1NOVEMBER 2020 S H A H I E T A L . 9391

Regional Variability and Trends of Temperature Inversions in Greenland

SONIKA SHAHI,JAKOB ABERMANN, AND GEORG HEINRICH Department of Geography and Regional Science, University of Graz, Graz, Austria

RAINER PRINZ Department of Atmospheric and Cryospheric Sciences, University of Innsbruck, Innsbruck, Austria

WOLFGANG SCHÖNER Department of Geography and Regional Science, University of Graz, Graz, Austria

(Manuscript received 25 December 2019, in final form 31 July 2020)

ABSTRACT: Strong and thick temperature inversions are key components of the Arctic climate system and it is important to study and better understand them. The present study quantifies the temporal and spatial variability of surface-based inversions (SBIs) and elevated inversions (EIs) over Greenland, as derived from the ERA-Interim (ERA-I) dataset for the period 1979–2017. The seasonal and multiannual variability of inversion strength, thickness, and frequency are examined. Our results clearly show regional as well as seasonal patterns of both SBIs and EIs. SBIs are more frequent and stronger than EIs, and the spatial variability of inversions is larger during winter and smaller during summer. Furthermore, during 2 2 2 summer, there has been a trend toward stronger (0.3 K decade 1), thicker (12 m decade 1), and more frequent (3% decade 1) SBIs in the southern part of Greenland, especially in the past two decades. Evidently, the strengthening of the anticyclone over Greenland causes a reduction of cloud cover, which manifests in an increase in SBI strength and thickness, particularly in the southern part of Greenland. KEYWORDS: Ice sheets; Anticyclones; Cloud cover; Inversions; Reanalysis data

1. Introduction troposphere, and they therefore play a key role in the Arctic The Arctic planetary boundary layer provides a favorable climate system. For instance, SBIs, which strongly decouple the condition for the formation of temperature inversions. These surface from the overlying free troposphere, are critical for are a form of atmospheric stratification and describe a situation understanding low cloud and fog formation (Gilson et al. of increasing air temperature with elevation, leading to a stable 2018b). It was also shown that low-level temperature inver- boundary layer. Two types of inversions are prominent for the sions, which lead to nonlinear air temperature lapse rates, can Arctic atmosphere: surface-based inversions (SBIs; i.e., inver- impact the vertical glacier mass balance gradient (Mernild sions whose base is at Earth’s surface) and elevated inversions et al. 2008; Hulth et al. 2010; Mernild and Liston 2010). (EIs; i.e., inversions whose base is above Earth’s surface). (See Correlations between low-level temperature inversions and the appendix for a list of abbreviations.) SBIs are forced by the episodes of increased sea ice and glacier melt have also been radiative imbalance between 1) emitted longwave radiation detected (Chutko and Lamoureux 2009; Tjernströmetal. from snow and ice surfaces and 2) incoming solar and longwave 2015). EIs are important for estimating the geostrophic drag radiation, particularly during the (polar) night. EIs are formed coefficients (Overland and Davidson 1992), which are needed by two mechanisms: by subsidence of air in anticyclones, or by in sea ice motion simulation (Hibler and Bryan 1987), and for warm air advection over underlying cold air masses (Kahl 1990; studying air pollution (Bridgman et al. 1989). Furthermore, Bradley et al. 1992). The intensity and thickness of EIs are Bintanja et al. (2011) described how inversion strength (i.e., considerably smaller than those of the SBIs (Przybylak 2016). temperature difference across the inversion layer) can affect Both SBIs and EIs can have important and complex effects Arctic climate change during winter, as strengthening the at- on the Arctic surface energy budget as well as in the lower mospheric stability leads to a positive temperature lapse rate feedback in the Arctic (Bintanja et al. 2012; Pithan and Mauritsen 2013). Detecting changes of inversions in the Arctic is thus crucial for understanding the impact of climate change on the Denotes content that is immediately available upon publica- tion as open access. cryosphere. Several existing Arctic inversion studies have focused on winter months (Boé et al. 2009; Medeiros et al. 2011; Pithan Supplemental information related to this paper is available at and Mauritsen 2013), owing to the high inversion frequency the Journals Online website: https://doi.org/10.1175/JCLI-D-19- during this period. Winter SBIs are primarily the result of the 0962.s1. large deficit in surface net radiation, whereas summer SBIs are governed by the interaction of melting (in coastal areas) and Corresponding author: Sonika Shahi, [email protected] warm-air advection from the south (Serreze et al. 1992; Palo

DOI: 10.1175/JCLI-D-19-0962.1 Ó 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses). Unauthenticated | Downloaded 10/02/21 02:01 AM UTC 9392 JOURNAL OF CLIMATE VOLUME 33 et al. 2017). Clouds may strongly influence inversions through spatially coherent studies, even in data-sparse regions such as their effect on the surface net radiation budget particularly Greenland. during summer (Serreze et al. 1992; Walsh and Chapman Several studies have extensively employed the different 1998). Several studies have attempted to relate changes in ECMWF reanalysis datasets [e.g., ERA-40 and ERA-Interim clouds to temperature inversions in different locations in the (herein ERA-I)] for studying the Arctic temperature stratifi- Arctic, such as along the northern Alaskan coast (Kahl 1990), cation (Graversen et al. 2008; Tjernström and Graversen 2009; over the central Arctic Ocean (Wetzel and Brümmer 2011; Screen and Simmonds 2010; Bintanja et al. 2011, 2012; Medeiros Palo et al. 2017; Nielsen-Englyst et al. 2019), in the Eurasian et al. 2011; Zhang et al. 2011; Graham et al. 2019). However, most of Arctic (Serreze et al. 1992), and in Greenland (Miller et al. these studies used vertical profiles based on pressure levels starting 2013, 2015; Gilson et al. 2018a,b; Nielsen-Englyst et al. 2019). from 1000 hPa as surface level, which in reality is extrapolated below Studies focusing on the Arctic temperature amplification Earth’s surface in high-altitude regions. Consequently, elevated re- (Graversen et al. 2008; Screen and Simmonds 2010; Bintanja gions such as the central GrIS are generally masked out and, et al. 2012; Pithan and Mauritsen 2013) have shown vertically therefore, such studies only partly reflect the spatial variability of nonuniform warming of the Arctic troposphere, with near- inversions in Greenland. Additionally, most of the studies defined surface warming occurring at a greater rate than at higher the Arctic region poleward of 648 or 708N(Boé et al. 2009; Screen levels. Bintanja et al. (2012) addressed the role of SBIs in and Simmonds 2010; Bintanja et al. 2011; Pavelsky et al. 2011), thus amplifying Arctic warming. missing the southernmost parts of Greenland (598–648N). Strong and thick temperature inversions are persistent in Another important advantage of using reanalysis data large parts of Greenland, because 80% of it is covered by the compared to the application of Automatic Weather Stations Greenland Ice Sheet (GrIS), and hence radiative cooling oc- (AWSs), or radiosonde data, is its ability to reach into the past, curs over a large region. These manifest themselves in the form and thus to provide us with the opportunity to investigate cli- of as a quasi-permanent anticyclone in the central and northern mate trends. Changes in Arctic inversion characteristics have parts of Greenland, which prevents advection of warmer been shown for different Arctic regions, such as for the North Atlantic air over the GrIS during winter (Steffen and Box American Arctic (Bradley et al. 1993; Walden et al. 1996), for 2001). In contrast, the southern part of Greenland is mainly the Arctic Ocean (Kahl et al. 1996; Pavelsky et al. 2011; Wetzel influenced by the Icelandic low, and shows comparatively less and Brümmer 2011), and for coastal stations in Greenland intense and shallower inversions than the central and northern (Zhang and Seidel 2011), using radiosonde, satellite, or re- part (Cappelen et al. 2001; Przybylak 2016). analysis datasets. Screen and Simmonds (2010) reported close The interaction between synoptic-scale climate drivers and agreement between the vertical structures of temperature the surface is governed by boundary layer processes, whose trends in ERA-I and several observations in various Arctic dynamics are important in modulating energy and moisture regions (including Danmarkshavn, a northeast coastal station exchange. At the Summit research station, Berkelhammer et al. in Greenland). Although several studies have increased our (2016) scrutinized the impacts of the SBI on boundary layer understanding of the vertical structures of temperature trends, dynamics, displaying that the stable atmosphere isolates the the relationship between changes in inversion characteristics surface from free-tropospheric moisture sources and limits ac- and clouds has not yet been fully explored. This is especially cumulation. Furthermore, Noël et al. (2019) showed the lat- true for the most recent period where major changes such as itudinal contrast in summertime meltwater runoff from GrIS in large positive Greenland blocking index (GBI) values (Hanna response to change in Arctic atmospheric circulation. This et al. 2016), as well as a reduction of the SMB, have been found. change in atmospheric dynamics can also lead to spatial and The temperature gradient within the lowest 2 m of the at- temporal variations in inversion characteristics in Greenland, mosphere plays a key role in the SEB (Adolph et al. 2018). and ultimately to variations in the associated impact on the GrIS Several studies using AWS data have described the presence of surface energy balance (SEB) and surface mass balance (SMB). stable near-surface-based inversions (NSBIs) in polar regions

This thus highlights the necessity of regionalization when studying as being the difference between 2-m air temperature (T2m) and the heterogeneity of inversion characteristics in Greenland. skin temperature (Tskin)(Hall et al. 2008; Good 2016; Adolph Several efforts have been undertaken in order to explain the et al. 2018; Nielsen-Englyst et al. 2019). Radiosondes are not climatology of temperature inversions in Greenland on the able to capture this near-surface process as they are launched point scale, especially in the coastal regions and at the Summit at levels higher than 2 m above ground level. In contrast, re- station using radiosonde (Gilson et al. 2018b), microwave ra- analysis data offer the potential for analyzing the lowest and diometer (MWR) (Miller et al. 2013), and near-surface mete- very decisive layers. Nevertheless, thorough evaluation by orological data (Adolph et al. 2018; Nielsen-Englyst et al. means of independent observations such as those from AWSs 2019). For example, Zhang and Seidel (2011) found increasing is still a crucial prerequisite. SBI frequency over the period 2000–09 for three coastal ra- In summary, previous studies on inversions in Greenland diosonde stations in Greenland. Unfortunately, as the spatial cov- either covered the region on a too-coarse spatial and vertical erage with in situ observations (e.g., from radiosondes) is generally resolution, or on the point scale only. Thus, what is lacking is a low for Greenland, this makes it hard to provide a consistent spatial more adequate and informative climatology of SBI, EI, and inversion characterization using such data alone. On the other hand, NSBI at the Greenland scale. To close this gap, the present reanalysis data such as from the European Centre Medium-Range study, using the ERA-I dataset, aims to analyze and better Weather Forecasts (ECMWF) reanalyses provide the possibility for understand the spatiotemporal variations and trends of

Unauthenticated | Downloaded 10/02/21 02:01 AM UTC 1NOVEMBER 2020 S H A H I E T A L . 9393 temperature inversion characteristics in Greenland for the past ERA-I cloud processes are described by prognostic equa- four decades (1979–2017). These inversion patterns exert a tions for cloud water/ice content and cloud cover based on the strong influence on many climate impacts and have important mass balance for total cloud condensate (Tiedtke 1993). Here implications for spatial variations in GrIS climate dynamics, and we use the TCC, which represents the fraction of a grid box are thus in clear need of quantification. Besides assessing the covered by cloud occurring at different model levels through climatological changes for various subregions in Greenland, this the atmosphere (ECMWF 2016). Only those days when in- study is also dedicated to detecting the mechanisms driving the versions occur are selected for further calculation. temperature inversion changes, especially in relation to changes Here, ERA-I is employed because it assimilates quality- in cloud cover and inversion strength. controlled in situ and remote sensing observations and ho- The paper is structured as follows: section 2 describes the mogenized radiosonde temperature observations (Haimberger datasets employed in the study. Section 3 explains the statis- 2007); furthermore, the modeled parameters are physically tical methods, the methodology for the calculation of inversion consistent with the observations (Dee et al. 2011). In addition, characteristics, and the applied regionalization of Greenland. the magnitude and vertical structure of temperature with re- Section 4 presents the characteristics of the SBIs, EIs, and spect to radiosonde observations over the Arctic have been NSBIs at the regional and seasonal scales, discusses potential improved by the use of bias correction in satellite radiances sources of uncertainty using comparisons to observational data (Dee and Uppala 2009), which further improved the simulation data, and relates inversion trends to the recent changes in cloud accuracy with respect to Arctic cloud properties and amounts cover. Finally, section 5 summarizes our main findings. (Walsh et al. 2009; Dee et al. 2011). Moreover, ERA-I shows smaller temperature errors (Graversen et al. 2008; Lindsay et al. 2014) 2. Data compared to other reanalysis products such as the National Centers for Environmental Prediction–National Center for Atmospheric a. ERA-I Research (NCEP–NCAR) reanalysis (Kalnay et al. 1996)orthe For the inversion analysis over all of Greenland we used the Japanese Meteorological Service 25-year Reanalysis (JRA-25) global climate reanalysis dataset, ERA-I, provided by ECMWF, (Onogi et al. 2007). In addition, ERA-I also includes a reasonable which covers the period 1979–2019 (Dee et al. 2011). In partic- depiction of inversion properties (Tjernström and Graversen ular, we used the analysis fields such as Tskin, T2m, and total cloud 2009) and trends (Screen and Simmonds 2010). cover (TCC), and also upper-air parameters such as model-level One major concern in using reanalysis data for trend estimation is air temperature and specific humidity. that changes in the observing system may reflect artifacts rather than Our study focuses on daily inversion statistics calculated true climate signals (Bengtsson et al. 2004). In the case of Greenland, from 0000, 0600, 1200, and 1800 UTC instantaneous values of such artifacts might be related to changes in the vertical resolution of air temperature at the lower 15 vertical levels of hybrid sigma- radiosondes or to the increase in the number of satellite observa- pressure coordinates out of 60 model levels from the surface to tions. To account for these deficiencies, ERA-I is the first assimila- 0.1 hPa. We used the ERA-I dataset with a horizontal resolu- tion scheme that adjusts for biases introduced by changes in the tion of 0.25830.258, which is bilinearly interpolated from the observation network (e.g., additional satellite observations) with native model grid of 0.75830.758, and the time period from the aim of removing potential inhomogeneities (Dee et al. 2011). January 1979 to December 2017 for all of Greenland. The Furthermore, compared to ERA-40, ERA-I shows large im- heights of the respective model levels were calculated by ap- provements in quantifying the Arctic warming trend. This im- plying the hydrostatic equation and are unevenly spaced at the provement may be related to a change in the processing of satellite following mean heights above ground level (AGL): 9, 31, 64, radiances in 1997 (Screen and Simmonds 2010; Dee et al. 2011). 111, 173, 254, 355, 477, 623, 792, 987, 1208, 1456, 1732, and We are aware of the successor of ERA-I, the fifth-generation

2037 m. We also include Tskin (as surface temperature) and T2m reanalysis, referred to as ERA5 (Hersbach and Dee 2016). to investigate SBIs and EIs. ERA-I Tskin is the theoretical ERA5 offer several improvements compared to ERA-I, most temperature of the uppermost surface (snow, ice, or soil) layer notably better spatial and temporal resolution, and more ex- with no heat capacity derived from closing the surface energy tensive observational inputs to the data assimilation system balance and consequently, not based on observations. By (Hennermann and Giusti 2018). Delhasse et al. (2020) com- contrast, ERA-I T2m is an independent assimilation product pared the near-surface climate in ERA-I and ERA5 over the derived by optimal interpolation using dry-bulb T2m station GrIS with the PROMICE (Programme for Monitoring of the observations and a background field coming from the lowest Greenland Ice Sheet) stations and concluded that ERA5 does model level (located at a height of about 10 m) and skin tem- not significantly outperform ERA-I. Graham et al. (2019) perature from the previous 6-h background forecasts (Simmons evaluated the skill of ERA-I and ERA5 in representing inde-

and Poli 2015; ECMWF 2016). The number of stations with T2m pendent radiosonde data in the Fram Strait and they concluded observations from Greenland and used for ERA-I is low, and that ERA5 performs best among five reanalyses. However, stations are almost exclusively close to the coast [see Simmons they also found that, in several cases, better simulations of SBIs and Poli (2015) for details]. Given the ERA-I approach, the and EIs were achieved by ERA-I. model level atmospheric fields, from which the background To raise confidence in our results, we compared the per- forecast for the next analysis in the assimilation sequence is formance of ERA-I to ERA5 using quality-controlled radio- derived, are only very weakly forced (via the land surface sonde observations from an enhanced version of the Integrated scheme) by the T2m analysis. Global Radiosonde Archive (IGRA) from Greenland (Durre and

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operated by the Geological Survey of Denmark and Greenland (GEUS), distributed in the ablation area of Greenland, and from the Zackenberg (ZAC) research station in northeast Greenland operated by the Greenland Ecosystem Monitoring program. These data are not assimilated within ERA-I (Delhasse et al. 2020) and thus provide an independent dataset for comparison. We also used hourly values of outgoing longwave radiation from

all stations in order to calculate Tskin, and only high-altitude sta- tions from the PROMICE network were selected in order to ensure perennial snow cover with known surface emissivity. Figure 1 shows the geographical distribution and corresponding abbreviations for all stations used in this study. Table 1 shows the temporal coverage and elevation difference between the stations

and the corresponding ERA-I subsampled grid cell value (ERA-Is) which denotes the median of the nearest four grid cells in order to take account of the effective model resolution (Laprise 1992).

3. Methods

a. Inversion layer identification and classification: Surface-based and elevated inversion For our study we implemented the objective inversion de- tection algorithm designed by Kahl (1990) to determine the temperature inversion characteristics, including embedded thin layers (thickness , 100 m) of negative lapse rate. The vertical temperature profile was scrutinized for inversions from 0 to 2086 m AGL for each grid point and for all daily time steps (0000, 0600, 1200, and 1800 UTC). First, four inversion characteristics were identified as de-

scribed by Kahl (1990): inversion base (zb), which is the ele- vation of the level above which the temperature increases; air

temperature at zb (Tb); inversion top (zt), which is the elevation of the level above (below) which the temperature decreases

(increases) with height; and air temperature at zt (Tt) (see Fig. 2). Second, the following inversion characteristics were then calculated: inversion thickness (Dz 5 z 2 z ), strength FIG. 1. Map of Greenland and the seven different regions used t b D 5 2 G5D D 21 for regionalization of inversion characteristics in this study. Regions ( T Tt Tb), gradient ( T z ), and frequency (f; are redrawn from Cappelen et al. (2001). Elevation contour lines are percentage of days with inversions) (Kahl 1990). Furthermore, shown in red. Regions are named as follows: Central (C), North (N), we labeled inversion as ‘‘surface-based’’ (SBI) if zb is 0 m AGL Northeast (NE), Southeast (SE), South (S), Southwest (SW), and (Fig. 2b), and SBI strength, thickness, and frequency as DTSBI, Northwest (NW). The location of stations used in this study is indi- DzSBI, and fSBI, respectively. Conversely, inversions were la- cated by white crosses representing the following stations: EGP in beled as ‘‘elevated’’ (EI) if zb is high above the ground (more than East Grip, KPC_U in Crown Prince Christian, ZAC in Zackenberg, or equal to 2 m AGL), and EI strength, thickness, frequency, and TAS_A in Tasiilaq, QAS_U in Qassimiut, NUK_U in Nuuk, and base as DTEI, DzEI, fEI,andzEIb, respectively (Fig. 2b). THU_U in Thule. In most of the existing studies inversions are referred to as SBIs if z is either at 2 m AGL (Zhang et al. 2011), or at 9 m AGL Yin 2008). Figure S1 in the online supplemental material reveals that b (Wetzel and Brümmer 2011; Palarz et al. 2018), or at any inversion both reanalyses products provide a close match to observations, with layer within a boundary layer depth of 25 m (Tjernströmand root-mean-square error (RMSE) values lower than 0.3 K, with the Graversen 2009). However, several other studies have demon- ERA5 match being slightly better than that of ERA-I. However, the strated the presence of strong near-surface temperature inversion general error pattern between ERA-I and ERA5 is consistent. This below 2 m (Hudson and Brandt 2005; Adolph et al. 2018; Nielsen- adds confidence to our choice of ERA-I as we expect that the signals Englyst et al. 2019) and scanned the temperature profile starting discussed in this study will not differ significantly between ERA-I from the ‘‘surface’’ (i.e., T of the surface at the snow–air in- and ERA5. skin terface). Consequently, we based our SBI nomenclature starting b. Observational data from the surface, while evaluating the potential of ERA-I to To evaluate the near-surface temperature of ERA-I, we used capture these surface inversions by comparing with independent ø hourly T2m from the PROMICE network (Ahlstr m et al. 2008), station observations (see section 3b).

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TABLE 1. Median absolute deviation (MAD) of daily near surface-based inversion strength (DTNSBI; K), between independent AWS and corresponding subsampled ERA-I (ERA-Is) at the AWS’s location for winter (DJF) and summer (JJA) of the given period. The absolute elevation difference between AWS (zAWS; m MSL) and ERA-Is (zERA; m MSL) is also shown in meters. See Fig. 1 for the station locations.

Na MAD (K)

Stations zAWS (m MSL) zERA (m MSL) jzAWS–zERAj (m) Period DJF JJA DJF JJA EGP 2660 2682 22 2016–17b 87 87 2.7 0.7 KPC_U 870 757 113 2009–17 681 794 1.2 1.3 TAS_A 890 674 216 2014–17 353 367 2.4 1.2 QAS_U 900 803 97 2009–17 591 763 1.6 1.0 NUK_U 1120 1199 79 2008–17 565 653 1.1 1.0 THU_U 760 340 420 2011–17 540 574 2.8 1.0 ZAC 38 568 530 2012–17 475 144 5.9 1.5 a Note that N represents the total number of inversion days when surface albedo is more than 0.3 and station data are available. b For EGP station, JJA and DJF data are available only for 2016 and 2017, respectively.

We apply the following criteria in defining inversions the lowest 2-m vertical profile (i.e., the difference between

(Fig. 2). If T2m is higher than Tskin, this profile is classified as T2m and Tskin). As previously mentioned, ERA-I Tskin is SBI as well as NSBI (see detailed explanation in section 3b) the numerical model result, which makes it necessary to

and hence Tb equals Tskin. If a noninversion layer (where assess the ability of the reanalysis system to represent the temperature decreases with height) of less than 100-m thick- near-surface layer. Even though ERA-I T2m is constrained ness is embedded between two inversion layers, then these by T2m observations in Greenland, T2m observations from two inversion profiles were combined and classified as SBI. the PROMICE network are not assimilated in ERA-I However, after such combinations, we encountered some profiles (Delhasse et al. 2020), thus providing an independent where the upper inversion layer Tt was less than the lowest inversion dataset. Keeping this in mind, we present the direct com- layer Tb, resulting in a negative lapse rate. Whenever this situation parison between ERA-Is Tskin, T2m, and the daily median arose, the highest level was skipped, and Tt was defined by the DTNSBI, with temporally, and spatially collocated AWS- subsequent lower inversion layer. Additionally, this skipped based DTNSBI. inversion layer was counted as EI and the same aforemen- The Tskin value for all stations was estimated from the out- tioned method was applied to determine the top of the given going longwave radiation applying the Stefan–Boltzmann law EI layer. If the thickness of the embedded noninversion layer using a surface emissivity of 0.97 for snow and ice (Van As was more than 100 m, then the lower inversion layer was 2011). To maintain procedural consistency, daily median

classed as SBI and the upper as EI (Fig. 2b). DTNSBI for all the stations was calculated in the same way as As the inversion characteristics are typically not normally dis- daily median DTNSBI from ERA-Is. To constrain the data to the tributed, we calculated median values to represent their central presence of snow and ice, only temperature differences (T2m tendency (Kahl 1990; Kahl et al. 1992; Tjernström et al. 2012; Palo minus Tskin) with a surface albedo greater than 0.3 were se- et al. 2017). First, we calculated the daily median of the inversion lected (Nielsen-Englyst et al. 2019). Furthermore, the median characteristics for each grid cell whenever more than one profile of the positive temperature differences at 0000, 0600, 1200, and

(0000, 0600, 1200, 1800 UTC) from a single day contained a tem- 1800 UTC was calculated to represent the DTNSBI for that day perature inversion. Whenever there was just one inversion profile a for every station.

day, we used this profile to represent that day (Palo et al. 2017). The corresponding daily DTNSBI for ERA-I was calculated Second, monthly median values were calculated by considering only first by extracting the hourly T2m and Tskin for each station’s those days with inversions. Furthermore, monthly f was calculated location by linear interpolation, and second, by computing the by dividing the number of days with at least one inversion by the median of the positive differences between the interpolated

number of days in a month. Finally, seasonal statistics of the inver- T2m and Tskin. Only the days with at least one inversion in both sion characteristics were calculated based on the meteorological datasets were included for comparison. seasons: winter [December–February (DJF); for December 1979 One source of systematic bias in ERA-I might be due to

data are not included], spring [March–May (MAM)], summer elevation differences between ERA-Is and stations (Delhasse [June–August (JJA)], and autumn [September–November (SON)]. et al. 2020). The ERA-I elevation (m) for Greenland is 2 To aid comparability across the subregions, we also show the spatial computed by dividing the surface geopotential (m2 s 2)by 2 distribution of the inversion characteristics. Earth’s gravity (i.e., 9.8 m s 2; Fig. S2) and the corresponding

ERA-Is elevation is extracted by linearly interpolating to the b. Deriving near-surface-based inversion from automatic respective station’s location. Figure S3 shows the linear fit

weather stations between the seasonal mean bias of Tskin, T2m,andDTNSBI as a To investigate the presence of near-surface-based in- function of the elevation bias between the ERA-Is and the versions, we analyzed the NSBI strength (DTNSBI)within respective AWS. Furthermore, the mean error (ME; Fig. S3)

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FIG. 2. Diagrammatic sketch of ERA-Interim vertical low model levels and idealized temperature profiles of different inversion types. (a) Cross-sectional schematic of Greenland and the orientation of the model levels above it, showing mean elevation of each level (black dotted lines; m AGL) following the Greenland orography repre- sented by the model (m MSL) with two temperature profiles [(i) and (ii)] above the ground. (b) Temperature (T) profiles above the ground (z), indicating different types of inversion layer such as (i) surface-based inversion (SBI) and near surface-based inversion (NSBI; red line) and (ii) elevated inversion (EI; green line). Thickness (Dz) is the elevation difference between inversion top (zt) and base (zb). Strength (DT) is the temperature difference between the inversion top (Tt) and base (Tb). SBI parameters are labeled as DzSBI and DTSBI. EI parameters are labeled as DzEI and DTEI. The embedded noninversion profile (black line) with Dz thickness is shown. Note that the SBI and EI can occur concurrently if embedded Dz is more than 100 m.

and the median absolute deviation (MAD; see Table 1)are (S), Southwest (SW), and Northwest (NW) (see Fig. 1). Regional used as a measure of accuracy. inversion characteristics presented here are spatially and tempo- rally aggregated grid cell values within the given regions. The C c. Regionalization of Greenland region mostly covers the GrIS, while all other marginal regions Greenland is characterized by extreme orographic and cli- along the coasts are influenced by the ice sheet, peripheral gla- matic heterogeneity (Steffen and Box 2001; Abermann et al. ciers, and the ocean, and therefore show a significant variety of 2017). Furthermore, due to the presence of wide and dense climate. polar pack ice drifting along the east coast with the East In the following sections, we report on the differences be- Greenland Current, the high Arctic zone extends much farther tween the inversion characteristics analyzed among these seven south on the east coast than on the west coast. Thus, the east subregions of Greenland. We applied statistical tests for each coast is characterized by a more continental climate, with very inversion characteristic in order to distinguish robust differ- cold winters and generally drier conditions (Stendel et al. ences. The collective statistical tests employed comprise the 2008), compared to its western counterpart. These differences nonparametric Wilcoxon rank-sum test and the Kruskal–Wallis create strong horizontal and vertical temperature gradients, test, followed by Dunn’s post hoc test with p adjustment using and, hence, gradient of temperature inversion conditions. the Benjamini-Hochberg method to control the false discovery To account for this strong regional variability, we divided rate (Benjamini and Hochberg 1995) at a 95% confidence level. Greenland into seven climatic regions as described in Cappelen Furthermore, we explore whether any significant trends can et al. (2001), namely Central (C) [called ‘‘Ice cap’’ in Cappelen be detected in the inversion parameters for the given periods. et al. (2001)], North (N), Northeast (NE), Southeast (SE), South Here, trends in all the subregions were estimated by applying

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FIG. 3. Climatology of surface-based inversions (SBIs) in FIG.4.AsinFig. 3, but for the median of elevated inversion (EI) Greenland over 1979–2017 shown by the median of SBI charac- characteristics for the different regions of Greenland. The inset teristics for the different regions. Each marker is for a region from image shows EI frequency (fEI; %) as a bar plot and median ele- Fig. 1 and represents the areal median of SBI characteristics, shown vation of EI base (zEIb; m AGL) as a marker for each region. The for winter (DJF), spring (MAM), summer (JJA), and autumn lower and upper end points of the vertical line passing through the D (SON). The 25th and 75th percentiles of SBI thickness ( zSBI) and marker denote the 25th and 75th percentiles, respectively, of zEIb strength (DTSBI) are indicated by the horizontal and vertical lines for each region. through each marker. Note that the values are on a logarithmic scale to base 10 to ease the comparison of regional inversion characteristics in response to skewness. The inset bar plots illus- 1) SEASONAL PATTERNS OF INVERSION FREQUENCY trate the median frequency of the SBI (fSBI; %) for each region SBIs occur more frequently and intensively than EIs, and (indicated by the respective marker above the corresponding bar). exhibit different characteristics in all the regions and seasons All the days with detected SBI contributes to f ; the vertical line SBI (Figs. 3 and 4). SBIs are more frequent during winter and au- spans the 25th and 75th percentiles of fSBI for each region. tumn (94%–100%, 25th–75th percentiles) than summer and

spring (fSBI 5 50%–100%), whereas EIs show an opposite the nonparametric Mann–Kendall test at the 5% significance pattern (for DJF and SON fEI 5 5%–22%; for JJA and MAM level in each grid cell and trends were quantified by applying fEI 5 9%–44%). SBIs are more frequent in autumn compared Sen’s slope estimator. Moreover, Spearman correlation is to spring (when also a clear north to south contrast is visible), computed between detrended variables in order to prevent the while EIs show the opposite pattern (Table S1). trend artificially affecting the correlation. Only statistically The high fSBI during the melt season in the low-lying coastal significant trends, correlation coefficients (r), and coefficients regions occurs due to surface melt consuming energy and of determination (R2) are presented, and the corresponding hence cooling the layers above the surface. In the interior of

significant areal percentages reported upon. To compute re- the GrIS however, where melting is rare, high fSBI is due to spective region area, we first calculated the area covered by strong radiation losses as a result of high albedo and a negative each grid cell within the given region, and then summed up the longwave radiation balance. This keeps the surface tempera- area of all the grid cells (denoted by the n symbol in Fig. S2) ture well below 08C and develops strong and shallow SBIs within the given region. This was done by using a general ap- (Busch et al. 1982; Palo et al. 2017). Additionally, Wetzel and proximation of 18 corresponding to 111 km. Since the longi- Brümmer (2011) suggest that the frequency of inversions tudes converge toward the poles, the distance between two during summer is additionally sustained by a downward di- longitudes were weighted by the cosine of the corresponding rection of the conductive heat flux in the cold glaciers.

latitude. The total area of each grid cell is then calculated by Miller et al. (2013) found a fSBI of 40% at the Summit station multiplying the latitude distance with weighted longitude in Greenland in July 2011 using MWR, whereas ERA-Is fSBI is distance. 17% higher for the summer (JJA median) of the same year.

This discrepancy between observed and ERA-Is fSBI could be 4. Results and discussion attributed to several factors, such as different time referencing, variations in the criteria for defining inversion (Zhang et al. a. Climatology of inversions in Greenland 2011), surface elevation difference (e.g., for Summit, the dif-

The results of seasonal median inversion strength (DTSBI ference with ERA-Is is 82 m), or subgrid cell spatial variability. and DTEI), thickness (DzSBI and DzEI), frequency (fSBI and fEI), However, despite the difference in magnitude, ERA-Is shows and height of EI base (zEIb) for the different climatic regions of an annual cycle of inversion frequency statistics similar to that Greenland, and for the different seasons are presented in observed at the Summit station; SBIs are more frequent in

Figs. 3 and 4. They show clear seasonal and regional patterns winter (fSBI 5 96%) compared to summer (fSBI 5 57%) in spanning from the low to the high Arctic regions in Greenland. ERA-Is for the same time period as in Miller et al. (2013) (see

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Miller et al. (2013): fEI for DJF is ;3% and for JJA is ;13%.

2) REGIONAL PATTERNS OF INVERSION FREQUENCY

Even though all the regions show high winter median fSBI (98%–100%), we find statistically significant regional differ- ences that are likely associated with a gradient in insolation, ranging from no polar night in the S region to more than four months of polar night in the N region (Abermann et al. 2017). During winter, the development and migration of the low pressure systems from S toward the area between the east coast of Greenland and Iceland (Turton et al. 2019) might addi- tionally cause differences in regional frequency. With the

transition from winter to spring and the onset of summer, fSBI decreases overall with maximum occurrence in the cold and FIG. 5. Frequency distribution of seasonal median of tempera- dry northern regions (N, NE, NW) (79%–98%) and lower 21 ture gradient (G;Km ) within the NSBI (GNSBI) and SBI (GSBI) occurrence in the warmer southern regions (S, SW, SE) (50%– layer over the period 1979–2017 for the given regions. Box-and- 93%). The lowest median summer fSBI is found in SW (72%) whisker plots show the median (white horizontal line), the 25th and and the highest in NE (96%). 75th percentiles (bottom and top of the box), and the 5th and 95th

Likewise, winter fEI shows a statistically significant regional percentiles (whiskers; black line) for seasonal median temperature difference with maximum occurrence in NE (10%–17%), fol- gradient within the NSBI (light gray box) and SBI (dark gray box) lowed by S and SW (9%–15%) (Fig. 4 and Table S1). Contrary layer for each region. The violin plot depicts the frequency distri- bution of the temperature gradient within each region. The y axis to f , f increases in all regions during summer, showing a SBI EI shows data with logarithms to the base 10, with the major tick labels maximum in the southern (S, SE, SW) and central regions on the original scale. The minor ticks on the y axis show increments 2 (13%–44%) and a minimum in the northern regions (N, NE, of 0.01, 0.1, and 1 K m 1 in every one-third of the major ticks from NW; 8%–33%). base to top. Noninversion days are excluded in median calculation. Based on the radiosonde data from Tasiilaq, Danmarkshavn, and Ittoqqortoormiit for the period 1980–2016, Gilson et al. 21 21 (2018b) found a higher fEI than fSBI in summer, while ERA-Is The temperature gradient (G5DT Dz ;Km ) within the shows the opposite. However, fEI derived from ERA-Is is higher inversion layer is larger during winter than during summer at NE stations (55%) than at the SE station (25%), which is in line (Fig. 5). However, the median winter G within the SBI layer

with the spatial differences presented by Gilson et al. (2018b) (GSBI) for different regions of Greenland ranges from 0.03 to 2 (79% at NE stations and 69% at the SE station). It has been 0.04 K m 1 within a thick inversion layer, whereas for summer 2 shown that it is very challenging to directly derive SBI charac- it lies between 0.04 and 0.05 K m 1 within a shallow inversion teristics from radiosondes as the lowest level of the radiosondes layer (Table 2). This pattern is in line with that observed by does not correspond to that of the ground level (Mernild and Bradley et al. (1992) for a coastal area in North American Liston 2010; Zhang et al. 2011). Another factor limiting the direct Arctic. Przybylak (2016) attributed such a strong G to the

comparability of radiosondes and ERA-Is is the difference in combination of subsidence-induced EI in the lower tropo- surface elevations between actual radiosonde stations and corre- sphere and strong SBI; we classified this kind of thermal profile sponding ERA-Is (difference is 2291 m MSL for Tasiilaq, 225 m as SBI. The G within the EI layer (GEI) is less steep than within MSL for Ittoqqortoormiit, and 275 m MSL for Danmarkshavn). the SBI layer for all the seasons (Table 2).

The EI base (zEIb) also shows a seasonality (Fig. 4); zEIb is 3) SEASONAL PATTERNS OF INVERSION STRENGTH, higher during winter and the base is lower (even close to the

THICKNESS, AND BASE ground) during summer; for example, zEIb is at 2 m in the SW Over an annual cycle, SBIs are strongest and thickest in during spring (Table S4). winter (6–24 K and 89–628 m) and get weaker in spring (4–18 K and 56–418 m) and even more so in summer (2.3–6.4 K and 60– 4) REGIONAL PATTERNS OF INVERSION STRENGTH, 179 m), then again increase during autumn (4.4–15.7 K and 79– THICKNESS, AND BASE

319 m). Values of DTEI and DzEI also show a similar seasonal Figures 3 and 4 clearly show that the spatial variability of DT pattern: DTSBI values are approximately one order of magni- and Dz is large during winter and small during summer. The tude higher than those of DTEI (Table S2), and DzEI is higher north–south differences of DT and Dz are higher during winter than DzSBI during summer (Table S3). To keep replicability (8.0 K and 351 m) compared to summer (1.5 K and 38 m) and comparability to other studies, we did not apply an in- (Tables S2 and S3). During winter, DTSBI and DzSBI are highest version strength criterion for defining inversion. So, the results in the N region (16.5 K, 462 m) and lowest in the SW region

for DzEI and DzSBI could differ depending on the use of such (7.2 K, 141 m) (Fig. 3). The reason for this spatial difference criteria as noted by Zhang et al. (2011). might be the dominance of anticyclonic activity in the N region,

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TABLE 2. Seasonal median of temperature gradient (G) for dif- with observations, the general agreement of frequency distri- ferent types of inversion for the period 1979–2017 averaged over butions is evident, in particular when the surface is covered Greenland. with snow or ice in both data sources. The mean error in

21 21 21 DTNSBI is a combination of biases in Tskin and T2m, which can at Seasons GNSBI (K m ) GSBI (K m ) GEI (K m ) least partly be explained by the elevation difference. Summer DJF 1.6–1.8 0.03–0.04 0.0008–0.001 mean error of DTNSBI shows a weak correlation with the mean MAM 1.0–1.2 0.03–0.04 0.001–0.003 error in elevation (i.e., R2 5 0.32) but is not statistically sig- JJA 0.8–0.9 0.04–0.05 0.003–0.005 nificant (Fig. S3). SON 1.3–1.5 0.03–0.04 0.001–0.002 Figure 6 shows the frequency distribution of DTNSBI for each station and its corresponding ERA-Is for the given period (see Table 1). Despite exhibiting a similar distribution, the MAD of resulting in very low cloudiness and precipitation, especially at the ERA-Is-based NSBI compared to the observations is the leeward side of the ice sheet (Przybylak 2016). Furthermore, comparatively small for stations located on the moderately flat during winter, the regions near the east coast (especially NE) ice sheet terrain (e.g., EGP; 2.7 K for DJF and 0.7 K for JJA) show stronger and thicker SBIs (9.3–18.1 K and 247–342 m) than and comparatively large for stations located in complex those near the west coast, particularly SW (7.2 K and 141 m) mountainous terrain (e.g., ZAC; 5.9 K for DJF and 1.5 K for (Fig. 3; Tables S2 and S3). This zonal difference can be attrib- JJA) (Table 1). This relatively large value can be attributed to uted to the influence of sea ice along the east coast during winter, various factors such as the elevation difference between the combined with clear, calm weather, and strong radiative cooling actual station location and corresponding interpolated grid cell leading to cold surface temperatures. This contrasts with the values (22 m for EGP; 530 m for ZAC), observation errors, southwestern region, which has much less sea ice (Cappelen model interpolation errors (Gao et al. 2012), and change in et al. 2001) along the coast and is therefore relatively warmer. measurement height of the AWS during snowfall and melt Additionally, different regional circulation regimes (e.g., the (Nielsen-Englyst et al. 2019). However, for station EGP, as the

Icelandic and the Baffin Bay lows) govern the eastern and winter MAD is based on only one winter (2017) of DTNSBI, this western regions of Greenland, respectively (Steffen and Box might explain the larger winter MAD value for this station. 2001), and may explain some of these regional differences. ERA-I-derived NSBIs show similar seasonal and regional

The G values are comparatively higher in the southern re- patterns similar to those of SBIs. In winter, the median DTNSBI 2 gions (0.06–0.08 K m 1; see Fig. 5) in all the seasons. This is is strongest (4.0–5.1 K), with greater spatial variability (2.6– likely to be due to their proximity to mildly warmer air that is 8.8 K) in all regions compared to its summer counterpart advected aloft (Bradley et al. 1992). This indicates that despite (median, 1.6–2.5 K; range, 1.4–3.1 K). This can be attributed to fewer and shallower inversions in the southern regions com- the frequent passages of cold and warm air masses in winter- pared to northern regions, those in the southern regions can be time compared to summertime (Steffen 1995) and the long

very stable. period of the midnight sun reducing the north–south DTNSBI The zEIb values also show a clear regional pattern (Table S4). spatial variation during summer (Cappelen et al. 2001). Near In the cold and dry northern regions of Greenland, where SBIs Summit station for the summer 2015, we found fNSBI of 69% are dominant, the inversion base is higher (116–561 m AGL) based on ERA-Is, which is in agreement with Adolph et al. than in the southern regions (2–285 m AGL), (Table S4). The (2018), who found a value of 68% based on near-surface me- zEIb values show largest regional variability during spring and teorological observations. The ERA-Is DTNSBI (;1.4 K) is summer. smaller compared with that of Adolph et al. (2018) [3.5 K

(62.4 K)]; however, the ERA-Is DTNSBI magnitude is not sig- b. Near-surface-based inversions in Greenland nificantly different when observation uncertainty is taken into We included the analysis of the ERA-I NSBIs in our study as account. they not only play a crucial role in near-surface atmospheric The seasonal and regional variation in temperature gradient processes, but also allow for comparison with independent within the NSBI layer (GNSBI) is presented in Fig. 5.Within observations from surface stations. Such a comparison is im- the lowest 2 km of troposphere temperature profiles, the portant as NSBIs derived from ERA-I combine different re- strongest temperature gradient is close to the surface (Fig. 5 analysis products (like T2m and Tskin). The accuracy of the and Table 2). inversion strength derived by ERA-I can be evaluated by di- rectly comparing it with the station-derived inversion strength. c. Multidecadal trends of temperature inversion However, stations represent point measurements, while ERA-I characteristics in Greenland represents larger grid cells and both may be at different eleva- We limit our further analysis to summer and winter seasons tions; because of the strong elevation dependency of tempera- considering the strong contrast in the seasonal pattern of in- ture, this could induce biases. To quantify these differences, we version characteristics. Taking this into account, we show the

compared the Tskin, T2m,andDTNSBI values derived from sta- multidecadal (1979–2017) change in the strength of different tions with those derived from ERA-Is, and analyzed the asso- inversion types (DTNSBI, DTSBI, and DTEI)inFig. 7. ciated elevation differences. There are distinct spatial and seasonal differences in the

While biases are apparent in ERA-Is Tskin, T2m (Figs. S4 and trends of all aforementioned variables. In winter, DTNSBI and S5; Table S5), and DTNSBI (Fig. 6 and Table 1) in comparison DTSBI decrease significantly with a median trend of 20.1

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FIG. 6. Frequency distribution of daily DTNSBI (T2m – Tskin) (K) calculated from ERA-Is and the automatic weather stations (AWSs) located in Greenland for winter (DJF) and summer (JJA) seasons of the given periods. All the components of box-and-whisker plots are as in Fig. 5, but for NSBI calculated from AWS (light gray) and ERA-Is (dark gray). The adjacent violin plot depicts the frequency distribution of the daily DTNSBI for the given station. The location of stations is indicated by the plus marker in the map inset. The minor ticks on the y axis show increments of 0.01, 0.1, and 1 K in every one-third of the major ticks from base to top. The periods used in analyzing each station are shown in Table 1.

2 and 20.8 K decade 1, in 24% and 12% of Greenland’s area for the most part, the decreasing trend is not statistically

(AG), respectively, over the period of 1979–2017 (Tables S6 significant. and S7). The negative trends of DTNSBI and DTSBI are strongest During winter, both DzSBI and DzEI show no significant in the warmer SE region (Tables S8 and S9). Additionally, trend for the largest part of Greenland, whereas during sum- 21 DTNSBI decreases significantly in 27% of the C region, whereas a mer they show the strongest positive trend (8–16 m decade ), negative trend of DTSBI is significant in less than 5% of the C especially in the western regions (Fig. S6, Tables S6 and S8). region. During the summer, on the other hand, there is a sig- The trends for fSBI and fEI are clear during summer, but during nificant increase in DTNSBI and DTSBI with median values of 0.15 winter no distinct trends are visible (Fig. S6). In summer, fSBI 21 21 and 0.3 K decade in 51% and 32% of AG, respectively, over increased significantly, with a median trend of 3% decade in 1979–2017, while these parameters exhibit a significant decrease 50% of AG, especially in the southern half and in the western in only 4% and 1% of AG. In the southern half of Greenland, regions. In contrast, the trend for summer fEI was significant in 21 both summer DTNSBI and DTSBI show a strong and significant the opposite direction (22% decade in 29% of AG) in the 21 positive trend (.0.3 K decade )(seeTablesS8andS9). areas where the fSBI trend is significant. Interestingly, during the summer, we find for both DTNSBI and DTSBI, trends with two opposite signs in the C region. d. Temperature inversion strength and clouds While the inversion strength is decreasing in the central part of Changes in cloudiness, such as that caused by variation in the the ice sheet, it increases toward the periphery and the south Greenland anticyclone (Hofer et al. 2017), may be an impor- dome. Nonuniform warming of the surface and the air above tant driver for the surface temperature trends (Miller et al. leads to different inversion trends depending on the surface 2015) and, consequently, for the inversions. To investigate the type: a warming of the air above a melting snow or ice surface potential relationship between inversion characteristics and

increases the DT as Tskin cannot warm to more than 08C(van TCC, we computed their correlations. There is a close corre- den Broeke et al. 2009). In the central part of the ice sheet, Tskin spondence between changes in TCC and changes in DT for is well below 08C and increasing at a higher rate than the air detrended data (Fig. S7) for the period 1979–2017. Notably, DT above, which weakens inversions there. (especially DTNSBI and DTSBI) shows a significant negative While trends in DTEI also show spatial variation, they exhibit correlation with TCC for both winter and summer seasons. We smaller seasonal variations; we find significant trends only in find a stronger anticorrelation between DTSBI and TCC during small areas of Greenland (a positive trend in the C and N re- summer than during winter (for DTSBI, JJA r 520.64, in 98% gions). The trend pattern reverses as one moves from the of AG compared to DJF r 520.5, in 65% of AG). While center of the ice sheet toward the periphery (Fig. 7). However, DTNSBI showed a significant, albeit weaker anticorrelation with

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21 FIG. 7. Linear trends of median strength (K decade ) of (left) NSBI (DTNSBI), (center) SBI (DTSBI), and (right) EI (DTEI) for (top) winter (DJF) and (bottom) summer (JJA) over Greenland for the period 1979–2017. The white dots and areas within the black mesh indicate statistically significant trends at the 0.05 and 0.1 significance levels, respectively. The solid gray line is the border between the regions.

TCC than DTSBI, no seasonal differences were found (r 520.4, Emphasizing the period 2002–15, it can be seen that the in- in 28% of AG). This indicates that decreasing cloud cover creasing trends for JJA SBI characteristics coincide with de- correlates more with increasing atmospheric temperature than creasing TCC (Fig. 8), suggesting that TCC and SBI may be with near-surface temperature. connected (note: no significant trend was found for DJF TCC For the period 2002–15, studies show a positive anomaly in and SBI; Fig. S8). On looking more closely, we find that TCC

the Greenland Blocking Index (GBI) (Hanna et al. 2016), and shows a strong anticorrelation with DTSBI for the time period accordingly, a decrease in cloud cover especially in southern 2002–15, explaining 35%–54% (in DJF for 44% of AG) and Greenland (Hofer et al. 2017; Noël et al. 2019). Furthermore, 41%–67% (in JJA for 75% of AG) of the variability in DTSBI Hofer et al. (2017) showed that the decreasing trend of summer (Fig. 9). The low R2 value (0.35–0.54) during DJF indicates that

cloud cover coincides with a reduction of the GrIS SMB, with TCC is only one of the factors causing the DTSBI to change, and the latter resulting from enhanced melt–albedo feedback. indicates that surface radiation deficit is likely to be the main

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21 21 21 FIG. 8. Linear trends of total cloud cover (TCC; % decade ), SBI strength (DTSBI; K decade ), thickness (DzSBI; m decade ), and 21 frequency (fSBI; % decade ) for summer (JJA) over Greenland for 2002–15. The white dots and areas within the black mesh indicate statistically significant trends at the 0.05 and 0.1 significance levels, respectively.

reason (Serreze et al. 1992). In contrast, for JJA over 2002–15, humidity deficit [HD; i.e., saturation specific humidity (qs)minus the reduction of TCC explains on average 54% of the vari- specific humidity (q)] is increasing at a smaller rate near the surface

ability in DTSBI. Likewise, during JJA, 32%–46% of DTNSBI compared to the air above, which further supports the decreasing variability can be explained by changes in TCC (r 520.62), TCC trend. Given that Berkelhammer et al. (2016) have already but this is significant only in 14% of AG. indicated the role of inversions in decoupling the surface from free- tropospheric moisture above the Summit station, the HD finding e. Vertical temperature and humidity trends in the tends to become even more relevant. In the ablation area, this southwestern part of Greenland confined moisture within the lower atmosphere may act to increase The relevance of the SBIs for the surface energy balance the latent heat flux toward the surface (Niwano et al. 2019). motivated us to look more closely into the potential differences Consequently, the vertical gradients in temperature and hu- in the inversion development on the accumulation and the midity between the surface and atmosphere, and hence, inver- ablation area of the GrIS. We focus on the southwestern part of sions (especially SBI) can play a significant role in modulating the GrIS for JJA. This shows statistically significant trends in surface–atmosphere energy and moisture exchange in the GrIS. summer TCC and DTSBI for 2002–15. Following Noël et al. (2019), we use an equilibrium line altitude of 1450 m MSL for 5. Conclusions the southern GrIS in order to distinguish between accumula- Our study has aimed at analyzing and better understanding tion and ablation areas. Figure 10 shows that in the accumu- the spatiotemporal variations and trends of temperature in- lation area of the southwestern part of the GrIS, the increase in version characteristics in Greenland over the past four decades

DTSBI and DzSBI during summer is forced by near-surface (1979–2017). For this purpose, we used ERA-I, as a physically cooling, as well as by warming of the layers above (starting coherent dataset covering the entirety of Greenland, and from ;100 m AGL). In contrast, for the ablation area, the in- compared this with station data and previous studies from crease in DTSBI and DzSBI is caused by an unchanged surface different regions of Greenland. Three types of inversions (SBI, temperature and by a temperature increase in the layers above. EI, and NSBI) were defined, and we analyzed the strength, These results are in line with what could be expected from an thickness, frequency, and base of each type in seven climatic increased GBI and reduced TCC for JJA. On average, an in- regions of Greenland for various seasons. In general, the ERA-I tensified Greenland anticyclone will increase the downward dataset proved to be reliable in capturing the spatiotemporal vertical motion of air, consequently increasing the atmospheric patterns of inversions in Greenland. This was confirmed by temperature by adiabatic warming. Other effects such as in- comparing ERA-I results with radiosonde data, independent creased outgoing longwave radiation from reduced TCC may ground stations data, and with results from other studies. also play an important role in enhancing surface cooling. Further conclusions of our study are presented below. Interestingly, the study from Westergaard-Nielsen et al. (2018) reported mostly the presence of cooling trends in surface air a. Regional and seasonal patterns of inversions temperature for the period 2001–15 for the ice-free part of SBIs occurred more frequently and intensively than EIs in Greenland, which is in line with our results (not shown). all climatic regions for all seasons. SBI frequency is higher Furthermore, both in the accumulation and ablation areas, the during winter (98%–100%) compared to summer (72%–96%),

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FIG. 9. Spearman correlation coefficient (r) between detrended TCC and median strength of (left) NSBI (DTNSBI), (center) SBI (DTSBI), and (right) EI (DTEI) for (top) winter (DJF) and (bottom) summer (JJA) over Greenland for 2002–15. The white dots and areas within the black mesh indicate statistically significant correlation at the 0.05 and 0.1 significance levels, respectively.

whereas EI frequency shows the opposite seasonal pattern The strongest temperature gradient is noticeable closest to the (winter: 7%–14%, summer: 15%–31%). During winter, SBIs surface (within 2 m AGL). Our results of spatial and temporal are stronger and thicker in the north (16.6 K and 462 m) com- patterns of inversions are generally consistent with previous pared to the south (8.5 K and 111 m), and stronger in the east studies for single locations or selected parts of Greenland, thus (10.8 K and 196 m) compared to the west (7.8 K and 151 m). providing a consistent overall picture. This is possibly due to the dominance of the Greenland anti- cyclone in the north, and to the influence of sea ice in the east. b. Trends in SBI However, in summer, north-to-south inversion variability de- We point out, that during the summer, SBIs become stronger 2 2 creases, which is probably related to the smaller latitudinal (0.3 K decade 1), thicker (12 m decade 1), and more frequent 2 differences in incoming solar radiation. Also, EI characteristics (3% decade 1) in the southern part of Greenland, especially in the depict similar but weaker regional variability compared to SBI. past two decades. The persistent, positive summer GBI, indicating a

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21 FIG. 10. Vertical structures of trends in (a) saturation specific humidity (qs;gkg ; circles), 2 2 specific humidity (q;ingkg 1; squares), and humidity deficit (HD; g kg 1; triangles), and (b) temperature (T; K; crosses) averaged over the accumulation (.1450 m MSL; blue) and ablation (,1450 m MSL; red) areas in sector SW (see map inset) for the summers (JJA) 2002– 15 from the ERA-I dataset. The error bars indicate the 25th and 75th percentiles. strengthening of the Greenland anticyclone, and hence less Paul Berrisford, and Gianpaolo Balsamo for their help in summer total cloud cover (along with several associated providing detailed information on the ECMWF datasets. We feedbacks), plays a significant role in increasing inversion would also like to express our deep gratitude to the editor, Shawn strength and thickness (mostly SBI), particularly in south- Marshall, and all three anonymous reviewers for their valuable west Greenland. While for the accumulation area of the time and for their constructive comments and suggestions. Greenland Ice Sheet the increased SBI strength is forced by near-surface cooling, the ablation area is characterized by APPENDIX constant surface temperatures. However, for both regions, the warming of the layers above is a key driver behind in- List of Abbreviations creased SBI strength during summer over the period 2002– 15. In this context, we also argue that SBI might play a a. Subregions of Greenland critical role in confining moisture within the lower atmo- C Central sphere by decreasing the height of the atmospheric bound- N North ary layer. NE Northeast It is clear that the large-scale analysis presented here cannot NW Northwest address all complex and interlinked processes with respect to S South inversions (e.g., the radiative effect due to changes in inver- SE Southeast sions). However, the present analysis does provide a solid SW Southwest footing for more detailed process-oriented studies and analysis concerning the latitudinal contrasting effects of inversion characteristics on the GrIS mass budget. b. Inversion types EI Elevated inversion Acknowledgments. We sincerely thank all providers of data. NSBI Near surface-based inversion ERA-Interim data were supported by ECMWF. Observational SBI Surface-based inversion data were provided by PROMICE (operated by the Geological Survey of Denmark and Greenland) and Asiaq (Greenland c. Inversion characteristics survey). The University of Graz is acknowledged for funding the article publication. We are grateful to Leopold Haimberger, f Inversion frequency

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Tb Air temperature at inversion base Berkelhammer, M., and Coauthors, 2016: Surface–atmosphere Tt Air temperature at inversion top decoupling limits accumulation at Summit, Greenland. Sci. zb Inversion base Adv., 2, e1501704, https://doi.org/10.1126/sciadv.1501704. Bintanja, R., R. G. Graversen, and W. Hazeleger, 2011: Arctic zt Inversion top DT Inversion strength winter warming amplified by the thermal inversion and con- Nat. Geosci. Dz Inversion thickness sequent low infrared cooling to space. , 4, 758–761, https://doi.org/10.1038/ngeo1285. G Temperature gradient within inversion layer ——, E. C. van der Linden, and W. Hazeleger, 2012: Boundary layer stability and Arctic climate change: A feedback study d. Surface-based and elevated inversion characteristics using EC-Earth. Climate Dyn., 39, 2659–2673, https://doi.org/ f Elevated inversion frequency 10.1007/s00382-011-1272-1. EI Boé, J., A. Hall, and X. Qu, 2009: Current GCMs’ unrealistic f Surface-based inversion frequency SBI negative feedback in the Arctic. J. Climate, 22, 4682–4695, z Elevated inversion base EIb https://doi.org/10.1175/2009JCLI2885.1. D TEI Elevated inversion strength Bradley, R. S., F. T. Keimig, and H. F. Diaz, 1992: Climatology of D TNSBI Near surface-based inversion strength surface-based inversions in the North American Arctic. J. Geophys. D TSBI Surface-based inversion strength Res., 97, 15 699–15 712, https://doi.org/10.1029/92JD01451. DzEI Elevated inversion thickness ——, ——, and ——, 1993: Recent changes in the North American DzSBI Surface-based inversion thickness Arctic boundary layer in winter. J. Geophys. Res., 98, 8851– GEI Temperature gradient within elevated inversion layer 8858, https://doi.org/10.1029/93JD00311. Bridgman, H. A., R. C. Schnell, J. D. Kahl, G. A. Herbert, and GNSBI Temperature gradient within near surface-based inversion layer E. Joranger, 1989: A major haze event near Point Barrow, Alaska: Analysis of probable source regions and transport GSBI Temperature gradient within surface-based inver- pathways. Atmos. Environ., 23, 2537–2549, https://doi.org/ sion layer 10.1016/0004-6981(89)90265-5. Busch, N., U. Ebel, H. Kraus, and E. Schaller, 1982: The structure of e. Others the subpolar inversion-capped ABL. Arch. Meteor. Geophys. Bioclimatol., 31A, 1–18, https://doi.org/10.1007/BF02257738. AG Greenland’s area Cappelen, J., B. V. Jørgensen, E. V. Laursen, L. S. Stannius, and ERA-I ERA-Interim subsampled grid cell value s R. S. Thomsen, 2001: The observed climate of Greenland, GBI Greenland Blocking Index 1958–99, with climatological standard normals, 1961–90. DMI GrIS Greenland Ice Sheet Tech. Rep. 00–18, 152 pp., https://www.dmi.dk/fileadmin/ HD Humidity deficit user_upload/Rapporter/TR/2000/tr00-18.pdf. q Specific humidity Chutko, K. J., and S. F. Lamoureux, 2009: The influence of low- qs Saturation specific humidity level thermal inversions on estimated melt-season character- SEB Surface energy balance istics in the central Canadian Arctic. Int. J. Climatol., 29, 259– SMB Surface mass balance 268, https://doi.org/10.1002/joc.1722. TCC Total cloud cover Dee, D. P., and S. Uppala, 2009: Variational bias correction of satellite radiance data in the ERA-Interim reanalysis. Quart. Tskin Skin temperature J. Roy. Meteor. Soc., 135, 1830–1841, https://doi.org/10.1002/ T2m Air temperature in 2 m above ground level qj.493. ——, and Coauthors, 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. REFERENCES Meteor. Soc., 137, 553–597, https://doi.org/10.1002/qj.828. Abermann, J., B. Hansen, M. Lund, S. Wacker, M. Karami, and Delhasse, A., C. Kittel, C. Amory, S. Hofer, D. van As, R. S. Fausto, J. Cappelen, 2017: Hotspots and key periods of Greenland and X. Fettweis, 2020: Brief communication: Evaluation of the climate change during the past six decades. Ambio, 46, 3–11, near-surface climate in ERA5 over the Greenland Ice Sheet. https://doi.org/10.1007/s13280-016-0861-y. Cryosphere, 14, 957–965, https://doi.org/10.5194/tc-14-957-2020. Adolph, A. C., M. R. Albert, and D. K. Hall, 2018: Near-surface Durre, I., and X. Yin, 2008: Enhanced radiosonde data for studies temperature inversion during summer at Summit, Greenland, of vertical structure. Bull. Amer. Meteor. Soc., 89, 1257–1262, and its relation to MODIS-derived surface temperatures. https://doi.org/10.1175/2008BAMS2603.1. Cryosphere, 12, 907–920, https://doi.org/10.5194/tc-12-907-2018. ECMWF, 2016: Part IV: Physical processes. IFS Documentation Ahlstrøm, A. P., and Coauthors, 2008: A new programme for CY43R1, ECMWF, 223 pp., https://www.ecmwf.int/node/17117. monitoring the mass loss of the Greenland ice sheet. Geol. Surv. Gao, L., M. Bernhardt, and K. Schulz, 2012: Elevation correction of Denmark Greenl. Bull., 15, 61–64, https://doi.org/10.34194/ ERA-Interim temperature data in complex terrain. Hydrol. Earth geusb.v15.5045. Syst. Sci., 16, 4661–4673, https://doi.org/10.5194/hess-16-4661-2012. Bengtsson, L., S. Hagemann, and K. I. Hodges, 2004: Can climate Gilson, G. F., H. Jiskoot, J. J. Cassano, I. Gultepe, and T. D. James, trends be calculated from reanalysis data? J. Geophys. Res., 2018a: The thermodynamic structure of Arctic coastal fog 109, D11111, https://doi.org/10.1029/2004JD004536. occurring during the melt season over East Greenland. Benjamini, Y., and Y. Hochberg, 1995: Controlling the false dis- Bound.-Layer Meteor., 168, 443–467, https://doi.org/10.1007/ covery rate: A practical and powerful approach to multiple s10546-018-0357-3. testing. J. Roy. Stat. Soc., 57B, 289–300, https://www.jstor.org/ ——, ——, ——, and T. R. Nielsen, 2018b: Radiosonde-derived stable/2346101. temperature inversions and their association with fog over 37

Unauthenticated | Downloaded 10/02/21 02:01 AM UTC 9406 JOURNAL OF CLIMATE VOLUME 33

melt seasons in East Greenland. J. Geophys. Res. Atmos., 123, Lindsay, R., M. Wensnahan, A. Schweiger, and J. Zhang, 2014: 9571–9588, https://doi.org/10.1029/2018JD028886. Evaluation of seven different atmospheric reanalysis products Good, E. J., 2016: An in situ-based analysis of the relationship in the Arctic. J. Climate, 27, 2588–2606, https://doi.org/ between land surface ‘‘skin’’ and screen-level air tempera- 10.1175/JCLI-D-13-00014.1. tures. J. Geophys. Res. Atmos., 121, 8801–8819, https://doi.org/ Medeiros, B., C. Deser, R. A. Tomas, and J. E. Kay, 2011: Arctic 10.1002/2016JD025318. inversion strength in climate models. J. Climate, 24, 4733– Graham, R. M., S. R. Hudson, and M. Maturilli, 2019: Improved 4740, https://doi.org/10.1175/2011JCLI3968.1. performance of ERA5 in Arctic gateway relative to four Mernild, S. H., and G. E. Liston, 2010: The influence of air temperature global atmospheric reanalyses. Geophys. Res. Lett., 46, 6138– inversions on snowmelt and glacier mass balance simulations, 6147, https://doi.org/10.1029/2019GL082781. Ammassalik Island, southeast Greenland. J. Appl. Meteor. Climatol., Graversen, R. G., T. Mauritsen, M. Tjernström, E. Källén, and 49, 47–67, https://doi.org/10.1175/2009JAMC2065.1. G. Svensson, 2008: Vertical structure of recent Arctic warm- ——, B. U. Hansen, B. H. Jakobsen, and B. Hasholt, 2008: Climatic ing. Nature, 451, 53–56, https://doi.org/10.1038/nature06502. conditions at the Mittivakkat Glacier catchment (1994–2006), Haimberger, L., 2007: Homogenization of radiosonde temperature Ammassalik Island, SE Greenland, and in a 109-year per- time series using innovation statistics. J. Climate, 20, 1377– spective (1898–2006). Dan. J. Geogr., 108, 51–72, https:// 1403, https://doi.org/10.1175/JCLI4050.1. doi.org/10.1080/00167223.2008.10649574. Hall, D. K., J. E. Box, K. A. Casey, S. J. Hook, C. A. Shuman, and Miller, N. B., D. D. Turner, R. Bennartz, M. D. Shupe, M. S. Kulie, K. Steffen, 2008: Comparison of satellite-derived and in-situ M. P. Cadeddu, and V. P. Walden, 2013: Surface-based in- observations of ice and snow surface temperatures over versions above central Greenland. J. Geophys. Res. Atmos., Greenland. Remote Sens. Environ., 112, 3739–3749, https:// 118, 495–506, https://doi.org/10.1029/2012JD018867. doi.org/10.1016/j.rse.2008.05.007. ——,M.D.Shupe,C.J.Cox,V.P.Walden,D.D.Turner,and Hanna, E., T. E. Cropper, R. J. Hall, and J. Cappelen, 2016: K. Steffen, 2015: Cloud radiative forcing at Summit, Greenland. Greenland blocking index 1851–2015: A regional climate J. Climate, 28, 6267–6280, https://doi.org/10.1175/JCLI-D-15- change signal. Int. J. Climatol., 36, 4847–4861, https://doi.org/ 0076.1. 10.1002/joc.4673. Nielsen-Englyst, P., J. L. Høyer, K. S. Madsen, G. Dybkjær, Hennermann, K., and M. Giusti, 2018: What are the changes from ERA- R. Tonboe, and E. Alerskans, 2019: In situ observed rela- Interim to ERA5? ECMWF, https://confluence.ecmwf.int/pages/ tionships between snow and ice surface skin temperatures and viewpage.action?pageId574764925. 2 m air temperatures in the Arctic. Cryosphere, 13, 1005–1024, Hersbach, H., and D. Dee, 2016: ERA5 reanalysis is in production. https://doi.org/10.5194/tc-13-1005-2019. ECMWF Newsletter, No. 147, ECMWF, Reading, United Niwano, M., A. Hashimoto, and T. Aoki, 2019: Cloud-driven Kingdom, 7, http://www.ecmwf.int/sites/default/files/elibrary/ modulations of Greenland ice sheet surface melt. Sci. Rep., 9, 2016/16299-newsletter-no147-spring-2016.pdf. 10380, https://doi.org/10.1038/s41598-019-46152-5. Hibler, W. D., III, and K. Bryan, 1987: A diagnostic ice–ocean Noël, B., W. J. van de Berg, S. Lhermitte, and M. R. van den model. J. Phys. Oceanogr., 17, 987–1015, https://doi.org/ Broeke, 2019: Rapid ablation zone expansion amplifies north 10.1175/1520-0485(1987)017,0987:ADIM.2.0.CO;2. Greenland mass loss. Sci. Adv., 5, eaaw0123, https://doi.org/ Hofer, S., A. J. Tedstone, X. Fettweis, and J. L. Bamber, 2017: 10.1126/sciadv.aaw0123. Decreasing cloud cover drives the recent mass loss on the Onogi, K., and Coauthors, 2007: The JRA-25 Reanalysis. J. Meteor. Greenland Ice Sheet. Sci. Adv., 3, e1700584, https://doi.org/ Soc. Japan, 85, 369–432, https://doi.org/10.2151/jmsj.85.369. 10.1126/sciadv.1700584. Overland, J. E., and K. L. Davidson, 1992: Geostrophic drag co- Hudson, S. R., and R. E. Brandt, 2005: A look at the surface-based efficients over sea ice. Tellus, 44A, 54–66, https://doi.org/ temperature inversion on the Antarctic Plateau. J. Climate, 18, 10.3402/tellusa.v44i1.17118. 1673–1696, https://doi.org/10.1175/JCLI3360.1. Palarz, A., D. Celinski-Mys ław, and Z. Ustrnul, 2018: Temporal Hulth,J.,C.Rolstad,K.Trondsen,andR.W.Rødby, 2010: and spatial variability of surface-based inversions over Europe Surface mass and energy balance of Sørbreen, Jan Mayen, based on ERA-Interim reanalysis. Int. J. Climatol., 38, 158– 2008. Ann. Glaciol., 51, 110–119, https://doi.org/10.3189/ 168, https://doi.org/10.1002/joc.5167. 172756410791392754. Palo, T., T. Vihma, J. Jaagus, and E. Jakobson, 2017: Observations Kahl, J. D., 1990: Characteristics of the low-level temperature in- of temperature inversions over central Arctic sea ice in sum- version along the Alaskan Arctic coast. Int. J. Climatol., 10, mer. Quart. J. Roy. Meteor. Soc., 143, 2741–2754, https:// 537–548, https://doi.org/10.1002/joc.3370100509. doi.org/10.1002/qj.3123. ——, M. C. Serreze, and R. C. Schnell, 1992: Tropospheric low-level Pavelsky, T. M., J. Boé, A. Hall, and E. J. Fetzer, 2011: Atmospheric temperature inversions in the Canadian Arctic. Atmos.–Ocean, inversion strength over polar oceans in winter regulated by sea 30, 511–529, https://doi.org/10.1080/07055900.1992.9649453. ice. Climate Dyn., 36, 945–955, https://doi.org/10.1007/s00382- ——, D. A. Martinez, and N. A. Zaitseva, 1996: Long-term vari- 010-0756-8. ability in the low-level inversion layer over the Arctic Ocean. Int. Pithan, F., and T. Mauritsen, 2013: Comments on ‘‘Current GCM’s J. Climatol., 16, 1297–1313, https://doi.org/10.1002/(SICI)1097- unrealistic negative feedback in the Arctic.’’ J. Climate, 26, 0088(199611)16:11,1297::AID-JOC86.3.0.CO;2-T. 7783–7788, https://doi.org/10.1175/JCLI-D-12-00331.1. Kalnay, E., and Coauthors, 1996: The NCEP/NCAR 40-Year Przybylak, R., 2016: The Climate of the Arctic. 2nd ed. Springer, 287 Reanalysis Project. Bull. Amer. Meteor. Soc., 77, 437–471, pp. https://doi.org/10.1175/1520-0477(1996)077,0437:TNYRP.2.0. Screen, J. A., and I. Simmonds, 2010: The central role of dimin- CO;2. ishing sea ice in recent Arctic temperature amplification. Laprise, R., 1992: The resolution of global spectral models. Bull. Nature, 464, 1334–1337, https://doi.org/10.1038/nature09051. Amer. Meteor. Soc., 73, 1453–1455, https://doi.org/10.1175/ Serreze, M. C., R. C. Schnell, and J. D. Kahl, 1992: Low-level 1520-0477-73.9.1453. temperature inversions of the Eurasian Arctic and comparisons

Unauthenticated | Downloaded 10/02/21 02:01 AM UTC 1NOVEMBER 2020 S H A H I E T A L . 9407

with Soviet drifting station data. J. Climate, 5, 615–629, https:// Van As, D., 2011: Warming, glacier melt and surface energy budget doi.org/10.1175/1520-0442(1992)005,0615:LLTIOT.2.0.CO;2. from weather station observations in the Melville Bay region Simmons, A. J., and P. Poli, 2015: Arctic warming in ERA-Interim of northwest Greenland. J. Glaciol., 57, 208–220, https:// and other analyses. Quart. J. Roy. Meteor. Soc., 141, 1147– doi.org/10.3189/002214311796405898. 1162, https://doi.org/10.1002/qj.2422. van den Broeke, M., P. Smeets, and J. Ettema, 2009: Surface layer Steffen, K., 1995: Surface energy exchange at the equilibrium line climate and turbulent exchange in the ablation zone of the on the Greenland ice sheet during onset of melt. Ann. Glaciol., west Greenland ice sheet. Int. J. Climatol., 29, 2309–2323, 21, 13–18, https://doi.org/10.3189/S0260305500015536. https://doi.org/10.1002/joc.1815. ——, and J. Box, 2001: Surface climatology of the Greenland Ice Walden, V. P., A. Mahesh, and S. G. Warren, 1996: Comment on Sheet: Greenland climate network 1995–1999. J. Geophys. Res., ‘‘Recent changes in the North American Arctic boundary 106, 33 951–33 964, https://doi.org/10.1029/2001JD900161. layer in winter’’ by R. S. Bradley et al. J. Geophys. Res., 101, Stendel, M., J. H. Christensen, and D. Petersen, 2008: Arctic climate 7127–7134, https://doi.org/10.1029/95JD03233. and climate change with a focus on Greenland. High Arctic Walsh, J. E., and W. L. Chapman, 1998: Arctic cloud–radiation– Ecosystem Dynamics in a Changing Climate: Ten Years of temperature associations in observational data and atmo- Monitoring and Research at Zackenberg Research Station, spheric reanalyses. J. Climate, 11, 3030–3045, https:// Northeast Greenland, Advances in Ecological Research Series, doi.org/10.1175/1520-0442(1998)011,3030:ACRTAI.2.0. Vol. 40, Elsevier, 13–43. CO;2. Tiedtke, M., 1993: Representation of clouds in large-scale models. ——, ——, and D. H. Portis, 2009: Arctic cloud fraction and radi- Mon. Wea. Rev., 121, 3040–3061, https://doi.org/10.1175/1520- ative fluxes in atmospheric reanalyses. J. Climate, 22, 2316– 0493(1993)121,3040:ROCILS.2.0.CO;2. 2334, https://doi.org/10.1175/2008JCLI2213.1. Tjernström, M., and R. G. Graversen, 2009: The vertical structure Westergaard-Nielsen, A., M. Karami, B. U. Hansen, S. Westermann, of the lower Arctic troposphere analysed from observations and B. Elberling, 2018: Contrasting temperature trends across and the ERA-40 reanalysis. Quart. J. Roy. Meteor. Soc., 135, the ice-free part of Greenland. Sci. Rep., 8,1586,https://doi.org/ 431–443, https://doi.org/10.1002/qj.380. 10.1038/s41598-018-19992-w. ——, and Coauthors, 2012: Meteorological conditions in the cen- Wetzel, C., and B. Brümmer, 2011: An Arctic inversion climatology tral Arctic summer during the Arctic Summer Cloud Ocean based on the European Centre Reanalysis ERA-40. Meteor. Z., Study (ASCOS). Atmos. Chem. Phys., 12, 6863–6889, https:// 20, 589–600, https://doi.org/10.1127/0941-2948/2011/0295. doi.org/10.5194/acp-12-6863-2012. Zhang, Y., and D. J. Seidel, 2011: Challenges in estimating trends ——, and Coauthors, 2015: Warm-air advection, air mass trans- in Arctic surface-based inversions from radiosonde data. formation and fog causes rapid ice melt. Geophys. Res. Lett., Geophys. Res. Lett., 38, L17806, https://doi.org/10.1029/ 42, 5594–5602, https://doi.org/10.1002/2015GL064373. 2011GL048728. Turton, J. V., T. Mölg, and D. van As, 2019: Atmospheric processes ——, ——, J. C. Golaz, C. Deser, and R. A. Tomas, 2011: and climatological characteristics of the 79N glacier (northeast Climatological characteristics of Arctic and Antarctic surface- Greenland). Mon. Wea. Rev., 147, 1375–1394, https://doi.org/ based inversions. J. Climate, 24, 5167–5186, https://doi.org/ 10.1175/MWR-D-18-0366.1. 10.1175/2011JCLI4004.1.

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