CMIP6 Projected Trend of Winter and Summer Variation in Arctic Cyclones over the 21st Century

doi:10.21203/rs.3.rs-4845760/v1

Download PDF

Research Article

CMIP6 Projected Trend of Winter and Summer Variation in Arctic Cyclones over the 21st Century

https://doi.org/10.21203/rs.3.rs-4845760/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

Using the simulation results from the CMIP6 global climate models, we calculate the projected changes of different kinds of Arctic cyclones (ACs)in the 21st century and examine the characteristics related to the Arctic cyclones under two shared socio-economic pathways (SSP1-2.6, SSP5-8.5). There is a significant decline of ACs during winter over southern Greenland, the Barents Sea, and the Gulf of Alaska. The number of Arctic cyclones has a significant circular decrease along the Arctic over most of the continent region in summer. By the end of the 21st century, the proportion of weaker, large-radius, and long-lifespan ACs will increase. The number of extreme Arctic cyclones will decrease in the future. However, the trend in the intensity of Arctic cyclones depends on the measure of cyclone intensity we use. Weaker baroclinic instability in the future is the primary reason for the decline of cyclone density in winter, but the situation in summer is more complicated. The number of Arctic cyclones in summer is affected by various factors like tropopause polar vortex and mid-latitude cyclones entering the Arctic. The positive anomaly of Eady growth rate can also cause the explosive growth of cyclones over the ocean.

CMIP6

Arctic cyclone

Global climate models

Cyclone tracking algorithm

The purpose of this study is to better understand how the different kinds of Arctic cyclones will change in the new CMIP6 models and contribute to understanding the range of possible futures and knowledge about the complex interactions between climate, emissions, and weather patterns, with the increase in the emission concentration of greenhouse gases from human activities. The melting of Arctic sea ice could potentially give rise to an upsurge in commercial shipping and the exploitation of resources along two routes traversing the Arctic Ocean. So it's important to explore future extreme changes in Arctic cyclones. This paper described that under the extreme greenhouse gas emissions, the proportion of long lifespan, deep and large radius of Arctic cyclones will increase in the future. Research on future trends of Arctic cyclones can be essential for comprehensive risk assessment, policy planning, and long-term preparedness. This research is highly valuable both in terms of addressing climate change and in disaster prevention and mitigation efforts.

Arctic cyclones play a significant role in the Arctic climate system, aiding the meridional transport of atmospheric heat and moisture from the mid-latitudes and influencing wind speed, temperature, precipitation, and sea ice distribution in the Arctic region (Crawford et al., 2017; Graham et al., 2019; Lukovich et al., 2021). In recent decades, the Arctic region has experienced twice the global warming rate, leading to an unprecedented reduction in the Arctic sea ice extent. This phenomenon has not only affected the large-scale atmospheric circulation and weather patterns in the middle and high latitudes of the Northern Hemisphere (Vihma et al., 2014; Semenov and Latif, 2015; Serreze and Meier, 2019; Schreiber and Serreze, 2020; Simmonds and Li, 2021), but has also impacted various open-air activities such as shipping, oil and gas exploitation, tourism, and fisheries, all of which are subject to weather conditions, including the impact of Arctic cyclones (Khon et al., 2017). The influence of climate change on the characteristics of cyclone activity is complex and depends on many dynamic and thermodynamic processes (Mokhov et al., 1992; Inoue et al., 2011; Akperov and Mokhov, 2013; Valkonen et al., 2021), highlighting the importance of investigating a range of possible future changes and their associated risks and opportunities in the characteristics of Arctic cyclones.

The physical processes simulated by the new-generation global climate models participating in the Coupled Model Intercomparison Project-Phase 6 (CMIP6) are more complex, compared to the models used in previous inter-comparison projects. The CMIP6 is dominated by earth system models that incorporate carbon and nitrogen cycle processes, and many models have achieved bi-directional coupling of atmospheric chemical processes and coupling with ice caps and permafrost, making it possible to use climate models to study future changes in cyclones. Moreover, the underestimation of cyclones in the Northern Hemisphere still exists, but there is an overall enhancement in the performance of the CMIP6 models when compared to the CMIP5 ensemble, and the number of climate models used to study cyclone variability has increased (Eyring et al., 2016; Andrews et al., 2019; Kawai et al., 2019). In general, CMIP6 provides a new and powerful method for assessing the evolution of Arctic cyclone characteristics under the influence of climate change.

Using the CMIP3 model ensemble from the SRES-A1B scenario, Lang and Waugh (2011) found that by 2100, the number of cyclones in the Norwegian Sea decreases significantly in winter, increases in the Barents Sea, Baffin Bay, Davis Strait, and near the southern of Greenland, and does not change significantly in summer. Colle et al. (2013) also used simulation results from CMIP5 global climate models under different RCP scenarios to project a reduction in the number of cyclones in the western Atlantic Ocean. In their study conducted by Akperov et al. (2015), HIRHAM regional climate model simulations were utilized to investigate the impact of the SRES-A1B anthropogenic scenario on cyclone frequency during the twenty first century, the results revealed a lack of significant decrease in cyclone occurrence during the cold seasons. (Preiestley et al., 2022) showed a reduction of cyclones of ~5 % by the end of the 21st century using CMIP6 models. However, some papers suggest that an increase in the number and intensity of cyclones entering the Arctic in the future (Orsolini and Sorteberg, 2009; Nishii et al., 2015). Day et al. (2018) used the CESM1-CAM5 climate model to predict Arctic cyclone patterns, also suggesting that cyclones will increase in Arctic summer. It is widely hypothesized that global warming will result in a decrease in the total number of cyclones in the Northern Hemisphere, as well as a poleward shift in their distribution (Zappa et al., 2013a; Chang et al., 2012; Sinclair et al., 2020; Priestley et al., 2022). However, the response of the cyclone to global warming is still uncertain due to deficiencies in climate models and the variations among different models in their predictions for the future (Ulbrich et al. 2009; Zappa et al. 2013; Chang et al. 2013; Feser et al. 2015). Collectively, there's still controversy over changes in Arctic cyclones. Most of the papers study the variation of mid-latitude cyclones, and there are still fewer analyses of Arctic cyclones. Due to the different mechanisms of occurrence and development of different Arctic cyclones, a comprehensive study of the different types of Arctic cyclones under the CMIP6 model has not yet been conducted.

In addition, Neu et al. (2013) suggested that statistics for extreme cyclones are more robust. However, there is disagreement in the variability of extreme cyclones, with some studies suggesting an increase (Vavrus 2013; Pfahl et al., 2015; Sinclair et al., 2020; Dolores-Tesillos et al., 2021; Priestley et al., 2022) and some a decrease in the number of cyclones (Chang et al 2012; Zappa et al., 2013b; Michaelis et al., 2017; Day et al., 2018). These differences may depend on the choice of model, the definition of a strong cyclone and the location of the region (Ulbrich et al., 2009, Chang et al., 2014; Catto et al., 2019). As for the intensity of cyclones, there is disagreement as to changes in the intensity of cyclones (Ulbrich et al., 2009; Chang et al., 2012), with different definition of intensity. The intensity of Cyclones are projected to decrease in the NH (North Hemisphere) (Zappa et al., 2013b; Chang et al., 2013; Colle et al., 2013), some are projected increase in NH (Mizuta et al., 2011; Priestley et al., 2022). Therefore, it is necessary to use different strength metrics to comprehensively evaluate the changing characteristics of Arctic cyclones.

There is considerable uncertainty regarding how Arctic cyclone characteristics will evolve in the future and what factors will be responsible for these changes due to the many influencing factors that are challenging to quantify ( Akperov and Mokhov, 2013; Shaw et al., 2016), such as changes in baroclinicity, horizontal temperature gradients, vertical stability and latent heat release (O’Gorman and Schneider, 2008; Pfahl et al, 2015). Some studies have suggested that an anomaly in the surface temperature gradient and the height of the tropopause due to polar warming could lead to a significant variation in all predicted cyclone activity (Butler et al., 2011; Chen et al., 2008; Chang et al., 2013; Lorenz and DeWeaver 2007). Orsolini and Sorteberg (2009) argued that the magnitude of response to cyclonic activity is closely related to the changes in the zonal mean winds and surface temperature gradient along the Eurasian coastline. Zappa et al. (2014) suggest that blocking has been shown to be a major influence on both the North Atlantic and North Pacific storm tracks. An increase in lower-tropospheric Potential vorticity (PV) in midlatitude cyclones has been detected in idealized model simulations (Sinclair et al, 2020). An adiabatically induced low-level PV increases contributes to an enhanced poleward motion of cyclones as the climate warms (Tamarin and Kaspi, 2017). Nevertheless, it is not clear how PV changes link to the change of the Arctic cyclone in the future. Valkonen et al. (2021) found that a negative correlation between the number of winter cyclones and sea ice concentration. Koyama et al. (2017) found no correlation between fall sea ice extent and cyclone intensity and frequency. Thus far, the impact of declining Arctic ice on Arctic cyclone trends is a topic of extensive discussion. The relationship between fluctuations in Arctic Sea ice and cyclones, along with their interaction with global warming, is a multifaceted issue (Clancy et al., 2022). Also, there is a strong positive correlation between cyclone activity and large-scale circulation, such as Arctic Oscillation (AO), North Atlantic Oscillation (NAO) (McCabe et al., 2001; Wei et al ., 2017). These factors do not change monotonically with warming, and so there are still uncertainties around the precise impact (Catto 2019). Therefore, understanding the variability of storms in the Arctic is important for the proper management of the risks associated with these events in a changing climate system.

Collectively, with the opening of the Arctic waterway, scientists are increasingly interested in the variation features of Arctic cyclones. To address the uncertainties above, this study aims to use the new CMIP6 models to project the characteristics of different kinds of Arctic cyclones in winter and summer across two future climate scenarios contribute to understanding the range of possible futures and knowledge about the complex interactions between climate, emissions, and weather patterns. The guiding questions are as follows:

How will the characteristic variables (e.g. frequency, intensity, radius, lifespan and so on) associated with Arctic cyclones change in the future and what regional variations can be observed in the changes to seasonal Arctic cyclones?
Will there be consistency in the variability of different definitions of extreme Arctic cyclones and different types of Arctic cyclones?
How do changes in the large-scale circulation affect Arctic cyclone variability?

a. Datasets

The simulation data used in this study were obtained from 12 global climate models participating in the Coupled Model Intercomparison Project-Phase 6 (CMIP6) initiated by the World Climate Research Programme (WCRP). These models provide variables such as 6-hourly sea level pressure, 2-meter temperature, sea ice concentration (SIC), terrain height, and sea level pressure (SLP) (although not all models provide data every 6 hours).The study period spans from 1981 to 2100, divided into a historical period (1981-2014) and a future period (2015-2100), with 1995-2014 serving as the reference period for future projections. The 21st century is further divided into near-term (2021-2040), medium-term (2041-2060), and long-term (2081-2100) periods. A Multimodel ensemble mean (MME) is employed, and Table 1 presents detailed information on the 12 CMIP6 global climate models used. Also, in order to compare with the CMIP6 models, the ERA5 reanalysis (Hersbach et al. 2020) is utilized as a real-world representation of atmospheric variability. However, it should be noted that the evaluation of ERA5 is not the main focus of the article, and the results are only presented in certain figures.

This paper uses the Shared Socio-economic Pathways (SSPs) greenhouse gas emission scenario developed by the IPCC Climate Change Impact Assessment Scenario Working Group (O'Neill et al., 2016; Riahi et al., 2017), which includes seven indicators: population and human resources, economic development, human development, technology, lifestyle, environment and natural resources, as well as policy and institutional management. Specifically, this paper analyzes the simulation tests by CMIP6 under the SSP1-2.6 and SSP5-8.5 pathways to project the future variation trend of Arctic cyclones.

Table. 1 The detailed information of 12 CMIP6 climate models.

Number	Model	Country	Ocean resolution(lat×lon)	Atmospheric resolution (lat×lon)		Resolution grouping (L: low; M: medium)
Number	Model	Country	Ocean resolution(lat×lon)	Horizontal	Vertical	Resolution grouping (L: low; M: medium)
1	ACCESS‒CM2	Australia	300 × 360	144 × 192	85 levels to 85 km	L (250 km)
2	BCC‒CSM2‒MR	China	232 × 360	160 × 320	46 levels to 1.46 hPa	M (100 km)
3	CNRM‒CM6‒1	France	294 × 362	128 × 256	91 levels to 78.4 km	L (250 km)
4	CNRM‒ESM2‒1	France	294 × 362	128 × 256	91 levels to78.4 km	L (250 km)
5	EC‒Earth3	European Union	292 × 362	256 × 512	91 levels to 0.01 hPa	M (100 km)
6	MIROC6	Japan	256 × 360	128 × 256	81 levels to 0.004 hPa	L (250 km)
7	MPI‒ESM1‒2‒HR	German	404 × 802	192 × 384	95 levels to 0.01 hPa	M (100 km)
8	MPI‒ESM1‒2‒LR	German	220 × 256	96 × 192	47 levels to 0.01 hPa	L (250 km)
9	MRI‒ESM2‒0	Japan	364 × 360	160 × 320	80 levels to 0.01 hPa	M (100 km)
10	NESM3	China	362 × 384	96 × 192	47 levels to 1 hPa	L (250 km)
11	NorESM2‒LM	Norway	384 × 360	96 × 144	32 levels to 3 hPa	L (250 km)
12	NorESM2‒MM	Norway	384 × 360	192 × 288	32 levels to 3 hPa	M (100 km)

b. Study Area

Various definitions of Arctic Cyclones (ACs) have been proposed by researchers. For instance, Tilinina (2014) only considers cyclones that track into the latitude circle of 55°N as ACs. In this study, we define the Arctic region as the area north of 60°N, and recognize ACs with a range that extends from 90°N southward to 30°N. As some cyclones reaching the Arctic may originate from lower latitudes. The mid-latitudes are defined as latitudes of 30°N-60°N, where the 60°N line roughly corresponds to the zero contour of the Arctic Oscillation (AO) mode. Moreover, we distinguish the "polar cap" from the mid-latitudes, following the approach of Zhang et al. (2004).

We divide Arctic Cyclones (ACs) into two categories: external Arctic Cyclones (EACs), which originate from the mid-latitudes (30°N- 60°N) and move into the Arctic region, and internal Arctic Cyclones (IACs), which are born inside the Arctic region (60°N-90°N) (Fig. 1). This division was justified due to the contrasting characteristics exhibited by these two groups, while those that EACs are considerably more intense (Sepp and Jaagus, 2011). Both Zhang et al. (2004) and Tanaka (2012) indicated that, compared with the IACs, the EACs have stronger energy, and longer life cycle and the EACs and IACs have a different structure, the latter typically exhibits a strong barotropic structure, characterized by a stationary vortex tube extending from the troposphere to the lower stratosphere.

Extreme cyclones in this study are defined by the intensity (min_p, max_dsqp, max_depth) exceeding the threshold, which corresponds to the most intense 10 percent calculated from the historical (1981-2014) in different seasons.

c. Cyclone detection and tracking method

The cyclone tracking algorithm used in this paper is a Lagrangian algorithm based on the method proposed by Serreze et al. (1993), which was further improved by Crawford et al. (2020). The algorithm involves several key steps. First, interpolation is performed to convert the 6-hour sea level pressure (SLP) data into polar equal-area grids of size 50 km x 50 km. The outcomes of this algorithm exhibit minimal sensitivity to spatial resolution, whether it is 25 km or 100 km (Crawford et al. 2021). Grid points located above an elevation of 1,500 m are treated as missing data. Next, candidate cyclone centers are identified by selecting grid points whose air pressure values are lower than the values of the 80 surrounding grid points, and whose mean pressure gradient is no lower than 7.5 hPa (1,000 km)^-1. Cyclone tracking is then carried out by predicting the location of each cyclone center at time t based on its past propagation, using the formula, where 0.75 is the attenuation coefficient (Hanley and Caballero, 2012). A cyclone center is considered to be still active if it has propagated to a location within the range of to where is the time interval (usually 6 h). If a cyclone fails to meet these conditions, it is considered to have dissipated. To ensure that only synoptic-scale cyclones are studied and thermal lows related to altitude are eliminated, cyclone tracks that persist for at least 24 hours, taking at least one time step (6 h) at elevations below 500 m and covering distances of at least 1,000 km, are selected for analysis (Tanaka et al., 2012; Aizawa and Tanaka, 2016). Based on the above tracking algorithm, the relevant characteristic variables of Arctic cyclones are obtained, as listed in Table 3.

According to Simmonds and Keay (2008), there are various limitations associated with each individual measure of cyclone intensity. Therefore, it is necessary to combine multiple measures in order to obtain a more comprehensive understanding of cyclone activity. Cyclone intensity can be quantified through four different methods: the deepening rate (dpdt, in hPa/day^-1), following Roebber (1984) and Serreze et al. (1997) the Laplacian of central pressure (Murray and Simmonds, 1991), cyclone depth (Simmonds et al., 2009), which is measured as the difference in pressure between a cyclone’s center and its edge and Lowest central pressure (min_p) during a cyclone life cycle. The Laplacian quantity of the central pressure of ACs averaged over a grid range of 50 km × 50 km (dsqp) which provides an index of cyclone intensity that is largely independent of changes in the background pressure field and is directly proportional to the geostrophic relative vorticity (Murray and Simmonds, 1991). While, the AC depth provides a direct measure of the kinetic energy of the system (Simmonds et al., 2008).

The occurrence frequency of cyclones is represented by cyclone track density, which is defined as the number of cyclone tracks passing through a 500 km × 500 km area during a given time (Roebber, 1984; Serreze, 1997). Similarly, cyclone events density (cyclogenesis, split, merger, cyclolysis) is defined as the number of cyclones that occur in a 500 km × 500 km area during a given period of time. The last-closed isobar method is used in this study, which means that the area a cyclone covers extends outward to higher and higher sea level pressure (SLP) isobars until the last closed isobar. Thus, the Arctic cyclone radius is defined as the square root of the cyclone's area divided by (km). The variables associated with Arctic cyclones in this study are displayed in Table 2.

Table. 2 The detailed information of variables of Arctic cyclones.

Abbreviation	Index	Unit
trkden	Spatial density of cyclone tracks	number (500 km)^-2per month
loc_lys	Spatial density of the locations of cyclolysis	number (500 km)^-2per month
loc_gen	Spatial density of the locations of cyclogenesis	number (500 km)^-2per month
loc_mrg	Spatial density of the locations of merge events	number (500 km)^-2per month
loc_spl	Spatial density of the locations of split events	number (500 km)^-2per month
center_v	Center velocity at all locations during each cyclone life cycle	km h^-1
dsqp	Cyclone intensities at all locations during each cyclone life cycle	hPa (50 km)^-2per month
radius	Cyclone radiuses at all locations during each cyclone life cycle	km per month
depth	The depth of Arctic cyclone	hPa
lifespan	Arctic cyclone life cycle	days
trlen	Distance traveled by an Arctic cyclone	km
min_p	Lowest central pressure during a cyclone life cycle	hPa
avg_dsqp	Averaged intensity during a cyclone life cycle	hPa (50 km)^-2
avg_radius	Averaged radius during a cyclone life cycle	km
num	Number of cyclone tracks during certain periods	number
Max_dpdt	Spatial density of the locations of Max deepening rate of Arctic cyclone events	number (500 km)^-2per month
max_dsqp	The max intensity during a cyclone life cycle	hPa (50 km)^-2
max_depth	The max cyclone depth during a cyclone life cycle	hPa

d. Verification method

In this study, the temporal and spatial correlation analysis is based upon the Pearson correlation coefficient (R). To assess the strength of trends, we evaluate their statistical significance by conducting a Student's t-test at a confidence level of 95% (P < 0.05).

e. Calculation of EGR

The maximum Eady growth rate (EGR) is commonly on used to measure the baroclinic instability of the atmosphere and also the ability of the atmospheric environment to promote the development of cyclonic systems (Simmonds and Rudeva, 2012), ERG was calculated as follows:

a. Density characteristics of Arctic cyclones

Assessing the ability of CMIP6 models to simulate Arctic cyclones using ERA5 reanalysis data has been discussed in Song et al. (2021). The results showed that the 12 CMIP6 models and their ensemble mean were able to simulate the spatial and temporal distribution features of variables of Arctic cyclones. Building on this assessment, we conduct a further analysis of the changes in Arctic cyclones during different periods of the 21st century relative to the changes observed during 1995–2014. We examine variations in Arctic cyclone density and multiple climate elements that are indicative of Arctic cyclones.

Figures 2a, 2b, 2c and 2e show the DJF and JJA mean track density responses for the SSP1-2.6 and SSP5-8.5 scenarios. The patterns are very similar between two scenarios. The areas with the highest density of Arctic cyclones are predominantly located in southern Greenland and the Gulf of Alaska.

Changes in track density are quiet different for SSP1-26 than SSP5-85, which reflects the sensitivity to increasing levels of anthropogenic climate change. All scenarios present a different pattern of storm track changes, especially over the land (Fig. 2c, 2d, 2g and 2h).

Future projections under the SSP1-2.6 scenario indicate a smaller variation trend in cyclone track density during winter and summer, with fluctuations ranging between − 0.3 to 0.3 (500 km)⁻² per month/10 year. However, an opposing trend is observed in winter and summer near the southern part of Greenland, where there is a significant decrease in the number of Arctic cyclones (ACs) during winter but an increase during summer. In the area of sea south of Alaska, there is a significant increase in ACs during winter. It is worth noting of in the medium-term periods of 21st century show a significant increase in the number of cyclones over north of the Queen Elizabeth Islands but disappeared in the long-term periods of the 21st century (Fig. S1).

On the other hand, the pattern that poleward shift of ACs seen in the winter gets larger in magnitude in SSP5-8.5. There is a significant decline in the number of ACs during winter over southern Greenland, the Barents Sea, and the Gulf of Alaska, while an opposite trend is observed over northern Greenland and the Bering Strait. In winter, ACs in the subtropical Pacific and northern Europe are projected to decrease significantly, while those in the eastern subtropical Atlantic tend to increase, with changes of around 0.1 (500 km)⁻² per month/10 year.

However, in JJA, under SSP5-8.5, the track density (trkden) of ACs is projected to decrease significantly around a majority of the Northern Hemisphere (NH), except for areas such as the Arctic Ocean, offshore Greenland, and around the subtropical Pacific. A significant decline is evident in various regions including North America, most parts of northern Europe, northern Asia, the North Atlantic, and the North Pacific, as observed in several previous studies (Zappa et al., 2013; Crawford et al., 2017; Akperov et al., 2019; Prestley et al., 2021). Compared to winter ACs, the track density (trkden) of Arctic cyclones is projected to decline slightly in most areas during summer (see Fig. 2). Looking at ACs in the three periods individually, the pattern gets significantly larger in magnitude beginning at medium-term periods of 21st century in DJF, but for JJA, significant changes begin at the long-term periods of 21st century (Fig. S1)

Additionally, the projected cyclogenesis (loc_gen) and cyclolysis (loc_lys) patterns are shown in Fig. 3. In SSP1-2.6, there are not clearly changes in the spatial distribution of the linear trend in loc_gen and loc_lys. An overall reduction in cyclogenesis and cyclolysis is larger for the higher emission climate change scenarios (Fig. 3e–h).

In the cold season, there is a significant decrease in ACs formed in southern Greenland, Alaska, Barents Sea and Norwegian Sea and a slight increase in Arctic Ocean and Northern Greenland. There is also a significant reduction in the number of ACs dying in the North Pacific, Alaska and Arctic Oceans in SSP5-8.5.

In the warm season, there is more evidence of a decline in cyclogenesis over the continents than over the oceans. This is evident in Asia, North America, Bering Sea and north Atlantic Ocean, where less cyclolysis are present.

However, in JJA (Fig. 3f), the patterns of loc_gen change are very similar to those of DJF (Fig. 3e) with the exception of the waters off southern Greenland. Summer stands in sharp contrast with winter of cyclolysis (loc_lys) in the Arctic Oceans and North Pacific.

As for the frequency of merging and splitting events, there is no significant change over NH under the SSP1-2.6 scenario. In DJF, the regions of note are the waters off southern Greenland, North Pacific Ocean and Alaska, where significant decreases in the number of merge (loc_mrg) and split (loc_spl) ACs are observed that are larger with increased future warming (as illustrated in Figs.S2). In JJA, there is a significant decrease over the Northeastern Asia, the North Pacific Ocean, Alaska, the Greenland Sea and the Denmark Strait which are opposite to DJF. All scenarios present a similar pattern of ACs merge and split.

b. Arctic cyclone intensity

Various metrics have been used to describe ACs intensity, but some studies found that different definitions of cyclone intensity yielded similar patterns and future changes (Chang 2017; Valkonen et al., 2023). However, our results demonstrate contrasting changes in some regions, which used the different intensity metrics.

In DJF, there is significant decrease in cyclone intensity (dsqp) over the waters off Greenland, Bering Strait, Alaska, Chukchi Sea and Chukotka Mountains, which gets larger in magnitude with the higher emission climate change scenarios. An overall reduction in North Pacific, Alaska and south of Greenland in ACs depth and an increase over Bering Strait, Chukchi Sea, North Atlantic and Arctic Ocean, which have almost opposite patterns compared to dsqp (Fig. 4).

In JJA, the feature of note is the significant decrease over Atlantic in dsqp that are larger in depth. Over Central Asia and Europe, an increase in ACs intensity is identified.

The magnitude of intensity change is greater in winter than in summer (Fig. 3). There is no significant change in Arctic cyclone intensity in SSP1-2.6. Looking at intensity (dsqp) of ACs in the three periods individually, the pattern gets significantly larger in magnitude beginning at near-term periods of the 21st century in DJF, but for JJA, the changes are not pronounced in three periods of the 21st century (Fig. S3).

Regarding the max_dpdt (Fig. S5) (Occurrences of maximum deepening rate density of Arctic cyclones), it is observed that cyclones with high values of maximum deepening rate are concentrated over the ocean during winter, whereas during summer, they are concentrated on land and over the south of Greenland. As greenhouse gas emissions increase, more Arctic cyclones are expected to intensify explosively in the Bering Strait and Chukchi Sea during the winter, and more cyclones are likely to reach their maximum deepening rates in the Greenland Sea and on the eastern side of the North American continent during the summer.

c. Arctic cyclone size and speed

Additionally, in DJF, the radius (loc_radius) is expected to increase in most parts of the Arctic region (as seen in Fig. S4), except in the North Pacific. However, in the summer months (JJA), there is a significant decrease in radius in the North Atlantic, with an increase in other areas. That is why several areas are showing an increase in cyclone depth but a decrease in the Laplacian of central pressure (Fig. 4). Cyclone depth is just the edge pressure minus the central pressure, so if the average storm size increases, the depth is also likely to increase even if the local intensity at the center of the storm (which is independent of size) weakens.

We have also shown that, in DJF, the center moving velocity (center_v) of ACs is projected to decrease in the vicinity of the land-sea interface under the SSP5-8.5 scenario (Fig. S4) and increase in ocean. In JJA, speeds of ACs generally decrease at mid-latitudes and increase at high latitudes, in contrast to the DJF.

Overall, the results suggest that with the increasing future warming, the number of ACs will decrease. The Arctic is likely to experience more slow-moving, large-scale, and weak-intensity ACs. And the reduction of merge or split of ACs inside the Arctic Circle is projected.

d. Percentage distribution of other characteristics

Figure 5 presents the percentage distribution of lifespan, minimum central pressure (min_p), radius (avg_radius), and track length (trlen) of Arctic cyclones during the end of the 21st century (2081–2100) (See Table 2 for a detailed definition).

Under the SSP5-8.5 scenario, in terms of the lifespan of Arctic cyclones during DJF and JJA at the end of the 21st century, the proportion of short-lifespan ACs (< 3 days) in winter is reduced by 2.2%, 1.9% respectively, while the model median increase in the proportion of ACs moving longer than 3 days is seen in Fig. 5a and 5b. The extreme lifespan of ACs is 8.5 days in DJF, albeit of a smaller magnitude than in JJA. Changes in lifespan are smaller for SSP1-2.6 than for SSP5-8.5. However, the percentage distribution of lifespan for both seasons and SSPs is similar to that during the evaluated period (1995–2014), they all show the largest spread for shortliving cyclones.

The analysis of the min_p percentage distribution (Figs. 5c, 5d) shows the largest spread for 980–990 hPa in historical and SSPs. The changes of min_p in contrasting climate scenarios are quite different. The deep ACs in DJF (JJA) (defined as the top 10% of the min_p distribution) is around 961 hPa (983 hPa). In DJF, a decrease (increase) as a percentage is evident with the min_p above (below) 980 hPa in SSP5-8.5, while for SSP1-2.6 shows less of a shift and even some increase in 990-1000hPa. Those with min_p ranging from 990 to 1000 hPa decrease by 1.4% for SSP5-8.5 in model mean. In summer there are no evident changes with increased warming. Additionally, the projections show that there will be a higher percentage of deep ACs in the future for SSP5-8.5 in both seasons.

Figures 5e and 5f illustrate the projected percentage changes in radius (avg_radius). The large ACs in DJF (JJA) (defined as the top 10% of the avg_radius distribution) is around 925 km (891 km). There is a greater increase of large ACs in both season and SSPs with the stronger climate change scenarios, with an increase by 1.8% and 1.1% in DJF and JJA for SSP5-8.5. The distribution of the avg_radius in DJF (JJA) also shows a somewhat higher proportion in the classes of 400–500 km (500–600 km) in the historical median. As for small ACs (300–400 km), in the median model for SSP5-8.5, there is a reduction in DJF and increases in JJA of 0.4% and 0.2% respectively. The results are not robust with regard to the model used.

The percentage distribution of track length (Figs. 3a, b) does not exhibit particularly large variations across different scenarios. The track length percentage distribution shows the largest spread for short-length (1000–2000 km) ACs. The long-length ACs in DJF (JJA) (defined as the top 10% of the trlen distribution) is around 8087 km (6790 km). There is a slight increase (decrease) of 0.2% (0.9%) in the percentage distribution of long-length ACs in DJF (JJA). The results are robust with regard to the model used, for the small range between maximum and minimum values.

Collectively, changes in the percentage distribution of values are all smaller for SSP1-26 than for SSP5-85, which demonstrates the model's sensitivity to escalating levels of anthropogenic climate change. For all seasons, and all future experiments, the model mean of lifespan and trlen are similar, indicating a robust across the models and a relatively small change in percentage distribution of track length and lifespan. min_p and avg_radius percentage distribution of Arctic cyclones differs in winter and summer and also varies under the two SSP scenarios. The proportion of long lifespan, deep and large radius of ACs will increase in the future.

a. Arctic cyclones generated in different regions

While Fig. 6, Fig. 7 and Fig. 8 illustrate the temporal changes of AC characteristics, the magnitude of changes of EACs and IACs are really distinctive.

The changes of the AC characteristics are exacerbated in the higher emission scenarios (SSP5-8.5). In DJF, there is a significant negative trend in IACs that is observed in the MME, which is decreased more versus EACs (Figs. 6a, 6c), with a decrease of -7.6% (-4.3%) for SSP5-8.5 (SSP1-2.6). As for EACs, at the end of the 21st century, the downward trend of EACs gradually slowed down. In JJA, the EACs reduced by about − 4.9%, with all models showing a future reduction in the range of − 1% to − 10% under the SSP5-8.5 scenario (Table 3).

The reduction of EACs are more pronounced in summer (trend = -0.09 number year^− 1 under SSP5-8.5) than in winter (trend = -0.04 number year^− 1 under SSP5-8.5). While, the number of IACs reduce more in winter than in summer, especially in the SSP5-8.5 scenario (Figs. 6c, 6d).

There is a significant correlation between the number of EACs and IACs (around 0.3 under SSP5-8.5, p < 0.05), which means a high number of IACs in years with a high number of EACs. This finding is inconsistent with zhang et al. (2004), which concluded that in 1948 to 2002 a significant negative correlation is shown between EACs and IACs.

In DJF, the 12 models project a decrease in intensity (avg_dsqp), MME is -6.1% (-3.8%) in IACs (EACs) under SSP5-8.5, which is significant. In JJA, a slight decrease in both EACs and IACs ranges from − 0.2%~-3.8%, this is less than in DJF (Fig. 7).

However, under the SSP1-2.6 scenario, the reduction in intensity for ACs is not as obvious as SSP5-8.5. ACs weaken more in winter than in summer. Looking at Table 3, to quantify differences in measures of cyclone intensity, we also use max_dsqp, max_depth and min_p (the definitions are in Table 2) to represent the intensity of ACs. The results of avg_dsqp and max_dsqp project a similar trend, however, the trend of max_depth is quite different. The trend is almost not significant in min_p and max_depth. The reduction of max_depth means that the cyclone becomes stronger, contrary to the intensity calculated by dsqp and min_p. So the changes in intensity of EACs and IACs depend on the measure of cyclone intensity we use.

Concerning the radius of ACs (Fig. 8), both the radius of EACs and IACs exhibit a significant increasing trend under SSP5-8.5 in all seasons, as the increase getting larger by the end of 21st. There is more increases in radius of IACs than EACs. In JJA, notably, the radius of IACs is projected to significantly increase by 2.3% and the MME projects radius of IACs will likely increase by 30 km for the long-term periods.

The analysis of changes in cyclone characteristics in the Arctic in the decades of the 21st century (2015–2100) simulated by MME under the SSP scenario relative to the historical (1995–2014) period is presented here (Table 3). The projected trlen of Arctic cyclones are projected to become shorter, with a maximum decrease of 1.8% in summer for EACs, followed by IACs in winter under the SSP5-8.5 scenario.

In DJF, the lifespan of EACs is projected to increase by 3.0%, which is significant, by contrast the EACs vary inversely in winter and summer under the SSP5-8.5.

Table 3

Changes of cyclone characteristics (“SSP”(2015–2100)–“HIST”) (%) (relative to the reference period 1995–2014). The boldface text represent the differences are statistically significant at the level of p < 0.05 using the two-sided t-test..
DJF/JJA	num	avg_dsqp	avg_radius	max_dsqp	max_depth	min_p	trlen	lifespan
SSP5-8.5(EAC)	-1.8/-4.9	-3.8/-3.8	1.5/1.5	-2.7/-1.0	0.003/-0.02	-0.06/0.02	-0.7/-1.8	1.1/-0.6
SSP5-8.5(IAC)	-7.6/-2.4	-6.1/-1.2	2.1/2.3	-7.4/-1.0	-0.02/-0.0003	-0.06/-0.06	-1.3/-0.04	3.0/0.6
SSP1-2.6(EAC)	-1.2/-0.7	-1.7/-0.5	0.03/0.9	-1.1/-0.3	-0.01/-0.01	0.002/0.007	-0.7/-0.6	-0.1/-0.2
SSP1-2.6(IAC)	-4.3/-1.3	-2.9/-0.2	0.9/0.5	-3.8/0.4	-0.006/0.001	0.03/-0.03	-0.7/-0.09	1.5/-0.3

b. Response of extreme Arctic cyclone

Extreme ACs play a very important role in the Arctic region, so an assessment considering a variety of intensity measures is therefore required to explore extreme cyclone variability.

The definitions of different intensities all yield a trend that extreme ACs will decrease, but the magnitude of the decrease varies between the different approaches. Extreme Arctic cyclone reductions calculated with max_dsqp are the largest (trend = -0.3/-0.4% year^− 1 under SSP5-8.5 (DJF/JJA), p < 0.01). Thus, our findings align with previous research indicating a decrease in the number of ACs, as observed in studies by Zappa et al. (2013) and Michaelis et al. (2017). Selecting the max_dsqp variable as the threshold, as shown in Figure S6, the number of extreme Arctic cyclones will significantly decrease in the North Atlantic and increase in the Chuukchi Sea and Bering Strait. There is an increasing number of extreme ACs in the Bering Strait (Parre Islands) in winter (summer).

Anthropogenic activities have the potential to significantly alter the Earth's climate, including atmospheric temperature, ocean and sea ice conditions, which can in turn affect the generation and development of Arctic cyclones (ACs) through changes in baroclinicity and diabatic processes. However, it is important to exercise caution when attributing changes in cyclone behavior to a specific climate component, as the different components are interdependent and can interact in complex ways. In this section, we want to know how the ACs activity changes in the strong warming scenario. How else will large-scale circulation systems continue to affect Arctic cyclone activity in the future?

a. Sea Ice extent and Temperature

The current CMIP6 models exhibit a reasonable ability to replicate the patterns of sea ice concentrations (SIC), as evidenced by the minimal disparities in SIC trends between ERA5 and the multi-model mean, as demonstrated by Valkonen et al. (2023), so it is worth investigating the relationship between changes of ACs, SIC and surface temperature (SST). Our findings suggest that, under the SSP1-2.6 and SSP5-8.5 scenarios, the Arctic surface temperature is projected to rise significantly, while the sea ice concentrations (SIC) will decline substantially. Correspondingly, the number of ACs is also projected to decrease. There is a significant negative correlation between the ACs frequency and Arctic SST, as well as a significant positive correlation between the ACs frequency and SIC. This correlation will be the largest with the greatest increase in climate forcing. In particular, under the SSP5-8.5 scenario, the correlation between the EACs and SIC is strongly positive, with a correlation coefficient reaching up to 0.6 (Fig. 11). This means that as SIC decreases, the EACs are likely to decrease as well. The correlation results are robust across a majority of the CMIP6 models.

b. Driving mechanisms of the cyclone under SSP585

Under the SSP5-8.5 scenario, the Arctic cyclones are significantly changed in some regions (Fig. 2). To further understand the reasons for ACs variability, we investigate the underlying physical mechanisms, which are externally driven by increasing greenhouse gas concentrations, including sea ice concentration (SIC), temperature gradient magnitude (Crawford and Serreze, 2015), the baroclinic instability (EGR), geopotential height (GHT) and the potential vorticity (PV). The response of ACs to increasing greenhouse gas concentrations is examined with MME. In this regard, the relationship between the background changes and ACs property changes can provide helpful insight into the future projection of ACs and the Arctic climate.

From Fig. 2, we found a large variation of ACs around Greenland and near the Chukchi Sea under the SSP5-8.5 scenario. In DJF, We first calculated the change in SIC, due to the warming of the Arctic, which has a large and significant decrease in SIC. With the melting of sea ice, the temperature gradient between sea ice and seawater has shifted to the north (Fig. 11, b). The temperature gradient and EGR decrease in the southern of Greenland and the Norwegian Sea (Fig. 11, c), significantly increasing from the north of Greenland to the North of the Barents Sea and Chukchi Sea. The poleward inflow of warm water from the North Atlantic can also enhance the thermal correlation of sea ice in the open ocean and decrease the static stability of the atmosphere, leading to an increase in EGR. The enhanced EGR has supported the increase in cyclone frequency over the same area (Fig. 2). Over the Pacific Ocean, the change of the ERG does not support the overlying increased cyclone frequency, The enhanced positive PV anomaly indicates a downward intrusion of the Planetary Stratospheric Polar Vortex (P-SPV) into the climatological troposphere promotes surface cyclogenesis and development (Haynes, 2005; Zhang et al., 2023). Some ACs do not originate the baroclinic instability, but the merging of smaller-scale mesocyclones like the tropical cyclones (Tanaka et al., 2012), this also explains that the increase in Bering Strait ACs are not related to EGR, but caused by positive PV anomalies and invading EACs. The positive anomaly of EGR can cause the stronger growth of cyclones over the ocean (Fig. S5). We also examined the large-scale atmospheric circulation in the troposphere and stratosphere. The winter 500 hPa GHT has shown a shallowed shifted, this statistically reflects the two-way interaction between large-scale atmospheric circulation variability and synoptic-scale cyclones. There is a high value center near the Kara Sea, and the trough in this area is weakened more, steering fewer cyclones to propagate northeastward to the Barents Sea (Fig. 2 and Fig. 3).

Also, the mechanism of change in Arctic cyclones in summer is more complex, with a large decrease in Arctic cyclones in summer over land associated with a decrease in EGR. However, in the southern Greenland and the northern part of the Americas, the positive anomaly in EGR does not correspond to the change in the number of cyclones but causes an greater intensification of ACs. Positive ERG anomalies in North America have also led to a significant increase in the number of extreme cyclones in the region (Fig. S6). The baroclinic instability is also not the sole dynamic mechanism during summer, changes in the troposphere polar vortex also affect the ACs, the distribution of 500hpa geopotential height changes is similar to the spatial distribution of summer Arctic cyclone changes. It is known that dynamic and thermodynamic processes at the tropopause are important for baroclinic development, and these intense surface systems are closely associated with strong upper-level structures (e.g. Thorncroft et al., 1993; Simmonds and rudeva, 2014). The main reason for the decrease in summer Arctic cyclones is from EACs (Fig. 6). Numerous studies have shown that PV in the upper troposphere is related to the development of low-level cyclone generation (Čampa and Wernli, 2012). Overall the decrease in the number and intensity of Arctic cyclones is associated with a significant decrease in the upper-level PV field in the Arctic (Fig. 11d, h). However, the decrease in the number of summer ACs are not only due to the influence of the upper and lower layers of the Arctic system, but also mainly due to the decrease in EACs.

a. comparison to past work

The new CMIP6 models and the revised cyclone tracking algorithm based on the minimum SLP is used for the first time for the analysis of different kinds of Arctic cyclones. Using different tracking methods have pronounced differences in patterns of track density changes in the North Pacific and the Arctic Ocean in the winter, comparing to other works (Priestley et al., 2021). For certain regions, such as the Arctic Ocean during the summer, there can be considerable variation in the outcomes (Neu et al. 2013; Rohrer et al. 2020; Vessey et al. 2020). Hence, conducting further research becomes necessary to investigate these same inquiries using additional cyclone detection and tracking algorithms. The study conducted by Harvey et al. (2020) demonstrated that the responses to different scenarios in the CMIP6 models are significantly larger than those in the CMIP5 models, which aligns with the higher climate sensitivities observed in the CMIP6 models, compared to CMIP5. Previous studies have shown that the total number of Arctic cyclones decreases in both DJF and JJA (Chang et al., 2018; Zappa et al., 2015) that is similar to CMIP6. Compared to CMIP5 models, the patterns of change in the NH are broadly consistent with the recent studies of Crawford et al. (2017), which used the same cyclone tracking methods. However, it remains necessary to investigate whether the same conclusion can be drawn based on the tracking method utilized in this particular paper.

The intensity of cyclones will weaken in western North America and eastern Asia in winter but not show significant changes in summer. Some previous studies have also estimated that the occurrence frequency and intensity of extratropical cyclones would decrease in winter, with weak cyclones occurring more frequently, which is consistent with our study's findings (Geng and Sugi, 2003; Akperov et al., 2015; Akperov et al., 2019). We have also investigated the radius of the cyclones, the conclusion that the occurrence of larger cyclones is projected to increase for both seasons, that is the same as other studies using of the HIRHAM regional climate model simulations with SRES-A1B anthropogenic scenario for the twenty-first century (Simmonds et al. 2008; Akperov et al., 2015).

In our results, there is a reduction in extreme ACs, with different definitions of intensities, which is the same as the studies that utilized the 90th percentile of 850-hPa wind speed during the historical period as a threshold to define extreme ETCs. Notably, projections from CMIP5 models indicate a decrease in extreme cyclones in the North Atlantic, as reported by Zappa et al. (2013b). Similarly, Chang et al. (2018) found similar results for the entire Northern Hemisphere. Harvey et al. (2014) observed a reduction in Arctic winter cyclones when using an Eulerian MSLP variance metric to analyze the future response of storms in the CMIP5 ensemble. Akperov et al. (2015) simulated a decrease in mean Arctic cyclone intensity based on a regional climate model but did not identify a significant change in extreme cyclones at high latitudes. By contrast, Vavrus (2013) suggested that extreme cyclones were becoming more frequent, particularly in the high-latitude North Atlantic, using an MSLP threshold. Priestley et al. (2022) found that the number of extreme cyclones in the Northern Hemisphere winter is projected to increase by 4%, it has been also concluded that the spatial extent of cyclones above a fixed wind speed threshold is projected to expand in the future.

We have applied three methods to define extreme Arctic cyclones: the extreme cyclones must exceed the 90th percentile of max_dsqp, min_p and max_depth for that season and models, we have shown that each of these measures gives different results. So, the projected changes in cyclone intensity may depend on the different intensity definitions, variables examined and the subset of cyclones examined (e.g. Ulbrich et al., 2009; Catto et al., 2019). Due to the increase of cyclone radius, the intensity of cyclone max_depth is enhanced, which is contrary to the result of max_dsqp. Moreover, due to the utilization of different models with distinct forcing protocols and diverse types of cyclones, it becomes challenging to ascertain the underlying source of this discrepancy.

b. Study limitations

In our study, we exclusively employed the tracking scheme put forth by Crawford et al. (2020). However, as stated by Neu et al. (2013), different algorithms yield varying results concerning cyclone characteristics. Given the lack of a universally agreed-upon definition for Arctic cyclones, it is imprudent to outrightly conclude that any particular algorithm produces incorrect outcomes. Therefore, further comparisons with observations are necessary to comprehensively assess the strengths and weaknesses of each algorithm.

Although the number of Arctic cyclones as a whole has decreased, there are large variations in different regions, which can be studied by focusing on key regions in the future. The resolution of the model and the degree of coupling between the atmosphere and ocean within the models have been identified as key factors influencing the accuracy of portraying Arctic cyclones (Small et al., 2019; Song et al., 2021), the resolution of the model we used is not enough, and a higher resolution regional model can be used to study for the Arctic cyclone in Arctic region.

Currently, there is no conclusive explanation for the underlying causes of future changes in Arctic cyclones. Moreover, only a limited number of studies have utilized the CMIP6 climate models to investigate the relationship between large-scale atmospheric circulation and cyclones under the SSPs scenario.

The interaction between the Arctic atmosphere and ACs is complex, with Arctic cyclones playing a crucial role. Considerable uncertainties persist regarding the influence of sea ice on cyclones, and apparent associations between ice and cyclones seem to be contingent on the resolution of the dataset and the specific identification method employed, as highlighted in the study by Rae et al. (2017). During the winter season, a substantial portion of snowfall on sea ice, particularly in the Atlantic sector, can be attributed to synoptic-scale cyclones (Webster et al. 2019). Research also indicates that increased activity of Arctic cyclones can lead to increased cloud cover, which weakens the transpolar drift current, thus inhibiting the melting of sea ice to some extent (Screen et al., 2011). Additionally, the displacement of cyclone genesis location can occur due to the movement of sea ice edges (Deser et al., 2000), and the loss of sea ice extent may impact Arctic cyclone activity by affecting changes in large-scale circulations (Crasemann et al., 2017; Wei et al., 2017).

Temperature also influences the characteristics of Arctic cyclones. For example, the decrease in the number of cyclones in the North Atlantic is due to the mid-troposphere warming faster than the ocean surface, which increases vertical instability (Zahn and von Storch, 2010). The genesis of synoptic-scale cyclones primarily arises from baroclinic instability, which is a consequence of horizontal temperature gradients and static stability. The diabatic process heavily influences the formation and development of Arctic cyclones, and changes in sea ice extent can affect both baroclinic and diabatic effects. Therefore, it is logical to expect that alterations in sea ice extent can influence the formation of cyclones. The change of baroclinicity over large areas of the Arctic and mid-latitudes suggests that atmospheric dynamics are more complex than the hypothesis that Arctic amplification causes a decline in the meridional temperature gradient (Zhang et al., 2023).

Arctic cyclone activity is driven by instability in the lower troposphere/surface baroclinic instability and alterations in the large-scale atmospheric circulation, which encompasses stratosphere-troposphere interactions. We just simply analyzed the correlations, and to accurately quantify and comprehend the thermodynamic and dynamic processes that contribute to the regional and temporal changes in in ACs, in-depth follow-up studies would be needed. Since the occurrence and development mechanism of Arctic cyclones is different from that of extratropical cyclones, it is necessary to conduct cluster analysis to analyze different types of Arctic cyclones in the future in order to obtain more reliable results.

In this paper, we utilized simulation results from 12 global climate models obtained from the CMIP6 models and a cyclone tracking algorithm based on the Lagrangian method to identify the Arctic cyclones during the 21st century. The first assessment of the changes across two shared socio-economic pathways on various characteristics of different kinds of ACs such as the intensity used different metrics, lifespan, radius and so on. Based on our findings, we have summarized the following conclusions:

(1) The number of Arctic cyclones shows a significant decrease across seasons, In winter, there is a significant decline in the number of ACs during winter over southern Greenland, the Barents Sea, and the Gulf of Alaska, while an opposite trend is observed over northern Greenland and the Bering Strait. In summer, the number of Arctic cyclones has a significant circular decrease along the Arctic over most of the region, with the decrease concentrated on the continent. For both seasons, the projected changes exhibit greater magnitudes when considering more aggressive climate change scenarios. (Fig. 2). Additionally, fewer Arctic cyclones will merge and split in the Pacific and Atlantic in the future (Fig. S2).

There is a significant reduction in the number of winter internal Arctic cyclones (IACs) and summer external Arctic cyclones (EACs), with a decrease of -7.6% and -4.9%, respectively (Fig. 6). The proportion of future extreme Arctic cyclones has increased (Fig.5), but the overall number of extreme ACs has decreased. Extreme Arctic cyclones will increase in the Bering Strait (Parre Islands) in winter (summer).

(2) In JJA, the radius of ACs is projected to increase significantly in most parts of the Arctic region (Fig. S4) except for north of the Pacific. In JJA, there is a significant decrease over north of Atlantic in radius. There is a greater increaing percentage of large ACs (> 891 km) in both seasons and SSPs (Fig. 5).

Both radius of EACs and IACs exhibit a significant increase trend under SSP5-8.5 in all seasons, as the increase gets larger by the end of the 21st century. There are more increases in radius of IACs than EACs (Fig. 8).

(3) As for the spatial distribution of ACs intensity trend, our results demonstrate contrasting changes in some regions, which used different intensity metrics. In DJF, the a significant decrease in cyclone intensity (dsqp) over the waters off Greenland, Bering Strait, Alaska, Chukchi Sea and Chukchi Mountains. while, an increased trend of ACs depth over Bering Strait, Chukchi Sea, North Atlantic and Arctic Ocean, which have almost opposite patterns compared to dsqp (Fig. 3).

In DJF, the MME projects a significant decrease in intensity (avg_dsqp), The weakening of ACs intensity is greater in winter than in summer. The different intensity metrics used all yield a different trend, and the magnitude of the decrease varies between the different approaches(Table 3).

As greenhouse gas emissions increase, more Arctic cyclones are expected to intensify greatly in the Bering Strait and Chukchi Sea during the winter, and more cyclones are likely to reach their maximum deepening rates in the Greenland Sea and on the eastern side of the North American continent during the summer (Fig. S5).

(4) We present the reasons for future changes in Arctic cyclones in this article. The number of Arctic cyclones is significantly correlated with both temperature and sea ice. Based on this, under the SSP5-8.5 scenario, the changes in the number of ACs near Greenland in winter are primarily caused by the changes in temperature gradient and baroclinic instability induced by sea ice in the future. Meanwhile, the changes in the number of ACs in the Chukchi Sea and Bering Strait are more complex, caused mainly by upper-level positive PV anomalies and the number of EACs.

In JJA, the decrease in Arctic cyclones over land is primarily caused by negative EGR anomalies, the tropopause polar vortex, and the reduced number of EACs. Also, the positive anomaly of EGR can cause the stronger growth of cyclones over the ocean (Fig. S5).

FUNDING

This research was jointly supported by the National Natural Science Foundation of China (No. 42141007), Key Innovation Team of China Meteorological Administration" Climate Change Detection and Response" (CMA2022ZD03).

COMPETING INTERESTS

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

AUTHOR CONTRIBUTIONS

Jianing Song: data curation; formal analysis; methodology; software; visualization; writing-original draft; writing-review & editing.

Ying Xu: Conceptualization; methodology; writing-original draft; writing-review & editing

Zhenyu Han: Data curation; software; writing – review and editing.

Jie Wu: Data curation; software; writing – review and editing

Data Availability

All data created or used during this study are openly available from the Coupled Model Intercomparison Project-Phase 6 (CMIP6) initiated by the World Climate Research Programme (WCRP) at https://esgf-node.llnl.gov/search/cmip6/.

Aizawa, T., and H. L. Tanaka, 2016: Axisymmetric structure of the long lasting summer Arctic cyclones. Polar Science, 10, 192-198.
Akperov, M., I. Mokhov, A. Rinke, K. Dethloff, and H. Matthes, 2015: Cyclones and their possible changes in the Arctic by the end of the twenty first century from regional climate model simulations. Theoretical and Applied Climatology, 122, 85-96.
Akperov, M., and Coauthors, 2019: Future projections of cyclone activity in the Arctic for the 21st century from regional climate models (Arctic-CORDEX). Global and Planetary Change, 182, 103005.
Akperov, M. G., and I. I. Mokhov, 2013: Estimates of the sensitivity of cyclonic activity in the troposphere of extratropical latitudes to changes in the temperature regime. Izvestiya, Atmospheric and Oceanic Physics, 49, 113-120.
Andrews, T., and Coauthors, 2019: Forcings, Feedbacks, and Climate Sensitivity in HadGEM3-GC3.1 and UKESM1. Journal of Advances in Modeling Earth Systems, 11, 4377-4394.
Aue, L., Vihma, T., Uotila, P., & Rinke, A., 2022: New insights into cyclone impacts on sea ice in the Atlantic sector of the Arctic Ocean in winter. Geophysical Research Letters, 49, e2022GL100051.
Butler, A. H., D. W. J. Thompson, and T. Birner, 2011: Isentropic slopes, downgradient eddy fluxes, and the extratropical atmospheric circulation response to tropical tropospheric heating. J. Atmos. Sci., 68, 2292–2305.
Catto, J. L., D.Ackerley, J. F., Booth, A. J. Champion, B. A. Colle, S. Pfahl, J. G. Pinto, J. F. Quinting, ,and C. Seiler, 2019: The Future of Midlatitude Cyclones, Curr. Clim. Change Rep., 5, 407–420.
Čampa, J., and H. Wernli, 2012: A PV perspective on the vertical structure of mature midlatitude cyclones in the Northern Hemisphere. Journal of the Atmospheric Sciences, 69, 725-740.
Carrió, D.S., V., Homar, Jansá, A., Romero, R. and M.A., Picornell, 2017: Tropicalization process of the 7 November 2014 Mediterranean cyclone: numerical sensitivity study. h, 197, 300–312.
Chang, E. K. M., Y. Guo, and X. Xia, 2012: CMIP5 multimodel ensemble projection of storm track change under global warming. Journal of Geophysical Research: Atmospheres, 117, D23118.
Chang, E. K. M., 2014: Impacts of background field removal on CMIP5 projected changes in Pacific winter cyclone activity. Journal of Geophysical Research: Atmospheres, 119, 4626-4639.
Chang, E. K. M., Y. Guo, X. Xia, and M. Zheng, 2013: Storm-Track Activity in IPCC AR4/CMIP3 Model Simulations. Journal of Climate, 26, 246-260.
Chang E. Projected change in Northern Hemisphere winter cyclones with associated extreme winds, 2018: J Clim. 31, 6527–42.
Chen, G., J. Lu, and D. M. W. Frierson, 2008: Phase speed spectra and the latitude of surface westerlies: Interannual variability and global warming trend. J. Climate, 21, 5942–5959.
Clancy, R., C. M. Bitz, E. Blanchard-Wrigglesworth, M. C. McGraw, and S. M. Cavallo, 2022: A cyclone-centered perspective on the drivers of asymmetric patterns in the atmosphere and sea ice during Arctic cyclones. Journal of Climate, 1-47.
Clark, M. P., M. C. Serreze, and D. A. Robinson, 1999: Atmospheric controls on Eurasian snow extent. Int. J. Climatol., 19, 27–40.
Colle, B. A., Z. Zhang, K. A. Lombardo, E. Chang, P. Liu, and M. Zhang, 2013: Historical Evaluation and Future Prediction of Eastern North American and Western Atlantic Extratropical Cyclones in the CMIP5 Models during the Cool Season. Journal of Climate, 26, 6882-6903.
Crasemann, B., D. Handorf, R. Jaiser, K. Dethloff, T. Nakamura, J. Ukita, and K. Yamazaki, 2017: Can preferred atmospheric circulation patterns over the North-Atlantic-Eurasian region be associated with arctic sea ice loss? Polar Science, 14, 9-20.
Crawford, A. D., and M. C. Serreze, 2015: A new look at the summer Arctic Frontal Zone. J. Climate, 28, 737–754.
Crawford, A. D., and M. C. Serreze, 2017: Projected changes in the Arctic frontal zone and smmer Arctic cyclone activity in the CESM large ensemble. Journal of Climate, 30, 9847-9869.
Crawford, A. D., K. E. Alley, and A. M. Cooke, 2020: Synoptic climatology of Rain‒on‒Snow events in Alaska. Mon. Wea. Rev., 148 , 1275‒1295.
Crawford, A. D., E. A. P. Schreiber, N. Sommer, M. C. Serreze, J. C. Stroeve, and D. G. Barber, 2021: Sensitivity of Northern Hemisphere Cyclone Detection and Tracking Results to Fine Spatial and Temporal Resolution using ERA5. Monthly Weather Review,149, 2581-2598
Day, J. J., M. M. Holland, and K. I. Hodges, 2018: Correction to: Seasonal differences in the response of Arctic cyclones to climate change in CESM1. Climate Dynamics, 50, 3905-3907.
Deser, C., J. E. Walsh, and M. S. Timlin, 2000: Arctic sea ice vari-ability in the context of recent atmospheric circulation trends.J. Climate, 13, 617–633.
Dolores-Tesillos, E., F. Teubler, and S. Pfahl, 2022: Future changes in North Atlantic winter cyclones in CESM-LE – Part 1: Cyclone intensity, potential vorticity anomalies, and horizontal wind speed. Weather Clim. Dynam., 3, 429-448.
Eyring, V., S. Bony, G. A. Meehl, C. A. Senior, B. Stevens, R. J. Stouffer, and K. E. Taylor, 2016: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geoscientific Model Development, 9, 1937-1958.
Feser, F., M. Barcikowska, O. Krueger, F. Schenk, R. Weisse, and L. Xia, 2015: Storminess over the North Atlantic and northwestern Europe—A review. Quarterly Journal of the Royal Meteorological Society, 141, 350-382.
Geng, Q., and M. Sugi, 2003: Possible Change of Extratropical Cyclone Activity due to Enhanced Greenhouse Gases and Sulfate Aerosols—Study with a High-Resolution AGCM. Journal of Climate, 16, 2262-2274.
Graham, R. M., and Coauthors, 2019: Winter storms accelerate the demise of sea ice in the Atlantic sector of the Arctic Ocean. Scientific Reports, 9, 9222.
Harvey, B. J., L. C. Shaffrey, and T. J. Woollings, 2014: Equator-to-pole temperature differences and the extra-tropical storm track responses of the CMIP5 climate models. Climate Dynamics, 43, 1171-1182.
Harvey, B. J., P. Cook, L. C. Shaffrey, and R. Schiemann, 2020: The Response of the Northern Hemisphere Storm Tracks and Jet Streams to Climate Change in the CMIP3, CMIP5, and CMIP6 Climate Models. Journal of Geophysical Research: Atmospheres, 125, e2020JD032701.
Hanley, J., and R. Caballero, 2012: Objective identification and tracking of multicentre cyclones in the ERA-Interim reanalysis dataset. Quarterly Journal of the Royal Meteorological Society, 138, 612-625.
Haynes, P., 2005: Stratospheric dynamics. Ann. Rev. Fluid Mech. 37, 263–293.
Inoue, J., and M. E. Hori, 2011: Arctic cyclogenesis at the marginal ice zone: A contributory mechanism for the temperature amplification? Geophysical Research Letters, 38, L12502.
Inoue, J, E. H.Masatake, T, Koutarou, 2012: The role of Barents sea ice in the wintertime cyclone track and emergence of a warm-arctic cold-Siberian anomaly. J Clim , 25,2561–2568.
Kawai, H., S. Yukimoto, T. Koshiro, N. Oshima, T. Tanaka, H. Yoshimura, and R. Nagasawa, 2019: Significant improvement of cloud representation in the global climate model MRI-ESM2. Geosci. Model Dev., 12, 2875-2897.
Khon, V. C., I. I. Mokhov, and V. A. Semenov, 2017: Transit navigation through Northern Sea Route from satellite data and CMIP5 simulations. Environmental Research Letters, 12, 024010.
Koyama, T., J. Stroeve, J. Cassano, and A. Crawford, 2017: Sea ice loss and Arctic cyclone activity from 1979 to 2014. Journal of Climate, 30, 4735-4754. Lang, C., and D. W. Waugh, 2011: Impact of climate change on the frequency of Northern Hemisphere summer cyclones. Journal of Geophysical Research, 116, D04103.
Lang, C., Waugh, D.W., 2011. Impact of climate change on the frequency of Northern
Hemisphere summer cyclones. J. Geophys. Res. Atmos. 116, 1–12
Lorenz, E. N., 1956: Empirical orthogonal functions and statistical weather prediction. MIT Department of Meteorology, Statistical Forecasting Project Scientific Rep. 1, 49 pp.
Lorenz, D. J., and E. T. DeWeaver, 2007: Tropopause height and zonal wind response to global warming in the IPCC scenario integrations. J. Geophys. Res., 112, D10119.
Lukovich, J. V., J. Stroeve, A. Crawford, L. Hamilton, M. Tsamados, H. Heorton, and F. Massonnet, 2021: Summer extreme cyclone impacts on Arctic sea ice. Journal of Climate, 12, 1-54.
McCabe, G. J., M. P. Clark, and M. C. Serreze, 2001: Trends in Northern Hemisphere Surface Cyclone Frequency and Intensity. Journal of Climate, 14, 2763-2768.
Michaelis, A. C., J. Willison, G. M. Lackmann, and W. A. Robinson, 2017: Changes in Winter North Atlantic Extratropical Cyclones in High-Resolution Regional Pseudo–Global Warming Simulations. Journal of Climate, 30, 6905-6925.
Mizuta, R., Matsueda, M., Endo, H., and Y ukimoto, S., 2011: Future Change in Extratropical Cyclones Associated with Change in the Upper Troposphere, J. Climate, 24, 6456–6470.
Mokhov, I. I., O. I. Mokhov, V. K. Petukhov, and R. R. Khayrullin, 1992: On trends of atmospheric cyclogenetic activity under global climate changes. Meteorol. Hydrol., 28,11-26.
Murray, R. J., and I. Simmonds, 1991: A numerical scheme for tracking cyclone centres from digital data. Part I: development and operation of the scheme. Australian Meteorolo3gical Magazine, 39, 155-166.
Neu, U., M. G. Akperov, and N. Bellenbaum, 2013: IMILAST: A community effort to intercompare extratropical cyclone detection and tracking algorithms. Bull. Amer. Meteor. Soc., 94, 529‒547.
Nishii, K., H. Nakamura, and Y. J. Orsolini, 2015: Arctic summer storm track in CMIP3/5 climate models. Climate Dynamics, 44, 1311-1327.
North, G. R., T. L. Bell, R. F. Cahalan, and F. J. Moenig, 1982: Sampling errors in the estimation of empirical orthogonal functions. Mon. Wea. Rev., 110, 669–706.
O’Gorman, P . A. and Schneider, T., 2008: Energy of midlatitude transient eddies in idealized simulations of changed climates, J. Climate, 21, 5797–5806.
O'Neill, B. C., and Coauthors, 2016: The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6. Geosci. Model Dev., 9, 3461-3482.
Orsolini, Y. J, A. Sorteberg, 2009: Projected changes in Eurasian and Arctic summer cyclones under global warming in the Bergen climate model. Atmos. Ocean Sci. Lett., 2,62–67
Overland, J. E. and M. Wang, 2010: Large-scale atmospheric circulation changes are associated with the recent loss of Arctic sea-ice, Tellus, 62, 1–9.
Pfahl, S., O’Gorman, P . A., and Singh, M. S. 2015: Extratropical Cyclones in Idealized Simulations of Changed Climates,J. Climate,28, 9373–9392.
Priestley, M. D. K., and J. L. Catto, 2022: Future changes in the extratropical storm tracks and cyclone intensity, wind speed, and structure. Weather Clim. Dynam., 3, 337-360.
Rae, J. G. L., A. D. Todd, E. W. Blockley, and J. K. Ridley, 2017: How much should we believe correlations between Arctic cyclones and sea ice extent? The Cryosphere, 11, 3023-3034
Riahi, K., and Coauthors, 2017: The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview. Global Environmental Change, 42, 153-168.
Rinke, A., and Coauthors, 2017: Extreme cyclone events in the Arctic: Wintertime variability and trends. Environmental Research Letters, 12, 094006.
Roebber, P. J., 1984: Statistical Analysis and Updated Climatology of Explosive Cyclones. Monthly Weather Review, 112, 1577-1589.
Rohrer, M., Martius, O., Raible, C. C., & Brönnimann, S., 2020: Sensitivity of Blocks and Cyclones in ERA5 to Spatial Resolution and Definition. Geophysical Research Letters, 47, e2019GL085582.
Schreiber, E. A. P., and M. C. Serreze, 2020: Impacts of synoptic-scale cyclones on Arctic sea-ice concentration: a systematic analysis. Annals of Glaciology, 61, 139-153.
Screen, J. A., I. Simmonds, and K. Keay, 2011: Dramatic interannual changes of perennial Arctic sea ice linked to abnormal summer storm activity. Journal of Geophysical Research, 116, D5105.
Semenov, Vladimir, A., Latif, Mojib, Park, and Wonsun, 2015: Super El Ninos in response to global warming in a climate model. Climatic Change, 132, 489-500.
Sepp, M., and J. Jaagus, 2011: Changes in the activity and tracks of Arctic cyclones. Climatic Change, 105, 577-595.
Serreze, M.C., Box, J.E., Barry, R.G., et al., 1993. Characteristics of Arctic synoptic activity. Meteorol. Atmos. Phys., 51, 147e164.
Serreze, M. C., F. Carse, R. G. Barry, and J. C. Rogers, 1997: Icelandic low cyclone activity: Climatological features, linkages with the NAO and relationships with recent changes in the Northern Hemisphere circulation. J. Climate, 10, 453-464.
Serreze, M. C, and A. P. Barrett, 2008: The summer cyclone maximum over the central Arctic Ocean. J. Climate, 21,1048–1065.
Serreze, M. C., and W. N. Meier, 2019: The Arctic's sea ice cover: trends, variability, predictability, and comparisons to the Antarctic. Ann N Y Acad Sci, 1436, 36-53.
Serreze, M. C., F. Carse, R. G. Barry, and J. C. Rogers, 1997: Icelandic Low Cyclone Activity: Climatological Features, Linkages with the NAO, and Relationships with Recent Changes in the Northern Hemisphere Circulation. Journal of Climate, 10, 453-464.
Shaw, T. A., and Coauthors, 2016: Storm track processes and the opposing influences of climate change. Nature Geoscience, 9, 656-664.
Simmonds, I., and M. Li, 2021: Trends and variability in polar sea ice, global atmospheric circulations, and baroclinicity. Ann N Y Acad Sci, 1504, 167-186.
Simmonds, I., and I. Rudeva, 2012: The great Arctic cyclone of August 2012. Geophysical Research Letters, 39, L23709.
Simmonds, I., and I. Rudeva, 2014: A comparison of tracking methods for extreme cyclones in the Arctic basin. Tellus A, 66, 25252.
Simmonds, I., C. Burke, and K. Keay, 2008: Arctic Climate Change as Manifest in Cyclone Behavior. Journal of Climate, 21, 5777-5796.
Simmonds, I., and K. Kevin, 2009: Extrodinary september arctic sea ice reduction and their relationships with storm behavior over 1979-2008. Geophys. Res. Lett., 36, 158‒168.
Small, R. J., R. Msadek, Y.-O. Kwon, J. F. Booth, and C. Zarzycki, 2018: Atmosphere surface storm track response to resolved ocean mesoscale in two sets of global climate model experiments. Climate Dynamics, 52, 2067-2089.
Small, R. J., R. Msadek, Y.-O. Kwon, J. F. Booth, and C. Zarzycki, 2019: Atmosphere surface storm track response to resolved ocean mesoscale in two sets of global climate model experiments. Climate Dynamics, 52, 2067-2089.
Sinclair, V . A., Rantanen, M., Haapanala, P ., Räisänen, J., and Järvinen, H. 2020: The characteristics and structure of extra-tropical cyclones in a warmer climate, Weather Clim. Dynam., 1, 1–25.
Sinclair, V . A., Rantanen, M., Haapanala, P ., Räisänen, J., and Järvi-nen, H., 2020: The characteristics and structure of extra-tropical cy-clones in a warmer climate, Weather Clim. Dynam., 1, 1–25.
Song, J. N., G. Fu, Y. Xu, Z.-Y. Han, Q.-Z. Sun, and H. Wang, 2021: Assessment of the capability of CMIP6 global climate models to simulate Arctic cyclones. Advances in Climate Change Research, 12, 660-676.
Tamarin, T. and Kaspi, Y.2017: The poleward shift of storm tracks under global warming: A Lagrangian perspective, Geophys. Res. Lett.,44, 10–666。
Tanaka, H. L., A. Yamagami, and S. Takahashi, 2012: The structure and behavior of the arctic cyclone in summer analyzed by the JRA-25/JCDAS data. Polar Science, 6, 55-69.
Thompson, D. W. J., and J. M. Wallace, 1998: The Arctic Oscillation signature in the wintertime geopotential height and temperature fields. Geophys. Res. Lett., 25, 1297–1300.
Thorncroft, C. D. , B. J. Hoskins, M. E. McIntyre , 1993: Two paradigms of baroclinic-wave life-cycle behaviour. Q. J. Roy. Meteorol. Soc, 119, 17–55.
Tilinina, N., S. K. Gulev, and D. H. Bromwich, 2014: New view of Arctic cyclone activity from the Arctic system reanalysis. Geophysical Research Letters, 41, 1766-1772.
Ulbrich, U., G. C. Leckebusch, and J. G. Pinto, 2009: Extra-tropical cyclones in the present and future climate: a review. Theoretical and Applied Climatology, 96, 117-131.
Ulbrich, U., and Coauthors, 2013: Are Greenhouse Gas Signals of Northern Hemisphere winter extra-tropical cyclone activity dependent on the identification and tracking algorithm? Meteorologische Zeitschrift, 22, 61-68.
Vavrus, S. J. 2013: Extreme Arctic cyclones in CMIP5 historical simulations, Geophys. Res. Lett., 40, 6208–12.
Valkonen, E., J. Cassano, and E. Cassano, 2021: Arctic Cyclones and Their Interactions With the Declining Sea Ice: A Recent Climatology. Journal of Geophysical Research: Atmospheres, 126, e2020JD034366.
Valkonen, E., J. Cassano, E. Cassano, and M. Seefeldt, 2023: Declining Sea Ice and Its Relationship with Arctic Cyclones in Current and Future Climate Part I: Current Climatology in CMIP6 Models. Weather Clim. Dynam. Discuss., 2023, 1-40.
Vessey, A. F., K. I. Hodges, L. C. Shaffrey, and J. J. Day, 2020: An inter-comparison of Arctic synoptic scale storms between four global reanalysis datasets. Climate Dyn., 54, 2 7 7 7–2795.
Vihma, T., and Coauthors, 2014: Linkages between Arctic sea ice cover, large-scale atmospheric circulation,and weather and ice conditions in the Gulf of Bothnia, Baltic Sea. Advances in Polar Science, 25, 289-299.
Wang, S., W. Chen, S. Chen, and S. Ding, 2020: Interdecadal change in the North Atlantic storm track during boreal summer around the mid-2000s: role of the atmospheric internal processes. Climate Dynamics, 55, 1929-1944.
Wei, L., T. Qin, and C. Li, 2017: Seasonal and inter-annual variations of Arctic cyclones and their linkage with Arctic sea ice and atmospheric teleconnections. Acta Oceanologica Sinica, 36, 1-7.
Webster, M. A., Parker, C., Boisvert, L., & Kwok, R., 2019: The role of cyclone activity in snow accumulation on Arctic sea ice. Nature Communications, 10, 5285.
Zahn, M., and H. von Storch, 2010: Decreased frequency of North Atlantic polar lows associated with future climate warming. Nature, 467, 309-312.
Zappa, G., L. C. Shaffrey, and K. I. Hodges, 2013a: The Ability of CMIP5 Models to Simulate North Atlantic Extratropical Cyclones. Journal of Climate, 26, 5379-5396.
Zappa, G., L. C. Shaffrey, K. I. Hodges, P. G. Sansom, and D. B. Stephenson, 2013b: A Multimodel Assessment of Future Projections of North Atlantic and European Extratropical Cyclones in the CMIP5 Climate Models. Journal of Climate, 26, 5846-5862.
Zappa, G., M. K. Hawcroft, L. Shaffrey, E. Black, and D. J. Brayshaw, 2015: Extratropical cyclones and the projected decline of winter Mediterranean precipitation in the CMIP5 models. Climate Dyn., 45, 1727‒1738.
Zappa, G., G. Masato, L. Shaffrey, T. Woollings, and K. Hodges, 2014: Linking Northern Hemisphere blocking and storm track biases in the CMIP5 climate models. Geophys. Res. Lett., 41, 135–139
Zhang, X., J. E. Walsh, J. Zhang, U. S. Bhatt, and M. Ikeda, 2004: Climatology and Interannual Variability of Arctic Cyclone Activity: 1948–2002. Journal of Climate, 17, 2300-2317.
Zhang, X., and Coauthors, 2023: Arctic cyclones have become more intense and longer-lived over the past seven decades. Communications Earth & Environment, 4.

SupplementalMaterial.docx

Download PDF

Reviewers agreed at journal
07 Aug, 2024
Reviewers invited by journal
07 Aug, 2024
Editor assigned by journal
06 Aug, 2024
First submitted to journal
05 Aug, 2024

You are reading this latest preprint version

CMIP6 Projected Trend of Winter and Summer Variation in Arctic Cyclones over the 21st Century

Status:

Version 1

Abstract

Figures

Significance Statement

1. Introduction

2. Data and methods

3. Projected changes in Arctic cyclones

4. Projected changes in different Arctic cyclones

5. Relation to the large-scale environment

6. Discussion

7. Conclusion

Declarations

References

Supplementary Files

Status:

Version 1