Evaluation of MODIS and VIIRS Cloud-Gap-Filled Snow-Cover Products for Production of an Earth Science Data Record

MODerate resolution Imaging Spectroradiometer (MODIS) cryosphere products have been available since 2000 following the 1999 launch of the Terra MODIS and the 2002 launch of the Aqua MODIS and include global snow-cover extent (SCE) (swath, daily, and 8 d composites) at 500 m and 5 km spatial resolutions. These products are used extensively in hydrological modeling and climate studies. Reprocessing of the complete snow-cover data record, from Collection 5 (C5) to Collection 6 (C6) and Collection 6.1 (C6.1), has provided improvements in the MODIS product suite. Suomi National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Collection 1 (C1) snow-cover products at a 375 m spatial resolution have been available since 2011 and are currently being reprocessed for Collection 2 (C2). Both the MODIS C6.1 and the VIIRS C2 products will be available for download from the National Snow and Ice Data Center beginning in early 2020 with the complete time series available in 2020. To address the need for a cloud-reduced or cloud-free daily SCE product for both MODIS and VIIRS, a daily cloud-gap-filled (CGF) snow-cover algorithm was developed for MODIS C6.1 and VIIRS C2 processing. MOD10A1F (Terra) and MYD10A1F (Aqua) are daily, 500 m resolution CGF SCE map products from MODIS. VNP10A1F is the daily, 375 m resolution CGF SCE map product from VIIRS. These CGF products include quality-assurance data such as cloud-persistence statistics showing the age of the observation in each pixel. The objective of this paper is to introduce the new MODIS and VIIRS standard CGF daily SCE products and to provide a preliminary evaluation of uncertainties in the gap-filling methodology so that the products can be used as the basis for a moderate-resolution Earth science data record (ESDR) of SCE. Time series of the MODIS and VIIRS CGF products have been developed and evaluated at selected study sites in the US and southern Canada. Observed differences, although small, are largely attributed to cloud masking and differences in the time of day of image acquisition. A nearly 3-month time-series comparison of Terra MODIS and S-NPP VIIRS CGF snow-cover maps for a large study area covering all or parts of 11 states in the western US and part of southwestern Canada reveals excellent correspondence between the Terra MODIS and S-NPP VIIRS products, with a mean difference of 11 070 sqkm, which is 0.45 % of the study area. According to our preliminary validation of the Terra and Aqua MODIS CGF SCE products in the western US study area, we found higher accuracy of the Terra product compared with the Aqua product. The MODIS CGF SCE data record beginning in 2000 has been extended into the VIIRS era, which should last at least through the early 2030s

Influence of snow properties on directional surface reflectance in Antarctica

The significance of the polar regions for the Earth’s climate system and their observed amplified response to climate change indicate the necessity for high temporal and spatial coverage for the monitoring of the reflective properties of snow surfaces and their influencing factors. Therefore, the specific surface area (SSA, as a proxy for snow grain size) and the hemispherical directional reflectance factor (HDRF) of snow were measured for a 2-month period in central Antarctica (Kohnen research station) during austral summer 2013/14. The SSA data were retrieved on the basis of ground-based spectral surface albedo measurements collected by the COmpact RAdiation measurement System (CORAS) and airborne observations with the Spectral Modular Airborne Radiation measurement sysTem (SMART). The snow grain size and pollution amount (SGSP) algorithm, originally developed to analyze spaceborne reflectance measurements by the MODerate Resolution Imaging Spectroradiometer (MODIS), was modified in order to reduce the impact of the solar zenith angle on the retrieval results and to cover measurements in overcast conditions. Spectral ratios of surface albedo at 1280 and 1100 nm wavelength were used to reduce the retrieval uncertainty. The retrieval was applied to the ground-based and airborne observations and validated against optical in situ observations of SSA utilizing an IceCube device. The SSA retrieved from CORAS observations varied between 29 and 96 m2 kg-1. Snowfall events caused distinct relative maxima of the SSA which were followed by a gradual decrease in SSA due to snow metamorphism and wind-induced transport of freshly fallen ice crystals. The ability of the modified algorithm to include measurements in overcast conditions improved the data coverage, in particular at times when precipitation events occurred and the SSA changed quickly. SSA retrieved from measurements with CORAS and MODIS agree with the in situ observations within the ranges given by the measurement uncertainties. However, SSA retrieved from the airborne SMART data underestimated the ground-based results. The spatial variability of SSA in Dronning Maud Land ranged in the same order of magnitude as the temporal variability revealing differences between coastal areas and regions in interior Antarctica. The validation presented in this study provided an unique test bed for retrievals of SSA under Antarctic conditions where in situ data are scarce and can be used for testing prognostic snowpack models in Antarctic conditions. The HDRF of snow was derived from airborne measurements of a digital 180° fish-eye camera for a variety of conditions with different surface roughness, snow grain size, and solar zenith angle. The camera provides radiance measurements with high angular resolution utilizing detailed radiometric and geometric calibrations. The comparison between smooth and rough surfaces (sastrugi) showed significant differences in the HDRF of snow, which are superimposed on the diurnal cycle. By inverting a semi-empirical kernel-driven model for the bidirectional reflectance distribution function (BRDF), the snow HDRF was parameterized with respect to surface roughness, snow grain size, and solar zenith angle. This allows a direct comparison of the HDRF measurements with BRDF products from satellite remote sensing

Community Review of Southern Ocean Satellite Data Needs

This review represents the Southern Ocean community’s satellite data needs for the coming decade. Developed through widespread engagement, and incorporating perspectives from a range of stakeholders (both research and operational), it is designed as an important community-driven strategy paper that provides the rationale and information required for future planning and investment. The Southern Ocean is vast but globally connected, and the communities that require satellite-derived data in the region are diverse. This review includes many observable variables, including sea-ice properties, sea-surface temperature, sea-surface height, atmospheric parameters, marine biology (both micro and macro) and related activities, terrestrial cryospheric connections, sea-surface salinity, and a discussion of coincident and in situ data collection. Recommendations include commitment to data continuity, increase in particular capabilities (sensor types, spatial, temporal), improvements in dissemination of data/products/uncertainties, and innovation in calibration/validation capabilities. Full recommendations are detailed by variable as well as summarized. This review provides a starting point for scientists to understand more about Southern Ocean processes and their global roles, for funders to understand the desires of the community, for commercial operators to safely conduct their activities in the Southern Ocean, and for space agencies to gain greater impact from Southern Ocean-related acquisitions and missions.The authors acknowledge the Climate at the Cryosphere program and the Southern Ocean Observing System for initiating this community effort, WCRP, SCAR, and SCOR for endorsing the effort, and CliC, SOOS, and SCAR for supporting authors’ travel for collaboration on the review. Jamie Shutler’s time on this review was funded by the European Space Agency project OceanFlux Greenhouse Gases Evolution (Contract number 4000112091/14/I-LG)

Understanding past and future changes in northern Fennoscandian snow cover.

In this project, a combination of field measurements, remote sensing data and regional climate model outputs were used to study recent and projected future changes in Northern Fennoscandian snow cover. The research questions considered in this thesis are: What are the uncertainties in remote sensing and climate modelling datasets used in snow studies? How has snow cover been changing since the 1960s and how will it change over the next century, at a regional level over Northern Fennoscandia? Field measurements were made over two field seasons in the Khibiny Mountains in Arctic Russia. This ground data was used to gain an understanding of snow cover behaviour in the Western Mountain Regions (WMR) of the Kola Peninsula and to ground-truth 500 m resolution satellite data (MODIS: Moderate Resolution Imaging Spectroradiometer) snow products. The overall root mean square error (RMSE) for both MODIS instruments was found to be less than 10 %. The ground-truthed MODIS snow product was then used with station data to analyse past changes in snow cover in the WMR over the past 16 years. Though there is high inter-annual and spatial variability in the long-term snow cover trends in the WMR, overall, the duration of the snow cover season has increased at lower elevations and decreased at higher elevations. Field measurements and MODIS data were used in the sensitivity analysis of the Weather Research and Forecasting (WRF) regional climate model. Twelve experiments with different physics parameterisations were run over the first field season, and a statistical scores evaluation was undertaken to determine the optimised parameter setup for modelling snow in the region. Three CMIP5 (Coupled Model Intercomparison Project 5) models were used to force WRF in historical (1990 - 1999) and two future climate (2090 - 2099) emission scenarios over Northern Fennoscandia. Outputs from the historical runs were compared to data from 10 stations across Northern Fennoscandia in order to further validate WRF. WRF makes excellent temperature estimates, with a mean bias in the yearly mean temperature outputs of the runs of -1.89 °C. The precipitation outputs are less accurate with values often higher than observations, especially for extreme precipitation events (CMIP5 ‘ensemble’ mean RMSE of 24.0 mm for 20 + mm precipitation events). Finally, the future runs were compared to historical runs to study projected future changes in temperature, precipitation, snowfall and snow cover. The three models give a range of different future predictions for regional climate change over Northern Fennoscandia. However, all CMIP5 models agree that in both emission scenarios mean snow cover duration will be lower over 2090 to 2099 than it was between 1990 and 1999. Importantly, changes in temperature, precipitation and snowfall are all higher, and snow cover is most impacted, in the higher emission scenario. RCP 8.5 consistently sees a higher decrease in solid precipitation than RCP 4.5 at all stations, and for all models and seasons, for example. Thus, aiming to reduce greenhouse gas emissions is still crucial to reducing anthropogenic impact on Northern Fennoscandian snow.Funded by NERC PhD studentship NE/L002507/

Diagnosis and Improvement of Cryosphere Shortwave Radiation Biases in Global Climate Models.

Faithful representation of cryospheric change is critical for accurate climate modeling, but there are complicating issues in representing snow extent and reflectance in physically realistic ways. This thesis is a collection of diagnostics and improvements of cryospheric shortwave radiation in climate models. Firstly, we incorporate a diagnostic called the cryosphere radiative effect (CrRE), the instantaneous influence of surface snow and sea ice on the top-of-model solar energy budget, into two released versions of the Community Earth System Model. CrRE offers a more climatically relevant metric of the cryospheric state than snow and sea ice extent and is influenced by factors such as the seasonal cycle of insolation, cloud masking, and vegetation cover. We evaluate CrRE during the late 20th century and over the 21st century, specifically diagnosing the CrRE contributions from terrestrial and marine sources. Present-day boreal CrRE compares well with observationally derived estimates. Similar present-day CrRE in the two model versions results from compensating differences in cloud masking and sea ice extent. Radiative forcing in future warming scenarios reduces boreal and austral sea ice cover, and boreal snow cover, which each contribute roughly 1 W/m-2 to enhancing global absorbed shortwave radiation. Similar global cryospheric albedo feedbacks between 0.41-0.45 W/m2/K indicate the models exhibit similar temperature-normalized CrRE change. Secondly, we incorporated a modified canopy scheme into the Community Land Model with snow interception as a prognostic variable and snow unloading tuned to in-situ measurements. The canopy radiation scheme has been updated from a direct temperature dependence of optical parameters to a dependence on the prognostic snow storage. With these improvements, boreal forest zones show large, significant albedo error reductions relative to MODIS observations. 13% gridcell RMSE reduction during spring results from a more gradual seasonal transition in albedo, while 27% reduction in winter is from a lower albedo. Over all North Hemisphere land area, error was also reduced. Thirdly, we assess the impacts of the snow canopy vegetation treatment in coupled model warming scenarios. Little change in global albedo feedback or climate sensitivity were shown, but significant alterations resulted that varied both regionally and temporally.PhDApplied PhysicsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113453/1/perketj_1.pd

Snow cover and snow albedo changes in the central Andes of Chile and Argentina from daily MODIS observations (2000-2016)

The variables of snow cover extent (SCE), snow cover duration (SCD), and snow albedo (SAL) are primary factors determining the surface energy balance and hydrological response of the cryosphere, influencing snow pack and glacier mass-balance, melt, and runoff conditions. This study examines spatiotemporal patterns and trends in SCE, SCD, and SAL (2000–2016; 16 years) for central Chilean and Argentinean Andes using the MODIS MOD10A1 C6 daily snow product. Observed changes in these variables are analyzed in relation to climatic variability by using ground truth observations (meteorological data from the El Yeso Embalse and Valle Nevado weather stations) and the Multivariate El Niño index (MEI) data. We identified significant downward trends in both SCE and SAL, especially during the onset and offset of snow seasons. SCE and SAL showed high inter-annual variability which correlate significantly with MEI applied with a one-month time-lag. SCE and SCD decreased by an average of ~13 ± 2% and 43 ± 20 days respectively, over the study period. Analysis of spatial pattern of SCE indicates a slightly greater reduction on the eastern side (~14 ± 2%) of the Andes Cordillera compared to the western side (~12 ± 3%). The downward SCE, SAL, and SCD trends identified in this study are likely to have adverse impacts on downstream water resource availability to agricultural and densely populated regions in central Chile and Argentina

Bedmap2: improved ice bed, surface and thickness datasets for Antarctica

We present Bedmap2, a new suite of gridded products describing surface elevation, ice-thickness and the seafloor and subglacial bed elevation of the Antarctic south of 60 S. We derived these products using data from a variety of sources, including many substantial surveys completed since the original Bedmap compilation (Bedmap1) in 2001. In particular, the Bedmap2 ice thickness grid is made from 25 million measurements, over two orders of magnitude more than were used in Bedmap1. In most parts of Antarctica the subglacial landscape is visible in much greater detail than was previously available and the improved datacoverage has in many areas revealed the full scale of mountain ranges, valleys, basins and troughs, only fragments of which were previously indicated in local surveys. The derived statistics for Bedmap2 show that the volume of ice contained in the Antarctic ice sheet (27 million km3) and its potential contribution to sea-level rise (58 m) are similar to those of Bedmap1, but the mean thickness of the ice sheet is 4.6% greater, the mean depth of the bed beneath the grounded ice sheet is 72m lower and the area of ice sheet grounded on bed below sea level is increased by 10 %. The Bedmap2 compilation highlights several areas beneath the ice sheet where the bed elevation is substantially lower than the deepest bed indicated by Bedmap1. These products, along with grids of data coverage and uncertainty, provide new opportunities for detailed modelling of the past and future evolution of the Antarctic ice sheets

Retrieval of Wintertime Sea Ice Production in Arctic Polynyas Using Thermal Infrared and Passive Microwave Remote Sensing Data

Precise knowledge of wintertime sea ice production in Arctic polynyas is not only required to enhance our understanding of atmosphere‐sea ice‐ocean interactions but also to verify frequently utilized climate and ocean models. Here, a high‐resolution (2‐km) Moderate Resolution Imaging Spectroradiometer (MODIS) thermal infrared satellite data set featuring spatial and temporal characteristics of 17 Arctic polynya regions for the winter seasons 2002/2003 to 2017/2018 is directly compared to an akin low‐resolution Advanced Microwave Scanning Radiometer‐EOS (AMSR‐E) passive microwave data set for 2002/2003 to 2010/2011. The MODIS data set is purely based on a 1‐D energy‐balance model, where thin‐ice thicknesses (≤ 20 cm) are directly derived from ice‐surface temperature swath data and European Centre for Medium‐Range Weather Forecasts Re‐Analysis‐Interim atmospheric reanalysis data on a quasi‐daily basis. Thin‐ice thicknesses in the AMSR‐E data set are derived empirically. Important polynya properties such as areal extent and potential thermodynamic ice production can be estimated from both pan‐Arctic data sets. Although independently derived, our results show that both data sets feature quite similar spatial and temporal variations of polynya area (POLA) and ice production (IP), which suggests a high reliability. The average POLA (average accumulated IP) for all Arctic polynyas combined derived from both MODIS and AMSR‐E are 1.99×105 km2 (1.34×103 km3) and 2.29×105 km2 (1.31×103 km3), respectively. Narrow polynyas in areas such as the Canadian Arctic Archipelago are notably better resolved by MODIS. Analysis of 16 winter seasons provides an evaluation of long‐term trends in POLA and IP, revealing the significant increase of ice formation in polynyas along the Siberian coast

The spectral and chemical measurement of pollutants on snow near South Pole, Antarctica

Remote sensing of light-absorbing particles (LAPs), or dark colored impurities, such as black carbon (BC) and dust on snow, is a key remaining challenge in cryospheric surface characterization and application to snow, ice, and climate models. We present a quantitative data set of in situ snow reflectance, measured and modeled albedo, and BC and trace element concentrations from clean to heavily fossil fuel emission contaminated snow near South Pole, Antarctica. Over 380 snow reflectance spectra (350–2500 nm) and 28 surface snow samples were collected at seven distinct sites in the austral summer season of 2014–2015. Snow samples were analyzed for BC concentration via a single particle soot photometer and for trace element concentration via an inductively coupled plasma mass spectrometer. Snow impurity concentrations ranged from 0.14 to 7000 part per billion (ppb) BC, 9.5 to 1200 ppb sulfur, 0.19 to 660 ppb iron, 0.013 to 1.9 ppb chromium, 0.13 to 120 ppb copper, 0.63 to 6.3 ppb zinc, 0.45 to 82 parts per trillion (ppt) arsenic, 0.0028 to 6.1 ppb cadmium, 0.062 to 22 ppb barium, and 0.0044 to 6.2 ppb lead. Broadband visible to shortwave infrared albedo ranged from 0.85 in pristine snow to 0.62 in contaminated snow. LAP radiative forcing, the enhanced surface absorption due to BC and trace elements, spanned from \u3c1 W m­–2 for clean snow to ~70 W m­–2 for snow with high BC and trace element content. Measured snow reflectance differed from modeled snow albedo due to specific impurity-dependent absorption features, which we recommend be further studied and improved in snow albedo models