Abstract. The NASA airborne Arctic Radiation-Cloud-aerosol-Surface-Interaction Experiment (ARCSIX) collected a unique data set providing a near-simultaneous characterization of radiative fluxes, surface, cloud, and aerosol particle properties to address science questions on the surface radiation budget, the processes governing the cloud lifecycle, atmospheric composition, and the interactions between the surface and atmosphere. The overarching goal of ARCSIX was to quantify the contributions of surface, clouds, aerosol particles, and precipitation to summer sea ice melt. ARCSIX consisted of two deployments in 2024 (Spring: 2024-05-28 through 2024-06-13 and Summer: 2024-07-25 through 2024-08-15) to capture pre- and post-melt conditions. ARCSIX provided coordinated remote sensing and in situ sampling using three aircraft in a high-flyer/low-flyer configuration. The NASA G-III served as the high-flying remote sensing platform with two lower flying in situ and near-target remote sensor observing platforms, NASA P-3B and SPEC Inc. Learjet. ARCSIX data are well-suited to improve satellite remote sensing capabilities in the Arctic. ARCSIX included an array of sea ice mass balance buoys deployed in the Lincoln Sea that were regularly overflown during the campaign. ARCSIX research flights spanned the Baffin Bay, Lincoln Sea, west and north of the Canadian Archipelago, and the Greenland north and northeast coasts. During the spring deployment, 19 research flights took place covering 114 flight hours: 10 flights and 68 hours by the P-3B and nine flights and 46 hours by the G-III. During summer, 24 research flights covered 136 flight hours: nine flights and 75 hours by the P-3B, five flights and 26 hours by the G-III, and 10 flights and 35 hours by the Learjet. A total of 13 coordinated flights with 2+ aircraft were carried out. This paper describes the ARCSIX flight strategy, instrumentation, and data set access, and usage details. ARCSIX data are publicly available at https://doi.org/10.5067/SUBORBITAL/ARCSIX/DATA001.

CryoSat-2 was launched in 2010, ICESat-2 in 2018, CRISTAL is scheduled for launch in 2027. These missions provide high precision altimetry measurements from space with an emphasis on measuring the heights of sea ice and the ice sheets of Greenland and Antarctica. While CryoSat-2 and CRISTAL are radar altimeters, ICESat-2 is a laser altimeter. We now have 5+ years of overlapping CryoSat-2 and ICESat-2 data and with an expected lifetime of ICESat-2 into the 2030s we likely will have coincident ICESat-2 and CRISTAL data as well. ICESat-2, a photon-counting lidar, provided a new concept for laser altimetry from space. Thus no precursor spaceborne data were available. In order to better understand expected data and to enable pre-launch algorithm development airborne simulators were developed and flown over the wide range of targets. These data were critical for having tested algorithms in place at the time of launch and distribute operational geophysical products (e.g., land ice elevation, sea ice freeboard, tree heights, inland water products etc.) to the community shortly after launch. To ensure proper post-launch calibration we picked Greenland Summit Station as the orbit anchor point as that station conduct routine GPS surveys underneath the ICESat-2 path. Similarly, for the southern hemisphere we have been conducting ground GPS survey along parts of the 88o S line in Antarctica. This is the ICESat-2 orbit inclination and thus the area where all ICESat-2 tracks converge. For post-launch cal/val airborne campaigns, first as part as of Operation IceBridge and later as dedicated flight over specific targets, we coordinated our flights with CryoSat-2 tracks as well and especially aligned flight lines with “Cryo2Ice” lines. Cryo2ice is a collaborative effort between NASA and ESA. ESA is routinely changing CryoSat-2’s orbit to align with ICESat-2’s. This is enabling scientists to fully explore the synergistic and complementary nature of those two missions. For the ESA CRISTAL mission, NASA will be providing a passive microwave radiometer as an additional instrument. While the primary purpose is to correct for potential path delays of the radar signal in the troposphere, the 19 and 34 GHz frequencies of that radiometer have been extensively used for cryospheric science. Thus there is a potential for ground breaking science using coincident radar and laser altimeter data together with the brightness temperatures at those frequencies. Joint airborne campaigns to enable pre-launch exploration are currently in the planning stage.

Satellite observations are essential for monitoring changes in vast inaccessible polar regions. CRISTAL is the next polar altimetry mission being developed by the European Space Agency (ESA) and planned for launch in 2027. The payload includes an Interferometric Radar altimeter for Ice and Snow (IRIS, provided by ESA) operating at Ku- and Ka- bands and a potential NASA contribution of an Advanced Microwave Radiometer (AMR-CR) developed at NASA-JPL. The mission will include alongside AMR-CR, a High-Resolution Microwave Radiometer (HRMR). Together they provide radiometric measurement channels ranging from 18 to 166 GHz for open ocean topography and ice/snow applications. Changes in the polar regions have been widely documented by satellite microwave radiometer data. Frequencies up to 40 GHz are routinely used to derive sea ice extent, concentration, type, and thickness, as well as snow depth on sea ice. While higher frequency channels (mm-wave) provide improved spatial resolution, their use has been limited due to their sensitivity to atmospheric conditions and decreased penetration depths. For the first time, the CRISTAL mission will provide active and passive measurements of the polar regions over a wide range of frequencies. This new sensing capability will open up new avenues to explore novel cryosphere science that exploits the unique sensitivities of microwave and mm-wave observations to changes in atmospheric conditions and surface properties across a wide range of frequencies.

Precipitation is a major component of the hydrologic cycle and plays a significant role in the sea ice mass balance in the polar regions. Over the Southern Ocean, precipitation is particularly uncertain due to the lack of direct observations in this remote and harsh environment. Here we demonstrate that precipitation estimates from eight global reanalyses produce similar spatial patterns between 2000 and 2010, although their annual means vary by about 250 mm yr −1 (or 26% of the median values) and there is little similarity in their representation of interannual variability. ERA-Interim produces the smallest and CFSR produces the largest amount of precipitation overall. Rainfall and snowfall are partitioned in five reanalyses; snowfall suffers from the same issues as the total precipitation comparison, with ERA-Interim producing about 128 mm less snowfall and JRA-55 about 103 mm more rainfall compared to the other reanalyses. When compared to CloudSat -derived snowfall, these five reanalyses indicate similar spatial patterns, but differ in their magnitude. All reanalyses indicate precipitation on nearly every day of the year, with spurious values occurring on an average of about 60 days yr −1 , resulting in an accumulation of about 4.5 mm yr −1 . While similarities in spatial patterns among the reanalyses suggest a convergence, the large spread in magnitudes points to issues with the background models in adequately reproducing precipitation rates, and the differences in the model physics employed. Further improvements to model physics are required to achieve confidence in precipitation rate, as well as the phase and frequency of precipitation in these products.

Quantifying changes in Earth's ice sheets and identifying the climate drivers are central to improving sea level projections. We provide unified estimates of grounded and floating ice mass change from 2003 to 2019 using NASA's Ice, Cloud and land Elevation Satellite (ICESat) and ICESat-2 satellite laser altimetry. Our data reveal patterns likely linked to competing climate processes: Ice loss from coastal Greenland (increased surface melt), Antarctic ice shelves (increased ocean melting), and Greenland and Antarctic outlet glaciers (dynamic response to ocean melting) was partially compensated by mass gains over ice sheet interiors (increased snow accumulation). Losses outpaced gains, with grounded-ice loss from Greenland (200 billion tonnes per year) and Antarctica (118 billion tonnes per year) contributing 14 millimeters to sea level. Mass lost from West Antarctica's ice shelves accounted for more than 30% of that region's total.

National Aeronautics and Space Administration's (NASA's) Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) mission was launched in September 2018 with the primary goal of monitoring our rapidly changing polar regions. The sole instrument onboard, the Advanced Topographic Laser Altimeter System, is now providing routine, very high-resolution, surface elevation data across the globe, including the Arctic and Southern oceans. In this study, we demonstrate our new processing chain for converting the along-track ICESat-2 sea ice freeboard product (ATL10) into sea ice thickness, focusing our initial efforts on the Arctic Ocean. For this conversion, we primarily make use of snow depth and density data from the NASA Eulerian Snow on Sea Ice Model. The coarse resolution (similar to 100 km) snow data are redistributed onto the high-resolution (approximately 30-100 m) ATL10 freeboards using relationships obtained from snow depth and freeboard data collected by NASA's Operation IceBridge mission. We present regional sea ice thickness distributions and highlight their seasonal evolution through our first winter season of data collection. We include ice thickness uncertainty estimates, while also acknowledging the limitations of these estimates. We generate a gridded monthly thickness product and compare this with various monthly sea ice thickness estimates obtained from European Space Agency's CryoSat-2 satellite mission, with ICESat-2 showing consistently lower thicknesses. Finally, we compare our February/March 2019 thickness estimates to ICESat February/March (19 February to 21 March) 2008 ice thickness estimates using the same input assumptions, which show an similar to 0.37 m or similar to 20% thinning across an inner Arctic Ocean domain in this 11-year time period. Plain Language Summary NASA's ICESat-2 mission was launched in September 2018 with the primary goal of monitoring our rapidly changing polar regions. The sole instrument onboard is a highly precise laser, which is now providing routine, very high-resolution, surface height measurements across the globe, including over the Arctic and Southern oceans. In this study, we show new estimates of Arctic sea ice thickness from the first winter season of data collected by ICESat-2. Sea ice thickness is calculated by combining the measured ICESat-2 freeboards-the extension of sea ice above sea level-with a new snow on sea ice model. Our derived thicknesses are consistently lower than the thicknesses calculated from ESA's CryoSat-2 data and the original ICESat mission, which ended in 2008. More work is needed to verify these new thickness estimates.

Surface height and total freeboard from the Ice, Cloud, and Land Elevation Satellite‐2 (ICESat‐2, IS‐2) sea ice data products (ATL07/ATL10) are assessed with near‐coincident retrievals from the Airborne Topographic Mapper (ATM) lidar in four dedicated underflights during the 2019 Operation IceBridge Arctic deployment. Over a mix of seasonal and older ice, we find remarkable correlations between the ATM and IS‐2 height profiles and roughness (in ninety‐nine 10‐km segments) that averages to >0.95 and > 0.97, respectively. Regression slopes near unity, between 0.93 and 0.99, indicate close agreement of the height estimates. Larger differences between the surface heights are seen in rougher areas where it is more difficult for the photon heights (used in IS‐2 surface finding) to capture the surface distributions at short length scales. Total freeboard in 10‐km segments, calculated using three different approaches, show variability of 0.02 to 0.04 m. Sources of residual variance, attributable to differences between the two instruments, are discussed.

Methods to radiometrically calibrate a non-imaging airborne visible-to-shortwave infrared (VSWIR) spectrometer to measure the Greenland ice sheet surface are presented. Airborne VSWIR measurement performance for bright Greenland ice and dark bare rock/soil targets is compared against the MODerate resolution atmospheric TRANsmission (MODTRAN®) radiative transfer code (version 6.0), and a coincident Landsat 8 Operational Land Imager (OLI) acquisition on 29 July 2015 during an in-flight radiometric calibration experiment. Airborne remote sensing flights were carried out in northwestern Greenland in preparation for the Ice, Cloud, and land Elevation Satellite 2 (ICESat-2) laser altimeter mission. A total of nine science flights were conducted over the Greenland ice sheet, sea ice, and open-ocean water. The campaign's primary purpose was to correlate green laser pulse penetration into snow and ice with spectroscopic-derived surface properties. An experimental airborne instrument configuration that included a nadir-viewing (looking downward at the surface) non-imaging Analytical Spectral Devices (ASD) Inc. spectrometer that measured upwelling VSWIR (0.35 to 2.5 µm) spectral radiance (Wm-2sr-1µm-1) in the two-color Slope Imaging Multi-polarization Photon-Counting Lidar's (SIMPL) ground instantaneous field of view, and a zenith-viewing (looking upward at the sky) ASD spectrometer that measured VSWIR spectral irradiance (W m−2 nm−1) was flown. National Institute of Standards and Technology (NIST) traceable radiometric calibration procedures for laboratory, in-flight, and field environments are described in detail to achieve a targeted VSWIR measurement requirement of within 5 % to support calibration/validation efforts and remote sensing algorithm development. Our MODTRAN predictions for the 29 July flight line over dark and bright targets indicate that the airborne nadir-viewing spectrometer spectral radiance measurement uncertainty was between 0.6 % and 4.7 % for VSWIR wavelengths (0.4 to 2.0 µm) with atmospheric transmittance greater than 80 %. MODTRAN predictions for Landsat 8 OLI relative spectral response functions suggest that OLI is measuring 6 % to 16 % more top-of-atmosphere (TOA) spectral radiance from the Greenland ice sheet surface than was predicted using apparent reflectance spectra from the nadir-viewing spectrometer. While more investigation is required to convert airborne VSWIR spectral radiance into atmospherically corrected airborne surface reflectance, it is expected that airborne science flight data products will contribute to spectroscopic determination of Greenland ice sheet surface optical properties to improve understanding of their potential influence on ICESat-2 measurements.

A first look at data from NASA’s laser altimeter mission ICESat-2 reveals very high resolution 3-D profiles of ice on land and sea, forests, and shallow bodies of water.

The Ice, Cloud, and land Elevation Satellite - 2 (ICESat-2) observatory was launched on 15 September 2018 to measure ice sheet and glacier elevation change, sea ice freeboard, and enable the determination of the heights of Earth's forests. ICESat-2's laser altimeter, the Advanced Topographic Laser Altimeter System (ATLAS) uses green (532 nm) laser light and single-photon sensitive detection to measure time of flight and subsequently surface height along each of its six beams. In this paper, we describe the major components of ATLAS, including the transmitter, the receiver and the components of the timing system. We present the major components of the ICESat-2 observatory, including the Global Positioning System, star trackers and inertial measurement unit. The ICESat-2 Level 1B data product (ATL02) provides the precise photon round-trip time of flight, among other data. The ICESat-2 Level 2A data product (ATL03) combines the photon times of flight with the observatory position and attitude to determine the geodetic location (i.e. the latitude, longitude and height) of the ground bounce point of photons detected by ATLAS. The ATL03 data product is used by higher-level (Level 3A) surface-specific data products to determine glacier and ice sheet height, sea ice freeboard, vegetation canopy height, ocean surface topography, and inland water body height.

NASA's Multiple Altimeter Beam Experimental LiDAR (MABEL) is an aircraft-based photon-counting laser altimeter designed as a simulator to test measurement techniques and algorithms for Advanced Topographic Laser Altimeter System (ATLAS), the sole instrument on NASA's Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) mission. By measuring the time of flight, pointing angle, and absolute position for individual photons, ICESat-2 provides detailed elevation measurements of earth's surface. Calculating accurate and precise elevations requires an understanding of how photons interact with surfaces, and characterization of the photon distribution after returning from surfaces. Neither MABEL nor ATLAS records the transmitted laser pulse shape, relying instead on aggregating several pulses worth of photons, often using histograms, to characterize the pulse shape. In this paper, we assess the limitations of using histograms and propose a more robust method to describe MABEL's system impulse-response function using an exponentially modified Gaussian distribution. We also provide standard error estimates for the arithmetic mean and standard deviation calculations, and for exponentially modified Gaussian parameters using a Monte Carlo sensitivity analysis. We apply this method to photon returns from a sea ice lead and from a dry salt lake bed as case studies for estimating the standard error associated with sample size for the arithmetic mean and standard deviation, and for the exponentially modified Gaussian parameters. We use these standard errors to calculate the minimum number of photons required to find both Gaussian and exponentially modified Gaussian distribution parameters within 3 cm of their parent population values.

We present the first winter season of surface height and sea ice freeboards of the Arctic Ocean from the new Ice, Cloud, and Land Elevation Satellite (ICESat-2; IS-2) mission. The Advanced Topographic Laser Altimeter System onboard has six photon-counting beams for surface profiling with a 10-kHz pulse rate (interpulse distance similar to 0.7 m) and footprints of similar to 17 m. Geolocated heights assigned to individual photons scattered from the surface allow significant flexibility in the construction of height distributions used in surface finding. For IS-2 sea ice products, a fixed 150-photon aggregate is used to control height precision and obtain better along-track resolution over high reflectance surfaces. Quasi-specular returns in openings as narrow as similar to 27 m, crucial for freeboard calculations, are resolved. The fixed photon aggregate results in unique variable along-track resolutions and nonuniform sampling (17 m x 27 m to 17 m x 200 m for the strong beams) of the surface. The six profiling beams-three pairs separated by 3.3 km with a strong and weak beam in each pair-provide correlated statistics at regional length scales for assessment of beam-to-beam retrieval consistency and accuracy. Analysis shows along-track height precisions of similar to 2 cm and agreement in the monthly freeboard distributions across the strong beams to 1-2 cm. In this paper, we describe briefly the approaches used in surface height and freeboard retrievals from Advanced Topographic Laser Altimeter System photon clouds and detail the key features of these along-track sea ice products, focusing on the first release of data collected over the Arctic Ocean, which spans the period between 14 October 2018-the start of data collection-and the end of March 2019.

The NASA Eulerian Snow On Sea Ice Model (NESOSIM) is a new, open-source snow budget model that is currently configured to produce daily estimates of the depth and density of snow on sea ice across the Arctic Ocean through the accumulation season. NESOSIM has been developed in a three-dimensional Eulerian framework and includes two (vertical) snow layers and several simple parameterizations (accumulation, wind packing, advection–divergence, blowing snow lost to leads) to represent key sources and sinks of snow on sea ice. The model is forced with daily inputs of snowfall and near-surface winds (from reanalyses), sea ice concentration (from satellite passive microwave data) and sea ice drift (from satellite feature tracking) during the accumulation season (August through April). In this study, we present the NESOSIM formulation, calibration efforts, sensitivity studies and validation efforts across an Arctic Ocean domain (100 km horizontal resolution). The simulated snow depth and density are calibrated with in situ data collected on drifting ice stations during the 1980s. NESOSIM shows strong agreement with the in situ seasonal cycles of snow depth and density, and shows good (moderate) agreement with the regional snow depth (density) distributions. NESOSIM is run for a contemporary period (2000 to 2015), with the results showing strong sensitivity to the reanalysisderived snowfall forcing data, with the Modern-Era Retrospective analysis for Research and Applications (MERRA) and the Japanese Meteorological Agency 55-year reanalysis (JRA-55) forced snow depths generally higher than ERAInterim, and the Arctic System Reanalysis (ASR) generally lower. We also generate and force NESOSIM with a consensus “median” daily snowfall dataset from these reanalyses. The results are compared against snow depth estimates derived from NASA’s Operation IceBridge (OIB) snow radar data from 2009 to 2015, showing moderate–strong correlations and root mean squared errors of ∼ 10 cm depending on the OIB snow depth product analyzed, similar to the comparisons between OIB snow depths and the commonly used modified Warren snow depth climatology. Potential improvements to this initial NESOSIM formulation are discussed in the hopes of improving the accuracy and reliability of these simulated snow depths and densities.

Precipitation over the Arctic Ocean has a significant impact on the basin-scale freshwater and energy budgets but is one of the most poorly constrained variables in atmospheric reanalyses. Precipitation controls the snow cover on sea ice, which impedes the exchange of energy between the ocean and atmosphere, inhibiting sea ice growth. Thus, accurate precipitation amounts are needed to inform sea ice modeling, especially for the production of thickness estimates from satellite altimetry freeboard data. However, obtaining a quantitative estimate of the precipitation distribution in the Arctic is notoriously difficult because of a number of factors, including a lack of reliable, long-term in situ observations; difficulties in remote sensing over sea ice; and model biases in temperature and moisture fields and associated uncertainty of modeled cloud microphysical processes in the polar regions. Here, we compare precipitation estimates over the Arctic Ocean from eight widely used atmospheric reanalyses over the period 2000–16 (nominally the “new Arctic”). We find that the magnitude, frequency, and phase of precipitation vary drastically, although interannual variability is similar. Reanalysis-derived precipitation does not increase with time as expected; however, an increasing trend of higher fractions of liquid precipitation (rainfall) is found. When compared with drifting ice mass balance buoys, three reanalyses (ERA-Interim, MERRA, and NCEP R2) produce realistic magnitudes and temporal agreement with observed precipitation events, while two products [MERRA, version 2 (MERRA-2), and CFSR] show large, implausible magnitudes in precipitation events. All the reanalyses tend to produce overly frequent Arctic precipitation. Future work needs to be undertaken to determine the specific factors in reanalyses that contribute to these discrepancies in the new Arctic.

ABSTRACTSea-ice thickness in the Sea of Okhotsk is estimated for 2004–2008 from ICESat derived freeboard under the assumption of hydrostatic balance. Total ice thickness including snow depth (htot) averaged over 2004–2008 is 95 cm. The interannual variability of htot is large; from 77.5 cm (2008) to 110.4 cm (2005). The mode of htot varies from 50–60 cm (2007 and 2008) to 70–80 cm (2005). Ice thickness derived from ICESat data is validated from a comparison with that observed by Electromagnetic Induction Instrument (EM) aboard the icebreaker Soya near Hokkaido, Japan. Annual maps of htot reveal that the spatial distribution of htot is similar every year. Ice volume of 6.3 × 1011 m3 is estimated from the ICESat derived htot and AMSR-E derived ice concentration. A comparison with ice area demonstrates that the ice volume cannot always be represented by the area solely, despite the fact that the area has been used as a proxy of the volume in the Sea of Okhotsk. The ice volume roughly corresponds to that of annual ice production in the major coastal polynyas estimated based on heat budget calculations. This also supports the validity of the estimation of sea-ice thickness and volume using ICESat data.

The potential of deriving snow depth estimates using differences in freeboard heights from CryoSat-2 (CS-2) and ICESat-2 (IS-2) is examined. In our analysis, we use lidar freeboard from the Airborne Topographic Mapper (ATM) on Operation IceBridge (OIB) as proxy of IS-2 total (snow+ice) freeboard. Snow depths are estimates from the OIB snow radar. Differences in height between the total (ATM) and ice (CS-2) freeboards are related to snow depth by the refractive index of the snow layer (ηs), which is dependent on snow density. For two years (2014 and 2015), regression of the ATM and CS-2 freeboard differences against OIB snow depth gives correlations of ∼0.80, estimated ηs of ∼1.21, and standard errors of ∼8cm. The resulting refractive index, ηs, can be compared to that expected of the Arctic snow cover in early spring (1.25±0.05). The expected biases and variability in the regression analysis are discussed. Results suggest that snow depth can be estimated from the freeboard differences. The benefits of adjusting the orbit of CS-2 for providing more optimized overlaps between IS-2 and CS-2 are considered.