The primary instruments used to characterize MPC are the ARM Ka-band zenith-pointing cloud radar (KAZR), the Vaisala CL31 cloud ceilometer (lidar), and a dual-frequency microwave radiometer (MWR). Moreover, atmospheric boundary layer (ABL) parameters and thermodynamic states are estimated from synergy between the ground-based infrared radiometer (GND_IRT) and regular radiosondes launched from the NSA site. Table  summarizes the instruments, relevant data products, spatial and temporal resolutions, and their corresponding references. A detailed timeline of data over the study period is provided in Fig. S14 (Sect. S4 in the Supplement). Additionally, satellite-derived sea ice observations are required to complement the atmospheric data for the application of the PSG23 method. The processing and merging of sea ice data with the atmospheric observations are explained in Sect. .

Figure  illustrate the synergy of KAZR, ceilometer, and MWR observations for a case on 19 January 2019. These data, combined with thermodynamic profiles from the ECMWF numerical weather model or radiosonde soundings, form the basis for classifying cloud thermodynamic phase and hydrometeor type. The retrieval algorithm used in this study is the state-of-the-art Cloudnet target classification algorithm version CloudnetPy 1.68.1 (Fig. d). Since ARM data are not natively compatible with CloudnetPy, the raw instrumentation data (Table ) were pre-processed and adapted to meet input requirements . Beyond standard Cloudnet products, additional cloud and atmospheric properties are computed: atmospheric boundary layer height, cloud base and top heights, and cloud top temperature for each cloud layer. Readers are referred to PSG23 for details.

Accurate characterization of low-level Arctic stratus clouds is compromised when the liquid-containing cloud base lies below the radar's lowest detection range. As noted by , if the lidar signal is attenuated by a low-level liquid layer and no higher liquid layer is detected, Cloudnet classifies the entire profile as ice, leading a complete omission of LWP from the MWR (e.g., Fig.  02:00–03:30 UTC). However, if a higher liquid layer is present, the MWR LWP is distributed accordingly. report that for the MOSAiC expedition such low-level stratus cases occur in over 25 % of observation time, with higher frequency in summer than winter. At the NSA site, the lidar can detect liquid clouds down to 10 m altitude, whereas the KAZR has a lowest detection range of 160 m (ARSCL) and 100 m (CORRGE), respectively (see references in Table ). The present study finds that low-level clouds below the radar's detection threshold (160 m for ARSCL, 100 m for CORRGE) account for approximately 26 % and 22.5 % of observations, respectively. To mitigate this limitation, the dataset is adjusted via a flat extrapolation of radar reflectivity down to the lidar's lowest liquid detection altitude (10 m) prior to cloud characterization.

A key limitation of the Cloudnet target classification algorithm is its reliance on valid lidar signal for liquid cloud detection. As shown in Fig.  (02:00–04:00 UTC), complete attenuation of the lidar signal by low-level liquid layers results in the misclassification of higher supercooled liquid layer as ice. Consequently, liquid water content is underestimated, and ice-only clouds are overestimated. More advanced cloud radar Doppler spectra-based tools – such as those introduced by and – can detect liquid beyond lidar signal attenuation, but their application would require extensive retraining on the NSA dataset and significant computation resources, which are beyond the scope of this study. As a result, the Cloudnet derived MPC dataset used here exhibits a low bias in liquid-phase detection, while ice-only clouds are overestimated.

The Cloudnet processing chain – applied to 14 years of NSA data – forms the basis for a merged dataset of macro- and micro-physical cloud properties, combined with sea ice conditions (see Sect. ). This approach follows , where MPC are retained only when both LWP and IWP exceed 5 g m⁻² in a single layer, minimizing uncertainties from retrieval algorithms due to instrument detection thresholds.

The PSG23 method originally used high-resolution (700 m) sea ice lead fraction (LF) derived from sea ice divergence from Synthetic Aperture Radar (SAR) satellite imagery specifically developed for the MOSAiC expedition . However, such high-resolution LF products are not available for the multi-year NSA site observations. Instead, we use sea ice concentration (SIC) from the University of Bremen's publicly available satellite-based Arctic sea ice products (https://www.seaice.uni-bremen.de/start/, last access: 9 May 2026, e.g. ) to characterize the ocean conditions surrounding the NSA site. The SIC product is retrieved from brightness temperatures measured by the Advanced Microwave Scanning Radiometer-2 (AMSR2) onboard the Global Change Observation Mission 1st GCOM-W1 satellite since autumn 2012. AMSR2 sea ice (ASI) provides daily SIC at 3.124 km polar stereographic grid resolution (see “Space-borne sensor” in Table ) . For this study, we use the “Chukchi-Beaufort” region (Fig. ), centred at the NSA site and covering the western and eastern part of the Chukchi and Beaufort Seas. To identify the most relevant sea ice conditions for cloud interaction, we defined a 50 km radius semi-circular sector centred at NSA site (red dashed line in Fig. ) spanning azimuths from -125 to 110°. This sector excludes the landward side (i.e. southerly inflow), ensuring that only clouds, with CTH ≤ 3 km, advected from open or ice-covered ocean areas are considered. This exclusion removes ∼ 8.1 % of the data, corresponding to clouds originating from land.

The mean and standard deviation of SIC are used. SIC is assumed to follow a truncated normal distribution with lower and upper truncation limits of 0 % and 100 %, respectively. The PSG23 method further restricts analysis to a ± 3° conical sector extending from the NSA site outwards up to 50 km and aligned with the upwind direction vw of maximum vertical gradient of water vapour transport (∇zWVT), as described in Sect. . The mean sea ice concentration within the conical sector is denoted by SIC, whereas the mean sea ice concentration over the full 50 km semi-circular sector is denoted as SIC⊙ (Fig. ). The annual differences between these two metrics are shown in Fig. S1 (Sect. S1.1 in the Supplement).

Between 2011 and 2025, the Chuckchi-Beafort region experienced several winters with extraordinary low SIC (≲50 % in Figs.  and S1), including 2012/13, 2016/17, 2017/18, 2019/20, 2023/24, and 2024/25. These anomalies are likely linked to climatological variability and/or low latitude forcing. For example, found that increased frequency of atmospheric rivers in winter correlates with reduced sea ice in the Barents-Kara seas. Atmospheric rivers prevent the sea ice recovering from summer melt and from growing to the climatologically expected sea ice coverage in winter. For the Chukchi-Beaufort region (2011 to 2020), atmospheric river frequency (averaged over November to April) has maximum values of 5.3 % in 2018/19, with intermediate value (2.6 %) in 2013/14, 2014/15, and 2019/20, and a minimum value of 0.2 % in 2015/16 (personal communication with Pengfei Zahng on 13 February 2024). The wintertime 2018–2019, despite moderate atmospheric river activity, saw a less frequent but still noticeable SIC⊙≲ 50 % (Fig. S1), likely due to anomalous warm ocean waters driven by southerly winds and episodic atmospheric blocking over the Bering Sea in September 2018 – directly observed by the RV Mirai Arctic Expedition in November 2018 .

Winter sea ice leads and polynyas act as localized sources of heat and moisture released to the atmosphere. Through convection and advection, these plumes can be transported downstream and influence cloud properties. To establish a physical link between sea ice and clouds, we apply the PSG23 methodology, using the water vapour transport (WVT) as a physical mechanism to link, akind a conveyor belt, the upstream sea ice conditions and the measured cloud properties above the NSA site . WVT is derived from radiosonde profiles and represents the magnitude and direction of water vapour flux and consists of several bands of different magnitudes along the vertical profile.

The PSG23 method identifies the most relevant WVT layer using its vertical profile gradient (∇zWVT [kg m⁻² s⁻¹]) within the planetary boundary layer height (PBLH). The PBLH is constrained when the bulk Richardson number Rib≈1. The vertical gradient of ∇zWVT is calculated using Eq. () (a detailed derivation is given in ). 1∇zWVT=-102g|qv⋅vw|dPdz where ∇z indicates the vertical gradient, g is the constant of gravity, with the specific humidity qv [g kg⁻¹], horizontal wind speed vw [m s⁻¹], and air pressure P [Pa] at altitudes z [m].

A cloud is classified as coupled (co) to the sea ice-WVT system if ∇zWVT has a local maximum within the cloud mixing layer – defined by the cumulative variance of virtual potential temperature θv from cloud base/top height downwards/upwards until a threshold is exceeded. Otherwise, the cloud is classified as decoupled (de).

Within the ASI grid cells a conical sector (± 3°) is aligned with the upwind direction at the altitude of maximum ∇zWVT (e.g., Fig.  where the radial arrows depict the direction of ∇zWVT and their colour the magnitude). Only SIC values within this sector are used for analysis. At a given time the azimuth of the conical sector is governed by the upwind direction obtained from the altitude where ∇zWVT reaches its maximum. Southerly winds (landward flow) are excluded. Figure S1 highlight a critical distinction: sea ice conditions in the full semi-circular sector differ significantly from those in the upwind conical sector. Thus, using large-scale SIC to infer cloud properties may lead to ill-posed correlations since not all sea ice conditions play a relevant role to explain observed cloud properties (e.g. ).

The NSA site climatology is influenced by the persistent cyclonic activity in the north-western Pacific Ocean and anticyclonic systems over the Beaufort/Chukchi seas . Surface pressure serves as a proxy for synoptic-scale high (H-) or low (L-) pressure systems over NSA site (Fig. ). Atmospheric air temperature and pressure are closely related to stability or instability in the atmosphere which strongly influences the formation of clouds. Local pressure systems are classified by the following steps: (i) a reference virtual isobaric surface P0 is assumed to separate the H- or L-pressure observations, (ii) from the measured surface pressure and temperature, the relative height of the virtual isobar P0 is determined. The relative height of P0 is expressed in terms of geopotential height difference derived from the equation of state : 2ΔZ=RT‾g0log⁡P0P where R is the specific gas constant 287.05 J kg⁻¹ K⁻¹, g0 the acceleration of gravity at the surface 9.81 m s⁻², T‾ the mean temperature within the layer [K], P0 the reference isobar surface (in this case the mean sea level pressure of 101.32 k Pa), and P is the measured local surface pressure [kPa] at a given time. ΔZ is negative for H-pressure systems (i.e. P>P0), and positive for L-pressure (i.e. P<P0). T‾ is assumed to be the measured 2 m air temperature, and its effect is to adjust the magnitude of ΔZ when P departures considerably from the reference P0.

From 2011 to 2024, surface pressures at NSA were slightly biased towards H-pressure (median: 101.69 kPa; and Inter-Quantile Region (IQR): 100.95–102.50 kPa), with H-pressure systems occurring 56.3 % of the time and L-pressure systems 43.7 % of the time. The distribution of ΔZ differs significantly between coupled and decoupled cases: Coupled: median ΔZ=-9.45 m (IQR: -65.05 to 44.72 m); Decoupled: median -30.98 m (IQR: -86.95 to 18.74 m). This indicates that H-pressure systems favour decoupled cloud formation. Note that ΔZ reflects local conditions and does not replace synoptic-scale pressure systems from Western Arctic weather maps.

For the considered time period the cloud frequency of occurrence (FoO) were analysed. The total cloud FoO (Fig. , left bars) remains approximately constant at ∼75 % throughout the winter, with a dependency on SIC in the monthly distribution. For MPC (Fig. , right bars) the FoO decreases from ∼56 % in November to ∼40 % in April, with the largest contribution from SIC > 95 % occurring in January. Total cloud FoO (left bars) is derived from all observations with CTH ≤ 3 km and either MWR-LWP > 0 g m⁻² or IWP > 0 g m⁻², indicating detection of liquid or ice clouds. MPC occurrence is defined by simultaneous detection of liquid and ice by both MWR and radar. The persistent presence of SIC = 0 % in every month indicates the continuous occurrence of polynyas and leads during winter along the SIC semi-cone around the NSA site. It is important to note that the SIC values in Fig.  represent the average within a 50 km radius, 6° wide semi-cone, with the azimuth determined by direction of the maximum WVT.

The absolute values for total cloud and MPC are consistent with previous studies: reported an annual total cloud FoO of ∼ 80 % at NSA, and reported a MPC FoO of ∼ 40 % in fall.

To investigate the relationship between sea ice and cloud properties, the data were binned by SIC with bins centred at every 20th percentile. MPC properties are represented by the median μ1/2 and its MAD σmad within each SIC bin. For the following analysis, MPC detection were restricted to cases with LWP and IWP above 5 g m⁻² to reduce noise from minimum detectable retrieval uncertainties, similar as in PSG23.

Figure  presents the liquid water path (LWP), cloud droplet effective radius reff, ice water path (IWP), and ice particle effective radius reff as functions of upwind sea ice concentration along ±3° of the maximum ∇zWVT direction. Observations under H-pressure systems are shown on the left (Fig. a, c, e, g), and L-pressure on the right (Fig. b, d, f, i). Coupled (blue-circles) and decoupled (orange triangles) cases are indicated, with data points from the same SIC bin offset by -3 % for clarity.

To describe the data behaviour, the best fit empirical model is shown as solid coloured lines, with the 95 % confidence interval (CI) shaded around them. The coefficient of determination r2 and goodness-of-fit χ2 are provided for coupled (blue) and decoupled (orange) cases, as defined in Appendix , Eqs. () and (), respectively.

The results in Fig.  reveal that several microphysical cloud properties increase in magnitude as SIC decreases. The best-fitting empirical model is given by Eq. (), which asymptotically approaches β1+β2 as SIC →0 %. This empirical model was specifically optimized for coupled cases; thus, poor fits for decoupled cases highlight differences between coupling regimes. For decoupled cases, only few cloud properties show a clear relationship with SIC, and many variables exhibit no significant correlation. 3Fvar(sic;β)=β1+β2exp⁡sic2 where sic∈{0,…,1} is normalized sea ice concentration, Fvar is the observed cloud property (e.g. LWP, IWP, droplet reff, ice particle reff), and β1, β2 are empirically derived coefficients from non-linear curve fitting. Equation () was preferred over the power-law form F=aSICb (PSG23) because the MOSAiC wintertime observations were predominantly above 90 % SIC, whereas this study spans SIC observations from 0 % to 100 % over 14 years, providing a more complete picture of cloud properties sensitive to SIC.

Specifically, Fig.  panels (a) and (b) reveal a clear increase in LWP with decreasing SIC in coupled cases (blue circles), levelling off at a median of ∼ 80 to 120 g m⁻² for SIC ≲ 30 %. When the observations are sorted according to weather system, the fit to Eq. () is better for H-pressure cases (r2=0.80 and χ2=0.1) than for L-pressure cases (r2=0.80 but χ2=0.34). For L-pressure, LWP exhibits higher variability, resulting in larger uncertainties in β1 and β2 in Eq. () (wider shaded CI in Fig. b), particularly due to outliers near 90 % SIC–80 % SIC. The decoupled LWP behaviour (orange triangles), align with , confirming no significant relationship with SIC. This is supported by reduced r2 of 0.51 (H-pressure) and 0.52 (L-pressure). Similarly, droplet effective radius (reff; Fig. c and d shows a strong negative correlation with SIC in coupled cases, but no significant relationship in decoupled cases.

Regarding IWP (Fig. e and f) no consistent change with SIC is observed in general. However, coupled cases show a strong positive correlation with decreasing SIC, particularly under H-pressure systems (r2=0.72). Decoupled cases show no statistically significant difference changes with SIC. These findings are consistent with pan-Arctic satellite observations by , who reported a negative correlation between SIC and ice cloud cover, and mixed-phase cloud cover, though differences in spatial and temporal scales exist.

For ice effective radius (Ice reff, Fig. g and i), a significant relationship with SIC is observed in coupled cases (r2=0.65/0.66 and χ2=0.09/0.12 for H- and L-pressure cases), though the magnitude of change is smaller than for droplet reff. In decoupled cases, a similar trend is observed but with greater scatter, reducing r2 to 0.24 for H-pressure systems. Notably, all MPC macro- and micro-physical properties are systematically lower in decoupled cases compares to their coupled counterparts.

Key MPC macrophysical properties are displayed in Fig. : Cloud geometrical depth (δh=CTH – CBH; Fig. a and b), liquid layer cloud base height (CBH; Fig. c and d), cloud top temperature (CTT; Fig. e and f), and cloud-layer temperature lapse-rate (Γcloud; Fig. g and i).

For decoupled MPC, no clear correlation with SIC is evident. This is reflected in low r2 values (0.38 for δh, 0.15 for CBH) under H-pressure systems. In contrast, coupled cases show a clear decrease in δh with decreasing SIC (r2=0.63), indicating shallower clouds. These results confirm findings from the MOSAiC expedition: coupled MPC are generally thicker and have lower CBH than decoupled cases, independent of SIC – except for higher liquid CBH in coupled cases under H-pressure systems.

CTT (Fig. e and f) shows significant differences between coupled and decoupled cases, with a noticeable dependency on SIC: colder CTT for sea ice-covered ocean and warmer CTT over regions with open water. The model (Eq. ) fits decoupled cases better (r2=0.81 and H-pressure) than coupled cases (r2=0.62). Coupled cases generally exhibit slightly higher CTT than decoupled cases.

The cloud-layer temperature lapse-rate (Γcloud=-∑z=CBHCTHTz+1-TzHz+1-Hz) is the only macrophysical property that showed pronounced modulation by SIC (Fig. g and i). Decoupled cases show systematically larger Γcloud values than coupled cases. The empirical model (Eq. ) fits the data with a robust coefficient of determination of r2=0.95 and χ2=0.03 for the L-pressure and coupled cases. For H-pressure and L-pressure systems with SIC ≳ 95 %, the median Γcloud (blue dots in Fig. g and i SIC > 95 %) shows that approximately half of the distribution is negative – indicating a temperature inversion within the cloud layer (Ttop>Tbase). As SIC decreases (e.g. SIC < 95 %), Γcloud becomes clearly positive (i.e. Ttop<Tbase), indicating cooling with height and a tendency towards a moist adiabatic lapse-rate.

The results suggesting an apparent contradiction that shallower clouds with increasing LWP and IWP as SIC decreases, is resolved by the concurrent increase in liquid- and ice reff (Fig. c, d, g, i). Furthermore, surface air temperature (T2m) shows a positive correlation with several micro- and micro-physical properties, particularly in coupled cases. The physical explanation for the observed increase in LWP and IWP with the decrease of SIC (i.e. warmer surface) lies in enhanced latent and sensible heat fluxes from sea ice openings, which promote convection and sea salt aerosol release. When coupled to clouds, this drives increased cloud water content. These findings align with in-situ measurements in Svalbard, which suggesting that a warming Arctic enhances ice nucleating particles, enabling formation of ice-containing clouds at warmer temperatures . Similarly, found in Arctic MPC simulations that a warming climate increases cloud ice water content via enhanced ice-nucleating particles and secondary ice production (e.g. rime splintering).

MPC properties are analysed as a time series across 14 years wintertime (WT) seasons WT={2011/12,2012/13,…,2024/25}, with each data point representing descriptive statistics (median, MAD) from observations within a given WT (e.g. “2012/13” = 1 November 2012 to 30 April 2013). No SIC-based separation is applied here.

A first attempt to analyse MPC time series observations at NSA site has been reported by . They previously analysed a 10 year subset of coupled/decoupled MPC at NSA site, reporting the oscillatory nature of some MPC properties with time but no statistically significant trends using Ordinary Linear Regression (OLR). However, OLR is limited by two major shortcomings: i.e. heteroskedasticity (increased variance with higher median values) and residuals' bias with skewed distributions due to oscillatory signals – e.g. a local maximum LWP was found at 2016/17 between two local minima in 2013/2014 and 2018/2019 – violating OLR's assumption of normally distributed residuals.

To address these shortcomings, two robust methods are applied, both using weighted linear regression (WLR) with weights inversely proportional to the variance (Eq. ) within wintertime : i.

A 10 year running window smooths the time series (e.g. 2016/17 averaged over ±5 winters), then a weighted linear regression is applied. This reduces noise and mitigates heteroskedasticity.

Section  along with Figs.  and are only partially comparable to the findings reported by PSG23 for the MOSAiC expedition. This is because the MOSAiC study mainly relies on the sea ice lead fraction (LF) to represent the sea ice states. For the present study, long-term and continues LF data are not available. Furthermore, SIC MOSAiC winter was above 90 %, in contrast to NSA site observations during the 14 years where a larger variation in SIC was covered. Keeping these caveats in mind, the following phenomena reported by can be confirmed:

LWP of coupled clouds increases as the sea ice states trend to larger openings, whereas LWP of decoupled clouds has no relationship with SIC. The present study confirms MOSAiC findings and extends its validity for SIC as low as 5 %.

Conversely, new findings that the study from the MOSAiC expedition did not conclude can be summarized as follows:

Under L-pressure systems and for all SIC, coupled clouds have a lower liquid cloud base height than decoupled clouds. This difference is only evident for H-pressure systems when SIC ≳ 95 %.

Two trend estimation methodologies highlight method-dependent biases. Method i, a standard approach for stochastic periodic signals, is sensitive to edge effects and moving window width. Disagreements arise, particularly in L-pressure decoupled LWP trends: method i shows a decline of -5.3±0.7 g m⁻² per decade, while method ii indicates a slight increase of +0.9±0.9 g m⁻² per decade. This discrepancy stems from Method i's fixed window size, which over-smooth the data, whereas method ii captures a dominant 6.5 years cycle -shorter than the 10 years window- leading to more accurate trend representation.

Disagreement also occurs for the liquid cloud base height of decoupled MPC. Method i detects a trend of +54.9±1.0 m per decade, while method ii reveals a negative trend of -228.1±1.5 m per decade – both statistically significant. Figure c shows how method ii captures a periodic signal of ∼3.6 years, which better fits the low CBH values observed during negative ENSO phases (e.g. 2018/19, 2022/23), whereas method i effectively suppresses these short-term variations.

Both methods agree on IWP trends under H-pressure, showing coupled case trends of +3.5±0.8 g m⁻² per decade (method i) and +4.4±1.0 g m⁻² per decade (method ii) with a 10.8 year period characteristic for ENSO and PDO (Sect. S2.2).

Based on time series of surface skin temperature as indicator of coupled clouds radiative feedback, we found that a continuing warming in the Western Arctic can only be attributed to L-pressure systems associated with observations under SIC < 95 %. In contrast, under H-pressure systems and SIC ≥ 95 %, our results show a decrease in cloud radiative-induced surface temperature.

Hypothesizing the influence of climate modes on MPC variability, we fit the data to Eq. (4b) with one relevant harmonic as a free parameter νk. The NSA site time series are best explained by ENSO's 5.4 and 10.8 years, PDO's 6.5 and 10.8 years, and AO's 2.3 and 2.9 year periods. ENSO and PDO (10.8, 5.4 year) dominate most MPC parameters, with AO (2.3, 2.9 year) and PDO (6.5 year) as secondary contributions. Decoupled MPC under H-pressure varies on intermediate periods (3.6, 4.0, 5.4 years), while coupled MPC shows longer cycles (6.5, 10.8 years). Under L-pressure, decoupled MPC oscillates at 2.3 to 6.5 years, and coupled MPC at 2.9, and 10.8 years, depending on the specific MPC property.

The MOSAiC's derived distinction between coupled and decoupled MPCs, based on water vapour transport-sea ice interaction, is confirmed at the NSA site. The multi-year NSA dataset further enabled the assessment of the influence of atmospheric pressure systems. Results show that MPC properties depend not only on coupling status but also on the prevailing weather pressure system. Under L-pressure systems, coupled LWP, cloud top temperature, and liquid layer base height exhibit positive long-term trends. In contrast, decoupled cases show no significant trend in LWP or CTT, while liquid layer base height was found to decrease. Under H-pressure systems, similar directional trends are observed, albeit with differing magnitudes.

A limitation of this study is that the current remote sensing capabilities at the NSA site do not provide vertical distribution of aerosols, which hinders the investigation of marine aerosol effects on LWP and IWP in conjunction to SIC changes. Moreover, only the local atmospheric pressure has been used to assess the relevant pressure system for NSA site, which does not necessary represent meso-scale synoptic weather pattern for the western Arctic.

This study uses empirical mathematical models derived from long-term observations to parameterize atmospheric boundary layer MPC ascribed to Arctic sea ice conditions; revealing that MPC properties are strongly modulated by sea ice concentration, cloud coupling status, atmospheric pressure systems, and intra-decadal climate variability – providing critical constraints for improving cloud and climate model representation. We argue that MPC in Arctic climate models need to capture the complexity revealed here – beyond small-scale horizontal moist air mass transport linking sea ice leads to downstream clouds – by incorporating weather patterns, large-scale circulation, intra- and multi-decadal climate oscillations, and Arctic-lower latitude teleconnections. Recent studies highlight the implications of this complexity for decadal climate predictability , with similar findings reported by , who linked long-term LWP and CRE (1999–2024) to large-scale circulation and atmospheric rivers. Our findings are aligned with those studies providing a robust observational verification, underscoring the critical role of sustained, long-term high-quality Arctic monitoring networks like the ARM NSA site for enabling such insights.