scholarly journals Warm Arctic Proglacial Lakes in the ASTER Surface Temperature Product

2021 ◽  
Vol 13 (15) ◽  
pp. 2987
Author(s):  
Adrian Dye ◽  
Robert Bryant ◽  
Emma Dodd ◽  
Fran Falcini ◽  
David M. Rippin
Keyword(s):  
Surface Temperature ◽  
High Spatial Resolution ◽  
The Arctic ◽  
Small Lakes ◽  
Lake Surface ◽  
Validation Data ◽  
Proglacial Lakes ◽  
Air Temperatures ◽  
Proglacial Lake

Despite an increase in heatwaves and rising air temperatures in the Arctic, little research has been conducted into the temperatures of proglacial lakes in the region. An assumption persists that they are cold and uniformly feature a temperature of 1 °C. This is important to test, given the rising air temperatures in the region (reported in this study) and potential to increase water temperatures, thus increasing subaqueous melting and the retreat of glacier termini from where they are in contact with lakes. Through analysis of ASTER surface temperature product data, we report warm (>4 °C) proglacial lake surface water temperatures (LSWT) for both ice-contact and non-ice-contact lakes, as well as substantial spatial heterogeneity. We present in situ validation data (from problematic maritime areas) and a workflow that facilitates the extraction of robust LSWT data from the high-resolution (90 m) ASTER surface temperature product (AST08). This enables spatial patterns to be analysed in conjunction with surrounding thermal influences, such as parent glaciers and topographies. This workflow can be utilised for the analysis of the LSWT data of other small lakes and crucially allows high spatial resolution study of how they have responded to changes in climate. Further study of the LSWT is essential in the Arctic given the amplification of climate change across the region.

scholarly journals Deriving Arctic 2 m air temperatures over snow and ice from satellite surface temperature measurements

2021 ◽  
Author(s):  
Pia Nielsen-Englyst ◽  
Jacob L. Høyer ◽  
Kristine S. Madsen ◽  
Rasmus T. Tonboe ◽  
Gorm Dybkjær ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Arctic Region ◽  
Satellite Observations ◽  
The Arctic ◽  
Clear Sky ◽  
Ice Surface ◽  
Air Temperatures ◽  
In Situ Observations ◽  
The Arctic Region

Abstract. The Arctic region is responding heavily to climate change, and yet, the air temperature of ice covered areas in the Arctic is heavily under-sampled when it comes to in situ measurements, resulting in large uncertainties in existing weather- and reanalysis products. This paper presents a method for estimating daily mean clear sky 2 meter air temperatures (T2m) in the Arctic from satellite observations of skin temperature, using the Arctic and Antarctic ice Surface Temperatures from thermal Infrared (AASTI) satellite dataset, providing spatially detailed observations of the Arctic. The method is based on a linear regression model, which has been tuned against in situ observations to estimate daily mean T2m based on clear sky satellite ice surface skin temperatures. The daily satellite derived T2m product includes estimated uncertainties and covers clear sky snow and ice surfaces in the Arctic region during the period 2000–2009, provided on a 0.25 degree regular latitude-longitude grid. Comparisons with independent in situ measured T2m show average biases of 0.30 °C and 0.35 °C and average root mean square errors of 3.47 °C and 3.20 °C for land ice and sea ice, respectively. The associated uncertainties are verified to be very realistic for both land ice and sea ice, using in situ observations. The reconstruction provides a much better spatial coverage than the sparse in situ observations of T2m in the Arctic, is independent of numerical weather prediction model input and it therefore provides an important supplement to simulated air temperatures to be used for assimilation or global surface temperature reconstructions. A comparison between in situ T2m versus T2m derived from satellite and ERA-Interim/ERA5 estimates shows that the T2m derived from satellite observations validate similar or better than ERA-Interim/ERA5 in the Arctic.

scholarly journals Deriving Arctic 2 m air temperatures over snow and ice from satellite surface temperature measurements

2019 ◽  
Author(s):  
Pia Nielsen-Englyst ◽  
Jacob L. Høyer ◽  
Kristine S. Madsen ◽  
Rasmus T. Tonboe ◽  
Gorm Dybkjær
Keyword(s):  
Surface Temperature ◽  
Arctic Region ◽  
Satellite Observations ◽  
The Arctic ◽  
Ice Surface ◽  
Air Temperatures ◽  
In Situ Observations ◽  
The Arctic Region ◽  
Snow And Ice

Abstract. The Arctic region is responding heavily to climate change, and yet, the air temperature of Arctic, ice covered areas is heavily under-sampled when it comes to in situ measurements, and large uncertainties exist in weather- and reanalysis products. This paper presents a method for estimating daily mean 2 meter air temperatures (T2m) in the Arctic from satellite observations of skin temperature, using the Arctic and Antarctic ice Surface Temperatures from thermal Infrared (AASTI) satellite dataset, providing spatially detailed observations of the Arctic. The method is based on a linear regression model which has been developed using in situ observations and daily mean satellite ice surface skin temperatures combined with a seasonal variation to estimate daily T2m. The satellite derived T2m product including estimated uncertainties covers clear sky snow and ice surfaces in the Arctic region during the period 2000–2009. Comparison with independent in situ measured T2m gives average correlations of 95.5 % and 96.5 % and average root mean square errors of 3.47 °C and 3.19 °C for land ice and sea ice, respectively. The reconstruction provides a much better spatial coverage than the sparse in situ observations of T2m in the Arctic, is independent of numerical weather prediction model input and it therefore provides an important alternative to simulated air temperatures to be used for assimilation or global surface temperature reconstructions. A comparison between in situ T2m versus T2m from satellite and ERA-Interim shows that the T2m derived from satellite observations validate similar or better than ERA-Interim estimates in the Arctic.

scholarly journals Arctic surface temperatures from Metop AVHRR compared to in situ ocean and land data

2012 ◽  
Vol 9 (2) ◽  
pp. 1009-1043 ◽  
Author(s):  
G. Dybkjær ◽  
R. Tonboe ◽  
J. Høyer
Keyword(s):  
Sea Ice ◽  
Surface Temperature ◽  
Temperature Data ◽  
Coupled Systems ◽  
The Arctic ◽  
In Situ Observation ◽  
Clear Sky ◽  
Ice Surface ◽  
Ice Surface Temperature

Abstract. The ice surface temperature (IST) is an important boundary condition for both atmospheric and ocean and sea ice models and for coupled systems. An operational ice surface temperature product using satellite Metop AVHRR infra-red data was developed for MyOcean. The IST can be mapped in clear sky regions using a split window algorithm specially tuned for sea ice. Clear sky conditions are prevailing during spring in the Arctic while persistent cloud cover limits data coverage during summer. The cloud covered regions are detected using the EUMETSAT cloud mask. The Metop IST compares to 2 m temperature at the Greenland ice cap Summit within STD error of 3.14 °C and to Arctic drifting buoy temperature data within STD error of 3.69 °C. A case study reveal that the in situ radiometer data versus satellite IST STD error can be much lower (0.73 °C) and that the different in situ measures complicates the validation. Differences and variability between Metop IST and in situ data are analysed and discussed. An inter-comparison of Metop IST, numerical weather prediction temperatures and in situ observation indicates large biases between the different quantities. Because of the scarcity of conventional surface temperature or surface air temperature data in the Arctic the satellite IST data with its relatively good coverage can potentially add valuable information to model analysis for the Arctic atmosphere.

scholarly journals Advances in altimetric snow depth estimates using bi-frequency SARAL/CryoSat-2 Ka/Ku measurements

2021 ◽  
Author(s):  
Florent Garnier ◽  
Sara Fleury ◽  
Gilles Garric ◽  
Jérôme Bouffard ◽  
Michel Tsamados ◽  
...  
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Ocean Modeling ◽  
The Arctic ◽  
Validation Data ◽  
Band Frequency ◽  
In Situ Data ◽  
Wide Range ◽  
Assimilation System

Abstract. Although snow depth on sea ice is a key parameter for Sea Ice Thickness (SIT), there currently does not exist reliable estimations. In Arctic, nearly all SIT products use a snow depth climatology (the Warren-99 modified climatology, W99m) constructed from in-situ data obtained prior to the first significant impacts of climate change. In Antarctica, the lack of information on snow depth remains a major obstacle in the development of reliable SIT products. In this study, we present the latest version of the Altimetric Snow Depth (ASD) product computed over both hemispheres from the difference of the radar penetration into the snow pack between the CryoSat-2 Ku-band and the SARAL Ka-band frequency radars. The ASD solution is compared against a wide range of snow depth products including model data (Pan-Arctic Ice-Ocean Modeling and Assimilation System (PIOMAS) or its equivalent in Antarctica the Global Ice-Ocean Modeling and Assimilation System (GIOMAS), the MERCATOR model and NASA's Eulerian Snow On Sea Ice Model (NESOSIM, only in Arctic)), the Advanced Microwave Scanning Radiometer 2 (AMSR-2) passive radiometer data, and the Dual-altimeter Snow Thickness (DuST) Ka-Ku product (only in Arctic). It is validated in the Arctic against in-situ and airborne validation data. These comparisons demonstrate that ASD provide a consistent snow depth solution, with space and time patterns comparable with those of the alternative Ka-Ku DuST product, but with a mean bias of about 6.5 cm. We also demonstrate that ASD is consistent with the validation data. Comparisons with Operation Ice Bridge's (OIB) airborne snow radar in Arctic during the period of 2014–2018 show a correlation of 0.66 and a RMSE of about 6 cm. Furthermore, a first-guess monthly climatology has been constructed in Arctic from the ASD product, which shows a good agreement with OIB during 2009–2012. This climatology is shown to provide a better solution than the W99m climatology when compared with validation data. Finally, we have characterised the SIT uncertainty due to the snow depth from an ensemble of SIT solutions computed for the Arctic by using the different snow depth products previously used in the comparison with the ASD product. During the period of 2013–2019, we found a spatially averaged SIT mean standard deviation of 20 cm. Deviations between SIT estimations due to different snow depths can reach up to 77 cm. Using the ASD data instead of W99m to estimate SIT over this time period leads to a reduction of the average SIT of about 30 cm.

Analyzing Near Surface Temperature Inversions Across the Greenland Ice Sheet Using In-situ, Remote Sensing, and Reanalysis Data

2020 ◽  
Author(s):  
Alden Adolph ◽  
Wesley Brown ◽  
Karina Zikan ◽  
Robert Fausto
Keyword(s):  
Surface Temperature ◽  
Energy Exchange ◽  
Ice Sheet ◽  
Greenland Ice Sheet ◽  
Near Surface ◽  
Surface Temperatures ◽  
Air Temperatures ◽  
Exchange Processes ◽  
Temperature Inversions

<p>As Arctic temperatures have increased, the Greenland Ice Sheet has exhibited a negative mass balance, with a substantial and increasing fraction of mass loss due to surface melt. Understanding surface energy exchange processes in Greenland is critical for our ability to predict changes in mass balance. In-situ and remotely sensed surface temperatures are useful for monitoring trends, melt events, and surface energy balance processes, but these observations are complicated by the fact that surface temperatures and near surface air temperatures can significantly differ due to the presence of inversions that exist across the Arctic. Our previous work shows that even in the summer, very near surface inversions are present between the 2m air and surface temperatures a majority of the time at Summit, Greenland. In this study, we expand upon these results and combine a variety of data sources to quantify differences between surface snow/ice temperatures and 2m air temperatures across the Greenland Ice Sheet and investigate controls on the magnitude of these near surface temperature inversions. In-situ temperatures, wind speed, specific humidity, and albedo data are provided from automatic weather stations operated by the Programme for Monitoring of the Greenland Ice Sheet (PROMICE). We use the Clouds and the Earth's Radiant Energy System (CERES) cloud area fraction data to analyze effects of cloud presence on near surface temperature gradients. The in-situ temperatures are compared to Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) and Moderate Resolution Imaging Spectrometer (MODIS) ice surface temperature data to extend findings across the ice sheet. Using PROMICE in-situ data from 2015, we find that these 2m temperature inversions are present 77% of the time, with a median strength of 1.7°C. The data confirm that the presence of clouds weakens inversions. Initial results indicate a RMSE of 3.9°C between MERRA-2 and PROMICE 2m air temperature, and a RMSE of 5.6°C between the two datasets for surface temperature. Improved understanding of controls on near surface inversions is important for use of remotely sensed snow surface temperatures and for modeling of surface mass and energy exchange processes.</p>

scholarly journals Evaluation of 2-m Air Temperature and Surface Temperature from ERA5 and ERA-I Using Buoy Observations in the Arctic during 2010–2020

2021 ◽  
Vol 13 (14) ◽  
pp. 2813
Author(s):  
Yining Yu ◽  
Wanxin Xiao ◽  
Zhilun Zhang ◽  
Xiao Cheng ◽  
Fengming Hui ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Air Temperature ◽  
The Arctic ◽  
Sea Ice Concentration ◽  
Weather Forecasts ◽  
Ice Concentration ◽  
Medium Range ◽  
In Situ Observations ◽  
Two Temperature

In data-sparse regions such as the Arctic, atmospheric reanalysis is one of the key tools for understanding rapid climate change at the regional and global scales. The utility of reanalysis datasets based on data assimilation is affected by their accuracy and biases. Therefore, it is important to evaluate their performance. Here, we conduct inter-comparisons of two temperature variables, namely, the 2-m air temperature (Ta) and the surface temperature (Ts), from the widely used ERA-I and ERA5 reanalysis datasets provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) against in situ observations from three international buoy programs (i.e., the International Arctic Buoy Programme (IABP), the Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC), and the Cold Regions Research and Engineering Laboratory (CRREL)) during 2010–2020 in the Arctic. Overall, the results show that both the ERA-I and ERA5 were well correlated with the buoy observations, with the highest correlation coefficient reaching 0.98. There were generally warm Ta biases for both ERA-I (2.27 ± 3.33 °C) and ERA5 (2.34 ± 3.22 °C) when compared with more than 3000 matching pairs of daily buoy observations. The warm Ta biases of both reanalysis datasets exhibited seasonal variations, reaching the maximum of 3.73 ± 2.84 °C in April and the minimum of 1.36 ± 2.51 °C in September. For Ts, both ERA-I and ERA5 exhibited good consistencies with the buoy observations, but have higher amplitude biases compared with those for Ta, with generally negative biases of −4.79 ± 4.86 °C for ERA-I and −4.11 ± 3.92 °C for ERA5. For both reanalysis datasets, the largest bias of Ts (−11.18 ± 3.08 °C) occurred in December, while the biases were rather small (less than −3 °C) in the warmer months (April to October). The cold Ts biases for ERA-I and ERA5 were probably overestimated due to the location of the surface temperature sensors on the buoys, which may have been affected by snow cover. Both the Ta and Ts biases varied for different buoy programs and different sea ice concentration conditions, yet they exhibited similar trends.

scholarly journals Near-surface thermal stratification during summer at Summit, Greenland, and its relation to MODIS-derived surface temperatures

2017 ◽  
Author(s):  
Alden C. Adolph ◽  
Mary R. Albert ◽  
Dorothy K. Hall
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Skin Temperature ◽  
Air Temperature ◽  
The Arctic ◽  
Cold Bias ◽  
Near Surface ◽  
Surface Temperatures ◽  
In Situ Ir

Abstract. As rapid warming of the Arctic occurs, it is imperative that climate indicators such as temperature be monitored over large areas to understand and predict the effects of climate changes. Temperatures are traditionally tracked using in situ 2 m air temperatures, but in remote locations where few ground-based measurements exist, such as on the Greenland Ice Sheet, temperatures over large areas are assessed using remote sensing techniques. Because of the presence of surface-based temperature inversions in ice-covered areas, differences between 2 m air temperature and the temperature of the actual snow surface (referred to as skin temperature) can be significant and are particularly relevant when considering validation and application of remote sensing temperature data. We present results from a field campaign extending from 8 June through 18 July 2015, near Summit Station in Greenland to study surface temperature using the following measurements: skin temperature measured by an infrared (IR) sensor, thermochrons, and thermocouples; 2 m air temperature measured by a NOAA meteorological station; and a MODerate-resolution Imaging Spectroradiometer (MODIS) surface temperature product. Our data indicate that 2 m air temperature is often significantly higher than snow skin temperature measured in-situ, and this finding may account for apparent biases in previous surface temperature studies of MODIS products that used 2 m air temperature for validation. This inversion is present during summer months when incoming solar radiation and wind speed are both low. As compared to our in-situ IR skin temperature measurements, after additional cloud masking, the MOD/MYD11 Collection 6 surface-temperature standard product has an RMSE of 1.0 °C, spanning a range of temperatures from −35 °C to −5 °C. For our study area and time series, MODIS surface temperature products agree with skin surface temperatures better than previous studies indicated, especially at temperatures below −20 °C where other studies found a significant cold bias. The apparent cold bias present in others’ comparison of 2 m air temperature and MODIS surface temperature is perhaps a result of the near-surface temperature inversion that our data demonstrate. Further investigation of how in-situ IR skin temperatures compare to MODIS surface temperature at lower temperatures (below −35 °C) is warranted to determine if this cold bias does indeed exist.

scholarly journals Modeling spatial structure of thermokarst lake fields in permafrost of Western Siberia based on satellite images

2019 ◽  
Vol 19 (1) ◽  
pp. 1-10
Author(s):  
Vladimir Yu. Polishchuk ◽  
Ildar N. Muratov ◽  
Yury M. Polishchuk
Keyword(s):  
Spatial Structure ◽  
Size Distribution ◽  
Spatial Resolution ◽  
Satellite Images ◽  
Western Siberia ◽  
High Spatial Resolution ◽  
The Arctic ◽  
Landsat 8 ◽  
Small Lakes ◽  
Thermokarst Lakes

Deciphering the satellite images of medium and high spatial resolution of the northern territories of Western Siberia has been carried out using geoinformation system ArcGIS 10.3. Images of medium resolution Landsat-8 and high resolution Kanopus-V were used. Kanopus-V images alluded to determine the number and areas of small lakes, which are considered as intensive sources of methane emission into the atmosphere from thermokarst lakes. Data on the spatial characteristics of thermokarst lakes were obtained. Based on the integration of images of medium and high spatial resolution, a synthesized histogram of the distribution of lakes in a wide range of sizes was constructed, taking into account small lakes. The obtained histogram was approximated by a lognormal distribution law by the Pearson criterion with a probability of 0.99. Based on the geo-simulation approach, a new model of the spatial structure of the fields of thermokarst lakes is presented, taking into account the lognormal law of the lake size-distribution. Algorithms for modeling the spatial structure of the fields of thermokarst lakes are described. An example of modeling the field of thermokarst lakes with a lognormal law of their size-distribution is given. The practical applicability of the previously developed model with an exponential distribution of lakes in size, based on data from Landsat images, has been experimentally confirmed. The results can be used to obtain predictions of the dynamics of methane emissions from the thermokarst lakes of the Arctic zone of Northern Eurasia for the coming decades in the context of climate changes.

scholarly journals Modeling spatial structure of thermokarst lake fields in permafrost of Western Siberia based on satellite images

2019 ◽  
Vol 19 (1) ◽  
pp. 1-10
Author(s):  
Vladimir Yu Polishchuk ◽  
Ildar N Muratov ◽  
Yury M Polishchuk
Keyword(s):  
Spatial Structure ◽  
Size Distribution ◽  
Spatial Resolution ◽  
Satellite Images ◽  
Western Siberia ◽  
High Spatial Resolution ◽  
The Arctic ◽  
Landsat 8 ◽  
Small Lakes ◽  
Thermokarst Lakes

Deciphering the satellite images of medium and high spatial resolution of the northern territories of Western Siberia has been carried out using geoinformation system ArcGIS 10.3. Images of medium resolution Landsat-8 and high resolution Kanopus-V were used. Kanopus-V images alluded to determine the number and areas of small lakes, which are considered as intensive sources of methane emission into the atmosphere from thermokarst lakes. Data on the spatial characteristics of thermokarst lakes were obtained. Based on the integration of images of medium and high spatial resolution, a synthesized histogram of the distribution of lakes in a wide range of sizes was constructed, taking into account small lakes. The obtained histogram was approximated by a lognormal distribution law by the Pearson criterion with a probability of 0.99. Based on the geo-simulation approach, a new model of the spatial structure of the fields of thermokarst lakes is presented, taking into account the lognormal law of the lake size-distribution. Algorithms for modeling the spatial structure of the fields of thermokarst lakes are described. An example of modeling the field of thermokarst lakes with a lognormal law of their size-distribution is given. The practical applicability of the previously developed model with an exponential distribution of lakes in size, based on data from Landsat images, has been experimentally confirmed. The results can be used to obtain predictions of the dynamics of methane emissions from the thermokarst lakes of the Arctic zone of Northern Eurasia for the coming decades in the context of climate changes.

scholarly journals Evapotranspiration Data Product from NESDIS GET-D System Upgraded for GOES-16 ABI Observations

2019 ◽  
Vol 11 (22) ◽  
pp. 2639 ◽  
Author(s):  
Li Fang ◽  
Xiwu Zhan ◽  
Mitchell Schull ◽  
Satya Kalluri ◽  
Istvan Laszlo ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Spatial Resolution ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Weather Prediction ◽  
Stress Index ◽  
Product System ◽  
Validation Data ◽  
Area Index

Evapotranspiration (ET) is a major component of the global and regional water cycle. An operational Geostationary Operational Environmental Satellite (GOES) ET and Drought (GET-D) product system has been developed by the National Environmental Satellite, Data and Information Service (NESDIS) in the National Oceanic and Atmospheric Administration (NOAA) for numerical weather prediction model validation, data assimilation, and drought monitoring. GET-D system was generating ET and Evaporative Stress Index (ESI) maps at 8 km spatial resolution using thermal observations of the Imagers on GOES-13 and GOES-15 before the primary operational GOES satellites transitioned to GOES-16 and GOES-17 with the Advanced Baseline Imagers (ABI). In this study, the GET-D product system is upgraded to ingest the thermal observations of ABI with the best spatial resolution of 2 km. The core of the GET-D system is the Atmosphere-Land Exchange Inversion (ALEXI) model, which exploits the mid-morning rise in the land surface temperature to deduce the land surface fluxes including ET. Satellite-based land surface temperature and solar insolation retrievals from ABI and meteorological forcing from NOAA NCEP Climate Forecast System (CFS) are the major inputs to the GET-D system. Ancillary data required in GET-D include land cover map, leaf area index, albedo and cloud mask. This paper presents preliminary results of ET from the upgraded GET-D system after a brief introduction of the ALEXI model and the architecture of GET-D system. Comparisons with in situ ET measurements showed that the accuracy of the GOES-16 ABI based ET is similar to the results from the legacy GET-D ET based on GOES-13/15 Imager data. The agreement with the in situ measurements is satisfactory with a correlation of 0.914 averaged from three Mead sites. Further evaluation of the ABI-based ET product, upgrade efforts of the GET-D system for ESI products, and conclusions for the ABI-based GET-D products are discussed.

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