The Earth climate system is out of energy balance, and heat has
accumulated continuously over the past decades, warming the ocean, the land,
the cryosphere, and the atmosphere. According to the Sixth Assessment Report
by Working Group I of the Intergovernmental Panel on Climate Change,
this planetary warming over multiple decades is human-driven and results in
unprecedented and committed changes to the Earth system, with adverse
impacts for ecosystems and human systems. The Earth heat inventory provides
a measure of the Earth energy imbalance (EEI) and allows for quantifying
how much heat has accumulated in the Earth system, as well as where the heat is
stored. Here we show that the Earth system has continued to accumulate
heat, with 381±61 ZJ accumulated from 1971 to 2020. This is equivalent to a
heating rate (i.e., the EEI) of 0.48±0.1 W m-2. The majority,
about 89 %, of this heat is stored in the ocean, followed by about 6 %
on land, 1 % in the atmosphere, and about 4 % available for melting
the cryosphere. Over the most recent period (2006–2020), the EEI amounts to
0.76±0.2 W m-2. The Earth energy imbalance is the most
fundamental global climate indicator that the scientific community and the
public can use as the measure of how well the world is doing in the task of
bringing anthropogenic climate change under control. Moreover, this
indicator is highly complementary to other established ones like global mean
surface temperature as it represents a robust measure of the rate of climate
change and its future commitment. We call for an implementation of the
Earth energy imbalance into the Paris Agreement's Global Stocktake based on
best available science. The Earth heat inventory in this study, updated from
von Schuckmann et al. (2020), is underpinned by worldwide multidisciplinary
collaboration and demonstrates the critical importance of concerted
international efforts for climate change monitoring and community-based
recommendations and we also call for urgently needed actions for enabling
continuity, archiving, rescuing, and calibrating efforts to assure improved
and long-term monitoring capacity of the global climate observing system. The data for the Earth heat inventory are publicly available, and more details are provided in Table 4.
Introduction
The Earth energy imbalance (EEI) is the most fundamental indicator for
climate change, as it tells us if, how much, how fast, and where the Earth's
climate is warming, as well as how this warming evolves in the future
(Hansen et al., 2011,
2005; von Schuckmann et al., 2016). The EEI is given by the difference
between incoming solar radiation and outgoing radiation, which determines
the net radiative flux at the top of the atmosphere (TOA). Today, the Earth
climate system is out of energy balance; consequently, heat has
accumulated continuously over the past decades, warming the ocean, the land,
the cryosphere, and the atmosphere, determining the Earth heat inventory
(Fig. 1, von Schuckmann et al., 2020). This planetary warming is
human-driven and results in unprecedented and committed changes to the Earth
system (Fig. 1)
(IPCC, 2021), with adverse impacts for ecosystems and human systems
(IPCC, 2022a). As long as this imbalance
persists (or even increases) planet Earth will keep gaining energy,
increasing planetary warming (Hansen et al., 2005, 2017). Today, the EEI can
be best estimated from the quantification of the Earth heat inventory,
complemented by direct measurements from space (von Schuckmann et al., 2016;
Loeb et al., 2021). In addition, the Earth heat inventory as derived from
multiple sources of measurements and models also allows researchers to unravel where the
energy – mostly in the form of heat – is stored in the Earth system across
all components (von Schuckmann et al., 2020). Results of the first
internationally driven initiative on the Earth heat inventory (von
Schuckmann et al., 2020) not only show how much and where heat has
accumulated in the Earth system but also show for the first time that
the Earth energy imbalance has increased over the recent decade. This
increase is expected to have fundamental implications for the Earth's climate, and
several potential drivers have been discussed recently
(Loeb
et al., 2021; Hakuba et al., 2021; Kramer et al., 2021).
The Earth system responds to an imposed radiative forcing through a number
of feedback mechanisms, which operate on various timescales. Earth's
radiative response is complex, comprising a variety of climate feedback mechanisms
(e.g., water vapor feedback, cloud feedback, ice–albedo feedback) (Forster
et al., 2021). Conceptually, the relationships between EEI, radiative
forcing, and surface temperature change can be expressed as the following
(Gregory and Andrews, 2016):
ΔNTOA=ΔFERF-αFPΔTS,
where ΔNTOA is the Earth's net energy imbalance at TOA (in Wm-2), ΔFERF is the effective radiative forcing (Wm-2), ΔTS is the global surface temperature anomaly (K)
relative to the equilibrium state, and αFP is the net total
feedback parameter (Wm-2 K-1), which represents the combined
effect of the various climate feedback mechanisms. 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 feedback associated
with surface temperature increase (e.g., Hansen et al., 2011).
Observation-based estimates of ΔNTOA are therefore crucial 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 sea-level
rise, ocean warming, and ice shelf loss
(Cheng
et al., 2019; Forster et al., 2021; Hansen et al., 2017; IPCC, 2021a; Nauels
et al., 2017). 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, in principle, 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 and its future commitment
(Cheng
et al., 2017b; Forster et al., 2021; Loeb et al., 2018; Palmer and McNeall,
2014; von Schuckmann et al., 2016).
Schematic overview on the central role of the Earth heat inventory
and its linkage to anthropogenic emissions, the Earth energy imbalance,
change in the Earth system, and implications for ecosystems and human
systems. The Earth heat inventory plays a central role for climate change
monitoring as it provides information on the absolute value of the Earth
energy imbalance, the total Earth system heat gain, and how much and where
heat is stored in the different Earth system components. Examples of
associated global-scale changes in the Earth system as assessed in
Gulev et
al. (2021) are drawn, together with major implications for the ecosystem and
human systems (IPCC, 2022b). Upward arrows
indicate increasing change, downward arrows indicate decreasing change, and
turning arrows indicate change in both directions. The percentages for heat stored
in the Earth system components are provided over the period 2006–2020 (see Sect. 6).
The heat gain in the Earth system from a positive EEI results in directly
and indirectly triggered changes in the climate system, with a variety of
implications for the environment and human systems (Fig. 1). One of the most
direct implications from a positive EEI is the rise of global mean surface
temperature. The accumulation and storage of surplus anthropogenic heat
leads to ocean warming and thermal expansion of the water column, which
together with terrestrial ice melt leads to sea-level rise
(WCRP Global Sea Level Budget Group, 2018). Moreover, there are
various facets of impacts from ocean warming such as on climate extremes,
which are provided in more detail in a recent review
(Cheng et al., 2022a). The heat accumulation in the
Earth system also leads to warming of the atmosphere, particularly to a
temperature increase in the troposphere, leading to water vapor increase and
changes in atmospheric circulation
(Gulev et
al., 2021).
On land, the heat accumulation leads to an increase in ground heat storage,
which in turn triggers an increase in ground surface temperatures that may
increase soil respiration and may lead to a decrease in soil water,
depending on the climatic and meteorological conditions and factors such as
land cover and soil characteristics
(Cuesta-Valero
et al., 2022; Gulev et al., 2021). Moreover, inland water heat storage
increases, leading to increases in lake water temperatures that may result
in algal blooms and lake stratification, and typically leads to a decrease
in lake ice cover. Heat gain in the Earth system also induces an increase in
permafrost heat content, which in turn leads to disruptive changes in ground
morphology, CH4 and CO2 emissions, and a decrease in permafrost
extent and ground ice volume. More details are synthesized in
Cuesta-Valero et al. (2023a). In the cryosphere,
associated changes include a loss of glaciers, ice sheets, and Arctic sea ice
(IPCC, 2019, 2021a).
These human-induced changes have already impacted ecosystems and have
adverse impacts on human systems (Fig. 1). Particularly, they have emerged
for ecosystem structure and species ranges and phenology (timing of life
cycles), and they include adverse impacts such as for water security and food
production; health and wellbeing; and cities, settlements, and infrastructure
(IPCC, 2022b; see their Fig. SPM.2).
Regularly assessing, quantifying, and evaluating the Earth heat inventory
creates a unique opportunity to support the call to action and solution
pathways as assessed during the sixth assessment cycle of the Intergovernmental Panel on Climate Change (IPCC).
Moreover, the Earth heat inventory allows for a regular stocktaking of the
implementation of the Paris Agreement
https://unfccc.int/sites/default/files/english_paris_agreement.pdf (last access: 29 March 2023)
while monitoring progress towards achieving the purpose of the agreement
and its long-term goals based on best available science. These assessment
outcomes further emphasize the need to extend the Global Climate Observing
System (GCOS) beyond the strict scientific observation of the climate state
to also support policy and planning (GCOS, 2021).
Science-driven studies driven by an Earth system view and backed by
concerted multidisciplinary and international collaborations play a
critical role to support these objectives
(Crisp et al., 2022;
Dorigo et al., 2021; von Schuckmann et al., 2020). With this second study, we
aim to contribute to a more frequent and regular science-driven update of
the state of the Earth heat inventory as an important indicator of climate
change.
Based on the quantification of the Earth heat inventory published in 2020
(von Schuckmann et al., 2020), we present the updated results of the Earth
heat inventory over the period 1960–2020, along with the long-term Earth
system's heat gain over this period, and the partitions of where the heat goes
for the ocean, atmosphere, land, and cryosphere. Section 2 provides the
updates for ocean heat content, which is based on improved evaluations
(e.g., trend evaluation method) and the addition of further international
data products of subsurface temperature. Updated estimates and refinements
for atmospheric heat content are discussed in Sect. 3. For the land
component in Sect. 4, an improved uncertainty framework is proposed for
the ground heat storage estimate, and new evaluations for inland freshwater
heat storage and thawing of permafrost have been included (Cuesta-Valero et
al., 2022). An update of the heat available to melt the cryosphere is
described in Sect. 5 based on re-enforced international collaboration. In
Sect. 6, the updated Earth heat inventory is established and discussed
based on the results of Sects. 2–5. In the final section, challenges and
recommendations for future improved estimates are discussed for each Earth
system component, with associated recommendations for future evolution of
the observing system.
Heat stored in the ocean
Global ocean heat content (OHC) can be estimated directly from subsurface
temperature measurements, which is one of the variables of the in situ
component of the Global Ocean Observing System (GOOS
https://www.goosocean.org/ (last access: 29 March 2023)
) and which has continued to evolve during the
past decades
(Abraham
et al., 2013; Gould et al., 2013; Moltmann et al., 2019). The evolution of
the ocean observing system for subsurface temperature measurements is
provided, for example, in Cheng et al. (2022a), leveraging the transition from
historical measures to modern autonomous techniques, which achieved
near-global coverage in the year 2006 (the so-called golden Argo era).
Different research groups have developed gridded products of subsurface
temperature fields and ocean heat content using different processing
methodologies
(Abraham
et al., 2022; Boyer et al., 2016; Cheng et al., 2022b; Gulev et al., 2021; Li
et al., 2022; Savita et al., 2022). Additionally, specific Argo-based
products are listed on the Argo web page (http://www.argo.ucsd.edu/, last
access: 12 July 2022). Near-global OHC can also be indirectly estimated from
spatial geodetic measurements by combining sea surface height from altimetry
and ocean mass from gravimetry to solve the sea-level budget equation
(Meyssignac
et al., 2019; Dieng et al., 2017; Llovel et al., 2014). Spatial geodetic OHC
is available since 2002 and provides full-depth OHC variations
(Marti et al., 2022; Hakuba et
al., 2021). Ocean reanalysis systems have also been used to deliver
estimates of near-global OHC (Trenberth et
al., 2016; von Schuckmann et al., 2018), and their international assessments
show increased agreement with increasing in situ data availability for the
assimilation, particularly when the Argo project had achieved nearly global-scale data
sampling (Fig. 2)
(Palmer
et al., 2017; Storto et al., 2018, 2019; Meyssignac et al., 2019).
This initiative relies on the availability of regular updates to data
products, their temporal extensions, and direct interactions with the
different research groups. A complete view of all subsurface ocean
temperature products can only be achieved through a concerted international
effort and over time, particularly accounting for the continued development
of new or improved OHC products. 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 global OHC. A first established
international ensemble mean and standard deviation of near-global OHC up to
2018 was established in von Schuckmann et al. (2020), which has now been
updated up to 2020 and further extended with the addition of five new products
(Fig. 3). The ensemble spread gives an indication of the agreement among
products and can be used as a proxy for uncertainty. Compared to the results
in von Schuckmann et al. (2020), the spread has increased, which can be
referred back to the additional use of data products, the impact of
year-to-year variations, and the refined use of the ensemble spread approach
(see below).
Although there has been a tremendous improvement in in situ subsurface temperature
measurements over time, estimates of global OHC remain an area of active
research to minimize the major effects from different data processing
techniques of the irregular (in space and time) in situ database and
associated sampling characteristics, followed by the choice of the
climatology used in the mapping process and data bias corrections, which
today induce discrepancies between the different estimates
(Boyer
et al., 2016; Cheng et al., 2014; Gouretski and Cheng, 2020; Cheng et al.,
2018; Good, 2017; Savita et al., 2022; Allison et al., 2019). Concerns about
common errors in the products remain. Accurate understanding of the
uncertainties of the product is an essential element in their use. So far, a
basic assumption is that the error distribution for the observations is
Gaussian with a mean of zero, which has been approximated by an ensemble of
various products. However, a more complete understanding of any apparent
trends requires the determination of systematic errors (e.g., systematic
calibration errors) (or the impacts of changing observation densities
through a synthetic profile approach; Allison et al., 2019)
and of instrument technologies (Wong et al., 2020). These elements can
result in biases across the ensemble, or produce artificial changes in the
energetics of the system (Wunsch, 2020). For example, Li et al. (2022) estimated that assuming linear vertical interpolation with sparse
historical vertical profiles results is an underestimation of global ocean
heat content (and ocean thermal expansion) trends since the 1950s on the order of
14 % compared with a more sophisticated vertical interpolation scheme
(Barker and McDougall, 2020; Li et al., 2022),
with the greatest systematic underestimates at latitudes 15–20∘ N,
and Li et al. (2022) also found that interannual differences between
various eXpendable BathyThermograph (XBT) corrections were similar to the differences when only higher-quality hydrographic data were included, implying the need for improved time-dependent XBT corrections. The uncertainty can also be estimated in other
ways, including some purely statistical methods
(Levitus
et al., 2012; MacIntosh et al., 2017; Cheng et al., 2019) or methods
explicitly accounting for the error sources
(Gaillard et al., 2016;
Lyman and Johnson, 2014; von Schuckmann and Le Traon, 2011). Each method
has its caveats; for example, the error covariances are mostly unknown and
must be estimated a priori. For this study, adopting a straightforward
method with a “data democracy” strategy (i.e., all OHC estimates have been
given equal weight) has been chosen as a starting point, which is different from
the ensemble approach adopted in the Sixth Assessment Report (AR6)
(Forster et al.,
2021).
Ensemble mean time series and ensemble standard deviation (95 %,
shaded) of global ocean heat content (OHC) anomalies relative to the
2005–2020 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. 3. 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.
Trends of global ocean heat content (OHC) as derived from
different products (colors) and using LOWESS (see text for more details).
References are given in the figure legend, except for CMEMS (CORA, Copernicus Marine Ocean Monitoring Indicator, 2023), EN.4.2.2.c14 (Good et
al., 2013) with Cheng et al. (2014), XBT
(Gouretski and Cheng, 2020), and Mechanical Bathythermograph (MBT) bias corrections, as well as for the method by Palmer et al. (2007). CSIRO-GEOMAR-NOC
(Argo) (Domingues et al., 2008; Wijffels et
al., 2016; Roemmich et al., 2015), CSIRO-GEOMAR-NOC (hist)
(Church et
al., 2011; Domingues et al., 2008), NOC (National Oceanographic Centre)
(Desbruyères et al., 2017), and the Argo dataset MOAA
GPV (Hosoda et al., 2008) are also included. Results from the
Copernicus Marine Service Global Reanalysis Ensemble Product have been added as well
(Copernicus Marine Ocean Monitoring Indicator: global ocean heat
content) for comparison but are not considered for the ensemble mean in
Fig. 1. The ensemble mean and standard deviation (95 % confidence
interval) are indicated 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 (1960–2020), altimeter era (1993–2020), and golden Argo
era (2006–2020). 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). References as indicated in the legend include the following:
Cheng
et al. (2017a); Gaillard et al. (2016); Good et al. (2013); Ishii et al. (2017);
Kuusela and Giglio (2022); Levitus et al. (2012); Li et al. (2017, 2022); Lyman and Johnson (2014); Roemmich and Gilson (2009); and von Schuckmann
and Le Traon (2011).
The continuity of this activity will help to further expand international
collaboration and to unravel uncertainties due to the community's collective
efforts on data quality as well as on detecting and reducing processing
uncertainties. It also provides up-to-date scientific knowledge of ocean
warming. Products used for this assessment are referenced in the caption of
Fig. 3. 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 (700 m for Li et al., 2022) 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. The ocean areas within
60∘ S–60∘ N include 91 % of the global ocean surface
area, and limiting to the 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 is estimated to account for more than 10 % for 0–2000 m OHC
trends (Savita et al., 2022; von Schuckmann et al., 2014) and about 4 %
for the Arctic area (J. Mayer et al., 2021). The
assessment is based on three distinct periods to account for the evolution
of the observing system, i.e., 1960–2020 (i.e., “historical”), 1993–2020
(i.e., “altimeter era”), and 2006–2020 (i.e., “golden Argo era”). All
time series go up to 2020 – which was one of the principal limitations for
the inclusion of some products. Our final estimates of OHC for the 0–300,
0–700, 700–2000, and 0–2000 m depth layers are the ensemble average of all
products, with the uncertainty range defined by the standard deviation
(2σ, 95 % confidence interval) of the corresponding ensemble used
(Fig. 2).
For the trend evaluation, we have followed the most recent study by Cheng et
al. (2022b) and used a locally weighted scatterplot smoothing (LOWESS)
approach to reduce the effect of high-frequency variability (e.g.,
year-to-year variability), data noise, or changes in the observing system as
it relies on a weighted regression (Cleveland, 1979) within
a prescribed span width of 25 years for the historical and altimeter era
and 15 years for the recent period (2006–2020). The change in OHC(t) over a
specific period, ΔOHC, is then calculated by subtracting the first
value from the last value of the fitted time series, OHCLOWESS(t), to obtain the trend while dividing by the considered period. To obtain an
uncertainty range on the trend estimate and to take into account the
sensitivity of the calculation to interannual variability, we implement a
Monte Carlo simulation to generate 1000 surrogate series OHCrandom(t),
under the assumption of a given mean (our “true” time series OHC(t))
(Cheng et al., 2022b). Each surrogate OHCrandom(t) consists of the
fitted true time series OHC(t) plus a randomly generated residual which
follows a normal (Gaussian) distribution and which is included in an
envelope equal to 2 times the uncertainty associated with the time series.
Then, a LOWESS fitted line is estimated for each of the 1000 surrogates. The
95 % confidence interval for the trend is then calculated based on ±2 times the standard deviation (±2σ) of all 1000 trends of
the surrogates. However, the use of either trend estimates following a
linear, LOWESS approach, or the approach discussed in
Palmer et al. (2021) leads to consistent results
within uncertainties (not shown).
In agreement with Cheng et al. (2019) and Gulev et al. (2021), our results confirm a continuous increase
in ocean warming over the entire study period (Fig. 2). Moreover, rates of
global ocean warming have increased over the three different study periods,
i.e., historical up to the recent decadal change. The trend values are all
given in Table 1. The major fraction of heat is stored in the upper ocean
(0–300 and 0–700 m depth). However, heat storage at intermediate depth
(700–2000 m) increases at a nearly comparable rate as reported for the
0–300 m depth layer (Table 1, Fig. 3). There is a general agreement among
the 16 international OHC estimates (Fig. 3). However, for some periods and
depth layers the standard deviation (95 % confidence level) reaches maxima
at about 0.3 W m-2. All products agree on the fact that global ocean
warming rates have increased in the past decades and doubled since the
beginning of the altimeter era (1993–2020 compared with 1960–2020) (Fig. 3). Moreover, there is a clear indication that heat sequestration took place
in the 700–2000 m depth layer over the past six decades linked to an increase
in OHC trends over time (Fig. 3). Ocean warming rates for the 0–2000 m
depth layer reached record rates of 1.03 (0.62) ±0.2 W m-2 over
the period 2006–2020 for the ocean (global) area, consistent with what has
been reported in Johnson et al. (2022).
OHC trends using LOWESS (locally weighted scatterplot smoothing;
see text for more details) as derived from the ensemble mean (Fig. 2) for
different time intervals, as well as different integration depths. The
regression was done for each time period (1960–2020, 1971–2020, 1993–2020, 2006–2020). A time window of 25 years was used for the periods that
allowed it (1960–2020, 1971–2020, 1993–2020). For the period 2006–2020, a time window of 15 years was used. Note that values are given in watts per square meter (Wm-2) relative to the global surface (a factor of 0.61 for the ocean
surface is used considering the area 60∘ S–60∘ N,
>300 m bathymetry). See also text and Figs. 2–3 for more details.
Additionally, values for satellite-derived estimates of OHC have been added
for the most recent period, which are updated after Hakuba et al. (2021) and Marti et
al. (2022).
Ocean heat content linear trends (Wm-2) 0–300 m0–700 m0–2000 m700–2000 m0–bottom0–bottom,0–bottom,(Hakuba et al., 2021)(Marti et al., 2022)1960–20200.14±0.040.21±0.10.32±0.10.11±0.040.35±0.11971–20200.18±0.10.27±0.10.40±0.10.13±0.030.43±0.11993–20200.24±0.10.37±0.10.55±0.20.18±0.040.61±0.22006–20200.27±0.10.39±0.10.62±0.20.23±0.10.68±0.30.88±0.240.87±0.2
For the deep OHC changes below 2000 m, we adapted an updated estimate from
Purkey and Johnson (2010)
(PG10 hereinafter) from 1992 to 2020, which is a constant linear trend
estimate (0.97±0.48 ZJ yr-1, 0.06±0.03 W m-2)
derived from a global integration of OHC below 2000 m using basin-scale deep-ocean temperature trends from repeated hydrographic sections. 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's function method and Estimating the Circulation and Climate of the Ocean (ECCO) reanalysis data, 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) show a
fairly weak global trend during the 1990s, which is different from observation-based
estimates. This mismatch might come from how surface–deep connections are
represented in ECCO reanalysis data and the use of time-mean Green's
functions in Zanna et al. (2019), as well as from the sparse coverage 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, and the rates of
warming may vary by ocean basin. Comparison of the results in Table 1 with
OHC estimates derived from the space geodetic approach (Hakuba et al., 2021; Marti
et al., 2022) shows overall agreement within uncertainties.
Before 1992, we assume zero OHC trend below 2000 m due to insufficient
global observations below 2000 m, following the methodology in some studies
(Cheng et al., 2017a, 2022b), IPCC AR5
(Rhein et al., 2013), and IPCC AR6
(Forster et al., 2021; Gulev et al., 2021). The deep warming is likely driven
by decadal variability in deep water formation rates, which could have been
in a non-steady-state mode prior to 1990, introducing additional uncertainty
to the pre-1990 OHC estimates. Using surface temperature observations and
assuming the heat is advected by mean circulation, Zanna et al. (2019) show
a near-zero (small cooling trend) OHC trend below 2000 m from the 1960s to
1980s, suggesting the trend before 1992 might be small. The derived time
series of PG10 after 1991 and zero-trend before 1992 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 by Cheng et al. (2017a), which
allows us to extract an uncertainty range over the period 1993–2018 within
the given (lower (0.96–0.48 ZJ yr-1), upper (0.96+0.48 ZJ yr-1)) range of the deep OHC trend estimate. We then extend the
obtained uncertainty estimate back from 1992 to 1960, with zero OHC anomaly.
Heat available to warm the atmosphere
The heat content of the atmosphere is small compared to those of the other
Earth subsystems. Yet it is by no means negligible, since, in relative terms,
the atmospheric heat gain is rapid over the recent decades and has a high
impact on human life (Fig. 1) (IPCC, 2021a). Atmospheric observations show a
warming of the troposphere and a cooling and contraction of the stratosphere
since at least 1979
(Pisoft
et al., 2021; Steiner et al., 2020a). In the tropics, the upper troposphere
has warmed faster than the near-surface atmosphere since at least 2001, as
seen with the new observation technique of GPS radio occultation
(Gulev
et al., 2021; Ladstädter et al., 2023; Steiner et al., 2020a, b), while observations based on microwave soundings have likely
underestimated tropospheric temperature trends in the past
(Santer et al., 2021; Zou et al., 2021).
Recently, a continuous rise in the height of the tropopause has been observed for 1980 to
2020 over the Northern Hemisphere (Meng et al.,
2022). The increase is equally due to tropospheric warming and stratospheric
cooling in the period 1980 to 2000, while the rise after 2000 resulted
primarily from enhanced tropospheric heat gain. Moreover, indications exist
of a widening of the tropical belt
(Grise
et al., 2019; Fu et al., 2019; Staten et al., 2020) as well as of changes in
the seasonal cycle (Santer et al., 2022). However, changes
in atmospheric circulation and related conditions for extreme weather are still
subject to uncertainty (Cohen et al., 2020), while the
occurrence of heat-related extreme weather events has clearly increased over
recent decades (Cohen et
al., 2020; IPCC, 2021b), with high risks for societies, economies, and the
environment (Fischer et al., 2021).
A regular assessment of atmospheric heat content changes is hence critical
for a complete overview of energy and mass exchanges with other climate
components and for a complete energy budgeting of Earth's climate system.
Atmospheric heat content
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 total energy
budget of the vertically integrated and globally averaged atmosphere
(indicated by the global averaging operator 〈.〉) reads
as follows (Mayer et al., 2017):
∂EA∂t=〈NTOA〉-〈Fs〉-〈Fsnow〉-〈FPE〉,
where the vertically integrated atmospheric energy content EA per unit
surface area (J m-2) reads as
EA=∫zszTOAρcvT+gz-zs+Leq+12V2dz.
In Eq. (2), NTOA is the net radiation at top of the atmosphere,
Fs is the net surface energy flux defined as the sum of net surface
radiation and latent and sensible heat fluxes, Fsnow denotes the latent
heat flux associated with snowfall, and FPE additionally accounts for
sensible heat of precipitation. See Mayer et al. (2017) or von Schuckmann et al. (2020) for a discussion of the last two terms, which are small on a
global scale and hence often neglected.
Equation (3), formulated in mean-sea-level altitude (z) coordinates and used here
for integrating over observational data, provides a decomposition of
EA 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 Lv or sublimation Ls (the latter relevant
below 0 ∘C), q the specific humidity of 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 computing EA for the purpose of this update to the von Schuckmann et al. (2020) heat storage assessment, we continued to use the formulations
described therein, including that we refer to (geographically
aggregated) EA as atmospheric heat content (AHC) in this context. This
acknowledges the dominance of the heat-related terms in Eq. (3). Briefly, in
deriving the AHC from observational datasets, we accounted for the intrinsic
temperature dependence of the latent heat of water vapor in formulating
Le (for details, see Gorfer, 2022), while the reanalysis derivations
approximated Le by constant values of Lv, as this simplification is
typically also made in the assimilating models (e.g.,
ECMWF-IFS, 2015). As another small difference, the observational estimations
neglected the kinetic energy term in Eq. (3), while the reanalysis
estimations accounted for it. However, the resulting differences in AHC anomalies
from any of these differences are negligibly small, especially when
considering trends over time.
Datasets and heat content estimation
Turning to the actual datasets used, the AHC and its changes and trends over
time can be quantified using various data sources. Reassessing possible data
sources, we extended the high-quality datasets that we used in the initial
von Schuckmann et al. (2020) assessment. In particular, we updated the time
period from 2018 to 2020 and improved the backward extension from 1980 to 1960.
Specifically, the adopted datasets and the related AHC data record
preparations can be summarized as follows.
Atmospheric reanalyses combine observational information from various
sources (radiosondes, satellites, weather stations, etc.) and a dynamical
model in a statistically optimal way. These data have reached a high level
of maturity, thanks to continuous improvement work since the early 1990s
(Hersbach et
al., 2018). Especially reanalyzed thermodynamic state variables, like
temperature and water vapor that are most relevant for AHC computation, are
of high quality and suitable for climate studies, although temporal
discontinuities introduced from changing observing systems continue to
deserve due attention
(Berrisford
et al., 2011; Chiodo and Haimberger, 2010; Hersbach et al., 2020; M. Mayer et
al., 2021).
We use the latest generation of reanalyses, including ECMWF's fifth-generation reanalysis ERA5
(Bell et al.,
2021; Hersbach et al., 2020), Japan Meteorological Agency (JMA)'s reanalysis JRA55
(Kobayashi et al., 2015), and NASA's
Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA2)
(Gelaro et al., 2017). ERA5 and JRA55
are both available over the full joint time frame of this heat storage
assessment from 1960 to 2020, while MERRA2 complements these from 1980 to
2020. The additional JRA55C reanalysis variant of JRA55, included for
initial intercomparison in von Schuckmann et al. (2020), is no longer used
since it is available to 2012 only, and due to its similarity to JRA55 it is not
adding appreciable complementary value.
In addition to these three reanalyses, the datasets from two climate-quality
observation techniques are used for complementary observational AHC
estimates. These include the Wegener Center (WEGC) multi-satellite radio
occultation (RO) data record (WEGC OPSv5.6; Angerer et al., 2017; Steiner et al., 2020b),
over 2002–2020, and a radiosonde (RS) data record derived from the
high-quality Vaisala sondes RS80/RS92/VS41 (WEGC Vaisala;
Ladstädter et al., 2015), covering 1996–2020. These RO
and RS datasets provide atmospheric profiles of temperature, specific
humidity, and density that are vertically completed by colocated ERA5
profiles in domains not fully covered by the data (e.g., in the lower
troposphere for RO or at polar latitudes for RS). Similar to dropping the
JRA55C reanalysis variant for no longer adding appreciable further value,
the simplified AHC proxy data based on microwave sounding unit (MSU)
observational data, intercompared in von Schuckmann et al. (2020), are no
longer used.
From the observational data, the AHC is estimated by first evaluating Eq. (3) (using all terms for total and the third term only for latent AHC) at
each available profile location and subsequently deriving it as volumetric
heat content, for up to the global scale, from vertical integration, temporal
averaging, and geographic aggregation according to the approach summarized
in von Schuckmann et al. (2020) and described in detail by
Gorfer (2022). For the reanalyses, the estimation is based on
the full gridded fields. Applying the approach for cross-check with reanalysis
profiles subsampled at observation locations only, confirms its validity as
it accurately leads to the same AHC results as from the full gridded fields.
Overall, the ensemble spread of all the atmospheric datasets used is deemed
a reasonable proxy for the uncertainty in the ensemble-mean annual AHC
anomaly data, in particular since 1980 during the “satellite observation
era” (e.g., Hersbach et al., 2020; Steiner et al., 2020a). However, the
uncertainties of the trend estimates, i.e., of the AHC increase rates (“AHC
gain”) obtained from linear fitting to the anomaly data over periods of
interest (see Sect. 3.3), are weakly depending on these data
uncertainties anyway, since the trend uncertainties are dominated
by the interannual natural variability in the data, which is significantly
larger than the data uncertainties expressed by the ensemble spread (see
Fig. 4).
Atmospheric heat content change since 1960 and its amplification
Figure 4 shows the resulting global AHC change inventory over 1960 to 2020
(61-year record), in terms of total AHC anomalies for each data type (Fig. 4a) and for the ensemble mean with trends for selected periods and
uncertainty estimates (Fig. 4c). The selected trend periods align with those
for ocean data and with availability of atmospheric datasets (see
Sect. 3.2 above) and represent a reference trend 1961–2000 plus recent
trends of the last about 30, 20, and 15 years. Latent AHC
anomalies, a key component of the AHC (Matthews
et al., 2022), are also shown (Fig. 4b and d). Compared to von Schuckmann
et al. (2020), the AHC data have the El Niño–Southern Oscillation (ENSO) signal removed (with ENSO
regressed out via the Niño 3.4 Index; and cross-check with
non-ENSO-corrected data is showing that trend differences are reasonably
small). However, variability due to volcanic eruptions is still included
and may somewhat influence the trends over 1993–2020, which start in the
cold anomaly after the Pinatubo eruption
(Santer et al., 2001).
The latent AHC (Fig. 4b and d), which accounts for about one-quarter of the
total AHC, exhibits a qualitatively similar temporal evolution as total AHC,
but with larger relative uncertainty compared to the total AHC. The RO
and RS datasets in Fig. 4b show some differences, particularly the low
latent AHC values in the 1990s and early 2000s from the RS WEGC Vaisala dataset likely stem from known dry biases of the RS80/RS90/RS92 humidity sensors
(Wang et al., 2002; Verver et al.,
2006; Vömel et al., 2007). Estimated trends based on these RS data are
thus likely too high, although the overall increase in latent AHC is
also substantial in the other datasets.
Annual-mean global AHC anomalies from 1960 to 2020 of total AHC (a, c) and latent-only AHC (b, d), respectively, of three different
reanalyses and two different observational datasets shown together with
their mean (a, b) and the mean AHC anomaly shown together with four
representative AHC trends and ensemble spread measures of its underlying
datasets (c, d). The in-panel legends identify the individual datasets (a, b) and the selected trend periods together with the associated trend
values (plus 90 % confidence range) and ensemble spread measures (c, d), with the latter including the time-average standard deviation and
minimum/maximum deviations of the individual datasets from the mean.
The results clearly show that the AHC trends have increased from the earlier
decades, represented by the 1961–2000 trend of near 1.7 TW. We find the mean
trend to be about 2.5 times higher over 1993–2020 (about 5.3 TW) and about 4
times higher in the most recent two decades (about 6–7 TW), a period that is
already covered by the RO and RS records. Latent AHC trends in the most
recent periods are about 3 times larger than the 1961–2000 reference period. Since
1971, the heat gain in the atmosphere amounts to 5±1 ZJ (see also
Fig. 8).
The remarkable amplification of total AHC and latent AHC trends is
highlighted in Fig. 5 and summarized in Table 2 for the representative
recent periods vs. the 1961–2000 reference period. The 1961–2000 and
1993–2020 periods were covered by reanalysis only, while the WEGC
Vaisala RS dataset additionally covers the 2001–2020 and 2006–2020
periods, and the RO dataset covers the most recent period (see dataset descriptions
in Sect. 3.2). The larger diversity of recent datasets induces more
spread; for example, the RS dataset shows an amplification factor of near
4.5 in the global total AHC gain for 2001–2020, while the amplification
factors from the reanalyses range from 2.6 to 3.8. Amplifications are
generally largest in the Southern Hemisphere extratropics, where the
1961–2000 reference gain is smallest, and weakest in the tropics. In the
most recent period (2006–2020), the amplification factors are strongest, with
the RS and RO datasets on the high end of the spread (near a factor of 5 in
global total AHC) and somewhat smaller but still high from the reanalyses
(around a factor of 4).
For the latent AHC amplification factors, we see moderate values in the
1993–2020 period in the global mean and tropics. In the tropics, the lower
uncertainty bound for amplification is slightly below 1 during all three
recent trend periods. The spread of the amplification factors increases for
the most recent periods, which is, on the one hand, due to the shorter
duration. The range increase is also related to the inclusion of the RS and RO datasets after 2000, which contribute the largest and smallest
latent AHC gain amplification factors. For 2006–2020, the global mean
amplification factor from RO is about 2, whereas from the RS dataset it is
near 5. Regarding latitudinal bands, the amplification factors are again
strongest in the extratropics, where the 1961–2000 reference gains are also
smallest, exhibiting a large spread, especially in the southern extratropics.
The relatively large amplification factors of the RS WEGC Vaisala dataset
are likely exaggerated due to the well-documented dry bias of the early RS
humidity sensors as noted above (Wang et al., 2002; Vömel et al., 2007;
Verver et al., 2006).
Despite the uncertainties and spread described, the overall message from
Fig. 5 and Table 2 is very clear and substantially reinforcing the
evidence from the initial von Schuckmann et al. (2020) assessment: the
trends in the AHC, including in its latent heat component, show that
atmospheric heat gain has strongly increased over the recent decades.
Amplification of long-term trends in AHC anomalies (“AHC gain”)
for total AHC (left) and latent-only AHC (right) in four geographic domains
(global, Northern Hemisphere extratropics, tropics, Southern Hemisphere
extratropics) for three recent time periods (legend upper left) expressed as
a ratio of the trend of each period relative to the trend in the
previous-century reference period (1961–2000) (noted below the reference line where the amplification
factor equals 1). The amplification factor for each
recent-trend case (for the four domains of both total and latent AHC) is
depicted for the mean anomaly serving as best estimate (larger black
circles) and the related recent trends in the individual-dataset anomalies
(colored circles as per upper-right legend). The related 90 % uncertainty
range (black error bar) is estimated from the spread (standard
deviation) of the individual-dataset amplification factors. The trend in the
mean anomaly over 1961–2000 is used as the reference AHC gain.
Long-term trend values in mean AHC anomalies (AHC gains; in units
of zettajoule per decade (ZJ per decade) and terrawatt (TW), the latter listed in parentheses) and amplification factors vs. the 1961–2000 reference gain
(lines marked “Ref.”), for total AHC (left block) and latent-only AHC (right
block) for the three recent time periods in four geographic domains as
illustrated in Fig. 5. The AHC gain and amplification values are listed
together with their 90 % confidence ranges.
Total AHC gain Latent AHC gain DomainTime rangeGain in ZJ per decade (TW)Amplification vs. Ref.Gain in ZJ per decade (TW)Amplification vs. Ref.GLOBAL1993–20201.68±0.24 (5.33±0.76)3.19 (2.63 to 3.34)0.50±0.06 (1.59±0.20)2.51 (2.05 to 2.91)2001–20201.91±0.34 (6.04±1.09)3.62 (2.27 to 4.73)0.60±0.09 (1.90±0.27)3.39 (1.79 to 5.13)2006–20202.29±0.54 (7.25±1.72)4.35 (3.33 to 5.36)0.65±0.13 (2.05±0.42)3.37 (1.55 to 5.18)Ref.1961–20000.53±0.18 (1.67±0.56)1.00.19±0.06 (0.61±0.18)1.0NH20-90N1993–20200.62±0.11 (1.97±0.35)5.44 (4.86 to 5.92)0.16±0.02 (0.50±0.08)4.57 (3.90 to 5.26)2001–20200.64±0.15 (2.03±0.47)5.62 (4.26 to 6.48)0.18±0.03 (0.58±0.11)5.50 (4.79 to 6.31)2006–20200.79±0.25 (2.49±0.80)6.89 (5.51 to 8.26)0.22±0.05 (0.70±0.17)6.32 (4.36 to 8.28)Ref.1961–20000.11±0.08 (0.36±0.24)1.00.03±0.02 (0.11±0.06)1.0TROPICS1993–20200.60±0.13 (1.90±0.41)1.72 (1.05 to 1.98)0.24±0.04 (0.75±0.12)1.58 (0.71 to 2.36)2001–20200.89±0.15 (2.82±0.47)2.56 (1.20 to 3.77)0.31±0.05 (1.00±0.16)2.52 (0.70 to 4.49)2006–20200.96±0.24 (3.04±0.77)2.76 (1.86 to 3.67)0.31±0.07 (0.99±0.22)2.22 (0.48 to 3.96)Ref.1961–20000.35±0.08 (1.10±0.25)1.00.14±0.03 (0.45±0.11)1.0SH20-90S1993–20200.46±0.09 (1.46±0.29)7.14 (5.49 to 7.86)0.11±0.02 (0.33±0.05)6.11 (3.02 to 9.02)2001–20200.37±0.17 (1.18±0.52)5.80 (3.76 to 7.58)0.10±0.03 (0.32±0.08)6.31 (2.81 to 9.95)2006–20200.54±0.25 (1.71±0.79)8.40 (6.99 to 9.81)0.11±0.04 (0.36±0.12)6.87 (3.52 to 10.22)Ref.1961–20000.07±0.06 (0.21±0.18)1.00.02±0.01 (0.05±0.03)1.0Heat available to warm land
In previous studies, the land term of the Earth heat inventory was considered as the heat used to warm the continental subsurface (Hansen et al., 2011;
Rhein et al., 2013; von Schuckmann et al., 2020). Temperature changes within
the continental subsurface are typically retrieved by analyzing the global
network of temperature–depth profiles, measured mostly in the Northern
Hemisphere, southern Africa, and Australia. Each temperature profile records
changes in subsurface temperatures caused by the heat propagated through the
ground due to alterations in the surface energy balance
(Cuesta-Valero et al., 2021b). Such
perturbations in the subsurface temperature profiles can be analyzed to
recover the changes in past surface conditions that generated the measured
profile, allowing a reconstruction of the evolution of ground surface
temperatures and ground heat fluxes at decadal to centennial timescales
(Beltrami
et al., 2002; Beltrami and Mareschal, 1992; Demezhko and Gornostaeva,
2015; Hartmann and Rath, 2005; Hopcroft et al., 2007; Jaume-Santero et al.,
2016; Lane, 1923; Pickler et al., 2016; Shen et al., 1992). Although
previous estimates only considered changes in ground temperatures for
representing the heat storage by exposed land, ground heat storage has been
found to be the second largest term of the Earth heat inventory, accounting
for 4 % to 6 % of the total heat in the Earth system (von Schuckmann
et al., 2020, Sect. 6).
The ground heat is, nevertheless, not the only energy component of the
continental landmasses. Other processes with large thermodynamic
coefficients, such as permafrost thawing and the warming of inland water
bodies, occur across large areas, leading to the exchange of large amounts
of heat with its surroundings over time. To account for those heat
exchanges, a recent study (Cuesta-Valero et al., 2023a)
has estimated the heat uptake by permafrost thawing and the warming of
inland water bodies, as well as ground heat storage from subsurface
temperature profiles, resulting in a comprehensive estimate of continental
heat storage. Therefore, our estimate is different to terrestrial or
land estimates, as we take into account the subsurface and water bodies of
the continental landmasses and thus not the land surface. The authors used the
same global network of subsurface temperature profiles as in von Schuckmann
et al. (2020) to estimate ground heat storage but applied an improved
inversion technique to analyze the profiles. This new technique is based on
combining bootstrap sampling with a widely used singular value
decomposition (SVD) algorithm (e.g., Beltrami and Mareschal, 1992) to retrieve past
changes in surface temperatures and ground heat fluxes, which also resulted
in smaller uncertainty estimates for global results
(Cuesta-Valero et al., 2022). Heat uptake from
permafrost thawing was estimated using a large ensemble of simulations
performed with the CryoGridLite permafrost model
(Nitzbon et al., 2022). Ground stratigraphies required
for this purpose, including ground ice distributions, were generated using
various global ground datasets. For soil properties, we used the datasets
described in Masson et al. (2003) and
Faroux et al. (2013); for soil organic carbon, we used the
dataset described in Hugelius et al. (2013), and for
excess ground ice content, we used Brown et al. (1997). Latent heat
storage due to melting of ground ice is evaluated to a depth of 550 m over
the Arctic region. Uncertainty ranges are evaluated using 100-parameter
ensemble simulations with strongly varied soil properties and soil ice
distributions. The climate forcing at the surface is based on a paleoclimate
simulation performed by the Commonwealth Scientific and Industrial Research
Organization (CSIRO), providing the initialization of the permafrost model,
and data from the ERA-Interim reanalysis since 1979 onwards. Heat storage by
inland water bodies was estimated by integrating water temperature anomalies
in natural lakes and reservoirs from a set of Earth system model (ESM)
simulations participating in the Inter-Sectoral Impact Model Intercomparison
Project phase 2b (ISIMP2b)
(Frieler et al., 2017; Grant et
al., 2021; Golub et al., 2022). Heat storage is then computed using
simulations with four global lake models following the methodology presented
in Vanderkelen et al. (2020) but
replacing the cylindrical lake assumption in that study for a more detailed
lake morphometry, which leads to a more realistic representation of lake
volume.
Continental heat storage from Beltrami et al. (2002) (black), von
Schuckmann et al. (2020) (gray), and Cuesta-Valero et al. (2023a) (red).
Gray and red shadows show the uncertainty range of the heat storage from von
Schuckmann et al. (2020) and Cuesta-Valero et al. (2023a), respectively.
Figure 6 shows the three main estimates of heat gain by the continental
landmasses since 1960. The first global estimate of continental heat storage
was provided by Beltrami et al. (2002), consisting of changes in ground heat
content for the period 1500–2000 as time steps of 50 years (black line in
Fig. 6). These estimates were retrieved by inverting 616 subsurface
temperature profiles constituting the global network of subsurface
temperature profiles in 2002, yielding a heat gain of 9.1 ZJ during the
second half of the 20th century. A comprehensive update was included in von
Schuckmann et al. (2020) using the results of
Cuesta-Valero et al. (2021b) (gray line in Fig. 6), with
the main difference consisting of the use of a larger dataset with 1079
subsurface temperature profiles. Since many of these new profiles were
measured at a later year than those in Beltrami et al. (2002), the
inversions from this new dataset were able to include the recent warming of
the continental subsurface, yielding higher ground heat content than those
from Beltrami et al. (2002). Concretely, the estimates in von Schuckmann et al. (2020) showed a heat gain of 24±5 ZJ from 1960 to 2018.
Recently, a new estimate of continental heat gain including the heat used in
permafrost thawing and in warming inland water bodies was presented in
Cuesta-Valero et al. (2023a) (red line in Fig. 6), achieving heat gains
of 24±2 ZJ since 1960 and 21±2 ZJ since 1971 (see also Fig. 8). Despite considering the heat stored in permafrost thawing, the warming
of inland water bodies, and the warming of the ground, the retrieved
continental heat storage is similar to the values from ground warming in von
Schuckmann et al. (2020). There is a difference of ∼3 ZJ
between the average ground heat storage in Cuesta-Valero et al. (2022)
(21.6±0.2 ZJ) and in von Schuckmann et al. (2020) (24±5 ZJ),
which is similar to the heat storage in inland water bodies and the heat
storage due to permafrost thawing together (see below). That is, the
decrease in ground heat storage in the new estimates is compensated by the
heat storage in inland water bodies and permafrost degradation. Another
important result is the narrower confidence interval in estimates from
Cuesta-Valero et al. (2023a), which is directly related to the new bootstrap
technique used to invert the subsurface temperature profiles (Cuesta-Valero
et al., 2022). This new bootstrap technique offers a more adequate
statistical framework than the technique used in von Schuckmann et al. (2020) as demonstrated in Cuesta-Valero et al. (2022); thus, we are
confident in the robustness of the lower uncertainty estimate for ground
heat storage presented here. Heat storage within inland water bodies has
reached 0.2±0.4 ZJ since 1960, with permafrost thawing accounting
for 2±2 ZJ. Therefore, ground heat storage is the main contributor
to continental heat storage (90 %), with inland water bodies accounting
for 0.7 % of the total heat and permafrost thawing accounting for 9 %. Despite the smaller proportion of heat stored in inland water bodies
and permafrost thawing, several important processes affecting both society
and ecosystems depend on the warming of lakes and reservoirs, as well as on the
thawing of ground ice
(Gädeke et al.,
2021). Therefore, it is important to continue quantifying and monitoring the
evolution of heat storage in all three components of the continental
landmasses.
Heat utilized to melt ice
Changes in Earth's cryosphere affect almost all other elements of the
environment including the global sea level, ocean currents, marine
ecosystems, atmospheric circulation, weather patterns, freshwater resources,
and the planetary albedo
(Abram
et al., 2019). The cryosphere includes frozen components of the Earth system
that are at or below the land and ocean surface: snow, glaciers, ice sheets,
ice shelves, icebergs, sea ice, inland water body ice (e.g., lake, river),
permafrost, and seasonally frozen ground (IPCC, 2019). In this study, we
estimate the heat uptake by the melting of ice sheets (including both
floating and grounded ice), glaciers, and sea ice at the global scale (Fig. 7).
Notwithstanding the important role that snow cover plays in the Earth's energy
surface budget as a result of changes in the albedo
(de Vrese et
al., 2021; Qu and Hall, 2007; Weihs et al., 2021), its influence on the
temperature of underlying permafrost
(Jan and Painter, 2020; Park et
al., 2015), or on sea ice in the Arctic
(Perovich et
al., 2017; Webster et al., 2021) and Antarctica
(Eicken
et al., 1995; Nicolaus et al., 2021; Shen et al., 2022), estimates of
changes in global snow cover are still highly uncertain and not included in
this inventory. However, they should be considered in future estimates.
Similarly, changes in lake ice cover (Grant et
al., 2021) are not taken into account here and warrant more attention in the
future. Permafrost is accounted for in the land component (see Sect. 4).
Heat uptake (in ZJ) and mass loss (in trillions of tonnes) for the
Antarctic Ice Sheet (grounded and floating ice, green), glaciers (orange),
Arctic sea ice (purple), Greenland Ice Sheet (grounded and floating ice,
red), and Antarctic sea ice (blue), together with the sum of the energy
uptake within each one of its components (total, black). Uncertainties are
95 % confidence intervals provided as shaded areas. See the text
for more details.
We equate the energy uptake by the cryosphere (glaciers, grounded and
floating ice of the Antarctic and Greenland ice sheets, and sea ice) with
the energy needed to drive the estimated mass loss. In doing so, we assume
that the energy change associated with the temperature change of the
remaining ice is negligible. As a result, the energy uptake by the
cryosphere is directly proportional to the mass of melted ice:
E=ΔM×(L+c×ΔT),
where, for any given component, ΔM is the mass of ice loss, L is the
latent heat of fusion, c is the specific heat capacity of the ice, and
ΔT is the rise in temperature needed to bring the ice to the melting
point. For consistency with previous estimates
(Ciais et al., 2014;
Slater et al., 2021; von Schuckmann et al., 2020), 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-1∘C-1, and a density of ice of 917 kg m-3. Estimating the energy used to warm the ice to its melting point
requires knowledge of the mean ice temperature for each component. Here we
assume a temperature of -15∘C for floating ice in Greenland, -2∘C for the floating ice in Antarctica, -20±10∘C for grounded ice in Antarctica and Greenland, and 0 ∘C for
sea ice and glaciers. Although this assumption is poorly constrained, the
energy required to melt ice is primarily associated with its phase
transition, and the fractional energy required for warming is a small
percentage (<1%∘C-1) of the total energy
uptake (Slater et al., 2021). Nevertheless, we include an additional
uncertainty of ±10∘C on the assumed initial ice
temperature within our estimate of the energy uptake. An overview of all
datasets used and their availabilities are provided in Table 3 and are
further described in the following.
Overview on data used and their availability for the estimate of
heat available to melt the cryosphere over the period 1979–2020. Backward
extension to 1971 for the heat inventory is based on the assumption of
negligible contribution. General specifications include constant values for
latent heat of fusion of 3.34×105 J kg-1, specific heat capacity
of 2.01×103 J kg-1∘C-1, density of ice of 917 kg m-3 for first-year ice and 882 kg m-3 for multiyear ice; see
also Ciais et al. (2014), Slater et al. (2021), and von Schuckmann et al. (2020).
Other component specifications are provided in the table.
ComponentsData type and informationPeriods coveredOther specifications:AntarcticIce SheetGrounded ice change from IMBIE (Shepherd et al., 2018, 2019)1992–2020;Mean ice temperature for floating ice (basal melting): -2∘C ±10∘C; floating ice (calving): -16∘C ±10∘C (Cloughand Hansen, 1979); grounded ice: -20±10∘CGrounded ice change before 1992 combining satellite and regional climate model data after Rignot et al. (2019)1972–1991Floating ice change from satellite altimetry reconstructions (Adusumilli et al., 2020)1994–2020 (extrapolated between 2017–2020); 1979–1993: zero mass loss assumedIce front retreat due to calving in the Amundsen Sea using ERS-1 radar altimetry (Adusumilli et al., 2020)1994–2020 (linear rate of energy uptake assumed)Antarctic Peninsula ice front retreat due to calving from imagery and remotely sensed data (Cook and Vaughan, 2010; Adusumilli et al., 2020)1979–2020 (linear rate of energy uptake assumed)Antarcticsea iceSea ice thickness from GIOMAS (Zhang and Rothrock, 2003)1979–2020Mean ice temperature:0 ∘C ±10∘CArctic sea iceSea ice thickness from PIOMAS model data (Schweiger et al., 2019; Zhang and Rothrock, 2003)1979–2011Mean ice temperature:0 ∘C ±10∘CCryoSat-2 satellite radar altimeter measurements (Slater et al., 2021; Tilling et al., 2018)2011–2020Glaciers (distinct from ice sheets)Geodetic and in situ glaciological observations after Zemp et al. (2019)1979–1996Mean ice temperature:0 ∘C ±10∘CIn situ glaciological observations after Zemp et al. (2020) and WGMS (2021)1997–2020Greenland Ice SheetGrounded ice change from IMBIE (Shepherd et al., 2018, 2019)1992–2020;Mean ice temperature for floating ice: -15∘C ±10∘C grounded ice: -20±10∘CGrounded ice change before 1992 from satellite velocity (Mankoff et al., 2019) and regional climate models (Mouginot et al., 2019)1979–1991Floating ice change (ice shelf collapse/thinning and tidewater glacier retreat) after Moon and Joughin (2008); Motyka et al. (2011); Mouginot et al. (2015); Münchow et al. (2014); Wilson et al. (2017); Carr et al. (2017)1979–2020
Grounded ice losses from the Greenland and Antarctic ice sheets from 1992 to
2020 are estimated from a combination of 50 satellite-based estimates of ice
sheet mass balance produced from observations of changes in ice sheet
volume, flow, and gravitational attraction, compiled by the Ice Sheet Mass
Balance Inter-comparison Exercise (IMBIE
http://imbie.org/imbie-3/ (last access: 29 March 2023)
)
(Shepherd et al., 2018, 2019). To extend
those time series further back in time, we use ice sheet mass balance
estimates produced using the input–output method, which combines estimates
of solid ice discharge with surface mass balance estimates. Satellite
estimates of ice velocity are available from the Landsat historical archive
from 1972, allowing for the calculation of ice discharge before the 1990s, while
surface mass balance is estimated from regional climate models. We extend
the IMBIE mass balance time series backwards to 1979 for Greenland using
Mouginot et al. (2019) and
Mankoff et al. (2019) and for Antarctica from 1972 to 1991
using Rignot et al. (2019).
Changes in Antarctic floating ice shelves due to thinning between 1994 and
2017 are derived from satellite altimetry reconstructions
(Adusumilli et al., 2020). There were no
estimates of ice shelf thinning between 1979 and 1993; therefore, we assume
zero mass loss from ice shelf thinning during that period. Changes in
Antarctic ice shelves due to increased calving in the Antarctic Peninsula
and the Amundsen Sea sector are derived from ERS-1 radar altimetry
(Adusumilli et al., 2020) for 1994–2017. For the 1979–1994 period, we only
have data for changes in the extent of the Antarctic Peninsula ice shelves
from Cook and Vaughan (2010). These are converted to changes in
mass using an ice shelf thickness of 140±110 m ice equivalent, which
represents the range of ice thickness values for the portions of Antarctic
Peninsula ice shelves that have collapsed since 1994 (Adusumilli et al.,
2020). Once icebergs break off from large Antarctic floating ice shelves, the
timescales of dissolution of the icebergs are largely unknown; therefore, we
assumed a linear rate of energy uptake between 1979–2020. For icebergs, we
use an initial temperature of -16∘C, which was the mean ice
temperature in the Ross Ice Shelf J-9 ice core (Clough
and Hansen, 1979). There are no large-scale observations or manifestations
of significant firn layer temperature change for the Antarctic Ice Shelf;
for example, there is no significant trend in the
observationally constrained model outputs of surface melt described in
Smith et al. (2020). Therefore, the
change in temperature of any ice that does not melt is assumed to be
negligible.
Changes in the floating portions of the Greenland Ice Sheet include ice
shelf collapse, ice shelf thinning, and tidewater glacier retreat. As in von
Schuckmann et al. (2020), we assume no ice shelf mass loss before 1997 and
estimate a loss of 13 Gt yr-1 after 1997 based on studies of the Zacharie Isstrom,
C. H. Ostenfeld, Petermann, Jakobshavn, 79N, and Ryder glaciers
(Mouginot
et al., 2015; Moon and Joughin, 2008; Münchow et al., 2014; Motyka et
al., 2011; Wilson et al., 2017). We assign a generous uncertainty of 50 %
to this value. For tidewater glacier retreat, we note a mean retreat rate of
37.6 m yr-1 during 1992–2000 and 141.7 m yr-1 during 2000–2010
(Carr et al., 2017). We assume that the
former estimate is also valid for 1979–1991 and that the latter estimate is valid
for 2011–2020. Assuming a mean glacier width of 4 km and thickness of 400 m,
we estimate mass loss from glacier retreat to be 9.3 Gt yr-1 during 1979–2000
and 35.1 Gt yr-1 during 2000–2020. 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).
The contributions from both the Antarctic and Greenland ice sheets to the
EEI are obtained by summing the mass loss from the individual components
(ice shelf mass, grounded ice mass, and ice shelf extent) for each ice sheet
separately, and, given that the datasets used for each component are
independent, the uncertainties were summed in quadrature. This is then
converted to an energy uptake according to the equation above.
Glaciers are another part of the land-based ice, and we here include
glaciers found in the periphery of Greenland and Antarctica (but distinct
from the ice sheets) in our estimate. We build our estimate on the
international efforts to compile and reconcile measurements of glacier mass
balance, under the lead of the World Glacier Monitoring Service
(WGMS
https://wgms.ch (last access: 29 March 2023)
). Up to 2016, the results are based on
Zemp et al. (2019), who combined
geodetic mass balance observations from digital elevation model (DEM) differencing on long temporal
and large spatial scales with in situ glaciological observations, which are
spatially less representative but provide information of higher temporal
resolution. Through this combination, they achieve coverage that is globally
complete yet retains the interannual variability well. For 2017 to 2021, the
numbers are based on the ad hoc method by Zemp et
al. (2020), which corrects for the spatial bias of the limited number of
recent in situ glaciological observations that are available with short
delay (WGMS, 2021), to derive globally representative
estimates. Error bars include uncertainties related to the in situ and
spaceborne observations, extrapolation to unmeasured glaciers, density
conversion, and to glacier area and its changes. For the conversion
from mass loss to energy uptake, only the latent heat uptake is considered,
which is based on the assumption of ice at the melting point, due to a lack of
glacier temperature data at the global scale. Moreover, since the absolute
mass change estimates are based on geodetic mass balances, mass loss of ice
below floatation is neglected. While this is a reasonable approximation
concerning the glacier contribution to sea-level rise, it implies a
systematic underestimation of the glacier heat uptake. While to our
knowledge there are no quantitative estimates available of glacier mass loss
below sea level on the global scale, it is reasonable to assume that this
effect is minor, based on the volume–altitude distribution of glacier mass
(Farinotti et al., 2019; Millan et
al., 2022). Further efforts are underway within the Glacier Mass Balance
Intercomparison Exercise (GlaMBIE
https://glambie.org (last access: 29 March 2023)
),
particularly to reconcile global glacier mass changes, including
estimates from gravimetry and altimetry, and to further assess related
sources of uncertainties (Zemp et al.,
2019).
Sea ice, formed from freezing ocean water and further thickened by snow
accumulation, is not only another important aspect of the albedo effect
(Kashiwase et al., 2017; Zhang et al.,
2019) and water formation processes (Moore et al.,
2022) but also provides essential services for polar ecosystems and human
systems in the Arctic (Abram et al., 2019). Observations of sea ice extent
are available over the satellite era, i.e., since the 1970s, but ice
thickness data – required to obtain changes in volume – have only recently
become available through the launch of CryoSat-2 and ICESat-2. For the
Arctic, we use a combination of sea ice thickness estimates from the
Pan-Arctic Ice-Ocean Modeling and Assimilation System (PIOMAS) between 1979
and 2011
(Schweiger
et al., 2019; Zhang and Rothrock, 2003) and CryoSat-2 satellite radar
altimeter measurements between 2011 and 2020 when they are available
(Tilling et al., 2018; Slater et
al., 2021). PIOMAS 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
(Labe
et al., 2018; Laxon et al., 2013; Wang et al., 2016). We note that the
PIOMAS domain does not extend sufficiently far south to include all regions
covered by sea ice in winter (Perovich et al.,
2017). Given that the entirety of the regions that are unaccounted for
(e.g., the Sea of Okhotsk and the Gulf of St Lawrence) are only seasonally
ice covered since the start of the record, this should not influence the
results. We convert monthly estimates of sea ice volume from CryoSat-2
satellite altimetry to mass using densities of 882 and 916.7 kg m-3 in
regions of multiyear and first-year ice, respectively (Tilling et al., 2018).
During the summer months (May to September), the presence of melt ponds on
Arctic sea ice makes it difficult to discriminate between radar returns from
leads and sea ice floes, preventing the retrieval of summer sea ice
thickness from radar altimetry (Tilling et al., 2018). As a result, we use
the winter-mean (October to April) mass trend across the Arctic for both
CryoSat-2 and PIOMAS estimates for consistency. According to PIOMAS, winter
Arctic sea ice mass estimates are 19 Gt yr-1 (6 %) smaller than the annual
mass trend between 1979 and 2011 (-324 Gt yr-1) and so are a conservative
estimate of Arctic sea ice mass change (Slater et al., 2021). The
uncertainty in monthly Arctic sea ice volume measurements from CryoSat-2
ranges from 14.5 % in October to 13 % in April
(Tilling et al., 2018; Slater et
al., 2021), and it is estimated as ±1.8×103 km3 for
PIOMAS (Schweiger et al., 2011).
Satellite radar altimeter retrievals of sea ice thickness in the Southern
Ocean are complicated by the presence of thick snow layers with unknown
radar backscatter properties on Antarctic sea ice floes. As a result, no
remote sensing estimates are available for Antarctic sea ice, and we use sea
ice volume anomalies from the Global Ice-Ocean Modeling and Assimilation
System (GIOMAS; Zhang and Rothrock, 2003), which is the global equivalent to PIOMAS.
GIOMAS output has been recently validated against in situ and satellite data
by Liao et al. (2022). We compute Antarctic sea ice trends as
annual averages between January and December. In the absence of a detailed
characterization of uncertainties for these estimates, we use the
uncertainty in GIOMAS sea ice thickness of 0.34 m (Liao et
al., 2022) to estimate the uncertainty in GIOMAS sea ice volume to be
±4.0×103 km3, using an annual mean
sea ice extent of 11.9×106 km2
(Lavergne et al., 2019). One caveat to this is
that the observational estimates have their own significant uncertainties
(Kern et al., 2019; Liao et al., 2022). For future
updates of the Earth heat inventory, we also aim to include
observation-based (remote sensing) estimates in the Southern Ocean
(Lavergne et al., 2019).
Our estimate of the total heat gain in the cryosphere amounts to 14±4 ZJ over the period 1971–2020 (see also Fig. 8 and Sect. 6) (assuming
negligible contribution before 1979 according to the data availability
limitation), which is consistent with the estimate obtained in von
Schuckmann et al. (2020) within uncertainties. 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). Compared to earlier
estimates and in particular the 8.83 ZJ estimate from Ciais et al. (2014),
this larger estimate is a result of both the longer period of time
considered and the improved estimates of ice loss across all
components, especially the ice shelves in Antarctica. Contributions to the
total cryosphere heat gain are dominated by the Antarctic Ice Sheet
(including the floating and grounded ice, about 33 %) and Arctic sea ice
(about 26 %), directly followed by the heat utilized to melt glaciers
(about 25 %). The Greenland Ice Sheet amounts to about 17 %, whereas
Antarctic sea ice is accounted for with a nonsignificant contribution of
about 0.2 %.
The Earth heat inventory: where does the energy go?
Evaluations of the heat storage in the different Earth system components as
performed in Sects. 2–5 now allow for the establishment of the Earth heat
inventory. Estimates for all Earth system components cover a core period of
1971–2020, except for the cryosphere where negligible contribution is
assumed before 1979. Our results reconfirm a continuous accumulation of heat
in the Earth system since our estimate begins (Fig. 8). The total Earth
system heat gain in this study amounts to 380±62 ZJ over the period
1971–2020. For comparison, IPCC AR6 obtained a total heat gain of 434.9
(324.5 to 545.5) ZJ for the period 1971–2018 and is hence consistent with
our estimate within uncertainties (Forster et al., 2021). However, it is
important to note that our estimate still excludes some aspects of Earth
heat accumulation, such as, for example, the shallow areas of the ocean, which
are challenging to be quantified with respect to gaps in the observing
system.
Total Earth system heat gain in ZJ (1 ZJ =1021 J) relative
to 1960 and from 1960 to 2020. 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). 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) and heating of the atmosphere (magenta shading). Uncertainty in
the ocean estimate also dominates the total uncertainty (dot-dashed lines
derived from the standard deviations (2σ) for the ocean, cryosphere,
land, and atmosphere). See Sects. 2–5 for more details of the different
estimates. The dataset for the Earth heat inventory is published at the
German Climate Computation Centre (DKRZ; https://www.dkrz.de/, last access: 29 March 2023) (see Sect. 7). Consistent with von Schuckmann et al. (2020), we obtain a total heat gain of 381±61 ZJ over the period
1971–2020, which is equivalent to a heating rate (i.e., the EEI) of
0.48±0.1 W m-2 applied continuously over the surface area of the
Earth (5.10×1014 m2). The corresponding EEI over the
period 2006–2020 amounts to 0.76±0.2 W m-2. The LOWESS method
and associated uncertainty evaluations have been used as described in
Sect. 2.
The estimate of heat storage in all Earth system components not only allows
for obtaining a measure of how much and where heat is available for inducing
changes in the Earth system (Fig. 1) but also to improve the accuracy of
the Earth system's total heat gain. In 1971–2020 and for the total heat
gain, the ocean accounts for the largest contributor with an about 89 %
fraction of the global inventory. The second largest component in the Earth
heat inventory relies on heat stored in land with an about 6 %
contribution. The cryosphere component accounts for about 4 % and the
atmosphere for about 1 %. For the most recent era of best available GCOS data
for the Earth heat inventory since the year 2006, the fractions amount to
about 89 % for the ocean, about 5 % for land, about 4 % for the
cryosphere, and about 2 % for the atmosphere.
The change of the Earth heat inventory over time allows for an estimate of
the absolute value of the Earth energy imbalance. Our results of the total
heat gain in the Earth system over the period 1971–2020 is equivalent to a
heating rate of 0.48±0.1 W m-2 and is 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 IPCC
AR6, the total heat rate has been assessed by 0.57 (0.43 to 0.72) W m-2
for the period 1971–2018 and 0.79 (0.52 to 1.06) W m-2 for the period
2006–2018 (Forster et al., 2021). Consistently, we further infer a total
heating rate of 0.76±0.2 W m-2 for the most recent era
(2006–2020).
Thus, the rate of heat accumulation across the Earth system has increased
during the most recent era as compared to the long-term estimate – an
outcome which reconfirms the earlier finding in von Schuckmann et al. (2020) and which had then been concurrently and independently confirmed in
Foster et al. (2021), Hakuba et al. (2021), Loeb et al. (2021), Liu et al. (2020), Raghuraman et al. (2021), and Kramer et al. (2021). The drivers of a larger EEI in the 2000s than in the long-term
period since 1971 are still unclear, and several mechanisms are discussed in
literature. For example, Loeb et al. (2021) argue for a decreased reflection
of energy back into space by clouds (including aerosol cloud interactions)
and sea ice and increases in well-mixed greenhouse gases (GHG) and water
vapor to account for this increase in EEI. Kramer
et al. (2021) refer to a combination of rising concentrations of well-mixed
GHG and recent reductions in aerosol emissions to be accounting for the increase,
and Liu et al. (2020) address changes in surface heat flux together with planetary
heat redistribution and changes in ocean heat storage. Future studies are
needed to further explain the drivers of this change, together with its
implications for changes in the Earth system.
Besides heat, which is the focus of this study, Earth also stores energy
chemically through photosynthesis in living and dead biomass with plant
growth. Recent studies
(Friedlingstein
et al., 2022; Denning, 2022; Crisp et al., 2022) on the Global Carbon Budget
and carbon cycle show that approximately 25 % of the added anthropogenic CO2 is
removed from the atmosphere by increased plant growth, which is a result of
fertilization by rising atmospheric CO2 and nitrogen inputs and of higher
temperatures and longer growing seasons in northern temperate and boreal
areas (Friedlingstein et al., 2022). This significant increase in carbon
uptake by the biosphere indicates that more energy is stored inside biomass,
together with the stored carbon. The quantification of the additional amount
of energy stored inside the biosphere is outside the scope of this study.
Data availability
The time series of the Earth heat inventory are published at DKRZ
(https://www.dkrz.de/, last access: 24 January 2023) under
https://www.wdc-climate.de/ui/entry?acronym=GCOS_EHI_1960-2020. More details are given below.
von Schuckmann et al. (2023): data for ocean
heat content (Sect. 2) and the total heat inventory as presented in
Sect. 6 are integrated.
Kirchengast et al. (2022): data for the atmospheric heat content are
distributed (Sect. 3).
Cuesta-Valero et al. (2023b): data for the ground heat
storage, together with the total continental heat gain, are provided (Sect. 4).
Vanderkelen and Thiery (2022): data for inland freshwater heat storage are
included (Sect. 4).
Nitzbon et al. (2022b): data for permafrost are delivered (Sect. 4).
Adusumilli et al. (2022): data for the cryosphere heat inventory are
provided.
The Digital Object Identifiers (DOIs) for data access are provided in Table 4.
Overview of Digital Object Identifiers (DOIs) for data access for the
components of the Earth heat inventory and associated references. The
results are presented in Fig. 8.
Earth heat inventory componentDOIReferenceOcean heat content; total Earth10.26050/WDCC/GCOS_EHI_1960-2020_OHC_v2von Schuckmann et al.heat inventory(2023)Atmospheric heat content10.26050/WDCC/GCOS_EHI_1960-2020_AHCKirchengast et al. (2022)Continental heat content10.26050/WDCC/GCOS_EHI_1960-2020_CoHC_v2Cuesta-Valero et al. (2023b)Inland water heat content10.26050/WDCC/GCOS_EHI_1960-2020_IWHCVanderkelen and Thiery (2022)Heat available to melt permafrost10.26050/WDCC/GCOS_EHI_1960-2020_PHCNitzbon et al. (2022b)Heat available to melt the cryosphere10.26050/WDCC/GCOS_EHI_1960-2020_CrHCAdusumilli et al. (2022)Conclusions
The Earth heat inventory is a global climate indicator integrating
fundamental aspects of the Earth system under global warming. Particularly,
the Earth heat inventory provides the best available current estimate of the
absolute value of the Earth energy imbalance
(Cheng
et al., 2017a, 2019; Hakuba et al., 2021; Hansen et al., 2011;
Loeb et al., 2012, 2022; Trenberth et al., 2016; von Schuckmann et al.,
2020). Moreover, its evaluation enables an integrated view of the effective
radiative climate forcing, Earth's surface temperature response, and the
climate sensitivity
(Forster
et al., 2021; Hansen et al., 2011, 2005; Palmer and McNeall,
2014; Smith et al., 2015). Additionally, its quantification informs about
the status of global warming in the Earth system as it integrates the heat
“in the pipeline” that will ultimately warm the deep ocean and melt ice
sheets in the long term (Hansen et al., 2011, 2005; IPCC,
2021b). The Earth heat inventory also reveals how much and where surplus
anthropogenic heat is available for melting the cryosphere and warming the
ocean, land, and atmosphere, which in turn allows for an evaluation of
associated changes in the climate system, and it is essential to improve
seasonal-to-decadal climate predictions and projections on century
timescales to enable improved planning for and adaptation to climate change
(Hansen et al., 2011; von Schuckmann et al., 2016, 2020). Regular
international assessment on the Earth heat inventory enables concerted
international and multidisciplinary collaboration and advancements in
climate science, including contributing to the development of
recommendations for the status and evolution of the global climate observing
system (GCOS, 2021; von Schuckmann et al., 2020).
This study builds on the first internationally and multidisciplinary
Earth heat inventory in 2020 (von Schuckmann et al., 2020) and provides an
update on total Earth system heat accumulation, heat storage in all Earth
system components (ocean, land, cryosphere, atmosphere), and the Earth energy
imbalance up to the year 2020. Moreover, this study improved earlier
estimates and further extended and fostered international collaboration,
allowing researchers to move towards a more complete view of where and how much heat is
stored in the Earth system through the addition of new estimates such as for
permafrost thawing, inland freshwater (Sect. 4), and Antarctic sea ice
(Sect. 5). Results obtained reveal a total Earth system heat gain of
381±61 ZJ over the period 1971–2020, with an associated total
heating rate of 0.48±0.1 W m-2. About 89 % of this heat is
stored in the ocean, about 6 % on land, about 4 % in the cryosphere,
and about 1 % in the atmosphere (Figs. 8, 9). The analysis additionally
reconfirms an increased heating rate which amounts to 0.76±0.2 W m-2 for the most recent era (2006–2020). The drivers for this
change still need to be elucidated, and they most likely reflect the interplay
between natural variability and anthropogenic change
(Loeb
et al., 2021; Kramer et al., 2021; Liu et al., 2020); their implications for
changes in the Earth system are reflected in the many record levels of
change in the 2000s reported elsewhere (e.g., Cheng
et al., 2022b; Forster et al., 2021; Gulev et al., 2021).
The Paris Agreement builds upon the United Nations Framework Convention on
Climate Change, 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 pre
industrial levels and to limit the temperature increase even further to 1.5 ∘C. 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
of the Paris Agreement (GST)
https://unfccc.int/topics/global-stocktake#:~:text=The global stocktake of the,term goals (Article 14)
(last access: 29 March 2023)
based on best available science. The Earth heat
inventory provides information on how much and where heat is accumulated and
stored in the Earth system. Moreover, it provides a measure of how much the
Earth is out of energy balance, and when combined with directly measured net
flux at the top of the atmosphere, it also enables us to understand the change of
the EEI over time. This in turn allows for assessing the portion of the
anthropogenic 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 human-induced forcing
(Hansen et al., 2017). The Earth heat inventory is thus
one of the key critical global climate change indicators defining the
prospects for continued global warming and climate change (Hansen et al.,
2011; von Schuckmann et al., 2016, 2020). Hence, we call for an
implementation of the Earth heat inventory into the Global Stocktake.
Schematic presentation on the Earth heat inventory for the current
anthropogenically driven positive Earth energy imbalance (EEI) at the top of the
atmosphere (TOA). The relative partition (in %) of the Earth heat
inventory presented in Fig. 8 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), atmosphere, and EEI for the
periods 2006–2020 and 1971–2020 (for the latter period, values are provided
in parentheses). The total heat gain (in red) over
the period 1971–2020 is obtained from the Earth heat inventory as presented
in Fig. 8.
The quantifications presented in this study are the result of
multidisciplinary global-scale collaboration and demonstrate the critical
importance of concerted international efforts for climate change monitoring
and community-based recommendations for the global climate observing system.
For the ocean observing system, the core Argo sampling needs to be sustained
– which includes the maintenance of shipboard collection of reference data
for validation – and complemented by remote sensing data. Extensions such as
into the deep-ocean layer need to be further fostered, 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, data
recovery, and processing of the historical dataset. Spatial geodetic
observations and the closure of the sea-level budget serve as a valuable
constraint for the full-column OHC. Although the independent estimates agree
within uncertainty, the geodetic approach suggest slightly larger OHC linear
trends, especially since 2016. Though efforts are underway to investigate
the emerging discrepancy (e.g., Barnoud
et al., 2021), the causes are not yet fully understood and require further
investigation.
For the ground heat storage, the estimate had been hampered by a lack of
subsurface temperature profiles in the Southern Hemisphere, as well as by
the fact that most of the profiles were measured before the 2000s.
Subsurface temperature data are direct and independent (not proxy)
measurements of temperature, yielding information on the temporal variation
of the ground surface temperature and ground heat flux at the land surface.
A larger spatial-scale dataset of the thermal state of the subsurface from
the last millennium to the present will aid in the continued monitoring of
continental heat storage, provide initial conditions for land surface model
(LSM) components of Earth system models (ESMs)
(Cuesta-Valero et al., 2019), and serve as a dataset for
validation of climate models' simulations
(Cuesta-Valero et al., 2021a, 2016). Progress in understanding climate variability through the last
millennium must lean on additional data acquisition as the only way to
reduce uncertainty in the paleoclimatic record and on changes to the current
state of the continental energy reservoir. Remote sensing data are expected
to be very valuable to retrieve recent, past, and future changes in ground
heat flux at short timescales with near-global coverage. However,
collecting subsurface temperature data is urgent as we must make a record of
the present thermal state of the subsurface before the subsurface climate
baseline is affected by the downward-propagating thermal signal from current
climate heating. Furthermore, an international organization should take
responsibility to gather and curate all measured subsurface temperature
profiles currently available and those that will be measured in the future,
as the current practices, in which individual researchers are responsible
for measuring, storing, and distributing the data, have led to fragmented
datasets, restrictions in the use of data, and loss of the original
datasets. Support from GCOS for international data acquisition and
curating efforts would be extremely important in this context.
For the permafrost estimates, the primary sources of uncertainty arise from
lacking information about the amount and distribution of ground ice in
permafrost regions, as well as measurements of liquid water content
(Nitzbon et al., 2022a). Permafrost heat storage is
defined as the required heat to change the mass of ground ice at a certain
location; thus, monitoring changes in ground ice and water contents would be
required to improve estimates of this component of the continental heat
storage. Nevertheless, the current monitoring system for permafrost soils is
focused on soil temperature, and the distribution of stations is still
relatively scarce in comparison with the vast areas that need to be surveyed
(Biskaborn et al., 2015). Due to the current limitations
in the observational data, a permafrost model was used to estimate the heat
uptake by thawing of ground ice. This approach retrieves latent heat fluxes
in extensive areas and at depths relevant to analyze the long-term change in
ground ice mass, but this is done at the cost of ignoring other relevant processes, such
as ground subsidence, to balance model performance with computational
resources. Including permafrost heat storage in the Tibetan Plateau is a
priority for the next iteration of this work, as well as to explore new
methods to evaluate model simulations using the available observations in
permafrost areas.
For inland water heat storage, a better representation of lake and reservoir
volume would be possible by better accounting for lake bathymetry using the
GLOBathy (Khazaei et al., 2022) dataset and
results from the upcoming Surface Water and Ocean Topography (SWOT) mission.
These improvements in the representation of lake volume and an updated lake
mask will be available in the upcoming ISIMIP3 simulation round, next to
improved meteorological forcing data (Golub et al., 2022). In
contrast to Vanderkelen et al. (2020),
the heat storage in rivers is not included in this analysis due to the high
uncertainties in simulated river water volume. To reduce the uncertainty in
river heat storage, the estimation of river water storage should be
improved, together with an explicit representation of water temperature in
the global hydrological models (Wanders et al., 2019). These
improvements will be incorporated into ISIMIP3 and will lead to better
estimates of inland water heat storage, thus enhancing future estimates of
continental heat storage. In the long run, these model-based estimates could
be supplemented or replaced by observation-based estimates, which would
however require a large, global-scale effort to monitor lake and river
temperatures at high spatial resolution and over long time periods.
Estimates for inland water heat storage and permafrost heat storage in this
analysis depend heavily on model simulations, which is a particular
challenge for analyzing and adding uncertainty ranges, as the sources of
uncertainty in model simulations differ from those in observational records
(Cuesta-Valero et al., 2023a). Future estimates should
hence focus on a hybrid approach considering in situ measurements,
reanalyses, remote sensing data, and model simulations, consistent with the
methods employed for deriving cryosphere and atmosphere heat storage for the
Earth heat inventory.
For the cryosphere, sustained remote sensing for all of the cryosphere
components is critical for quantifying future changes over these vast and
inaccessible regions; in situ observations are also needed for process
understanding and in order to properly calibrate and validate them. For sea
ice, observations of the albedo, area, and ice thickness are all
essential – the continuation of satellite altimeter missions with high
inclination, polar-focused orbits is critical for our ability to monitor sea
ice thickness in particular. Observations of snow thickness with
multifrequency altimeters and microwave radiometers are essential for
further constraining sea ice thickness estimates. For ice sheets and
glaciers, reliable gravimetric, geodetic, and ice velocity measurements;
knowledge of ice thickness and extent; snow/firn thickness and density; and
the continuation of the now three-decade long satellite altimeter records are
essential for understanding changes in the mass balance of grounded and
floating ice. The recent failure of Sentinel-1b, which in tandem with
Sentinel-1a could be used to systematically measure ice speed changes every
6 d, means that images are now being acquired every 12 d and thus an
earlier launch of Sentinel-1c should be encouraged to regain the ability to
monitor ice speed changes over short timescales. The estimate of glacier
heat uptake is particularly affected by lacking knowledge of ice melt below
sea level and, to a lesser degree, lacking knowledge of firn and ice
temperatures. This lack of observations is likely related to most studies on
glaciers focusing on their contribution to sea-level rise or seasonal water
availability, where melt below sea level and warming of ice do not matter
much. However, it becomes obvious here that this gap introduces a systematic
bias in the estimate of cryospheric energy uptake, which is presumably small
compared to the other components but unconstrained. Although the Antarctic
sea ice change and the warming of Greenland and Antarctic firn are poorly
constrained or have not significantly contributed to this assessment, they
may become increasingly important over the coming decades. Similarly, there
exists the possibility for rapid change associated with positive ice
dynamical feedback at the marine margins of the Antarctic Ice Sheet.
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 its related energy uptake.
For the atmosphere, there is a need to sustain and enhance a coherent
operational long-term monitoring system for the provision of climate data
records of essential climate variables. Observations from radiosonde
stations within the GCOS Reference Upper-Air Network (GRUAN) and from
satellite-based Global Navigation Satellite System (GNSS) radio occultation deliver thermodynamic profiling
observations of benchmark quality and stability from surface to stratopause.
For climate monitoring, it is of critical importance to ensure continuity of
such observations with global coverage over all local times. This continuity
of radio occultation observations in the future is not sufficiently
guaranteed as we are facing an imminent observational gap in the middle to high
latitudes for most local times (IROWG, 2021), which is a major
concern. Thus, there is an urgent need for satellite missions in high-inclination orbits to provide full global and local-time coverage in order
to ensure global climate monitoring. Operational radio occultation missions
need to be maintained as support for a global climate observing system, and
long-term availability and archiving of measurement data, metadata, and
processing information need to be ensured.
In summary, we also call for urgently needed actions for enabling
continuity, archiving, rescuing and calibrating efforts to assure improved
and long-term monitoring capacity of the global climate observing system for
the Earth heat inventory and to complement with measurements from space for
assessing the changes in EEI (e.g., Loeb et al., 2021; von Schuckmann et
al., 2016). Particularly, the summarized recommendations include the following:
We need to sustain, reinforce, and even to establish data repositories for
historical climate data (archiving).
We need to reinforce efforts for recovery projects for historical data and
associated metadata information (rescuing).
We need to sustain and reinforce the global climate observing system for
assuring the monitoring of the Earth heat inventory targets, such as for the
polar, deep, and shallow ocean, as well as of top-of-the-atmosphere radiation fluxes
(continuity).
We need to foster calibration measurements (in situ) for assuring the quality and
reliability of large-scale measurement techniques (e.g., remote sensing) and
autonomous components (e.g., Argo) (calibrating).
A continuous effort to regularly update the Earth heat inventory is
important as this global climate indicator 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 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 into the inventory of heat in the Earth system, its evolution over
time, and the absolute values. The data product of this effort is made
available and thus can be used for climate model validation purposes. The
results also demonstrate that further efforts are needed for uncertainty
evaluations, such as, for example, the use of synthetic profile analyses.
Indeed, improving the climate observing system will allow for reduced
uncertainties for estimating the Earth heat inventory. However, further
evaluations are needed to unravel uncertainties in the different components
of the Earth heat inventory, which rely for example on nonhomogeneous data
sampling and large data gaps, the use of different measurement types and
statistical approaches, instrumental bias corrections, and their joint
analysis of mode-based quantifications.
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 updated attempt presented here has 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 further monitor the Earth's cycles and the global
energy budget (GCOS, 2021). The outcome of this study will therefore
directly feed into GCOS assessments of the status of the global climate
observing system, and the identified observation requirements will guide the
development of the next generation of in situ and satellite global climate
observations as specified by GCOS by all national meteorological services
and space agencies and other oceanic and terrestrial networks.
Author contributions
KvS, AM, FCV, GK, AS, SA, FS have worked on the conceptualization and the writing of the original draft preparation; FG, AM, AS, FCV, KvS, FS, GK, TB, LC, DG, MG, SH, RK, BK, NK, JN, ML, JL, BM, SP, KS, AS, TS, IV, MZ have supported the work with formal analysis and software developments; FG, GK, KvS have worked on the visualization, and all authors have worked on the writing, particularly for review and editing.
Competing interests
The contact author has declared that none of the authors has any competing interests.
Disclaimer
Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
Ocean: the OHC estimate from the product ISAS (Gaillard et al., 2016) was
provided by “Service National d'Observation Argo France” (INSU/CNRS) at Observatoire des Sciences de l'Univers (OSU) IUEM (https://www.argo-france.fr/, last access: 29 March 2023).
Atmosphere: we acknowledge the WEGC EOPAC (Earth Observation Processing and Analysis Center for Atmosphere and Climate) team for providing the OPSv5.6 RO
data (available online at 10.25364/WEGC/OPS5.6:2021.1, EOPAC Team, 2021) as
well as quality-processed Vaisala RS data, UCAR/CDAAC (Boulder, CO, USA) for
access to RO phase and orbit 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 (Global Modeling and Assimilation Office) (Greenbelt, MD, USA) for access to the MERRA-2 reanalysis data.
Financial support
Maximilian Gorfer was supported by WEGC atmospheric remote sensing and
climate system research group young scientist funds. Michael Mayer was
supported by the Austrian Science Fund (project P33177).
Donata Giglio and Mikael Kuusela were supported by NOAA (awards
NA21OAR4310261 and NA21OAR4310258).
Lijing Cheng was financial supported by the Strategic Priority Research
Program of the Chinese Academy of Sciences (XDB42040402) and the National Natural
Science Foundation of China (grant numbers 42122046, 42076202).
John Church and Yuehua Li were supported by the Centre for Southern Hemisphere Oceans
Research (CSHOR), jointly funded by the Qingdao National Laboratory for
Marine Science and Technology (QNLM, China) and the Commonwealth Scientific
and Industrial Research Organisation (CSIRO, Australia), and the Australian
Research Council's Discovery Project funding scheme (project DP190101173)
and the Australian Research Council Special Research Initiative, Australian
Centre for Excellence in Antarctic Science (Project Number SR200100008).
TMcD and PMB gratefully acknowledge Australian Research Council support
through grant FL150100090. This paper contributes to the tasks of the Joint
SCOR/IAPSO/IAPWS Committee on the Thermophysical Properties of Seawater.
Hugo Beltrami 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). Hugo Beltrami holds a
Canada Research Chair in Climate Dynamics.
Francisco José Cuesta-Valero is an Alexander von Humboldt Research
Fellow at the Helmholtz Centre for Environmental Research (UFZ).
Richard P. Allan is funded by the National Centre for Earth Observation (Research Councils UK)
(grant NE/RO16518/1).
Felix W. Landerer and Maria Z. Hakuba were supported by the Jet Propulsion Laboratory,
California Institute of Technology, under a contract with the National
Aeronautics and Space Administration (80NM0018D0004).
Rachel Killick was supported by the Met Office Hadley Centre Climate Programme funded by BEIS.
Axel Schweiger was supported by NSF Grant NSF-OPP-1744587 and NASA Grant 80NSSC20K1253.
Review statement
This paper was edited by David Carlson and reviewed by two anonymous referees.
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