Full Terms & Conditions of access and use can be found athttp://www.tandfonline.com/action/journalInformation?journalCode=tato20

Download by: [Kimberly Strong] Date: 14 September 2016, At: 18:34

Atmosphere-Ocean

ISSN: 0705-5900 (Print) 1480-9214 (Online) Journal homepage: http://www.tandfonline.com/loi/tato20

Distributions of Downwelling Radiance at 10 and20 µm in the High Arctic

Zen Mariani, K. Strong & J. R. Drummond

To cite this article: Zen Mariani, K. Strong & J. R. Drummond (2016): Distributions ofDownwelling Radiance at 10 and 20 µm in the High Arctic, Atmosphere-Ocean

To link to this article: http://dx.doi.org/10.1080/07055900.2016.1216825

Published online: 14 Sep 2016.

Submit your article to this journal

View related articles

View Crossmark data

Distributions of Downwelling Radiance at 10 and 20 μm inthe High Arctic

Zen Mariani1,*, K. Strong1, and J. R. Drummond2

1Department of Physics, University of Toronto, Toronto, Ontario, Canada2Department of Physics, Dalhousie University, Halifax, Nova Scotia, Canada

[Original manuscript received 22 December 2015; accepted 29 June 2016]

ABSTRACT In the Arctic, most of the infrared (IR) energy emitted by the surface escapes to space in two atmos-pheric windows centred at 10 and 20 μm. As the Arctic warms and its water vapour burden increases, the 20 μmcooling-to-space window, in particular, is expected to become increasingly opaque (or “closed”), trapping moreIR radiation, with implications for the Arctic’s radiative energy balance. Since 2006, the Canadian Network for theDetection of Atmospheric Change has measured downwelling IR radiation with Atmospheric Emitted RadianceInterferometers at the Polar Environment Atmospheric Research Laboratory at Eureka, Canada, providingmeasurements of the 10 and 20 μm windows in the High Arctic. In this work, measurements of the distributionof downwelling 10 and 20 µm brightness temperatures at Eureka are separated based on cloud cover, providinga comparison to an existing 10 µm climatology from the Southern Great Plains. The downwelling radiance at both10 and 20 μm exhibits strong seasonal variability as a result of changes in cloud cover, temperature, and watervapour. Given the 20 µm window’s limited transparency, its ability to allow surface IR radiation to escape tospace is found to be highly sensitive to changes in atmospheric water vapour and temperature. When separatedby season, brightness temperatures in the 20 µm window are independent of cloud optical thickness in thesummer, indicating that this window is opaque in the summer. This may have long-term consequences, particularlyas warmer temperatures and increased water vapour “close” the 20 μm window for a prolonged period each year.

RÉSUMÉ [Traduit par la rédaction] Dans l’Arctique, la majeure partie de l’énergie infrarouge (IR) qu’émet lasurface s’échappe dans l’espace par deux fenêtres atmosphériques centrées sur les longueurs d’onde de 10 et 20μm. À mesure que l’Arctique se réchauffe et que sa charge en vapeur d’eau augmente, cette fenêtre particulière à20 μm, qui laisse passer l’énergie vers l’espace, pourrait devenir opaque (ou se fermer), piégeant davantage derayonnement IR et perturbant ainsi l’équilibre de l’énergie radiative de l’Arctique. Depuis 2006, le Réseau cana-dien de détection des changements atmosphériques mesure le rayonnement IR descendant à l’aide d’interféro-mètres d’émission de luminance atmosphérique au Laboratoire de recherche atmosphérique dansl’environnement polaire (Eureka, Canada), qui enregistre des mesures pour les fenêtres de 10 et 20 μm, dans l’Arc-tique. Dans cette étude, les mesures de la distribution de la température de luminance descendante à 10 et 20 µm àEureka sont classées selon le couvert nuageux, et servent de comparaison à la climatologie (à 10 µm) qui existepour les Grandes plaines du sud. La luminance descendante à 10 et 20 μm montre une forte variabilité saisonnièreen raison des différences de nébulosité, de température et de contenu en vapeur d’eau. Étant donné la transparencelimitée de la fenêtre de 20 µm, sa capacité à laisser échapper le rayonnement IR de la surface vers l’espace s’avèrehautement sensible aux changements du contenu en vapeur d’eau et de la température de l’atmosphère. Quand onles classe par saison, les températures de luminance à 20 µm sont indépendantes de l’épaisseur optique des nuagesestivaux. Ce qui indique que cette fenêtre demeure opaque en été. Cette situation pourrait avoir des conséquences àlong terme, notamment si l’augmentation des températures et du contenu en vapeur d’eau « ferme » la fenêtre de 20μm pendant une période prolongée chaque année.

KEYWORDS climate variability; clouds; Arctic; remote sensing; climate change

1 Introduction

The effects of climate change on the Arctic are profound:increases in surface temperature and atmospheric watervapour are greater for the Arctic than for southern latitudesand are expected to continue, with a corresponding increase

in downwelling infrared radiance (ACIA, 2004; Bekryaev,Polyakov, & Alexeev, 2010; Richter-Menge et al., 2006).As shown in Francis and Hunter (2007), the causes for theincrease in the downwelling longwave flux vary

*Corresponding author’s email: zen.mariani@canada.ca; current affiliation: Cloud Physics and Severe Weather Research Section, Environment and ClimateChange Canada, Toronto, Canada

ATMOSPHERE-OCEAN iFirst article, 2016, 1–12 http://dx.doi.org/10.1080/07055900.2016.1216825Canadian Meteorological and Oceanographic Society

geographically and also depend on micro- and macrophysicalcloud properties. The percentage increase in downwellingradiance due to an increase in cloud optical depth has beenshown to be more significant in the Arctic than in morehumid regions (Intrieri et al., 2002; Mariani et al., 2012),and 40% of the projected warming in the Arctic is due tocloud changes that enhance greenhouse warming (Vavrus,2004). Given the high frequency of cloud cover in the HighArctic (Shupe et al., 2011), this increase in surface radiativeforcing due to clouds can have important consequences forthe surface energy balance, as shown in Curry, Rossow,Randall, and Schramm (1996). To characterize changes indownwelling radiance in the High Arctic due to watervapour, temperature, and clouds, there is a need for long-term measurements.Long-term cloudy-sky radiation measurements are also

crucial for evaluating general circulation model (GCM) simu-lations. As argued in Webb, Senior, Bony, and Morcrette(2001), “if we are to have confidence in predictions fromclimate models, a necessary (although not sufficient) require-ment is that they should be able to reproduce the observedpresent-day distribution of clouds and their associated radia-tive fluxes.” Since infrared (IR) spectra can be generatedfrom GCMs using radiative transfer models, ground-basedmeasurements of downwelling radiance can be used to evalu-ate models as was done by the International Satellite CloudClimatology Project (Klein & Jakob, 1999; Webb et al., 2001).Measurements of radiation can also provide information

about changes in the 10 µm surface cooling-to-spacewindow (�800 to 1250 cm−1 or 8 to 12 µm, where the atmos-phere is relatively transparent), where upwelling surface IRenergy escapes to space and cools the planet. Note that inthis paper, “cooling-to-space” refers to radiation emittedfrom the surface that reaches the top of the atmosphere. Inthe Arctic, significant IR surface cooling-to-space can alsooccur in the 20 µm “dirty window” (�400 to 600 cm−1 or17 to 25 µm) (Stamnes, Ellingson, Curry, Walsh, & Zak,1999; Tobin et al., 1999). At lower latitudes, surfacecooling-to-space in the 20 μm window is less significantbecause of strong absorption by water vapour; however, the20 μm window is semi-transparent in the extremely cold anddry Arctic atmosphere and, hence, important for climate andenergy balance (Tobin et al., 1999). The lower temperaturesalso shift the Planck function to longer wavelengths, increas-ing the impact of the 20 μm window in the Arctic (Harrieset al., 2008; Tobin et al., 1999; Turner & Mlawer, 2010).The transition of regimes from surface cooling-to-space at20 μm to surface cooling-to-space at 10 μm in the Arctic isof interest because of the possible impacts of continuedwarming. A recent study by Bintanja, Graversen, and Hazele-ger (2011) found that Arctic winter warming is amplified as aresult of decreased IR cooling-to-space, indicating the signifi-cance of these cooling-to-space windows.In order to perform long-term measurements of these

cooling-to-space windows, the Canadian Network for theDetection of Atmospheric Change (CANDAC) has equipped

the Polar Environment Atmospheric Research Laboratory(PEARL) at Eureka, Canada (80°N, 86°W), with two Atmos-pheric Emitted Radiance Interferometers (AERIs) (Knutesonet al., 2004a, 2004b). These two instruments have measuredthe absolute downwelling IR radiation spectrum at Eurekafor studies of the High Arctic atmospheric composition andradiation budget (Cox, Walden, & Rowe, 2012; Marianiet al., 2012). Various cloud properties have been measuredby AERI instruments in several previous studies (Collardet al., 1995; Mace, Ackerman, Minnis, & Young, 1998;Mahesh, Walden, & Warren, 2001; Smith, Ma, Ackerman,Revercomb, & Knuteson, 1993; Turner, 2005, 2007).Measurements of downwelling radiance at Eureka com-menced in March 2006 and are ongoing, providing a relativelylong-term dataset.

The first AERI installed at Eureka was the Polar-AERI (P-AERI) (Walden, Town, Halter, & Storey, 2005), whose spec-tral range includes both the 10 µm window and a portion of the20 µm window from approximately 500 to 600 cm−1 (17 to20 µm). In October 2008, installation of the Extended-rangeAERI (E-AERI) provided additional coverage in the 20 µmwindow down to 400 cm−1 (25 µm). Simulations using aline-by-line radiative transfer model (not shown) indicate theimportance of this extended region. For a typical clear-skyspringtime Arctic atmospheric profile, approximately 30% ofthe radiance lost to space from 17 to 25 µm is lost between20 and 25 µm.

Although there are numerous satellite measurements ofradiative fluxes, they do not directly measure surface radiativefluxes, making ground-based measurements critical for deter-mining the effect of clouds on surface fluxes. In the Arctic, sig-nificant surface radiative forcing from different types of cloudswas observed by a suite of ground-based instruments duringthe Surface Heat Budget of the Arctic (SHEBA) experiment(Perovich et al., 1999), including an older E-AERI (Tobinet al., 1999; Uttal et al., 2002). The longest relevant studywas performed by Dong et al. (2010); a decade of observationswere used to determine the cloud fraction and radiative forcingover Barrow, Alaska (71°N, 156°W), where an earlier E-AERIwas deployed in 1998 and continues to operate under theAtmospheric Radiation Measurement (ARM) program (Knu-teson et al., 2004a; Marty et al., 2003; Stamnes et al., 1999;Stokes & Schwartz, 1994). Studies by Cox et al. (2012) andCox, Turner, Rowe, Shupe, & Walden (2014) have usedAERI radiances measured at Eureka from 2006 to 2008 toinvestigate clear- and cloudy-sky fluxes and retrieve cloudproperties. Significant differences in the downwelling long-wave cloud radiative forcing and the seasonal cycles of thelongwave radiation flux were found between Barrow andEureka (Cox et al., 2012), indicating that surface measure-ments at more locations are needed to characterize surfaceradiative forcing across the Arctic—particularly since nolong-term measurements of this type exist in the High Arctic(>75°N).

A climatology of the downwelling 10 µm brightness temp-eratures (sometimes referred to as “radiance temperature” or

2 / Zen Mariani et al.

ATMOSPHERE-OCEAN iFirst article, 2016, 1–12 http://dx.doi.org/10.1080/07055900.2016.1216825La Société canadienne de météorologie et d’océanographie

“apparent temperature”) separated based on cloud cover andtrends in the downwelling radiance were provided for theSouthern Great Plains (SGP) in Turner and Gero (2011) andGero and Turner (2011). The purpose of this work is topresent a similar analysis for the High Arctic. In Section 2,the combined Eureka AERI dataset extending over nineyears is presented. Section 3 presents methods, includingclassification of spectra based on cloud cover and spectralregions chosen for analysis. Section 4 presents results, includ-ing the first measurements of the distributions of downwellingbrightness temperatures in both the 10 and 20 μm regions inthe High Arctic. Arctic downwelling brightness temperaturesfor clear-sky, thin-cloud, and thick-cloud are compared withthe SGP climatology. Conclusions are provided in Section 5.

2 Instrumentation

Two AERI instruments have been deployed at differentPEARL sites. The AERI instrument was originally developedby the University of Wisconsin to take measurements thatwould improve longwave radiative transfer models (Knutesonet al., 2004a; Turner, Mlawer, & Revercomb, 2016). Initiallydeveloped at the University of Wisconsin Space Science andEngineering Centre (UW-SSEC), AERIs are Fourier transformIR spectrometers that measure downwelling spectral radiance(Knuteson et al., 2004a, 2004b). The maximum optical pathdifference is 1 cm, providing a spectral resolution of 1 cm−1.Measurements from the AERI are useful not only for studiesof radiation budgets but also for determining concentrationsof atmospheric trace gases (Feltz et al., 1998, 2003; Marianiet al., 2013; Turner & Löhnert, 2014; Yurganov et al., 2010)and for investigating cloud forcing (e.g., Mariani et al.,2012; Turner, 2005).

One AERI measurement cycle includes a zenith-skymeasurement as well as views of two blackbodies (the hotblackbody stabilized at 310 K and ambient blackbody atambient temperature) to ensure accurately calibrated zenith-sky spectra. Measurements are only interrupted during precipi-tation events to prevent damage to the fore optics. The twocalibration blackbodies are of identical construction, each con-sisting of a thermally isolated cavity that is painted with ahigh-emissivity diffuse black paint. The radiometric cali-bration absolute accuracy is less than 1% of the ambient black-body radiance (Knuteson et al., 2004b; Mariani et al., 2012).Both instruments’ noise-equivalent radiances were relativelyconstant over their respective measurement periods, indicatingconsistency throughout the dataset. The usefulness of this cali-bration approach with respect to combining measurementsfrom different AERIs for a single long-term dataset is dis-cussed in more detail in Turner and Gero (2011). The com-bined nine-year (and continuing) Eureka AERI datasetconsists of over 300,000 quality-controlled spectra startingin March 2006 with the P-AERI and extending to January2015 (in this study) with the E-AERI, as illustrated inFig. 1. More information about each AERI is provided inthe following subsections.

a The Polar Atmospheric Emitted Radiance Interferometer(P-AERI)The University of Idaho’s P-AERI was deployed at the Zero-altitude PEARL Auxiliary Laboratory (0PAL), at an altitudeof 10 m above sea level and 15 km away from the PEARLRidge Lab (which is at an altitude of 610 m), from March2006 to June 2009. The P-AERI is a second-generation AERIinstrument with a spectral range of approximately 500 to

Fig. 1 Number of AERI observations (seven-minute spectra) for each month in the combined Eureka AERI dataset (up to 2015). Bars are coloured black for winter(October–April), blue for fall/spring (May and September), and red for summer (June–August). See Section 3 for descriptions of seasons. The overlap periodwhen the two AERIs operated at different altitudes is indicated. Gaps exist because of instrument repair or maintenance.

Distributions of Downwelling Radiance at 10 and 20 µm in the High Arctic / 3

ATMOSPHERE-OCEAN iFirst article, 2016, 1–12 http://dx.doi.org/10.1080/07055900.2016.1216825Canadian Meteorological and Oceanographic Society

3000 cm−1 (3.3 to 20 µm). The instrument has been on numer-ous field campaigns at different remote sites and extensivelycharacterized, and its data have undergone quality control(Rowe, Miloshevich, Turner, & Walden, 2008; Rowe,Neshyba, & Walden, 2011; Rowe, Neshyba, Cox, & Walden,2011; Shupe et al., 2013; Town, Walden, & Warren, 2005;Town & Walden, 2007; Walden et al., 2005). Measurementsfrom the P-AERI were recently used to identify significantdifferences in downwelling fluxes between Barrow andEureka, motivating the need for additional measurements ofdownwelling radiance throughout the Arctic (Cox et al., 2012).There were only two data gaps during this measurement

period (June 2007 and April–May 2008), resulting in near-consistent measurement distribution from 2006 to 2009.Noise-filtered radiance data from the P-AERI were used inthis analysis (Turner, Knuteson, Revercomb, Lo, & Dedecker,2006). The P-AERI operates in rapid sampling mode, whichproduces calibrated spectra approximately every 40 s. Sincethe E-AERI operates in a slower sampling mode (every �7minutes), the P-AERI spectra were averaged within each sim-ultaneous E-AERI measurement, which matches the approachused to produce the SGP climatology in Turner and Gero(2011). Thus, the combined Eureka AERI dataset has a tem-poral resolution of approximately seven minutes.

b The Extended-range Atmospheric Emitted RadianceInterferometer (E-AERI)The E-AERI is similar to the P-AERI with the exception that itis the latest (fourth) generation AERI instrument manufacturedby ABB Inc. under commercial license from UW-SSEC andhas an extended spectral range of 400 to 3000 cm−1 (3.3 to25 µm). Details of the E-AERI’s design, calibration, installa-tion, field-of-view, non-linearity, blackbody characterization,and performance evaluations are provided in Mariani et al.(2012). Prior to its installation at the PEARL Ridge Lab, theE-AERI was operated side-by-side with the P-AERI at0PAL for several days to ensure agreement within measure-ment error between the two instruments. Measured radiancesagreed within noise and indicated consistency between thetwo instruments (Mariani et al., 2012). The E-AERI wasinstalled at the PEARL Ridge Lab in October 2008, where itrecorded continuous measurements until it was moved to0PAL in October 2009, after the P-AERI was removed.Thus, between October 2008 and June 2009, there were twoAERIs operating at two altitudes, which provided an opportu-nity to characterize the differences between the two measure-ment sites (Mariani et al., 2012).From a climatology of radiosonde data, there exists between

8% (winter) and 15% (summer) additional water vapour at the0PAL site than at the Ridge Lab. While large differences indownwelling radiance were found in various spectral regions(e.g., the CO2 band around 15 μm), the micro-windows(radiances in small spectral regions of interest are calledmicro-windows) selected to sample the two cooling-to-spacewindows included minimal interfering species and no strong

water vapour emission lines. Despite this, line-by-line radia-tive transfer calculations estimate the sensitivity of downwel-ling brightness temperatures to be from 3.6 to 7.3 K (DavidTurner, personal communication, 2016) for an increase in pre-cipitable water vapour (PWV) from 8 to 15% in the 20 µmwindow (529.9–532 cm−1). To correct for this difference,E-AERI brightness temperature measurements in the 20 µmwindow recorded at the Ridge Lab were increased from3.6 K for winter months to 7.3 K for summer months. Thebrightness temperature corrections were scaled using themicrowave radiometer (MWR) PWV measurements shownin Fig. 2; minimum PWV values (winter) correspond to thesmaller increase (3.6 K) and maximum PWV values(summer) correspond to the larger increase (7.3 K). Intermedi-ate PWV values (during fall/spring) were linearly scaled sothat a value between 3.6 and 7.3 K was used as the correctionfactor. A similar correction from 0.8 to 1.5 K (smaller becauseof the lower measured radiances in this window) was appliedto Ridge Lab E-AERI brightness temperatures in the 10 µm(985–998 cm−1) window.

No measurements were made from October 2009 to Febru-ary 2011; during this time the E-AERI had componentsrepaired and upgraded, and its mercury cadmium telluride(MCT) detector replaced. It then began measurements at0PAL, where the instrument underwent post-repair calibrationand acceptance testing by ABB personnel on-site at Eureka on12 February 2011 according to UW-SSEC standards and pro-cedures (side-by-side comparison tests were not performedbecause there was only one AERI onsite) and again on 27October 2011 to ensure instrument performance stability andmeasurement reliability (Mariani et al., 2013). This confirmedthat the instrument’s specifications and performance at 0PALmatched its performance at the PEARL Ridge Lab (and theperformance of the P-AERI). Note that the E-AERI underwentadditional calibration and performance evaluations per UW-SSEC standards during the spring of 2015 that certified E-AERI measurements; the E-AERI will regularly undergosuch tests to ensure measurement reliability.

Measurements at 0PAL started on 16 February 2011 andcontinue as of the date of this publication. A standard-rangephotoconductive MCT detector (520–1800 cm−1) was usedin conjunction with the original photovoltaic indium antimo-nide (InSb) detector (1800–3000 cm−1) in February 2011until a new extended-range MCT detector was installed inOctober 2011; during these nine months no measurementswere taken beyond 20 μm. In 2011, there were three periodswith limited or no measurements because of minor instrumentissues and maintenance. At the end of 2012 and beginning of2013, the instrument’s MCT channel was offline for twomonths because of the failure of an analog-to-digital conver-ter. It should be noted that the P-AERI and E-AERI havedifferent noise levels, and each change in the E-AERI’sMCT detector resulted in a change in its noise level over themeasurement period. The E-AERI’s noise-equivalent spectralradiance (NESR), calculated from the root-mean-square of thevariation in 284 spectra, averaged in the 20 μmmicro-window

4 / Zen Mariani et al.

ATMOSPHERE-OCEAN iFirst article, 2016, 1–12 http://dx.doi.org/10.1080/07055900.2016.1216825La Société canadienne de météorologie et d’océanographie

changed from 0.19 to 0.15 to 0.17 mW (m2 sr cm−1)−1 for theoriginal, standard-range replacement and current extended-range MCT detectors, respectively. The NESR averaged inthe 10 μm micro-window changed from 0.16 to 0.02 to0.15 mW (m2 sr cm−1)−1 for the original standard-range repla-cement and current extended-range MCT detectors, respect-ively. However, these changes do not have a large effect onthe measured radiance because 248 co-added spectra areperformed for each seven-minute scan, increasing the signal-to-noise ratio for all three detectors to greater than 375 andgreater than 63 for clear-sky spectra averaged in the 20 and10 μm micro-windows, respectively.As a result of these measurement gaps, mainly a result of

failures and issues associated with the E-AERI being one ofthe first fourth-generation AERIs, the E-AERI dataset is notas evenly distributed as that of the P-AERI; 51, 31, and 18%of P-AERI measurements occurred during winter, summer,and fall/spring, respectively, whereas 61, 22, and 17% ofE-AERI measurements occurred during these same periods.Seasons are defined (see Section 3) so that winter, summer,and fall/spring take up 58, 25, and 17% of the year.

3 Methodsa Characterizing Sky ScenesMeasurements of radiance by the AERI can be grouped basedon cloud cover to ascertain the corresponding forcing producedby different types of cloud cover. The cloud radiative forcing,which is the difference between the radiative budget (down-welling minus upwelling radiation) of clear-sky and all-sky

conditions, can be quantified either across the entire spectrumas a flux density (watts per square metre) (Cox et al., 2012) orover a specific spectral region of interest, such as the surfacecooling-to-space windows for the Arctic. The sensitivity ofthe AERI radiance to cloud radiative forcing provides infor-mation about the presence and properties of clouds, as demon-strated in a number of studies (Cox et al., 2012, 2014; Marianiet al., 2012; Rathke, Fischer, Neshyba, & Shupe, 2002; Turner,2005). This section describes the method used to classifycloudy scenes as thin (optically thin or high-altitude) or thick(opaque or low-altitude) clouds consistent with the approachof Turner and Gero (2011).

Characterizing sky scenes using AERI spectra is simplifiedin the extremely dry and cold atmosphere of the Arctic. To dis-tinguish between clear skies and thin or thick clouds at theSGP where the high humidity obscures clouds, a neuralnetwork algorithm was applied to AERI spectra (Turner &Gero, 2011). At Eureka, however, the colder and drierArctic atmosphere makes it easier to detect weak cloud emis-sion with an AERI. Figure 3 illustrates the high sensitivity ofthe E-AERI to clouds at Eureka; radiances measured on 1November 2008 are shown for early morning thin-cloud,mid-day clear skies, and evening thick-cloud.

The average radiance over the 850–950 cm−1 spectralregion was used to identify clear skies as given in Marianiet al. (2013); this spectral region is highly sensitive toclouds and has limited interfering species (e.g., away fromthe ozone band around 1050 cm−1). This method has beenused in previous studies to eliminate cloudy scenes duringtrace gas retrievals (Mariani et al., 2013). Note that

Fig. 2 MWR PWV (blue, left y-axis) and Environment and Climate Change Canada surface (11.25 m a.s.l.) temperature (red, right y-axis) records taken at Eurekafrom 2006 to 2014.

Distributions of Downwelling Radiance at 10 and 20 µm in the High Arctic / 5

ATMOSPHERE-OCEAN iFirst article, 2016, 1–12 http://dx.doi.org/10.1080/07055900.2016.1216825Canadian Meteorological and Oceanographic Society

determining clear skies using the 850–950 cm−1 averagedradiance is season dependent. The timing of the changeswith season is correlated with changes in PWV measured byan MWR at Eureka, characterized by a sharp increase inMay and sharp decrease in September that reflects the increasein background continuum emission in May and the decrease inSeptember due to changes in water vapour as well as tempera-ture. As a result, the seasons are not of equal length. Thisreflects the unique environmental conditions experienced inthe High Arctic (a long, cold and dry winter followed by arapid transition to a short summer).Distinguishing between optically thick and/or low-altitude

“warm” clouds and optically thin and/or high-altitude “cold”clouds was done using a modified version of the Turner andGero (2011) method, whereby average brightness tempera-tures at 10 µm > 225 K over a 60-minute duration were classi-fied as thick clouds. Utilizing the same methodology providesconsistency when comparing the Eureka and SGP datasets.“Thin” clouds correspond to clouds that are either semi-trans-parent in the IR (e.g., producing a reflectivity backscatter> −50 dBZ), high-altitude (hence, colder) opaque clouds, orlow-altitude clouds that were only partially in the AERI’sfield-of-view during the sky measurement period (Turner &Gero, 2011); the latter introduces some, albeit very few,cases for which the wrong designation was given. Becausethe AERI automatically closes the hatch and does not takemeasurements during precipitation, precipitating (presumablythick) clouds are excluded from this analysis.Measurements of reflectivity by the Millimeter Cloud Radar

(MMCR), stationed at 0PAL, were used to validate AERI

cloud classifications. Elevated reflectivity (>−50 dBZ) abovethe surface indicates the presence of thin clouds, with highermeasured reflectivity (>−20 dBZ) corresponding to thickclouds. Note that MMCR measurements detect approximately70% of the total cloud optical depth for clouds with an opticaldepth less than 2.0 (Borg, Holz, & Turner, 2011). This causesa small portion of very thin cloud observations to be incor-rectly classified as clear-sky, producing a small clear-skybias in the AERI dataset. Results from the entire AERIcloudy scene designation dataset were compared withMMCR and Eureka Weather Station operator observationsthroughout the measurement period to verify the accuracyand stability of the sky scene characterization. An exampleof the verification exercise is shown for one day (1 November2008) in Fig. 4 in which AERI sky classifications are com-pared with radar reflectivity measurements by the MMCR.The AERI cloud classification correctly classifies thick-cloud cover during periods of enhanced reflectivity (0.5–2 km thick-cloud layer with reflectivity > −20 dBZ) measuredby the MMCR between approximately 0200 and 0800 UTC,which corresponds to thick-cloud cover. The accuracy of ourcloud classification based on these thresholds is found to begreater than 94% when compared with the MMCR dataset.

The percentage of each type of cloud cover that occurredsince 2006 provides insight into the variability of cloudcover in the Arctic and is shown in Table 1. Only measure-ments at 0PAL were included in this analysis and some ofthe year-to-year variability is a result of the uneven measure-ment distribution. Note that on rare occasions the AERI’shatch closure was caused by blowing snow, which could

Fig. 3 E-AERI radiances measured from the Ridge Lab on 1 November 2008 during clear-sky (cyan), thin-cloud (blue), and thick-cloud (magenta) conditions.Grey regions indicate the 20 and 10 µm (529.9–532 and 985–998 cm−1) micro-windows representative of the cooling-to-space windows. The shadedlight-blue region indicates the 850–950 cm−1 spectral range (enlarged in the inset panel) used to identify sky scenes.

6 / Zen Mariani et al.

ATMOSPHERE-OCEAN iFirst article, 2016, 1–12 http://dx.doi.org/10.1080/07055900.2016.1216825La Société canadienne de météorologie et d’océanographie

occur during all-sky scenes. Despite this, the cloud coveragedetermined by AERI measurements is in very close agreementwith other studies of cloud cover performed at Eureka. Forinstance, a study performed by Lesins et al. (2009) using theArctic High Spectral Resolution Lidar (AHSRL) measure-ments found that in January, February, March, and December2006, cloudy skies existed 65% of the time at Eureka, whileour method indicates 66% cloud coverage during this sameperiod. Most clear skies are found to occur in the summer(49%) and spring/fall (40%) periods, which is in agreementwith the annual cycle of cloud cover for Eureka described inShupe et al. (2011). The large fraction of thin clouds observedduring winter months (41%) is in agreement with a study ofsimulated and observed Arctic cloud cover in winter thatfound a large increase in high-altitude clouds, which wouldbe classified as “thin” in this study, as well as consistentnearly-overcast conditions (Vavrus, 2004). Note that actualcloudiness is likely to be higher because the AERI instrumentdoes not take measurements during precipitation events.

b Radiances and Brightness TemperaturesWith cloud types defined for each season, radiances in acooling-to-space region in each of the 10 and 20 μmwindows can be analyzed. Radiances were averaged in twomicro-windows to determine the brightness temperature.These micro-windows illustrated in Fig. 3 are (i) 529.9–532 cm−1, which is within the 20 μm window where emissionis primarily due to water vapour and clouds, and (ii) 985–998 cm−1, which is within the 10 μm window, is a match forthe window used to produce the SGP climatology in Turnerand Gero (2011), and is where emission is primarily due towater vapour and clouds. Note that the main conclusionsdrawn from using these micro-windows are insensitive tosmall changes in their location or width. For instance, widermicro-windows of 525–535 cm−1 and 980–998 cm−1 pro-duced nearly identical brightness temperature distributions(<5% change in peak values) as those shown in Section 4.

4 Results and discussiona Eureka Downwelling Brightness TemperatureDistributionsThe distribution of the Eureka AERI brightness temperaturesfor both micro-windows representative of the 10 and 20 μmsurface cooling-to-space windows is provided in Fig. 5.These distributions have been further separated into theirsky scene classification using the method described inSection 3. Both the 10 and 20 μm distributions exhibit abimodal character for all-sky, clear-sky, and (to a lesserextent) thin-cloud classifications, agreeing with the bimodaldistribution in downwelling all-sky flux found at Eureka byCox et al. (2014). Both distributions have a single mode forthick clouds, peaking at 260 K at 10 μm and 270 K at20 μm. The bimodal distribution for clear-sky observationsis a seasonal effect; the warmer mode is almost entirely attrib-uted to measurements in summer and the colder mode tomeasurements in winter.

There are distinct differences between these two cooling-to-space micro-windows. The 20 μm micro-window has broader

TABLE 1. Clear-sky, thin-cloud, and thick-cloud coverage at Eureka foreach year determined by AERI measurements at 0PAL. Alsoshown are the percentages of each type of cloud cover duringeach season and from 2006 to 2014. The percentage is calculatedas the number of times the given cloud designation existeddivided by the total number of AERI seven-minute spectra in thattime period. Measurements from the PEARL Ridge Lab were notincluded in this analysis.

Year Clear-Sky (%) Thin-Cloud (%) Thick-Cloud (%)

2006 31 28 412007 42 34 242008 34 35 312009 26 23 512010 – – –

2011 42 30 282012 24 30 462013 38 25 372014 39 30 31All measurements 34 31 35Summer months 49 19 32Spring/Fall months 40 12 48Winter months 25 41 34

Fig. 4 Example of AERI cloud classification scheme validation using MMCR observations. Results from the AERI cloud classification scheme are shown as a bar(clear-sky: grey, thin-cloud: purple, thick-cloud: black), with the corresponding cloud cover measured by the MMCR shown below. Measurements weretaken 1 November 2008 using spectra from the E-AERI.

Distributions of Downwelling Radiance at 10 and 20 µm in the High Arctic / 7

ATMOSPHERE-OCEAN iFirst article, 2016, 1–12 http://dx.doi.org/10.1080/07055900.2016.1216825Canadian Meteorological and Oceanographic Society

distributions than the 10 μm micro-window with a slightlylarger spread in temperatures. The bimodal distributions aresignificantly more spread out in the 20 μm micro-window,with clear-sky peaks separated by approximately 85 K andthin-cloud peaks separated by approximately 65 K, indicatingextreme seasonal differences. This seasonal effect has asmaller impact in the 10 μm micro-window because theclear-sky peaks differ by only approximately 15 K. Addition-ally, clear-sky and thin-cloud observations do not occur athigher temperatures in the 10 μm micro-window, whereasthey are commonplace in the 20 μm micro-window. Thisdifference in the high-temperature range is indicative ofenhanced water vapour emission in warmer atmospheresaffecting the 20 µm window more than the 10 µm window(note that there are also large differences in the refractiveindices of ice compared with water between the 10 and20 µm regions (Turner, 2005)). Thus the 20 µm window ismore sensitive to changes in water vapour and temperaturecompared with the 10 µm window.The distributions are further separated based on season

and are shown as a percentage of occurrence (number ofX occurrences at that brightness temperature divided bythe sum of all X occurrences in that season, where X =all-sky, clear-sky, thin-cloud, or thick-cloud) in Fig. 6. Adistinct increase in brightness temperatures is observedfrom winter to fall/spring to summer for both the 10 and20 μm micro-windows. For instance, the distribution ofthick clouds is wide and smoothed out in winter (�5%occurrence from 200 to 250 K for 10 μm, 5–8% from 225to 260 K for 20 μm) while it peaks much more sharply inthe summer (15% at 270 K for 10 μm, 28% at 275 K for20 μm). This indicates a sharp transition between thesetwo seasons, typical of the High Arctic atmosphere, whichexperiences a sudden and large increase in surface tempera-ture and water vapour (by �75 K and a factor >10, respect-ively, from winter to summer, according to Eurekaclimatologies discussed in Mariani et al. (2013)). In Figs 5

and 6, the relative sizes of the two modes differ for thetwo micro-windows, with generally more cases in thehigh-temperature mode for the 20 μm micro-window relativeto the 10 μm micro-window.

There is almost no separation of brightness temperaturesamong sky scenes in the 20 µm micro-window during thesummer months in Fig. 6f (even in the fall/spring a clear sep-aration is difficult) indicating that the 20 µm window is satu-rated and essentially closed during this time. Because watervapour is the most radiatively active gas in these windowregions, during peak PWV periods the limited transparencyin the 20 μm region is decreased, resulting in a narrow distri-bution of downwelling brightness temperatures (all of whichare >250 K) independent of sky cover. This behavioursuggests that the 20 µm window is opaque, as is the case atlower latitudes, preventing surface cooling-to-space fromoccurring at this wavelength.

The seasonal cycle of surface temperature and PWV atEureka provided in Fig. 2 correlates with the seasonalchanges in observed brightness temperatures. Surface temp-erature has been increasing overall at Eureka since 1941 (by�2 K during December–February and by almost 1 K duringMarch–May) as has humidity according to meteorologicalmeasurements at the Eureka Weather Station and Lesins,Duck, and Drummond (2010). Given this trend of increasingtemperatures in the Arctic and the consequent increase inwater vapour during prolonged Arctic summers (ACIA,2004; Bekryaev et al., 2010; Richter-Menge, 2006), thesurface radiative forcing will continue to increase, whichwill have long-term consequences for the Arctic’s energybalance.

b Comparison of Eureka and SGP DownwellingBrightness Temperature DistributionsNotable similarities exist between the SGP (see Fig. 5 and Fig.7 in Turner & Gero, 2011) and Eureka 10 μm brightness

Fig. 5 The distribution of brightness temperatures measured at (a) 10 μm (985–998 cm−1) and (b) 20 μm (529.9–532 cm−1) from the combined Eureka AERI datasetusing altitude-corrected Ridge Lab E-AERI brightness temperatures. Histograms of all-sky conditions (black), clear-sky (cyan), thin-cloud (dark blue), and thick-cloud (magenta) are shown. Note the scale of the y-axis is different for the 20 μm brightness temperatures because there are fewer measurements in the far IR.

8 / Zen Mariani et al.

ATMOSPHERE-OCEAN iFirst article, 2016, 1–12 http://dx.doi.org/10.1080/07055900.2016.1216825La Société canadienne de météorologie et d’océanographie

temperature distributions, as well as some differences. First,both the clear-sky and thin-cloud brightness temperaturesexhibit somewhat bimodal distributions in the Eurekadataset, similar to the SGP. The small spread in their

distributions is also similar: the result of summer versuswinter observations. Second, Eureka brightness temperaturesare lower because it is colder in the Arctic than at the SGP.Third, Fig. 6 reveals that, unlike at the SGP, at Eureka the

Fig. 6 As in Fig. 5, except shown as a percentage of occurrence (number of X occurrences at that brightness temperature divided by the sum of all X occurrences inthat season, where X = all-sky, clear-sky, thin-cloud, or thick-cloud) and separated by season: (a) and (b) winter, (c) and (d) fall/spring, and (e) and (f)summer.

Distributions of Downwelling Radiance at 10 and 20 µm in the High Arctic / 9

ATMOSPHERE-OCEAN iFirst article, 2016, 1–12 http://dx.doi.org/10.1080/07055900.2016.1216825Canadian Meteorological and Oceanographic Society

peaks of the fall/spring clear-sky and winter thin-cloud distri-butions are narrow. Fourth, the SGP 10 μm all-sky classifi-cation distribution is tri-modal while at Eureka it is bimodal.This is due to winter thin-cloud observations being predomi-nately colder than summer clear-sky observations at Eureka;when summed, this results in a single, wide, cold mode anda bimodal all-sky distribution. Conversely, at the SGP, thebrightness temperatures of a significant number of winterthin-cloud observations overlap with those of summer clear-sky observations, producing the narrow all-sky mode.

5 Conclusions

Brightness temperature measurements have been collected byAERI instruments in the Canadian High Arctic over a nine-year period, providing measurements of the two surfacecooling-to-space windows at 10 μm and at 20 μm (the dirtywindow) in the High Arctic (>75°N). Because an increase inemission allows for a clear distinction between spectra recordedduring clear skies and spectra recorded when thin or thickclouds are present, sky scenes could be classified. This skyscene characterization was tested and applied to AERIspectra, permitting designation of three distinct meteorologicalconditions: (i) clear skies, (ii) thin clouds (optically thin or high-altitude), and (iii) thick clouds (opaque or low-altitude). Thisdataset was compared with a similar dataset for the SGP andcan be used to assess GCM simulations of brightness tempera-ture distributions, cloud cover, and trends in radiance for theHigh Arctic’s two surface cooling-to-space windows.When separated by season, a distinct increase in brightness

temperatures is observed from winter to fall/spring to summerfor both cooling-to-space windows. When water vapour is amaximum during summer, many scenes in the 20 μm micro-window are warm independent of sky cover; hence, distri-butions are narrow and do not vary with cloud thickness, indi-cating that this cooling-to-space window is mostly opaque (or“closed”) in the summer. Thus the 20 µm window is found tobe more sensitive to changes in water vapour (primarily) andtemperature compared with the 10 µm window. If the Arcticcontinues to warm at its current pace (ACIA, 2004; Bekryaevet al., 2010; Richter-Menge, 2006), the consequent increase inatmospheric water vapour will increase the duration of the20 µm window being closed, with implications for theArctic’s energy balance; a shift towards warmer, “summer-like” brightness temperatures will occur for other seasons,resulting in a net increase in year-round downwelling bright-ness temperatures regardless of cloud cover.The brightness temperatures in both the 10 and 20 μm

cooling-to-space micro-windows exhibit strong seasonal varia-bility for all cloud thicknesses as a result of changes in atmos-pheric temperature and water vapour. Distributions ofbrightness temperatures at 10 μm at Eureka are similar tothose found at the SGP in Turner and Gero (2011), withminor differences in the all-sky distributions. Eureka brightnesstemperatures are found to occur at lower temperatures, as thesecond (warmer) mode of the bimodal distributions is shifted

to colder temperatures relative to the SGP. This is expectedbecause it is significantly colder in the Arctic than at the SGP.

These surface-based measurements of radiative forcing canbe used to quantify changes in the energy budget of the HighArctic, analyze long-term trends, and evaluate GCM simu-lations. Continued measurements are necessary to determinestatistically significant correlations between the two cooling-to-space windows and surface measurements of temperatureand PWV. A trend analysis of the downwelling radiationusing the growing AERI dataset could be used to determinewhether the closing of the 20 μm window is acceleratingand whether changes in cloud cover are occurring. Quantify-ing trends will be challenging because it will require the con-sideration of (i) observations at two altitudes and (ii) differentnoise levels and integration periods for each instrument. Along-term record of integrated radiances throughout theentire 20 µm window (17–25 μm) could be compared withthe entire 10 µm window (8–12 μm) to determine the effectsof climate change (i.e., the shifting of cooling-to-spaceregimes) on the Arctic’s energy balance, particularly as the20 µm window closes, and provide additional data for com-parisons with climate models.

Acknowledgements

The Polar Environment Atmospheric Research Laboratory(PEARL) is operated by the Canadian Network for the Detec-tion of Atmospheric Change (CANDAC). Thanks to VonWalden and Penny Rowe for the use of P-AERI data, DavidHudak at Environment and Climate Change Canada (ECCC)for providing MMCR data, UW-SSEC for AERI calibration,PEARL site manager Pierre Fogal, and CANDAC operatorsAshley Harrett, Alexei Khmel, Paul Loewen, Mike Maurice,Peter McGovern, Oleg Mikhailov, Keith MacQuarrie, andMatt Okraszewski, who have helped with the E-AERImeasurements at PEARL, and the staff at the EurekaWeather Station for their support and hospitality. Thanks tothe Cloud Physics and Severe Weather Research Section atECCC for enabling ZM to conclude this study. Thanks to Ste-phane Lantagne and Guillaume Gamache from ABB for theirwork installing and repairing the E-AERI.

Disclosure statement

No potential conflict of interest was reported by the authors.

Funding

CANDAC/PEARL funding partners are the Arctic ResearchInfrastructure Fund, Atlantic Innovation Fund/Nova ScotiaResearch Innovation Trust, Canadian Foundation forClimate and Atmospheric Science, Canada Foundation forInnovation, Canadian Space Agency (CSA), Environmentand Climate Change Canada (ECCC), Government ofCanada International Polar Year, Natural Sciences and Engin-eering Research Council (NSERC), Ontario Innovation Trust,Ontario Research Fund, Indian and Northern Affairs Canada,

10 / Zen Mariani et al.

ATMOSPHERE-OCEAN iFirst article, 2016, 1–12 http://dx.doi.org/10.1080/07055900.2016.1216825La Société canadienne de météorologie et d’océanographie

and the Polar Continental Shelf Program. Spring visits toPEARL were made as part of the Canadian Arctic ACE Vali-dation Campaigns led by Kaley A. Walker and supported by

CSA, ECCC, NSERC, and the Northern Student TrainingProgram. This work was also supported by the NSERCCREATE Training Program in Arctic Atmospheric Science.

ReferencesACIA. (2004). Arctic climate impact assessment. NewYork: CambridgeUniversity Press.

Bekryaev, R., Polyakov, I., & Alexeev, V. (2010). Role of polar amplificationin long-term surface air temperature variations and modern Arctic warming.Journal of Climate, 23(14), 3888–3906.

Bintanja, R., Graversen, R., & Hazeleger, W. (2011). Arctic winter warmingamplified by the thermal inversion and consequent low infrared cooling tospace. Nature Geoscience, 4, 758–761.

Borg, L., Holz, R., & Turner, D. D. (2011). Investigating cloud radar sensi-tivity to optically thin cirrus using collocated Raman lidar observations.Geophysical Research Letters, 38, L05807. doi:10.1029/2010GL046365

Collard, A. D., Ackerman, S. A., Smith, W. L., Ma, H. E., Revercomb, H. E.,Knuteson, R. O., & Lee, S. C. (1995). Cirrus cloud properties derived fromhigh spectral resolution infrared spectrometry during FIRE II. Part III:Ground-based HIS results. Journal of the Atmospheric Sciences, 52,4264–4275.

Cox, C., Turner, D. D., Rowe, P. M., Shupe, M. D., & Walden, V. P. (2014).Cloud microphysical properties retrieved from downwelling infrared radi-ance measurements made at Eureka, Nunavut, Canada (2006–2009).Journal of Applied Meteorology and Climatology, 53, 772–791.

Cox, C., Walden, V., & Rowe, P. (2012). A comparison of the atmosphericconditions at Barrow, Alaska and Eureka, Canada (2006–2008). Journalof Geophysical Research, 117, D12204. doi:10.1029/2011JD017164

Curry, J. A., Rossow, W. B., Randall, D., & Schramm, J. L. (1996). Overviewof Arctic cloud and radiation characteristics. Journal of Climate, 9, 1731–1764.

Dong, X., Xi, B., Crosby, K., Long, C. N., Stone, R. S., & Shupe, M. D.(2010). A 10 year climatology of Arctic cloud fraction and radiativeforcing at Barrow, Alaska. Journal of Geophysical Research, 115,D17212. doi:10.1029/2009JD013489

Feltz, W., Smith, L., Knuteson, R. O., Revercomb, H. E., Woolf, H. M., &Howell, H. B. (1998). Meteorological applications of temperature andwater vapor retrievals from the ground-based atmospheric emitted radi-ance interferometer (AERI). Journal of Applied Meteorology, 37, 857–875.

Feltz, W. F., Howell, H. B., Knuteson, R. O., Woolf, H. M., & Revercomb, H.E. (2003). Near continuous profiling of temperature, moisture, and atmos-pheric stability using the Atmospheric Emitted Radiance Interferometer(AERI). Journal of Applied Meteorology, 42, 584–597.

Francis, J. A., & Hunter, E. (2007). Changes in the fabric of the Arctic’s green-house blanket. Environmental Research Letters, 2, 045011. doi:10.1088/1748-9326/2/4/045011

Gero, P. J., & Turner, D. D. (2011). Long-term trends in downwelling spectralinfrared radiance over the U.S. Southern Great Plains. Bulletin of theAmerican Meteorological Society, 24, 4831–4843.

Harries, J. E., Carli, B., Rizzi, R., Serio, C., Mlynczak, M., Palchetti, L.,…Masiello, G. (2008). The far-infrared earth. Reviews of Geophysics, 46,RG4004, 1–34.

Intrieri, J., Fairall, C., Shupe, M., Persson, P., Andreas, E., Guest, P., &Moritz, R. (2002). An annual cycle of Arctic surface cloud forcing atSHEBA. Journal of Geophysical Research, 107(C10), 8039. doi:10.1029/2000JC000439

Klein, S., & Jakob, C. (1999). Validation and sensitivities of frontal cloudssimulated by the ECMWF model. Monthly Weather Review, 127(10),2514–2531.

Knuteson, R., Revercomb, H., Best, F., Ciganovich, N., Dedecker, R., Dirkx,T.,… Tobin, D. (2004a). Atmospheric Emitted Radiance Interferometer.Part I: instrument design. Journal of Atmospheric and OceanicTechnology, 21, 1763–1776.

Knuteson, R., Revercomb, H., Best, F., Ciganovich, N., Dedecker, R., Dirkx,T.,… Tobin, D. (2004b). Atmospheric Emitted Radiance Interferometer.Part II: Instrument performance. Journal of Atmospheric and OceanicTechnology, 21, 1777–1789.

Lesins, G., Bourdages, L., Duck, T., Drummond, J., Eloranta, E., & Walden,V. (2009). Large surface radiative forcing from topographic blowing snowresiduals measured in the High Arctic at Eureka. Atmospheric Chemistryand Physics, 9, 1847–1862.

Lesins, G., Duck, T., & Drummond, J. (2010). Climate trends at Eureka in theCanadian High Arctic. Atmosphere-Ocean, 48(2), 59–80.

Mace, G. G., Ackerman, T. P., Minnis, P., & Young, D. F. (1998). Cirrus layermicrophysical properties derived from surface-based millimeter radar andinfrared radiometer data. Journal of Geophysical Research: Atmospheres,103, 23207–23216.

Mahesh, A., Walden, V., & Warren, S. (2001). Ground-based infrared remotesensing of cloud properties over the Antarctic Plateau. Part II: Cloud opticaldepths and particle sizes. Journal of Applied Meteorology, 40, 1279–1294.

Mariani, Z., Strong, K., Palm, M., Lindenmaier, R., Adams, C., Zhao, X.,…Drummond, J. R. (2013). Year-round retrievals of trace gases in the Arcticusing the extended-range atmospheric emitted radiance interferometer.Atmospheric Measurement Techniques, 6, 1549–1565.

Mariani, Z., Strong, K., Wolff, M., Rowe, P., Walden, V., Fogal, P. F.,…Lindenmaier, I. A. (2012). Infrared measurements in the Arctic using twoatmospheric emitted radiance interferometers. Atmospheric MeasurementTechniques, 5, 329–344.

Marty, C., Philipona, R., Delamere, J., Dutton, E. G., Michalsky, J., Stamnes,K.,…Mlawer, E. J. (2003). Downward longwave irradiance uncertaintyunder Arctic atmospheres: Measurements and modeling. Journal ofGeophysical Research, 108(D12), 4358. doi:10.1029/2002JD002937

Perovich, D. K., Andreas, E., Curry, J., Eiken, H., Fairall, C., Grenfell, T.,…Uttal, T. (1999). Year on ice gives climate insights. Eos, TransactionsAmerican Geophysical Union, 80, 481–486. doi:10.1029/EO080i041p00481-01

Rathke, C., Fischer, J., Neshyba, S., & Shupe, M. (2002). Improving IR cloudphase determination with 20 microns spectral observations. GeophysicalResearch Letters, 29(8), 50-1–50-4. doi:10.1029/2001GL014594

Richter-Menge, J., Overland, J., Proshutinsky, A., Romanovsky, V.,Bengtsson, L., Brigham, L.,…Walsh, J. (2006). State of the Arctic.NOAA, 1–36.

Rowe, P., Miloshevich, L., Turner, D., &Walden, V. (2008). Quantification ofa dry bias in radiosonde humidity profiles over Antarctica. Journal ofAtmospheric and Oceanic Technology, 25, 1529–1541.

Rowe, P., Neshyba, S., Cox, C., & Walden, V. (2011). A responsivity-basedcriterion for accurate calibration of FTIR emission spectra: Identification ofin-band low-responsivity wavenumbers.Optics Express, 19(7), 5930–5941.

Rowe, P., Neshyba, S., &Walden, V. (2011). Responsivity-based criterion foraccurate calibration of FTIR emission spectra: theoretical development andbandwidth estimation. Optics Express, 19(6), 5451–5463.

Shupe, M., Walden, V., Eloranta, E., Uttal, T., Campbell, J., Starkweather, S.,& Shiobara, M. (2011). Clouds at Arctic atmospheric observatories. Part 1:Occurrence and macrophysical properties. Journal of Applied Meteorologyand Climatology, 50, 626–644.

Shupe, M. D., Turner, D. D., Walden, V. P., Bennartz, R., Cadeddu, M.,Castellani, B.,…Rowe, P. (2013). High and dry: New observations of tro-pospheric and cloud properties above the Greenland Ice Sheet. Bulletin ofthe American Meteorological Society, 94, 169–186. doi:10.1175/BAMS-D-11-00249.1

Smith, W. L., Ma, X. L., Ackerman, S. A., Revercomb, H. E., & Knuteson, R.O. (1993). Remote sensing cloud properties from high spectral resolution

Distributions of Downwelling Radiance at 10 and 20 µm in the High Arctic / 11

ATMOSPHERE-OCEAN iFirst article, 2016, 1–12 http://dx.doi.org/10.1080/07055900.2016.1216825Canadian Meteorological and Oceanographic Society

infrared observations. Journal of the Atmospheric Sciences, 50(12), 1708–1720.

Stamnes, K., Ellingson, R., Curry, J., Walsh, J., & Zak, B. (1999). Review ofscience issues, deployment strategy, and status of the ARM North Slope ofAlaska-Adjacent Arctic Ocean climate research site. Journal of Climate, 12,46–63.

Stokes, G., & Schwartz, S. (1994). The atmospheric radiation measurement(ARM) program: Programmatic background and design of the cloud and radi-ation test bed. Bulletin of the AmericanMeteorological Society, 75, 1201–1221.

Tobin, D. C., Best, F. A., Brown, P. D., Clough, S. A., Dedecker, R.,Ellingson, R.,…Walden, V. (1999). Downwelling spectral radiance obser-vations at the SHEBA ice station: Water vapour continuum measurementsfrom 17 to 26 µm. Journal of Geophysical Research: Atmospheres, 104(D2), 2081–2092.

Town, M., & Walden, V. (2007). Cloud cover over the South Pole from visualobservations, satellite retrievals, and surface-based infrared radiationmeasurements. Journal of Climate, 20(3), 544–559.

Town, M., Walden, V., & Warren, S. (2005). Spectral and broadband long-wave downwelling radiative fluxes, cloud radiative forcing, and fractionalcloud cover over the South Pole. Journal of Climate, 18(20), 4235–4252.

Turner, D. D. (2005). Arctic mixed-phase cloud properties from AERI-lidarobservations: Algorithm and results from SHEBA. Journal of AppliedMeteorology, 44, 427–444.

Turner, D. D. (2007). Improved ground-based liquid water path retrievalsusing a combined infrared and microwave approach. Journal ofGeophysical Research, 112, D15204. doi:10.1029/2007JD008530

Turner, D. D., & Gero, P. J. (2011). Downwelling 10 μm radiance temperatureclimatology for the Atmospheric Radiation Measurement Southern GreatPlains site. Journal of Geophysical Research, 116. doi:10.1029/2010JD015135

Turner, D. D., Knuteson, R. O., Revercomb, H. E., Lo, C., & Dedecker, R. G.(2006). Noise reduction of atmospheric emitted radiance interferometer(AERI) observations using principal component analysis. Journal ofAtmospheric and Oceanic Technology, 23, 1223–1238.

Turner, D. D., & Löhnert, U. (2014). Information content and uncertainties inthermodynamic profiles and liquid cloud properties retrieved from theground-based atmospheric emitted radiance interferometer (AERI).Journal of Applied Meteorology and Climatology, 53, 752–771.

Turner, D. D., & Mlawer, E. J. (2010). Radiative heating in underexploredbands campaigns (RHUBC). Bulletin of the American MeteorologicalSociety, 91, 911–923.

Turner, D. D., Mlawer, E. J., & Revercomb, H. E. (2016). Water vaporobservations in the ARM program. The Atmospheric RadiationMeasurement program: The First 20 Years. Meteorological Monographs,57, 13.1–13.18. doi:10.1175/AMSMONOGRAPHS-D-15-0025.1

Uttal, T., Curry, J. A., McPhee, M. G., Perovich, D., Moritz, R., Maslanik, J.,…Grenfeld, T. (2002). The surface heat budget of the Arctic Ocean.Bulletin of the American Meteorological Society, 83, 255–275.

Vavrus, S. (2004). The impact of cloud feedbacks on Arctic climate undergreenhouse forcing. Journal of Climate, 17, 603–615.

Walden, V. P., Town, M., Halter, B., & Storey, J. (2005). First measurementsof the infrared sky brightness at Dome C, Antarctica. Publications of theAstronomical Society of the Pacific, 117(829), 300–308.

Webb, M., Senior, C., Bony, S., & Morcrette, J. J. (2001). Combining ERBEand ISCCP data to assess clouds in the Hadley Centre, ECMWF and LMDatmospheric climate models. Climate Dynamics, 17, 905–922.

Yurganov, L., McMillan, W., Wilson, C., Fischer, M., Biraud, S., & Sweeney,C. (2010). Carbon monoxide mixing ratios over Oklahoma between 2002and 2009 retrieved from Atmospheric Emitted Radiance Interferometerspectra. Atmospheric Measurement Techniques, 3, 1319–1331.

12 / Zen Mariani et al.

ATMOSPHERE-OCEAN iFirst article, 2016, 1–12 http://dx.doi.org/10.1080/07055900.2016.1216825La Société canadienne de météorologie et d’océanographie