Shallow clouds covering vast areas of the world's middle- and high-latitude oceans play a key role in dampening the global temperature
rise associated with CO2. These clouds, which contain both ice and
supercooled water, respond to a warming world by transitioning to a state
with more liquid water and a greater albedo, resulting in a negative
“cloud-phase” climate feedback component. Here we argue that the magnitude
of the negative cloud-phase feedback component depends on the amount and
nature of the small fraction of aerosol particles that can nucleate ice
crystals. We propose that a concerted research effort is required to reduce
substantial uncertainties related to the poorly understood
sources, concentration, seasonal cycles and nature of these ice-nucleating
particles (INPs) and their rudimentary treatment in climate models. The
topic is important because many climate models may have overestimated the
magnitude of the cloud-phase feedback, and those with better representation
of shallow oceanic clouds predict a substantially larger climate warming. We
make the case that understanding the present-day INP population in shallow
clouds in the cold sector of cyclone systems is particularly critical for
defining present-day cloud phase and therefore how the clouds respond to
warming. We also need to develop a predictive capability for future INP
emissions and sinks in a warmer world with less ice and snow and potentially
stronger INP sources.
Introduction
Projections of global warming due to increased anthropogenic greenhouse gas
concentrations is of central importance for our society. We need these
projections to be sufficiently accurate in order to effectively plan
adaptation and mitigation strategies and also to provide a robust basis for
plans to curb carbon emissions. However, substantial and poorly defined
uncertainties exist in our climate models. While it is unambiguous that
greenhouse gas emissions are leading to a warmer climate, our climate models
are hugely divergent in how much the world will warm in future (see Fig. 1
and Box 1). Apart from the obvious societal benefits of reducing
uncertainty, improvements to climate predictions are estimated to carry a
multi-trillion dollar value (Hope, 2015).
The equilibrium climate sensitivity plotted against cloud feedback
parameter for CMIP5 and CMIP6 models. The left plot is for total cloud
feedback parameter, while the right one is for shallow clouds (< 680 hPa) that are poleward of 45∘. The data are from
Zelinka et al. (2020). The correlation between low cloud
feedback and ECS that has emerged in CMIP6 models indicates that the
treatment of mixed-phase low clouds is critical for driving inter-model ECS
variability.
The way that clouds respond to forcing by CO2 in models is one of the
key sources of uncertainty in climate projections. These feedbacks on
climate can either dampen (negative feedback) or amplify (positive feedback)
climate warming. In fact, some models predict a negative overall cloud
feedback, whereas others predict an overall positive feedback (Fig. 1).
The divergence of the treatment of clouds amongst models correlates with the
predicted amounts of warming for a doubling of CO2 (known as
equilibrium climate sensitivity, ECS; see Box 1), with negative feedbacks
resulting in smaller ECS values and vice versa.
Definition and relevance of some key climate science terms.
There has been a shift amongst some more recent models to larger ECS values.
In the 2013 IPCC assessment the estimates of ECS ranged from 1.5 to
4.5 ∘C (Collins et al., 2014), whereas 10
out of 27 models that will inform the next IPCC assessment have ECS values
greater than 4.5 ∘C (Zelinka et al., 2020).
Whether these high ECS values (Gettelman et al., 2019; Zelinka et al.,
2020; Bodas-Salcedo et al., 2019) are possible or probable is a matter of
debate (Palmer, 2020; Forster et al., 2020; Sherwood et al., 2020).
Nevertheless, one of the key differences between the older CMIP5 (Coupled
Model Intercomparison Project Phase 5) models and the new CMIP6 models is the
treatment of clouds in the middle to high latitudes where clouds can persist
in a mixed-phase state. Many of the CMIP6 models have a much more positive
cloud feedback at latitudes poleward of 45∘, which correlates
with higher ECS values (Fig. 1b). This illustrates the key role that
clouds, particularly shallow marine clouds in the middle and high latitudes,
play in inter-model variations in ECS (Ceppi et al., 2017; Zelinka et
al., 2020; Gettelman et al., 2019; Andrews et al., 2019). We argue that this
issue has to be addressed urgently.
Liquid-only clouds in the marine boundary layer at low latitudes are
generally expected to decrease in amount in a warmer world, exerting a
positive feedback (Ceppi et al., 2017). However, for clouds at
higher latitudes or higher altitudes where the temperature is below the
freezing point of water, the response to warming can be entirely different
(see Fig. 2). The key difference in “mixed-phase” clouds is that the
formation and precipitation of ice crystals can strongly reduce the amount
of supercooled liquid water, which accounts for most of the cloud
reflectivity. If aerosol particles capable of nucleating ice, ice-nucleating
particles (INPs), are present and are active at the local cloud temperature,
then the supercooled liquid water content and albedo of these clouds can be
dramatically reduced through ice-related microphysical processes
(Vergara-Temprado et al., 2018; Komurcu et al., 2014; Storelvmo, 2017).
In a warmer future climate, water will replace ice and therefore the cloud
will have a greater albedo. For clouds over dark surfaces such as oceans,
the cloud-phase feedback caused by this simple thermodynamic change is
negative, but its magnitude is highly uncertain (Storelvmo, 2017;
Storelvmo et al., 2015; Tan et al., 2016; Frey and Kay, 2018).
The cloud-phase feedback and its relationship with ice-nucleating
particles (based on Storelvmo et al., 2015). For shallow marine
clouds, the replacement of ice by liquid water leads to more reflective
clouds and less shortwave radiation reaching the low albedo ocean surface,
resulting in a negative climate feedback.
Here we argue that although temperature changes are the primary driver of
changes in ice formation, the magnitude of the cloud-phase feedback is
directly related to the spatial and temporal distribution of the atmospheric
INP population and also how this INP population may change in the future.
While we have learnt a great deal from recent field and laboratory work
about INPs in middle to high latitudes (∼ 45–70∘),
the region critical for the cloud-phase feedback, we need a much better
understanding of sources and sinks of INPs as well as the nature of INPs in
both hemispheres. We finish by outlining what research needs to be
undertaken to reduce the uncertainty associated with the cloud-phase
feedback.
The cloud-phase feedback and the importance of ice-nucleating particles
The first description of the cloud-phase feedback in the literature was over
30 years ago by Mitchell et al. (1989). They found that by
including a treatment of cloud phase in their model the global mean
temperature change on a doubling of CO2 decreased from 5.2 to
2.7 ∘C. This and more recent work point to a strong but highly
uncertain negative feedback focused in the middle and lower high latitudes
(Storelvmo et al., 2015; Tan et al., 2016; Ceppi et al., 2017; Ceppi et
al., 2016; McCoy et al., 2018; Frey and Kay, 2018). The divergent
representation of cloud feedbacks at these latitudes leads to huge
variability in middle- to high-latitude cloud feedback (-0.63 to +0.68 in
CMIP6 models) and a strong positive correlation with ECS (see Fig. 1b). In
climate models, which probably do not represent all the key processes, these
uncertainties in feedbacks stem from what assumptions are made about the
existence and radiative properties of mixed-phase cloud.
The core physical process that drives the cloud-phase feedback is the
transition to clouds with more liquid water and less ice as the isotherms
shift upwards in a warmer world (see Fig. 2). The
shortwave (SW) cloud radiative effect (CRE) of clouds is strongly dependent
on their liquid water content since liquid clouds tend to be made up of many
cloud droplets of tens of micrometres in diameter, which scatter shortwave
radiation very effectively. In contrast, glaciation of a supercooled cloud
results in far fewer particles of larger sizes and consequently shorter
lifetimes that reflect much less sunlight. Hence, the microphysical
processes that lead to glaciation and depletion of liquid water content are
important for cloud feedbacks (Vergara-Temprado et al., 2018; Storelvmo
et al., 2015).
The shift to fewer, albeit larger, hydrometers when a supercooled cloud
glaciates is a result of the abundance of aerosol available for nucleating
cloud droplets and ice crystals, as well as the various ice-related
microphysical processes that occur subsequent to ice nucleation. The
aerosol particles that form cloud droplets, cloud condensation nuclei (CCN),
are relatively common with tens to hundreds per cubic centimetre over the remote oceans
(and much greater in air with continental influence). In contrast the
concentration of INPs are typically many orders of magnitude smaller
(DeMott et al., 2010; Kanji et al., 2017). Hence, a small subset of cloud
droplets may contain INPs (after serving as CCN themselves) and if these
droplets are sufficiently cold, they will freeze (Koop and Mahowald,
2013). These frozen droplets then find themselves in an environment that is
strongly supersaturated with respect to ice (∼ 10 % in a
liquid cloud at -10 ∘C); hence they grow rapidly. Within minutes
they reach hundreds of micrometres in diameter, depleting liquid water through
diffusional growth (Wegener–Bergeron–Findeisen process, WBF) and accretion
of droplets (riming) as they grow and precipitate. In some situations the
impact of INPs will be amplified through secondary ice production (SIP) where
a range of mechanisms are thought to result in the production of additional
ice crystals (Field et al.,
2017). It should be borne in mind that these processes (SIP, WBF, riming)
subsequent to ice nucleation are relatively poorly understood and need attention (Komurcu et al., 2014). Nevertheless, primary ice
production initiates these subsequent ice-related processes, and it is the
role of INPs in the cloud-phase feedback that is the focus of this paper.
Modelling work suggests that at concentrations of ice crystals above about 1 L-1 there are dramatic reductions in liquid water, but smaller
concentrations also deplete the liquid water path and reduce albedo
(Vergara-Temprado et al., 2018; Stevens et al., 2018). However, the
relationship between INP concentration and cloud glaciation is complex and
governed by the WBF process (Desai et al., 2019). In some
publications, CCN and INPs are collectively referred to as “cloud-forming
nuclei”. In fact, for INPs, the opposite is true: they should be regarded as
cloud-destroying (or at least albedo-destroying) agents in shallow supercooled clouds.
The underpinning physical principles of the cloud-phase feedback are
illustrated in Figs. 2 and 3. Generally, a warmer
world results in a larger proportion of the marine boundary layer containing
clouds at temperatures that do not support ice formation and growth. The
greater prevalence of reflective droplets in these warmer clouds combined
with less precipitation leads to less shortwave radiation being absorbed by
the ocean and a negative climate feedback. The strength of this feedback
depends on the balance between ice and supercooled water in the present and
future climate (Fig. 3); however, the cloud-phase
feedback is treated in climate models with varying levels of detail.
Cartoons illustrating how the response of mixed-phase clouds to a
changing climate is controlled by the ice-nucleating particle concentration.
(a) With a relatively high INP concentration ([INP]), there is a large
potential for liquid to replace ice as climate warms and isotherms shift
upwards, resulting in a strong negative shortwave feedback. (b) With a
relatively low INP concentration, clouds contain relatively little ice in the
present climate, so there is less ice to replace with liquid water and a
relatively small negative feedback. (c) Setting the temperature changes
aside, there may be either increases or decreases in INP concentration in
the future that clouds will respond to. We have shown the effect of an
increase in INP concentration where we would expect a decrease in liquid
water path and a positive feedback.
We hypothesize three ways in which the nature and concentration of INPs can
directly modulate the strength of the feedback
(Fig. 4). Firstly, the more ice in clouds in the
present climate, the stronger the negative cloud-phase feedback, while in
clouds that are mainly composed of supercooled water the cloud-phase
feedback will be relatively weak (Fig. 4a). Since
the amount of ice in many shallow clouds is strongly influenced by the INP
population, there are likely to be regional and seasonal variations in the
cloud-phase feedback. If our understanding is correct, then regions with
strong INP sources should have a more negative cloud feedback than regions
with weaker INP sources (also see Fig. 3a and b). At present we have insufficient measurement and modelling data to
test this hypothesis.
Illustration of how the cloud-phase feedback depends on the
concentration and nature of ice-nucleating particles. The strength of the
feedback depends on (a) the present-day INP concentration – the cloud phase
feedback is more negative with a higher INP concentration since there is a
greater potential to replace ice with water on warming (this is
mechanistically illustrated in Fig. 3a and b); (b) the type of INP –
different INP types have different temperature dependences (dlog[INP]/dT) and
for those with steeper temperature dependencies there is a greater potential
to replace ice with liquid; (c) INP concentrations may change in the future
– increases or decreases in INP concentration will feedback on climate
positively or negatively, respectively (this relates to Fig. 3c).
Secondly, the magnitude of the cloud-phase feedback will depend on the
nature of INPs because different types of INPs have very different temperature
dependencies, and this directly affects how the mixed-phase part of the
cloud responds to warming (Fig. 4b). The increase
in INP concentration, and hence ice particle formation, per degree of
cooling is greater for a material with a steep slope, such as mineral dust
(Atkinson et al., 2013; Harrison et al., 2019), than a material with a
shallower slope, such as fertile soil dust (Steinke et al., 2016;
O'Sullivan et al., 2014). In the case of a steep slope, a warming climate
will cause a greater reduction in the concentrations of INPs active at cloud
temperatures than in the case of a shallow slope. Hence, there will be a
stronger feedback in the case of a steep slope. However, the temperature
dependence of INPs from different sources relevant for the cloud-phase
feedback is poorly understood, and our understanding of how clouds respond
to variations in the nature of INPs is the subject of ongoing research.
Thirdly, INP sources, processing and removal in the atmosphere are likely to change with a changing climate (Fig. 4c). For example, it has been suggested that less snow and ice cover may
lead to more widespread emission sources and higher dust emissions rates at
high latitudes (Tobo et al., 2019; Prospero et al., 2012;
Sanchez-Marroquin et al., 2020; Amino et al., 2020) (we discuss this further
in Sect. 6). In addition, INP emissions have been linked to environmental factors
such as rainfall; hence a warmer, wetter world may lead to enhanced INP
emission rates from some terrestrial sources (Conen et al., 2017; Huffman
et al., 2013; Hara et al., 2016). Higher INP concentrations would lead to
more ice in cold clouds, which would lead to a positive feedback (see
Fig. 3c). But it is also conceivable that INP
sources might weaken if, for example, dust sources become vegetated.
Alternatively, loss mechanisms might be enhanced in a warmer world with more
precipitation. This would lead to a stronger negative feedback. Furthermore,
biological processes that result in very active biogenic INPs (primary
biological particles, by-product fragments and macromolecules) (Hill et
al., 2016; O'Sullivan et al., 2015) may also respond to a changing climate.
Hence, a correct representation of INPs and a link to the type of aerosol and
the sources is necessary to represent this aspect of the cloud-phase
feedback process.
It has become clear over the last few years that many models may
overestimate the magnitude of the cloud-phase feedback, especially in the
Southern Ocean. There are well-known model biases in the Southern Ocean with
too much SW radiation making it to the surface due to shallow clouds not
being sufficiently reflective (Bodas-Salcedo et al., 2012; Trenberth and
Fasullo, 2010). In many models, these shallow clouds contain too little
supercooled water, exposing the dark ocean underneath and resulting in sea
surface temperatures around 2 ∘C too warm (Wang et al.,
2014). This bias has profound implications for the strength of the
cloud-phase feedback. Tan et al. (2016) demonstrated that the
strength of the cloud-phase feedback was strongly dependent on the amount of
supercooled liquid water in present-day clouds (Fig. S1 in the Supplement). The ECS in
their control case, where the model was run in its default configuration, was
4.0 ∘C. But, when the amount of supercooled water in the
present-day climate was increased to be more consistent with satellite data, the ECS increased to 5.3 ∘C. Similarly, Frey and Kay (2018) showed that ECS increased from 4.1 to 5.6 ∘C when they increased the
amount of supercooled water to better match observations of absorbed
shortwave radiation over the Southern Ocean. The fact that ECS is sensitive
to the balance between supercooled water and ice in clouds means that we
have to improve our understanding of ice-related microphysical processes. In
particular, we need a concerted effort to understand the atmospheric
abundance of INPs, the aerosol type that catalyses ice formation in mixed
phase clouds and plays a major role in defining the cloud-phase feedback.
To what extent is the persistence of supercooled liquid clouds related to
ice nucleation?
In the absence of collisions with ice crystals, water droplets can freeze
both homogeneously, i.e. spontaneously, or heterogeneously, where an
impurity catalyses freezing. Homogeneous nucleation defines the lower limit
to which supercooled clouds can persist in the absence of INPs. The exact
temperature limit depends on dynamics and microphysics, but homogeneous
nucleation becomes increasingly important below about -33 ∘C
(Herbert et al., 2015; Koop and Murray, 2016), which is consistent with
the lack of supercooled water in shallow clouds below about -35 ∘C
(Kanitz et al., 2011; Morrison et al., 2011; Hu et al., 2010) (also
Fig. S2).
There are many aerosol particle types that possess the capability to
nucleate ice, from mineral dusts to biological particles and combustion
aerosol to fertile soil dusts (see the reviews of Kanji et
al., 2017, Murray et al., 2012, and
Hoose and Möhler, 2012). One of the striking and
important aspects of INPs is that particles with the capacity to serve as
immersion mode INPs are rare in comparison to those capable of serving as
cloud condensation nuclei. Even within a specific category of INPs, not all
particles with a particular composition will nucleate ice. For example, ice
nucleation by desert dust is thought to depend on the presence of K-feldspar
(Harrison et al., 2019; Atkinson et al., 2013; Peckhaus et al., 2016), and
even then only a fraction of K-feldspar grains possess active sites capable
of nucleating ice at around a particular characteristic freezing temperature
(Holden et al., 2019). The fact that ice nucleation, at least on some
materials, is a site-driven process means that it is not possible to define
the ice-nucleating ability of an aerosol population using macroscopic
properties in a manner that is analogous to droplet formation on soluble
particles, which depends solely on the bulk chemical composition. Hence, we
have to empirically quantify the ability of specific particle types by
describing the distribution of sites across the particle population using
quantities such as the INP concentration spectrum or the active site density
spectrum.
In general, the INP concentrations in air masses associated with land are
higher than those with a strong marine influence (McCluskey et al.,
2018b; Vergara-Temprado et al., 2017; DeMott et al., 2016; Welti et al.,
2020). This terrestrial–marine divide is related to the sources in the two
environments. There is clearly a source of highly active INPs in sea water
(Wilson et al., 2015; Schnell and Vali, 1975; Irish et al., 2019b), but
the sea spray production process only produces rather low INP concentrations
(Vergara-Temprado et al., 2017; McCluskey et al., 2018a; DeMott et al.,
2016). In contrast, there are a plethora of potential INP sources on land
including mineral dusts, biogenic particles and combustion particles
(Kanji et al., 2017; Murray et al., 2012).
This divide between terrestrially influenced regions and remote oceans is
reflected in the extent to which shallow clouds supercool. For example,
satellite data indicate that liquid clouds over the Southern Ocean
supercool extensively, whereas clouds over Europe, where there are stronger
INP sources, supercool much less (Choi et al., 2010; Storelvmo et al.,
2015; Kanitz et al., 2011; Hu et al., 2010). Furthermore, it has been shown
that the degree of supercooling correlates with the presence of specific
aerosol species such as mineral dust (Tan et al., 2014; Choi et al.,
2010). Also, it has been shown using satellite data that there is a large
contrast in the contribution of cloud phase changes to changes in cloud
optical depth with temperature between land and ocean, which points to the
importance of INPs (Tan et al., 2019). Hence, there is a clear
link between the degree of supercooling and aerosol type, which needs to be
represented routinely in climate models.
How well do models represent phase partitioning in climate models?
Current models are hugely divergent in their representation of the amounts
of supercooled water (Komurcu et al., 2014; Zelinka et al., 2020; McCoy
et al., 2015; McCoy et al., 2018; Cesana et al., 2015). For example, in an
intercomparison of cloud water between several climate models
Komurcu et al. (2014) found that in some models liquid water as warm as -10 ∘C had
largely been removed, while in other models
unrealistically high amounts of liquid water persisted down to
-35 ∘C. Some of these models also deviate strongly from satellite
measurements of the cloud-top phase (Cesana et al., 2015; Komurcu et al.,
2014).
The reasons for the model discrepancies are complex. Cesana
et al. (2015) conclude that models with more complex microphysics tend to
have a better representation of ice phase, while Komurcu et al. (2014) conclude that the inter-model variability they report was related in
part to the specifics of the ice nucleation scheme, but also to the
representation of other ice-related microphysical process. However, it is
important to bear in mind that many of the relevant processes occur on
scales finer than the grid resolution of climate models, and
parametrizations of these processes can affect the distribution and amounts
of ice and liquid (Kay et al., 2016). Nevertheless, the amount
of supercooled liquid water in climate models is highly sensitive to the
treatment of primary ice production (Vergara-Temprado
et al., 2018). Overall, the representation of phase partitioning in models
is massively divergent and this likely contributes to the variable cloud
feedbacks and ECS values (Bodas-Salcedo et al., 2019). In
the future, models need to improve their representation of ice-related
microphysical processes; in particular, they need to include a direct link
to aerosol type, specifically INPs, in order to improve the representation of
cloud phase and the response of clouds to a warming world.
What are the meteorological conditions most important for the cloud-phase
feedback?
Detailed analysis of model biases over the Southern Ocean have shown that
the cold-air outbreaks (CAOs) are of central importance to the cloud-phase
feedback (Bodas-Salcedo et al., 2016; Bodas-Salcedo et al., 2014). Marine
COAs are high-impact events where cold air flows from higher latitudes over
a warmer ocean (Fig. S3). This creates the conditions for shallow
supercooled cloud systems as heat, moisture and aerosol are mixed into cold
air. The strongest CAOs are associated with the cold sector of extratropical
cyclone systems, which tend to draw air from the polar or cold continental
regions (Fletcher et al., 2016; Pithan et al., 2018).
Modelling work has shown that CAO cloud systems are strongly impacted by
INPs, with low INP concentrations leading to more extensive highly reflective
stratus clouds, whereas high INP concentrations tends to lead to much
patchier convective cloud with local albedos many hundreds of watts per square metre lower
(Vergara-Temprado et al., 2018). This is illustrated
in Fig. 5 where a cyclone system was simulated by
nesting a high-resolution (7 km) region within a global model
(Vergara-Temprado et al., 2018). Two cases are shown
in Fig. 5, one with INP concentrations
representative of the terrestrial mid-latitudes ([INP]-15=4 L-1) and one
representative of the Southern Ocean ([INP]-15=6×10 L-1). The mean cloud
reflectivity in the cold sector is lower by hundreds of watts per square metre in the high
[INP] case relative to the low [INP] case, and
Vergara-Temprado et al. (2018) shows that the
reflected shortwave flux increases with increasing INP concentration. This
illustrates that correctly representing primary ice production, and INPs, is
critical for maintaining the amount of supercooled water in clouds and their
albedo. More importantly, although various processes in models could be
adjusted to match present-day measurements, this would not address how INPs
influence the response of the clouds to warming.
The effect of INP concentration on model clouds in the cold-air
sector of a cyclone system over the Southern Ocean with a cloud top
temperature of around -15 ∘C (adapted from
Vergara-Temprado et al., 2018). The case is from
1 March 2015 at 14:00 with 0.07∘ grid spacings (roughly 7.7 km on a rotated grid). The left map shows a case with a relatively high INP
concentration (4 L-1 active at -15 ∘C; based on
Meyers et al., 1992) and the right map is for a relatively low
INP concentration (6×10-4 L-1 at -15 ∘C, based on
Vergara-Temprado et al., 2017). The
low INP concentration is a good match to measurements in the Southern Ocean
(Schmale et al., 2019; McCluskey et al., 2018a; Welti et al., 2020) and the higher
concentration is within the range of measured INP concentrations elsewhere
(see Fig. 6).
Vergara-Temprado et al. (2018) demonstrate that the
lower INP concentration produces clouds which are consistent with satellite
measurements of SW flux, whereas the high-INP case suffers a large low bias.
In the image, the Antarctic peninsula is visible in the lower left and the
Antarctic continent is on the bottom right. The x axis of these plots is
approximately 4500 km, while the y axis is approximately 3900 km.
What do we currently know about atmospheric INPs in the regions important
for the cloud-phase feedback?
Our knowledge of the global distribution, seasonal cycle and sources of
these enigmatic particles is in its infancy. However, we argue that the
documented importance of CAO clouds (Bodas-Salcedo et
al., 2016) allows us to focus on understanding aerosol and INP sources in
these specific environments. The air flow in CAOs is well defined, with air
streaming out of the colder high latitudes into the mid-latitudes. These
cloud systems are therefore impacted by (i) high-latitude aerosol and
terrestrial biogenic INP sources, (ii) sea spray that carries biogenic INPs,
and (iii) INPs in the free troposphere from more distant sources entrained
into the boundary layer. Hence, middle- and high-latitude sources of INPs may
have a disproportionate effect on climate through their influence on shallow
clouds.
What do we know about INPs at middle to high latitudes, and specifically in
environments that have the potential to directly impact CAOs? Measurements
of INP concentrations in regions that may influence CAOs are summarized in
Fig. 6. It is striking how variable INP
concentrations are, both in space and time. If we take 1 INP L-1 as a
reference value, where ice formation is thought to substantially reduces the
liquid water path and albedo, then this threshold is reached anywhere from
around -10 ∘C to temperatures where we expect homogeneous freezing
to dominate primary ice production (<-35 ∘C). This
temperature range is not an uncertainty, but rather a range of atmospheric
states that we need to understand because it is relevant to present-day
mixed-phase clouds and future feedbacks.
INP concentration measurements in the middle- to high-latitude
Northern Hemisphere (yellows–reds) and the Southern Hemisphere (blues).
Given INP sources in the middle to high latitudes are likely to be of central
importance to CAOs, we have only presented measurements in either coastal
regions or in the open ocean from latitudes greater than about 43∘. At present we have no means of predicting this variability in INP
concentration, because we are only beginning to quantitatively understand
the sources relevant for these regions. Data were taken from multiple
sources (Schmale et al., 2019; McCluskey et al., 2018a; Bigg, 1973; Bigg
and Hopwood, 1963; Bird et al., 1961; Belosi et al., 2014; Kikuchi, 1971;
Sanchez-Marroquin et al., 2020; Wex et al., 2019; Irish et al., 2019a;
Creamean et al., 2019; Creamean et al., 2018; Bigg, 1996; Bigg and Leck,
2001; Borys, 1989; Rogers et al., 2001; DeMott et al., 2016; Flyger and
Heidam, 1978), and more details are given in Table S1. While there is clearly
a great deal of natural variability there are also differences in sampling
and instrumentation that will cause some variability. A discussion of known
artefacts associated with the technique employed by Bigg and co-workers has
been given previously (Mossop and Thorndike, 1966; McCluskey et al.,
2018a).
INPs in the northern middle to high latitudes
The limited INP concentration data in Fig. 6
indicate that the INP concentrations in the Northern Hemisphere are
generally higher than in the Southern Hemisphere. This may be related to the
proximity of terrestrial sources in the Northern Hemisphere that are less
common in the Southern Hemisphere. Over recent years it has become
increasingly apparent that there are significant dust emissions from a
plethora of high-latitude sources, such as pro-glacial deposits (Bullard
et al., 2016; Prospero et al., 2012). Samples from a handful of these
sources have been shown to nucleate ice (Tobo et al., 2019; Paramonov et
al., 2018; Sanchez-Marroquin et al., 2020), and dust from Iceland's deserts
has been shown to be an important INP type across the North Atlantic and low
Arctic (Sanchez-Marroquin et al., 2020).
Further evidence for a strong terrestrial source of INPs in the Arctic was
found by Irish et al. (2019a), with a correlation between INP concentrations and the time that air spent
over bare land during late summer. In addition to mineral dust, which tends
to control the INP population only below about -15 ∘C
(Murray et al., 2012), there is evidence that there are
strong sources of terrestrial biogenic material active at much warmer
temperatures across the Arctic (Tobo et al., 2019; Wex et al., 2019).
Terrestrial biogenic material might be associated with sediments from rivers
(Tobo et al., 2019) or vegetated areas (Conen et al., 2016;
Schnell and Vali, 1976). In fact, it has been suggested that biogenic
ice-nucleating material may account for the INPs active at the highest
temperatures in Fig. 6
(Wex et al., 2019).
In addition to terrestrial sources, there are multiple studies showing that
there is a biogenic source of INPs in sea water that can become aerosolized
through the action of waves and subsequent bubble bursting (Schnell,
1977; Schnell and Vali, 1975; Wilson et al., 2015; Irish et al., 2019b;
DeMott et al., 2016; Irish et al., 2017; Creamean et al., 2019). Modelling
(Vergara-Temprado et al., 2017) and
measurements (McCluskey et al., 2018b; McCluskey et al., 2018a) suggests
that this source produces sea spray aerosol that are relatively ineffective
INPs, with activities orders of magnitude smaller (on a per-surface-area basis) than
mineral dust. Marine biogenic INPs may define a baseline INP concentration in
environments that lack other more active INP types (McCluskey et al.,
2018a; Vergara-Temprado et al., 2017; Schill et al., 2020), and it is
conceivably an important source in windy CAOs. In the Northern Hemisphere,
even the small quantities of dust transported from the low-latitude source
regions may dominate over marine sources of INPs much of the time
(Vergara-Temprado et al., 2017), and
local terrestrial sources may episodically swamp both marine and low-latitude sources (Sanchez-Marroquin et al.,
2020). Taking all this together, the INP population in the Northern
Hemisphere high latitudes appears to be a complex mixture of different INP
types from the marine and terrestrial environment.
The observed strong seasonal dependence of high-latitude Northern Hemisphere
INP concentrations could give us a clue to how INPs might change with climate
(our third hypothesis; Fig. 4c). These
dependencies are clearest in the multi-season data presented by
Wex et al. (2019) for four locations
around the Arctic. The highest INP concentrations occur in the spring,
summer and autumn when high-latitude marine and terrestrial sources become
ice-free and when biological activity is at its maximum. The implications of
these data are that there is a local biogenic source of INPs in the northern
high latitudes and that as the ice and snow season shortens with a warmer
climate, these sources may be active for more of the year and more sources
may become available, which would cause positive feedback on climate through
increased ice production in clouds (Fig. 3c). In
addition to this, it has been argued that high-latitude dust sources
associated with glaciers will become more active in the future
(Bullard et al., 2016), and it was recently shown that mineral
dust emissions from the coastal areas of Greenland have increased in the
last few decades (Amino et al., 2020). However, paleo-records
indicate that warmer periods are generally less dusty than dry periods,
although this may reflect a combination of lower-latitude sources being more
active and increased transport to high latitudes during glacial periods
(Lamy et al., 2014; Fischer et al., 2007). Hence, it may be that the
glacial–interglacial trends in high-latitude dust sources relevant for CAOs
are decoupled from low-latitude dust sources and the general dust loading of
the atmosphere. More work in identifying the sources of INPs in the high
latitudes and how these sources will respond to a changing climate is
clearly required.
INPs in the southern middle to high latitudes
It is unclear whether there are similarly strong INP sources in the Southern
Hemisphere. Recent measurements over open ocean or in sea ice indicate that
INP concentrations are generally very low (Schmale et al., 2019;
McCluskey et al., 2018a; Welti et al., 2020); in fact, these are amongst the lowest INP
concentrations that have been measured anywhere on Earth. However,
measurements at the Antarctic coastal stations of McMurdo (Bigg and
Hopwood, 1963) and Syowa (Kikuchi, 1971) indicate
concentrations in excess of 1 L-1 at -20 ∘C. There are
reports of dust uplift on the Antarctic peninsula (Bory et al., 2010;
Asmi et al., 2018) and also in the dry McMurdo valleys
(Lancaster, 2002). There are also dust sources more generally
across the Southern Hemisphere; in particular dust from New Zealand and
Patagonia is transported to the higher-latitude Southern Ocean
(Neff and Bertler, 2015) and dust from Patagonia has been
shown to be effective at nucleating ice (López et al.,
2018). A significant input of INPs to clouds in the Southern Ocean in the
present climate would imply a strong negative cloud-phase feedback and that
these clouds have a strong buffering effect on warming by anthropogenic
CO2. Conversely, if the INP source is weak, as contemporary
measurements suggest (McCluskey et al., 2018a; Schmale et al., 2019; Welti et al., 2020),
then the cloud-phase feedback would be far less negative than over the
Northern Hemisphere. In addition, there is the potential that sources of INPs
in the Southern Hemisphere become more prominent in the future as a response
to warming, which would lead to a positive feedback. Clearly, more work
needs to be done to assess the sources, transport and nature of INPs in both
hemispheres.
Important areas of future research
The field of atmospheric ice nucleation and its role in defining the
cloud-phase feedback is rapidly evolving. We have come a long way in recent
years in defining the problem, improving our understanding of ice nucleation
and building the capacity in our models to deal with ice processes. However,
while we can see that the climate system is very sensitive to the
cloud-phase feedback, there are substantial knowledge, technology and skills
gaps that need to be addressed in order to make quantitative predictions.
Here we highlight some of the frontiers in the field that need to be
addressed in order to reduce the uncertainty associated with the cloud-phase
feedback.
Control of primary ice production by INPs in global climate models
Many global climate models do not represent the basic physical processes
relevant for the cloud-phase feedback. For example, it has been shown that
linking primary ice production to aerosol concentrations, amongst other
changes, improved the representation of cold oceanic clouds (Gettelman et
al., 2019; DeMott et al., 2010). This is an important result, but it must be
acknowledged that there are many, sometimes more important, INP sources than
low-latitude mineral dust, especially at high latitudes. Global climate
models need to couple with a full model of INPs, including sources and
removal processes relevant to specific cloud systems. Inclusion of INPs in
climate models would open up the opportunity to simulate the number
concentration of primary ice particles, which is required for a realistic
simulation of the chain of processes that control precipitation and cloud
reflectivity (Vergara-Temprado et al., 2018).
An INP measurement network
While aerosol properties such as their ability to activate to cloud droplets
are measured routinely around the world, INP concentrations are not. To improve
the representation of the cloud-phase feedback we have to be able to
represent INP concentrations in our models. This can only come from suitable
measurements in the right places. We need a global network of INP
measurement sites making year-round measurements across the full range of
mixed-phase cloud conditions, with high priority in regions where CAOs are
particularly important (i.e. ∼ 45 to 70∘).
Instrument development
Until very recently, the INP measurement community has lacked instruments
that can operate on an autonomous basis and can access the full range of INP
concentrations and temperatures relevant for the cloud-phase feedback. In
order to access the full range of INP concentrations, this will most likely
require several separate instruments operating in parallel, targeting the
full range of temperature and saturations over which clouds form in the
atmosphere. Developments such as a new semi-autonomous portable expansion
chamber INP counter (Möhler et al.,
2020), the application of microfluidics technology (Tarn et al., 2020;
Porter et al., 2020) and autonomous continuous flow diffusion chambers
(Bi et al., 2019; Brunner and Kanji, 2020) may offer routes to much
improved instrumentation for routinely quantifying INP concentrations.
Quantifying INP sources and their physical, chemical and biological controls
We have to understand quantitatively where INPs relevant for the
cloud-phase feedback come from and what drives their emission. Sources in
the Arctic appear to be strongly seasonal and are likely to respond to a
changing climate. Sources in the Southern Hemisphere are even less well
defined. Terrestrial high-latitude sources associated with pro-glacial
deposits may be very important, but we are only just starting to quantify
them.
Dedicated field campaigns
We need field campaigns focused on quantifying the relationship between
aerosol (INP and CCN), mixed-phase clouds and boundary layer dynamics. We
need to understand how the processes in these cloud systems depend upon the
sea surface temperature and changes in aerosol availability. As well as
being key to the cloud-phase feedback, cloud systems in CAOs offer an
opportunity to study a relatively repeatable weather regime that has a
well-defined transition from mixed-phase stratus to shallow convective
clouds.
Development of global INP models that include all relevant sources
Many models create ice as a function of temperature but lack the link to
aerosol; this has been shown to be inadequate (DeMott et al., 2010;
Vergara-Temprado et al., 2018). We have begun to build models of the global
distribution of atmospheric INPs (Vergara-Temprado et al., 2017; Hoose et
al., 2010; Spracklen and Heald, 2014; Schill et al., 2020), but we currently
lack an understanding of middle- and high-latitude sources. We must also
represent the INP removal processes, which in turn depend on a correct
representation of the microphysics. It is only with INP models where there
is a link to surface properties in key source regions that we can expect to
be able to predict how INP distributions will change in response to climate
change.
Cloud microphysics and dynamics
In addition to ice nucleation, other microphysical and dynamical processes
are also extremely important for clouds and their response to a changing
climate. Many of these other processes are also very uncertain and are the
topics of extensive review articles in themselves. For example, secondary
ice production remains a major challenge and has the potential to amplify
the effect of a small concentration of INPs. However, even the basic
mechanisms leading to ice multiplication are unclear (Field et al., 2017;
Korolev and Leisner, 2020).
Final comments
As a global civilization striving to secure its future prosperity, wellbeing
and sustainability, we need accurate predictions of our impact on Earth's
climate. It is clear that our understanding of the cloud-phase feedback and
ice-nucleating particles, as well as the representation of these processes
in climate models, is limiting our ability to do this accurately. There is
substantial evidence that the cloud-phase feedback has been too negative in
climate models, and the correction of this will lead to larger ECS values.
Whether these large ECS values are plausible is a topic of hot debate, but
if they are not feasible then it seems some other feedback is (or feedbacks
are) too positive. Nevertheless, it is becoming very clear that the
cloud-phase feedback contributes substantially to the uncertainty in
predictions of the rate at which our planet will warm in response to
CO2 emissions.
We argue that a concerted effort is needed from scientists working on
different scales, from the detailed microphysical, biological and chemical
processes associated with INP sources to those who can implement this
knowledge to build a global understanding using state-of-the-art modelling
tools. Without this underpinning knowledge and its suitable representation
in our models, ECS will remain highly uncertain. But if it turns out that
the larger ECS reported by some new climate models is correct, then society
will need to act even more assertively to limit the accumulation of CO2
in our atmosphere. Hence, resolving the role of INPs in the cloud-phase
feedback needs to be a research priority for the coming years.
Data availability
No data sets were used in this article.
The supplement related to this article is available online at: https://doi.org/10.5194/acp-21-665-2021-supplement.
Author contributions
BJM led the writing of this paper and the design of the figures. KSC and PRF contributed to the writing and editing of the text and the design of the figures as well as defining key concepts.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
We thank Daniel McCoy, Mark Zelinka and Trudy Storelvmo for helpful
discussions in relation to climate feedbacks in models. In particular, we
thank Mark Zelinka for providing access to ECS and feedback data associated
with Zelinka et al. (2020). Conversations about historic
data sets with Keith Bigg were invaluable. We are grateful to Heike Wex,
Jessie Creamean, Jingwei Yun and Allan Bertram for sending us data used in
Fig. 6.
Financial support
This research has been supported by the European Research Council (MarineIce (grant no. 648661)), the Horizon 2020 (PRIMAVERA (grant no. 641727)), and the Natural Environment Research Council (grant no. NE/T00648X/1).
Review statement
This paper was edited by Thomas Koop and reviewed by Trude Storelvmo and Paul DeMott.
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