Equal-Area Scalable Earth Grid Research Articles

A climate data record of year-round global sea-ice drift from the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI SAF)

Abstract. Sea ice in the polar regions can move several tens of kilometres per day under the actions of winds, ocean currents, and internal stresses. Long-term observations of the rate and patterns of this motion are needed to characterize the full response of the polar environment to climate change. Here, we introduce a new climate data record (CDR) of year-round, global, daily sea-ice drift vectors covering 1991–2020. The motion vectors are computed from series of passive microwave imagery in the winter months and from a parametric free-drift model in the summer months. An evaluation against on-ice buoy trajectories reveals that the RMSEs of the sea-ice drift CDR are small and vary with hemisphere and seasons (2.1 km for Arctic winters, 2.6 km for Arctic summer, 3 to 4 km for the Antarctic sea ice). The CDR is un-biased for Arctic winter conditions. The bias is larger for Antarctic and for summer sea-ice motion. The CDR consists of daily product files holding the dX and dY components of the drift vectors on an Equal-Area Scalable Earth (EASE2) grid with 75 km spacing as well as associated uncertainties and flags. It is prepared in the context of the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI SAF) and is readily available at https://doi.org/10.15770/EUM_SAF_OSI_0012 (OSI SAF, 2022).

Evaluating the effective resolution of enhanced resolution SMAP brightness temperature image products

The MEaSUREs Calibrated Enhanced-Resolution Passive Microwave Daily Equal-Area Scalable Earth Grid 2.0 Brightness Temperature (CETB) Earth System Data Record (ESDR) includes conventional- and enhanced-resolution radiometer brightness temperature (TB) images on standard, compatible grids from calibrated satellite radiometer measurements collected over a multi-decade period. Recently, the CETB team processed the first 4 years of enhanced resolution Soil Moisture Active Passive (SMAP) L-band (1.41 GHz) radiometer TB images. The CETB processing employs the radiometer form of the Scatterometer Image Reconstruction (rSIR) algorithm to create enhanced resolution images, which are posted on fine resolution grids. In this paper, we evaluate the effective resolution of the SMAP TB image products using coastline and island crossings. We similarly evaluate the effective resolution of the SMAP L1C_TB_E enhanced resolution product that is based on Backus-Gilbert processing. We present a comparison of the spatial resolution of the rSIR and L1C_TB_E enhanced resolution products with conventionally-processed (gridded) SMAP data. We find that the effective resolution of daily CETB rSIR SMAP TB images is slightly finer than that of L1C_TB_E and about 30% finer than conventionally processed data.

Evaluation of Global Surface Water Temperature Data Sets for Use in Passive Remote Sensing of Soil Moisture

Inland open water bodies often pose a systematic error source in the passive remote sensing retrievals of soil moisture. Water temperature is a necessary variable used to compute water emissions that is required to be subtracted from satellite observation to yield actual emissions from the land portion, which in turn generates accurate soil moisture retrievals. Therefore, overestimation of soil moisture can often be corrected using concurrent water temperature data in the overall mitigation procedure. In recent years, several data sets of lake water temperature have become available, but their specifications and accuracy have rarely been investigated in the context of passive soil moisture remote sensing on a global scale. For this reason, three lake temperature products were evaluated against in-situ measurements from 2007 to 2011. The data sets include the lake surface water temperature (LSWT) from Global Observatory of Lake Responses to Environmental Change (GloboLakes), the Copernicus Global Land Operations Cryosphere and Water (C-GLOPS), as well as the lake mix-layer temperature (LMLT) from the European Centers for Medium-Range Weather Forecast (ECMWF) ERA5 Land Reanalysis. GloboLakes, C-GLOPS, and ERA5 Land have overall comparable performance with Pearson correlations (R) of 0.87, 0.92 and 0.88 in comparison with in-situ measurements. LSWT products exhibit negative median biases of −0.27 K (GloboLakes) and −0.31 K (C-GLOPS), whereas the median bias of LMLT is 1.56 K. When mapped from their respective native resolutions to a common 9 km Equal-Area Scalable Earth (EASE) Grid 2.0 projection, similar relative performance was observed. LMLT and LSWT data are closer in performance over the 9 km grid cells that exhibit a small range of lake cover fractions (0.05–0.5). Despite comparable relative performance, ERA5 Land shows great advantages in spatial coverage and temporal resolution. In summary, an integrated evaluation on data accuracy, long-term availability, global coverage, temporal resolution, and regular forward processing with modest data latency led us to conclude that LMLT from the ERA5 Land Reanalysis product represents the most optimal path for use in the development of a long-term soil moisture product.

Enhanced-Resolution SMAP Brightness Temperature Image Products

The NASA-sponsored Calibrated Passive Micro- wave Daily Equal-Area Scalable Earth Grid 2.0 Brightness Temperature (CETB) Earth System Data Record Project team has generated a multisensor, multidecadal time series of high-resolution radiometer products designed to support climate studies. This project uses image reconstruction techniques to generate conventional and enhanced-resolution daily brightness temperature images on a standard set of map projections. Sensors included in CETB are the Aqua Advanced Microwave Scanning Radiometer–Earth Observing System (AMSR-E), Scanning Multichannel Microwave Radiometer, and all Special Sensor Microwave/Imager and Special Sensor Microwave Imager/Sounder radiometers. These span frequencies between 6 and 89 GHz. This paper considers the issues of adding the L-band (1.6 GHz) Soil Moisture Active Passive (SMAP) radiometer measurements to the CETB climate record, with emphasis on optimizing the reconstruction to provide the highest possible spatial resolution at the lowest noise level. SMAP radiometer reconstruction on SMAP-standard grids is also considered. Simulation is used to optimize the reconstruction, and the results confirmed using actual data. A comparison of the performance of the Backus–Gilbert approach and the radiometer form of the Scatterometer Image Reconstruction algorithm is provided. These are compared to the conventional drop-in-the-bucket gridded imaging.

Northern Hemisphere surface freeze–thaw product from Aquarius L-band radiometers

Abstract. In the Northern Hemisphere, seasonal changes in surface freeze–thaw (FT) cycles are an important component of surface energy, hydrological and eco-biogeochemical processes that must be accurately monitored. This paper presents the weekly polar-gridded Aquarius passive L-band surface freeze–thaw product (FT-AP) distributed on the Equal-Area Scalable Earth Grid version 2.0, above the parallel 50° N, with a spatial resolution of 36 km × 36 km. The FT-AP classification algorithm is based on a seasonal threshold approach using the normalized polarization ratio, references for frozen and thawed conditions and optimized thresholds. To evaluate the uncertainties of the product, we compared it with another satellite FT product also derived from passive microwave observations but at higher frequency: the resampled 37 GHz FT Earth Science Data Record (FT-ESDR). The assessment was carried out during the overlapping period between 2011 and 2014. Results show that 77.1 % of their common grid cells have an agreement better than 80 %. Their differences vary with land cover type (tundra, forest and open land) and freezing and thawing periods. The best agreement is obtained during the thawing transition and over forest areas, with differences between product mean freeze or thaw onsets of under 0.4 weeks. Over tundra, FT-AP tends to detect freeze onset 2–5 weeks earlier than FT-ESDR, likely due to FT sensitivity to the different frequencies used. Analysis with mean surface air temperature time series from six in situ meteorological stations shows that the main discrepancies between FT-AP and FT-ESDR are related to false frozen retrievals in summer for some regions with FT-AP. The Aquarius product is distributed by the U.S. National Snow and Ice Data Center (NSIDC) at https://nsidc.org/data/aq3_ft/versions/5 with the DOI https://doi.org/10.5067/OV4R18NL3BQR.

Spatiotemporal variability of snow cover and snow water equivalent in the last three decades over Eurasia

Changes in the extent and amount of snow cover in Eurasia are of great interest because of their vital impacts on the global climate system and regional water resource management. This study investigated the spatial and temporal variability of the snow cover extent (SCE) and snow water equivalent (SWE) of the continental Eurasia using the Northern Hemisphere Equal-Area Scalable Earth Grid (EASE-Grid) Weekly SCE data for 1972–2006 and the Global Monthly EASE-Grid SWE data for 1979–2004. The results indicated that, in general, the spatial extent of snow cover significantly decreased during spring and summer, but varied little during autumn and winter over Eurasia in the study period. The date at which snow cover began to disappear in spring has significantly advanced, whereas the timing of snow cover onset in autumn did not vary significantly during 1972–2006. The snow cover persistence period declined significantly in the western Tibetan Plateau as well as partial area of Central Asia and northwestern Russia, but varied little in other parts of Eurasia. “Snow-free breaks” (SFBs) with intermittent snow cover in the cold season were principally observed in the Tibetan Plateau and Central Asia, causing a low sensitivity of snow cover persistence period to the timings of snow cover onset and disappearance over the areas with shallow snow. The averaged SFBs were 1–14 weeks during the study period and the maximum intermittence could even reach 25 weeks in certain years. At a seasonal scale, SWE usually peaked in February or March, but fell gradually since April across Eurasia. Both annual mean and annual maximum SWE decreased significantly during 1979–2004 in most parts of Eurasia except for eastern Siberia as well as northwestern and northeastern China. The possible cross-platform inconsistencies between two passive microwave radiometers may cause uncertainties in the detected trends of SWE here, suggesting an urgent need of producing a long-term, more homogeneous SWE product in future.

Persistent artifacts in the NSIDC ice motion data set and their implications for analysis

AbstractIn this study we evaluate weekly Equal‐Area Scalable Earth Grid (EASE‐Grid) sea ice velocities released by the National Snow and Ice Data Centre (NSIDC). We identify persistent Eulerian and Lagrangian features that arise solely as an artifact of the method used in the incorporation of buoy data. This, in turn, significantly impacts calculation of sea ice motion gradients, including divergence, convergence, and shear. Our numerical experiments and comparison with observations further demonstrate the impact of these artificial features on climatological assessments, including age‐of‐ice studies. In particular, we find that age‐of‐ice studies using this data set significantly underestimate multiyear ice extent by an average of 0.8 × 106 km2 in the month of March.

Multiyear Arctic Ice Classification Using ASCAT and SSMIS

The concentration, type, and extent of sea ice in the Arctic can be estimated based on measurements from satellite active microwave sensors, passive microwave sensors, or both. Here, data from the Advanced Scatterometer (ASCAT) and the Special Sensor Microwave Imager/Sounder (SSMIS) are employed to broadly classify Arctic sea ice type as first-year (FY) or multiyear (MY). Combining data from both active and passive sensors can improve the performance of MY and FY ice classification. The classification method uses C-band σ0 measurements from ASCAT and 37 GHz brightness temperature measurements from SSMIS to derive a probabilistic model based on a multivariate Gaussian distribution. Using a Gaussian model, a Bayesian estimator selects between FY and MY ice to classify pixels in images of Arctic sea ice. The ASCAT/SSMIS classification results are compared with classifications using the Oceansat-2 scatterometer (OSCAT), the Equal-Area Scalable Earth Grid (EASE-Grid) Sea Ice Age dataset available from the National Snow and Ice Data Center (NSIDC), and the Canadian Ice Service (CIS) charts, also available from the NSIDC. The MY ice extent of the ASCAT/SSMIS classifications demonstrates an average difference of 282 thousand km - + from that of the OSCAT classifications from 2009 to 2014. The difference is an average of 13.6% of the OSCAT MY ice extent, which averaged 2.19 million km2 over the same period. Compared to the ice classified as two years or older in the EASE-Grid Sea Ice Age dataset (EASE-2+) from 2009 to 2012, the average difference is 617 thousand km2 . The difference is an average of 22.8% of the EASE-2+ MY ice extent, which averaged 2.79 million km2 from 2009 to 2012. Comparison with the Canadian Ice Service (CIS) charts shows that most ASCAT/SSMIS classifications of MY ice correspond to a MY ice concentration of approximately 50% or greater in the CIS charts. The addition of the passive SSMIS data appears to improve classifications by mitigating misclassifications caused by ASCAT's sensitivity to rough patches of ice which can appear similar to, but are not, MY ice.

SMOS prototype algorithm for detecting autumn soil freezing

A prototype algorithm for hemispheric scale detection of autumn soil freezing using space-borne L-band passive microwave observations is presented. The methodology is based on earlier empirical and theoretical studies of L-band emission properties of freezing and thawing soils. We expand a method originally developed for soil freeze–thaw (F/T) state detection from L-band tower based observations to satellite scale, applying observations from the European Space Agency's Soil Moisture and Ocean Salinity (SMOS) mission. The developed algorithm is based on first establishing spatially variable thresholds for L-band brightness temperatures representing frozen and thawed states of soil, and comparing these to current values of different indicators of soil freezing, calculated based on observed brightness temperature at different polarizations and incidence angles. An exponential relation between the freezing indicators and the depth of soil frost is developed based on a large amount of manual soil frost tube observations across Finland. An additional processing filter based on observed physical temperature and snow cover information is used to flag obvious F/T detection errors. The estimated soil F/T-states provided in this study are limited to the autumn freezing period, as melting snow in spring effectively prevents acquisition of information from the soil surface using microwaves for large areas in Northern latitudes. The F/T estimate is produced as daily information and provided in the equal-area scalable Earth (EASE) grid. Soil F/T-state is categorized into three discrete levels: ‘frozen’, ‘partially frozen’, and ‘thawed’, and accompanied with a quality data matrix estimating the data reliability for each freezing season separately. Comparisons to in situ data were conducted at 10 different locations in Finland, Northern America and Siberia. These comparison results indicate that the onset of autumn soil freezing can be estimated from SMOS observations to within 1 to 14 days, depending on the freezing indicator applied and the in situ data used in comparison. Although the initial results are encouraging, more comprehensive assessment of SMOS based soil F/T estimates still requires further comparison to other reference sites, particularly to sites with measurements available for all locally representative land cover types, as well as other satellite-based soil freezing products.

Optimum Image Formation for Spaceborne Microwave Radiometer Products.

This paper considers some of the issues of radiometer brightness image formation and reconstruction for use in the NASA-sponsored Calibrated Passive Microwave Daily Equal-Area Scalable Earth Grid 2.0 Brightness Temperature Earth System Data Record project, which generates a multisensor multidecadal time series of high-resolution radiometer products designed to support climate studies. Two primary reconstruction algorithms are considered: the Backus-Gilbert approach and the radiometer form of the scatterometer image reconstruction (SIR) algorithm. These are compared with the conventional drop-in-the-bucket (DIB) gridded image formation approach. Tradeoff study results for the various algorithm options are presented to select optimum values for the grid resolution, the number of SIR iterations, and the BG gamma parameter. We find that although both approaches are effective in improving the spatial resolution of the surface brightness temperature estimates compared to DIB, SIR requires significantly less computation. The sensitivity of the reconstruction to the accuracy of the measurement spatial response function (MRF) is explored. The partial reconstruction of the methods can tolerate errors in the description of the sensor measurement response function, which simplifies the processing of historic sensor data for which the MRF is not known as well as modern sensors. Simulation tradeoff results are confirmed using actual data.

Estimating errors in cloud amount and cloud optical thickness due to limited spatial sampling using a satellite imager as a proxy for nadir‐view sensors

AbstractCloud climatologies from space‐based active sensors have been used in climate and other studies without their uncertainties specified. This study quantifies the errors in monthly mean cloud amount and optical thickness due to the limited spatial sampling of space‐based active sensors. Nadir‐view observations from a satellite imager, the Moderate Resolution Imaging Spectroradiometer (MODIS), serve as a proxy for those active sensors and observations within 10° of the sensor's nadir view serve as truth for data from 2003 to 2013 in the Arctic. June–July monthly mean cloud amount and liquid water and ice cloud optical thickness from MODIS for both observations are calculated and compared. Results show that errors increase with decreasing sample numbers for monthly means in cloud amount and cloud optical thickness. The root‐mean‐square error of monthly mean cloud amount from nadir‐view observations increases with lower latitudes, with 0.7% (1.4%) at 80°N and 4.2% (11.2%) at 60°N using data from 2003 to 2013 (from 2012). For a 100 km resolution Equal‐Area Scalable Earth Grid (EASE‐Grid) cell of 1000 sample numbers, the absolute differences in these two monthly mean cloud amounts are less than 6.5% (9.0%, 11.5%) with an 80 (90, 95)%chance; such differences decrease to 4.0% (5.0%, 6.5%) with 5000 sample numbers. For a 100 km resolution EASE‐Grid of 1000 sample numbers, the absolute differences in these two monthly mean cloud optical thicknesses are less than 2.7 (3.8) with a 90% chance for liquid water cloud (ice cloud); such differences decrease to 1.3 (1.0) for 5000 sample numbers. The uncertainties in monthly mean cloud amount and optical thickness estimated in this study may provide useful information for applying cloud climatologies from active sensors in climate studies and suggest the need for future spaceborne active sensors with a wide swath.

Artificial neural network coupled with wavelet transform for estimating snow water equivalent using passive microwave data

Snow Water Equivalent (SWE) is an important parameter in hydrologic engineering involving the streamflow forecasting of high-elevation watersheds. In this paper, the application of classic Artificial Neural Network model (ANN) and a hybrid model combining the wavelet and ANN (WANN) is investigated in estimating the value of SWE in a mountainous basin. In addition, k-fold cross validation method is used in order to achieve a more reliable and robust model. In this regard, microwave images acquired from Spectral Sensor Microwave Imager (SSM/I) are used to estimate the SWE of Tehran sub-basins during 1992–2008 period. Also for obtaining measured SWE within the corresponding Equal-Area Scalable Earth-Grid (EASE-Grid) cell of SSM/I image, approach of Cell-SWE extraction using height–SWE relations is applied in order to reach more precise estimations. The obtained results reveal that the wavelet-ANN model significantly increases the accuracy of estimations, mainly because of using multi-scale time series as the ANN inputs. The Nash–Sutcliffe Index (NSE) for ANN and WANN models are respectively 0.09 and 0.44 which shows a firm improvement of 0.35 in NSE parameter when WANN is applied. Similar trend is observed in other parameters including RMSE where the value is 0.3 for ANN and 0.07 for WANN.

The summer snow cover anomaly over the Tibetan Plateau and its association with simultaneous precipitation over the mei-yu-baiu region

The summer snow anomalies over the Tibetan Plateau (TP) and their effects on climate variability are often overlooked, possibly due to the fact that some datasets cannot properly capture summer snow cover over high terrain. The satellite-derived Equal-Area Scalable Earth grid (EASE-grid) dataset shows that snow still exists in summer in the western part and along the southern flank of the TP. Analysis demonstrates that the summer snow cover area proportion (SCAP) over the TP has a significant positive correlation with simultaneous precipitation over the mei-yu-baiu (MB) region on the interannual time scale. The close relationship between the summer SCAP and summer precipitation over the MB region could not be simply considered as a simultaneous response to the Silk Road pattern and the SST anomalies in the tropical Indian Ocean and tropical central-eastern Pacific. The SCAP anomaly has an independent effect and may directly modulate the land surface heating and, consequently, vertical motion over the western TP, and concurrently induce anomalous vertical motion over the North Indian Ocean via a meridional vertical circulation. Through a zonal vertical circulation over the tropics and a Kelvin wave-type response, anomalous vertical motion over the North Indian Ocean may result in an anomalous high over the western North Pacific and modulate the convective activity in the western Pacific warm pool, which stimulates the East Asia-Pacific (EAP) pattern and eventually affects summer precipitation over the MB region.

Potential for hydrologic characterization of deep mountain snowpack via passive microwave remote sensing in the Kern River basin, Sierra Nevada, USA

Snow plays a critical role in hydrology and water resources, but snow properties in mountainous areas vary dramatically in space, making characterization difficult: in situ measurements represent a single point of spatially-variable snow properties. Spaceborne passive microwave (PM) remote sensing has spatially continuous coverage, but coarse spatial resolution. PM spatial footprints are nominally elliptical, with the spatial orientation changing relative to a given basin on the ground from one satellite pass to the next. The widely used Equal-Area Scalable Earth Grid (EASE-Grid) resamples the raw PM observations to a 25km×25km grid; this is far coarser than The Advanced Microwave Scanning Radiometer for EOS (AMSR-E) 37GHz Level 2A (L2A) footprints with an area of 87.9km2. This paper presents methods for processing the L2A data in order to illustrate that PM measurements contain information about snow accumulation and ablation cycles in mountainous regions. Methods are presented 1) to calculate the average Tb over a hydrologic basin for monitoring SWE accumulation amount, and 2) to interpolate Tb to a point for monitoring SWE ablation timing. Across the six-year study period, the range of the Tb was 50K, while the range of the air temperature was only 30K, indicating significant surface emissivity variations. The minimum Tb of each water year (WY), which starts from October 1 of a calendar year and ends at September 30 of the next, showed a strong inverse relationship to SWE measured in situ; the correlation coefficient between Tb and an in situ basin average SWE was −0.94. The L2A data is three times more sensitive to than the EASE-Grid data to in situ SWE. The diurnal amplitude variability (DAV, or difference between daytime and nighttime Tb) was used to identify the day of melt onset and compared with in situ estimates of onset derived from daily SWE measurements. L2A data corresponded well with the in situ onset dates with a correlation coefficient of 0.94 and an RMSE of 5.04days. When using EASE-Grid data, the RMSE in onset date was 11.7days. In addition, Tb DAV was found to be correlated with Tair DAV during winter, with an average correlation coefficient of 0.72. Future work will explore methods to extract this information in order to improve estimates of snow accumulation and ablation patterns.

Investigating correlations between snowmelt and forest fires in a high latitude snowmelt dominated drainage basin

AbstractHigh latitude drainage basins are experiencing increases in temperature higher than the global average, with snowmelt dominated basins most sensitive to effects in winter because of the snowpack's integration of these changes over the season. This may influence the timing of snowmelt onset, the melt‐refreeze period and snowpack accumulation resulting in changes in spring runoff, associated flooding and drought conditions later in the year, possibly enhancing forest fire potential. Large burned areas cleared of vegetation change discharge dynamics and may affect snowmelt characteristics and discharge in subsequent seasons. Correlations are tested by comparing forest fire occurrence with spring melt onset, the end of the melt‐refreeze period (after which snow rapidly depletes) and early snowmelt events. Snow characteristics are derived from brightness temperature (Tb) data from the Advanced Microwave Scanning Radiometer for EOS (AMSR‐E) for 2003–2010. Dates of melt onset, end of melt‐refreeze and early melt events are defined with Tb and diurnal amplitude variation thresholds. Areas and intensities of forest fires are from the Moderate Resolution Imaging Spectroradiometer (MODIS) thermal anomaly data (MOD14), and all data are mapped to an Equal‐Area Scalable Earth Grid to assess spatial correlations. Earlier melt onset and end of melt‐refreeze are found in years and areas of high forest fire occurrence by comparing high (2004–2005) and low (2006–2007) fire years in the Porcupine sub‐basin of the Yukon River in northeastern Alaska and the Yukon Territory. The burned areas also correlate with relatively later melt onset and later end of melt‐refreeze in subsequent low fire years. Copyright © 2012 John Wiley & Sons, Ltd.

EASE-Grid 2.0: Incremental but Significant Improvements for Earth-Gridded Data Sets

Defined in the early 1990s for use with gridded satellite passive microwave data, the Equal-Area Scalable Earth Grid (EASE-Grid) was quickly adopted and used for distribution of a variety of satellite and in situ data sets. Conceptually easy to understand, EASE-Grid suffers from limitations that make it impossible to format in the widely popular GeoTIFF convention without reprojection. Importing EASE-Grid data into standard mapping software packages is nontrivial and error-prone. This article defines a standard for an improved EASE-Grid 2.0 definition, addressing how the changes rectify issues with the original grid definition. Data distributed using the EASE-Grid 2.0 standard will be easier for users to import into standard software packages and will minimize common reprojection errors that users had encountered with the original EASE-Grid definition.

Linking pan-Arctic human and physical data

The Arctic Observing Network–Social Indicators (AON-SI) project, building upon earlier work, developed an approach for linking social with physical science data across the Northern pan-Arctic. The first iteration of social data involves time series of demographic indicators in more than 100 separate regions, such as boroughs of Alaska, census divisions of Northern Canada, and oblast or autonomous regions of Northern Russia. Its geographical scope covers all of Alaska, Greenland, Iceland, the Faroe Islands, Norway, Sweden, and Finland, along with Northern parts of Canada and Russia. Administrative subdivisions within these areas define the regions. Key features of this data framework are (1) the list of region names, (2) a unified system of numerical identification codes, and (3) a region-year organization. We approximated the geographical area of each region as a particular set of 25 × 25 km grid cells, following the Equal-Area Scalable Earth Grid (EASE-Grid) scheme widely used in high-latitude natural science. Physical data in this format are accessible through the Arctic RIMS website. The linkage opens doors for integrated analysis of social and natural-science data, as illustrated by an analysis of Alaska community electricity use. Current versions of the database are published online.

Solar partitioning in a changing Arctic sea-ice cover

AbstractThe summer extent of the Arctic sea-ice cover has decreased in recent decades and there have been alterations in the timing and duration of the summer melt season. These changes in ice conditions have affected the partitioning of solar radiation in the Arctic atmosphere–ice–ocean system. the impact of sea-ice changes on solar partitioning is examined on a pan-Arctic scale using a 25 km × 25 km Equal-Area Scalable Earth Grid for the years 1979–2007. Daily values of incident solar irradiance are obtained from NCEP reanalysis products adjusted by ERA-40, and ice concentrations are determined from passive microwave satellite data. the albedo of the ice is parameterized by a five-stage process that includes dry snow, melting snow, melt pond formation, melt pond evolution, and freeze-up. the timing of these stages is governed by the onset dates of summer melt and fall freeze-up, which are determined from satellite observations. Trends of solar heat input to the ice were mixed, with increases due to longer melt seasons and decreases due to reduced ice concentration. Results indicate a general trend of increasing solar heat input to the Arctic ice–ocean system due to declines in albedo induced by decreases in ice concentration and longer melt seasons. the evolution of sea-ice albedo, and hence the total solar heating of the ice–ocean system, is more sensitive to the date of melt onset than the date of fall freeze-up. the largest increases in total annual solar heat input from 1979 to 2007, averaging as much as 4%a–1, occurred in the Chukchi Sea region. the contribution of solar heat to the ocean is increasing faster than the contribution to the ice due to the loss of sea ice.

Daily Microwave-Derived Surface Temperature over Canada/Alaska

Abstract The land surface temperature variation over northern high latitudes in response to the increase in greenhouse gases is challenging because of the lack of meteorological stations. A new method to derive the surface temperature from satellite microwave measurements that improves the frequency of measurements relative to that of infrared data is presented. The daily Special Sensor Microwave Imager 25 km × 25 km Equal-Area Scalable Earth Grid (EASE-Grid) dataset provided by the National Snow and Ice Data Center in Boulder, Colorado, is processed to derive the surface temperature using the method proposed by Fily et al. A normalization approach based on the 40-yr ECMWF reanalysis (ERA-40; 2.5°) temperature diurnal cycle fitted for each pixel is applied to overcome the time acquisition variation of measurements as well as to interpolate missing data. An adaptive mask for discriminating between ice-free pixels and snow-free pixels is also applied. The resulting database is thus a new consistent hourly series of near-surface air temperatures during the summer (without snow). The mean accuracy is on the order of 2.5–3 K when compared with the synchronous in situ air temperature and different gridded datasets over Canada and Alaska. The trend over the last 10 yr confirms observed climate evolution: an increase in summer surface temperature of +0.09° ± 0.04°C yr−1, at the 90% confidence level, for Canada between 1992 and 2002, whereas a decrease of −0.15° ± 0.05°C yr−1, at the 95% confidence level, is observed for Alaska. Spatial and temporal anomalies show regional impacts of meteorological phenomena such as the El Niño extreme warm summer episode of 1998, the decrease in temperatures in 1992 in Canada following the volcanic eruption of Mount Pinatubo in June 1991, and the strong drought in the prairies in 2001. The annual sum of positive degree-days (thawing index) has been related to the permafrost distribution. The lower values of the derived thawing index (<1400 degree-days) are related well to the presence of continuous and dense discontinuous permafrost. The observed increase in the thawing index during the 1992–2002 period represents a decrease of classified permafrost area of 7%.

Identification of systematic bias in the cross-platform (SMMR and SSM/I) EASE-Grid brightness temperature time series

Vertically polarized 18-, 19-, and 37-GHz brightness temperatures from the Scanning Multichannel Microwave Radiometer (SMMR) and Special Sensor Microwave/Imager (SSM/I) are examined for the August 2-20, 1987 period when data from both sensors are available in the Equal-Area Scalable Earth Grid (EASE-Grid) projection. Colocated measurements over terrestrial surfaces of central North America are compared because of a previously observed inconsistency in derived winter season snow water equivalent across the cross-platform passive microwave time series. The results of this comparison show that SSM/I brightness temperatures systematically exceed SMMR measurements, with the magnitude of this difference dependant on overpass time and brightness temperature magnitude. Regression relationships are determined for adjusting EASE-Grid SMMR data to an SSM/I F8 baseline and are compared to the results of a previous study that examined daily averaged data for the polar regions. These results suggest that adjustment factors are not globally applicable; rather the region and application must be considered.