Role of atmospheric rivers in shaping long term Arctic moisture variability

Domain: earth_science
Published: 2024-06-29
Editorial outcome: major_revision

Abstract
Atmospheric rivers (ARs) reaching high-latitudes in summer contribute to the majority of climatological poleward vapor transport into the Arctic. This transport has exhibited long term changes over the past decades, which cannot be entirely explained by anthropogenic forcing according to ensemble model responses. Here, through observational analyses and model experiments in which winds are adjusted to match observations, we demonstrate that low-frequency, large-scale circulation changes in the Arctic play a decisive role in regulating AR activity and thus inducing the recent upsurge of this activity in the region. It is estimated that the trend in summertime AR activity may contribute to 36% of the increasing trend of atmospheric summer moisture over the entire Arctic since 1979 and account for over half of the humidity trends in certain areas experiencing significant recent warming, such as western Greenland, northern Europe, and eastern Siberia. This indicates that AR activity, mostly driven by strong synoptic weather systems often regarded as stochastic, may serve as a vital mechanism in regulating long term moisture variability in the Arctic.

Introduction
Arctic surface air temperatures have shown a warming trend at a rate more than twice that of the global average in recent decades, attributed to various Arctic Amplification (AA) processes driven by both anthropogenic and natural climate forcing1–9. As constrained by the Clausius–Clapeyron (CC) relationship, Arctic atmospheric warming also leads to atmospheric moistening, resulting in higher specific humidity, greater cloud cover and cloud water content, and more precipitation across the Arctic10–13. This moisture increase has substantially altered the Arctic hydrological and cryospheric variability over the past few decades14,15, owing to various moisture-related positive feedbacks connected to changing radiative properties of the atmosphere, clouds, and surface conditions.

This increase in moisture is prominent throughout the year, with the most significant rise occurring during the summer months (June–July–August, abbreviated as JJA)16,17, which has been attributed to multiple sources triggered by global warming, including increased evaporation from local ocean and surrounding continents18, enhanced sublimation of ice and snow within the Arctic19,20, and intensified moisture transport from lower latitudes15,16,21–27. Climate models forced by historical anthropogenic emissions can replicate a warmer and more humid Arctic in summer, featuring a rather uniform rise in atmospheric temperature and specific humidity across most of the Arctic. Future climate projections suggest that this wetting trend will intensify under continued global warming scenarios, and bring about a number of cascading effects in the Arctic, such as a precipitation regime shift from snow to rain that will substantially change local ecosystems in the coming decades28–31. While these modeling studies highlight the dominance of the CC relationship in shaping Arctic temperature and specific humidity, it is evident that over recent decades summertime changes in these parameters are influenced by large-scale circulation variability, manifested as a long-term trend toward the local barotropic high-pressure anomaly situated over Greenland over the past four decades. This summertime circulation trend pattern is thought to have origins, in part, from internal climate variability and contribute to warming in the mid-to-lower troposphere of the Arctic, as well as the melting of sea ice and the Greenland ice sheet (GrIS) through adiabatic warming processes32–37. Thus, both anthropogenic forcing and internal variability should be factored in when considering the underlying mechanisms behind recent moisture trends in the Arctic.

While the CC equation governs the temperature–humidity relationship on a global scale, internal atmospheric circulation variability also plays a key role in the Arctic. Circulation variability regulates the distribution and transport of moisture across a wide range of timescales through its effects on weather systems including extratropical storms such as Arctic cyclones and atmospheric rivers (ARs). ARs are characterized by elongated, ribbon-like plumes of intense water vapor, driven by synoptic-scale cold-frontal systems, which normally move northeastward and persist for a few days in the extratropcis12,38,39. ARs typically form as a result of the interaction between a cold front, which transports a substantial amount of water vapor, and a warm front40. Thus, ARs represent a unique and intensely extreme phenomenon linked to the storm tracks41,42, closely tied to extratropical cyclone and anticyclone activity43.

Over the annual cycle, ARs contribute to more than 90% of poleward transport of water vapor into the Arctic and are observed across the Arctic for about 1–3 days per month throughout the year, with their highest occurrence from June to August (Supplementary Fig. 1a, which can be found in the Supplementary Information section). This is mainly due to the northward shift of the subtropical jet in these warmer months relative to the cold season, resulting in more ARs propagating into the Arctic44–46 (Supplementary Fig. 1b, c). Thus, boreal summer stands out as a critical period during which ARs have a greater potential of altering moisture distribution in the Arctic and consequently impacting the local climate.

It is worth noting that AR frequency in the Arctic has increased in recent decades, particularly over western Greenland, where stronger ARs have contributed to significant melt over the GrIS’ ablation zone47. These AR-related changes have complex origins, and their causes remain uncertain. Suggestions have been made that mid- and high-latitude AR characteristics (i.e., frequency, intensity, trajectory and duration) are sensitive to thermodynamic effects related to global warming42,48–54 and/or anthropogenic and natural aerosol forcing55–57. Sufficient moisture and eddy kinetic energy are necessary conditions for the formation and activity of ARs. Under global warming, the available potential energy and atmospheric moisture are expected to increase, particularly in the Arctic, consequently increasing the occurrence of ARs there58–60. These AR changes could also be attributed to atmospheric internal variability61–63. It is unclear whether a combination of these mechanisms can fully account for the recent observed features of ARs and the moistening trend in the Arctic. A particularly unaddressed question is how global warming and the low-frequency circulation variability, both separately and in concert, have moistened the Arctic in the past decades through AR activity. This is an emerging topic of interest given the significant potential impacts of summertime ARs in destabilizing the GrIS in recent decades. For instance, extreme GrIS melt in the summer of 2012, which contributed 1.2 mm global mean sea level rise64, was exacerbated by AR activity65,66 connected with amplified JJA anticyclonic circulation over the region67,68. More recently, ARs prompted a mid-summer melt event in northeast Greenland in 2014 and a late summer melt event across much of the GrIS ablation area in 202169.

One hurdle in understanding the interplay between ARs and large-scale circulation change lies in the inability of models in capturing the observed circulation pattern in the Arctic over the past decades when they are forced by anthropogenic radiative forcing or observed boundary SST and sea ice forcing70–72. This poses a significant challenge toward precisely investigating the governing role of circulation on ARs. In this study, we overcome this challenge by employing a nudging approach in Community Earth System Model version 1 (CESM1)73 to examine how ARs respond when observed wind trends are introduced into the model. We then compare these simulated AR changes against observations and AR responses to anthropogenic forcing in the same model. Through this approach, we aim to disentangle the relative roles and underlying mechanisms of anthropogenic forcing and large-scale circulation in regulating the moisture distribution in the Arctic via AR activity. Our goal is to develop physical and qualitative insights about the contribution of ARs to the Arctic moistening trends. We focus our analysis on the summer months (JJA) when the Pan-Arctic moistening trend is most notably positive and significant over the period (Fig. 1b and Supplementary Figs. 1a and 2).

Results
Concurrent changes in large-scale atmospheric circulation and AR characteristics
Shaped by the mean atmospheric circulation pattern (Supplementary Fig. 3c), the average frequency of summertime ARs in each grid point is about 3–4 days per month over western Greenland, northern Europe, from Scandinavia to western Russia and northward to the Barents Sea, and eastern Siberia (Supplementary Fig. 3a). Climatologically, specific humidity, circulation, and air temperature in summer exhibit a consistent pattern, with a minimum value centered at the pole surrounded by higher values in the subarctic (Supplementary Fig. 3b–d).

Over the past few decades, coinciding with the period of enhanced AA and rapid global warming, the frequency of JJA AR events reaching the Arctic exhibits an overall increase of 0.1 days month−1 decade−1 from 1979 to 2019. However, this increase is not uniform. AR frequency increases significantly over northern Canada extending to western Greenland, eastern Siberia, and small portions of the central Arctic and northern Europe, while the North Atlantic Ocean, western Alaska, and central Siberia witness weak negative trends (Fig. 1a). Patterns of long-term variations in AR frequency reveal notable similarities to the observed trends in large-scale JJA circulation (Fig. 1c). Upper-level geopotential heights significantly increase across western Greenland, northern Europe, and eastern Siberia, (highlighted in Supplementary Fig. 3e) but over the North Atlantic Ocean, Alaska, and central Siberia the increase is relatively small. In line with these atmospheric circulation trends, both specific humidity and temperature display pronounced rises over western Greenland, northern Europe, and eastern Siberia (Fig. 1b–d).

However, these observed changes are not fully captured by our best estimate of the climate response to anthropogenic forcing, as derived from ensemble means of historical simulations in the Coupled Model Intercomparison Project Phase 6 (CMIP6) and the CESM2 large ensemble. The ensemble mean model simulations show a slight change or nearly uniform rise of geopotential height (200 hPa), air temperature (surface to 200 hPa average), and specific humidity (surface to 500 hPa average) over Greenland and Eurasia, respectively, which strongly contrasts observational findings (Fig. 1 vs. Supplementary Fig. 4) and may signify the role of internal variability in shaping the patterns. It has been extensively discussed in earlier studies that this discrepancy is a common issue across most historical simulations, partially attributed to models’ limitations in replicating the observed low-frequency variability of tropical–extratropical teleconnections33,36,37,71,72,74,75. In particular, the ensemble mean of JJA ARs displays only an increasing trend, with the highest values located over the Arctic Ocean close to the North Pacific, rather than primarily centered atop and around Greenland as observed (Supplementary Fig. 5a). This suggests that the storm track shift over the North Pacific close to the Bering Strait has a larger contribution to the increasing trend of ARs in the Arctic under the influence of AA in models.

A better understanding of these discrepancies between observations and the model response to external forcing is necessary to accurately assess the sensitivity of ARs to global warming and to account for other potential forcing factors. The above-described alignment between the observed trends in atmospheric circulation, temperature and specific humidity fields, and AR frequency suggests that large-scale circulation plays a key role in regulating poleward moisture transport through the circulation’s control on ARs76–78 (Fig. 1a, c). We assess these connections from a statistical perspective by employing maximum covariance analysis (MCA)79 to objectively examine the primary coupled patterns among climatic variables. Figure 1e, f shows the spatial pattern of the leading MCA mode of detrended JJA 200 hPa geopotential heights (Z200, m) and JJA frequency of ARs between 1979 and 2019. The leading MCA mode (MCA1) explains 41.3% of the squared covariance and its spatial pattern clearly exhibits a zonal wavenumber 2 structure along 70°N. Regarding the MCA1 pattern, the positive signals of Z200 and ARs are found over western Greenland and the Siberian Arctic, while negative loadings of the two variables are observed over western Europe/western Siberia and areas from western Canada to the Bering Strait. Apparently, these two patterns bear some resemblance to the long-term trends of ARs and circulation, respectively, particularly over Greenland. This resemblance suggests that these secular trends of Z200 and ARs over Greenland may be physically connected, as indicated by their interannual connections identified by MCA1.

MCA1-related 200 hPa wind anomalies demonstrate how JJA mean circulation anomalies shape ARs on interannual timescales. During the summer season, westerly winds prevail in the Arctic troposphere across the region in the climatology (Supplementary Fig. 3c, vectors). ARs are more frequent over the regions marked by strong southerly wind anomalies, while decreased AR frequency is seen over the regions dominated by anomalous JJA northerly or easterly flows (Fig. 1f, indicated by wind vectors). This suggests a strong governing forcing of anomalous seasonal mean flow on AR activities within the season on interannual timescales. In light of the strong impacts of seasonal mean circulation on ARs, if large-scale circulation also varies on low-frequency timescales in specific areas, ARs may respond to these changes and reflect long-term variability.

Moreover, we observe a consistent coupling pattern in the MCA mode between any two fields from Fig. 1, whether trends are removed or not (figures not shown). To further link the change of this MCA mode to other fields of the Arctic climate system, we correlate the time series of MCA1-ARs (or MCA1-Z200) with the detrended JJA low cloud, downward longwave radiation, surface air temperature, and sea ice. We obtain significant correlations among these variables, indicating the importance of the large-scale circulation trend in creating a warmer, cloudier, and more humid atmosphere, characterized by increased AR incursions into many areas over the past decades. This, in turn, amplifies downward longwave radiation and leads to accelerated Arctic sea ice and land ice melt (Supplementary Fig. 6).

Contribution of ARs to long-term changes in specific humidity
While ARs are statistically connected with large-scale atmospheric variables, such as geopotential height, specific humidity and temperature, in the Arctic on interannual and lower frequency timescales (see Fig. 1), it remains unclear how ARs are physically driven by these large-scale climate variables and potentially feedback on these fields. In this section, we use 6-hourly changes in specific humidity to examine how moisture changes in some regions are associated with 6-hourly AR propagation into the Arctic. This analysis will enable us to develop an approach to estimate the contribution of ARs to the increasing humidity observed over the past 41 years. We first focus our analysis on the western Greenland region, given that it has witnessed the most significant increases in AR frequency and lower tropospheric specific humidity. The year 2012 is particularly noteworthy as it marked the most extreme melt season of the GrIS and Pan-Arctic sea ice since 197964. Therefore, our first examination centers on that year. An MCA analysis between daily AR frequency and specific humidity anomalies during JJA of 2012 is computed to understand their day-to-day connection (for more details, refer to the Methods section). Climatological seasonal cycles and interannual variability are removed from each grid cell to ensure that the identified MCA pattern primarily reflects the connection on shorter timescales.

The leading MCA mode, explaining 58.3% of the squared covariance, displays a large positive AR loading west of Greenland that is collocated with a widespread increase in specific humidity (Fig. 2a, b). The time series of the ARs and moisture modes closely covary throughout the season (Fig. 2d). In particular, in mid-July, a strong AR preceded a significant surge of atmospheric moisture over most of Greenland65,66,80 and impacted Summit Station, where exceptional surface air temperatures reached 2.2 °C81. This moisture intrusion resulted in widespread ice sheet melt, a phenomenon not observed since 188980,82,83. An MCA analysis for all summers (by connecting all summertime daily data together) from 1979 to 2019 over the region yields a similar coupling pattern (Supplementary Fig. 7a, b). Likewise, MCA for the regions over northern Europe and eastern Siberia exhibits a highly similar coupled mode and a close coherence in the temporal variations for individual summers from 1979 to 2019 (Supplementary Figs. 8 and 9). This highlights the consistent influence of ARs in driving specific humidity changes on synoptic timescales. To illustrate this effect across all summers, we make composites of specific humidity associated with the strongest ARs centered in three key regions (western Greenland, northern Europe, and eastern Siberia, Supplementary Fig. 3e). In each of these regional centers, specific humidity begins to increase approximately 1 day prior to the onset of ARs. Subsequently, following the occurrence of ARs, specific humidity experiences a rapid increase and peaks approximately 1 day after the AR onset, and then swiftly returns to values similar to those observed before the onset of ARs (Fig. 2c and Supplementary Fig. 10). We also create similar composites for areas near the North Pole and southwestern Alaska (Supplementary Fig. 3e), where long-term trends of ARs show a strong increase and decrease over the past four decades, respectively. A similar temporal progression of ARs and specific humidity changes is observed in all of these areas (Supplementary Fig. 11). This analysis suggests that specific humidity changes across most Arctic regions are partly determined by AR activity within the season. Possibly, changes in long-term trends of atmospheric moisture are also mirrored by long-term changes in Arctic AR frequency to some extent.

To quantify the impacts of ARs on moisture changes over the past four decades, we use a statistical method to differentiate AR impacts on the long-term changes of JJA-specific humidity in the Arctic (see “Methods”). As shown in Fig. 3a, the long-term trend of JJA-specific humidity unrelated to AR activity has only increased in some subarctic areas, especially in the North Atlantic. The difference between this trend pattern (Fig. 3a) and the original one (Fig. 1b) is empirically attributed to long-term impacts of ARs in summer (Fig. 3b). More specifically, the moisture trends related to and unrelated to ARs in the Arctic (north of 60°N) are 0.017 g kg−1 decade−1 and 0.03 g kg−1 decade−1, respectively, indicating that the trend of ARs activity may contribute to 36% of the overall increase in atmospheric moisture across the Arctic since 1979. In addition, the impact of ARs is most pronounced in specific regions, including western Greenland, northern Europe, and eastern Siberia, where ARs account for 57.1%, 47.1%, and 67.8% of the corresponding increases in moisture over the period, respectively.

Role of circulation variability in shaping AR activity in CESM1
The CMIP6 and CESM2 model response to historical radiative forcing indicates that the observed AR changes cannot be fully explained by anthropogenic forcing (Supplementary Fig. 5). Based on the statistical connection of ARs and circulation revealed by the aforementioned MCA analysis, we hypothesize that the large-scale circulation may direct more ARs into the Arctic region in recent decades. To further assess the plausibility of this linkage, we conduct three sets of simulations using CESM1. The first one is a CMIP6-type simulation with CO2 forcing set at constant values of the year 2000 (CO2 = 370 ppm) (referred to as “CTL” hereafter). This run aids in understanding how ARs behave in a climate state without changes in any radiative forcing. In the second set, in order to evaluate how long-term trends of JJA circulation exert a forcing on ARs in the Arctic, 3-D wind anomalies (m/s/decade) of JJA ERA5 circulation over the past 41 years (U and V) are added to the model during summer, only within the Arctic (north of 60°N) (see “Methods”) (referred to as “WIN” (with wind nudging only)). These anomalous 3-D winds (constant values in each grid cell over the 3 months) are incorporated in the model at each timestep through the season, introducing seasonal mean wind anomalies without introducing any synoptic or intraseasonal variability in the wind fields. The goal of this design is to assess whether the model’s response to these imposed wind trends can generate conditions conducive to ARs varying in a manner consistent with observations. The third set is the same as the second one except that anthropogenic forcing is imposed at the values of the year 2020 (CO2 = 415 ppm) to include impacts of both anthropogenic forcing and anomalous winds (referred to “WIN+CO2”). The decadal change rate of CO2 concentration from 2000 to 2020 ((415 ppm − 370 ppm) / 2 decades ≈ 20 ppm/decade) is very similar to changes observed over the past four decades (~20 ppm/decade). This enables us to examine how anthropogenic forcing, along with the influence of the long-term wind trend pattern, regulated ARs from 1979 to 2019. Each set is continuously integrated for 40 years and the 40-year means of each set are used to estimate a relatively stable model response to the imposed forcing.

Figure 4a shows the difference of AR frequency between WIN and CTL simulations. It is clear that ARs occur more frequently over northwestern Greenland and the northern part of Eurasia, but less frequently in southern and eastern Greenland and the Bering Strait, similar to observations (Figs. 4a and 1a). The simulated ARs appear to be closely related to the large-scale circulation pattern. Southerly wind anomalies to the west of the high pressure over Greenland and central Siberia increase the frequency of ARs, while northerly wind anomalies to the east of the high pressure reduce AR activity (Fig. 4b, vectors). The positive AR response in northwestern Greenland and northern Europe is approximately 0.5 days per month, which is consistent with changes in observations (Figs. 4a and 1a). Around southern and eastern Greenland and the Bering Strait, the magnitude of the negative AR response is around –0.5 day per month (Fig. 4a). This implies that a wind field change in magnitude around 1 m/s can lead to significant JJA AR changes. The simulated patterns of ARs and Z200 are consistent with the observed covariability of ARs and Z200 detected by MCA (Figs. 1e, f and 4a, b).

The difference in AR frequency and circulation between WIN+CO2 and CTL is similar to that between WIN and CTL (Fig. 4a–d), suggesting that anthropogenic forcing in this model has a minor effect on AR activity in the Arctic. Since the response of ARs to anthropogenic forcing may be model dependent, we cannot exclude the possibility that some observed AR signals are indeed a consequence of global warming effects over the past few decades.

Role of circulation variability in shaping AR activity using a fingerprint analysis
To further examine the impact of large-scale circulation on ARs we adapt a distinct approach, referred to as fingerprint analysis63, based on the CESM2 large ensemble. In this approach, we use the AR and Z200 trends in the ensemble mean of 40 members to represent the forced response of the two fields to anthropogenic forcing, and the difference between each member with the ensemble mean as long-term trends due to internal variability. The ensemble mean of the ARs (Supplementary Fig. 5b) and Z200 trends (Supplementary Fig. 12) exhibit markedly different patterns from those in observations (Fig. 1a, c), echoing our findings derived from the CMIP6 result (Supplementary Fig. 5a), and reinforcing the notion that the observed AR and circulation trend patterns over the past decades cannot be fully explained by anthropogenic forcing.

To further understand how internal variability has contributed to the observed changes, we select a subset of 8 members from the 40-member ensemble, if their simulated AR frequency trends exhibit an increase (>80% percentile of simulated AR trends) over western Greenland. We then compare the large-scale circulation trend from this subgroup to examine whether the high-pressure pattern also emerges over Greenland, as observed. As we expect, the subgroup, possessing a better chance of replicating an increasing trend of ARs over western Greenland, also replicates the observed high-pressure trend over Greenland, indicating a strong constraint of large-scale JJA circulation trends on long-term changes of AR frequency over the region (Fig. 5a, b). Similar constraints of circulation on ARs are evident when focusing on northern European and eastern Siberia (Supplementary Fig. 13). Furthermore, two of the 40 members demonstrate an AR frequency increases trend in both western Greenland and eastern Siberia, and the corresponding large-scale circulation clearly exhibits a chain of high pressure in the above two areas as observed (Fig. 5c, d). This result suggests that long-term trends of large-scale circulation, particularly in forms similar to those observed, have a capacity to strongly modulate AR activity on low-frequency timescales. The establishment of this governing effect is attributed to a strong internal origin, as it is only discernible in a subset of the 40 members, rather than the ensemble means. This approach, emphasizing the internal variability of the model system, facilitates us to reasonably use model results to interpret observed features.

Our model experiments collectively support our initial hypothesis that the large-scale wind trend is able to apply a forcing to regulate changes of AR frequency, particularly over Greenland, northern Europe, and part of Eurasia. Over eastern Siberia, the wind-induced increase of AR frequency is shifted toward the interior of the continent in our nudging simulation results. However, the results obtained by the fingerprint method show that the large-scale circulation may still have some influences on ARs in that area (Supplementary Fig. 13c, d), indicating the need for further studies.

Methods
Reanalysis and observational data
We examine changes in Arctic atmospheric circulation (Z200), specific humidity (For brevity, in some parts of the text, we use “humidity” and “specific humidity” interchangeably.), temperature, cloud cover, and radiation over the period from 1979 to 2019 using 6-hourly and monthly reanalysis data from ERA585 and ERA-Interim86. Our results are consistent between the two reanalysis datasets. Therefore, in this work, we focus on the findings derived from ERA5. All variables are regridded to a 1.5° × 1.5° resolution. We primarily utilize monthly reanalysis data to calculate the trend (calculated by the least-square regression method) of variables in our study (or for MCA). However, when identifying AR variability, 6-hourly data is ideal due to the transient and extreme nature of ARs, where higher temporal resolution data can better capture their temporal characteristics. Unfortunately, only a few CMIP6 models provide daily data (U, V, and specific humidity over multiple representative vertical levels) for the public, and 6-hourly data for these variables are not available. Consequently, we employ a remedial approach using daily data to detect ARs for these models. Monthly sea ice concentrations are acquired from NSIDC87 for the analysis of the impact of ARs on sea ice variations.

Large ensemble CESM2 simulations and CMIP6 experiments
We use 6-hourly data from 40 realizations of the CESM2 historical run88 for the period spanning 1979-2014, each with slightly different initial atmospheric states. These 40-member CESM2 (CESM2-LEN) simulations are employed to explore the relationship between ARs and circulation and to assess how ARs respond to atmospheric internal variability. In addition, the multi-model ensemble mean of monthly output from the historical runs of 34 climate models archived in CMIP689 (Supplementary Table 1) is used to investigate the response of the atmosphere to historical anthropogenic forcing. We also incorporated daily data from the multi-model ensemble mean of 10 (as daily outputs needed for AR calculations are only accessible in these 10 members) climate models stored in CMIP6 historical runs (Supplementary Table 2) to calculate AR frequency and estimate the AR response to historical anthropogenic forcing. All of the CMIP6 model output mentioned above is focused on the period from 1979 to 2014. To facilitate comparison with the reanalysis data, we regrid all model datasets to a 1.5° × 1.5° resolution.

Nudging experiments with the CESM1
To examine the regulation of large-scale circulation on AR activity in the Arctic in the fully coupled CESM173, we incorporate long-term trend wind patterns (the 200 hPa winds pattern is shown in Fig. 1c) from ERA5 to constrain the Arctic (north of 60°N) circulation in the WIN (with wind nudging only) and WIN+CO2 (with wind nudging and anthropogenic forcing) runs from the surface to the top of the atmosphere. The magnitude of these wind trends is those changes spanning over 10 years (per decade). In each model step from June 1 to August 31 for these two runs (WIN and WIN+CO2), within the nudging region, additional wind tendency terms, equal to the observed long-term wind trends, are added on top of the model’s tendencies of zonal and meridional winds at each grid point. Since we impose constant wind anomalies each summer, this method acts to numerically incorporate observed wind trends into the model without introducing artificial short term variability. The same method has been used in a number of previous studies37,70,71, which indicates the reliability of this approach in replaying observed circulation changes in the model. In addition, CO2 concentration is held constant in CTL and WIN at the year 2000 level (370 ppm). In the WIN+CO2 run, CO2 is held constant at the value of the year 2020 (415 ppm). Other radiative and anthropogenic forcing, such as solar constant, other types of greenhouse gases, ozone concentration, various aerosols, land-use/land cover conditions are exactly the same between the two runs.

AR detection
The ARs are defined via a detection algorithm based on integrated water vapor transport (IVT)90. In this algorithm, the 85th percentile of the climatological monthly IVT magnitude at each grid point is used as the threshold to identify ARs. IVT is calculated from ERA5 6-hourly wind and specific humidity fields integrated from 1000 to 300 hPa on 0.25° × 0.25° global grids. Additional criteria include the requirement that the length of the identified AR areas should be greater than or equal to 2000 km and that the length-to-width ratio should be greater than or equal to 2. The ARs dataset is regridded to a 1.5° × 1.5° spatial resolution for analysis. This algorithm is recommended by the Atmospheric River Tracking Method Intercomparison Project (ARTMIP)91, and has been extensively used in previous studies in detecting ARs in the Arctic and Antarctic78,92–94. In this study, ARs in the Arctic refer to all AR activity north of 60°N. Given that ARs represent transient and extreme moisture transport events, data with higher temporal resolution better describe their characteristics. Therefore, 6-hourly ERA5 reanalysis data are typically utilized to detect their signatures. We note that relatively few CMIP6 models (10 total) provide daily data as their highest temporal resolution data. As such, we only use this group to detect changes in ARs within the models. To study the year-to-year variability of ARs in the Arctic, a monthly mean AR field is created based on 6-hourly AR data. This field counts the number of AR activity in each grid point within each calendar month, with the value representing the monthly AR frequency (unit: days/month) in each grid point.

AR frequency and specific humidity composites
To assess the impact of daily ARs on Arctic water vapor changes, we focus on strong AR events that occurred since 1979. These strong ARs are defined based on their continuous activity at the central points of three key regions (western Greenland, northern Europe, and eastern Siberia) for a minimum duration of 24 h. It is important to note that the water vapor (specific humidity) anomalies associated with ARs are found to last behind the end of ARs by approximately 1 day. To ensure that the selected AR cases were not influenced by the prior water vapor changes resulting from preceding AR activity, we apply a criterion that requires these selected ARs to have no other AR activity on the day before their occurrence. Using this approach, we identify a total of 97 cases of ARs in the central point of western Greenland, 90 cases in northern Europe, and 83 cases in eastern Siberia. We then conduct composite analyses to examine the specific humidity anomalies associated with the AR events. Specifically, we analyze the changes in specific humidity (after detrending and removing the climatological seasonal cycles) for 4 days preceding and 5 days following the onset of ARs.

Quantification of the contribution of ARs to specific humidity trends
We apply an AR removal method to isolate water vapor changes associated with 6-hourly AR activity. Initially, vertically integrated specific humidity fields for each summer (1979 to 2019) with 6-hourly resolution are generated. For each 6-hourly field from June 1st to August 31st, grid points with AR activity are determined by the “shape” of ARs as defined by the detection algorithm. When reconstructing the JJA seasonal mean humidity values in these grid points for any given summer, specific humidity values within these AR impact areas are excluded (with humidity over AR-shape grids set as zero). Conversely, averaging those 6-hourly grid points impacted by ARs (with humidity over non-AR grids set to zero) will form a JJA-specific humidity field related to ARs. This approach allowed us to reconstruct two JJA-specific humidity fields over the past 41 years, representing humidity variability unrelated to ARs and related to ARs, respectively.

Description of the MCA approach
Maximum Covariance Analysis (MCA), often referred to as Singular Value Decomposition (SVD) analysis, is a valuable statistical method utilized in meteorological and oceanographic research to capture coupled modes of covariability between two fields79. Typically, this method operates on two distinct 2-D fields, X(m, t) and Y(n, t), where m and n represent spatial points and t represents the temporal dimension. MCA aims to compute a SVD of X × Y’ (transpose matrix of Y), where each element in the matrix (X × Y’) represents the covariance of a pair of points from X and Y, respectively (one spatial point from X and one spatial point from Y). This enables MCA to identify the dominant coupling modes between variations in X and Y. Specifically, we use MCA to extract the dominant coupled modes between the AR frequency and atmospheric variables over the Arctic in summer.

Significance of correlation
The assumption for the statistical significance test of linear correlation is that the time series used to calculate the correlation consists of completely independent samples characterized by white-noise processes95. However, if the data contain strong autocorrelation (indicating some red-noise processes), this assumption may not hold well. To address this issue, it is necessary to consider the effect of effective sample size when assessing the significance of correlation by using conventional T test96. Accordingly, the statistical significance of the correlation coefficient is estimated by using an effective sample size N*, which is given by:

N* = N (1 − r1r2) / (1 + r1r2) (1)

where N is the number of available time steps, r1 and r2 are lag-one autocorrelation coefficients of each variable97.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This manuscript explores the trend in atmospheric rivers in the Arcfic in boreal summerfime and the drivers of this change. Using observafions and models, including experiments with winds nudged to observafional trends, they found that large-scale circulafion trends played a key role in atmospheric rivers in the Arcfic and that they contribute to the increased wefting trend in the Arcfic summer. Understanding the variability and long term trends of humidity in the Arcfic is vitally important in terms of changes precipitafion, temperature, sea ice etc and so an analysis of this is necessary. However this paper at fimes seems a liftle disjointed, jumping between indicafing that humidity is a driver of AR to AR driving humidity or that the paper is about the trends of humidity. I think clarifying the message of the paper befter is necessary. Furthermore I feel that there should have been more analysis done on other sources of humidity in the Arcfic or simply remove those completely and focus only on AR related humidity in summer fime. While I find the model experiments with nudging the winds in the model interesfing, more work is required to make the manuscript publishable.\nI have outlined a number of concerns/quesfions about this below.\nMajors\nI am curious about the role of other sources of moisture in the Arcfic namely as Figure 3a shows that there is a large significant porfion of the Arcfic in summer that is experiencing increasing humidity that is not related to ARs and this should be invesfigated more thoroughly or have more separafion between AR induced humidity relafive to influence of remote sources (sea ice loss) for example.\nModel bias was never discussed at all which I think is important. If the observafions are showing that we are gefting an increase of moisture flux from ARs through changes in circulafion pafterns, but the models do not show this, then an explorafion into the wind/circulafion trends in models relafive to observafions would be a first step to understanding the differences between the models and the observafions rather than stafing it is solely due internal variability.\nThis is all assessing the trends from 1979-2014 but I feel a brief analysis of the climatology is needed to assess what these trends are in relafion to parficularly for the circulafion, is more negafive trend that it is more negafive or a shift in the centres of acfion? for example AR trends show a decrease trend over Alaska were in the mean this region has the most AR’s climatologically. I think it would be worth also referencing this within your study, is the circulafion changes contribufing to this negafive trend in ARs in that locafion?\nDetails\nLine 82: You state that summer months get more ARs because the jet is further poleward but further poleward relafive to what. Supp fig 1b just shows climatological winds and AR so not evident that it is more poleward e.g. is this relafive to DJF – then maybe say or show that too.\nLine 91: What are the thermodynamic effects from global warming the ARs are sensifive to?\nLine 95: Are the “recent observed features” the increase in ARs?\nLine 107: Is there any references/papers to support this claim that models lack the ability to capture the observed circulafion pafterns in the Arcfic?\nLine 110: Why did you decide to go for wind rather than geopotenfial height for example for the circulafion trends?\nLine 120: Counfing the number of AR acfivity by grid – what do you mean by grid here, is it West Greenland or by gridcell as this is what is usually thought when one refers to ‘grids’ in a modelling paper. This lack of disfincfion in present throughout the manuscript parficularly in the methods (e.g. lines 398 and 422) and is confusing so I suggest changing the name e.g. region.\nLine 133: atmospheric temperature is fig 1d not 1c as stated here.\nLine 143: Relafing to the uniform rise in z200 over Greenland and Eurasia, I would not say that this is uniform, its much greater in Europe than Greenland, and with the white colour even make it appear, at first glance to have no trend. This could be wriften befter to address this slight differences which are likely from internal variability.\nLine 147-149: I am not sure how storm track shifts over the North Pacific may have impacted increasing trends of AR, has this been documented somewhere before? Have you does analysis to show this?\nLine 165-168: I find this difficult to read, you say that “Z200-AR may share common mechanisms” which to me suggests that something else controls BOTH Z200 and AR but isn’t it that the circulafion aka Z200 that drives AR? And the second half ‘suggesfing a deeper understanding” is very dense to read.\nLine 170: The text says tropospheric winds but figure 1f says 200hPa winds so have you assessed winds throughout the verfical column or just at the top of the troposphere? If lafter state that this is the upper level westerly winds and remove the suggesfion that this is throughout the whole troposphere.\nLine 174: Can you explain how this shows interannual fime scales if it is days/month, is it also per year?\nLine 188-193: Have you shown that AR are stafisfically connected to humidity and temperature, figure 1 a-d shows only the linear trends but does not link them stafisfically? And was there ever a suggesfion previous to this line that humidity and temperature drove AR trends, especially when in lines 192 you state the humidity is associated with AR propagafion into the Arcfic indicafing that AR therefore is a driver of humidity. Revise this secfion and be very specific by what you mean.\nLine 204: How many ARs were there in 2012 over Greenland?\nLine 213-214: As seen in supplementary figure 7.\nLine 222-224: This is a liftle crypfic, what other regions are these? Are there figures for them?\nLine 230-233: This commentary is confusing to me, you say that figures 3a and 1b are similar (I agree here) but that this is similar to figure 3b, which I do not see. Figures 3a and 1b show increases of humidity everywhere while humidity related to ARs (figure 3b) shows negafive trends so can you explain this a liftle more.\nFurthermore, is a sum of Figs 3a and 3b result in fig1b – meaning that you get increase trend of humidity everywhere.\nLine 241: I do not understand this, you don’t actually look at AR changes in CMIP6 models but just humidity, Z200 and temperature?\nLine 270: Are you able to actually show the wind anomalies in Figure 4b rather than infer based on the Z200 circulafion?\nLines 287-293: Have you plots made to show this? If so, reference them as I am not sure what you are talking about/showing here.\nLine 295: What is the size of the subgroup?\nLine 303: You state here that you only use 2 of the 40 members but don’t state this in the figure capfion so just want to check what you are actually showing in figure 5c and d.\nLine 309: You have shown previously that the models do not do a good job replicafing the trends of AR and Z200 and yet here are saying that they agree with your results and so I am confused about this?\nFigures\nSupp fig 5 and 8: In my version of documents, these plots are cut off and so difficult to aftest to claims made about these figures in your manuscript.\nMethods:\nLine 389: Be specific here, what are the anthropogenic forcings that are held constant?\nLine 416: I would just note that you are examining the specific humidity anomalies here, even though it is menfioned in following line, will avoid any confusion about what is meant.\nMuch more detail is needed about the nudged model specifically demonstrafing what the nudged field looks like.\nSignificance of correlafion, you never explain why it is important to account for autocorrelafion in the fime series", "reviewer_2": "Review of the manuscript NCOMMS-23-52552 enfitled “Role of atmospheric rivers in shaping long-term Arcfic moisture variability” by Wang et al.\nThe manuscript shows the influence of the large-scale atmospheric circulafion in modulafing the atmospheric rivers moistening impact on the Arcfic using both the ERA5 reanalysis and idealised simulafions with a climate model, in order to explain the trends in the observed atmospheric rivers frequency. The paper is relafively well wriften. However, I find that many aspects (methods and results) could be befter explained, especially the maximum covariance analysis, on which I am not an expert myself. Therefore, I recommend major revisions of the manuscript.\nHere below, are my comments listed in the order of the manuscript followed by a few technicalifies.\nFig. S1a and line 82: The figure represents a mean frequency of atmospheric rivers (AR frequency), not a mean. Please modify the plot and the capfion. Moreover, it is not clear how this AR frequency in the Arcfic is defined. Is it the mean of the AR frequency north of 60N? Is it the mean of AR frequency at 60N? In this lafter case, are only the poleward ARs considered or are all ARs considered? Please explain befter in the capfion as well as in the Methods secfion.\nFig. S1b and lines 82-84: To show that the AR frequency and the jet are shifted northward in summer compared to winter, the authors should have represented the difference between winter and summer and not summer only. This behaviour of the jet is well known, but it maybe less known for the ARs. Please consider adding this plot (difference or superimposifion of the winter and summer fields in contours).\nFig. S2: In the capfion, (blue fan-shaped covered area, → (blue fan-shaped covered area)\nLines 115-117: the authors menfion “moistening trends”, but they do not show any specific humidity trends, for example. It seems that they assimilate this wording to the AR frequency trend. However, it is not fully correct as ARs are not the only source of humidity in the Arcfic. The authors also wrote “the Pan-Arcfic moistening trend is the most posifive and significant (Fig. S1a)”, but they do not show any significance calculafion and they should rather refer to Fig. S2b, which shows a trend, shouldn’t they? Ideally, the authors should rather represent the specific humidity trend in summer and winter.\nLine 130: The authors should also add Western Alaska here because it seems to be one of the most prominent pathways of AR into the Arcfic in summer, with the largest AR mean frequency north of 60N as seen in Fig. S2a.\nLine 133: Why using the geopotenfial at 200 hPa? It is in the stratosphere at those high lafitudes. Could the authors explain why they use this pressure level and not at a lower level, such as 400 or 500 hPa?\nLine 133: Fig. 1b-1c → Fig. 1b-d (to including panels b, c, and d)\nLine 136: The authors should describe the methodology of the maximum covariance analysis in the Methods secfion and what results it will give so that readers not familiar with this method can understand it.\nLines 130-137 could be wriften more concisely. Consider rewrifing.\nLines 138-144: What about adding SSP5-8.5 to complete the historical simulafions and calculate the trend over the exact same period as in the reanalysis (same as what has be performed for CESM2 in Fig. S4b it seems)? Or what about using only 1979-2014 in the reanalysis to match the CMIP6 historical period? A few more or less years can make a difference when calculafing trends (see for example Fig. 2 of Ouften et al. 2022 hftps://wcd.copernicus.org/arficles/4/95/2023/wcd-4-95-2023.pdf).\nLine 142-143: why not using the same verfical extent for all variables? Moreover, these verfical extents are different than the one used to calculate the IVT (line 399). Can the authors jusfify their choice?\nLine 144: Could the authors add a measure of significance on Fig. S3? An easy one could be the number of models agreeing on the sign of the trend. Moreover, are there some CMIP6 models in befter agreement with ERA5 than others? Could the answer to this quesfion be menfioned in the manuscript?\nFig. S4a: Why is this figure the mean of only 10 CMIP6 models when Fig. S3 includes 40 models? Could Fig. S3 be redrawn with the 10 same models?\nIn the capfion: “and 40-member” → and (b) 40-member\nLine 148: What are the SST changes the authors menfion here? Please be more precise. Why would it be the only factor influencing the AR trend found in CMIP6?\nLine 165: What are “the long term changes of ARs and circulafion”? Do the authors mean the trend?\nLine 181: Shouldn’t it be MCA1 ARs-Z200 instead of MCA1-ARs or MCA1-Z200 since the maximum covariance analysis is performed using both the AR frequency and Z200? Do the authors expect different results by using MCA1-ARs instead of MCA1-Z200? Please clarify the MCA methodology.\nLine 195: Greenland → the Western Greenland region\nLines 196-198: Could the authors add a reference to this statement?\nLine 199: What do the authors mean with “daily AR frequency”? Aren’t the daily fields binary masks (0-1 fields) of atmospheric rivers?\nWhy do the authors use both CESM1 and CESM2 in their study? Would it be possible to use only one model version so that the results of their two analysis are comparable? Do the authors expect the two versions of CESM to give similar results for their idealised experiments? This uncertainty could be acknowledged somewhere in the manuscript.\nLines 206-207: Isn’t that result coming directly from the MCA? Would the authors expect another result? Also, the figure referred to should be Fig. 2d not 2c, right?\nFigure 2d: - Could the authors give more details on what is represented on panel d? Why is the y-axis label saying “Standard deviafion” whereas the capfion text says “standardized fime series”? Please clarify.\n- Moreover, the straight horizontal parts in the blue curve highlight the days with no AR. Is that right? Isn’t it an issue to perform the MCA with some fime series having 0s?\n- Finally, it is not clear to me if this fime series (panel d) is for the central point of the region or an average over the region or something else. Please add this precision.\nLines 207-208: Again, what does the value of the fime series peak on 11 July (Fig. 2d) mean? I understand that the fact above 0 values mean that there is an atmospheric river and that many days around the 11th experienced an atmospheric river. Is that correct?\nLine 209: remove ).\nFigure 2c and Figure S7: Why do the two figures have different y-axis label (AR change (days) for Fig. 2c and AR anomaly (case) for Fig. S7). Please adjust and make it consistent (both the label and the unit).\nLines 228-230: The methodology is not clear at all to me. I suggest a major rewrifing of the method secfion for this analysis.\nLines 233-234: How are those trends calculated? Please explain in the text.\nFigure 3: My understanding is that Fig. 3b is obtained by subtracfing Fig. 3a from Fig. 1b. Is that correct? In Eastern Siberia, it looks like the sum of Fig. 3a and b would be higher than Fig. 1b. Could the authors check? In any case, please add some explanafions to the methods (as also wriften above).\nLines 241-242: I am sorry, but I do not understand how the authors can say that from Fig. S3a. Could the authors elaborate a bit here?\nLine 254: Does the addifion of the anomalies introduce noise or disconfinuifies in the wind field?\nLine 276: How small is this wind change?\nLine 291: “...and again, and reinforcing…” needs rewrifing.\nLine 366: add (Z200) after “Arcfic atmospheric circulafion”\nLines 367-368: Why do the authors use both 6-hourly and monthly reanalysis data? Moreover, since data from models are available daily, why not also use daily reanalysis, instead of 6-hourly? It looks inconsistent, especially as the ARs are detected from daily fields for the models but 6-hourly fields for the reanalysis.\nLine 398 (and others, e.g. 425, 426): “grid” → grid point\nFollowing the Oxford dicfionary, the noun grid means “An arrangement of parallel bars with openings between them”. Therefore, the authors seem to misuse this noun. Please correct where appropriate.\nLine 410: “lag” or last?\nLines 422-423: “Inifially … algorithm”. This sentence is not understandable to me. Please adjust and clarify.\nLine 424: “extended spafiotemporally”. The spafial extension is of 6 degrees, but how long in fime is the AR impact extended?\nLine 426: What do the authors mean with “fime point”?\nLines 428-429: How can the AR impacts be removed? Please add an explanafion.\nFigures:\n- Please represent the coastlines in black. They are barely visible in gray.\n- Figure 1 would benefit a lot from larger panels with for example only 3 panels on the top row.\n- How is the significance calculated (the black dots in the figures)?\nTechnicalifies (probably not exhausfive):\nLine 53: altered Arcfic → altered the Arcfic\nLine 78: move eastward → move (north)eastward\nLine 135: in the Fig. S2b, Fig. 1c → in Fig. S2b, Fig. 1c\nLine 399: 0.25° x .25° → 0.25° x 0.25°\nLine 442: is → are\nLine 655: The dofted → Black dots\nLines 681, 690, 699: The dofted grids → Black dots"}, "author_response": "We express our gratitude to all the reviewers for dedicating their time to evaluate our work,\nproviding both positive feedback and constructive criticism on our manuscript. Their\ninsightful comments and suggestions have significantly contributed to enhancing the\npresentation and clarity of our key arguments. We have fully integrated the reviewers’\nrecommendations into the revised manuscript, encompassing the following three key changes\noutlined below.\n1. To address the concerns raised by the reviewers, we have made several revisions to the text.\nIn the introduction, we have rewritten various sections to discuss multiple sources\ncontributing to the recent moistening trend in the Arctic (Lines 58-62) and have provided\nexplanations for why ARs are sensitive to thermodynamic effects of global warming (Lines\n103-106). Furthermore, we have emphasized the connection between extratropical storm\ntracks and ARs (Lines 84-88). In the Methods section, we have clarified why data with\ndifferent temporal resolutions were utilized (Lines 413-418) and elucidated the methodology\nfor distinguishing moisture variability related to ARs from that unrelated to ARs (Lines 482-\n495). Additionally, we have introduced a new segment in the Methods to elucidate the\nfundamental principles of the MCA method and have modified the discussion on the\nsignificance test of correlation in response to the reviewer’s concern.\n2. Some parts of the main text and supplementary materials have been reorganized, and\nnumerous plots have been redrawn to streamline our presentation and improve clarity of\nreasoning.\n3. Additional analyses have been conducted, and if the results are not shown in the text, they\nare provided for the reviewers’ reference along with our response to their inquiries.\nBelow, you will find our comprehensive responses (highlighted in blue font) to the reviewers’\ncomments. We hope that we have effectively addressed all concerns raised in the revised\nmanuscript.\nREVIEWER COMMENTS\nReviewer #1 (Remarks to the Author):\nThis manuscript explores the trend in atmospheric rivers in the Arctic in boreal summertime\nand the drivers of this change. Using observations and models, including experiments with\nwinds nudged to observational trends, they found that large-scale circulation trends played a\nkey role in atmospheric rivers in the Arctic and that they contribute to the increased wetting\ntrend in the Arctic summer. Understanding the variability and long term trends of humidity in\nthe Arctic is vitally important in terms of changes precipitation, temperature, sea ice etc and\nso an analysis of this is necessary. However this paper at times seems a little disjointed,\njumping between indicating that humidity is a driver of AR to AR driving humidity or that the\npaper is about the trends of humidity. I think clarifying the message of the paper better is\nnecessary. Furthermore I feel that there should have been more analysis done on other\nsources of humidity in the Arctic or simply remove those completely and focus only on AR\nrelated humidity in summer time. While I find the model experiments with nudging the winds\nin the model interesting, more work is required to make the manuscript publishable.\nI have outlined a number of concerns/questions about this below.\nWe extend our gratitude to the reviewer for his/her positive remarks and valuable suggestions,\nwhich have significantly improved the clarity and coherence of our manuscript. We hope that\nthe reviewer will find the modifications we have made satisfactory in response to the\ncomments and suggestions.\nMajors\nI am curious about the role of other sources of moisture in the Arctic namely as Figure 3a\nshows that there is a large significant portion of the Arctic in summer that is experiencing\nincreasing humidity that is not related to ARs and this should be investigated more\nthoroughly or have more separation between AR induced humidity relative to influence of\nremote sources (sea ice loss) for example.\nThank you for your constructive comment. Indeed, the increasing moisture over the Arctic in\nrecent decades has been attributed to multiple sources, as documented in previous studies.\nThese sources include local ocean and land surface evaporation, sublimation of ice and snow,\nas well as transport from lower latitudes (Bintanja and Selten, 2014; Vihma et al., 2015). It is\nwidely believed that this increase is primarily due to the warming induced by anthropogenic\nforcing, which leads to higher moisture levels in the atmosphere and contributes to climate\nchange in the Arctic (Serreze et al., 2012; Boisvert and Stroeve, 2015; Olonscheck et al.,\n2019; Bokuchava and Semenov, 2021; Choi et al., 2023). However, what has received less\nattention is how changes in circulation patterns regulate moisture transport, particularly\nthrough alterations in AR characteristics, which is the main focus of this study.\nWe have incorporated an additional sentence to offer a brief review on other sources of\nmoisture in the Arctic as listed above in lines 58-62 of the manuscript:\n“This increase in moisture is prominent throughout the year, with the most significant rise\noccurring during the summer months (June-July-August, abbreviated as JJA)14,15, which has\nbeen attributed to multiple sources triggered by global warming, including increased\nevaporation from local ocean and surrounding continents16, enhanced sublimation of ice and\nsnow within the Arcitc17,18, and intensified moisture transport from lower\nlatitudes19,14,20,21,22,23,24,25,26.”\nModel bias was never discussed at all which I think is important. If the observations are\nshowing that we are getting an increase of moisture flux from ARs through changes in\ncirculation patterns, but the models do not show this, then an exploration into the\nwind/circulation trends in models relative to observations would be a first step to\nunderstanding the differences between the models and the observations rather than stating it\nis solely due internal variability.\nThis aspect has been addressed in a series of previous studies, including published work from\nour group (Ding et al., 2017; Baxter and Ding, 2022; Li et al., 2022; Topál et al. 2022, 2023;\nFeng et al. 2023), and by other colleagues (Athanase et al., 2022; Roach and Blanchard,\n2022). In brief, due to the model's limitations in simulating the mean state, tropical-Arctic\nteleconnections (Feng et al., 2023), and certain low-frequency SST modes, the models\ngenerally exhibit weaknesses in capturing observed circulation variability in the Arctic. By\nnudging observed wind trends within a model (Ding et al., 2022, Roach and Blanchard, 2022),\nit becomes feasible to accurately simulate the response of local climate fields, such as\nsummer sea ice melting. Furthermore, utilizing a fingerprint analysis (emphasizing both\ninternal and forced components of model simulations), we can achieve a better match in\nsimulating observed changes in Arctic climate over the past decades. Additional clarification\nhas been integrated to elaborate on this point further (Lines 164-167).\nThis is all assessing the trends from 1979-2014 but I feel a brief analysis of the climatology is\nneeded to assess what these trends are in relation to particularly for the circulation, is more\nnegative trend that it is more negative or a shift in the centres of action? for example AR\ntrends show a decrease trend over Alaska were in the mean this region has the most AR’s\nclimatologically. I think it would be worth also referencing this within your study, is the\ncirculation changes contributing to this negative trend in ARs in that location?\nThank you for your suggestion. In response, we have revised Supplementary Fig. 3 to include\nthe summer climatology of four variables (1000-500 hPa specific humidity, 200 hPa\ngeopotential height and winds, and 1000-200 hPa air temperature) in the Arctic from 1979 to\n2019, and we have incorporated relevant discussions in the manuscript (Lines 141-143).\nAs suggested, we emphasize the negative trend in AR activity over Alaska, which is an area\nof the Arctic where these features are climatologically prevalent. Our analysis indicates that\ncirculation patterns may also contribute to this trend in Alaska (Lines 151, 155-156).\nDetails\nLine 82: You state that summer months get more ARs because the jet is further poleward but\nfurther poleward relative to what. Supp fig 1b just shows climatological winds and AR so not\nevident that it is more poleward e.g. is this relative to DJF – then maybe say or show that too.\nThank you for your suggestion. What we meant is that during summer, the jet stream is\npositioned further north compared to its location in other seasons. To clarify, we have\nincluded the climatological ARs and u200 for DJF in Supplementary Fig. 1 as panel c.\nAdditionally, we have adjusted the sentence accordingly (Lines 92-94, Fig S1c).\nLine 91: What are the thermodynamic effects from global warming the ARs are sensitive to?\nThe formation and action of ARs rely on adequate kinetic energy and abundant moisture.\nWith global warming, the available potential energy (a source of baroclinic instability) and\natmospheric moisture are expected to increase, consequently enhancing the mean occurrence\nof ARs and leading to long term variability in their frequency, intensity, trajectory, and\nduration characteristics. However, this is a complex area of study. Recent research suggests\nthat the most intense midlatitude storms will shift poleward due to tropical expansion and\nincreased atmospheric instability (Tamarin-Brodsky and Kaspi, 2017; Yang et al., 2020;\nGentile et al., 2023). Whether this poleward shift will extend to the Arctic remains an open\nquestion, and we have provided a better summary of this point in the introduction (Lines 103-\n106).\nLine 95: Are the “recent observed features” the increase in ARs?\nIt refers to both ARs and the moistening trend in the Arctic. We have rephrased this part to\nensure clarity.\nLine 107: Is there any references/papers to support this claim that models lack the ability to\ncapture the observed circulation patterns in the Arctic?\nSupplementary Fig. 3b supports this point. We have cited a number of references to support\nthis claim in the revision (Line 121).\nLine 110: Why did you decide to go for wind rather than geopotential height for example for\nthe circulation trends?\nGeopotential height changes reflect both temperature and wind field variations, as these two\nvariables are closely interlinked physically. However, the main distinction between the two\nfields lies in the fact that temperature changes can directly result from both external and\ninternal factors, whereas wind patterns, particularly regional wind patterns, are primarily\ninfluenced by pressure changes. In a first-order approximation, anthropogenic forcing does\nnot significantly alter the horizontal distribution of air pressure. Therefore, changes in wind\npatterns are less sensitive to anthropogenic forcing. This is evident in many models' responses\nto anthropogenic forcing, which often prioritize long-term trends in temperature over changes\nin wind patterns. Given that the response of ARs in most models' forced simulations does not\nclosely resemble observed changes, we speculate that observed AR changes could be\ninternally driven. To further examine this idea, our primary focus is on examining the\ninfluence of wind patterns on ARs.\nLine 120: Counting the number of AR activity by grid – what do you mean by grid here, is it\nWest Greenland or by grid cell as this is what is usually thought when one refers to ‘grids’ in\na modelling paper. This lack of distinction in present throughout the manuscript particularly\nin the methods (e.g. lines 398 and 422) and is confusing so I suggest changing the name e.g.\nregion.\nThank you for your suggestion. The AR frequency data is derived from the ERA5 reanalysis,\nwhich has a resolution of 1.5°×1.5°, resulting in a total of 5040 (21×240) grid points covering\nthe Arctic region. To assess the frequency of AR occurrence across the Arctic, the mean AR\nactivity (from all 5040 grid points) within the region was calculated. Therefore, the sentence\nshould read as 'this field counts the number of AR occurrences at each grid point'. We have\nalso made wording adjustments to address several other similar issues (new lines 136, 454,\n484).\nLine 133: atmospheric temperature is fig 1d not 1c as stated here.\nWe modified the sentence, so it only refers to geopotential height in the revised manuscript.\nLine 143: Relating to the uniform rise in z200 over Greenland and Eurasia, I would not say\nthat this is uniform, its much greater in Europe than Greenland, and with the white colour\neven make it appear, at first glance to have no trend. This could be written better to address\nthis slight differences which are likely from internal variability.\nThank you for your suggestion. You are right that the trend of 200 hPa geopotential height\n(Z200) is relatively weak over Greenland, but there are noticeable and relatively uniform\nincreases of Z200 over Europe. We have revised the sentence to provide a more accurate\ndescription (Lines 160-164).\nLine 147-149: I am not sure how storm track shifts over the North Pacific may have impacted\nincreasing trends of AR, has this been documented somewhere before? Have you does\nanalysis to show this?\nStorm tracks, recognized as regions of intense synoptic eddy activity, are closely tied to\nextratropical cyclone and anticyclone activity (Blackmon 1976). ARs form as a result of the\ninteraction between a cold front, which transports a substantial amount of water vapor into\nthe warm sector, a warm front (Dacre et al. 2015). Thus, ARs represent a unique and extreme\nphenomenon linked to the extratropical storm tracks (Lavers et al. 2020). We have added\nsentences and citations to relevant literature to support this explanation (Lines 84-88).\nLine 165-168: I find this difficult to read, you say that “Z200-AR may share common\nmechanisms” which to me suggests that something else controls BOTH Z200 and AR but\nisn’t it that the circulation aka Z200 that drives AR? And the second half ‘suggesting a deeper\nunderstanding” is very dense to read.\nThe sentence is too long, which may cause some confusion. We modified it and showed it in\nthe last sentence of the previous paragraph as follows: “This resemblance suggests that the\nsecular trends of Z200 and ARs over Greenland may physically connected, as indicated by\ntheir interannual connections identified by MCA1.” We hope our edit improves the\nreadability of this sentence (Lines 190-192).\nLine 170: The text says tropospheric winds but figure 1f says 200hPa winds so have you\nassessed winds throughout the vertical column or just at the top of the troposphere? If latter\nstate that this is the upper level westerly winds and remove the suggestion that this is\nthroughout the whole troposphere.\nThe sentence refers to the summertime climatological westerly winds that prevail in the\nArctic troposphere. We have added a figure in supplementary material (Supplementary Fig.\n3c, vectors) and a few words to clarify the sentence (Line 198).\nLine 174: Can you explain how this shows interannual time scales if it is days/month, is it\nalso per year?\nThe unit of ARs frequency is days per month, indicating the number of days in a month when\nARs occur at a grid point. When we calculate the JJA mean, we average the values from June,\nJuly, and August (JJA) for each year, and then compare year-to-year AR changes.\nLine 188-193: Have you shown that AR are statistically connected to humidity and\ntemperature, figure 1 a-d shows only the linear trends but does not link them statistically?\nAnd was there ever a suggestion previous to this line that humidity and temperature drove AR\ntrends, especially when in lines 192 you state the humidity is associated with AR propagation\ninto the Arctic indicating that AR therefore is a driver of humidity. Revise this section and be\nvery specific by what you mean.\nThe relationship between ARs and humidity is evident across various time scales. Figure 1\nillustrates the long-term trend relationship of variables, while Figures 2a, 2b, 2d, S7, S8, and\nS9 depict the day-to-day relationship between ARs and moisture. To further elucidate the\nstatistical connection between ARs and humidity, we composited individual AR events and\ncorresponding specific humidity in specific regions (western Greenland, northern Europe,\neastern Siberia, near the North Pole, and southwestern Alaska, Figs. 2c, S10, and S11,\nrespectively) in the subsequent part of this section (lines 245-256). A series of MCA analyses\nis done to further show a close relationship between ARs and specific humidity and ARs with\ntemperature on year to year time scales in the Arctic (Figs. Rsp1 and Rsp2, which were not\nshown in the main text). These figures clearly demonstrate the relationships among ARs,\nspecific humidity, and air temperature.\nFigure Rsp1. a-b, Spatial pattern of the leading MCA mode of summer ARs (a, in\ndays/month) and specific humidity (f, in g/kg) (detrended). c, Standardized time series of the\nleading MCA mode of summer ARs (blue line) and specific humidity (red line). ‘SCF’ in c\nindicates the squared covariance fraction of the leading MCA mode between summer ARs\nand specific humidity. ‘r’ in c indicates the correlation coefficients between the time series of\nthe leading MCA mode (summer ARs and specific humidity).\nFigure Rsp2. Same as Fig. Rsp1, but for the variables between ARs and temperature.\nLine 204: How many ARs were there in 2012 over Greenland?\nWe have observed a total of 29 AR events that reached western Greenland during the summer\nof 2012. Specifically, there were 7 events in June, 13 in July, and 9 in August. It's notable that\nthe number of AR events in the summer of 2012 significantly surpasses the summer\nclimatology (average 2-3 events in a month) of ARs from 1979 to 2019 (Supplementary Fig.\n1a-1b)\nLine 213-214: As seen in supplementary figure 7.\nSupplementary Fig. 7 supplements Figure 2c in the text, but extends the period to 1979-2019.\nPreviously, we did not include the MCA for the regions over northern Europe and eastern\nSiberia in the original supplementary material. In the revision, we have added these two\nfigures to the supplementary material as Supplementary Fig. 7 and Supplementary Fig. 8,\nrespectively, and reorganized the numbering of supplementary figures accordingly.\nLine 222-224: This is a little cryptic, what other regions are these? Are there figures for them?\nThe regions showing notable increase trends in AR frequency, are evident near the North Pole,\nwhile a significant reduction in ARs is observed on southwestern Alaska. We present the\ncorresponding figure below (Fig. Rsp3, Supplementary Fig. 11). We modified the sentences\nof the specific figure (Lines 252-256).\nFigure Rsp3. a-b, Composite 6-hourly ARs (days) and lower and middle tropospheric\nspecific humidity (g/kg) anomalies (from four days before to five days after the outbreak of\nARs) near the North Pole (a) and southwestern Alaska (b) regions from 1979 to 2019 (all\ndata are detrended and climatological seasonal cycles are removed).\nLine 230-233: This commentary is confusing to me, you say that figures 3a and 1b are similar\n(I agree here) but that this is similar to figure 3b, which I do not see. Figures 3a and 1b show\nincreases of humidity everywhere while humidity related to ARs (figure 3b) shows negative\ntrends so can you explain this a little more.\nFurthermore, is a sum of Figs 3a and 3b result in fig1b meaning that you get increase trend of\nhumidity everywhere.\nFigures 1b, 3a, and 3b represent the total humidity trend, humidity trend unrelated to ARs,\nand humidity trend related to ARs, respectively. You are right that the total humidity trend\n(Figure 1b) is equivalent to the sum of Figures 3a and 3b. To enhance clarity, we have\nrephrased the sentence accordingly (Lines 262-265).\nLine 241: I do not understand this, you don’t actually look at AR changes in CMIP6 models\nbut just humidity, Z200 and temperature?\nThis is a typo, it should be Supplementary Fig. 5a in the revision. We corrected it (Line 274).\nLine 270: Are you able to actually show the wind anomalies in Figure 4b rather than infer\nbased on the Z200 circulation?\nSince the winds in the model are nudged from ERA5, they are exactly the same as those from\nERA5. We have included this information for reference. We have shown both wind anomalies\nand Z200 anomalies between nudging run and control run in Fig. 4b and modified the figure\ncaption in the revision (Fig. 4b, Lines 828-826).\nLines 287-293: Have you plots made to show this? If so, reference them as I am not sure\nwhat you are talking about/showing here.\nIn the original version, we presented the trends of ARs and Z200 in observations (Figs. 1a,\n1c), along with the ARs trend of the 40-member CESM2 ensemble mean (Supplementary Fig.\n5b). Here, we have included the Z200 trend of the 40-member CESM2 ensemble mean in the\nsupplementary material as Supplementary Fig. 12 (Fig. Rsp4) and reordered the numbering of\nsupplementary figures accordingly. In this revision, we refer to these figures within the main\ntext (Line 327).\nFigure Rsp4. Linear trends of summer (JJA) Z200 (m/decade) in the Arctic based on 40-\nmember CESM2 simulations (40-CESM2) from 1979 to 2019. The trends of Z200 are\nsignificant across all grid points at the 95% confidence level.\nLine 295: What is the size of the subgroup?\nThere is a total of 40 members. Subgroups, each comprising 8 members, were selected based\non AR frequency trends that exceeded the 80th percentile of simulated AR trends for each\nspecific region. We have provided clarification on this issue in the revision (Line 332).\nLine 303: You state here that you only use 2 of the 40 members but don’t state this in the\nfigure caption so just want to check what you are actually showing in figure 5c and d.\nThank you for your suggestion. We have included additional clarification in the Figure 5c\ncaption to address this point that only two members are shown (Line 841).\nLine 309: You have shown previously that the models do not do a good job replicating the\ntrends of AR and Z200 and yet here are saying that they agree with your results and so I am\nconfused about this?\nWhat we meant is that when using the ensemble mean forced solely by anthropogenic forcing,\nthe model does not perform well since it cannot accurately simulate observed wind patterns.\nHowever, when we consider the role of internal variability, we can better interpret\nobservations using model results. We have rephrased some of the text to eliminate any\npotential confusion (lines 345-349).\nFigures\nSupp fig 5 and 8: In my version of documents, these plots are cut off and so difficult to attest\nto claims made about these figures in your manuscript.\nMy apologies for our oversight. We have double-checked to ensure that there are no issues\nwith our plots in the supplementary figures.\nMethods:\nLine 389: Be specific here, what are the anthropogenic forcings that are held constant?\nThank you for your suggestion. The anthropogenic forcings that are held constant refer to the\naerosol concentrations from multiple sources, solar constant, ozone concentration, and all\nother necessary forcings used in the CMIP6 protocol. The CO2 values for the years 2000\n(Exp: WIN) and 2020 (Exp: WIN+CO2) are 370 ppm and 415 ppm, respectively. We have\nclarified this in the text (Lines 447-449).\nLine 416: I would just note that you are examining the specific humidity anomalies here,\neven though it is mentioned in following line, will avoid any confusion about what is meant.\nThank you for your suggestion. To emphasize this, we have added the following sentence in\nthe method part: 'For brevity, in some parts of the text, we use \"humidity\" and \"specific\nhumidity\" interchangeably.' (Lines 407-408).\nMuch more detail is needed about the nudged model specifically demonstrating what the\nnudged field looks like.\nA new paragraph has been added to describe how we nudge winds in the model. In brief, we\napply a constrained nudging of winds during the summer months (JJA) in the Arctic region,\nfrom the surface to the TOA. This wind pattern remains constant and mirrors the long-term\nwind trends observed over the past 41 years. The objective is to understand how ARs respond\nwhen this anomalous wind pattern is incorporated into the model. We have shown the nudged\n200 hPa wind simulation in the Figure 1c (vectors) and clarified this in the methods section\n(line 437).\nSignificance of correlation, you never explain why it is important to account for\nautocorrelation in the time series.\nThe assumption for a significance test of correlation, according to the principal of the T-test,\nis that the time series used to calculate the correlation consist of completely independent\nsamples characterized by white-noise processes (Wilks et al., 2011). However, if the data\ncontain strong auto-correlation (indicating some red-noise process), this assumption may not\nhold well. To address this issue, it is necessary to consider the effect of effective sample size\nwhen testing the significance of correlation using conventional T-tests (Bretherton et al.\n1999). We have clarified this point in the revision (Lines 504-510).\nReferences:\nAthanase, M., Schwager, M., Streffing, J., Andrés-Martínez, M., Loza, S. N., & Goessling, H.\nF. (2022). Impact of the atmospheric circulation on the Arctic snow cover and ice\nthickness variability. Geosci. Model Dev. 6, 849–859\nBaxter, I., & Ding, Q. (2022). An optimal atmospheric circulation mode in the Arctic favoring\nstrong summertime sea ice melting and ice–albedo feedback. Journal of Climate, 35(20),\n6627-6645.\nBintanja, R., & Selten, F. M. (2014). Future increases in Arctic precipitation linked to local\nevaporation and sea-ice retreat. Nature, 509(7501), 479-482.\nBlackmon, M. L. (1976). A climatological spectral study of the 500 mb geopotential height of\nthe Northern Hemisphere. Journal of the Atmospheric Sciences, 33(8), 1607-1623.\nBoisvert, L. N., & Stroeve, J. C. (2015). The Arctic is becoming warmer and wetter as\nrevealed by the Atmospheric Infrared Sounder. Geophysical Research Letters, 42(11),\n4439-4446.\nBokuchava, D. D., & Semenov, V. A. (2021). Mechanisms of the early 20th century warming\nin the Arctic. Earth-Science Reviews, 222, 103820.\nBretherton, C. S., Widmann, M., Dymnikov, V. P., Wallace, J. M., & Bladé, I. (1999). The\neffective number of spatial degrees of freedom of a time-varying field. Journal of\nclimate, 12(7), 1990-2009.\nChoi, H. J., Min, S. K., Yeh, S. W., An, S. I., & Kim, B. M. (2023). Seasonally distinct\ncontributions of greenhouse gases and anthropogenic aerosols to historical changes in\nArctic moisture budget. npj Climate and Atmospheric Science, 6(1), 189.\nDacre, H. F., Clark, P. A., Martinez-Alvarado, O., Stringer, M. A., & Lavers, D. A. (2015).\nHow do atmospheric rivers form?. Bulletin of the American Meteorological\nSociety, 96(8), 1243-1255.\nDing, Q., Schweiger, A., L’Heureux, M., Battisti, D. S., Po-Chedley, S., Johnson, N. C., ... &\nSteig, E. J. (2017). Influence of high-latitude atmospheric circulation changes on\nsummertime Arctic sea ice. Nature Climate Change, 7(4), 289-295.\nFeng, X., Ding, Q., Wu, L., Jones, C., Wang, H., Bushuk, M., & Topál, D. (2023).\nComprehensive representation of tropical–extratropical teleconnections obstructed by\ntropical Pacific convection biases in CMIP6. Journal of Climate, 36(20), 7041-7059.\nGentile, E. S., Zhao, M., & Hodges, K. (2023). Poleward intensification of midlatitude\nextreme winds under warmer climate. npj Climate and Atmospheric Science, 6(1), 219.\nGimeno, L., Vázquez, M., Eiras‐Barca, J., Sorí, R., Algarra, I., & Nieto, R. (2019).\nAtmospheric moisture transport and the decline in Arctic Sea ice. Wiley Interdisciplinary\nReviews: Climate Change, 10(4), e588.\nJohnsen, S. J., Dansgaard, W., & White, J. W. C. (1989). The origin of Arctic precipitation\nunder present and glacial conditions. Tellus B: Chemical and Physical\nMeteorology, 41(4), 452-468.\nLavers, D. A., Ralph, F. M., Richardson, D. S., & Pappenberger, F. (2020). Improved\nforecasts of atmospheric rivers through systematic reconnaissance, better modelling, and\ninsights on conversion of rain to flooding. Communications Earth & Environment, 1(1),\n39.\nLi, Z., Ding, Q., Steele, M., & Schweiger, A. (2022). Recent upper Arctic Ocean warming\nexpedited by summertime atmospheric processes. Nature communications, 13(1), 362.\nListon, G. E., & Sturm, M. (2004). The role of winter sublimation in the Arctic moisture\nbudget. Hydrology Research, 35(4-5), 325-334.\nLiu, C., & Barnes, E. A. (2015). Extreme moisture transport into the Arctic linked to Rossby\nwave breaking. Journal of Geophysical Research: Atmospheres, 120(9), 3774-3788.\nOlonscheck, D., Mauritsen, T., & Notz, D. (2019). Arctic sea-ice variability is primarily\ndriven by atmospheric temperature fluctuations. Nature Geoscience, 12(6), 430-434.\nRoach, L. A., & Blanchard‐Wrigglesworth, E. (2022). Observed winds crucial for september\nArctic sea ice loss. Geophysical Research Letters, 49(6), e2022GL097884.\nSerreze, M. C., Barrett, A. P., & Stroeve, J. (2012). Recent changes in tropospheric water\nvapor over the Arctic as assessed from radiosondes and atmospheric reanalyses. Journal\nof Geophysical Research: Atmospheres, 117(D10).\nTamarin-Brodsky, T., & Kaspi, Y. (2017). Enhanced poleward propagation of storms under\nclimate change. Nature geoscience, 10(12), 908-913.\nTopál, D., & Ding, Q. (2023). Atmospheric circulation-constrained model sensitivity\nrecalibrates Arctic climate projections. Nature Climate Change, 1-9.\nTopál, D., Ding, Q., Ballinger, T. J., Hanna, E., Fettweis, X., Li, Z., & Pieczka, I. (2022).\nDiscrepancies between observations and climate models of large-scale wind-driven\nGreenland melt influence sea-level rise projections. Nature Communications, 13(1),\n6833.\nVihma, T., Screen, J., Tjernström, M., Newton, B., Zhang, X., Popova, V., ... & Prowse, T.\n(2016). The atmospheric role in the Arctic water cycle: A review on processes, past and\nfuture changes, and their impacts. Journal of Geophysical Research:\nBiogeosciences, 121(3), 586-620.\nWilks, D. S. (2011). Statistical methods in the atmospheric sciences (Vol. 100). Academic\npress.\nYang, H., Lohmann, G., Lu, J., Gowan, E. J., Shi, X., Liu, J., & Wang, Q. (2020). Tropical\nexpansion driven by poleward advancing midlatitude meridional temperature gradients.\nJournal of Geophysical Research: Atmospheres, 125(16), e2020JD033158.\nReviewer #2 (Remarks to the Author):\nReview of the manuscript NCOMMS-23-52552 entitled “Role of atmospheric rivers in\nshaping long-term Arctic moisture variability” by Wang et al.\nThe manuscript shows the influence of the large-scale atmospheric circulation in modulating\nthe atmospheric rivers moistening impact on the Arctic using both the ERA5 reanalysis and\nidealised simulations with a climate model, in order to explain the trends in the observed\natmospheric rivers frequency. The paper is relatively well written. However, I find that many\naspects (methods and results) could be better explained, especially the maximum covariance\nanalysis, on which I am not an expert myself. Therefore, I recommend major revisions of the\nmanuscript.\nHere below, are my comments listed in the order of the manuscript followed by a few\ntechnicalities.\nWe thank the reviewer for the constructive comments, particularly the suggestion to provide a\nmore comprehensive explanation of the usefulness of MCA and the interpretation of its\nresults. In the revision, we have thoroughly revised our manuscript to address all raised\nconcerns.\nFig. S1a and line 82: The figure represents a mean frequency of atmospheric rivers (AR\nfrequency), not a mean. Please modify the plot and the caption. Moreover, it is not clear how\nthis AR frequency in the Arctic is defined. Is it the mean of the AR frequency north of 60N?\nIs it the mean of AR frequency at 60N? In this latter case, are only the poleward ARs\nconsidered or are all ARs considered? Please explain better in the caption as well as in the\nMethods section.\nThank you for this valuable suggestion. We have replotted the figures, rewritten the figure\ncaptions, and further clarified the definition of Arctic ARs in both the figure caption and the\nMethods section (Lines 461-462 and Supplementary Fig. 1). The time series represents the\narea-weighted mean of AR frequency north of 60°N, where area weights are considered. All\nAR occurrences north of 60°N are taken into account.\nFig. S1b and lines 82-84: To show that the AR frequency and the jet are shifted northward in\nsummer compared to winter, the authors should have represented the difference between\nwinter and summer and not summer only. This behaviour of the jet is well known, but it\nmaybe less known for the ARs. Please consider adding this plot (difference or\nsuperimposition of the winter and summer fields in contours).\nIn response to this suggestion, we have included the mean AR frequency and jet in winter as\nSupplementary Fig. 1c for comparison with the variables in summer (Lines 95-96 and\nSupplementary Fig. 1c). Additionally, the difference between the two fields has been added to\nbetter illustrate their shift in the supplementary figures.\nFig. S2: In the caption, (blue fan-shaped covered area, → (blue fan-shaped covered area)\nWe have made the suggested change to the figure caption of Supplementary Fig. 3.\nLines 115-117: the authors mention “moistening trends”, but they do not show any specific\nhumidity trends, for example. It seems that they assimilate this wording to the AR frequency\ntrend. However, it is not fully correct as ARs are not the only source of humidity in the Arctic.\nThe authors also wrote “the Pan-Arctic moistening trend is the most positive and significant\n(Fig. S1a)”, but they do not show any significance calculation and they should rather refer to\nFig. S2b, which shows a trend, shouldn’t they? Ideally, the authors should rather represent the\nspecific humidity trend in summer and winter.\nThank you for your suggestion. In Fig. 1b, we now present the summer-specific humidity\ntrend over the Arctic from 1979 to 2019. Additionally, in response to your feedback, we have\nincluded the winter trend of specific humidity as Supplementary Fig. 2. Furthermore, we have\nadjusted the reference figures in the main text accordingly (Line 132).\nLine 130: The authors should also add Western Alaska here because it seems to be one of the\nmost prominent pathways of AR into the Arctic in summer, with the largest AR mean\nfrequency north of 60N as seen in Fig. S2a.\nThanks for your suggestion. We have commented on western Alaska trends in the text (Line\n149).\nLine 133: Why using the geopotential at 200 hPa? It is in the stratosphere at those high\nlatitudes. Could the authors explain why they use this pressure level and not at a lower level,\nsuch as 400 or 500 hPa?\nThe reviewer is correct. In the Arctic during JJA, the tropopause typically resides around 300\nhPa, which is relatively high compared to other seasons. Additionally, the circulation trend\nover the Arctic in summer exhibits a barotropic structure, with its long term geopotential\nheight trends more clear at either 300 hPa or 200 hPa than at lower altitudes. Therefore, we\nutilized Z200 to represent changes in atmospheric circulation in the upper troposphere.\nFurthermore, we conducted calculations using Z300 or Z500, revealing similar trends to those\nobserved at 200 hPa.\nLine 133: Fig. 1b-1c → Fig. 1b-d (to including panels b, c, and d)\nWe reorganized these sentences (Line 150-154).\nLine 136: The authors should describe the methodology of the maximum covariance analysis\nin the Methods section and what results it will give so that readers not familiar with this\nmethod can understand it.\nThank you for your suggestion. We have included additional explanations regarding MCA in\nthe Methods section (Lines 496-506).\nMCA is conceptually similar to principal components analysis (PCA) but is applied to two\ndifferent data fields. Consider a 2-D (spatial and temporal) data field X(m,t), where m\nrepresents the number of total spatial points and t represents the total temporal steps. In PCA,\nwe calculate eigenvectors of the covariance matrix X×X′ (transpose matrix of X), where each\nelement corresponds to the covariance of any pair of two spatial points in X.\nIn MCA, we work with two different 2-D fields, X(m,t) and Y(n,t), where m and n represent\nspatial points and t still represents the temporal dimension. MCA aims to compute a singular\nvector decomposition of X×Y′, where each element in the matrix represents the covariance of\na pair of points from X and Y, respectively (one spatial point from X and one spatial point\nfrom Y). This enables MCA to identify the dominant coupled modes between variations in X\nand Y. Due to its widespread use in climate and meteorology since its introduction in the\n1990s, we provide a concise explanation in the text without going into more extensive detail.\nLines 130-137 could be written more concisely. Consider rewriting.\nWe have rewritten it as you suggested. It now reads as follows:\n“Patterns of long term variations in AR frequency reveal notable similarities to the observed\ntrends in large-scale JJA circulation (Fig. 1c). Upper-level geopotential heights significantly\nincrease across western Greenland, northern Europe, and eastern Siberia, but over the North\nAtlantic Ocean, Alaska, and central Siberia the increase is relatively small (three regions are\nshown in Supplementary Fig. 3e). In line with these atmospheric circulation trends, both\nspecific humidity and temperature display pronounced rises over western Greenland,\nnorthern Europe and eastern Siberia (Fig. 1b and 1d)” (Lines 150-166).\nLines 138-144: What about adding SSP5-8.5 to complete the historical simulations and\ncalculate the trend over the exact same period as in the reanalysis (same as what has be\nperformed for CESM2 in Fig. S4b it seems)? Or what about using only 1979-2014 in the\nreanalysis to match the CMIP6 historical period? A few more or less years can make a\ndifference when calculating trends (see for example Fig. 2 of Outten et al.\n2022 https://wcd.copernicus.org/articles/4/95/2023/wcd-4-95-2023.pdf).\nWe have used the three variables only from 1979 to 2014 in the reanalysis to calculate the\ntrends, which are consistent with the results from 1979 to 2019 (Fig. Rsp5 vs Fig. 1a-1d). We\nalso added an analysis of the trend of variable Z200 under the SSP5-8.5 scenario. From 2015\nto 2099, the magnitude of the trends became significantly larger, with a similar spatial pattern\nas that from the historical simulation over the period of 1979-2014 (Fig. Rsp6 vs\nSupplementary Fig. 4b). Thank you for this suggestion.\nFigure Rsp5. a-d, Linear trends of JJA ARs (days/month/decade) (a), lower and middle\ntropospheric (surface to 500 hPa average) specific humidity (g/kg/decade) (b), 200 hPa\ngeopotential height (Z200) (m/decade) (c), and tropospheric (surface to 200 hPa average) air\ntemperature (K/decade) (d) in the Arctic from 1979 to 2019. Black dots in a-d denotes\nstatistically significant trends at the 95% confidence level.\nFigure Rsp6. Linear trend of summer 200 hPa geopotential height (Z200) (m/decade) (a) in\nthe Arctic from 2015 to 2099 based on 40 CMIP6 models under SSP 5-8.5 scenario. The\ntrend of Z200 significant everywhere at the 95% confidence level.\nLine 142-143: why not using the same vertical extent for all variables? Moreover, these\nvertical extents are different than the one used to calculate the IVT (line 399). Can the authors\njustify their choice?\nThank you for your suggestion. In fact, our results are not sensitive to how we conduct\nvertical integration, given that these features typically exhibit quite a barotropic structure\n(uniformly vertically). However, we provide specific reasons for why, for some plots, we\nonly focus on certain levels or average for certain levels.\nFor the humidity and air temperature variables, we focus on different vertical extents: an\naverage from the surface to 500 hPa, and an average from the surface to 200 hPa, respectively.\nWe find changes in Z200 are quite similar to changes in geopotential heights at other levels.\nFor the simplicity of presentation, we use Z200 to analyze trends in upper-level atmospheric\ncirculation. Moreover, since most of the moisture in the atmosphere is concentrated in the\nmiddle and lower troposphere, we integrate humidity from the surface to 500 hPa to represent\nvariations in atmospheric moisture (Sun and Lindzen, 1993). Moisture content above 500 hPa\nis almost zero in the Arctic. Additionally, as Z200 change also represents a thickness change\n(equivalent to temperature) of the entire air column from 200 hPa to the surface, we integrate\nvertically from the surface to 200 hPa when analyzing air temperature changes. When\ncalculating IVT, we integrate moisture across the entire troposphere as required by the\nexisting AR scheme. In fact, using IVT integrated from 500 hPa to the surface would not\nchange our result.\nLine 144: Could the authors add a measure of significance on Fig. S3? An easy one could be\nthe number of models agreeing on the sign of the trend. Moreover, are there some CMIP6\nmodels in better agreement with ERA5 than others? Could the answer to this question be\nmentioned in the manuscript?\nWe have conducted a significance test on the trend of the variables in the Supplementary Fig.\n4 (Supplementary Fig. 4 in the revision). Since all grid points across all figures are significant,\nwe made note of this in the figure caption.\nWe also examine each individual model among the 10 models used, revealing that most\nsimulations fail to capture the observed trend of JJA Z200 in ERA5. The results across the 10\nmodels exhibit significant diversity (Fig. Rsp7).\nFigure Rsp7. Linear trend of summer 10 models (a-j) 200 hPa geopotential height (Z200)\n(m/decade) in the Arctic from 1979 to 2014.\nFig. S4a: Why is this figure the mean of only 10 CMIP6 models when Fig. S3 includes 40\nmodels? Could Fig. S3 be redrawn with the 10 same models?\nWe used monthly data from 40 CMIP6 models to generate Supplementary Fig. 4\n(Supplementary Fig. 4 in revision). However, when calculating ARs, daily or even higher\ntime-resolution specific humidity and wind fields are necessary. Here, only 10 models\nprovide such high temporal resolution data. Therefore, when examining changes in ARs, we\nonly considered data from these 10 models. In response, we have added a sentence to the\nMethods section to clarify this (Lines 415-418).\nIn the caption: “and 40-member” → and (b) 40-member\nWe have made the suggested change in the caption of Supplementary Fig. 5.\nLine 148: What are the SST changes the authors mention here? Please be more precise. Why\nwould it be the only factor influencing the AR trend found in CMIP6?\nSince this is not a key point of our paper and to avoid further confusion, we removed this part\nof the sentence.\nLine 165: What are “the long term changes of ARs and circulation”? Do the authors mean the\ntrend?\nHere we refer to the leading mode of MCA for the interannual variation of two variables,\nwhich is similar to the spatial patterns of those long-term trends. We modified the sentence to\n'the long-term trends of ARs and circulation' (Lines 189-190)\nLine 181: Shouldn’t it be MCA1 ARs-Z200 instead of MCA1-ARs or MCA1-Z200 since the\nmaximum covariance analysis is performed using both the AR frequency and Z200? Do the\nauthors expect different results by using MCA1-ARs instead of MCA1-Z200? Please clarify\nthe MCA methodology.\nThe leading mode of MCA (which contains two spatial patterns for each field) has two time\nseries (referred to as MCA1-ARs and MCA-Z200, respectively). As these two time series are\nhighly correlated (Corr. = 0.83), utilizing either of the time series as an index to calculate\ncorrelations with other variables yields very consistent results. In response, we added a\nsection in the Methods to introduce the MCA (Lines 496-506).\nLine 195: Greenland → the Western Greenland region\nWe have made the suggested change (Line 224).\nLines 196-198: Could the authors add a reference to this statement?\nWe have added a reference:Van den Broeke et al. 2016 (Line 227).\nLine 199: What do the authors mean with “daily AR frequency”? Aren’t the daily fields\nbinary masks (0-1 fields) of atmospheric rivers?\nYes, this number indicates the percentage of the one-day period (24h) during which the grid\nbox is governed by ARs.\nWhy do the authors use both CESM1 and CESM2 in their study? Would it be possible to use\nonly one model version so that the results of their two analysis are comparable? Do the\nauthors expect the two versions of CESM to give similar results for their idealised\nexperiments? This uncertainty could be acknowledged somewhere in the manuscript.\nSince the data (6-hour 3-D U, V, and specific humidity) used to calculate ARs is not available\nin CESM1-LEN (which was conducted almost 8 years ago), but is available in CESM2-LEN,\nwe strategically utilized both CESM1 and CESM2 in this study. For our fingerprint analysis,\nwe relied on CESM2-LEN since ARs can be obtained from this dataset. However, for our\nnudging run, given the more efficient calculation speed of CESM1 compared to CESM2, we\nrelied on CESM1. Actually, CESM2 did not exhibit significant changes compared to CESM1\nwhen the nudging scheme was employed on winds (Topál et al., 2023). Thus, we believe that\nour results were not sensitive to the choice of model. We have clarified this point in the paper.\n(Lines 445-447)\nLines 206-207: Isn’t that result coming directly from the MCA? Would the authors expect\nanother result? Also, the figure referred to should be Fig. 2d not 2c, right?\nThe results are derived from MCA after detrending the data and removing climatological\nseasonal cycles. To facilitate easier comparison of the time series, we standardized them after\napplying MCA. We were not surprised to see the MCA result at the beginning, as the\ncoherent spatial features of trends in these variables already suggested that these fields are\nclosely coupled on some time scales. Thanks for bringing the figure reference error to our\nattention. It has been corrected. (Line 236)\nFigure 2d: - Could the authors give more details on what is represented on panel d? Why is\nthe y-axis label saying “Standard deviation” whereas the caption text says “standardized\ntime series”? Please clarify.\n- Moreover, the straight horizontal parts in the blue curve highlight the days with no AR. Is\nthat right? Isn’t it an issue to perform the MCA with some time series having 0s?\n- Finally, it is not clear to me if this time series (panel d) is for the central point of the region\nor an average over the region or something else. Please add this precision.\nFigure 2d depicts the standardized time series of the leading MCA mode of ARs and lower\nand middle tropospheric specific humidity over western Greenland in summer 2012.\nThe time series in Figure 2d illustrate the temporal changes in the spatial pattern of the\nleading MCA mode between ARs and specific humidity over western Greenland in summer\n2012.\nIn the revision, we have included a new section in the Methods to introduce the MCA method\n(Lines 496-506).\nLines 207-208: Again, what does the value of the time series peak on 11 July (Fig. 2d) mean?\nI understand that the fact above 0 values mean that there is an atmospheric river and that\nmany days around the 11th experienced an atmospheric river. Is that correct?\nYou are correct. When there is ARs activity at some grid points over western Greenland, the\nARs value will be higher than 0. However, on July 11, the ARs activity area reached its\nmaximum over western Greenland, and the humidity anomaly also reached the highest. We\nhave marked July 11 in red font and added a sentence in the figure caption of Fig. 2 to clarify\nthis point (Lines 815-816).\nLine 209: remove ).\nWe have made the suggested change. (Line 238)\nFigure 2c and Figure S7: Why do the two figures have different y-axis label (AR change (days)\nfor Fig. 2c and AR anomaly (case) for Fig. S7). Please adjust and make it consistent (both the\nlabel and the unit).\nThanks for your suggestion. The label should be “days” in Supplementary Fig. 10\n(Supplementary Fig. 10 in the revision). We have made the correction.\nLines 228-230: The methodology is not clear at all to me. I suggest a major rewriting of the\nmethod section for this analysis.\nWe have rephrased many parts of the methodology section to improve its readability (Lines\n482-495).\nLines 233-234: How are those trends calculated? Please explain in the text.\nFollowing our 'spatiotemporal extension' method, we extract 6-hourly specific humidity data\nrelated to and unrelated to AR activity during summer over the Arctic. We then aggregate\nthese datasets into JJA averages and calculate their respective trends from 1979 to 2019. In\nthe revised version, we have provided a more detailed explanation of this approach (Lines\n482-495).\nFigure 3: My understanding is that Fig. 3b is obtained by subtracting Fig. 3a from Fig. 1b. Is\nthat correct? In Eastern Siberia, it looks like the sum of Fig. 3a and b would be higher than\nFig. 1b. Could the authors check? In any case, please add some explanations to the methods\n(as also written above).\nFig. 1b, Fig. 3a, and Fig. 3b depict the trends of total humidity, humidity unrelated to ARs,\nand humidity trend related to ARs, respectively. Therefore, the sum of Fig. 3a and Fig. 3b equals\nFig. 1b. We have verified these results to be accurate. Additionally, we have redrawn Fig. 1b,\nand below, we present the new Fig. 1b along with the sum of Fig. 3a and Fig. 3b as Fig. Rsp8.\nFigure Rsp8. Linear trends of JJA lower and middle tropospheric (surface to 500 hPa average)\nspecific humidity (g/kg/decade) associated with ARs (left panel), and the sum of specific\nhumidity unrelated and related to ARs (right panel) in the Arctic from 1979 to 2019. Stippling\nindicates statistical significance?\nLines 241-242: I am sorry, but I do not understand how the authors can say that from Fig. S3a.\nCould the authors elaborate a bit here?\nWe apologize for our figure referencing error, which should be Supplementary Fig. 5a. We\nhave corrected this error in the revision. (Line 274)\nLine 254: Does the addition of the anomalies introduce noise or discontinuities in the wind\nfield?\nSince this method is adapted from a widely used assimilation method developed by NCAR\nDART team (https://journals.ametsoc.org/view/journals/clim/25/18/jcli-d-11-00395.1.xml),\nwe do not anticipate significant errors in our calculations using this approach. However, it's\nstill possible that some subtle noise may be introduced at certain levels, but it should be very\ntrivial given the focus on the long-term trends in our results.\nLine 276: How small is this wind change?\nWind changes in the model between the nudging run and the control run are approximately\n0.5-1 m/s (see Fig. 4b, vectors), which is relatively small compared to the climatology wind\nspeed of JJA, ranging from 6-12 m/s (see Supplementary Fig. 3c, vectors) within the Arctic.\nLine 291: “...and again, and reinforcing…” needs rewriting.\nWe have rewritten the phrase as recommended (Line 328).\nLine 366: add (Z200) after “Arctic atmospheric circulation”\nWe have made the suggested change (Line 407).\nLines 367-368: Why do the authors use both 6-hourly and monthly reanalysis data? Moreover,\nsince data from models are available daily, why not also use daily reanalysis, instead of 6-\nhourly? It looks inconsistent, especially as the ARs are detected from daily fields for the\nmodels but 6-hourly fields for the reanalysis.\nThank you for your comment. We primarily utilize monthly reanalysis data to calculate the\ntrend of variables in our study (or for MCA). However, when identifying AR variability, 6-\nhour data is ideal due to the transient and extreme nature of ARs, where higher temporal\nresolution data can better capture their characteristics. Unfortunately, only a few CMIP6\nmodels provide daily data (3-D U, V, and specific humidity over multiple representative\nvertical levels) for the public, and 6-hour data for these variables are not available.\nConsequently, we have to employ a remedial approach using daily data to detect ARs. In\nresponse, we have added sentences to the Methods section to clarify our approach (Lines\n412-418).\nLine 398 (and others, e.g. 425, 426): “grid” → grid point\nFollowing the Oxford dictionary, the noun grid means “An arrangement of parallel bars with\nopenings between them”. Therefore, the authors seem to misuse this noun. Please correct\nwhere appropriate.\nWe appreciate the reviewer’s recommendation and have made several corrections (Lines 136,\n454, 484).\nLine 410: “lag” or last?\nHere, using ‘last’ is more appropriate, and we have made the suggested change (Line 473).\nLines 422-423: “Initially … algorithm”. This sentence is not understandable to me. Please\nadjust and clarify.\nThanks for your suggestion. We have made the following modification:\n‘Initially, grid points that have AR activity are determined by the 'shape' of ARs as defined by\nthe detection algorithm.’ (Lines 484-485).\nLine 424: “extended spatiotemporally”. The spatial extension is of 6 degrees, but how long in\ntime is the AR impact extended?\nWater vapor anomalies associated with ARs are observed to precede the onset of ARs by\napproximately 1 day and to lag behind the termination of ARs by approximately 1 day (see\nFigs. 2c, Supplementary Fig.10, and Supplementary Fig.11). To account for these lead-lags,\nwe have extended the time window by one day on both ends. Additionally, we have included\nsome text to elucidate this process (Lines 487-490).\nLine 426: What do the authors mean with “time point”?\nThis should be ‘time,’ and we have made the suggested correction (Line 492).\nLines 428-429: How can the AR impacts be removed? Please add an explanation.\nFollowing the 'spatiotemporal extension' method (details see Methods section, lines 480-493),\nwe have differentiated the humidity trends related to ARs from those unrelated to ARs. To\nmake it clearer, we have modified the sentence (Lines 493-495).\nTo provide a quick overview of our approach, we briefly summarize it here. AR events\nreaching the Arctic exhibit a sporadic pattern during the JJA season. At each time step (every\n6 hours) over grids within the Arctic region, if certain grids are identified as influenced by\nARs based on our AR detection scheme, we mask these grid points in subsequent calculations.\nThis screening process is applied across all 40 years at 6-hourly intervals. The processed 6-\nhour specific humidity data are aggregated for each JJA season. We then recalculate the linear\ntrend of this newly constructed JJA-specific humidity. Through this process, we can isolate\nthe linear trend of specific humidity without the influence of ARs.\nFigures:\n- Please represent the coastlines in black. They are barely visible in gray.\nThanks for your suggestion. To distinguish it from the black dots of the significance test, we\nused darker gray for the map outline.\n- Figure 1 would benefit a lot from larger panels with for example only 3 panels on the top\nrow.\nWe appreciate the suggestion and have modified the panels accordingly (Fig. 1).\n- How is the significance calculated (the black dots in the figures)?\nThe significance of trend was calculated by Mann-Kendall Test (Mann 1945; Kendall 1948;\nGilbert 1987; and Hamed and Rao 1998). The Mann-Kendall Test analyzes difference in\nsigns between earlier and later data within a time series. The idea is that if a trend is present,\nthe sign values will tend to increase constantly, or decrease constantly. The detail of the\ncalculation can be found here:\n(https://vsp.pnnl.gov/help/vsample/design_trend_mann_kendall.htm)\nTechnicalities (probably not exhaustive):\nLine 53: altered Arctic → altered the Arctic\nWe have made the suggested correction (Line 53).\nLine 78: move eastward → move (north)eastward\nWe have made the suggested correction (Line 84).\nLine 135: in the Fig. S2b, Fig. 1c → in Fig. S2b, Fig. 1c\nWe have made the suggested correction, which should be Supplementary Fig. 3e in the\nrevision (Line 154).\nLine 399: 0.25° x .25° → 0.25° x 0.25°\nWe have made the suggested correction (Line 456).\nLine 442: is → are\nWe have made the suggested correction (Line 525).\nLine 655: The dotted → Black dots\nWe have made the suggested correction (Line 792).\nLines 681, 690, 699: The dotted grids → Black dots\nWe have made the suggested corrections (Lines 821, 835, 844. Supplementary Fig. 5,\nSupplementary Fig. 6, and Supplementary Fig. 13).\nReferences:\nSun, D. Z., & Lindzen, R. S. (1993). Distribution of tropical tropospheric water vapor.\nJournal of Atmospheric Sciences, 50(12), 1643-1660.\nWallace, J. M., Smith, C., & Bretherton, C. S. Singular value decomposition of wintertime\nsea surface temperature and 500-mb height anomalies. J. Clim. 5, 561-576 (1992).\nMo, R. (2003). Efficient algorithms for maximum covariance analysis of datasets with many\nvariables and fewer realizations: A revisit. Journal of Atmospheric and Oceanic\nTechnology, 20(12), 1804-1809.\nMann, H. B. (1945). Nonparametric tests against trend. Econometrica: Journal of the\neconometric society, 245-259.\nKendall, M. G. (1948). Rank correlation methods.\nGilbert, R. O. (1987). Statistical methods for environmental pollution monitoring. John Wiley\n& Sons.\nHamed, K. H., & Rao, A. R. (1998). A modified Mann-Kendall trend test for autocorrelated\ndata. Journal of hydrology, 204(1-4), 182-196.\nREVIEWERS' COMMENTS\nReviewer #1 (Remarks to the Author):\nThis manuscript explores the role of large-scale circulafion variability on AR frequency within the Arcfic\nin observafions and large-ensemble and bespoke models forced with wind anomalies. The authors find\nthat the increased trend of AR and moisture is related to the circulafion over the Arcfic bringing more AR\nand moisture into Greenland, Siberia and northern Europe.\nI want to thank the authors for the fime and energy spent to comprehensively address my comments\nand quesfions and for all the addifional work that went into the manuscript. I have only minor\ncomments on this iterafion of the manuscript.\nLine 53: Can you give examples of how moisture increase has altered the Arcfic hydrology and\ncryosphere?\nLine 170: Rogue underscore\nLine 171: Have -> Has\nLine 321: Is the ensemble mean of the “two fields” you talk about here supplemental figure 12? I would\njust state that first and then menfion the other figures that you are comparing it too.\nLines 379-381: Seems like a word or phrase is missing or perhaps needs reworded to emphasis that the\nincrease in moisture can impact sea ice if that is the purpose of these lines.\nLine 421: Outline what WIN and WIN+CO2 are before you reference them mid-sentence. The text in lines\n274-294 that describe the models in detail I feel should be in the methods secfion\nLine 427: numerically incorporafing? Should it be numerically incorporate?\nLine 456: Would it be befter to say that the IVT anomalies lag behind rather than “last”?\nLine 491: Consist -> Consists\nFigures 1 and supplemental figure 4 – I think it might be good to have these on the same colorbar, that\nway it will really emphasise the lack of change in CMIP compared to ERA5\nReviewer #2 (Remarks to the Author):\nReview of revised manuscript NCOMMS-23-52552A enfitled “Role of atmospheric rivers in shaping long\nterm Arcfic moisture variability” by Wang et al.\nThe authors have addressed most of my previous comments. Overall, I think that the manuscript could\nsfill be improved, especially the first part that is slightly difficult to read. The authors can find my\ncomments on their revised version here below, with my specific comments and minor/technical\ncomments. In light of these comments, I suggest to revise again the manuscript.\nSpecific comments:\nLine 39: What do the authors mean with “wefting trends”? More moisture or more precipitafion? Please\nclarify in the manuscript.\nLine 42: “AR acfivity, driven by extreme synopfic weather systems” Not all ARs are associated with very\nstrong cyclones or are they? Can’t they be associated with more moderately intense cyclones? Please\nadjust text.\nLine 70-74: This is a confusing sentence. It seems that the authors just want to say that the anficyclonic\nanomaly favours adiabafic warming, hence melfing of ice. Please rephrase.\nLines 77-81: “While the CC relafionship governs the temperature-humidity relafionship on a global scale,\ninternal atmospheric circulafion variability at various fime scales also plays a key role in the Arcfic,\nregulafing the distribufion and transport of moisture across a wide range of fime scales through its\neffects on weather systems including intense storms such as Arcfic cyclones and atmospheric rivers\n(ARs).” This is a very long and not clear sentence. I suggest to split it in two and to clarify the\nmessage. Note also the repefifion of “relafionship” at he beginning of the sentence.\nLine 80: “intense storms such as Arcfic cyclones” Are Arcfic cyclones intense storms? Are they stronger\n(in pressure or in wind intensity) than mid-lafitudes cyclones?\nLines 83-85: “ARs typically form as a result of the interacfion between a cold front, which transports a\nsubstanfial amount of water vapor in the warm sector, and a warm front.” I do not think this is correct.\nARs are collocated with the cold front, which separates the warm sector from the cold sector. It is not\nlinked to the warm front.\nLine 92: “more AR propagafion into the Arcfic” > more ARs propagafing into the Arcfic\nLines 134-137: this part belongs to the Methods.\nLine 177: “AR characterisfics” The authors only analyse the frequency of atmospheric rivers. Therefore, I\nsuggest to change it to “AR frequency”.\nLines 197-199: I have a bit of trouble with the “steering forcing” menfioned here. By definifion, the\natmospheric rivers, via the integrated water vapour transport, are highly dependent on the wind.\nTherefore, it is normal that the atmospheric rivers are associated with the wind. In my view, the\natmospheric rivers which are most often associated with cyclones at those lafitudes rather depend on\nwhat drives the cyclones in one direcfion or another. Please modify this sentence.\nFig. 2d: Please explain somewhere (main text or figure capfion) what the horizontal blue lines in this\nfime series mean.\nLines 254-255: 1) The trend does not change with fime in this study. I think the authors mean here long-term trends in Arcfic ARs frequency.\n2) Please acknowledge that there could be other sources of moisture in the Arcfic not linked to\natmospheric rivers, so that only most of the trend can be explained by changes in moisture.\nLine 256: What are “these impacts”? Please clarify here.\nLine 259: I would not say that the AR acfivity is “rather uniform”. There are maxima west of Greenland,\nSiberia and in the North Atlanfic.\nLine 269: Why don’t the authors also comment CESM2 here (Fig. S5b)?\nLines 305-306: “a small wind field change”: is it smaller than the northerly wind in line 301?\nLine 353: “ARs are primarily driven by the atmosphere’s baroclinic instability of the mean state” I do not\nthink this is true. It is the cyclone to which the AR “belongs” that is controlled by baroclinic instability.\nPlease change and/or clarify.\nLine 354: Could the authors explain here why atmospheric rivers “exhibit stochasfic behaviour on shorter\nfimescales”? “Shorter” than what? Why “stochasfic”?\nLine 356: “Arcfic ARs have exhibited a well-organised increase” I suppose the authors mean an increase\nin frequency (and not an increase in intensity for example). Please add “increase in frequency”.\nMethods: Although it is the main topic of the study, the authors sfill do not explain how they calculate\nthe trends. Do they use least-square regression, a Theil-Sen esfimator, another method? Please add a\ndescripfion of the method in this secfion.\nFigures 1d and 2d: Again, why is the y-axis label “Standard deviafion”? Is it a spafial standard deviafion?\nTo my understanding, if the authors were using EOFs, they would represent here the principal\ncomponents (which are not a standard deviafion). Do the authors mean expansion coefficients? Please\nchange the y-axis label with the appropriate name or explain befter what is represented with this fime\nseries.\nTechnical and minor comments:\nLine 31: “This” → The\nLine 60: “Arcitc” → Arcfic\nLine 81: “characterized as” → characterized by\nLine 95: “leading dramafic impacts on the local climate” → impacfing the local climate\nSupplementary Figure 1b,c: “Climatological mean ARs” → AR frequency, also in the legend “AR mean”\ndoes not mean anything. Please change to AR frequency.\nLine 105: “consequently enhancing the more occurrence of ARs there” → consequently increasing the\noccurrence of ARs there\nLine 109: “and low-frequency” → and the low-frequency\nLine 116: 68. -> 68.\nLine 129: “the role of ARs in contribufing to recent” -> the contribufion of ARs to the\nLine 134: “To study year-to-year” -> To study the year-to-year\nLines 137, 138: “grid” -> grid point\nLine 171: “have” -> has\nLine 173: “model” -> models\nLine 184: “its spafial pafterns clearly exhibit” -> its spafial paftern clearly exhibit\nLine 190: “may physically connected” -> may be physically connected\nLine 194: “in climatology” -> in the climatology\nFig. 2c: The x-axis label is missing. Should be “Days” as in the Supplement.\nLine 251: “The similar” -> A similar\nLine 262: Please add that Arcfic is north of 60°N here.\nLine 272: “that large-scale circulafion” -> that the large-scale circulafion\nLine 278: “to top of” -> to the top of\nLine 297-298: Remove “, which is characterized by a zonal wave number 2 structure,”\nLine 300: Where is “the high pressure”? Over Canada/Canadian archipelago?\nLines 302-303: Is the response of northern Europe really above 0.5 days per month?\nLines 336, 346, 347, 365: “AR”: AR frequency\nLine 400: “6-hour” -> 6-hourly\nLine 470: “spafiotemporally” -> spafially\nLines 474, 475: “grids” -> grid points\nCapfion of Figure 1:\nLines 783, 788, 789: “ARs” -> ARs frequency\n“lower and middle” -> lower to middle\nLines 785-786: remove “, or m/s/10a”\nLine 789: “denotes” -> denote\nLine 790: “for wind” -> for the wind\nCapfion of Figures 4 and 5:\nLine 829, 839, 840: “ARs” -> AR frequency\nPlease also check the capfions of the Supplement Figures.\nREVIEWERS' COMMENTS\nReviewer #1 (Remarks to the Author):\nThis manuscript explores the role of large-scale circulation variability on AR\nfrequency within the Arctic in observations and large-ensemble and bespoke models\nforced with wind anomalies. The authors find that the increased trend of AR and\nmoisture is related to the circulation over the Arctic bringing more AR and moisture\ninto Greenland, Siberia and northern Europe.\nI want to thank the authors for the time and energy spent to comprehensively address\nmy comments and questions and for all the additional work that went into the\nmanuscript. I have only minor comments on this iteration of the manuscript.\nWe appreciate the reviewer for their valuable suggestions, which provide us with a\nnew opportunity to improve our manuscript. We hope that the modifications we have\nmade in response to the comments and suggestions will meet the reviewer's\nexpectations.\nLine 53: Can you give examples of how moisture increase has altered the Arctic\nhydrology and cryosphere?\nIn the Arctic, the increase in moisture in the atmosphere regulates hydrology and the\ncryosphere in several ways, including changing atmospheric radiative properties by\naltering moisture content and cloud characteristics, surface albedo conditions by\ndepositing different types of snow on the surface, and the mass balance of ice sheets\nand glaciers through precipitation processes, among many others (Bring et al., 2016;\nVihma et al., 2016). We have added relevant references covering these aspects to\nsupport that sentence (Line 52).\nLine 170: Rogue underscore\nWe have made the suggested correction (Line 165).\nLine 171: Have -> Has\nWe have made the suggested correction (Line 166).\nLine 321: Is the ensemble mean of the “two fields” you talk about here supplemental\nfigure 12? I would just state that first and then mention the other figures that you are\ncomparing it too.\nRegarding that part, we compared the trends of ARs and Z200 between observations\nand the ensemble mean. To improve clarity of that sentence, we have replaced 'two\nfields' with 'ARs and Z200 trends' and reorganized how we reference these figures in\nsupporting that statement (Lines 318-319).\nLines 379-381: Seems like a word or phrase is missing or perhaps needs reworded to\nemphasis that the increase in moisture can impact sea ice if that is the purpose of these\nlines.\nWe have rephrased the latter part of this sentence as follows: ‘... sea ice and land ice.’\n(Line 379).\nLine 421: Outline what WIN and WIN+CO2 are before you reference them\nmid-sentence. The text in lines 274-294 that describe the models in detail I feel should\nbe in the methods section.\nWe have implemented the suggested correction at lines 419-420. As we consider the\ntext from original lines 274-294 crucial for aiding readers' understanding of the\nsubsequent section, we have retained the majority of it.\nLine 427: numerically incorporating? Should it be numerically incorporate?\nIt shoud be ‘incorporate’. We have made the suggested correction (Line 426).\nLine 456: Would it be better to say that the IVT anomalies lag behind rather than\n“last”?\nHere, we want to emphasize that the moisture anomalies will persist for one day after\nARs end. Therefore, using 'last' may be more appropriate in that context.\nLine 491: Consist -> Consists\nIt should be ‘consist’ to agree with the plural subject ‘time series’ of that sentence.\nFigures 1 and supplemental figure 4 – I think it might be good to have these on the\nsame colorbar, that way it will really emphasise the lack of change in CMIP compared\nto ERA5\nThank you for your suggestion. We have redrawn Supplementary Figure 4 using the\nsame color bar as Figure 1 (Supplementary Fig. 4).\nReferences:\nBring, A., Fedorova, I., Dibike, Y., Hinzman, L., Mård, J., Mernild, S. H., ... & Woo,\nM. K. (2016). Arctic terrestrial hydrology: A synthesis of processes, regional effects,\nand research challenges. Journal of Geophysical Research: Biogeosciences, 121(3),\n621-649.\nVihma, T., Screen, J., Tjernström, M., Newton, B., Zhang, X., Popova, V., ... &\nProwse, T. (2016). The atmospheric role in the Arctic water cycle: A review on\nprocesses, past and future changes, and their impacts. Journal of Geophysical\nResearch: Biogeosciences, 121(3), 586-620.\nReviewer #2 (Remarks to the Author):\nReview of revised manuscript NCOMMS-23-52552A entitled “Role of atmospheric\nrivers in shaping long term Arctic moisture variability” by Wang et al.\nThe authors have addressed most of my previous comments. Overall, I think that the\nmanuscript could still be improved, especially the first part that is slightly difficult to\nread. The authors can find my comments on their revised version here below, with my\nspecific comments and minor/technical comments. In light of these comments, I\nsuggest to revise again the manuscript.\nWe deeply appreciate the comprehensive review conducted by the reviewer,\nrecognizing the significant time and effort involved. Therefore, we have carefully\nintegrated all the comments and suggestions into the manuscript, aiming to ensure the\npaper meets expectations in its current form. We hope the reviewer finds the revisions\nsatisfactory.\nSpecific comments:\nLine 39: What do the authors mean with “wetting trends”? More moisture or more\nprecipitation? Please clarify in the manuscript.\nWe thank the reviewer for this suggestion. It refers to the upward trend of atmospheric\nmoisture. We have replaced ‘wetting’ with ‘humidity’ (Line 39).\nLine 42: “AR activity, driven by extreme synoptic weather systems” Not all ARs are\nassociated with very strong cyclones or are they? Can’t they be associated with more\nmoderately intense cyclones? Please adjust text.\nThank you for your suggestion. Given that ARs are associated with two key features:\nstrong cyclonic rotation and large-scale moisture transport, their occurrence is often\ndriven by strong synoptic-scale systems associated with moisture transport originating\nfrom the tropics. This is because the abundance of moisture and the dynamic intensity\nof low-pressure systems (such as extratropical storms and cyclones) mostly tend to\nvary in-phase. To refine the sentence, we replaced 'extreme' with 'strong' and added\n'mostly' there (Line 41).\nLine 70-74: This is a confusing sentence. It seems that the authors just want to say\nthat the anticyclonic anomaly favours adiabatic warming, hence melting of ice. Please\nrephrase.\nWe provide additional information before that sentence to improve the readability of\nboth sentences. The new sentence reads ‘…manifested as a long term trend toward the\nlocal barotropic high pressure anomaly situated over Greenland over the past four\ndecades.’ (Lines 68-69).\nLines 77-81: “While the CC relationship governs the temperature-humidity\nrelationship on a global scale, internal atmospheric circulation variability at various\ntime scales also plays a key role in the Arctic, regulating the distribution and transport\nof moisture across a wide range of time scales through its effects on weather systems\nincluding intense storms such as Arctic cyclones and atmospheric rivers (ARs).” This\nis a very long and not clear sentence. I suggest to split it in two and to clarify the\nmessage. Note also the repetition of “relationship” at he beginning of the sentence.\nThank you for your suggestion. We have split the sentence into two parts (Lines\n75-79). Additionally, we replaced 'relationship' with 'equation' in the first sentence\n(Lines 75-77).\nLine 80: “intense storms such as Arctic cyclones” Are Arctic cyclones intense storms?\nAre they stronger (in pressure or in wind intensity) than mid-latitudes cyclones?\nNot all cyclones can reach intense storm status. To better reflect this point, we have\nmodified the sentence to read ‘...including extratropical storms such as Arctic\ncyclones and atmospheric rivers (ARs)’ (Lines 78-79).\nLines 83-85: “ARs typically form as a result of the interaction between a cold front,\nwhich transports a substantial amount of water vapor in the warm sector, and a warm\nfront.” I do not think this is correct. ARs are collocated with the cold front, which\nseparates the warm sector from the cold sector. It is not linked to the warm front.\nWe have removed ‘the warming sector’ from the sentence (Line 83). Thanks for\nraising this issue.\nLine 92: “more AR propagation into the Arctic” > more ARs propagating into the\nArctic\nWe have made the suggested correction (Line 90).\nLines 134-137: this part belongs to the Methods.\nWe agree. We have relocated this part to Methods section under ‘AR detection’ (Lines\n449-452).\nLine 177: “AR characteristics” The authors only analyse the frequency of atmospheric\nrivers. Therefore, I suggest to change it to “AR frequency”.\nWe have made the suggested correction (Line 172).\nLines 197-199: I have a bit of trouble with the “steering forcing” mentioned here. By\ndefinition, the atmospheric rivers, via the integrated water vapour transport, are highly\ndependent on the wind. Therefore, it is normal that the atmospheric rivers are\nassociated with the wind. In my view, the atmospheric rivers which are most often\nassociated with cyclones at those latitudes rather depend on what drives the cyclones\nin one direction or another. Please modify this sentence.\nWe have modified ‘steering forcing’ to 'governing forcing’ since we aim to emphasize\nthat the JJA mean anomalies provide a relatively long-term background flow in which\nsynoptic-scale ARs can receive some impacts (Line 193). This scenario is very similar\nto how the summer mean flow regulates the activity of hurricanes over the tropical\nAtlantic or western Pacific.\nFig. 2d: Please explain somewhere (main text or figure caption) what the horizontal\nblue lines in this time series mean.\nThe horizontal blue line in Figure 2d indicates the absence of AR activity during the\nperiod. We added the relevant words in the figure caption to explain this point (Lines\n818-819).\nLines 254-255: 1) The trend does not change with time in this study. I think the\nauthors mean here long-term trends in Arctic ARs frequency.\nYes. We haved rephrased that sentence as ‘Possibly, changes in long term trends of\natmospheric moisture are also mirrored by long term changes in Arctic AR frequency\nto some extent.’ (Lines 248-250).\n2) Please acknowledge that there could be other sources of moisture in the Arctic not\nlinked to atmospheric rivers, so that only most of the trend can be explained by\nchanges in moisture.\nThanks for your comment. We have changed ‘primarily’ to ‘partly’ and ‘to some\nextent’ to make this point clearer (Lines 248-250).\nLine 256: What are “these impacts”? Please clarify here.\nThis refers to impacts of ARs on moisture changes in the Arctic. We have added a few\nwords there to clarify this point (Lines 251).\nLine 259: I would not say that the AR activity is “rather uniform”. There are maxima\nwest of Greenland, Siberia and in the North Atlantic.\nAgree. We have modified the sentence as ‘… has increased in most Arctic areas,\nespecially in western Greenland, Siberia, and the North Atlantic.’ (Lines 254-255)\nLine 269: Why don’t the authors also comment CESM2 here (Fig. S5b)?\nThanks for raising this comment. We have rephrased the sentence to include CESM2\nthere (Lines 265-267).\nLines 305-306: “a small wind field change”: is it smaller than the northerly wind in\nline 301?\nWe understand your concern regarding our vague use of 'small' in the sentence. We\nhave removed 'small' and added the magnitude (1 m/s) of the wind change in that\nsentence (Lines 302-303).\nLine 353: “ARs are primarily driven by the atmosphere’s baroclinic instability of the\nmean state” I do not think this is true. It is the cyclone to which the AR “belongs” that\nis controlled by baroclinic instability. Please change and/or clarify.\nAgree. ARs are also influenced by the availability of atmospheric moisture. We\nchanged 'primarily' to 'mainly' since it is still reasonable to use 'mainly' here, as these\nfrontal low-pressure systems (due to baroclinic instability) serve as the backbone of\nARs (Line 350).\nLine 354: Could the authors explain here why atmospheric rivers “exhibit stochastic\nbehaviour on shorter timescales”? “Shorter” than what? Why “stochastic”?\nAR activity over the synoptic scale appears to be stochastic, given the nature of\nbaroclinic instability and the supply of moisture from the tropics. To avoid any\npossible confusion generated by this word, we have modified 'stochastic' to ‘highly\nvariable’ (Line 351).\nLine 356: “Arctic ARs have exhibited a well-organised increase” I suppose the\nauthors mean an increase in frequency (and not an increase in intensity for example).\nPlease add “increase in frequency”.\nWe have made the suggested correction (Line 353).\nMethods: Although it is the main topic of the study, the authors still do not explain\nhow they calculate the trends. Do they use least-square regression, a Theil-Sen\nestimator, another method? Please add a description of the method in this section.\nWe used the least-square regression method to estimate linear trends in this study. We\nhave emphasized this method in the Methods section (Lines 393).\nFigures 1d and 2d: Again, why is the y-axis label “Standard deviation”? Is it a spatial\nstandard deviation? To my understanding, if the authors were using EOFs, they would\nrepresent here the principal components (which are not a standard deviation). Do the\nauthors mean expansion coefficients? Please change the y-axis label with the\nappropriate name or explain better what is represented with this time series.\nThanks very much for your suggestion. We have replaced “Standard deviation” with\n“Standardized time coefficient” in all relevant figures, including Figures 1 and 2, and\nSupplementary Figures 7, 8, and 9.\nTechnical and minor comments:\nLine 31: “This” → The\nWe have rewritten the sentence (Lines 31-33).\nLine 60: “Arcitc” → Arctic\nWe have made the suggested correction (Line 58).\nLine 81: “characterized as” → characterized by\nWe have made the suggested correction (Line 79).\nLine 95: “leading dramatic impacts on the local climate” → impacting the local\nclimate\nWe have made the suggested correction (Line 93).\nSupplementary Figure 1b,c: “Climatological mean ARs” → AR frequency, also in the\nlegend “AR mean” does not mean anything. Please change to AR frequency.\nWe have made the suggested correction (Supplementary Figure 1).\nLine 105: “consequently enhancing the more occurrence of ARs there” →\nconsequently increasing the occurrence of ARs there\nWe have made the suggested correction (Line 103).\nLine 109: “and low-frequency” → and the low-frequency\nWe have made the suggested correction (Line 107).\nLine 116: 68. -> 68.\nWe have made the suggested correction, and now it is Ref 69 in the revision (Line\n114).\nLine 129: “the role of ARs in contributing to recent” -> the contribution of ARs to the\nWe have made the suggested correction (Line 127).\nLine 134: “To study year-to-year” -> To study the year-to-year\nWe have made the suggested correction (Line 449).\nLines 137, 138: “grid” -> grid point\nWe have made the suggested correction (Lines 452, 133).\nLine 171: “have” -> has\nWe have made the suggested correction (Line 166).\nLine 173: “model” -> models\nWe think we should use the singular subject 'model' there.\nLine 184: “its spatial patterns clearly exhibit” -> its spatial pattern clearly exhibit\nWe have made the suggested correction (Line 179).\nLine 190: “may physically connected” -> may be physically connected\nWe have made the suggested correction (Line 185).\nLine 194: “in climatology” -> in the climatology\nWe have made the suggested correction (Line 189).\nFig. 2c: The x-axis label is missing. Should be “Days” as in the Supplement.\nThank you very much! Added (Figure 2, Line 801).\nLine 251: “The similar” -> A similar\nWe have made the suggested correction (Line 245).\nLine 262: Please add that Arctic is north of 60°N here.\nWe have made the suggested correction (Line 258).\nLine 272: “that large-scale circulation” -> that the large-scale circulation\nWe have made the suggested correction (Line 268).\nLine 278: “to top of” -> to the top of\nWe have made the suggested correction (Line 275).\nLine 297-298: Remove “, which is characterized by a zonal wave number 2\nstructure,”\nWe have made the suggested correction (Line 294).\nLine 300: Where is “the high pressure”? Over Canada/Canadian archipelago?\nWe referred to the high-pressure anomalies over Greenland and central Siberia. We\nhave added a few words there to clarify this point (Line 297).\nLines 302-303: Is the response of northern Europe really above 0.5 days per month?\nThe AR response over northern Europe is around 0.5 days per month. To make the\ndescription more accurate, we have modified the sentences (Line 299).\nLines 336, 346, 347, 365: “AR”: AR frequency\nWe have made the suggested correction (Lines 333, 343, 345, and 362).\nLine 400: “6-hour” -> 6-hourly\nWe have made the suggested correction (Lines 395, 398).\nLine 470: “spatiotemporally” -> spatially\nWe have made the suggested correction (Line 472).\nLines 474, 475: “grids” -> grid points\nWe have made the suggested correction (Lines 476, 477).\nCaption of Figure 1:\nLines 783, 788, 789: “ARs” -> ARs frequency\nWe have made the suggested correction (Lines 787, 792, and 794).\n“lower and middle” -> lower to middle\nWe have made the suggested correction (All figure captions containing 'lower and\nmiddle').\nLines 785-786: remove “, or m/s/10a”\nWe have made the suggested correction (Line 795).\nLine 789: “denotes” -> denote\nWe have made the suggested correction (Line 797).\nLine 790: “for wind” -> for the wind\nWe have made the suggested correction (Lines 790-804).\nCaption of Figures 4 and 5:\nLine 829, 839, 840: “ARs” -> AR frequency\nWe have made the suggested correction (Lines 827, 834, 840, 841, and 843).\nPlease also check the captions of the Supplement Figures.\nThanks for your suggestion! We have double-checked the figure captions for the\nsupplementary figures and have corrected all existing typos and errors.\nREVIEWER COMMENTS\nReviewer #2 (Remarks to the Author):\nThe authors have rather safisfactorily answered my comments and I believe the manuscript was\nimproved. I sfill have one major comment along with minor issues listed here below. I recommend that\nthe authors make the suggested modificafions before acceptance of the manuscript for publicafion.\nMajor comment:\nFigure 1g, 2d, and Supplementary Figures 7c, 8c: Following my comments on what is represented in\nthese panels, the authors have changed the y-axis label from “Standard deviafion” to “Standardized fime\ncoefficient”. This lafter is befter but sfill not precise to my opinion. If the authors were using EOFs, they\nwould represent in these plots the fime series of the principal component of EOF1. Please correct me if I\nhave misunderstood what is actually represented here. However, the authors use the Maximum\nCovariance Analysis and the principal component is now called expansion coefficient or fime expansion\ncoefficient as for example menfioned in An (2003). Therefore, I strongly suggest to the authors to use\n“Expansion coefficient” or “Standardized expansion coefficient”.\nTechnical and minor comments:\nLine 100: It seems that the paper linked to reference 56 deals mostly with actual rivers over land and not\nmuch with atmospheric rivers. Are you sure that this citafion is appropriate here?\nLine 125: “regulafing moisture” → regulafing the moisture\nSupplementary Figure 3: the labels of the color bars should just contain the variable name, that is\nremove the word “mean”. Also “AR mean” should AR frequency, as in the capfion.\nLine 142: “northeastern” → northern. It looks to me that the whole Arcfic side of Canada experience an\nincrease in AR frequency.\nLines 199-200: “we use the fime series of MCA1-ARs (or MCA1-Z200) to correlate with detrended JJA low\ncloud” → we correlate the fime series of MCA1-ARs (or MCA1-Z200) with the detrended JJA low cloud\nLine 218-219: “our first examinafion centers in an analysis of that year” → our first examinafion centers\non that year\nLine 219-220: “MCA analysis [...] are computed” → An MCA analysis […] is performed\nFigure 1: Panel (a) shows that the atmospheric river pathway into the Arcfic west of Greenland will be\nstrengthened with more ARs in this region. However, the trend in cyclone frequency is quite weak as\nseen in Fig. 3f in Zhang et al. (2023) (hftps://www.nature.com/arficles/s43247-023-01003-0/figures/3)\nand not significant. Moreover, the cyclone frequency seems also to increase over the Nordic Seas\n(Greenland, Norwegian, and Barents Seas) although the AR frequency trend the authors find is negafive.\nHow do the authors explain these two different behaviors? This could maybe be included in the\ndiscussion secfion.\nReferences:\nAn, S.-I. (2003) Condifional Maximum Covariance Analysis and Its Applicafion to the Tropical Indian\nOcean SST and Surface Wind Stress Anomalies. J. Climate, 16, 2932–2938. hftps://doi.org/10.1175/1520-\n0442(2003)016<2932:CMCAAI>2.0.CO;2\nZhang, X. et al. Arcfic cyclones have become more intense and longer-lived over the past seven decades.\nCommun Earth Environ 4, 348 (2023). hftps://doi.org/10.1038/s43247-023-01003-0\nZhang, Z., Ralph, F. M., & Zheng, M. (2019). The relafionship between extratropical cyclone strength and\natmospheric river intensity and posifion. Geophysical Research Lefters, 46, 1814–1823.\nhftps://doi.org/10.1029/2018GL079071\nREVIEWER COMMENTS\nReviewer #2 (Remarks to the Author):\nThe authors have rather satisfactorily answered my comments and I believe the\nmanuscript was improved. I still have one major comment along with minor issues\nlisted here below. I recommend that the authors make the suggested modifications\nbefore acceptance of the manuscript for publication.\nWe are grateful to the reviewer again for the valuable suggestions, which offer us\nanother opportunity to improve our manuscript. We hope the changes we have made\nin response to the reviewer’s feedback meet the expectations.\nMajor comment:\nFigure 1g, 2d, and Supplementary Figures 7c, 8c: Following my comments on what is\nrepresented in these panels, the authors have changed the y-axis label from “Standard\ndeviation” to “Standardized time coefficient”. This latter is better but still not\nprecise to my opinion. If the authors were using EOFs, they would represent in these\nplots the time series of the principal component of EOF1. Please correct me if I have\nmisunderstood what is actually represented here. However, the authors use the\nMaximum Covariance Analysis and the principal component is now called expansion\ncoefficient or time expansion coefficient as for example mentioned in An (2003).\nTherefore, I strongly suggest to the authors to use “Expansion coefficient” or\n“Standardized expansion coefficient”.\nThank you for your suggestion. We have replaced “Standardized time coefficient”\nwith “Expansion coefficient” in Figure 1g, 2d, and Supplementary Figures 7c, 8c, 9c.\nTechnical and minor comments:\nLine 100: It seems that the paper linked to reference 56 deals mostly with actual rivers\nover land and not much with atmospheric rivers. Are you sure that this citation is\nappropriate here?\nThat paper primarily discusses the impact of human activities on river runoff, but it\nalso analyzes the contribution of ARs to changes in river runoff. It was found that the\ncontribution of ARs to changes in the flow intensity of some rivers, such as the Amur,\nZhujiang (Pearl), and Columbia, exceeded 30%. Therefore, we believe it is\nappropriate to cite that work to support our main point of that sentence.\nLine 125: “regulating moisture” → regulating the moisture\nWe have made the suggested correction (Line 125).\nSupplementary Figure 3: the labels of the color bars should just contain the variable\nname, that is remove the word “mean”. Also “AR mean” should AR frequency, as\nin the caption.\nWe thank the reviewer for this suggestion. We have replaced “AR mean” with “AR\nfrequency” in Supplementary Figure 3a.\nLine 142: “northeastern” → northern. It looks to me that the whole Arctic side of\nCanada experience an increase in AR frequency.\nWe have made the suggested correction (Line 142).\nLines 199-200: “we use the time series of MCA1-ARs (or MCA1-Z200) to correlate\nwith detrended JJA low cloud” → we correlate the time series of MCA1-ARs (or\nMCA1-Z200) with the detrended JJA low cloud\nWe have made the suggested correction (Lines 199-200).\nLine 218-219: “our first examination centers in an analysis of that year” → our first\nexamination centers on that year\nWe have made the suggested correction (Line 219).\nLine 219-220: “MCA analysis [...] are computed” → An MCA analysis […] is\nperformed\nThanks for your suggestion. We have made the suggested correction (Lines 219-220).\nFigure 1: Panel (a) shows that the atmospheric river pathway into the Arctic west of\nGreenland will be strengthened with more ARs in this region. However, the trend in\ncyclone frequency is quite weak as seen in Fig. 3f in Zhang et al. (2023)\n(https://www.nature.com/articles/s43247-023-01003-0/figures/3) and not significant.\nMoreover, the cyclone frequency seems also to increase over the Nordic Seas\n(Greenland, Norwegian, and Barents Seas) although the AR frequency trend the\nauthors find is negative. How do the authors explain these two different behaviors?\nThis could maybe be included in the discussion section.\nThanks for pointing us to this paper. In Zhang et al. (2023), the authors compared the\nfrequency of strong cyclones between two epochs (1950-1985 vs 1986-2021), which\nis different from our timeframe focusing on long-term changes in AR frequency from\n1979 to 2019. Thus, the two papers examine changes in two different variables over\ndifferent periods. Additionally, it is known that the occurrence of ARs requires two\nkey elements: strong cyclonic activity and an abundance of moisture from lower\nlatitudes. In Zhang et al. (2023), the main criterion to determine strong cyclones is the\npresence of an isolated low sea level pressure and the strength of geotrophic winds\nassociated with cyclones. The influence of moisture is not considered in their\ndetection method. Given the different physical emphasis of our AR detection\napproach and their cyclone detection method, and the different time frames between\nthe two studies, we can't expect that the long-term change in two fields should\nresemble each other. Lastly, I would like to use the key conclusion of another study by\nZhang et al. (2019) to reinforce this point : in extratropical regions, ARs and cyclones\ndo not always coexist. Therefore, the occurrence of ARs cannot be simply determined\nby the presence of cyclones.\nReferences:\nZhang, Z., Ralph, F. M., & Zheng, M. (2019). The relationship between extratropical\ncyclone strength and atmospheric river intensity and position. Geophysical Research\nLetters, 46, 1814–1823. https://doi.org/10.1029/2018GL079071"}]

Decision letter