Responses of Arctic cyclones to biogeophysical feedbacks under future warming scenarios in a regional Earth system model

The cyclone characteristics were assessed based on the simulations with RCA-GUESS (Smith et al 2011, Zhang et al 2014b), which implements a synchronous coupling between the land surface scheme of a regional dynamically-downscaled atmosphere model (RCA4) (Samuelsson et al 2011, 2015) with an individual-based dynamic vegetation and ecosystem biogeochemistry model, LPJ-GUESS (Smith et al 2011, 2014). RCA4 provides the daily air temperature, precipitation, and incoming shortwave radiation to LPJ-GUESS to simulate vegetation competition, succession, and ecosystem biogeochemistry. LPJ-GUESS in turn provides RCA4 with the daily leaf area index and the annual vegetation cover fractions of broad-leaved trees/shrubs, needle-leaved trees/shrubs and grasses/herbaceous vegetation to calculate turbulent fluxes based on adjusted canopy resistance, surface roughness length and grid-averaged albedo. Six plant functional types (PFTs) representing boreal and arctic woody and herbaceous ecosystems were defined and represented in LPJ-GUESS (table S1). Tree PFTs were taken also to represent coverage by Arctic tall and short shrubs, characteristic of extensive shrub tundra. RCA-GUESS was set up and applied to the Coordinated Regional Climate Downscaling Experiment Arctic domain according to the specifications (http://wcrp-cordex.ipsl.jussieu.fr/). The domain (figure S2) covers 150 × 156 rotated longitude-latitude coordinates at a resolution of 0.44° (approximately 50 km) and 40 vertical levels for atmospheric dynamics. Biogeochemical feedbacks are not accounted for in this coupling, but vegetation cover, composition and associated land surface properties were affected by the perturbed CO₂ through 'CO₂ fertilization' in addition to direct climate effects.

The lateral boundary conditions used in RCA-GUESS were based on the CMIP5 simulations with the EC-Earth GCM (Hazeleger et al 2010, 2012) under three RCP scenarios (RCP2.6, 4.5 and 8.5). To disentangle BF, we conducted simulations with and without interactive vegetation–atmosphere coupling, hereinafter denoted as the feedback run (FB) and non-feedback run (NoFB). The coupled model ran two stages of spin-up to give initial states for vegetation structure and composition, carbon pools and climate conditions consistent with 1961–1990 forcing. In the first spin-up stage, the vegetation model was forced for 300 years with observation-based climatology from the CRU TS3.0 data set (Mitchell & Jones 2005) to obtain realistic vegetation distributions and C pools. The second spin-up employed the fully-coupled model based on the initial 30 years of lateral boundary conditions and the starting vegetation and carbon states from the first spin-up stage. Detailed evaluation of RCA-GUESS for the historical (hist) period (1961–1990) was performed by Zhang et al 2014a). NoFB was based on the same procedure as FB, which implemented interactive vegetation dynamics in the land surface scheme for the entire simulation period (1961–2100). Land surface properties of NoFB were fixed by using the state variables averaged from 1961 to 1990 to run the simulation for 1991–2100. Our analysis was based on a comparison of 30 year (2070–2099) time slices of relevant outputs from FB, NoFB and hist period (1970–1999). The difference 'NoFB minus hist' shows the changes due to the RCP scenario under no-feedback conditions, while effects of BF are quantified by 'FB minus NoFB'. The details of how the hist, FB and NoFB runs were designed are illustrated in figure S1. All the outputs were aggregated for spring (March, April and May) and summer (June, July and August) seasons.

The algorithm used to identify cyclones is based on a method by Bardin and Polonsky (2005) and Akperov et al (2007). This method has been further modified for the Arctic regions (Akperov et al 2015). This algorithm has been used in a number of inter-comparison studies dealing with changes in cyclone activities in extratropical and high latitudes (e.g. Neu et al 2013, Ulbrich et al 2013, Simmonds and Rudeva 2014, Akperov et al 2018, 2019).

Based on this approach, cyclone depth, size, and frequency were calculated. Cyclones were identified as low-pressure regions enclosed by closed isobars on 6-hourly maps of mean sea level pressure (MSLP). The size (radius) was defined as the average distance from the geometric center to the outermost closed isobar. The depth was defined as the difference between the pressure in the cyclone geometric center and the outermost closed isobar. The cyclone frequency was defined as the number of cyclone events per season.

To map spatial patterns of cyclone depth, size and frequency, we used a grid with circular cells of a 2.5° latitude radius. To select the robust cyclone systems in the Arctic, cyclones with a size less than 100 km and a depth less than 1 hPa were excluded. All cyclones over regions with surface elevations higher than 1000 m were also excluded from the analysis due to larger uncertainty in the MSLP fields resulted from the extrapolation to the sea level. The significance of differences in cyclone characteristics between 'FB minus NoFB' and 'NoFB minus hist' was determined by the Student's t-test (P < 0.05). More details of this algorithm and its application for detection of the variability and changes in the cyclone activity over the Arctic are provided in previous studies (Akperov et al 2015, Zahn et al 2018).

Baroclinic instability, as the major mechanism for extratropical cyclone genesis, is often used to interpret formation, intensification and persistence of Arctic cyclones in the atmosphere (e.g. Akperov et al 2020, Yanase and Niino 2006). Eady growth rate (EGR) is a frequently used measure of the baroclinic instability (Lindzen and Farrell 1980, Hoskins and Valdes 1990). EGR is defined using the vertical wind shear and the Brunt–Väisälä frequency:

$$\begin{equation}{\text{EGR}} = 0.31f\left| {\frac{{\partial V}}{{\partial z}}} \right|{N^{ - 1}}\end{equation} \tag{ 1 }$$

where f is the Coriolis parameter, N is the Brunt–Väisälä frequency and ∂V/∂z the vertical wind shear. Because of the thermal wind balance, the vertical wind shear is connected to the meridional temperature gradient (∂θ/∂y). Furthermore, the Brunt–Väisälä frequency (static stability) depends on the vertical gradient of the potential temperature (∂θ/∂z)

$$\begin{equation}N = {\left( {\frac{g}{\theta }\frac{{{\text{d}}\theta }}{{{\text{d}}z}}} \right)^{1/2}}.\end{equation} \tag{ 2 }$$

where g is the gravity acceleration and θ is the potential temperature. We used 6-hourly data for wind, temperature, and geopotential height for two levels in the lower atmosphere (500 and 850 hPa) to calculate EGR and explain Arctic cyclone response to climatic warming and BF. These vertical levels have been chosen to cover the lower half of the tropospheric column but above the planetary boundary layer, the top of which is typically around the 850 hPa level in spring and summer.

Impacts of BF on temporal variations of the land-sea temperature difference for RCP2.6, RCP4.5 and RCP8.5 scenarios show contrasting patterns in spring and summer (figure 1). In all the RCP scenarios, BF-induced changes in land-sea temperature difference in spring show a rising trend, leading to a higher temperature difference between land and sea by 1 °C–1.9 °C from 2006 to 2099 (figure 1(a)). The slopes of linear fits of the trends in RCP2.6, RCP4.5 and RCP8.5 are 0.106 °C decade⁻¹, 0.132 °C decade⁻¹, 0.193 °C decade⁻¹ respectively. On the contrary, declining trends in land-sea temperature difference are found in summer. The slopes of linear fits of the trends in RCP2.6, RCP4.5 and RCP8.5 are 0.002 °C decade⁻¹, −0.006 °C decade⁻¹, −0.025 °C decade⁻¹ respectively (figure 1(b)).

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These temporal variations of land-temperature difference can be attributed to the changes in spatial patterns of near-surface air temperature (figure S2) and surface energy fluxes (figures S3 and S4) during spring and summer. The noticeable changes in near-surface air temperature due to BF in spring and summer are well observed across tundra areas of Siberia, northern Canada, Alaska and Scandinavian Mountains (figure S2). In general, BF-induced warming (e.g. most of them occurring in spring) or cooling effects (e.g. most of them occurring in summer) get reinforced in warmer RCPs, although the fraction of the BG-induced warming relative to the anthropogenic warming (i.e. (FB—NoFB)/(NoFB—hist)× 100) becomes smaller. Meanwhile, BF have increased upward (i.e. negative values) latent heat flux for both spring and summer in three RCPs (figure S3), these increased latent heat fluxes are mostly found in the areas where the vegetation changes from herbaceous species to woody species (figures S5(a)–(c)). In spring, the sensible heat flux is downward (e.g. positive values) in tundra areas and upward (i.e. negative values) in forest areas (figures S4(b), (f) and (j)). BF have reduced both upward and downward sensible heat flux (figures S4(a), (e) and (i)). In summer, the sensible heat flux is upward, and BF have decreased upward sensible heat flux or even turned them downward in the warmer RCPs (figures S4(k) and (l)). However, increased upward sensible fluxes due to BF are also found in tundra areas in Siberia for all RCPs (figures S4(c), (g) and (k)). Overall, the abovementioned changes in near-surface temperatures, and surface energy fluxes are associated with the major changes of the normalized physiognomy index (higher values indicate more woody species) and the normalized phenology index changes in three RCPs. For instance, the relative fraction of woody species increased in the taiga-tundra transition zones in the warmer scenarios, indicating that taller shrubs may encroach into the herbaceous tundra (figures S5(a)–(c)). Some deciduous forests (Larix) will be displaced by evergreen coniferous forests (e.g. Pinus) in Siberia (figures S5(d)–(f)).

Figure 2 shows differences of MSLP between FB and NoFB runs for spring and summer during the last three decades for the different RCP scenarios. BF results in statistically significant circulation changes over the land in spring and over the Arctic Ocean in summer (figure 2). These changes become more evident in the RCP8.5 for both seasons. In spring, MSLP shows the largest reduction over the whole Arctic in the RCP8.5 scenario relative to the other two RCP scenarios (figure 2(e)). In summer, MSLP shows a significant increase over the Central Arctic, though this increase emerges already in the RCP4.5 scenario (figure 2(d)). Therefore, in summer the circulation over the Arctic Ocean becomes more anticyclonic/less cyclonic with the opposite occurring in spring in the 'FB' run. The circulation changes for the different atmospheric levels (850, 500 and 300 hPa) for the different RCP scenarios are shown in supplementary figures S6–S8. In spring, there is an increase of the geopotential height over the continents that become more prominent in the higher level. Under the RCP4.5 scenario, additionally, there is a decrease of the geopotential height over the Arctic Ocean. This decrease of the geopotential height over the ocean, coincident with an increase over the continents (dipole structure), is also observed in the RCP8.5 scenario. In summer, the same dipole structure is apparent in the RCP2.6 scenario. This dipole structure is also present under the RCP4.5 scenario, however, its position has changed. In contrast, under the RCP8.5 scenario, the geopotential height changes become positive over the whole Arctic Ocean. It should be noted that the geopotential height changes are partly insignificant, except in summer and under RCP8.5 in both seasons. Thus, we note that most pronounced and significant changes in the atmospheric circulation occur under the RCP8.5 scenario. Therefore, in the following section, we examine the factors that influence the baroclinic instability as triggers for cyclone genesis and growth with the focus on the RCP8.5 scenario results.

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The primary factor for the generation and evolution of cyclones in northern high latitudes is the baroclinicity of the atmosphere. As outlined in section 2.4, a simple parameter measuring the baroclinic instability is the EGR, which depends on both the vertical static stability and the horizontal temperature gradients (through the thermal wind balance) in the atmosphere. Therefore, here we investigate the response of baroclinic instability to BF with focus on the RCP8.5 scenario. Spatial changes 'FB minus NoFB' for the other two RCP2.6 and RCP4.5 scenarios are presented in supplementary figures (figures S9 and S10).

In spring, the RCP8.5 warming scenario under no feedback conditions ('NoFB minus hist') leads to significant changes in EGR (figure 3(a)). EGR mostly increases over Alaska, Beaufort and Barents-Kara Seas, and decreases over the Eurasian continent, Norwegian Sea and over the Arctic Ocean. The static stability (Brunt–Vaisala frequency), which is a component of the EGR, decreases over most areas of the Arctic (figure 3(c)). Wind shear changes are in line with EGR changes (figure 3(e)). The BF ('FB minus NoFB') in spring increases EGR over parts of the Arctic Ocean (figure 3(b)). This is related with concurrent wind shear increase over the Norwegian and Laptev Seas, the Canadian Arctic and parts of the Eurasian continent (figure 3(f)). Further, the BF lead to a significant decrease of the static stability in the Arctic (figure 3(d)), i.e. the feedback triggers a further destabilization initiated by the warming scenario (figure 3(c)). These changes become more evident in the RCP8.5 scenario compared to the other two RCP4.5 and RCP2.6 scenarios (figures S9 and S10). However, the signal is not always statistically significant.

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In summer, the RCP8.5 warming ('NoFB minus hist') effect on EGR (figure 4(a)) is similar strong as in spring. EGR increases over the Arctic Ocean as well as over the American continent (Alaska and Canadian Arctic). However, there are also negative significant changes seen over the Eurasian continent, Central Arctic and over the Barents-Kara Seas. In contrast to spring, the static stability increases everywhere in the Arctic in summer, with the smallest changes over the Barents-Kara seas (figure 4(c)). The wind shear increase follows as in spring, the pattern of the EGR change (figure 4(e)).

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The BF in summer ('FB minus NoFB') lead to an increase of EGR over Siberia and a strong decrease over the Canadian archipelago (figure 4(b)). The static stability is due to the feedbacks further increased over large areas of the Arctic Ocean, but is decreased over Siberia (figure 4(d)). The wind shear changes (figure 4(f)) again follow the regional changes in EGR.

The results show that BF noticeably modify the baroclinicity of the atmosphere in the warmer climate. Consequently, we may expect significant changes in cyclone characteristics in the Arctic in spring and summer due to BF.

Comparing the NoFB to hist runs, the cyclone frequency in the Arctic (>65° N) as a whole increases in both seasons with the greatest and significant changes in summer over the Arctic Ocean in the RCP8.5 scenario (table 1). In spring, the cyclone mean depth and size decrease in the Arctic including the Arctic Ocean. In summer, cyclone depth and size increase in the Arctic. Cyclone depth increase reaches its maximum over the Arctic Ocean in RCP8.5. Comparing the FB to NoFB runs, the cyclone frequency in the Arctic increases in spring and decreases in summer (table 2). Cyclone frequency increases six-fold in the Arctic in the RCP8.5 scenario relative to RCP2.6 in spring. In summer, a decrease in cyclone frequency is observed in RCP8.5. Cyclone mean depth decreases in FB in both seasons. While cyclone mean size decreases in RCP8.5, it increases under RCP2.6, for both spring and summer. Averaged over the Arctic Ocean (>75° N), BF increase the cyclone frequency with the greatest change in the RCP8.5 scenario in spring. In spring, cyclone frequency differences vary from +1% (RCP2.6) to +10% (RCP8.5). Impacts of BF on temporal variations of the cyclone characteristics for the Arctic Ocean (>75° N) and Arctic (>65° N) for different RCP scenarios in spring and summer are shown in supplementary figure S11. Impact of BF on cyclone characteristics are evident, and the impact varies depending on RCP scenario. The Arctic wide changes (tables 1 and 2) are aggregated from the regionally different changes in cyclone characteristics, and thus may not be representative for individual subregions. Therefore, it is important to highlight the regional pattern of cyclone changes.

The spatial differences in the cyclone characteristics for 'NoFB minus hist' and 'FB minus NoFB' under RCP8.5 scenario for spring and summer are shown in figures 5 and 6. The figures show that the regional changes due to BF can be of a similar order as those due to the warming scenario, but the regional patterns are different. Under no feedbacks, the RCP8.5 leads in spring to a cyclone frequency increase over large areas in the Arctic. A decrease of cyclones is found over Barents-Kara Seas, in some parts of Canadian archipelago and western Siberia (figure 5(a)). Cyclone mean depth and size increase over the Beaufort and Chukchi Seas and eastern Siberia (figures 5(c) and (e)). BF lead to increase in the cyclone frequency over large parts of the Arctic Ocean and over Alaska and Yakutia, which become stronger in the RCP8.5 scenario (figure 5(b)), compared to RCP4.5 and RCP2.6 (figures S13(a) and S14(a)). A small decrease of cyclone frequency is seen over the Kara Sea and Canadian archipelago in all three RCP scenarios and additionally over the Norwegian Sea and eastern Yakutia (figure 5(b)). For cyclone mean depth and size, the BF can also lead to changes of comparable magnitude as the warming scenario, but with a counteracting effect in a few regions (figure 5). This is most prominent in the Beaufort and Chukchi Seas, where the RCP8.5 lead to an increased cyclone depth and size, but the BF work against it.

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In summer, the cyclones increase over large parts of the Arctic Ocean in NoFB compared to hist runs under the RCP8.5 scenario (figure 6(a)), with stronger increase compared to spring. A decrease of cyclone frequency is observed over the continents. Similar patterns are seen for cyclone mean depth and size changes (figures 6(c) and (e)).

Spatial behavior of cyclone characteristics changes is quite patchy in the FB run in all three RCP scenarios (figures 6(b), S13 and S14). However, the overall cyclone frequency decreases in the Arctic including the Arctic Ocean in the RCP8.5 scenario (as shown in table 2). Like in spring, the changes in cyclone frequency, depth and size due to BF can be of similar order as those by the RCP8.5 warming scenario for specific regions with potential counteracting effects. The increased cyclone frequency, depth and size in the western Arctic Ocean due to RCP8.5 is reduced due to BF. In general, the spatial changes in cyclone size due to BF are in line with the changes of cyclone mean depth, i.e. reduced cyclone depth is accompanied with reduced cyclone size and increased depth goes hand in hand with increased size in summer.

There is a significant correlation between changes in cyclone characteristics and changes in land-sea thermal contrast induced by BF (figure S12). Importantly, its spatial patterns agree with those of changes in cyclone characteristics 'FB minus NoFB' particularly in spring (figure 5). This confirms that the mechanism through which the changes in cyclone characteristics are triggered by BF are associated with the changed land-sea thermal contrast. The latter is related to changes of baroclinic instability (EGR). The results show that changes in cyclone characteristics triggered by BF in both seasons are in line with changes in EGR over some areas but not all. In case of cyclone frequency, increase of EGR may lead to increased cyclone frequency, which is seen in parts over the Arctic Ocean in spring and summer and over continental regions in summer. As noted in Day and Hodges (2018b), increase of sea-land thermal contrast may lead also to increase of cyclone intensity (i.e. cyclone depth). Accordingly, over some areas the cyclone mean depth changes are in line with changes in EGR (and wind shear) in RCP8.5 (figures 3–6), such as over the Laptev Sea and north of the Canadian archipelago in spring and over the central Arctic Ocean and Laptev Sea in summer.