1. Introduction
Proxy data and modeling studies have demonstrated that the early Cretaceous (∼145–100 million years ago) and Paleogene (∼66–23 million years ago) periods had considerably warmer Northern Hemisphere high latitudes compared to the preindustrial period, while the tropical sea surface temperatures (SSTs) were only slightly warmer (Huber and Caballero 2011; Lunt et al. 2021; Tierney et al. 2020; Hollis et al. 2019). Such climates with reduced equator-to-pole SST gradients and polar-amplified surface air temperatures are known as equable climates.
Arctic amplification can occur as a result of several regional positive feedbacks including surface albedo (Liang et al. 2020; Serreze et al. 2009), lapse-rate feedbacks (Graversen et al. 2014; Pithan and Mauritsen 2013), and large-scale atmospheric and ocean heat transport changes (Alekseev et al. 2019; Alexeev et al. 2005). Increases in boreal autumn and winter high cloud fractions that support Arctic warming in equable climates with high carbon dioxide (CO2) concentrations is also supported by simple models (Abbot and Tziperman 2008a,b) and atmosphere-only general circulation models (GCMs) (Abbot et al. 2009; Dutta et al. 2021). High clouds trap outgoing longwave (LW) radiation, which is re-emitted to the surface as downwelling radiation and produces atmospheric warming after surface turbulent and radiative adjustments (Colman 2015; Andrews et al. 2009). In a warmer Arctic, changes in the vertical distribution and microphysics of clouds also play important roles in Arctic amplification (Koenigk et al. 2013; Zhu and Poulsen 2020; Zhu et al. 2019). Comparing a set of idealized CO2 sensitivity experiments to a preindustrial simulation, Zhu and Poulsen (2020) report positive high- and low-latitude cloud feedbacks associated with changes in optical properties.
While the above studies examined clouds in the troposphere, clouds also occur in the lower stratosphere. Due to very low winter (June–July–August) temperatures in the present-day Antarctic region, synoptic-scale polar stratospheric clouds (PSCs) commonly occur over Antarctica (Peter 1997; Solomon et al. 1986; Tritscher et al. 2021) when temperatures drop below a critical point (approximately 192–195 K) (Molleker et al. 2014; Steele et al. 1983; Pitts et al. 2011, 2018). However, the Arctic polar vortex is weaker with a higher variability in strength and location than its Southern Hemisphere counterpart. This results in fewer PSCs in the Arctic, although observational studies suggest an increase in Arctic PSCs in recent years (2006/07–2017/18), mainly caused by a large drop in stratospheric temperatures (Pitts et al. 2018; Tritscher et al. 2021; Voigt et al. 2018). The Sixth Assessment Report (AR6) of the Intergovernmental Panel on Climate Change indicates a strong likelihood of widespread stratospheric cooling (Lee et al. 2021). AR6 also suggests an increase in stratospheric water vapor content associated with changes in the Brewer–Dobson circulation (BDC; Dobson 1956; Brewer 1949) but shows low confidence in any stratospheric water vapor trend over the instrumental period (Gulev et al. 2021). The BDC is characterized by upwelling of tropical tropospheric air into the stratosphere which then moves poleward into the winter hemisphere before descending in the mid- to high latitudes (Butchart 2014; Dobson 1956; Brewer 1949). Stratospheric cooling and an increase in water vapor concentration raise the potential of PSC formation in the Arctic (Khosrawi et al. 2016; Tabazadeh et al. 1994).
Despite some improvements shown in some recent studies (Lunt et al. 2021; Zhu et al. 2019), state-of-the-art GCMs are unable to simulate the full magnitude of Arctic amplification as suggested by proxy data in past warm climates (Haywood et al. 2016; Spicer et al. 2008; Tierney et al. 2020; Lunt et al. 2021). This suggests that the GCMs might be missing some physical processes or feedback mechanisms responsible for Arctic amplification.
To explain this discrepancy between proxies and GCM simulations of equable climates, Sloan and Pollard (1998) suggest a wintertime increase in PSCs, which were not well represented in GCMs used in the 1990s, as one possible factor that could explain Arctic warming. The authors argue that in the early Eocene, optical depth and coverage of PSCs in the Arctic might have been much higher than today due to an increase in the stratospheric water vapor concentration caused by enhanced methane (CH4) oxidation from massive biogenic CH4 emissions (Dickens 2011; Katz et al. 2001; Frieling et al. 2016; Sloan et al. 1992). Sloan and Pollard (1998) impose PSCs with 100% fractional coverage and set optical thickness to 0.999 in the Genesis version 2 atmospheric GCM coupled to a mixed-layer ocean model using early Eocene geography. These imposed PSCs increased winter Arctic temperatures by up to 20°C, thereby reducing the equator-to-pole surface temperature gradient. However, the amount of imposed PSCs and their optical thickness (Sloan and Pollard 1998) is extreme and was not justified by any physical cloud model.
Changes in the BDC can impact the formation of PSCs by changing both the Arctic stratospheric temperature and water vapor entering the stratosphere. In response to higher greenhouse gases (GHGs) and/or warmer-than-preindustrial SSTs, a large number of GCMs and chemistry-climate models (CCMs) show an acceleration of the BDC (Lin and Fu 2013; Chiodo and Polvani 2017; Chrysanthou et al. 2020; Butchart 2014; Xie et al. 2020). However, studies do not agree on the magnitude and underlying causes of BDC change (Butchart 2014; Bunzel and Schmidt 2013).
Using 12 CCMs participating in the Chemistry-Climate Model Validation Activity phase 2, Lin and Fu (2013) show an acceleration of transition (100–70 hPa), shallow (70–30 hPa), and deep (above 30 hPa) branches of the BDC by the end of the twenty-first century. For abrupt quadrupling of CO2, Chrysanthou et al. (2020) and Chiodo and Polvani (2017) also report BDC acceleration using the atmospheric component of the Hadley Centre Global Environment Model and the Whole Atmosphere Community Climate Model (WACCM), respectively. In contrast, there are only a small number of studies that have explored the state of the stratosphere (Unger and Yue 2014; Szopa et al. 2019; Winterstein et al. 2019) and the importance of GHGs other than CO2 in past climates (Unger and Yue 2014; Beerling et al. 2011; Szopa et al. 2019). Szopa et al. (2019) find a strengthening of the BDC in the early Eocene using an Earth system model with interactive chemistry forced by quadrupled CO2, higher-than-preindustrial CH4 and nitrous oxide. Additionally, a sharp increase in ozone concentration was found near the poles and at tropical latitudes above 50 and 10 hPa, respectively. Using a CCM, Unger and Yue (2014) report a strong surface warming due to non-CO2 GHGs associated with changes in terrestrial ecosystem emissions in the mid-Pliocene. Unger and Yue (2014) also find an increased tropical upwelling and colder stratospheric temperatures in their mid-Pliocene simulation.
Clearly, there is a link between the meridional SST gradient and BDC strength, which in turn may impact the formation of PSCs in the Arctic. Using a two-dimensional energy balance model that includes a linear dependence of the stratospheric circulation on the meridional surface temperature gradient, Kirk-Davidoff et al. (2002) force a weakening of the BDC when the meridional SST gradient is reduced. This leads to an increase in water vapor content in the Arctic stratosphere resulting in optically thick PSCs which increase surface radiative warming by more than 30 W m−2 in boreal winter. In contrast, Korty and Emanuel (2007) find a small strengthening of the BDC and an increase in stratospheric temperatures at 80°N when the Community Atmosphere Model version 3 (CAM3) is forced by a reduced equator-to-pole SST gradient. Korty and Emanuel (2007) use an extended version of CAM3 with the model-top in the middle-mesosphere and an increased resolution above 100 hPa; however, CAM3 does not include an interactive chemistry module, which is important for understanding CH4 oxidation, tracer transport and consequently PSC changes. Finally, using a simple two-dimensional model, Kirk-Davidoff and Lamarque (2008) show that optically thick PSCs might still form without a reduction in BDC strength if ice-formation microphysics change during equable climates.
Beerling et al. (2011) simulate 4–5 times higher CH4 concentrations in the early Eocene compared to the preindustrial using a fully coupled ocean–atmosphere–vegetation model. Their tropospheric chemistry-transport model prognostically calculates the concentration of CH4 in response to increased CO2 and changes in vegetation cover. Note that Beerling et al. (2011) do not account for the impact of CH4 change on the stratosphere. There are large uncertainties associated with CH4 sources, sinks and concentrations during past warm climates because paleoproxies do not provide any direct constraints on CH4. The CH4 sources (wetlands, forests, biomass burning, oceans, geothermal/volcanic activities) and sinks (tropospheric oxidation by OH, biological CH4 oxidation in dry soil, and loss to the stratosphere) are highly sensitive to the meteorological conditions (Denman et al. 2007). Additionally, other factors such as an increase in the global area of wetlands, forest cover, sea level rise, etc., might have directly impacted the CH4 cycle during past high greenhouse climates.
Although CH4 is the main chemical source of stratospheric water vapor, some water vapor is also transported to the stratosphere from the troposphere. The amount of this is mainly determined by the tropical cold-point temperature (Mote et al. 1996; Brewer 1949; Randel and Park 2019) in addition to cumulus clouds and aerosol concentrations in the tropics (Sherwood 2002; Zhou et al. 2017; Hardiman et al. 2015). The cold point is the location of the minimum temperature between the troposphere and the stratosphere (Highwood and Hoskins 1998; Seidel et al. 2001). As air crosses the tropical cold point, it freeze dries before entering the stratosphere and is transported to the mid- to high latitudes (Mote et al. 1996). The tropical cold-point temperature itself is strongly influenced by the midlatitude meridional surface temperature gradient (Jucker and Gerber 2017; Hu et al. 2014) and the stratospheric meridional circulation, both of which play important roles in determining stratospheric temperatures, water vapor concentrations and consequently PSCs in the Arctic (Brewer 1949; Dobson 1956; Butchart 2014; Mote et al. 1996; Plumb 2002).
Therefore, changes in Arctic PSCs are mainly determined by changes in Arctic stratospheric temperature and water vapor content, which are impacted by several interconnected factors, such as the equator-to-pole SST gradient, GHGs, the BDC, and the tropical cold-point temperature. Previous studies that looked at the physical processes causing PSC changes used simple models that do not represent stratospheric processes and simple idealized experimental configurations. Therefore, we revisit this topic here using several sensitivity experiments integrated with WACCM, which is a high-top CCM with a well-resolved middle atmosphere extensively used to study stratospheric processes. Coupled models underestimate polar climate changes. Additionally, it is difficult to isolate the roles of different factors affecting Arctic amplification in coupled models. As such, we examine a simplified configuration in which the atmosphere responds to prescribed polar-amplified surface warming representative of available proxy data. We aim to examine if the atmospheric changes, particularly in the stratosphere, act to damp or enhance the imposed warming. In a further set of experiments, we isolate the roles of GHGs on BDC change, which in turn may impact PSCs in the Arctic, by varying the concentrations of CO2 and CH4. Finally, an offline radiative balance model is used to isolate the top of the atmosphere (TOA) and surface radiative fluxes associated with the simulated changes in stratospheric and tropospheric clouds. In this study, we will address the following questions:
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What are the roles of CH4 and CO2 increase in changing PSCs in the Arctic when the model is forced with a strong polar-amplified SST?
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How do the resulting changes in stratospheric and tropospheric clouds affect the TOA and surface energy balance thereby feeding back onto Arctic amplification?
2. Methodology
a. Model description
We conduct a set of sensitivity experiments using WACCM version 4 (WACCM4) (Marsh et al. 2013) of the Community Earth System Model (CESM) version 1.2.2 (Hurrell et al. 2013). WACCM4 has 66 vertical levels extending from the surface to the lower thermosphere (140 km) and a horizontal resolution of 1.9° latitude × 2.5° longitude. WACCM4 is a CCM with a fully interactive chemistry module based on the Model for Ozone and Related Chemical Tracers (MOZART), version 3 (Kinnison et al. 2007) and includes all of the physical parameterizations of CAM4 of CESM (Hurrell et al. 2013). Along with CH4 and its degradation products, MOZART includes chemical species within the Ox, NOx, HOx, ClOx, and BrOx families and treats 18 PSC heterogenous chemical reactions on aerosols and PSCs (Kinnison et al. 2007; Horowitz et al. 2003).
The PSC microphysics scheme simulates supercooled ternary solution (STS) droplets, nitric acid trihydrate particles (NAT), and ice PSCs (Wegner et al. 2013; Solomon et al. 2015; Kinnison et al. 2007). The prescribed particle number density of STS and NAT is set to 5 and 0.01 cm−3, respectively. Ice PSCs form when temperature in the stratosphere is close to the ice frost point (∼188 K) depending on the abundance of water vapor, and results in dehydration in the stratosphere. For simplicity, it is assumed that ice PSCs are in thermodynamic equilibrium with the gas phase of water and only vary with relative humidity, which is determined by the equilibrium temperature (Solomon et al. 2015; Wegner et al. 2013). In WACCM4, the formation of ice PSCs is dependent on the mass of condensed water and water vapor which is prognostically calculated in CAM4 (Wegner et al. 2013; Solomon et al. 2015). Observational studies suggest that the size and particle number density of ice particles vary in PSCs (Dye 1992; Zasetsky et al. 2007). However, the simplified parameterization scheme in WACCM4 only tracks the distribution of ice mass in PSCs. Further details of the WACCM4 PSC microphysical scheme are provided in Solomon et al. (2015).
In CAM4 cloud fraction is diagnostically evaluated using simple equations based on temperature and water vapor concentration, and cloud layers form when relative humidity in an atmospheric grid box exceeds a predefined threshold. Similarly, the optical properties of PSCs are diagnosed using highly simplified parameterization schemes which are functions of temperature. More details on the evaluation of cloud microphysics and radiative properties in CAM4 can be found in Neale et al. (2012).
Temperature in the stratosphere plays a significant role in determining the rate of heterogeneous chemical reactions in the stratosphere (Brakebusch et al. 2013), therefore impacting PSC formation (Snels et al. 2019). Previous studies show that use of the specified dynamics version of WACCM4 (SD-WACCM4) reduces the bias in simulated stratospheric temperature and abundance of different tracers in the upper atmosphere and therefore provides a good representation of present-day PSCs (Wegner et al. 2013; Solomon et al. 2015; Snels et al. 2019). SD-WACCM4 uses the same PSC microphysical scheme as WACCM4. However, the model simulated fields of temperature, zonal and meridional winds, and surface pressure are nudged toward reanalysis data in SD-WACCM4. In this study we use the free-running version of WACCM4 as we do not have a realistic reference field for polar-amplified climates for the meteorological variables, which are nudged in the SD-WACCM version (the quasi-biennial oscillation package is not activated in this study). Given the importance of temperature, we evaluate the model temperatures to the fifth generation of atmospheric reanalysis products of the European Centre for Medium-Range Weather Forecasts (ERA5) for the period 1979–2020. The free running version of WACCM4 used in this study has a warm temperature bias of ∼5 K in the lower stratosphere in the Arctic in SON and DJF compared to ERA5 (Fig. 1), which possibly leads to underestimation of PSCs.
Seasonal mean stratospheric temperature difference WACCM4 (CTL) minus ERA5 in (a) SON and (b) DJF in shades. The contours overlaid on top show the ERA5 seasonal mean for 1979–2020.
Citation: Journal of Climate 36, 8; 10.1175/JCLI-D-22-0497.1
The Community Land Model version 4.0 is used without dynamic vegetation or an active carbon-nitrogen cycle. For simplicity, a one-dimensional thermodynamical sea ice model is used without sea ice dynamics (Bailey et al. 2011).
b. Experimental setup
The CTL experiment is integrated under preindustrial conditions and is forced by climatological monthly varying preindustrial observed SSTs and sea ice concentrations (Hurrell et al. 2008). The CO2 and CH4 concentrations vary around a mean value of 280 ppm and 791 ppb, respectively, and follow the seasonal cycle of year 1850. The 11-yr solar cycle is not included in this setup and all solar parameters (e.g., solar irradiance, wavelength) are based on an average climatology over the years 1834–67.
To understand the response of the stratospheric circulation to a reduced SST gradient, we conduct two idealized SST perturbation experiments, Pol10 and Pol20 (Table 1), based on the Cloud Feedback Model Intercomparison Project (CFMIP) protocols (https://www.cfmip.org/experiments/informal-experiments; see AMIP polar amplification therein). Contrary to the CFMIP protocol, we completely remove sea ice in both experiments to avoid unrealistic temperature gradients at high latitudes as in Dutta et al. (2021). The ice sheets, vegetation type and phenology are kept the same as in the CTL experiment, but the land surface processes are interactive with the atmosphere in the perturbation experiments.
List of experiments.
SST anomalies (°C) for the Pol10 (blue), Pol20 (red), and Pol10_3K (magenta) experiments.
Citation: Journal of Climate 36, 8; 10.1175/JCLI-D-22-0497.1
We perform a series of additional high GHG experiments with the Pol20 SST and increase the surface level CO2 and CH4 mixing ratios from that of preindustrial (Table 1). CO2 is increased by a factor of 4 and 7 from that of preindustrial in Pol20_4C and Pol20_7C, respectively (Table 1). In another set of sensitivity experiments, the CH4 mixing ratio at the surface is ramped up by 16×, 64×, 128×, and 256× (Table 1) that of preindustrial to see how PSCs scale with CH4. In the GHG perturbation experiments, the WACCM4 chemical model responds to the increase in mixing ratios of CO2 and CH4 at the surface and prognostically calculates the changes elsewhere. While paleoproxies do not provide any direct constraints on CH4 concentrations in past warm climates, the CH4 concentrations in these sensitivity experiments are much larger compared to the range suggested by a model-based study by Beerling et al. (2011) for the early Eocene. However, given the uncertainty in CH4 concentrations, our experiments provide an opportunity to test the impact of potentially large CH4 concentrations.
The Parallel Offline Radiative Transfer (PORT) model of CESM is used to calculate the change in radiative fluxes associated with changes in different components of the atmosphere, in particular tropospheric and stratospheric clouds (Conley et al. 2013). PORT is run in offline mode using the data from the WACCM4 simulations, and diagnoses radiative forcing and heating rates. Radiative effect is usually calculated by assuming that the dynamical heating of the stratosphere remains constant in response to surface warming or GHG emissions (Fels et al. 1980; Kiehl and Boville 1988; Ramaswamy et al. 2001). Under this assumption, stratospheric temperatures are adjusted to a new radiative equilibrium. However, we are interested in understanding the radiative impact associated with stratospheric changes; therefore, we disable the stratospheric temperature adjustments in PORT. Radiative forcing is calculated using the following steps:
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We output the high-frequency instantaneous radiation every 73 model time steps (1 time step = 30 min) for different WACCM4 experiments (CTL, Pol20, Pol20_4C_64M, Pol20_4C_128M, Pol20_4C_256M in Table 1). This sampling frequency is chosen as it provides a good balance between data size, accuracy and enables even sampling of all seasons (Conley et al. 2013).
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Using the radiation data from step 1, we perform offline radiation calculations in PORT and compare the TOA and surface radiative fluxes obtained from PORT to that of the sampling data for the CTL experiment. PORT outputs match with the sampling data obtained in step 1, which assures proper functioning of PORT.
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To isolate the radiative effect of individual variables, we replaced the Pol20 value of the variable under investigation in PORT with the associated value from different experiments. For example, the radiative impact of stratospheric clouds in Pol20_4C_64M (Table 1) is computed by replacing the cloud variables between 100 and 1 hPa from Pol20 with those from Pol20_4C_64M and processing this dataset with PORT. The cloud radiative variables replaced in these experiments are cloud fraction, cloud water and ice content, optical depth, and emissivity. We integrated the offline radiation code for the last 5 years and the output of last 4 years are used in the analysis here.
Previous works have shown that radiative forcing calculated using the forward partial radiative perturbation (PRP) by itself (a one-sided PRP technique) may not tell the whole story as the changes in radiative fluxes associated with different variables are sensitive to the background climate (Soden et al. 2008; Colman and McAvaney 1997). Therefore, a “reverse PRP” calculation is also performed. In this case the base state is from the perturbed experiments. Variable values are then individually set to their Pol20 values and the associated radiative change calculated using PORT. Use of the Pol20 series of experiments with the same prescribed SST ensures relatively small dependence on background climate of forward and backward PRP calculations.
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Finally, we compare the PORT outputs obtained from step 3 to those of step 2 to obtain the radiative effect of different variables.
A large PSC change only occurred in the high CH4 experiments (64×, 128×, and 256× preindustrial) and we therefore use PORT only in these experiments.
3. Polar dynamics
a. Background
The transformed Eulerian-mean (TEM; for details refer to the methods in the supplemental document) residual circulation is used as a proxy to understand the strength of the BDC (Andrews and McIntyre 1978, 1976). Additionally, planetary-scale stationary waves generated in the troposphere propagate to the stratosphere in the presence of zonal westerlies, a process that can be diagnosed with the help of Eliassen–Palm (EP) fluxes (Tanaka et al. 2004; Eliassen and Palm 1960; Andrews et al. 1983). Propagation of waves into the stratosphere is sensitive to the distribution of zonal mean zonal wind, its vertical shear and the stability of the atmosphere (Chen and Robinson 1992; Liu et al. 2014; Hu et al. 2014; Li et al. 2007). Breaking of planetary waves in the stratosphere decelerates the zonal winds and dissipation of energy warms the stratosphere (Mcintyre and Palmer 1983). Wave-breaking is characterized by convergence of EP fluxes. We have followed the procedure of Jucker (2021) to compute and scale the EP flux vectors. Positive values of omega (in Pa s−1) and streamfunction denote downwelling and clockwise circulation, respectively.
b. Impacts of SST gradients
We examine the changes in the Arctic polar vortex (between 100 and 1 hPa) resulting from the polar-amplified SSTs and test if these changes increase PSC formation. The meridional SST gradient directly affects the tropospheric temperature gradient and hence the midlatitude jet, which in turn changes the BDC and influences the vertical structure of stratospheric zonal wind via the thermal wind balance (∂zu ∝ −∂yT) (Figs. 3a–c). In DJF, the strong westerly winds form the boundary of the NH polar vortex (Figs. 3a–c). With a weaker SST gradient in Pol10 and Pol20, the tropical lower stratosphere warms while the Arctic cools compared to CTL (Figs. 3b,c). This increased mid- to high-latitude meridional temperature gradient in the stratosphere causes a strengthening of zonal wind shear in the Arctic stratosphere (Figs. 3b,c). Zonal winds are stronger in Pol20 (Fig. 3c) than in Pol10 (Fig. 3b) due to a stronger meridional temperature gradient in the stratosphere in Pol20.
(a)–(c) DJF zonal mean stratospheric temperature (shading; K) and zonal wind (contours; m s−1), and (d)–(f) TEM residual streamfunction (contours; 109 kg s−1) and vertical velocity (shading; Pa s−1). Positive values of vertical velocity and streamfunction denote downwelling and clockwise circulation, respectively. (g)–(i) EP flux vectors (arrows; m s−1 day−) and EP flux divergence (shading; m). The results of (top) CTL, (middle) Pol10 minus CTL, and (bottom) Pol20 minus CTL. Stippling in (b), (c), (e), (f), (h), and (i) shows regions where the temperature, vertical velocity, and EP flux divergence differences are statistically significant (p < 0.05, based on n = 40 winters; calculated using a two-sided t test).
Citation: Journal of Climate 36, 8; 10.1175/JCLI-D-22-0497.1
A reduction in BDC strength supports the temperature and zonal wind changes in the stratosphere in Pol10 and Pol20 (Figs. 3e,f). Poleward of 60°N, Pol20 has a larger EP flux reduction (Fig. 3i) and stronger cooling in the Arctic stratosphere (Fig. 3c) compared to Pol10 (Figs. 3b,h). TEM momentum equation [Eq. (S2) in the supplemental material] relates the changes in the BDC to EP fluxes. The differences in the EP flux (Figs. 3h,i) are consistent with the zonal wind changes in Pol10 and Pol20 (Figs. 3b,c), that is, stronger westerlies in the Arctic vortex are associated with a larger reduction in upward wave propagation poleward of 60°N in Pol20 (Fig. 3i). Earlier studies show that stationary waves are refracted by low values of the refractive index (Simpson et al. 2009; Matsuno 1970; Charney and Drazin 1961). In Pol10 and Pol20 changes in the EP fluxes are consistent with those of refractive index such that waves propagate away from regions of low refractive index toward high refractive index see Fig. S3 in the online supplemental material). The overall similarity in the BDC strengths between Pol10 and Pol20 (Figs. 3e,f) can be explained by the stronger upward EP fluxes from the troposphere in the midlatitudes in Pol20 (Fig. 3i), which compensate for the weaker EP fluxes poleward of 60°N. These stronger upward EP fluxes also explain the negative zonal wind anomalies in Pol20 around 30°N (Fig. 3c).
In summary, a reduced equator-to-pole SST gradient reduces BDC strength and upward propagation of planetary waves in Pol10 and Pol20. These changes strengthen the Arctic vortex by cooling the stratosphere and strengthening the zonal westerlies, thereby providing favorable conditions for PSC formation. The resulting changes in PSCs and their radiative impact are investigated in section 5.
c. Impacts of GHGs and uniform surface warming
Past equable climates are characterized by higher GHGs and warmer SSTs than preindustrial (Tierney et al. 2020; Zachos et al. 2007; Rae et al. 2021; Zachos et al. 2003; Hollis et al. 2019), which impact BDC strength (Lin and Fu 2013; Chiodo and Polvani 2017; Chrysanthou et al. 2020; Butchart 2014; Xie et al. 2020), thereby changing the thermodynamic state for PSC formation in the Arctic. Here, we isolate the relative roles of higher GHGs and warmer SSTs on BDC strength and PSC formation.
To understand the impact of higher tropical SSTs without changing the meridional SST gradient, a globally uniform 3 K SST anomaly is added to the Pol10 SST. This enables us to separate the relative roles of higher GHGs and warmer SSTs from the meridional SST gradient on BDC strength. The additional uniform warming results in a very weak cooling in the tropical stratosphere and a warming poleward of 60°N compared to Pol10 (Figs. 4a,c). The zonal winds weaken poleward of 60°N (Fig. 4c) indicating a weaker Arctic vortex in the warmer simulation (Fig. 4a). Stronger westerlies in the midlatitudes (Fig. 4c) allow for more upward propagation of planetary waves (Fig. 5c), which in turn intensifies the BDC. The stronger BDC explains the cooler tropical stratosphere, the warming of the Artic stratosphere and the weaker polar vortex. This indicates one possible reason why the response of the Arctic vortex in Korty and Emanuel (2007) is similar to Pol10_3K, but in the opposite direction to Pol10 and Pol20 despite a similar experimental setup. Korty and Emanuel (2007) impose high tropical SSTs (between 28° and 30°C) with an equator-to-pole temperature gradient of approximately 10°C. By comparing their result with Pol20_3K, we speculate that the high tropical SST in Korty and Emanuel (2007) might cause increased upward wave propagation and BDC strengthening, leading to an increase in wave breaking in the stratosphere thereby warming the Arctic stratosphere.
DJF zonal mean stratospheric temperature (shading) and zonal wind (contours) in (a) Pol10 minus CTL, (b) Pol20 minus CTL, (c) Pol10_3K minus Pol10, (d) Pol20_4C minus Pol20, and (e) Pol20_4C_64M minus Pol20. Stippling in the plots shows regions where temperature differences are statistically significant (p < 0.05).
Citation: Journal of Climate 36, 8; 10.1175/JCLI-D-22-0497.1
DJF mean EP flux vectors (arrows) and EP flux divergence (shading) in (a) Pol10 minus CTL, (b) Pol20 minus CTL, (c) Pol10_3K minus Pol10, (d) Pol20_4C minus Pol20, and (e) Pol20_4C_64M minus Pol20. The differences in EP flux divergence are statistically significant (p < 0.05).
Citation: Journal of Climate 36, 8; 10.1175/JCLI-D-22-0497.1
High GHG concentrations may directly impact the Arctic PSCs through radiative cooling in the stratosphere. Additionally, higher CH4 concentrations result in larger water vapor concentration in the stratosphere, also increasing the potential for PSC formation. With the increased GHGs prescribed in Pol20_4C and Pol20_4C_64M, the upper stratosphere cools by more than 15 K at all latitudes (Figs. 4d,e) and the zonal winds in the Arctic vortex weaken progressively with increases in the prescribed GHG concentration (Figs. 4d,e). An increase in upward propagation of waves into the stratosphere causes a convergence of EP flux (Figs. 5d,e) and decelerates the zonal winds in the Arctic vortex (Figs. 4d,e). However, the radiative cooling in the polar stratosphere dominates over the dynamic heating associated with BDC strengthening in the GHG sensitivity experiments. This large radiative cooling in the stratosphere leads to a global increase in stratospheric ozone (supplemental Fig. 1) and is in agreement with Winterstein et al. (2019), who show an increase in total column ozone exceeding 40% in response to a fivefold CH4 increase from that of present day. The cooling in the stratosphere is quite uniform at all latitudes in our experiments, which suggests that GHG-induced radiative cooling plays a more important role than dynamic heating in the Arctic stratosphere (Fig. 4), which would produce opposite changes at low and high latitudes. Note that the stratospheric ozone response depends on the background state, and the use of modern-day conditions in CTL instead of preindustrial might have resulted in a colder stratosphere due to ozone depleting substances.
In summary, radiative cooling associated with increased GHGs plays a dominant role over dynamic heating and causes Arctic stratospheric cooling in the enhanced GHG experiments. Conversely, an increase in tropical SST has an opposite effect and causes a weak warming of the Arctic stratosphere via BDC strengthening.
4. Changes in water vapor transport
Changes in the tropical cold-point temperature influence the amount of water vapor entering the tropical stratosphere and therefore regulate the amount of water vapor transported into the Arctic. In Pol10 and Pol20 the tropical mean cold-point temperatures increase by more than 3 K (area weighted average between 10°N and 10°S) compared to CTL (Fig. 6). While other factors such as tropical deep convection, aerosol concentrations and modes of climate variability (Sherwood 2002; Zhou et al. 2017; Hardiman et al. 2015) play important roles in tropical cold-point temperature change, observational studies show a direct link between BDC strength and cold-point temperature in the tropics (Han et al. 2017; Randel et al. 2006). Warming of tropical cold-point temperature (Fig. 6) with BDC weakening (Figs. 3e,f) in Pol10 and Pol20 support these findings. Warmer tropical cold-point temperatures increase the water vapor concentration transported into the stratosphere in Pol10 and Pol20 (Fig. 7). In Pol20 there is an approximately 100% increase in water vapor concentration near the tropical cold point in SON and DJF compared to CTL (Fig. 7). Consequently, water vapor concentration in the Arctic stratosphere increases (Fig. 7).
Zonal mean cold-point temperature in SON and DJF in the different experiments. Colors of the lines indicate different SST experiments with black, green, red, and blue representing the CTL, Pol10, Pol10_3K, and Pol20, respectively. Circular and triangular markers show 4× and 7× CO2 concentrations, respectively, with different colored markers indicating different CH4 concentrations.
Citation: Journal of Climate 36, 8; 10.1175/JCLI-D-22-0497.1
Scatter diagram of seasonal mean water vapor mixing ratio at 70 hPa in the Arctic (70°–90°N area weighted mean) vs tropical (10°N–10°S area weighted mean) cold-point mixing ratio. The circular and triangular markers show different SST and CO2 experiments, while the diamond and square markers are related to low (16×) and high (64×, 128×, 256×) CH4 experiments, respectively.
Citation: Journal of Climate 36, 8; 10.1175/JCLI-D-22-0497.1
The additional 3-K SST anomaly globally associated with Pol10_3K, increases the mean tropical cold-point temperature by approximately 1 K compared to Pol10 (Fig. 6) which in turn causes a weak increase in water vapor transport into the tropical stratosphere (Fig. 7). An increase in CO2 alone (Pol20_4C and Pol20_7C) has negligible impact on the tropical cold-point temperature compared to Pol20 (Fig. 6). Conversely, increases in CH4 to 16× and 64× shows a larger effect than any other modifications relative to Pol20. Note that CH4 is increased by a much larger factor than CO2; therefore, we would expect a much larger impact of CH4 in our experiments. Water vapor mixing ratio in the tropics and in the Arctic increases monotonically with increases in CH4 concentration (Fig. 7). This increase in water vapor concentration results partly from the warmer tropical cold-point temperature and CH4 oxidation. However, it is difficult to quantify the individual contributions of cold point temperature and CH4 oxidation as they necessarily happen at the same time. A comparatively lower rate of stratospheric water vapor increase in the 256× experiment suggests an increase in ice cloud content compared to other CH4 sensitivity experiments. In summary, at very high CH4 concentrations, large increases in the stratospheric water vapor content and radiative cooling in the Arctic stratosphere provide favorable conditions for PSC formation.
5. Changes in clouds
a. Cloud fraction
In all polar-amplified experiments, total tropospheric cloud cover increases compared to CTL (Fig. 8). Pol10 and Pol10_3K show the largest increases in low- and midlevel clouds in SON and DJF. In Pol20, high clouds increase more compared to Pol10 and Pol10_3K. GHGs, on the other hand, have negligible impact on tropospheric clouds. WACCM4 uses all the physical parameterizations of CAM4 including cloud microphysics and we see similar changes in tropospheric clouds as Dutta et al. (2021). This indicates that the interaction between the upper troposphere and lower stratosphere has negligible impact on tropospheric clouds. More details about tropospheric cloud changes can be found in Dutta et al. (2021).
Seasonal mean cloud fraction in the Arctic (70°–90°N mean) in different experiments. Color coding and markers are as in Fig. 6.
Citation: Journal of Climate 36, 8; 10.1175/JCLI-D-22-0497.1
While tropospheric clouds show a large response to polar-amplified SSTs, the increase in PSC fraction is very small (<0.002) when only SSTs are changed (Fig. 8). Very low average relative humidity (<5% in SON and <18% in DJF) explains the small amounts of PSCs in these experiments (Fig. 9). While temperatures reduce by more than 15 K in Pol20_4C and Pol20_7C (Fig. 4d), a water vapor mixing ratio lower than 10 ppm (Fig. 7) in the Arctic stratosphere results in very low relative humidity (Fig. 9). Therefore, Arctic PSC fractions remain low in these experiments (Fig. 9). On the other hand, increases in CH4 to 16×, 64×, 128×, and 256× from preindustrial CH4 increase DJF mean PSC fractions in the Arctic by 75×, 241×, 242×, and 290×, respectively, compared to CTL (Fig. 9). In these experiments, PSC formation increases with increases in stratospheric water vapor and decreases in temperature as relative humidity reaches up to 48%. The different simulations show that PSC fraction closely follows a power law with respect to average relative humidity, with a best fit exponent of 2.7 for SON (Fig. 9a) and 3.1 in DJF (Fig. 9b). Note that a power law approximation works well down to a PSC fraction of 10−4 but overestimates PSCs below this.
Scatter diagram of stratospheric volume weighted average (between 100 and 1 hPa) PSC fraction and relative humidity in the Arctic (70°–90°N area weighted mean) in SON and DJF. Curves following y = (0.01 × x)k fits the PSC variation with respect to relative humidity.
Citation: Journal of Climate 36, 8; 10.1175/JCLI-D-22-0497.1
Analysis of DJF mean cloud ice content provides a possible explanation for the relatively small increase in Arctic water vapor mixing ratio in Pol20_4C_256M (Fig. 7). The latitudinal extent and magnitude of cloud ice content increase with increasing CH4 (Fig. 10j). Thus, relatively high stratospheric water vapor content in the 64×, 128×, and 256× experiments result in considerable growth of ice clouds (Fig. 10), and thus promote precipitation and dehydration of the Arctic stratosphere.
(a)–(e) DJF mean cloud fraction, (f)–(j) cloud ice content, and (k)–(o) water vapor concentration in the stratosphere. The color bars at the bottom are shared for (b)–(e), (g)–(j), and (m)–(o). Note the different color bar in (l).
Citation: Journal of Climate 36, 8; 10.1175/JCLI-D-22-0497.1
In summary, a large radiative cooling and water vapor increase result in higher relative humidity in the CH4 sensitivity experiments, thereby causing an increase in Arctic PSCs in SON and DJF. In the next section, we use PORT to understand the radiative impact of these PSC increases and compare them with the impact of changes in tropospheric clouds.
b. Radiative impact of clouds
We use an offline radiative transfer model, PORT, to quantify the local radiative impact of PSCs in the Arctic for the experiments in which PSC fractions are considerably higher than CTL and Pol20 (i.e., Pol20_4C_64M, Pol20_4C_128M, and Pol20_4C_256M). PORT uses WACCM4 outputs to isolate the radiative effect of PSCs as described in section 2a. For comparison we also analyzed the radiation changes due to tropospheric clouds and GHGs (CO2 and CH4) in these experiments.
In CTL, the OLR in the Arctic is approximately 186 W m−2 in SON (Table 2). Despite an increase in total Arctic tropospheric clouds that provides a larger greenhouse effect in Pol20 (Fig. 11a), there is an overall 40 W m−2 increase in OLR (Table 2) compared to CTL. The increase in OLR with polar-amplified surface warming is also seen in the model intercomparison study of Dutta et al. (2021). There is a negligible PSC change in the Pol20 experiment (Fig. 11).
Scatter diagram of the average perturbation in seasonal-mean PORT-processed OLR in different simulations associated with the cloud properties and individual GHGs obtained from forward and reverse PRP in (a) SON and (b) DJF. The top and bottom x axis in each panel show the volume weighted average (between 100 and 1 hPa and between 1000 and 100 hPa for stratospheric and tropospheric clouds, respectively) cloud fraction and the GHG mixing ratio (between the surface and the TOA) in the Arctic (between 70° and 90°N), respectively. (c),(d) As in (a) and (b), but for surface downwelling LW radiation. Different markers are associated with tropospheric clouds, PSCs, and GHGs, with colors showing the different experiments. The error bars extend between forward and reverse PRP. Note the difference in scale for the y axis between seasons.
Citation: Journal of Climate 36, 8; 10.1175/JCLI-D-22-0497.1
All-sky OLR and net surface LW radiation (W m−2) associated with different experiments from WACCM4.
The TOA radiative cooling reduces in high GHG experiments compared to Pol20 due to increases in PSCs, direct radiative effect of CH4, and CO2 (Figs. 11a,b). An increase in CH4 to 64× to 256× of preindustrial levels and CO2 to 4× preindustrial levels in addition to Pol20 SST reduces the OLR by 17–25 W m−2 in SON (Table 2), with a gradually increasing contribution from the greenhouse effect of PSCs (Fig. 11). Approximately 30% of the reduction in OLR in the 128× and 256× CH4 experiments results from PSCs, with the remainder mostly arising from a combination of the greenhouse effects of CH4 and water vapor, with a relatively smaller contribution from tropospheric clouds (Fig. 11). While the volume weighted mean tropospheric cloud fraction is similar in the 64× to 256× CH4 experiments, the greenhouse effect of tropospheric clouds increases (Fig. 11). This is associated with an increase in cloud fraction between 150 and 100 hPa (Fig. 8). Increases in cloud-top height (Fig. 8) indicate a reduction in average cloud-top temperature, which lowers the OLR in these experiments compared to Pol20 (Fig. 11).
The net surface LW radiation is the difference between upwelling and downwelling surface LW radiative fluxes. Despite an increase in surface downwelling LW radiation due to tropospheric clouds and PSCs (Fig. 11c), there is an increase in net surface LW radiation of 4 W m−2 (Table 2) in Pol20 compared to CTL in SON. With higher GHGs, the surface downwelling LW radiation increases which lowers the net surface LW radiation (Table 2). The impact of PSCs on surface downwelling LW radiation increases with increases in CH4 concentration (Fig. 11c). Similar changes in the TOA and surface LW radiation due to PSCs are also evident in DJF (Fig. 11b).
Using two-way PRP analysis, we show that changes in LW radiation due to PSCs reinforce the imposed polar-amplified SST in our experiments. These results suggest that the direct radiative effect of CH4 is similar to the indirect effect resulting from a change in PSCs. Despite this, the combined greenhouse effect of PSCs and tropospheric clouds is not large enough to counterbalance the increase in OLR caused by changes in other atmospheric variables in particular the temperature change associated with polar-amplified SSTs. In our simulations GHGs play important role in PSC changes while tropospheric clouds are mainly influenced by SST.
6. Discussion and conclusions
Previous modeling studies suggest that an increase in wintertime Arctic PSCs might be able to reduce the Arctic warming gap between proxy data and climate models for past warm climates like the early Cretaceous and Paleogene periods (Sloan and Pollard 1998; Sloan et al. 1992; Kirk-Davidoff and Lamarque 2008). In this study we investigated this possibility with an interactive chemistry–climate model (WACCM4) using a set of GHG sensitivity experiments and idealized polar-amplified warming scenarios.
The schematic in Fig. 12 summarizes the interconnected physical processes expected to be important for PSC formation in a warmer-than-pre-industrial climate with high GHGs. Note that this study is not able to quantify every mechanism indicated in the schematic. Based on WACCM4 simulations, the major findings of this study are as follows:
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The strength of the BDC is sensitive to the imposed boundary conditions.
The schematic highlights dynamical and radiative processes in the stratosphere for warmer-than-modern-day climates characterized by polar-amplified SST and high GHG concentrations. The flowchart summarizes the processes important for PSC formation with red and blue arrows showing positive and negative feedbacks, respectively. The dotted arrows show physical processes that have a smaller impact.
Citation: Journal of Climate 36, 8; 10.1175/JCLI-D-22-0497.1
Enhanced polar amplification reduces the BDC strength, while a uniform increase in global temperature or GHG concentration tends to increase it. Strengthening of the BDC is caused by an increase in planetary wave breaking in the stratosphere, while a reduction in wave breaking weakens the BDC. Additionally, previous studies suggest a reduction in the BDC strength associated with changes in planetary-scale waves in response to sea ice loss in the Arctic (Kim et al. 2014; Chripko et al. 2021; Sun et al. 2015). Therefore, care must be taken when designing numerical experiments aiming at isolating the causes of BDC changes in past warm climates. Differences in boundary conditions such as a warmer global mean temperature, different GHG concentrations, and/or different geographic configurations may have resulted in a strengthening of the BDC in previous studies (Korty and Emanuel 2007; Szopa et al. 2019; Unger and Yue 2014). Additionally, unavailability of paleoproxies make it difficult to constrain the BDC changes in deep past climates.
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Radiative processes are more important than dynamical processes in changing stratospheric temperature in the Arctic
An increase in planetary wave breaking in the stratosphere increases the dissipation of energy thereby warming the Arctic stratosphere in the GHG sensitivity experiments. However, radiative cooling plays a more important role than this BDC-induced heating when we increase GHG concentrations in our simulations.
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PSCs increase with more stratospheric water vapor or reduced stratospheric temperature
Previous studies show that an increase in water vapor concentration increases the persistence and frequency of PSCs in the Arctic (Khosrawi et al. 2016; Tabazadeh et al. 1994). We come to a similar conclusion that in addition to radiative cooling, a large increase in water vapor concentration is necessary for a large increase in relative humidity that increases PSC cover in the Arctic. Increases in PSCs closely follow a power law with respect to average relative humidity in our experiments (formula given in section 5a).
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Large increases in water vapor concentration from CH4 oxidation are necessary to produce significant PSC cover in the Arctic
Water vapor enters the stratosphere in the tropics and is transported to the Arctic by the BDC. In the tropics, the troposphere-to-stratosphere water vapor flux is determined by the cold-point temperature, which itself is influenced by several factors including the BDC upwelling strength, GHG concentrations and tropical SSTs. A stronger BDC adiabatically cools the tropical cold-point temperature thereby reducing the water vapor entering the stratosphere, while increases in GHGs and SST increase the cold-point temperature. In our high CH4 experiments, we saw a small increase in troposphere-to-stratosphere water vapor transport which is then transported to the mid and high latitudes via the BDC. We found that water vapor in the Arctic stratosphere increases monotonically with increases in CH4 concentration. However, we could not isolate the separate roles of the BDC and CH4 oxidation in changing the water vapor concentration.
Increasing CH4 by factors of 16–256 from that of the preindustrial value results in increases in DJF mean PSC fractions in the Arctic by factors of 75–290, respectively, compared to preindustrial in our experiments. The PSC changes in our experiments build on the findings of Kirk-Davidoff and Lamarque (2008) who used WACCM3 forced with Permian–Triassic (∼251 million years ago) boundary conditions. We find an approximately exponential increase in Arctic PSC cover with increasing CH4 concentrations in boreal autumn and winter. Although our results support the hypothesis of Sloan and Pollard (1998) that PSCs increase with an increase in CH4 concentration, the maximum PSC cover in SON/DJF is much lower than the imposed 100% fractional coverage implemented by Sloan and Pollard (1998) in their numerical experiments.
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PSCs reduce OLR loss in the elevated CH4 experiments
Polar amplification with preindustrial GHG concentrations results in a large increase in OLR, which then reduces again with increasing CH4 concentrations. We computed OLR changes due to PSCs and individual GHG by changing them one at a time with an offline radiation model. The greenhouse effect of PSCs accounts for approximately 30% of the reduction in OLR relative to Pol20 in our high CH4 experiments (128× and 256× preindustrial), which is similar to the direct radiative effect of CH4. While increase in CO2 concentration accounts for approximately 20% of OLR reduction, the rest is caused by a combination of the greenhouse effect from the increases in water vapor and tropospheric clouds due to the CH4 increase.
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PSC changes simulated here might play an important role, but more work is required to reduce the uncertainty in CH4 concentrations during past warm climates
With the two-way PRP technique we show that greenhouse warming effects of PSCs increase in a polar-amplified climate with increasing CH4 concentrations. A reduction in OLR by approximately 7–8 W m−2 is large enough to be potentially important for the Arctic energy budget and might play an important role in winter polar amplification during past equable climates. However, this reduction in OLR can only be obtained with a large increase in CH4 concentrations. As CH4 levels approach 64× preindustrial, the OLR reduction due to PSCs begin to match the direct radiative forcing due to CH4.
There is to date no proxy-based evidence for CH4 in past warm climates. Estimates from modeling studies suggest a four- to fivefold increase in atmospheric CH4 concentrations in the Eocene compared to preindustrial (Beerling et al. 2009; Bartdorff et al. 2008; Beerling et al. 2011). Beerling et al. (2011) simulated this using a fully coupled ocean–atmosphere–vegetation model, but their chemistry model excludes the stratosphere. Beerling et al. (2009) and Bartdorff et al. (2008)’s estimates are based on a 2D model forced with records of coal basin sedimentation rates. These studies excluded the changes in biogenic emissions of NOx and volcanic organic compounds, oxidation of which influences the abundance of OH− and thus changes tropospheric CH4 concentrations. Neither of these three studies include all the chemical processes needed to accurately evaluate potential changes to the lifetime of CH4. In addition, coal basin sedimentation rates can only be reconstructed at a very low time resolution and with considerable uncertainties. While these studies advanced our knowledge with the best tools and data available, the estimates of past CH4 concentrations are unfortunately still highly uncertain. This shows the importance of constraining the CH4 concentrations during past warm climates.
The results of this study depend on past methane concentrations, and we can therefore neither confirm nor rule out a role for PSCs in past polar amplification. There are also additional limitations to our study that could be addressed by further work. The current study does not take into account differences in geographic configuration and orography in the Eocene (Herold et al. 2014; Xiong et al. 2020) which could have influenced the BDC. The BDC change can directly impact polar stratospheric temperatures, and indirectly change PSC occurrence via modulation of meridional water vapor and methane transport. Finally, other factors not addressed in this study might also play important roles in Arctic warming, such as the presence of fresh, stratified waters in the Arctic Ocean (Miller et al. 2010; Sluijs et al. 2006; Schmitz et al. 1996), different vegetation cover in the northern high latitudes (Loptson et al. 2014; Harrington et al. 2012), and a higher-than-present-day climate sensitivity during past warm climates (Zhu et al. 2019; Anagnostou et al. 2020).
Acknowledgments.
The authors acknowledge the support from the Australian Research Council Centre of Excellence for Climate Extremes (CE170100023). DD acknowledges the support from the Australian Government Research Training Program Scholarship. SCS acknowledges ARC Grant FL150100035. KJM acknowledges ARC Grant DP180100048. Computational resources were provided by the NCI National Facility at the Australian National University, through awards under the Merit Allocation Scheme, the Intersect Allocation Scheme, and the UNSW HPC at NCI Scheme.
Data availability statement.
Model output is available upon request. Analysis was performed with the help of the aostools package available at https://github.com/mjucker/aostools.
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