Figure 11. Historical model biases in warm-season MHW
properties vs. the projected future changes over the global cells of
kelp under SSP2-4.5 by the end of 21st century for the
a.-c. the continuous warm-season MHW (Dc, day), d.-f.
the accumulated heat stress over the continuous period
(Ac, °C·day).
Over the coral and kelp cells, the pattern of future changes in the five
thermal properties are similar to the changes globally, with increases
across most cells in Dc, HSpeak and
Ac as well as many decreases in HRc and
Dp, and higher magnitude of changes under the higher
emission scenarios (Figure 8, 10; Table S1-6). The mean changes over the
kelp cells are close to the global levels, while those over the coral
cells are greater than the global averages in all three models (Table
S1-6), in part because warm-water corals exist in the tropics and
subtropics where the limited seasonality cause greater chance of
continuous heat stress occurrence, and that contribute to higher
accumulation of heat stress. For example, the mean level of
Dc and Ac under SSP2-4.5 in GFDL-ESM4 is
178 day and 281 °C·day, respectively, while that across global ocean is
143 day and 246 °C·day (Figure 8; Table S1).
For HSpeak,
HRc and Dp, however, the differences
between the projected changes over the coral cells and the whole ocean
varies among the models, ranging from negative to positive (Table S1-3).
While it is unknown to what extent corals and kelp could adapt to recent
thermal history in the future, the projected changes might be smaller if
the thermal threshold increases due to acclimation and or adaptation.
For example, the global mean increase in Dc by 2100 in
the coral and kelp cells is 152 and 101 day shorter, respectively, using
a thermal threshold based on the previous 60 years (2041-2100) rather
than the historical period (Figure 8, 10).
Similar to the global patterns, the regional changes in these thermal
properties also tend to be larger in CESM2-WACCM and MRI-ESM2 than those
in GFDL-ESM4, which could be related to processes driving larger biases
in those models (Figure 8, 10, S11-14). The projected increases of
Dc and Ac across the coral and kelp
cells show statistically significant positive relationships with the
present-day model biases in each of those cells (Figure 9, 11, Table
S7-12). The positive relationships, particularly for CESM2-WACCM and
MRI-ESM2, indicates that coral and kelp cells with large positive biases
are also exhibiting large projected changes in those same variables.
This suggests that every 1 day increase in the model biases, for example
in Dc, the projected values increase by 0.5, 1.2, 1.8
days in GFDL-ESM4, CESM2-WACCM and MRI-ESM2, respectively. However, the
relationships between the projected changes and model biases for
HSpeak, HRc and Dp vary
among the models with some of them not statistically significant (Table
S7-12).
5. Discussion and conclusions
In this study, we evaluate the model biases in simulating five thermal
properties of warm-season MHWs from three CMIP6 models, and analyze
their potential role in the projected future changes with specific focus
over the global regions of warm-water corals and kelp forests. By the
end of 21st century, the duration, accumulated heat
stress and peak intensity show systematic increases under all the future
emission scenarios considered. Conversely, heating rate and the priming
period display systematic decreases in the tropics and subtropics. The
projected changes in warm-season MHW properties are broadly consistent
in global patterns among the three models. However, there are regional
disagreements on future MHW properties among the models as well as
between present-day model simulations and the observations.
Understanding the drivers of biases in the models is important in
interpreting MHW projections and responsibly employing MHW projections
in the studies of their ecological impacts. In the following section, we
discuss the potential drivers of present-day model biases and the
implications for future warm-season MHW projections.
The large model biases during the historical period and inter-model
spread in the spatial pattern of the future projections are likely
caused by different model representations of atmospheric and oceanic
processes including 1) cloud formation; 2) deep convection,
precipitation and storms, 3) surface winds and associated oceanic heat
transport, and 4) ENSO dynamics. First, cloud representation in the
tropics and subtropics help determine the peak intensity of MHWs by
inducing anomalous radiation balance and surface heat flux that can
cause anomalously warm SSTs. For example, the negative low cloud biases
off the coast of California and Peru and in the Benguela current system
lead to large warm bias in HSpeak observed in GFDL-ESM4
output (Dunne et al., 2020).
Second, heavy precipitation and storms may affect MHW duration, as
associated strong winds and anomalous surface heat flux can cool the sea
surface and terminate a MHW. Overestimated precipitation in parts of the
tropical Pacific in GFDL-ESM4 due to Double Intertropical Convergence
Zone (ITCZ) problem could contribute to a shorter MHW duration than in
the other two models (Danabasoglu et al., 2020; Dunne et al., 2020;
Yukimoto et al., 2019). Tropical storms as well as deep convection
associated with Madden Julian Oscillation (MJO) can also drive MHW
dissolution; how accurately models simulate tropical storms and
behaviors could therefore affect the simulation of MHW duration and the
associated accumulated heat stress (Shin & Park, 2020).
Third, surface winds over the
ocean influence SSTs and trigger anomalous heat stress at regional
scales through affecting air-sea heat flux, wind-driven anomalous zonal
advection and turbulent mixing (Bond et al., 2015; Sen Gupta et al.,
2020; Holbrook et al., 2019; Oliver et al., 2017). In CESM2-WACCM,
underestimated upwelling due to damped wind stress in the eastern
boundary current regions could contribute to the positive biases in MHW
duration off the west coasts of California, South Africa and South
America (Danabasoglu et al., 2020). In GFDL-ESM4, the positive biases in
the equatorial cyclonic wind stress and the negative biases in the zonal
surface winds off the equator could enhance the surface heat loss at the
equator and weaken surface heat loss off the equator by affecting
vertical mixing of the warm surface later with cooler waters at depth
(Dunne et al., 2020). This may partially explain the pattern of negative
biases in MHW duration in the equatorial Pacific and more poleward
positive biases. Shorter durations in the tropics in GFDL-ESM4 compared
with the other models could also be driven by the shallower mixed layer
in GFDL-ESM4, which would allow sea surface to warm or cool faster and
stronger through air-sea heat flux. Faster and stronger warming due to
the shoaled representation of the mixed layer depth might contribute to
the higher peak temperatures (HSpeak) and larger rate of
anomalous heat stress development (HRc) in the tropics,
relative to the other models.
Fourth, the inter-model variability in the thermal properties over the
tropics are also related to model disagreements on the magnitude,
location and timing of ENSO-driven SST anomalies. Though the simulations
of ENSO dynamics have been improved with the latest generation of GCMs
included in CMIP6, many uncertainties that can affect ENSO-driven heat
stress remain (Beobide-Arsuaga et al., 2021; Brown et al., 2020; Jiang
et al., 2021). Large warm biases for HSpeak in the
eastern tropical Pacific by GFDL-ESM4 may be related to the
underestimated convection in the western equatorial Pacific and stronger
thermal stratification in the Pacific cold tongue region that could
drive overestimated SSTs during ENSO events (Dunne et al., 2020). In
contrast, there is no warm bias across the tropical Pacific in
CESM2-WACCM and MRI-ESM2, which have improved representation of the
stratocumulus clouds, and ocean mixing and stratification (Danabasoglu
et al., 2020; Yukimoto et al., 2019).
Another fundamental source of model bias in simulating warm-season MHWs
is the spatial resolution of the atmosphere and ocean components of the
models. The spatial resolution of the atmospheric module in a GCM can
affect many processes, notably cloud formation. Though this factor
cannot explain the differences among the models we examined, as the
models employed the same resolution the atmosphere (Danabasoglu et al.,
2020; Dunne et al., 2020; Yukimoto et al., 2019), it could influence the
models’ performance relative to observations. In addition, the spatial
resolution of the ocean module in most GCMs is not fine enough to
resolve small-scale processes, like boundary currents and mesoscale
eddies, which may drive underestimates of heat stress which arise from
variations in these oceanic processes. For example, in coastal and
boundary current regions, underestimated magnitude of mesoscale eddies
could lead to a negative heat stress bias due to its effects on heat
transport (Guo et al., 2022; Hayashida et al., 2020; Oliver et al.,
2019; Pilo et al., 2019), as shown in the negative biases of
HSpeak in these regions across all models.
Coarse-spatial resolution can also cause unrealistically smooth SST time
series due to high serial autocorrelation (Oliver et al., 2019), thereby
leading to overestimated duration of continuous heat stress and
underestimated duration of priming. The spatial resolution of GFDL-ESM4
is finer than that of CESM2-WACCM and MRI-ESM2 (Danabasoglu et al.,
2020; Dunne et al., 2020; Yukimoto et al., 2019), which may partially
explain the lower magnitude of model biases in GFDL-ESM4 and smaller
coefficient of the relationship between historical model bias and
projected change. While the resolution in the tropics and subtropics is
similar among the models, the C-grid employed by GFDL-ESM4 could
represent more realistic boundary features than the B-grid employed by
the other two models; for example, the equatorial undercurrent could be
represented up to twice as accurately using the C-grid as opposed to the
B-grid at the same spatial resolution (Dunne et al., 2020). The positive
biases for the duration and negative biases for peak intensity in most
of the ocean by CESM2-WACCM and MRI-ESM2 might also be related to the
limited spatial resolution of their ocean couplers. The systematic cold
bias in the subpolar North Atlantic is known as a common error feature
in GCMs due to the poor representation of mesoscale eddies (Danabasoglu
et al., 2020; Dunne et al., 2020; Yukimoto et al., 2019).
Uncertainty in projected MHW properties may be larger in the tropics
where SST can be more sensitive to model ability to simulate the
aforementioned driving processes. In the tropics, a small positive bias
in the mean state of SST could lead to large bias in the duration and
accumulated heat stress due to the limited seasonality, which is
reflected in the large positive biases for Dc and
Ac in the tropical Pacific by CESM2-WACCM and MRI-ESM2.
This is also shown in the large positive relationship between the future
model projections and present-day model bias for Dc and
Ac in the warm-water coral reef cells. Given this likely
amplification of model bias, the future projections of the duration and
accumulated heat stress, two metrics used for exploring ecological
impacts of MHWs, need to be interpreted carefully in impacts modelling
and research.
Future MHW studies can focus on examining the role of above driving
processes using high resolution models with a large model ensemble of
SST outputs, as it may advance the predictability of MHWs. Modelling
experiments could be designed to examine the role of each of the
physical processes which we identified might drive the model biases in
simulating warm-season MHW properties. Given the essential contribution
of high spatial resolution to the accuracy of MHW projections, future
research characterizing MHW properties and their ecological impacts
would benefit from using outputs from GCMs with finer ocean and
atmospheric grids, although employing finer resolution SST output
requires greater computational resources. Future work could also
incorporate outputs from more GCMs and ESMs when data is available,
considering the fact that this study is restricted in examining
systematic biases for warm-season MHW properties due to the limited
availability of daily SST model outputs. This would not only create a
more robust ensemble of future projection, it would enable a more
thorough analysis of the processes that systematically drive model
biases in simulating MHWs. Meanwhile, including more ensemble member
projections for each model may further constrain the uncertainties in
terms of internal variability (e.g., ENSO) that could influence heat
stress conditions.
It should be noted that the choice of thermal threshold for defining
MHWs is fundamental to computing MHW projections and their ecological
impacts. Most of the MHW projection studies to date used a fixed,
historical thermal threshold to define MHWs (e.g., Hobday et al., 2016).
As the ocean warms, marine organisms and ecosystems may adjust to a
warmer baseline via physiological acclimatization, direction selection
and changes in community structure, such that heat stress calculated
from historical conditions is not representative. For example, there is
evidence that coral reefs exposed to frequent heat stress may acquire
higher thermal resistance (Hughes et al., 2018; Morikawa et al., 2019),
though it may come with reduced coral diversity and structural
complexity (Donner & Carilli, 2019; Magel et al., 2019). To better
examine the projections of MHWs and their ecological impacts, more
studies need to incorporate the role of acclimatization and adaptation
into the definition of the heat stress baseline (Logan et al., 2014;
McManus et al., 2020, 2021). We conducted a simple additional analysis
here by quantifying MHWs relative to a rolling MMM threshold that
represents theoretical adjustment to warming over time. Though the
results are intuitive, the projection of less severe MHW properties
assuming the rolling thermal threshold demonstrate the high sensitivity
of MHW projections to the choice of threshold. This highlights the
necessity of incorporating variable thermal thresholds, based on
research into acclimation and adaptation in marine organisms and
ecosystems (Alsuwaiyan et al., 2021), into future MHW projection and
impact research.
With continued climate change and associated ocean warming, MHWs will
continue to, or even more substantially, threaten marine ecosystems and
the associated cultures, fisheries and incomes of local and Indigenous
peoples (Cooley et al., 2022). To best understand the impact of
increasing warm-season MHWs on marine organisms and ecosystems, we need
to look beyond the accumulated intensity and examine the thermal
properties like duration, heating rate and priming period. We also need
to consider the ability of models to describe the processes driving MHW
development and dissolution, as well as the extent to which organisms
and ecosystems may adjust to warming. Considering these factors, and the
biases they may create in model output, is important for researchers
studying the impact of MHWs on ecosystems. This cautious analysis of MHW
projections is necessary to better inform policymakers and marine
resource managers tasked with protecting marine life, and the people who
depend on marine life, from the rising threat of MHWs.