Arctic radiation-icebridge sea and ice experiment: the arctic radiant energy system during the critical seasonal ice transition. (2024)

ABSTRACT

The National Aeronautics and Space Administration (NASA)'sArctic Radiation-IceBridge Sea and Ice Experiment (ARISE) acquiredunique aircraft data on atmospheric radiation and sea ice propertiesduring the critical late summer to autumn sea ice minimum andcommencement of refreezing. The C-130 aircraft flew 15 missions over theBeaufort Sea between 4 and 24 September 2014. ARISE deployed a shortwaveand longwave broadband radiometer (BBR) system from the Naval ResearchLaboratory; a Solar Spectral Flux Radiometer (SSFR) from the Universityof Colorado Boulder; the Spectrometer for Sky-Scanning, Sun-TrackingAtmospheric Research (4STAR) from the NASA Ames Research Center; cloudmicroprobes from the NASA Langley Research Center; and the Land,Vegetation and Ice Sensor (LVIS) laser altimeter system from the NASAGoddard Space Flight Center. These instruments sampled the radiantenergy exchange between clouds and a variety of sea ice scenarios,including prior to and after refreezing began. The most critical andunique aspect of ARISE mission planning was to coordinate the flighttracks with NASA Cloud and the Earth's Radiant Energy System(CERES) satellite sensor observations in such a way that satellitesensor angular dependence models and derived top-of-atmosphere fluxescould be validated against the aircraft data over large grid-box domainsof order 100-200 km. This was accomplished over open ocean, over themarginal ice zone (MIZ), and over a region of heavy sea iceconcentration, in cloudy and clear skies. ARISE data will be valuable tothe community for providing better interpretation of satellite energybudget measurements in the Arctic and for process studies involvingice-cloud-atmosphere energy exchange during the sea ice transitionperiod.

**********

Through ARISE, NASA acquired unique aircraft data on clouds,atmospheric radiation and sea ice properties during the critical periodbetween the sea ice minimum in late summer and autumn and thecommencement of refreezing.

Arctic sea ice decline is one of the most profound manifestationsof contemporary climate change, and the loss has been accelerating inrecent years as seen by regular extreme September minima and lengtheningof the melt season by 5 days [decade.sup.-1] (Stroeve et al. 2012,2014). This overall decline, combined with a shift toward entirelyseasonal ice (Perovich and Polashenski 2012), implies the action ofnumerous feedbacks involving thinner and darker ice, changing cloudcover, and increasing energy input to the upper water column. Radiationfeedbacks are a necessary mechanism to drive this decline (Perovich etal. 2008), although anomalous winds and preconditioning also play amajor role in both trends and variability (Zhang et al. 2008). At thesame time, it is expected that this large-scale decrease in Arctic seaice will drive circulation anomalies throughout the troposphere (Cassanoet al. 2014). There is a need to diagnose these changes empirically, andto validate climate model simulations, on a pan-Arctic basis.

Ultimately, this need is most satisfactorily addressed withwell-characterized satellite remote sensing data. Several sensors fromthe National Aeronautics and Space Administration (NASA)'s Terraand Aqua spacecraft and A-Train constellation (https://atrain.gsfc.nasa.gov/) have provided observations of key components of theArctic climate system for more than a decade, including atmosphericstructure, cloud optical properties, and sea ice concentration (sea icebeing available in the passive microwave satellite record going back to1979). Concurrently, the Cloud and the Earth's Radiant EnergySystem (CERES) sensors, and their predecessors from the Earth RadiationBudget Experiment (ERBE), retrieve the net shortwave and longwave fluxesthat reveal the combined action of the radiative and dynamical feedbacksinvolving Arctic sea ice. Hartmann and Ceppi (2014) use CERES data toshow that every [10.sup.6] [km.sup.2] decrease in September Arctic seaice in recent years corresponds to an annual-mean increase in absorbedshortwave radiation of 2.5 W [m.sup.2] between 75[degrees] and90[degrees]N. Further progress in our understanding of the whole Arcticclimate system requires understanding how the individual components ofthe Arctic ocean-atmosphere system manifest in the CERES-measured fluxesand how well they are retrieved by other satellite sensors.

In addition, high-quality spectral and broadband radiometric datafrom above sea ice, and below, within, and above Arctic stratiformclouds, can provide a valuable resource for testing the overalleffectiveness of parameterizations for cloud and sea ice evolution inclimate models. For example, if a regional model is initialized with themeteorological conditions pertaining to a given flight mission, then thesimulated energy fluxes at the surface and below, within, and abovecloud can be compared with the data to note where agreement ordiscrepancies occur. If general model-data agreement appears in themicrophysics, for example, then discrepancies in measured irradiance maybe related to the radiative transfer parameterization (e.g.,three-dimensional effects vs a plane-parallel model). Comparison ofArctic surface radiation measurements with climate model simulations hasproven valuable (Tjernstrom et al. 2008); however, to date most Arcticaircraft studies related to climate model parameterizations haveconcentrated on cloud microphysics (e.g., Fridlind et al. 2007,2012).Here we describe a unique aircraft campaign focused on cloud propertiesand radiative effects that can benefit both the remote sensing andclimate modeling approaches to the study of Arctic change.

EXPERIMENT DESIGN AND EXECUTION.

One remarkable aspect of the Arctic Radiation-IceBridge Sea and IceExperiment (ARISE) is the short timeline from experiment conception tosuccessful execution in September 2014. NASA funding became available inMarch of 2014 to supplement Operation IceBridge (OIB) with sea iceobservations during the September transition in the Beaufort-ChukchiSeas, and a C-130 aircraft (N439NA) was also available that was capableof carrying advanced instrumentation for cloud and atmospheric energybudget observations during a time frame that is relatively undersampledin the high Arctic compared with spring and midsummer. OIB is an ongoingairborne science campaign to characterize sea ice, glaciers, and icesheets in unprecedented detail while bridging the gap in polarobservations between NASA's Ice, Cloud, and Land ElevationSatellite (ICESat) missions. The sea ice, radiation, cloud microprobe,and meteorological instruments are listed in Table 1, and their aircraftinstallation is depicted in Fig. 1. Because of the unusually shortplanning timeline, much of the instrument selection was based on proventrack records and uncomplicated installation in the C-130. Nevertheless,the instrument suite was comprehensive and advanced, yielding a timelydataset, preliminary results of which are presented here.

While NASA satellites are making routine observations, an accurateinterpretation of the data required to track Arctic climate change canbe difficult. Uncertainties in atmospheric temperature and humidity,heterogeneity in surface conditions (including sea ice properties), anddifficulties detecting and characterizing clouds over sea ice allcontribute to the uncertainty associated with the CERES-derivedirradiances, which is currently larger over sea ice than any other scenetype (Su et al. 2015b). Thus, the evaluation of CERES top-of-atmosphere(TOA) and surface (SFC) radiative fluxes over the Arctic with data fromthe C-130 payload is a unique and important ARISE scientific objective.A number of ARISE flight plans were designed specifically to accomplishthis objective over a wide range of conditions. Other flight plans weredesigned to characterize the composition of low-level clouds and theirradiative effects over various sea ice conditions and to support OIBwith sea and land ice characterizations. Recent work has shown thatheterogeneity and small-scale interactions are important to consider,particularly in leads and over open water adjacent to sea ice (Vihma etal. 2014). The high time resolution of both the radiometric suite andsurface remote sensors provides direct observation of heterogeneity.

ARISE was based at Eielson Air Force Base (AFB) near Fairbanks,Alaska. Weather prediction and regional modeling resources were usedon-site for flight planning. Aircraft mission planning fell into threemajor categories: 1) CERES collocation and validation, 2) sea iceobservation, and 3) cloud sampling. The missions that were accomplishedare detailed in Table 2, and the associated flight tracks areillustrated in Fig. 2. Figure 3, obtained from the nadir andforward-looking cameras, shows examples of the wide variety of sea iceconditions sampled during ARISE, including thick multiyear ice, a widerange of broken and scattered ice conditions, melt ponds, and frazil andblack ice upon refreezing.

The dates for the CERES experiments were fixed in advance, based onthe known intersection of several satellite overpasses sufficientlywithin the range of the aircraft to allow for extensive gridbox flightpatterns over the Beaufort Sea. Outside of those dates, sea ice andcloud radiation sampling missions were organized in near-real time basedon the comprehensive weather data and forecasting available in thefield. There was some advance planning given to within-cloud stackedtransects, but due to the dynamic nature of the cloud cover, the cloudradiation missions more often adapted to the conditions on the spot. Onthese occasions, satellite meteorology observations and updatedforecasts were transmitted to the aircraft en route to the Beaufort Sea,to help vector the mission to the most interesting scenes.

METEOROLOGICAL CONDITIONS. Supporting weather forecasts for theARISE flights were conducted with the NASA Goddard Earth ObservingSystem Model, version 5 (GEOS-5; Molod et al. 2015), and Polar WeatherResearch and Forecasting (WRF) Model, version 3.5.1(http://polarmet.osu.edu/PWRF/; Hines et al. 2015). Output fields fromthe forecasts are used here along with atmospheric reanalyses torepresent synoptic meteorological conditions during the field program.Meteorology during ARISE maybe categorized by two distinct regimes.During the first seven flights over the Arctic Ocean (4-11 September),the meteorological state was dominated by a surface high pressure overthe southern Chukchi and/or Beaufort Seas. Figure 4 shows a compositeset of 21-h Polar WRF forecasts valid at 1300 Alaska daylight time(AKDT), roughly at the midtimes of the C-130 flights. This resulted innortheasterly low-level flow over the Arctic coast and northern andcentral Alaska. There was considerable low-level cloudiness over thesouthern Beaufort Sea, consistent with the seasonal climatology (e.g.,Intrieri et al. 2002). However midlevel and precipitating clouds werenot extensive. Temperatures over central Alaska were mild with limitedcloud cover--as indicated by the GEOS-5 cloud fraction (Fig. 5a),providing excellent flying weather.

A key synoptic shift occurred near 13 September that accompanied anorthward advance and deepening of low pressure over Bristol Bay.Surface pressures fell over Alaska and the southern Beaufort Sea. Duringthis second regime of 13-21 September, the region of surface highpressure was now located several hundred kilometers farther north overthe Arctic Ocean (Fig. 4b). This resulted in east-northeasterlylow-level flow over the flight target regions of the Arctic Oceanoriginating from a cold source region over sea ice. Simulated surfacetemperatures over the sea ice suggest surface freezing and thickening ofthe ice pack, consistent with reports from the C-130 staff (Fig. 4b). Aweak time-averaged minimum pressure was located over the northwestcorner of Alaska, as a series of weak mesoscale lows propagated eastwardthrough the region. This is consistent with increased cloud cover overthe North Slope of Alaska and the southern Beaufort Sea (Fig. 5b).Increased cloud cover and some light precipitation occurred in centralAlaska during the second regime, and daily average temperatures droppedfrom near 15[degrees]C at Eielson on 13 September to 5[degrees]C on 21September. During the later stages of this regime, dense fogoccasionally appeared in the morning over central Alaska, limiting theC-130 flights from Eielson. Time series of Polar WRF low-leveltemperature over open ocean and sea ice in the Beaufort Sea indicatefluctuations on mesoscale and fast synoptic time scales between coldperiods of strong low-level static instability and warmer periods ofnear-neutral low-level static stability (Fig. 6). Low-level temperatureswere several degrees colder over sea ice than over open water. Moreover,the Polar WRF simulations show that during the ARISE field programfaster net seasonal cooling occurred over sea ice than over open water.

The Polar Meteorology Group at The Ohio State University has doneextensive Arctic testing of Polar WRF, including in the northern Alaskaand Beaufort Sea regions. Specific to the ARISE campaign, we compared aPolar WRF, version 3.6, run against near-surface observations fromBarrow, Nome, Prudhoe, and Red Dog in Alaska, and buoys in the ChukchiSea. Polar WRF was run on a 283 x 312 cell grid with 70 vertical levelsand 8-km horizontal resolution. Table 3 shows that the model reasonablyproduces the near-surface air temperature, wind speed, wind direction,and surface pressure during September 2014. The multiday sea levelpressure averages for regime 1 and regime 2, shown by Figs. 4a and 4b,respectively, are highly consistent with the summer and fall seasonallow-level wind climatologies near northern Alaska as shown by Figs. 3cand 3d in Zhang et al. (2016), respectively. Early analysis of the PolarWRF simulations suggest that ARISE meteorology during September 2014yielded less low cloud liquid water and more cloud ice than during theAugust-September 2008 Arctic Summer Cloud Ocean Study (ASCOS; Tjernstromet al. 2012).

PRELIMINARY RESULTS. CERES. CERES is a key component of the EarthObserving System (EOS) and Suomi National Polar-Orbiting Partnership(SNPP) observatory. During ARISE, four CERES instrument flight models(FM) were fully functional on the EOS Terra (FMI and FM2), Aqua (FM3),and the SNPP (FM5) satellites. The CERES program strives for consistentinstrument performance, calibration, and data products across satelliteplatforms to the extent possible. CERES products provide the mostaccurate spatially complete depiction of radiant energy exchanges in theArctic. However, the uncertainty associated with the CERES-derivedirradiances is currently larger over sea ice than any other scene type(Su et al. 2015b). The CERES Science Team provides instantaneoussatellite footprint (level 2) and the hourly gridded mean (level 3) TOAand surface irradiance data products. ARISE observations provide anopportunity to evaluate irradiances for both of these products over theArctic. Two CERES objectives are 1) to evaluate the level 2CERES-derived top-of-atmosphere irradiance over areas with different seaice conditions and 2) to evaluate hourly gridded mean irradiances in thelevel 3 CERES radiative flux data products.

The CERES instrument measures reflected and emitted shortwave (SW;0.2-5 [micro]m) and longwave (LW; 5-50 [micro]m) radiances at afootprint size of ~20 x 20 km at nadir. Loeb et al. (2012) demonstrateexcellent stability of the CERES instrument to better than 0.3 W[m.sup.-2] [decade.sup.-1] and an absolute accuracy (2 [sigma]) of theCERES TOA fluxes of 2% in the SW and 1% in the LW (Loeb et al. 2009).After properly accounting for the spectral response of the radiometricfilters (Loeb et al. 2001), the CERES radiances are converted toirradiances using angular distribution models (ADMs; Su et al. 2015a;Loeb et al. 2005). An ADM is a set of anisotropic factors that relatesthe radiance measured at a certain viewing geometry to a radiant flux.The anisotropy of the radiation field varies significantly underdifferent surface types and cloud conditions. Thus, ADMs vary with scenetype, especially for the shortwave, and accurate scene typeidentification is critical. The scene properties of each footprint aredetermined using a combination of satellite imager-derived cloud andsurface properties (Minnis et al. 2011) and microwave-derived sea iceinformation. Temperature and humidity profiles required for the cloudretrievals are obtained from the NASA Global Modeling and AssimilationOffice (GMAO) data assimilations system (Rienecker et al. 2008). Scenetypes in the Arctic are complex due to widely variable surface (e.g.,Fig. 3) and cloud conditions.

To better evaluate the ADM performance and associated uncertaintiesin the instantaneous fluxes, one of the two CERES instruments on theTerra satellite--FM2--was placed in programmable azimuthal plane (PAP)scan mode during the ARISE campaign. The PAP mode was set to rotate FM2for continuous targeting of a specific area as Terra passed over theregion. This mode significantly increases the CERES sampling density andprovides irradiance estimates over a wider range of viewing geometriesin the area of interest. The other CERES instrument on Terra--FMI--wasset to scan in the nominal cross-track direction. The difference in thespatial and angular sampling patterns for the FMI and FM2 instruments isillustrated in Fig. 7. FMI samples the broader area with a narrowerviewing geometry, while FM2 samples over a more limited area but with awider range of viewing geometries. This combination of coincidentinformation from the PAP and cross-track scan modes, along with theaircraft measurements, provides a unique capability to test the CERESADMs and thus evaluate the uncertainties associated with CERES level 2TOA data products.

Collocated aircraft measurements with level 2 satelliteobservations have been previously used to evaluate instantaneousirradiances and retrievals from satellite instruments. However, theseoccur only over a short time window for a given satellite overpass,leading to a small sample size and significant noise in the comparisons.Even under a best-case scenario, where instantaneous satellite-derivedirradiances are found to agree with aircraft measurements, thecorresponding uncertainty for hourly 1[degrees] x [degrees] griddedradiant fluxes is not clear. Thus, the direct evaluation of level 3 TOAand surface irradiances is a major goal and a unique concept of theARISE mission.

To create the level 3 data products, the level 2 CERES fluxes areaggregated to construct hourly 1[degrees] x [degrees] gridded mean TOAradiant fluxes (Doelling et al. 2013). The CERES Synoptic (SYN) level 3data (CERES level 3) also contain hourly 1[degrees] x [degrees]gridbox-mean surface irradiances (Rutan et al. 2015). CERES level 3atmospheric and surface irradiances are computed hourly. Surface radiantfluxes are evaluated using radiant flux measurements at surface sites(Rutan et al. 2015; Kato et al. 2013). Uncertainty in level 3 surfaceradiant fluxes is described in Kato et al. (2013). Over the ArcticOcean, conventional observations of the surface and atmosphere arescarce and there are few opportunities to evaluate irradiances.Furthermore, the characterization of cloud and atmospheric conditionsrequired for CERES irradiance computations is more uncertain over theArctic than over other regions of the world. Thus, larger errors inCERES surface irradiances are also likely. ARISE observations enable anevaluation of CERES input datasets and the subsequent TOA and surfacelevel 3 irradiances, which are extensively used in model evaluation(e.g., Pincus et al. 2008; Wang and Su 2013; Itterly and Taylor 2014;English et al. 2014).

To acquire the necessary data, the NASA C-130 flew "lawnmower" patterns (Fig. 2) over ~200 km x 100 km or ~100 km x 100 kmgrid boxes at a nearly constant altitude, either ~6 km (TOA experiment)or near the surface (surface experiment), for 2-3 h. TOA experimentflight paths consisted of five legs of 200-km length, spaced 20 kmapart. The surface flight paths consisted of seven 100-km-length legs,spaced 15 km apart. The flight paths corresponding to the TOAexperiments are shown in Figs. 8a-c. TOA and surface experiments wereconducted in pairs over a particular region, separated by 2 days. Thispairing strategy allowed ARISE to capture aircraft measurements of TOAand surface irradiances along with other data over similar surfaceconditions, and with the most optimal coincidence with CERES and othersatellite overpasses.

One advantage of the Arctic compared to lower-latitude areas is thehigh frequency of polar-orbiting satellite overpasses that occur over agiven region since the satellite orbits spatially converge. For ARISE,three "gridbox" locations were selected based upon theexpected sea ice conditions and the most coincident satellite overpasstimes for the following spacecraft: Terra, Aqua, SNPP, Cloud-AerosolLidar and Infrared Pathfinder Satellite Observations (CALIPSO), andCloudSat. One flight leg of the lawn-mower pattern was always alignedwith the CALIPSO/CloudSat ground track (Fig. 7, dashed red line). Theseactive sensor observations, collocated with the aircraft data, providedetailed vertical profiles of clouds (Fig. 9) that are important to theevaluation of CERES irradiances, Moderate Resolution ImagingSpectroradiometer (MODIS) cloud retrievals, and the attribution ofirradiance errors. For example, the MODIS cloud-top heights shown inFig. 9d are retrieved with a single-layer assumption, which leads tounderestimates when compared to CloudSat/CALIPSO retrievals inmultilayered conditions. The MODIS cloud optical properties are alsomore uncertain over snow and ice for thinner clouds. Kato et al. (2011)demonstrate improvements in surface radiation budget estimates overpolar regions when combining cloud properties from CALIPSO and CloudSatwith MODIS data. More detailed analyses to determine how MODIS cloudretrieval errors contribute to the surface irradiance uncertainties,particularly when active sensor data are not available, remain as futurework. Multilayer retrieval methods (e.g., demonstrated later in Fig. 14)and other improvements in MODIS cloud retrievals are being developed andevaluated with ARISE and A-train data.

Each of the three sets of CERES level 3 evaluation experiments wereperformed over different surface conditions: over open ocean (15 and 17September), over the marginal ice zone (MIZ; 7 and 9 September), andover an area of high sea ice concentration (11 and 13 September). Allthree regions were well sampled, with at least four satellite overpasses(from a combination of Terra, Aqua, and SNPP) during each 2.5-3-haircraft flight. Figures 7d-f show the distribution of instantaneousCERES-derived SW and LW irradiances at TOA from within each of theorange grid boxes that bound the flight pattern. The distributions of LWand SW irradiances are noticeably different for each of the days. Thedifferences can largely be understood by the cloud and surfaceconditions present in each of the grid boxes. On 7 September, thesurface consisted of marginal ice and open ocean with a very low andquite optically thin overcast cloud layer. This results in a SWirradiance distribution that is skewed toward lower values with the longtail toward higher values due to the marginal sea ice and some cloudoptical depth variability. Because the cloud tops were so low, there islittle variation in the emission height, resulting in a narrow LWirradiance distribution. On 11 September, the surface consisted of highsea ice concentration with a combination of clear sky and low thinclouds. This creates a bright scene and correspondingly higher SWfluxes. The low cloud tops and cold sea ice results in a narrow LWirradiance distribution. While the surface on 15 September was openocean, the cloud conditions were overcast, high, and very opticallythick (see Fig. 9). This results in the comparatively high SW and low LWfluxes shown in Fig. 8f. These distributions will be compared with thebroadband radiometer (BBR) irradiance measurements obtained from theC-130 (with suitable atmospheric correction). BBR irradiances taken nearthe surface will be compared with computed irradiances from the SYNproduct. The spectral surface albedo derived from the Solar SpectralFlux Radiometer (SSFR) will be used to evaluate the surface albedo usedin the computations.

BBR. BBRs were mounted on the top and bottom of the aircraft tomeasure the down- and upwelling global solar (SW) irradiance (0.2-3.6[micro]m); the downwelling global, direct, and diffuse SW irradiance(0.4-42 [micro]m); and the down- and upwelling infrared (LW) irradiance(4.5-42 [micro]m; see Table 1). Kipp & Zonen pyranometers (Kipp& Zonen 2004) and pyrgeometers (Kipp & Zonen 2001), modified tomake them better suited for use on an aircraft, measured the SW and LWirradiances. Modifications included new hermetically sealed backhousings with the connector on the bottom that prevented condensationand freezing inside the domes and simplified the mounting of the sensorsto the aircraft. The front-end optics and electronics of the originalinstruments were retained but an amplifier was added right below thesensors and the instruments were operated in current loop mode, awell-established technique to minimize electronic noise.

A Delta-T Devices sunshine pyranometer (SPN-1) was mounted on topof the aircraft to measure the downwelling global, direct, and diffuseSW irradiance. To accomplish this, the SPN-1 has a custom-designedhemispheric "shadowmask" that lies just under the protectiveglass dome that covers the instrument's seven thermopile sensors,each topped with a cosine-corrected diffuser and each with a spectralbandpass of 0.4-2.7 [micro]m. The shadowmask is designed to ensure thatat least one sensor is always exposed to the direct solar radiation, andat least one sensor is always shaded from the direct beam, independentof the orientation of the instrument to the sun. The global, direct, anddiffuse SW irradiances are then derived from these maximum and minimumreadings (Delta-T Devices 2007). Although there is some uncertaintyregarding the absolute accuracy of the SPN-1 (Badosa et al. 2014), thesedata are particularly useful to obtain the direct-diffuse ratio neededto correct the downwelling SW irradiances for the attitude of theaircraft (Long et al. 2010; Bucholtz et al. 2008).

The SW radiometers were calibrated using the standard alternatingsun-shade method (ASTM 2005), where the given sensor is compared to thetrue direct solar irradiance measured by an Eppley automaticHickey-Frieden (AHF) absolute cavity radiometer. The sensitivities forthe SW radiometers from pre- and postmission calibrations agreed towithin 1%. The LW radiometers were calibrated by comparison of themeasured signals to the irradiance of a blackbody immersed in a variabletemperature alcohol bath. The calibration coefficients for the LWradiometers from pre- and postmission calibrations agreed to within 2%.Thus, the stability of the SW and LW radiometers during ARISE wasexcellent. For the SPN-1 the calibration from the manufacturer was used(8% estimated accuracy). This is sufficient here, since the SPN-1measurements will be mainly used to correct the downwelling BBR SWirradiances for the attitude of the aircraft, which requires only therelative values of the global, direct, and diffuse SW irradiance.

Figures 10a and 10b show the CERES lawn-mower pattern flown on 7September overlaid on the NOAA-19 red-green-blue (RGB) and IR satelliteimages taken during the flight at 2150 UTC. A uniform, optically thinlow-level cloud deck blanketed the area. The pinker area, apparent inthe RGB image of the southeastern half of the pattern, indicates heavyconcentrations of sea ice, while the darker areas in the northwesternhalf of the box indicate mostly open ocean beneath the clouds. Theinfrared image (Fig. 10b) indicates that the area was mostly clear ofhigh clouds, although some thin scattered cirrus are seen in thenorthwestern portion of the box. These conditions were confirmed by theonboard flight scientist's notes and the forward video on theaircraft. Figure 10c is an image grab from the forward video taken atapproximately the midpoint of the first leg of the pattern, showing themostly clear skies aloft and a uniform low-level cloud deck. Figure 10dshows the order in which the lawn-mower pattern was flown. This flightis a good case for comparisons between the CERES and BBR SW and LWirradiances because, while there was some variation in the cloud andsurface properties within the box, they remained nearly constant whilethe aircraft sampled the area. In fact, a particular advantage inconducting this type of experiment in the Arctic in late summer/earlyfall is that the sun, though low in the sky, remains at a nearlyconstant elevation angle and thus the incoming solar irradiance at theTOA is nearly constant for a long time during the day. Figure lOe showsthat the solar zenith angle [theta] remained nearly constant (average[[theta].sub.o] = 69.75[degrees] [+ or -] 0.62[degrees]) during theentire pattern. This simplifies the interpretation of the aircraftirradiances, which take about 2 h to survey over the region, whencompared to the nearly instantaneous CERES satellite measurements.

The corresponding BBR LW and SW irradiances are shown in Fig. 11.Figure 11a shows the measured down- and upwelling LW irradiances. Thedata during turns has been removed. Little variation in the down- orupwelling LW irradiances from leg to leg is apparent during the pattern.The mean downwelling LW for is 70.17 [+ or -] 5.74 W [m.sup.-2], whilethe average upwelling LW is 251.90 [+ or -] 4.60 W [m.sup.-2],confirming the uniformity of the conditions with respect to LWirradiance. Figure lib shows the measured down- and upwelling SWirradiances. The downwelling SW fluxes require correction for theattitude of the aircraft because changes in the pitch, roll, or headingof the aircraft can cause changes in the zenith angle of the sun withrespect to the SW radiometer on top of the aircraft. This causesartificial offsets in the downwelling SW measurements (Bucholtz et al.2008). This can be seen in Fig. lib for the uncorrected downwelling SWirradiances shown in black. Dramatic shifts in the data are seen fromone leg to the next as the aircraft changes heading. Using the pitch,roll, and heading from the aircraft's navigational system, thedownwelling SW fluxes are corrected back to the true solar zenith angleand are found also to remain fairly constant during the flight, as shownin red in Fig. lib. In this case, the SW irradiances are normalized tothe mean solar zenith angle during the pattern ([[theta].sub.o] =69.75[degrees]) to make the SW measurements consistent throughout theflight pattern. In future analyses, other solar zenith angle (SZA)normalization strategies will be employed (e.g., to the CERESobservation time). Most of the variability in downwelling SW isattributed to the scattered thin cirrus that occasionally occurredoverhead. The mean downwelling SW irradiance is 399.35 [+ or -] 16.87 W[m.sup.-2]. The upwelling SW irradiances show more variation, withincreases or decreases within a given leg. This is attributed to thechange in the sea surface conditions beneath the low-cloud deck. Forexample, the upwelling SW irradiances shown in Fig. lib are smaller atthe northwestern end of each leg because of the darker ocean compared tothe brighter surfaces found over the southeastern end, where there wasmuch more sea ice. The average upwelling SW irradiance for the entirepattern was 207.33 [+ or -] 32.48 W [m.sup.-2]. The upwelling SW and LWirradiances are consistent with earlier Arctic aircraft campaigns (Curryand Herman 1985; Herman and Curry 1984; Pinto 1998; Curry et al. 2000),while the downwelling LW irradiance is smaller due to theaircraft's higher altitude during this particular flight pattern.This initial analysis is encouraging and supports the sampling strategydevised and employed during ARISE for evaluating CERES TOA and surfaceirradiances over the Arctic with aircraft measurements. More detailedanalyses and comparisons between BBR and CERES are planned for all ofthe ARISE gridbox experiments.

SSFR. The SSFR (Pilewskie et al. 2003) measures downwelling(zenith: [F.sup.[down arrow].sub.[lambda]]) and upwelling (nadir:[F.sup.[up arrow].sub.[lambda]]) SW spectral irradiance from 350 to 2150nm with a spectral resolution of 6-12 nm. Since its development, it hasbeen used for deriving the radiative effect of cloud and aerosols, andfor determining their properties in conjunction with remote sensing andin situ instruments (e.g., Schmidt and Pilewskie 2012). The SSFR hasbeen used to validate satellite data (e.g., Coddington et al. 2008,2010) and to develop cloud retrievals based on relative spectralinformation (McBride et al. 2012; Coddington et al. 2013; LeBlanc et al.2015).

The instrument consists of two light collectors at the top andbottom of the aircraft fuselage, as well as a rack-mounted radiometerunit that is connected to the light collectors through fiber-opticbundles. For ARISE, the zenith light collector was mounted on an activeleveling platform to keep the receiving plane of the light collectorsaligned with the horizon during attitude changes of the airplane. Theradiometer box contains two identical pairs of grating spectrometerscovering the spectral range: (a) 350-1000 nm (Zeiss grating spectrometerwith silicon linear diode array) and (b) 950-2200 nm (Zeiss gratingspectrometer with InGaAs linear diode array). More instrument detailscan be found in Wendisch et al. (2013, chapter 7). The radiometric andangular responses were determined in the laboratory before and after thefield deployment; the drift of the radiometric calibration was trackedwith a portable field calibrator over the course of the mission(accuracy of 3%), and the horizontal alignment of the leveling platformwas adjusted before each flight (accuracy of 0.2[degrees]). Because ofthe low sun elevation in the Arctic, minor misalignments of theinstrument with respect to the horizon increase the absolute uncertainty(Wendisch et al. 2001) and low signal levels lead to elevated noise. Inaddition, reflections and obstructions from the aircraft itself or otherinstruments affect the measurements under these conditions. Overall, theabsolute uncertainty was increased to about 7% for [[theta].sub.o] <75[degrees].

Collocated legs above and below a cloud field can be used to derivereflected, transmitted, and absorbed radiation above the open ocean andice, providing "ground truth" to satellite-derived estimatesof these quantities. The aircraft platform is the only way to get theperspectives from "above," "below," and"within" a cloud almost all at once. Figure 12 demonstratesthis for a case from 19 September, where a cloud field in the MIZ wassampled above both a clear area and an ice-covered area. It shows thatthe albedo [green spectra: ([F.sup.[up arrow].sub.[lambda]]/[F.sup.[down arrow].sub.[lambda]]) x 100% , derived from a high-levelleg, is almost identical for the cloud above ice (large symbols) and theone above open ocean (small symbols), even though the surface albedo(red), derived from a low-level leg, is very different. The smalldifferences of the albedo spectra can be explained by different cloudproperties (optical thickness and effective radius) for the two cases.On the other hand, the cloud transmittance [blue: ([F.sup.[down arrow],below.sub.[lambda]]/ [F.sup.[down arrow],above.sub.[lambda]]) x 100%] issubstantially higher above ice than over open ocean because part of theenhanced upwelling radiation over ice is reflected down by the cloud.The distinct spectral shape in the albedo (decreasing toward theshortest wavelengths) is mirrored by the apparent absorptance (fluxdivergence), the difference between net irradiances above and below thelayer normalized by incident irradiance

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]

This is indicative of the presence of horizontal transport ofradiation (Schmidt et al. 2010; Song et al. 2016). In this case, theclouds act as net recipients of radiation from surrounding areas, whichresults in higher transmittance (and/or reflectance) than predicted byone-dimensional radiative transfer. Studies for reconciling measured insitu microphysics profiles with the corresponding irradiances above thecontrasting surface types are underway and will be published separately.

This example begs the question whether such three-dimensional cloudeffects remain significant when averaging over larger domains. A furtherinteresting question concerns the relative magnitude of cloud and watervapor absorption for different types of clouds (thermodynamic phase andaltitude) above different surface types. In our example, the water vaporabsorption features (relative to the negative baseline caused byhorizontal photon transport) are much more prominent than the weak cloudabsorption features. For high clouds, the situation may be reverse. Thiswill be quantified in future work, using spectral partitioning of theabsorption by constituents (Kindel et al. 2011).

SSFR data will provide spectral surface albedo as a boundarycondition for satellite and airborne remote sensing--a first example isshown in Fig. 12. From the measured albedo, transmittance, andabsorptance spectra, cloud properties (optical thickness, thermodynamicphase, effective radius) can be derived that are averaged over the SSFRhemispherical footprint.

These can be compared with satellite retrievals. The collection ofaircraft and satellite cloud retrievals, in situ measurements, andspectral and broadband irradiances is expected to lead to a deeperunderstanding of the radiative effects of clouds in the MIZ.

4STAR. The Spectrometer for Sky-Scanning, Sun-Tracking AtmosphericResearch (4STAR) instrument combines airborne sun tracking and skyscanning with spectroscopy by incorporating asun-tracking-sky-scanning-zenith-pointing head with fiber-optic Signaltransmission to rack-mounted grating spectrometers (Dunagan et al. 2013)that cover the ultraviolet-visible (210-995 nm, spectrometer I) and SWinfrared (950-1703 nm, spectrometer II) spectral regions, with a spectraacquisition rate of 1 Hz. During ARISE, 4STAR was operated in its threeoperation modes: sun tracking, sky scanning, and zenith pointing. The4STAR tracking head was installed in a modified escape hatch in thezenith port at flight station 220 on the NASA C-130. The dataacquisition, motion control, and spectrometers were installed furtheraft at a flight operator station.

In sun-tracking mode, two motors and a quadrant photodiode detectorprovide active tracking of the solar disk for measurements of directsolar beam transmittance. Dark counts are measured every 20 min with ashutter mechanism. Atmospheric transmittance is derived by dividing thedark-subtracted photon counts by a TOA reference spectrum, accountingfor measurement integration time. The TOA reference spectrum isdetermined by the refined Langley plot method (Shinozuka et al. 2013).In ARISE, we obtained the 4STAR TOA calibration spectrum (SegalRosenhaimer et al. 2014) using measurements from a dedicatedhigh-altitude flight on 2 October. Direct sun products include aerosoloptical depth (AOD; Shinozuka et al. 2013), total column water vapor(CWV), [O.sub.3], and N[O.sub.2], (Segal Rosenhaimer et al. 2014) underclear sky and cirrus optical depth under thin cirrus cases (SegalRosenhaimer et al. 2013).

In sky-scanning mode, 4STAR measures the diffuse sky radiance atprescribed scattering angles from the sun in the almucantar or principalplane to retrieve aerosol properties (single-scattering albedo, sizedistribution, and refractive index; see Kassianov et al. 2012). InARISE, a special modification of this mode was applied under cloudyscenes, with the goal of extracting scattering phase function propertiesfrom the various cloud types.

In the zenith mode, the instrument points in the zenith directionand measures diffuse radiances, for the retrieval of cloud phase,optical depth, and effective radii, following the method of LeBlanc etal. (2015). This mode is used under cloudy skies and accounts for 18% ofthe data collected by 4STAR during ARISE. Figure 13 shows an exampleillustrating the sensitivity to the zenith radiances to cloud opticalproperties. Modeled radiances closely match the two example measuredspectra, with small differences owing to the possible inclusion of cloudparticles of mixed phase. The sky radiance measurements were calibratedbefore and after the 4STAR ARISE deployment to a National Institute ofStandards and Technology (NIST)-traceable integrating sphere at the NASAAmes Research Center, and throughout the deployment with afield-portable 15.24-cm-diameter integrating sphere referenced againstthe same NIST-traceable source.

Cirrus cloud optical thickness (COT) was calculated based on themethod detailed in Segal Rosenhaimer et al. (2013). This retrievalapproach is based on the generation of lookup tables (LUTs) of totaltransmittance for the sun photometer's field of view (FOV) due tothe direct and scattered irradiance over the spectral range measured,for a range of cirrus COT (0-4), and a range of ice cloud effectivediameters (10-120 [micro]m) by using explicit cirrus optical propertymodels from Baum et al. (2011). To calculate the total transmittanceseen by the instrument, which includes both the direct andforward-scattered components, we use a function suggested by Shiobaraand Asano (1994), generated by a three-dimensional (3D) Monte Carloradiative transfer model. Our measurements are then corrected for theappropriate gas absorption and solar zenith angle at the time ofmeasurement and compared to the modeled values over a range ofwavelengths, spanning both visible and infrared spectrometers, and arechosen by the best-fit approach. Cirrus locations were adjusted fromaircraft coordinates, since the 4STAR tracks the sun and does not viewthe clouds in zenith directly above the aircraft. The new location wascalculated based on distance derived by the estimated cirrus height, thesolar zenith angle, and the sun azimuth. Cirrus top height wasapproximated from MODIS and was ~9 km (300 mb for cirrus top height).The latter adjusted cirrus location is about 8 km from the aircraftcoordinates.

The 4STAR cirrus retrievals will not only aid the interpretation ofthe aircraft irradiance measurements but also be useful for validatingsatellite cloud property retrievals, such as COT (by direct comparison),and cloud-top height (CTH; indirectly). For example, Fig. 14 shows theCTH derived from the MODIS imager on Terra at 2140 UTC 15 September2014. Two sets of MODIS CTH retrievals are shown. The first, shown inFig. 14a, is based on a single-layer (SL) cloud assumption for allice-phase clouds (Minnis et al. 2011), which often underestimates CTH(Chang et al. 2010a, and references therein). The second is based on amultilayer (ML) cloud algorithm (Chang et al. 2010b) and shown in Fig.14b for the upper layer (Fig. 14b). While the satellite CTH estimatesfrom the SL method are near or below the altitude of the aircraft, theupper-level CTHs determined from the ML algorithm are consistentlyhigher than the aircraft, which is corroborated by numerous 4STARobservations of overhead cirrus. Figure 14c shows the total column COTderived from the MODIS SL method. For areas with clouds beneath theaircraft these retrievals are not comparable to 4STAR, since 4STAR ispointing at the overhead sun. However, the MODIS ML COT retrieval forthe upper-level cloud should be more comparable to the 4STAR retrievalsof cirrus above the aircraft. This statistical comparison is shown inFig. 15. The CERES-MODIS upper-layer COTs, derived from the 2140 UTCTerra overpass, were spatially interpolated to match the 4STAR cirruslocations (found between 2100 and 2200 UTC). While the overall mean andmedian values of COT along the flight track are found to agree quitewell as shown in Fig. 15 (0.84 and 0.77 for 4STAR and MODIS,respectively), the 4STAR data suggest more widespread cirrus may havebeen occurring than were detected with the CERES-MODIS method. Only 164valid CERES points were found in comparison to 664 from 4STAR. Onepossible contributing factor to this difference is the relatively large4STAR instrument FOV (compared to MODIS), which spans about 2[degrees],allowing for coverage of the entire solar disk plus about 0.5[degrees]from each side. Thus, as the box plots indicate, 4STAR appears to bemore sensitive to the optically thinner cirrus clouds, which aredifficult to detect from MODIS. A comparison between only the coincidentpositive cirrus COT retrievals (not shown) indicates that the MODIS meanvalue of 0.77 is considerably lower than the mean value of 1.3 foundfrom the corresponding 4STAR points. This is useful information that canbe used to improve the skill of the satellite method. Table 4 describesthe full range of 4STAR data products that will be available from ARISE.

LARGE cloud probes. Cloud droplet microphysical properties weremeasured in situ by the C-130 using multiple probes operated by the NASALangley Aerosol Research Group (LARGE). The probes were mounted on thestarboard side of the aircraft just forward of the propeller line (Fig.1).

The multielement water content system (WCM2000; SEA Inc.) is athree-wire probe based on commonly used and proven technologies that arecombined to measure the total and liquid water content (TWC and LWC,respectively) simultaneously. The ice water content (IWC) is inferredfrom the difference between TWC and LWC. During ARISE, most of the massmeasured with this instrument was liquid. Typically the ratio LWC/TWC ison the order of 90%-95%, and this is consistent with an earlier aircraftstudy of autumnal Arctic clouds sampled approximately a month later inthe season (Pinto 1998). Uncertainties of 20% have been found acrossdifferent Johnson-Williams LWC probes in a wind tunnel testing (Strappand Schemenauer 1982), which lend support to the premise that these aresupercooled liquid rather than ice clouds. In our preliminary inspectionof the dataset, there does not seem to be a dependence of LWC/TWC acrosssurface types.

Cloud droplet number and size distribution (2-50-[micro]m diameter)are measured with a cloud droplet probe [CDP; Droplet MeasurementTechnologies (DMT)]. The CDP measures the forward-scattered light fromcloud particles that pass through a laser beam. The intensity of thescattered light is related to the cloud particle size assuming sphericalparticles and is verified using NIST-traceable glass spheres (ThermoFisher Scientific, Inc.). Liquid water and water vapor path above theaircraft are measured using a G-band (183 GHz) water vapor radiometer(GVR; ProSensing, Inc.). The GVR measures the brightness temperature offour receiver channels centered on the water vapor absorption line at183.31 [+ or -] 1, [+ or -]3, [+ or -]7, and [+ or -]14 GHz. Twointernal references (i.e., hot and warm targets at 333 and 293K,respectively) are used to calibrate the receivers once every 10 s duringflight.

Low-level Arctic stratus clouds were sampled in situ during each ofthe ARISE science flights and were consistently observed within theshallow boundary layers spanning 0-350 m in altitude. An example of thisvertical structure is shown in Fig. 16 for the research flight on 15September. For this flight, the C-130 initially transited northwesttoward the sea ice edge at approximately 7000 m before descending to thesurface to profile three cloud layers centered at approximately 5500,4000, and 300 m. The aircraft then ascended and descended through thelow-cloud layer, for which vertical profiles are shown in Fig. 16,indicating that the cloud layer extended from 30 to 90 m at cloud baseup to 490-550 m at cloud top. Mean droplet number concentrations wereobserved to be relatively constant throughout the cloud layer atapproximately 100 [cm.sup.-3], while both droplet mean diameters andliquid water content increased with altitude (from 4 to 14-16 [micro]mand from 0.15 to 0.4-0.5 g [m.sup.3], respectively). Despite being nearthe monthly mean sea ice extent for September 2014, it was noted at thetime that these aircraft maneuvers were conducted over a mostly seaice-free surface with only the occasional patch of broken sea ice below.

This low-cloud structure contrasts that seen for a cloud samplingpattern carried out on 19 September considerably to the east of that on15 September, where the aircraft flew vertically stacked legs across thesea ice edge from approximately 136[degrees] to 129[degrees]W longitude.As shown in the top-left panel of Fig. 17, the aircraft initiallyascended from west to east while skirting the ever-increasing top of thecloud layer (black trace), then retraced its transect from east to westin a descending/ascending porpoise maneuver (red, blue, gold), andfinally turned back west to east for a low-level horizontal leg throughthe lowest portion of the cloud. The western portion of the low-cloudlayer (gold traces in Fig. 17) spanned 150-1200 m altitude, while theeastern portion (red traces) was shifted higher (600-2100 m). Despitethese differences, typical droplet number concentrations, mean dropletdiameter, and liquid water content were of similar magnitude across allthree profiles.

In addition to the vertical cloud structure, level flight legs(green and cyan in Fig. 17) show a marked amount of horizontalvariability. The cloud droplet number concentration and LWC traces inthe topright panel of Fig. 17 show an alternating pattern of cloud andcloud-free air as both LWC and the cloud droplet number concentration(CDNC) drop quickly to zero for brief periods of time. This cloudstructure was clearly visible from the aircraft during this (and other)flight--the ocean surface could be discerned when looking at angles nearnadir, while the view at lower angles was entirely opaque. Finally, wenote the strong increase in cloud droplet number and correspondingdecreases in both LWC and mean droplet diameter as the aircraft passedover the ice sheet edge. This transition may be explained by a shift inthe dynamics controlling these clouds or, possibly, by an increase incloud condensation nuclei over the open waters.

LVIS. NASA's Land, Vegetation and Ice Sensor (LVIS) is awide-swath scanning laser altimeter (lidar) system that digitallyrecords the shapes of the outgoing and reflected laser pulses (Blair etal. 1999). Information extracted from the laser waveforms is combinedpostflight with precise laser pointing, scanning, and positioning datato precisely and accurately measure surface elevation and 3D surfacestructure relative to a reference surface, such as the World GeodeticSystem 1984 (WGS-84) reference ellipsoid (Hofton et al. 2008). Operatingat a wavelength of 1064 nm and at a data rate of 1500 Hz, typical dataprecision and accuracy are at the 10-cm level over ice surfaces (Hoftonet al. 2008). The sensor is used to collect data for cryospheric,ecological, biodiversity, and solid-Earth applications, providing acharacterization of the three-dimensional nature of overflown surfaces.An atmospheric channel, implemented for the first time for the ARISEmission, provided a record of the returns at 1064 nm along the fulllaser path from the airplane to the ground. During data processing,these waveforms were combined over 1-s intervals within a commonelevation range to provide the vertical distribution of reflectedsurfaces between the laser and the ground.

During ARISE, the sensor operated in two principal configurationsthat defined the data swath width. From medium to high flight altitudes,the full laser swath width was used. For example, from a 7-km flightaltitude the laser swath was ~1400 m wide with an 18-m-wide footprint.From lower altitudes, in order to prevent overstressing of systemcomponents, an 80-mrad-wide laser swath was used (e.g., from a 0.45-kmflight altitude, the laser swath was -4.5 m wide with eight ~1-m-widefootprints). Data products include the geolocated return laser waveform,defining the vertical distribution of the reflecting surfaces within thelaser footprint relative to the reference ellipsoid (level 1B), andelevation data products extracted from the level IB laser waveform usingstandard waveform interpretation algorithms, in this case the locationsof the lowest and highest reflecting surfaces with the laser footprint(level 2).

Data were typically collected throughout each ARISE flight even ifthe surface was not discernible through clouds in order to enable bothradiation and ice target objectives to be met. Mission highlightsincluded a 1,000-km-long transect from open water to sea ice along the140[degrees]W longitude line (Fig. 18); a 600-km-long transect of anorbit track of the European Space Agency (ESA)'s Cryosat-2 with thesatellite passing directly overhead at the start of the line; repeatedpasses over the MIZ throughout the ARISE campaign over the time of thesea ice minimum; data swaths along several Alaskan glaciers, includingthe Columbia, Portage, Spencer, Trail, and Wolverine glaciers; andcharacterization of cloud-top heights throughout each flight tointerpret the radiation measurements (Fig. 19). The LVIS team isdeveloping a cloud-top height product based on the laser returns. Aslong as the laser beam is not fully attenuated, there is information onthe top height of multiple cloud layers.

SUMMARY AND FUTURE WORK. ARISE was a uniquely successful experimentin three respects. First, the experiment collected advanced radiometric,laser altimeter, and in situ atmospheric data during the critical periodof late summer and early autumn sea ice transition in the Beaufort Sea.Second, the aircraft measurements were effectively coordinated withmultiple intersecting satellite overpasses, allowing for thoroughValidation of CERES climate data record products plus a greaterunderstanding of the subgrid-scale variability that influences satelliteproducts at high northern latitudes. Third, the experiment wasconceived, planned, and executed in a remarkably short time--6 monthsfrom concept to flight missions, whereas many other experiments of thiscomplexity often take several years to realize. This third success alsoentails a challenge for the ARISE Science Team: our expertise is almostexclusively within the domains of the flight instruments and datainterpretation specific to the instruments and satellite remote sensing.We therefore invite and encourage as wide a collaboration as possiblewith the broader community, particularly researchers interested in 1)applying the resulting well-tested CERES data products to global andregional climate modeling and climate change studies and 2) applying thecombination of spectrally resolved and radiometric data and sea icestructure data to process studies involving radiant ice-ocean-atmosphereenergy exchange during the sea ice transition. Already we have noticedone potentially important aspect of the clouds sampled throughout ARISE:there is a pronounced tendency toward liquid water in lower- andmidtropospheric clouds, with relatively little radiative influence ofcloud ice particles as compared with the geometrically extensivemixed-phase clouds observed over the region later in autumn (Verlinde etal. 2007). In this sense the cloud cover during the critical sea icetransition may be more typical of summer (e.g., Tjernstrom et al. 2012)than autumn. This merits further investigation because ice water contentin Arctic mixed-phase clouds exerts a significantly contrastingradiative forcing compared with clouds that are almost entirely liquidwater (Lubin and Vogelmann 2011). At the same time, the apparentsimplicity of a cloud possibly dominated by a single thermodynamic phasemay be offset by the 3D radiative transfer effects noted above (Fig.12), and the high-time-resolution spectral radiometric data from ARISEcan address these complexities.

The ARISE data, which are available at the NASA Langley AtmosphericScience Data Center and in the NASA OIB archive, contain a wealth ofinformation on the Arctic sea ice transition from in situ process tosatellite spatial scales. In addition to data analysis from the campaignitself, ARISE can help motivate future work. The average SeptemberArctic sea ice extent exhibits large interannual variability ofapproximately 1,000,000 [km.sup.2], in addition to the pronounceddownward trend over the past three decades (Stroeve et al. 2012).Additional missions during this transition season with similarinstrumentation could provide insight into the precise radiative andthermodynamic precursors for onset of seasonal ice recovery. Stroeve etal. (2014) show that the timing of the melt onset impacts the amount ofinsolation absorbed during summer, which in turn influences the timingof the autumn ice recovery. Similar attention, perhaps an additionalcampaign, should focus on the springtime melt onset in the Beaufort Sea.Finally, for both the satellite and in situ objectives presented here, afollow-on aircraft mission would benefit from additional active sensors,such as polarized cloud lidar and cloud radar; a more complete cloudmicroprobe suite, including aerosol composition and microphysics; anddropsondes, to provide measurements of atmospheric thermodynamicstructure specifically over ice of varying concentrations versus openwater during a given mission. ARISE has demonstrated what is possiblefrom long-range research aircraft; over the next decade, enhancements toinstrumentation combined with a focus on timing of sea ice melt onsetand autumn recovery can provide a foundation for thorough understandingof mechanisms for Arctic sea ice trends.

ACKNOWLEDGMENTS. ARISE was sponsored and supported by the EarthScience Division of NASAs Science Mission Directorate. We thank theprogram managers at NASA headquarters: Jack Kaye, Hal Maring, BruceTagg, and Tom Wagner. Their support and inspiration were critical in theplanning and successful execution of ARISE given the urgency for themission and the short time frame to accomplish it. We are grateful tothe personnel at the NASA Wallops Flight Facility, who provided supportfor the C-130. We thank the C-130 flight crew and integration engineersfor their support and significant accomplishes in readying andmaintaining the aircraft. We particularly thank the pilots of theC-130--John Long and Jeff Chandler--for their expertise and genuineinterest in helping us to accomplish our science objectives. We alsothank the NASA Ames Earth Science Project Office for a number ofcontributions, including logistics support; the National SuborbitalEducation and Research Center (NSERC) for its support of the aircraftdata system; Aaron Duley and his colleagues at the NASA Ames ResearchCenter for configuring and maintaining the NASA Mission Tools Suite; andNathan Eckstein and his colleagues at the Alaska Aviation Weather Unitfor providing valuable meteorological support. Finally, we are gratefulto the staff members at Eielson Air Force Base for all of their supportin hosting the C-130 and the ARISE experiment team.

REFERENCES

ASTM, 2005: Standard test method for calibration of a pyranometerusing a pyrheliometer. ASTM G167-05, ASTM International, 21 pp,doi:10.1520/G0167-05.

Badosa, J., J. Wood, P. Blanc, C. N. Long, L. Vuilleumier, D.Demengel, and M. Haeffelin, 2014: Solar irradiances measured using SPN1radiometers: Uncertainties and clues for development. Atmos. Meas.Tech., 7, 4267-4283, doi:10.5194/amt-7-4267-2014.

Barker, H. W., M. P. Jerg, T. Wehr, S. Kato, D. P. Donovan, R. J.Hogan, 2011: A 3D cloud-construction algorithm for the EarthCAREsatellite mission. Q. J. R. Meteor. Soc., 137, 1042-1058,doi:10.1002/qj.824.

Baum, B. A., P. Yang, A. J. Heymsfield, C. G. Schmitt, Y. Xie, A.Bansemer, Y. X. Hu, and Z. Zhang, 2011: Improvements in shortwave bulkscattering and absorption models for the remote sensing of ice clouds.J. Appl. Meteor. Climatol, 50, 1037-1056, doi: 10.1175/2010JAMC2608.1.

Blair, J. B., D. L. Rabine, and M. A. Hofton, 1999: The LaserVegetation Imaging Sensor (LVIS): A medium-altitude, digitization-only,airborne laser altimeter for mapping vegetation and topography. ISPRS J.Photogramm. Remote Sens., 54,115-122, doi:10.1016/S0924-2716(99)00002-7.

Bucholtz, A., R. T. Bluth, B. Kelly, S. Taylor, K. Batson, A. W.Sarto, T. P. Tooman, and R. F. McCoy Jr., 2008: The StabilizedRadiometer Platform (STRAP)--An actively stabilized horizontally levelplatform for improved aircraft irradiance measurements. J. Atmos.Oceanic Technol., 25, 2161-2175, doi:10.1175/2008JTECHA1085.1.

Cassano, E. N., J. J. Cassano, M. E. Higgins, and M. C. Serreze,2014: Atmospheric impacts of an Arctic sea ice minimum as seen in theCommunity Atmosphere Model. Int. J. Climatol., 34, 766-779, doi:10.1002/joc.3723.

Chang, F.-L., P. Minnis, B. Lin, M. Khaiyer, R. Palikonda, and D.Spangenberg, 2010a: A modified method for inferring upper tropospherecloud top height using the GOES 12 imager 10.7 and 13.3 [micro]m data.J. Geophys. Res., 115, D06208, doi:10.1029/2009JD012304.

--,--, J. K. Ayers, M. J. McGill, R. Palikonda, D. A. Spangenberg,W. L. Smith Jr., and C. R. Yost, 2010b: Evaluation of satellite-basedupper troposphere cloud top height retrievals in multilayer cloudconditions during TC4. J. Geophys. Res., 115, D00J065,doi:10.1029/2009JD013305.

Coddington, O. M., and Coauthors, 2008: Aircraft measurements ofspectral surface albedo and its consistency with ground-based andspace-borne observations. J. Geophys. Res., 113, D17209,doi:10.1029/2008JD010089.

--,-- and Coauthors, 2010: Examining the impact of overlyingaerosols on the retrieval of cloud optical properties from passiveremote sensing. J. Geophys. Res., 115, D10211, doi:10.1029/2009JD012829.

--,-- P. Pilewskie, K. S. Schmidt, P. J. McBride, and T. Vukicevic,2013: Characterizing a new surface-based shortwave cloud retrievaltechnique, based on transmitted radiance for soil and vegetated surfacetypes. Atmosphere, 4, 48-71, doi:10.3390/atmos4010048.

Curry, J. A., and G. F. Herman, 1985: Infrared radiative propertiesof summertime Arctic stratus clouds. J. Climate Appl. Meteor., 24,525-538, doi:10.1175/1520 -0450(1985)024<0525:IRPOSA>2,0.CO;2.

--,-- and Coauthors, 2000: FIRE Arctic Clouds Experiment. Bull.Amer. Meteor. Soc., 81, 5-29,doi:10.1175/1520-0477(2000)081<0005:FACE>2.3.CO;2.

Delta-T Devices, 2007: User manual for the Sunshine pyranometer,type SPN-1.43 pp. [Available online atftp://ftp.dynamax.com/manuals/SPNl_Manual.pdf.]

Doelling, D. R., and Coauthors, 2013: Geostationary enhancedtemporal interpolation for CERES flux products. J. Atmos. OceanicTechnol, 30,1072-1090, doi:10.1175/JTECH-D-12-00136.1.

Dunagan, S. E., and Coauthors, 2013: Spectrometer for Sky-ScanningSun-Tracking Atmospheric Research (4STAR): Instrument technology. RemoteSens., 5, 3872-3895, doi:10.3390/rs5083872.

English, J. M., J. E. Kay, A. Gettelman, X. Liu, Y. Wang, Y. Zhang,and H. Chepfer, 2014: Contribution of clouds, surface albedos andmixed-phase ice nucleation schemes to Arctic radiation biases in CAM5.J. Climate, 27, 5174-5197, doi:10.1175/JCLI-D-13-00608.1.

Fridlind, A. M., A. S. Ackerman, G. McFarquhar, G. Zhang, M. R.Poellot, P. J. DeMott, A. J. Prenni, and A. J. Heymsfield, 2007: Iceproperties of single-layer stratocumulus during the Mixed-Phase ArcticCloud Experiment: 2. Model results. J. Geophys. Res., 112, D24202,doi:10.1029/2007JD008646.

--, B. van Diedenhoven, A. S. Ackerman, A. Avramov, A. Mrowiec, H.Morrison, P. Zuidema, and M. D. Shupe, 2012: A FIRE-ACE/SHEBA case studyof mixed-phase Arctic boundary layer clouds: Entrainment ratelimitations on rapid primary ice nucleation processes. J. Atmos. Sci.,69,365-389, doi:10.1175/JAS-D-11-052.1.

Hartmann, D. L., and P. Ceppi, 2014: Trends in the CERES dataset,2000-13: The effect of sea ice and jet shifts and comparison to climatemodels. J. Climate, 27, 2444-2456, doi:10.1175/JCLI-D-13-00411.1.

Herman, G. F., and J. A. Curry, 1984: Observational and theoreticalstudies of solar radiation in Arctic stratus clouds. J. Climate ApplMeteor., 23, 5-24, doi:10.1175/1520-0450(1984)023<0005:OATSOS>2,0.CO;2.

Hines, K. M., D. H. Bromwich, L. Bai, C. M. Bitz, J. G. Powers, andK. W. Manning, 2015: Sea ice enhancements to Polar WRF. Mon. Wea. Rev.,143, 2363-2385, doi:10.1175/MWR-D-14-00344.1.

Hofton, M. A., J. B. Blair, S. B. Luthcke, and D. L. Rabine, 2008:Assessing the performance of 20-25 m footprint waveform lidar datacollected in ICESat data corridors in Greenland. Geophys. Res. Lett.,35, L24501, doi:10.1029/2008GL035774.

Intrieri, J. M., C. W. Fairall, M. D. Shupe, P. O. G. Persson, E. LAndreas, P. S. Guest, and R. E. Moritz, 2002: An annual cycle of Arcticsurface cloud forcing at SHEBA. J. Geophys. Res., 107, 8039,doi:10.1029/2000JC000439.

Itterly, K., and P. C. Taylor, 2014: Evaluation of the tropical TOAflux diurnal cycle in MERRA and ERA-Interim retrospective analyses. J.Climate, 27, 4781-4796, doi:10.1175/JCLI-D-13-00737.1.

Kassianov, E., C. Flynn, J. Redemann, B. Schmid, P. B. Russell, andA. Sinyuk, 2012: Initial assessment of the Spectrometer forSky-Scanning, Sun-Tracking Atmospheric Research (4STAR)-based aerosolretrieval: Sensitivity study. Atmosphere, 3, 495-521,doi:10.3390/atmos3040495.

Kato, S., and Coauthors, 2011: Improvements of top-of-atmosphereand surface irradiance computations with CALIPSO-, CloudSat-, andMODIS-derived cloud and aerosol properties. /. Geophys. Res., 116,D19209, doi:10.1029/2011JD016050.

--, N. G. Loeb, F. G. Rose, D. R. Doelling, D. A. Rutan, T. E.Caldwell, L. Yu, and R. A. Weiler, 2013: Surface irradiances consistentwith CERES-derived top-of-atmosphere shortwave and longwave irradiances.J. Climate, 26, 2719-2740, doi:10.1175/JCLI-D-12-00436.1.

Kindel, B. C., P. Pilewskie, K. S. Schmidt, O. Coddington, and M.D. King, 2011: Solar spectral absorption by marine stratus clouds:Measurements and modeling. J. Geophys. Res., 116, D10203,doi:10.1029/2010JD015071.

Kipp & Zonen, 2001: CG4 pyrgeometer: Instruction manual. 64 pp.[Available online at www.kippzonen,com/ Download/33/CG-4-Manual.]

--, 2004: CM22 precision pyranometer: Instruction manual. 65 pp.[Available online at www.kippzonen.com/Download/55/CM-22-Pyranometer-Manual.]

LeBlanc, S. E., P. Pilewskie, K. S. Schmidt, and O. Coddington,2015: A spectral method for discriminating thermodynamic phase andretrieving cloud optical thickness and effective radius usingtransmitted solar radiance spectra. Atmos. Meas. Tech., 8, 1361-1383,doi:10.5194/amt-8-1361-2015.

Loeb, N. G., K. J. Priestley, D. P. Kratz, E. B. Geier, R. N.Green, B. A. Wielicki, P. O'Rawe Hinton, and S. K. Nolan, 2001:Determination of unfiltered radiances from the Clouds and theEarth's Radiant Energy System instrument. J. Appl. Meteor., 40,822-835, doi:10.1175/1520-0450(2001)040<0822:DOURFT>2.0.CO;2.

--, S. Kato, K. Loukachine, and N. Manalo-Smith, 2005: Angulardistribution models for top-of-atmosphere radiative flux estimation fromthe Clouds and the Earth's Radiant Energy System instrument on theTerra satellite. Part I: Methodology. J. Atmos. Oceanic Technol.,22,338, doi:10.1175/JTECH1712.1.

--, B. A. Wielicki, D. R. Doelling, G. L. Smith, D. F. Keyes, S.Kato, N. Manalo-Smith, and T. Wong, 2009: Toward optimal closure of theEarth's top-of-atmosphere radiation budget. J. Climate, 22,748-766,doi:10.1175/2008JCLI2637.1.

--, S. Kato, W. Su, T. Wong, F. G. Rose, D. R. Doelling, J. R.Norris, and X. Huang, 2012: Advances in understanding top-of-atmosphereradiation variability from satellite observations. Surv. Geophys., 33,359-385, doi:10.1007/s10712-012-9175-1.

Long, C. N., A. Bucholtz, H. Jonsson, B. Schmid, A. Vogelmann, andJ. Wood, 2010: A method of correcting for tilt from horizontal indownwelling shortwave irradiance measurements on moving platforms. OpenAtmos. Sci. J., 4, 78-87, doi:10.2174/1874282301004010078.

Lubin, D., and A. M. Vogelmann, 2011: The influence of mixed-phaseclouds on surface shortwave irradiance during the Arctic spring. J.Geophys. Res., 116, D00T05, doi:10.1029/2011JD015761.

McBride, P. J., K. S. Schmidt, P. Pilewskie, A. Walther, A. K.Heidinger, D. E. Wolfe, C. W. Fairall, and S. Lance, 2012: CalNex cloudproperties retrieved from a ship-based spectrometer and comparisons withsatellite and aircraft retrieved cloud properties. J. Geophys. Res.,117, D00V23, doi:10.1029/2012JD017624.

Minnis, P., and Coauthors, 2011: CERES Edition-2 cloud propertyretrievals using TRMM VIRS and Terra and Aqua MODIS data--Part I:Algorithms. IEEE Trans. Geosci. Remote Sens., 49, 4374-4400,doi:10.1109/TGRS.2011.2144601.

Molod, A. M., L. L. Takacs, M. Suarez, and J. Bacmeister, 2015:Development of the GEOS-5 atmospheric general circulation model:Evolution from MERRA to MERRA2. Geosci. Model Dev., 8, 1339-1356,doi:10.5194/gmd-8-1339-2015.

Perovich, D. K., and C. Polashenski, 2012: Albedo evolution ofseasonal Arctic sea ice. Geophys. Res. Lett., 39, L08501,doi:10.1029/2012GL051432.

--, J. A. Richter-Menge, K. F. Jones, and B. Light, 2008: Sunlight,water, and ice: Extreme Arctic sea ice melt during the summer of 2007.Geophys. Res. Lett., 35, LI 1501, doi:10.1029/2008GL034007.

Pilewskie, P. J., and Coauthors, 2003: Solar spectral radiativeforcing during the Southern African Regional Science Initiative. J.Geophys. Res., 108, 8486, doi:10.1029/2002JD002411.

Pincus, R., C. P. Batstone, R. J. P. Hofmann, K. E. Taylor, and P.J. Glecker, 2008: Evaluating the present-day simulation of clouds,precipitation, and radiation in climate models. J. Geophys. Res., 113,D14209, doi:10.1029/2007JD009334.

Pinto, J. O., 1998: Autumnal mixed-phase cloudy boundary layers inthe Arctic. J. Atmos. Sci., 55, 2016-2038,doi:10.1175/1520-0469(1998)055<2016:AMPCBL>20.CO;2.

Rienecker, M. M., and Coauthors, 2008: The GOES-5 data assimilationsystem--Documentation of versions 5.0.1, 5.1.0, and 5.2.0. M. J. Suarez,Ed., Technical Report Series on Global Modeling and Data Assimilation,Vol. 27, NASA Tech. Memo. NASA/TM-2008-104606, 101 pp. [Available onlineat https://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/20120011955.pdf.]

Rutan, D. A., S. Kato, D. R. Doelling, F. G. Rose, L. T. Nguyen, T.E. Caldwell, and N. G. Loeb, 2015: CERES synoptic product: Methodologyand validation of surface radiant flux. /. Atmos. Oceanic Technol., 32,1121-1143, doi:10.1175/JTECH-D-14-00165.1.

Schmidt, K. S., and P. Pilewskie, 2012: Airborne measurements ofspectral shortwave radiation in cloud and aerosol remote sensing andenergy budget studies. Light Scattering Reviews 6: Light Scattering andRemote Sensing of Atmosphere and Surface, A. A. Kokhanovsky, Ed.,Springer, 239-288, doi:10.1007/978-3-642-15531-4_6.

Schmidt, K. S., and Coauthors, 2010: Apparent absorption of solarspectral irradiance in heterogeneous ice clouds. J. Geophys. Res., 115,D00J22, doi:10.1029/2009JD013124.

Segal Rosenhaimer, M., P. B. Russell, J. M. Livingston, S.Ramachandran, J. Redemann, and B. A. Baum, 2013: Retrieval of cirrusproperties by Sun photometry: A new perspective on an old issue. J.Geophys. Res. Atmos., 118,4503-4520, doi:10.1002/jgrd.50185.

--, and Coauthors, 2014: Tracking elevated pollution layers with anewly developed hyperspectral Sun/Sky spectrometer (4STAR): Results fromthe TCAP 2012 and 2013 campaigns. J. Geophys. Res. Atmos., 119,2611-2628, doi:10.1002/2013JD020884.

Shinozuka, Y., and Coauthors, 2013: Hyperspectral aerosol opticaldepths from TCAP flights. J. Geophys. Res., 118, 12 180-12 194,doi:10.1002/2013JD020596.

Shiobara, M., and S. Asano, 1994: Estimation of cirrus opticalthickness from sun photometer measurements. J. Appl. Meteor.,33,672-681, doi:10.1175/1520-0450(1994)033 <0672:EOCOTF>2.0.CO;2.

Song, S., and Coauthors, 2016: The spectral signature of cloudspatial structure in shortwave irradiance. Atmos. Chem. Phys., 16, 13791-13 806, doi:10.5194/acp-16-13791-2016.

Strapp, J. W., and R. S. Schemenauer, 1982: Calibrations ofJohnson-Williams liquid water content meters in a high-speed icingtunnel. /. Appl. Meteor., 21, 98-108, doi:10.1175/1520-0450(1982)021<0098: COJWLW>2.0.CO;2.

Stroeve, J. C., M. C. Serreze, M. M. Holland, J. E. Kay, J.Maslanik, and A. P. Barrett, 2012: The Arctic's rapidly shrinkingsea ice cover: A research synthesis. Climatic Change, 11, 1005-1027,doi:10.1007/s10584-011-0101-1.

--, T. Markus, L. Boisvert, J. Miller, and A. Barrett, 2014:Changes in Arctic melt season and implications for sea ice loss.Geophys. Res. Lett., 41, 1216-1225, doi:10.1002/2013GL058951.

Su, W., J. Corbett, Z. Eitzen, and L. Liang, 2015a: Next-generationangular distribution models for top-of-atmosphere radiative fluxcalculation from CERES instruments: Methodology. Atmos. Meas. Tech., 8,611-632, doi:10.5194/amt-8-611-2015.

--, --, --, and --, 2015b: Next-generation angular distributionmodels for top-of-atmosphere radiative flux calculation from the CERESinstruments: Validation. Atmos. Meas. Tech., 8,3297-3313,doi:10.5194/amt-8-3297-2015.

Tjernstrom, M., J. Sedlar, and M. D. Shupe, 2008: How well doregional climate models reproduce radiation and clouds in the Arctic? Anevaluation of ARCMIP simulations. /. Appl. Meteor. Climatol., 47,2405-2422, doi:10.1175/2008JAMC1845.1.

--, and Coauthors, 2012: Meteorological conditions in the centralArctic summer during the Arctic Summer Cloud Ocean Study (ASCOS). Atmos.Chem. Phys., 12, 6863-6889, doi:10.5194/acp-12-6863-2012.

Verlinde, J., and Coauthors, 2007: The Mixed-Phase Arctic CloudExperiment. Bull. Amer. Meteor. Soc., 88, 205-221,doi:10.1175/BAMS-88-2-205.

Vihma, T., and Coauthors, 2014: Advances in understanding andparameterization of small-scale physical processes in the marine Arcticclimate system: A review. Atmos. Chem. Phys., 14, 9403-9450,doi:10.5194/acp-14-9403-2014.

Wang, H., and W. Su, 2013: Evaluating and understanding top of theatmosphere cloud radiative effects in Intergovernmental Panel on ClimateChange (IPCC) Fifth Assessment Report (AR5) Coupled ModelIntercomparison Project Phase 5 (CMIP5) models using satelliteobservations. J. Geophys. Res. Atmos., 118, 683-699,doi:10.1029/2012JD018619.

Wendisch, M., D. Muller, D. Schell, and J. Heintzenberg, 2001: Anairborne spectral albedometer with active horizontal stabilization. /.Atmos. Oceanic Technol., 18, 1856-1866, doi:10.1175/1520-0426(2001)018<1856:AASAWA>2,0.CO;2.

--, and Coauthors, 2013: Atmospheric radiation measurements.Airborne Measurements for Environmental Research: Methods andInstruments, M. Wendisch and J.-L. Brenguier, Eds., Wiley, 343-412.

Zhang, J., R. Lindsay, M. Steele, and A. Schweiger, 2008: Whatdrove the dramatic retreat of arctic sea ice during summer 2007?Geophys. Res. Lett., 35, LI 1505, doi:10.1029/2008GL034005.

Zhang, J.-L., F. Liu, W. Tao, J. Krieger, M. Shulski, and X. Zhang,2016: Mesoscale climatology and variation of surface winds over theChukchi-Beaufort coastal areas. J. Climate, 29, 2721-2739,doi:10.1175/JCLI-D-15-0436.1.

AFFILIATIONS: Smith, Anderson, Kato, Moore, Barrick, Chen, Loeb,Nguyen, Stackhouse, and Taylor--NASA Langley Research Center, Hampton,Virginia; Hansen, Cullather, Blair, and Rabine--NASA Goddard SpaceFlight Center, Greenbelt, Maryland; Bucholtz and Reid--Naval ResearchLaboratory, Monterey, California; Beckley and Cornejo--SGT, Lanham,Maryland; Corbett, Ham, Palikonda, Spangenberg, Thornhill, andWinstead--SSAI, Hampton, Virginia; Hines and Bromwich-Byrd Polar andClimate Research Center, The Ohio State University, Columbus, Ohio;Hofton and Kittleman--University of Maryland, College Park, CollegePark, Maryland; Lubin and Scott--Scripps Institution of Oceanography,University of California, San Diego, La Joila, California; SegalRosenhaimer and Shinozuka--Bay Area Environmental Research Institute,Petaluma, California; Redemann--NASA Ames Research Center, MoffettField, California; Schmidt, Song, and Pilewskie--Department ofAtmospheric and Oceanic Sciences, and Laboratory for Astrophysics andSpace Physics, University of Colorado Boulder, Boulder, Colorado;Brooks--SSAI, Greenbelt, Maryland; Corr-Oak Ridge AssociatedUniversities, Oak Ridge, Tennessee, and NASA Langley Research Center,Hampton, Virginia; Knappmiller and Miller--Laboratory for Astrophysicsand Space Physics, University of Colorado Boulder, Boulder, Colorado;LeBlanc-Oak Ridge Associated Universities, Oak Ridge, Tennessee, andNASA Ames Research Center, Moffett Field, California; RichterMenge--ColdRegions Research and Engineering Laboratory, U.S. Army Corps ofEngineers, Hanover, New Hampshire; Van Gilst-National SuborbitalEducation and Research Center, University of North Dakota, Grand Forks,North Dakota

CORRESPONDING AUTHOR: William Smith, [emailprotected]

The abstract for this article can be found in this issue, followingthe table of contents.

DOI: 10.1175/BAMS-D-14-00277.1

Caption: Fig. 1. The NASA C-130 research aircraft as configured forARISE, showing the location of each instrument described in Table 1.

Caption: Fig. 2. Map of the ARISE mission flight tracks asdescribed in Table 2.

Caption: Fig. 3. Surface conditions during the 2014 sea icetransition period over the Beaufort Sea photographed from the ARISEaircraft using the nadir camera, (a) 5 Sep, 80[degrees]N, 140[degrees]W;(b) 10 Sep, 77[degrees]N, 128[degrees]W; (c) 17 Sep, 74[degrees]N,152[degrees]W; (d) 16 Sep, 77[degrees]N, 143[degrees]W; (e) 16 Sep,76[degrees]N, 141[degrees]W; and (f) 24 Sep, 73[degrees]N,129[degrees]W.

Caption: Fig. 4. Average sea level pressure (contours, interval 2hPa) and 2-m temperature ([degrees]C; shaded) from Polar WRF 21 -hforecasts (1300 AKDT) for (a) the first seven flights (4-11 Sep 2014)and (b) the next seven flights (13-21 Sep 2014).

Caption: Fig. 5. Total cloud fraction from NASA's Modern-EraRetrospective Analysis for Research and Applications, version 2(MERRA-2), for 2200 UTC, averaged for (a) 4-11 Sep and (b) 13-21 Sep2014.

Caption: Fig. 6. Time series of skin temperature (solid lines) and2-m temperature (dashed) for Polar WRF grid points at 73[degrees]N,133[degrees]W (blue) and 73[degrees]N, 150 [degrees]W (green).Temperatures are 21 -h forecasts valid at 1300 AKDT, near the centertimes of ARISE flights.

Caption: Fig. 7. (a)TOA SW irradiance derived from the CERES FMIinstrument (operated in the cross-track mode) on Terro. The orange boxindicates the area where the TOA gridbox experiment took place on 11 Sep2014. (b) As in (a), but for the FM2 instrument on Terra that wasoperated in a programmable azimuthal-plane mode, (c) Viewing zenith andrelative azimuth angles of CERES FMI (red) and FM2 (blue) measurementsinside the orange grid box in (a) and (b).

Caption: Fig. 8. (a)-(c) Sea ice cover derived from AdvancedMicrowave Scanning Radiometer 2 (AMSR2) [Arctic Radiation and TurbulenceInteraction Study (ARTIST) sea ice (ASI) algorithm] with NASA C-130flight-track overlays on 3 days when TOA gridbox experiments wereconducted. The CALIPSO ground track is indicated by the dashed redlines. The P and Q markers in (c) correspond to the P and Q points,respectively, in Fig. 9. (d)-(f) Distribution of CERES-derived LW and SWirradiances over the grid box encompassed by the orange solid linesshown in (a)-(c), respectively.

Caption: Fig. 9. Cloud-layer mask derived on 15 Sep 2014 from (a)CALIPSO vertical feature mask (VFM), (b) CloudSat 2B-CLDCLASS, (c)clouds detected by both CALIPSO and CloudSat, and (d) merged clouds,that is, clouds detected by CALIPSO or CloudSat. (e)CERES-CALIPSO-CloudSat- MODIS (CCCM)-merged 0.64-[micro]m cloudextinction profiles derived at the CERES footprint scale (~20 km), (f) Adownlooking view of 0.64-[micro]m COT constructed with the methoddescribed in Barker et al. (20II). The C-I30 flight track is shown in(a) and (b) by the black lines for the entire pattern and by the dashedred line for the period collocated to CERES observations with a timedifference <30 min and a distance <20 km. MODIS-derived cloud-topand effective heights are shown in (d) by red and blue dots,respectively. The P and Q markers in (a) correspond to the locationsshown of 15 Sep in Fig. 7c. Caption: Fig. 10. Example of BBR datacollection parameters on 7 Sep 2014. Aircraft flight pattern overlaid onthe NOAA-19 satellite (a) RGB and (b) IR images from 2150 UTC, (c) animage from the forward video camera, (d) the order of the lawn mowerflight pattern, and (e) SZA.

Caption: Fig. 11. BBR (a) LW and (b) SW fluxes from the flighttracks in Fig. 10.

Caption: Fig. 13. 4STAR spectra of zenith radiance transmittedthrough cloud over open water and over sea ice (black) on 19 Sep 2014compared with radiative transfer model simulations for varioussingle-phase clouds (colors). Radiative transfer calculations used thescattering phase functions for ice particles described by Baum et al.(2011) and flight-level spectral albedo measured by SSFR. The green andred curves show the extremes of considered cloud properties, while blueand purple curves represent modeled radiances matching closely measuredradiances. Gray areas indicate wavelength regions where the spectralshape can be used to retrieve cloud properties (optical depth, effectiveparticle radius, thermodynamic phase).

Caption: Fig. 14. 4STAR retrievals of cirrus COT on IS Sep 2014compared with MODIS retrievals from the nearest satellite overpass, (a)All ice clouds' top heights derived by CERES, overlaid by aircraftaltitude (open circles), (b) Top-layer cloud height of ML clouds,derived by CERES, overlaid by aircraft altitude (open circles) for 15Sep flight, (c) COT for all ML ice clouds with top above 5-km heightderived by CERES (solid circles), overlaid by direct sun cirrusretrievals [based on procedure developed in Segal-Rosenheimer et al.(2013)] from the 4STAR instrument on board C-130 (open circles), (d) COTfor only upper-layer clouds, as derived by CERES, overlaid by direct suncirrus retrievals from 4STAR (open circles) for 15 Sep flight. Note thedifferent color bar scales for (c) and (d).

Caption: Fig. 15. Cirrus COT statistics from CERES-MODISupper-layer cloud retrievals (yellow) and those derived from 4STAR(blue) using data taken on 15 Sep 2014 along the C-130 flight trackshown in Fig. 14. Solid black lines indicate the median values, whilethe top and bottom numerical values indicate the mean values and thenumber of samples, respectively.

Caption: Fig. 16. Vertical profiles of cloud microphysicalproperties for three cloud penetrations on 15 Sep 2014. (left) Thelocation of each profile is highlighted (red: 75.60[degrees]N,156.04[degrees]W, blue: 74.82[degrees]N, 155.43[degrees]W, gold:76.33[degrees]N, 156.83[degrees]W). The complete flight track is shownin black, the National Snow and Ice Data Center (NSIDC) monthly mean seaice extent is shown as solid white, and the MODIS visible imagery isshown for the non-ice region, (top right) Traces of droplet numberdensity, mean droplet size, and LWC at I-Hz resolution during eachprofile, (bottom right) Droplet number size distributions binned byaltitude.

Caption: Fig. 17. Sampling of cloud properties across the ice edgecentered near 72.3[degrees]N, 133.5[degrees]W on 19 Sep 2014. (top left)The altitude vs longitude trace shows the aircraft sampling strategyalong (bottom left) the parallel tracks. Initially, the aircraftascended from west to east following just above the cloud top (blacktrace). Then, three vertical profiles were carried out to map thevertical extent of the clouds (red, blue, gold). The vertical profile ofdroplet number density, mean diameter and LWC are shown in the lowerright. Finally, a series of horizontal legs was performed at 800 ft(~245 m) along the same track. The cloud properties along one of theselegs is shown in the upper right. Green (cyan) denotes the underflightof the gold (blue) profiles at 800 ft.

Caption: Fig. 18. Surface elevation data derived from the scanningLVIS superimposed on digital camera imagery taken near 76.4[degrees]N,140[degrees]W on 5 Sep 2014 to help characterize sea ice properties andvariability.

Caption: Fig. 19. LVIS return pulse intensity (a) along the C-130flight track and (b) on 15 Sep 2014, depicting surface and cloud-topaltitudes. An LVIS level 2 cloud altitude product is in development tocomplement and help interpret the cloudy-sky radiative flux measurementsobtained during ARISE.

Table 1. Parameters measured from the C-130 (NASA 439) during ARISE.NRL = Naval Research Laboratory. NSERC = Natural Sciences andEngineering Research Council of Canada. GSFC = Goddard Space FlightCenter. LaRC = Langley Research Center.Parameters Instrument Manufacturer (mentor)Broadband SW Pyranometer Kipp & Zonen,radiative flux, modified CM22 (NRLupwelling and BBR suite)downwellingBroadband LW Pyrgeometer Kipp & Zonen,radiative flux, modified CG4 (NRLupwelling and BBR)downwellingGlobal, direct, and Sunshine pyranometer Delta-T Devices SPN-diffuse SW radiative I (NRL BBR)flux, downwellingSpectral SW radiance, 4STAR (NASA Ames Researchdownwelling Center)Spectral SW SSFR (University ofirradiance, upwelling Colorado Boulder)and downwellingCloud and surface Pyrometer Heitronics KT-19.85temperature, up- and series II (NSERC anddownlooking NRL)Surface topography, LVIS (NASA GSFC)vertical extent andstructureIWC, LWC WCM-2000 SEA, Inc. (NASA LaRC)Cloud droplet size CDP DMT, Inc (NASA LaRC)distributionLiquid water path GVR (183 ghz) ProSensing, Inc.(LWP), precipitablewater vaporLocation, attitude, Digital air data Aventech ARIM200,meteorological probes Rosemount package,variables EdgeTech Vigilant[precipitation P, (NSERC)temperature T,relative humidity(RH), winds u and v]Video and imagery, Digital cameras (NSERC and NASA GSFC)forward and nadirlookingParameters Range (accuracy)Broadband SW 0.2-3.6 [micro]m (3%)radiative flux,upwelling anddownwellingBroadband LW 4.5-42 [micro]m (3%)radiative flux,upwelling anddownwellingGlobal, direct, and 0.4-2.7 [micro]m (5%)diffuse SW radiativeflux, downwellingSpectral SW radiance, 380-1700 nm, 6-12-nmdownwelling resolution (3%)Spectral SW 350-2150 nm, 6-12-nmirradiance, upwelling resolution (3%-5%)and downwellingCloud and surface 9.6-11.5 [micro]mtemperature, up- and (0.5[degrees]C)downlookingSurface topography, 1064 nm (10-cmvertical extent and vertical and l-mstructure horizontal precision)IWC, LWC Water contents 0-10 g [m.sup.-3]Cloud droplet size Sizes 2-50 [micro]mdistributionLiquid water path LWP 0-300 g [m.sup.-(LWP), precipitable 2] (20 g [m.sup.-2])water vaporLocation, attitude, Static P (0.25 hPa),meteorological dynamic P (0.5 hPa),variables static T[precipitation P, (0.2[degrees]C), RHtemperature T, (5%), u, v (l mrelative humidity [s.sup.-1])(RH), winds u and v]Video and imagery, 1080 pixelsforward and nadirlookingTable 2. ARISE mission summary: select satellite overpass times (A:Aqua, C: Cryosat-2, T: Terra, S: Suomi National Polar-OrbitingPartnership), dominant surface type, and flight description. KWAL =Wallops Flight Facility, Wallops Island, VA. KTCM = McChord AFB,Tacoma, WA. PAEI = Eielson AFB. SCT = scattered. BKN = broken.Start date (focal Satellite Surfacelocation) overpasses (UTC) type1 Sep 2014 -- Land(KWAL to KTCM)2 Sep 2014 -- Land(KTCM to PAEI)4 Sep 2014 A: 2035, 2214 Ocean, sea(72.8[degrees]-75[degrees]N, 7: 2013, 2155 ice (set)142[degrees]-I59[degrees]W) S: 2013, 21475 Sep 2014 A: 2119, 2258 Sea ice(70.5[degrees]-80[degrees]N, 7: 2136, 2317140[degrees]W) S: 2054, 22306 Sep 2014 A: 2023, 2202, 2341 Sea ice(72.5[degrees]-74.5[degrees]N, 7: 1935, 2117, 2258135[degrees]-140[degrees]W) S: 2001, 2134, 23137 Sep 2014 A: 1927, 2106, 2245 Ocean(74.I[degrees]-76.5[degrees]N, 7: 1916, 2058, 2239140[degrees]-148[degrees]W) S: 2042, 2218, 23579 Sep 2014 A: 1915,2054, 2233 Sea ice(73.5[degrees]-75.2[degrees]N, 7: 2019, 2201,2342 (bkn)138[degrees]-145[degrees]W) S: 2031, 2205, 234410 Sep 2014 A: 1958, 2137, 2316 Sea ice(75.2[degrees]-76.5[degrees]N, T: 2000,2142,2323134[degrees]-140[degrees]W) S: 1936,2112,224911-Sep-2014 A: 2042, 2221,2359 Sea ice(72.2[degrees]-74.5[degrees]N, T: 1941, 2123, 2304130[degrees]-136.5[degrees]W) S: 2019,2153,233213 Sep 2014 A: 2029, 2208, 2347 Sea ice(72.7[degrees]-74.5[degrees]N, T: 1903, 2045, 2226130[degrees]-137[degrees]W) S: 2007, 2141, 232015 Sep 2014 A: 2017, 2156, 2335 Ocean(72.5[degrees]-76.5[degrees]N, 7: 2006, 2148, 2329149[degrees]-159[degrees]W) S: 1955, 2129, 230716 Sep 2014 A: 1921, 2100, 2239 Sea ice(74.7[degrees]-77[degrees]N, T: 1947, 2129, 2310136.5[degrees]-141[degrees]W) S: 2037, 2212, 235017 Sep 2014 A: 2005, 2143, 2322 Ocean, sea(73.2[degrees]-74.8[degrees]N, T: 1928, 2110, 2251 ice (bkn)150.5[degrees]-156[degrees]W) S: 1942, 2117, 225518 Sep 2014 A: 1909, 2048, 2227 Sea ice(75.5[degrees]-77.5[degrees]N, T: 1909, 2051, 2232137[degrees]-149[degrees]W) S: 2025, 2159, 2338 C: 185219 Sep 2014 A: 1952, 2131, 2310 Sea ice,(71.8[degrees]-73.2[degrees]N, T: 2032, 2213, 2355 ocean128[degrees]-137[degrees]W) S: 1930, 2106, 224221 Sep 2014 A: 1940, 2119, 2258 Sea ice(73[degrees]-76.5[degrees]N, T: 1953, 2135, 2316125[degrees]-131[degrees]W) S: 1918, 2054, 223024 Sep 2014 A: 2011, 2150, 2329 Sea ice(73[degrees]-75[degrees]N, T: 2038, 2219128[degrees]-133.5[degrees]W) S: 1948, 2123, 23012 Oct 2014 -- Land, ocean(southwest Alaska,Bristol Bay)4 Oct 2014 -- Land(PAEI to KTCM)Start date (focal Takeoff Landlocation) (UTC) (UTC)1 Sep 2014 1415 2257(KWAL to KTCM)2 Sep 2014 1600 2235(KTCM to PAEI)4 Sep 2014 1815 0050 (a)(72.8[degrees]-75[degrees]N,142[degrees]-I59[degrees]W)5 Sep 2014 2015 0320 (a)(70.5[degrees]-80[degrees]N,140[degrees]W)6 Sep 2014 1910 0215 (a)(72.5[degrees]-74.5[degrees]N,135[degrees]-140[degrees]W)7 Sep 2014 1815 0240 (a)(74.I[degrees]-76.5[degrees]N,140[degrees]-148[degrees]W)9 Sep 2014 1820 0200 (a)(73.5[degrees]-75.2[degrees]N,138[degrees]-145[degrees]W)10 Sep 2014 1710 0155 (a)(75.2[degrees]-76.5[degrees]N,134[degrees]-140[degrees]W)11-Sep-2014 1835 0205 (a)(72.2[degrees]-74.5[degrees]N,130[degrees]-136.5[degrees]W)13 Sep 2014 1705 0125 (a)(72.7[degrees]-74.5[degrees]N,130[degrees]-137[degrees]W)15 Sep 2014 1748 0156 (a)(72.5[degrees]-76.5[degrees]N,149[degrees]-159[degrees]W)16 Sep 2014 1719 0135 (a)(74.7[degrees]-77[degrees]N,136.5[degrees]-141[degrees]W)17 Sep 2014 1815 0127 (a)(73.2[degrees]-74.8[degrees]N,150.5[degrees]-156[degrees]W)18 Sep 2014 1655 0130 (a)(75.5[degrees]-77.5[degrees]N,137[degrees]-149[degrees]W)19 Sep 2014 1653 0111 (a)(71.8[degrees]-73.2[degrees]N,128[degrees]-137[degrees]W)21 Sep 2014 1650 0100 (a)(73[degrees]-76.5[degrees]N,125[degrees]-131[degrees]W)24 Sep 2014 1952 0208 (a)(73[degrees]-75[degrees]N,128[degrees]-133.5[degrees]W)2 Oct 2014 2127 0602 (a)(southwest Alaska,Bristol Bay)4 Oct 2014 0838 1814(PAEI to KTCM)Start date (focallocation) Flight description1 Sep 2014 Transit from NASA Wallops to(KWAL to KTCM) McChord AFB. LVIS canopy measurements.2 Sep 2014 Transit from McChord AFB to Eileson(KTCM to PAEI) AFB. Southern Alaska glacier mapping4 Sep 2014 Arctic Ocean survey near ice edge,(72.8[degrees]-75[degrees]N, low-cloud profiling142[degrees]-I59[degrees]W)5 Sep 2014 I40[degrees]W sea ice survey from(70.5[degrees]-80[degrees]N, 70.5[degrees] to 80[degrees]N140[degrees]W)6 Sep 2014 MIZ survey, radiative flux(72.5[degrees]-74.5[degrees]N, profiles, ML cloud characterization135[degrees]-140[degrees]W)7 Sep 2014 CERES TOA gridbox experiment,(74.I[degrees]-76.5[degrees]N, full column profiles,140[degrees]-148[degrees]W) low-cloud characterization9 Sep 2014 CERES SFC gridbox experiment,(73.5[degrees]-75.2[degrees]N, full column profiles,138[degrees]-145[degrees]W) low-cloud characterization10 Sep 2014 MIZ survey, low-cloud(75.2[degrees]-76.5[degrees]N, characterization and radiative134[degrees]-140[degrees]W) fluxes along ice edge11-Sep-2014 CERES TOA gridbox experiment,(72.2[degrees]-74.5[degrees]N, full column profiles, low-cloud130[degrees]-136.5[degrees]W) characterization13 Sep 2014 CERES SFC gridbox experiment, full(72.7[degrees]-74.5[degrees]N, column profiles, sea ice albedo and130[degrees]-137[degrees]W) low-cloud characterization15 Sep 2014 CERES TOA gridbox experiment,(72.5[degrees]-76.5[degrees]N, full column profiles,149[degrees]-159[degrees]W) ML cloud characterization16 Sep 2014 Low-cloud radiative closure(74.7[degrees]-77[degrees]N, experiment, diffuse and136.5[degrees]-141[degrees]W) clear-sky albedo measurements17 Sep 2014 CERES SFC gridbox experiment,(73.2[degrees]-74.8[degrees]N, low-cloud characterization,150.5[degrees]-156[degrees]W) ML cloud sampling18 Sep 2014 Cryosat-2 underflight,(75.5[degrees]-77.5[degrees]N, characterization of sea ice and137[degrees]-149[degrees]W) surface albedo, MIZ repeat line, low-cloud profiling19 Sep 2014 Low-cloud radiative closure(71.8[degrees]-73.2[degrees]N, experiment, cloud and surface128[degrees]-137[degrees]W) characterization across sea ice edge21 Sep 2014 MIZ sea ice characterization,(73[degrees]-76.5[degrees]N, low ML cloud profiling125[degrees]-131[degrees]W)24 Sep 2014 MIZ sea ice characterization,(73[degrees]-75[degrees]N, low-cloud profiling128[degrees]-133.5[degrees]W)2 Oct 2014 Alaskan glacier mapping, radiometer(southwest Alaska, calibration maneuversBristol Bay)4 Oct 2014 Return transit to KWAL(PAEI to KTCM)(a) Flight completed following day.Table 3. Demonstration of monthly mean Polar WRF (<PWRF>), version3.6, simulation agreement with Alaska and Chukchi Sea monthly meanobservations (<Obs>) of near-surface temperature ([degrees]C), windspeed (m [s.sup.-1]), wind direction ([degrees]), and mean sea levelpressure (MSLP, hPa), for Sep 2014. The surface observation stationsare Prudhoe Bay (70.40[degrees]N, 148.53[degrees]W), Nome(64.50[degrees]N, 165.43[degrees]W), Klondike buoy (70.87[degrees]N,168.25[degrees]W), Red Dog Dock (67.58[degrees]N, 164.07[degrees]W),Burger buoy (71.50[degrees]N, 164.13[degrees]W), and Barrow(71.29[degrees]N, 156.79[degrees]W).Station variable Correlation rmse BiasPrudhoe Bay buoy temperature 0.7726 1.307 -0.213Nome temperature 0.9256 2.058 -1.485Klondike buoy temperature 0.7938 0.771 0.112Red Dog Dock temperature 0.8694 1.923 0.054Burger buoy temperature 0.7391 1.068 0.790Barrow 2-m temperature 0.8650 1.415 -1.055Barrow 10-m temperature 0.8295 1.122 -0.445Prudhoe Bay buoy wind speed 0.8904 3.054 -1.982Nome wind speed 0.7359 2.062 -0.648Klondike buoy wind speed 0.9044 1.352 0.276Red Dog Dock wind speed 0.7989 2.149 0.197Burger buoy wind speed 0.8773 1.807 1.056Barrow 10-m wind speed 0.8372 1.984 -1.201Prudhoe Bay buoy wind direction 0.7834 37.34 -1.13Nome wind direction 0.5065 66.69 -20.35Klondike buoy wind direction 0.8377 34.72 17.45Red Dog Dock wind direction 0.6904 55.48 0.08Burger buoy wind direction 0.8059 39.77 8.60Barrow 10-m wind direction 0.9072 25.24 -10.52Prudhoe Bay buoy MSLP 0.9983 0.56 -0.01Nome MSLP 0.9978 0.66 -0.33Klondike buoy MSLP 0.9927 1.49 1.11Red Dog Dock MSLP 0.9965 0.76 0.29Burger buoy MSLP 0.9976 1.11 -0.92Barrow 2-m RH 0.6732 10.87 -7.29Station variable <Obs> <PWRF>Prudhoe Bay buoy temperature 1.528 1.315Nome temperature 8.330 6.845Klondike buoy temperature 2.738 2.850Red Dog Dock temperature 6.668 6.722Burger buoy temperature 1.299 2.089Barrow 2-m temperature 2.019 0.965Barrow 10-m temperature 1.385 0.940Prudhoe Bay buoy wind speed 9.100 7.118Nome wind speed 4.434 3.787Klondike buoy wind speed 8.040 8.317Red Dog Dock wind speed 5.349 5.546Burger buoy wind speed 7.018 8.073Barrow 10-m wind speed 6.695 5.494Prudhoe Bay buoy wind direction 150.04 148.91Nome wind direction 170.97 150.62Klondike buoy wind direction 142.17 159.62Red Dog Dock wind direction 175.46 175.54Burger buoy wind direction 149.16 157.76Barrow 10-m wind direction 167.67 157.15Prudhoe Bay buoy MSLP 1,010.13 1,010.12Nome MSLP 1,009.45 1,009.13Klondike buoy MSLP 1,010.92 1,009.81Red Dog Dock MSLP 1,008.91 1,009.19Burger buoy MSLP 1,010.98 1,010.05Barrow 2-m RH 90.65 83.36Table 4. 4STAR data products during ARISEProduct name Description Data levelAerosol optical depth Total column AOD above the 2 aircraft at 14 discrete wavelengths (a)Column water vapor Total column water vapor above 2 the aircraftOzone Total column ozone above the 2 aircraftZenith cloud radiances Zenith cloud radiances at 24 1 discrete wavelengthsSky radiances Sky radiances at four 1 wavelengths (440, 673, 873, and 1020 nm) for selected casesCloud properties Cloud phase, cloud optical 2b depth, and effective radiusCirrus properties Thin cirrus (0.01-4) optical 2b depthsProduct name Accuracy 4STAR modeAerosol optical depth [+ or -]0.02 Direct sunColumn water vapor [+ or -]0.05 Direct sunOzone 1% Direct sunZenith cloud radiances 3%-5% ZenithSky radiances 3%-5% Sky scanningCloud properties -- ZenithCirrus properties [+ or -]0.05 Direct sun1 These 14 wavelengths were chosen for window regions from thehyperspectral AOD measured.b These products are still under development and are being processed forselected cases.