light blue shading) accounts for the largest amount of heat gain, together with the intermediate ocean (700–2000 m, blue shading) and the deep ocean below 2000 m depth (dark blue shading). Although much lower, the second largest contributor is the storage of heat on land (orange shading), followed by the gain of heat to melt grounded and floating ice in the cryosphere (gray shading). Due to its low heat capacity, the atmosphere (magenta shading) makes a smaller contribution. Uncertainty in the ocean estimate also dominates the total uncertainty (dot-dashed lines derived from the standard deviations (2σ) for the ocean, cryosphere and land; atmospheric uncertainty is comparably small). Deep ocean (>2000 m) is assumed to be zero before 1990 (see Sect. 1 for more details). The dataset for the Earth heat inventory is published at the German Climate Computing Centre (DKRZ, https://www.dkrz.de/) under the DOI https://doi.org/10.26050/WDCC/GCOS_EHI_EXP_v2. The net flux at TOA from the NASA CERES program is shown in red (https://ceres.larc.nasa.gov/data/, last access: 7 August 2020; see also for example Loeb et al., 2012) for the period 2005–2018 to account for the golden period of best available estimates. We obtain a total heat gain of 358±37 ZJ over the period 1971–2018, which is equivalent to a heating rate (i.e., the EEI) of 0.47±0.1 W m-2 applied continuously over the surface area of the Earth (5.10×1014 m2). The corresponding EEI over the period 2010–2018 amounts to 0.87±0.12 W m-2. A weighted least square fit has been used taking into account the uncertainty range (see also von Schuckmann and Le Traon, 2011).
In agreement with previous studies, the Earth heat inventory based on most
recent estimates of heat gain in the ocean (Sect. 1), the atmosphere
(Sect. 2), land (Sect. 3) and the cryosphere (Sect. 4) shows a
consistent long-term heat gain since the 1960s (Fig. 6). Our results show a
total heat gain of 358±37 ZJ over the period 1971–2018, which is
equivalent to a heating rate of 0.47±0.1 W m-2, and it applied
continuously over the surface area of the Earth (5.10×1014 m2). For comparison, the heat gain obtained in IPCC AR5 amounts to 274±78 ZJ and 0.4 W m-2 over the period 1971–2010 (Rhein et al.,
2013). In other words, our results show that since the IPCC AR5 estimate has
been performed, heat accumulation has continued at a comparable rate. The
major player in the Earth inventory is the ocean, particularly the upper
(0–700 m) and intermediate (700–2000 m) ocean layers (see also Sect. 1, Fig. 2).
Although the net flux at TOA as derived from remote sensing is anchored by
an estimate of global OHC (Loeb et al., 2012), and thus does not provide a
completely independent result for the total EEI, we additionally compare net
flux at TOA with the Earth heat inventory obtained in this study (Fig. 6).
Both rates of change compare well, and we obtain 0.7±0.1 W m-2
for the remote sensing estimate at TOA and 0.8±0.1 W m-2 for
the Earth heat inventory over the period 2005–2018.
Overview on EEI estimates as obtained from previous publications; references are listed in the figure legend. For IPCC AR5, Rhein et al. (2013) is used. The color bars take into account the uncertainty ranges provided in each publication, respectively. For comparison, the estimates of our Earth heat inventory based on the results of Fig. 6 have been added (yellow lines) for the periods 1971–2018, 1993–2018 and 2010–2018, and the trends have been evaluated using a weighted least square fit (see von Schuckmann and Le Traon, 2011, for details on the method).
Rates of change derived from Fig. 6 are in agreement with previously
published results for the different periods (Fig. 7). Major disagreements
occur for the estimate of Balmaseda et al. (2013) which is obtained from an
ocean reanalysis and known to provide higher heat gain compared to results
derived strictly from observations (Meyssignac et al., 2019). Over the last
quarter of a decade this Earth heat inventory reports – in agreement with
previous publications – an increased rate of Earth heat uptake reaching up
to 0.9 W m-2 (Fig. 7). This period is also characterized with an
increase in the availability and quality of the global climate observing
system, particularly for the past 2 decades. The heat inventory as obtained
in this study reveals an EEI of 0.87±0.12 W m-2 over the period
2010–2018 – a period which experienced record levels of Earth surface
warming and is ranked as the warmest decade relative to the reference
period 1850–1900 (WMO, 2020). Whether this increased rate can be attributed
to an acceleration of global warming and Earth system heat uptake (e.g.,
Cheng et al., 2019a; WMO, 2020; Blunden and Arndt, 2019), an induced
estimation bias due to the interplay between natural and anthropogenically
driven variability (e.g., Cazenave et al., 2014), or underestimated
uncertainties in the historical record (e.g., Boyer et al., 2016) needs
further investigation.
The new multidisciplinary estimate obtained from a concerted international
effort provides an updated insight in where the heat is going from a
positive EEI of 0.47±0.1 W m-2 for the period 1971–2018. Over
the period 1971–2018 (2010–2018), 89 % (90 %) of the EEI is stored in
the global ocean, from which 52 % (52 %) is repartitioned in the upper
700 m depth, 28 % (30 %) at intermediate layers (700–2000 m) and 9 %
(8 %) in the deep ocean layer below 2000 m depth. Atmospheric warming
amounts to 1 % (2 %) in the Earth heat inventory, the land heat gain
amounts to 6 % (5 %) and the heat uptake by the cryosphere amounts to 4 % (3 %).
These results show general agreement with previous estimates (e.g., Rhein et
al., 2013). Over the period 2010–2018, the EEI amounts to 0.87±0.12 W m-2, indicating a rapid increase in EEI over the past decade. Note
that a near-global (60∘ N–60∘ S) area for the ocean heat
uptake is used in this study, which could induce a slight underestimation,
and needs further evaluation in the future (see Sect. 1). However, a test
using a single dataset (Cheng et al., 2017b) indicates that the ocean
contribution within 1960–2018 can increase by 1 % if the full global ocean
domain is used (not shown).
Data availability
The time series of the Earth heat inventory
are published at DKRZ (https://www.dkrz.de/, last access: 7 August 2020) under the DOI
10.26050/WDCC/GCOS_EHI_EXP_v2 (von Schuckmann et al., 2020). The data contain an
updated international assessment of ocean warming estimates as well as new and updated estimates of heat gain in the atmosphere, cryosphere and land over
the period 1960–2018. This published dataset has been used to build the
basis for Fig. 6 of this paper. The ocean warming estimate is based
on an international assessment of 15 different in situ data-based ocean
products as presented in Sect. 1. The new estimate of the atmospheric heat
content is fully described in Sect. 2 and is based on a combined use
of atmospheric reanalyses, multisatellite data and radiosonde records, and
microwave sounding techniques. The land heat storage time series as
presented in Sect. 3 relies on borehole data. The heat available to
account for cryosphere loss is presented in Sect. 4 and is based on a
combined use of model results and observations to obtain estimates of major
cryosphere components such as polar ice sheets, Arctic sea ice and glaciers.
Conclusions
The UN 2030 Agenda for Sustainable Development states that climate change is
“one of the greatest challenges of our time …” and warns
“… the survival of many societies, and of the biological
support systems of the planet, is at risk” (UNGA, 2015). The
outcome document of the Rio+20 Conference, The Future We Want, defines climate change as
“an inevitable and urgent global challenge with long-term implications for
the sustainable development of all countries” (UNGA, 2012). The
Paris Agreement builds upon the United Nations Framework Convention on
Climate Change (UN, 1992) and for the first time all nations
agreed to undertake ambitious efforts to combat climate change, with the
central aim to keep global temperature rise this century well below
2 ∘C above preindustrial levels and to limit the temperature
increase even further to 1.5 ∘C (UN, 2015). Article 14
of the Paris Agreement requires the Conference of the Parties serving as the meeting of the Parties to the Paris Agreement (CMA) to periodically take stock of the
implementation of the Paris Agreement and to assess collective progress
towards achieving the purpose of the agreement and its long-term goals
through the so-called global stocktake based on best available science.
The EEI is the most critical number defining the prospects for continued
global warming and climate change (Hansen et al., 2011; von Schuckmann et
al., 2016), and we call for an implementation of the EEI into the global
stocktake. The current positive EEI is understood to be foremost and
primarily a result of increasing atmospheric greenhouse gases (IPCC, 2013),
which have – according to the IPCC special report on Global Warming of 1.5 ∘C – already “caused approximately 1.0 ∘C of
global warming above preindustrial levels, with a likely range of
0.8 ∘C to 1.2 ∘C” (IPCC, 2018). The IPCC special report
further states with high confidence that “global warming is likely to reach
1.5 ∘C between 2030 and 2052 if it continues to increase at the
current rate”. The EEI is the portion of the forcing that the Earth's
climate system has not yet responded to
(Hansen et al., 2005) and defines additional
global warming that will occur without further change in forcing
(Hansen et al., 2017). Our results show that EEI is not
only continuing, but also increasing. Over the period 1971–2018 average EEI
amounts to 0.47±0.1 W m-2, but it amounts to 0.87±0.12 W m-2 during 2010–2018 (Fig. 8). Concurrently, acceleration of sea-level
rise (WCRP, 2018; Legelais et al., 2020), accelerated surface warming,
record temperatures and sea ice loss in the Arctic (Richter-Menge et al.,
2019; WMO, 2020; Blunden and Arndt, 2020) and ice loss from the Greenland
ice sheet (King et al., 2020), and intensification of atmospheric warming
near the surface and in the troposphere (Steiner et al., 2020) have been – for
example – recently reported. To what degree these changes are intrinsically
linked needs further evaluations.
Schematic presentation on the Earth heat inventory for the current anthropogenically driven positive Earth energy imbalance at the top of the atmosphere (TOA). The relative partition (in %) of the Earth heat inventory presented in Fig. 6 for the different components is given for the ocean (upper: 0–700 m, intermediate: 700–2000 m, deep: >2000 m), land, cryosphere (grounded and floating ice) and atmosphere, for the periods 1971–2018 and 2010–2018 (for the latter period values are provided in parentheses), as well as for the EEI. The total heat gain (in red) over the period 1971–2018 is obtained from the Earth heat inventory as presented in Fig. 6. To reduce the 2010–2018 EEI of 0.87±0.12 W m-2 towards zero, current atmospheric CO2 would need to be reduced by -57±8 ppm (see text for more details).
Global atmospheric CO2 concentration reached 407.38±0.1 ppm averaged over
2018 (Friedlingstein et al., 2019) and 409.8±0.1 ppm in 2019 (Blunden and
Arndt, 2020). WMO (2020) reports CO2 concentrations at the Mauna Loa
measurement platform of 411.75 ppm in February 2019 and 414.11 ppm in
February 2020. Stabilization of climate, the goal of the universally agreed
UNFCCC (UN, 1992) and the Paris Agreement (UN, 2015),
requires that EEI be reduced to approximately zero to achieve Earth's system
quasi-equilibrium. The change of heat radiation to space for a given
greenhouse gas change can be computed accurately. The amount of CO2 in
the atmosphere would need to be reduced from 410 to 353 ppm (i.e., a
required reduction of -57±8 ppm) to increase heat radiation to space
by 0.87 W m-2, bringing Earth back towards energy balance (Fig. 8),
where we have used the analytic formulae of Hansen et al. (2000) for this
estimation. Atmospheric CO2 was last 350 ppm in the year 1988, and
the global Earth surface temperature was then +0.5∘C relative to
the preindustrial period (relative to the 1880–1920 mean) (Hansen et al.,
2017; Friedlingstein et al., 2019). In principle, we could reduce other
greenhouse gases and thus require a less stringent reduction of CO2.
However, as discussed by Hansen et al. (2017), some continuing increase in
N2O, whose emissions are associated with food production, seems
inevitable, so there is little prospect for much net reduction of
non-CO2 greenhouse gases, and thus the main burden for climate
stabilization falls on CO2 reduction. This simple number, EEI, is the
most fundamental metric that the scientific community and public must be
aware of as the measure of how well the world is doing in the task of
bringing climate change under control (Fig. 8).
This community effort also addresses gaps for the evolution of future
observing systems for a robust and continued assessment of the Earth heat
inventory and its different components. Immediate priorities include the
maintenance and extension of the global climate observing system to assure a
continuous monitoring of the Earth heat inventory and to reduce the
uncertainties. For the global ocean observing system, the core Argo sampling
needs to be sustained and complemented by remote sensing data. Extensions
such as into the deep ocean layer need to be further fostered
(Desbruyères
et al., 2017; Johnson et al., 2015), and technical developments for the
measurements under ice and in shallower areas need to be sustained and
extended. Moreover, continued efforts are needed to further advance bias
correction methodologies, uncertainty evaluations and data processing of the
historical dataset.
In order to allow for improvements on the present estimates of changes in
the continental heat and to ensure that the database is continued into the
future, an international, coordinated effort is needed to increase the
number of subsurface temperature data from BTPs at additional locations
around the world, in particular in the Southern Hemisphere. Additionally,
repeated monitoring (after a few decades) of existing boreholes should help
reduce uncertainties at individual sites. Such data should be shared through
an open platform.
For the atmosphere, the continuation of operational satellite- and
ground-based observations is important, but the foremost need is sustaining
and enhancing a coherent long-term monitoring system for the provision of
climate data records of essential climate variables. GNSS radio occultation
observations and reference radiosonde stations within the Global Climate
Observing System (GCOS) Reference Upper Air Network (GRUAN) are regarded as
climate benchmark observations. Operational radio occultation missions for
continuous global climate observations need to be maintained and expanded,
ensuring global coverage over all local times, as the central node of a
global climate observing system.
For the cryosphere, sustained remote sensing for all of the cryosphere
components is key to quantifying future changes over these vast and
inaccessible regions but must be complemented by in situ observations for
calibration and validation. For sea ice, the albedo, the area and ice
thickness are all essential, with ice thickness being particularly
challenging to quantify with remote sensing alone. For ice sheets and
glaciers, reliable gravimetric measurements, ice thickness and extent,
snow/firn thickness and density are essential to quantify changes in mass
balance of grounded and floating ice. We highlight Antarctic sea ice change
and warming of firn as terms that are poorly constrained or have not
significantly contributed to this assessment but may become important over
the coming decades. Similarly, there exists the possibility for rapid change
associated with positive ice dynamical feedbacks at the marine margins of
the Greenland and Antarctic ice sheets. Sustained monitoring of each of
these components will, therefore, serve the dual purpose of furthering
the understanding of the dynamics and quantifying the contribution to Earth's
energy budget. In addition to data collection, open access to the data and
data synthesis products as well as coordinated international efforts are key
to the continued monitoring of the ice loss from the cryosphere and related
energy uptake.
Sustained and improved observations to quantify Earth's changing energy
inventory are also critical to the development of improved physical models
of the climate system, including both data assimilation efforts that help us
to understand past changes and predictions
(Storto et al., 2019) and climate models
used to provide projections of future climate change
(Eyring et al., 2019). For example, atmospheric reanalyses
have shown to be a valuable tool for investigating past changes in the EEI
(Allan et al., 2014) and ocean reanalyses have proven
useful in estimating rates of ocean heating on annual and subannual
timescales by reducing observational noise
(Trenberth et al., 2016).
Furthermore, both reanalyses and climate models can provide information to
assess current observing capabilities
(Fujii et al., 2019) and improve
uncertainty estimates in the different components of Earth's energy
inventory (Allison et al., 2019). Future priorities for
expanding the observing system to improve future estimates of EEI should be
cognizant of the expected evolution of the climate change signal, drawing on
evidence from observations, models and theory
(Meyssignac et
al., 2019; Palmer et al., 2019).
A continuous effort to regularly update the Earth heat inventory is
important to quantify how much and where heat accumulated from climate
change is stored in the climate system. The Earth heat inventory crosses
multidisciplinary boundaries and calls for the inclusion of new science
knowledge from the different disciplines involved, including the evolution
of climate observing systems and associated data products, uncertainty
evaluations, and processing tools. The results provide indications that a
redistribution and conversion of energy in the form of heat is taking place
in the different components of the Earth system, particularly within the
ocean, and that EEI has increased over the past decade. The outcomes have
further demonstrated how we are able to evolve our estimates for the Earth
heat inventory while bringing together different expertise and major climate
science advancements through a concerted international effort. All of these
component estimates are at the leading edge of climate science. Their union
has provided a new and unique insight on the inventory of heat in the Earth
system, its evolution over time and a revision of the absolute
values. The data product of this effort is made available and can be thus
used for model validation purposes.
This study has demonstrated the unique value of such a concerted
international effort, and we thus call for a regular evaluation of the Earth
heat inventory. This first attempt presented here has been focused on the
global area average only, and evolving into regional heat storage and
redistribution, the inclusion of various timescales (e.g., seasonal,
year to year) and other climate study tools (e.g., indirect methods, ocean
reanalyses) would be an important asset of this much needed regular
international framework for the Earth heat inventory. This would also
respond directly to the request of GCOS to establish the observational
requirements needed to monitor the Earth's cycles and the global energy
budget. The outcome of this study will therefore directly feed into GCOS'
assessment of the status of the global climate observing system due in 2021,
which is the basis for the next implementation plan. These identified
observation requirements will guide the development of the next generation
of in situ and satellite global climate observations by all national
meteorological services and space agencies and other oceanic and terrestrial
networks.
Author contributions
Overall coordination of this initiative has been driven by KvS, LC, MDP, CT and VA.
Author contributions to the ocean section include KvS, LC, TB, DD, CD, JG, MI, GCJ, REK, BAK, NK, JL, MM, DPM, SP, DR and SEW.
Author contributions to the atmosphere section include GK, MG, AKS, MM and LH.
Author contributions for the land section include AGG, FJCV, HB, SIS and PG.
Author contributions for the cryosphere section include FS, SA,
DAS, MLT, BM, AS and AS.
All authors have contributed to the Earth energy inventory section, with specific contributions from KvS, AGG, FJCV, JH and MM.
All authors have contributed to the conclusion, with specific contributions from KvS, JH, CT, VA, LC, MDP, GK, AKS, AGG, FJCV, HB and FS.
Acknowledgements
This research benefited from long-term attention by GCOS and builds on
initial work carried out under the WCRP core projects CLIVAR, GEWEX, CliC
and SPARC.
We would like to thank the anonymous reviewers for their comments, which helped to improve the manuscript.
We also would like to thank Marianne Nail (https://girlsmakesense.com/, last access: 7 August 2020) for
her support to the graphical development of Figs. 7 and 8.
We acknowledge the WEGC EOPAC team for providing the OPSv5.6 RO data (available online at 10.25364/WEGC/OPS5.6:2019.1, EOPAC Team, 2019) as well as quality-processed Vaisala RS data, UCAR/CDAAC (Boulder, CO, USA) for access to RO phase and orbit data, RSS (Santa Rosa, CA, USA) for providing MSU V4.0 data, ECMWF (Reading, UK) for access to operational analysis and forecast data, ERA5 reanalysis data, and RS data from the ERA-Interim archive, JMA (Tokyo, Japan) for provision of the JRA55 and JRA55C reanalysis data, and NASA GMAO (Greenbelt, MD, USA) for access of the MERRA-2 reanalysis data.
Financial support
Matthew D. Palmer and Rachel E. Killick were supported by the Met Office Hadley Centre Climate Programme funded by the BEIS and Defra. PML authors were supported by contribution number 5053. Catia M. Domingues was supported by an ARC Future Fellowship (FT130101532). Lijing Cheng is supported by the Key Deployment Project of Centre for Ocean Mega-Research of Science, CAS (COMS2019Q01). Maximilian Gorfer was supported by WEGC atmospheric remote sensing and climate system research group young scientist funds. Michael Mayer was supported by Austrian Science Fund project P33177. This work was supported by grants from the National Sciences and Engineering Research Council of Canada Discovery Grant (NSERC DG 140576948) and the Canada Research Chairs Program (CRC 230687) to Hugo Beltrami. Almudena García-García and Francisco José Cuesta-Valero are funded by Beltrami's CRC program, the School of Graduate Studies at Memorial University of Newfoundland and the Research Office at St. Francis Xavier University. Fiamma Straneo was supported by NSF OCE 1657601. Susheel Adusumilli was supported by NASA grant 80NSSC18K1424.
Review statement
This paper was edited by David Carlson and reviewed by two anonymous referees.
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