AVHRR Polar Pathfinder Research Articles

A New Perspective on Four Decades of Changes in Arctic Sea Ice from Satellite Observations

Arctic sea ice characteristics have been changing rapidly and significantly in the last few decades. Using a long-term time series of sea ice products from satellite observations—the extended AVHRR Polar Pathfinder (APP-x)—trends in sea ice concentration, ice extent, ice thickness, and ice volume in the Arctic from 1982 to 2020 are investigated. Results show that the Arctic has become less ice-covered in all seasons, especially in summer and autumn. Arctic sea ice thickness has been decreasing at a rate of −3.24 cm per year, resulting in an approximate 52% reduction in thickness from 2.35 m in 1982 to 1.13 m in 2020. Arctic sea ice volume has been decreasing at a rate of −467.7 km3 per year, resulting in an approximate 63% reduction in volume, from 27,590.4 km3 in 1982 to 10,305.5 km3 in 2020. These trends are further examined from a new perspective, where the Arctic Ocean is classified into open water, perennial, and seasonal sea ice-covered areas based on sea ice persistence. The loss of the perennial sea ice-covered area is a major factor in the total sea ice loss in all seasons. If the current rates of sea ice changes in extent, concentration, and thickness continue, the Arctic is expected to have ice-free summers by the early 2060s.

Assessing satellite-derived net surface radiative flux in the Arctic

Satellite-derived surface radiative fluxes have been recently improved and extended. However, the accuracy of recent satellite-derived surface radiative fluxes in the Arctic is not well characterized. Here, the authors assess the accuracy of the net surface radiative flux (NETSRF) in the Arctic, focusing on the ice-covered ocean, for three satellite products against four in situ measurements collected from different areas in the Arctic. The three satellite products are the Surface Radiation Budget project (SRB), the International Satellite Cloud Climatology Project (ISCCP), and the Extended AVHRR Polar Pathfinder version-2 (APP-x). Our comparisons suggest that: (1) in terms of the overall bias, root-mean-square error, and correlation, the NETSRF of ISCCP is closer to in situ observations than that of SRB and APP-x; (2) in terms of the diurnal variation of the biases, it is not very clear which satellite product is superior to the others; and (3) in terms of the interannual variability of the bias,...

The AVHRR Polar Pathfinder Climate Data Records

With recent, dramatic changes in Arctic sea ice and the Antarctic ice sheets, the importance of monitoring the climate of the polar regions has never been greater. While many individual global satellite products exist, the AVHRR Polar Pathfinder products provide a comprehensive set of variables that can be used to study trends and interactions within the Arctic and Antarctic climate systems. This paper describes the AVHRR Polar Pathfinder (APP), which is a fundamental climate data record that provides channel reflectances and brightness temperatures, and the AVHRR Polar Pathfinder—Extended (APP-x), which is a thematic climate data record that builds on APP to provide information on surface and cloud properties and radiative fluxes. Both datasets cover the period from 1982 through the present, twice daily, over both polar regions. APP-x has been used in the study of trends in surface properties, cloud cover, and radiative fluxes, interactions between clouds and sea ice, and the role of land surface changes in summer warming.

A Satellite-Derived Climate-Quality Data Record of the Clear-Sky Surface Temperature of the Greenland Ice Sheet

Abstract The authors have developed a climate-quality data record of the clear-sky surface temperature of the Greenland Ice Sheet using the Moderate-Resolution Imaging Spectroradiometer (MODIS) ice-surface temperature (IST) algorithm. Daily and monthly quality-controlled MODIS ISTs of the Greenland Ice Sheet beginning on 1 March 2000 and continuing through 31 December 2010 are presented at 6.25-km spatial resolution on a polar stereographic grid along with metadata to permit detailed accuracy assessment. The ultimate goal is to develop a climate data record (CDR) that starts in 1981 with the Advanced Very High Resolution Radiometer (AVHRR) Polar Pathfinder (APP) dataset and continues with MODIS data from 2000 to the present, and into the Visible Infrared Imager Radiometer Suite (VIIRS) era (the first VIIRS instrument was launched in October 2011). Differences in the APP and MODIS cloud masks have thus far precluded merging the APP and MODIS IST records, though this will be revisited after the APP dataset has been reprocessed with an improved cloud mask. IST of Greenland may be used to study temperature and melt trends and may also be used in data assimilation modeling and to calculate ice sheet mass balance. The MODIS IST climate-quality dataset provides a highly consistent and well-characterized record suitable for merging with earlier and future IST data records for climate studies. The complete MODIS IST daily and monthly data record is available online.

Development of a water clear of sea ice detection algorithm from enhanced SeaWinds/QuikSCAT and AMSR-E measurements

We develop and evaluate water clear of sea ice (open water following ice cover) detection algorithms that make use of Scatterometer Image Reconstruction (SIR) SeaWinds/QuikSCAT (QuikSCAT) backscatter (σ°) and Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) brightness temperature (T B) measurements. Algorithm validation was performed within Canadian Arctic waters using the Canadian Ice Service Digital Archive (CISDA) ice charts, NASATeam ice concentration estimates, extended AVHRR Polar Pathfinder (APP-x) albedo data, RADARSAT-1 imagery, and MODIS imagery. Results indicate that the temporal evolution of QuikSCAT σ°, AMSR-E polarization ratio (PR18), and AMSR-E vertical spectral gradient ratio (GR3618) can detect water clear of sea ice events, however mean differences due to frequency dependent characteristics of the data (spatial resolution; sensitivity to open water) were apparent. All water clear of sea ice algorithms are in good agreement with the timing and clearing patterns given by the CISDA. The QuikSCAT algorithm provided a more representative ice edge and more details on the ice clearing process due to higher spatial resolution, however, transient clearing events were better represented by the AMSR-E PR(18) or (GR3618) algorithm. By exploiting the strengths of each sensor, we found that a QuikSCAT and AMSR-E fused algorithm provide improved open water area estimates by as much as 11%. The fusion of QuikSCAT and AMSR-E PR(18) yielded in the most spatially representative open water detection. The residual surface of the water clear of sea ice algorithms was found to provide another measure of the average September minimum pan-Arctic sea ice extent within 6% of the NASATeam algorithm estimates.

Daily snow cover estimation from Advanced Very High Resolution Radiometer Polar Pathfinder data over Northern Hemisphere land surfaces during 1982–2004

The Global Climate Observing System has identified the need for systematic global daily snow cover data sets over land. Current in situ snow cover data sets have limited spatial coverage while satellite‐based snow cover records have either limited historical extent or limited temporal and spatial resolution because of cloud cover or specific sensor availability. NOAA Advanced Very High Resolution Radiometer (AVHRR) data offers nearly complete daily global coverage of the Northern Hemisphere, extending back to the early 1980s with successors slated to continue into the next decade. In this paper, we apply a new algorithm, Snowcover, to estimate daily snow cover, including periods of cloudy conditions, from AVHRR Polar Pathfinder (APP) data over Northern Hemisphere land surfaces. This new snow cover product is compared to snow cover estimates derived from long‐term in situ snow depth measurements over Canada and the northern Eurasia. The APP snow cover maps showed an 80% agreement rate or better at 95% of the in situ sites. This performance was comparable to the agreement of MODIS 0.05 degree snow cover products over the same sites; although the MODIS product was only retrieved ∼20% of the time corresponding to clear sky conditions in contrast to over 95% of the time with the APP snow product. The almost continuously temporal and spatial coverage for the past 23 years from 1982 to 2004 over Northern Hemisphere makes the new daily snow cover product especially suitable for analysis of large‐scale patterns of spring snowmelt in association with variability of circumpolar climate and ecological parameters.

Assessment of Cloud Cover Characteristics in Satellite Datasets and Reanalysis Products for Greenland

Abstract Clouds have an important controlling influence on the radiation balance, and hence surface melting, over the Greenland ice sheet and need to be classified to derive reliable albedo estimates from visible imagery. Little is known, however, about the true cloud cover characteristics for the largest island on Earth, Greenland. Here, an attempt is made to address this knowledge gap by examining cloud characteristics, as determined by three complementary satellites sensors: the Advanced Very High Resolution Radiometer (AVHRR), the Along Track Scanning Radiometer-2 (ATSR-2), and the Moderate Resolution Imaging Spectroradiometer (MODIS). The first provides a multidecadal time series of clouds, albedo, and surface temperature, and is available, in the form of the extended AVHRR Polar Pathfinder dataset (APP-x), as a homogeneous, consistent dataset from 1982 until 2004. APP-x data, however, are also the most challenging to cloud classify over snow-covered terrain, due to the limited spectral capabilities of the instrument. ATSR-2 permits identification and classification using stereophotogrammetric techniques and MODIS has enhanced spectral sampling in the visible and thermal infrared but over more limited time periods. The spatial cloud fractions from the three sensors are compared and show good agreement in terms of both magnitude and spatial pattern. The cloud fractions, and inferred patterns of accumulation, are then assessed from three commonly used reanalysis datasets: NCEP–NCAR, the second NCEP–Department of Energy (DOE) Atmospheric Model Intercomparison Project (AMIP-II), and the 40-yr ECMWF Re-Analysis (ERA-40). Poor agreement between the reanalysis datasets is found. NCEP–DOE AMIP-II produces a cloud fraction similar to that observed by the satellites. NCEP–NCAR and ERA-40, however, bear little similarity to the cloud fractions derived from the satellite observations. This suggests that they may produce poor accumulation estimates over the ice sheet and poor estimates of radiation balance. Using these reanalysis data to force a mass balance model of the ice sheet, without appropriate downscaling and correction for the substantial biases present, may, therefore, produce substantial errors in surface melt rate estimates.

Antarctic ice sheet and sea ice regional albedo and temperature change, 1981–2000, from AVHRR Polar Pathfinder data

Spring–summer (November, December, January) ice sheet and sea ice regional surface albedo, surface temperature, sea ice concentration and sea ice extent averages and trends from 1981 to 2000 have been calculated for the Antarctic area. In this research the AVHRR Polar Pathfinder 5-km EASE-Grid Composites and the combined SMMR and SSMI data sets from the National Snow and Ice Data Center (NSIDC), Boulder, Colorado have been employed. A regional analysis has been made for five longitudinal sectors around Antarctica: the Weddell Sea (WS), the Indian Ocean (IO), the Pacific Ocean (PO), the Ross Sea (RS) and the Bellingshausen–Amundsen Sea (BS). The IO and PO sectors show ice sheet albedos of 0.85 and temperatures of − 25 °C. The corresponding values in the RS and BS sectors are 0.80 and − 16 °C respectively. The sea ice albedo is about 0.60 in the RS, BS and WS sectors and 0.55 in the IO and PO sectors. The average sea ice temperature varies around − 12 °C. All the sectors show slight increasing spring–summer albedo trends and decreasing spring–summer temperature trends and similar interannual variability in albedo and surface temperature. The steepest ice sheet albedo trend of 0.0019 ± 0.0009/yr is found in the RS sector. The steepest sea ice albedo trend of 0.0044 ± 0.0017 /yr occurs in the PO sector. The steepest temperature trends for both the ice sheet and sea ice occur in the BS sector, having values of − 0.075 ± 0.040 °C/yr and − 0.107 ± 0.027 °C/yr respectively. The sea ice concentration shows slight increasing trends, the highest being in the PO sector (0.3 ± 0.12%/yr), whereas the sea ice extent trends are near zero with the exception of the RS sector (14,700 ±8600 km 2/yr) and the BS sector (− 13,000 ± 6400 km 2/yr).

Validation of Avhrr and Modis ice Surface temperature products using in Situ radiometers

AbstractShip-borne and airborne infrared radiometric measurements during the Arise cruise of September–October 2003 permitted in Situ validation Studies of two Satellite-based ice Surface Skin temperature algorithms: the AVHRR Polar Pathfinder Ice Surface Temperature and the MODIS Sea Ice Surface Temperature. Observations of Sea ice from theAurora AustralisShip’s rail using a KT-19.82 radiometer were conducted between 25 September and 21 October during clear-sky overflights by AVHRR (41 passes) and MODIS (17 passes) on their respective Satellite platforms. Data from both Sensors Show highly linear fits to 1 min integrated radiometer Spot measurements, Spanning the range 245–270 K with a ±1.4˚C, 1σ(AVHRR) and ±1.0˚C (MODIS) variation relative to a 1: 1 relationship. There was no Significant offset. Helicopter observations made with a KT-19.85 radiometer on three dates (8, 19 and 20 October) provided more data (236 gridcell Sites total), but over a more limited Sea-ice Skin temperature range (252–268 K), with higher variation (±1.7˚C, 1σ) due to mixed-pixel issues. Comparison of MODIS and AVHRR algorithms directly, with both images acquired during a helicopter flight, indicates very high correlation and near-unity Slope for the two Satellite-based algorithms. Ship air-temperature data during the validation indicated moderate to Strong inversions over Sea ice under clear Skies. These formed and decayed rapidly (tens of minutes) as clouds moved out of and into the zenith area.

Arctic Surface, Cloud, and Radiation Properties Based on the AVHRR Polar Pathfinder Dataset. Part II: Recent Trends

Abstract Over the past 20 yr, some Arctic surface and cloud properties have changed significantly. Results of an analysis of satellite data show that the Arctic has warmed and become cloudier in spring and summer but has cooled and become less cloudy in winter. The annual rate of surface temperature change is 0.057°C for the Arctic region north of 60°N. The surface broadband albedo has decreased significantly in autumn, especially over the Arctic Ocean, indicating a later freeze-up and snowfall. The surface albedo has decreased at an annual rate of −0.15% (absolute). Cloud fraction has decreased at an annual rate of −0.6% (absolute) in winter and increased at annual rates of 0.32% and 0.16% in spring and summer, respectively. On an annual time scale, there is no trend in cloud fraction. During spring and summer, changes in sea ice albedo that result from surface warming tend to modulate the radiative effect of increasing cloud cover. On an annual time scale, the all-wave cloud forcing at the surface has decreased at an annual rate of –0.335 W m−2, indicating an increased cooling by clouds. There are large correlations between surface temperature anomalies and climate indices such as the Arctic Oscillation (AO) index for some areas, implying linkages between global climate change and Arctic climate change.

Changes in seasonal cloud cover over the Arctic seas from satellite and surface observations

Winter and spring changes in cloudiness are compared over the arctic seas (ocean areas north of 60°N) from the TOVS (TIROS Operational Vertical Sounder) Polar Pathfinder retrievals and two separate datasets derived from the Advanced Very High Resolution Radiometer (AVHRR). All satellite products exhibit significant decreases in cloud fraction over the arctic seas during winter (December, January, February) on the order of 5 %/decade. An equally striking increase in spring (March, April, May) cloudiness is evident from the TOVS Pathfinder (TPP) and the extended AVHRR Polar Pathfinder (APP‐x) projects. In the Central Arctic these positive trends can be as large as 15 %/decade. Surface observations from the Russian drifting meteorological stations are consistent with satellite‐ observed changes during the 1980s. Negative trends in spring cloudiness reported by Comiso [2003] are in conflict with these findings. Spring changes in cloudiness are associated with changes in the atmospheric circulation. These dramatic, large‐scale changes may have substantial impacts on the surface energy balance.

Antarctic Cloud Radiative Forcing at the Surface Estimated from the AVHRR Polar Pathfinder and ISCCP D1 Datasets, 1985–93

Abstract Surface cloud radiative forcing from the newly extended Advanced Very High Resolution Radiometer (AVHRR) Polar Pathfinder (APP-x) dataset and surface cloud radiative forcing calculated using cloud and surface properties from the International Satellite Cloud Climatology Project (ISCCP) D-series product were used in this 9-yr (1985–93) study. On the monthly timescale, clouds were found to have a warming effect on the surface of the Antarctic continent every month of the year in both datasets. Over the ocean poleward of 58.75°S, clouds were found to have a warming effect on the surface from March through October in the ISCCP-derived dataset and from April through September in the APP-x dataset. Net surface fluxes from both datasets were validated against net surface fluxes calculated from measurements of upwelling and downwelling shortwave and longwave radiation at the Neumayer and Amundsen–Scott South Pole Stations in the Antarctic. The net all-wave surface flux from the ISCCP-derived dataset was ...

Ground-Based and Satellite-Derived Measurements of Surface Albedo on the North Slope of Alaska

Abstract Spatial and temporal variations of surface albedo on the North Slope of Alaska were investigated using both ground-based tower measurements and satellite remote sensing data. Ground-based measurements of incident and reflected solar radiation at several stations along the Dalton Highway over the period 1985–98 are used to determine in situ surface albedo. Advanced Very High Resolution Radiometer (AVHRR)-derived surface albedo were obtained from AVHRR Polar Pathfinder products, available from the National Snow and Ice Data Center, using a modified cloud mask. AVHRR-derived surface albedo agrees closely with in situ measurements. Results from this study indicate that surface albedo varies from greater than 0.9 for a snow-covered land surface under overcast conditions to less than 0.1 for a wet tundra land surface. Five distinct temporal periods are discerned, based on seasonal variations of surface albedo: winter stationary, spring snowmelt, postsnowmelt, summer stationary, and autumn freeze-up per...

Intercomparison between In Situ and AVHRR Polar Pathfinder-Derived Surface Albedo over Greenland

The Advanced Very High Resolution (AVHRR) Polar Pathfinder (APP) data provides the first long-time series of consistent, calibrated surface albedo and surface temperature data for the polar regions. Validations of these products have consisted of individual studies that analyzed algorithm performance for limited regions and or time periods. This article reports on comparisons made between the APP-derived surface albedo and that measured at 14 automatic weather stations (AWS) around the Greenland ice sheet from January 1997 to August 1998. Results show that satellite-derived surface albedo values are on average 10% less than those measured by the AWS stations. However, the station measurements tend to be biased high by about 4% and thus the differences in absolute albedo may be less (e.g., 6%). In regions of the ice sheet where the albedo variability is small, such as the dry snow facies, the APP albedo uncertainty exceeds the natural variability. Further work is needed to improve the absolute accuracy of the APP-derived surface albedo. Even so, the data provide temporally and spatially consistent estimates of the Greenland ice sheet albedo.