New satellites are needed to quantify the upwards of 170 trillion microplastic particles floating on the ocean surface that concentrate in the convergence zones of the ocean gyres. Past research on cleaned microplastic pieces harvested from the ocean gyres indicates a highly consistent endmember across the spectrum with distinct absorption features at 931, 1215, 1417, 1732 and 2313 nm. Here, surface-floating microplastic pieces with natural biofilm were collected at 21 stations across 4700 km of the North Pacific gyre convergence zone, also referred to as the "Great Pacific Garbage Patch," to assess the impact of biofilm on spectral properties. Reflectance of damp, biofilmed microplastic pieces were measured in bulk from 350 to 2500 nm shortly after collection with ambient sunlight on the ship deck and the biofilm was separated and stored for DNA sequencing. Biofilm was found on all collected samples and the red algal genus Tsunamia alone accounted for similar to 53% of the eukaryotic microbiome of the harvested biofilm. Biofilm absorbed light broadly across blue wavelengths (400-500 nm) and in a narrowband at 674 nm consistent with chlorophyll-a and other photosynthetic pigments in red algae. Hence, biofilm could impact ocean color chlorophyll-a and fluorescence line height retrievals if particle concentrations were high enough. No significant differences were found in band-depths estimated at 1215 and 1732 nm and only minor differences at 931 nm between biofilmed and biofilm-removed samples. This new endmember of damp, biofilmed microplastic most closely resembles the spectral properties of microplastic pieces as they naturally occur in the ocean gyres. New sensors for marine debris detection may consider narrow bands between 670 and 680 nm to discriminate red algal biofilmed microplastic from phytoplankton, in addition to the NIR and SWIR bands characteristic of floating marine microplastics.

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.

A multiple-stream radiative transfer scheme for sea ice suitable for GCM applications is introduced. The algorithm explicitly considers the refraction at the air-ice and air-water interfaces and the multiple scattering by inclusions entrapped in the ice, such as brine pockets and air bubbles. The integrated brine and air volumes are derived from the ice physical properties (salinity, density and temperature) based on phase equilibrium relationships. Thus, the AOPs are linked to the sea ice IOPs through the ice physical properties, which are used as the input variables for the radiative transfer computations. This physically based approach provides a sophisticated and complete treatment for the radiation transport in sea ice, and facilitates its inclusion in climate models. The new radiative transfer scheme is implemented into the GISS climate model to calculate the sea ice albedo, solar radiation transmission and internal ice absorption. These radiative variables are fed back to the sea ice thermodynamic module to simulate the ice properties, so that radiation, ice properties and thermodynamics are interactively coupled. Model experiments show that the new sea ice radiation physics significantly influence the solar radiation distribution in the atmosphere-sea ice-ocean system, especially the shortwave attenuation in the ice and the transmission into the ocean beneath. The melting at the ice top is highly correlated with the net shortwave radiation at the surface, whereas the basal melting is highly correlated with the shortwave transmission. The modeled albedo is generally consistent with surface- and satellite-based observations.

A multiple-stream radiative transfer scheme for sea ice suitable for GCM applications is introduced. The algorithm explicitly considers the refraction at the air-ice and air-water interfaces and the multiple scattering by inclusions entrapped in the ice, such as brine pockets and air bubbles. The integrated brine and air volumes are derived from the ice physical properties (salinity, density and temperature) based on phase equilibrium relationships. Thus, the sea ice optical properties are represented as a function of its physical properties, which are used as the input variables for the radiative transfer computations. This physically based approach provides a sophisticated and complete treatment for the radiation transport in sea ice, and facilitates its inclusion in climate models. The new radiative transfer scheme is implemented into the GISS climate model to calculate the sea ice albedo, solar radiation transmission and internal ice absorption. These radiative variables are fed back to the sea ice thermodynamic module to simulate the ice properties, so that radiation, ice properties and thermodynamics are interactively coupled. Model experiments show that the new sea ice radiation physics significantly influence the solar radiation distribution in the atmosphere-sea ice-ocean system, especially the shortwave attenuation in the ice and the transmission into the ocean beneath. The melting at the ice top is highly correlated with the net shortwave radiation at the surface, whereas the basal melting is highly correlated with the shortwave transmission. The modeled albedo is generally consistent with surface- and satellite-based observations.

A set of dedicated radiative kernels are generated to quantify the individual contributions of sea ice and clouds on the interannual variation of absorbed solar radiation (ASR) observed over the Arctic ocean. From spring to early summer, changes in ASR are associated more with sea ice than with the clouds. Conversely, the cloud contribution explains more of the ASR anomaly after June. Overall, variations in cloud cover and sea ice extent can explain 50%-93% of the ASR variability in each sunlit month. Both the sea-ice- and cloud-associated ASR variations are positively correlated with the observed ASR anomaly, but their trends over the 20-year observational period are opposite. The positive ASR trend revealed by the kernel decomposition, primarily driven by the observed decline in sea ice extent, is dampened by clouds, particularly in June. Annually, clouds counteract 55% of the ASR trend induced by Arctic sea ice loss.

Global retrievals of ocean parameters greatly benefit from spaceborne missions equipped with passive multispectral, multiangle and polarimetric capabilities. Here we present a compendium of advanced radiative transfer simulations of such observations at the top of the atmosphere, where the total reflectance and its polarization counterpart are partitioned among the scattering contributions from the atmosphere, a rough ocean surface, and the ocean body. The focus is on the spectral contributions of different water types in an extensive wavelength range, which impact the retrievability of their descriptive parameters. Beside accurately quantifying such contributions for observations in heritage ocean-color bands, the results highlight the detectability of the ocean signal in the total reflectance measured in the ultraviolet range of the spectrum. Also, polarization signatures emerge for highly-complex waters in the green region and at larger wavelengths. This textbook exercise in radiative transfer provides the basis for a correct interpretation of spaceborne measurements, and can be exploited in studies related to instrument design and spectral information content.

Ocean surface radiation measurement best practices have been developed as a first step to support the interoperability of radiation measurements across multiple ocean platforms and between land and ocean networks. This document describes the consensus by a working group of radiation measurement experts from land, ocean, and aircraft communities. The scope was limited to broadband shortwave (solar) and longwave (terrestrial infrared) surface irradiance measurements for quantification of the surface radiation budget. Best practices for spectral measurements for biological purposes like photosynthetically active radiation and ocean color are only mentioned briefly to motivate future interactions between the physical surface flux and biological radiation measurement communities. Topics discussed in these best practices include instrument selection, handling of sensors and installation, data quality monitoring, data processing, and calibration. It is recognized that platform and resource limitations may prohibit incorporating all best practices into all measurements and that spatial coverage is also an important motivator for expanding current networks. Thus, one of the key recommendations is to perform interoperability experiments that can help quantify the uncertainty of different practices and lay the groundwork for a multi-tiered global network with a mix of high-accuracy reference stations and lower-cost platforms and practices that can fill in spatial gaps.

This study presents a detailed theoretical assessment of the information content of passive polarimetric observations over snow scenes, using a global sensitivity analysis (GSA) method. Conventional sensitivity studies focus on varying a single parameter while keeping all other parameters fixed. In contrast, the GSA correctly addresses the covariance of state parameters across their entire parameter space, hence favoring a more correct interpretation of inversion algorithms and the optimal design of their state vectors. The forward simulations exploit a vector radiative transfer model to obtain the Stokes vector emerging at the top of the atmosphere for different solar zenith angles, when the bottom boundary consists of a vertically resolved snowpack of non-spherical grains. The presence of light-absorbing particulates (LAPs), either embedded in the snow or aloft in the atmosphere above in the form of aerosols, is also considered. The results are presented for a set of wavelengths spanning the visible (VIS), near-infrared (NIR), and shortwave infrared (SWIR) region of the spectrum. The GSA correctly captures the expected, high sensitivity of the reflectance to LAPs in the VIS–NIR and to grain size at different depths in the snowpack in the NIR–SWIR. With adequate viewing geometries, mono-angle measurements of total reflectance in the VIS–SWIR (akin to those of the Moderate Resolution Imaging Spectroradiometer, MODIS) resolve grain size in the top layer of the snowpack sufficiently well. The addition of multi-angle polarimetric observations in the VIS–NIR provides information on grain shape and microscale roughness. The simultaneous sensitivity in the VIS–NIR to both aerosols and snow-embedded impurities can be disentangled by extending the spectral range to the SWIR, which contains information on aerosol optical depth while remaining essentially unaffected when the same particulates are mixed with the snow. Multi-angle polarimetric observations can therefore (i) effectively partition LAPs between the atmosphere and the surface, which represents a notorious challenge for snow remote sensing based on measurements of total reflectance only and (ii) lead to better estimates of grain shape and roughness and, in turn, the asymmetry parameter, which is critical for the determination of albedo. The retrieval uncertainties are minimized when the degree of linear polarization is used in place of the polarized reflectance. The Sobol indices, which are the main metric for the GSA, were used to select the state parameters in retrievals performed on data simulated for multiple instrument configurations. Improvements in retrieval quality with the addition of measurements of polarization, multi-angle views, and different spectral channels reflect the information content, identified by the Sobol indices, relative to each configuration. The results encourage the development of new remote sensing algorithms that fully leverage multi-angle and polarimetric capabilities of modern remote sensors. They can also aid flight planning activities, since the optimal exploitation of the information content of multi-angle measurements depends on the viewing geometry. The better characterization of surface and atmospheric parameters in snow-covered regions advances research opportunities for scientists of the cryosphere and ultimately benefits albedo estimates in climate models.

Climate warming has a stronger impact on Arctic climate and sea ice cover (SIC) decline than previously thought. Better understanding and characterization of the relationship between sea ice and clouds and the implications for surface radiation is key to improving our confidence in Arctic climate projections. Here we analyze the relationship between sea ice, cloud phase and surface radiation over the Arctic, defined as north of 60° N, using active- and passive-sensor satellite observations from three different datasets. We find that all datasets agree on the climatology of and seasonal variability in total and liquid-bearing (liquid and mixed-phase) cloud covers. Similarly, our results show a robust relationship between decreased SIC and increased liquid-bearing clouds in the lowest levels (below 3 km) for all seasons (strongest in winter) but summer, while increased SIC and ice clouds are positively correlated in two of the three datasets. A refined map correlation analysis indicates that the relationship between SIC and liquid-bearing clouds can change sign over the Bering, Barents and Laptev seas, likely because of intrusions of warm air from low latitudes during winter and spring. Finally, the increase in liquid clouds resulting from decreasing SIC is associated with enhanced radiative cooling at the surface. Our findings indicate that the newly formed liquid clouds reflect more shortwave (SW) radiation back to space compared to the surface, generating a cooling effect of the surface, while their downward longwave (LW) radiation is similar to the upward LW surface emission, which has a negligible radiative impact on the surface. This overall cooling effect should contribute to dampening future Arctic surface warming as SIC continues to decline.

We examine the changes in clouds and cloud feedback between Phase 5 (AMIP5) and Phase 6 (AMIP6) of the Atmospheric Model Intercomparison Project. Each model is perturbed by uniformly increasing the sea surface temperature by 4 K. The simulated cloud fraction, the perturbed states and cloud radiative kernels are used to derive cloud feedback in the shortwave (SW), longwave (LW) and their sum (Net). Compared to AMIP5, the cloud fraction in AMIP6 increases by 9.1%, while the perturbation leads to a 0.25% decrease. The Net cloud feedback at the top of the atmosphere (TOA) is almost double (174%). Statistical tests support that this change is mainly due to an increase in the surface SW cloud feedback caused by optically thick, middle and low clouds. The contribution of the atmospheric Net component (12%) stems from the increase in the atmospheric LW cloud feedback, likely to play a role in weakening (strengthening) the northward (southward) meridional atmospheric energy transport, while the opposite is true for the surface LW and Net cloud feedback in the meridional oceanic energy transport. The substantial increase in cloud feedback at the TOA primarily contributes to the higher climate sensitivity. The cloud feedback spread in AMIP6 is comparable to that in AMIP5.

The design and the calibration of the Beam-attenuation, b and bb Laser Underwater Environment Sensor (4BLUES), which borrows from advanced designs developed by the authors’ individual groups in the course of the last two decades. The sensor features unparalleled accuracy in determining the optical scattering and backscattering coefficients, which are critical parameters for the remote sensing of water constituents and in-water bio-optical applications. Calibration procedures performed in the laboratory with spheres of known properties are presented together with an assessment of the performance in two field experiments in coastal waters. The more complex calibration of the scattering channel includes a novel inversion approach, which accounts for scattering and absorption losses along the laser-beam path. Strong agreement was observed with current advanced sensors for extinction and the backscattering measurement for one of the field experiments. Total scattering comparison with the current state of the art showed reasonable agreement (within 25%) for all stations except one, where the interpretation for the higher discrepancies is unresolved. These preliminary results suggest further assessment is warranted.

A rigorous treatment of the sea ice medium has been incorporated in the advanced Coupled Ocean-Atmosphere Radiative Transfer (COART) model. The inherent optical properties (IOPs) of brine pockets and air bubbles over the 0.25-4.0 µm spectral region are parameterized as a function of the sea ice physical properties (temperature, salinity and density). We then test the performance of the upgraded COART model using three physically-based modeling approaches to simulate the spectral albedo and transmittance of sea ice, and compare them with measurements collected during the Impacts of Climate on the Ecosystems and Chemistry of the Arctic Pacific Environment (ICESCAPE) and the Surface Heat Budget of the Arctic Ocean (SHEBA) field campaigns. The observations are adequately simulated when at least three layers are used to represent bare ice, including a thin surface scattering layer (SSL), and two layers to represent ponded ice. Treating the SSL as a low-density ice layer yields better model-observation agreement than treating it as a snow-like layer. Sensitivity results indicate that air volume (which determines the ice density) has the largest impact on the simulated fluxes. The vertical profile of density drives the optical properties but available measurements are scarce. The approach where the scattering coefficient for the bubbles is inferred in lieu of density leads to essentially equivalent modeling results. For ponded ice, the albedo and transmittance in the visible are mainly determined by the optical properties of the ice underlying the water layer. Possible contamination from light-absorbing impurities, such as black carbon or ice algae, is also implemented in the model and is able to effectively reduce the albedo and transmittance in the visible spectrum to further improve the model-observation agreement.

This conceptual study presents advanced radiative transfer computations of light polarization originating from a snowpack consisting of nonspherical grains and variable content of light-absorbing impurities, either embedded in the snowpack or (with the same optical properties) lofted above it in the form of atmospheric aerosols. The results highlight the importance of considering shapes other than spherical for the snow grains, which otherwise can lead to non-negligible errors in the retrieval of snow albedo from remote sensing observations. More importantly, it is found that polarimetric measurements provide a means to partition light-absorbing impurities embedded in the snowpack from absorbing aerosols aloft, a task traditionally prohibitive for sensors capable exclusively of measurements of total reflectance. Heritage techniques to obtain snow grain size from shortwave infrared observations of total reflectance are well established, as are those that leverage polarimetric, multiangular observations across the entire optical spectrum to characterize the optical and microphysical properties of atmospheric aerosols. The polarization signatures of near-infrared (e.g., 864 nm) observations carry critical information on snow grain shape. The prospected launch of space-borne polarimeters with proven accuracy, therefore, advocates for the development of data inversion schemes, to boost the accuracy of simultaneous retrievals of atmospheric and surface parameters in the polar and snow-covered regions, critical to climate studies.

In early 2013, three airborne polarimeters were flown on the high altitude NASA ER-2 aircraft in California for the Polarimeter Definition Experiment (PODEX). PODEX supported the pre-formulation NASA Aerosol-Cloud-Ecosystem (ACE) mission, which calls for an imaging polarimeter in polar orbit (among other instruments) for the remote sensing of aerosols, oceans, and clouds. Several polarimeter concepts exist as airborne prototypes, some of which were deployed during PODEX as a capabilities test. Two of those instruments to date have successfully produced Level 1 (georegistered, calibrated radiance and polarization) data from that campaign: the Airborne Multiangle Spectropolarimetric Imager (AirMSPI) and the Research Scanning Polarimeter (RSP). We compared georegistered observations of a variety of scene types by these instruments to test whether Level 1 products agreed within stated uncertainties. Initial comparisons found radiometric agreement, but polarimetric biases beyond measurement uncertainties. After subsequent updates to calibration, georegistration, and the measurement uncertainty models, observations from the instruments now largely agree within stated uncertainties. However, the 470 nm reflectance channels have a roughly +6% bias of AirMSPI relative to RSP, beyond expected measurement uncertainties. We also find that observations of dark (ocean) scenes, where polarimetric uncertainty is expected to be largest, do not agree within stated polarimetric uncertainties. Otherwise, AirMSPI and RSP observations are consistent within measurement uncertainty expectations, providing credibility for the subsequent creation of Level 2 (geophysical product) data from these instruments, and comparison thereof. The techniques used in this work can also form a methodological basis for other intercomparisons, for example, of the data gathered during the recent Aerosol Characterization from Polarimeter and Lidar (ACEPOL) field campaign, carried out in October and November of 2017 with four polarimeters (including AirMSPI and RSP).

The research frontiers of radiative transfer (RT) in coupled atmosphere-ocean systems are explored to enable new science and specifically to support the upcoming Plankton, Aerosol, Cloud ocean Ecosystem (PACE) satellite mission. Given (i) the multitude of atmospheric and oceanic constituents at any given moment that each exhibits a large variety of physical and chemical properties and (ii) the diversity of light-matter interactions (scattering, absorption, and emission), tackling all outstanding RT aspects related to interpreting and/or simulating light reflected by atmosphere-ocean systems becomes impossible. Instead, we focus on both theoretical and experimental studies of RT topics important to the science threshold and goal questions of the PACE mission and the measurement capabilities of its instruments. We differentiate between (a) forward (FWD) RT studies that focus mainly on sensitivity to influencing variables and/or simulating data sets, and (b) inverse (INV) RT studies that also involve the retrieval of atmosphere and ocean parameters. Our topics cover (1) the ocean (i.e., water body): absorption and elastic/inelastic scattering by pure water (FWD RT) and models for scattering and absorption by particulates (FWD RT and INV RT); (2) the air-water interface: variations in ocean surface refractive index (INV RT) and in whitecap reflectance (INV RT); (3) the atmosphere: polarimetric and/or hyperspectral remote sensing of aerosols (INV RT) and of gases (FWD RT); and (4) atmosphere-ocean systems: benchmark comparisons, impact of the Earth's sphericity and adjacency effects on space-borne observations, and scattering in the ultraviolet regime (FWD RT). We provide for each topic a summary of past relevant (heritage) work, followed by a discussion (for unresolved questions) and RT updates.

The linear polarization of sunlight reflected by ocean surfaces in the short-wave infrared (SWIR), at geometries where specular reflection dominates the signal, is a direct function of the refractive index of the surface microlayer (SML). This simple physical concept is at the base of a novel technique presented in this study. We invert observations obtained by the airborne Research Scanning Polarimeter (RSP) in the sunglint region, where each pixel's radiance is dominated by the signal originating from the wave slopes oriented precisely to cause specular reflection. The SWIR wavelength ensures minimization of aerosol interference when radiance travels through the atmosphere; strong absorption by the water body then limits the penetration depth to the first micrometer or so, effectively probing the SML. The resulting Degree of Linear Polarization (DoLP) is then governed by the refractive index via the Fresnel law for the specific pixel geometry, independently of the windspeed. The selected dataset concerns several field deployments from both low- and high-altitude aircraft, including total reflectance measurements with the sole purpose of accounting for the residual aerosol effect. Stable retrievals from transects above pure seawater yielded values of refractive index that match the values published in the literature within an accuracy of 5 x 10(-4). Flying over the oil spill caused by the explosion of the Deepwater Horizon platform, detected variations were found compatible with the presence of an oil slick. The robustness of the results, guaranteed by the high RSP polarimetric accuracy (<= 0.2%), opens the possibility for remote-sensing detection of other entities that similarly affect the refractive index including whitecaps, microplastics, biological gels, seaweed and grass mats.